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

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.

1. 食物网（food web）稳定性与韧性是解析生态系统结构与功能的核心议题。过往研究表明，相较于随机食物网模型，实证食物网模型的稳定性显著更高，这源于多数弱相互作用中嵌入了少量强相互作用。针对营养相互作用环（trophic interaction loops）的分析显示，实证食物网中相互作用强度的分布模式可避免失稳重环的形成，从而提升系统韧性。然而，目前仍未明确究竟是何种生物机制催生了这些能够提升食物网韧性的相互作用模式。
2. 本研究以黄河三角洲（Yellow River Delta, YRD）湿地海草床的食物网为研究对象，利用该食物网雅可比矩阵（Jacobian matrix）的最大特征值（eigenvalue）实部量化食物网韧性，该矩阵可依托该食物网的实证数据完成参数化。
3. 研究发现，基于黄河三角洲海草床食物网构建的雅可比矩阵，其展现出的韧性远高于元素取值相同但排布随机的对照矩阵。对营养相互作用环的分析显示，该食物网的高韧性源于三物种种内正反馈环（positive feedback loops）中，负相互作用强度与正相互作用强度（分别对应个体水平下行调控与上行调控效应）之间存在负相关关系。该负相关关系表明，当负相互作用强度较高时，正相互作用强度则较弱，反之亦然。
4. 本研究以生物量与比生长速率为基础对环权重进行创新性重构，结果表明，营养类群的能量特性以及质量平衡约束（mass-balance constraints，例如食物摄取量需抵消所有代谢损耗），是导致相互作用强度间产生负相关关系的核心机制。本研究通过构建动态集团内捕食（intraguild predation）模型，可将该结果推广至更广泛的场景，该模型在广泛的参数范围内均能得到一致的相互作用模式。
5. 本研究结果揭示了营养类群与食物网层面的能量约束，如何通过塑造营养相互作用环内的相互作用强度模式，进而提升食物网的整体韧性。