Scale-dependence of biotic interactions: Empirical evidence and implications during climate change
Understanding the scales over which biotic interactions operate is critical for forecasting species distributional changes now and during future climate change. In terms of species distributions, biotic interactions are thought to operate predominately at fine scales, and environmental filtering is expected to operate at broader scales, yet this paradigm has received relatively little empirical scrutiny. We ask whether the relative influence of different types of biotic interactions (e.g., competition, commensalism, predator-prey) changes with grain and extent, when compared to environmental suitability and geographic proximity to intraspecific occurrence sites. To characterize biotic interactions, we compiled a large interaction matrix with positive, negative, and neutral interactions for North American woodpeckers and their interacting avian species from published literature. Using a logistic mixed effects model framework, we modeled the occurrence of each woodpecker in the conterminous United States across 6 grains and 4 extents as a function of biotic interactions, environmental covariates, and geographic proximity.
We found that when a species experienced positive interactions, the importance of those interactions decreased with increased grain, yet these positive interactions still remained important at a grain size of 40km. Negative interactions had no clear scale-dependence. Geographic proximity became a stronger predictor of woodpecker occurrence with increasing grain, but environment was grain-invariant. Our findings show that although biotic interactions have the strongest influence on species distributions at fine scales, they remain important predictors of species distributions at very coarse scales and are thus important to include when forecasting changes to species distributions. We suggest investigators incorporate different types of biotic interactions and scales when forecasting changes to species distributions.