Rapid monitoring of ecological persistence

Author:

Song Chuliang123ORCID,Simmons Benno I.4,Fortin Marie-Josée2ORCID,Gonzalez Andrew1ORCID,Kaiser-Bunbury Christopher N.4ORCID,Saavedra Serguei5ORCID

Affiliation:

1. Department of Biology, Quebec Centre for Biodiversity Science, McGill University, Montreal, QC H3A 0G4, Canada

2. Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON M5S 3B2, Canada

3. Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544

4. Centre for Ecology and Conservation, University of Exeter, Cornwall Campus, Penryn TR10 9FE, United Kingdom

5. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02138

Abstract

Effective conservation of ecological communities requires accurate and up-to-date information about whether species are persisting or declining to extinction. The persistence of an ecological community is supported by its underlying network of species interactions. While the persistence of the network supporting the whole community is the most relevant scale for conservation, in practice, only small subsets of these networks can be monitored. There is therefore an urgent need to establish links between the small snapshots of data conservationists can collect, and the “big picture” conclusions about ecosystem health demanded by policymakers, scientists, and societies. Here, we show that the persistence of small subnetworks (motifs) in isolation—that is, their persistence when considered separately from the larger network of which they are a part—is a reliable probabilistic indicator of the persistence of the network as a whole. Our methods show that it is easier to detect if an ecological community is not persistent than if it is persistent, allowing for rapid detection of extinction risk in endangered systems. Our results also justify the common practice of predicting ecological persistence from incomplete surveys by simulating the population dynamics of sampled subnetworks. Empirically, we show that our theoretical predictions are supported by data on invaded networks in restored and unrestored areas, even in the presence of environmental variability. Our work suggests that coordinated action to aggregate information from incomplete sampling can provide a means to rapidly assess the persistence of entire ecological networks and the expected success of restoration strategies.

Publisher

Proceedings of the National Academy of Sciences

Subject

Multidisciplinary

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