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No complexity-stability relationship in empirical ecosystems.

ABSTRACT: Understanding the mechanisms responsible for stability and persistence of ecosystems is one of the greatest challenges in ecology. Robert May showed that, contrary to intuition, complex randomly built ecosystems are less likely to be stable than simpler ones. Few attempts have been tried to test May's prediction empirically, and we still ignore what is the actual complexity-stability relationship in natural ecosystems. Here we perform a stability analysis of 116 quantitative food webs sampled worldwide. We find that classic descriptors of complexity (species richness, connectance and interaction strength) are not associated with stability in empirical food webs. Further analysis reveals that a correlation between the effects of predators on prey and those of prey on predators, combined with a high frequency of weak interactions, stabilize food web dynamics relative to the random expectation. We conclude that empirical food webs have several non-random properties contributing to the absence of a complexity-stability relationship.

SUBMITTER: Jacquet C

PROVIDER: S-EPMC4999500 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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No complexity-stability relationship in empirical ecosystems.

Jacquet Claire C Moritz Charlotte C Morissette Lyne L Legagneux Pierre P Massol François F Archambault Philippe P Gravel Dominique D

Nature communications 20160824

Understanding the mechanisms responsible for stability and persistence of ecosystems is one of the greatest challenges in ecology. Robert May showed that, contrary to intuition, complex randomly built ecosystems are less likely to be stable than simpler ones. Few attempts have been tried to test May's prediction empirically, and we still ignore what is the actual complexity-stability relationship in natural ecosystems. Here we perform a stability analysis of 116 quantitative food webs sampled wo ...[more]

PMID: 27553393

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Project description:Ecosystems degradation represents one of the major global challenges at the present time, threating people's livelihoods and well-being worldwide. Ecosystem restoration therefore seems no longer an option, but an imperative. Restoration challenges are such that a dialogue has begun on the need to re-shape restoration as a science. A critical aspect of that reshaping process is the acceptance that restoration science and practice needs to be coupled with socio-economic research and public engagement. This inescapably means conveying complex ecosystem's information in a way that is accessible to the wider public. In this paper we take up this challenge with the ultimate aim of contributing to making a step change in science's contribution to ecosystems restoration practice. Using peatlands as a paradigmatically complex ecosystem, we put in place a transdisciplinary process to articulate a description of the processes and outcomes of restoration that can be understood widely by the public. We provide evidence of the usefulness of the process and tools in addressing four key challenges relevant to restoration of any complex ecosystem: (1) how to represent restoration outcomes; (2) how to establish a restoration reference; (3) how to cope with varying restoration time-lags and (4) how to define spatial units for restoration. This evidence includes the way the process resulted in the creation of materials that are now being used by restoration practitioners for communication with the public and in other research contexts. Our main contribution is of an epistemological nature: while ecosystem services-based approaches have enhanced the integration of academic disciplines and non-specialist knowledge, this has so far only followed one direction (from the biophysical underpinning to the description of ecosystem services and their appreciation by the public). We propose that it is the mix of approaches and epistemological directions (including from the public to the biophysical parameters) what will make a definitive contribution to restoration practice.

Dataset Information

No complexity-stability relationship in empirical ecosystems.

Publications

No complexity-stability relationship in empirical ecosystems.

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