Community structure determines the predictability of population collapse 

Baruah et al., 2022.

1. Early warning signals (EWS) are phenomenological tools that have been proposed as predictors of the collapse of biological systems. Although a growing body of work has shown the utility of EWS based on either statistics derived from abundance data or shifts in phenotypic traits such as body size, so far this work has largely focused on single species populations.
2. However, to predict reliably the future state of ecological systems, which inherently could consist of multiple species, understanding how reliable such signals are in a community context is critical.
3. Here, reconciling quantitative trait evolution and Lotka–Volterra equations, which allow us to track both abundance and mean traits, we simulate the collapse of populations embedded in mutualistic and multi-trophic predator–prey communities. Using these simulations and warning signals derived from both population- and community-level data, we showed the utility of abundance-based EWS, as well as metrics derived from stability-landscape theory (e.g. width and depth of the basin of attraction), were fundamentally linked. Thus, the depth and width of such stability-landscape curves could be used to identify which species should exhibit the strongest EWS of collapse.
4. The probability a species displays both trait and abundance-based EWS was dependent on its position in a community, with some species able to act as indicator species. In addition, our results also demonstrated that in general trait-based EWS were less reliable in comparison with abundance-based EWS in forecasting species collapses in our simulated communities. Furthermore, community-level abundance-based EWS were fairly reliable in comparison with their species-level counterparts in forecasting species-level collapses.
5. Our study suggests a holistic framework that combines abundance-based EWS and metrics derived from stability-landscape theory that may help in forecasting species loss in a community context.

https://bit.ly/3BjTBZM