Predicting Species Distributions Using Record Centre Data: Multi-Scale Modelling of Habitat Suitability for Bat Roosts.
Conservation increasingly operates at the landscape scale. For this to be effective, we need landscape scale information on species distributions and the environmental factors that underpin them. Species records are becoming increasingly available via data centres and online portals, but they are of...
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Online Access: | https://eprints.whiterose.ac.uk/87609/ https://eprints.whiterose.ac.uk/87609/1/Predicting%20Species%20Distributions%20Using%20Record%20Centre%20Data%3A%20Multi-Scale%20Modelling%20of%20Habitat%20Suitability%20for%20Bat%20Roosts.pdf https://doi.org/10.1371/journal.pone.0128440 |
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ftleedsuniv:oai:eprints.whiterose.ac.uk:87609 2023-05-15T17:48:38+02:00 Predicting Species Distributions Using Record Centre Data: Multi-Scale Modelling of Habitat Suitability for Bat Roosts. Bellamy, C Altringham, J 2015-07-08 text https://eprints.whiterose.ac.uk/87609/ https://eprints.whiterose.ac.uk/87609/1/Predicting%20Species%20Distributions%20Using%20Record%20Centre%20Data%3A%20Multi-Scale%20Modelling%20of%20Habitat%20Suitability%20for%20Bat%20Roosts.pdf https://doi.org/10.1371/journal.pone.0128440 en eng Public Library of Science https://eprints.whiterose.ac.uk/87609/1/Predicting%20Species%20Distributions%20Using%20Record%20Centre%20Data%3A%20Multi-Scale%20Modelling%20of%20Habitat%20Suitability%20for%20Bat%20Roosts.pdf Bellamy, C and Altringham, J (2015) Predicting Species Distributions Using Record Centre Data: Multi-Scale Modelling of Habitat Suitability for Bat Roosts. PloS one, 10 (6). e0128440 - e0128440. ISSN 1932-6203 Article NonPeerReviewed 2015 ftleedsuniv https://doi.org/10.1371/journal.pone.0128440 2023-01-30T21:33:45Z Conservation increasingly operates at the landscape scale. For this to be effective, we need landscape scale information on species distributions and the environmental factors that underpin them. Species records are becoming increasingly available via data centres and online portals, but they are often patchy and biased. We demonstrate how such data can yield useful habitat suitability models, using bat roost records as an example. We analysed the effects of environmental variables at eight spatial scales (500 m - 6 km) on roost selection by eight bat species (Pipistrellus pipistrellus, P. pygmaeus, Nyctalus noctula, Myotis mystacinus, M. brandtii, M. nattereri, M. daubentonii, and Plecotus auritus) using the presence-only modelling software MaxEnt. Modelling was carried out on a selection of 418 data centre roost records from the Lake District National Park, UK. Target group pseudoabsences were selected to reduce the impact of sampling bias. Multi-scale models, combining variables measured at their best performing spatial scales, were used to predict roosting habitat suitability, yielding models with useful predictive abilities. Small areas of deciduous woodland consistently increased roosting habitat suitability, but other habitat associations varied between species and scales. Pipistrellus were positively related to built environments at small scales, and depended on large-scale woodland availability. The other, more specialist, species were highly sensitive to human-altered landscapes, avoiding even small rural towns. The strength of many relationships at large scales suggests that bats are sensitive to habitat modifications far from the roost itself. The fine resolution, large extent maps will aid targeted decision-making by conservationists and planners. We have made available an ArcGIS toolbox that automates the production of multi-scale variables, to facilitate the application of our methods to other taxa and locations. Habitat suitability modelling has the potential to become a standard tool for ... Article in Journal/Newspaper Nyctalus noctula Pipistrellus pipistrellus White Rose Research Online (Universities of Leeds, Sheffield & York) PLOS ONE 10 6 e0128440 |
institution |
Open Polar |
collection |
White Rose Research Online (Universities of Leeds, Sheffield & York) |
op_collection_id |
ftleedsuniv |
language |
English |
description |
Conservation increasingly operates at the landscape scale. For this to be effective, we need landscape scale information on species distributions and the environmental factors that underpin them. Species records are becoming increasingly available via data centres and online portals, but they are often patchy and biased. We demonstrate how such data can yield useful habitat suitability models, using bat roost records as an example. We analysed the effects of environmental variables at eight spatial scales (500 m - 6 km) on roost selection by eight bat species (Pipistrellus pipistrellus, P. pygmaeus, Nyctalus noctula, Myotis mystacinus, M. brandtii, M. nattereri, M. daubentonii, and Plecotus auritus) using the presence-only modelling software MaxEnt. Modelling was carried out on a selection of 418 data centre roost records from the Lake District National Park, UK. Target group pseudoabsences were selected to reduce the impact of sampling bias. Multi-scale models, combining variables measured at their best performing spatial scales, were used to predict roosting habitat suitability, yielding models with useful predictive abilities. Small areas of deciduous woodland consistently increased roosting habitat suitability, but other habitat associations varied between species and scales. Pipistrellus were positively related to built environments at small scales, and depended on large-scale woodland availability. The other, more specialist, species were highly sensitive to human-altered landscapes, avoiding even small rural towns. The strength of many relationships at large scales suggests that bats are sensitive to habitat modifications far from the roost itself. The fine resolution, large extent maps will aid targeted decision-making by conservationists and planners. We have made available an ArcGIS toolbox that automates the production of multi-scale variables, to facilitate the application of our methods to other taxa and locations. Habitat suitability modelling has the potential to become a standard tool for ... |
format |
Article in Journal/Newspaper |
author |
Bellamy, C Altringham, J |
spellingShingle |
Bellamy, C Altringham, J Predicting Species Distributions Using Record Centre Data: Multi-Scale Modelling of Habitat Suitability for Bat Roosts. |
author_facet |
Bellamy, C Altringham, J |
author_sort |
Bellamy, C |
title |
Predicting Species Distributions Using Record Centre Data: Multi-Scale Modelling of Habitat Suitability for Bat Roosts. |
title_short |
Predicting Species Distributions Using Record Centre Data: Multi-Scale Modelling of Habitat Suitability for Bat Roosts. |
title_full |
Predicting Species Distributions Using Record Centre Data: Multi-Scale Modelling of Habitat Suitability for Bat Roosts. |
title_fullStr |
Predicting Species Distributions Using Record Centre Data: Multi-Scale Modelling of Habitat Suitability for Bat Roosts. |
title_full_unstemmed |
Predicting Species Distributions Using Record Centre Data: Multi-Scale Modelling of Habitat Suitability for Bat Roosts. |
title_sort |
predicting species distributions using record centre data: multi-scale modelling of habitat suitability for bat roosts. |
publisher |
Public Library of Science |
publishDate |
2015 |
url |
https://eprints.whiterose.ac.uk/87609/ https://eprints.whiterose.ac.uk/87609/1/Predicting%20Species%20Distributions%20Using%20Record%20Centre%20Data%3A%20Multi-Scale%20Modelling%20of%20Habitat%20Suitability%20for%20Bat%20Roosts.pdf https://doi.org/10.1371/journal.pone.0128440 |
genre |
Nyctalus noctula Pipistrellus pipistrellus |
genre_facet |
Nyctalus noctula Pipistrellus pipistrellus |
op_relation |
https://eprints.whiterose.ac.uk/87609/1/Predicting%20Species%20Distributions%20Using%20Record%20Centre%20Data%3A%20Multi-Scale%20Modelling%20of%20Habitat%20Suitability%20for%20Bat%20Roosts.pdf Bellamy, C and Altringham, J (2015) Predicting Species Distributions Using Record Centre Data: Multi-Scale Modelling of Habitat Suitability for Bat Roosts. PloS one, 10 (6). e0128440 - e0128440. ISSN 1932-6203 |
op_doi |
https://doi.org/10.1371/journal.pone.0128440 |
container_title |
PLOS ONE |
container_volume |
10 |
container_issue |
6 |
container_start_page |
e0128440 |
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