{"type": "FeatureCollection", "features": [{"id": "10.1002/ecy.2199", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:13:54Z", "type": "Journal Article", "created": "2018-02-27", "title": "Temperature and aridity regulate spatial variability of soil multifunctionality in drylands across the globe", "description": "Abstract<p>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\uffc2\uffa0modelling was clearly higher for the spatial variability of N\uffe2\uff80\uff90 than for C\uffe2\uff80\uff90 and P\uffe2\uff80\uff90related 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. 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These changes create abiotic stresses, including desiccation and nutrient limitation, which act on microbes. However, our understanding of the responses of microbial communities to co-occurring climate change drivers is limited. We surveyed soil bacterial and fungal diversity and composition after a 1-year warming and altered precipitation manipulation in the Tibetan plateau alpine grassland. In isolation, warming and decreased precipitation treatments each had no significant effects on soil bacterial community structure; however, in combination of both treatments altered bacterial community structure (p = 0.03). The main effect of altered precipitation specifically impacted the relative abundances of Bacteroidetes and Gammaproteobacteria compared to the control, while the main effect of warming impacted the relative abundance of Betaproteobacteria. In contrast, the fungal community had no significant response to the treatments after 1-year. Using structural equation modeling (SEM), we found bacterial community composition was positively related to soil moisture. Our results indicate that short-term climate change could cause changes in soil bacterial community through taxonomic shifts. Our work provides new insights into immediate soil microbial responses to short-term stressors acting on an ecosystem that is particularly sensitive to global climate change.", "keywords": ["Abiotic component", "Microbial population biology", "Climate Change", "Soil Science", "Precipitation", "soil microbial community structure", "Microbiology", "Mathematical analysis", "Environmental science", "Agricultural and Biological Sciences", "Meteorology", "11. Sustainability", "FOS: Mathematics", "Genetics", "Climate change", "alpine grassland", "Biology", "Ecosystem", "2. 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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\uffc2\uffa0modelling was clearly higher for the spatial variability of N\uffe2\uff80\uff90 than for C\uffe2\uff80\uff90 and P\uffe2\uff80\uff90related soil variables. In the case of N cycling, the effects of temperature and aridity were both direct and indirect via changes in soil properties. 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