A hierarchical model to estimate the abundanceand biomass of salmonids by using removalsampling and biometric data from multiplelocations
2010
Ruiz, Philippe | Laplanche, Christophe
We present a Bayesian hierarchical model to estimate the abundance and the biomass of brown trout (Salmotrutta 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) variabilityof the catchability with fish length (as a logit regression model), (iv) spatial variability of the catchability, and (v) residualvariability of the catchability with fish. Model measured variables are the areas of the stream sections as well as thelength 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 andfit by using the field dataset. The selected model accounts for variability of the abundance with fish length and stream sectionand variability of the catchability with fish length. By using the selected model, 95% credible interval estimates ofthe abundances at the three stream sections are (0.46,0.59), (0.90,1.07), and (0.56,0.69) fish/m2. Respective biomass estimatesare (9.68, 13.58), (17.22, 22.71), and (12.69, 17.31) g/m2.
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