Data from: Quantifying apart what belongs together: a multi-state species distribution modeling framework for species using distinct habitats
1. Species distribution models (SDMs) have been used to inform scientists and conservationists about the status and change of occurrence patterns in threatened species. Many mobile species use multiple functionally distinct habitats, and cannot occupy one habitat type without the other being within...
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ftdryad:oai:v1.datadryad.org:10255/dryad.149556 2023-05-15T16:06:10+02:00 Data from: Quantifying apart what belongs together: a multi-state species distribution modeling framework for species using distinct habitats Frans, Veronica F. Augé, Amélie A. Edelhoff, Hendrik A. Erasmi, Stefan Balkenhol, Niko Engler, Jan O. Enderby Island 2001-2003 2017-06-29T16:17:47Z http://hdl.handle.net/10255/dryad.149556 https://doi.org/10.5061/dryad.14mt7 unknown doi:10.5061/dryad.14mt7/1 doi:10.1111/2041-210x.12847 doi:10.5061/dryad.14mt7 Frans VF, Augé AA, Edelhoff H, Erasmi S, Balkenhol N, Engler JO (2018) Quantifying apart what belongs together: A multi-state species distribution modelling framework for species using distinct habitats. Methods in Ecology and Evolution 9(1): 98-108. 2041-210X http://hdl.handle.net/10255/dryad.149556 terrestrial habitat use Article 2017 ftdryad https://doi.org/10.5061/dryad.14mt7 https://doi.org/10.5061/dryad.14mt7/1 https://doi.org/10.1111/2041-210x.12847 2020-01-01T15:52:54Z 1. Species distribution models (SDMs) have been used to inform scientists and conservationists about the status and change of occurrence patterns in threatened species. Many mobile species use multiple functionally distinct habitats, and cannot occupy one habitat type without the other being within a reachable distance. For such species, classical applications of SDMs might lead to erroneous representations of habitat suitability, as the complex relationships between predictors are lost when merging occurrence information across multiple habitats. To better account for the spatial arrangement of complementary—yet mandatory—habitat types, it is important to implement modeling strategies that partition occurrence information according to habitat use in a spatial context. Here, we address this issue by introducing a multi-state SDM framework. 2. The multi-state SDM framework stratifies occurrences according to the temporal or behavioral use of distinct habitat types, referred to as “states.” Multiple SDMs are then run for each state and statistical thresholds of presence are used to combine these separate predictions. To identify suitable sites that account for distance between habitats, two optional modules are proposed where the thresholded output is aggregated and filtered by minimum area size, or through moving windows across maximum reachable distances. 3. We illustrate the full use of this framework by modeling the dynamic terrestrial breeding habitat preferences of the New Zealand sea lion (NZSL; Phocarctos hookeri), using Maxent and trialing both modules to identify suitable sites for possible recolonization. 4. The Maxent predictions showed excellent performance, and the multi-state SDM framework highlighted 36 to 77 potential suitable breeding sites in the study area. 5. This framework can be applied to inform management when defining habitat suitability for species with complex changes in habitat use. It accounts for temporal and behavioral changes in distribution, maintains the individuality of each partitioned SDM, and considers distance between distinct habitat types. It also yields one final, easy-to-understand output for stakeholders and managers. Article in Journal/Newspaper Enderby Island Dryad Digital Repository (Duke University) New Zealand |
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Dryad Digital Repository (Duke University) |
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unknown |
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terrestrial habitat use |
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terrestrial habitat use Frans, Veronica F. Augé, Amélie A. Edelhoff, Hendrik A. Erasmi, Stefan Balkenhol, Niko Engler, Jan O. Data from: Quantifying apart what belongs together: a multi-state species distribution modeling framework for species using distinct habitats |
topic_facet |
terrestrial habitat use |
description |
1. Species distribution models (SDMs) have been used to inform scientists and conservationists about the status and change of occurrence patterns in threatened species. Many mobile species use multiple functionally distinct habitats, and cannot occupy one habitat type without the other being within a reachable distance. For such species, classical applications of SDMs might lead to erroneous representations of habitat suitability, as the complex relationships between predictors are lost when merging occurrence information across multiple habitats. To better account for the spatial arrangement of complementary—yet mandatory—habitat types, it is important to implement modeling strategies that partition occurrence information according to habitat use in a spatial context. Here, we address this issue by introducing a multi-state SDM framework. 2. The multi-state SDM framework stratifies occurrences according to the temporal or behavioral use of distinct habitat types, referred to as “states.” Multiple SDMs are then run for each state and statistical thresholds of presence are used to combine these separate predictions. To identify suitable sites that account for distance between habitats, two optional modules are proposed where the thresholded output is aggregated and filtered by minimum area size, or through moving windows across maximum reachable distances. 3. We illustrate the full use of this framework by modeling the dynamic terrestrial breeding habitat preferences of the New Zealand sea lion (NZSL; Phocarctos hookeri), using Maxent and trialing both modules to identify suitable sites for possible recolonization. 4. The Maxent predictions showed excellent performance, and the multi-state SDM framework highlighted 36 to 77 potential suitable breeding sites in the study area. 5. This framework can be applied to inform management when defining habitat suitability for species with complex changes in habitat use. It accounts for temporal and behavioral changes in distribution, maintains the individuality of each partitioned SDM, and considers distance between distinct habitat types. It also yields one final, easy-to-understand output for stakeholders and managers. |
format |
Article in Journal/Newspaper |
author |
Frans, Veronica F. Augé, Amélie A. Edelhoff, Hendrik A. Erasmi, Stefan Balkenhol, Niko Engler, Jan O. |
author_facet |
Frans, Veronica F. Augé, Amélie A. Edelhoff, Hendrik A. Erasmi, Stefan Balkenhol, Niko Engler, Jan O. |
author_sort |
Frans, Veronica F. |
title |
Data from: Quantifying apart what belongs together: a multi-state species distribution modeling framework for species using distinct habitats |
title_short |
Data from: Quantifying apart what belongs together: a multi-state species distribution modeling framework for species using distinct habitats |
title_full |
Data from: Quantifying apart what belongs together: a multi-state species distribution modeling framework for species using distinct habitats |
title_fullStr |
Data from: Quantifying apart what belongs together: a multi-state species distribution modeling framework for species using distinct habitats |
title_full_unstemmed |
Data from: Quantifying apart what belongs together: a multi-state species distribution modeling framework for species using distinct habitats |
title_sort |
data from: quantifying apart what belongs together: a multi-state species distribution modeling framework for species using distinct habitats |
publishDate |
2017 |
url |
http://hdl.handle.net/10255/dryad.149556 https://doi.org/10.5061/dryad.14mt7 |
op_coverage |
Enderby Island 2001-2003 |
geographic |
New Zealand |
geographic_facet |
New Zealand |
genre |
Enderby Island |
genre_facet |
Enderby Island |
op_relation |
doi:10.5061/dryad.14mt7/1 doi:10.1111/2041-210x.12847 doi:10.5061/dryad.14mt7 Frans VF, Augé AA, Edelhoff H, Erasmi S, Balkenhol N, Engler JO (2018) Quantifying apart what belongs together: A multi-state species distribution modelling framework for species using distinct habitats. Methods in Ecology and Evolution 9(1): 98-108. 2041-210X http://hdl.handle.net/10255/dryad.149556 |
op_doi |
https://doi.org/10.5061/dryad.14mt7 https://doi.org/10.5061/dryad.14mt7/1 https://doi.org/10.1111/2041-210x.12847 |
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1766402076534898688 |