Evaluating the influence of spatially varying catchability on multispecies distribution modelling. (9th May 2020)

• Record Type: Journal Article
• Authors: Zhang, Chongliang; Chen, Yong; Xu, Binduo; Xue, Ying; Ren, Yiping
• Editors: Yates, Katherine
• Is Part Of: ICES journal of marine science. Volume 77:Number 5(2020)
• Page Start: 1841
• Page End: 1853
• Publication Date: 2020-05-09
• Subjects: density dependence -- HMSC -- JSDM -- predictive ability -- spatial pattern -- trawl survey; Ocean -- Periodicals; Fisheries -- Periodicals; Fishes -- Periodicals; Marine biology -- Bibliography -- Periodicals; 551.4605
• DOI: 10.1093/icesjms/fsaa068
• Languages: English
• ISSNs: 1054-3139

Abstract

Abstract: Varying catchability is a common feature in fisheries and has great impacts on fisheries assessments and species distribution models. However, spatial variations in catchability have been rarely evaluated, especially in the multispecies context. We advocate that the need for multispecies models stands for both challenges and opportunities to handle spatial catchability. This study evaluated the influence of spatially varying catchability on the performance of a novel joint species distribution model, namely Hierarchical Modelling of Species Communities (HMSC). We implemented the model under nine simulation scenarios to account for diverse spatial patterns of catchability and conducted empirical tests using survey data from Yellow Sea, China. Our results showed that ignoring variability in catchability could lead to substantial errors in the inferences of species response to environment. Meanwhile, the models' predictive power was less impacted, yielding proper predictions of relative abundance. Incorporating a spatially autocorrelated structure substantially improved the predictability of HMSC in both simulation and empirical tests. Nevertheless, combined sources of spatial catchabilities could largely diminish the advantage of HMSC in inference and prediction. We highlight situations where catchability needs to be explicitly accounted for in modelling fish distributions, and suggest directions for future applications and development of JSDMs.