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Temperature and aridity regulate spatial variability of soil multifunctionality in drylands across the globe

Durán, Jorge, Delgado‐Baquerizo, Manuel, Dougill, Andrew J., Guuroh, Reginald T., Linstädter, Anja, Thomas, Andrew D., Maestre, Fernando T.
Ecology 2018 v.99 no.5 pp. 1184-1193
arid lands, climate change, climatic factors, dry environmental conditions, edaphic factors, microorganisms, prediction, sand fraction, structural equation modeling, temperature, terrestrial ecosystems, texture, vegetation
The relationship between the spatial variability of soil multifunctionality (i.e., the capacity of soils to conduct multiple functions; SVM) and major climatic drivers, such as temperature and aridity, has never been assessed globally in terrestrial ecosystems. We surveyed 236 dryland ecosystems from six continents to evaluate the relative importance of aridity and mean annual temperature, and of other abiotic (e.g., texture) and biotic (e.g., plant cover) variables as drivers of SVM, calculated as the averaged coefficient of variation for multiple soil variables linked to nutrient stocks and cycling. We found that increases in temperature and aridity were globally correlated to increases in SVM. Some of these climatic effects on SVM were direct, but others were indirectly driven through reductions in the number of vegetation patches and increases in soil sand content. The predictive capacity of our structural equation modelling was clearly higher for the spatial variability of N‐ than for C‐ and P‐related soil variables. In the case of N cycling, the effects of temperature and aridity were both direct and indirect via changes in soil properties. For C and P, the effect of climate was mainly indirect via changes in plant attributes. These results suggest that future changes in climate may decouple the spatial availability of these elements for plants and microbes in dryland soils. Our findings significantly advance our understanding of the patterns and mechanisms driving SVM in drylands across the globe, which is critical for predicting changes in ecosystem functioning in response to climate change.