FN Archimer Export Format PT J TI Combining Regional Habitat Selection Models for Large-Scale Prediction: Circumpolar Habitat Selection of Southern Ocean Humpback Whales BT AF Reisinger, Ryan R. Friedlaender, Ari S. Zerbini, Alexandre N. Palacios, Daniel M. Andrews-Goff, Virginia Dalla Rosa, Luciano Double, Mike Findlay, Ken Garrigue, Claire How, Jason Jenner, Curt Jenner, Micheline-Nicole Mate, Bruce Rosenbaum, Howard C. Seakamela, S. Mduduzi Constantine, Rochelle AS 1:1;2:2;3:3,4;4:5;5:6;6:7;7:6;8:8,9;9:10;10:11;11:12;12:12;13:5;14:13;15:14;16:15; FF 1:;2:;3:;4:;5:;6:;7:;8:;9:;10:;11:;12:;13:;14:;15:;16:; C1 Institute of Marine Sciences, University of California Santa Cruz, 115 McAllister Way, Santa Cruz, CA 95060, USA Ecology and Evolutionary Biology, University of California Santa Cruz, 115 McAllister Way, Santa Cruz, CA 95060, USA Marine Mammal Laboratory, Alaska Fisheries Science Center, NOAA Fisheries, 7600 Sand Point Way NE, Seattle, WA 98115, USA Marine Ecology and Telemetry Research, 2468 Camp McKenzie Trail NW, Seabeck, WA 98380, USA Marine Mammal Institute, Oregon State University, 2030 SE Marine Science Dr, Newport, OR 97365, USA Australian Marine Mammal Centre, Australian Antarctic Division, 203 Channel Highway, Kingston, TAS 7050, Australia Instituto de Oceanografia, Universidade Federal do Rio Grande—FURG, Av. Itália km 8 s/n, Rio Grande RS 96203-900, Brazil Cape Peninsula University of Technology, Cape Town 8000, South Africa Department of Zoology and Entomology, Mammal Research Institute, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa UMR ENTROPIE, IRD, Université de La Réunion, Université de la Nouvelle-Calédonie, CNRS, IFREMER, Laboratoire d’excellence-CORAIL, BP A5, 98848 Nouméa, New Caledonia Department of Primary Industries and Regional Development, Perth, WA 6000, Australia Centre for Whale Research, P.O. Box 1622, Fremantle, WA 6959, Australia Wildlife Conservation Society, Ocean Giants Program, 2300 Southern Boulevard Bronx, New York, NY 10460, USA Department of Forestry, Fisheries and the Environment, Branch Oceans and Coasts, P.O. Box 52126, V&A Waterfront, Cape Town 8000, South Africa School of Biological Sciences & Institute of Marine Science, University of Auckland, Private Bag 92019, Auckland 1142, New Zealand C2 UNIV CALIF SANTA CRUZ, USA UNIV CALIF SANTA CRUZ, USA NOAA, USA MARECOTEL, USA UNIV OREGON STATE, USA AUSTRALIAN ANTARCTIC DIV, AUSTRALIA UNIV FED RIO GRANDE, BRAZIL UNIV CAPE PENINSULA TECHNOL, SOUTH AFRICA UNIV PRETORIA, SOUTH AFRICA IRD, FRANCE DPT PRIMARY INDUSTRIES REGIONAL DEVELOPMT, AUSTRALIA CTR WHALE RES, AUSTRALIA WILDLIFE CONSERVAT SOC, USA OCEANS AND COASTS, SOUTH AFRICA UNIV AUCKLAND, NEW ZEALAND UM ENTROPIE IN WOS Cotutelle UMR DOAJ copubli-int-hors-europe copubli-sud IF 5.349 TC 18 UR https://archimer.ifremer.fr/doc/00696/80845/84431.pdf https://archimer.ifremer.fr/doc/00696/80845/84432.zip LA English DT Article DE ;ensembles;habitat selection;machine learning;prediction;resource selection functions;telemetry;humpback whale;Megaptera novaeangliae AB Machine learning algorithms are often used to model and predict animal habitat selection—the relationships between animal occurrences and habitat characteristics. For broadly distributed species, habitat selection often varies among populations and regions; thus, it would seem preferable to fit region- or population-specific models of habitat selection for more accurate inference and prediction, rather than fitting large-scale models using pooled data. However, where the aim is to make range-wide predictions, including areas for which there are no existing data or models of habitat selection, how can regional models best be combined? We propose that ensemble approaches commonly used to combine different algorithms for a single region can be reframed, treating regional habitat selection models as the candidate models. By doing so, we can incorporate regional variation when fitting predictive models of animal habitat selection across large ranges. We test this approach using satellite telemetry data from 168 humpback whales across five geographic regions in the Southern Ocean. Using random forests, we fitted a large-scale model relating humpback whale locations, versus background locations, to 10 environmental covariates, and made a circumpolar prediction of humpback whale habitat selection. We also fitted five regional models, the predictions of which we used as input features for four ensemble approaches: an unweighted ensemble, an ensemble weighted by environmental similarity in each cell, stacked generalization, and a hybrid approach wherein the environmental covariates and regional predictions were used as input features in a new model. We tested the predictive performance of these approaches on an independent validation dataset of humpback whale sightings and whaling catches. These multiregional ensemble approaches resulted in models with higher predictive performance than the circumpolar naive model. These approaches can be used to incorporate regional variation in animal habitat selection when fitting range-wide predictive models using machine learning algorithms. This can yield more accurate predictions across regions or populations of animals that may show variation in habitat selection PY 2021 PD JUL SO Remote Sensing SN 2072-4292 PU MDPI AG VL 13 IS 11 UT 000660599100001 DI 10.3390/rs13112074 ID 80845 ER EF