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

Varela S, Lima-Ribeiro MS, Terribile LC - PLoS ONE (2015)

Bottom Line: Ecological niche models are widely used for mapping the distribution of species during the last glacial maximum (LGM).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.

View Article: PubMed Central - PubMed

Affiliation: Department of Ecology, Faculty of Science, Charles University, Praha, Czech Republic; Museum für Naturkunde. Leibniz Institute for Evolution and Biodiversity Science, Berlin, Germany.

ABSTRACT
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/.

No MeSH data available.


Related in: MedlinePlus

Boxplot showing the correlation values for the temperature variables (in red) and the precipitation variables (in blue), of each General Circulation Model (GCM) compared with the rest of the layers from the other GCMs.Although there are discrepancies in certain variables, temperature variables are highly congruent between models. On the other hand, precipitation variables show more discrepancies between models. COSMOS is the most different model in relation to its predictions about precipitation. Points are outliers (located 1.5 times the interquartile range above the upper quartile and bellow the lower quartile, which is the default definition of outlier in the R function boxplot).
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pone.0129037.g004: Boxplot showing the correlation values for the temperature variables (in red) and the precipitation variables (in blue), of each General Circulation Model (GCM) compared with the rest of the layers from the other GCMs.Although there are discrepancies in certain variables, temperature variables are highly congruent between models. On the other hand, precipitation variables show more discrepancies between models. COSMOS is the most different model in relation to its predictions about precipitation. Points are outliers (located 1.5 times the interquartile range above the upper quartile and bellow the lower quartile, which is the default definition of outlier in the R function boxplot).

Mentions: After calculating the correlations between the model’s predictions for each climatic layer, we observed that temperature variables are highly correlated; some models have more extreme temperature predictions for the temperate and cold areas, but all the temperature predictions are highly correlated. Precipitation variables show more discrepancies between models; correlations between the predicted precipitation layers between models are not high, and the COSMOS climatic model showed the most different predictions (Fig 4 and S2 Table).


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

Varela S, Lima-Ribeiro MS, Terribile LC - PLoS ONE (2015)

Boxplot showing the correlation values for the temperature variables (in red) and the precipitation variables (in blue), of each General Circulation Model (GCM) compared with the rest of the layers from the other GCMs.Although there are discrepancies in certain variables, temperature variables are highly congruent between models. On the other hand, precipitation variables show more discrepancies between models. COSMOS is the most different model in relation to its predictions about precipitation. Points are outliers (located 1.5 times the interquartile range above the upper quartile and bellow the lower quartile, which is the default definition of outlier in the R function boxplot).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4466021&req=5

pone.0129037.g004: Boxplot showing the correlation values for the temperature variables (in red) and the precipitation variables (in blue), of each General Circulation Model (GCM) compared with the rest of the layers from the other GCMs.Although there are discrepancies in certain variables, temperature variables are highly congruent between models. On the other hand, precipitation variables show more discrepancies between models. COSMOS is the most different model in relation to its predictions about precipitation. Points are outliers (located 1.5 times the interquartile range above the upper quartile and bellow the lower quartile, which is the default definition of outlier in the R function boxplot).
Mentions: After calculating the correlations between the model’s predictions for each climatic layer, we observed that temperature variables are highly correlated; some models have more extreme temperature predictions for the temperate and cold areas, but all the temperature predictions are highly correlated. Precipitation variables show more discrepancies between models; correlations between the predicted precipitation layers between models are not high, and the COSMOS climatic model showed the most different predictions (Fig 4 and S2 Table).

Bottom Line: Ecological niche models are widely used for mapping the distribution of species during the last glacial maximum (LGM).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.

View Article: PubMed Central - PubMed

Affiliation: Department of Ecology, Faculty of Science, Charles University, Praha, Czech Republic; Museum für Naturkunde. Leibniz Institute for Evolution and Biodiversity Science, Berlin, Germany.

ABSTRACT
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/.

No MeSH data available.


Related in: MedlinePlus