Final Report Summary - WEBOFLIFE (Robustness of The Web of Life in the Face of Global Change)
Previous work by our lab and others had described the architecture of networks of ecological interactions. This paved to road to incorporate ecological interactions as a main component of what we know as biodiversity. Through the Advanced Grant, we moved beyond this research program by considering the robustness of these networks in the face of global change. The rationale for this was that anthropogenic effects have been identified to affect the number of species of a community, its total biomass, and the length of food chains. However, it was less clear how these human-induced effects could affect the entire network of species interdependencies. We have shown that influences such as overfishing and human population density can decrease the stability of marine coastal food webs describing who-eats-whom in a community. This suggests that human activities may have a previously unnoticed effect on natural communities by eroding their resilience in the face of further perturbations. Because of these pervading effects, the next question was to what degree the architecture of observed ecological networks affects their resistance to anthropogenic influences. For this goal, we developed a quantitative framework based on the notion of structural stability. In short, it allows addressing the question of how broad is the range of perturbations that the system can withstand before one or more species are driven extinct. Our results show that the observed architecture of mutualistic networks between plants and their insect pollinators are structured in a way increasing structural stability. As ongoing global change has most likely already driven many species extinct, it is also interesting to study how the network will collapse as one crosses these tipping points. Our results show that there may be an abrupt collapse where further perturbations lead these networks crossing a tipping point. Interestingly enough, we have developed novel tools to detect how close a network is to these points of no return.
This research has been published in the top interdisciplinary journals such as Nature (3), Science (4), and PNAS (3).
This research has been published in the top interdisciplinary journals such as Nature (3), Science (4), and PNAS (3).