Ontario Commercial Fisheries Association

Blenheim, Canada

Ontario Commercial Fisheries Association

Blenheim, Canada
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Yu H.,Virginia Polytechnic Institute and State University | Jiao Y.,Virginia Polytechnic Institute and State University | Reid K.,Ontario Commercial Fisheries Association
Fisheries Research | Year: 2012

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. © 2011 Elsevier B.V.


Jiao Y.,Virginia Polytechnic Institute and State University | Reid K.,Ontario Commercial Fisheries Association | Nudds T.,University of Guelph
Scientia Marina | Year: 2010

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 vitreous) 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.


Li Y.,Virginia Polytechnic Institute and State University | Jiao Y.,Virginia Polytechnic Institute and State University | Reid K.,Ontario Commercial Fisheries Association
North American Journal of Fisheries Management | Year: 2011

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 decline when fish accumulation was 6-8 fish/m2. The decline in CPUE with increasing soak time for walleyes and with increasing fish accumulation levels for both walleyes and yellow perch indicates that gill-net saturation did exist in Lake Erie gill-net surveys for these two species and that the gill nets were saturated faster by walleyes than by yellow perch.We suggest that gill-net saturation be considered when applying CPUE from gill-net surveys to stock assessment and that the generalized linear additive-based modeling approach be considered as an alternative in gill-net saturation analyses. © American Fisheries Society 2011.


Li Y.,Virginia Polytechnic Institute and State University | Jiao Y.,Virginia Polytechnic Institute and State University | Reid K.,Ontario Commercial Fisheries Association
Journal of Great Lakes Research | Year: 2011

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. © 2011 Elsevier B.V.

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