by Schneider, F.D., Kéfi, S.
Published: 19 December 2015
Ecosystems may exhibit catastrophic shifts, i.e. abrupt and irreversible responses of ecosystem functions and services to continuous changes in external conditions. The search for early warning signs of approaching shifts has so far mainly been conducted on theoretical models assuming spatially-homogeneous external pressures (e.g. climatic).
Here, we investigate how a spatially-explicit pressure may affect ecosystems’ risk of catastrophic shifts and the associated spatial early-warning signs. As a case study, we studied a dryland vegetation model assuming ‘associational resistance’, i.e. the mutual reduction of local grazing impact by neighboring plants sharing the investment in defensive traits. Consequently, grazing pressure depends on the local density of plants and is thus spatially-explicit. We focus on the distribution of vegetation patch sizes, which can be assessed using remote sensing and are candidate early warning signs of catastrophic shifts in drylands.
We found that spatially-explicit grazing affected both the resilience and the spatial patterns of the landscape. Grazing impact became self-enhancing in more fragmented landscapes, disrupted patch growth and put apparently ‘healthy’ drylands under high risks of catastrophic shifts. Our study highlights that a spatially explicit pressure may affect the nature of the spatial pattern observed and thereby change the interpretation of the early warning signs. This may generalize to other ecosystems exhibiting self-organized spatial patterns, where a spatially-explicit pressure may interfere with pattern formation.
- Alexandre Génin, Sabiha Majumder, Sumithra Sankaran, Florian D. Schneider, Alain Danet, Miguel Berdugo, Vishwesha Guttal, Sonia Kéfi (2018), Spatially heterogeneous stressors can alter the performance of indicators of regime shifts, Ecological Indicators, 94:520-533 (pdf)
- Alexandre Génin, Sabiha Majumder, Sumithra Sankaran, Alain Danet, Vishwesha Guttal, Florian D. Schneider, Sonia Kéfi (2018), Monitoring ecosystem degradation using spatial data and the R package spatialwarnings, Methods in Ecology and Evolution, 9:2067-2075 (pdf)