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 regi...
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Online Access: | https://doi.org/10.3390/rs13112074 https://doaj.org/article/3f3f9ff3d5644b22ac7bf74534ab932e |
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ftdoajarticles:oai:doaj.org/article:3f3f9ff3d5644b22ac7bf74534ab932e 2023-05-15T16:35:54+02:00 Combining Regional Habitat Selection Models for Large-Scale Prediction: Circumpolar Habitat Selection of Southern Ocean Humpback Whales Ryan R. Reisinger Ari S. Friedlaender Alexandre N. Zerbini Daniel M. Palacios Virginia Andrews-Goff Luciano Dalla Rosa Mike Double Ken Findlay Claire Garrigue Jason How Curt Jenner Micheline-Nicole Jenner Bruce Mate Howard C. Rosenbaum S. Mduduzi Seakamela Rochelle Constantine 2021-05-01T00:00:00Z https://doi.org/10.3390/rs13112074 https://doaj.org/article/3f3f9ff3d5644b22ac7bf74534ab932e EN eng MDPI AG https://www.mdpi.com/2072-4292/13/11/2074 https://doaj.org/toc/2072-4292 doi:10.3390/rs13112074 2072-4292 https://doaj.org/article/3f3f9ff3d5644b22ac7bf74534ab932e Remote Sensing, Vol 13, Iss 2074, p 2074 (2021) ensembles habitat selection machine learning prediction resource selection functions telemetry Science Q article 2021 ftdoajarticles https://doi.org/10.3390/rs13112074 2022-12-31T00:44:49Z 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 Directory of Open Access Journals: DOAJ Articles Southern Ocean Remote Sensing 13 11 2074 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
ensembles habitat selection machine learning prediction resource selection functions telemetry Science Q |
spellingShingle |
ensembles habitat selection machine learning prediction resource selection functions telemetry Science Q Ryan R. Reisinger Ari S. Friedlaender Alexandre N. Zerbini Daniel M. Palacios Virginia Andrews-Goff Luciano Dalla Rosa Mike Double Ken Findlay Claire Garrigue Jason How Curt Jenner Micheline-Nicole Jenner Bruce Mate Howard C. Rosenbaum S. Mduduzi Seakamela Rochelle Constantine Combining Regional Habitat Selection Models for Large-Scale Prediction: Circumpolar Habitat Selection of Southern Ocean Humpback Whales |
topic_facet |
ensembles habitat selection machine learning prediction resource selection functions telemetry Science Q |
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 |
Ryan R. Reisinger Ari S. Friedlaender Alexandre N. Zerbini Daniel M. Palacios Virginia Andrews-Goff Luciano Dalla Rosa Mike Double Ken Findlay Claire Garrigue Jason How Curt Jenner Micheline-Nicole Jenner Bruce Mate Howard C. Rosenbaum S. Mduduzi Seakamela Rochelle Constantine |
author_facet |
Ryan R. Reisinger Ari S. Friedlaender Alexandre N. Zerbini Daniel M. Palacios Virginia Andrews-Goff Luciano Dalla Rosa Mike Double Ken Findlay Claire Garrigue Jason How Curt Jenner Micheline-Nicole Jenner Bruce Mate Howard C. Rosenbaum S. Mduduzi Seakamela Rochelle Constantine |
author_sort |
Ryan R. Reisinger |
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 |
publisher |
MDPI AG |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13112074 https://doaj.org/article/3f3f9ff3d5644b22ac7bf74534ab932e |
geographic |
Southern Ocean |
geographic_facet |
Southern Ocean |
genre |
Humpback Whale Southern Ocean |
genre_facet |
Humpback Whale Southern Ocean |
op_source |
Remote Sensing, Vol 13, Iss 2074, p 2074 (2021) |
op_relation |
https://www.mdpi.com/2072-4292/13/11/2074 https://doaj.org/toc/2072-4292 doi:10.3390/rs13112074 2072-4292 https://doaj.org/article/3f3f9ff3d5644b22ac7bf74534ab932e |
op_doi |
https://doi.org/10.3390/rs13112074 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
11 |
container_start_page |
2074 |
_version_ |
1766026216132837376 |