Phd-thesis - David Balayla

ABSTRACT Shallow waters are globally important, for their uses (e.g. fisheries, water purification, water for human consumption), amenity values and ecological importance, despite their generally small size and volume. Shallow lakes apparently exist in either of two alternative states, one dominated by aquatic plants and characterized by clear water, the other turbid and often considered undesirable. Either state can be found across a wide range of nutrient concentrations. It is postulated that each state is stabilized by a suite of mechanisms. One of the mechanisms stabilizing the plant-dominated state may be the grazing of cladocerans living in tight or loose association to plants. Little Mere (Cheshire) is a small (2.3 ha) and very shallow lake (average depth 0.7 m) in North-West England, which received a poor standard effluent prior to 1991. In that year, sewage was diverted for treatment elsewhere. The lake seems to have stabilized, about ten years after diversion in June 1991, with aquatic plants, both nymphaeids and submerged, covering almost its entirety. Lily beds now cover circa 40 % of the total surface area. In Little Mere, top-down processes (i.e. cladoceran herbivory and fish predation on zooplankton) are thought to be more important than bottom-up forces (i.e. nutrient limitation) in the regulation of the maximum size of algal crops. However, little is known on the specific mechanisms stabilizing the plant-dominated state. Indeed, occasionally high levels of water clarity are maintained in this lake even in the absence of significant populations of the common open-water cladoceran Daphnia. The main hypothesis examined in this study is that plant-associated cladocerans are important grazers helping to maintain clear water in Little Mere during the growing season (April to November). The lake was intensively sampled for Cladocera and other variables related to chemistry and phytoplankton during the period April 1998-April 2000. Samples were collected at five sites across the lake, three of them located within lily beds, the other two over submerged plant beds of mixed composition. Specific sampling techniques were developed for floating lily leaves, petioles, submerged plants and water. Significant horizontal differences were identified for most cladoceran species, both open-water and plant-associated, chydorid periphyton scrapers and filter-feeders. Daphnia and Ceriodaphnia were significantly more abundant in lily beds than in more open water in both growing seasons, suggesting these beds are an effective refuge against fish predation. Size-structure and egg-ratio data support this contention. Specific differences with respect to micro-habitat association in lily beds (i.e. floating lily beds vs petioles) were also identified. Spatial distributions of most cladocerans were very heterogeneous, particularly in the case of chydorids. Patchiness was driven by resource availability and intraspecific competition when populations were dense, but fish predation was apparently the main regulator when numbers were scarce (i.e. in growing season 1999). Egg-ratio models were examined for Daphnia and Simocephalus vetulus (O.F.Muller), a plant-associated cladoceran. These models were difficult to apply to the latter species, probably as a result of large spatial heterogeneity. In situ feeding experiments using a dual radio-isotope technique, labelling planktonic and periphytic algae with H-3 and C-14, respectively, were conducted during one growing season in Little Mere, to gauge measures of feeding ´intensity´ for the main cladoceran species in the lake. Sida crystallina (O.F.Muller), a plant-associated

cladoceran, was found to be an efficient filterer (though not as efficient as Daphnia hyalina L.). Periphyton inhibited its filtering, but less so in young than adults. Ceriodaphnia and, especially, Simocephalus vetulus, were relatively inefficient filterfeeders in Little Mere. Measures of areal plant biomass and filtering rates (ml d-1 ind.-1), in conjunction with density estimates for the main cladoceran species (both open water and plant-associated) were used to calculate measures of grazing rate (% lake volume filtered per day), for filter-feeders in Little Mere. Consideration of the multiple sources of error in these estimates showed that they are a valid method for comparison of different large components of the grazer ´community´ of a lake such as Little Mere. The comparison between Daphnia and the main plant-associated filterers (Simocephalus vetulus and Sida crystallina) showed that the former species is the main grazer during most of the growing season with grazing rates often above 100 %d-1. However, Sida crystallina was a significant filter-feeder both at the beginning and end of growing seasons, and sometimes the main grazer in the lake, with grazing rates occasionally exceeding 500 %d-1. These impacts were short-lived, although the duration of peaks was much larger in 1999 than in 1998, and very localized in space. Simocephalus vetulus had a maximum grazing rate of 15-30 %d-1 in both years, but generally its impact is considered small, albeit not negligible when considered together with the other grazers. Mesocosm experiments were conducted in each of the growing seasons. Treatments were nutrient loadings (nitrogen and phosphorus) and applied fish densities (three-spined sticklebacks, Gasterosteus aculeatus L.). Fish predation was found to be the main regulator of the grazing impact of Daphnia. Herbivory by Daphnia could be sufficient to control the size of algal populations in Little Mere, too. A threshold of less, but close to, 4 gm-2 (or circa 3 fry m-2) was the maximum fish density that allowed Daphnia populations to persist in enclosures. In the lake this threshold may be larger than 4gm-2, given lily beds act as refuge for the zooplankton against fish predation. Plant-associated filter-feeders are a by no means negligible grazer ‘community’ in plantdominated lakes such as Little Mere, with a large plant cover. Generally, biomanipulation success can be aided by their buffering of negative effects on plant-bed persistence. Indirect effects on nutrient cycling in plant beds by chydorid periphyton scrappers may be another important link, although scarce data on periphyton ingestion rates and natural periphyton concentrations hamper the testing of this idea. In conclusion, plant-associated microcrustacea are important components of plant beds and merit further study. These studies need intensive sampling, to capture effects that are often short-lived (i.e. less than two weeks), and efforts should be distributed spatially, as conclusions may vary substantially if spatial heterogeneity is not taken into account.

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Chapter 6 Beklioglu, M. & Moss, B. (1996), Existence of a macrophyte-dominated clear-water state over a wide range of nutrient concentrations in a small shallow lake, Hydrobiologia 337:93-106. Benndorf, J. (1988), Objectives and Unsolved Problems in Ecotechnology and Biomanipulation: A Preface, Limnologica (Berl.) 19(1):5-8. ----------------- (1990), Conditions for effective biomanipulation; conclusions derived from whole-lake experiments in Europe, Hydrobiologia 200/201:187-203. Brooks, J.L. & Dodson, S.I. (1965), Predation, body size, and composition of plankton, Science 150:28-35. Burns, C.W. (1968), The relationships between body size and filter-feeding Cladocera and the maximum size of particle ingested, Limnol.Oceanogr. 13:675-8 Carpenter, S.R. et al. (1987), Regulation of lake primary productivity by food-web structure, Ecology 68:18637. Crisman, T.L. & Beaver, J.R. (1990), Applicability of planktonic biomanipulation for managing eutrophication in the subtropics, Hydrobiologia 200/201:177-85. Cryer, M. et al. (1986), Reciprocal interactions between roach (Rutilus rutilus) and zooplankton in a small lake: prey dynamics and fish growth and recruitment, Limnol.Oceanogr. 31:1022-38. DeBernardi, R. & Giussani, G. (1995)(eds.), “Guidelines of Lake Management” Vol. 7- Biomanipulation in Lakes and Reservoirs Management, International Lake Environment Committee (ILEC), UN Env. Programme (UNEP). ---------------------------------------- (1990), Are blue-green algae a suitable food for zooplankton?. An overview, Hydrobiologia 200/201:29-41. DeMelo, R.; France, R. & McQueen, D.J. (1992), Biomanipulation: hit or myth?, Limnol.Oceanogr. 37(1):192-207. DeMott, W.R. (1998), Utilization of a cyanobacterium and a phosphorus-deficient green alga as complementary resources by daphnids, Ecology 79(7):2463-81. DeStasio, B. Jr. (1990), The role of dormancy and emergence patterns in the dynamics of a freshwater zooplankton community, Limnol.Oceanogr. 35:1079-90. Edmondson, W.T. & Abella, S.E.B. (1988), Unplanned biomanipulation of Lake Washington, Limnologica 19:73-9. Elser, J.J. et al. (1990), The zooplankton-phytoplankton interface in lakes of contrasting trophic status: an experimental comparison, Hydrobiologia 200/201:69-82. Faafeng, B.A. et al. (1990), Biomanipulation and food-web dynamics- the importance of seasonal stability, Hydrobiologia 200/201:119-28. Fowler, J. & Cohen, L. (1990), “Practical statistics for field biology”, Wiley & Sons, NY (US), 1993. Forsberg, C. et al. (1990), Absence of allelopathic effects of Chara on phytoplankton in situ, Aquatic Botany 38:289-94. Gliwicz, Z.M. (1990), Why do cladocerans fail to control algal blooms?, Hydrobiologia 200/201:83-95. Gulati, R. (1995), Manipulation of fish population for lake recovery from eutrophication in the temperate region, in: “Guidelines of Lake Management” Vol. 7- Biomanipulation in Lakes and Reservoirs Management, International Lake Environment Committee (ILEC), UN Env. Programme (UNEP), Chapter 4, pp.53-79. Hall, D.J. et al. (1976), The size-efficiency hypothesis and the size-structure of zooplankton communities, Ann.Rev.Ecol.Syst. 7:177-208. Heisenberg, W. (1927), in Wheeler, J.A. & Zurek, H. (eds.), “Quantum Theory and Measurement”, Princeton Univ. Press, 1983, pp.62-84. Horppila, J. & Kairesalo,T. (1990), A fading recovery: the role of roach (Rutilus rutilus L.) in maintaining high algal productivity and biomass in Lake Vesijarvi, southern Finland. Hydrobiologia 200/201:153-65.

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Hosper, S.H. & Jagtman, E. (1990), Biomanipulation additional to nutrient control for restoration of shallow lakes in The Netherlands, Hydrobiologia 200/201:523-34. Jeppesen, E. et al. (1990), Fish manipulation as a lake restoration tool in shallow, eutrophic temperate lakes 1: cross-analysis of three Danish case-studies, Hydrobiologia 200/201:205-18. Jeppesen, E. et al. (1991), Recovery resilience following a reduction in external phosphorus loading of shallow, eutrophic Danish lakes: duration, regulating factors and methods for overcoming resilience, Memoria dell' Istituto Ital. Idrobiol. 48:127-48. Kairesalo, T. et al. (1999), Direct and indirect mechanisms behind successful biomanipulation, Hydrobiologia 395/396:99-106. Kankaala, P. et al. (1990), Zooplankton of Lake Ala-Kitka (NE-Finland) in relation to phytoplankton and predation by vendace (Coregonus albula), Aqua Fennica 20:81-94. Kasprzak, P. (1995), Objectives of Biomanipulation, in: “Guidelines of Lake Management” Vol. 7Biomanipulation in Lakes and Reservoirs Management, International Lake Environment Committee (ILEC), UN Env. Programme (UNEP), Chap. 2, pp.15-32. Kerfoot, W.C. & DeAngelis, D.L. (1989), Scale-dependent dynamics: zooplankton and the stability of freshwater food webs, TREE 4(6):167-71. Lammens, E.H.H.R. et al. (1990), The first biomanipulation conference: a synthesis, Hydrobiologia 200/201:619-27. Lampert, W. (1988), The relationship between zooplankton biomass and grazing: a review, Limnologica (Berl.) 19(1):11-20. Lauridsen, T.L. & Buenk, I. (1996), Diel changes in the horizontal distribution of zooplankton in the littoral zone of two shallow eutrophic lakes, Arch. Hydrobiol. 137(2):161-76. Lauridsen, T.L. et al. (1996), The importance of macrophyte bed size for cladoceran compostion and horizontal migration in a shallow lake, J.Plankton Res. 18(12):2283-94. Leah, R.T. et al. (1980), The role of predation in causing major changes in the limnology of a hyper-eutrophic lake, Int.Rev.Gesamten Hydrobiol. 65:223-47. Lehman, J.T. (1986), The goal of understanding in Limnology, Limnol.Oceanogr. 31(5):1160-6. Lindman, H.R. (1974), Analysis of Variance in Complex Experimental Designs, W.H. Freeman & Co., San Francisco. In: Sokal, R.R. & Rohlf, F.J. (1981). Loehle, C. (1988), Philosophical tools: potential contributions to Ecology, Oikos 51:97-104. Luokkanen, E. (1995), The species composition, biomass and production of the pelagic cladoceran community in the Enonselka basin of Lake Vesijarvi. Helsingin yliopiston Lahden tutkimus- ja koulutuskeskuksen selvityksia 25/1995 (in Finnish). Lurling, M. (1999), “The smell of water. Grazer-induced colony formation in Scenedesmus”, Thesis Wageningen Agricultural University, 1999. Lurling, M. & Van Donk, E. (1997), Morphological changes in Scenedesmus induced by infochemicals released in situ from zooplankton grazers, Limnol.Oceanogr. 42:783-88. Lynch, M. (1979), Predation, competition, and zooplankton community structure: An experimental study, Limnol.Oceanogr. 24:253-72. McQueen, D.J. (1990), Manipulating lake community structure: where do we go from here?, Freshwat. Biol. 23:613-20. McQueen, D.J. et al. (1990), Effects of planktivore abundance on chlorophyll-a and Secchi depth, Hydrobiologia 200/201:337-41. Meijer, M.L. et al. (1994), The consequences of a drastic fish stock reduction in the large and shallow lake Wolderwijd, The Netherlands- can we understand what happened?, Hydrobiologia 276:31-42. Mills, E.L. et al. (1987), Fish predation and its cascading effect on the Oneida Lake food chain, in: “Predation: Direct and Indirect Impacts on Aquatic Communities”, W.C.Kerfoot & A.Sih (eds.), Univ. Press of New England, Hanover, pp118-31. Moss, B. (1989), Water pollution and the management of ecosystems: a case-study of science and scientist, Toward a more exact Ecology pp.401-22, 30th Symp of the BES, London 1988, Blackwell Sci.Publ. Moss, B. et al. (1991), Development of daphnid communities in diatom- and cyanophyte-dominated lakes and their relevance to lake restoration by biomanipulation, J.Applied Ecology 28:586-602. Moss, B. et al. (1996), Progressive restoration of a shallow lake: a 12-year experiment in isolation, sediment removal and biomanipulation, J.Applied Ecology 33:71-86. Moss, B. et al. (1997), Vertically-challenged limnology; constrast between deep and shallow lakes, Hydrobiologia 342/343:257-67. Moss, B.; Kornijow, R. & Measey, G.J. (1998), The effects of nymphaeid (Nuphar lutea) density and predation by perch (Perca fluviatilis) on the zooplankton communities in a shallow lake, Freshwat. Biol. 39:68997.

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Perrow, M.R.; Moss, B. & Stansfield, J. (1994), Trophic interactions in a shallow lake following a reduction in nutrient loading: a long-term study, Hydrobiologia 275/276:43-52. Peters, R.H. (1986), The role of prediction in Limnology, Limnol.Oceanogr. 31:1143-59. Phillips, G. et al. (1994), The importance of sediment phosphorus release in the restoration of very shallow lakes (The Norfolk Broads, England) and implications for biomanipulation, Hydrobiologia 275/276:445-56. Popper, K. R. (1959), “The Logic of Scientific Discovery”, Hutchinson & Co. Ltd., London. Porter, H. (1973), Selective grazing and differential digestion of algae by zooplankton, Nature 44:179-80. Ripl, W. et al. (1994), A holistic approach to the structure and function of wetlands, and their degradation, pp.16-35. In: Eiseltova, M. (ed.), 1994. Restoration of lake ecosytems. A holistic approach, IWRB Publ. 32. 182 pp. Samuels, M.L. (1991), Statistics for the Life Sciences, 1st ed., Maxwell MacMillan Internat. Ed., Singapore, 1991. Scheffer, M. (1998), “Ecology of shallow lakes”, Chapman & Hall, 1st edition, 1998. Scheffer, M. & Beets, J. (1994), Ecological models and the pitfalls of causality, Hydrobiologia 275/276: 11524. Schoenberg, S.A. & Carlson, R.E. (1984), Direct and indirect effects of zooplankton grazing on phytoplankton in a hypereutrophic lake, Oikos 42:291-302. Schriver, P. et al. (1995), Impact of submerged macrophytes on fish-zooplankton-phytoplankton interactions: large-scale enclosure experiments in a shallow eutrophic lake, Freshwat.Biol. 33:255-70. Shapiro, J. (1990), Biomanipulation: the next phase-making it stable, Hydrobiologia 200/201:13-27. Shapiro, J. & Wright, D.I. (1984), Lake restoration by biomanipulation in Round Lake, Minnesota, the first two years, Freshwat.Biol. 14:371-84. Sokal, R.R. & Rohlf, F.J. (1981), “Biometry”, 2nd edition, W.H. Freeman & Co., NY, 1981. Sommer, U. (1989), Nutrient status and nutrient competition of phytoplankton in a shallow, hypertrophic lake, Limnol.Oceanogr. 34(7):1162-73. Sondergaard, M. et al. (1990), Phytoplankton biomass reduction after planktivorous fish reduction in a shallow, eutrophic lake: a combined effect of reduced internal P-loading and increased zooplankton grazing, Hydrobiologia 200/201:229-40. Stephen, D.; Moss, B. & Phillips, G. (1997), Do rooted macrophytes increase sediment phosphorus release?, Hydrobiologia 342/343:27-34. ------------------------------------------------ (1998), The relative importance of top-down and bottom-up control of phytoplankton in a shallow macrophyte-dominated lake, Freshwat. Biol. 39:699-713. Timms, R.M. & Moss, B. (1984), Prevention of growth of potentially dense phytoplankton populations by zooplankton grazing, in the presence of zooplanktivorous fish, in a shallow wetland ecosystem, Limnol. Oceanogr. 29(3):472-86. Urabe, J. & Watanabe, Y. (1992), Possibility of N and P limitation for planktonic cladocerans: An experimental test, Limnol.Oceanogr. 37:244-51. Van Donk, E. et al. (1994), Use of mesocosms in a shallow eutrophic lake to study the effects of different restoration measures, Arch. Hydrobiol. Ergeb.Limnol. 40:283-94. Vanni, M.J. (1986), Fish predation and zooplankton demography: indirect effects, Ecology 67(2):337-54. --------------- (1987), Indirect effects of predators on age-structured prey populations: planktivorous fish and zooplankton, in: “Predation: Direct and Indirect Impacts on Aquatic Communities”, W.C.Kerfoot & A.Sih (eds.), Univ. Press of New England, Hanover. Vanni, M.J. et al. (1990), Effects of planktivorous fish mass mortality on the plankton community of Lake Mendota, Wisconsin: implications for biomanipulation, Hydrobiologia 200/201:329-36. Wium-Andersen, S.U. et al. (1982), Allelopathic effects on phytoplankton by substances isolated from aquatic macrophytes (Charales), Oikos 39:187-90.

Chapter 7 Brancelj, A. & Blejec, A. (1994), Diurnal vertical migration of Daphnia hyalina (Leydig, 1860) (Crustacea, Cladocera) in Lake Bled (Slovenia) in relation to temperature and predation, Hydrobiologia 284:125-36. Cushing, D.H. (1951), The vertical migration of planktonic Crustacea, Biol. Rev. 26:158-92. Dagg, M.J. (1985), The effects of food limitation on diel migratory behavior in marine zooplankton, Archiv Hydrobiol.Bei.Ergeb.Limnol. 21:247-55. DeMeester, L. (1990), Evolutionary potential and local genetic differentiation in a phenotypically plastic trait of a cyclical parthenogen, Daphnia magna. Evolution 50:1293-8. Dodson, S. (1988), The ecological role of chemical stimuli for the zooplankton predator-avoidance behavior in Daphnia, Limnol. Oceanogr. 33:1431-9.

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Chapter 8 Aloi, J.E. (1990), A critical review of recent freshwater periphyton field methods, Can.J.Fish Aquat.Sci 47:65670. Bailey, N.T.J. (1992), Statistical methods in Biology, 2nd ed., Cambridge Univ. Press, 1992. Balls, H.; Moss, B. & Irvine, K. (1989), The loss of submerged plants with eutrophication. I. Experimental design, water chemistry, aquatic plant and phytoplankton biomass in experiments carried out in ponds in the Norfolk Broadland, Freshwat.Biol. 22:71-87. Barko, J.W. & James, W.F. (1997), Effects of submerged aquatic macrophytes on nutrient dynamics, sedimentation, and resuspension, In: Jeppesen et al. (eds.) (1997), “The structuring role of submerged macrophytes in lakes”, Springer-Verlag, New York. Beklioglu, M. & Moss, B. (1995), The impact of pH on interactions among phytoplankton algae, zooplankton and perch (Perca fluviatilis) in a shallow, fertile lake. --------------------------------- (1996), Existence of a macrophyte-dominated clear-water state over a wide range of nutrient concentrations in a small shallow lake, Hydrobiologia 337:93-106. Blindow, I. et al. (1993), Long-term pattern of alternative stable states in two shallow eutrophic lakes, Freshwat.Biol. 30:159-67. Blindow, I. et al. (2000), How important is the crustacean plankton for the maintenance of water clarity in shallow lakes with abundant submerged vegetation?, Freswat. Biol. 44:185-97. Brendelberger, H. (1991), Filter mesh size of cladocerans predicts retention efficiency for bacteria, Limnol.Oceanogr. 36(5):884-94. -----------------(1968), The relationships between body size and filter-feeding Cladocera and the maximum size of particle ingested, Limnol.Oceanogr. 13:675-8 Cattaneo, A. (1983), Grazing on epiphytes, Limnol.Oceanogr. 28(1):124-32. Chambers, P.A. & Kalff, J. (1985), Depth distribution and biomass of submersed aquatic macrophyte communities in relation to Secchi depth, Can.J.Fish Aquat. Sci. 42:701-9. Descartes, R. (1637), “Discurso del metodo”, Alianza Editorial, Madrid (Spain). DiFonzo, C. & Campbell, J. (1988), Spatial partioning of microhabitats in littoral Cladoceran communities, J.Freshwater Ecology 4(3):303-13.

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Moss, B. et al. (1996), A guide to the restoration of nutrient-enriched shallow lakes. Environment Agency and the Broads Authority, W W Hawes UK. Moss, B. et al. (1997), Vertically-challenged limnology; constrast between deep and shallow lakes, Hydrobiologia 342/343:257-67. Moss, B.; et al. (1998), The effects of nymphaeid (Nuphar lutea) density and predation by perch (Perca fluviatilis) on the zooplankton communities in a shallow lake, Freshwat. Biol. 39:689-97. Mullin, M.M. & Brooks, E.R. (1976), Some consequences of distributional heterogeneity of phytoplankton and zooplankton, Limnol.Oceanogr. 21(6):784-96. Nakai, S. et al. (1999), Growth inhibition of blue-green algae by allelopathic effects of macrophytes, Wat.Sci.Tech. 39(8):47-53. Pennak, R.W. (1962), Quantitative zooplankton sampling in littoral vegetation areas, Limnol.Oceanogr. 7:4879. Persson, L. & Eklov, P. (1995), Prey refuges affecting interactions between piscivorous perch and juvenile perch and roach, Ecology 76:70-81. Peters, R.H. (1986), The role of prediction in limnology, Limnol.Oceanogr. 31:1143-59. Phillips, G.L.; Eminson, D. & Moss, B. (1978), A mechanism to account for macrophyte decline in progressively eutrophicated freshwaters, Aquatic Botany 4:103-26. Porter, H. (1973), Selective grazing and differential digestion of algae by zooplankton, Nature 44:179-80. Prepas, E. & Rigler, F.H. (1978), The enigma of Daphnia death rates, Limnol.Oceanogr. 23(5):970-88. Reynolds, C.S. (1983), ‘What is phytoplankton?’, and ‘Spatial and temporal distribution of phytoplankton’ in: “Ecology of freshwater phytoplankton”, …, Chapters 1 & 3. Scheffer, M. (1989), Alternative stable states in eutrophic, shallow freshwater systems: a minimal model, Hydrobiological Bull. 23:73-83. ----------------- (1990), Multiplicity of stable states in freshwater systems, Hydrobiologia 200/201:475-86. ----------------- (1997), On the implications of predator avoidance, Aquatic Ecology 31:99-107. Scheffer, M. et al. (1994), Vegetated areas with clear water in turbid, shallow lakes, Aquatic Botany 49:193-6. Scheffer, M. et al. (1997), Seasonal dynamics of Daphnia and algae explained as a periodically forced predatorprey system, Oikos 80:519-32. Scheffer, M. & DeBoer, R.J. (1995), Implications of spatial heterogeneity for the paradox of enrichment, Ecology 76:2270-7. Schindler, J.E. (1971), Food quality and zooplankton nutrition, J.Anim.Ecol. 40:589-95. Shapiro, J. (1990), Biomanipulation: the next phase-making it stable, Hydrobiologia 200/201:13-27. Spence, D.H.N. (1982), The zonation of plants in freshwater lakes, Adv.Ecol.Res. 12:37-125. Timms, R.M. & Moss, B. (1984), Prevention of growth of potentially dense phytoplankton populations by zooplankton grazing, in the presence of zooplanktivorous fish, in a shallow wetland ecosystem, Limnol.Oceanogr. 29(3):472-86. Tollrian, R. (1995), Predator-induced morphological defenses: Costs, life-history shifts, and maternal effects in Daphnia pulex, Ecology 76:1691-1705. Wetzel, R.G. (1995), Death, detritus, and energy flow in aquatic ecosystems, Freshwat.Biol. 33:83-9. Whiteside, M.C.; Williams, J.B. & White, C.P. (1978), Seasonal abundance and pattern of chydorid Cladocera in mud and vegetative habitats, Ecology 59(6):1177-88. Wium-Andersen et al. (1982), Allelopathic effects on phytoplankton by substances isolated from aquatic macrophytes (Charales), Oikos 39:187-90.

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CHAPTER 1

General introduction

CHAPTER 1

1.1.- Introduction. Human life is directly related to freshwater availability. The distribution of human populations is closely related to the distribution of a freshwater supply, in the form of lakes, rivers or underground sources of water. Water is very scarce. Only about 0.014 % of the blue planet’s water is fresh and easily available (i.e. excluding ice and ground waters), furthermore, only a small part of this amount is potable. More than half of this water is in lakes, mostly in large deep lakes. However, in many areas of the world, both in the temperate zone and in tropical areas (Howard-Williams & Lenton 1975), shallow waters are an important source of water for human consumption too. Interest in shallow lakes also rests on their economic importance as fisheries or for their amenity values. However, their rich diversity and role as reservoirs for flora and fauna in the face of human development are perhaps the main reasons why we should endeavour to better understand and protect these ecosystems.

The proportionally large area of sediment-water interface and, often, the growth of aquatic plants, make shallow lakes a particularly complex kind of lake (Moss et al. 1997; see Table 1.1). Surfaces of sediments or plants, with their great opportunities for interactions, are also where life can display its maximal diversity (Fryer 1968). Equally, aquatic plants can be thought to be the backbone of a suite of inter-linked mechanisms stabilising the state of shallow lakes (see Figure 1.1).

It seems a good start to define the object of study; what is meant by ‘shallow lake’?. Indeed it is conventional wisdom we cannot talk about anything fruitfully if we first do not agree on the meaning of our words (Plato-Socrates, IV century B.C.). Shallow lakes can be characterised by three main features.

First, they are not permanently thermally stratified during summer. Heat from the sun is mostly absorbed by the top one or two metre layers of water in temperate lowland lakes (this depth will vary with latitude and incident radiation). Cold water is denser and will, therefore, tend to sink in relation to warmer water. In shallow lakes the entire water column is frequently mixed, even during the summer. Because of the complete mixing, internal cycling at the sediment-water interface is more efficient than in deep lakes, at least during the growing season. Both these factors, mixing and efficient cycling, determine that littoral areas of deep

2

GENERAL INTRODUCTION

lakes and shallow lakes as a whole (effectively a whole-lake ‘littoral area’) are ‘virtual hothouses’ (Wesenberg-Lund 1912, in Lindeman 1942), absorbing more radiant energy per unit volume than more pelagial environments. Last, but not least, the bottom of shallow waters has a considerable coverage of aquatic plants (see Moss 1990, 1995). The extent of this cover will vary a lot with trophy, depth profile (e.g. Duarte, Kalff & Peters 1986; Gasith & Hoyer 1997), sediment type, average water temperature and length of the growing season (for the classic review of these factors see Spence 1982). Then, by ‘shallow’ we mean the group of temperate lakes with a mean depth of around 3 metres or less.

Figure 1.1. Plants are the centre of a suite of stabilizing mechanisms of the state of shallow temperate lake ecosystems. Plus and minus symbols on the connecting arrows represent the impact of an increase in the source factor (i.e. start of the arrow) on the variable in question (end of arrow). Carp and bream may cause inorganic turbidity through sediment resuspension due to their bottom-feeding habit (e.g. Meijer et al. 1990). From Stephen (1997).

In general, aquatic plants will have an important structuring role (Jeppesen, Sondergaard & Christofferson 1997). In shallow lakes there are many processes simultaneously at work, mediated or directly driven by plants (see Figure 1.1). Many of these appear to stabilise the plant-dominated state, maintaining clear water and an adequate light climate for the plants. A good classification of these multiple mechanisms (Table 1.1) could be as follows (modified from Gasith & Hoyer 1997):

3

CHAPTER 1

1) Limnological effects: changes in physico-chemistry of soil and water. In Table 1.1: mechanisms 1, 2, 5, 6, 7, 9 and 10. 2) Production and processing of organic matter and nutrient cycling. Table 1.1: 6 and 8. 3) Effects on biotic interactions and community structure related to plants providing a structured habitat for links in the food web. Table 1.1: 3 and 4. Table 1.1.- List of possible stabilizing-buffering mechanisms of plant-dominated lakes.

FEEDBACK STABILIZING MECHANISMS IN ‘PLANT-DOMINATED’ SHALLOW LAKES 1) Nutrient uptake from the plant parts in the water. A percentage volume infested (P.V.I.) of 40 % has been put forward as a minimum for macrophytes being able to clarify the whole lake hypothetically isolated from any other contributory factor (Howard-Williams 1981; Canfield et al. 1984; Schriver et al. 1995). 2) Higher denitrification rates of microbial biota in plant beds (see, for example, Karjalainen et al. 1998). 3) Lower fish zooplanktivory in the ‘refugium’ afforded against visual predators (e.g. Timms & Moss 1984). Zooplankton migrate in and out of lily beds in response to fish predation (e.g. Lauridsen & Buenk 1996). Similar patterns are observed among planktivorous fish in response to piscivory (Persson & Eklov 1995). It is not known how vertical migration can influence predation in relation to the refuge effect afforded by different plant densities. 4) Plant-associated microcrustacean communities can have an impact on both phytoplankton (filterfeeders such as Sida crystallina and Simocephalus, but also facultative microfiltrators such as Eurycercus; Figure 1.4), and impacted phytoplankton or periphyton (the ‘scraper community’). They are apparently ‘immune’ to fish predation (e.g. Irvine, Balls & Moss 1990; Beklioglu & Moss 1995; 1996). 5) Faster sinking rates of phytoplankton in the quieter waters of plant beds (non-motile algae species). 6) Periphyton growth on plant surfaces can sequester considerable amounts of nutrients otherwise available to phytoplankton (Wetzel 1964; Hansson 1992). However, abundant periphyton growth has been related to macrophyte decline and switches to an algae-dominated state (Phillips et al. 1978; Howard-Williams 1981, Bronmark & Weisner 1992, but see Balls et al. 1989). 7) Less resuspension of sediment, particularly if flocculent and in large lakes. Plants stabilize it (see, for example, Barko & James 1991). 8) Nutrient cycling: upon senescence, plants break up into phosphorus-rich organic particles which, via bacterial decomposition and chemical leaching, release phosphorus (Landers 1982). This phosphorus may be taken up in part by detritivores (e.g. Eurycercus) but mostly goes to sediment. 9) ‘Allelopathy’: plant species possibly secrete compounds that inhibit the growth of phytoplankton (Wium-Andersen et al. 1982; Forsberg et al. 1990; Gross 1999) 10) Shading: plant beds create an unfavourable light climate to phytoplankton growth (Frodge et al. 1990).

Under certain circumstances, overcoming enough of these mechanisms will lead to a lake becoming dominated by planktonic algal growth. Interest in how phytoplankton crops are maintained in lakes is a very applied issue with strong economic and even political implications (see Lehman 1986; Peters 1986; Shapiro 1990). For example, water treatment for human consumption is more expensive in a eutrophicated lake because of the need to remove large algal crops. Blue-green algae, which can sometimes develop as nutrient concentrations increase, are sometimes toxic for cattle. Amenity values may be lost as water transparency

4

GENERAL INTRODUCTION

decreases and aquatic plants die back or do not grow in the unfavourable light climate of algae-dominated lakes. Initially, plant loss during eutrophication was thought to be a direct consequence of nutrient enrichment and shading by large algal biomass (ref.). Evidence is growing that rejects the idea that eutrophication in shallow lakes is a linear process solely controlled by nutrients. In shallow lake ecosystems at least, there seem to be two different possible states (Holling 1973; May 1977) (see Figure 1.2), particularly with respect to aquatic plant coverage and water transparency (Balls et al. 1989; Scheffer 1989; Moss 1990).

In shallow lakes one of these alternative states is the ‘plant-dominated’ state, stabilized by multiple mechanisms, both biological and physico-chemical (see Table 1.1). The alternative, and often undesired state (see Moss 1986) is dominated by algal growth. Both states can be found over a wide range of phosphorus concentrations (Figure 1.2). Total phosphorus concentration (micrograms per litre) 25

50

100

1000

Alternative states of plant or plankton dominance

Clear water dominance by plants

Clear water, dominance by taller plants, stabilized by buffers

Clear water with sparser plants

PLANT DOMINANCE

REVERSE SWITCHES (BIOMANIPULATION)

FORWARD SWITCHES

Turbid water, dominance by phytoplankton algae stabilized by buffers

PHYTOPLANKTON DOMINANCE

Possibly unique phytoplankton dominance (highest conc.)

Increasing stability of phytoplankton dominance Increasing stability of plant dominance

Figure 1.2. Diagram representing the two stable states in shallow lakes, which are possible across a very wide range of total phosphorus concentrations. A ‘switch’ towards either state may occur if the mechanisms apparently stabilizing each state are overcome. See text for details. From Moss et al. 1996.

When the stabilizing mechanisms are finally overcome (often due to human influences), the system ‘snaps’ and the algal community takes over, leading to a ‘phytoplankton-dominated’

5

CHAPTER 1

state, characterized by high algal biomass and low transparency. This can happen through excessive plant management, damage to the plants by boat propellers or pesticides leaching into the water. Often the exact mechanism/s causing the switch are unknown. However, they all have in common the removal or death of large amounts of aquatic plant growth. Rarely, ‘catastrophic’ climatic events may be determinant, such as a freak hot summer reducing greatly water levels, or an extremely cold winter freezing the lake down to the sediment (see, for example, Blindow, Hargeby & Andersson 1997). In both cases the resting parts of plants cannot sprout back next season. Light is often the limiting factor to aquatic plant growth, hence the correlations between plant coverage and lake depth (Chambers & Kalff 1985; Canfield et al. 1985). In very turbid water, vegetation cannot develop at the start of the growing season (i.e. April to October). Conversely, once plant growth is established, high water transparency is maintained and buffered from change.

Perhaps the best evidence for two possible states comes from the observation of the coexistence of the two states in the same body of water. In the English Norfolk Broads, Hoveton Great Broad and Hudsons Bay (Timms & Moss 1984) are two connected lakes with areas of similar depth and that receive comparable nutrient loads. Fish can freely move between these two basins, but while in Hudsons Bay there was lily growth and extremely clear water one summer (less than 5 µg l-1 chlorophyll-a), Hoveton Great Broad was very turbid. In this case it was the grazing of zooplankton, Daphnia and perhaps other, plantassociated, grazers, that kept phytoplankton densities low in Hudsons Bay. It was postulated this occurred because they were predated less in the lily beds, which somehow interfered with fish predation.

When a lake switches to an algae-dominated system, restoration may be possible by reestablishing the buffers of the plant-dominated state (see Figure 1.2). In lakes with sufficient plant coverage and density, for instance, the plant beds are thought to be used as refugia for herbivorous zooplankton against fish predation (Timms & Moss 1984). It has been suggested that these refugia could be provided artificially (Shapiro 1990), though tests with netting, rope and twigs have had mixed success (Irvine, Moss & Stansfield 1990). On the other hand, restoration is facilitated if those mechanisms perpetuating the undesired state are removed first (Shapiro 1990). In addition, the desired switch may be easier to effect when nutrient

6

GENERAL INTRODUCTION

loads are reduced. Though this has not been experimentally tested yet, there is circumstantial evidence for this being true (e.g. Benndorf 1990). Biomanipulation: lake restoration and management using the structure of biological communities. The switch may be provoked artificially by manipulating the biological community of the lake (i.e. ‘biomanipulation’). When predation pressure on the herbivores is released by eliminating their predators, the increased grazing pressure on phytoplankton results in a marked increase in water transparency (Shapiro & Wright 1984; Shapiro 1990). The same effect can be accomplished in many ways (see Benndorf 1988), though it is usually by the mass removal of zooplanktivorous fish (Briand & McCauley 1978; Shapiro & Wright 1984; Carpenter et al. 1987; Post & McQueen 1987; Threlkeld 1988; Lampert 1988; McQueen et al. 1990; Lammens et al. 1990; Jeppesen et al. 1990; Kasprzak 1995). The ratio of piscivorous to planktivorous fish can be increased, as a way to maintain naturally low planktivore densities (e.g. Shapiro & Wright 1984). On the other hand, Edmondson and Abella (1988) suggested enhancing the reproductive capacity of herbivorous populations by providing their requirements in addition to removing their main mortality causes. SMALL-SHALLO W LAKES

LARGE-DEEP LAKES

Figure 1.3.- Representation of the differential response of different types of lake to biomanipulation. In the extreme, biomanipulated large deep lakes are said to be more stable, as are their biological communities, than shallower, and smaller lakes. The broken line expresses the manipulated state (from Gophen 1995).

Nutrient control and biomanipulation as restoration measures developed in parallel. Predation is an important force in the shaping of ecosystem structure (e.g. Hrbacek et al. 1961, Brooks & Dodson 1965, Carpenter et al. 1987). Biomanipulation, understood as the deliberate change in the lake fish communities, is a relatively old technique (e.g. Thereinen and Helm 1954). However, it has had a broad use only from the 1980s. For some workers, though, grazing was

7

CHAPTER 1

an additional factor explaining algal biomass in different lakes (e.g. Schindler 1978; Canfield et al. 1984). For other workers, by contrast, transparency was mainly controlled by the food web and the emphasis was on providing the best conditions for the success of the manipulation (e.g. Benndorf 1988, 1990; Edmondson & Abella 1988; McQueen 1990).

With internal loading (e.g. Jeppesen et al. 1991; Phillips et al. 1994; Stephen et al. 1997) or when nutrient control plans are in place, recovery may be speeded up by manipulation of the food web. However, a combination of these strategies is probably the most cost-effective approach (Benndorf 1988). Moreover, nutrient-load threshold limits, which are often specific to each lake, should not be exceeded if biomanipulation is to be successful (e.g. Benndorf 1990; DeBernardi & Giussani 1995). Knowledge of both the actors and plays enacted in, especially, shallow lakes is still very fragmentary. Trophic links and indirect effects are not uncommonly discovered during and after biomanipulation, as opposed to during the planning step (Meijer et al. 1994; Lammens et al. 1990 and references therein). There are gaps in our knowledge of many of the mechanisms buffering the plant-dominated state. Much is known about nitrogen transformations (e.g. Howard-Williams 1981), though little about the relative contribution of plant uptake when in competition with algae. Some plant species are thought to produce compounds that may inhibit algal growth, or allelopathic compounds (Gross 1999) but the effectiveness of allelopathy in situ (e.g. Forsberg et al. 1990) is not understood

On the other hand, the relative importance of nutrients and fish predation (i.e. ‘bottom-up’ and ‘top-down’ processes, respectively) explaining water transparency remains a fundamental question in Aquatic Ecology.

8

GENERAL INTRODUCTION

Figure 1.4.- From left to right, top to bottom: Sida crystallina, an ovigerous Simocephalus vetulus, an ephippial Simocephalus vetulus, Eurycercus lamellatus (ovigerous), an adult Daphnia hyalina and an ephippial Daphnia hyalina. The first three species are the larger-bodied plant-associated Cladocera.

Little attention has been paid to the contributory role that microcrustacean grazers other than Daphnia species, particularly plant-associated genera such as Sida and Simocephalus (see Figure 1.4), may have in biomanipulation success in eutrophic shallow lakes. There have been many studies investigating particular aspects of their biology. For example, spatial distribution (e.g. Whiteside 1974; Whiteside, Williams & White 1978; Campbell, Clark & Kosinski 1982; DiFonzo & Campbell 1988), feeding and other behaviour (e.g. Cannon 1921; Smirnov 1962, 1964; Shan 1968; Szlauer 1973; Fairchild 1981), habitat preference and general ecology (e.g. Fryer 1968; Quade 1969; Goulden 1971; Whiteside 1974; Paterson 1993), population dynamics of species (e.g. Sharma & Pant 1982; Fairchild 1983; Perrin 1989), effects of fish predation on their abundance (Fairchild 1982), or specific feeding rates (Downing & Peters 1980; Downing 1981; Pant & Sharma 1982), have been studied. Strikingly, and as far as I am aware, the issue of their trophic role has not been addressed. The scarcity in studies has been highlighted repeatedly (e.g. Irvine, Balls & Moss 1990; Moss 1990; Beklioglu & Moss 1995, 1996; Jeppesen et al. 1997; Stephen 1997; Stephen, Moss & Phillips 1998). Two factors explain the relative lack of quantitative work on this community. Methodologies have never been standardized in the same way as they have in pelagic research (Edmondson & Winberg 1971; Bottrell et al. 1976) and there are considerable difficulties of

9

CHAPTER 1

interpretation because of the patchy distributions of these species in plant beds (Irvine, Balls & Moss 1990). Results are consequently hardly comparable across studies, let alone lakes. Nevertheless, some very ingenious sampling methods have been devised, often for particular cases and purposes (e.g. Whiteside 1975, Campbell, Clark & Kosinski 1982). Refer to section 3.1 for a brief review of sampling methods.

This study aims to contribute to filling this gap by quantifying the grazing impact of the plantassociated microcrustacean community (Cladocera) of a plant-dominated shallow lake. A subsidiary aim is to look at the extent to which conventional models, i.e. nutrient loading and open water herbivory, explain transparency.

1.2.- Outline of research. In particular years and within periods of the growing season (i.e. April to October), low chlorophyll-a concentration and high water transparency cannot be accounted for by Daphnia grazing in the experimental lake, Little Mere, in Cheshire (UK) (see Chapter 2 for details). There are other processes in operation related to plant cover, either removing phytoplankton faster than it is produced (‘top-down’ alternative control, but not by Daphnia), or mechanisms depleting nutrient resources otherwise available to algal growth (‘bottom-up’ control mediated by plants, directly or indirectly).

The null working hypothesis is that ‘the plant-associated microcrustacea community (Cladocera) has NO significant effect on water clarity in the experimental lake’. To test this the grazing rate of the plant-associated Cladocera community has been estimated in order to compare it with that of more ‘open-water’ cladoceran species, particularly Daphnia spp. This rate is expressed in percentage of volume of lake water (litres) filtered per day by the populations of each species in given areas centred around the sites sampled (i.e. % day-1; see Map in section 4.2). In order to estimate grazing rates (% day-1), the following variables were estimated (see section 4.2.3 for details):

a) Densities of species, both in number of individuals per gram of plant (plant-associated) and per litre of lake water (‘open-water’).

10

GENERAL INTRODUCTION

b) Plant biomass across areas (grams plant m-2 lake bottom) c) Filtering rates (filter-feeders) or ingestion rates (periphyton scrapers) for each species (per individual: µl water day-1 or µg periphyton day-1). d) Plant cover and depth profile of the lake.

The relative role of nutrients and grazing in the control of algal biomass (particularly in summer months) remains an open question. A controlled mesocosm experiment running separately during two growing seasons was conducted in Little Mere (Cheshire), testing both nutrients levels and zooplanktivorous fish (hence grazer densities) and their effects on algal biomass and water transparency. 1.3.- Thesis layout. Chapter 2 to introduces the experimental lake, Little Mere. Chapter 3 describes the densities of the cladoceran species in Little Mere. It is divided into four: distribution of vegetation, zooplankton (i.e. ‘open-water’ species), periphyton scrapers in lily beds, and lily-associated filter-feeders. Patterns of abundance of the main zooplankton species are examined through seasonal size-structure of the populations of the main filter-feeders, Daphnia spp. and Simocephalus vetulus. Two simple egg-ratio models have been calculated for these two species.

In Chapter 4, field abundances of the main grazers (Chapter 3) are combined with clearance rate estimates, to estimate grazing rates. Clearance rates are from short-term feeding experiments made in '99 using radioactively-labelled foods.

Chapter 5 investigates the issue of whether nutrients of grazing controls the summer phytoplankton in Little Mere, using the results of two separate enclosure (mesocosm) experiments..

Chapter 6 brings together the information presented in the previous chapters to defend the thesis that plant-associated Cladocera are by no means negligible grazers in Little Mere, particularly during April to October.

11

CHAPTER 2 The experimental lake: Little Mere. A summary of its recent history.

CHAPTER 2

The experimental lake: Little Mere The experimental lake, Little Mere (53°20’N, 2°24’W; 50 metres above sea level; NGR SJ733823) is a small, shallow fertile lake situated in Cheshire, North West England (details in Table 2.1). It belongs to a group of over 100 lakes (area <1 ha to about 50 ha), the West Midland Meres (Figure 2.4). Most of them have originated from the deposition of glacial moraines in a low-lying area at the end of the last glaciation (circa 13,000 B.P.) (Sinker 1962). Little Mere, however, was formed by damming in the 18th century the outflow stream of another (natural) lake located just upstream (Figure 2.3; Stephen, Moss & Phillips 1998). Table 2.1. Main physical features of Little Mere. Surface area1 Catchment area1 Depth (max./mean)2 Retention time/ Flushing rate3 Approximate volume4 1 3

0.028 km2 (2.8 ha) 3.2 km2 2.8 m / 0.7 m 11-146 days 9-0.5 % day-1 21 x 103 m3

Carvalho (1994); 2Carvalho (1994) and own measurements Stephen (1997); 4Stephen (1997) and own measurements

Little Mere is immediately surrounded by a narrow fringe of mixed woodland and partly by private housing. The relatively large catchment area is occupied by a golf-course and agricultural land, as can be seen from an aerial view taken in 1992 (Figure 2.1).

Figure 2.1. Aerial photograph of Little Mere taken in 1992. Mere Mere can be seen to the right. A golfcourse is located on its west bank (further away in the photograph). A road separates the lake from pasture fields, on the other bank. Large nymphaeid beds can be seen in the lake, after a period when nutrient loads from a sewage treatment works located closer to the golf-course discharged large amounts of organic sewage directly into it (see also Figure 2.5).

14

RECENT HISTORY OF LITTLE MERE

The North West Midland Meres have been described as Britain’s naturally eutrophic lakes (Reynolds & Sinker 1976). Moreover, there is paleolimnological evidence for blue-green algal blooms having occurred thousands of years ago (McGowan et al. 1999). Deposits of apatite, a phosphorus-rich mineral, are present in the glacial drift (Reynolds 1979; but see Moss et al. 1997). Nitrogen-to-phosphorus ratios are low in many cases during most of the year (frequently less than 5, suggesting nitrogen rather than phosphorus-limitation; see Figure 2.2). Moss et al. (1994) found that in the West Midland Meres the relative importance of grazing versus nutrients in the maintenance of algal crops was a function of depth. In deeper meres (i.e. deeper than 3 metres) it was nitrogen which limited phytoplankton growth, while in the shallower meres inverse correlations between peak chlorophyll-a concentrations and cladoceran density (ind l-1), particularly of Daphnia species, were taken as indicative of topdown regulation of algae. Little Mere. TDIN:TP during the period 1990-1999

25

100

Rostherne Mere. TDIN:TP during the period 1990-1999

160

90

20

80

60

TDIN:TP

TDIN:TP

70

50 40

15

10

30 20

5

10 0 ene-90

ene-91

ene-92

ene-93

ene-94

ene-95

ene-96

ene-97

ene-98

ene-99

ene-00

0 ene-90

ene-91

ene-92

ene-93

ene-94

ene-95

ene-96

ene-97

ene-98

ene-99

ene-00

Figure 2.2. TDIN:TP ratios in Little Mere (left) and Rostherne Mere (right) in the period 1990 to 1999. Large seasonal oscillations are evident in Little Mere. In Rostherne Mere ratios are maintained below 5 during most of the year.

15

CHAPTER 2

Figure 2.3. Map of the group of linked lakes Little Mere, Mere Mere and Rostherne Mere. The three lakes have been monitored for the past ten years (i.e. since 1990), for basic limnological information (water chemistry, temperature, phytoplankton and zooplankton) every three weeks or less. The location of the sampling station for this monitoring in each lake is shown in the map. Two sewage treatment works were located on the west bank of Little Mere and on Rostherne brook, both operating until June 1991 (see text for details).

16

RECENT HISTORY OF LITTLE MERE

Figure 2.4- Little Mere, Mere Mere and Rostherne Mere are located in the northern part of a plain which includes well over 100 lakes of varying depth and size in the West Midlands (England). The estuary of the river Mersey and Liverpool city lie to the north of this map. Mean annual conductivities (µS cm-1), mean winter dissolved inorganic nitrogen concentrations (nitrate-N plus ammonium-N) (mg l-1) and mean annual total phosphorus concentrations (µg l-1) are shown in that order, below the names of a selection of meres. Asterisks indicate lakes known to be polluted by stock or sewage effluent. Shaded areas show relief above sea level. The map is orientated in a north to south direction. Scaled to 1:400,000. From Moss et al. (1997).

17

CHAPTER 2

Pollution history of Little Mere. Until June 1991 this lake was being used effectively as an oxidation pond for the sewage effluent of a treatment plant located on one of its banks (Carvalho 1994). There was an ample supply of phosphorus, particularly during the growing season (April to October). This plant had been operating since ca 1932, overloaded at three times its design capacity. Indeed, considerable ammonium and phosphate concentrations were measured at the outflow of the lake before diversion (Carvalho 1994). Sewage was finally diverted from this plant for treatment elsewhere in June 1991.

There is no information on the biology of Little Mere prior to 1990. The limnology of the group of three lakes has been followed since that year, providing an excellent opportunity to follow the differential response of a shallow lake (Little Mere), and a deep one (Rostherne Mere, Zmax= 28 m) to nutrient load reduction. In addition, Mere Mere, upstream, has never received effluent nutrient loads and diffuse inputs are probably unchanged from pre-diversion levels. Mere Mere, thus, can act for comparison with Little Mere (see Carvalho et al. 1995).

After diversion of the sewage effluent, Rostherne Mere, which received inputs from the Little Mere works through the Rostherne Brook inflow as well as from a smaller plant discharging directly into the brook (see Figure 2.3), has responded only weakly to the reduction in loads. Load reduction on the lake is manifest from measurements made at the inflow itself (Carvalho et al. 1995). The lack of response in the lake is surprising considering this input accounted for 71 % of the nutrient budget (Moss et al. 1997). Two possibilities are that either the nutrient budget is flawed, missing out important inputs or grossly underestimating those identified, or that there are mechanisms of internal release of the phosphorus accumulated in the sediment during the high load periods (Moss et al. 1997). In contrast, Little Mere responded almost immediately (see Figure 2.5), with a drastic reduction in measured total phosphorus and spring chlorophyll-a concentrations in the water. Reducing conditions in the pre-diversion period were reflected in extremely high concentrations of ammonia (i.e. 5-10 mg l-1). Since diversion, both ammonia and nitrate levels have been virtually undetectable, particularly during growing seasons (April to October). It is possible nitrogen becomes limiting to phytoplankton growth during summer months. What is not known is if limitation comes about through algal growth or if other processes such as, for example, plant uptake from the water, are important.

18

RECENT HISTORY OF LITTLE MERE

Conductivity and pH, on the other hand, have not experienced significative variations in relation to sewage diversion or in the nine years after diversion (see Figure 2.6). Water levels have been relatively stable, with minima generally in June-July-August, months when both inflow and outflow run dry. Despite the large potential for algal growth, phytoplankton density has remained low and measured chlorophyll-a concentrations have seldom exceeded about 30-50 µg l-1 (see Figure 2.5).

Mean Total Phosphorus and chlorophyll-a (growing seasons 1990-1999)

micrograms TP / litre

3500

100 90

Total Phosphorus Chlorophyll-a

3000

80 70

2500

60

2000

50

1500

40 30

1000

20

500

micrograms chl-a/litre

4000

10

0

0 1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

Figure 2.5. Diversion of sewage input from June 1991 had a clear effect on mean total phosphorus concentrations. Chlorophyll-a values are very variable among years and do not seem to follow changes in TP concentration. Plotted are growing season (i.e. April to October) means with one standard error at either side of the mean. Conductivity and pH in Little Mere during the period 1990-1999

10.5

700

pH conductivity

10

Conductivity (microS cm-1)

600

9.5

500

pH

9 8.5

400

8

300

7.5

200 7

100

6.5 dic-99

jul-99

feb-99

sep-98

abr-98

nov-97

may-97

nov-96

jul-96

abr-96

nov-95

jun-95

ene-95

jul-94

mar-94

nov-93

ago-93

may-93

feb-93

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jul-92

abr-92

ene-92

sep-91

jun-91

mar-91

nov-90

jul-90

abr-90

ene-90

6

0

Figure 2.6. Conductivity (µS cm-1) and pH in Little Mere over the period 1990-1999. The pH has remained quite stable, in a range of values between 7 to 8 in most dates, with occasional peaks relating to a larger photosynthetic activity during periods of high algal biomass. Conductivity was generally low, with values generally around 400 µS cm-1.

19

CHAPTER 2

Changes in the biota over the past ten years in Little Mere. Prior to diversion, Little Mere preserved a surprisingly large cover of lily growth, even with total phosphorus (TP) concentrations in the order of mg l-1 (see Figure 2.5). Plant coverage, particularly nymphaeids, expanded considerably, judging from an aerial photograph taken in 1992 (see Figure 2.1), soon after diversion, to the circa 40 % of total surface found in 1996 (Beklioglu & Moss 1996) and at present (Table 2.2 & section 4.2.2). Submerged vegetation covered around 30 to 70 % of the lake bottom in the first period after diversion (1991-1993), mainly the fine-leaved Potamogeton berchtoldii Fieber, with some scattered patches of Elodea canadensis Michaux (Beklioglu & Moss 1996). Plant dominance seems to have switched annually between the former species and Callitriche hermaphroditica L. (Stephen, Moss & Phillips 1998). Submerged plant coverage has increased to near 100 % in the past three years. Lately (i.e. 1998 and 1999), Ceratophyllum demersum L. has been clearly more abundant than in previous years, in combination with dense and heterogeneously distributed patches of C. hermaphroditica (see Table 2.2 and Appendix-D). Table 2.2. Submerged aquatic flora in Little Mere in the period 1998-99. Percentage cover and species composition of the submerged aquatic flora are shown for both years, separately for the periods between the beginning and middle, and middle and end of the growing season (April to October). See text for details. Maximum macrophyte cover Maximum lily cover Aquatic flora 1998 composition* (submerged only) 1999

c98 % c40 % Beginning-Middle of growing season C; P; E; Cal; Lt Middle-End of growing season C+; Cal+; Lt; E; P Beginning-Middle of growing season C+; Cal+; E; Lt; P Middle-End of growing season C+; Cal; Lt; E; P + *Key for abbreviations: C = sparse Ceratophyllum demersum; C abundant; P = sparse Potamogeton berchtoldii; Cal = sparse Callitriche hermaphroditica; Lt = sparse Lemna trisulca; E = sparse Elodea canadensis. Sparse/abundant refer to coverage, not percentage-volume-infested (PVI) or biomass.

The zooplankton community has changed considerably from year to year. Before diversion (i.e. 1990), the oxygen-depleted conditions produced by decomposition of the poorly treated sewage precluded many fish from living in the lake (Carvalho 1994). The virtual absence of predation pressure allowed large populations of the large-bodied, and otherwise vulnerable, Daphnia magna Strauss to develop. This species can live under very low O2 concentrations thanks to the production of haemoglobin, a very efficient carrier of oxygen (Carvalho 1984). Extremely low chlorophyll-a concentrations were measured during summer 1990 (i.e. less than 5 µg l-1 chlorophyll-a; see Figure 2.7) despite the huge potential for algal growth reflected in nutrient concentrations, suggesting intense herbivory by this large Daphnia

20

RECENT HISTORY OF LITTLE MERE

species (Carvalho 1994). In the two summers immediately after diversion (i.e. 1991 and 1992), the peak size of populations of Daphnia magna became smaller as the lake reoxygenated and fish reinvaded from Mere Mere upstream, intensifying predation on their populations (Carvalho 1994). Average summer chlorophyll-a concentrations increased slightly as the Daphnia magna populations were replaced by the smaller and less efficient Daphnia species (mainly D. hyalina L.). Water clarity was generally maintained.

Despite the re-entrance of fish, predominantly of perch (Perca fluviatilis L.), following diversion (Beklioglu & Moss 1996), significant, though decreased, populations of D.magna were maintained for some time, perhaps because of the presence of sufficient plant bed cover. These plant beds, it is suggested, acted as ‘refuge’ for the zooplankton against fish predation. Indeed, as Daphnia magna populations were replaced by the smaller Daphnia species, significantly higher numbers were found in lily beds than in more open waters. In contrast, submerged plants, especially the fine-leaved P.berchtoldii, did not seem to constitute an effective refuge (Beklioglu & Moss 1996; Moss, Kornijow & Measey 1998). The role of aquatic plants as stabilizers of the clear-water state in Little Mere has been repeatedly emphasized (e.g. Carvalho 1994; Moss et al. 1994; Beklioglu & Moss 1996; Stephen et al. 1998; Moss et al. 1998). The exact mechanisms mediated by plants (or caused by the plants themselves) that are relevant in Little Mere, though, are not known.

Summer peak Daphnia density against chlorophyll-a

Total Daphnia/litre

450 400 350

total Daphnia/litre chlorophyll-a

25 20

300 15

250 200

10

150 100

5

micrograms chl-a/litre

30

500

50 0

0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Figure 2.7. Spring-early summer maxima (columns) of Daphnia spp (ind l -1) plotted against the cooccurring value of chlorophyll-a (µg l-1; line). Top-down effects are generally evident prior to 1996. See text for details.

21

CHAPTER 2

In general, top-down control by Daphnia seems to have been the most powerful influence on phytoplankton (Stephen, Moss & Phillips 1998). When few Daphnia were present, chlorophyll-a values were considerably higher, for example during 1995 (see Figure 2.7). In 1996, on the other hand, low and unexplained values of chlorophyll-a cannot be accounted for by Daphnia grazing. Although, the ecosystem seems to have stabilized in a clear water plant-dominated state, there is considerable variation in chlorophyll-a among years (see Figure 2.5). Likewise, there is large variability in the seasonal behaviour of Daphnia spp populations from year to year. For example, in 1996 there was no Daphnia summer peak at all, and in early summer 1995 and, especially, during the summer of 1999, the open-water grazer community was dominated by small cladocerans such as Ceriodaphnia spp and Bosmina longirostris. The size of peaks, and perhaps the extent of top-down control by Daphnia, varies considerably among years too. The remarkable resilience of the lake even in the absence of grazers such as Daphnia spp. suggests other buffering mechanisms of the plant-dominated system are in operation. The expansion of rooted macrophyte stands has been related to this resilience (Beklioglu & Moss 1996). What is not known is what are the relative roles of direct mechanisms mediated by plants, for example the nutrient uptake by plants in competition with planktonic algae, and indirect ones, for instance the grazing of phytoplankton by the plant-associated cladoceran genera (Sida and Simocephalus; see Figure 1.4). Plant-associated genera (Eurycercus lamellatus O.F.Muller, Sida crystallina O.F.Muller, Simocephalus spp O.F.Muller/Koch) were first detected in 1993, two years after diversion (Carvalho 1994), though sampling had been strictly planktonic (i.e. only in ‘open waters’; Figure 2.3). In 1993 these species constituted 21 % of total zooplankton numbers, while in 1994 none were recorded and in 1995 only 2 % were of plant-associated species (Stephen 1997). Only open waters were routinely sampled, and it is possible low densities of these genera were simply due to the lack of suitable habitat around the sampling station (see, for example, Table 2.3). It seems, however, expansion particularly of lily bed coverage, but also submerged vegetation (reported in Beklioglu & Moss 1995, 1996; see Table 2.2) has been accompanied by a growing role for these plant-associated grazers.

22

RECENT HISTORY OF LITTLE MERE Table 2.3. Diversity of cladoceran species found in Little Mere in the period April 1998-April 2000. Frequency (number of sampling occasions when detected of a total of 36) and relative abundance of each species during the sampling period are also indicated. Species/genera

Acroperus harpae Alona spp Alonella spp Bosmina longirostris Ceriodaphnia spp Chydorus spp Daphnia cucullata Daphnia hyalina-longispina Daphnia pulex Daphnia magna Diaphanosoma brachyurum Eurycercus lamellatus Graptoleberis testudinaria Peracantha truncata Pleuroxus denticulatus Polyphemus pediculus Scapholeberis mucronata Sida crystallina Simocephalus vetulus Simocephalus spp

Frequency (number of sampling occasions when detected; total: 30) Very scarce to scarce (16) Scarce (29) Very scarce (29) Very abundant (22) Very abundant (25) Very abundant (29) Very scarce Very abundant (28) Very scarce Very scarce (occasional) Very scarce (occasional) Scarce to medium (15) Scarce to medium (28) (occasionally abundant) Abundant (27) Very scarce (occasional) Abundant (25) Abundant (27) Abundant (20) Abundant (27) Very scarce (occasional)

Relative abundance in Little Mere (1998-2000) 0-209 ind g-1 DW lily 0-450 ind g-1 DW lily Occasional 0-1264 ind l-1 0-1358 ind l-1 0-418 ind g-1 DW lily Occasional 0-406 ind l-1 Occasional Occasional Occasional 0-76 ind g-1 DW submerged plant 0-551 ind g-1 DW lily 0-380 ind g-1 DW lily Occasional 0-2700 ind g-1 DW lily 0-270 ind g-1 DW lily 0-1731 ind g-1 DW lily 0-386 ind g-1 DW lily Occasional

Thus, in particular years and within periods of the growing season (i.e. April to October), low chlorophyll-a concentration and high water transparency cannot be accounted for by Daphnia grazing in Little Mere (see Figure 2.7). There are other processes in operation related to plant cover, either removing phytoplankton faster than it is produced (‘top-down’ control, but not by Daphnia), or mechanisms depleting nutrient resources otherwise available to algal growth (‘bottom-up’ control mediated by plants, directly or indirectly).

23

CHAPTER 3 Population and community dynamics of the plantassociated Cladocera in Little Mere over two years.

CHAPTER 3

3.1. Introduction. Intensive and detailed studies of the plant-associated cladoceran community are few. Sampling techniques introduce a large amount of error when sampling from plants. The same sampling techniques have not been widely used very often, making comparability a problem. Thus, few patterns have emerged from sampling studies on this community (Scheffer 1998).

High levels of spatial heterogeneity, particularly for periphyton scrapers, both in numbers and species composition, are expected across short distances because the animals very often live in close association with the plant surfaces (Fryer 1968; Quade 1969; Whiteside 1970; Fairchild 1981). Because of large variability, a higher sampling effort is needed to detect effects over ‘noise’ (Downing 1986). One solution may be to use surrogates (for example, macrophyte species composition and/or biomass; Downing 1986) that are ‘well correlated’ with population density (i.e. r>0.7; Cassie, in Edmondson & Winberg (eds.) 1971). A mathematical relationship was found by regression analysis, allowing subsequent use of the ‘easier-to-sample variable’ as a predictor of animal density, instead of directly sampling for the animals (Downing & Cyr 1985; Downing 1986).

±10 cm.

±100 cm.

±1 cm.

± 15 cm.

Figure 3.1. ‘Pattern’ sampler design. A number of inverted funnels facing down towards the lake bottom are held in a frame, as shown. Each funnel leads to a small bottle containing all animals caught while migrating upwards. This sampling method is only useful for relatively homogeneous substrates such as Chara plant beds and mud habitats. In the photo, this sampler is used to assess the small-scale distribution of chydorid populations in the mud habitat of Lake Naardermeer (The Netherlands).

In the search for better sampling methods two basic approaches have been followed. An example of the first, a non-interference method, is given by Whiteside (1974). He used square funnels fitted head downwards in a frame that can be positioned over the plant bed. As the animals migrate upwards at night, they are collected in the funnel which leads to a plastic collecting bottle (see Fig. 3.1). In this way, detailed information can be obtained on both

26

PLANT-ASSOCIATED CLADOCERA DYNAMICS

density and close spatial distribution (“pattern”, in Whiteside’s terms; see Whiteside & Williams 1975; Whiteside, Williams & White 1978). However, Paterson (1993) showed in his study of the littoral microcrustacea of a small oligotrophic lake, that estimates of cladoceran and cyclopoid density obtained using funnel traps are only about a tenth of estimates from traditional cores. Moreover, he could not find evidence of vertical migration, although he suggested this may be a lake-specific effect of low edible-phytoplankton concentrations and relatively high night-oxygen concentrations in the plant beds. This method has not been widely used and is inferior to the second approach.

The second approach to sampling these populations involves taking a representative “section” of the plant-animal system, with a “jaw” or box sampler, generally around the stem. Many types of these samplers have been used (see review by Downing & Cyr 1985) varying in the way the plant is collected (e.g. mesh bags, Vuille 1991).

These variations in method across researchers add to those difficulties due to the nature of the samples themselves. Samples are of the biota associated with a living plant surface of diverse characteristics (e.g. age, amount of periphyton, quality of this periphytic community and plant species). The complexity of this system means that the size of the sample is more of an issue than in samples taken in more “open” waters (i.e. zooplankton samples). In addition, the scales of change in population density may vary with the animal species in question, depending on their habitat preference and, linked with this, their feeding mode (Fryer 1968). For example, Sida crystallina, a filter-feeder, will probably have a larger spatial scale of change (a ‘coarser grain’; see, for example, Fairchild 1981) than Graptoleberis testudinaria (e.g. Quade 1969), a periphyton scraper, which lives in a finer-grain habitat. The latter species may be more dependent on leaf quality (i.e. species composition of the periphytic community, proximity to substrate, light climate, presence and abundance of macroinvertebrates, etc.), which will most likely change in the space of centimetres, rather than metres.

Spatio-temporal scales of change have often been totally ignored in modelling. For example, Edmondson’s (1960) original egg-ratio model assumes populations are distributed uniformly across space. However, Elster & Schwoerbel (1970) have found differences of more than a factor of 100 in population numbers when sampling in different stations in the same lake. Furthermore, animals may migrate horizontally in a diel cycle in response to predators or “shore-avoidance”, Gliwicz & Rykowska 1992; Davies 1985; Lauridsen & Buenk 1996).

27

CHAPTER 3

Fairchild (1981) found 30 metres to be the limit of representativeness of point samples of population density of Sida crystallina. He also found juveniles and adult individuals to exhibit different swimming behaviours. Vertical migration may also be important. Densities obtained using common destructive methods tend to refer to either only one part of the plant, generally close to the water surface, or to the whole plant block, ignoring potential vertical differences in density (e.g. Downing & Cyr 1985; Downing 1986). Densities may even change with different plant parts (DiFonzo and Campbell 1988). With respect to sampling frequency and intensity, a compromise must be reached between what is practical within the time and resources available (Downing & Cyr 1985; Peters & Downing 1984), and what level of accuracy is satisfactory given the purpose of the investigation. A large literature was generated during the 1970s and early 1980s communicating advances and criticisms on zooplankton population modelling, based on the egg-ratio method (Edmondson 1960, 1968, 1974). The repeated census of a population yields counts that can then be related to population growth using this method. The egg-ratio is the number of eggs born on average by females in the population. Because cladoceran species are usually parthenogenetic, no correction is needed to account for unequal sex ratios in the population; it is effectively composed of females, at least during episodes of population growth. Some of the egg-ratio equations have helped to direct sampling programmes, so important properties of populations were taken into account depending on the aim of each study. Seitz (1979) assessed the influence of the variances of birth and death estimates on the accuracy of different models by comparing the correlation coefficients between “true” values, and the calculated values given by them. He found Paloheimo’s model to be valid if size-selective predation was not to be expected (Paloheimo 1974). Gabriel et al. (1987) showed the sampling interval can strongly influence reliability of estimates, by comparing different formulae in computer simulations of growth. Indeed, faster growing populations reach stable age distribution faster and, therefore, a more frequent sampling will be required. Keen & Nasaar (1981) suggest frequency of sampling should be chosen such that 95 % confidence intervals, calculated for birth rate, overlap for successive collections. The general assumption of all egg-ratio equations is that rates are constant (i.e. the population is growing

28

PLANT-ASSOCIATED CLADOCERA DYNAMICS

exponentially). If births and deaths were “time-dependent” and could not be well approximated by an intensive sampling effort (Lynch 1982), or when not all members of a population are expected to experience similar temperatures and, thus, have different developmental periods (Hall 1964; Prepas & Rigler 1978), then estimates of growth rate, birth and death, cannot be reliably provided by egg-ratio methods. “Substrate-associated” cladocerans such as chydorids, and some other species of the order such as Simocephalus spp or Sida crystallina, may have growth rates dependent on very variable habitat conditions, such as periphyton abundance and composition, or availability of plant substrate. In such cases doubt remains whether egg-ratio methods are applicable. In this chapter, the distribution and seasonality of cladoceran species over a two-year period in Little Mere are closely examined. Size-structure changes and a population dynamics model using the egg-ratio method are then examined for the main filter-feeders in the lake. Distribution patterns of species, emerging from the analysis of data on densities across the lake, are complemented with an analysis of the small-scale variability of these densities. Results are discussed in relation to both potential regulatory forces of distribution patterns (e.g. fish predation, food availability), and ‘habitat preference’ of different cladoceran species.

The main aims for this chapter are 1) To describe sampling techniques used, particularly for plant-associated Cladocera.

2) To present population sizes of main grazers in Little Mere so that, in conjunction with measures of ‘feeding intensity’ (see Chapter 4), grazing rates can be estimated for both the ‘open water Cladocera community’ and the plant-associated. The ultimate aim is to compare the trophic role of Daphnia and other, plant-associated species, in the maintenance of clear water.

3) To show spatio-temporal differences in population densities and dynamics of cladoceran species in Little Mere and discuss reasons for these.

29

CHAPTER 3

3.2. Materials and Methods. 3.2.1. Sampling programme description. Lily beds and more open waters (i.e. with only submerged vegetation growth) were sampled with different techniques (section 3.2.1). Samples were taken of floating lily leaves and of petioles (5 replicates) at three sites in the lake (for Map see Fig. 3.2). No samples were taken of the submerged leaves of yellow lilies (Nuphar lutea). This was forced by time constraints. Uncertainty remains whether this habitat holds important reservoirs of grazers. Nevertheless, samples were taken of petioles reaching close to the bottom (see section 3.2.4 for details). Both leaves and petioles and the water around them were collected using purpose-designed samplers (section 3.2.3).

4 3

2 1

5 Figure 3.2. Map of Little Mere with the outline of lily bed growth, generally growing close to the shoreline and sampling sites location. Sites 1, 2 and 5 are located within lily beds while sites 3 and 4 are over submerged plant beds (relatively ‘open waters’).

At the other two sites, with only submerged vegetation (generally beds of Potamogeton berchtoldii, Ceratophyllum demersum mixed with Elodea canadensis, or rooted and halffloating Callitriche hermaphroditica), samples were taken at surface level and at arm’s length depth (i.e. about 30 cm deep).

Generally two 10-l depth-integrated samples of the water in the plant beds were also collected at the five sampling sites. For open water grazers (i.e. mainly Daphnia spp, Ceriodaphnia spp and Bosmina longirostris) units were calculated by extrapolating the number of litres sampled to the volume of water in a hypothetical column of base 1 m2 and height the depth around the sampling site (see depth map in Figure 4.14).

30

PLANT-ASSOCIATED CLADOCERA DYNAMICS

Densities are first expressed per unit of plant biomass sampled (i.e. number gram-1 of leaf, petiole or submerged plant part). Extrapolation for plant-associated grazers required independent estimates of areal plant biomass across sites. Details of plant surveys conducted to estimate these are given in Chapter 4. Little Mere was intensively sampled from April 1998 to April 2000. However, there is information on mainly open-water cladocerans sampled at a central location in the lake for a longer period (from 1990 onwards; see Figure 2.3) as part of an ongoing monitoring programme of the system of three lakes (see Chapter 2). During the intensive programme, samples were taken generally every week during the growing season (April to October) in both years, and generally every two to three weeks the rest of each year. In addition to animal samples, a 1-litre sample of unfiltered water and another of water filtered through a 25 µm nylon mesh were taken at each site, in order to estimate chlorophylla concentrations and suspended solid parameters, all related to the food concentrations in the water experienced by the biota. Cladocerans can probably only ingest particles smaller than 100 µm, depending on shape, and more likely smaller than 25 µm (Burns 1968b). The fraction sampled represents most of the “more edible” algae available to these grazers. Generally temperature was also measured at each site at the surface and half a metre down. Only one reading for the whole lake was taken outside the growing season (i.e. November to March). Developmental times vary with temperature, influencing birth rates as inputs of eggratio models (Bottrell 1974, Paloheimo 1974). Temperature is also thought to influence filtering rates (e.g. Mourelatos & Lacroix 1990).

Sampling was stratified (sensu Cassie, in Edmondson 1971, e.g. Lampert & Taylor 1984). From the outset a decision was taken to sample both lily beds and submerged plant beds. Further stratification of sampling involved selecting the lily beds to sample on the basis of ‘size’ and location in the lake. One lily bed sampled grows in very shallow water (average depth 0.5 m) dominated by yellow lily (Nuphar lutea). A second, deeper, bed sampled (average depth approximately 0.7 m) is a monospecific white-lily stand (Nymphaea alba), and the third bed selected contains a mixture of both lily species. See Table 3.1 for details.

31

CHAPTER 3

Table 3.1. Size (m2) and relative proportions (%) of different lily-bed types in Little Mere, Cheshire (survey July ’98). Each type of bed is represented by one sampling site, as shown.

Surface area (m2) % of total lily bed area % of Little Mere Sampling site code number

Yellow water lily (Nuphar lutea) 4,600 42 16.8 2

Lily bed type White water lily Mixed yellow & (Nymphaea alba) white water lilies 1,100 5,200 10 48 4 19.2 1 5

TOTAL 10,900 100 40

The number of sampling sites was limited to five in order to intensify replication per site. It was felt a larger number of sites would not add significantly to the coverage, while it was paramount to monitor variation due to sampling methodology and the biology of species as much as possible within time constraints. The number of replicates taken was set by time constraints. Indeed, sampling more replicates would perhaps include differences related to different timing of sampling within each sampling occasion. Sampling generally began at around midday and was completed in about 3-4 hours during the growing seasons.

3.2.2. Sample processing and information recorded. Samples from plants (i.e. leaves, petioles and parts of submerged vegetation of various species) were washed of any attached animals into a bucket by means of a strong jet of water from a bottle, using a hand to help detach tightly associated animals. Animals retained by meshes were carefully washed into the bucket, too. The plant part was then bagged for further processing in the laboratory. The water and animals in the bucket were strained through a fine mesh (Nytex 50 µm) to remove most of the water, and the animals were washed into a sample bottle. These samples were briefly narcotized with chloroform (2 minutes) and preserved with absolute ethanol (in at least 70 ethanol : 30 sample proportions). Each sample bottle was kept in the bag with its corresponding plant part.

Once in the laboratory, plant parts were placed on labelled pieces of aluminium foil and left to dry in a dessicator oven (T=60°C, 24 hours). A series of variables was recorded for each plant part, as described in Table 3.2.

32

PLANT-ASSOCIATED CLADOCERA DYNAMICS

Table 3.2. Variables recorded for the plant parts of samples taken during a sampling programme for microcrustacea in Little Mere, Cheshire, during growing seasons ’98 and ‘99. PLANT PART

Variable name

LEAF

PETIOLE

SUBMERGED PLANT PART

Meaning of codes

Species

Codes adopted for each variable y/w

Approximate age

1/2/3

Young/Mature/Senescent

Degree of attack by macroinvertebrates Length

0/1/2/3/4 8-79 cm

Not eaten at all/Very slightly eaten/Quite eaten/Very much eaten/Heavily eaten -

‘Redness’

0/1/2/3

Light green/Mature green/Reddish/Very red

Approximate age

1/2/3

Young/Mature/Senescent

Species

C/E/P/Cal.

Ceratophyllum demersum Elodea canadensis Potamogeton berchtoldii Callitriche hermaphroditica

Yellow/white lily

All animals were counted under a dissecting microscope at 30X magnification. To concentrate the sample, most of the liquid was decanted, the residue left to settle and further liquid removed using a syringe. Five millilitres at a time of this concentrate were placed in a groove cut in a plastic disc. This circle could be rotated on a platform fixed to the base of the microscope. On the few occasions when the sample was extremely dense or a lot of sediment had been inadvertently collected, the sample was thoroughly mixed and a subsample taken. Either 100+ individuals of the commonest species were counted, when the sample was not too dense, or at least three subsamples counted and values averaged, when the sample contained a lot of sediment. Carapace lengths of at least 30 individuals of each of the largest species (i.e. Daphnia spp– generally Daphnia hyalina-longispina–, Simocephalus vetulus, Sida crystallina and Eurycercus lamellatus) were measured, using an ocular micrometer (max. precision 10 µm).

3.2.3. Construction of samplers. Floating lily leaves were sampled using a small rectangular box (capacity ~3 litres) from which the plastic bottom had been removed and replaced by a 50 µm Nylon mesh (see Fig. 3.3). A commercially available water-resistant cement glue was used to attach the mesh to the edges. To enhance adhesion these were made rough by filing.

Sampling the petioles of the lilies posed many problems. It was necessary to take the stem itself along with the water around it by cutting it as close to the bottom as possible without

33

CHAPTER 3

disturbing the very flocculent sediment of the lake. This had to be done quickly and with minimal disturbance, both to avoid losing any animals swimming away from around the petiole, and to minimize unwanted effects on subsequent sampling of other nearby petioles. After some trials it was decided to attach a pair of shears to a 10 cm-diameter plastic tube enclosing the stem so the blades could be remotely closed from the boat (see Fig. 3.4 and section 3.2.4).

To sample the microfauna associated to the submerged vegetation, particularly outside the lily beds, a decision had to be taken to restrict this part of sampling to the period when maximal or near-maximal growth was attained. The sampler consisted of a short cylinder (30 cm-long) from which large windows had been removed and replaced by fine mesh (50 µm). The piece of tube was cut in half lengthwise and hinged. Two windows were cut out on either side and covered with the fine mesh (see Fig. 3.5). Both ends on either half were closed with Perspex semi-circles so closure was as water-tight as possible. To aid this, very thin strips of closedpore neoprene were sealed on closing edges (original design by Kórnijow, U. of Lublin, Poland).

The zooplankton among lily beds cannot be easily sampled with traditional methods such as nets or depth bottles. This is because of the shallow depth (often less than one metre) and the physical obstacle of the plant growth itself. Swarming behaviour of many zooplankton species, perhaps more frequent in more heterogeneous environments (Folt et al. 1993), reduces the representativeness of single samples using traditional methods, too (Wiebe & Holland 1968). Thus, a 1-m tube was used to collect a depth-integrated sample by covering the surface end with a bung as it was lifted and retrieving the contents into a bucket (see Fig. 3.6). Generally around 4 or 5 tubefuls were sufficient to collect a 10-l sample (see section 3.2.4, also).

Methods for areal plant biomass samples and estimates are explained in Chapter 4. 3.2.4. Trades-off of sampling techniques used. Lily floating leaf sampler. Floating leaves were sampled using a small box (see Fig. 3.3), collecting the leaf from beneath it as it was cut off the petiole with a pair of scissors.

34

PLANT-ASSOCIATED CLADOCERA DYNAMICS

Extreme spatial heterogeneity of plant-associated microcrustacea distributions (e.g. Whiteside 1975) is a major sampling problem (Frost et al. 1988). Multiple small samples allow a measure of very small-scale variability that is missed by larger samples but cover only a small part of the population. In practice, leaves were taken as the unit ‘patch’ and the size and shape of the sampler was guided by the range of sizes of leaves.

Figure 3.3. Sampler for floating lily leaves (capacity ~3 litres). The photograph on the right also shows the filter (50 µm) and bottle used to concentrate and wash the collected animals into the sample bottle. The ruler in the picture is 50 cm long.

Some disturbance during sampling is inevitable. A record was kept of the order in which samples were collected, and leaf samples, involving relatively low disturbance, were collected before petiole samples. However, it is difficult to know whether the repeated disturbance (i.e. between sampling dates) in a small area around the sampling buoy used to fix the location, had a significant effect on actual densities (see Fig. 4.23).

The sampler allowed water but not animals to flow through the mesh. The flow of water into the box facilitated leaf collection, but at the price of letting in a variable amount of water containing pelagic animals. While this is not thought to be important, particularly compared with other sources of variability (e.g. spatial heterogeneity of species’ distributions), densities

35

CHAPTER 3

of pelagic animals (Daphnia, Ceriodaphnia and Bosmina) were gauged from tubes rather than from leaf samples. Petiole sampler (Figure 3.4). Each sample was taken by enclosing the petiole with the tube, cutting the petiole close to the bottom. The top end of the tube when full of water was then covered with a bung and the contents of the tube (i.e. petiole and surrounding water) emptied into a bucket for processing (see section 3.2.2).

Figure 3.4. Lily petiole sampler. The sampler was made of a 1-m long PVC tube attached by means of a conveniently shaped piece of wood to a pair of shears. These were placed so the cutting parts closed around the bottom end of the tube to cut the lily petiole, but without interfering with the retrieving of the water contents. A stopper is used to cover the surface end. The ruler in the picture is 50 cm long. See text for details.

36

PLANT-ASSOCIATED CLADOCERA DYNAMICS

‘Kórnijow’ jaw sampler (see Figure 3.5). Samples were taken by submerging the sampler and carefully enclosing the plant, then cutting it. The sample was put in a bucket for further processing (see section 3.2.2). The main limitation was access to sample plants growing beyond arm’s length. Depth differences in animal densities were examined by comparing samples at surface level and at about 30-60 cm depth (i.e. arm’s length). In addition, samples were taken continuously throughout the sampling period using the tube sampler (see Fig. 3.6), partially offsetting the drawback of not sampling submerged plants during dates with little growth.

Figure 3.5. Sampler for submerged vegetation. The ruler in the picture is 50 cm long. Also shown in the picture are the mesh and bottle used to collect the animals in the sample. The concentrated sample was then washed into a 300-ml bottle for preservation (see text for details). Based on an original design by Ryszard Kornijow (U. of Lublin, Poland).

Tube sampler (Figure 3.6). A depth-integrated sample was taken by repeatedly submerging the tube, covering one end of the tube with a bung, and retrieving the contents into a bucket. Taking 4 or 5 tubefuls made the sample less sensitive to bias due to aggregations of animals. Two such samples were taken per site.

37

CHAPTER 3

Figure 3.6. Tube sampler. A depth-integrated zooplankton sample was collected by submerging a tube and covering the surface end with a rubber bung. A 10-litre sample was made up of 4-5 tubefuls. See text for details.

3.2.5. Statistical analyses. 1. Spatio-temporal patterns in animal densities: A) Open-water species. Data from each of the two years’ growing seasons (April to end of October) were separately analyzed. The significance of site differences in density (individuals/litre) of open water species (i.e. Daphnia spp, Bosmina longirostris, Ceriodaphnia spp, Cyclops spp, Eudiaptomus gracilis and total copepod nauplii) was analyzed by a univariate ANOVA. The F-statistic was used to assess the magnitude and significance of date and site effects and their first-order interactions in relation to density of each species. To identify the main sources of differences among the levels of a variable (i.e. among sites or between dates) when these were statistically

significant,

Tukey

honestly

significant

difference

(HSD)

tests

were

simultaneously run on the data.

Analysis of variance (ANOVA) is robust to departures from normality, although the data must be at least symmetric (Kinnear & Gray 1997). Density data were plotted on an imaginary axis to make sure the overall distribution fitted this assumption. On the other hand, ANOVA assumes variances are equal across means (i.e. variance ‘homogeneity’). To investigate this assumption, plots of observed by standardized residual plots were generated for the dependent variable (i.e. density of the given species). Density data were logarithm-transformed when any

38

PLANT-ASSOCIATED CLADOCERA DYNAMICS

of these two conditions were not satisfactorily fulfilled (i.e. when Levene’s test hypothesis for equality of variances was rejected). Only densities larger than 1 individual/litre were included in analyses. This is because large numbers of low values may lead to spuriously narrow errors for predicted means, therefore overestimating the significance of differences between these (Bailey 1992).

In summary tables, F-tests evaluating the significance of main effects and first-order interactions are shown.

B) Plant-associated species: To investigate temporal patterns in animal densities (individuals gram-1 of lily leaf or petiole) within each year, across sites with lilies, and between lily leaves and petioles, I used a threefactor ‘mixed’ ANOVA with repeated measures. The dependent variable (density) is considered in this ‘mixed’ approach to be the response to the levels of the ‘within-subjects’ factors (i.e. ‘site’ and ‘leaf-petiole’; Norusis 1994). ‘Date’ is the grouping variable (i.e. the ‘between-subjects’ factor). Replicates can be considered ‘repeated measures’ because the levels of factors (e.g. for ‘site’, 1, 2 and 5, the three lily-bed sites, analyzed in this section) do not change among replicates. Thus, the power of the test can be increased considerably as degrees of freedom increase (five-fold as there are five replicates). In addition, sampling error is explicitly included in tests. In this type of analysis, ‘date’ is the grouping variable, and differences due to location (‘site’) or type of sample (leaf or petiole) are analyzed within each date.

Analogously to the analysis of open-water species densities, data were plotted to make sure the overall distribution was at least approximately symmetric. For each species’ density data, plots of observed values against residuals of their fit to the ANOVA model were examined to check the homogeneity of variance assumption of ANOVA. Density data were logarithmtransformed when any of these two conditions were not satisfactorily fulfilled (i.e. when Levene’s test hypotheses for equality of variances was rejected).

Comparisons were made only on non-zero data, by strictly excluding all zero-values found on leaves in the three lily-bed sites (Table 3.3). This is because large numbers of low values may lead to spuriously narrow errors for predicted means, therefore overestimating the

39

CHAPTER 3

significance of differences between these (Bailey 1992). Exceptionally, data for Eurycercus lamellatus, Chydorus spp and Graptoleberis testudinaria in growing season 1999 and Acroperus harpae, Eurycercus lamellatus and Sida crystallina in growing season 1998, were so scarce as to ‘soften’ the criterion of exclusion in order to perform a valid analysis. Thus, data included allowed the comparison of zero values with non-zero values, but never the comparison of zero-values with zero-values. The biological interpretation of this procedure is that comparisons are made only during periods of species presence. Absence of a species throughout the lake cannot inform about differences in density between sites or between leaves and petioles, thus warranting the exclusion of these data. Table 3.3. Number of samples and proportion of total number (%) collected during the sampling programme (April-October of 1998 and 1999) that were included in statistical analyses (see text for details on exclusion criteria). Number of samples & % of total number Acroperus harpae Alona sp Chydorus spp Eurycercus lamellatus Graptoleberis testudinaria Peracantha truncata Polyphemus pediculus Scapholeberis mucronata Sida crystallina Simocephalus vetulus

1998

N 198 150 168 222 150 150 90 90 126 114

1999

% 55.0 41.7 46.7 61.7 41.7 41.7 25.0 25.0 35.0 31.2

N 0 120 282 96 252 102 138 150 108 216

% 0 22.2 52.2 17.6 46.7 18.9 25.6 27.8 20.0 40.0

In addition, for the full interpretation of repeated measures ANOVA the validity of the Fstatistic used must be assured (Norusis 1994). A conservative adjustment of F-ratio degrees of freedom was sometimes necessary (Greenhouse-Geisser’s correction).

ANOVA tables are given for main factor effects (date, site and leaf/petiole) and all their possible interactions. Changes across dates and interactions can also be visually examined in plots. Within each main factor (except date), levels were compared using the deviation method. This method compares the mean of each level to the mean of all of the levels (i.e. the grand mean). The Bonferroni correction, based on Student’s t-statistic, was applied to adjust the observed significance level for the fact that multiple comparisons are made. Multiple comparisons between dates used Tukey’s Honestly Significant Difference (HSD) test.

40

PLANT-ASSOCIATED CLADOCERA DYNAMICS

Differences arising between each year’s patterns are compared by testing with a t-test (α=0.05) the significance of differences between grand means, site means and means for leaf and petiole densities in both years.

Data from submerged plants merit a separate section (see section 3.3.3). Comparisons between data for lilies and submerged plants were made by separately examining plots of density in both types of vegetation. Densities expressed on a number per gram basis are very much confounded by other factors unrelated to plant biomass (for example, plant growth structure; see Discussion). Indeed, this problem arises also when comparing densities (in ind.gram-1 of lily part) on lily leaves and petioles. The key variable in this case may be available attachment surface rather than biomass (see Discussion for details). Attachment area is related to plant weight in a different manner in leaves and petioles (i.e. there is much less surface per unit weight in petioles than in leaves). On the other hand, calculation of densities per m2 of lake bottom introduces probably much larger errors (see Chapter 4), reducing the sensitivity of tests to the questions posed. Errors in calculation are also time-dependent. Thus, data analyzed are in numbers per gram units. Apparent differences are discussed in relation to these methodological difficulties. 2. Submerged vegetation and plant-associated Cladocera. Data were densities (ind g-1 of submerged plant) of the main plant-associated Cladocera species found in samples, i.e. Acroperus harpae, Alona sp, Chydorus spp, Eurycercus lamellatus, Graptoleberis testudinaria, Peracantha truncata, Polyphemus pediculus, Scapholeberis mucronata, Sida crystallina and Simocephalus vetulus.

Differences between years in mean densities of top and bottom samples separately were compared statistically using t-tests (0.05 significance level). Analogously, t-tests were also used to assess the significance of differences between means of top and bottom samples within each year. 3. Size-structure of populations of Daphnia spp and Simocephalus vetulus. Size-structure changes were examined for populations of the two most common species in Little Mere, Daphnia spp (mainly Daphnia hyalina-longispina; see Table 2.3 for details) and Simocephalus vetulus.

41

CHAPTER 3

To examine differences between years, plots of percentages of the total population in each size-class were plotted for each date. In the case of Daphnia the comparison between sites was done by grouping lily-bed sites in one plot-series, and open-water sites in a separate series. For Simocephalus vetulus, only size data from lily leaf samples could be used. Densities on petioles were too low to allow any size-structure analysis. A detailed time-series for site 2 is shown for both species. Site 2 was the lily-bed site where Simocephalus vetulus was more abundant in both years (see section 3.3.2 for details). Size-structure changes for Daphnia at site 2 are shown in order to compare these with changes observed in Simocephalus populations at the same site.

4. Egg-ratio models for Daphnia spp and Simocephalus vetulus. Finite and instantaneous birth rates were calculated for Simocephalus vetulus and Daphnia spp (mainly Daphnia hyalina-longispina; see Table 2.3 for details) in the two growing seasons sampled. Data on number of eggs per individual and animal size in replicate samples were pooled and the following parameters calculated, for each date: NT

Total number of individuals in the sample.

Novigerous

Number of ovigerous individuals in the sample.

% ovigerous

Percentage of ovigerous individuals in the sample.

Eggs (total)

Total number of eggs in ovigerous individuals in each sample.

Brood size

Mean number of eggs in ovigerous individuals in each sample.

Egg-ratio

Calculated as Eggs(total in sample) / NT.

‘b’

Instantaneous birth rate, i.e. b = D-1 * Ln (Egg-ratio + 1) (Edmondson 1968; Paloheimo 1974).

‘D’

is the egg developmental period, estimated from regression equations relating it to water temperature (see Table 3.4).

‘B’

Finite birth rate (i.e. number of newborn per individual per day), i.e. B = (1/r) * b * (er – 1), where ‘r’ is:

‘r’

the average growth rate in the sampling period t Æ t+1, i.e. r = (1/∆t) * Ln (Nt+1 / Nt)

‘d’

Instantaneous death rate, i.e. d = b – r.

42

PLANT-ASSOCIATED CLADOCERA DYNAMICS

Table 3.4. Egg developmental period (D, in days) at different temperatures for Simocephalus vetulus (Bottrell 1975) and Daphnia spp (Bottrell et al. 1976). Temperature (°C) 13 17 19 21

Temperatures at surface at five sites (1999)

Temperature at surface at five sites (1998) 25

25

20

20

degrees centigrade

degrees centigrade

Daphnia spp (LnD=3.4+0.22LnT-0.34LnT2) 5.5 days 4.0 2.9 2.3

Simocephalus vetulus (LnD=3.68-0.16T+0.0019T2) 7.2 days 4.9 4.1 3.5

15 10 5

15 10 5 0

0

8-Oct

27-Sep

15-Aug

5-Aug

22-Jul

15-Jul

26-May

19-May

13-May

5-May

7-Oct

17-Sep

2-Sep

17-Aug

29-Jul

10-Jul

2-Jul

25-Jun

18-Jun

Figure 3.7. Instantaneous temperatures recorded during the relevant dates in growing seasons ’98 and ’99 across sampling sites in Little Mere (Cheshire). Day-night differences were never of more than about 3º. For a Map with site locations, see Fig. 3.2

Measured animals were those first encountered when counting a well-mixed sample (see section 3.2.2 for details). For Daphnia spp at least 30 individuals were measured per replicate sample (1 or 2 replicates, depending on dates, see section 3.2.2) where possible. For Simocephalus vetulus at least 50 individuals were measured per replicate (N=5 replicates) where possible. Only samples from lily leaves were analyzed, as they generally contained larger densities of this species (see section 3.3.2). Information is presented only for dates with at least 20 measured individuals, for both species. 5. Small-scale distribution of plant-associated species. Spatial distribution is sometimes measured by an index relating mean abundance to the variance of the mean estimate (‘mean crowding’ (MC), Lloyd 1967; see Equation 1).

MC = Mean density + [( s2 – 1 ) / mean density]

Equation 1.

where ‘s’ is the standard error of mean density. Unfortunately, there is a general trend to increased variance as means increase (Samuels 1991). Thus, when ‘mean crowding’ is standardized by dividing it by the mean, a measure of

43

CHAPTER 3

‘patchiness’ is generated that is quite independent of sampling variability (Lloyd 1967; see Equation 2). ‘Patchiness’ = MC / Mean density

Equation 2.

Lily leaf density data (ind g-1DW leaf) was used to calculate these two indices whenever mean density was more than 1 ind g-1. Density values lower than 1 ind g-1 would lead to inordinately large values of mean crowding and patchiness, impossible to interpret biologically.

3.3. Results. 3.3.1. Zooplankton. In general, much higher densities of Daphnia were found in the first year, while the second year it was the smaller species, such as Bosmina longirostris and Ceriodaphnia sp that were most abundant (Table 3.6). Copepods were also more abundant in the second year, particularly cyclopoids. Cyclops spp was about twice as abundant in the second year. Total copepod nauplii (i.e. both cyclopoid and calanoid) presented similar densities in both years. There was considerable heterogeneity in Daphnia spp densities across the lake in both years. (Fig. 3.8). Higher densities were generally found in the lily beds than in open waters in both years (1998: F=5.4, p<0.05; 1999: F= 13.1, p<0.01; Table 3.7). In 1999, Daphnia populations abruptly collapsed at the end of May after a brief peak (Figure 3.9), reappearing as an isolated population at the end of the growing season.

44

PLANT-ASSOCIATED CLADOCERA DYNAMICS

900

cumulative no./ litre

800

Site 1

700

Site 2

Site 3

Site 4

Site 5

Growing season

600 500 400 300 200 100 0 1

4

7

10 13 16 19 22 25 28 31 34 37 40 43 46 Date

Figure 3.8. Daphnia cumulative density (ind l-1) during the two years sampled. Underlined are growing season dates (April to October). The size of areas gives a measure of the changing relative importance of each site’s populations.

Ceriodaphnia and Bosmina, on the other hand, were found at comparable densities across sites in the first year (F-test, p>0.05; Table 3.7). In the second year Ceriodaphnia densities in lily-bed sites were about double those in more open waters (F=35.6, p<0.001; Tables 3.6 & 3.7) with variance in the order of 600 times the mean (Table 3.5). Populations, particularly of Daphnia and Ceriodaphnia, aggregated more among lilies. Table 3.5. Variance-to-mean ratios in lilies and open water for the five open-water taxa (Daphnia, Ceriodaphnia, Bosmina longirostris, Eudiaptomus gracilis, Cyclops sp and total copepod nauplii) from samples taken during growing seasons ’98 and ’99 (April to October) in Little Mere (Cheshire). To calculate ratios all data were included.

Variance/Mean Daphnia spp Ceriodaphnia spp Bosmina longirostris Eudiaptomus gracilis Cyclops spp Total nauplii

1998

1999

Lilies

Open water

Lilies

Open water

118 557 76 7 49 198

74 341 9 68 58 38

191 94 658 7 47 50

91 43 611 218 77 43

45

CHAPTER 3

Daphnia spp.

Daphnia spp.

Growing season 1998

Growing season 1999 200

Type of site Open water

27-Apr 18-May 1-Jun 18-Jun 25-Jun 2-Jul 10-Jul 23-Jul 29-Jul 17-Aug 2-Sept 9-Sept 17-Sept 25-Sept 7-Oct 26-Oct

0

Lily bed

100

Type of site Open water

0

Lily bed

1-Apr 21-Apr 28-Apr 5-May 13-May 19-May 26-May 10-Jun 17-Jun 1-Jul 8-Jul 15-Jul 22-Jul 5-Aug 15-Aug 13-Sept 27-Sept 8-Oct 26-Oct

100

individuals/litre

individuals/litre

200

Figure 3.9. Daphnia density (ind l-1) across sites in Little Mere (Cheshire), grouped as lily or open water sites during growing seasons (April to October) '98 (left) and '99 (right).

Eudiaptomus gracilis, Cyclops spp and total copepod nauplii (i.e. both cyclopoid and calanoid nauplii) were more abundant in the open water (F=31.0 & F=198.7, respectively, p<0.01; Tables 3.6 & 3.7). Ceriodaphnia and Bosmina, which were scarce during this growing season, were found at comparably low densities in both lily beds and more open waters (see Table 3.6).

46

PLANT-ASSOCIATED CLADOCERA DYNAMICS

1999

1998

Table 3.6. Marginal densities of pelagic species in lily or open water (OW) areas during growing seasons ’98 and ’99 (April to October) in Little Mere (Cheshire). Means ± standard error are of densities higher than 1 ind l-1. For a Map, see Figure 3.2. Means ± S.E. (ind l-1)

Daphnia spp

Ceriodaphnia sp

Bosmina longirostris

Eudiaptomus gracilis

Cyclops sp

Total copepod nauplii

Lilies

78.8 ± 16.4

38.6 ± 11.4

18.3 ± 8.3

5.0 ± 1.4

25.3 ± 8.5

31.5 ± 16.3

Open water

53.4 ± 11.0

37.5 ± 8.2

3.1 ± 1.3

28.1 ± 24.8

33.5 ± 12.1

48.8 ± 7.6

Lilies

32.4 ± 7.8

203.1 ± 48.2

167.8 ± 55.9

6.2 ± 1.3

35.3 ± 5.1

32.5 ± 5.1

Open water

54.0 ± 16.5

74.9 ± 23.5

120.9 ± 45.4

30.3 ± 7.3

61.9 ± 7.9

52.0 ± 5.5

47

CHAPTER 3

Table 3.7. Summary of significance tests for main factor effects (date, site and ‘lily-open’, or grouped lily-sites versus grouped openwater sites) and first-order interactions in Univariate ANOVA models for open-water taxa densities (ind l-1). Analyses are of results deriving from samples taken at 5 sites across Little Mere (Cheshire) during growing seasons ’98 and ’99 (April to October). *F-test is significant at the 0.05 level, **at the 0.01 level; ***at the 0.001 level.

1999

1998

Full factorial models (SS-typeIII) Source

48

Daphnia spp

Ceriodaphnia sp

Bosmina longirostris

Eudiaptomus gracilis

Cyclops sp

Copepod nauplii

SS

MS

Fsig.

SS

MS

Fsig.

SS

MS

Fsig.

SS

MS

Fsig.

SS

MS

Fsig.

SS

MS

Fsig.

DATE

17.9

1.19

17.6***

26.67

1.78

33.9***

4.54

0.38

9.4**

3.34

0.28

7.3*

9.38

0.63

37.8***

13.07

0.87

6.4**

SITE

0.97

0.32

4.7*

0.05

0.02

0.33

0.38

0.13

3.2

3.11

1.04

27.1**

5.26

1.75

106.0***

1.04

0.35

2.5

DATE*SITE

7.8

0.21

3.03*

4.16

0.14

2.64

4.26

0.28

7.1**

4.24

0.24

6.2*

5.95

0.21

12.4***

6.41

0.15

1.1

2.57

2.57

18.9**

*

0.01

0.01

0.17

0.03

0.03

0.774

1.19

1.19

31.0

LILY-OPEN

0.37

0.37

5.4

Source

SS

MS

Fsig.

SS

MS

Fsig.

SS

MS

Fsig.

SS

MS

DATE

21.75

1.45

19.0***

109.65

6.09

140.3***

63.73

3.54

35.2***

5.90

SITE

1.08

0.36

4.7**

0.67

0.22

5.2**

2.02

0.67

6.7**

DATE*SITE

9.26

0.27

3.6***

7.03

0.14

3.2***

10.83

0.27

LILY*OPEN

1.0

1.0

13.1**

1.55

1.55

35.6***

0.24

0.24

**

***

3.29

3.29

198.7

Fsig.

SS

MS

Fsig.

SS

MS

Fsig.

0.33

11.2***

20.98

1.17

32.9***

29.25

1.63

36.5***

16.70

5.57

191.1***

0.34

0.11

3.2*

0.24

0.08

1.8

2.7***

4.96

0.12

4.3***

11.01

0.20

5.8***

9.95

0.18

4.1***

2.4

12.63

12.63

433.5***

1.05

1.05

29.5***

3.14

3.14

70.6***

PLANT-ASSOCIATED CLADOCERA DYNAMICS

Ceriodaphnia followed bimodal dynamics in the lily beds, with peaks separated by about 2-3 weeks but only displayed one population peak in the open water. Bosmina and copepods (Eudiaptomus gracilis and Cyclops sp) had unimodal dynamics. In Cyclops and copepod nauplii no time pattern was evident, but populations fluctuated irregularly, though within the non-overlapping ranges of lily or open sites. Eudiaptomus, on the contrary, slowly built a population maximum that subsequently declined across two dates.

3.3.2. Plant-associated cladocerans on lilies. The years sampled were very different with respect to densities of particular plant-associated species and community composition (Table 3.8). Table 3.8. Overall comparisons across years of grand means (i.e. mean of all data together), site means and lily leaf / petiole means for each plant-associated cladoceran in Little Mere (Cheshire). Data are from growing seasons ’98 & ’99. Results summarize significance of t-tests (0.05-level); +significant; -not significant. Between-year differences in grand means

Sites were significantly different across years

Between-year differences in mean densities on lily petioles*

Between-year differences in mean densities on lily leaves*

Cannot + + compare** + Site 2 + Alona + Sites 1, 2 & 5 + + Chydorus Sites 1 & 2 Eurycercus + Sites 1, 2 & 5 + + Graptoleberis + Site 2 + + Peracantha Site 2 + Polyphemus Site 2 + Scapholeberis + Sites 1 & 5 + + Sida Sites 2 & 5 Simocephalus *for details on the direction of these differences, see Table 3.10. **only trace densities were detected in 1999 and, therefore, no comparison for sites can be done. Acroperus

+

In particular, all periphyton scrapers with the exception of Eurycercus, which was very scarce both years, had significantly lower densities during the second year sampled. Scapholeberis mucronata and Polyphemus pediculus had comparable densities both years. The main plantassociated filter-feeders, i.e. Sida crystallina and Simocephalus vetulus, were more abundant the second year, although this difference was only statistically significant in the case of Sida (Table 3.9).

49

CHAPTER 3

Table 3.9. Grand means (ind g-1DW of lily), standard errors and conventional confidence bounds for the mean of the 10 species recorded during growing seasons ’98 and ’99 in Little Mere (Cheshire). All data are included for the calculation of means (see section 3.2.5.1 for details on methods). GRAND MEANS (ind g-1 DW of lily)

1998

1999

95 % confidence limits

95 % confidence limits

Food habit

Species

Mean

Std. error

Lower bound

Upper bound

Mean

Periphyton scrapers

Acroperus harpae

6.6 9.5 19.6 1.1 14.2 11.6 15.6 4.2 0.8 7.1

1.8 2.4 2.3 0.15 1.0 1.6 8.5 0.7 0.2 0.5

2.9 4.7 15.0 0.8 12.2 8.3 0 2.6 0.4 6

10.3 14.4 24.3 1.4 16.3 15.0 32.8 5.7 1.2 8.2

Trace densities recorded 2.8 6.0 2.2 3.4 0.8 0.1 0.6 1.1 0.2 0.03 0.1 0.2 0.7 0.1 0.4 0.9 3.4 0.4 2.5 4.2 23.8 4.9 14.1 33.6 8.3 1.0 6.2 10.4 22.1 3.8 14.6 29.7 9.4 0.8 7.9 10.9

Alona sp Chydorus spp Eurycercus lamellatus Graptoleberis testudinaria Peracantha truncata

Raptorial

Polyphemus pediculus

Filterfeeders

Scapholeberis mucronata Sida crystallina Simocephalus vetulus

Std. error

Lower bound

Upper bound

A) Periphyton scrapers: chydorids. More animals were generally found on leaves than on petioles in both years (Table 3.10). Table 3.10. Mean densities (ind.g-1 DW of lily) comparing leaves and petioles in each year separately, for the main periphyton scraper species. In bold significantly larger values (F-test, significance level: *0.05; **0.01; ***0.001). All data have been used for the estimation of means. Standard errors indicate variation in the mean estimate, but cannot be used to compare means statistically (see section 3.2.5.1 for details). See Table 3.11 for the filter-feeder species’ means ± SE. Mean ± Std. error (ind.g-1 DW lily) Acroperus harpae Alona sp Chydorus spp Eurycercus lamellatus Graptoleberis testudinaria Peracantha truncata

1998

1999

Leaves

Petioles

3.8±0.8 9.6±2.7** 21.0±2.2** 0.8±0.1 18.5±1.4*** 17.4±3.4***

9.5±3.0 9.5±3.0 18.2±3.4 1.4±0.3 10.0±1.5 5.9±1.3

Leaves

Petioles

Trace densities recorded 2.8±0.3 2.8±0.5 1.0±0.2 0.7±0.2 0.1±0.03 0.3±0.06 0.4±0.1 0.9±0.2 1.1±0.3 5.7±0.7***

B) Filter-feeders. Plant-associated filter-feeders were found in higher densities near or on lily leaves than on petioles (Table 3.11), although differences were only statistically significant in the second year, for both Simocephalus vetulus and Sida crystallina (Table 3.11).

The behaviour of Sida populations in the two years sampled was quite unpredictable. In 1998, an important population peak was detected in spring (i.e. 250+ ind g-1DW leaf of Sida; Fig. 3.10). Although much larger densities were found on leaves than on petioles in this first year of sampling, these differences could not be separated from sampling error (F=2.36, p>0.05).

50

PLANT-ASSOCIATED CLADOCERA DYNAMICS

Differences in densities across sites were swamped by the very large standard errors when estimating means. Sida crystallina , 1998

50

Sida crystallina , 1999 500

45

450

40

400 350

30

ind g DW

leaves petioles

25 20

-1

ind g-1 DW

35

15

leaves petioles

300 250 200 150

10

100

5

50

0

08-oct

27-sep

13-sep

15-ago

05-ago

22-jul

15-jul

08-jul

01-jul

17-jun

10-jun

26-may

19-may

13-may

05-may

28-abr

21-abr

01-abr

17-sep

02-sep

17-ago

29-jul

10-jul

02-jul

25-jun

18-jun

01-jun

18-may

27-abr

06-abr

0

Figure 3.10. Sida crystallina mean densities (ind.g-1DW) with standard error bars (N=5) in leaf and petiole samples collected across the three lily beds sampled in Little Mere (Cheshire) (April-October). Left: 1998; right: 1999.

In 1999, leaf samples were significantly richer (by a factor of two) in Sida crystallina than petiole samples (F=30.96, p<0.001). Table 3.11. Mean densities (ind.g-1 DW lily) comparing leaves and petioles in each year separately, for the main plant-associated filter-feeding species in Little Mere during growing seasons ’98 and ‘99. In bold significantly larger values (F-test, significance level: *0.05; **0.01;***0.001, as in Table 3.14). All data were used to estimate means (1998: 170 leaves and 150 petioles; 1999: 270 leaves and 270 petioles). Standard errors indicate variation in the mean estimate, but cannot be used to infer differences between means (see section 3.2.5.1 for details). Mean ± S.E. (ind g-1DW lily) Scapholeberis mucronata Sida crystallina Simocephalus vetulus Polyphemus pediculus

1998

1999

Leaves

Petioles

Leaves

Petioles

5.0±1.4 0.5±0.09 9.2±0.9** 23.8±17.1

3.3±0.9 1.1±0.4 5.1±0.8 7.4±1.8

8.1±1.5 23.8±4.6*** 12.6±1.1*** 18.3±5.3

8.5±1.7 20.5±7.2 6.1±0.8 29.4±7.7

Numbers of Simocephalus on leaves were 50-100 % larger than those on petioles in both years (1998: F=4.571, p<0.01; 1999: F=17.87, p<0.001).

51

CHAPTER 3

Simocephalus vetulus , 1998

80

60

leaves petioles

70 60

ind g-1 DW

50 ind g-1 DW

Simocephalus vetulus , 1999

90

70

40 30

leaves petioles

50 40

20

30

10

20 10

0

08-oct

27-sep

13-sep

15-ago

05-ago

22-jul

15-jul

08-jul

01-jul

17-jun

10-jun

26-may

19-may

13-may

05-may

28-abr

21-abr

01-abr

17-sep

02-sep

17-ago

29-jul

10-jul

02-jul

25-jun

18-jun

01-jun

18-may

27-abr

06-abr

0

Figure 3.11. Simocephalus vetulus mean densities (ind.g-1DW) with one standard error at either side of the mean (N=5) on lily leaves and petioles across the three lily beds sampled in Little Mere (Cheshire) (AprilOctober). Left: 1998, right: 1999.

Densities achieved were higher in 1999 than in 1998. While maxima were reached in midsummer in growing season ’98, peaks were reached by the end of the summer in 1999.

52

PLANT-ASSOCIATED CLADOCERA DYNAMICS

Table 3.12. Summary of three factor mixed General Linear Models (ANOVA) for periphyton scraper species in Little Mere (Cheshire) during growing seasons (April to October) ’98 and ‘99. Results are for main factor effects (site, leaf or petiole and date) and interactions. Date is the grouping variable. See section 3.2.5.1 for details. 1998

Acroperus harpae

Alona sp

Chydorus spp

Eurycercus lamellatus

Graptoleberis testudinaria

Peracantha truncata

1

1999

Source

SITE

TYPE OF SAMPLE (Leaf or petiole)

SITE* DATE

TYPE OF SAMPLE* DATE

SITE* TYPE OF SAMPLE

SITE* TYPE OF SAMPLE* DATE

SITE* TYPE OF SAMPLE

SITE* TYPE OF SAMPLE* DATE

SS1

3.525

0.119

14.031

1.81

0.061

2.716

MS

1.763

0.119

0.702

0.181

0.0308

0.136

F

24.65***

1.623

9.811***

0.463*

0.411

1.813*

SS

2.753

1.73

8.64

2.11

0.0894

5.77

0.793

0.416

1.655

4.384

0.477

1.822

MS

1.372

1.73

0.54

0.264

0.0447

0.361

0.396

0.416

0.138

0.731

0.238

0.152

F

15.913***

9.092**

6.242***

1.387

0.52

4.192***

3.774*

1.868

1.315

3.278*

1.818

1.158

SS

14.229

0.885

11.296

2.132

0.638

1.206

0.202

0.346

2.228

0.714

0.525

4.51

MS

7.115

0.885

0.807

0.305

0.319

0.0861

0.101

0.346

0.0795

0.0509

0.263

0.161

F

50.909***

9.964**

5.774***

3.337*

1.827

0.424

1.169

4.799*

0.92

0.708

3.641*

2.233**

SS

0.612

0.0182

3.709

1.001

0.388

0.661

0.462

0.0433

0.919

0.146

0.011

0.348

MS

0.306

0.0182

0.232

0.125

0.194

0.0413

0.231

0.0433

0.919

0.0292

0.005

0.0348

F

5.484**

0.267

4.154***

1.833

2.468

0.526

13.909***

1.070

5.529**

0.722

0.124

0.759

SS

13.493

4.092

14.224

0.607

0.915

1.803

1.875

0.0757

5.066

0.37

0.014

0.916

MS

6.747

4.092

1.423

0.121

0.457

0.18

0.938

0.0757

0.181

0.0264

0.008

0.0327

F

48.484***

35.304***

10.226***

1.048

3.325*

1.311

18.448***

1.592

3.56***

0.556

0.223

0.987

SS

3.475

5.237

5.919

1.4

0.529

2.259

0.409

4.337

2.625

1.077

0.31

0.497

MS

1.737

5.237

0.592

0.28

0.264

0.226

0.204

4.337

0.336

0.269

0.155

0.062

F

11.149***

23.336***

3.798**

1.248

2.174

1.857

2.635

35.095***

4.329**

2.179

1.16

0.465

SITE

TYPE OF SAMPLE (Leaf or petiole)

SITE* DATE

TYPE OF SAMPLE* DATE

Trace densities recorded

Type III; *0.05 significance level; **0.01; ***0.001

53

CHAPTER 3

Table 3.13. Summary of three factor mixed General Linear Models (ANOVA) for filter-feeding species and Polyphemus pediculus in Little Mere (Cheshire) during growing seasons ’98 and ‘99. Results are for main factor effects (site, leaf or petiole and date) and interactions. Date is the grouping variable. See section 3.2.5.1 for details. 1998

Scapholeberis mucronata

Sida crystallina

Simocephalus vetulus

Polyphemus pediculus

1

Source

SITE

TYPE OF SAMPLE (Leaf or petiole)

SITE* DATE

TYPE OF SAMPLE* DATE

SITE* TYPE OF SAMPLE

SITE* TYPE OF SAMPLE* DATE

SITE

TYPE OF SAMPLE (Leaf or petiole)

TYPE OF SAMPLE* DATE

LEAFPET* DATE

SITE* TYPE OF SAMPLE

SITE* TYPE OF SAMPLE* DATE

SS1

0.693

0.557

6.757

0.172

0.676

1.664

2.232

0.972

12.764

0.506

0.209

2.038

MS

0.347

0.557

0.845

0.0429

0.338

0.208

1.116

0.972

0.912

0.0722

0.105

0.146

F

3.677*

4.3

8.963***

0.332

3.836*

2.362

4.326*

4.191

3.534**

0.311

0.549

0.763

SS

0.0861

0.148

1.424

0.149

0.0398

0.584

4.95

3.131

9.467

0.982

0.0613

1.063

MS

0.043

0.148

0.102

0.0212

0.0199

0.0417

2.475

3.131

0.947

0.196

0.0307

0.106

F

0.602

2.365

1.422

0.339

0.248

0.52

16.204***

30.955***

6.198***

1.941

0.253

0.878

SS

0.953

6.856

2.294

0.565

0.366

2.211

9.354

2.021

5.313

2.175

0.106

1.646

MS

0.477

0.571

2.294

0.0941

0.183

0.184

4.677

2.021

0.266

0.217

0.0528

0.0823

F

3.814*

4.571**

18.599**

0.763

1.038

1.046

30.36***

17.869***

1.72

1.923

0.559

0.871

SS

0.628

0.0782

4.944

0.502

0.302

1.87

3.041

0.332

23.538

1.602

0.652

2.902

MS

0.314

0.0782

0.494

0.100

0.151

0.187

1.52

0.332

1.962

0.267

0.326

0.242

F

1.030

0.229

1.623

0.293

0.670

0.83

4.841*

1.058

6.246***

0.851

1.329

0.986

Type III; *0.05 significance level; **0.01; ***0.001

54

1999

PLANT-ASSOCIATED CLADOCERA DYNAMICS

3.3.3. Submerged vegetation and plant-associated Cladocera. There were appreciable differences in densities of the plant-associated Cladocera sampled from submerged vegetation between both years (see Table 3.14). These differences, though, were generally not statistically significant due to large replicate variability. Table 3.14. Mean density (ind.g-1 DW of plant) of all plant-associated Cladocera species recorded in submerged plant samples (N=5) collected at surface (‘top’) and at arm’s length (‘bottom’) in Little Mere (Cheshire) during two growing seasons (April to October; ’98 and ’99). Significantly larger densities in a top to bottom comparison using a t-test (0.05 level) are indicated with an asterisk and in bold.

Top vs. Bottom Statistical comparison t-tests (0.05 level) Acroperus harpae Alona sp Chydorus spp Eurycercus lamellatus Graptoleberis testudinaria Peracantha truncata Polyphemus pediculus Scapholeberis mucronata Sida crystallina Simocephalus vetulus

1998 Top 69±20 21.0±5.8 81±58 318±206 116+±22 47±22 43*±40 23.4±10.6 6.8±3.4 637±1001

1999

Bottom 384*+±108 19.6±5.0 74±34 236±56 103±58 30±26 0±0 7.6±6.4 7.7±7.2 673±921

Top Bottom 29.2±6.6 8.9±5.4 183±94 67.4±22 538±172 179±38 48±36 111±32 2.6±1.6 38.7*±15.2 308±152 45.9±15.2 None recorded 8.2±2.8 7.1±2.4 604±334 248+±60 594±354 832±334

*significantly larger in a top-to-bottom comparison (t-test, at the 0.05 level); +significantly larger in a between-year comparison.

Sida crystallina. In the second year, Sida was found at high densities in vegetation close to the surface when populations in lilies had either sharply decreased (sites 1 and 5) or were falling rapidly (site 2). Compare Figures 3.10 and 3.12. Conversely, only small populations were found when the species was abundant in the lily beds (Sept.13th, 1999). 1000

16

12 10

Sida crystallina SITE 4, SUBMERGED VEGETATION, 1998

900

Top

number / gram plant

number / gram plant

14

Bottom

8 6 4 2

800 700 600

Sida crystallina Site 4 Submerged vegetation 1999

Top Bottom

500 400 300 200 100

0

0 18-Jun

25-Jun

29-Jul

17-Aug

02-Sep

17-Sep

17-Jun

13-Sep

27-Sep

8-Oct

Figure 3.12. Mean densities (ind g-1 DW plant) of Sida crystallina with standard error bars (N=5) in submerged vegetation samples (‘top’ and ‘bottom’) taken in Little Mere (Cheshire) during two growing seasons (April to October). Left:1998; Right:1999.

55

CHAPTER 3

In the first year, Sida maintained small populations associated with submerged plants when the species had totally disappeared from lily beds (see Fig. 3.12). Simocephalus vetulus. Dense patches of Simocephalus (i.e. around 1,000 ind g-1 DW plant, or around 20,000 ind m-2 lake bottom), were sporadically found at the height of the summer in 1998, and at the end of the season in 1999 (see Fig. 3.13).

Simocephalus vetulus SITE 4, SUBMERGED VEGETATION, 1998

number / gram plant

1600 1400 1200 1000 800

Top

600

Bottom

400

1200 1000

Simocephalus vetulus Site 4 Submerged vegetation 1999

Top Bottom

800 600 400 200

200 0

1400 number / gram plant

1800

*

*

18-Jun

25-Jun

0 29-Jul

17-Aug

02-Sep

17-Sep

17-Jun

13-Sep

27-Sep

8-Oct

Figure 3.13. Mean densities of Simocephalus vetulus (ind g-1 DW plant) with standard error bars (N=5) in submerged plant samples taken in Little Mere (Cheshire) during two growing seasons (April to October). Left: 1998; Right: 1999. Asterisks indicate bottom samples were not taken those dates (see Discussion for details).

3.3.4. Population size-structure changes: Daphnia spp (mainly Daphnia hyalinalongispina) and Simocephalus vetulus. Size-structure changes of Daphnia populations were generally periodic and clearly identifiable (see, for example, Fig. 3.14). Simocephalus vetulus, in contrast, presented either apparently random changes in size-structure (i.e. growing season ’98), or few significant changes (i.e. growing season ’99) (see Figs. 3.16 & 3.17).

56

PLANT-ASSOCIATED CLADOCERA DYNAMICS

50

1-6-98

45

40

40

35

35

lily beds open waters

25

20

20

15

15

10

10

5

5

0

0 0.5

0.7

0.9

45

1.1 size (mm)

1.3

1.5

1.7

0.5

0.7

0.9

45

18-6-98

40

1.1 size (mm)

1.3

1.5

1.7

29-7-98

40

35

35 lily beds open waters

30

30

25 %

%

lily beds open waters

30

25

%

%

30

lily beds open waters

25

20

20

15

15

10

10

5

5

0

0 0.5

0.7

0.9

50

1.1 size (mm)

1.3

1.5

1.7

0.5

0.7

0.9

90

25-6-98

45

1.1 size (mm)

1.3

1.5

1.7

17-8-98

80

40

70

35

lily beds open waters

30

lily beds open waters

60

25

%

%

10-7-98

45

50

20

40

15

30

10

20

5

10

0

0 0.5

0.7

0.9

1.1 size (mm)

1.3

1.5

1.7

0.5

0.7

0.9

1.1 size (mm)

1.3

1.5

1.7

2-7-98

35 30

lily beds open waters

25

%

20 15 10 5 0 0.5

0.7

0.9

1.1 size (mm)

1.3

1.5

1.7

Figure 3.14. Size-structure of Daphnia populations in lily beds and more open waters in Little Mere (Cheshire) during growing season ’98 (April to October).

57

CHAPTER 3

50

5-5-99

60

26-5-99

45 50

40 35

30

lily beds open waters

30

lily beds open waters

%

%

40

25 20

20

15 10

10

5 0

0 0.5

0.7

0.9

1.1 size (mm)

1.3

1.5

1.7

0.5

0.9

50

13-5-99

60

0.7

1.1 size (mm)

1.3

1.5

1.7

13-9-99

45 50

40 35 lily beds open waters

30 %

%

40 lily beds open waters

30

25 20

20

15 10

10

5 0

0 0.5

0.7

0.9

1.1

1.3

1.5

1.7

size (mm) 60

0.5

0.7

0.9

1.1 size (mm)

1.3

1.5

1.7

19-5-99

50

%

40 30

lily beds open waters

20 10 0 0.5

0.7

0.9

1.1 size (mm)

1.3

1.5

1.7

Figure 3.15. Size-structure of Daphnia populations in lily beds and more open waters in Little Mere (Cheshire) during growing season ’99 (April to October).

Daphnia spp (mainly Daphnia hyalina-longispina). Populations of Daphnia inhabiting lily beds and those sampled from open waters had clearly different size-structures (see Figs 3.14 & 3.15). Changes in size-structure of populations in lily beds and open waters paralleled those of density in both habitats. In 1998, there were proportionally more of the larger individuals in lily beds than in open waters during most of June and then again in August. During July, in contrast, the larger individuals were found in the open water. Most animals were 0.7-1.1 mm in size. In 1999, there were large changes in size-structure. Open waters had populations of smaller individuals than those in lily beds. This difference intensified as the month progressed. By late May, Daphnia populations in lily beds were largely made up of animals of 1.5 mm in size

58

PLANT-ASSOCIATED CLADOCERA DYNAMICS

or bigger. In stark contrast, populations in open waters on this date included 80 % of individuals smaller than 0.7 mm. Populations declined after May.

Populations recovered in September (Figure 3.15). Size-structure after recovery was similar in lily beds and open waters, with a predominance of very small animals.

Simocephalus vetulus. Large animals (i.e. >1.1 mm) were sampled throughout summer ‘98. However, animals were generally smaller in June than in July. Small animals were again proportionally abundant in August. In September, a high percentage of the population was made up of animals larger than 1.0 mm (see Fig. 3.16).

Simocephalus vetulus , 25th June, 1998

Simocephalus vetulus , 1st June, 1998

40

60

35 50

30

40

20

%

%

25

30

15 20

10 5

10

0 0.5

0.7

0.9

1.1

1.3

1.5

1.7

1.9

0

2.1

0.5

Body length (mm)

0.7

0.9

1.1

1.3

1.5

1.7

1.9

2.1

Body length (mm)

Simocephalus vetulus , 10th July, 1998

45 40 35 30 %

25 20 15 10 5 0 0.5

0.7

0.9

1.1

1.3

1.5

1.7

1.9

2.1

Body length (mm)

Figure 3.16. Changes in size-structure of Simocephalus vetulus populations in Little Mere (Cheshire) midsummer ’98.

59

CHAPTER 3

Simocephalus vetulus , 8th July, 1999

Simocephalus vetulus , 5th August, 1999

60

45 40

50

35 40

30 %

%

25 30

20 15

20

10 10

5 0

0 0.5

0.7

0.9

1.1

1.3

1.5

1.7

1.9

0.5

2.1

0.7

0.9

1.1

1.3

1.5

1.7

1.9

2.1

1.9

2.1

Body length (mm)

Body length (mm)

Simocephalus vetulus , 15th August, 1999

Simocephalus vetulus , 15th July, 1999 45

40

40

35

35

30

30

25

25 %

%

45

20

20

15

15

10

10

5

5 0

0 0.5

0.7

0.9

1.1

1.3

1.5

1.7

1.9

0.5

2.1

0.7

0.9

Body length (mm)

1.1

1.3

1.5

1.7

Body length (mm)

Simocephalus vetulus, 13th September, 1999 45 40 35 30 %

25 20 15 10 5 0 0.5

0.7

0.9

1.1

1.3

1.5

1.7

1.9

2.1

Body length (mm)

Figure 3.17. Size-structure changes of Simocephalus vetulus populations in Little Mere (Cheshire) during growing season ’99.

Populations of Simocephalus vetulus were late developing in 1999. Significant numbers were only sampled in July. Size-structure was, compared with 1998, remarkably consistent across lily beds and very stable (Fig. 3.17). In general, animals were small, with a predominance of the 0.7 mm size-class in all dates. A larger proportion of big animals was observed in populations sampled in August and September. At the end of the growing season (i.e. 8th of October) most animals were small and few were larger than about one millimetre.

60

PLANT-ASSOCIATED CLADOCERA DYNAMICS

3.3.5. Egg-ratio models: Simocephalus vetulus and Daphnia. Daphnia Birth rates were higher in lily beds than in open waters, although there was much variation in any given year. Large differences in birth rate between lily beds and more open waters were particularly evident in 1999 (Fig. 3.18, plots on the right). In open waters, growth rates were generally negative. With few exceptions, lily-bed populations had large birth rates, but also much larger death rates, as estimated from the difference between apparent growth and recruitment (i.e. births). In 1998, death rates were fairly constant in lily beds throughout the growing season, while in more open waters, births and population growth diminished into the summer (black bars in Fig. 3.18). 01-jun

18-jun

25-jun

02-jul

10-jul

29-jul

17-ago

05-may

25-sep

200

0.300

Daphnia , lily beds, 1998

13-may

19-may

26-may

13-sep

08-oct 180

Daphnia , lilies, 1999

180

0.250

27-sep

0.300

160 0.200

160

140

0.200

100

0.100

80

0.050

120

0.100

100 0.000 80

-0.100

60

60 0.000

40

40

-0.200 20

-0.050

20

Birth rate (b)

Growth rate (r)

Density

-0.100

-0.300

0

01-jun

18-jun

25-jun

02-jul

10-jul

29-jul

0 Birth rate (b)

17-ago

05-may

120

0.250

13-may

Growth rate (r)

Density

19-may

26-may

0.300

25

Daphnia , open water, 1998

Daphnia , open water, 1999

0.200 0.200

100

20

0.150 0.100

0.000

-0.100 10

40

Density (ind l-1)

0.000

15

b and r

b and r

60

0.050

Density (ind l-1)

80 0.100

-0.200

-0.050 5

20 -0.100

Birth rate (b) -0.150

Growth rate (r)

-0.300

Density 0

Density (ind l-1)

120

b and r

b and r

0.150

Density (ind l-1)

140

-0.400

Birth rate (b)

Growth rate (r)

0

Density

Figure 3.18. Birth and growth rates of Daphnia populations in Little Mere (Cheshire) during growing seasons ’98 (left plots) and ’99 (right plots) among lilies (top plots) and in the open water (bottom plots). Death rates are represented by the difference between birth rate and growth rate lines. White bars indicate periods when population growth exceeds apparent birth rates; black bars indicate periods when birth rate is lower than growth of the population. This could happen as a result of deaths, but also if birth rate had been underestimated (see Discussion in Chapter 3 for details).

61

CHAPTER 3

Simocephalus vetulus. The fertility of Simocephalus vetulus (i.e. proportion of ovigerous individuals) was low throughout the growing season of 1998 (Fig. 3.19). Periods with larger individuals also had a larger proportion of ovigerous individuals. Site differences in egg-ratios were not apparent from these data. In 1999, birth rates greatly exceeded growth rates on many occasions (Fig. 3.19).

Comparisons between lily beds as regards growth dynamics are hampered by the scarcity of data. There were very large differences in the percentage of ovigerous in different lily-bed sites in any given date (Tables 3.15 & 3.16). 1st June 18th June 25th June 2nd July

10th July 29th July 17th Aug. 2nd Sept. 17th Sept.

8th July

50

0.350

Simocephalus vetulus Lily leaves, 1998

15th July

22nd July

5th Aug.

15th Aug.

13th Sept.

27th Sept.

0.200

70

Simocephalus vetulus Lily leaves, 1999

45

60

0.150

0.250

8th Oct.

25

20 -0.050 15

10 -0.150

40 0.050 30 0.000 20

-0.050

Density (ind g-1 DW leaf)

-1

b and r

30

0.050

50 0.100

b and r

35 0.150

Density (ind g DW leaf)

40

10

5

Birth rate (b)

Growth rate (r)

Density

-0.250

0

Birth rate (b)

-0.100

Growth rate (r)

Density

0

Figure 3.19. Birth and growth rates of Simocephalus vetulus populations in Little Mere (Cheshire) during growing seasons ’98 (left) and ’99 (right) (April to October). Death rates are represented by the difference between these two lines. White bars indicate periods when population growth exceeds apparent birth rates; black bars indicate periods when birth rate is lower than growth of the population. This could happen as a result of deaths, but also if birth rate had been underestimated (see Discussion for details). Table 3.15. Sample size and egg-ratio variables (number of ovigerous individuals in each sample, % ovigerous, total number of eggs and egg-ratio; see 3.2.5.5 for details on calculations) for Simocephalus vetulus sampled on floating lily leaves, estimated for each date in three lily beds in Little Mere (Cheshire) during growing season ’98.

1st June 18th June 25th June 2nd July 10th July 29th July 17th Aug. 2nd Sept. 17th Sept.

62

Sample size

Ovigerous individuals

% ovigerous

Eggs (total)

Egg-ratio

192 16 446 684 346 59 16 65 82

0 0 13 10 39 2 1 15 0

0 0 2.9 1.5 11.3 3.4 6.3 23.1 0.0

0 0 75 36 142 7 5 51 0

0.000 0.000 0.168 0.053 0.410 0.119 0.313 0.785 0.000

PLANT-ASSOCIATED CLADOCERA DYNAMICS

Table 3.16. Sample size and egg-ratio variables (number of ovigerous individuals in sample, % ovigerous, total number of eggs and egg-ratio) for Simocephalus vetulus sampled on floating lily leaves, estimated for each date in three lily beds in Little Mere (Cheshire) during growing season ’99.

8th July 15th July 22nd July 5th Aug. 15th Aug. 13th Sept. 27th Sept. 8th Oct.

Sample size

Ovigerous individuals

% ovigerous

Eggs (total)

Egg-ratio

442 247 76 676 380 743 267 295

14 37 1 62 71 128 17 3

3.2 15.0 1.3 9.2 18.7 17.2 6.4 1.0

108 184 8 417 424 599 48 8

0.244 0.745 0.105 0.617 1.116 0.806 0.180 0.027

3.3.6. Small-scale distribution of species. No clear relationship between crowding levels and patchiness, as indicated by Lloyd’s index, was apparent across species. However, some species with large seasonal changes in numbers (e.g. Graptoleberis testudinaria and Eurycercus lamellatus) showed highest patchiness when populations were scarce. Plant-associated filterers often showed very regular distributions (i.e. patchiness index close to 1.0), despite large variations in mean crowding. Sida crystallina was very regularly distributed at its population peaks in both years (1998 & 1999).

Simocephalus vetulus showed the strongest consistency in patchiness across dates and sites. Mean crowding was large at times of population growth (max. 134 ind ind-1 in 1998; 162 in 1999).

A general classification of “habitats” for microcrustacean species in Little Mere is summarized in Tables 3.17-3.19. Filter-feeders were generally more abundant on lily leaves than on petioles, while the pattern was not as clear for chydorid species (Tables 3.17 & 3.18). Chydorid species were generally abundant on submerged plant parts (Table 3.17). Daphnia and Ceriodaphnia were generally more abundant in lily beds. Bosmina and copepods were found at higher densities in the open water (Table 3.19). In all cases there was large variability in numbers both across years and across sampling sites.

63

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Table 3.17. Variables summarizing the ‘habitat’ association of the 6 chydorid species (Acroperus harpae, Alona sp, Chydorus spp, Eurycercus lamellatus, Graptoleberis testudinaria and Peracantha truncata) sampled from Little Mere over the two-year sampling programme. Tabled are the lily part (leaf or petiole) where denser, and the maximum densities (in individuals sample-1) found in both parts in the two-year period, the part of submerged plant (top or bottom, see section 3.2.1 for details) where densities were frequently more abundant and the max. density found, the across-year variability in densities, judged from the differences in grand means (see Table 3.9) and size of population peaks in each year, the percentage decrease or increase in average density between years (in brackets, year when maximum), and the month when the maximum density of each species was reached (i.e. ‘seasonality). Concept

Acroperus harpae

Alona sp

Chydorus spp

Eurycercus lamellatus

Graptoleberis testudinaria

Peracantha truncata

Lily leaf- petiole ‘preference’ *

Petioles (n.s.)

Leaves

Petioles (n.s.)

Leaves

Leaves

Max. density on leaves (ind. sample-1) Max. density on petioles (ind. sample-1) Part of submerged plant where more frequently abundant * Max. density on submerged plant (ind. sample-1) Across-year variability % of each year grand mean

209

Leaves-sites 1,5 Petioles-site 2 450

418

33

551

380

173

128

193

12

64

37

Bottom

Top (n.s.) 15 Very scarce Variable ± 300 % (1998) September

No pref.

Bottom (n.s.) 76 Very abundant Variable ± 2000 % (1998) June & Sept. ’98 June ‘99

Top-Bottom (n.s. 1998) 67 Abundant Extremely var. ± 500 % (1998)

Top (n.s.) 27 Scarce Variable ± 300 % (1998) September

Seasonality (month of max.)

55 Abundant Extremely var. ± 600 % (1998) September

15 Very scarce Variable ± 2000 % (1998) July ’98 September ‘99

July ’98 June ‘99

Table 3.18. Variables summarizing the ‘habitat’ association of the main plant-associated filter-feeders (Scapholeberis mucronata, Sida crystallina and Simocephalus vetulus) and of the raptorial species Polyphemus pediculus, sampled from Little Mere over the two-year sampling programme. Tabled are the lily part (leaf or petiole) where denser, the maximum densities (in individuals sample-1) found in both parts in the two-year period, the part of submerged plant (top or bottom, see section 3.2.1 for details) where densities were frequently more abundant and the max. density found, the across-year variability in densities, judged from the differences in grand means (see Table 3.9) and size of population peaks in each year, the percentage decrease or increase in average density between years (in brackets, year when maximum), and the month when the maximum density of each species was reached (i.e. ‘seasonality). Concept Lily leaf- petiole ‘preference’ * Max. density on leaves (ind. sample-1) Max. density on petioles (ind. sample-1) Part of submerged plant where more frequently abundant Max. density on submerged plant (ind. sample-1) Across-year variability % of each year grand mean Seasonality (month of max.)

Polyphemus pediculus

Scapholeberis mucronata

Sida crystallina

Simocephalus vetulus

Leaves (n.s.) 2700

Leaves (n.s.) 270

Leaves

Leaves

1731

386

259

87

876

133

No pref.

Top (n.s.) 5 Very scarce Variable ±200 % (1999)

Top

No pref.

171 Abundant Extremely var. ±2700 % (1999)

353 Very abundant Not variable ±30 % (1999)

April ’98 Sept.Oct. ‘99

June-July ’98 Aug. ‘99

10 Very scarce Variable ±150 % (1999) June-July ‘98 July-Aug. ’99

July

‘n.s.’ indicates the differences in densities between leaves and petioles are not significant (see Table 3.14, for details).

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PLANT-ASSOCIATED CLADOCERA DYNAMICS

Table 3.19. Variables summarizing the ‘habitat’ association and seasonality of the main ‘open-water’ species in Little Mere (Bosmina longirostris, Ceriodaphnia spp, Daphnia spp, Cyclops spp and Eudiaptomus gracilis) over the two-year sampling programme. Tabled are the lily-bed or open-water ‘preference’, the site where maximum densities were generally reached, the maximum density (in individuals l-1) in each year, the across-year variability in densities, as judged from the increase/decrease in densities in both lily beds and open waters, the percentage increase or decrease in density in either habitat (lily beds, LB, and open waters, OW) and the month when population peaks were reached (i.e. ‘seasonality’). Concept

Bosmina longirostris

Ceriodaphnia spp

Daphnia spp

Cyclops spp

Eudiaptomus gracilis

Lily bed or open water*

Open water (n.s.)

Lily beds

Lily beds

Open water

Site ‘preference’

3, 4 > 1, 2, 5

1, 2, 5 > 3, 4

3, 4 > 1, 2, 5

1998: 135 1999: 1264 Extremely var. 1998: 350 % OW 1999: 2 % OW 1998: October 1999: July-Aug.

1998: 204 1999: 1358 Extremely var. 1998: 20 % LB 1999: 250 % LB 1998: Sept.Oct. 1999: July-Aug.

5 in 1998 1 in 1999 1998: 409 1999: 456 Variable 1998: 37 % LB 1999: 61 % LB 1998: July 1999: May & Sept.

Open water (occasionally more in lily beds) 3, 4 > 1, 2, 5 1998: 146 1999: 264 Variable 1998: 100 % OW 1999: 60 % OW 1998: Sept.Oct. 1999: July-Aug.

1998: 127 1999: 610 Extremely var. 1998: 600 % OW 1999: 1400 % OW 1998: November 1999: April

Max. density (ind. l-1) Across-year variability % more in lily beds (LB) or in open waters (OW) ** Seasonality (month of max.)

‘n.s.’ indicates densities in lily beds or open waters are not significantly different (see Table 3.7, for details); ** percentages have been calculated from the marginal means for lily beds and open waters (see Table 3.6).

65

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3.4. Discussion. A few very clear patterns in spatial distribution of species were observed. Daphnia populations were significantly larger in lily beds in Little Mere, lending support to the idea that lilies provide a refuge against fish predation for the zooplankton. ‘Open waters’ (i.e. outside lily beds) had little submerged vegetation that could act as refuge during most of both growing seasons sampled. Differences in the size of populations between lilies and open waters increased towards mid-summer, perhaps coinciding with a late recruitment of planktivorous fish.

Gliwicz & Rykowska (1992) interpreted the onshore-offshore gradient in pelagic species’ densities as the result of predator-induced behaviour of the zooplankton. However, the smaller, not the larger, populations were found in the littoral areas, dominated by a belt of common reed (Phragmites australis). Lauridsen & Buenk (1996) found that nighttime densities of Daphnia increased in the open water, whilst in the littoral zone, vegetated with the fine-leaved submerged Potamogeton pectinatus, densities were much lower. The highest daytime densities were at the edge of the plant beds, and not inside them. Lauridsen et al. (1996) compared the dynamics of zooplankton in and out of different-diameter plant beds. Beds with large edge-to-size ratios were the most effective daytime refugia against fish predation.

Refuge effectiveness has been found to be dependent on a balance between predator pressure (as measured by areal fish density) and submerged plant density (Schriver et al. 1995). Little is known about the differential refuge effectiveness for zooplankton of floating and submerged plants. There has been controversy concerning the potential refuge role of nymphaeids as opposed to completely submerged vegetation (Venugopal & Winfield 1993; Scheffer 1998, Blindow et al. 2000). Venugopal & Winfield (1993) found higher perch densities in large yellow lily stands (Nuphar lutea) in Priest Pot, English Lake district. However, these differences were not significantly different (F-test, p>0.05), perhaps because results were based on a small sample size (i.e. 61 perch over five weeks). Lower light-levels under the floating leaf canopy may provide a relative refuge for zooplankton in lily beds (Scheffer 1998). However, the foraging behaviour of most planktivorous fish is hardly impaired by the reduction in ambient light due to shading in lily beds (Wright & Shapiro 1990). Timms & Moss (1984) proposed it is the direct ‘physical interference’ with the feeding

66

PLANT-ASSOCIATED CLADOCERA DYNAMICS

behaviour of planktivores that may explain the apparent refuge role of nymphaeids (see also Moss, Kórnijow & Measey 1998). However, Winfield (1986) found that the foraging performances of rudd (Scardinius erythropththalmus) and perch (Perca fluviatilis) were actually increased in a dense stand of Nuphar spp (albeit an artificial stand) relative to those shown in open water, perhaps owing to a reduction in ‘perceived’ predation risk from piscivores. Moss et al. (1998) have shown that lilies (Nuphar lutea) in Little Mere (England) reduced predation by perch (Perca fluviatilis) on Daphnia spp at high plant densities.

The clearest evidence for the refuge role of lily beds was obtained here in the second year of sampling. Daphnia was only dominant in the first two months of this growing season (i.e. April & May). By the beginning of June only residual populations could be found. The severity of the impact, as compared to this species’ population dynamics the previous year, and the fact it concerned the whole lake, suggest it could have been the result of heavy predation by young-of-the-year (YOY) fish once they reached the size to have any significant effect on Daphnia populations. These yearly variations in predation pressure from planktivorous fish on zooplankton are common in shallow lakes, especially those of high trophy (see Scheffer et al. 1997). Only residual populations were maintained until significant numbers were detected at a single sampling station by the end of the growing season (i.e. September ’99; see Figure 3.8).

This observation has implications for the estimation of zooplankton grazing impacts as calculated from single measurements of density, generally from open-water sampling stations (e.g. Blindow et al. 2000). Also, Daphnia have been shown to migrate vertically even in such shallow depths as those in Little Mere (F-test; p<0.05), albeit only in enclosure studies (see Chapter 5). It is unlikely Daphnia gains any refuge from visually-predating fish by migrating vertically in this lake and the persistence of this behaviour in Little Mere may simply be a relict trait of populations with strong vertical migrations and that migrated in from the deep Mere Mere, upstream (see Chapter 2 for details; Fig. 2.3).

Two small cladocerans, Bosmina longirostris and Ceriodaphnia spp dominated the zooplankton community in the absence of Daphnia species in July and part of August in the second year of sampling (Figure 3.8). Daphnia is a more efficient filter-feeder than smaller cladocerans (Brooks & Dodson 1965; Chow-Fraser & Knoechel 1985; Mourelatos & Lacroix 1990). In the presence of intense planktivory by visually-predating fish, smaller cladocerans 67

CHAPTER 3

would come to predominate over the larger, and thus more vulnerable, Daphnia species. This could be because the fish would deplete invertebrate predators feeding preferentially on smaller zooplankton, or, more directly, because fish would predate preferentially on larger zooplankton. In Little Mere, the apparent shift in community composition lends further support to the idea intense fish predation was responsible for the temporary disappearance of Daphnia from the lake in growing season ’99.

Larger-bodied Daphnia were consistently found in lily beds, too (Figures 3.14 & 3.15). Very few individuals larger than 1 mm were found in the two open-water sites in 1998, with the exception of two dates in July, when very large densities were found throughout the lake. This would be expected if individuals of different cohorts had a chance to grow in the face of fish predation. The magnitude of this apparent refuge effect in 1998, was variable. In July, larger Daphnia could be found in open waters, and densities were high in both lilies and open waters. By contrast, in August 17th virtually all Daphnia in the lake were found among the lilies. The few Daphnia sampled from outside lily beds were smaller than 0.7 mm in size, while about half the populations in lily beds were larger than 0.9 mm (see Fig. 3.14). Again, all these facts are consistent with the idea lily beds offer a very powerful refuge for Daphnia in the face of fish predation.

Changes in size-structure of Daphnia populations in 1999 give further support to the idea lily beds act as refuge. Before hatching of young-of-the-year in May, size-structure across sites was comparable across sites, both in lily beds and out. In May, most animals in open waters were in small size-classes. The potential for population growth at the end of May was very large. Most of the populations were in larger size classes and 55-84 % of the populations in lily beds (but not in open waters) were made up of large ovigerous individuals (see section 3.3.5). This could be explained by larger Daphnia migrating less out of relatively protective lily beds in response to a very high predation risk outside the beds or that size-selective fish predation was more intense outside lily beds. Densities then were substantially lower in the two open-water sites (see Fig. 3.9). Further support for this hypothesis would need direct evidence that the overall decline of Daphnia populations was related to fish moving into the lily beds. Large numbers of small perch (Perca fluviatilis) were found in surveys with seine nets, conducted in Little Mere in November ’99 (see Appendix A). Evidently, lily beds only delayed the demise of populations, but a threshold fish density can be imagined below which this ‘refuge effect’ would be sufficient to maintain relatively stable

68

PLANT-ASSOCIATED CLADOCERA DYNAMICS

Daphnia populations, other things being equal (Schriver et al. 1995; see, for example, Moss, Kornijow & Measey 1998; see also Discussion in Chapter 5). Large-scale distribution of plant-associated cladocerans in Little Mere. The plant-associated filter-feeders were never very abundant in the open-water (see Table 3.18), although, occasionally, Simocephalus vetulus was found in considerable numbers on Ceratophyllum demersum. Large numbers were found when Daphnia populations were strongly increasing in the waters above the submerged plant beds. Daphnia were seldom found in submerged plant parts (a max. of 451 ind sample-1 were present in a sample on July 29th, 1998; 21 individuals were counted in one sample on October 8th, 1999). However, these Daphnia could have been inadvertently introduced from the above waters during collection of the sample. In any case large numbers of Simocephalus were never found along with large numbers of Daphnia in submerged plant beds, suggesting these two species favour different niches.

In lily beds, Simocephalus was associated with leaves, and few individuals were caught in water samples taken among the lilies. High Daphnia densities were found in lily beds together with considerable numbers of Simocephalus (e.g. compare Figures 3.9 & 3.11). Again, this seems to point towards both species partitioning resources available in adjacent ‘habitats’, i.e. lily leaves for Simocephalus, and water among the lily structure for Daphnia spp.

Simocephalus is an extremely poor swimmer (Sharma & Pant 1984). Movements of populations out of lily beds have not been observed (Lauridsen et al. 1996). In submerged plant beds in Little Mere, dense patches of Simocephalus (i.e. around 1,000 ind g-1DW, or around 20,000 ind m-2) were sporadically found, at the height of summer ‘98, and at the end of growing season ‘99 (Fig. 3.13). Populations in submerged plant beds are probably quite independent of those in lily beds.

Size-structure changes of Simocephalus were more conspicuous in the first year. There was little pattern to these changes, though. During the second year, size-structure did not change visibly even during dates with maximal fish presence (as implied from Daphnia disappearance; i.e. July mainly). This would be expected if Simocephalus was less vulnerable to fish, as predation pressures were apparently stronger the second year. The species in the plant-associated cladoceran community do not seem to be subject to the same predation 69

CHAPTER 3

pressures of ‘open-water’ grazers (Beklioglu & Moss 1995, 1996), perhaps owing to their lower visibility while remaining still (Fairchild 1981, but see Fairchild 1982). However, it is difficult to imagine what factors may drive size-structure dynamics of Simocephalus in Little Mere. Fish predation, as with Daphnia, is probably not a negligible influence, even if at lower intensities than over the faster-moving, and therefore perhaps more visible, Daphnia. In support of this, more of the larger Simocephalus were found in late summer ’98. In 1999, Simocephalus individuals slowly grew from August onwards, and most of the September peaks were made up of small animals (i.e. <0.7 mm), offspring of the larger August animals.

Sida crystallina, on the other hand, seldom co-occurred with high Daphnia densities in lily beds. The extreme seasonality of Sida (Table 3.18; Figs. 3.10 & 3.12) may be an alternative pathway to co-existence with competing filter-feeders such as Daphnia. Thus, this species may increase before Daphnia’s spring peak in April-May (e.g. in 1998), or after the demise of Daphnia populations, at the end of the growing season (e.g. in 1999). It may be a pioneer species. It thrives when it has no competition from other species, suggesting it takes advantage from occupying competitor-free space (Fairchild 1981). Daphnia and Sida did occasionally co-occur, however, albeit in localized areas of the lake (e.g. site 5 at the end of growing season ’99). Indeed, very high densities of Sida occurred occasionally in some lily beds, but not others. In addition, large populations developed in submerged plant beds in the second growing season. Lily beds seem to be the preferred ‘habitat’, however, as large numbers first developed there. Only when populations reached densities well in excess of 30,000 ind m-2 (e.g. Sept. 27th, 1999), was Sida also observed in more open waters (e.g. Oct. 8th, 1999).

Unfortunately, little can be said on top-bottom differences in submerged plant beds because samples could only be taken of both fractions on two occasions. In addition, it would have been interesting to extend sampling to other sites with submerged plants in order to gauge an estimate on horizontal variability in Sida’s density estimates. This may be crucial when extrapolating point figures to whole areas, for example when evaluating grazing impacts of whole populations, (see Chapter 4). Important plant-associated filter-feeders such as Sida may find submerged plant surfaces an attractive option, particularly when ‘sources’ (i.e. lily beds) become food-depleted.

70

PLANT-ASSOCIATED CLADOCERA DYNAMICS

Sida crystallina is largely confined to the littoral zone as it is a sessile phytoplankton filterfeeder typically attached to plant surfaces. Availability of attachment areas is not thought to limit Sida’s distribution as both fish predation (Fairchild 1983) and food conditions (Fairchild 1981) generally regulate population size below a size at which attachment would be scarce. However, in this study Sida was found at very high densities on some dates in 1999 (i.e. >800 ind g-1DW, or 40,000+ ind m-2; see Fig. 3.10). Attachment space could then have been scarce, to the extent of animals having to swim despite their known preference for living directly attached to surfaces (Szlauer 1973). Attachment to sub-optimal surfaces such as the underside of lily leaves (Frodge et al. 1990) may also have been forced by crowding. Fairchild (1981) found Sida crystallina in stands of Nymphaea to swim more in search for reattachment in areas with fewer lily leaves than in denser parts of the same lily bed. A higher density of Sida was found on isolated plants or sparsely distributed ones, particularly those on margins of stands of aquatic vegetation. This implies density (as expressed per m2) was uniform across areas and not denser where plant biomass was higher. Indeed from my data, Sida density can be seen to increase with decreasing plant availability. It can be shown density (in ind g-1DW) increased as lilies died down at the end of the growing season, particularly at sites where populations were not very crowded.

Fairchild (1981) also found density of this species was higher on younger plants. Significant differences in densities across depths were observed in immature individuals but not in adults. Juvenile Sida were significantly more abundant near the surface. The behaviour of Sida was not observed to change at night (Fairchild 1981). However, it is not known what determines the distribution of this species at a larger scale, tens of metres (e.g. between sampling stations in this study). The only published detailed study on the spatial distribution on this species is that of Fairchild (1981) and was based on a restricted number of samples of mainly emergent plant species (Scirpus). Few references have been made to population movements of this species, for instance between plant beds, except a passing reference to 30 metres being a good approximation to the range covered by any given population (Fairchild 1983). Confusingly, Lauridsen et al. (1996) found density differences between lily beds and more open waters were significant during the day but not at night. Fairchild (1981), however, did not observe diel behaviour changes of this species.

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Population dynamics. Standard error bars are large, particularly when densities are highest, and therefore it could be we are actually comparing different ‘patches’ in egg-ratio models (see section 3.3.6 for details (see Keen & Nasaar 1981). Occasionally, apparent birth rates exceeded actual population growth rates. These inconsistencies indicate the necessity of drawing error bars for parameters used in egg-ratio models. On the other hand, the predictably higher yield of young of Simocephalus after the 10th of July could not be detected, as perhaps 3 or 4 generations of young would have succeeded at the temperatures found those dates. This highlights the need for sampling at periods close to the egg developmental period, which is shorter at higher temperatures, in order to detect all significant dynamics of populations.

There was a tendency in both years towards a larger proportion of big Simocephalus towards late summer. On many occasions actual growth rates of Simocephalus populations cannot be justified by new births suggesting a source of mortality. Dynamics was more irregular in 1999 than in the previous year, perhaps as a result of larger fish predation pressure.

In 1998, both Simocephalus and Daphnia presented relatively high birth rates at the end of June (see Figs. 3.18 & 3.19), particularly in lily beds. Simocephalus maintained the highest birth rates of the two species, perhaps gaining advantage of the added food source of periphyton, generally unavailable to Daphnia. Birth rates of Daphnia were larger within lily beds than in more open waters. A reduction in perceived predation risk among lilies could release time and effort to feeding, increasing population growth.

Small-scale distribution of species. Crowding levels were generally correlated with population size. However, crowded populations were found to be very regularly distributed. Lloyd (1967) thought patchiness should be lower at higher densities, as crowding pressures lead to homogenisation. However, Whiteside (1974) found that increased densities of chydorid populations at Elk Lake (Minnesota) did not lead to more uniform arrangements.

There are two main contesting hypotheses for patchiness generation:

72

PLANT-ASSOCIATED CLADOCERA DYNAMICS

1) Patches are the result of availability and search for best food patches. Crowded patches will be increasingly unattractive to foraging animals and relatively empty spaces will be at a premium (Lloyd 1967). 2) Patchiness is the consequence of low-intensity and unspecific fish predation (Whiteside 1974).

Fish pressures mid-summer ’99 were likely so intense that even chydorid populations were affected. Patchiness of these populations was extremely variable. In contrast, the larger populations developing by late summer ’99 were regularly distributed, in agreement with Whiteside’s hypothesis of predation-driven patchiness patterns.

The limited mobility of chydorids, as they generally crawl rather than swim (see Fryer 1968), would make the strategy of searching and then staying in ‘best food patches’ even more advantageous than for swimmers. Moreover, delays in how spatial arrangement would reflect underlying resource competition would be expected in chydorids, especially when food conditions changed rapidly. Data from 1999, however, seem to indicate chydorids’ patchiness was in direct relation to fish predation.

In contrast with chydorids, distribution of plant-associated filterers was independent of fish predation pressure, and crowded populations were very regularly distributed. Extraordinarily crowded conditions were experienced locally by Sida populations at the end of growing season ’99. The dense aggregations were not patchily distributed. Again this observation is in support to the idea it is intraspecific, and perhaps interspecific (e.g. Daphnia & Simocephalus), competition that mainly regulated the small-scale distribution of cladocerans in Little Mere during the two years sampled, rather than fish.

The comparison between microcrustacean populations in lily beds and more open waters in this chapter will be extended to a comparison of their respective trophic role in the following chapter. The main hypothesis tested is that plant-associated microcrustacea have an important role to play in the maintenance of water transparency in Little Mere.

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Conclusions. •

There were significantly more Daphnia in lily beds than in more open waters. The individuals in lily-bed populations were also larger. Both observations lend support to the hypothesis lily beds constitute an effective refuge for large-bodied Cladocera in the face of fish predation risk.

•

Sida crystallina developed large but short-lived populations at the beginning of growing season ’98 (April) and at the end of the season in 1999 (September-October), maintaining residual populations during the rest of both summers (Fig. 3.10).

•

Sida crystallina was occasionally very abundant in Little Mere, reaching maximum densities of 1205 ind g-1DW lily leaf (1731 Sida in one leaf sample; see Table 3.18).

•

Simocephalus vetulus reached maximum densities in July and August in both years, even when high fish densities were apparent (as inferred from Daphnia’s low numbers). This suggests this species is not as exposed to fish predation as more pelagial species such as Daphnia.

•

Large populations of most chydorid species developed in the first growing season. During growing season ’99, by contrast, numbers were between 3 and 20 times lower (see Table 3.17).

•

Lowest patchiness values were found at periods of maximum crowding in all species particularly during the first growing season, suggesting food limitation and intra-specific competition were the main drivers of the spatial arrangement of populations at the small scale (Lloyd 1967).

•

During growing season ’99 (April to October), spatial distributions of chydorid species seemed to be driven by fish predation pressure. Patchiness was extremely variable during periods of high fish predation. By contrast, the generally much larger populations in late summer ’99 were very regularly distributed (i.e. not very patchy). This is in agreement with Whiteside’s hypothesis of predation-driven patchiness patterns (Whiteside 1974).

74

CHAPTER 4 Grazing rates of the plant-associated and open-water cladoceran communities in Little Mere.

CHAPTER 4

4.1. Introduction. Water transparency is influenced by multiple processes (Frodge et al. 1990; Barko & James 1991; Hansson 1992; Schriver et al. 1995; Karjalainen 1998; Gross 1999; see Chapter 1 for details). Many of these processes are particularly intense within plant beds in shallow lakes, and are thought to buffer the whole lake from a “switch” towards a turbid, phytoplanktondominated state (Leah et al. 1980, Timms & Moss 1984, Scheffer 1989, Blindow et al. 1993). The relative importance of grazing and nutrient limitation may be some function of lake depth (Moss et al. 1997). Moss et al. (1994) found in the West Midland Meres (Cheshire) that in the deep meres (i.e. > 3 metres) it was nitrogen which limited phytoplankton growth. In the shallow meres, inverse correlations between peak chlorophyll-a concentrations and cladoceran density (number of individuals per litre), particularly of Daphnia species, were taken as indicative of top-down regulation of algae. Elser et al. (1990) suggested algal biomass in oligotrophic lakes is regulated by nutrient availability rather than by zooplankton grazing, and Carpenter et al. (1997) proposed a nutrient loading gradient to organize the strength of the main regulatory forces of primary and secondary production in shallow lakes. In this gradient, grazing is considered a negligible control of algae under high nutrient loadings. In Little Mere, a shallow fertile lake (see Chapter 2 for details), top-down control by Daphnia seems to have been the most powerful influence on phytoplankton (Stephen, Moss & Phillips 1998). During the 1991-1999 period, Daphnia density was inversely related to chlorophyll-a values. However, in particular years and within periods of growing seasons (i.e. April to October), low chlorophyll-a concentration and high water transparency cannot be accounted for by Daphnia grazing. What is not known is what are the relative roles of Daphnia and plant-associated grazers. Very little attention has been paid to the role of microcrustacean grazers other than Daphnia species, particularly plant-associated genera such as Sida and Simocephalus, may have in the maintenance of clear water in shallow lakes.

Also, information is needed on the relative contributions of 'direct' mechanisms mediated by plants, for example the nutrient uptake by plants in competition with planktonic algae, and 'indirect' ones, for instance the grazing of phytoplankton by the plant-associated genera (e.g. Sida and Simocephalus). Planktonic zooplankton and even more so their plant-associated counterparts, exist in a bewildering array of forms and sizes in shallow lakes (Fryer 1968). This diversity may mean

76

GRAZING RATES

we cannot easily identify what fractions of the biota are the main grazers of phytoplankton (Frost 1984). Furthermore, accurate estimation of grazing rates in natural communities is difficult, and several ways of estimating these have been devised (see, for example, Gliwicz 1968, Enright 1969, Haney 1971, Porter 1973, Gulati et al. 1982). In broad terms, methods can be grouped into two classes.

The first group of methods estimates grazing rates directly, from field death rates of algae, estimated from repeated sampling of phytoplankton populations (e.g. Allen 1922), or through the in situ incubation of phytoplankton and zooplankton assemblages (Porter 1973). These methods are subject to large sampling variability and algae cell division rates applicable to natural environments must be known with a degree of accuracy. The second type of estimates involves estimating herbivory indirectly, from field abundance and clearance rates of grazers (e.g. Gulati et al. 1982). The main disadvantage of the latter type of estimates derives from the large number of variables normally involved in calculations (e.g. field abundance of grazers, calculated volumes of water and/or areal plant biomass estimates, clearance rates). Thus, errors are multiplied and interpretation of results can be complicated (see Discussion for details). Indeed, the feeding rates of microcrustacea are influenced by many interacting environmental factors. Species compete for resources (Horton et al. 1978) and predation influences the spatial distribution of grazing pressures (Kerfoot 1987). Many of these influences and factors change spatially, seasonally and even daily, quantitative and qualitatively. Therefore it was hardly surprising that results obtained in feeding experiments under controlled conditions in the laboratory were generally poorly representative of natural conditions (see Peters & Downing 1984).

The first in situ attempt at measuring feeding rates of particular species involved the comparison of algae, bacteria and detritus food in a plankton trap submerged in the lake water with an anaesthetising agent to stop grazing activity at a given time (Gliwicz 1968). The feeding rate was then estimated from the difference in cell counts generally in a simple medium, before and after the timed period during which the animals had been left to feed (e.g. Fuller & Clarke 1936). Methodological problems with this technique (e.g. resuspension of faeces, varying food concentrations) prompted a series of seminal papers in which a technique was developed that

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was less labour-intensive than the cell-count method. This method involved uptake of radioactively-labelled food items in short-term experiments (Marshall & Orr 1955; Nauwerck 1959 cited in Rigler 1971; Malovitskaya & Sorokin 1961; Monakov & Sorokin 1961, cited in Burns & Rigler 1967). Relatively accurate instantaneous feeding rates could be obtained quickly and easily.

Schindler (1971) stressed the necessity of using more than one food type with respect to both size and shape, or “taste”, as ingestion rates seemed to vary with food species presented to the animals. The issue of particle size feeding selection was first approached by Lampert (1974) and Gophen et al. (1974). Lampert used populations of Daphnia pulex, offering tritiumlabelled bacteria and carbon-14 labelled algae as food organisms, then calculating an index of food preference from the relative proportions of these two isotopes in the animal compared with the feeding medium. Haney (1971, 1973) designed an in situ method improving on Gliwicz’s 1968 apparatus, bringing together the advantages of in situ experimentation and the short-term radioactive technique. This innovative approach relied on using a plankton trap, or other means of enclosing a volume of lake water with its natural biota (i.e. a “grazing chamber”), with a piston that could rapidly introduce a small amount of highly radioactive food once the chamber had been lowered at a certain depth into the lake. A typical experiment would last 5 minutes to an hour (depending on the gut passage time of the animal species concerned), during which the zooplankton were allowed to feed. A critical step in this procedure is to kill the animals before food has had time to complete gut passage. Moreover, the short feeding period reduces the chance of relevant biological and chemical processes having a significant effect on rates. On the other hand, the precision and ease of use of radioactivity counting apparatus (e.g. liquid scintillation counters) greatly reduced the amount of time previously needed to estimate rates from cell-count differences. Used with in situ experiments, this technique allowed study of variables thought to play a role in the clearance rates on a spatial and temporal scale under natural conditions (e.g. food concentration, temperature, vertical migration of feeding animals). Downing and Peters (1980) modified Haney’s method by enclosing plants in the chamber. Thus, they were able to study the effect of body size and food concentration on the in situ feeding of a plant-associated cladoceran, Sida crystallina. Littoral cladocerans apparently can feed on food in suspended algae or scrape periphyton (Fryer 1968). In a later paper, Downing

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GRAZING RATES

(1981) investigated whether littoral cladoceran species (i.e. Alona affinis, Chydorus sphaericus and Sida crystallina) feed on these different food items on the basis of availability or if other mechanisms are in play. Both food compartments were labelled with different isotopes as in Lampert’s (1974) technique.

In this chapter, data on field abundance of grazers (reported in Chapter 3), plant biomass estimates, and clearance rate estimates from in situ experiments carried out in Little Mere are brought together in order to calculate measures of grazing rate (% of lake volume filtered per day, i.e. % day-1). Clearance rates were estimated from in situ short-term feeding experiments using a modified radio-labelling technique in which the animals were presented with both periphyton and planktonic algae labelled with different isotopes (H-3 and C-14). The aim of the differential labelling was to pick up differences in uptake related to food selectivity of the animals (see Fryer 1968). Periphyton ingestion rates (in mass of periphyton ingested per day) were also estimated from the carbon-14 activity taken up by animals. The main aim of this chapter is to compare the grazing rates (% day-1) of the ‘open-water’ microcrustacean grazer community with those of the Cladocera associated with plants, in order to establishing the relative roles of Daphnia and of hitherto often ignored plantassociated filter-feeders in Little Mere. Multiple and interacting mechanisms may be responsible for variations in phytoplankton density and water transparency (see Figure 1.1). Because of this complexity, I am directly addressing the question of what is the absolute contribution to these variations of cladoceran grazing. The validity of grazing rate estimates is discussed in relation to sources of error (i.e. estimates of field abundance of main grazers, clearance rates and areal plant biomass estimates).

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4.2. Materials and Methods. 4.2.1. In situ short-term feeding experiments. A series of nine short-term experiments was conducted between the 26th of April 1999 and 29th of October 1999 in Little Mere (Cheshire, England), see Table 4.1 for details of each experiment. Table 4.1. Date, weather and number of samples of the 9 in situ experiments. Experiment number 1 2 3 4 5 6 7 8 9

Date

Weather

26th April 12th May 1st June 7th July 28th July 13th Aug. 23rd Sept. 5th Oct. 29th Oct.

Sunny spells, slight breeze. Showery, mild, no wind. Sunny spells, no wind. Rainy, windy. Sunny, no wind. Showery, mild, no wind. Overcast, breezee. Sunny, no wind. Variable, slight breeze.

Total number of samples per experiment 51 65 69 43 68 61 100 93 100

In a typical in situ grazing experiment using radio-labelled algal food, a plankton trap or other means of enclosing a volume of lake water with their natural biota (i.e. a “grazing chamber”) is submerged in the lake. The radioactively labelled food is injected rapidly and left for a short period (i.e. 5 minutes to an hour, depending on animal’s gut passage time) during which the animals are allowed to feed. I used a 7 mm thick perspex chamber of capacity 6 litres (see Figure 4.1), based on an original design by Downing & Peters (1980).

Figure 4.1. Photo and schematic picture of the grazing chamber used in experiments. Dimensions are 10 x 20 x 30 cm. The chamber was constructed by sealing perspex pieces. The closure was made water-tight by using strips of neoprene and silicone around the inside. The chamber can open allowing a thorough cleaning before a new experiment is initiated. The sampling port allows the retrieval of the strips with periphyton before the sampling of the suspended algae and the animals at the end of each experiment. Adapted from an original design by Downing & Peters (1980).

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GRAZING RATES

To approach the issue of periphyton versus suspended algae feeding selectivity of animals, radioactive periphyton and planktonic algae, both of which had been previously labelled in the laboratory with different isotopes, were introduced into the grazing chamber along with the animals. 1. Preparation of periphyton food. - Carbon-14 labelled periphyton. An artificial, plastic, substrate with a rough surface was chosen for periphyton colonization for a series of reasons. In the first place, it facilitates enormously the labelling process. The natural substrate for the algae, a plant, would be expected to compete for the uptake of label. Many aquatic plant species are known to use bicarbonate (i.e. the carbon-14 carrier used in these experiments) as a carbon source. It is also easier when using plastic strips to standardise rates to unit area. A smooth and bright polythene plastic was an option discarded because it is thought to be more “unnatural” from the point of view of periphyton growth (Eminson & Moss 1980).

The periphytic community colonizing plastic substrates resembles that on more natural surfaces (Eminson & Moss 1980, Hansson 1992, but see Young 1945, Tippett 1970). Also, the potential problem of periphyton availability to feeding animals within the box can be minimized by increasing the number of strips with periphyton presented to them. The problem remains, however, of how natural the strips may be to potential microcrustacean scrapers.

Four small strips (i.e. 10 x 2 cm; see Fig. 4.3) were used. A set of strips was left for at least three weeks prior to each experiment, dangling from wires attached to a raft anchored in the experimental lake (see Fig. 4.2). To label the periphyton, generally 50 µCi of carbon-14 activity (54 mCi mmol-1, aqueous solution; ICN Pharmaceuticals, Inc.) was added to each of four 250 ml flasks in 200 ml of GF/C-filtered lake water collected from the experimental lake. The flasks were then left under continuous tungsten bulb light for at least 72 hours prior to each in situ experiment. Details on final strip activities achieved for each in situ experiment are shown in Table 4.2.

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Figure 4.2. Moment when the raft for periphyton in situ colonization is placed into the lake.

Strips were transported within 2 hours to Little Mere in a plastic bottle with GF/C-filtered lake water, to be used in the in situ experiment. Table 4.2. Carbon-14 activities, incubation periods (weeks) and labelling times (hours) for strips used in each in situ experiment. Radioactivity of strips is shown as both total C-14 average activity (N=4) per strip (MBq) and C-14 average activity (Bq) per µg of periphytic chlorophyll-a. Experiment (date)

Incubation period (weeks)

Labelling time (hours)

Mean activity per strip (MBq)

Mean activity per strip (Bq µg-1 periphytic chl-a)

1. 26th April 2. 12th May 3. 1st June 4. 7th July 5. 28th July 6. 13th August 7. 23rd Sept. 8. 5th October 9. 29th October

3 2 3 2 2.5 2.5 5 2.5 4

4 5 5 5 5 5 5 5 2

0.258 0.054 0.081 0.040 0.017 0.099 0.033 0.036 0.004

8.5 1.6 0.9 2.4 1.5 12.7 2.7 2.4 0.1

- Estimation of periphyton concentrations. In order to compare feeding rates of animals on periphyton across experiments we need a specific measure of carbon-14 activity (i.e. radioactive counts per minute CPM µg-1 periphytic chlorophyll-a, or CPM µg-1 dry weight of periphyton). To obtain these measures, each strip was cut in half lengthwise (see Fig. 4.3 for a flow chart of procedures). A set of halves was

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processed for the estimation of chlorophyll-a content (phaeopigment acid-correction method on SFS 5772 Finnish standard, based on ISO 10260, 1992) and dry weight. The periphyton was scraped off the plastic strip using a steel wire brush into a known volume of distilled water and sieved through a 140 µm mesh to remove larger elements, such as filamentous algae (see Figure 4.4).

In situ INCUBATION (3-4 weeks) Strips: 4 x (10 x 42cm.) cm.)

To lake RADIOLABELLING (2-4 days in the lab.)

In situ EXPERIMENT

SAMPLE PREPARATION AND COUNTING

Strips: 2 x (10 x 21 cm.) cm.)

CHLOROPHYLL-a ESTIMATION Strips: 2 x (10 x 21cm.) cm.)

Figure 4.3. Sequence of steps to prepare radio-labelled strips and take samples at the end of each in situ experiment. See text for details.

To estimate the chlorophyll-a content, a sample of the resulting periphyton suspended algae was collected onto a GF/C filter and extracted using ethanol 90 % in a hot bath (T=75 °C) for 5 minutes (SFS 5772 Finnish standard). Absorbance was read at 750 and 665 nanometres. Another sample collected onto a tared GF/C filter was oven-dried for 24 hours at 105 °C and weighed on an electronic balance to estimate dry weight of the periphytic algae. This was calculated by subtraction from the final weight of the tared filter. Other contributions to the final weight of the filter, such as inorganic solids, were considered negligible and therefore not included in dry weight calculations. - Periphyton activity. At the end of the timed feeding period, the sampling port on the grazing box was opened and the strips retrieved with forceps and placed in four separate bottles with distilled water. Once on shore the radioactive periphyton is thoroughly scraped off using a steel wire brush, as was used in the laboratory for the estimation of concentration parameters (see previous section). Again, the resulting periphyton suspended algae were sieved through a 140 µm mesh to remove larger particles and the whole volume was collected onto GF/C filters with the aid of a hand pump.

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Carbon-14 radioactivity measured from periphyton on strips, detached by shaking or by whole-strip assays

6000

Strip assay (activity per strip)

Counts per minute (103)

Shaking and filter (activity per strip)

5000 4000 3000 2000 1000 0 6

24 48 72 Labelling period (hours)

168

Figure 4.4. Carbon-14 activity of periphyton colonizing plastic strips, measured in a time-course uptake experiment at times 6, 24, 48, 72 and 168 hours (1 week). Plotted are means (N=3 strips) and one standard error at either side of the mean. Two methods for measuring carbon-14 on the periphyton were compared in this test. In the first, the plastic strip was cut into small pieces which were then introduced into scintillation vials and radioactivity measured. In the second, periphyton was detached from strips by shaking in water during one minute. The periphyton was then collected onto GF/C filters and these placed into vials for activity measurement. Direct assay of strips showed generally higher activities than assays with GF/C filters. Because of large variability, however, differences between these two treatments were not statistically significant.

Each strip was cut into small pieces and both the filter and its corresponding scraped strip (see Fig. 4.3) were introduced into marked scintillation glass vials with 1 ml of tissue solubilizer (soluene-350 and toluene, quaternary ammonium hydroxide mixture, Canberra-Packard). These vials were left for digestion to take place for around 24 hours at 40 °C, before about 10 ml of scintillation cocktail were added (toluene-based) with 50 µl of glacial acetic acid as a buffer. Swings in pH have been shown to interfere with radioactivity counting (Downing & Peters 1980).

600

Average strip carbon-14 activities in in situ experiments

CPM (103) microg-1 chl-a

500 400 300 200 100 0 26-abr 12-may 01-jun

07-jul

28-jul 13-ago 23-sep 05-oct 29-oct

Figure 4.5. Average strip activity (CPM (103) µg-1 chlorophyll-a), bars are of one standard error at either side of the mean (N=4 strips).

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GRAZING RATES

Differences in uptake cannot be related to labelling period or input activity, but are datespecific. The suggestion is that different components of the periphytic community are more efficient at using bicarbonate, the carbon-14 carrier, as a carbon source. 2. Planktonic algae food. The natural seston in the chamber was labelled using as “tracer” a dense culture of Scenedesmus spp. This species was chosen as a “tracer” of phytoplankton ingestion because of the ease with which it can be cultured and maintained without significant contamination. Experiments were short (i.e. around 15 minutes; see Table 4.4) and significant losses due to sinking within the chamber were thus not expected. It was assumed the “tracer species” was representative of the range of sizes ingested by the animals under natural conditions. This assumption is appropriate and avoids the problem of unequal label uptake by different algae species in the natural assemblage (see, for example, Lampert 1988). In addition, the small volumes injected into the 6-l chamber are comparatively small, thus not changing the natural food conditions in the lake. - Preparation of the H-3 labelled Scenedesmus “tracer”. The minimum volume of Scenedesmus culture to be injected in the chamber for ingestion to be detectable by the scintillation counter depends on culture density. Downing (1981) using the yeast species Rhodotorula as food organism for Sida crystallina judged 1000 cells ml-1 to be a minimum. Considering the capacity of the chamber (6 litres), the much smaller size of the Scenedesmus cell and the small volume needed for fast injection and mixing (40 ml), a density would be required of about 2 x 105 cells ml-1-tracer. Trial tests using 500 ml flasks and Daphnia spp showed an increased chance of detectable ingestion using around 10,000 cells ml-1 in the flask, as opposed to the 1000 cells ml-1 used by Downing (1981).

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Figure 4.6. Flow chart showing the sequence of steps followed to prepare the H-3 labelled Scenedesmus ‘tracer’ and take samples at the end of each in situ experiment. See text for details.

Another important parameter of the labelled culture is the specific activity of the label at the start of the experiment. For the uptake to be measurable after the time spans employed in short-term feeding experiments (5 minutes to an hour) the in-chamber activity should be at least 1,500 CPM ml-1, or around 1 CPM cell-1 if densities are 1000 cells ml-1 (Downing & Peters 1980). At least 5000 CPM ml-1 were shown to be necessary for ingestion by lake Daphnia spp to be detectable in preliminary tests in the laboratory. However, and given the higher “tracer” concentrations used in the in situ experiments here, a higher per millilitre activity was achieved (around 15,000 CPM ml-1, see Fig. 4.7).

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Suspension radioactivities sampled in the grazing chamber at the end of each feeding experiment 25000

GF/C means GF/F means

CPM ml-1

20000 15000 10000 5000 0 29-oct

05-oct

23-sep

13-ago

28-jul

07-jul

01-jun

12-may

26-abr

Figure 4.7. Final in-box activities of suspended material achieved in experiments (in CPM ml-1). Bars plotted are the averages of three 50 ml aliquots, collected on either GF/C (1.2 µm pore diameter) or GF/F filters (0.7 µm). Error bars are of one standard deviation either side of the mean. Details in text.

To obtain a fast estimate of culture density, a curve relating density (i.e. cells ml-1) to direct spectrophotometric readings can be used (see Fig. 4.8).

12000

Prediction of approximate cell density (cells ml-1) from direct spectrophotometric readings (abs. 665 nm) 1.2

cells ml-1 (x103)

10000 8000 0.55

6000 4000

0.39

Density = 8048.9 * Abs665 + 970.48

0.27

2000

r2 = 0.9945, p<0.001

0.19 0

0 0

0.2

0.4

0.6 0.8 1 Absorbance (665 nm)

1.2

1.4

Figure 4.8. Curve relating cell density (cells ml-1) with absorbance at 665 nm for a Scenedesmus culture.

The culture of known density (1 or 2 x 106 cells ml-1) was prepared by dilution with an inorganic nutrient-enriched commercially available solution (7 % vol. total N; 1.3 % P, as water-soluble pentaoxide; 6 % potassium oxide; 50 mgKg-1 Zn; 30 mgKg-1 B; 20mgKg-1 Mo; 10mgKg-1 Mn; ASB-Greenworld Ltd., Lincolnshire, UK). About 40 ml of this culture were then introduced into a 50 ml. flask and 59.2 MBq (1.6 mCi) of tritiated sodium acetate added

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(ICN Pharm. Inc., sodium salt in ethanol). The culture was left to label under continuous light on a fly rotator for 48-72 hours (see Table 4.3). Table 4.3. Labelling periods and input activity for the Scenedesmus ‘tracer’ in the 9 experiments. Also shown are measures of the residual activity (expressed as counts per minute, CPM and as a percentage of the total initial activity) left at the end of the labelling period in the medium used for labelling the algae. This residual activity is calculated as an average of 3 x 100 µl aliquot samples of the supernatant, after centrifugation of the medium to isolate the algae. See Fig. 4.7 and text for details. Experiment

Labelling period (hours)

Initial input activity (MBq)

H-3 activity left in labelling medium (100 µl)

% input activity remaining at the end of the labelling period

1 2 3 4 5 6 7 8 9

48 48 48 72 96 72 96 72 96

59.2 59.2 59.2 59.2 59.2 59.2 59.2 59.2 59.2

445748 169495 193481 220422 250728 295092 236071 147402 141636

8.1 3.1 3.5 4.0 4.6 5.4 4.3 2.7 2.6

This culture was shown to take up a maximum of 53 % of the initial activity after 48 hours (see Fig. 4.9). However, after around 72 hours the label was apparently lost from the cells, suggesting either the uptake concerns mainly labile compartments of cell function, rather than structural parts, or that once cell lysis starts, label is released to the medium. %INITIAL H-3 TAKEN UP BY Scenedesmus 60 X

50

X

% initial H-3 activ ity

40 X

30 20 X 10

X

X X

0X -10 0

20

40

60 80 100 120 140 160 180 Labelling time (hrs.)

Figure 4.9. Tritium uptake curve for a mixed Scenedemus culture during a time-course experiment with a mixed Scenedesmus culture. Uptake is expressed as percentage of total tritium activity introduced at the beginning of the labelling period measured in the algae. Plotted are means and standard deviations of three aliquot samples taken from the algae culture at times 0, 1, 3, 6, 24, 48 and 72 hours.

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TRITIATED LABELLING MEDIUM TIME-COURSE 100 90 80

X X X X

103 CPM/mL

70

X

X

60

X

X

50 40 30 20 10 0 0

20

40

60 80 100 Labelling time (hrs.)

120

140

160

180

Figure 4.10. Tritium activity remaining in the labelling medium during a time-course experiment with a mixed Scenedesmus culture. Plotted are means (in 103 of CPM ml-1 of labelling medium) with standard deviations of three aliquot samples taken from the supernatant after centrifugation to isolate the algae (see also Fig. 4.9). Samples were taken at times 0, 1, 3, 6, 24, 48 and 72 hours after introduction of H-3-sodium acetate. Most of the tritium is taken up by the algae in the first 24 hours. Note that there was little loss of activity in the labelling medium due to the exchange with the air and water during the 7-day time-course.

At the end of the labelling period the volume of labelled culture was double-centrifuged at 1,500 rpm for 10 minutes each time, resuspending once in fresh, unlabelled, medium. The centrifugation was repeated to eliminate any remaining unincorporated label. Samples of the first supernatant always presented very low activities, suggesting most of the label was within the cell. The labelled culture was kept in a 50 ml closed-cap centrifuge tube until the moment it was introduced into the 40 ml syringe for the experiment in situ. It was shown specific activity did not decrease significantly after two hours from the time the labelling medium was removed from the labelled algae. - Lake suspended algae food measures prior to each in situ experiment. Clearance rates have been shown to be strongly dependent on body size of the filter-feeder (Chow-Fraser & Knoechel 1985; Knoechel & Holtby 1986a & b). Effects of temperature (Lampert & Taylor 1985; Bogdan & Gilbert 1982; Mourelatos & Lacroix 1990), and food concentration (e.g. Downing & Peters 1980), are equivocal in in situ experiments, although the influence of food conditions on rates in laboratory experiments is well documented. These effects, however, rarely account for more than 30 % of variation in feeding rates (Peters & Downing 1984).

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I assumed that the highly radioactive Scenedesmus culture injected in the chamber as “tracer” did not change significantly the within-chamber concentrations of algae. Thus, samples were taken directly from the lake to estimate chlorophyll-a and dry weight of suspended matter prior to each experiment. Two one-litre samples of unfiltered water and two of water passed through a 25 µm mesh were taken at the experimental site using 1-l Pyrex bottles. Cladocerans can probably only ingest particles smaller than 100 µm, depending on shape, and more likely smaller than 25 µm (Burns 1968). The fraction sampled represents most of the “more edible” algae available to these grazers. In the laboratory, three 0.5 litre aliquots of filtered and three of unfiltered water were passed through pre-combusted and tared GF/C filters and the filters dried (T=105° C, 24 hours) and weighed to a precision of 10 µg in an electronic balance. They were then combusted at 400° C for 0.5 hours and re-weighed for ashfree weight estimations. Preliminary tests showed all organic matter combusted in the first half hour. A measure of chlorophyll-a concentration was also taken for the filtered and unfiltered water by collecting two 300-ml aliquots onto GF/C filters and extracting the pigment with hot 90 % ethanol in a water bath (T=75°C for 5 minutes). -Within-chamber suspended algae sampling after each in situ experiment (i.e. planktonic algae activity). At the end of each experiment three 50 ml aliquot samples of the suspended algae were taken from within the box for radioactivity counting. The assumption is that this activity does not change significantly because of the grazer activity, or for any other reason, in the short time of the experiment (generally about 15 minutes, see Table 4.6). The samples were taken by collecting 50 millilitres of the suspended algae onto glass fibre filters in the field by means of a hand pump, then placing the filter into a glass vial with tissue solubilizer. As with periphyton samples, filters were introduced into marked glass vials with one millilitre of tissue solubilizer and left to digest overnight at 40°C before scintillation cocktail and buffer were added, prior to counting. The filtering of the suspended algae samples onto glass fibre filters and the processing of the periphyton samples was done in the field. This reduced the time during which the highly labile tritium could exchange with the water and other living fractions in the water, and minimized the slow detachment of the carbon-14 labelled periphyton.

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GRAZING RATES

3. Field procedures. All experiments were conducted at the same site, located in more open and deeper waters (i.e. outside the lily beds) to facilitate the submersion of the box.

Before the experiment began, samples of the lake water were taken at this site (see site location on Map; ‘site 3’ in Fig. 4.14). The animals to be used in each experiment were collected from neighbouring plants using both the sampler especially designed for lily leaves (see section 3.2.3 for details) and a “tube sampler” for taking depth-integrated samples of the water within the lily bed. The aim was to maximize the representation of species present in the lake at the experimental date (see Table 4.6). See Fig. 4.11 for a flow chart of field procedures.

The box was filled with lake water by submersion. The previously sampled animals and the carbon-14 labelled strips with periphyton were then introduced into the box through the sampling port (see Fig. 4.1), the port was tightly closed and the box lowered into the lake attached to the side of the boat with a rope. The beginning of each experiment was marked by the injection into the box of the radioactive tracer algae. The injection was effected by means of a small syringe connected through a plastic tube to an inlet on the top of the chamber. The water in the chamber was then mixed using the same syringe for around 30 seconds (i.e. 6 or 7 syringe-fulls). Mixing tests in the laboratory using acrydine as a dye have demonstrated effective mixing after this time.

Given that periphyton ingestion rates are much lower than suspended algae feeding rates (e.g. Downing 1981), the time before experiment initiation, when animals were left with the carbon-14 labelled strips, is considered negligible compared with the feeding period. In addition, some time was assumed for food location by periphyton scrapers. Although an inlet was prepared for introduction of the experimental animals, it was subsequently preferred to use the sampling port directly to minimize potential disturbance to their feeding behaviour through injection.

Strips were retrieved and a suspended algae sample taken before the experiment was terminated, with about two minutes allowed for this (see Table 4.6 for experimental periods). The animal sample was then taken (see section 4.2.1) and submerged in liquid nitrogen for

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transport back to the laboratory. Back on the shore, samples of periphyton and suspended algae were collected on glass fibre filters, as described in the appropriate sections.

Collection of animals from plant bed

To open-water site

Lowered into water

•

Carbon-14 labelled strips

•

Animals

Injection of the H-3 labelled Scenedesmus culture + mixing (30 seconds)

FEEDING PERIOD (12-15 minutes)

• • •

Periphyton strips removed. Suspension activity sample. Collection of feeding animals on mesh + N2 freezing.

Figure 4.11. Field procedures during each in situ experiment. Each experiment started with the collection of animals from neighbouring lily beds and ended with the collection of samples of periphyton, suspended algae and the feeding animals from the grazing chamber. See text for details.

4. Animal samples. In the laboratory, the mesh was defrosted and the animals, algae and other material, washed into a bottle with distilled water (Burns & Rigler 1967). Losses of radioactivity into the sorting fluid (i.e. distilled water) are not thought to be large because isotonical concentrations can be reached rapidly.

Individual animals were measured, under a stereomicroscope with an ocular micrometer (maximum precision 10 µm) and placed in marked glass vials into which 0.5 ml of distilled

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GRAZING RATES

water had been added. Animals were sorted for at least two hours, but often only a fraction of those collected on the mesh were radio-assayed. A record was kept of size and species and other features of potential interest, for example presence of ephippia or asexual eggs in the brood pouch. One mililitre of tissue solubilizer was added and the samples incubated for 24 hours at 40°C in an oven, as were periphyton and suspended algae samples collected during the experiment, as described in previous sections.

Counts were made on both C-14 and H-3 channels simultaneously. Radioactivity was measured on 650 animals of 5 cladoceran species (see Table 4.4). -Radioactivity counting: quench and spillover corrections. When using radioactively labelled food we are essentially comparing the differences between the activity levels derived from the animals with those in the feeding medium, then attributing these differences to the ingestion of radioactive food by the animals during the experimental period (Rigler 1971). The comparison is made using simple arithmetic relationships: C.R. = Ra * 24 / Rm * t

‘C.R.’ is the clearance rate, in ml ind-1day-1

P = (Ra’ – residual) * 24 / (Rm’ * t)

‘P’ is the periphyton ingestion rate, in µg periphyton ind-1day-1.

where

Ra and Ra’ are the H-3 and carbon-14 radioactivities of one animal (cpm or counts per minute per individual). Rm and Rm’ are the H-3 and carbon-14 radioactivities of the feeding media (cpm per ml, and cpm per µg of periphytic chlorophyll-a, respectively).

‘t’ is the experimental period (hours), and Residual = (F*resid.*t/24) (in cpm per individual); ‘resid.’ is the residual carbon-14 activity in the suspended algae (i.e. detached carbon-14 labelled periphyton). The residual calculated by the expression represents the detached periphyton filtered, not ingested by the animals after scraping off a surface such as that of the plastic strips used in the experiments here. Carbon-14 activities in animals may increase through filtering this naturally detached periphyton, a process different from scraping or ‘browsing’ (see Horton et al. 1978). Thus, periphyton ingestion was corrected for this “accidental” uptake (see Downing 1981). In dual isotope experiments both carbon-14 and H-3 contribute to ‘Ra’ in one single sample. Activities from both can be counted in a liquid scintillation analyzer simultaneously because

93

CHAPTER 4

the energy profiles of the particles emitted hardly overlap. Likewise, residual carbon-14 activity may be found in ‘Rm’, from detached labelled periphytic cells.

Before clearance rates can be calculated, values for ‘Ra’ and ‘Rm’ should be corrected for their overlapping energy profiles and for a measure of interference with mainly the scintillation liquids, called ‘quench’.

Efficiency of detection by the counter is reduced by interference of the scintillation cocktail and by absorption of particles by the tissues in animal samples (i.e. “self-absorption”, see, for example, Wang & Williams 1965; Wang, Willis & Loveland 1975). Quench may be also due to plastic in some of the periphyton samples, or partially digested glass fibre in suspended material samples.

The energy profiles of both isotopes used overlap slightly (around 7-14 % in an unquenched sample). Moreover, quench influences the degree of overlap. A curve relating “quench level” (as determined by the liquid scintillation analyser) to efficiency was drawn (Fig. 4.12). From the quench level given for each sample an approximate correction was made.

tSIE and % of unquenched

% of unquenched CPM

70 60 50 40 30

R2 = 0.9195

20 10 0 908

748

559

479

351 270 tSIE

204

165

131

104

Figure 4.12. Curve relating quench index (i.e. tSIE, or Transformed Spectral Index of the internal standard spectrum, an index generated by the counter and measuring ‘quench’; high values indicate little quench) to efficiency (percentage of theoretical activity detected in spite of interference or ‘quench’).

94

GRAZING RATES

4.2.2. Plant surveys. To estimate total population sizes of the main plant-associated grazers (densities in ind g-1DW plant), areal measures of plant biomass (in gm-2DW) were needed. Thus, two surveys were conducted in each of two growing seasons (1998 and 1999). Each year, the first survey was in mid-summer, when the plant communities were at maximal growth, and the second in late summer, when senescence had set in. Each survey generally lasted two or three days.

About 50 plant samples were collected in each survey, by removing all the plant biomass enclosed by a cylinder of known base area (0.105 m2; see Figure 4.13). Samples were sorted into species, and the lily parts further classified into petiole and leaf parts. Plants were then dried at 60°C. Plant biomass was then expressed as dry weight per m2 of lake bottom (i.e. g m-2). Sampling locations were randomized by drawing 10-12 transects across the lake with a rope that was marked with tape every 10 metres (for transect maps see Appendix C). Every mark was assigned a number and a subset of about 50 samples of all possible numbers along transects was then selected at random.

Figure 4.13. Sampler used to isolate a known area from within which all plant biomass is removed using a rake (see text for details). The base area is 0.105 m2 (diameter = 36 cm).

Diameter = 36 cm

95

GRAZING RATES

Plant cover. Coverage of the different plant species in Little Mere was visually assessed in the first survey by noting the vegetation along the transects. In subsequent surveys gross changes in the vegetation communities and lily bed coverage were assessed in large sectors of the lake. Depth. The depth of the water column at each sampling point was determined using a marked pole. Two lines of stakes were set at 10-m intervals in the lake. Stakes were numbered and with the aid of conspicuous landmarks on the shoreline, a reference was kept of the approximate position while taking measurements. A detailed depth map was then drawn by connecting sites of similar depth.

96

GRAZING RATES

[Here "fused" Map] Map on paper (A4 landscape) with lily bed outline, extrapolation sectors and sampling site location + overlaid A4 transparency with bathymetry.

Figure 4.14. Map of Little Mere (Cheshire) showing the lily beds’ outline (black interior line), equal depth lines, sampling site location and extrapolation sectors for each sampling site. Note that the sector represented by site 5 is made up of three separate areas. The boundaries of each sector approximate the actual lily bed outline.

97

GRAZING RATES

98

GRAZING RATES

4.2.3. Estimation of grazing rates. Grazing rates for the main filter-feeding species in Little Mere were separately estimated for the two years sampled (i.e. 1998 and 1999). Large spatial variation in grazing rates has for long been acknowledged (e.g. Gulati, Siewertsen & Postema 1982). For Daphnia spp, sites were grouped into either lily or “open water” sites, while for Simocephalus vetulus and Sida crystallina data for the three lily bed sites were grouped. Table 4.4. Approximate parameters used for extrapolation of density data and grazing rate calculation (see text for details). Values are given for the area (m2), volume (m3), mean depth (m) of each sector of the lake used in extrapolations and represented by each sampling point. TYPE OF SITE Lily bed sites

SITE*

Area (m2)

Volume (m3)

Mean depth (m)

1 1,100 770 2 2,500 1,750 5 6,900 4,300 ‘Open 3 7,900 6,500 water’ 4 9,600 7,600 sites *see Fig. 4.14 for a map of sampling site locations.

0.7 0.7 0.6 0.8 0.8

Surface (m2)

10,500 (38 %) 17,500 (62 %)

The following equation gives the grazing rate for any given species (G, in % lake volume filtered per day, i.e. % day-1), for each sector separately (see Figure 4.14 for a Map of sectors):

≅

G

T

×

V

×

F 1000

ind. × ml day ind. -1

-1

% day

=

Equation 1.

-1

l × ml l

-1

See Table 4.5 for the meaning of variables used in equation 1.

Variability in clearance rates with body size of the animal was taken into account when calculating grazing rates by applying a different rate for each size class. A ‘weighted’ clearance rate is calculated for each date and species. The weights are on the basis of sizeclass proportions in the populations sampled from lily leaves in the case of Simocephalus vetulus, and from tube samples in the case of Daphnia spp. Three size classes were used, small individuals (0.5-0.9 mm), medium-sized (0.91-1.5 mm) and larger individuals (> 1.51 mm). When no size-structure information was available (e.g. when densities were too small to

99

GRAZING RATES

obtain reliable information), sizes were assumed to be those of the closest date or/and site. For Sida, an average clearance rate for a 1.5 mm individual was used. Table 4.5. Variables used in the calculation of grazing rates (% day-1). VARIABLE T T’

UNITS individuals (ind) ind -1

F D

ml day ind ind g-1

D’ P

ind l-1 g m-2

C

m2

V

l

z

m

-1

DESCRIPTION Total number of individuals of a given species (plant-associated) in a lake sector. Total number of individuals of a given species (‘open-water’) in a lake sector. Clearance rate. Number of individuals per gram of lily (leaf + petiole). Number of individuals per litre of lake water. Grams of lily (leaf + petiole) per unit area of lake bottom. Surface area of a sector of the lake around each sampling section (see Fig. 4.15 for a Map). Approximate volume of lake water in a given sector of the lake. Average depth of the water column in any given sector of the lake.

ESTIMATION T = D * ind g-1

ind

T’

=

P

*

D’

m2

*

V

ind l-1

ind

C

g m-2 l

See section 4.2.1 for details. See Chapter 3 for details. ditto. See section 4.2.2 for details. ditto.

V l

=

C m2

*

z

* 1000

m

l m-3

See section 4.2.2 for details.

For the estimation of total population sizes ('T' in equation 1), data from lily leaf and petiole samples were added together to calculate a ‘lily-integrated’ density for each animal species. Likewise, errors in density estimates were calculated from the pooled data (i.e. leaf + petiole samples). When calculating whole population sizes from densities (i.e. ind g-1DW) two values of plant biomass were used per season, corresponding to the two surveys conducted (section 4.2.2). The first factor is used for April to the end of July density data, while the second value helped extrapolate the rest of densities. I will restrict the analysis to a comparison of filter-feeders of both ‘communities’ (i.e. openwater and plant-associated). The estimation of periphyton ingestion rates and the total removal by whole populations involves much larger variability and, therefore, results are difficult to interpret in terms of potential effects on the ecosystem.

4.3. Results. Clearance rates. The data consist of 530 individual animal clearance and periphyton ingestion rates (Table 4.10). Highly significant (F-ratio test, p<0.001, except Scapholeberis mucronata; p=0.014)

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GRAZING RATES

regression models for clearance rates were obtained for five of the six species (Table 4.9). For periphyton ingestion rates highly significant models were found for only four of the six species, Eurycercus lamellatus, Simocephalus vetulus, Sida crystallina, and Daphnia spp. Ten species were collected in experiments (Table 4.6), but breadth of response data only allowed analysis on six (Daphnia, Simocephalus vetulus, Sida crystallina, Eurycercus lamellatus and Ceriodaphnia sp.) Table 4.6. Experimental periods and composition of animal samples of each in situ experiment. The period to freezing the filter is taken as the feeding period. Experiment

Species’ names* Feeding period (mins.) Number To box To filterAnimals Species retrieval freezing 1 14 18 51 4 D/Ch/B/Sim 2 14 19 65 6 Sim/D/B/C/P/A 3 15 69 6 Sim/D/C/E/P/A 4 14 43 4 D/C/B/Sim 5 15 68 4 Sim/Sc/C 6 15 61 3 Sim/Sc/C 7 15 100 4 Sim/S/D/E 8 9 12 93 5 Sim/S/D/G/E 9 9 13 100 4 Sim/S/D/E *Species: A-Alona spp; B- Bosmina longirostris; C- Ceriodaphnia spp; Ch-Chydorus spp; D-Daphnia spp; E-Eurycercus lamellatus; P-Peracantha truncata; Sc-Scapholeberis mucronata; Sim. Simocephalus vetulus; S-Sida crystallina.

Table 4.7. Ranges for clearance rate (CR), periphyton ingestion rate (P) and the main independent variables in models for each species in in situ dual-isotope feeding experiments in Little Mere (Cheshire, UK). Simocephalus vetulus* 264 Observations 7 Number of dates 0.3-26.3 CR (ml ind-1day-1) 0.1-429 P (µg ind-1day-1) 0.44-2.00 Size (mm) 9.9-24.4 Temperature (°C) 0.8-8.9 Periphyton chl-a (µg cm-2) 6.9-45 <25 µm planktonic chl-a (µg l-1) 11.3-93.6 Total planktonic chl-a (µg l-1) 2.5-14.7 Total suspended solids (mg l-1) *after exclusion of outliers. See text for details.

Variable

Daphnia spp* 130 8 0.4-29.4 0.1-252 0.25-2.94 8.7-23.9 1.2-8.9 6.9-45 11.3-93.6 2.5-13

Sida crystallina* 78 3 0.59-260.0 0.2-656 0.75-3.44 10.8-14.7 1.2-4.8 6.9-45 13.7-57.7 2.5-5.6

Eurycercus lamellatus 35 5 0.0-1.6 0.1-768 0.56-2.25 10.8-14.7 1.2-8.9 6.9-45 13.7-57.7 2.5-7.4

Scapholeberis mucronata 23 2 1.2-9.3 0.4-23 0.38-0.69 20.8-24.4 0.8-1.1 40.1-44 58.3-93.6 13-14.7

See Tables 4.9 & 4.10 for the variation accounted for by each model. All accounted for 21.769.7 % of variance in clearance rate measurements and 11.4-73.2 % of periphyton ingestion estimates. The most important of the variables tested was body size, accounting alone for 2262 % of the total variance observed in clearance rates. Results for variables selected for periphyton ingestion rates were quite variable (Table 4.10).

101

GRAZING RATES

Table 4.8. Transformation of response data (clearance rate and periphyton ingestion rate estimates) from in situ dual-isotope experiments conducted in Little Mere (Cheshire) during growing season ‘99 (April to October) for each species and linearizing effect of transformations, as expressed by the coefficient of determination (r2) when regressed against body size (mm). See text for selection criteria for type of transformation. Species

Type of transformation for Clearance rate response data (CR)

Periphyton ingestion rate data (P)

Daphnia spp1 Log Log Scapholeberis mucronata Log Log Sida crystallina1 Log Log Simocephalus vetulus1 Log Sqrt Eurycercus lamellatus Sqrt Untransformed 1 Outliers suppressed (see text for details). * p<0.05; ** p<0.01.

Effect of transformation (P) r2

Effect of transformation (CR) r2 Before 0.46** 0.238* 0.46** 0.51** 0.6**

After 0.499** 0.26* 0.57** 0.54** 0.62**

Before 0.0005 0.018 0.007 0.17** 0.55**

After 0.047* 0.038 0.125** 0.231** -

Food conditions for the nine experiments were spread across quite a wide range, allowing the relationship of clearance rate to food concentration to be investigated (see Fig. 4.15). 2

Periphyton (micrograms DW cm )

16

Total chlorophyll-a <25 micrometer chl-a

90

14

chl-a (micrograms l-1)

TSS

80

<25 micrometer SS

12

70 60

10

8.9

8

50 40

6

30 20 10

3.3

4

3.0 4.8 1.6

0 26-Apr 12-May 01-Jun 07-Jul

1.1

0.8

Suspended solids (mg l -1)

100

2

1.2 1.5

0 28-Jul 13-Aug 23-Sep 05-Oct 29-Oct

Figure 4.15. Food conditions during feeding experiments at the experimental site. Plotted are total and less than 25 micrometer fractions of both chlorophyll-a (µg l-1) and suspended solids (mg l-1). Periphyton concentrations (in µg dry weight cm-2) within the grazing chamber have been plotted as histograms (see text for details).

102

GRAZING RATES

Table 4.9. Clearance rate best linear regression models relating clearance rate to size and other variables in experiments carried out in Little Mere (Cheshire). Basic statistics and ANOVA tables for the species analysed are given. The top table shows the multiple linear regression models obtained for Simocephalus vetulus and Daphnia spp. The best linear model for the other four species includes only one predictor (size), for details on the transformation chosen for the response data, see Table 4.8. See text for details. Simocephalus vetulus

MODEL

Predictor variable

Coeff.

SE

Part. F

Adj.r

Size (mm)

0.691

0.035

305.2

-0.008

0.002

19.1

-2

Strip chl-a (µg cm )

ANOVA

Daphnia spp 2

Predictor variable

Coeff.

SE

Part. F

Adj.r

0.538

Size

0.248

0.031

120.5

0.481

Signif. Fincrease <0.001

0.568

<0.001

Temperature (°C)

-0.048

0.006

58.8

0.642

<0.001

-1

-0.006

0.002

18.05

0.685

<0.001

-2

6.25

0.697

0.014

MS

F

p

75.28

<0.001

Temperature (°C)

0.015

0.003

12.1

0.586

0.001

<25µm chl-a (µg l )

<25 µm ashfree SS -1 (mgl ) constant

-0.032

0.01

9.4

0.599

0.002

Strip chl-a (µg cm )

0.09

0.037

-0.146

0.057

constant

0.08

0.09

df

SS

df

SS

Total

261

23.48

Total

129

14.06

Regression

4

14.21

3.55

Regression

4

9.93

2.48

Residual

257

9.27

0.036

Residual

125

4.12

0.033

MS

F

98.5

Sida crystallina Var.

Coeff.

SE

Size

0.511

0.05

constant

0.619

0.098

df

SS

Total

79

11.81

Regression

1

11.7

11.7

Residual

78

0.11

0.0014

ANOVA

2

p

105.14 0.569 <0.001

MS

p

<0.001

Eurycercus lamellatus

Part. F Adj.r

F

p

105.14 <0.001

2

Signif. Fincrease <0.001

Scapholeberis mucronata 2

2

Coeff.

SE

F

Adj.r

p

Coeff.

SE

F

Adj.r

p

2.5

0.33

56.2

0.62

<0.001

3.18

1.19

7.11

0.217

0.014

-0.03

0.37

0.28

0.65

df

SS

df

SS

MS

F

p

34

47.6

22

6.16

1

30

30

1

1.56

1.56

7.11

0.014

33

17.6

0.53

21

4.6

0.22

MS

F

56.2

p

<0.001

103

GRAZING RATES

Table 4.10. Best periphyton ingestion linear regression models relating periphyton ingestion rates to size and other variables in experiments carried out in Little Mere (Cheshire). Basic statistics and ANOVA tables for the species analysed are given. The top table shows the multiple regression models. The second table shows the simple models found for two species. Simocephalus vetulus

MODEL

ANOVA

Predictor variable Temperature (°C) <25µm chl-a -1 (µg l ) Size (mm)

Daphnia spp 2

Coeff.

SE

Part. F Adj.r

-0.456

0.03

0.141

0.011 202.92 0.576 <0.001

3.06

0.426

41.15

0.637 <0.001

Periphyton -2 (mg cm ) Constant

-0.124

0.029

17.99

0.656 <0.001

4.585

0.683

Source

df

SS

Total

261

3962

Regression

4

Residual

257

86.85

p

0.248 <0.001

MS

F

p

2619.9 654.97 125.4 <0.001 1342.3

Predictor variable Periphyton -2 (mg cm ) <25µm chl-a -1 (µg l ) constant

Coeff.

SE

Part. F

Adj.r

0.091

0.067

436.04

0.771

0.024

0.003

86.9

0.863

-0.17

0.029

Source

df

SS

Total

129

25.59

Regression

2

22.124

11.07

Residual

127

3.45

0.027

5.22

Eurycercus lamellatus 2

MS

F

p

Predictor Coeff. variable <0.001 Size 303.4 (mm) <0.001 83.68 <25µm ashfree SS Temperature 23.13 (°C) constant -754.04

p

407.75 <0.001

Scapholeberis mucronata

Part. F

Adj.r

p

36.36

40.62

0.54

<0.001

16.6

18.58

0.7

<0.001

10.33

5.01

0.732

0.032

MS

F

p

31.99

<0.001

177.05

Source

df

SS

Total

34

719573

Regression

3

543915

181305

Residual

31

175659

5666

Sida crystallina 2

2

Predictor

Coeff.

SE

Part. F

Adj.r

p

Predictor

Coeff.

SE

Part. F

Adj.r

p

Size (mm) constant

*

*

0.841

-0.007

n.s.

Size (mm) constant

-0.357

0.107

11.116

0.114

0.001

1.873

0.211

df

SS

MS

F

p

Total

79

45.61

Regression

1

5.69

5.69

11.116

0.001

Residual

78

39.92

0.512

ANOVA

104

df

SS

MS

Total

22

1.975

Regression

1

0.076

0.076

Residual

21

1.899

0.009

F

0.841

p

n.s.

2

SE

GRAZING RATES

Simocephalus vetulus yielded the most complex models. Size accounted for 53.8 % of clearance rate variation (Table 4.9). An additional 3 % was explained by the depressive effect of periphyton concentration on rates. With respect to the range of values predicted, values are about 3 times larger than those predicted by Ivanova & Klekowsky (1972), but about half those estimated by Sharma & Pant (1982) using cell-count methods (see Table 4.11). Periphyton ingestion of this species could be explained in 65.6 % of cases, with four variables (see Table 4.10). Temperature had a strong depressive effect on periphyton ingestion. Periphyton concentration (mg cm-2 dry weight) was also inversely correlated with ingestion rate. Larger animals tended to ingest larger amounts of periphyton. Table 4.11. Clearance rate estimates (ml ind-1day-1) for the main filter-feeders in Little Mere (Cheshire). Tabled are own estimates (CR) and values gleaned from the literature (CR', CR''). The ratios between own estimates and literature values are also shown. Clearance rate comparisons (ml ind-1day-1) and ratios

Size (mm)

CR

CR'

CR''

CR/CR'

CR/CR''

0.7 4.0 3.1 0.8 0.97 3.0 1.2 11.6 14.6 0.5 0.4 5.7 1.8 23.2 40.3 0.4 0.2 9.7 2.1 86.1 0.1 12.1 1.2 29.5 19.1 0.17 0.26 Sida crystallina 5.0 1.8 84.3 45.5 0.22 0.4 18.2 2.1 130.5 61.0 0.23 0.5 30.5 1.2 10.3 1.8 0.6 3.2 Simocephalus vetulus 5.7 1.8 36.6 6 0.3 2.1 12.6 2.1 10.8 1.6 17.4 CR'. Daphnia spp: Mourelatos & Lacroix (1990); Sida crystallina: Downing & Peters (1980); Simocephalus vetulus: Brendelberger (1991). CR''. Daphnia spp: Burns & Rigler (1967); Sida crystallina: Downing (1981); Simocephalus vetulus: Ivanova & Klekowski (1972). Daphnia spp

Following are the equations drawn by back-logging regressions between body length (mm) and clearance rate estimates (ml ind-1day-1), from in situ feeding experiments in Little Mere: Clearance rate (CR: ml ind-1day-1) vs. Length (L: mm): Simocephalus vetulus: CR = (102.96* Log (L+1) - 0.19) - 1 Daphnia spp: CR = (101.97* Log (L+1) + 0.15) - 1 Sida crystallina: CR = (104.84* Log (L+1) - 0.88) -1 In Little Mere, Daphnia apparently filtered less efficiently than Simocephalus and Sida, particularly larger-bodied animals (see Table 4.11 & Fig. 4.16).

105

GRAZING RATES

Periphyton ingestion rates of this species were positively correlated with the amount of periphyton presented to the animals in the box. On the other hand, suspended algae food (<25 µm chlorophyll-a) also had a positive effect on ingestion of periphyton.

Sida crystallina filtered about double the volume of similar-sized Daphnia, particularly the larger individuals (Table 4.11). Strikingly, the larger Sida ingested less periphyton than small individuals (see Table 4.10).

Eurycercus lamellatus. Clearance rates (CR) were only significantly correlated with size of the animal (Table 4.9). This species is thought to be mainly a detritivore and periphyton scraper (Smirnov 1962). However, a surprising 62 % of CR variation could be explained by carapace length, in a similar fashion to filter-feeders. For periphyton ingestion it was possible to build a multiple regression model with two additional variables representing suspended algae food concentration (<25 µm ashfree weight of suspended solids) and temperature, accounting for 73.2 % of total variation.

Only a small amount of variation in clearance rates of Scapholeberis mucronata could be explained by models (27 %), and no valid model was found for periphyton ingestion, given the small sample size (N=23). Size was the only variable with sufficient range of values for meaningful analysis, but its effects on ingestion were not significant. No significant clearance rate or periphyton ingestion models for Ceriodaphnia spp could be found, as very few countings were made on this species (Table 4.7).

106

GRAZING RATES

1.6

Daphnia spp : clearance rate vs. body size

1.4

log(CR+1) = 1,97* log (L+1) + 0,15 2 r = 0,499

-1

1.0

-1

log(ml day ind +1)

1.2

0.8 0.6 0.4 0.2 0.0 0.0

0.1

0.2

0.3

0.4

0.5

0.6

log (mm + 1)

Simocephalus vetulus : clearance rate vs. body size

1.6

log(CR+1) = 2.9583 * log (L+1) + 0.1864

1.4

2

R = 0.5569 1.2 1.0 0.8 0.6 0.4 0.2 0.0 0.0

0.1

0.2

0.3

0.4

0.5

0.6

log(mm + 1)

Sida crystallina : clearance rate vs. body size

3.0

log (CR+1) = 4.8441*log(L+1) - 0.8769 2.5

2

r = 0.56

2.0

1.5

1.0

0.5

0.0 0.2

0.3

0.4

0.5

0.6

0.7

log (L+1)

Figure 4.16. Clearance rate (in ml ind-1day-1; log-transformed) vs. animal body length (L(mm); log-transf.) for Daphnia spp (top), Simocephalus vetulus (middle) and Sida crystallina (bottom). Lines represent the linear regression models shown on each plot (see Table 4.8).

107

GRAZING RATES

Least-squares multiple regression analyses were performed to establish the relationship between feeding (i.e. clearance rates or periphyton ingestion rates; CR or P, respectively), and the individual body length, algal concentration, periphyton concentration and temperature (see Table 4.12). Any response, however complex, may be more than adequately described by a polynomial if most of the relevant variables are known. The question, then, is really what order polynomial are we prepared to accept as workable, given, of course, that the model describes a statistically significant effect. Linear models were preferred to those involving power or higher order polynomials, because of their generally better fit to response data. All species’ data had to be fitted to a same-shape regression model too, i.e. linear, so comparisons between species could be easily made. Table 4.12. Candidate predictor variables in stepwise multiple regression analysis. Suspended algae chlorophyll-a • Total • <25 µm fraction

Suspended solids • • • •

Total <25 µm fraction Total ashfree <25 µm ashfree

Periphyton density • •

Temperature

Size

Per cm2 periphytic chlorophyll-a Per cm2 periphyton dry weight

Equations were fitted for the main species found in experiments, Simocephalus vetulus, Daphnia spp, Ceriodaphnia spp, Sida crystallina, Scapholeberis mucronata and Eurycercus lamellatus. The simplest equation was found for each species by sequentially adding terms with significant coefficients (F-ratio test at the 0.05 significance level). Further refinement of models involved the consideration of the explanatory value of each term, as expressed by the additional variation in feeding rates accounted for in the model (i.e. the r2).

Initially, all variables were included in the dataset. Sometimes the forward stepwise procedure added variables that were highly correlated or biologically related (e.g. total suspended solids and total chlorophyll-a). The analysis was then re-run with the first variable entering in the stepwise procedure as representative of all other related variables (e.g. total suspended solids would now represent total and small chlorophyll, <25 µm suspended solids, total suspended solids ashfree weight and <25 µm suspended solids ashfree weight). The model selected was that one having the maximum significant (F-ratio test, alfa=0.05) predictive power (i.e. max. r2). However, very small step increases were not added to the model, even if the final model was highly significant (maximum simplicity principle).

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In addition, variables were only considered candidate predictors in each model if more than three values were possible for any given species (i.e. three or more dates). Thus, only ‘size’ was considered a potential predictor for Scapholeberis mucronata (2 dates) and Sida crystallina (3 dates). Analogously, no periphyton ingestion model was drawn for Ceriodaphnia spp because of insufficient breadth of response data. Unstable variance of filtering rate measurements (Simocephalus vetulus )

20 15

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Figure 4.17. Plot of residuals of the linear regression model of clearance rate (ml ind-1day-1) of Simocephalus vetulus on size (mm) before transformation of data. Variance of response data was found to follow a pattern of increasing variance with increasing means. Details on the type of transformation chosen for each model and species are given in Table 4.8.

For the full interpretation of regression models, two basic conditions of data must be met. In the first place, response for given values of the predictors must be about normally distributed. Secondly, the variance of these responses across the range of values adopted by the predictors must be similar (i.e. variance is said to be ‘stable’). Variance in feeding rates was shown to be unstable, with a tendency to a larger scatter of response in larger animals, which is a common pattern (Samuels 1991; see Fig. 4.17). To make the response linear and alleviate error unstability, both periphyton ingestion and clearance rates were transformed. For details on the type of transformation see Table 4.8. The logarithmic transformation was often chosen over the commonly used power-transformation (see, for example, Downing & Peters 1980). The type of transformation was chosen based on three criteria. First, it maximized the amount of response accounted by the linear model (i.e. max. r2). It also stabilized variance and normality of response, while at the same time lowering the value of residuals (i.e. minimum mean sum of residuals for the transformations tested). Regression models can sometimes describe data well, but it is perhaps their predictive function which is more often highlighted (Bailey 1992). Thus, extreme values (with respect to the regression model, i.e. ‘outliers’) weighing heavily on predictive power were identified by 109

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visual inspection of residual plots and removed from subsequent analysis (see Table 4.8). Outliers were always found to be the maximal values of clearance rate, perhaps explained by the inadvertent inclusion of labelled algae material into animal vials. Therefore their suppression is justified because they do not belong to the distribution but respond to a methodological error (Samuels 1991). Plant cover and biomass. Lily growth the first year was about half that in the second year (Fig. 4.18). However, mean estimates are not accurate enough to estimate growth rates in the interval between the first and second surveys. Years were very different with respect to average changes between surveys, though. Thus, in the second year, the cycle of lily growth was shifted later in the season, and peaks of biomass were generally larger in the second survey. However, differences between years could not be tested statistically due to large variation in biomass. Leaf average weight across sites in 1998

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60 50 40 30 20

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Figure 4.18. Lily leaf and petiole average weights (gm-2DW) in plant surveys conducted in Little Mere during two growing seasons (1998 & 1999). Top and bottom plots are 1998 and 1999 surveys, respectively. First and second ‘replicate’ surveys are left and right, respectively. Bars are of one standard error each side of the mean. See text and Appendix B for details.

As regards plant cover, in the ‘open water’ (i.e. outside the lily-covered littoral area; for a Map see Fig. 4.14), both years were characterized by an increasing dominance of Ceratophyllum demersum as the growing season proceeded. Maximum biomass of this species was comparable in both years.

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Callitriche hermaphroditica was found in late summer the first year, in very dense patches (i.e. up to 265 g m-2DW). In the second year, in contrast, it was not found at all among the 67 samples taken in late summer, but was present in June, albeit at lower densities than in the previous year.

Potamogeton berchtoldii was present in the first year, although no biomass estimates are available. The second growing season it was only occasionally spotted. On the other hand, Elodea canadensis was hardly seen the first year, but was relatively abundant by September in the second year (i.e. 40-80 gm-2DW; Appendix C) even if patchily distributed. Grazing. The grazing role of the plant-associated filter-feeders in Little Mere, represented by Simocephalus vetulus and Sida crystallina, was very small compared with that of Daphnia (Figs. 4.19 to 4.21). Daphnia spp grazing rates were much higher and persistent in 1998 than in 1999 (Fig. 4.19). In the second growing season, grazing rates of this species were virtually zero from the beginning of June to mid-August. A high peak was measured in spring within the lily beds this year. Daphnia grazing rates during growing season 1998

Daphnia grazing during growing season 1999

250

90 Grazing in lily beds

80

-1

Grazing rate (% day )

-1

60

150

50 40

100

30 20

50

10

*

0

Figure 4.19. Daphnia spp grazing rates (% day –1; i.e. percentage site-area volume (litres) filtered per day) during the two growing seasons (left: 1998, right: 1999).

Simocephalus vetulus had a maximum grazing rate of 8-10 % day-1 at certain times of the growing season, with a localized peak of 15-30 % day-1 (Fig. 4.20). There were important

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differences in the timing of maxima between years, and in the first growing season maximum grazing impact occurred mid-summer while in the second year it was restricted to the end of the season. 4

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Figure 4.20. Simocephalus vetulus grazing rates (in % day-1, i.e. percentage volume filtered per day) across lily beds in Little Mere (Cheshire) (Left: 1998; right: 1999). Different-colour bars indicate rates estimated from the min. and max. plant biomass. Total populations of the species are calculated using the min. and max. values found during plant surveys. Bars are one standard error at either side of the mean, calculated from density estimate errors (for details see section 4.2.3).

In both years, Simocephalus density differences detected across lily beds paralleled those of grazing rates. Rates were about twice larger in 1999 than in 1998 (Fig. 4.20). In 1998 the largest grazing rates occurred in June and July, while in 1999 rates were larger in late summer. 30

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Figure 4.21. Sida crystallina grazing rates (% day-1; i.e. percentage volume filtered per day) across lily beds in Little Mere (Cheshire) (Left: 1998; right: 1999). Different-colour bars indicate rates estimated from the min. and max. plant biomass at each site. Total populations of the species are calculated using the min. and max. values found during plant surveys. Bars are one standard error at either side of the mean, calculated from density estimate errors (for details see section 4.2.3).

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Sida crystallina showed important differences in grazing rate both between sites and between years. Highest grazing rates in the first year occurred at the beginning of the growing season (i.e. April 1998), while during the second year only small peaks were detected in April. In contrast, very high grazing rates were measured at the end of this growing season (i.e. September-October ‘99) (Fig. 4.21). Maximum grazing rates in the second year were an order of magnitude higher than in 1998 in some sites, too. Higher rates were located around site 1 in both years (for a Map see Fig. 4.14). Significant reductions in algal biomass concomitant with high grazing rates of this species were apparent in September-October 1999 (Fig. 4.21). Unfortunately, no chlorophyll data are available for the beginning of growing season ‘98, when relatively high rates were measured for this species. During approximately two months at the end of this growing season, very low chlorophyll-a values were measured in Little Mere, particularly within the lily beds (see Table 4.14). Implications for a top-down regulation of algae by this grazer are discussed below. 4.4. Discussion. Clearance and periphyton ingestion estimates. Estimation of accurate feeding rates is fraught with difficulty. For instance, complete tracer mixing in the grazing chamber is necessary for uptake to be representative. Very small species and small individuals of larger species may encounter labelled cells at random in the short feeding period, reflecting, rather than filtering rate, cell concentration in the box and/or medium heterogeneity (e.g. Chow-Fraser & Knoechel 1984; Mourelatos & Lacroix 1990). However, tests with a dye in the laboratory showed 30 seconds to be enough for complete mixing. On the other hand, to obtain the minimum specific activity for in situ experiments I had to be sure the algal density was neither too high compared with natural lake algal densities, nor so low that homogeneous mixing in the box might become more of an issue, given experiments are short-term. During the labelling period (48-72 hours) the algae increased by at least an order of magnitude, making the culture denser than originally intended. Thus, relationships between clearance rate and food conditions should be regarded with caution. Periphyton activity in the box during each experiment had relatively narrow bounds (Fig. 4.5). It is still matter of controversy whether cladocerans feed continuously under natural conditions. The short spans of time employed by necessity in radio-tracer feeding studies may compound this effect but are difficult to detect. Haney (1973) brought attention to the fact diel 113

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rate depressions are overlooked in short-term experiments. Changes in animal behaviour related to diel migratory rhythms are not taken into account either (see Peters & Downing 1984). Albeit important, these types of error are thought to be smaller than those controlled in the experiment (e.g. tracer mixing, experiment fast termination).

Crowding is thought to explain a lot of the across-author variability in clearance rate estimates (Peters & Downing 1984; Szlauer 1973). To avoid crowding, few animals were introduced into the box (see section 4.2.1 for details). Clearance rate models. Slopes of relationships between clearance rates of species and body size are within the range reported in the literature (see Table 4.11 & Fig. 4.16). Detailed cross-study comparisons are not easily made on the basis of predictive regressions (Chow-Fraser & Knoechel 1984), however they can point towards significant trends (see, for example, Peters & Downing 1984). Solutions of equations are subject to large uncertainty (see r2 values in Table 4.8) and, thus, their primary use is heuristic (see Peters & Downing 1984). Simocephalus vetulus emerged as a relatively unefficient filter-feeder, particularly in the larger size range (i.e. > 1.2 mm; see Fig. 4.16 & Table 4.11). There is very large acrossworker variability in clearance rates estimated for this species. The trend, though, is for a relatively low filtering efficiency as compared with other cladoceran filter-feeders. Simocephalus vetulus is a slow-growing species (Bottrell 1975) and phytoplankton ingestion at the rates measured may be sufficient to provide for this growth. In addition, one would expect this species, given its strong preference for living in close proximity to plants (see, for example, Lauridsen et al. 1996), to apportion a considerable part of its diet to alternative food sources, such as periphyton, abundant within plant beds.

Sida crystallina, on the other hand, was found to be quite an efficient filterer, although rates obtained were considerably lower than those presented by Downing & Peters (1980) and Downing (1981). Values obtained by these authors for this species exceed by an order of magnitude common values of clearance rate estimated for Daphnia (Table 4.11). In this study, Sida crystallina was found to filter over twice as fast as similar-sized Daphnia. Because the relationship between length and biomass of animals is similar for both species (Dumont 1975; Downing & Peters 1980), Sida is considered to have a superior filtering capacity than

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Daphnia. However, predominance, or even abundance, of Sida has been seldom reported (see Discussion in Chapter 3 for details). In Little Mere, Sida clearance rates were higher with lower suspension and periphyton concentrations (e.g. October 5th, 1999; see Fig. 4.10). Downing & Peters (1980) suggested periphyton had some regulatory effect on the nutrition of Sida, as clearance rates decreased with increasing periphyton concentration. Furthermore, Downing (1981) found this 'inhibitory' effect of periphyton on clearance rates to be enhanced at high suspension concentrations. Clearance rates would be expected to decrease with increasing particle availability anyway, irrespective of periphyton concentration (McMahon & Rigler 1965). This highlights the difficulty of separating the influences on clearance rate of periphyton and planktonic algae.

Clearance rates for Daphnia are conservative estimates. The small number of individuals larger than 1.2 mm in experiments (see Fig. 4.16, top) may explain in part the relatively low estimates for this species (see Table 4.13). Periphyton had a slightly 'negative' effect on Simocephalus filtering activity, while the reverse was true for Daphnia. The latter result should be regarded with caution, though, as 55 % of Daphnia samples were collected in the first two experiments, biasing correlations towards the values of periphyton presented on the first two dates (see Figure 4.10 and Table 4.15). Periphyton ingestion models. In Little Mere, larger Simocephalus ingested more periphyton than smaller individuals. Whether these age-differences can be related or not to feeding habit can only be guessed at. On the other hand, Simocephalus vetulus was inhibited in its periphyton ingestion activity by the presence of the strips. Simocephalus may either actively swim away from plastic strips colonized by periphyton (e.g. Lauridsen & Lodge 1996) or choose to filter-feed when periphyton concentrations increase. Perhaps only the larger individuals were repelled by the periphyton-covered strips, thereby filter-feeding on more carbon-14-free algae, while smaller individuals not avoiding so much the proximity of the carbon-14 labelled algae, filtered more of the detached algae in the process. However, this could also come about through more swimming, in which case a more pelagic habit could be attributed to the younger animals,

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similarly to Sida crystallina (Fairchild 1981). To test these ideas feeding experiments are needed which present both foods while they are closely monitored.

With regards to multivariate periphyton ingestion models (see Table 4.10), there is hardly any consistency in variables selected across species. One would expect larger Daphnia individuals to switch to browsing more frequently than smaller animals (Horton et al. 1979), collecting in the process periphyton from strips. However, no significant correlation with size was detected for periphyton ingestion rates. Periphyton concentration actually increased ingestion rate, as would be expected if Daphnia utilized this source normally, which adds to the inconsistency.

Large Sida fed less on periphyton than smaller animals. Again this is an unexpected result, as juveniles have been reported to swim faster and more frequently than older individuals (Fairchild 1981), thereby probably associating less with periphyton-burdened surfaces. The relationship is significant, albeit with a very flat slope, but very difficult to interpret biologically. On the other hand, the most common methodological error involves not washing away labelled cells attached to body surfaces. Larger animals would be expected to be more prone to produce this error, giving a positive slope, rather than the one observed. Clearance rates are the basis for the estimation of measures of grazing “impact” (% day-1; i.e. % lake volume filtered per day). However, these measures are subject to further error, as new variables come into play during calculations (see section 4.2.3 for details on methods). Errors associated with each variable are multiplied when we calculate grazing rates. Potential sources of error or natural variability when estimating grazing rates are listed in Table 4.13.

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Table 4.13. Potential sources of error in grazing rate estimation (see Equation 1).

Source of error*

Units

Description

Animal density (D & D’)

ind g-1 ind l-1

Areal plant biomass (P)

g m-2

Total population size of animal species (T & T’)

ind

Cover and Depth (C & z)

m2 m

Extrapolation errors: point-samples density in areas. Large variability across replicates. Natural spatial variation. Sampling error. Potential disturbance during sampling operations, producing differences between replicates. producing differences between species. producing differences between samplers. Unaccounted spatial heterogeneity. Seasonal changes. Sampling procedures and estimate accuracy. Extrapolation errors: point-samples whole-bed averages. Different sampling frequency of plant biomass and animal density. In-bed plant biomass differences and extrapolated total bed animal population sizes. Error implicit in the linear regression between plant biomass and density of associated animals. Seasonality of plant cover, particularly of submerged plants. Averaged depth for each lake sector. Season and inter-annual differences in water level (i.e. depth, ‘z’). See section 4.2.1 for details.

Clearance rate (CR) ml day-1 ind-1 *see meaning of variable codes in Table 4.5.

Estimates of field abundance of cladoceran species. Plankton sampling techniques are bound to underestimate densities of microcrustacea living attached or in close association to plant surfaces, particularly when these are low (Fig. 4.22). Also, there is large variability in densities across replicate samples (Figure 4.23). Plantassociated species densities are especially variable (see section 3.3.6 for details).

6,0

Plankton sampling technique and lily leaf sampler Simocephalus vetulus , site 2 (1999)

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Figure 4.22. Comparison of the effectiveness of plant-associated Cladocera sampling using conventional plankton techniques and a specifically designed lily leaf sampler. Densities are in ind.m-2. Plotted are results for a population peak of Simocephalus vetulus sampled from a very shallow lily bed (site 2) during growing season ’99. For details on extrapolations, see text.

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Sampling procedures are bound to affect how representative samples are of real populations. It is important, though, that errors due to sampling procedures are not systematic errors. No apparent pattern of disturbance was detected in my dataset (see Fig. 4.23).

Extrapolation of point-samples to estimate densities in whole areas is an important source of error (De Bernardi 1984). In littoral areas and in shallow lakes in general, swarming and contagious distribution of populations, are likely to be more important than in pelagial environments (Folt 1987; Folt et al. 1993; see Discussion in Chapter 3). 450 400 350

% of mean

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Figure 4.23. Replicate order and % of mean density sampled with each replicate during a population peak of Simocephalus vetulus (site 1, growing season 1999). No trend for a disproportionate amount of animals in the first sample suggests that there was no systematic effect of disturbance during sampling operations.

Areal plant biomass and cover estimates . During the height of the growing season, errors overall are smaller, simply because there is generally less time between plant survey and sampling of the animals. This pattern of error may apply to whole plant beds, but within beds it is likely that growth and/or senescence proceeds at different rates in different areas (Spence 1982). A main assumption in the extrapolation of point-measures of animal density (ind g-1DW) to whole areas is that plant biomass is linearly related to the density of associated animals. No significant relationship exists between leaf weight and number of Simocephalus vetulus (or Sida crystallina) per leaf. The unit of relevance is probably surface area, but estimating such a parameter routinely is difficult. However, because leaf weight in samples is a perfectly random variable (i.e. it fits the Poisson distribution very well), the large number of leaves sampled allow us to consider the variable ‘leaf weight’ as normally distributed (Bailey 1992). Thus, leaf weight, when thought of in terms of populations of leaves, is an excellent predictor

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of total number of associated animals (see Fig. 4.24). Analogous considerations apply to lily petiole weight. Extrapolation errors: cumulative numbers of Simocephalus vetulus (both years) and cum. lily leaf weight against number of lily leaves 10000 500

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Figure 4.24. Prediction of total numbers of Simocephalus vetulus on lily leaves from the weight (grams) of a large population of lily leaves. See text for details.

Grazing rates and water clarity in Little Mere. Daphnia was generally the main grazer in Little Mere (Figs. 4.19 to 4.21). Variations in chlorophyll-a are apparently in an inverse relation to Daphnia density during most of the growing season. On numerous occasions, however, high water transparency (i.e. low chl-a concentration) coincided with small Daphnia populations, suggesting other processes aside from strict Daphnia top-down control may have been in operation during these periods (see Chapter 1 for details). Sida crystallina was the most important plant-associated filter-feeder during the two years sampled. Simocephalus vetulus, by contrast, was generally not an important grazer in Little Mere, although, in combination with Sida, could account for changes in water clarity (see Fig. 4.21).

No conclusions can be drawn on when and where precisely is the large grazing impact of Sida going to occur. However, population peaks and, therefore, high grazing rates, seem to occur every growing season. This species appeared at the very beginning and end of each growing season, perhaps exploiting the window of opportunity oferred by Daphnia´s generally low numbers during these periods. The suggestion is these two species compete, either through direct interference or through competition for common resources (i.e. planktonic algae). Sida's strategy seems to be to rapidly outnumber other potentially competing species,

exhausting resources and, thus, disappearing after episodes of extreme population crowding (see section 3.3.6 for details). 119

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The question remains, however, of what actual effects on water clarity the plant-associated filter-feeder community are having during these periods of calculated high grazing rate. The discussion following will be centred around Sida crystallina and the clear-water phase occurring at the end of growing season ‘99 (see Table 4.14 for details).

It is difficult to predict how relatively localized impacts will affect whole lake water transparency, particularly as exchange rates between water masses are not known (see James & Barko 1991). Indeed on particular dates with high plant-associated herbivory, mainly of Sida crystallina (see Figure 4.21), a clear-cut difference in transparency and chlorophyll-a levels was apparent between open-water sites and lily bed sites (see Table 4.14). For example, at the end of growing season ‘99, and during about two months, very low chlorophyll-a values were measured (i.e. less than 20 µg l-1) concomitantly with low Daphnia densities (i.e. less than 10 ind l-1 in lily beds; less than 20 ind l-1 in open-water sites; see Fig. 3.8). On the other hand, grazing rates of the plant-associated filter-feeders (i.e. Simocephalus vetulus and Sida crystallina) were sufficient to account for the low chlorophyll-a values, while those of Daphnia, be it in plants beds or in more open-waters, were not (see Fig. 4.21 for Sida crystallina, Fig. 4.19 for Daphnia).

There is much evidence to support the idea that Sida was the main regulator of water transparency during these dates. For instance, during the first two dates of clear-water (i.e. September 13th and Sept. 27th, 1999; see Table 4.14) relatively low Sida densities were found in the open-water sites, dominated by submerged plant beds (i.e. sites 3 and 4, with no lilies; for a Map see Fig. 4.14). Chlorophyll-a measured in these sites these dates was twice to four times higher than in the lily beds. Very high Sida densities (17,000-40,000 ind m-2) were found on October 8th in submerged vegetation, coinciding with low chlorophyll-a concentrations (< 10 µg l-1), suggesting Sida was indeed the main grazer. Negligible grazing rates of Daphnia were measured in the open water during the clear-water phase (see Fig. 4.19). The clear-water phase is difficult to explain by other processes aside from herbivory. Direct plant competition with the algae for nutrients in the water is one possibility (see, for example, Howard-Williams 1981). Ceratophyllum demersum, a completely submerged plant species, increased in cover and biomass towards the end of the growing season (see section 4.3). This

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mechanism, however, cannot account for the fact water was clearer in lily beds than in more open waters. In addition, nutrients (both nitrogen and phosphorus) were not limiting during the clear-water phase. For instance, 234 µg l-1 of NH4+ and 129 µg l-1 total phosphorus were measured on October 5th, with values of less than 10 µg l-1 of chlorophyll-a (see Table 4.14). Without an estimation of rates of exchange of water masses between the quieter lily areas and more open waters and resulting nutrient exchange, the importance of plant uptake cannot be properly assessed, though.

Ceratophyllum demersum is thought to have strong allelopathic effects (Gross 1999; see Chapter 1 for details). The increase in biomass of this species coincided with the clear water (see Table 2.2), but again, plant beds were largely confined to outside the lily-covered areas, while the clearer water was found in the lily beds (see Table 4.14).

Wash-out of algae cells was most likely not an important factor in maintaining water transparency during the observed clear-water phase, with relatively low outflows and sinking rates within and outwith lily beds probably comparable throughout the growing season (see Table 4.14). Nevertheless, sinking may explain differences across sites to a certain extent, particularly during some dates. For instance, proportionally more small algae (i.e. < 25 µm) could be found at the sheltered site 2 (for a Map see Fig. 4.14), than in other lily areas, and clearly smaller ratios were consistently found in deeper areas, without lilies. Table 4.14. Clear-water phase at the end of the 1999 growing season. Small to total chlorophyll-a (%) and total chlorophyll-a values (µg l-1) across sampling sites in Little Mere (Cheshire). % small/large chlorophyll-a

Clearwater phase Site 13th Sept. 27th Sept. 8th Oct.

Lily bed 1 91.6 84.0 77.6

2 100 100 71.1

Total chlorophyll-a

Open water 5 92.5 62.5 77.4

3 55.5 68.8 62.1

4 38.3 42.9 82.0

Lily bed 1 9.5 26.2 10.7

2 6.3 15.5 9.7

Open water 5 10.7 61.5 11.5

3 28 33.7 8.7

4 90 83.7 13.3

In conclusion, top-down effects of Daphnia are important in Little Mere, and, in combination with plant-associated herbivory, could explain most changes in water clarity in this lake. Plant-associated grazers such as Sida crystallina are a by no means negligible factor in controlling algal biomass, at least during certain periods of the growing season (April to October). Experiments would be needed, however, in order to assess the absolute 121

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contributions of these important grazers, isolating any confounding effects of, for instance, nutrient uptake by plants, in competition with algae.

Herbivory as a regulating factor is under a set of varying nutrient regimes and fish predation pressures in natural conditions. Next chapter presents the results of two mesocosms experiments looking into how herbivory, and thereby water transparency, is regulated by nutrients and fish, and how these factors may interact in Little Mere.

4.5. Conclusions. Clearance rates (ml ind-1day-1) and periphyton ingestion rates (µg ind-1day-1):

•

Significant clearance rate (CR) regression models for five of the six cladoceran species in experiments were drawn, i.e. Daphnia spp, Simocephalus vetulus, Sida crystallina, Eurycercus lamellatus and Scapholeberis mucronata.

•

All models accounted for 21.7-69.7 % of variance in CR measurements and 11.4-73.2 % of periphyton ingestion estimates.

•

Simocephalus vetulus: body length accounted for 53.8 % of CR variation. An additional 3 % was explained by the deppresive effect of periphyton concentration (mg cm-2 periphytic chl-a). Temperature had a strong deppresive effect on periphyton ingestion. Periphyton concentration was also inversely correlated with periphyton ingestion. Larger animals ingested more periphyton.

•

Daphnia spp: this species filtered less efficiently than Simocephalus and Sida, particularly larger-bodied animals. Periphyton ingestion was positively correlated with periphyton concentrations in the grazing chamber. Suspended algae food also had a positve effect on periphyton ingestion.

•

Sida crystallina filtered about double the volume of similar-sized Daphnia, particularly the larger individuals. Both high suspension and periphyton concentrations depressed clearance rates. Larger Sida ingested less periphyton than small individuals.

122

GRAZING RATES

Grazing rates (% day-1):

•

Plant-associated filter-feeders (‘alternative grazers’) are a generally minor grazer component in Little Mere as compared to open-water filterers such as Daphnia spp.

•

At the beginning and end of the growing season alternative grazers may have had a significant impact on phytoplankton, or even been the main grazers during short periods.

•

The magnitude of these short-term effects is very variable across years.

•

Timing of maximum grazing impact of alternative grazers is also very variable across sites within any given year.

123

CHAPTER 5 The relative role of nutrients and fish predation on zooplankton dynamics in Little Mere

CHAPTER 5

5.1. Introduction. The relative weight of physico-chemical ('bottom-up') and biological ('top-down') factors influencing communities and ecosystem function remains a fundamental issue in aquatic ecology (e.g. Kairesalo et al. 1999; Blindow et al. 2000). Efforts, however, have often bypassed the basic questions in search of direct, fast applications (Kitchell et al. 1988; Gophen 1990; Kitchell & Carpenter 1993).

For instance, biomanipulation, or "the deliberate exploitation of the interactions between the components of the aquatic ecosystem in order to reduce the algal biomass" (Shapiro 1990), has had a controversial history (see De Melo et al. 1992 for comment), with periods of an abundance of success cases being reported (e.g. Briand & McCauley 1978; Shapiro & Wright 1984; Carpenter et al. 1987; Post & McQueen 1987; Threlkeld 1988; McQueen et al. 1990), soon followed by case-studies acknowledging its multiple limitations (for a review see Gulati 1995).

Some of these limitations come from its short-lived effects (e.g. Vijverberg 1984; Mills et al. 1987; Shapiro 1990; McQueen et al. 1990), from unknown complexities of natural ecosystems interfering with restoration (e.g. invertebrate predation, e.g. Edmondson & Abella 1988; Benndorf 1990; benthivorous fish and sediment resuspension, Meijer et al. 1994; see Lammens et al. 1990 and references therein), or from blue-green algae, which are thought to be relatively unedible (Porter 1973, Gliwicz 1990, but see DeBernardi & Giussani 1995) coming to predominate as more favoured algae are grazed down by zooplankton (e.g. Gliwicz & Siedlar 1980; Moss et al. 1991). Moreover, it is now recognized, algae may quickly develop morphological defenses in response to infochemicals exuded by zooplankton (Lurling 1999).

However, biomanipulation seems to be very effective in shallow lakes (Lammens et al. 1990; Jeppesen et al. 1990; Kasprzak 1995). The success of this restoration technique relies on the strength of top-down interactions in the lake (e.g. piscivory, herbivory by microcrustacean grazers). Generally, the aim is to sustain populations of large herbivores (Lampert 1988). Indeed, smaller zooplankton are thought to be less efficient at controlling algal biomass (Dawidowicz 1990; Crisman & Beaver 1990).

126

NUTRIENTS, FISH AND ZOOPLANKTON DYNAMICS

By contrast, nutrient control alone has seldom worked as a restoration tool in shallow lakes (see, for example, Jeppesen et al. 1990; Hosper & Jagtman 1990; Jeppesen et al. 1991) and one of the reasons for this could be the increased internal loading from organic-rich sediments as external loads are reduced (Moss et al. 1991; Phillips et al. 1994; Stephen et al. 1997). In addition, bioturbation by benthivorous fish (Meijer et al. 1990; Meijer et al. 1994) or chironomids (Phillips et al. 1994) particularly in already eutrophicated waters (see, for example, Vanni et al. 1990), may hamper restoration efforts.

Nutrient control, although often insufficient for complete restoration, is always necessary (see, for example, Moss et al. 1996). Trophy of the lake strongly affects outcomes of biomanipulation, although it is still not well known to what extent (Carpenter et al. 1997). Elser et al. (1990), for example, proposed lakes of ‘intermediate productivity’ would be the best candidates for successful biomanipulation because links between zooplankton and algal biomass are stronger. For the same nutrient concentration, different herbivore species have been shown to have a different "pattern of nutrient recycling" (Sterner 1989; Urabe & Watanabe 1992; Urabe 1993), in part explaining the observed shifts of community structure with eutrophication toward small, relatively unefficient, herbivores (Pace 1984). On the other hand, the response to nutrient enrichment depends on trophy too, and the more nutrient-rich, the less susceptible to nutrient effects (Allan 1980). The need for combining nutrient control measures with manipulation of the trophic structure of shallow lakes was emphasized at the Biomanipulation Conference held in Amsterdam in 1989 (Lammens et al. 1990; see, for example, Benndorf 1988, 1990; Hosper & Jagtman 1990; Sondergaard et al. 1990).

McQueen (1990), for instance, found that a surprisingly low 64 % of manipulations (in a set of 36 analysed) reported significant effects of zooplankton on algae, while 100 % reported significant effects of piscivores on fish. He explained this as implying that the trophic cascade was "damped". Smaller-sized species are less efficient at utilizing available food particles than could be, for example, piscivorous fish feeding on planktivores. Thus, eutrophication would determine a wider gap between primary production and algae biomass utilization by the herbivorous zooplankton (see De Bernardi & Giussani 1995).

De Melo et al.'s (1992) cross-analysis of 50 papers shows that in those cases where biomanipulation was reported to have been successful, conclusive evidence was not put forward as to what were the main causal mechanisms for clarification of the water, whether 127

CHAPTER 5

zooplankton herbivory alone or grazing in combination with other processes. Moreover, in their analysed set, 56 % of enclosure studies, and 50 % of lake or pond studies were qualified as 'undecided' as regards zooplankton grazing being the main causal agent of increased transparency following manipulation. In fact, examples exist of successful restoration plans based on biomanipulation where the algae community biomass and structure was shown to depend mainly on nutrients (Kitchell & Carpenter 1992; Gophen 1995). Table 5.1. Some problems and indirect effects encountered in the literature during restoration attempts of shallow lakes using biomanipulation (modified from Kasprzak 1995 & Gulati 1995). Examples

Vijverberg 1984

DIFFICULTIES IN APPLYING BIOMANIPULATION Planktivorous fish tend to reestablish high standing stocks.

Examples

INDIRECT AND UNEXPECTED EFFECTS

Meijer et al. 1994

Nutrient recycling due to increased sediment resuspension by benthivorous fish. Daphnia is released from Neomysis predation because of increase of longfin smelt (Spirinchus thaleichthys) Increase in macrophyte biomass and cover with higher water transparency and indirect macrophyte effects, i.e.: Shading of phytoplankton Zooplankton refugia Plant competition for nutrients in the water Allelopathy Faster sinking, … See Chapter 1 for details.

See DeMelo et al. 1992 for criticisms

The response is sometimes weak (‘damped’).

Edmondson & Abella

Mills et al. 1987

Nutrient limitation of zooplankton through ‘over’biomanipulation.

Leah et al. 1980

Gliwicz & Siedlar 1980

Phytoplankton develops morphological defence mechanisms. Some planktivorous fish are more resilient to biomanipulation in part owing to different feeding behaviour and time-scheduling of this. Effectiveness depends on nutrient status of lake. Nutrient control is often necessary.

Benndorf 1990

Vanni et al. 1990

Control of indirect mechanisms playing a role in algal growth may be key to successful biomanipulation (see, for example, Kairesalo 1999; see Table 5.1). In shallow lakes, many of these mechanisms are mediated by plants (Jeppesen et al. 1997; see Chapter 1 for details). Indeed, as water becomes clearer and plant coverage increases, a series of 'buffering mechanisms' of the plant-dominated state are thought to set in (Ozimek et al. 1990; Moss 1990; Grimm & Backx 1990). 128

NUTRIENTS, FISH AND ZOOPLANKTON DYNAMICS

Because field enclosures ('mesocosms') can be easily controlled and replicated (see Table 5.11) they are a powerful tool for investigating some of these mechanisms and ecosystem function (Carpenter & Kitchell 1992, but see Frost et al. 1988). Issues that have been profitably addressed in a mesocosms approach are: •

The relative importance of top-down and bottom-up processes on water clarity maintenance. The interactions between these types of processes are perhaps more interesting than a simplistic assessment of their relative importance (see, for example, Carpenter et al. 1987; Vanni 1987).

•

The role of macrophytes in the maintenance of water clarity (e.g. Beklioglu & Moss 1995; Stephen, Moss & Phillips 1998; Moss, Kornijow & Measey 1998).

•

Selected interactions between zooplankton and their predators, and between herbivorous zooplankton and the algae they graze (see Carpenter & Kitchell 1993).

•

Effectiveness of the biomanipulation technique in a particular lake (e.g. Van Donk et al. 1994; Faafeng et al. 1990).

Enclosure studies were conducted in Little Mere (Cheshire) at the height of two summers with the main objective of understanding how fish presence and different nutrient regimes may affect, a. the biomass build-up of different zooplankton species and, b. water transparency, potentially regulated by herbivory, in this lake. A graded series of nutrient loadings was applied at varying levels of fish predation (i.e. fish density) and the changes in various variables monitored (see detailed Methods in section 5.2). A further aim was to look at how the system would respond to increased nutrient loads once plants had been removed. In this case, the hypothesis was that the “switch” towards an ‘algae-dominated state’ would occur more easily when plants were removed (see Chapter 1).

Unfortunately, there was not much plant growth inside our enclosures, or even in the lake itself during the experiments (see Discussion for details). The focus in this chapter is therefore on the population dynamics of planktonic Crustacea (Cladocera), as opposed to plantassociated cladocerans, and on water transparency, under the conditions imposed by treatments (nutrient loadings and applied fish densities) .

129

CHAPTER 5

Also, the experiments conducted here were part of a larger project at the European scale (SWALE, namely, "Shallow Wetland and Lake Function and Restoration in a Changing European Environment"). Effectively the same experiment was reproduced six times in five different countries across a latitudinal gradient, i.e. Finland, Sweden, The Netherlands, United Kingdom and Spain, at the heights of two growing seasons (’98 and ’99). In Spain, two experiments were performed, one located in the northwestern region, and the other on the central Mediterranean coast.

The main hypothesis tested in these experiments was that the stability of a shallow lake ecosystem, defined as its resilience in the face of increasing nutrient loads, would deteriorate as the current warming trend reflected in water temperatures. Specifically, higher average temperatures would facilitate a ‘switch’ (see Chapter 1) to the algae-dominated state in many shallow lakes. This could happen with:

a) increasing average water temperatures (across experiments). b) increased fish activity at higher temperatures, removing the ‘top-down control’ of algae exerted by the zooplankton grazers (within each experiment).

Thus, to test this two-pronged hypothesis, experiments were ‘replicated’ across a latitudinal gradient. The average temperature differences experienced by shallow lakes in very different latitudes mimicked the changes that could be experienced by these systems as a consequence of global warming.

5.2. Materials and Methods. Two experiments using mesocosms were conducted mid-summer 1998 and 1999 in Little Mere (Cheshire, England; see Chapter 2). The main design differences between them are summarized in Table 5.2.

130

NUTRIENTS, FISH AND ZOOPLANKTON DYNAMICS

Table 5.2. Experimental designs and treatments of both experiments.

Dates (total number of weeks). Replicates (per treatment combination). Number of treatment combinations. Applied nutrient loadings (Nitrogen/Phosphorus) (in mgl-1 of enclosure water*) Target nutrient loadings (Nitrogen/Phosphorus). Number of fish added per enclosure in the different fish treatment levels* Target fish density treatment levels.

1998

1999

June 8th-August 3rd (9) 3 12 0, 8.3, 41.5 & 83.1 mgl-1 NO3 0, 0.44, 2.2 & 4.4 mgl-1 PO4 0, 1, 5 and 10 mg1-1 NO3 0, 100, 500 and 1000 µg1-1 PO4 0, 3 & 15 fish

June 28th-August 9th (7) 2 18 0, 0.13, 0.25, 0.39, 0.55 & 1.32 mgl-1 0, 2.53, 5.05, 7.58. 12.54 & 25.28 mgl-1 0, 0.5, 1.2, 1.8, 2.4 and 3 mgl-1 NO3 0, 50, 120, 180, 240 and 300 µg1-1 PO4 0, 3 & 15 fish

0, 4 & 20 gm-2

0, 4 & 20 gm-2

*to calculate these weekly additions, we assumed an average depth of 0.95 metres and a total volume of water enclosed by each enclosure of 750 litres; fish are assumed to have an average wet weight of 1 gram.

The plastic used for the enclosures was soft polyethylene of 125 µm thickness. Each end of these bags of soft plastic was held onto 1-m diameter plastic rings using cable ties. The enclosures were then set in place holding them by the frames (see Fig. 5.2), and weighted to the bottom with bricks (Fig. 5.3). A plastic, coarse mesh was attached covering the whole outer surface of each block of enclosures, to minimize disturbance from fish and birds (see Fig. 5.1). See details of the construction of frames and enclosures in Figure 5.2.

Figure 5.1. Materials needed for the experimental setup in the lake. At the front of the picture, the netting use to cover the outside of the frames can be seen. Frames, stakes and enclosures are behind.

131

CHAPTER 5

Figure 5.2. Construction of each frame for four enclosures. Tbe frame was made of PVC tubing. Also shown are the attachments of each frame to stakes, which were used to fix the frame into position in the lake. Attachment of the tubing to stakes used cable ties, as shown in the diagram.

Both experiments followed a randomised block design. Each block of enclosures contained one replicate of each treatment combination, in a random position within the block, so “position effects” could be ironed out in averaging across treatments. The 1998 experiment consisted of 3 blocks of 12 enclosures, with 3 levels of fish density applied (one no-fish control), and 4 nutrient loadings (one no-nutrient addition control). In 1999 the number of replicates was only two, as 5 different nutrient loads were applied (see Table 5.2 & Figure 5.4).

Figure 5.3. Arrangement of the in situ enclosures into blocks. Two blocks of 12 mesocosms can be seen in the picture, as the plastic enclosures are set in place.

132

NUTRIENTS, FISH AND ZOOPLANKTON DYNAMICS

Figure 5.4. Arrangement of enclosures in groups of four and in blocks. Each treatment combination was represented by one enclosure in each block. The position of each treatment combination (e.g. no nutrients, high fish density) was randomized within each block to ‘iron out’ potential unwanted effects of enclosure position on a variable sediment. In 1998 there were 3 blocks and 12 treatment combinations. In 1999, 2 blocks, with 18 treatment combinations each.

The fish used were small sticklebacks (Gasterosteus aculeatus) 4-5 cm long, captured with a benthos net (1 mm mesh) from streams in Ince marshes (Cheshire). Some fish were kept in a continuous flow aquaria to replenish enclosures where mortality was observed, in order to maintain desired treatment densities. Fish were added to obtain approximate densities of 0, 4 and 20 gm-2 (0, 3 and 15 one-gram fish, approximately). Phosphorus and nitrogen loadings were applied as KH2PO4 and Ca(NO3)2.4H2O. Treatments were applied weekly, following the first, ‘pre-treatment’ week, to increase PO43--P and NO3--N concentrations by 0, 0.1, 0.5 & 1 mgl-1 and 0, 1, 5 & 10 mgl-1, respectively. Treatment differences between experiments are shown in Table 5.4. The loadings in the 1999 experiment were lower, as results from the first year suggested the ecologically meaningful range for our lakes was well below 5 mg l-1 for NO3--N and 0.5 mgl-1 for PO43--P. No significant differences were generally detected across treatments for the zooplankton groups analysed in this week (see Table 5.5) and, therefore,

133

CHAPTER 5

subsequent analyses were performed only on ‘treatment weeks’, thus assuming a common baseline for all enclosures. Zooplankton data were organized either directly as species or in 7 large groups classified according to size and habit of species (see Table 5.3). Table 5.3. Pre-treatment week: significance of differences in means (GLM-ANOVA, Sum of Squares type III) across enclosures grouped as either ‘to-be-treatment-combinations’ or as ‘experimental blocks’. Blocks are included as a ‘random factor’. Results are for the main seven zooplankton groups analysed in both years (in µg-C l-1). These groups are: 1- Small Cladocera (<500 µm); 2- Large Cladocera (>500 µm); 3- Cyclopoid copepods; 4- Calanoid copepods; 5- Total copepod nauplii; 5- Raptorial Cladocera & Copepoda; 7- Filter-feeders. For the species composition of each group refer to Table 5.3. Number of cases analysed when zero-cases were excluded is shown. There were not enough valid cases for analysis in group 2 (1999) once zero-values were excluded (see text for details). Significance

Group

N (1998/1999)

1 2 3 4 5 5 7 All

15/35

n.s.

n.s.

15/*

n.s.

cannot compute

13/35

n.s.

n.s.

35/35

n.s.

n.s.

35/35

n.s.

n.s.

35/35

n.s.

n.s.

35/35

n.s.

n.s.

35/35

n.s.

n.s.

1 2 3 4 5 5 7 All

15/35

n.s.

n.s.

15/*

n.s.

cannot compute

13/35

n.s.

*

35/35

**

n.s.

35/35

n.s.

n.s.

35/35

**

*

35/35

*

n.s.

35/35

n.s.

n.s.

(*p<0.05; **p<0.01; ***p<0.001)

Grouped by treatment combination (1998: 12; 1999: 18)

Experimental blocks (1998: 3; 1999: 2)

1998

1999

A 10-litre weekly zooplankton sample was taken per enclosure using a tube sampler (see Fig. 3.6). Seven of these 10 litres were strained through a 50 µm mesh to collect the cladoceran zooplankton. The remaining three litres were separately strained through a 25 µm mesh to estimate the density of rotifers. Cladocera were counted under a stereomicroscope at 30x magnification, and rotifers under an inverted microscope at 100x. Data are abundances (ind l-1) or biomass (in µg-C l-1) for each species and biomass (µg-C l-1) for groups. The percentages of total zooplankton biomass represented by each group were also calculated and analysed. Biomass was estimated using regression equations relating body size to biomass (in µg-C l-1) provided by Luokkanen (1995), Lehtovaara (unpubl.) and Kankaala et al. (1990). Average size estimates for each species in each experiment were

134

NUTRIENTS, FISH AND ZOOPLANKTON DYNAMICS

drawn from a lumped sample bringing together extreme treatment combinations and enclosures with largest numbers of any given species. Size-structure of populations and changes in average size were not taken into account in these estimates. However, differences due to size changes were expected to be smaller than, for example, sampling errors. Thus, the extra measuring effort for more precise size estimates was considered unnecessary. Table 5.4. Species composition of zooplankton groups, classified on the basis of size and feeding habit.

GROUP 1-Small Cladocera (< 500 µm)

2-Large Cladocera (> 500 µm) 3-Cyclopoid copepods 4-Calanoid copepods 5- Total copepod nauplii 5-Raptorial Cladocera + Copepoda 7- Filter-feeders

SPECIES REPRESENTED Bosmina longirostris, Ceriodaphnia spp, Polyphemus pediculus and Scapholeberis mucronata. Daphnia spp and Simocephalus vetulus. mainly Cyclops spp mainly Eudiaptomus gracilis. Both cyclopoid and calanoid nauplii. Cyclops spp and Polyphemus pediculus. Bosmina longirostris, Ceriodaphnia spp, Daphnia spp, Eudiaptomus gracilis, Scapholeberis mucronata, Simocephalus vetulus and copepod nauplii.

Scarce plant growth had implications on the data finally included in analyses. The data collected after plant removal in the first year were analysed separately from weeks before cutting. Thus, results of analyses concerning abundances and biomass before the plant cutting are tabled separately from those after the cutting. The intensity of perturbation represented by the plant cutting was deemed to be stronger than any direct effect of plant removal, given the low plant biomass present in enclosures (see Appendix D). Therefore, in the second year no plant removal was attempted and, thus, data of all experimental weeks are analysed together. Statistical analyses. Nutrient load and fish predation effects on zooplankton density and biomass. Reliability of means increases as the experiment ‘matures’. The outcome of treatment application, at the end of the experimental period, is of more value than changes occurring throughout the experiment. Thus, data were weighted on the basis of the number of weeks passed from the start. The time-variability of treatment effects was examined separately for this

time-averaged

analysis,

by

means

of

plots

of

the

three-way

interaction

nutrient*fish*week.

135

CHAPTER 5

Analyses were conducted using three-way mixed-effect analyses of variance on each species/group, to identify significant treatment effects (i.e. nutrient loadings and/or fish densities). Effects of position of treatments within experimental blocks were accounted for by including ‘block’ as a ‘random variable’ (see Lindman 1974). The power of the test is considerably increased with respect to totally randomized designs (i.e. ignoring block effects) (Lindman 1974; Sokal & Rohlf 1981). Furthermore, if significant differences between blocks with respect to fixed treatments are not taken into account, conclusions can be radically different. In general, treatment effects are overestimated when blocks are not included in the design. When a significant effect was detected by the F-test, Tukey’s honestly significant difference tests were conducted to identify which levels of the treatment were mainly responsible for the significant effect.

Normality and stability of variance of the dependent variables were visually checked. The normality assumption was checked by inspection of a plot of the residuals of data against the cumulative predicted normal line. On the other hand, any patterns in variance across the data were checked by plotting means versus their standard deviations. Biomass and count data were generally log-transformed to ‘pull in the long tails’, and percentage data were arcsin(square-root)-transformed, particularly when patterns in variance were detected.

Abundance of values close to zero may lead to computational problems. This is because the estimated variance will be unduly low on dates with many zeros, generating a pattern of unstable variance, with variance much larger for high means than for means close to zero (i.e. from means estimated from data with many zeros). Transformation is often insufficient to correct this pattern of unstable variance (Samuels 1991). Because variance cannot be assumed to be equal across different mean estimates, the F-test has no power to discern differences between means from those occurring in variances (Fowler & Cohen 1990). Therefore, when more than half the data were made up of zeros, only non-zero data were analyzed. By this procedure we re-scale data and assume nil values do not respond to treatment effects. This is not an unreasonable assumption given treatment effects are assessed in terms of differences in numbers, and not as producing the presence or absence of any given species. Indeed, differences in the zooplankton community composition may be more influenced by differential recruitment from different sediment patches covered by enclosures (DeStasio Jr. 1990), than by any treatment applied. Data from the pre-treatment week were used to assess whether there were significant differences between enclosures and/or blocks as regards any of 136

NUTRIENTS, FISH AND ZOOPLANKTON DYNAMICS

the variables of interest (i.e. zooplankton density, chlorophyll-a,…). As no significant differences were detected (see Table 5.3), the pre-treatment week data was excluded from subsequent analyses. Effect of a provoked perturbation on zooplankton dynamics. The effect of plant-cutting on zooplankton dynamics in the 1998 enclosures was also looked at. To analyse this effect an ANOVA of biomass of each group (in µg-C l-1) was performed separately on data before and after the cutting. Significances of effects before and after the perturbation were then compared for each group. All enclosures were included in these analyses, i.e. both enclosures where plants were removed (i.e. disturbed) and enclosures where no plant growth was apparent and therefore had not been disturbed. 5.3. Results. Experiments in the separate years were characterised by very different zooplankton communities (see Table 5.5). Table 5.5. Ranges of biomass (in µg-C l-1) and relative importance (in % of total zooplankton biomass) of main groups of zooplankton in both experiments. The minimum and maximum in each range refer to the extremes in any given enclosure.

Group

Small Cladocera Large Cladocera Cyclopoid copepods Calanoid copepods Nauplii Raptorial feeders Open-water filterers Rotifers Total biomass

1998

1999

µg-C l-1

%(over total zooplankton biomass)

µg-C l-1

% (over total zooplankton biomass)

0-298 0-885 0-135 0-443 0-221 0-135 5-1,159 0-933 7-1,288

0-57 0-98 0-55 0-97 0-58 0-55 44-100 0-100 -

0-2,539 0-314 0-1,013 0-53 0-495 0-1,013 9-10,025 0-9,983 34-10,055

0-95 0-59 0-85 0-9 0-53 0-85 14-100 0-99.8 -

The experiment in 1998 was dominated by large Cladocera, mainly Daphnia spp, and among copepods, calanoids were more frequent and abundant than cyclopoids. In the second year experiment, small Cladocera (i.e. Bosmina longirostris and Ceriodaphnia spp) and rotifers were prevalent and populations of larger species only started developing late in the experiment. Calanoid copepods were scarce in this second experiment, while cyclopoid copepods were frequently the dominant group of copepods. Rotifer biomass (in µg-C l-1) was one order of magnitude larger overall in the second year experiment (see Table 5.5). In

137

CHAPTER 5

general, the second year experiment yielded much larger biomass for most groups, particularly small Cladocera, cyclopoid copepods and rotifers. The latter were by far the dominant zooplankton group in the 1999 experiment (Table 5.5). Differences in community composition across experiments and a discussion of possible factors explaining these differences are given in section 5.3.2 and in the Discussion section. Results for each experiment are separately described in the following two sections. 5.3.1. The 1998 experiment. Fish decreased the biomass of large and small Cladocera in significant amounts, both before and after the plant-cutting (see Table 5.7). In this experiment, large Cladocera (>500 µm) were largely represented by Daphnia spp. Fish effects on Daphnia biomass were generally very conspicuous (F=125.0, p<0.001 & F=218.8, p<0.001 in the pre- and post-cutting periods, respectively; see Table 5.9). Indeed, at the highest fish density, Daphnia populations were close to being wiped out. At the low fish density treatment, on the other hand, Daphnia populations were stable at about two thirds of the biomass of those in enclosures with no fish (see Fig. 5.5). The nutrient treatment interacted with this strong fish predation on Daphnia, particularly during the post-cutting period (see Table 5.9). The dynamics of this interaction are examined in detail in the Discussion section. Contrary to the cladoceran zooplankton groups, rotifer biomass was significantly greater in the fish treatments than in enclosures without fish (F=2171, p<0.001 and F=25.4, p<0.01, in the pre- and post-cutting periods, respectively; see Fig. 5.6). Nutrient effects on the biomass of most zooplankton groups were not significant (see Table 5.9). The general tendency, though, was for enclosures with a higher nutrient loading to have a larger maximum zooplankton biomass. However, these effects were not significant against the background of fish predation effects. Nutrient effects were only significant on the biomass of the smallest zooplankton, i.e. copepod nauplii and rotifers, but not on the relative proportions (i.e. % of total zooplankton mass) of these groups (see Table 5.9).

138

NUTRIENTS, FISH AND ZOOPLANKTON DYNAMICS

Daphnia hyalina

Daphnia hyalina

1998, Pre-cutting period

1998, Post-cutting period 400

200

100 No fish

micrograms-C/litre

micrograms-C/litre

300

300

200

No fish

100

Fish- medium

Fish- medium

Fish- high

0 0

1

2

Fish- high

0

3

0

1

Nutrient loadings

2

3

Nutrient loadings

Figure 5.5. Treatment (nutrient and fish) interaction plot for Daphnia spp in the 1998 experiment (preand post-cutting periods, left and right, respectively). Plotted are mean biomass values (in µg-C l-1) across the nutrient treatments (control: no addition; level 1: 8.3 mgl-1 N and 0.44 mgl-1 P; level 2: 41.5 mgl-1 N and 2.2 mgl-1 P and level 3: 83.1 mgl-1 N and 4.4 mgl-1 P; all values are in mg-added per litre of enclosure water). Each bar shows the results for one fish density (control: no fish; level 1: 4 grams m-2 or ~3 fish per enclosure, and level 2: 20 grams m-2, or ~15 fish per enclosure). Total rotifer biomass

Total rotifer biomass

1998, Pre-cutting period

1998, Post-cutting period 300

200

No fish

100

Fish-medium Fish- high

0 0

1

2

Nutrient loadings

3

micrograms-C/litre

micrograms-C/litre

300

200

No fish

100

Fish- medium Fish- high

0 0

1

2

3

Nutrient loadings

Figure 5.6. Effect of treatments (nutrient loadings and applied fish densities) on total rotifer biomass (in µg-C l-1) in the pre- and post-cutting periods. Plotted are means (N=3) grouped into the three different fish treatments (controls, 4 gm-2 of fish (~3 fish per encl.) or ‘medium density’, and 20 gm-2 or ‘high density’ (~15 fish per enclosure). On the x-axis are nutrient loadings (see Table 5.1 for details).

The last 4 weeks of the experiment (i.e. after plant cutting) show a similar pattern of response to that before the plant cutting, with fish effects being more important in the determination of the biomass of most zooplankton groups than nutrients (see, for example, Fig. 5.7). Fish effects were stronger with respect to the pre-cutting period. However, nutrients were more important during this period in determining many groups’ biomass than they appeared to be in the first phase of the experiment (see Table 5.9). Especially during the post-cutting period, the effects of nutrients on zooplankton biomass were apparently dependent on the fish treatment considered. Generally, increased nutrient loads lead to higher zooplankton biomass up to a level, above which, additions were reflected in a decrease in biomass of most groups. This level seemed to be lower in enclosures with fish (see, for example, Fig. 5.5). In addition, this

139

CHAPTER 5

interaction between the effect of fish and nutrients on zooplankton biomass was stronger in the post-cutting period (e.g. compare, for example, left and right plots in Fig. 5.5). Because of this, fish effects are not detected when both periods are lumped together (see Table 5.8), warranting the separation of analyses on the periods before and after plant cutting. These time-averaged effects are discussed in detail in the appropriate section.

Table 5.6. Significance of main treatments and interactions on the biomass and the percentage of total biomass of zooplankton groups (see Table 5.5) when all weeks (i.e. before and after the plant cutting) are lumped together (1998). All are results of ANOVA tests with balanced design except the group ‘Small Cladocera’, which was analysed using a GLM-ANOVA for unbalanced designs. Significance of block effects and of the interaction block * treatment (nutrients or fish) are also indicated. ZOOPLANKTON GROUP µg-C l-1 & % total mass AND TREATMENTS Biomass Small Cladocera (zeros excluded) % Biomass Large Cladocera % Biomass Cyclopoid copepods % Biomass Calanoid copepods % Biomass Copepod nauplii % Biomass Raptorial feeders (Cladocera) % Biomass Open-water filterers % Rotifers Total zooplankton

Stability of variance1

All weeks Block

N

F

N*F

Block interactions

n.s.

n.s.

n.s.

n.s.

n.s.

None

n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s.

n.s. n.s. n.s. * n.s. n.s. * n.s. n.s. * n.s. n.s. n.s. * n.s.

* *** *** ** ** n.s. * * ** ** ** n.s. ** ** n.s.

n.s. n.s. n.s. n.s. * ** ** n.s. * n.s. n.s. * n.s. n.s. n.s.

n.s. n.s. n.s. F: **. n.s. F: **. F: * n.s. n.s. F: *. n.s. n.s. n.s. n.s. n.s.

None

1

2 2 2 2 2 2 2 2 2 2 Both 2 Both Both

’n.s’. not significant; *p<0.05; **p<0.01; ***p<0.001. The two criteria for assessing stability of variance of the data are: 1- Levene’s test of the hypothesis variances across groups are equal. Rejection of this hypothesis implies the variance is NOT stable, and 2- Means are regressed against variances. Significance of this relationship is stated, for each group. Fulfillment of these criteria are indicated as 'both’, ‘none’ or 1 or 2, whichever the case.

140

NUTRIENTS, FISH AND ZOOPLANKTON DYNAMICS

Table 5.7. Significance of main treatments and interactions on the biomass and relative biomass proportions (in %) of zooplankton groups (for a definition of these groups see Table 5.5) before and after the cutting of plants (1998). All are results of ANOVA tests with balanced design (i.e. same number of data points for each column and row) except the group ‘Small Cladocera’, which was analysed using a GLM-ANOVA for unbalanced designs (type III Sum of Squares). Significance of block effects and of the interaction block * treatment (nutrients or fish) are also indicated. ZOOPLANKTON GROUP µg-C l-1 & % of total zooplankton mass and TREATMENTS Small Cladocera

Biomass (zeros excluded) % Biomass % Biomass % Biomass % Biomass % Biomass % Biomass %

Pre-cutting

Post-cutting

Block

N

F

N*F

Block & treatment

Block

N

F

N*F

Block & treatment

n.s.

n.s.

*

n.s.

n.s.

n.s

n.s.

*

n.s.

n.s.

Stability of variance1 PREPOSTCUTTING CUTTING Both

2

* n.s. * n.s. n.s. * n.s. ** n.s. n.s. None 2 n.s. n.s. *** n.s. n.s. n.s. n.s *** n.s n.s. Both 2 n.s. n.s. * n.s. n.s. n.s. n.s. *** n.s. n.s. 2 2 n.s. n.s. *** n.s. n.s. n.s. n.s * * N:*; F:* 2 None Cyclopoid copepods n.s. n.s. ** n.s. n.s. n.s. n.s. ** ** n.s. Both None n.s. n.s. ** * N: *; F: * n.s. n.s. n.s. * F:* OK OK Calanoid copepods n.s. n.s. ** ** n.s. n.s. n.s. n.s. ** F:* 2 None n.s. * *** n.s. n.s. n.s n.s. n.s. n.s n.s. None None Copepod nauplii n.s. n.s. ** n.s. n.s. n.s. n.s. n.s. n.s. n.s. Both Both n.s. n.s. *** n.s. n.s. n.s * * n.s F: * None None Raptorials n.s. n.s. ** n.s. n.s. n.s. n.s. * * n.s. 2 2 n.s. n.s. ** n.s. n.s. n.s. n.s. n.s. * n.s. Both Both Open-water filterers n.s. n.s. ** n.s. n.s. n.s. n.s. * * n.s. 2 2 n.s. * *** n.s. n.s. n.s. n.s. ** n.s. n.s. Both 2 Rotifers n.s. n.s. ** n.s. n.s. n.s. n.s. n.s * n.s. Both 2 Total zooplankton ’n.s’. not significant; *p<0.05; **p<0.01; ***p<0.001. 1 The two criteria for assessing stability of variance of the data are: 1- Levene’s test of the hypothesis variances across groups are equal. Rejection of this hypothesis implies the variance is NOT stable, and 2- Means are regressed against variances. Significance of this relationship is stated, for each group. Fulfillment of these criteria are indicated as 'both’, ‘none’ or 1 or 2, whichever the case. Large Cladocera

141

CHAPTER 5 Table 5.8. ANOVA table for significance of main treatment effects (N: nutrient additions; F: fish densities) and their interactions on species’ biomass (in µg-C l-1) in the 1998 experiment. For unbalanced designs (i.e. when zeros are excluded), a GLM-ANOVA with type III Sum of Squares was run (see text for details). Some statistical comparisons cannot be computed because the exclusion of zeros may eliminate from analysis all values of one or more terms of the statistical comparison. Significance of block effects and of the interaction block * treatment (nutrients or fish) are also indicated.

SPECIES’ BIOMASS (µg-C l-1) AND TREATMENTS

Pre-cutting period Block

N

F

N*F

Bosmina longirostris (zeros excluded) Ceriodaphnia spp (zeros excluded) Cyclops spp

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

***

Cannot compute Cannot compute n.s.

Daphnia spp Eudiaptomus gracilis

n.s. n.s.

n.s. n.s.

*** **

n.s. ***

Polyphemus pediculus (zeros excluded) Total copepods Rotifers Total zooplankton

n.s.

*

*

n.s.

n.s. n.s. n.s.

* * n.s.

** *** **

* n.s. n.s.

Stability of variance1

Post-cutting period Block interactions

Block

N

F

N*F

Block i.nteractions

PRE-

POSTBoth

Cannot compute

n.s.

n.s.

n.s.

n.s.

n.s.

Cannot compute

Cannot compute

n.s.

n.s.

n.s.

n.s.

n.s.

2

Both

n.s.

n.s.

n.s.

*

*

2

2

n.s. N: ** F: ** N: cannot compute; F: n.s. n.s. n.s. n.s.

n.s. n.s.

n.s. n.s.

*** n.s.

* *

N: *; F: * n.s. F: *

n.s.

n.s.

n.s.

n.s. n.s. n.s.

n.s. n.s. n.s.

n.s. ** n.s.

* n.s. *

F: * n.s. n.s.

2

2

Both

2

None

2

2

2

Both

2

Both

2

’n.s’. not significant; *p<0.05; **p<0.01; ***p<0.001. 1The two criteria for assessing stability of variance of the data are: 1- Levene’s test of the hypothesis variances across groups are equal. Rejection of this hypothesis implies the variance is NOT stable, and 2- Means are regressed against variances. Significance of this relationship is stated, for each group. Fulfillment of these criteria are indicated as 'both’, ‘none’ or 1 or 2, whichever the case.

142

NUTRIENTS, FISH AND ZOOPLANKTON DYNAMICS

5.3.2. The 1999 experiment. In stark contrast with the previous experiment, few effects of treatments were detected across the zooplankton groups analysed. Effects of nutrients and fish were only significant on openwater filterers’ biomass (see Table 5.9 & Fig. 5.7). Open water filter-feeders 1999

micrograms-C/litre

3000

2000

1000 No fish Fish- medium 0

Fish- high 0

1

2

3

4

5

Nutrient loadings

Figure 5.7. Biomass (in µg-C l-1) of the group ‘open water filter-feeders’ under the different treatment combinations of fish and nutrients in the 1999 experiment. Mean biomass (N=2) estimates are plotted separately for the three fish treatments (no fish, medium and high density of fish) as different shaded bars. The x-axis gives increasing nutrient loadings.

When all species were grouped (i.e. total zooplankton biomass) only nutrient effects were statistically significant (Table 5.9). No significant effects of the treatments were detected on the biomass of zooplankton species taken separately, this year (see Table 5.10). Bosmina longirostris

Bosmina longirostris

1999. Experimental Block One

1999. Experimental Block Two. 700

800

600

400 Fish- high 200

Fish- medium No fish

0 0

1

2

3

Nutrient loadings

4

5

cumulative micrograms-C/l

cumulative micrograms-C/l

1000

600 500 400 300 200

Fish- high

100

Fish- medium No fish

0 0

1

2

3

4

5

Nutrient loadings

Figure 5.8. Effect of treatments (nutrient loads and applied fish densities) on Bosmina longirostris biomass µg-C l-1) in the two experimental blocks during the 1999 experiment. The size of shaded sectors at any given nutrient loading, on the x-axis, shows the differential effect of the three fish treatments (no fish, medium and high densities) on this species' biomass (in cumulative µg-C l-1). There is considerable variability across enclosures, though not related directly to block (see ‘block’ effect in Table 5.10).

143

CHAPTER 5

Treatment effects varied significantly across the two experimental blocks (see Tables 5.95.10). Thus, when a completely randomized design (i.e. ignoring block effects) is analysed with a two-way ANOVA, both nutrient and, especially, fish effects are generally highly significant. For example, small cladocerans (e.g. Bosmina and Ceriodaphnia), which were the dominant species during this experiment, were particularly abundant in enclosures with fish (Figure 5.8). There were considerable differences between the two experimental blocks in this respect, though. For example, the highest biomass of Bosmina longirostris was reached in the high fish density enclosures in one block, but in medium-density treatments in the other (see Fig. 5.8). The fish treatment increased the percentage of total biomass of both raptorials and open water filter-feeders (see Table 5.9). Enclosures with high densities of fish also had the highest biomass proportions of open water filter-feeders (mainly rotifers, but also Bosmina and Ceriodaphnia). Raptorials, on the contrary, seemed to be favoured by the low density fish treatment, but were found in proportions similar to those in controls in enclosures with high fish density (see Figure 5.9). Open water filter-feeders

Rotifer biomass 1998, Post-cutting period 60

90

50

80 70 60

No fish

50

Fish- medium Fish- high

40 0

1

2

3

4

Nutrient loadings

5

% of total mass

% of total mass

1999. Percentage of total biomass 100

40 30 20

No fish

10

Fish- medium Fish- high

0 0

1

2

3

4

5

Nutrient loadings

Figure 5.9. Effect of treatments (nutrient loads and applied fish densities) on the percentage of total zooplankton biomass (in µg-C l-1) of open-water filter-feeders and raptorials (Cladocera and Copepoda) in the 1999 experiment. Fish effects were not very clear in either case (Table 5.9) and the direction of these effects differs among the two groups.

Overall, significance of treatment effects was considerably muted with respect to the 1998 experiment. When fish effects were detected, generally these were of increasing biomass. This effect of increasing zooplankton biomass was particularly evident at the highest fish density (see, for example, Figure 5.10). Some possible reasons for this anomaly are discussed in next section.

144

NUTRIENTS, FISH AND ZOOPLANKTON DYNAMICS

Table 5.9. ANOVA table for significance of main treatment effects (N: nutrient additions; F: fish densities) and their interactions on each groups’ biomass (in µg-C l-1) and percentage of total zooplankton mass in the 1999 experiment. Significance of block effects and of the interaction block * treatment (nutrients or fish) are also indicated. Some statistical comparisons and tests cannot be computed because the exclusion of zeros may eliminate from analysis all values of one or more terms of the statistical comparison.

ZOOPLANKTON GROUP µg-C l-1 & % of total zooplankton mass and TREATMENTS Small Cladocera Biomass Large Cladocera

with zeros without zeros (N=75)

Cyclopoid copepods Calanoid copepods Copepod nauplii Raptorial feeders (Cladocera + Copepoda) Open water filter-feeders

% Biomass % Biomass % Biomass % Biomass % Biomass % Biomass % Biomass %

Block

N

F

N*F

Block interactions

Stability of variance1

n.s.

n.s.

n.s.

n.s.

N: ***; F: ***

2

n.s.

n.s.

n.s.

n.s.

N:**; F:***

None

**

n.s.

n.s.

n.s.

n.s.

2

*

n.s.

n.s.

n.s.

n.s.

None

n.s.

n.s.

n.s.

n.s.

n.s.

Both

n.s.

n.s.

n.s.

n.s.

Cannot compute

Cannot compute

n.s.

n.s.

n.s.

n.s.

n.s.

Both

n.s.

n.s.

*

n.s.

n.s.

Both

n.s.

n.s.

n.s.

n.s.

F: *

Both

n.s.

n.s.

n.s.

n.s.

F:*

2

n.s.

n.s.

n.s.

n.s.

n.s.

Both

n.s.

n.s.

n.s.

n.s.

n.s.

Both

n.s.

n.s.

n.s.

n.s.

n.s.

Both

n.s.

n.s.

*

n.s.

n.s.

Both

n.s.

*

*

n.s.

n.s.

Both

n.s.

n.s.

*

n.s.

n.s.

Both

n.s. * n.s. n.s. n.s. Both Total zooplankton 1 ’n.s’. not significant; *p<0.05; **p<0.01; ***p<0.001. The two criteria for assessing stability of variance of the data are: 1- Levene’s test of the hypothesis variances across groups are equal. Rejection of this hypothesis implies the variance is NOT stable, and 2- Means are regressed against variances. Significance of this relationship is stated, for each group. Fulfillment of these criteria are indicated as 'both’, ‘none’ or 1 or 2, whichever the case.

145

CHAPTER 5 Table 5.10. ANOVA table for significance of main treatment effects (N: nutrient additions; F: fish densities) and their interactions on each species’ biomass (in µgC l-1) in the 1999 experiment. A General Linear Model-ANOVA with type III Sum of Squares was run, allowing for the analysis of datasets with missing cells as a result of the exclusion of zeros. Effects and interactions with insufficient degrees of freedom cannot be computed and are thus indicated.

SPECIES’ BIOMASS µg-C l-1 Bosmina longirostris Ceriodaphnia spp Daphnia spp

Block

N

F

N*F

Block interactions

Stability of variance1

n.s. n.s. n.s. n.s. N: ***; F: *** 2 ** n.s. n.s. n.s. n.s. 2 with zeros (N=215) ** n.s. n.s. n.s. n.s. Cannot compute without zeros (N=55) n.s. n.s. n.s. n.s. n.s. None Scapholeberis mucronata with zeros (N=215) n.s. n.s. n.s. * N: ** None without zeros (N=71) n.s. n.s. n.s. n.s. n.s. None Simocephalus vetulus (N=17) cannot n.s. n.s. cannot cannot compute None compute compute Eudiaptomus gracilis n.s. n.s. n.s. n.s. F: *. Both Cyclops spp n.s. n.s. n.s. n.s. n.s. Both Nauplii n.s. n.s. n.s. n.s. n.s. Both Total copepods n.s. n.s. n.s. n.s. N: * 2 Rotifers n.s. n.s. n.s. n.s. n.s. Both ’n.s’. not significant; *p<0.05; **p<0.01; ***p<0.001.1The two criteria for assessing stability of variance of the data are: 1- Levene’s test of the hypothesis variances across groups are equal. Rejection of this hypothesis implies the variance is NOT stable, and 2- Means are regressed against variances. Significance of this relationship is stated, for each group. Fulfillment of these criteria are indicated as 'both’, ‘none’ or 1 or 2, whichever the case.

146

NUTRIENTS, FISH AND ZOOPLANKTON DYNAMICS

Differences between the 1998 and the 1999 experiment. The main difference between both experiments concerns the degree to which zooplankton biomass was controlled by fish predation. In the 1998 experiment, strong effects were apparent, with much larger total zooplankton biomass in enclosures without fish than with fish (see Fig. 5.10). Effects were strong throughout this experiment, but particularly pronounced during the post-cutting period. In the 1999 experiment, there were no appreciable fish effects (see Table 5.9 & Fig. 5.10). Nutrient effects on zooplankton biomass were not significant in both experiments. The significance of differences between experiments is discussed below. Total zooplankton

Total zooplankton

1998, Pre-cutting period

1998, Post-cutting period 500

micrograms-C/litre

micrograms-C/litre

400

300

No fish

200

Fish- medium

100

400 300 200 100

Fish- high

0

1

2

No fish Fish- medium

0

3

Fish- high

0

Nutrient loadings

1

2

3

Nutrient loadings

Total zooplankton 1999

micrograms-C/litre

3000

2000

No fish

1000

Fish- medium Fish- high

0 0

1

2

3

4

5

Nutrient loadings

Figure 5.10. Total zooplankton biomass (in µg-C l-1) and treatments (nutrient loads and applied fish densities) in both experiments (top left: 1998, pre-cutting period; top right: 1999, post-cutting period; bottom: 1999). Fish effects are apparent in the 1998 experiment but are not so clear in the 1999 experiment.

Clear top-down effects of fish on algal density, as measured by chlorophyll-a, were apparent in the 1998 experiment. By contrast, in the 1999 experiment there were no effects of fish on chlorophyll-a (see Fig. 5.11, bottom). In the 1998 experiment, fish effects were particularly clear in the post-cutting period (see Fig. 5.10, top). Nutrient effects on chlorophyll-a were not

147

CHAPTER 5

statistically significant (marginally significant in the post-cutting period; see Table 5.12), although there was a trend for increasing algal biomass with higher nutrient loadings. This trend seemed to be interrupted at the highest nutrient loading in both periods. This truncated trend or ‘saturation effect’ was more evident in the post-cutting period. Possible causes for this are discussed below. Chlorophyll-a

Chlorophyll-a

Pre-cutting period, 1998

Post-cutting, 1998

120

200

100 micrograms/litre

micrograms/litre

150 80 60 40 No fish 20

100

No fish

50

Fish- medium

Fish- medium

Fish- high

0 No load

1

2

Fish- high

0

3

No load

1

Nutrient loadings

2

3

Nutrient loadings

Figure 5.11. Response of the phytoplankton (i.e. chlorophyll-a, in µgl ) to treatments (nutrient loads and applied fish densities) in the 1998 experiment. Plotted are means of N=3 enclosures, left: before plant cutting and right: after plant-cutting, for each fish treatment (no fish controls, 4 gm-2 of fish and 20 gm-2). The x-axis gives nutrient loadings (see Table 5.1 for details). -1

Chlorophyll-a 1999 400

micrograms/litre

300

200

No fish

100

Fish- medium 0

Fish- high No load

1

2

3

4

5

Nutrient loadings

Figure 5.12. Response of the phytoplankton (i.e. chlorophyll-a, in µgl-1) to treatments (nutrient loads and applied fish densities) in the 1999 experiment. Plotted are means of N=2 enclosures for each fish treatment (no fish controls, 4 gm-2 of fish and 20 gm-2). The x-axis gives nutrient loadings (see Table 5.2 for details).

148

NUTRIENTS, FISH AND ZOOPLANKTON DYNAMICS

5.4. Discussion. These experiments, with all their limitations (see Table 5.11), have been frequently used to assess the potential for biomanipulation, i.e. the removal/reduction of predation pressure on grazer populations with an aim to increasing grazing on phytoplankton. (see, for example, Lampert 1988; the term ‘biomanipulation’, however, was originally coined with a much wider meaning, see Shapiro 1990). The main conclusion that can be drawn from experiments here, is that fish strongly regulate the maximum biomass of large-bodied grazers (Cladocera) in Little Mere (1998 experiment, pre-cutting F=125.2, p<0.001; post-cutting F=459.9, p<0.001). Moreover, zooplankton populations cannot survive in the face of fish predation pressure in excess of about 4 g m-2 (see, for example, Fig. 5.10), which is equivalent to about 2 or 3 small (1 gram) sticklebacks, at least within enclosures in our experiments (see Discussion below). In addition, the effects of predation on grazer populations translated immediately into larger algal biomass (see Table 5.12). More generally, this has obvious implications on the idea algal biomass can be controlled through ‘biomanipulation’. Results from experiments conducted in Little Mere in growing seasons ’98 & ‘99 will be discussed in favour and against biomanipulation as a restoration tool in shallow lakes in general.

Data and theory should be assessed together (see Lehman 1985), and mesocosms experiments conducted in Little Mere and reported here provided an opportunity to compare the dynamics of zooplankton in enclosures and in the natural situation (i.e. in the lake), under the treatments applied (fish densities, and nutrient loadings).

Experiments with enclosures are vulnerable to the cumulative effects of treatments resulting in effects amplified by time and application of treatments, rather than direct effects of the experimental manipulation (see Table 5.11 for details). On the other hand, high nutrient concentrations achieved in enclosures give a unique opportunity to look at the interaction of nutrient limitation (e.g. Urabe & Watanabe 1992) and fish predation on zooplankton. The dynamics of this interaction is discussed in detail, below. Plant beds are thought to act as a ‘refuge’ for zooplankton against fish predation (e.g. Leah et al. 1980). Open-sediment mesocosm allow plant growth to develop, and cutting of plants midway through the experiment was aimed at testing the effect of submerged plant growth removal on zooplankton and water transparency, again under the treatments applied.

149

CHAPTER 5

Unfortunately, few enclosures developed significant plant biomass. On the other hand, this apparently random behaviour of enclosures as regards plant growth (see, for example, Fig. 5.14) provides an opportunity to compare the response of enclosures with and without plants, before and after the perturbation constituted by the plant removal.

Table 5.11. Pros and intrinsic shortcomings of open-sediment mesocosms. See text for details.

ADVANTAGES

SHORTCOMINGS

1) They allow the isolation of effects of controlled experimental treatments (see Kitchell & Carpenter 1993) 2) Small size permits easy replication which can be used, with appropriate statistical techniques, to tease out spurious effects (e.g. sampling error and initial conditions differences).

1) Problem of representativeness (see Frost et al. 1988) Shallow bottom lakes are often spatially very heterogeneous… 2) Problem of extrapolation. Are mesocosms a good ‘mimic’ of lakes, or are important factors left out of designs?.

3) Within the context of their appropriate aims (see Introduction for details), they are a powerful method to obtain reliable answers to some specific research problems.

3) Problem of generality. Shallow lakes are very heterogeneous and varied environments. Results from one lake are often not applicable to other shallow lakes. Both data and theory must be assessed together. To be aware of particular features affecting experimental results should be a prerrogative of any attempt to apply results to other lakes. 4) Algae sedimentation rates may be significantly greater inside mesocosms than in more natural conditions, where turbulence and waves continually resuspend cells. 5) Artificially high zooplankton densities and unnaturally high grazing rates (e.g. Faafeng et al. 1990). Therefore top-down effects of herbivores are, potentially, overestimated. 5) Seasonality of particular species population dynamics is often not in phase with that of the lake. 7) Higher water temperatures with respect to lake and altered light climate, especially further down the water column. 8) Magnified effect of periphytic uptake of nutrients. Inner enclosure walls provide a large colonizing surface for periphyton. 9) Variable sediment ephippial recruitment and “pioneer colonizer” effects. A water column and a possibly atypical patch of lake bottom are isolated by each enclosure. Plankton communities in enclosures, both phytoplanktonic and zooplanktonic, may be atypical of the lake. 10) Limited to one or two different fish species. Higher-order trophic interactions and competition between species, as those existing in nature, cannot be easily reproduced in an in situ mesocosms approach.

The comparison between the two years’ experiments has provided some information on the differential response to treatments of very different zooplankton communities. Some of the

150

NUTRIENTS, FISH AND ZOOPLANKTON DYNAMICS

potential causes of these two very different outcomes as regards zooplankton community composition in different years are discussed.

Finally, lessons learnt from these experiments can be used to frame an understanding of algal biomass regulation in Little Mere, and the maximum levels of nutrient load and fish density that can be expected to support this regulation in the more complex environment of the lake. Top-down control of zooplankton by fish predation in Little Mere. The effect of fish predation on zooplankton biomass was clearly stronger in the 1998 experiment than in the 1999 experiment (see Fig. 5.10, top plots), as shown by the generally lower biomass at high fish density. The composition of the zooplankton community in both years probably had a strong influence on this outcome. During the 1999 experiment, over 90 % of the total zooplankton biomass was rotifer biomass. Rotifers are generally small species, probably not under the same predation pressure cladocerans are generally exposed to, under the assumption fish forage preferentially, or better, on larger zooplankton (Brooks & Dodson 1955; Lynch 1979; Kerfoot & DeAngelis 1989). Indeed no fish predation effect was found on rotifer biomass in the 1998 experiment. In this experiment, populations of Daphnia spp developed in all enclosures in the week before fish were added (the ‘pre-treatment week’; see Fig. 5.13). As fish predation pressure was introduced, differences in Daphnia biomass became evident between enclosures with fish and without (Fig. 5.13). This had practically immediate consequences on phytoplankton biomass, as measured by chlorophyll-a, and enclosures with fewer Daphnia, i.e. those with fish, had significantly more chlorophyll-a (see Table 5.12). This suggests grazing by this species has indeed a strong control over algal biomass in Little Mere. Results reported in Chapter 4 concerning algal biomass (as measured by chlorophyll-a) and Daphnia grazing across areas in Little Mere are consistent with this suggestion.

The coupling between phytoplankton and zooplankton is not always as close as in Little Mere. Negative feedback effects may dampen the interaction considerably in some lakes (e.g. Cryer et al. 1985; see McQueen 1990). For instance, increased grazing pressure may reduce the fast nutrient cycles (i.e. sedimentation of algae cells, phosphorus release from sediments as dead algae cells promote reducing conditions at the microzone; see, for example, Phillips et al. 1994). Grazing will also increase light penetration and potentially algal growth, and grazers may become food-limited in crowded conditions (e.g. Urabe & Watanabe 1992). The short spans of our experiments (7-9 weeks; see Table 5.3), though, perhaps precluded the

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possibility of these feedback mechanisms operating, or being strong, in enclosures. Faafeng et al. (1990), for instance, found in bag experiments in the pelagial zone, that grazing was more important during summer than in spring or autumn, and attributed this to seasonal changes in the proportion of edible algae in the phytoplankton community. Moss et al. (1991) also propose phytoplankton seasonal succession should be considered when assessing the potential for successful biomanipulation.

In the experiments reported here the influence of changes in the phytoplankton assemblage over zooplankton production cannot be adequately assessed because the effects of large algal biomass (see Discussion below) are confounded with high cyanophyte densities in enclosures, particularly during the 1999 experiment. Nevertheless, few cyanophytes developed in the 1998 experiment (an occasional peak of 17 % of total numbers in week 4 in one enclosure, but generally less than 7 %) and, thus, cannot be responsible for the changes in secondary production occurring over time. In the 1999 experiment, zooplankton abundances, mainly of small cladocerans (see Table 5.6), increased irrespective of treatment and it is unlikely the phytoplankton community composition had any effect on zooplankton abundance. Overall, seasonal changes and the fact experiments are short-term, are not thought to have affected the validity of conclusions. Indeed, field observations support the contention that top-down control of zooplankton in Little Mere is strong, at least under the nutrient concentrations that were found in the lake’s waters. Table 5.12. Summary of ANOVA results on treatment effects (applied fish densities and nutrient loadings) on chlorophyll-a concentration (µg l-1) in the pre- and post-cutting periods of the 1998 experiment. Chlorophyll-a vs. Treatments Block FISH NUTRIENT FISH*NUTRIENT + 0.07
PRE-CUTTING PERIOD SS MS F 0.529 0.255 1.408 4.209 2.104 15.401* 0.895 0.299 1.301 1.003 0.157 0.938

POST-CUTTING PERIOD SS MS F 0.153 0.077 0.585 9.297 4.548 18.04* 0.820 0.273 4.14+ 0.845 0.141 0.555

The interaction of nutrient and fish effects, or the relative contribution of top-down and bottom-up control of zooplankton and phytoplankton in Little Mere. The effect of nutrients on zooplankton biomass, and particularly on Daphnia biomass, during the 1998 experiment seemed to be modulated by fish density. The overall pattern of dependency between Daphnia spp biomass (in µg-C l-1) and treatments (i.e. nutrient additions and different fish applied densities) in the 1998 experiment is summarized in Figure 5.13.

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Following, the effects of nutrients on Daphnia biomass (experiment 1998) are separately examined at the three levels of fish density (no fish, medium and high density). Potential nutrient effects in the 1999 experiment will be examined when discussing the differences in zooplankton community structure between the two experiments. •

In the absence of fish, nutrient levels apparently increased the ceiling of maximum zooplankton production as the experiment progressed (Figure 5.13), although this effect was not statistically significant (see Table 5.7). However, above ‘level 2’ nutrient loadings (see Table 5.2 for details), biomass reached a maximum and further additions had little effect on both the phytoplankton, as measured by chlorophyll-a readings (see Fig. 5.11), and Daphnia biomass. Moreover, further additions may have had deleterious effects on zooplankton. Three mechanisms can be thought of, accounting for this levelling off and posterior decline:

a) Lower assimilation efficiency at high nutrient concentrations. Nitrogen and phosphorus may become limiting to zooplankton growth as N:C and P:C ratios in the algae cells decrease as carbon availability increases (i.e. at higher nutrient loadings). Although zooplankton has considerable homeostasis capacities, ratios in body tissues also become lower at higher nutrient concentrations (Sterner 1990; Urabe & Watanabe 1992) and this is suggested to be caused by lower assimilation efficiencies at high food concentrations (Urabe & Watanabe 1991). This could explain the maximum reached at high loadings in fishless enclosures. No direct evidence of a decrease in C/P ratios is available to directly test this idea. In any case, declines in Daphnia production occurring in some fishless enclosures above level 2 loadings (see Fig. 5.13) cannot be explained by limiting factors. Deleterious effects must be invoked.

b) Deleterious effects on Daphnia, either limiting survival and/or preventing growth. The decline of Daphnia populations over time parallels that of the populations of this species in the lake itself. What differs is the rapidity of this decline, which seems to depend on the nutrient treatment. This could be related to the grazing capacity of the zooplankton populations. The highest nutrient loads in the 1998 experiment were probably unrealistic, promoting levels of algal biomass that could have interferred with Daphnia’s filtering machinery. This effect, though, is not related to filamentous algae as these were generally scarce during experiments, but to high green algae densities. Lower nutrient loads were 153

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tested in the 1999 experiment, also examining a more detailed range of nutrient applications (see Table 5.2). Unfortunately, this effort was obscured by initial conditions (compare Figs. 5.11 & 5.12), of high algal densities. ‘Baseline conditions’ in mesocosm experiments may be of paramount importance. Once the system is isolated from the rest of the lake, its capacity for change may be linked, and treatment effects may be swamped if the system is already heavily eutrophicated.

c) Self-shading of algae. The algae community may depend on the nutrient regime (Sommer 1989). Differences in the phytoplankton assemblage and the different chlorophyll-a content of algal species (Reynolds 1983) may explain some of the chlorophyll-a variation, but it is hard to believe that the very large differences can be accounted for by only species composition of the algae community. A likely possibility is that nutrients were not limiting in many enclosures with the highest nutrient load, exceeding the growth capacity of the algae. Thus, comparable algal biomass (i.e. chlorophyll-a) was estimated in the high nutrient load enclosures, irrespective of fish and grazer effects. Analogous experiments conducted further south in the latitudinal gradient (see section 5.3 for details) did not report such effects. The southernmost experiment, in Valencia, Spanish central mediterranean coast, is a case in point. There, even the highest nutrient additions reflected in higher chlorophyll-a values. This could happen under the higher light levels of the more southern latitudes. Furthermore, in León, northwest Spain, water temperatures during the experiment were comparable to those in England. However, no "saturation effect" was apparent either. Apparently, light may have been the limiting factor at the very high nutrient loadings in our experiment (see Carpenter et al. 1997).

On the other hand, ‘time-effects’ may have been important. As nutrient loads were progressively incorporated into the system, lower and lower additions were capable of decreasing Daphnia biomass (see Figure 5.7). A possible mechanism for these deleterious effects could be the mechanical interference of the feeding apparati of animals, particularly at the algal densities promoted by the highest nutrient loadings (Burns 1958; Porter 1973; Gliwicz & Siedlar 1980; Gliwicz 1990). Significant herbivory effects of Cladocera are, thus, only expected when grazer populations are able to reach a certain threshold biomass. Below this, phytoplankton is not grazed down fast enough to compensate for algal growth. Turbidity due to the algae, then, gradually increases.

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-1 Daphnia biomass (µg-C l ) Daphnia spp. ug-C/litre 500

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Figure 5.13. Daphnia spp mean biomass (in µg-C l-1; N=3) across weeks in all treatment combinations. Different lines are of biomass in the three fish density treatments (no fish, 4gm-2 and 20 gm-2). Each line represents mean Daphnia biomass across the nutrient levels (control, N1, N2 and N3; see Table 5.2 for details).

Cyanobacteria are not good food for daphnids. Laboratory growth experiments by De Mott (1998) have conclusively shown reduced growth and reproduction of 5 daphnid species when fed on pure diets of the cyanobacterium Synechococcus elongatus. The issue is of import, as attempts at controlling algae populations through biomanipulation fail if zooplankton cannot utilize cyanobacteria as food (see De Bernardi & Giussani 1990). Toxic effects of these algae may be overcome, though, as various authors have documented top-down effects of filterfeeders on blue-green algae (e.g. De Bernardi et al. 1992). Gliwicz (1990) suggested grazer control of blue-green algae would be limited to those instances when a large grazing potential (i.e. large zooplankton biomass) coexisted with small populations of blue-green algae species.

Results from the 1998 experiment suggest large phytoplankton densities (as measured by chlorophyll-a) occurring in high loading enclosures are sufficient to inhibit feeding and growth of Daphnia (point b., above). Toxic effects are difficult to argue, as most enclosures had few cyanobacteria. Thus, direct mechanical interference seems the most likely mechanism (see Burns 1958; Gliwicz & Siedlar 1980). The occurrence of colonial forms of algal species

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at higher nutrient concentrations (Holtmann & Hegewald 1985) could enhance this effect. Recently, colony formation in Scenedesmus, a green algae species, has been related to grazer presence, cued by infochemicals exuded by the grazers themselves (Lurling & Van Donk 1997), giving support to the idea that mechanical interference may indeed offer some degree of protection to the algal cell from herbivory. •

In the presence of relatively low fish densities (i.e. 4 grams of fish per m2). More nutrient reflected in larger Daphnia biomass. This effect, though, was slighter than in fish controls. Furthermore, at the greater nutrient loads, Daphnia populations died out sooner and never reached the biomass seen in fishless enclosures. Fish predation on zooplankton resulted in drastic swings in population size across weeks. At the lower nutrient loads, populations seemed to be more stable across weeks (see Fig. 5.13), even in the face of comparable fish predation pressures, although large error bars across enclosures with same treatment combinations do not allow refinement of this tentative conclusion.

This result can be interpreted as lending support to the idea it is the grazing potential (as given by zooplankton biomass; see Lampert 1988) which sets a limit to the potentially deleterious effects of phytoplankton on the grazers (Gliwicz 1990). Thus, in the presence of fish, the ensuing smaller Daphnia populations would not be able to keep algal populations in check, and at high nutrient loads, phytoplankton would be so dense as to seriously interfere with feeding mechanisms. This is also consistent with the cumulative effect of nutrient loadings in progressively lowering the threshold addition capable of decreasing Daphnia biomass. However, it is also likely Daphnia populations were slowly reduced by fish predation pressure along weeks. This would suggest the balance of fish predation and Daphnia biomass is struck somewhere below the 4 g m-2 threshold (i.e. equivalent to about 3 one-gram fish per enclosure), rather than above it.

Benndorf (1988) emphasizes the necessity of reaching an optimum zooplanktivorous fish density, in order to maximize the ‘filtration capacity’ of herbivorous zooplankton. Lampert (1988) calculated the biomass of zooplankton necessary to attain a given algal biomass reduction, but gave no suggestion as to what fish pressure would allow such a grazing capacity to be attained. Indeed lakes may differ with respect to zooplankton community structure, seasonality of this, and other biotic factors (e.g. mysid invertebrate predators on zooplankton, in Benndorf et al. 1990) and generalizations are therefore difficult to reach. In 156

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addition, plant beds are thought to act as refuge for zooplankton in the face of fish predation (Leah et al. 1980; Timms & Moss 1984). As far as I am aware, the most detailed study of the interaction fish-zooplankton-phytoplankton in relation to plant refuge availability is that of Schriver and colleagues (1995). In their large-scale enclosure experiment in Lake Stigsholm (Denmark), a very shallow lake (mean depth 0.8 m), they estimated ~2 fry m-2 to be the maximum fish density that large-bodied cladoceran populations could withstand, as long as the percentage volume infested of aquatic vegetation (PVI) exceeded 15-20 %. This figure of 2 fry m-2 approximates well that found in Little Mere, particularly in the 1998 experiment, of close to, but less than, 4 gm-2 (approx. 3 fry m-2). Perhaps had our experiment continued, predation effects on Daphnia would have become more obvious, even at the medium fish treatment (i.e. 4 g m-2). •

At the highest fish density (i.e. 20 g m-2). Daphnia populations were only briefly sustained at the lowest nutrient treatments (see pre-treatment week and weeks 1 & 2 in Fig. 5.13). It could be that the nutrient level at which grazer populations disappear is dependent on the presence of fish and, perhaps, on fish density. Again, the smaller grazer populations that can survive in the face of low levels of fish predation are those capable of keeping in check the algae community. This can only happen, perhaps, when the phytoplankton reproductive capacity, as given by nutrient loads, is below a threshold (Gliwicz 1990).

Overall, the interaction between nutrient loadings and the fish treatment on Daphnia production (µg-C l-1) was not statistically significant (F-test, p>0.05). However, visual trends in Daphnia production in relation to treatments and its changes (see, for example, Fig. 5.13) are easily interpreted in terms of the grazing capacity of Daphnia populations in relation to fish predation pressure and the ‘grazability’ of phytoplankton (given by its density), as discussed above. Thus, algal biomass in the 1998 experiment was mainly regulated by Daphnia herbivory. Nutrients had only a small, modulating, effect on zooplankton biomass during the 1998 experiment. However, an understanding of population biomass dynamics cannot be reached if nutrient concentrations and algal densities experienced by these populations, are not taken into account. No apparent effect of nutrient loadings was observed in the 1999 experiment. Differences between the zooplankton community structure in both experiments are discussed below.

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Plant refugia and fish predation on zooplankton in Little Mere. The refuge role of plant beds (see Leah et al. 1980; Timms & Moss 1984) might vary with plant cover (Lauridsen & Buenk 1995; Lauridsen et al. 1995), plant density (Schriver et al. 1995), plant types dominating the lake (i.e. floating-leaved, stemmed emergent, totally submerged; Carpenter et al. 1997; Scheffer 1998), or bathymetry, although these variations in refuge role have not, so far, been specifically addressed in an experimental way. Few plants were present in enclosures in both experiments (i.e. generally less than 1g m-2DW in the 1998 experiment; see Fig. 5.14, too). The threshold value of 3 fry m-2, suggested as a threshold-maximum fish density that can sustain large Daphnia populations, could be higher in the lake, where refugia are available. There is strong evidence from field observations in Little Mere that lily beds do act as refugia against predation, mainly by perch (Perca fluviatilis) (see Chapter 3 for details).

Plant biomass (gDWm-2)

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Figure 5.14. Plant biomass (gm-2DW) and chlorophyll-a (µg l-1) in the 36 enclosures, measured in the last week of the 1999 experiment (Aug. 13th). Enclosures have been grouped into the three fish treatments (no fish, medium density and high density) and are arranged so from left to right on the x-axis. Bars indicating plant biomass are shaded accordingly.

No refuge effect, however, was apparent in enclosures in both experiments. In the 1998 experiment hardly any plants grew at all, with yields larger than 1 g m-2DW in only two enclosures. In the 1999 experiment, plant biomass was generally larger (see Fig. 5.14). However, few grazer effects were apparent anyway, regardless of plant biomass in the enclosures, as discussed above, and thus few refuge effects can be expected. Interestingly, though, the relationship between plant biomass and chlorophyll-a was statistically significant this year (r2=0.12, p<0.05), albeit with little explanatory value. The apparent effect of plants

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on phytoplankton was especially clear in the enclosures with relatively dense macrophyte growth, and these had sensibly less chlorophyll-a, irrespective of whether the enclosure contained fish or not (see different-shaded bars in Fig. 5.14).

Macrophytes may have some relevance in the regulation of algal biomass in this experiment, although it is unlikely the mechanism was through the harbouring of the small cladoceran grazers in the presence of fish. More likely, plant competition for nutrients may have contributed to limiting growth of phytoplankton (see, for example, Howard-Williams 1981). Allelopathy cannot be discarded as an hypothesis, either. Both these effects, nutrient competition and allelopathy, are confounded and specific experiments testing different levels of plant biomass and different species with known allelopathic activity (e.g. Chara; see Wium-Andersen et al. 1982; but see Forsberg et al. 1990) would be needed to separate their relative influence on phytoplankton biomass. Differences in zooplankton community structure between the two experiments. In the 1998 experiment, the highest rotifer biomass was found in the high fish density treatment, where few Daphnia could be found. This could be interpreted as Daphnia generating unfavourable conditions for the development of large rotifer populations, perhaps competing for resources (Lynch 1979; Vanni 1985, 1987). This suggestion is consistent with results from the 1999 experiment, when generally high rotifer biomass were found coinciding with few or no Daphnia. Again this could be interpreted as small herbivores (Ceriodaphnia, Bosmina) being normally outcompeted by the more efficient filter-feeder Daphnia (Dodson 1974; Lynch 1978, 1979). Low interspecific competition following elimination of the large Daphnia would allow the smaller-bodied Cladocera to develop (Vanni 1985).

The question remains, though, why did Daphnia not develop in the 1999 experiment as it did in the 1998 experiment. No block or treatment effects were appreciable at the outset of both experiments (see Table 5.4). However, the second experiment was initiated when the lake phytoplankton community measured up to 80 µg l-1 of chlorophyll-a, about four times as much as that at the outset of the 1998 experiment (compare Figs. 5.11 & 5.12). Lake phytoplankton densities sharply decreased soon after the 1999 experiment was set up. However, dense algae communities were sustained within the enclosures. Nutrient additions in subsequent treatment weeks added to this abnormally-high-for-the-lake algal density.

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Fish effects, for example on small cladocerans such as Bosmina longirostris, were very variable and significantly different in both blocks (see Table 5.10 and Fig. 5.8). Block effects may have obscured main treatment effects, but the general trend was for enclosures to behave ‘independently’ of applied treatments. One reason for these anomalies could have been a higher fish mortality induced by the dense algal populations developing in many enclosures, sometimes with considerable densities of cyanobacteria, as discussed in detail above. Large Cladocera populations (e.g. Daphnia spp) only started developing late in the experiment, in some enclosures and not consistently across treatment combinations. Indeed, the zooplankton community developing in the 1999 experiment was a reflection of that developing in the lake itself. Large-bodied zooplankton in the lake was probably subject to intense predation. In addition, this may have freed up food resources for the smaller cladocerans as a consequence of the bigger mortality inflicted by visually-predating fish on the larger and thus more vulnerable (Brooks & Dodson 1955; Hall et al. 1975), though superior resource-competitor Daphnia (Vanni 1985, 1987).

Although considerable densities of small cladocerans developed in enclosures (e.g. 7000 Bosmina and 3000 Ceriodaphnia per litre in some enclosures) no strong effects of grazing on algae were apparent as measured by decreases in chlorophyll-a (small cladoceran log(biomass) vs. log(chlorophyll-a) regression: r2 = 0.30; p<0.001). Weak top-down effects of small Cladocera have been observed in numerous occasions, particularly in lowland temperate lakes of low latitudes but also in more northern lakes (e.g. Schoenberg & Carlson 1984; Crisman & Beaver 1990). Biomanipulation in shallow lakes: what do these experiments tell us?. From a management perspective, results from these experiments suggest maintenance of water clarity in an environment effectively devoid of aquatic plant cover by herbivory of zooplankton in the presence of fish is only feasible within a range of nutrient loads and under a relatively low fish pressure. Only large cladocerans seem to be capable of exerting any control on phytoplankton populations, under the set of high nutrient regimes imposed in these experiments. With regard to biomanipulation as an effective restoration technique in shallow lakes, two restrictions can be made, supported by the data reported here and extensively covered by recent scientific literature. The first is that nutrient concentrations at the time of biomanipulation are important (Lammens et al. 1990 and references therein; Benndorf 1990;

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Jeppesen et al. 1991; Perrow et al. 1994; DeBernardi & Giussani 1995; Carpenter et al. 1997; Kairesalo et al. 1999). Indeed the highest loading treatment in the 1998 experiment supported a relatively low zooplankton production in the presence of fish. The second is that populations of large cladocerans can only coexist with fish under relatively low predation regimes (e.g. Brooks & Dodson 1955; Halls et al. 1975; Lynch 1979; Vanni 1987, etc.), at least in the conditions provided by mesocosms. Sustainable herbivory could perhaps be maintained below a maximum fish biomass of 4 g m-2 (fresh weight).

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CHAPTER 6 Summarizing discussion

CHAPTER 6

The resilience of the plant-dominated state (see CHAPTER 1) is considerable, and much research effort has gone into elucidating the specific mechanisms contributing to the maintenance of clear water. Comparatively little is known, however, about the abundance of plant-associated grazers, although their potential role as important buffers of the plant-dominated state has been hypothesized for long. In addition, few investigations into the role of grazing have taken into account, or even acknowledged, the implications of aggregation of grazers, either into ‘clumps’ (i.e. horizontal distribution) or ‘layers’ (i.e. vertical migration).

Seasonal cycles of both chydorids and other plant-associated cladocerans (e.g. Simocephalus and Sida) as well as important horizontal differences were identified in this study (CHAPTER 3). Information on features of species' populations, such as size-structure, and clearance rate (CHAPTER 4), in turn strongly dependent on animal body size, complement data provided in CHAPTER 3 on animal densities. Grazing rates (in % lake volume filtered per day) were then

estimated (CHAPTER 5), providing insight into the trophic role of different cladoceran species and communities.

Daphnia was generally the main grazer in Little Mere. However, water clarity may be maintained in its absence in this lake. A very large biomass of plant-associated filter-feeders was also found, at the beginning and end of the two growing seasons sampled (1998 & 1999), always coinciding with low chlorophyll-a. High localized grazing rates were estimated for these species during these periods. On the other hand, the fit of regressions between Daphnia and chlorophyll-a was often poor, albeit inverse correlations were apparent from visual inspection of data (CHAPTER 4). Lags could not always explain this poor fit.

Studies with a similar approach to that used in this study have reached very different conclusions as regards the importance of herbivory in their systems. However, this highlights the usefulness of grazing rates as measures of trophic role of particular grazer components in a lake.

The role of Daphnia in the control of algal crops was also investigated in relation to fish predation pressure on populations and the potentially modulating effects of nutrient regimes using data from two field mesocosm experiments conducted in two separate summers (1998-

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2000) (CHAPTER 5). Top-down effects were apparent in the 1998 experiment, stronger than effects of nutrients on algal biomass (i.e. chl-a concentration).

There is much scope for investigation of the relative contribution to water transparency of plantbed processes, for example through parallel experiments isolating the effect of each mechanism as much as possible, within the same lake. The grazing on phytoplankton of plant-associated Cladocera, for one, is a clearly important buffer of the plant-dominated state (see CHAPTER 4), but also grazing of periphyton by chydorids and other scrapers in Little Mere may have important feedback links.

Nutrients captured in periphyton and then scraped from surfaces by these species will reach the sediment with their death. In addition, plant growth may be favoured by the cleaning of plant surfaces. Thus, chydorids may be an important link that helps sustain plant beds in the face of increasing nutrient loads and in-lake concentrations, and therefore perpetuate the plant-dominated state (see CHAPTER 1 for details). Very scarce reliable data are presently available about the ingestion rates of chydorids (e.g. Alona), estimation of periphyton is difficult and extremely variable across short distances. Animal abundances can be considerable (i.e. 500-5000 Chydorus m-2). Despite their generally small size (< 1 mm, but frequently less than 0.7 mm) and probably low ingestion rates, they are worth investigating in as much as they may be important buffers of the clear-water state in lakes where plants are widespread and abundant. Predictions have never, as far as I am aware, been made on the potential trophic role in plant beds and in shallow lakes, of these periphyton scrapers.

Shallow lake management will greatly benefit from understanding what processes are important in the maintenance of clear water. This study has shown another mechanism by which plant beds can indirectly perpetuate the conditions under which they may continue to grow (i.e. mainly high water transparency), through grazing on phytoplankton by harboured filter-feeders. Periphyton grazing by chydorids, and other scrapers (e.g. snails) has not been approached at an ecosystem level and in a quantitative way yet, and their abundance and distribution suggest this line of research may be worth pursuing in the future.

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On the other hand, results from this study may help to understand better some cases in which biomanipulation in shallow lakes has not been successful. Plant-associated grazers may contribute to the sustenance of plant growth and thereby to the buffering of the plant-dominated state in the face of eutrophication, at least during limited periods of the growing season (April to October). This highlights, again, the importance of taking into account plant beds and their structuring role when designing a restoration strategy for a given shallow lake. Plant beds may act as refugia for zooplankton against fish predation and indeed the potential of providing the refugia artificially has been explored in the past, albeit with mixed success. However, other processes mediated by the living surfaces provided by aquatic plants, and the physico-chemical conditions within natural beds, are probably very important. This study has shown that grazers making their habitat in aquatic plant beds can be a by no means negligible buffer mechanism of the plant-dominated state of shallow lakes.

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