This blog post is provided by Xiaoxiao Li and tells the #StoryBehindThePaper for the paper ‘Energetic constraints imposed on trophic interaction strengths enhance resilience in empirical and model food webs’, which was recently published in the Journal of Animal Ecology. The authors explored the underlying biological mechanisms behind the complex trophic interactions that enhance food web resilience.

Food webs depict the trophic interactions, i.e. who eats whom, in biological communities, which occupy a central position in community ecology. The immense complexity of food webs arises from the multitude of positive and negative trophic interactions within the web, and this has stimulated ecologists to identify the mechanisms of food web resilience.

In an earlier paper in Marine Biology, we used our measurements of species biomasses and stable carbon and nitrogen isotope values, and a mass-balanced model to obtain the trophic structure and energy flows of the food web from a seagrass bed in the Yellow River Delta wetland, China (Figure 1). In our new article available in Journal of Animal Ecology, we aim to investigate the level of resilience of the empirical food web and find out what causes the resilience.

We first quantified the resilience of the food web following the classic approach of theoretical ecology, i.e. using the real part of the maximum eigenvalue (Re(λmax)) of the Jacobian matrix of the food web. We were curious to find out whether the empirical food web has a high level of resilience. Therefore, we did randomizations in the empirical food web, i.e. generating multiple random food webs by randomly swapping trophic interactions between predators and prey. We then found that the empirical food web was much more resilient than the random food webs (Figure 2), implying that the trophic interactions in the empirical food web capture a pattern that is important to food web resilience.

To get more insight into how and why the pattern in interaction strengths was important to food web resilience, we analyzed the structure of the food web in terms of trophic interaction loops. A trophic interaction loop connects the interactions among several trophic groups in a closed circuit, where a group is connected with any other group only once along the same path. The weight of a loop is the geometric mean of the component interaction strengths. The higher the maximum loop weight of the food web the less resilient the food web. We found that the maximum loop weight in the empirical food web was indeed much lower than that in the random food webs (Figure 2), which is consistent with the low value of Re(λmax).

The high level of resilience of the empirical food web compared to that of the random food webs encouraged us to investigate the underlying mechanisms of food web resilience. We found that high resilience was due to a negative correlation between the negative and positive interaction strengths (per capita top-down and bottom-up effects, respectively) within positive feedback loops with three species. We further found that the energetic properties of the trophic groups in the loop (e.g. biomasses, production to biomass ratios) and mass-balance constraints, e.g. the food uptake has to balance all losses, created the negative correlation between the interaction strengths. This result was generalized using a dynamic intraguild predation model, which delivered the same pattern for a wide range of model parameters.

Knowledge of patterns and processes underlying the resilience of food webs is important to understand how species-rich communities can withstand environmental disturbance. Our study contributes to this understanding by revealing how energetic constraints at the trophic group and food web level enhance food web resilience by dampening the strength of destabilizing positive feedback loops.

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