EDI Staging Environment
Many biotic interactions influence community structure, yet most distribution models for plants have focused on plant competition or used only abiotic variables to predict plant abundance. Furthermore, biotic interactions are commonly context-dependent across abiotic gradients. For example, plant-plant interactions can grade from competition to facilitation over temperature gradients. We used a hierarchical Bayesian framework to predict the abundances of 12 plant species across a mountain landscape and test hypotheses on the context-dependency of biotic interactions over abiotic gradients. We combined field-based estimates of six biotic interactions (foliar herbivory and pathogen damage, fungal root colonization, fossorial mammal disturbance, plant cover, and plant diversity) with abiotic data on climate and soil depth, nutrients, and moisture. All biotic interactions were significantly context-dependent along temperature gradients. Results supported the stress gradient hypothesis: As abiotic stress increased, the strength or direction of the relationship between biotic variables and plant abundance generally switched from negative (suggesting suppressed plant abundance) to positive (suggesting facilitation/mutualism). For half of the species, plant cover was the best predictor of abundance, suggesting that the prior focus on plant-plant interactions is well-justified. Explicitly incorporating the context-dependency of biotic interactions generated novel hypotheses about drivers of plant abundance across abiotic gradients and may improve the accuracy of niche models.
EDI is a collaboration between the University of New Mexico and the University of Wisconsin – Madison, Center for Limnology:
