A total of 47 explanatory variables in five categories were used in random forests models to predict local tree species richness and quantify LDG
According to standardized variable importance values (horizontal bar plots to the left), bioclimatic variables contributed the most to LDG, followed by vegetation and survey, topographic, anthropogenic and soil variables. The correlogram to the right illustrates correlations between any two variables by the colour and size of a disk. The partial dependence plots to the left (see Extended Data Fig. 3) show the effect of each predictor variable on the species richness, while all the other predictors remained constant at their sample mean. The metadata of the training dataframe provide a detailed description of the explanatory variables. Min., minimum; Temp., temperature; Precip., precipitation; t., temperature; Pot., potential; Max., maximum; dbh, diameter at breast height; part. deriv., partial derivative; Topo., topographic; HF, human footprint; El., electrical.

A total of 47 explanatory variables in five categories were used in random forests models to predict local tree species richness and quantify LDG According to standardized variable importance values (horizontal bar plots to the left), bioclimatic variables contributed the most to LDG, followed by vegetation and survey, topographic, anthropogenic and soil variables. The correlogram to the right illustrates correlations between any two variables by the colour and size of a disk. The partial dependence plots to the left (see Extended Data Fig. 3) show the effect of each predictor variable on the species richness, while all the other predictors remained constant at their sample mean. The metadata of the training dataframe provide a detailed description of the explanatory variables. Min., minimum; Temp., temperature; Precip., precipitation; t., temperature; Pot., potential; Max., maximum; dbh, diameter at breast height; part. deriv., partial derivative; Topo., topographic; HF, human footprint; El., electrical.

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The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we...

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... While, a very less tree species richness (10 spp.) with a comparable total tree density of 255 ind. ha −1 and slightly high dominance (10.11 m 2 ha −1 ) were reported in high fire zones in tropical deciduous forests of Bhoramdeo Wildlife Sanctuary, Chhattisgarh (Jhariya et al., 2014) and are primarily driven by latitudinal gradients (Liang et al., 2022). Altered species composition, diversity, and reduced seedling density were also been reported in areas of dry deciduous forest with the shortest fire return intervals compared to forest patches with lower fire frequency in the moist deciduous forests of Nilgiri Biosphere Reserve, Western Ghats (Kodandapani et al., 2009). ...
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