Multilabel variable selection revealed the overriding importance of topography, soils, and temperature extremes (rather than averages) in determining the distribution of plant species in the study area and their response to climate change. Our model explained presences and absences of 175 plant species in 608 plots with >87% cross-validated accuracy, predicted decreases in α, β, and γ diversity by 2040 under both moderate and extreme climate scenarios, and identified likely advantaged and disadvantaged plant species under climate change. We applied our model to a large set of plant communities in the Swiss Alps.
To this purpose, we first used multilabel algorithms in order to convert the task of explaining a large assemblage of plant communities into a classification framework able to capture with high cross-validated accuracy the pattern of species distributions under a composite set of biotic and abiotic factors. We devised a method to reliably predict the impact of climate change on large assemblages of plant communities, while also considering competing biotic and environmental factors. There is increasing interestin broad-scale analysis, modeling, and prediction of the distribution and composition of plant species assemblages under climatic, environmental, and biotic change, particularly for conservation purposes.
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