2
GENERAL REVIEW OF NORTHEAST GROUNDFISH STOCK ASSESSMENTS

But that dread of something after death, the undiscover'd country from whose bourn no traveler returns, puzzles the will and makes us rather bear the ills we have than fly to others that we know not of.

William Shakespeare, Hamlet

Stock assessment is the science of data collection, analysis, and modeling that provides the basis for prudent, sustainable exploitation of fishery resources. It includes the provision of scientific advice about management strategies used to exploit fish stocks and the integration of science and scientific advice into the management process. In particular, the feedback between stock assessment and fisheries management has to be included to manage fisheries effectively.

A recent National Research Council (NRC) report Improving Fish Stock Assessments (NRC, 1998) reviewed the state of existing knowledge about stock assessment and made ten recommendations to improve the process (Box 2.1). The first recommendation is that a complete stock assessment should include five major topics: stock definition; data; assessment model; policy evaluation; and communication of results to managers and stakeholders. A checklist of items that should be in a stock assessment is given in Box 2.2. These recommendations provide a benchmark against which fishery stock assessments can be measured. The committee considered the framework of recommendations from the earlier NRC report presented in Boxes 2.1 and 2.2.

The approach of this committee was to examine the Northeast groundfish stock assessments against well-defined standards of quality. This investigation was a multistage process:

  1. The committee first examined the data collection protocols and assessment models used. In particular, the following issues were evaluated: whether appropriate data were collected; whether stock assessments could be replicated; whether alternative models were used or should have been used; and whether forecasts of future populations were appropriate. In this chapter, the general results of this examination are presented, which are of interest to a general audience. Recommendations regarding technical details of stock assessments of interest to specialists are given in Chapter 3.

  2. The committee then compared the assessments against approaches used around the world. The idea behind this comparison was to determine whether other stock assessment processes were qualitatively better than those for the Northeast fishery.

  3. The committee evaluated the Northeast groundfish stock assessments against the NRC (1998) recommendations for improving stock assessment. Given that the earlier report was in press and had not been seen by stock assessment scientists, ours was a particularly severe test of the Northeast fishery assessments, which did not have the NRC guidelines.



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2 GENERAL REVIEW OF NORTHEAST GROUNDFISH STOCK ASSESSMENTS But that dread of something after death, the undiscover'd country from whose bourn no traveler returns, puzzles the will and makes us rather bear the ills we have than fly to others that we know not of. William Shakespeare, Hamlet Stock assessment is the science of data collection, analysis, and modeling that provides the basis for prudent, sustainable exploitation of fishery resources. It includes the provision of scientific advice about management strategies used to exploit fish stocks and the integration of science and scientific advice into the management process. In particular, the feedback between stock assessment and fisheries management has to be included to manage fisheries effectively. A recent National Research Council (NRC) report Improving Fish Stock Assessments (NRC, 1998) reviewed the state of existing knowledge about stock assessment and made ten recommendations to improve the process (Box 2.1). The first recommendation is that a complete stock assessment should include five major topics: stock definition; data; assessment model; policy evaluation; and communication of results to managers and stakeholders. A checklist of items that should be in a stock assessment is given in Box 2.2. These recommendations provide a benchmark against which fishery stock assessments can be measured. The committee considered the framework of recommendations from the earlier NRC report presented in Boxes 2.1 and 2.2. The approach of this committee was to examine the Northeast groundfish stock assessments against well-defined standards of quality. This investigation was a multistage process: The committee first examined the data collection protocols and assessment models used. In particular, the following issues were evaluated: whether appropriate data were collected; whether stock assessments could be replicated; whether alternative models were used or should have been used; and whether forecasts of future populations were appropriate. In this chapter, the general results of this examination are presented, which are of interest to a general audience. Recommendations regarding technical details of stock assessments of interest to specialists are given in Chapter 3. The committee then compared the assessments against approaches used around the world. The idea behind this comparison was to determine whether other stock assessment processes were qualitatively better than those for the Northeast fishery. The committee evaluated the Northeast groundfish stock assessments against the NRC (1998) recommendations for improving stock assessment. Given that the earlier report was in press and had not been seen by stock assessment scientists, ours was a particularly severe test of the Northeast fishery assessments, which did not have the NRC guidelines.

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Box 2.1: Recommendations of the NRC 1998 Report, Improving Fish Stock Assessments Stock assessment scientists should conduct complete assessments using a checklist such as given in Box 2.2. Scientists from state and federal governments and from independent fisheries commissions should continue to conduct fish stock assessments with periodic peer review. At the minimum, at least one reliable abundance index should be available for each stock. Fishery-independent surveys offer the best choice for achieving a reliable index if designed well with respect to location, timing, sampling gear, and other statistical survey design considerations. Because there are often problems with the data used in assessments, a variety of different assessment models should be applied to the same data; new methods may have to be developed to evaluate the results of such procedures. The different views provided by different models should improve the quality of assessment results. Greater attention should also be devoted to including independent estimates of natural mortality in assessment models. The committee recommended that fish stock assessments include realistic measures of the uncertainty in the output variables whenever feasible. Although a simple model can be a useful management tool, more complex models are needed to better quantify all the unknown aspects of the system and to address the long-term consequences of specific decision rules adequately. Implementation of this recommendation could follow the methods discussed in Chapter 3 (of the earlier report.) Precautionary management procedures should include management tools specific to the species managed, such as threshold biomass levels, size limits, gear restrictions, and area closures (for sedentary species). Assessment methods and harvesting strategies have to be evaluated simultaneously to determine their ability to achieve management goals. Ideally, this involves implementing them both in simulations of future stock trajectories. For complex assessment methods, this may prove very computationally intensive, and an alternative is to simulate only the decision rule while making realistic assumptions about the uncertainty of future assessments. Simulation models should be realistic and should encompass a wide range of possible stock responses to management and natural fluctuations consistent with historical experience. The performance of alternative methods and decision rules should be evaluated using several criteria, including the distribution of yield and the probabilities of exceeding management thresholds. NMFS (National Marine Fisheries Service) and other bodies responsible for fishery management should support the development of new techniques for stock assessment that are robust to incomplete, ambiguous, and variable data and to the effects of environmental fluctuations on fisheries. The committee recommended that NMFS conduct (at reasonable intervals) in-depth, independent peer review of its fishery management methods to include (1) the survey sampling methods used in the collection of fishery and fishery-independent data, (2) stock assessment procedures, and (3) management and risk management strategies. The committee recommended that a standardized and formalized data collection protocol be established for commercial fisheries data nationwide. The committee further recommends that a complete review of methods for collection of data from commercial fisheries be conducted by an independent panel of experts. NMFS and other bodies that conduct fish stock assessments should ensure a steady supply of well-trained stock assessment scientists to conduct actual assessments and carry out associated research. NMFS should encourage partnerships among universities, government laboratories, and industry for their mutual benefit. This can be accomplished by exchanging personnel and ideas and by providing funding for continuing education at the graduate, postdoctoral, and professional levels, including elements such as cooperative research projects and specialized courses, workshops, and symposia. SOURCE: NRC, 1998.  

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Box 2.2 Checklist for Conducting or Reviewing Stock Assessments   Step Important Considerations 1.0 Stock Definition What is spatial definition of a ''stock"? Should the assessment be spatially structured or assumed to be spatially homogeneous? Stock Structure Choose single-species or multispecies assessment? Single or multispecies Use tagging, microconstituents, genetics, and/or morphometrics to define stock structure? 2.0 Data     2.1 Removals   Catch Are removals included in the assessment? Discarding Are biases and sampling design documented? Fishing-induced mortality   2.2 Indices of abundance For all indices, consider whether an index is absolute or relative, sampling design, standardization, linearity between index and population abundance, what portion of stock is indexed (spawning stock, vulnerable biomass). Catch per unit effort (CPUE) What portions of fleet should be included and how should data be standardized? How are zero catches treated? What assumptions are made about abundance in areas not fished? Spatial mapping of CPUE is especially informative. Gear surveys (trawl, longline, pot) Is gear saturation a problem? Does survey design cover the entire range of the stock? How is gear selectivity assessed? Acoustic surveys Validate species mix and target strength. Egg surveys Estimate egg mortality, towpath of nets, and fecundity of females. Line transect, strip counting   2.3 Age, size, and sex-structure information Consider sample design, sample size, high-grading selectivity, and ageing errors. Catch at age   Weight at age   Maturity at age     Size at age   Age-specific reproductive information  

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2.4 Tagging data Consider both tag loss and shedding and tag return rates. Was population uniformly tagged or were samples recovered? 2.5 Environmental data How should such data be used in the assessment? What are the dangers of searching databases for correlates? 2.6 Fishery information   Are people familiar with the fishery, who have spent time on fishing boats, consulted and involved in discussions of the value of different data sources? 3.0 Assessment Model   3.1 Age-, size-, length-, or sex-structured model?   Are alternative structures considered? 3.2 Spatially explicit or not?   3.3 Key model parameters     Natural mortality Vulnerability Fishing mortality Catchability   Are these parameters assumed to be constant or are they estimated? If they are estimated, are prior distributions assumed? Are they assumed to be time invariant? Recruitment   Is a relationship between spawning stock and recruitment assumed? If so, what variance is allowed? Is depensation considered as a possibility? Are environmentally driven reductions (or increases) in recruitment considered? 3.4 Statistical formulation   What process errors? What observation errors? What likelihood distributions?   If the model is in the form of weighted sum of squares, how are terms weighted? If the model is in the form of maximum likelihood, are variances estimated or assumed known? 3.5 Evaluation of uncertainty   Asymptotic estimates of variance Likelihood profile Bootstrapping Bayes posteriors How is uncertainty in model parameters or between alternative models calculated? What is actually presented, a distribution or only confidence bounds? 3.6 Retrospective evaluation Are retrospective patterns evaluated and presented?

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4.0 Policy Evaluation   4.1 Alternative hypotheses   What alternatives are considered: parameters for a single model or different structural models? How are the alternative hypotheses weighted? What assumptions are used regarding future recruitment, environmental changes, stochasticity, and other factors?   Is the relationship between spawners and recruits considered? If so, do future projections include autocorrelation and depensation? 4.2 Alternative actions   What alternative harvest strategies are considered? What tactics are assumed to be used in implementation? How do future actions reflect potential changes in future population size? Is implementation error considered? Are errors autocorrelated? How does implementation error relate to uncertainty in the assessment model? 4.3 Performance indicators   What is the real "objective" of the fishery? What are the best indicators of performance? What is the time frame for biological, social, and economic indices? How is "risk" measured? Are standardized reference points appropriate? Has overfishing been defined formally? 5.0 Presentation of Results   How are uncertainties in parameters and model structure presented? Can decision tables be used to summarize uncertainty and consequences? Is there explicit consideration of the trade-off between different performance indicators?   Do the decisionmakers have a good understanding of the real uncertainty in the assessment and the trade-offs involved in making a policy choice? SOURCE: NRC, 1998.    

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Finally, the committee interpreted the stock assessments to determine the appropriate scientific advice to be drawn from them. In particular, it determined stock size and condition, whether exploitation rates were high, whether current regulations have reduced fishing mortality, whether lower fishing mortality would diminish the yield obtained from a fixed amount of recruitment, and whether there appeared to be a relationship between recruitment and spawning biomass or between recruitment and fishing mortality. These factors are routinely examined in providing scientific advice about acceptable catch levels, as explained below. INVESTIGATION OF NORTHEAST FISHERY STOCK ASSESSMENTS For the most part, the Stock Assessment Review Committee (SARC) and auxiliary reports contained information about the five major topics in stock assessment (Box 2.2): stock definition; data; assessment model; policy evaluation; and presentation of results to managers. The more detailed recommendations follow: Stock definition: Stock identification issues have been considered in the stock assessments, leading to independent assessments for Georges Bank cod, haddock, and yellowtail flounder; Gulf of Maine cod; and southern New England yellowtail flounder; as well as for some 50 other stocks in the general area. It should be noted that the stock boundaries for the U.S. and Canadian assessments are different because the Canadians estimate the biomass of fish inhabiting their waters and set a total allowable catch (TAC) as a proportion of this biomass. U.S. assessments cover the stock in both U.S. and Canadian waters to provide information on total stock. As a consequence, the information in U.S. and Canadian assessments is complementary, not contradictory, and scientists from each country participate in both assessments. The committee notes that better information on genetics and migrating behavior of these populations is needed in order to establish causal mechanisms for changes in stock size by area Data: The National Marine Fisheries Service (NMFS) assessments contain information and documentation of a variety of data, including landings, discards, logbook information, age and other biological sampling information, survey indices of abundance, and various analyses of the data to provide standardization. There are problems with aspects of data collection (see Chapter 3), but these problems are identified (see Appendix D). It was beyond the scope of the committee to conduct an in-depth review of the raw data. Assessment model: The ADAPT assessment model used in the assessments is documented, and in the case of Gulf of Maine cod, an alternative analysis was conducted using concepts developed by Fournier and Archibald (1982) and others (Ianelli, 1997; see NRC, 1998 for additional information). The main source of variability considered is in survey indices of abundance. The committee accepted the use of ADAPT as the primary assessment model but provided comments on its features and alternatives in Chapter 3. Methodology for projecting the future population under alternative scenarios is documented and allows for variation in recruitment and starting abundance. The committee examined spawner-recruit data from the five stocks (see the section "Status of the Five Stocks", (pp. 40-60) and Appendix F) and noted that a wide variety of models could be fitted to the data and that alternative interpretations of what will happen with future recruitment are valid. Policy evaluation: Alternative hypotheses for policy evaluation are considered to a limited extent, mainly in the pattern of expected recruitment. Alternative actions considered include a range of different fishing mortalities (Fs) without consideration of implementation error (deviations from fishing mortalities due to the dynamic nature of fishing and regulation). The main performance indicator is the statistical distribution of spawning biomass over a 10-year period, which is condensed into a risk measure related to the probability of reaching a rebuilding target set by the Northeast Fishery Management

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Council (NEFMC). The committee is concerned that uncertainty tends to be underestimated in these projections, with the consequence that probabilities of not reaching the rebuilding targets may be higher than suggested. Consequently, in "Evaluating the Consequences of Alternative Management Actions," the committee gives a rationale for considering a wider range of alternatives and illustrates this approach by reexamining the Georges Bank haddock assessment. Presentation of results: Results are presented in a concise advisory report, an in-depth report of the Stock Assessment Workshop (SAW), and related technical documents and reports (see Appendix C for a list of material provided to the committee). Improvements could be made in all of these areas, but it is clear that considerable documentation and analysis are available. Replication of Assessment Results The committee's consultant, Marine Resources Assessment Group (MRAG) Americas Inc., reran the ADAPT models, using workspaces provided by NMFS and was able to replicate the NMFS stock assessment results. The committee did not have the resources to investigate raw data sources, so it utilized NMFS data summaries. In addition, the consultant used a different ADAPT program, frequently employed in Canadian assessments (Gavaris, 1991; Gavaris et al., 1996, Gavaris and VanEeckhaute, 1997), that treats the oldest age group somewhat differently. The results were very similar to the NMFS assessments, with average annual differences in age 1 and total abundance (numbers of fish) being less than 3% for all five stocks. Evaluating Consequences of Alternative Management Actions A primary objective of fish stock assessments is to evaluate the possible consequences of alternative management decisions. This evaluation is accomplished by simulating future stock projections under different management options and assessing gains and risks associated with each. The ability to predict future stock responses to management interventions is, in general, very limited, and this fact should be reflected in the simulations. Uncertainty about future stock projections has several sources: (1) uncertainty about the current status of the stock (assessment error); (2) variability of future stock dynamics, such as environmental effects on recruitment (process error); (3) uncertainty about how to model the future dynamics of the population, including recruitment, growth, mortality and other relevant processes (model uncertainty); and (4) errors in the implementation of management strategies (implementation error). Errors in the implementation of management strategies only compound the inherent biological uncertainties. Managers can only attempt to control fishing mortality indirectly through effort and/or catch regulations or, more directly, by closing grounds to fishing. In either case, the link between regulatory tactics and the resulting fishing mortality is uncertain. One of the problems is that it is difficult to predict how the fishing industry will respond to a given set of regulations. Delays or adjustments in management strategies in the Northeast due to the industry's response have been typical. As discussed in Chapter 4, these problems can sometimes be reduced when stakeholders are involved in developing management strategies early in the process. Assessments of the Northeast groundfish stocks include an evaluation of the consequences of setting different fishing mortality over a 10-year period (see Appendix D and NEFSC, 1997a). Among the management alternatives considered for the five stocks were to maintain the current fishing mortality and to implement a mortality of F0.1. In addition, the effects of closing down the fishery were evaluated for Gulf of Maine cod, and F = 0.1 was explored for Georges Bank haddock. Overall, the committee believes that these projections have underestimated the uncertainty inherent in predicting future stock responses to different management regulations.

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NMFS scientists did incorporate uncertainty about the current stock status in simulations. However, as is standard practice in fishery stock assessments, the level of uncertainty was evaluated on the assumption that the assessment model was correct. Especially problematic in the case of Northeast groundfish assessments are the assumptions that natural mortality is fixed and known and that catches-at-age are observed without error. These assumptions about mortality and catch-at-age result in overly optimistic assessments of possible estimation errors and, in turn, an underestimation of the variability of possible stock sizes at the start of the simulations (see Chapter 3). An alternative model that incorporates these two factors (Ianelli, 1997) showed a greater range of uncertainty, and further efforts of this type are desirable. With respect to uncertainty in the stock dynamics, a wide range of possible stock responses is usually consistent with historical experience and should be considered in the simulations. In many situations (e.g., in the Northeast groundfish complex), a discrete set of alternative scenarios can be identified to characterize possible future trends in recruitment. The projections conducted by NMFS are instead based on a single, "best-fit" stock-recruitment model. A Beverton-Holt model was fitted to the time series of estimates of spawning biomass and recruitment provided by the assessment model, with assessment errors ignored. Residual variability in the stock-recruitment process was incorporated into the simulations, but no uncertainty in the specification of the model was considered. In general, the results of these projections indicate that stocks, recruitment, and future catches will increase if fishing mortalities are reduced substantially relative to recent high values. Although, in most cases, stock and recruitment time series indicate low recruitment levels on average in recent years when the stocks were depleted, there is no guarantee that these trends would reverse if the stocks recovered. The possibility that historical trends in recruitment were driven by changes in the ecosystem cannot be ruled out. So the alternative that recruitment may not recover to historical high levels when and if stocks rebuild should be considered. Also, an evaluation of risks under high fishing mortality hinges on how future recruitment may be affected if the stock is kept at very low levels. Some stock-recruitment (S-R) plots appear to be consistent with a depensatory relationship, in which the number of recruits produced per unit of spawning biomass decreases as the stock becomes more severely depleted. This depensation alternative should be considered in such cases. An interesting approach that may be used to postulate alternative hypotheses about the relationship between spawning biomass and subsequent recruitment is to compare stock-recruitment patterns across populations of a single species or groups of similar species. These comparisons may be used to evaluate the possibility of depensation (e.g., Liermann and Hilborn, 1997; Myers et al., 1995), the capacity of the stock to recover from low abundance levels (e.g., Myers et al., 1997), or simply to see how parameters estimated for a particular stock fit in relation to other similar stocks. To illustrate how future stock responses to management can change for different stock-recruitment scenarios, the committee conducted a limited set of simulations using Gulf of Maine cod, the most problematic stock, which is still subject to high levels of fishing mortality. Four alternative recruitment scenarios were postulated based on assessment results: (1) recruitment will increase in proportion to stock size if spawning biomass is allowed to increase; (2) recruitment will stay constant on average at the historical mean value independent of stock size; (3) the stock-recruitment relationship shows depensation at very low stock size; and (4) the same stock-recruitment model used in NMFS assessments. In all cases, residual variance was assumed to be uncorrelated from year to year. Further details about the methods and results are provided in the section "Status of the Five Stocks" (pp. 40-60) and Appendix F. The Beverton-Holt recruitment function did not fit the data particularly well for Gulf of Maine cod, as well as some other stocks, which motivated the use of the depensatory model. Whether the lack of fit in the recruitment data were due to variability related to environmental conditions or to depensation in the spawner-recruitment relationship cannot easily be resolved. In the NMFS analysis, the age at

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maturity for George Bank cod is assumed to be stable throughout the past 10 years, whereas for Gulf of Maine cod, age at maturity has increased. This type of population response at low stock sizes is contrary to what could be expected: if the carrying capacity of the stocks is constant, one would rather expect a decrease in age at maturity when stocks decline. If the assumed increase in age at maturity were artificial, the spawning biomass would be underestimated in recent years, and the spawner-recruit relationship would have even stronger depensation than estimated. Other hypotheses related to carrying capacity changes also are possible. Understanding possible processes affecting recruitment at small stock sizes would require a more refined formulation of the spawning process, where the sex and age structure of the stock are taken into account as they affect egg production. The simulations show that reducing fishing mortality to the F0.1 level resulted in increases in stock size in all recruitment scenarios (see the section "Status of the Five Stocks" (pp. 40-60) and Appendix F). Increases were only moderately larger when recruitment was assumed to increase in proportion to spawning biomass than when recruitment was independent of stock size. This occurred because most of the increase in adult biomass is due to a reduction in mortality of recruits, and very little is due to increases in recruitment. Larger gains derived from improved recruitment would be realized later if this scenario is correct. Maintaining fishing mortalities at the current high levels, on the other hand, would have very contrasting effects depending on the stock-recruitment scenario. On one extreme, under the depensatory stock-recruitment relationship, the stock continued to decline and collapsed in all trials after six to nine years. Similar results, although the stocks did not become extinct, were obtained when recruitment was proportional to stock size. On the other extreme, when recruitment was assumed to be independent of spawning biomass, predicted stock size increased only slightly, since average recruitment was set equal to the average of the last 15 years, which is somewhat higher than the most recent recruitment estimates. Results of this type can be summarized in a decision-analysis table in which the consequences of alternative actions can be evaluated across different recruitment scenarios (Table 2.1). To be even more useful in decisionmaking, such tables should be constructed using actual management tactics that could be employed to implement different target fishing mortalities, rather than using target fishing mortalities themselves. How conservative management actions should be depends on the probabilities assigned to alternative scenarios. For example, assigning a high probability to the depensatory model would prompt severe restrictions in fishing effort to minimize the possibility of stock collapse. On the other hand, if depensation was considered unlikely and recruitment was assumed to be driven mostly by the environmental conditions, the motivations to rebuild stocks rapidly would not be as strong. In many cases, assigning probabilities is difficult when views contrast on how nature may operate based solely on the stock-recruitment data. Independent observations of similar stocks could, for instance, be used to assess how likely depensation may be (Myers et al., 1995). Also, studies addressing possible links between recruitment and environmental changes may provide some evidence for or against hypotheses involving environmental change. Ecosystem changes have been studied extensively in the area, and these studies could have a more prominent role in assessment, especially in the construction of alternative recruitment scenarios. Because of the underestimation of uncertainty and the limited set of management options explored, the analysis presented cannot be used to evaluate trade-offs between possible gains and risks under more or less stringent management regulations. The committee recommends that a more comprehensive analysis of the effects of alternative management options incorporate the three main sources of uncertainty discussed above. In addition, socioeconomic factors may be included and would also be subject to similar peer review as the stock assessments.

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TABLE 2.1 Consequences of Implementing Different Rates of Fishing Mortality Under Alternative Stock-Recruitment (S-R) Scenarios for Gulf of Maine Cod.   Fishing Mortality Recruitment (R) scenarios F = 0.16 (F0.1) F = 0.29 (Fmax) F = 1.04 (10-year mean) R proportional to stock size SSB increases by 274 to 1488% Catches increase by 247 to 1315% SSB increases by 107 to 850% Catches increase by 85 to 801% SSB continues to decline -66 to -92% Substantial drop in catches -67 to -92 R independent of stock size SSB increases by 306 to 1240% Catches increase by 288 to 1214% SSB increases by 175 to 867% Catches increase by 154 to 840% Slight increase in SSB Slight increase in catches Depensatory S-R relationship SSB increases by 365 to 1503% Catches increase by 329 to 1450% SSB increases by 14 to 568% Catches increase by 2 to 534% Stock collapses with probability = 1 S-R model used in NMFS projections SSB increases by 307 to 1204% Catches increase by 272 to 1158% SSB increases by 142 to 760% Catches increase by 122 to 718% SSB increases by 142 to 760% Catches increase by 122 to 718% NOTE: Results are percent change in spawning stock biomass (SSB) and catch at the end of a 10-year projection. Range corresponds to 2.5 and 97.5 percentiles of 1000 trials as a percentage of the median at the start of projections. COMPARISON WITH ASSESSMENTS AROUND THE WORLD Input Data In the Northeast, as is common worldwide, assessments are based on age-structured assessment methods, in this case using a particular age-structured model calibrated to time series of survey catch rates. The data necessary to conduct such analyses are available in the Northeast and are comparable to the data routinely used in stock assessments elsewhere, although Northeast data quantity and quality could be improved. It would be particularly useful to collect additional biological samples, to increase the number of sets made during the surveys, and to improve the reliability of catch-and-effort data, as explained in Chapter 3. Inaccurate landing statistics are a widespread problem in stock assessments around the world that can be particularly acute in TAC-managed fisheries, where the incentives to misreport catches are greater. Method and Calibration Several methods can be used in stock assessments that utilize catch-at-age information, and particular methods tend to be associated with specific geographic areas. For example, stocks assessed by the International Council for Exploration of the Sea (ICES) most often are analyzed by the Extended Survivor Analysis method (Anonymous, 1992). Those covered by the International Commission for the Conservation of Atlantic Tunas (ICCAT) are assessed with the ADAPT methodology (Gavaris,

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1993), whereas stocks in the northeast Pacific are assessed with statistical models developed for several data sources (e.g., Stock Synthesis [Methot, 1989]; CAGEAN [Deriso et al., 1985]). Some of these methods have been evaluated (Patterson and Kirkwood, 1993; NRC, 1998), and the results indicate that their performance is generally comparable. Northeast stock assessments use ADAPT in a standard formulation where stock size is estimated by minimizing the square of the difference between the natural log of predicted stock size index (indices) minus the observed stock size index. Northeast stock assessments use ADAPT in a standard formulation where stock size is estimated by minimizing the sum of squared differences between the natural logarithms of predicted and observed stock size indices. Analyses Included in the Assessment The Northeast groundfish stock assessments include all the analyses expected by the committee: regression analysis to standardize catch rates (when CPUE [catch per unit effort] data are available and considered useful), ADAPT analysis to estimate stock size yield, spawner-per-recruit analyses, and stock-recruitment analyses to estimate biological reference points. Short-term projections are made with recruitment estimates when available, and stochastic medium-term projections are made to compare the effects of various fishing mortality rates over a 10-year period (see discussion of assessment models in Chapter 3). Uncertainties are likely greater than indicated in the assessment, but this is a problem shared by most other assessments based on virtual population analysis (VPA). The medium-term projections presented are all based on a Beverton-Holt stock-recruitment model, constrained in some cases so that the number of recruits per unit of spawning biomass did not exceed the median observed value when spawning biomass dropped below the historical minimum. No alternative recruitment scenarios were explored. In addition, the primary management measures used, days at sea and closed areas, are not explicitly taken into account in making the projections. Provision of Advice The provision of advice involves the collection of data, data analysis, documentation, peer review of the analyses formulation, and communication of advice. Ways in which these steps are performed around the world vary. Methods for Regional Stock Assessment U.S. Northeast Coast On the U.S. Northeast coast, these steps are performed by SARC, implemented in the region in 1985. The assessments under review are conducted collaboratively by NMFS and Canadian Department of Fisheries and Oceans (DFO) analysts and are peer reviewed in both Canada and the United States. In the United States, a peer review is provided first at working group meetings by federal, state, and academic scientists (see Figure 1.5). A second peer review takes place at the Stock Assessment Workshop (SAW) where draft advice is formulated. The assessments and advice are then presented at a SAW plenary, where the final advice is formulated. Canadian Maritimes In the Canadian Maritimes Region, multidisciplinary stock assessment teams, including assessment scientists, oceanographers, and in some cases, individuals from the fishing industry, prepare an assessment. Preparation of the assessment may involve several meetings of each assessment team.

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FIGURE 2.12 Results of stochastic projection runs for Gulf of Maine cod using a depensatory S-R relationship recruitment model and three target fishing mortalities (F0.1 = 0.16, Fmax = 0.29, and a target fishing mortality F = 1.04 equal to the 10 year mean).

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FIGURE 2.13 Results of a stochastic projection for Gulf of Maine cod spawning stock biomass (SSB) using a spawner-recruit model with recruitment proportional to spawning biomass and a high target fishing mortality F=1.04 equal to the 10 year mean and removing the constraints on recruitment used in the NMFS analysis. Georges Bank Cod (NEFSC, 1997a; pp. 108-170) Stock Size and Condition CPUE and survey trends from NMFS show that the spawning stock biomass of Georges Bank cod, in 1996, was roughly one-fifth the level of the late 1970s (NEFSC, 1997a, Figures B2, B4). The ADAPT estimate of spawning stock at its lowest, in 1994, was roughly one-third of the 1980 estimates, the highest in the historical record (Figure 1.2). Analysis of the long-term catch data (see Appendix F) suggests the stock in 1980 amounted to perhaps one-half the potential unfished spawning stock biomass (SSB). This indicates that the 1994 spawning stock biomass was likely in the range of 10-20% of unfished stock size. The latest ADAPT runs indicate some rebuilding of spawning stock (Figure 1.2). Canadian data from the most recent stock assessment in the 5Zj,m areas show a similar trend in the SSB of Georges Bank cod (Hunt and Buzeta, 1997). Recent Exploitation Rates The age distribution and effort suggest a high fishing mortality rate until 1995, which is consistent with the ADAPT outputs (Figure 2.14). The current F is estimated to be 0.18 (NEFSC, 1997a), and estimates of F in 1995 and 1996 are much lower than in previous years (Figure 2.14). Have Current Regulations Reduced Fishing Mortality (F)? Reductions in effort and landings (Figure 2.14) are consistent with a significant drop in F in the last two years. Again, this drop is also indicated by the ADAPT output. The model-independent EFI shows a strong drop in the last two years (Figure 2.7), suggesting that fishing mortality has been reduced. The Canadian assessments show a similar decrease in F, corroborating the U.S. assessment results, although there may have been a slight increase in F in the most recent year. Nevertheless, the exploitation rate in the last two years is dramatically lower than the 1978-1994 exploitation rates (Hunt and Buzeta, 1997).

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FIGURE 2.14 Commercial landings (metric tons, live) and fishing mortality of Georges Bank (GB) cod (ages 4-8). Based on ADAPT-tuned VPA. SOURCE: Serchuk et al., 1994; NEFSC, 1997a. Will Yield-Per-Recruit Change with Lower Fishing Mortality? The yield-per-recruit analysis indicates minor gains in yield-per-recruit by reducing F from high values before 1995 to Fmax. An expected reduction of less than 10% of the maximum is predicted when F is reduced to F0.1 (see Appendix E for definition). Spawning biomass per recruit is only about 15% of the unfished level at the high values of F before 1995 and increases to about 25% at Fmax and 40% at F0.1 (which is near F40%). Will Recruitment Increase with Increasing Spawning Biomass or Decline with Current Fishing Mortality (F)? The spawner-recruit analysis for Georges Bank cod suggests a near linear spawner-recruit relationship (Figure 2.2), and as in the case of Gulf of Maine cod, U.S. data show that recruitments in the most recent years are particularly weak (Figure 2.8). Canadian data also show similarly poor recruitment in the most recent years (Hunt and Buzeta, 1997). This analysis suggests the possibility of depensation at low spawning stock sizes; it also provides support for the hypothesis that larger spawning stocks will result in larger recruitments. The committee suggests that increases in recruitment are not likely at spawning stock levels higher than those seen in the early 1980s. The possibility of depensation raises the concern of stock collapse if spawning biomasses were to decline below the levels of 1994.

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Georges Bank Haddock (NEFSC, 1997a; pp. 171-223) Stock Size and Condition The haddock stock is much less abundant now than it was before 1960. Although survey and CPUE data do not extend back before 1960, catch and survey data from the early 1960s provide evidence of higher historical levels of abundance (Figure 1.2, Appendix F; NEFSC, 1997a, Figures C2, C5). According to a long-term VPA (1931-1986), the spawning stock was between 100,000 and 300,000 metric tons prior to 1960 and was as low as 11,000 metric tons in 1993. The most recent NMFS assessment shows an increase from this historical low in abundance to 32,400 metric tons in 1996 (NEFSC, 1997a). Canadian results from the smaller 5Zj,m area assessment show a similar increase in spawning biomass since 1993 (Gavaris and VanEeckhaute, 1997). It appears that the recent small increases in spawning biomass (Figure 1.2) are due to lower fishing mortality on the existing biomass; recent recruitments for this stock are low (Figures 2.3, 2.17). The committee concludes that the Georges Bank haddock stock has collapsed. Recent Exploitation Rates The age structure and total effort suggest that F was very high prior to 1995. Current regulations have reduced F in 1995 and 1996 (Figure 2.15). The current F is estimated to be 0.18. FIGURE 2.15 Commercial landings (metric tons, live) and fishing mortality of Georges Bank (GB) haddock (ages 4-7). Based on ADAPT-tuned VPA. SOURCE: O'Brien and Brown, 1997; NEFSC, 1997a.

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Have Current Regulations Reduced Fishing Mortality (F)? Reductions in effort and implementation of closed areas are consistent with the drastic declines in fishing mortality that emerge from ADAPT runs. The model-independent EFI shows a strong drop in the last two years (Figure 2.16), supporting a decrease in fishing mortality. Will Yield-Per-Recruit Improve with Lower Fishing Mortality (F)? The yield-per-recruit analysis shows that the expected yield continues to increase slightly when fishing mortality increases to more than one. An expected reduction of about 20% of the maximum yield is predicted when F is reduced to F0.1. Spawning biomass per recruit is less than 10% of the unfished level at the highest values of F shown on the yield-per-recruit graph. Spawning biomass per recruit increases to about 40% at F0.1 (which is near F40%). Will Recruitment Increase with Increasing Spawning Biomass or Decline with Recent High Fishing Mortality (F)? The spawner-recruit analysis for haddock is particularly complex. The data show that recruitment has varied since 1968 (Figures 2.3, 2.17, Appendix F). It has fluctuated without a trend about an average of 13.5 million recruits, with two large year classes in 1975 and 1978. Thus, at first sight, if the data prior to 1968 are ignored, there are no indications that higher spawning stock biomass produced larger recruitment from 1968 to 1996. However, since 1968, the spawning stock biomass has never been higher than 80,000 tons, whereas it had never been below that value from 1931 to 1967, the period during which substantially higher recruitment was observed. Potentially, there would be significant losses in not rebuilding the stock if 80,000 tons of spawning stock biomass were in fact a real biological threshold below which the average productivity is substantially lower. Of the two strong year classes produced since 1968, the 1975 year class was apparently a result of the particularly good survival of the spawning products from a small spawning stock biomass, whereas the 1978 spawning stock biomass was one of the highest during 1968-1996. As a result of the strong 1975 and 1978 year classes, the SSB remained higher than 40,000 tons from 1977 to 1982, but no other strong year classes were produced during that period. Recent studies (Marshall and Frank, 1994; Chambers and Trippel, 1997) strongly suggest that reproductive success may be a function of the quality of spawners, not just their quantity. Therefore, it is possible to imagine a scenario in which the spawning stock biomass during 1979-1982 consisted mostly of first-time spawners whose spawning products have a low probability of survival. There are indications that the higher recruitments recorded prior to 1968 may have been produced from two major spawning aggregations, one on the northeast peak of Georges Bank and the other in Nantucket Shoals-West Gulf of Maine (McCracken, 1960; Grosslein, 1961; Clark et al., 1982). One hypothesis is that the Nantucket Shoals/West Gulf of Maine spawning unit may have been severely overexploited and that a larger proportion of the recruits are now produced from the northeast peak spawning unit. Recent work suggests that elimination of local stocks is a major problem for Gulf of Maine cod (Ames, 1997), and that serious attention needs to be given to this situation. Hydroclimatic changes have occurred in this area, but their magnitude has been substantially lower than in the northern areas off Newfoundland and Labrador, where they have been invoked as one of the causative factors in stock collapses (Myers et al., 1996). However, haddock in this area are at the southern limit of their distributional range, and small hydroclimatic changes may have a proportionately greater effect on stock dynamics.

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FIGURE 2.16 The ratio of commercial landings to spring survey index spawning stock biomass (SSB) for Georges Bank (GB) haddock. SOURCE: NEFSC, 1997a. FIGURE 2.17 Recruitment of Georges Bank (GB) haddock in millions of fish at age 1. SOURCE: NEFSC, 1997a.

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Although it is possible that the low recruitments since 1968 have been caused primarily by hydroclimatic or environmental changes, it would seem to be extremely important to rebuild the spawning stock biomass to more than 80,000 to 100,000 metric tons. It should be noted that the historically smaller haddock stock on Browns Bank and in the Bay of Fundy (Northwest Atlantic Fisheries Organization Division 4X) has consistently produced higher year classes since 1978, perhaps because spawning stock biomass there has been maintained closer to an optimal value. Georges Bank Yellowtail Flounder (NEFSC, 1997a; pp. 224-259) Stock Size and Condition The four survey indices of stock size available for Georges Bank yellowtail flounder indicate that the lowest stock sizes were observed from 1987 to 1989 (Figure 2.18). The U.S. spring and fall surveys, which have been conducted since the 1960s, suggest that stock sizes during this period were considerably smaller than those of the late 1960s. The trends of the various indices are inconsistent for the recent period: the scallop and Canadian surveys suggest increases in stock biomass since at least 1993 (Neilson et al., 1997), the 1995 U.S. spring and fall surveys indicate that there has not been any increase in stock size in recent years. VPA results (NEFSC, 1997a, Figure D14) also suggest substantially lower biomass in the late 1980s (less than 3,000 tons in 1987-1988) than in the early 1970s (21,000 tons in 1973). Recent Exploitation Rates The high fishing mortality rates estimated by the VPA prior to 1995 are consistent with the almost total lack of individuals older than 5 years observed in the survey data. The current F is estimated to be 0.25, and estimates in 1995 and 1996 are much lower than in previous years (Figure 2.19). FIGURE 2.18 Commercial survey indices for Georges Bank yellowtail flounder (mean catch [kg] per tow for all age classes).

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FIGURE 2.19 Commercial landings (metric tons, live) and fishing mortality of Georges Bank (GB: ages 3-7) and southern New England (SNE: ages 3-6) yellowtail flounder. Based on ADAPT-tuned VPA. SOURCE: yellowtail flounder (GB): NEFSC, 1994b, 1997a; yellowtail flounder (SNE): NEFSC, 1994c; 1997a. Have Current Regulations Reduced Fishing Mortality (F)? Reductions in effort and implementation of closed areas are consistent with the drastic declines in fishing mortality that emerge from ADAPT runs. The model-independent EFI shows a strong decline in the last two years (Figure 2.20), suggesting that fishing mortality has been reduced. However, these data should be interpreted with care: there are indications, at least for the Canadian survey, that the catchability or availability may have gone up in 1996. Will Yield-Per-Recruit Improve with Lower Fishing Mortality (F)? The yield-per-recruit graph is shown in the SARC advisory report (NEFSC, 1997b). Cadrin et al. (1997) showed Fmax = 0.6 and a 12% reduction in predicted yield per recruit when F is reduced to F0.1 = 0.24. The reduction may be inconsequential if recruitment increases due to lower F (see next section). Spawning biomass per recruit is only about 10% of the unfished level at the high values of F before 1995 and increases to about 20% at Fmax and 40% at F0.1 (which is near F40%). Will Recruitment Increase with Increasing Spawning Biomass or Decline with Current Fishing Mortality (F)? On average, larger spawning stock sizes have produced more recruits in Georges Bank yellowtail flounder, and both Canadian and U.S. data indicate that increasing spawning stock sizes should provide a higher probability of producing good year classes (Neilson et al., 1997). All four strong year classes

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FIGURE 2.20 Ratio of commercial landings to spring survey index spawning stock biomass (SSB) for Georges Bank (GB) and southern New England (SNE) yellowtail flounder. SOURCE: NEFSC, 1997a. since 1973 have been formed from spawning stocks in excess of 7,000 metric tons (Figures 2.4, 2.21, Appendix F). The 1995 year class is among the weakest in the series, and it was produced from a spawning stock close to 7,000 metric tons. If fishing mortality had not been reduced, spawning biomass would have been lower, and there is no indication whether this would have resulted in even poorer recruitment. Thus, the major choice is between a future similar to the recent past or larger recruitments based on improved spawning stocks. It would be particularly important, in this case, to extend the assessment periods back to the early 1960s, when survey catch rates indicate substantially higher adult biomass than estimated for 1973-1996, the period considered in the current assessment (see Appendix F). Southern New England Yellowtail Flounder (NEFSC, 1997a; pp. 260-290) Stock Size and Condition Catch and survey data and ADAPT results all show a major decline in the abundance of southern New England yellowtail flounder (Figures 1.1, 1.2; NEFSC, 1997a, Tables E1, E8). Autumn survey abundances for the mid-1990s are less than 5% of the values observed in the late 1960s (Appendix F). Catch data show similar strong reductions (see Figure 1.1), and recruitment has been weak for a number of years (Figure 2.21). The committee considers the southern New England yellowtail flounder stock to have collapsed. Mid-1990s Exploitation Rates A truncated age distribution indicates high exploitation rates in the 1990s consistent with model outputs. The current F is estimated to be 0.12 (Figure 2.19).

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FIGURE 2.21 Recruitment of Georges Bank (GB) and southern New England (SNE) yellowtail flounder in millions of fish at age 1. SOURCE: NEFSC, 1997a. Have Current Regulations Reduced Fishing Mortality (F)? Reductions in effort and implementation of closed areas are consistent with major declines in fishing mortality that emerge from ADAPT runs. The model-independent EFI shows a strong drop in the last two years (Figure 2.20). Will Yield-Per-Recruit Improve with Lower Fishing Mortality (F)? Yield-per-recruit analysis shows that expected yield continues to increase slightly when fishing mortality increases well beyond 1. An expected reduction of about 15% of the maximum is predicted when F is reduced to F0.1. The reduction may be inconsequential if recruitment increases due to lower F (see next section). Spawning biomass per recruit is about 20% of the unfished level at the highest values of F shown on the yield per recruit graph (which are near the levels of F before 1995). Spawning biomass per recruit increases to about 40% at F0.1 (which is near F40%). Will Recruitment Increase with Increasing Spawning Biomass or Decline with Current Fishing Mortality (F)'s? Spawner-recruit data show that recruitment has been fluctuating without a clear trend over a broad range of spawning stocks, with indications that the most recent years (at low spawning stock biomass) have produced poor recruitments (NEFSC, 1997a; Overholtz et al., 1997; see Appendix F). One of the largest year classes, 1987, was formed from a small spawning stock. Most of the largest year classes, 1976-1981, came in a sequence of years. Recruitments larger than the recent average may not occur from increased spawning stock sizes. Except for the 1987 year class, no strong year class has been produced at spawning stock biomass less than 5,000 metric tons. So there are indications that

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spawning stock biomass should be kept greater than 5,000 metric tons. In addition, most of the largest year classes (1976-1981) came in a sequence of years. Thus, if only the period covered by the assessment is examined, the two most likely recruitment hypotheses are: (1) although strong recruitment is not necessarily associated with the largest spawning stocks but rather with favorable environmental conditions, recruitment will decline if high F is maintained; and (2) recruitment would stay reasonably unchanged if F is maintained. However, earlier survey catch rates at age that extend back to 1963 (NEFSC, 1997a; see Appendix F) indicate that much larger year classes might have been recruited in the 1960s when biomass was substantially larger. The appearance of large year classes in years when biomass was greater would provide support for a third hypothesis, namely, that stronger recruitments may be possible under favorable environmental conditions when spawning biomass is higher.