An holistic approach to fish stock identification

Fisheries Research 43 (1999) 35±44

An holistic approach to ®sh stock identi®cation Gavin A. Begga,*, John R. Waldmanb a

National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Northeast Fisheries Science Center, 166 Water Street, Woods Hole MA 02543, USA b Hudson River Foundation for Science and Environmental Research, 40 West 20th Street, 9th Floor, New York, NY 10011, USA

Abstract The concept of the `stock' is fundamental for both ®sheries and endangered species management. We review different approaches used in identifying and classifying stocks and advocate that an holistic approach (e.g., involving a broad spectrum of complementary techniques) be used in future stock identi®cation studies. Stock de®nitions should evolve as management requirements do and be re-evaluated when the need arises or when break-through technologies become available. # 1999 Elsevier Science B.V. All rights reserved. Keywords: Stock; Identi®cation; Population; Discrimination; Integration

1. Introduction The concept of the `stock' is fundamental to ®sheries management. Stocks are arbitrary groups of ®sh large enough to be essentially self-reproducing, with members of each group having similar life history characteristics (Hilborn and Walters, 1992). To manage a ®shery effectively, it is important to understand the stock structure of a species and how ®shing effort and mortality are distributed (Grimes et al., 1987). An understanding of stock structure is vital to designing appropriate management regulations in ®sheries where multiple stocks are differentially exploited (Ricker, 1981). We offer a review and perspective on different approaches to ®sh stock identi®cation *

Corresponding author. Present address: Marine Research Institute, P.O. Box 1390, 121 Reykjavik, Iceland. E-mail address: [email protected] (G.A. Begg)

problems in ®sheries management, and discuss the utility of an holistic (multiple technique) approach as a solution to these problems. In ®sheries science, `stock' ®rst referred to any group of a ®sh species that was available for exploitation in a given area (Milton and Shaklee, 1987). Marr (1957) explicitly differentiated stocks and subpopulations, considering the subpopulation to be a genetically self-sustaining entity, i.e., a deme, or the smallest self-perpetuating unit. He de®ned the stock as a population or portion of a population, all members of which are characterized by similarities which are not heritable, but are induced by the environment, and which may include members of several different subpopulations. Race also has been used to describe categories now considered to be stocks or populations (e.g., Raney et al., 1954). Larkin (1972) emphasized practical aspects and considered a stock to be a production or management unit. Saila and Jones

0165-7836/99/$ ± see front matter # 1999 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 5 - 7 8 3 6 ( 9 9 ) 0 0 0 6 5 - X

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G.A. Begg, J.R. Waldman / Fisheries Research 43 (1999) 35±44

(1983) de®ned unit stocks as characteristic populations or sets of subpopulations within subareas of the geographic range of a species, but noted that the taxonomic status of unit stocks may vary or remain unclear. However, a stock is often taken to mean a lower category than that of the population recognized by taxonomists (Cushing, 1968). Modern stock de®nition studies often integrate genetic knowledge, recognizing the importance of information on the number and geographic limits of non-interbreeding, self-recruiting populations within an exploited species (Ovenden, 1990). In essence, the stock concept describes the characteristics of the units assumed homogeneous for particular management purposes. There is a surprising degree of parallelism between the problems in de®ning species (e.g., Mayden and Wood, 1995) and stocks: both have shifted and diversi®ed over time partly in response to changes in evolutionary thinking and technological advances. Yet, on the operational level, the taxon levels of species and stocks are usually differentiated without debate. In contrast, the terms `stock' and `population' are presently used rather interchangeably, although there are exceptions. Regardless of its precise de®nition and biological underpinnings, the stock concept really has to do with the interaction between a ®sh species and its management. Consequently, the purpose of such de®nitions is to direct management efforts to taxon levels below that of the species. This leads us to an important distinction to be made between endangered species management and ®sheries management with respect to stock identi®cation. Endangered species management aims to ensure that populations that exhibit unique life history traits to particular areas are not irreversibly reduced by harvest or habitat destruction (Dizon et al., 1992), and is concerned with the evolutionary basis of stock de®nition. Fisheries management, generally aims to achieve maximum (or optimum) sustainable production from ®sh stocks usually de®ned on the basis of the same vital rate parameters that are used for productivity calculations. Therefore, the techniques used in de®ning these two types of management needs are fundamentally different. However, stock de®nitions have tended not to be revisited in ®sheries management because considerable ®scal and personnel resources are normally required to conduct stock identi®cation studies. Stock structure information

can often create more uncertainty in ®sheries management, particularly when it contradicts historically established stock and management boundaries, however, ignoring such information can contribute to erroneous, ineffective ®sheries management. There are many cases in which high exploitation, combined with ineffective ®sheries management, have resulted in depletion of ®sh stocks. These include anchovy Engraulis ringens (Hilborn and Walters, 1992), capelin Mallotus villosus (Tjelmeland and Bogstad, 1993), Atlantic cod Gadus morhua (Hutchings, 1996), haddock Melanogrammus aegle®nus (Clark et al., 1982), herring Clupea harengus (Dragesund et al., 1980), orange roughy Hoplostethus atlanticus (Smith et al., 1991), Atlantic salmon Salmo salar (Cook and McGaw, 1991), and sardine Sardinops sagax (Shannon et al., 1993). Such depletions can result in a loss to the total gene pool of a species (Nelson and SouleÂ, 1987; Smith et al., 1991), now a consideration in the precautionary approach to ®sheries management (ICES, 1997). Disregard of stock structure in ®sheries management can also lead to signi®cant changes in the biological characteristics of a ®sh species (Altukhov, 1981; Ricker, 1981). Identi®cation of ®sh stocks may be simple and qualitative, such as observation of different timings of runs of an anadromous species in a single river, or it may be highly technical and quantitative, requiring laboratory techniques and complex statistical analyses. Across this spectrum, scientists favor the former approach for situations involving low numbers of putative stocks exhibiting large characteristic interstock differences, and favor the latter approach for situations involving larger numbers of stocks and where differences are slight between stocks. The interplay of the particular questions posed, resources available, and improvements in scienti®c understanding and technology has resulted in a suite of approaches applied to ®sh stock identi®cation. For instance, in a review of the stock identi®cation history of striped bass Morone saxatilis, Waldman et al. (1988) found that stock identi®cation was based upon: catch data, tag recoveries, meristics, morphometrics, scale morphology, parasites, cytogenetics, protein electrophoresis (isoelectric focusing), immunogenetics, mitochondrial DNA, and nuclear DNA. Heart tissue fatty acids (Grahl-Nielsen and Mjaavatten, 1992), the elemental composition of otoliths (Mulli-


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gan et al., 1987), and osteological interdigitation features (Waldman and Andreyko, 1993) have also been used. Additional approaches such as stable isotope measurements, and otolith microstructure, shape analysis and thermal marking, have been applied to other ®shes (e.g., Campana and Casselman, 1993; Roelke and Cifuentes, 1997; Volk et al., 1999 these proceedings). Stocks are useful taxonomic groupings to the extent that they are important for some explicit management purpose. Many stock de®nitions used in ®sheries management date back to when statistical data collection programs were ®rst implemented, and were often based on geographical or statistical area boundaries (e.g., in the northwest Atlantic, see Halliday and Pinhorn, 1990). However, stock de®nitions should evolve as management requirements do, and should be revisited when the need arises or when breakthrough technologies become available.

when putative stocks are marked when they are geographically discrete in order to determine whether they subsequently intermingle. Alternatively, stock mixtures may be marked to ®nd out if ®sh later separate geographically. The success of mark-recapture for stock identi®cation purposes is dependent on representative tagging and recapture efforts (a condition rarely met). Such studies are generally costly and time-consuming.

2. Fundamental `signals' from alternative stock identification approaches

Consistent differences in life history characteristics such as growth rates or reproductive characteristics (e.g., fecundity-at-age, spawning time, etc.) have frequently been used to separate stocks.

Fish stocks are identi®ed on the basis of differences in characteristics between stocks. Quantitative bases for demonstration of stock structure may occur as discrete differences in some physical attribute, or as a statistically signi®cant distribution. Investigation of any single characteristic will not necessarily reveal stock differences even when `true' stock differences exist (Type 1 error). Characteristics vary between ®sh stocks due to environmental and genetic factors. Environmental factors tend to in¯uence phenotypic characteristics within stocks; phenotypic variation between stocks is usually associated with the geographical region occupied by a species throughout its range. Although phenotypic differences do not provide direct evidence of genetic isolation between stocks, they can indicate the prolonged separation of postlarval ®sh in different environmental regimes (Campana et al., 1995). A brief review underlying the techniques for stock identi®cation follows:

2.2. Catch data Catch data can often be used as crude indicators of stock structure. Strong geographic differences in ageor size-composition, if not re¯ective of gear differences and other factors, suggest independence of recruitment or other biological or ®shery factors as a basis for assuming stocks are discrete. 2.3. Life history characteristics

2.4. Parasites Parasite `tags' can also differentiate stocks of ®sh. The species composition and abundance of parasites may differ between ®sh stocks due to biogeography, differential environmental tolerances of parasites, differences in availability of intermediate hosts, and different life history characteristics of the ®sh stocks themselves. An advantage of this approach is that the parasites are applied by nature at no cost to researchers (Williams et al., 1992). Disadvantages include the need for considerable biological information on the parasites, and intra-stock variation due to adoption of different life history modes within a ®sh stock (not to mention the assumption that parasitic fauna are stable over time, and the dif®culties in doing quantitative parasitology).

2.1. Mark-recapture

2.5. Otolith microchemistry

Marking of ®sh can be used to infer stock structure. For stock identi®cation purposes, best results occur

Otoliths are predominantly composed of calcium and trace elements that are derived from the ambient

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waters inhabited by the ®sh (Campana et al., 1995). Because water bodies often differ in the concentrations of trace elements, stocks may often be distinguished by the chemical `signatures' retained in otoliths (assayed using spectrometric or related techniques). A further advantage of this approach is that by analyzing selected portions of an otolith, the trace element signals can be associated with particular growth stages. This approach has potential to discriminate between stocks where an environmental signal is pronounced (e.g., where substantial reproductive interchange diminishes genetic differences). However, results from these techniques are often dif®cult to interpret because of the combined effects of physiological, ontogenetic, and environmental in¯uences on the deposition of trace elements, not to mention a posteriori sampling problems with handling and elemental contamination (Fowler et al., 1995; Thresher, 1999 these proceedings). 2.6. Morphology 2.6.1. Meristics Countable, morphological structures (e.g., ®n rays, gill rakers, scales in rows) have historically served as an important basis for identifying ®sh stocks. Count data are discrete, thus facilitating statistical analysis. Meristics are controlled by both genetic and environmental factors, in unknown proportions (Barlow, 1961). Meristic characters are generally set early in ontogeny and remain stable throughout life; thus re¯ecting environmental effects over a relatively brief time of larval development. Because of this, signi®cant statistical differences can occur within a stock among year classes or geographic subgroups subjected to varying environmental conditions. However, consistent environmental in¯uences have the potential to provide stock discrimination where there is weak genetic divergence between actual stocks. 2.6.2. Morphometrics Morphometrics include the analysis of body shape, or the shape of particular morphological features of various body dimensions or parts. These data are continuous, and must be corrected for size differences among specimens. As with meristics, morphometric expression is under the simultaneous control of genetic and environmental factors.

2.6.3. Scale and otolith analyses Geometric analysis of scales and otoliths provide sources of variation amenable to morphometric and other forms of analysis (e.g., Campana and Casselman, 1993; Richards and Esteves, 1997). Features containing stock-speci®c information such as annuli spacing are biologically interpretable (i.e., related to age and growth), whereas other features such as perimeter shape are not easily interpretable. 2.7. Genetics Genetic differences between individuals, stocks and populations are the basis for ascertaining the degree of reproductive isolation, which is the fundamental mechanism structuring differences between these taxonomic groups. The strengths of genetic signals between stocks are positively associated with time since divergence of stocks (mediated by generation time, with shorter generation time accelerating genetic differentiation), and their degree of isolation (i.e., reproductive exchange between stocks eroding genetic differences) (Adkinson, 1996). Thus, recent divergence, or substantial secondary reproductive contact result in no apparent differences in gene frequencies between groups, even when it exists. Genetic techniques used in stock identi®cation include the following: 2.7.1. Protein variation Variation in proteins or enzymes is an indirect expression of nucleotide base differences between groups. Until the advent of molecular techniques, allelic frequency differences revealed by protein electrophoresis were the primary markers used to assess genetic differences between stocks (Wirgin and Waldman, 1994). However, the usefulness of this approach is undermined by the fact that the same protein may be coded by multiple nucleotide sequences. 2.7.2. Mitochondrial DNA Mitochondrial DNA (mtDNA) is a cytoplasmically inherited circular molecule of approximately 16 000± 20 000 base pairs. Inheritance of mtDNA is haploid and is either strictly or overwhelmingly maternal. Different regions of the mtDNA molecule evolve at different rates (Meyer, 1993), with differences in base pairs used to separate stocks.


2.7.3. Nuclear DNA Base sequences of nuclear DNA (nDNA) partitioned among chromosomes present large numbers of potential markers (typically > 3 billion nucleotide pairs) in any individual (Wirgin and Waldman, 1994). Nuclear DNA is diploid; recombination re¯ects genetic characteristics of both sexes. Nuclear DNA that codes for gene products and functions is thought to evolve substantially slower than mtDNA, but certain non-coding markers such as minisatellites and microsatellites may evolve faster than mtDNA (Dowling et al., 1990). Interpretation of nDNA data may be complicated by polyploidy, as is common in sturgeons and salmonids (Wirgin and Waldman, 1994). 3. Integrated `signals' from multiple stock identification approaches The strongest inferences on stock structure are drawn from a suite of complementary techniques that cover multiple aspects of the biology of a ®sh species. This is partly because the de®nition of a `stock', for management purposes, is not strictly a genetic construct, but represents a semi-discrete group of ®sh with some de®nable attributes of interest to managers. Integration of the results of each method used in a multiple or `holistic' stock identi®cation approach maximizes the likelihood of correctly de®ning stocks ± , however, de®ned by management (Hohn, 1997). An holistic approach to ®sh stock identi®cation, involving a broad spectrum of techniques, appears to be particularly pertinent for those ®sh species with complex stock identities. From an operational perspective we de®ne an `holistic approach' as one that utilizes multiple techniques for ®sh stock identi®cation, and can include: collating all available stock identi®cation information previously known into a single review to infer stock structure; using two or more different stock identi®cation techniques in a single study on a range of samples; or ideally, for more direct comparisons using a wide range of stock identi®cation techniques on the same samples. Although, the latter approach is the preferred option to resolving stock identi®cation problems, only one study has speci®cally examined this approach (Waldman et al., 1997). The paucity of studies utilizing such an approach is due to a number of reasons including

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the different expertise of individual scientists conducting the research, the speci®c stock identi®cation issues and purposes being addressed, and the logistical costs in utilizing multiple, complementary techniques in a single study. Incorporating different stock identi®cation methods into a single study often allows apparent discrepancies implied by each method to be resolved. This is illustrated by Kinsey et al. (1994). Electrophoretic data revealed a single panmictic population of Spanish sardine, Sardinella aurita, in coastal Florida, however, morphometric and meristic data indicated regional patterns. The morphological variations were habitatspeci®c and diminished as the ®sh got older and migrated throughout the species range. Leslie and Grant (1990) found no genetic differences between samples of angler, Lophius vomerinus, from different areas along the South African coast, while variation in meristic and morphometric characters was evident. Differences in these characters were thought to be environmentally driven and not related to segregation of speci®c genes. Safford and Booke (1992) concluded that the phenotypic variation in Atlantic herring, Clupea harengus harengus, stocks throughout the northwest Atlantic was most likely due to environmental conditions, as they found no genetic differentiation. Likewise, Pepin and Carr (1993) found no evidence for genetic discrimination of Atlantic cod, Gadus morhua, off Newfoundland, in contrast to their meristic and morphometric data. Extensive egg and larval drift throughout the region was considered to have limited the potential for genetic isolation, despite variation in environmental regimes that may have affected phenotypic features of larvae as they settled. When the results of different stock identi®cation studies are not consonant, the default management scenario should be to use a precautionary approach to ensure resource sustainability and maintenance of genetic biodiversity. In the above mentioned studies, such an approach was encompassed where each stock component was recommended to be managed as a separate unit. Without the information from multiple techniques an inaccurate determination of stock structure is possible, although it really depends on why stock identi®cation is required in the ®rst place (i.e., genetic stocks vs. management units). Relying on a single approach for stock identi®cation may enable temporal and spatial patterns in stock

40


structure of a species to be followed, but the accuracy of the technique remains unknown without the use of additional con®rmatory evidence (Waldman et al., 1997). For example, Roby et al. (1991) found congruent results of an east±west separation of capelin stocks in the Gulf of St. Lawrence using truss morphometric and electrophoretic analyses. The concomitant use of different stock identi®cation approaches improved their con®dence in the results of the stock identi®cation. Melvin et al. (1992) observed similar results using mark-recapture, meristic and morphometric data in the discrimination of American shad, Alosa sapidissima, stocks in the Bay of Fundy. Integrating the meristic and morphometric variables enabled them to achieve relatively high rates of classi®cation success in mixed-stock analyses. O'Connell et al. (1995) found signi®cant genetic heterogeneity between Atlantic salmon, Salmo salar, stocks throughout Wales using mtDNA and allozyme variation. They concluded that alternative DNA approaches (as with other comparative stock identi®cation techniques) should be compared with results from other techniques to assess the relative power of stock identi®cation. Johnson et al. (1994) compared electrophoretic data of king mackerel, Scomberomorus cavalla, in the Gulf of Mexico to tagging, catch data and spawning seasonality to help interpret stock identi®cation patterns. Few stock identi®cation studies have explicitly examined a comprehensive range of techniques (Casselman et al., 1981; Waldman et al., 1988; Grif®ths, 1997; Begg et al., 1998a), and only one has speci®cally assessed the results of different stock identi®cation approaches in a statistically rigorous, comparative, fashion (Waldman et al., 1997). Casselman et al. (1981) examined the stocks of lake white®sh, Coregonus clupeaformis, in Lake Huron using a combination of tag-recapture, life history, morphometric, meristic, and electrophoretic data. Multiple stocks were identi®ed, with tagging data providing the best evidence of discreteness between the stocks. Other characteristics provided additional information on the speci®c details of the separations. Mark-recapture data delineated ®ve stocks, while the life history parameters indicated that another six groups were relatively discrete and probably constituted separate stocks, even though some of the differences may have been in¯uenced by changes in environmental conditions.

Waldman et al. (1988), in a review of stock identi®cation techniques used for striped bass, concluded that the best overall resolution between stocks occurred for a combination of approaches that have high individual resolutions, but low mutual congruence (i.e., techniques that do not have a substantial degree of parallelism in the type of variation that is measured). Grif®ths (1997) found con®rmatory evidence from several life history parameters, morphometric relationships, and mark-recapture data used to identify South African stocks of silver kob, Argyrosomus inodorus. Begg et al. (1998a) investigated the stock structure of two mackerels, Scomberomorus queenslandicus, and, S. munroi, off the Australian east-coast using tag-recapture, life history, otolith chemistry, electrophoretic and commercial catch data. Integrating complementary information from the different approaches was essential in interpreting the results from any one character, particularly for the phenotypic-based attributes, where a number of hypotheses (e.g., environmental differences) could have been speculated to have caused the spatial (stock) patterns, rather than simply genetic factors. Only one empirical comparison of alternative stock identi®cation approaches ± utilizing the same specimens for each technique has been conducted to date (Waldman et al., 1997). In that study the morphometric-based or genotypic approach best characterized mixed-stocks of striped bass. Their results also suggested that estimates of stock composition may be inaccurate if a phenotypic-based approach does not have relatively high rates of classi®cation success, despite the use of correction factors. A recommended protocol for integrated stock identi®cation, therefore, is the use of at least a genetic procedure and at least one phenotypic-based approach. Genetics may be used to provide a direct basis for stock structuring and to interpret phenotypic-based patterns (Ihssen et al., 1981; Smith, 1990). As described above, morphometric and meristic characteristics have been frequently used in association with different genetic approaches (e.g., Claytor and MacCrimmon, 1987; Lindholm and Maxwell, 1988; O'Maoleidigh et al., 1988; Riget et al., 1992). The application of morphological and meristic characteristics in resolving stock identi®cation problems, however, is complicated as phenotypic variation may not be due to genomic differences (Clayton, 1981), but by


environmental factors (Lindsey, 1964; Todd et al., 1981). Mark-recapture has also been commonly used in conjunction with genetic-based studies. Owing to the logistics and expense of large-scale tagging projects, these have been usually conducted as separate studies, or as precursors to genetic analyses (Johnson et al., 1986, 1994; Ross and Sullivan, 1987). Markrecapture data can provide information about ®sh movements that can assist in de®ning individual spawning aggregates, which are prerequisites for genetic-based de®nitions of stock structure. Markrecapture data are also useful not only in providing estimates of movements and stock mixing, but also growth, mortality and stock size, which are important parameters for ®shery management (Pawson and Jennings, 1996). Parasite markers have been used in combination with genetic methods to differentiate stocks and can provide an indication of stock mixing similar to mark-recapture techniques (MacKenzie, 1983; Belyaev and Ryabov, 1987; Lester, 1990). Genetic methods are useful for determining evolutionary history related to stock structure (particularly for endangered species management), in contrast with phenotypic markers that are more applicable for studying short-term environmentally-induced variation (which are more often used for purposes of ®shery management) (Roby et al., 1991; Kinsey et al., 1994). The spatial and temporal scales of management interest de®ne the choice of stock identi®cation techniques. Genetic-based differences re¯ect evolutionary time scales and may produce more conservative groupings (i.e., fewer stocks) than those based on chemical, morphometric, parasitic or tagging studies (Elliott et al., 1995). 4. Discussion Despite the need to reliably identify management units of the same species, such classi®cation cannot be accomplished with a single technique (Edmonds et al., 1989; Campana et al., 1995). Combining the results obtained with several techniques may provide considerable insight to the possible stock structure of a species (Elliott et al., 1995). The ability to readily characterize the identity of an individual ®sh or group of ®sh taken in a particular area and/or time, to a particular stock remains a major challenge (Kutkuhn,

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1981), and will always be probabilistic lacking a technique or protocol that unambiguously identi®es the origin of individual ®sh (Waldman et al., 1988). An holistic approach to ®sh stock identi®cation is highly desirable owing to the limitations and conditions associated with any particular method and the requirements of ®shery management for separating units based on genotypic or phenotypic differences. Procedures are now available that can provide a suite of information on the biology, distribution, and implied stock structure of a species. The utility of stock identi®cation techniques should be considered on a case-by-case basis depending also on the degree of resolution required. Tagging and parasitic data generally provide broad-scale stock identi®cation information, but may be inadequate for determining more complex multi-stock structures, unless greater emphasis is placed on obtaining more thorough recapture information than is typical (MacKenzie, 1983; Begg et al., 1997). Morphometrics, meristics and life history characteristics have been used successfully for stock identi®cation at a range of different scales (Casselman et al., 1981; Elliott et al., 1995; Cadrin and Friedland, 1999, these proceedings), but are often limited by their possible alteration by environmental variation (Lindsey, 1964; Todd et al., 1981). Chemical methods enable elemental `®ngerprints' to be discerned for multiple stock complexes (Campana et al., 1995; Begg et al., 1998b; Thresher, 1999, these proceedings), although environmental variation may exist within the distribution of a single genetic stock. Molecular procedures can provide a genetic basis for stock identi®cation (Smith, 1990; Shaklee et al., 1999, these proceedings). Consequently, the different circumstances related to each stock identi®cation technique, and the scale at which stocks can be detected vary depending on the situation. Application of multiple stock identi®cation techniques (i.e., an holistic approach) to stock identi®cation problems may con®rm a particular stock structure ®rst detected by a single procedure used in isolation. Overlaying all available information from a range of techniques will enable a generalized, consistent and de®nitive pattern of stock structure to be developed, relative to the needs of ®shery management. Such an approach would enable a higher degree of con®dence in a particular stock structure, rather than that for one that had been generated by a single

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procedure. This would be particularly relevant for situations where different techniques provide different interpretations of stock af®nities. In such cases, reliance on a single procedure could provide an inaccurate representation of the underlying stock structure of a species, which could have signi®cant management implications in the utilization and conservation of its stocks. Importantly, the various means of obtaining sources of samples and data for alternate stock identi®cation approaches may often be complementary, thereby minimizing time and collection costs (Hohn, 1997). The process of de®ning ®sh stocks is essential for effective ®sheries management, and will continue to undergo re®nement as new tools and technologies are developed (Kutkuhn, 1981). Although, a precise determination of stock identi®cation remains a major challenge, and will not necessarily be suf®cient if ®sheries occur on mixed stocks, our best opportunity at identi®cation between these mixed catches relies on examining all available stock identi®cation information using the most up-to-date technologies when logistically feasible. The sampling of ®sheries vs. the sampling of ®sh is a real challenge in linking the results of laboratoryintensive stock identi®cation techniques to ®sheries operations (and ®sheries management), and subsequent impacts on the ®sh stocks. Stock identi®cation should thus be recognized as a continuing process, evolving as management needs for stock assessment change, but always viewed against the background of a critical examination of all available data and new studies as dictated by changing resource condition and experimental technologies (Brown et al., 1987). Acknowledgements Thanks to Fred Serchuk, Steve Murawski, Kevin Friedland, Jon Gibson, and William Overholtz for reviews and critical comments of this paper. This work was performed while G.A. Begg held a National Research Council (NOAA/NMFS/NFSC) Research Associateship. References Adkinson, M.D., 1996. Population differentiation in Pacific salmon: local adaptation, genetic drift, or the environment. Can. J. Fish. Aquat. Sci. 52, 2762±2777.

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