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AuthorFerrarini, Alessandro
AuthorDai, Junhu
AuthorBai, Yang
AuthorAlatalo, Juha M.
Available date2019-05-13T09:02:44Z
Publication Date2019-03-27
Publication NameScience of The Total Environmenten_US
Identifierhttp://dx.doi.org/10.1016/j.scitotenv.2019.03.353
ISSN0048-9697
ISSN1879-1026
URIhttp://hdl.handle.net/10576/11537
AbstractClimate change is increasingly affecting plant species distributions, in ways that need to be predicted. Here, in a novel prediction approach, we developed the relevant climate niche (RCN) of plants, based on thorough selection of climate variables and implementation of a non-parametric Bayesian network for climate simulations. The RCN was conditionalized to project the fate of Silene acaulis in North America under moderate (Representative Concentration Pathway 4.5; RCP4.5) and extreme (RCP8.5) short-term (2011–2040) climate scenarios. We identified a three-variable climate hypervolume for S. acaulis. Within 20 years >50% of current locations of the species will be outside the defined climate hypervolume. It could compensate for climate change in 2011–2040 through a poleward shift of 0.97 °C or an upshift of 138 m in the RCP4.5 scenario, and 1.29 °C or 184 m in the RCP8.5 scenario. These results demonstrate the benefits of redefining the climate niche of plant species in the form of a user-defined, data-validated, hierarchical network comprising only variables that are consistent with species distribution. Advantages include realism and interpretability in niche modeling, and new opportunities for predicting future species distributions under climate change.
SponsorQatar Petroleum (QUEX-ESC-QP-RD-18/19) supported J.M.A. J.D. was supported by National Key R & D Program of China (2018YFA0606100).
Languageen
PublisherElsevier
SubjectClimate change compensation
SubjectNetwork-like climate niche
SubjectNon-parametric Bayesian network
SubjectNorth America
SubjectReverse climate simulations
Subjectilene acaulis L
TitleRedefining the climate niche of plant species: A novel approach for realistic predictions of species distribution under climate change
TypeArticle
Pagination1086-1093
Volume Number671


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