Fish Stock Assessment

Ang Nagkakaisang Mamamayang Kostal ng Balayan [ANAKBALAYAN], Inc.

Tolentino Boulevard, Barangay District #10, Balayan, 1st District Batangas, Philippines 4213 [email protected], [email protected] (+63)(043) 921-1910 (+63)9109192842 http://www.freewebs.com/anakbalayan/ http://anakbalayanfill.webs.com http://anakbalayan.multiply.com _____________________________________________________________________

INTRODUCTION TO FISH STOCK ASSESSMENT What is FSA? Fish Stock Assessment or FSA is a method of determining presence and stocking of fish in any particular area regardless of its specie, volume, fishing ground, weather, size, age or maturity. Forms and techniques in conducting FSA which is also a type of fishery researches and studies are being developed or improved in different countries. The aim of Fish Stock Assessment is to establish the status of a resource and to determine the level at which it may be sustainably exploited. Age data is one of the fundamental input parameters to a model used to perform stock assessments. The management of marine fisheries presents a complex mixture of biological, economic and social problems. Fisheries management may be regarded as everything done to maintain or improve fisheries resources and their utilization. Fisheries management ranges from managing the resource to understanding activities that promote fisheries development (exploratory fishing, gear and fleet development, environmental maintenance, processing and marketing). Initial efforts at fisheries management have focused on the conservation of wild fish stocks. However, in recent years, there has been a growing recognition that fisheries have to be viewed as a total system from the fish in the water to the fish on the table. This system includes harvesting, processing and the marketing sectors as well as the resource, and it combines economic, diverse social and political as well as biological factors. To undertake a fish stock monitoring and knowing the result thereof may led us all to better understanding of the "total system". “Most countries that exploit marine fish stocks conduct stock monitoring programs that provide data on the current status of the fish stocks. These monitoring programs consist of the sampling of daily fish catch data, or data from commercial landings, sea based sampling of discards, research vessel surveys (ground fish, young fish, egg and larval), ageing programs, analysis of fishing effort, catch per unit effort (CPUE) and landings statistics. The results from various national stock monitoring programs are combined with other international data to carry out annual stock assessments. These assessments are vital to the provision of scientific advice on the status of fish stocks and form the basis of management advice on the fisheries, essential in the management of marine fish stocks as sustainable and renewable resources. In the EU, most assessments are conducted by the International Council for the Exploration of the Seas (ICES). The assessments are conducted at various ICES Working Groups for stocks in different ICES fishing areas. The results are used to give scientific advice to the EU which manages the stocks using Total Allowable Catches (TAC's), supplemented by various Technical Measures (i.e. mesh sizes, minimum landing sizes, closed areas).” Fish Specie Fish species are relative to their characteristics and habitats. Oftentimes fishes are associated to their way of feeding, bone and fin structures, and physical shapes. For fishers especially at the local areas, fish specie is of no importance. The very reason for this is their lack of scientific knowledge of determining the species of their catch. Therefore, the determination lies on the person/s ability to research the appropriate specie base on the local name supplied in the FSA forms. Validation of the specie may done by actual looking at sample catches.

Volume of Catch The recorded number of fishes in one catch or its equivalent in weight denoted by its kilograms plays an important role in determining fish stocks in any given and particular area. Increase and decrease in volume of any specie in the area where fisher usually catch them may mean problem in the system, habitat or recruitment.

Fish Size Size of the fish catch also is an important part of FSA. Fish size is related to the specie maturity and fecundity. Like specie, fish size is of no importance to fisher, the bigger the size the better income they

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Ang Nagkakaisang Mamamayang Kostal ng Balayan [ANAKBALAYAN], Inc.

Tolentino Boulevard, Barangay District #10, Balayan, 1st District Batangas, Philippines 4213 [email protected], [email protected] (+63)(043) 921-1910 (+63)9109192842 http://www.freewebs.com/anakbalayan/ http://anakbalayanfill.webs.com http://anakbalayan.multiply.com _____________________________________________________________________ could get thus the probability of proper resource-use and maintaining yield capacity in a stock is sometimes put aside. Fishing Ground Fishing grounds denotes specie and at times fish value as well as sustained stocks due to the systems reigning in the area such as in reefs and other habitats. In some fishing grounds, the marine spaces in between two or more habitats, fish catch depends on the occurring recruitment transport system of the habitats. Fishing grounds are susceptible to coastal environment thus the mismanaged environment meant death of the fishing grounds. Weather Though basically less important in FSA, weather may explain or attribute to the increase or decrease of fish catch. Sometimes size and volume of fish catch may not be sufficient enough to establish fish stocks fact in a certain fishing ground or area. The changes in temperature and current may influence the existence of fish specie in any particular area most probably in non-habitat places. Age Estimation in Fish “Age estimation is a fundamental part of studies of the life history of fish. In commercial marine fisheries, age estimation is a key area in the monitoring, assessment and management of fish stocks because age determined parameters underlie the population dynamics models used to perform fish stock assessments. These ages determined parameters include age profiles, age at first maturity, spawning frequency, recruitment success and growth and mortality. The ability to estimate the age of a fish is an important tool in fisheries biology and, in conjunction with length and weight measurements, provides valuable information on stock composition, age at maturity, life span, mortality, and production of a fish stock. A variety of structures have been used to estimate the age of fish including opercular bones, fin rays, scales and sagittal otoliths. In elasmobranchs which are composed of a cartilaginous skeleton, centra and dorsal spines have been traditionally used in age estimation studies. Age estimation plays a fundamental role in the assessment of commercial marine fish species. Most of the models used in stock assessment rely on age data and if these data are inaccurate, scientific advice on the state of stocks will be flawed leading to inappropriate management advice. It is therefore essential to ensure that age data are precise and accurate. Three broad approaches to age estimation have evolved and can be categorized as follows: an empirical approach, based on direct observations of individual fish held in captivity, or from fish marked and recaptured; a statistical approach, based on length frequency distributions; an anatomical approach, based on ageing individual fish from body structures, such as scales, opercular bones, vertebrae, fin rays, and otoliths. The empirical approach to ageing was initially used by fish culturists. It relies on measuring the size of confined fish at different ages, and is by far the oldest method of age estimation. Today, the empirical approach is the least used method, because the confined fish seldom experience the same conditions (temperature, day length, food availability, predation) that fish in natural conditions experience. As a result, captive fish often have different sizes at a given age than wild fish. A logical extension of this method was the discovery that the ages of wild fish could be determined if the fish had been marked, released, and recaptured. These methods depend on observing individuals and extrapolating the results to populations. The second basic approach, the statistical analysis of length frequency distribution, has been used to estimate the age of fish since the late nineteenth century. In 1892, the Danish biologist John Petersen showed that the number of age groups in a length frequency distribution could be determined by counting the number of peaks in the distribution. More sophisticated methods of modal analysis are still in use today, especially for species which cannot be aged using hard body parts (e.g., Nephrops norvegicus).

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Ang Nagkakaisang Mamamayang Kostal ng Balayan [ANAKBALAYAN], Inc.

Tolentino Boulevard, Barangay District #10, Balayan, 1st District Batangas, Philippines 4213 [email protected], [email protected] (+63)(043) 921-1910 (+63)9109192842 http://www.freewebs.com/anakbalayan/ http://anakbalayanfill.webs.com http://anakbalayan.multiply.com _____________________________________________________________________ Nowadays, the most popular method of age estimation in fish involves the use of body parts, the anatomical method. This method of age estimation relies on the existence of regular, periodic growth markings on the hard body parts to which a regular timescale can be assigned. This concept is analagous to determining the age of a tree by counting annual rings in a cross section of the trunk. Seasonal changes in the growth of fish in temperate waters are generally recorded as contrasting bands in body parts such as scales, fin rays, spines, opercular bones and otoliths. In bivalve molluscs, contrasting bands found on the shell can be used to estimate age.”

Strengths and weaknesses of fish stock assessment techniques. Accurate stock assessment is of growing importance as the human population and demand for fish increases. Continued technological advances in fishing fleets increase efficiency directly effecting natural fish stocks. Attempting to match natural stock fluctuation with fishing effort may help to avoid any further long term damage of exploited species; this is of great importance as fish provide vital contributions to food supplies and influence employment in coastal areas. Various methods are applied to calculate estimates of recruitment, stock sizes, and age groups. It is apparent that stock assessment techniques are highly dependent on available data, whether long or shortterm predictions are the aim, both strengths and weaknesses are influenced by the abundance of this information. For correct predictions many techniques require large inputs of unbiased data, therefore the strength of any stock biomass prediction will be influenced by the weakness of the available inputs; validating final modal estimates of a fishery. Rose (1997) offers another view for these problems, indicating that fisheries scientists have lost track of their science by becoming 'keyboard ecologists' whom rarely, if ever work directly with real fisheries. This of course does not reflect on the collection of fisheries data but more the interpretation and wisdom required to gain results. This diversion may lead to incorrect long-term analysis; potentially undermining fishery techniques, therefore it is crucial that all stakeholders in a fishery increase an understanding and trust in stock assessment procedures (Anon 1998). Cortes's (1998) study of shark stock assessment concluded no accurate results could be gained without increased collections of biological and fishery data, coinciding with a better understanding of stock recruitment relationships. Many fisheries are in dire straits due to data collection leaving them with a retrospective problem for stocks. This is pinpointed by Mohn (1999), who studied data on the East Scotian Shelf cod fishery. He concludes that failure to correct the problems encountered by traditional analysis techniques, leads to catch level advice twice or more the intended level. Stock and recruitment data sets should not be published or used unless estimates of error variance are shown; without this information Walters & Ludwig (1981) believe they are meaningless and misleading. Data types can be split into two groups, dependent or independent of the fishery. Fishery dependent data comprises of four usable types, the total catch, amount of fishing, (the combination known as) catch per unit effort (CPUE) and age or size compositions. Catch data is essential for most stock production models, inaccurate or biased collection can have damaging long term effects. Importantly, this data should be totaled over ages, fleets, and nations with longer term information helping to predict the past life of the fishery. Problems arise with data collection as some fleet members find financial rewards in discarding initial catches, searching for larger or higher valued cohorts. When total catches are calculated these discarded fish that obviously made up part of the initial stock are rarely accounted for. The result can be higher biomass predictions, thus allocation of total allowable catches are higher than the available stock. This incorrect estimation of stocks can result in either continued over exploitation or economic hardship when quotas are cut. Some models include discard data, but as Mesnil (1996) points out, the general assumption is that all discards die even though there is proof that in some fisheries (usually shellfish) a significant fraction are able to survive. Either way the incorrect analysis of discarded fish will result in wrong estimates of the fishery. The 'amount of fishing' or 'effort data' on its own is less important to fishery scientists unlike economists who study activity trends. The collection of days fished at sea and number of boats operating in conjunction with the previously mentioned catch data is much more valuable. This information known as catch per unit effort (CPUE), if based on age and composition, can be a very important factor in

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Ang Nagkakaisang Mamamayang Kostal ng Balayan [ANAKBALAYAN], Inc.

Tolentino Boulevard, Barangay District #10, Balayan, 1st District Batangas, Philippines 4213 [email protected], [email protected] (+63)(043) 921-1910 (+63)9109192842 http://www.freewebs.com/anakbalayan/ http://anakbalayanfill.webs.com http://anakbalayan.multiply.com _____________________________________________________________________ fisheries modelling. Effort based production models (PM) use this catch effort data with catches recorded in weight, in an attempt to estimate parameters as a stock production curve. They also assume that effort is closely related to fishing mortality (Kimura et al. 1984). When writers such as Roff (1983) suggest the catch/effort data is only reliable to detect major fluctuations in population size and "attempts to determine equilibrium yields from catch/effort data are as likely to be successful as finding the pot of gold at the end of the rainbow", doubts over the strengths of models using CPUE data must arise. Collie & Sissenwine (1983) were more liberal in their views but still express difficulties in the standardising of CPUE data from commercial and recreational fisheries. They pinpoint the of 'constant catchability coefficients' and continued 'technical improvements' as the main areas for biased data. Many models rely on accurate age, length, and age-length calculations of a stock. The difficult nature of collecting this data probably has the most influence hence the most accurate sources are usually from survey vessels. If capable industry collects this data, but usually samples are taken from the catch when landed; the two usual techniques of ageing these fish are via scale and otolith readings. Otolith ageing is less adaptable to a fishery wide sampling program than scale readings, due to the difficult and timeconsuming nature of collection. Otolith morphology has also been shown to be an effective tool for stock discrimination in certain species (Freidland and Reddin 1994). Independent data collection via fishing surveys helps limitations apparent from actual fishery dependent data. As with commercial methods, variable catches and weather conditions affect surveys; technical influences such as mesh size and vessel efficiency may not coincide with actual fleet averages. Changes in vessel efficiency or shifts in effort may not accurately reflect trends of abundance or fishing mortality. Therefore, in the determining of age structures, growth, mortality rates and historical trends, survey techniques may provide the only basis of data collection (Clark 1979). Simplistic assumptions that areas have been swept clean of fish, and common assumptions that trawls are giving unbiased samples both as to species and size of the local demersal fish abundance, may prove inaccurate and damaging in the long-term. Acoustic methods can be used to either estimate population sizes of pelagic species directly or in conjunction with survey vessels when beam trawling for demersal fish. Engas & Vold Soldal (1992) believe trawl catch rates cannot be relied upon to provide representative estimates and any bias will therefore affect the equivalent acoustic estimates. This may be due to unrealistic requirements such as the confinement of a stock in an area small enough to be surveyed in a set time, at a required intensity, in mid-water not to close to the shore (MacLennan & Forbes 1987). Further assumptions are limited numbers of other species detected, the target strength of the species is accurately known, acceptable weather conditions and no response from the fish to the vessel. Fish densities estimated by horizontal beaming can be up to fifty times higher than vertical beaming due to boat avoidance creating large errors in final data (Kubecka & Wittingerova 1998). Although much interference may be apparent, Pope (1982) still believes the data gained may be valuable when setting precautionary catch levels. Methods of tagging can be applied to gain imprecise levels of natural mortality (Shepherd 1988), main weaknesses being the sometimes over-expectant assumptions that need to be made. A fixed population with an equal capture rate and no change in catchability level, coinciding with no loss of marks or tags, all seems a little un-realistic. Peterson's closed population method cannot even test these assumptions whereas the Scnabel open population system can, but still with uncertainties. These methods are applied throughout the world indicating that they must work with certain species under perfect conditions. Uncertainties, as with many other techniques do not seem unique, but the methodology of tagging does seem to have greater assumptions than any other method applied to fisheries. The choice of assessment type will depend on the biology of the species, the time scale required, the area and purpose of the assessment and any specific goal of the fisheries manager. This choice may be difficult as stock production models used for long-term management are frequently no better in the forecasting of the following years CPUE than is the previous years CPUE (Stocker & Hilborn 1981). Long-term assessments aid strategic decisions by managers, information such as maximum sustainable yield (MSY) can be estimated and relationships between stock and recruits can be found. Short-term assessments can reveal information on the likely catch in the next or following years (CPUE), as well as consequences of recruitment in the near future. The latter relates to the suggestion and tactics of longterm strategy. Describing of uncertainties in these strategies is of great importance to managers when

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Ang Nagkakaisang Mamamayang Kostal ng Balayan [ANAKBALAYAN], Inc.

Tolentino Boulevard, Barangay District #10, Balayan, 1st District Batangas, Philippines 4213 [email protected], [email protected] (+63)(043) 921-1910 (+63)9109192842 http://www.freewebs.com/anakbalayan/ http://anakbalayanfill.webs.com http://anakbalayan.multiply.com _____________________________________________________________________ weighing the benefits and losses of different techniques. Rosenberg & Restrepo (1994) suggest methods of analysing and assessing risk in management strategies implying that every possible analysis of risk should be undertaken. Hilborn (1992) pinpoints three dominant approaches to fisheries stock assessment: the investigation into catch at age data, uninvolved models of biomass dynamics, and examination of length-frequency data. He suggests that these methods ignore what is known about the biology of the fish and tend to rely on single types of data. This point is of importance as natural mortality, assumed in many modals may increase via predation or reduced food sources causing large errors in calculations. Age based methods such as Virtual population analysis (VPA), require catches recorded in numbers at age on an individual cohort basis to solve the exponential form of the catch equations (Kimura et al. 1984). The dependency of knowing the catch at age in numbers is a downfall as age data is costly and technically difficult to obtain. VPA or simpler cohort analysis needs data from various other sources, any of which could be bias or incorrect. Catch in weight, natural and fishing mortality, weights at cohort, and proportion of mature fish are all required for cohort analysis. Although these methods are the most commonly applied to stock assessment, the large variety of necessary information will have any final say on the weakness of this technique. Interestingly, Agnew et al. (1998) believes that cohorts of certain species have differing dynamics, and therefore should be considered as different stocks. This would render total stock calculation models redundant, with very few other options available to fishery scientists this opinion seems to be alone. Some typical problems arising with these methods includes the missing of year data, changes in survey techniques and age determination methodologies; Richards et al. (1997) suggests some graphical techniques to portray these uncertainties. The errors encountered in age structure data can to some extent be cancelled by using mean age calculations in the assessment models (Richards & Schnute 1998). These of course are statistical problems that may be lost in complicated calculations. Important and essentially undetectable problems arise with discard levels, the guessing of terminal fishing mortality, and predation mortality (Christensen 1996). The statistical problems can be corrected with the application of more accurate data collection, but these biological influences need highly intensive studies before a complete understanding can be hoped for. The effects of various percentage errors in the population of a year class, due to incorrect values of fishing mortality are shown in figure 1. Figure 1: This graph plots percentage error of Ni (population of year class at the ith birthday), against cumulative fishing mortality. The under estimation of Ft (fishing mortality at the last age of a year class to which catch data are available) will result in guesses of Nt that are to small, overestimation has the reverse effect. Interestingly, as the cumulative fishing mortality increases errors in both Ft and Ni decrease. If the cumulative fishing mortality is greater than 2 and Ft can be estimated within the given range many users will find errors in Ni and Fi small enough to work with. Accurate estimations of Ni and Fi require careful choices of Ft if the cumulative fishing mortality is small. This case may arise when numbers of recruits to a year class is guessed from catches of partially recruited age groups. Similar graphs allow fishery scientists to produce the error range of their calculations that will aid assessment of their value. Source: Pope (1983) When age data is sparse or the species cannot easily be aged, length based assessments are an alternative. Chen's (1997) comparison between age and length structured yield-per-recruit models showed length structured techniques better incorporated information observed from fisheries, but age structured

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Ang Nagkakaisang Mamamayang Kostal ng Balayan [ANAKBALAYAN], Inc.

Tolentino Boulevard, Barangay District #10, Balayan, 1st District Batangas, Philippines 4213 [email protected], [email protected] (+63)(043) 921-1910 (+63)9109192842 http://www.freewebs.com/anakbalayan/ http://anakbalayanfill.webs.com http://anakbalayan.multiply.com _____________________________________________________________________ gave more precise and conservative estimates of yield-per-recruit. This is the main reason why age structured models are chosen from the conservation perspective in fisheries management. The obvious difference between age and length is that age is a linear measure of time whereas length is non-linear. This makes data interpretations more difficult, more assumptions of growth reductions due to age must be made. Assumptions removed from a model increase accuracy, this is why age methods are preferred if feasible. A potential strength of fishery science will be the adoption of multi-species models to fisheries that currently utilise single species methods. These models, although essential for future management purposes, seem unreliable and more imprecise than the currently used methods. They require more data that could lead to inaccurate assumptions, thus leaving fisheries in a worse state. The key area that multi-species models address is predation. It is often assumed that fishing mortality alone is responsible for the variation in fish survival, but in some fisheries, losses to predation can exceed losses to fisheries (Bax 1998). This could indicate that assumptions of natural mortality in single species models are drastically misleading. Mertz & Myers (1997) point out that if bad estimations of natural mortality are used in calculations of cohort strength derived from catch data, the accuracy may be greatly corrupted. Pereiro (1995) supports that where species are not linked to a specific substratum natural mortality will always predominate over fishing mortality thus fishing mortality is not the subsidiary factor. Either way the addition of accurate natural mortality estimations into models must be welcomed. This review has shown some major problems encountered when estimating populations from a fishery. Strengths seem sparse, maybe the biggest being that these techniques are the only available methods for estimating stock dynamics. Assessment techniques have strengths over each other and it is imperative the correct method is paired to its purpose. Weaknesses seemed over bearing and many writers have tried to remove errors from previously presented models resulting in a claim that theirs is now the most accurate. Until data collection methods have improved there will always be inaccuracies in results. The addition of computer programs should aid time-consuming calculations allowing scientists to return to the field of study to uncover new methods of improving the currently used stock assessment techniques.

Participatory Fisheries Stock Assessment (ParFish) ParFish is an approach to fisheries stock assessment and a tool for involving fishers in the development of management actions. It is a particularly suitable methodology for small-scale fisheries in developing countries as it: Allows a rapid assessment; Encourages participation of fishers; Does not require data recorded over a long time series. At the centre of ParFish is a software for stock assessment that can use a number of different sources of information including fisher interviews. The ParFish software gives an indication of the level of exploitation of fisheries stocks. It also indicates what the optimal level of control (e.g. catch quota, the number of boats fishing or area of refuge) should be, to sustain fish stocks and give catch rates acceptable to fishers. However ParFish is not only about stock assessment but is an holistic approach that assists fishers and other stakeholders to enter a cycle of learning, management planning and implementation.

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