All ecosystems are subjected to chronic disturbances, such as harvest, pollution, and climate change. The capacity to forecast how species respond to such press perturbations is limited by our imprecise knowledge of pairwise species interaction strengths and the many direct and indirect pathways along which perturbations can propagate between species. Network complexity (size and connectance) has thereby been seen to limit the predictability of ecological systems. We define a network's predictability as the directional consistency by which species respond to press perturbations of their community despite intrinsic variation or estimation uncertainty in species' pairwise direct interaction strengths. Community matrices reflecting networks of varying complexity were used to infer species' net effects on each other and were parameterized using simulations of allometrically-scaled bioenergetic network models.
Results/Conclusions
Although the number of indirect effects increases exponentially with network complexity to reduce network predictability, our results demonstrate a counteracting mechanism in which the influence of indirect effects declines with increasing network complexity when species interactions are governed by universal allometric constraints. With these constraints, network size and connectance interact to produce a skewed distribution of interaction strengths whose skew becomes more pronounced with increasing complexity. Randomization tests show that network predictability increases when a target network’s interaction strength values are redrawn from more complex source networks with more skewed distributions of interaction strengths. Together, the increased prevalence of weak interactions and the increased relative strength and rarity of strong interactions in complex networks limit disturbance propagation and preserve the qualitative predictability of net effects even when pairwise interaction strengths exhibit substantial variation or uncertainty.