Quantifying long-term population growth rates of threatened bull trout: challenges, lessons learned, and opportunities

Canadian Journal of Fisheries and Aquatic Sciences
By: , and 

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Abstract

Temporal symmetry models (TSM) represent advances in the analytical application of mark–recapture data to population status assessments. For a population of char, we employed 10 years of active and passive mark–recapture data to quantify population growth rates using different data sources and analytical approaches. Estimates of adult population growth rate were 1.01 (95% confidence interval = 0.84–1.20) using a temporal symmetry model (λTSM), 0.96 (0.68–1.34) based on logistic regressions of annual snorkel data (λA), and 0.92 (0.77–1.11) from redd counts (λR). Top-performing TSMs included an increasing time trend in recruitment (f) and changes in capture probability (p). There was only a 1% chance the population decreased ≥50%, and a 10% chance it decreased ≥30% (λMCMC; based on Bayesian Markov chain Monte Carlo procedure). Size structure was stable; however, the adult population was dominated by small adults, and over the study period there was a decline in the contribution of large adults to total biomass. Juvenile condition decreased with increasing adult densities. Utilization of these different information sources provided a robust weight-of-evidence approach to identifying population status and potential mechanisms driving changes in population growth rates.

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Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Quantifying long-term population growth rates of threatened bull trout: challenges, lessons learned, and opportunities
Series title Canadian Journal of Fisheries and Aquatic Sciences
DOI 10.1139/cjfas-2016-0336
Volume 74
Issue 12
Year Published 2017
Language English
Publisher NRC Research Press
Contributing office(s) Coop Res Unit Seattle
Description 13 p.
First page 2131
Last page 2143
Country United States
State Oregon
Other Geospatial South Fork Walla Walla River