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A Short Guide to the Climatic Variables of the Last Glacial Maximum for Biogeographers

Varela, Sara ; Lima-Ribeiro, Matheus S ; Terribile, Levi Carina Rebelo, Hugo

PloS one, 2015-06, Vol.10 (6), p.e0129037-e0129037 [Periódico revisado por pares]

United States: Public Library of Science

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  • Título:
    A Short Guide to the Climatic Variables of the Last Glacial Maximum for Biogeographers
  • Autor: Varela, Sara ; Lima-Ribeiro, Matheus S ; Terribile, Levi Carina
  • Rebelo, Hugo
  • Assuntos: Bioclimatology ; Climate ; Climate change ; Climate prediction ; Cluster Analysis ; Coefficient of variation ; Ecological niches ; Ecosystem ; General circulation models ; Geospatial data ; Hemispheres ; Last Glacial Maximum ; Mapping ; Models, Theoretical ; Niche (Ecology) ; Niches (Ecology) ; Precipitation ; Precipitation (Meteorology) ; Predictions ; Rainfall ; Seasonal variations ; Species ; Temperature ; Tropical environments ; Uncertainty
  • É parte de: PloS one, 2015-06, Vol.10 (6), p.e0129037-e0129037
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
    Conceived and designed the experiments: SV MSLR LCT. Performed the experiments: SV. Analyzed the data: SV MSLR LCT. Contributed reagents/materials/analysis tools: SV MSLR LCT. Wrote the paper: SV MSLR LCT.
    Competing Interests: The authors have declared that no competing interests exist.
  • Descrição: Ecological niche models are widely used for mapping the distribution of species during the last glacial maximum (LGM). Although the selection of the variables and General Circulation Models (GCMs) used for constructing those maps determine the model predictions, we still lack a discussion about which variables and which GCM should be included in the analysis and why. Here, we analyzed the climatic predictions for the LGM of 9 different GCMs in order to help biogeographers to select their GCMs and climatic layers for mapping the species ranges in the LGM. We 1) map the discrepancies between the climatic predictions of the nine GCMs available for the LGM, 2) analyze the similarities and differences between the GCMs and group them to help researchers choose the appropriate GCMs for calibrating and projecting their ecological niche models (ENM) during the LGM, and 3) quantify the agreement of the predictions for each bioclimatic variable to help researchers avoid the environmental variables with a poor consensus between models. Our results indicate that, in absolute values, GCMs have a strong disagreement in their temperature predictions for temperate areas, while the uncertainties for the precipitation variables are in the tropics. In spite of the discrepancies between model predictions, temperature variables (BIO1-BIO11) are highly correlated between models. Precipitation variables (BIO12-BIO19) show no correlation between models, and specifically, BIO14 (precipitation of the driest month) and BIO15 (Precipitation Seasonality (Coefficient of Variation)) show the highest level of discrepancy between GCMs. Following our results, we strongly recommend the use of different GCMs for constructing or projecting ENMs, particularly when predicting the distribution of species that inhabit the tropics and the temperate areas of the Northern and Southern Hemispheres, because climatic predictions for those areas vary greatly among GCMs. We also recommend the exclusion of BIO14 and BIO15 from ENMs because those variables show a high level of discrepancy between GCMs. Thus, by excluding them, we decrease the level of uncertainty of our predictions. All the climatic layers produced for this paper are freely available in http://ecoclimate.org/.
  • Editor: United States: Public Library of Science
  • Idioma: Inglês
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