Table 2. Assessment of the importance of predictors used to model forest points within montane forest grassland mosaics across the Western Ghats.

Predictor a	Summed Akaike weight	Standardized, model-averaged beta (SE)	Model averaged 95% CI
elevation	~1	-1.014 (0.094)	-1.199: -0.829
dry.prec	~1	0.92 (0.117)	0.69: 1.15
tpi3.10	0.999	-0.449 (0.085)	-0.616: -0.283
anntmprng	0.999	0.36 (0.08)	0.204: 0.516
anntmprng:dry.prec	0.999	0.323 (0.056)	0.214: 0.432
curve90	0.579	-0.259 (0.063)	-0.383: -0.136
cos.aspect	0.379	0.204 (0.05)	0.107: 0.301
elevation:tpi3.10	0.029	0.19 (0.055)	0.082: 0.298
annprec	0.008	-0.304 (0.099)	-0.498: -0.109
coast.dist	0.002	0.202 (0.087)	0.031: 0.372
elevation:dry.prec	0.0009	-0.173 (0.077)	-0.323: -0.022
tci90	0.0003	0.091 (0.056)	-0.018: 0.2
tpi10.67	0.0002	-0.091 (0.062)	-0.211: 0.03
dry.prec:tpi3.10	0.0001	0.061 (0.056)	-0.05: 0.171
rugged	0.0001	0.003 (0.05)	-0.096: 0.102
dist.coast:elevation	0.0001	-0.571 (0.113)	-0.792: -0.35
cos.aspect:elevation	~0	0.163 (0.051)	0.063: 0.263
elevation:tci90	~0	-0.209 (0.055)	-0.318: -0.1
curve90:elevation	~0	0.051 (0.058)	-0.063: 0.165
dry.prec:tci90	~0	-0.108 (0.06)	-0.226: 0.01
cos.aspect:coast.dist	~0	0.062 (0.052)	-0.04: 0.164

Columns show summed Akaike weights, standardized beta coefficients averaged across models and unconditional standard errors (SE) in parentheses and 95% confidence intervals (CI) based on the unconditional SEs.

^aPlease refer to Table 1 for explanation of predictor codes