Combining Regional Habitat Selection Models for Large-Scale Prediction: Circumpolar Habitat Selection of Southern Ocean Humpback Whales.
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 reg...
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ftoceandocs:oai:aquadocs.org:1834/42799 2023-10-25T01:39:19+02:00 Combining Regional Habitat Selection Models for Large-Scale Prediction: Circumpolar Habitat Selection of Southern Ocean Humpback Whales. 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, Mduduzi Constantine, Rochelle 2021 http://hdl.handle.net/1834/42799 https://doi.org/ 10.3390/rs13112074 en eng https://www.mdpi.com/2072-4292/13/11/2074 https://doi.org/ 10.3390/rs13112074 http://hdl.handle.net/1834/42799 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ Ensembles Habitat selection Machine learning Resource selection functions Telemetry Humpback whales Megaptera novaeangliae Journal Contribution 2021 ftoceandocs 2023-09-27T22:24:55Z 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 ... Article in Journal/Newspaper Humpback Whale Megaptera novaeangliae Southern Ocean IODE-UNESCO: OceanDocs - E-Repository of Ocean Publications Southern Ocean |
institution |
Open Polar |
collection |
IODE-UNESCO: OceanDocs - E-Repository of Ocean Publications |
op_collection_id |
ftoceandocs |
language |
English |
topic |
Ensembles Habitat selection Machine learning Resource selection functions Telemetry Humpback whales Megaptera novaeangliae |
spellingShingle |
Ensembles Habitat selection Machine learning Resource selection functions Telemetry Humpback whales Megaptera novaeangliae 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, Mduduzi Constantine, Rochelle Combining Regional Habitat Selection Models for Large-Scale Prediction: Circumpolar Habitat Selection of Southern Ocean Humpback Whales. |
topic_facet |
Ensembles Habitat selection Machine learning Resource selection functions Telemetry Humpback whales Megaptera novaeangliae |
description |
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 ... |
format |
Article in Journal/Newspaper |
author |
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, Mduduzi Constantine, Rochelle |
author_facet |
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, Mduduzi Constantine, Rochelle |
author_sort |
Reisinger, Ryan R. |
title |
Combining Regional Habitat Selection Models for Large-Scale Prediction: Circumpolar Habitat Selection of Southern Ocean Humpback Whales. |
title_short |
Combining Regional Habitat Selection Models for Large-Scale Prediction: Circumpolar Habitat Selection of Southern Ocean Humpback Whales. |
title_full |
Combining Regional Habitat Selection Models for Large-Scale Prediction: Circumpolar Habitat Selection of Southern Ocean Humpback Whales. |
title_fullStr |
Combining Regional Habitat Selection Models for Large-Scale Prediction: Circumpolar Habitat Selection of Southern Ocean Humpback Whales. |
title_full_unstemmed |
Combining Regional Habitat Selection Models for Large-Scale Prediction: Circumpolar Habitat Selection of Southern Ocean Humpback Whales. |
title_sort |
combining regional habitat selection models for large-scale prediction: circumpolar habitat selection of southern ocean humpback whales. |
publishDate |
2021 |
url |
http://hdl.handle.net/1834/42799 https://doi.org/ 10.3390/rs13112074 |
geographic |
Southern Ocean |
geographic_facet |
Southern Ocean |
genre |
Humpback Whale Megaptera novaeangliae Southern Ocean |
genre_facet |
Humpback Whale Megaptera novaeangliae Southern Ocean |
op_relation |
https://www.mdpi.com/2072-4292/13/11/2074 https://doi.org/ 10.3390/rs13112074 http://hdl.handle.net/1834/42799 |
op_rights |
Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
_version_ |
1780734595375824896 |