Combining regional habitat selection models for large-scale prediction : circumpolar habitat selection of southern ocean humpback whales
SUPPLEMENTARY MATERIALS : Table S1: Tracking data. Table summarizing the tracking data collated for this study. Supplementary Figure S1: Partial dependence plots. Relationship between the regional model predictions and the four most important environmental covariates, in order of decreasing mean imp...
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Online Access: | https://doi.org/10.3390/rs13112074 https://repository.up.ac.za/handle/2263/88126 |
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ftunivpretoria:oai:repository.up.ac.za:2263/88126 2023-06-11T04:12:30+02:00 Combining regional habitat selection models for large-scale prediction : circumpolar habitat selection of southern ocean humpback whales Reisinger, Ryan Rudolf Friedlaender, Ari S. Zerbini, Alexandre N. Palacios, Daniel M. Andrews-Goff, Virginia Rosa, Luciano Dalla Double, Mike Findlay, Kenneth Pierce Garrigue, Claire How, Jason Jenner, Curt Jenner, Micheline-Nicole Mate, Bruce Rosenbaum, Howard C. Seakamela, S. Mduduzi Constantine, Rochelle 2021-05-25 application/pdf https://doi.org/10.3390/rs13112074 https://repository.up.ac.za/handle/2263/88126 en eng MDPI 2072-4292 doi:10.3390/rs13112074 https://repository.up.ac.za/handle/2263/88126 © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Ensembles Habitat selection Machine learning Prediction Resource selection functions Telemetry Megaptera novaeangliae Humpback whale (Megaptera novaeangliae) Article 2021 ftunivpretoria https://doi.org/10.3390/rs13112074 2023-05-02T00:24:37Z SUPPLEMENTARY MATERIALS : Table S1: Tracking data. Table summarizing the tracking data collated for this study. Supplementary Figure S1: Partial dependence plots. Relationship between the regional model predictions and the four most important environmental covariates, in order of decreasing mean importance: ICEDIST, SLOPEDIST, SST, and SHELFDIST. Partial dependence plots show the predicted response probability, here p(Observed track), on the vertical axis, over values of the environmental covariate in question while accounting for the average effect of the other predictors in the model [114]. DATA AVAILABILITY STATEMENT : Computer code and derived data are in the paper’s Github repository: https://github.com/ryanreisinger/megaPrediction (accessed on 21 May 2021) 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 ... Article in Journal/Newspaper Humpback Whale Megaptera novaeangliae Southern Ocean University of Pretoria: UPSpace Southern Ocean Remote Sensing 13 11 2074 |
institution |
Open Polar |
collection |
University of Pretoria: UPSpace |
op_collection_id |
ftunivpretoria |
language |
English |
topic |
Ensembles Habitat selection Machine learning Prediction Resource selection functions Telemetry Megaptera novaeangliae Humpback whale (Megaptera novaeangliae) |
spellingShingle |
Ensembles Habitat selection Machine learning Prediction Resource selection functions Telemetry Megaptera novaeangliae Humpback whale (Megaptera novaeangliae) Reisinger, Ryan Rudolf Friedlaender, Ari S. Zerbini, Alexandre N. Palacios, Daniel M. Andrews-Goff, Virginia Rosa, Luciano Dalla Double, Mike Findlay, Kenneth Pierce 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 |
topic_facet |
Ensembles Habitat selection Machine learning Prediction Resource selection functions Telemetry Megaptera novaeangliae Humpback whale (Megaptera novaeangliae) |
description |
SUPPLEMENTARY MATERIALS : Table S1: Tracking data. Table summarizing the tracking data collated for this study. Supplementary Figure S1: Partial dependence plots. Relationship between the regional model predictions and the four most important environmental covariates, in order of decreasing mean importance: ICEDIST, SLOPEDIST, SST, and SHELFDIST. Partial dependence plots show the predicted response probability, here p(Observed track), on the vertical axis, over values of the environmental covariate in question while accounting for the average effect of the other predictors in the model [114]. DATA AVAILABILITY STATEMENT : Computer code and derived data are in the paper’s Github repository: https://github.com/ryanreisinger/megaPrediction (accessed on 21 May 2021) 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 ... |
format |
Article in Journal/Newspaper |
author |
Reisinger, Ryan Rudolf Friedlaender, Ari S. Zerbini, Alexandre N. Palacios, Daniel M. Andrews-Goff, Virginia Rosa, Luciano Dalla Double, Mike Findlay, Kenneth Pierce Garrigue, Claire How, Jason Jenner, Curt Jenner, Micheline-Nicole Mate, Bruce Rosenbaum, Howard C. Seakamela, S. Mduduzi Constantine, Rochelle |
author_facet |
Reisinger, Ryan Rudolf Friedlaender, Ari S. Zerbini, Alexandre N. Palacios, Daniel M. Andrews-Goff, Virginia Rosa, Luciano Dalla Double, Mike Findlay, Kenneth Pierce Garrigue, Claire How, Jason Jenner, Curt Jenner, Micheline-Nicole Mate, Bruce Rosenbaum, Howard C. Seakamela, S. Mduduzi Constantine, Rochelle |
author_sort |
Reisinger, Ryan Rudolf |
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 |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13112074 https://repository.up.ac.za/handle/2263/88126 |
geographic |
Southern Ocean |
geographic_facet |
Southern Ocean |
genre |
Humpback Whale Megaptera novaeangliae Southern Ocean |
genre_facet |
Humpback Whale Megaptera novaeangliae Southern Ocean |
op_relation |
2072-4292 doi:10.3390/rs13112074 https://repository.up.ac.za/handle/2263/88126 |
op_rights |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. |
op_doi |
https://doi.org/10.3390/rs13112074 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
11 |
container_start_page |
2074 |
_version_ |
1768388410216546304 |