|Document Type:||Journal Article|
|Title:||Spatial, semi-parametric models improve estimates of species abundance and distribution|
|Author:||Andrew O. Shelton, James T. Thorson, E. J. Ward, Blake E. Feist|
|Journal:||Canadian Journal of Fisheries and Aquatic Sciences|
|Keywords:||hierarchical Bayesian models, hurdle model, Sebastes crameri, essential fish habitat, spatial abundance estimation.,|
Accurate estimates of abundance are imperative for successful conservation and management. Classical, stratified abundance estimators provide unbiased estimates of abundance but such estimators may be imprecise and impede assessment of population status and trend when the distribution of individuals is highly variable in space. Model-based procedures that account for important environmental covariates can improve overall precision, but frequently there is uncertainty about the contribution of particular environmental variables and a lack of information about variables that are important determinants of abundance. We develop a general semi-parametric mixture model that incorporates measured habitat variables and a non-parametric smoothing term to account for unmeasured variables. We contrast this spatial-habitat approach with two stratified abundance estimators and compare the three models using an intensively managed marine fish, darkblotched rockfish (Sebastes crameri). We show that the spatial habitat model yields more precise, biologically reasonable, and interpretable estimates of abundance than the classical methods. Our results suggest that while design-based estimators are unbiased, they may exaggerate temporal variability of populations and strongly influence inference about population trend. Furthermore, when such estimates are used in broader meta-analyses such imprecision may affect the broader biological inference (e.g. the causes and consequences of the variability of populations).
|Theme:||Recovery, Rebuilding and Sustainability of Marine and Anadromous Species|
Develop methods to use physiological and biological information to predict population-level processes.