Animal and plant space-use drive plant diversity-productivity relationships

Plant community productivity generally increases with biodiversity, but the strength of this relationship exhibits strong empirical variation. In meta-food-web simulations, we addressed if the spatial overlap in plants' resource access and animal space-use can explain such variability. We found that spatial overlap of plant resource access is a prerequisite for positive diversity-productivity relationships, but causes exploitative competition that can lead to competitive exclusion. Space-use of herbivores causes apparent competition among plants, resulting in negative relationships. However, space-use of larger top predators integrates sub-food webs composed of smaller species, offsetting the negative effects of exploitative and apparent competition and leading to strongly positive diversity-productivity relationships. Overall, our results show that spatial overlap of plants' resource access and animal space-use can greatly alter the strength and sign of such relationships. In particular, the scaling of animal space-use effects opens new perspectives for linking landscape processes without effects on biodiversity to productivity patterns.

Inferring spatially varying animal movement characteristics using a hierarchical continuous-time velocity model

Understanding the spatial dynamics of animal movement is an essential component of maintaining ecological connectivity, conserving key habitats, and mitigating the impacts of anthropogenic disturbance. Altered movement and migratory patterns are often an early warning sign of the effects of environmental disturbance, and a precursor to population declines. Here, we present a hierarchical Bayesian framework based on Gaussian processes for analysing the spatial characteristics of animal movement. At the heart of our approach is a novel covariance kernel that links the spatially varying parameters of a continuous-time velocity model with GPS locations from multiple individuals. We demonstrate the effectiveness of our framework by first applying it to a synthetic data set and then by analysing telemetry data from the Serengeti wildebeest migration. Through application of our approach, we are able to identify the key pathways of the wildebeest migration as well as revealing the impacts of environmental features on movement behaviour.