How to predict community responses to perturbations in the face of imperfect knowledge and network complexity

TL;DR

This paper presents an iterative method for efficiently predicting how complex food webs respond to perturbations by focusing measurements on key species, reducing the effort needed to understand system dynamics.

Contribution

It introduces a novel iterative approach to identify which elements of a complex system should be measured most precisely to improve response predictions.

Findings

Prioritizing long-lived, generalist top predators enhances prediction accuracy.

Focusing on specific key species reduces measurement effort in complex food webs.

Efficient measurement strategies improve understanding of system responses to perturbations.

Abstract

It is a challenge to predict the response of a large, complex system to a perturbation. Recent attempts to predict the behaviour of food webs have revealed that the effort needed to understand a system grows quickly with its complexity, because increasingly precise information on the elements of the system is required. Here, we show that not all elements of the system need to be measured equally well. This suggests that a more efficient allocation of effort to understand a complex systems is possible. We develop an iterative technique for determining an efficient measurement strategy. Finally, in our assessment of model food webs, we find that it is most important to precisely measure the mortality and predation rates of long-lived, generalist, top predators. Prioritizing the study of such species will make it easier to understand the response of complex food webs to perturbations.