Abstract

Patterning of vegetation in drylands is a consequence of localized feedback mechanisms. Such feedbacks also determine ecosystem resilience-i.e. the ability to recover from perturbation. Hence, the patterning of vegetation has been hypothesized to be an indicator of resilience, that is, spots are less resilient than labyrinths. Previous studies have made this qualitative link and used models to quantitatively explore it, but few have quantitatively analysed available data to test the hypothesis. Here we provide methods for quantitatively monitoring the resilience of patterned vegetation, applied to 40sites in the Sahel (a mix of previously identified and new ones). We show that an existing quantification of vegetation patterns in terms of a feature vector metric can effectively distinguish gaps, labyrinths, spots, and a novel category of spot-labyrinths at their maximum extent, whereas NDVI does not. The feature vector pattern metric correlates with mean precipitation. We then explored two approaches to measuring resilience. First we treated the rainy season as a perturbation and examined the subsequent rate of decay of patterns and NDVI as possible measures of resilience. This showed faster decay rates-conventionally interpreted as greater resilience-associated with wetter, more vegetated sites. Second we detrended the seasonal cycle and examined temporal autocorrelation and variance of the residuals as possible measures of resilience. Autocorrelation and variance of our pattern metric increase with declining mean precipitation, consistent with loss of resilience. Thus, drier sites appear less resilient, but we find no significant correlation between the mean or maximum value of the pattern metric (and associated morphological pattern types) and either of our measures of resilience.

Highlights

  • Ecosystems with strong internal feedback mechanisms can exhibit multiple stable states

  • We utilize a remotely sensed resilience monitoring Python toolkit for patterned vegetation, with an initial focus on drylands, called pyveg (Barlow et al, 2020). This draws on a number of existing tools and insights. It requires (1) a source of remotely sensed data of patterned vegetation, derived from the Sentinel-­2 satellite accessed through Google Earth Engine (GEE); (2) a method of turning the qualitative observation of pattern into a quantitative metric called Offset50, based upon feature vector analysis used in Mander et al (2017); and (3) additional data on the potential environmental determinants of resilience, using precipitation data from the ERA5 dataset

  • Assessing and understanding vegetation patterning morphology is an important step towards understanding

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Summary

| INTRODUCTION

Ecosystems with strong internal feedback mechanisms can exhibit multiple stable states. Some of these patterns have previously been studied within the context of wider Sahelian precipitation trends and human influence (Barbier et al, 2006; Leblanc et al, 2008). Model studies have shown that in addition to changes in rainfall, overgrazing can decrease the resilience of patterned vegetation and induce tipping points at rainfall levels that would otherwise be stable (Siero et al, 2019). We investigate the spatial distribution of pattern trends across the Sahel in the context of the North–­South rainfall gradient and changes in the East–­West precipitation regime (Nicholson et al, 2018)

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| Limitations and future work
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