Landscape quantifies the intermediate state and transition dynamics in ecological networks

Jinchao Lv, Jin Wang and Chunhe Li

PLOS Computational Biology, 2024, vol. 20, issue 1, 1-23

Abstract: Understanding the ecological mechanisms associated with the collapse and restoration is especially critical in promoting harmonious coexistence between humans and nature. So far, it remains challenging to elucidate the mechanisms of stochastic dynamical transitions for ecological systems. Using an example of plant-pollinator network, we quantified the energy landscape of ecological system. The landscape displays multiple attractors characterizing the high, low and intermediate abundance stable states. Interestingly, we detected the intermediate states under pollinator decline, and demonstrated the indispensable role of the intermediate state in state transitions. From the landscape, we define the barrier height (BH) as a global quantity to evaluate the transition feasibility. We propose that the BH can serve as a new early-warning signal (EWS) for upcoming catastrophic breakdown, which provides an earlier and more accurate warning signal than traditional metrics based on time series. Our results promote developing better management strategies to achieve environmental sustainability.Author summary: Exploring the evolutionary characteristics of complex ecological networks is an important issue. Here, using a plant-pollinator network as an example, we investigate the stochastic dynamical mechanism of the ecological system. The multistable energy landscape characterizes high, low, and intermediate abundance stable states. Interestingly, we observe the intermediate states during pollinator decline and reveal their vital role in state transitions. The barrier height calculated from landscape provides a global measure to quantify the feasibility of transitions. We propose that the barrier height can serve as a new early warning signal (EWS) for anticipating catastrophic breakdown, which is more accurate and prompt than traditional metrics based on time-series. Our findings highlight the need for improved management strategies to achieve environmental sustainability.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1011766

DOI: 10.1371/journal.pcbi.1011766

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