Gillnet data enhance performance of rockfishes habitat suitability index model derived from bottom-trawl survey data: A case study with Sebasticus marmoratus

Abstract

Bottom-trawl surveys are widely used to generate data for developing habitat suitability models for rockfish species. However, bottom trawl selectivity varies with substrate type and can lead to biased and unreliable model outputs. Bottom-set gillnets provide more efficient and reliable data in untrawlable substrates, and fine-tuning bottom-trawl survey indices with supplementary gillnet survey data could enhance performance of rockfish habitat models. We applied this approach with bottom trawl and gillnet data for Sebasticus marmoratus in Ma’an Archipelago, China. Abundance indices and environmental data collected by the gillnet surveys were used to tune the bottom trawl survey data for empirical habitat suitability modeling of S. marmoratus. Under cross-validation, the habitat model based on tuned trawl data generated ecologically reliable suitability indices (SIs) and improved habitat suitability prediction for Sebasticus marmoratus compared to the habitat model developed using untuned trawl data. The data-tuning method developed in this study can be used to enhance the performance of habitat models of other demersal species whose habitats are not adequately sampled by bottom-trawl surveys.