Remotely-sensed slowing down in spatially patterned dryland ecosystems

Abstract

Regular vegetation patterns have been predicted to indicate a system slowing down and possibly desertification of drylands. However, these predictions have not yet been observed in dryland vegetation due to the inherent logistic difficulty to gather longer-term in situ data. Here, we use recently developed methods using remote-sensing EVI time-series in combination with classified regular vegetation patterns along a rainfall gradient in Sudan to test these predictions. Overall, three temporal indicators (responsiveness, temporal autocorrelation, variance) show slowing down as vegetation patterns change from gaps to labyrinths to spots towards more arid conditions, confirming predictions. However, this transition exhibits non-linearities, specifically when patterns change configuration. Model simulations reveal that the transition between patterns temporarily slows down the system affecting the temporal indicators. These transient states when vegetation patterns reorganize thus affect the systems resilience indicators in a non-linear way. Our findings suggest that spatial self-organization of dryland vegetation is associated with critical slowing down, but this transition towards reduced resilience happens in a non-linear way. Future work should aim to better understand transient dynamics in regular vegetation patterns in dryland ecosystems, because long transients make regular vegetation patterns of limited use for management in anticipating critical transitions.

A resilience sensing system for the biosphere

“…Regular patterns of dryland vegetation are a manifestation of competing positive and negative feedbacks on fine spatial scales of order approximately 10–100 m, so require fine spatial resolution satellite remote sensing to be resolved. Spatial skewness may track vegetation patterning along environmental gradients but at questionably coarse spatial resolution (400 m) [30]. Instead, feature vectors applied to 10 m resolution data allow the nature of patterning to be converted to a metric [29,93], which can then be tracked temporally [29].…”