Leveraging Angler Effort to Inform Fisheries Management: Using Harvest and Harvest Rate to Estimate Abundance of White Sturgeon

Abstract

Abstract Traditional methods for estimating abundance of fish populations are not feasible in some systems due to complex population structure and constraints on sampling effort. Lincoln’s estimator provides a technique that uses harvest and harvest rate to estimate abundance. Using angler catch data allows assumptions of the estimator to be addressed without relying on methods that could be prohibitively field-intensive or costly. Historic estimates of White Sturgeon Acipenser transmontanus abundance in the Sacramento-San Joaquin River basin have been obtained using mark-recapture methods. However, White Sturgeon population characteristics often cause violations of model assumptions, such as population closure and independent capture probabilities. We developed a version of Lincoln’s estimator using a joint likelihood, estimated abundance of White Sturgeon in the Sacramento-San Joaquin River basin in 2015 using this method and empirical data, and assessed accuracy and precision of estimates in a simulation study. Estimating abundance using harvest and harvest rate, as represented by our model framework, has the potential to be precise and accurate. The joint likelihood-based approach fitted using Bayesian methods is advantageous because all sources of variation are included in a single model. Precision of abundance estimates was low when the model was applied to White Sturgeon in the Sacramento-San Joaquin River basin and to similar conditions in a simulated dataset. Using simulation, precision and accuracy increased with increases in the number of high-reward and standard tags released, tag reporting rate, tag retention rate, and harvest rate. Results demonstrate potential sources of error when using this approach and suggest that increasing the number of tagged fish and tag reporting rate are potential actions to improve precision and accuracy of abundance estimates of the model.