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Evaluating a fish monitoring protocol using state-space hierarchical models

Open Fish Science Journal

By:
, , , , ,
DOI: 10.2174/1874401X01205010001

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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.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Evaluating a fish monitoring protocol using state-space hierarchical models
Series title:
Open Fish Science Journal
DOI:
10.2174/1874401X01205010001
Volume
5
Year Published:
2012
Language:
English
Publisher:
Bentham Open
Publisher location:
Oak Park, IL
Contributing office(s):
Montana Cooperative Fishery Research Unit
Description:
8 p.
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Open Fish Science Journal
First page:
1
Last page:
8
Country:
United States
State:
Montana