Can trait-based analyses of changes in species distribution be transferred to new geographic areas?

Aim Anthropogenic environmental change is having a major impact on biodiversity. By identifying traits that correlate with changes in species range, comparative studies can shed light on the mechanisms driving this change; but such studies will be more useful for conservation if they have true predictive power, i.e. if their trait-based models can be transferred to new regions. We aim to examine the ability of trait-based models to predict changes in plant distribution across seven geographic regions that varied in terms of land cover and species composition. Location Britain and Flanders (Belgium). Methods We estimated distribution change for more than 1000 species for over 70 years of data (1930s to 2004), using data from published plant atlases. We identified regional trait-based models of plant distribution change. Traits included morphological characteristics, Ellenberg values and distribution-based traits. The trait models were then used to predict change in all other regions, with the level of linear correlation between predicted and observed changes in range used as a measure of transferability. We then related transferability to land cover and species similarity between regions. Results We found that trait correlates of range change varied regionally, highlighting the regional variation in the drivers of range change in plants. These trait models also varied in the amount of variation explained, with r 2 values ranging from 0.05 to 0.17. A key cross-regional difference was the variation in the relationship between soil nutrient association (Ellenberg N) and distribution change, which was strongly positive in Flanders and southern England but significantly negative in northern Scotland. We found that transferability between regions was significantly correlated with the level of similarity in land cover. Main conclusions We conclude that trait-based models can predict broad-scale changes in species distributions in regions that share similar land-cover composition; however, predictions between regions with differing land-cover cover tend to be poor.

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