Network-based metrics of resilience and ecological memory in lake ecosystems

Abstract

17Some ecosystems can undergo abrupt critical transitions to a new regime after passing 18 a tipping point, such as a lake shifting from a clear to turbid state as a result of eutrophication. 19Metrics-based resilience indicators acting as early warning signals of these shifts have been 20 developed but have not always been reliable in all systems. An alternative approach is to 21 focus on changes in the structure and composition of an ecosystem, but this can require long-22 term food-web observations that are typically unavailable. Here we present a network-based 23 algorithm for estimating community resilience, which reconstructs past ecological networks 24 from palaeoecological data using metagenomic network inference. Resilience is estimated 25 using local stability analysis and eco-net energy (a neural network-based proxy for 26 "ecological memory"). The algorithm is tested on model (PCLake+) and empirical (lake 27 Erhai) data. We find evidence of increasing diatom community instability during 28 eutrophication in both cases, with changes in eco-net energy revealing complex eco-memory 29 dynamics. The concept of ecological memory opens a new dimension for understanding 30 ecosystem resilience and regime shifts, and further work is required to fully explore its drivers 31 and implications. 32 33 Networks 35 36

Quantifying the impact of ecological memory on the dynamics of interacting communities

“…The modeling of real systems using models that incorporate memory would benefit from the ability to gather empirical evidence for the presence, strength, and type of memory in the system. Recent literature suggests that it might be possible to empirically detect the presence of memory based on the broad properties of a time series: it has been shown that longitudinal time series of microbial communities may carry detectable signatures of underlying ecological processes [ 7 , 70 ], and recently Bayesian hierarchical models [ 11 , 19 ], random forests [ 12 ], neural networks [ 71 ], and unsupervised Hebbian learning [ 24 ] have been proposed to detect signatures of memory in other contexts. Furthermore, specifically designed longitudinal experiments could be used to characterize memory in real communities.…”

Quantifying the impact of ecological memory on the dynamics of interacting communities

“…Recent literature suggests that it might indeed be possible to empirically detect the presence of memory based on the broad properties of a time series. It has been shown that longitudinal time series of microbial communities may carry detectable signatures of underlying ecological processes [4,58]; and recently, Bayesian hierarchical models [10,14], Random Forests [11], neural networks [59], and unsupervised Hebbian learning [60] have been proposed to detect signatures of memory in other contexts.…”

“…It has been shown that longitudinal time series of microbial communities may carry detectable signatures of underlying ecological processes [7, 70]. Recently, Bayesian hierarchical models [11, 19], random forests [12], neural networks [71], and unsupervised Hebbian learning [24] have been proposed to detect signatures of memory in other contexts. Furthermore, specifically designed longitudinal experiments could be used to characterize memory in real communities.…”