Abstract

Our understanding of the complexity of forces at play in the rise of major angiosperm lineages remains incomplete. The diversity and heterogeneous distribution of most angiosperm lineages is so extraordinary that it confounds our ability to identify simple drivers of diversification. Using machine learning in combination with phylogenetic modelling, we show that five separate abiotic and biotic variables significantly contribute to the diversification of Cactaceae. We reconstruct a comprehensive phylogeny, build a dataset of 39 abiotic and biotic variables, and predict the variables of central importance, while accounting for potential interactions between those variables. We use state-dependent diversification models to confirm that five abiotic and biotic variables shape diversification in the cactus family. Of highest importance are diurnal air temperature range, soil sand content and plant size, with lesser importance identified in isothermality and geographic range size. Interestingly, each of the estimated optimal conditions for abiotic variables were intermediate, indicating that cactus diversification is promoted by moderate, not extreme, climates. Our results reveal the potential primary drivers of cactus diversification, and the need to account for the complexity underlying the evolution of angiosperm lineages.
Original languageEnglish
Article number7282
Number of pages11
JournalNature Communications
Volume15
Issue number1
Early online date23 Aug 2024
DOIs
Publication statusPublished - 23 Aug 2024

Data Availability Statement

All Supplementary Data underlying our results are available at https://github.com/jamie-thompson/cactaceae, which includes GenBank accession numbers (in the file named “AccessionsMatrix.csv”) and the entire dataset of 39 variables. Data used to make Fig. 1 is in folders “Alignment, accessions and tree” and “XGBoost and GBIF Data Assembly”. Data used to make Fig. 2 is available in the folder “XGBoost relative importance tables”. Data used to make Fig. 3 is available in the folder “QuaSSE model fits”.

Funding

Roger and Sue Whorrod PhD studentship

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