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Evaluating Alternative Hierarchical Modeling Approaches for the Estimation of Salmonid Smolt Abundance
Author(s) -
Payton Quinn,
Som Nicholas A.
Publication year - 2021
Publication title -
north american journal of fisheries management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 72
eISSN - 1548-8675
pISSN - 0275-5947
DOI - 10.1002/nafm.10621
Subject(s) - abundance (ecology) , abundance estimation , mark and recapture , statistics , bayesian probability , population , econometrics , ecology , mathematics , environmental science , biology , demography , sociology
Calibrated estimates of fisheries population abundance are vital in the development and appraisal of management actions. Capture–recapture (CR) experiments are invaluable monitoring tools for estimating abundance of biological populations in general. Many researchers, including those studying out‐migrating juvenile Chinook Salmon Oncorhynchus tshawytscha in the Klamath Basin of Oregon and California, have attempted to employ Bayesian B‐splines to smooth temporal variation in abundance estimates. However, concerns about overfitting and reduced precision are common with this approach. We conducted a simulation study assessing the relative fit of the standard Bayesian B‐spline implementation—a B‐spline model of abundance with a random effects (RE) binomial model of recapture probabilities—in comparison with that of three alternative model options: a B‐spline model of abundance with an autoregressive (AR) model of recapture; an AR model of abundance with an RE model of recapture, and an AR model of abundance with an AR model of recapture. We analyzed 1–3 years of CR data from three rotary screw trap sites and simulated data sets of variable completeness to assess each model’s strengths and weaknesses in estimating the underlying data. Results demonstrated that in general, an AR model of abundance coupled with an RE binomial model of recapture was the least biased model, consistently exhibiting no greater than 5% bias in abundance estimates across data sets. The B‐spline abundance models were able to produce narrower 95% credible intervals (CRIs), ranging from 15% to 30% of the parameterized annual abundance value, compared to CRI widths between 23% and 54% from the AR models. However, these narrower CRI widths were commonly errant, with coverage probability rates as low as 50–70% in half of the simulated data set collections compared to over 90% coverage for the AR models. These analyses provide valuable insight and caution for researchers employing modern methods of modeling abundance in natural populations.