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Rapid monitoring of ecological persistence

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 netwo...

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Autores principales: Song, Chuliang, Simmons, Benno I., Fortin, Marie-Josée, Gonzalez, Andrew, Kaiser-Bunbury, Christopher N., Saavedra, Serguei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10194002/
https://www.ncbi.nlm.nih.gov/pubmed/37155860
http://dx.doi.org/10.1073/pnas.2211288120
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author Song, Chuliang
Simmons, Benno I.
Fortin, Marie-Josée
Gonzalez, Andrew
Kaiser-Bunbury, Christopher N.
Saavedra, Serguei
author_facet Song, Chuliang
Simmons, Benno I.
Fortin, Marie-Josée
Gonzalez, Andrew
Kaiser-Bunbury, Christopher N.
Saavedra, Serguei
author_sort Song, Chuliang
collection PubMed
description 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.
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spelling pubmed-101940022023-11-08 Rapid monitoring of ecological persistence Song, Chuliang Simmons, Benno I. Fortin, Marie-Josée Gonzalez, Andrew Kaiser-Bunbury, Christopher N. Saavedra, Serguei Proc Natl Acad Sci U S A Biological Sciences 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. National Academy of Sciences 2023-05-08 2023-05-16 /pmc/articles/PMC10194002/ /pubmed/37155860 http://dx.doi.org/10.1073/pnas.2211288120 Text en Copyright © 2023 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Biological Sciences
Song, Chuliang
Simmons, Benno I.
Fortin, Marie-Josée
Gonzalez, Andrew
Kaiser-Bunbury, Christopher N.
Saavedra, Serguei
Rapid monitoring of ecological persistence
title Rapid monitoring of ecological persistence
title_full Rapid monitoring of ecological persistence
title_fullStr Rapid monitoring of ecological persistence
title_full_unstemmed Rapid monitoring of ecological persistence
title_short Rapid monitoring of ecological persistence
title_sort rapid monitoring of ecological persistence
topic Biological Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10194002/
https://www.ncbi.nlm.nih.gov/pubmed/37155860
http://dx.doi.org/10.1073/pnas.2211288120
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