Model-Based Essential Fish Habitat Definitions for Bering Sea Groundfish Species
The Magnuson-Stevens Fishery Conservation and Management Act (MSFCMA) mandates that the NationalMarine Fisheries Service (NMFS) identify habitats essential for managed species and conserve them from adverse effects of fishing and other anthropogenic activities. Essential fish habitat (EFH) is defined in the MSFCMA as “those waters and substrates necessary for fish to spawn, breed, feed or grow to maturity.” As part of this mandate, EFH descriptions are necessary for all species listed under a Fishery Management Plan (FMP) in Alaska. In addition, these descriptions must be reviewed periodically to update species descriptions and EFH information from new data and research.
In Alaska, most EFH descriptions for groundfish have been limited to qualitative statements on the distribution of adult life stages (i.e., based on presence-absence alone). These consist generally of EFH maps and written descriptions which can be improved by the use of species distribution models (SDM) that we detail here. The SDM approach demonstrated provides an opportunity to utilize previously unavailable data to create more empirically-derived and detailed maps depicting the distribution of species' life-history stages and season-specific habitat associations.
Species distribution models have been widely used in conservation biology and terrestrial systems to define the potential habitat for organisms of interest (e.g., Bio et al. 2002, Cutler et al. 2007, Elith et al. 2008, Kumar and Stohlgren 2009, Lozier et al. 2009), but have been less commonly applied in marine systems (e.g., DeLong and Collie 20041, Elith et al. 2011, Robinson et al. 2011, Sagarese et al. 2014). Recently, SDMs have been developed for coral and sponge species in the eastern Bering Sea, Gulf of Alaska, and Aleutian Islands (Rooper et al. 2014, Sigler et al. 2015, Rooper et al. 2016). These models can take a number of forms, from relatively simple frameworks such as generalized linear or additive models to complex techniques like maximum entropy modeling. These SDM scan be used to predict potential habitat, probability of presence, or abundance.