Michael H. Prager

Using otolith shape analysis to distinguish eastern Gulf of Mexico and Atlantic Ocean stocks of king mackerel

Abstract In winter, king mackerel from eastern Gulf of Mexico and Atlantic stocks mix off southeast Florida, where they support a large fishery. Neither tagging nor genetics has yielded a way to estimate mixing rates accurately. For management purposes, and based on tagging data from the mid-1970s, all of these fish have been considered to be from the Gulf stock. Our objectives were to examine the feasibility of using otolith shape data to distinguish the two stocks, and if the method proved feasible, to use it to estimate stock composition in the winter mixed-stock fishery. In the feasibility phase of the study, we collected shape data from sagittae of 355 female king mackerel taken during summer spawning seasons, 1986–1993, outside the winter mixing area. Gulf fish were taken off the Florida panhandle and Atlantic fish from waters north of Cape Canaveral, FL. Shape data, obtained from the posterior half of the sagitta, included area, perimeter, and standardized Fourier amplitudes. Using a training set of 250 fish and a stepwise discriminant procedure, we selected a set of variables that correctly classified 80.4% of Atlantic and 85.7% of eastern Gulf fish of the remaining 105 individuals. In the application phase of the study, we estimated mixed-stock composition in the 1996–1997 winter fishery. Shape data were extracted from sagittae of 363 females taken from distinct areas during the 1996 spawning season. A training subset of the data and a stepwise discriminant procedure were used to select a set of variables that correctly classified 71.1% of Atlantic and 77.5% of eastern Gulf fish in the remaining data (test set). We used that set of variables and a maximum likelihood method to estimate composition of the ensuing mixed-stock fishery from a sample of 463 females taken in that fishery. The resulting estimate was that 99.8% of individuals in the winter landings were from the Atlantic stock and only 0.2% were from the eastern Gulf stock, with a standard error of 3.4%. Using otolith shape data, it is possible to distinguish individuals from eastern Gulf and Atlantic stocks of king mackerel and to estimate stock composition in the mixed-stock fishery. Our estimate of that stock composition is consistent with other recent studies, but is markedly different from the composition presently assumed in management. Because management can be ineffective if based on inaccurate estimates of stock composition, we recommend that such information be used regularly in management and that corresponding estimates be made in future years. Such continued analysis would serve both to confirm our results and to estimate year-to-year variability in stock composition of the mixed fishery.

Superposed Epoch Analysis: A Randomization Test of Environmental Effects on Recruitment with Application to Chub Mackerel

Abstract Superposed epoch analysis, a nonparametric technique, can be used to test the statistical significance of associations between extreme environmental events and recruitment success. A test statistic, similar to a paired t-statistic, is used to compare recruitment in years with extreme events to recruitment in the immediately surrounding years. Because statistical significance is determined by a randomization test, superposed epoch analysis does not rely on the usual assumptions (random sampling, normality, homogeneity of variance, independence of observations) of parametric testing. Thus, the method can be used when regression analysis, correlation, or a t-test would be inappropriate. As an example, we tested the association between elevated sea level (often associated with El Nino events) and high recruitment success of chub mackerel Scomber japonicus off the coast of southern California. The association was statistically significant (P < 0.01) for the period preceding the collapse of the chub ma...

Application of a Surplus Production Model to a Swordfish-Like Simulated Stock with Time-Changing Gear Selectivity

Abstract Should a surplus production model be applied to a stock that exhibits pronounced age structure? Should it be applied to a stock that has experienced changing fishing mortality rates on fish of different sizes over time (i.e., changing selectivity)? These questions are of general interest to those engaged in stock assessment and of particular interest in the assessment of North Atlantic swordfish Xiphias gladius. In an attempt to answer them, we simulated an age-structured population, with fishery, similar to that of swordfish in the North Atlantic. The 30-year simulation included biological characteristics from the literature on swordfish and simulated fishing with increasing mortality of young fish over time; simulated catches approximated the actual catches of swordfish from 1962 through 1991. We fit a lumped-biomass, dynamic surplus production model to summary non-age-structured data from the simulated fishery. The resulting parameter estimates were compared to management benchmarks, including...

Better Catch Curves: Incorporating Age-Specific Natural Mortality and Logistic Selectivity

Abstract Catch-curve analysis is one of the simplest methods for stock assessment and is widely applied in data-poor fisheries. Conventional catch-curve methods rely on the strong assumptions of constant fishing and natural mortality rates above some fully selected age that is usually estimated by visually inspecting a plot of catch at age. Here, we evaluate the performance of three catch-curve methods that relax or modify these assumptions by (1) estimating logistic selectivity parameters, (2) assuming Lorenzen-form natural mortality (natural mortality that decreases with weight), and (3) using both methods simultaneously. We used simulation modeling and decision tables to compare estimates of fishing mortality from four catch-curve methods, including the conventional method, across a variety of observable and unobservable data characteristics. We then applied the methods to catch-at-age data for Atlantic menhaden Brevoortia tyrannus from the U.S. South Atlantic fishery management region and compared the...

Group Method of Data Handling: A New Method for Stock Identification

Abstract The group method of data handling (GMDH) is described and applied to the problem of fish stock separation (classification). The method, which increases model complexity in a stepwise fashion, finds an empirical polynomial model of a complex system that satisfies any of several optimality criteria. By basing the models structure largely on the data, rather than a priori assumptions, GMDH can reduce the number of assumptions that must be made about the system under study. I analyzed data from two previously published data sets with GMDH to arrive at stock identification models for American shad Alosa sapidissima and striped bass Morone saxatilis. Each model was verified with data not used in model construction. The GMDH-based models were as good as conventional discriminant models at classifying the verification samples of fish. In addition, GMDH was able to identify excellent models that used fewer characteristics (features) of the samples.

Can We Determine the Significance of Key-Event Effects on a Recruitment Time Series?—A Power Study of Superposed Epoch Analysis

Abstract A persistent question in fishery research is whether extreme environmental events, such as climatic perturbations or discharges of toxic substances, influence recruitment. Superposed epoch analysis has been proposed as a statistical test to address such questions. In a superposed epoch analysis of recruitment, a test statistic is computed from the differences between recruitments in years with extreme environmental events (“key years”) and recruitments in immediately surrounding years; the significance of the test statistic can be determined either parametrically or nonparametrically. Here we examine the power of two parametric and four nonparametric test statistics to detect, in a variety of simulated data sets analyzed by the superposed epoch method, associations between key events and unusual values of recruitment. The statistical significance of the nonparametric test statistics is determined by randomization, the significance of the parametric statistics by consulting tabled distributions. U...

Closing the Loop in Fishery Management : the Importance of Instituting Regular Independent Management Review

Herein we argue that lack of a regular independent review process in U.S. federal fishery management is a serious shortcoming in policy and that such a system should be implemented. Although our arguments specifically refer to federal fishery management in the United States, we believe they may be applicable to other areas of ecological management here and in other countries. Fish-stock assessments are among the most influential scientific contributions to marine resource management. In the United States, where, for legal reasons, assessment results may trigger unavoidable fishing restrictions, assessments of federally managed resource stocks are subject to extensive peer review, making these assessments among the most highly reviewed examples of applied science worldwide. Within the National Marine Fisheries Service (NMFS), each of the 6 science centers, working with local Fishery Management Councils, has established a peer-review process. Typically, an expert panel spends 3–6 days reviewing 1–3 stock assessments and several days writing reports. Panel members are scientists not connected with the assessment or the fishery who have