Extinction risk from climate change
Chris D. Thomas,Alison Cameron,Rhys E. Green,Rhys E. Green,Michel Bakkenes,Linda J. Beaumont,Yvonne C. Collingham,Barend F.N. Erasmus,Marinez Ferreira de Siqueira,Alan Grainger,Lee Hannah,Lesley Hughes,Brian Huntley,Albert S. van Jaarsveld,Guy F. Midgley,Lera Miles,Lera Miles,Miguel A. Ortega-Huerta,A. Townsend Peterson,Oliver L. Phillips,Stephen E. Williams +20 more
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Estimates of extinction risks for sample regions that cover some 20% of the Earth's terrestrial surface show the importance of rapid implementation of technologies to decrease greenhouse gas emissions and strategies for carbon sequestration.Abstract:
Climate change over the past approximately 30 years has produced numerous shifts in the distributions and abundances of species and has been implicated in one species-level extinction. Using projections of species' distributions for future climate scenarios, we assess extinction risks for sample regions that cover some 20% of the Earth's terrestrial surface. Exploring three approaches in which the estimated probability of extinction shows a power-law relationship with geographical range size, we predict, on the basis of mid-range climate-warming scenarios for 2050, that 15-37% of species in our sample of regions and taxa will be 'committed to extinction'. When the average of the three methods and two dispersal scenarios is taken, minimal climate-warming scenarios produce lower projections of species committed to extinction ( approximately 18%) than mid-range ( approximately 24%) and maximum-change ( approximately 35%) scenarios. These estimates show the importance of rapid implementation of technologies to decrease greenhouse gas emissions and strategies for carbon sequestration.read more
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Interactions between climate change and land use change on biodiversity: attribution problems, risks, and opportunities
TL;DR: In this article, the authors highlight the influence that attribution errors can have on biodiversity projections and discuss experimental designs and analytical tools suited to this challenge, summarise the risks and opportunities provided by the existence of interaction effects and discuss appropriate experimental and analytical designs for attribution.
Journal ArticleDOI
Forecasting Regional to Global Plant Migration in Response to Climate Change
Ronald P. Neilson,Louis F. Pitelka,Allen M. Solomon,Ran Nathan,Guy F. Midgley,José M. V. Fragoso,Heike Lischke,Ken Thompson +7 more
TL;DR: Simulation of plant migration and local vegetation change by dynamic global vegetation models (DGVMs) is critical, yet fraught with challenges because theories about climate change and migration are limited by inadequate data.
Journal ArticleDOI
Building Statistical Models To Analyze Species Distributions
TL;DR: It is demonstrated that making distribution models spatially explicit can be essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results.
Journal ArticleDOI
Species richness changes lag behind climate change
Rosa Menéndez,Adela González Megías,Jane K. Hill,Brigitte Braschler,Stephen G. Willis,Yvonne C. Collingham,Richard Fox,David B. Roy,Chris D. Thomas +8 more
TL;DR: It is confirmed that the average species richness of British butterflies has increased since 1970–82, but much more slowly than predicted from changes of climate: on average, only one-third of the predicted increase has taken place.
Journal ArticleDOI
Life history and spatial traits predict extinction risk due to climate change
Richard G. Pearson,Richard G. Pearson,Jessica C. Stanton,Kevin T. Shoemaker,Matthew E. Aiello-Lammens,Peter J. Ersts,Ned Horning,Damien A. Fordham,Christopher J. Raxworthy,Hae Yeong Ryu,Jason McNees,H. Resit Akçakaya +11 more
TL;DR: In this article, the authors developed a simulation approach based on generic life history types to show that extinction risk due to climate change can be predicted using a mixture of spatial and demographic variables that can be measured in the present day without the need for complex forecasting models.
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