Model-Based Essential Fish Habitat Definitions for Aleutian Island Groundfish Species

The 1996 reauthorization of the Magnuson-Stevens Fishery Conservation and Management Act (MSFCMA) mandates NOAA Fisheries to identify habitats essential for managed fish and invertebrate species and conserve habitats from adverse effects of fishing and other anthropogenic activities. Essential Fish Habitat (EFH) is defined under the act as those waters and substrates necessary to fish for spawning, breeding, feeding, or growth to maturity. As part of this mandate, EFH descriptions for all species listed under a Fisheries Management Plan in Alaska waters are needed. In addition, these descriptions are routinely revisited under a 5-year cycle that reviews and updates EFH information (including species descriptions) with new data and research. Essential fish habitat descriptions consist of maps of EFH and text descriptions. Although useful, they could be easily refined both in terms of spatial extent and life history stage using species distribution models and available data from a variety of sources. Distribution models have been widely used in conservation biology and terrestrial systems to define the potential habitat for organisms of interest (e.g., Delong and Collie 2004, Lozier et al. 2009, Elith et al. 2011, Sagarese et al. 2014). Recently species distribution models 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).

Species distribution models themselves can take a number of forms, from relatively simple frameworks such as generalized linear or additive models to more complex methods including computer learning methods (e.g., boosted regression trees, maximum entropy models, random forest models) or multi-stage models. The models can be used to predict potential habitat, probability of presence or even abundance, but they all have some features in common: 1) the underlying data consists of some type of independent variables (predictors) and a dependent response variable (presence, presence/absence, or abundance), 2) raster maps of independent variables are used to predict a response map, 3) confidence bounds on the predictions and partitioning of the data can produce test statistics useful for evaluating the model. The outputs of species distribution models are designed to be raster maps that can show the predicted abundance of a species at each of the raster cells. This type of product is useful for EFH descriptions, as it lends itself to producing maps of areas of high abundance or hotspots of distribution and the models themselves can be used to refine the required text descriptions.