The global biogeography of semi‐arid periodic vegetation patterns

TLDR

Vegetation exhibiting regular spatial patterns has been reported worldwide in arid and semi‑arid regions, a phenomenon linked to low productivity and self‑organization, and the correlations identified here will need to be reproduced in future models. The study aimed to test the relationship between periodic pattern occurrence and environmental factors at a global scale and to parametrize a predictive distribution model. We trained a Maxent predictive model using environmental predictors and Landsat‑verified occurrences to map periodic vegetation across global arid and semi‑arid areas. The model uncovered new pattern sites, including the first spotted patterns, confirming that periodic vegetation is widespread at arid–semi‑arid boundaries and reflects a biome‑scale response to environmental conditions.

Abstract

ABSTRACT Aim Vegetation exhibiting landscape‐scale regular spatial patterns has been reported for arid and semi‐arid areas world‐wide. Recent theories state that such structures are bound to low‐productivity environments and result from a self‐organization process. Our objective was to test this relationship between periodic pattern occurrence and environmental factors at a global scale and to parametrize a predictive distribution model. Location Arid and semi‐arid areas world‐wide. Methods We trained an empirical predictive model (Maxent) for the occurrence of periodic vegetation patterns, based on environmental predictors and known occurrences verified on Landsat satellite images. Results This model allowed us to discover previously unreported pattern locations, and to report the first ever examples of spotted patterns in natural systems. Relationships to the main environmental drivers are discussed. Main conclusions These results confirm that periodic patterned vegetations are ubiquitous at the interface between arid and semi‐arid regions. Self‐organized patterning appears therefore to be a biome‐scale response to environmental conditions, including soil and topography. The set of correlations between vegetation patterns and their environmental conditions presented in this study will need to be reproduced in future modelling attempts.