Cargando…

Uncertainty of future projections of species distributions in mountainous regions

Multiple factors introduce uncertainty into projections of species distributions under climate change. The uncertainty introduced by the choice of baseline climate information used to calibrate a species distribution model and to downscale global climate model (GCM) simulations to a finer spatial re...

Descripción completa

Detalles Bibliográficos
Autores principales: Tang, Ying, Winkler, Julie A., Viña, Andrés, Liu, Jianguo, Zhang, Yuanbin, Zhang, Xiaofeng, Li, Xiaohong, Wang, Fang, Zhang, Jindong, Zhao, Zhiqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5761832/
https://www.ncbi.nlm.nih.gov/pubmed/29320501
http://dx.doi.org/10.1371/journal.pone.0189496
_version_ 1783291596340461568
author Tang, Ying
Winkler, Julie A.
Viña, Andrés
Liu, Jianguo
Zhang, Yuanbin
Zhang, Xiaofeng
Li, Xiaohong
Wang, Fang
Zhang, Jindong
Zhao, Zhiqiang
author_facet Tang, Ying
Winkler, Julie A.
Viña, Andrés
Liu, Jianguo
Zhang, Yuanbin
Zhang, Xiaofeng
Li, Xiaohong
Wang, Fang
Zhang, Jindong
Zhao, Zhiqiang
author_sort Tang, Ying
collection PubMed
description Multiple factors introduce uncertainty into projections of species distributions under climate change. The uncertainty introduced by the choice of baseline climate information used to calibrate a species distribution model and to downscale global climate model (GCM) simulations to a finer spatial resolution is a particular concern for mountainous regions, as the spatial resolution of climate observing networks is often insufficient to detect the steep climatic gradients in these areas. Using the maximum entropy (MaxEnt) modeling framework together with occurrence data on 21 understory bamboo species distributed across the mountainous geographic range of the Giant Panda, we examined the differences in projected species distributions obtained from two contrasting sources of baseline climate information, one derived from spatial interpolation of coarse-scale station observations and the other derived from fine-spatial resolution satellite measurements. For each bamboo species, the MaxEnt model was calibrated separately for the two datasets and applied to 17 GCM simulations downscaled using the delta method. Greater differences in the projected spatial distributions of the bamboo species were observed for the models calibrated using the different baseline datasets than between the different downscaled GCM simulations for the same calibration. In terms of the projected future climatically-suitable area by species, quantification using a multi-factor analysis of variance suggested that the sum of the variance explained by the baseline climate dataset used for model calibration and the interaction between the baseline climate data and the GCM simulation via downscaling accounted for, on average, 40% of the total variation among the future projections. Our analyses illustrate that the combined use of gridded datasets developed from station observations and satellite measurements can help estimate the uncertainty introduced by the choice of baseline climate information to the projected changes in species distribution.
format Online
Article
Text
id pubmed-5761832
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-57618322018-01-23 Uncertainty of future projections of species distributions in mountainous regions Tang, Ying Winkler, Julie A. Viña, Andrés Liu, Jianguo Zhang, Yuanbin Zhang, Xiaofeng Li, Xiaohong Wang, Fang Zhang, Jindong Zhao, Zhiqiang PLoS One Research Article Multiple factors introduce uncertainty into projections of species distributions under climate change. The uncertainty introduced by the choice of baseline climate information used to calibrate a species distribution model and to downscale global climate model (GCM) simulations to a finer spatial resolution is a particular concern for mountainous regions, as the spatial resolution of climate observing networks is often insufficient to detect the steep climatic gradients in these areas. Using the maximum entropy (MaxEnt) modeling framework together with occurrence data on 21 understory bamboo species distributed across the mountainous geographic range of the Giant Panda, we examined the differences in projected species distributions obtained from two contrasting sources of baseline climate information, one derived from spatial interpolation of coarse-scale station observations and the other derived from fine-spatial resolution satellite measurements. For each bamboo species, the MaxEnt model was calibrated separately for the two datasets and applied to 17 GCM simulations downscaled using the delta method. Greater differences in the projected spatial distributions of the bamboo species were observed for the models calibrated using the different baseline datasets than between the different downscaled GCM simulations for the same calibration. In terms of the projected future climatically-suitable area by species, quantification using a multi-factor analysis of variance suggested that the sum of the variance explained by the baseline climate dataset used for model calibration and the interaction between the baseline climate data and the GCM simulation via downscaling accounted for, on average, 40% of the total variation among the future projections. Our analyses illustrate that the combined use of gridded datasets developed from station observations and satellite measurements can help estimate the uncertainty introduced by the choice of baseline climate information to the projected changes in species distribution. Public Library of Science 2018-01-10 /pmc/articles/PMC5761832/ /pubmed/29320501 http://dx.doi.org/10.1371/journal.pone.0189496 Text en © 2018 Tang et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Tang, Ying
Winkler, Julie A.
Viña, Andrés
Liu, Jianguo
Zhang, Yuanbin
Zhang, Xiaofeng
Li, Xiaohong
Wang, Fang
Zhang, Jindong
Zhao, Zhiqiang
Uncertainty of future projections of species distributions in mountainous regions
title Uncertainty of future projections of species distributions in mountainous regions
title_full Uncertainty of future projections of species distributions in mountainous regions
title_fullStr Uncertainty of future projections of species distributions in mountainous regions
title_full_unstemmed Uncertainty of future projections of species distributions in mountainous regions
title_short Uncertainty of future projections of species distributions in mountainous regions
title_sort uncertainty of future projections of species distributions in mountainous regions
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5761832/
https://www.ncbi.nlm.nih.gov/pubmed/29320501
http://dx.doi.org/10.1371/journal.pone.0189496
work_keys_str_mv AT tangying uncertaintyoffutureprojectionsofspeciesdistributionsinmountainousregions
AT winklerjuliea uncertaintyoffutureprojectionsofspeciesdistributionsinmountainousregions
AT vinaandres uncertaintyoffutureprojectionsofspeciesdistributionsinmountainousregions
AT liujianguo uncertaintyoffutureprojectionsofspeciesdistributionsinmountainousregions
AT zhangyuanbin uncertaintyoffutureprojectionsofspeciesdistributionsinmountainousregions
AT zhangxiaofeng uncertaintyoffutureprojectionsofspeciesdistributionsinmountainousregions
AT lixiaohong uncertaintyoffutureprojectionsofspeciesdistributionsinmountainousregions
AT wangfang uncertaintyoffutureprojectionsofspeciesdistributionsinmountainousregions
AT zhangjindong uncertaintyoffutureprojectionsofspeciesdistributionsinmountainousregions
AT zhaozhiqiang uncertaintyoffutureprojectionsofspeciesdistributionsinmountainousregions