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Abstract:
The environmental filtering hypothesis predicts that the abiotic environment selects species with similar trait values within communities. Testing this hypothesis along multiple – and interacting – gradients of climate and soil variables constitutes a great opportunity to better understand and predict the responses of plant communities to ongoing environmental changes.
Based on two key plant traits, maximum plant height and specific leaf area (SLA), we assessed the filtering effects of climate (mean annual temperature and precipitation, precipitation seasonality), soil characteristics (soil pH, sand content and total phosphorus) and all potential interactions on the functional structure and diversity of 124 dryland communities spread over the globe. The functional structure and diversity of dryland communities were quantified using the mean, variance, skewness and kurtosis of plant trait distributions.
The models accurately explained the observed variations in functional trait diversity across the 124 communities studied. All models included interactions among factors, i.e. climate–climate (9% of explanatory power), climate–soil (24% of explanatory power) and soil–soil interactions (5% of explanatory power). Precipitation seasonality was the main driver of maximum plant height, and interacted with mean annual temperature and precipitation. Soil pH mediated the filtering effects of climate and sand content on SLA. Our results also revealed that communities characterized by a low variance can also exhibit low kurtosis values, indicating that functionally contrasting species can co-occur even in communities with narrow ranges of trait values.
Synthesis. We identified the particular set of conditions under which the environmental filtering hypothesis operates in drylands world-wide. Our findings also indicate that species with functionally contrasting strategies can still co-occur locally, even under prevailing environmental filtering. Interactions between sources of environmental stress should be therefore included in global trait-based studies, as this will help to further anticipate where the effects of environmental filtering will impact plant trait diversity under climate change.
