Integrating Dynamic Subsurface Habitat Metrics Into Species Distribution Models

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

doi:10.3389/fmars.2018.00219

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Integrating Dynamic Subsurface Habitat Metrics Into Species Distribution Models

Journal article

Open access

Peer reviewed

Integrating Dynamic Subsurface Habitat Metrics Into Species Distribution Models

S Brodie, Michael G Jacox, Steven J Bograd, Heather Welch, Heidi Dewar, Kylie L Scales, Sara M Maxwell, Dana M Briscoe, Christopher A Edwards, Larry B Crowder, …

Frontiers in Marine Science, Vol.5, 219

2018

DOI: https://doi.org/10.3389/fmars.2018.00219

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Abstract

Species distribution models (SDMs) have become key tools for describing and predicting species habitats. In the marine domain, environmental data used in modeling 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.

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Title: Integrating Dynamic Subsurface Habitat Metrics Into Species Distribution Models
Authors: S Brodie (Author) - University of California Santa Cruz, United States
Michael G Jacox (Author) - University of California Santa Cruz, United States
Steven J Bograd (Author) - University of California Santa Cruz, United States
Heather Welch (Author) - University of California Santa Cruz, United States
Heidi Dewar (Author) - NOAA Southwest Fisheries Science Center
Kylie L Scales (Author) - University of the Sunshine Coast - Faculty of Science, Health, Education and Engineering
Sara M Maxwell (Author) - Old Dominion University, United States
Dana M Briscoe (Author) - University of California Santa Cruz, United States
Christopher A Edwards (Author) - University of California Santa Cruz, United States
Larry B Crowder (Author) - Stanford University, United States
Rebecca L Lewison (Author) - San Diego University, United States
Elliott L Hazen (Author) - University of California Santa Cruz, United States
Publication details: Frontiers in Marine Science, Vol.5, 219; 13
Publisher: Frontiers Research Foundation
Date published: 2018
DOI: 10.3389/fmars.2018.00219
ISSN: 2296-7745
Copyright note: Copyright © 2018 Brodie, Jacox, Bograd, Welch, Dewar, Scales, Maxwell, Briscoe, Edwards, Crowder, Lewison and Hazen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Organisation Unit: School of Science and Engineering - Legacy; University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering
Language: English
Record Identifier: 99451450302621
Output Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Web Of Science research areas: Environmental Sciences; Marine & Freshwater Biology; Oceanography

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