Monitoring ecosystem degradation using spatial data and the R package spatialwarnings. Issue 10 (31st July 2018)

• Record Type: Journal Article
• Authors: Génin, Alexandre; Majumder, Sabiha; Sankaran, Sumithra; Danet, Alain; Guttal, Vishwesha; Schneider, Florian D.; Kéfi, Sonia
• Editors: Goslee, Sarah
• Is Part Of: Methods in ecology and evolution. Volume 9:Issue 10(2018)
• Page Start: 2067
• Page End: 2075
• Publication Date: 2018-07-31
• Subjects: alternative stable states -- early warning signals -- ecological indicator -- ecosystem shifts -- R package -- remote sensing -- spatial data -- spatial patterns; Ecology -- Periodicals; Evolution -- Periodicals; 577
• DOI: 10.1111/2041-210X.13058
• Languages: English
• ISSNs: 2041-210X

Abstract

Abstract : Abstract: Some ecosystems show nonlinear responses to gradual changes in environmental conditions, once a threshold in conditions—or critical point—is passed. This can lead to wide shifts in ecosystem states, possibly with dramatic ecological and economic consequences. Such behaviours have been reported in drylands, savannas, coral reefs or shallow lakes for example. Important research effort of the last decade has been devoted to identifying indicators that would help anticipate such ecosystem shifts and avoid their negative consequences. Theoretical and empirical research has shown that, as an ecosystem approaches a critical point, specific signatures arise in its temporal and spatial dynamics; these changes can be quantified using relatively simple statistical metrics that have been referred to as "early warning signals" (EWS) in the literature. Although tests of those EWS on experiments are promising, empirical evidence from out‐of‐laboratory datasets is still scarce, in particular for spatial EWS. The recent proliferation of remote‐sensing data provides an opportunity to improve this situation and evaluate the reliability of spatial EWS in many ecological systems. Here, we present a step‐by‐step workflow along with code to compute spatial EWS from raster data such as aerial images, test their significance compared to permutation‐based null models, and display their trends, either at different time steps or along environmental gradients. We created the R ‐package spatialwarnings (MIT license) to help achieve all these steps in a reliable and reproducible way, and thereby promote the application of spatial EWS to empirical data. This software package and associated documentation provides an easy entry point for researchers and managers into spatial EWS‐based analyses. By facilitating a broader application, it will leverage the evaluation of spatial EWS on real data, and eventually contribute to providing tools to map ecosystems' fragility to perturbations and inform management decisions.