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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ftsouthampton:oai:eprints.soton.ac.uk:455515 2023-12-03T10:23:57+01:00 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, S. Mduduzi Constantine, Rochelle 2021-06-01 text https://eprints.soton.ac.uk/455515/ https://eprints.soton.ac.uk/455515/1/remotesensing_13_02074.pdf en English eng https://eprints.soton.ac.uk/455515/1/remotesensing_13_02074.pdf 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 and Constantine, Rochelle (2021) Combining regional habitat selection models for large-scale prediction: Circumpolar habitat selection of southern ocean humpback whales. Remote Sensing, 13 (11), [2074]. (doi:10.3390/rs13112074 <http://dx.doi.org/10.3390/rs13112074>). cc_by_4 Article PeerReviewed 2021 ftsouthampton https://doi.org/10.3390/rs13112074 2023-11-03T00:04:00Z 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 Southern Ocean University of Southampton: e-Prints Soton Southern Ocean Remote Sensing 13 11 2074 |
institution |
Open Polar |
collection |
University of Southampton: e-Prints Soton |
op_collection_id |
ftsouthampton |
language |
English |
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, S. Mduduzi Constantine, Rochelle |
spellingShingle |
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 Combining regional habitat selection models for large-scale prediction: Circumpolar habitat selection of southern ocean humpback whales |
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, S. 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 |
https://eprints.soton.ac.uk/455515/ https://eprints.soton.ac.uk/455515/1/remotesensing_13_02074.pdf |
geographic |
Southern Ocean |
geographic_facet |
Southern Ocean |
genre |
Humpback Whale Southern Ocean |
genre_facet |
Humpback Whale Southern Ocean |
op_relation |
https://eprints.soton.ac.uk/455515/1/remotesensing_13_02074.pdf 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 and Constantine, Rochelle (2021) Combining regional habitat selection models for large-scale prediction: Circumpolar habitat selection of southern ocean humpback whales. Remote Sensing, 13 (11), [2074]. (doi:10.3390/rs13112074 <http://dx.doi.org/10.3390/rs13112074>). |
op_rights |
cc_by_4 |
op_doi |
https://doi.org/10.3390/rs13112074 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
11 |
container_start_page |
2074 |
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1784272239485517824 |