Modeling demersal fish and benthic invertebrate assemblages in support of marine conservation planning

Abstract

Marine classification schemes based on abiotic surrogates often inform regional marine conservation planning in lieu of detailed biological data. However, these schemes may poorly represent ecologically relevant biological patterns required for effective design and management strategies. We used a community-level modeling approach to characterize and delineate representative mesoscale (tens to thousands of kilometers) assemblages of demersal fish and benthic invertebrates in the Northwest Atlantic. Hierarchical clustering of species occurrence data from four regional annual multispecies trawl surveys revealed three to six groupings (predominant assemblage types) in each survey region, broadly associated with geomorphic and oceanographic features. Indicator analyses identified 3–34 emblematic taxa of each assemblage type. Random forest classifications accurately predicted assemblage distributions from environmental covariates (AUC > 0.95) and identified thermal limits (annual minimum and maximum bottom temperatures) as important predictors of distribution in each region. Using forecasted oceanographic conditions for the year 2075 and a regional classification model, we projected assemblage distributions in the southernmost bioregion (Scotian Shelf-Bay of Fundy) under a high emissions climate scenario (RCP 8.5). Range expansions to the northeast are projected for assemblages associated with warmer and shallower waters of the Western Scotian Shelf over the 21st century as thermal habitat on the relatively cooler Eastern Scotian Shelf becomes more favorable. Community-level modeling provides a biotic-informed approach for identifying broadscale ecological structure required for the design and management of ecologically coherent, representative, well-connected networks of Marine Protected Areas. When combined with oceanographic forecasts, this modeling approach provides a spatial tool for assessing sensitivity and resilience to climate change, which can improve conservation planning, monitoring, and adaptive management.