Network-based metrics of community resilience and dynamics in lake ecosystems

Some ecosystems can undergo abrupt critical transitions to a new regime after passing a tipping point, such as a lake shifting from a clear to turbid state as a result of eutrophication. Metrics-based resilience indicators acting as early warning signals of these shifts have been developed but have not always been reliable in all systems. An alternative approach is to focus on changes in the structure and composition of an ecosystem, but this can require long-term food-web observations that are typically unavailable. Here we present a network-based algorithm for estimating community resilience, which reconstructs past ecological networks from palaeoecological data using metagenomic network inference. Resilience is estimated using local stability analysis and eco-net energy (a neural network-based proxy for “ecological memory”). The algorithm is tested on model (PCLake+) and empirical (lake Erhai) data. We find evidence of increasing diatom community instability during eutrophication in both cases, with changes in eco-net energy revealing complex eco-memory dynamics. The concept of ecological memory opens a new dimension for understanding ecosystem resilience and regime shifts, and further work is required to fully explore its drivers and implications.