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Aim To use a fine-grained global model of ant diversity to identify the limits of our knowledge of diversity in the context of climate change.

Location Global.

Methods We applied generalized linear modelling to a global database of local ant assemblages to predict the species density of ants globally. Predictors evaluated included simple climate variables, combined temperature X precipitation variables, biogeographic region, elevation, and interactions between select variables. Areas of the planet identified as beyond the reliable prediction ability of the model were those having climatic conditions more extreme than what was represented in the ant database.

Results Temperature was the most important single predictor of ant species density, and a mix of climatic variables, biogeographic region and interactions between climate and region yielded the best overall model. Broadly, geographic patterns of ant diversity match those of other taxa, with high species density in the wet tropics and in some, but not all, parts of the dry tropics. Uncertainty in model predictions appears to derive from the low amount of standardized sampling of ants in Asia, in Africa and in the most extreme (e. g. hottest) climates. Model residuals increase as a function of temperature. This suggests that our understanding of the drivers of ant diversity at high temperatures is incomplete, especially in hot and arid climates. In other words, our ignorance of how ant diversity relates to environment is greatest in those regions where most species occur - hot climates, both wet and dry.

Main conclusions Our results have two important implications. First, temperature is necessary, but not sufficient, to explain fully the patterns of ant diversity. Second, our ability to predict ant diversity is weakest exactly where we need to know the most, the warmest regions of a warming world. This includes significant parts of the tropics and some of the most biologically diverse areas in the world.