A hierarchical model to estimate the abundance and biomass of salmonids by using removal sampling and biometric data from multiple locations
Résumé
We present a Bayesian hierarchical model to estimate the abundance and the biomass of brown trout (Salmo trutta fario) by using removal sampling and biometric data collected at several stream sections. The model accounts for (i) variability of the abundance with fish length (as a distribution mixture), (ii) spatial variability of the abundance, (iii) variability of the catchability with fish length (as a logit regression model), (iv) spatial variability of the catchability, and (v) residual variability of the catchability with fish. Model measured variables are the areas of the stream sections as well as the length and the weight of the caught fish. We first test the model by using a simulated dataset before using a 3-location, 2-removal sampling dataset collected in the field. Fifteen model alternatives are compared with an index of complexity and fit by using the field dataset. The selected model accounts for variability of the abundance with fish length and stream section and variability of the catchability with fish length. By using the selected model, 95% credible interval estimates of the abundances at the three stream sections are (0.46,0.59), (0.90,1.07), and (0.56,0.69) fish/m2. Respective biomass estimates are (9.68, 13.58), (17.22, 22.71), and (12.69, 17.31) g/m2.
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