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Energetic constraints imposed on trophic interaction strengths enhance resilience in empirical and model food webs

Citation

Li, Xiaoxiao; Yang, Wei; Gaedke, Ursula; de Ruiter, Peter (2021), Energetic constraints imposed on trophic interaction strengths enhance resilience in empirical and model food webs, Dryad, Dataset, https://doi.org/10.5061/dryad.brv15dv92

Abstract

1. Food web stability and resilience are at the heart of understanding the structure and functioning of ecosystems. Previous studies show that models of empirical food webs are substantially more stable than random ones, due to a few strong interactions embedded in a majority of weak interactions. Analyses of trophic interaction loops show that in empirical food webs the patterns in the interaction strengths prevent the occurrence of destabilizing heavy loops and thereby enhances resilience. Yet, it is still unexplored which biological mechanisms cause these patterns that enhance food web resilience.

2. We quantified food web resilience using the real part of the maximum eigenvalue of the Jacobian matrix of the food web from a seagrass bed in the Yellow River Delta (YRD) wetland, that could be parameterized by the empirical data of the food web.

3. We found that the empirically based Jacobian matrix of the YRD food web indicated a much higher resilience than random matrices with the same element values but arranged in random ways. Investigating the trophic interaction loops revealed that the 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. The negative correlation showed that when the negative interaction strengths were strong the positive was weak, and vice versa.

4. Our invented reformulation of loop weight in terms of biomasses and specific production rates showed that energetic properties of the trophic groups in the loop and mass-balance constraints, e.g. the food uptake has to balance all losses, created the negative correlation between the interaction strengths. This result could be generalized using a dynamic intraguild predation model, which delivered the same pattern for a wide range of model parameters.

5. Our results shed light on how energetic constraints at the trophic group and food web level create a pattern in interaction strengths within trophic interaction loops that enhances food web resilience.

Methods

This dataset represents the fluxes, the negative and positive interaction strengths, resilience, and trophic interaction loops of the food web in the Yellow River Delta wetland, China. Data were processed as described in the associated manuscript.

Usage Notes

Files:

1_Interaction strengths, biomass ratios, and fluxes in the food web of Yellow River Delta wetland: Representing the biomass ratio between the preator and the prey, the negative interaction strength of the predator on the prey, the positive interaction strength of the prey on the predator, the flux from the prey to the predator for each trophic link in the food web.

2_Resilience of all random food webs based on the four randomizations: Representing the food web resilience quantified by the real part of the maximum eigenvalue of all random food webs based on the four randomizations described in the associated manuscript. 

3_Maximum loop weights of all random food webs based on the four randomizations: Representing the maximum loop weight of all random food webs based on the four randomizations described in the associated manuscript. 

4_Trophic interaction loops of the food web in Yellow River Delta wetland: Representing the results of trophic interaction analysis of the food web in the Yellow River Delta wetland. Please note that the loop length means the number of species in the loop. Here we analysed all loops with length from 3 to 8.

5_Output of the intraguild predation modelling: Representing the results of trophic interaction analysis based on the intraguild predation model.

Funding

National Key Research and Development Program of China, Award: 2018YFC1406404

National Key Research and Development Program of China, Award: 2017YFC0404505

National Natural Science Foundation of China-Shandong Joint Fund, Award: U1806217