Current trends indicate a five-fold increase in extinction rates in recent years, and these extinctions have an impact on ecosystem functioning via ecological interactions (Johnson et al. 2017). Recent research have compared alternative measures in the analysis of networks with ecological relevance using graph theory (Delmas et al. 2019; Strydom et al. 2021), but many problems remain. The inclusion of species-specific features, on the other hand, can improve the prediction of an ecological interaction network. This study looks at ecological interaction networks across multiple temporal and geographic gradients, with the goal of quantifying and qualitatively assessing these interactions to infer the loss of ecological connections throughout the Anthropocene. I hypothesize that the amount and speed with which interactions are lost depend on species features, and that there is an interaction loss tipping point that can be predicted. This project has three particular goals: 1) to quantify the relationship between species extinction and interaction loss in different communities; 2) to identify patterns of change in the structure of the ecological network that lead to a tipping point; and 3) to evaluate different conservation strategies in the human-altered community.
The intellectual merit of this research lies in the understanding of the behaviors of ecological interactions in geographic space, achieved through a virtual biodiversity modeling approach, addressing the issue that networks captured and modeled from real life may misinterpret the results due to the difficulty of sampling these links. On the other hand, modeling a meta-web of ecological interactions along a geographic gradient will enable for subsampling and testing of sections of this network in real situations. The ramifications of this research include the prediction of extinctions as well as the construction of natural protected zones that include ecological interactions as another measure of biodiversity. Early detection of tipping points will improve decision making on critical conservation concerns and foresee in a priori well-conserved areas.