Evaluating a fish monitoring protocol using state-space hierarchical models

Russell, Robin E.; Schmetterling, David A.; Guy, Chris S.; Shepard, Bradley B.; McFarland, Robert; Skaar, Donald

Abstract:

Using data collected from three river reaches in Montana, we evaluated our ability to detect population trends and predict fish future fish abundance. Data were collected as part of a long-term monitoring program conducted by Montana Fish, Wildlife and Parks to primarily estimate rainbow (Oncorhynchus mykiss) and brown trout (Salmo trutta) abundance in numerous rivers across Montana. We used a hierarchical Bayesian mark-recapture model to estimate fish abundance over time in each of the three river reaches. We then fit a state-space Gompertz model to estimate current trends and future fish populations. Density dependent effects were detected in 1 of the 6 fish populations. Predictions of future fish populations displayed wide credible intervals. Our simulations indicated that given the observed variation in the abundance estimates, the probability of detecting a 30% decline in fish populations over a five-year period was less than 50%. We recommend a monitoring program that is closely tied to management objectives and reflects the precision necessary to make informed management decisions.

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Article

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70004720

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Russell, Robin E.; Schmetterling, David A.; Guy, Chris S.; Shepard, Bradley B.; McFarland, Robert ; Skaar, Donald

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Montana Cooperative Fishery Research Unit

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English

Citation Larger Work Title:

Open Fish Science Journal

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8 p.

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Bentham Open

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Evaluating a fish monitoring protocol using state-space hierarchical models; 2012; Article; Journal; Open Fish Science Journal; Russell, Robin E.; Schmetterling, David A.; Guy, Chris S.; Shepard, Bradley B.; McFarland, Robert ; Skaar, Donald