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Overexploitation and collapse of major fisheries raises important concerns about effects of harvest on fish populations. We tested for ecological and evolutionary mechanisms by which harvest could affect exploited fish populations in Lake Erie over the last four decades, over most of which intensive fisheries management was implemented. We did not detect evidence of long-term negative effects of harvest on yellow perch (Perca flavescens), walleye (Sander vitreus), white perch (Morone americana), or white bass (Morone chrysops) populations, either through recruitment success or through alteration of maturation schedules. Current fisheries management in Lake Erie has been relatively successful with respect to minimizing negative harvest effects, such that the dynamics of exploited fish populations in Lake Erie were more strongly affected by environment than harvest. Our study adds to the evidence that effective fisheries management is capable of rebuilding depleted fisheries and (or) maintaining healthy fisheries. Nevertheless, fisheries management needs to move beyond the ecological dimension to incorporate economic, social, and institutional aspects for society to be better assured of the sustainability of fisheries in rapidly changing ecosystems.

Stock-recruitment relationships (SRRs) may vary over time due to temporal variation in ecological conditions, reducing confidence in projections from stock-recruitment models. We examined whether the time-varying SRRs detected for yellow perch (Perca flavescens) in the western basin of Lake Erie between 1977 and 2013 could be attributed to variation in yearling walleye (Sander vitreus) predation, indexed by variation in density. Annual variation in yearling walleye density was strongly associated with yellow perch recruitment dynamics, and positively correlated with temporal variation in density-dependence of yellow perch SSRs. However, non-stationary SRRs persisted after accounting for effects of yearling walleye density, and the extent of temporal variation in SRRs actually increased. In simulations, we showed that time-varying SRRs may result more from variation in low-frequency ecological factors on the order of decades, than from variation in high-frequency ecological factors on the order of years (e.g., yearling walleye density) and thus may not be distinguishable from noise. Hence, of these two types of factors, the systematic identification, characterization and incorporation of those of low-frequency factors into stock-recruitment models (e.g., exotic mussel invasions and eutrophication, in the case of Lake Erie) may offer greater promise to improve the reliability of long-term forecasts for sustainable harvests in this and other fisheries in dynamic ecosystems.

Exploring species-environment relationships to identify essential habitat areas is recognized as an effective way for resource conservation and management. Because of ecological and economic importance of Yellow Perch (Perca flavescens) in Lake Erie, it is essential to develop appropriate habitat models to analyze habitat supply of this species. Empirical species-environment models for two age-groups (juveniles and adults) at two depth strata were developed based on a long-term (27 years) gillnet survey data. Models incorporated interactive terms indicate a high degree of prediction accuracy and satisfied the assumption of residual independence. As a benthivorous species, high-quality habitat is mainly distributed in the bottom waters. Suitable habitat for adults is also found in the mid-waters in the central and east basins. Weighted suitability index per unit area differs significantly among years, basins, and vertical strata. Currently, quota allocation among jurisdictions is determined by using GIS applications of jurisdictional surface area of waters within each management unit. The outputs from our study can be used to revise the current quota allocation schemes and habitat-based quota allocation protocol will provide more scientific and rational total allowable catch for each jurisdiction.

Fish stock–recruitment relationships (SRRs) may vary in response to ecosystem change, increasing uncertainty for fisheries management. We defined three periods between 1975 and 2015 over which Lake Erie, a Laurentian Great Lake, underwent significant ecosystem changes: before zebra mussel (Dreissena polymorpha) establishment, after zebra mussel establishment and before re-eutrophication, and after re-eutrophication. To examine the extent to which SRRs of Lake Erie yellow perch (Perca flavescens) also varied over these periods, we compared the performance of Baseline (constant recruitment), Ricker (constant SRR), Periodic Ricker (different SRRs among three periods) and Random-walk Ricker (annually varying SRRs) models fitted to data for yellow perch stocks corresponding to three lake basins. Periodic and Random-walk Ricker models performed better for stocks in the western and eastern basins, but the Baseline model performed best in the central basin. Annual variation in the SRRs coincided with the timing of zebra mussel establishment and re-eutrophication in the shallower western basin, but not in the deeper eastern basin, where quagga mussels (Dreissena bugensis) established later and conditions are less eutrophic. These results underscore that temporally and spatially varying SRRs associated with ecosystem change should be taken into account in models of fish population dynamics.

The relative effects of biotic and abiotic factors, and the life-history stages upon which they act to affect fish recruitment, vary among species and ecosystems. We compared the effects of spawning stock biomass, and factors operating at early-term (encompassing the egg, yolk-sac larval, and first few days of swim-up larval stages), middle-term (including the swim-up larval and pelagic juvenile stages), and late-term (over the benthic juvenile stage) on recruitment by yellow perch (Perca flavescens) in the western basin of Lake Erie between 1999 and 2013. Variation of recruitment was mainly driven by middle-term effects. Then, abiotic factors, such as warming rate and wind speed, more strongly affected recruitment than did biotic factors. Among middle-term biotic factors, the top-down effect of yearling walleye (Sander vitreus) abundance was stronger than the bottom-up effect of zooplankton abundance. Similar to marine species, physical processes appear to strongly affect recruitment dynamics of Lake Erie yellow perch over its pelagic larval and juvenile stages, demonstrating the importance of physical and biological processes in understanding fish population dynamics in large lakes.

Older spawning fish were often observed to contribute more recruits to harvested stocks than younger fish. However, effects of spawning stock age structure on recruitment were not universally detected. We tested effects of age-dependent absolute fecundity, age-dependent relative fecundity, and age-related maternal effects on recruitment by Lake Erie yellow perch (Perca flavescens). No effects of age-dependent absolute fecundity and age-dependent relative fecundity, nor positive age-related maternal effects, were detected at multidecadal or finer temporal scales of analysis, between 1975 and 2013. Instead, we found evidence of negative age-related maternal effects, of spatially and temporally varying strength; older cohorts in the spawning stock were sometimes associated with lower offspring survival. Such counterintuitive results imply that biotic and abiotic factors, such as interspecific predation, during larval and juvenile life stages might strongly affect recruitment of Lake Erie yellow perch.

Background Global regression models under an implicit assumption of spatial stationarity were commonly applied to estimate the environmental effects on aquatic species distribution. However, the relationships between species distribution and environmental variables may change among spatial locations, especially at large spatial scales with complicated habitat. Local regression models are appropriate supplementary tools to explore species-environment relationships at finer scales. Method We applied geographically weighted regression (GWR) models on Yellow Perch in Lake Erie to estimate spatially-varying environmental effects on the presence probabilities of this species. Outputs from GWR were compared with those from generalized additive models (GAMs) in exploring the Yellow Perch distribution. Local regression coefficients from the GWR were mapped to visualize spatially-varying species-environment relationships. K-means cluster analyses based on the t-values of GWR local regression coefficients were used to characterize the distinct zones of ecological relationships. Results Geographically weighted regression resulted in a significant improvement over the GAM in goodness-of-fit and accuracy of model prediction. Results from the GWR revealed the magnitude and direction of environmental effects on Yellow Perch distribution changed among spatial locations. Consistent species-environment relationships were found in the west and east basins for adults. The different kinds of species-environment relationships found in the central management unit (MU) implied the variation of relationships at a scale finer than the MU. Conclusions This study draws attention to the importance of accounting for spatial nonstationarity in exploring species-environment relationships. The GWR results can provide support for identification of unique stocks and potential refinement of the current jurisdictional MU structure toward more ecologically relevant MUs for the sustainable management of Yellow Perch in Lake Erie.

Delayed quota adjustments, and/or lagged fishing effort and catch by harvesters, to changes in stock abundance may induce unstable population dynamics and exacerbate the risk of fishery collapse. We examined a 39-y time series of change to quotas by managers, and to effort and catch by both commercial harvesters and anglers, in response to changes in Lake Erie walleye abundance (Sander vitreus) estimated both contemporaneously and retrospectively. Quotas, commercial effort and catch were entrained by contemporaneous estimates of stock abundance. Recreational effort and harvest were not; they had better tracked abundance, as better estimated today, than did the commercial fishery. During the 1990s, a significant mismatch developed between the quota-driven commercial harvest and stock abundance that persisted until a new assessment process obtained. The quasi-open access recreational fishery, instead, freed anglers to respond better to stock abundance. Further elaboration of adaptive risk governance processes, including multi-model inference for stock assessments, may bode well to further reduce risk to fisheries imposed by lagged adjustments to variation in stock abundance.

We compared the performance of two traditional sampling designs with three adaptive sampling designs using simulated data based on fishery-independent surveys for yellow perch in Lake Erie. Traditionally, the fishery-independent survey has been conducted with a stratified random sampling design based on basin and depth strata; however, adaptive sampling designs are thought to be more suitable for surveying heterogeneous populations. A simulation study was conducted to compare these designs by examining the accuracy and precision of the estimators. Initially in the simulation study, we used bias, variance of the mean, and mean squared error (MSE) of the estimators to compare simple random sampling (SRS), stratified random sampling (StRS), and adaptive two-phase sampling (ATS). ATS was the best design according to these measurements. We then compared ATS, adaptive cluster sampling (ACS), adaptive two-stage sequential sampling (ATSS), and the currently used stratified random sampling design. ATS performed better than the other two approaches and the current stratified random sampling design. We concluded that ATS is preferable for yellow perch fishery-independent surveys in Lake Erie. Simulation study is a preferred approach when we seek an appropriate sampling design or evaluate the current sampling design.

Theoretical by-catch (including landed and non-landed bycatch) of walleye (S. vitreus), yellow perch (Perca flavescens), and white perch (Morone americana) from the Lake Erie commercial gillnet fisheries during 1994–2007, was predicted by a delta model developed on the fishery-independent survey data (Lake Erie Partnership Index Fishing Survey). The delta model consisted of one generalized additive model and one AdaBoost model. The generalized additive model was used to predict non-zero catches of the by-catch species, and the AdaBoost model was used to predict the probability of obtaining non-zero catches. Non-landed by-catch was estimated as the difference between the theoretical by-catch predicted from the delta model and the landed by-catch recorded in the commercial fishery data. The theoretical by-catch of walleye was relatively higher in the west basin in October. A higher theoretical by-catch of yellow perch occurred in the west central basin in November, and a higher theoretical by-catch of white perch occurred in the west central basin in October. We observed higher levels of non-landed by-catch of walleye in the west basin during August to September, higher levels of non-landed by-catch of yellow perch in the west central and east central basins in November, and higher levels of non-landed by-catch of white perch in the west basin in August and November. The combination of the AdaBoost model with the delta model provided an alternative model in by-catch analyses when the percentage of zero observations was high.

Gill-net saturation was analyzed through a delta model (i.e., two-stage model) by examining the effects of soak time and fish accumulation (number of fish of all species enmeshed per square meter of a given gill net, including the species of interest) on catch per unit effort (CPUE) of walleyes Sander vitreus and yellow perch Perca flavescens in Lake Erie. The analysis was based on fishery-independent survey data for 1989–2003. In the delta model, the positive values of CPUE were estimated by a generalized additive model (GAM) assuming a log-gamma distribution, and the probability of obtaining nonzero values of CPUE was estimated by a GAM assuming a binomial distribution. Soak time and fish accumulation had significant effects on CPUE. The CPUE of walleyes decreased in gill nets soaked for 10 h and started to decline when fish accumulation was around 2 fish/m2. We did not observe a substantial decline in the CPUE of yellow perch within the soak time interval we examined, but we did observe a decl...

We present a graphical method that uses a statistical likelihood approach to summarize evidence and risk in fishery status evaluation. The graphical method is based on the surplus production model and shows the fishery status as regions and colors on graphs. Two fishery status graphs are used: one is based on the comparison between observed catch and catch at the fishing mortality level corresponding to maximum sustainable yield; the other one is based on the comparison between observed catch and catch at the surplus production level. The graphical statistical likelihood approach quantifies the strength of evidence in supporting different hypotheses of fishing status as regions in the status graphs. A simulated hypothetical fishery is given as an example. Results are graphically presented to show the exploitation status of the fishery, and they compare favorably with those from a composite risk assessment method. This graphical statistical likelihood approach may improve the communication of know...

Global regression techniques that assume spatial stationarity are usually used to study the interaction between aquatic species distribution and habitat variables. In the present study, a local regression model, named geographically weighted regression (GWR), was used to question the spatial stationarity assumption in exploring the relationships between walleye (Stizostedion vitreum) distribution and habitat variables in Lake Erie. The GWR model resulted in a significant improvement of model performance over the two global linear and non-linear regression methods (a generalised least-squares (GLS) model and a generalised additive mixed model (GAMM)), accounting for residual spatial autocorrelation using the same response and explanatory variables as in the GWR model. The values of local regression coefficients from the GWR model changed among spatial locations significantly, implying spatially varying and scale-dependent relationships between walleye distribution and habitat variables. The k-means cluster analyses based on the t-values of local regression coefficients of GWR model characterised special zones of species–environment relationships of walleye in Lake Erie. In conclusion, spatial stationarity needs to be questioned in studying the relationships between aquatic species distribution and habitat variables and a non-stationary approach, such as GWR, is recommended as a complementary tool.

Harvest control rules are widely used by management agencies for decision-making and for promoting public awareness of the status of marine and freshwater fisheries. Many current control rules combine fishing mortality and biomass-based biological reference points. Control rules were introduced as a precaution against the influence of uncertainty and to decrease the risk of overfishing, but are compromised if the uncertainties of the biological reference points are not explicitly considered. Uncertainty has been widely acknowledged but has not been incorporated into control rule design and application. In this paper, we used a Bayesian statistical catch-at-age model to estimate uncertainties in the indicators of fishing mortality, population size, and biological reference points. We apply this model to the Lake Erie walleye (Sander vitreus) fishery, and by fully considering the uncertainty of the indicators, the risk of overfishing and the risk of the population being overfished can be explicitly estimated in the control rules. We suggest short and long-term approaches to incorporate uncertainty in the design of control rules. We also suggest that control rules for specific fisheries should be designed with explicit consideration of the uncertainty of the biological reference points, based on a risk level that the management agency and stakeholders agree upon. Las reglas de control de producción se usan generalmente por los organismos de gestión de recursos pesqueros para tomar decisiones y para promover la concienciación pública sobre el estado de conservación de pesquerías marinas y de aguas dulces. Muchas de las reglas de control actuales combinan puntos de referencia biológicos basados en los indicadores mortalidad por pesca y biomasa. Las reglas de control fueron introducidas como precaución contra la influencia de la incertidumbre en las evaluaciones y para reducir el riesgo de sobrepesca, pero se ven comprometidas si la incertidumbre en los puntos de referencia biológicos no se consideran explícitamente. El papel de la incertidumbre se reconoce ampliamente, pero no ha sido incorporado hasta ahora en el diseño y aplicación de reglas de control. En la presente contribución se aplica un modelo bayesiano de capturas por edad para estimar la incertidumbre en los indicadores de mortalidad por pesca, tamaño de la población y puntos de referencia biológicos. Se aplica este modelo a la pesquería de Sander vitreus del Lago Erie, y, mediante la completa incorporación de la incertidumbre de los indicadores, el riesgo de sobrepesca puede ser estimado explícitamente en las reglas de control. Se sugieren aproximaciones a corto y largo plazo para la incorporación de la incertidumbre en el diseño de reglas de control. Se sugiere también que las reglas para pesquerías particulares deben diseñarse incorporando explícitamente la incertidumbre en los puntos de referencia biológicos, en base al nivel de riesgo que se acuerde entre el organismo encargado de la gestión del recurso y sus usuarios.

Stock recruitment (SR) modeling is the central part of fisheries popula- tion dynamics analysis. Models and modeling techniques on SR relationships have been evolving for decades, and have moved from traditional SR models, such as the Ricker and Beverton-Holt models to measurement error models, and Kalman filter time series models. Though SR models are evolving, people still typically select a specific model and then proceed as if the selected model had generated the data. This approach ignores the uncertainty in the model selection, leading to overconfident inferences and decisions with higher risk than expected. Bayesian model averaging (BMA) provides a coherent mechanism for accounting for this model uncertainty. In this study, Lake Erie walleye (Sander vitreus) fishery was used as an example. Six mathematical models were developed, which included a Ricker model, a hierarchical Ricker model, a residual auto-regressive model, a Kalman filter random walk model, a Kalman filter autoregressive model, and a Ricker measurement error model. The posterior distributions of estimated productivity and recruitment from these mod- els were weighted based on the Deviance Information Criterion (DIC) to provide our predictive posterior distribution of population productivity and recruitment over time. To test the efficiency of the Bayesian averaging approach and the uncertainty from model selection, a further simulation study was done based on the example fishery. Our results showed that model selection uncertainty is high and BMA explained the data reasonably well. We suggest that BMA is more appropriate in simulating SR models. The framework developed here can be used for other species population SR analysis. We also suggest that the model selection uncertainty be considered and the