Cargando…

Matching expert range maps with species distribution model predictions

Species’ range maps based on expert opinion are a critical resource for conservation planning. Expert maps are usually accompanied by species descriptions that specify sources of internal range heterogeneity, such as habitat associations, but these are rarely considered when using expert maps for an...

Descripción completa

Detalles Bibliográficos
Autores principales: Mainali, Kumar, Hefley, Trevor, Ries, Leslie, Fagan, William F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7540670/
https://www.ncbi.nlm.nih.gov/pubmed/32115748
http://dx.doi.org/10.1111/cobi.13492
_version_ 1783591253045149696
author Mainali, Kumar
Hefley, Trevor
Ries, Leslie
Fagan, William F.
author_facet Mainali, Kumar
Hefley, Trevor
Ries, Leslie
Fagan, William F.
author_sort Mainali, Kumar
collection PubMed
description Species’ range maps based on expert opinion are a critical resource for conservation planning. Expert maps are usually accompanied by species descriptions that specify sources of internal range heterogeneity, such as habitat associations, but these are rarely considered when using expert maps for analyses. We developed a quantitative metric (expert score) to evaluate the agreement between an expert map and a habitat probability surface obtained from a species distribution model. This method rewards both the avoidance of unsuitable sites and the inclusion of suitable sites in the expert map. We obtained expert maps of 330 butterfly species from each of 2 widely used North American sources (Glassberg [1999, 2001] and Scott [1986]) and computed species‐wise expert scores for each. Overall, the Glassberg maps secured higher expert scores than Scott (0.61 and 0.41, respectively) due to the specific rules (e.g., Glassberg only included regions where the species was known to reproduce whereas Scott included all areas a species expanded to each year) they used to include or exclude areas from ranges. The predictive performance of expert maps was almost always hampered by the inclusion of unsuitable sites, rather than by exclusion of suitable sites (deviance outside of expert maps was extremely low). Map topology was the primary predictor of expert performance rather than any factor related to species characteristics such as mobility. Given the heterogeneity and discontinuity of suitable landscapes, expert maps drawn with more detail are more likely to agree with species distribution models and thus minimize both commission and omission errors.
format Online
Article
Text
id pubmed-7540670
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-75406702020-10-15 Matching expert range maps with species distribution model predictions Mainali, Kumar Hefley, Trevor Ries, Leslie Fagan, William F. Conserv Biol Conservation Methods Species’ range maps based on expert opinion are a critical resource for conservation planning. Expert maps are usually accompanied by species descriptions that specify sources of internal range heterogeneity, such as habitat associations, but these are rarely considered when using expert maps for analyses. We developed a quantitative metric (expert score) to evaluate the agreement between an expert map and a habitat probability surface obtained from a species distribution model. This method rewards both the avoidance of unsuitable sites and the inclusion of suitable sites in the expert map. We obtained expert maps of 330 butterfly species from each of 2 widely used North American sources (Glassberg [1999, 2001] and Scott [1986]) and computed species‐wise expert scores for each. Overall, the Glassberg maps secured higher expert scores than Scott (0.61 and 0.41, respectively) due to the specific rules (e.g., Glassberg only included regions where the species was known to reproduce whereas Scott included all areas a species expanded to each year) they used to include or exclude areas from ranges. The predictive performance of expert maps was almost always hampered by the inclusion of unsuitable sites, rather than by exclusion of suitable sites (deviance outside of expert maps was extremely low). Map topology was the primary predictor of expert performance rather than any factor related to species characteristics such as mobility. Given the heterogeneity and discontinuity of suitable landscapes, expert maps drawn with more detail are more likely to agree with species distribution models and thus minimize both commission and omission errors. John Wiley and Sons Inc. 2020-08-23 2020-10 /pmc/articles/PMC7540670/ /pubmed/32115748 http://dx.doi.org/10.1111/cobi.13492 Text en © 2020 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Conservation Methods
Mainali, Kumar
Hefley, Trevor
Ries, Leslie
Fagan, William F.
Matching expert range maps with species distribution model predictions
title Matching expert range maps with species distribution model predictions
title_full Matching expert range maps with species distribution model predictions
title_fullStr Matching expert range maps with species distribution model predictions
title_full_unstemmed Matching expert range maps with species distribution model predictions
title_short Matching expert range maps with species distribution model predictions
title_sort matching expert range maps with species distribution model predictions
topic Conservation Methods
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7540670/
https://www.ncbi.nlm.nih.gov/pubmed/32115748
http://dx.doi.org/10.1111/cobi.13492
work_keys_str_mv AT mainalikumar matchingexpertrangemapswithspeciesdistributionmodelpredictions
AT hefleytrevor matchingexpertrangemapswithspeciesdistributionmodelpredictions
AT riesleslie matchingexpertrangemapswithspeciesdistributionmodelpredictions
AT faganwilliamf matchingexpertrangemapswithspeciesdistributionmodelpredictions