Predictive value of plant traits to grazing along a climatic gradient in the Mediterranean.
Abstract
In the face of large-scale environmental changes, predicting the consequences for species composition from species' traits can be a major step towards generalizing ecological patterns and management. Few studies, however, have explored the applicability of this tool in relation to different climatic conditions. Here, the changes in species composition along a gradient of sheep-grazing pressure (high, low, abandonment) were used to test whether a common set of plant functional traits (PFT) would provide consistent predictions of species' responses to grazing in different biogeographical regions. Data were collected across an altitudinal and climatic gradient from Mediterranean rangelands to subalpine grasslands in north-eastern Spain. Species' responses were calculated using partial constrained ordination to account only for the effect of grazing intensity. Regression trees and general linear models were applied to identify traits that could predict species' responses. Results were mostly consistent with the ruderal vs. competitive strategy (sensu Grime), in terms of life cycle, life form and plant height, and their expected responses to repeated disturbance. However, the predictive capacity of the investigated traits changed with climatic conditions. Traits generally related to grazing did not show a strong repeatability across the climatic gradient. Convergent selection of climatic conditions and grazing indicated that plant features might reflect an adaptation to multiple selective forces. The climatic conditions acted as filters on the pool of PFT available and shifted the relevance of plant traits as potential predictors. Results were not substantially different after applying phylogenetically independent contrasts (PIC). Synthesis and applications. At a local scale, plant functional traits are useful tools in predicting species' responses to grazing and, for conservation purposes, identifying species vulnerable to land-use changes. However, predictions cannot be extrapolated from one climatic region to another. The methodology proposed in this study to detect predicting traits can be applied more generally. Regression trees, in particular, appear to be a useful tool because they account for non-additive effects and allow visualizations of trait combinations.