Trait-based climate change predictions of plant community structure in arid steppes

Authors


Correspondence author. E-mail: cedric.frenette.dussault@usherbrooke.ca

Summary

  1. Global climate change is possibly one of the most important challenges for current and future human populations due to its wide-ranging effects on ecosystems. Global prediction models suggest that in some areas of the world (e.g. Northern Africa, Central America) an increase in aridity might strongly disturb agricultural production and affect food security. To counterbalance these negative effects, reliable predictive models are needed to anticipate ecosystem changes.
  2. We tested the ability of the Community Assembly by Trait-based Selection (CATS) model that is based on the principle of maximum entropy and trait-based environmental filtering, to predict actual and future plant community composition in the arid steppes of eastern Morocco. Specifically, we asked whether this model was adequate for predicting actual community composition, based on predicted community-weighted mean (CWM) traits, and what would be the changes in community composition under various scenarios of climate change for the period 2080—2099.
  3. The CATS model could predict > 90% of actual community composition if the actual CWM traits were known but only ˜40% if the CWM values were predicted from estimated aridity and grazing values. The predictions of community composition for 2080—2099 suggested that, regardless of the climate change scenario considered, the dominant group in grazed and ungrazed sites would shift from ruderal species to stress-tolerant sub-shrubs, which would constitute up over 80% of total community composition in some cases.
  4. Synthesis. Our findings suggest that effects of climate change will strongly modify plant community structure in arid steppes, possibly accentuating the process of desertification, and reducing the pastoral value of the vegetation. Future research efforts should concentrate on the identification of strong trait–environment relationships to improve model predictions.

Ancillary