Identifying the multiple drivers of Cactus diversification

Abstract

Many drivers of diversification have been identified across the angiosperm Tree of Life, ranging from abiotic factors, such as climate change, to biotic factors such as key adaptations. While this provides invaluable evolutionary insight into the rise of major angiosperm lineages, our understanding of the complexity underlying this remains incomplete. In species-rich families such as Cactaceae, simple explanations of triggers of diversification are insufficient. Their sheer morphological and ecological diversity, and wide distribution across heterogeneous environments, render the identification of key forces difficult. Cactus diversification is likely shaped by multiple drivers, which themselves interact in complex ways. This complexity is extremely difficult to disentangle, but applying modern analytical methods to extensive datasets offers a solution. Here, we investigate the heterogeneous diversification of the iconic Cactus family. We reconstruct a comprehensive phylogeny, build a dataset of 39 abiotic and biotic variables, and predict the variables of central importance to tip-speciation rate variation using Machine Learning. State-dependent diversification models confirm that a rich range of eleven abiotic and biotic variables filtered as important by Machine Learning shape Cactus diversification. Of highest importance is an atypical latitudinal gradient in speciation rates, which is spatially decoupled from richness hotspots. Of medium importance is plant size, shaped by growth form. Of lesser, but significant, importance is soil composition, bioclimate, topography, geographic range size, and chromosome count. However, it is unlikely that any one of these eleven variables is of primary importance without the complex interactions formed with others. Our results contribute to our understanding of one of the most iconic angiosperm families, while revealing the need to account for the complexity underlying macroevolution.