Table 1. Mean species richness of marsupials in Brazil (S) projected for current and future climatic conditions, different between future & current species richness (Δ), mean turnover, and percent variation (median) of species range size and its interquartile deviation obtained in each Green house gases emission scenario, modeling method, and climate model.
| Emission scenario | Modeling method | Climate Model | Species richness (current climate) | Species richness (future climate) | Δ species richness | Turnover | % Range size variation (interquartile deviation) |
| A2a | GAM | CCCMA-CGCM2 | 11.28 | 10.79 | 0.49 | 0.50 | −44.35 (51.98) |
| CSIRO-MK2 | 11.28 | 10.63 | 0.65 | 0.37 | −26.75 (30.59) | ||
| HCCPR-HadCM3 | 11.28 | 9.88 | 1.40 | 0.55 | −52.76 (40.44) | ||
| NIES99 | 11.28 | 10.01 | 1.27 | 0.48 | −30.77 (41.22) | ||
| GBM | CCCMA-CGCM2 | 10.85 | 9.46 | 1.39 | 0.45 | −44.97 (44.41) | |
| CSIRO-MK2 | 10.85 | 9.85 | 1 | 0.38 | −26.93 (30.14) | ||
| HCCPR-HadCM3 | 10.85 | 8.27 | 2.57 | 0.54 | −49.66 (28.88) | ||
| NIES99 | 10.85 | 8.97 | 1.87 | 0.48 | −39.03 (30.43) | ||
| GLM | CCCMA-CGCM2 | 14.2 | 15.4 | −1.2 | 0.49 | −33.19 (41.91) | |
| CSIRO-MK2 | 14.2 | 14.78 | −0.58 | 0.35 | −7.76 (26.26) | ||
| HCCPR-HadCM3 | 14.2 | 15.05 | −0.85 | 0.53 | −34.12 (60.03) | ||
| NIES99 | 14.2 | 14.21 | −0.01 | 0.49 | −21.18 (42.1) | ||
| MARS | CCCMA-CGCM2 | 11.65 | 10.34 | 1.31 | 0.52 | −52.04 (41.18) | |
| CSIRO-MK2 | 11.65 | 10.83 | 0.82 | 0.40 | −28.72 (26.03) | ||
| HCCPR-HadCM3 | 11.65 | 8.86 | 2.79 | 0.57 | −46.32 (29.14) | ||
| NIES99 | 11.65 | 9.21 | 2.44 | 0.50 | −34.75 (40.05) | ||
| RF | CCCMA-CGCM2 | 8.53 | 8.22 | 0.31 | 0.43 | −21.17 (38.52) | |
| CSIRO-MK2 | 8.53 | 8.36 | 0.16 | 0.36 | −7.83 (33.13) | ||
| HCCPR-HadCM3 | 8.53 | 7.34 | 1.19 | 0.54 | −35.58 (49.38) | ||
| NIES99 | 8.53 | 8.27 | 0.25 | 0.45 | −0.21 (0.39) | ||
| ANN | CCCMA-CGCM2 | 14.42 | 11.83 | 2.59 | 0.45 | −21.17 (38.52) | |
| CSIRO-MK2 | 14.42 | 12.15 | 2.27 | 0.37 | −7.83 (33.13) | ||
| HCCPR-HadCM3 | 14.42 | 10.16 | 4.26 | 0.51 | −35.58 (49.38) | ||
| NIES99 | 14.42 | 11.66 | 2.76 | 0.47 | −21.04 (38.79) | ||
| B2a | GAM | CCCMA-CGCM2 | 11.28 | 10.77 | 0.51 | 0.32 | −4.21(23.94) |
| CSIRO-MK2 | 11.28 | 10.86 | 0.42 | 0.35 | −20.24 (30.20) | ||
| HCCPR-HadCM3 | 11.28 | 9.64 | 1.64 | 0.52 | −38.78 (39.34) | ||
| NIES99 | 11.28 | 10.53 | 0.75 | 0.42 | −30.70 (32.26) | ||
| GBM | CCCMA-CGCM2 | 10.85 | 10 | 0.85 | 0.38 | −22.36 (23.24) | |
| CSIRO-MK2 | 10.85 | 9.97 | 0.88 | 0.37 | −29.68 (27.66) | ||
| HCCPR-HadCM3 | 10.85 | 8.47 | 2.37 | 0.53 | −42.72 (35.93) | ||
| NIES99 | 10.85 | 9.38 | 1.47 | 0.45 | −34.01 (26.55) | ||
| GLM | CCCMA-CGCM2 | 14.2 | 15.05 | -0.85 | 0.31 | 1.49 (19.73) | |
| CSIRO-MK2 | 14.2 | 14.44 | -0.24 | 0.35 | −13.91 (26.46) | ||
| HCCPR-HadCM3 | 14.2 | 14.85 | -0.65 | 0.52 | −25.3 (56.3) | ||
| NIES99 | 14.2 | 13.59 | 0.61 | 0.44 | −22.71 (34.9) | ||
| MARS | CCCMA-CGCM2 | 11.65 | 10.98 | 0.67 | 0.37 | −10.82 (20.23) | |
| CSIRO-MK2 | 11.65 | 10.97 | 0.68 | 0.38 | −21.31 (23.62) | ||
| HCCPR-HadCM3 | 11.65 | 8.9 | 2.76 | 0.55 | −36.47 (27.53) | ||
| NIES99 | 11.65 | 10 | 1.65 | 0.45 | −32.35 (25.84) | ||
| RF | CCCMA-CGCM2 | 8.53 | 8.49 | 0.04 | 0.38 | −0.03 (0.23) | |
| CSIRO-MK2 | 8.53 | 8.42 | 0.11 | 0.37 | −10.63 (31.19) | ||
| HCCPR-HadCM3 | 8.53 | 7.49 | 1.04 | 0.54 | −34.87 (41.99) | ||
| NIES99 | 8.53 | 8.63 | −0.1 | 0.42 | −12.87 (38.57) | ||
| ANN | CCCMA-CGCM2 | 14.42 | 12.60 | 1.82 | 0.39 | −3.34 (23.3) | |
| CSIRO-MK2 | 14.42 | 12.12 | 2.30 | 0.37 | −10.63 (31.19) | ||
| HCCPR-HadCM3 | 14.42 | 10.62 | 3.80 | 0.52 | −34.87 (41.99) | ||
| NIES99 | 14.42 | 11.99 | 2.43 | 0.45 | −12.77 (38.57) |
Generalized Additive Models, GAM; Generalized Boosting Regression Models, GBM; Generalized Linear Models, GLM; Multivariate Adaptive Regression Splines, MARS; Artificial Neural Networks, ANN; and Random Forest, RF.