Ecological niche model comparison under different climate scenarios: a case study of Olea spp. in Asia

Ecological niche modeling (and the related species distribution modeling) has been used as a tool with which to assess potential impacts of climate change processes on geographic distributions of species. However, the factors introducing variation into niche modeling outcomes are not well understood...

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Published inEcosphere (Washington, D.C) Vol. 8; no. 5
Main Authors Ashraf, Uzma, Peterson, A. Townsend, Chaudhry, Muhammad Nawaz, Ashraf, Irfan, Saqib, Zafeer, Rashid Ahmad, Sajid, Ali, Hassan
Format Journal Article
LanguageEnglish
Published Washington John Wiley & Sons, Inc 01.05.2017
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ISSN2150-8925
2150-8925
DOI10.1002/ecs2.1825

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Abstract Ecological niche modeling (and the related species distribution modeling) has been used as a tool with which to assess potential impacts of climate change processes on geographic distributions of species. However, the factors introducing variation into niche modeling outcomes are not well understood: To this end, we used seven algorithms to develop models (Maxent, GARP, BIOCLIM, artificial neural networks, support‐vector machines, climate envelope, and environmental distance) to estimate the potential geographic distribution of olives (Olea europaea sensu lato, including Olea ferruginea) under two climatic data sets (current 2000 and future 2050). Five general circulation models and two representative concentration pathway scenarios were used as predictor variables in future projections of the geographic potential of this species; models were fit at global extents (10′ spatial resolution) but transferred and interpreted for a region of particular interest in Central Asia, which largely avoids problems with truncation of niche estimates. We found marked differences among approaches in predicted distributions and model performance, as well as in the future distributional pattern reconstructed, from one algorithm to another. These general approaches, when model‐to‐model variation is managed appropriately, appear promising in predicting the potential geographic distribution of O. europaea sensu lato and thus can be an effective tool in restoration and conservation planning for wild populations, as well as possible commercial plantations of this species.
AbstractList Ecological niche modeling (and the related species distribution modeling) has been used as a tool with which to assess potential impacts of climate change processes on geographic distributions of species. However, the factors introducing variation into niche modeling outcomes are not well understood: To this end, we used seven algorithms to develop models (Maxent, GARP, BIOCLIM, artificial neural networks, support‐vector machines, climate envelope, and environmental distance) to estimate the potential geographic distribution of olives (Olea europaea sensu lato, including Olea ferruginea) under two climatic data sets (current 2000 and future 2050). Five general circulation models and two representative concentration pathway scenarios were used as predictor variables in future projections of the geographic potential of this species; models were fit at global extents (10′ spatial resolution) but transferred and interpreted for a region of particular interest in Central Asia, which largely avoids problems with truncation of niche estimates. We found marked differences among approaches in predicted distributions and model performance, as well as in the future distributional pattern reconstructed, from one algorithm to another. These general approaches, when model‐to‐model variation is managed appropriately, appear promising in predicting the potential geographic distribution of O. europaea sensu lato and thus can be an effective tool in restoration and conservation planning for wild populations, as well as possible commercial plantations of this species.
Ecological niche modeling (and the related species distribution modeling) has been used as a tool with which to assess potential impacts of climate change processes on geographic distributions of species. However, the factors introducing variation into niche modeling outcomes are not well understood: To this end, we used seven algorithms to develop models (Maxent, GARP, BIOCLIM, artificial neural networks, support‐vector machines, climate envelope, and environmental distance) to estimate the potential geographic distribution of olives (Olea europaea sensu lato, including Olea ferruginea) under two climatic data sets (current 2000 and future 2050). Five general circulation models and two representative concentration pathway scenarios were used as predictor variables in future projections of the geographic potential of this species; models were fit at global extents (10′ spatial resolution) but transferred and interpreted for a region of particular interest in Central Asia, which largely avoids problems with truncation of niche estimates. We found marked differences among approaches in predicted distributions and model performance, as well as in the future distributional pattern reconstructed, from one algorithm to another. These general approaches, when model‐to‐model variation is managed appropriately, appear promising in predicting the potential geographic distribution of O. europaea sensu lato and thus can be an effective tool in restoration and conservation planning for wild populations, as well as possible commercial plantations of this species.
Ecological niche modeling (and the related species distribution modeling) has been used as a tool with which to assess potential impacts of climate change processes on geographic distributions of species. However, the factors introducing variation into niche modeling outcomes are not well understood: To this end, we used seven algorithms to develop models (Maxent, GARP , BIOCLIM , artificial neural networks, support‐vector machines, climate envelope, and environmental distance) to estimate the potential geographic distribution of olives ( Olea europaea sensu lato, including Olea ferruginea ) under two climatic data sets (current 2000 and future 2050). Five general circulation models and two representative concentration pathway scenarios were used as predictor variables in future projections of the geographic potential of this species; models were fit at global extents (10′ spatial resolution) but transferred and interpreted for a region of particular interest in Central Asia, which largely avoids problems with truncation of niche estimates. We found marked differences among approaches in predicted distributions and model performance, as well as in the future distributional pattern reconstructed, from one algorithm to another. These general approaches, when model‐to‐model variation is managed appropriately, appear promising in predicting the potential geographic distribution of O. europaea sensu lato and thus can be an effective tool in restoration and conservation planning for wild populations, as well as possible commercial plantations of this species.
Author Chaudhry, Muhammad Nawaz
Saqib, Zafeer
Peterson, A. Townsend
Ashraf, Irfan
Ashraf, Uzma
Ali, Hassan
Rashid Ahmad, Sajid
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Snippet Ecological niche modeling (and the related species distribution modeling) has been used as a tool with which to assess potential impacts of climate change...
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SubjectTerms Algorithms
Calibration
Climate change
Climatic data
ecological niche model
Environmental impact
Genetic algorithms
Geographical distribution
Introduced species
invasive species
Neural networks
Niches
partial receiver‐operating characteristic (ROC)
Precipitation
species distribution model
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Title Ecological niche model comparison under different climate scenarios: a case study of Olea spp. in Asia
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