Evaluating a Fish Monitoring Protocol Using State-Space Hierarchical Models
Robin E. Russell1, *, David A. Schmetterling2, Chris S. Guy3, Bradley B. Shepard2, #, Robert McFarland1, Donald Skaar4
Identifiers and Pagination:Year: 2012
First Page: 1
Last Page: 8
Publisher Id: TOFISHSJ-5-1
Article History:Received Date: 23/06/2011
Revision Received Date: 07/10/2011
Acceptance Date: 10/10/20
Electronic publication date: 12/1/2012
Collection year: 2012
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.