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

• Published in: Canadian journal of fisheries and aquatic sciences Vol. 74; no. 12; pp. 2131 - 2143
• Main Authors: Budy, Phaedra E, Bowerman, Tracy, Al-Chokhachy, Robert, Conner, Mary, Schaller, Howard
• Format: Journal Article
• Language: English
• Published: Ottawa NRC Research Press 01.12.2017; Canadian Science Publishing NRC Research Press
• Subjects: Adults; analytical methods; Animal populations; biomass; Computer simulation; confidence interval; Confidence intervals; Fish populations; Fisheries management; Growth; Growth rate; Information sources; juveniles; Markov analysis; Markov chain; Markov chains; Monte Carlo method; Monte Carlo simulation; Population growth; Population status; Regression analysis; Salvelinus confluentus; Statistical analysis; Symmetry; Trout; weight-of-evidence
• ISSN: 0706-652X; 1205-7533; 1205-7533
• DOI: 10.1139/cjfas-2016-0336

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 (λ TSM ), 0.96 (0.68–1.34) based on logistic regressions of annual snorkel data (λ A ), and 0.92 (0.77–1.11) from redd counts (λ R ). 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% (λ MCMC ; 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.