Search

Aim Theory suggests that as ecological systems approach regime shifts, they become increasingly slow in recovering from perturbations. This phenomenon, known as critical slowing down [CSD], leads to spatial and temporal signatures in ecological state variables, thus potentially offering early indicators of regime shifts. Indicators using temporal dynamics have been empirically validated in laboratory microcosms and other well-mixed systems, but tests of spatial indicators of regime shifts at large spatial scales in the field are rare due to the relative absence of high-resolution data and difficulties in experimental manipulations. Here, we test theoretical predictions of CSD-based spatial indicators using large-scale field data from the Serengeti-Mara grassland-woodland system. Location Serengeti-Mara ecosystem, Tanzania and Kenya. Time period Year 2000 Major taxa studied Vegetation Method We used a space-for-time substitution method to empirically test the validity of CSD-based spatial indicators, i.e., we computed indicators along a spatial [in lieu of temporal] gradient of ecological states. First we used a model of vegetation dynamics to determine if our space-for-time substitution method was appropriate. Then we tested for CSD-based spatial indicators using high-resolution spatial vegetation [30 m] and rainfall [2.5 km] data from the Serengeti-Mara ecosystem. Results Our model predicts that CSD-based indicators increase along a spatial gradient of alternative vegetation states. Empirical analyses suggest that grasslands and woodlands occur as alternative stable states in the Serengeti-Mara ecosystem with rainfall as one of the potential drivers of transitions between these states. We found that four indices of CSD showed the theoretically expected increasing trends along spatial gradients of grasslands to woodlands: spatial variance, spatial skewness, spatial correlation at lag-1 and spatial spectra at low frequencies. Main conclusions Our results suggest that CSD-based spatial indicators can offer early warning signals of critical transitions in large-scale ecosystems. [ABSTRACT FROM AUTHOR]