Modelling the spatial population structure and distribution of the queen conch, Aliger gigas, on the Pedro Bank, Jamaica

Abstract

The estimation of reliable indices of abundance for sedentary stocks requires the incorporation of the underlying spatial population structure, including issues arising from the sampling design and zero inflation. We applied seven spatial interpolation techniques [ordinary kriging (OK), kriging with external drift (KED), a negative binomial generalized additive model (NBGAM), NBGAM plus OK (NBGAM+OK), a general additive mixed model (GAMM), GAMM plus OK (GAMM+OK) and a zero-inflated negative binomial model (ZINB) ] to three survey datasets to estimate biomass for the gastropod Aliger gigas on the Pedro Bank Jamaica. The models were evaluated using 10-fold cross-validation diagnostics criteria for choosing the best model. We also compared the best model estimations against two common design methods to assess the consequences of ignoring the spatial structure of the species distribution. GAMM and ZINB were overall the best models but were strongly affected by the sampling design, sample size, the coefficient of variation of the sample and the quality of the available covariates used to model the distribution (geographic location, depth and habitat). More reliable abundance indices can help to improve stock assessments and the development of spatial management using an ecosystem approach.