Estimating distance to tipping point from dryland ecosystem images
Resource-limited ecosystems, such as drylands, can exhibit self-organized spatial patterns. Theory suggests that these patterns can reflect increasing degradation levels as ecosystems approach possible tipping points to degradation. However, we still lack ways of estimating a distance to degradation points that is comparable across sites. Here, we present an approach to do just that from images of ecosystem landscapes. After validating the approach on simulated landscapes, we applied it to a global dryland dataset, estimated the distance of each of the sites to their degradation point and investigated the drivers of that distance. Crossing this distance with aridity projections makes it possible to pinpoint the most fragile sites among those studied. Our approach paves the way for a risk assessment method for spatially-organized ecosystems.