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Range map datasets for terrestrial vertebrates across Taiwan

Accurate data describing the geographic distribution of specific species form the basis for effective conservation management policies. However, for most species the freely available distributional information is usually confined to either expert maps or purely theoretical maps constructed by using...

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Autores principales: Chang, An-Yu, Chen, Wan-Jyun, He, Rui-Yang, Lin, Da-Li, Lin, Yong-Lun, Lin, Te-En, Chou, Shih-Ping, Lin, Chun-Fu, Lin, Ruey-Shing, ChangChien, Lin-Wen, Chang, Shih-Wei, Cheng, Hsi-Chi, Lin, Yu-Hsiu, Tsai, Jo-Szu, Lee, Pei-Fen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8956915/
https://www.ncbi.nlm.nih.gov/pubmed/35345840
http://dx.doi.org/10.1016/j.dib.2022.108060
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author Chang, An-Yu
Chen, Wan-Jyun
He, Rui-Yang
Lin, Da-Li
Lin, Yong-Lun
Lin, Te-En
Chou, Shih-Ping
Lin, Chun-Fu
Lin, Ruey-Shing
ChangChien, Lin-Wen
Chang, Shih-Wei
Cheng, Hsi-Chi
Lin, Yu-Hsiu
Tsai, Jo-Szu
Lee, Pei-Fen
author_facet Chang, An-Yu
Chen, Wan-Jyun
He, Rui-Yang
Lin, Da-Li
Lin, Yong-Lun
Lin, Te-En
Chou, Shih-Ping
Lin, Chun-Fu
Lin, Ruey-Shing
ChangChien, Lin-Wen
Chang, Shih-Wei
Cheng, Hsi-Chi
Lin, Yu-Hsiu
Tsai, Jo-Szu
Lee, Pei-Fen
author_sort Chang, An-Yu
collection PubMed
description Accurate data describing the geographic distribution of specific species form the basis for effective conservation management policies. However, for most species the freely available distributional information is usually confined to either expert maps or purely theoretical maps constructed by using a variety of modeling frameworks. These maps usually do not provide enough resolution for conservation applications or do not accurately describe the current distribution status. In this study, we constructed a novel workflow designed to integrate data from various species distribution models and expert knowledge into a single unified modeling process. Under this workflow, we systematically constructed current distribution maps for a selection of terrestrial vertebrates found across Taiwan. We used species distribution modeling as the base and then aggregated multiple open datasets describing species occurrence and environmental factors as data sources. Thereafter, we estimated the primary broad-scale and high spatial resolution species range maps using the MaxEnt modeling algorithm, and then consulted experts on each taxa to refine these maps. This dataset provides up-to-date species distribution maps for 379 terrestrial vertebrates in Taiwan, with members from across four taxa (27 amphibians, 52 reptiles, 264 birds, and 36 mammals). This dataset helps to fill the spatial knowledge gaps for conservation concerns and improves our understanding of the geographic distribution of more than half (61%) of the vertebrate species of Taiwan. Furthermore, by stacking the range maps of multiple species, we can identify vertebrate diversity hotspots and identify priority areas for conservation.
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spelling pubmed-89569152022-03-27 Range map datasets for terrestrial vertebrates across Taiwan Chang, An-Yu Chen, Wan-Jyun He, Rui-Yang Lin, Da-Li Lin, Yong-Lun Lin, Te-En Chou, Shih-Ping Lin, Chun-Fu Lin, Ruey-Shing ChangChien, Lin-Wen Chang, Shih-Wei Cheng, Hsi-Chi Lin, Yu-Hsiu Tsai, Jo-Szu Lee, Pei-Fen Data Brief Data Article Accurate data describing the geographic distribution of specific species form the basis for effective conservation management policies. However, for most species the freely available distributional information is usually confined to either expert maps or purely theoretical maps constructed by using a variety of modeling frameworks. These maps usually do not provide enough resolution for conservation applications or do not accurately describe the current distribution status. In this study, we constructed a novel workflow designed to integrate data from various species distribution models and expert knowledge into a single unified modeling process. Under this workflow, we systematically constructed current distribution maps for a selection of terrestrial vertebrates found across Taiwan. We used species distribution modeling as the base and then aggregated multiple open datasets describing species occurrence and environmental factors as data sources. Thereafter, we estimated the primary broad-scale and high spatial resolution species range maps using the MaxEnt modeling algorithm, and then consulted experts on each taxa to refine these maps. This dataset provides up-to-date species distribution maps for 379 terrestrial vertebrates in Taiwan, with members from across four taxa (27 amphibians, 52 reptiles, 264 birds, and 36 mammals). This dataset helps to fill the spatial knowledge gaps for conservation concerns and improves our understanding of the geographic distribution of more than half (61%) of the vertebrate species of Taiwan. Furthermore, by stacking the range maps of multiple species, we can identify vertebrate diversity hotspots and identify priority areas for conservation. Elsevier 2022-03-16 /pmc/articles/PMC8956915/ /pubmed/35345840 http://dx.doi.org/10.1016/j.dib.2022.108060 Text en © 2022 The Author(s). Published by Elsevier Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Chang, An-Yu
Chen, Wan-Jyun
He, Rui-Yang
Lin, Da-Li
Lin, Yong-Lun
Lin, Te-En
Chou, Shih-Ping
Lin, Chun-Fu
Lin, Ruey-Shing
ChangChien, Lin-Wen
Chang, Shih-Wei
Cheng, Hsi-Chi
Lin, Yu-Hsiu
Tsai, Jo-Szu
Lee, Pei-Fen
Range map datasets for terrestrial vertebrates across Taiwan
title Range map datasets for terrestrial vertebrates across Taiwan
title_full Range map datasets for terrestrial vertebrates across Taiwan
title_fullStr Range map datasets for terrestrial vertebrates across Taiwan
title_full_unstemmed Range map datasets for terrestrial vertebrates across Taiwan
title_short Range map datasets for terrestrial vertebrates across Taiwan
title_sort range map datasets for terrestrial vertebrates across taiwan
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8956915/
https://www.ncbi.nlm.nih.gov/pubmed/35345840
http://dx.doi.org/10.1016/j.dib.2022.108060
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