Integrating Dynamic Subsurface Habitat Metrics Into Species Distribution Models

Stephanie Brodie,Michael G Jacox,Christopher A Edwards,Sara M Maxwell,Heidi Dewar,Heather Welch,Elliott L Hazen,Steven J Bograd,Rebecca L Lewison,Dana M Briscoe,Kylie L Scales,Larry B Crowder

doi:10.3389/fmars.2018.00219

Abstract

Species distribution models (SDMs) have become key tools for describing and predicting species habitats. In the marine domain, environmental data used in modelling species distributions are often remotely sensed, and as such have limited capacity for interpreting the vertical structure of the water column, or are sampled in situ, offering minimal spatial and temporal coverage. Advances in ocean models have improved our capacity to explore subsurface ocean features, yet there has been limited integration of such features in SDMs. Using output from a data-assimilative configuration of the Regional Ocean Modeling System, we examine the effect of including dynamic subsurface variables in SDMs to describe the habitats of four pelagic predators in the California Current System (swordfish Xiphias gladius, blue sharks Prionace glauca, common thresher sharks Alopias vulpinus, and shortfin mako sharks Isurus oxyrinchus). Species data were obtained from the California Drift Gillnet observer program (1997-2017). We used boosted regression trees to explore the incremental improvement enabled by dynamic subsurface variables that quantify the structure and stability of the water column: isothermal layer depth and bulk buoyancy frequency. The inclusion of these dynamic subsurface variables significantly improved model explanatory power for most species. Model predictive performance also significantly improved, but only for species that had strong affiliations with dynamic variables (swordfish and shortfin mako sharks) rather than static variables (blue sharks and common thresher sharks). Geospatial predictions for all species showed the integration of isothermal layer depth and bulk buoyancy frequency contributed value at the mesoscale level (<100 km) and varied spatially throughout the study domain. These results highlight the utility of including dynamic subsurface variables in SDM development and support the continuing ecological use of biophysical output from ocean circulation models.

Highlights

Species distribution models (SDMs) have become a common method to study species spatial ecology, often to support environmental management and conservation (Robinson et al, 2011, 2017)
Species occurrence data were obtained from the NOAA fisheries observer program from the California drift gillnet fishery, which operates at night along the US West Coast (Figure 1)
The prevalence of vertical movement behavior in pelagic top predators substantiates the importance of integrating vertical water column structure into SDMs

Summary

Introduction

Species distribution models (SDMs) have become a common method to study species spatial ecology, often to support environmental management and conservation (Robinson et al, 2011, 2017). In the marine realm, such corresponding environmental information can be obtained from satellite platforms, in situ sources (i.e., data loggers, moorings, under sea vehicles, surveys), and ocean circulation models. Data-assimilative ocean circulation models incorporate available environmental information from satellite and in situ platforms while adding value in the form of increased spatial and temporal data resolution and elimination of data gaps. Ocean circulation models can provide spatiotemporal resolution of the vertical structure of the ocean. These benefits have resulted in the increasing use of ocean circulation models in SDM development (Becker et al, 2016; Scales et al, 2017b)

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