Abstract

As the third most landed species (by total pounds – as determined by Puerto Rico commercial landings 2012-2017), queen snapper (Etelis oculatus) is of interest from an ecological and management perspective yet little is known about its detailed distribution patterns and the habitats it utilizes. As a relatively deep-water species (100 - 500 m), it is difficult to develop expansive occurrence datasets due to limited opportunities and costly field sampling. With fishing activities progressively expanding into deeper waters, its critical to gather data on deep-water fish populations to identify essential fish habitats (EFH). The first step in determining EFH requires simple presence/absence data for a species for either some or all portions of its geographic range. However, in the U.S. Caribbean, the critically data-deficient nature of this species has made this challenging. In this work, we investigated the use of ensemble species distribution modeling to predict queen snapper distribution along the coast of Puerto Rico, specifically the western, northeastern, and southeastern regions. Terrain attributes derived from multibeam bathymetric data were combined with occurrence data collected through a NOAA National Marine Fisheries Service fishery-independent video and hook and line survey to develop ensemble models unique to each sampling region. The models facilitate the analysis of fish distribution and fish-habitat relationships at different spatial scales. Our results are a first step in determining EFH and distribution of this species in Puerto Rico, and further highlight the importance of investigating the role spatial scale and other contributing factors play in our understanding of species-environment relationships.

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