Monitoring ecosystem degradation using spatial data and the R package spatialwarnings

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.

Inferring critical thresholds of ecosystem transitions from spatial data

“…We compute spatial variance and spatial ACF‐1 using methods from Génin et al. () for each N × N nonoverlapping window of the rectangular matrix. While our method is conceptually derived based on analyses of partial differential equation (PDE) models that describe ecosystem states at a coarse scale ( Maxima of Spatial Variance and Autocorrelation Occur at the Bifurcation Point ), for numerical simulations we have employed a CA model of ecosystem transitions.…”

Exogenous disturbances and endogenous self-organized processes are not mutually exclusive drivers of spatial patterns in macroalgal assemblages

“…A gap in C. compressa stands (or a patch of algal turfs) was defined as a set of empty (or occupied) cells sharing at least one side with their neighbours. We described, for each experimental transect, the frequency of gap and patch sizes using an inverse-cumulative distribution, which is the probability that the size of gaps (S) was larger than or equal to a given value s, P(S ≥ s), as a function of size (Kéfi et al 2014, Génin et al 2018. Note that in contrast to previous studies, this procedure is not based on binning, which ignores the information of gap-(or patch-) size distribution within each bin.…”

“…Transects with less than 4 unique gap (patch) sizes were excluded from the fitting procedure (Génin et al 2018). Estimates of models parameters were obtained using the Maximum Likelihood estimation method following the Clauset procedure (Clauset et al, 2009).…”

“…The best-fitting model, among those tested, was selected on the basis of the lowest Akaike Information Criterion (AIC) (Clauset et al, 2009). The core steps of the analysis were performed using R package 'spatialwarnings' (Génin et al 2018). Coefficients and confidence intervals are reported in Supplementary material Appendix 1 Tables A1 and A2.…”

Spatial early warning signals for impending regime shifts: A practical framework for application in real‐world landscapes

“…Such Spatial Early Warning Signals (SEWS) including patch size distributions (Kéfi et al, ), spatial variance and skewness (Guttal & Jayaprakash, ), spatial autocorrelation (Dakos, van Nes, Donangelo, Fort, & Scheffer, ), wavelength analyses (Carpenter & Brock, ), recovery length (Dai, Korolev, & Gore, ), cross‐scale connectivity (Zurlini, Jones, Riitters, Li, & Petrosillo, ), spatial heteroscedasticity (Seekell & Dakos, ) and Fisher information (Sundstrom et al, ). Compared to temporal indicators, SEWS have the advantage that they can be applied on spatial data with irregular and infrequent temporal resolution (Génin, Majumder, Sankaran, Danet et al, ). The increasing availability and resolution of remotely sensed gridded data (Gómez, White, & Wulder, ) therefore provides a unique opportunity to monitor ecosystem resilience and detect impending regime shifts induced by global change all around the globe.…”