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Slow Recovery from Local Disturbances as an Indicator for Loss of Ecosystem Resilience
A range of indicators have been proposed for identifying the elevated risk of critical transitions in ecosystems. Most indicators are based on the idea that critical slowing down can be inferred from changes in statistical properties of natural fluctuations and spatial patterns. However, identifying...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6954009/ https://www.ncbi.nlm.nih.gov/pubmed/31983890 http://dx.doi.org/10.1007/s10021-017-0154-8 |
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author | van de Leemput, Ingrid A. Dakos, Vasilis Scheffer, Marten van Nes, Egbert H. |
author_facet | van de Leemput, Ingrid A. Dakos, Vasilis Scheffer, Marten van Nes, Egbert H. |
author_sort | van de Leemput, Ingrid A. |
collection | PubMed |
description | A range of indicators have been proposed for identifying the elevated risk of critical transitions in ecosystems. Most indicators are based on the idea that critical slowing down can be inferred from changes in statistical properties of natural fluctuations and spatial patterns. However, identifying these signals in nature has remained challenging. An alternative approach is to infer changes in resilience from differences in standardized experimental perturbations. However, system-wide experimental perturbations are rarely feasible. Here we evaluate the potential to infer the risk of large-scale systemic transitions from local experimental or natural perturbations. We use models of spatially explicit landscapes to illustrate how recovery rates upon small-scale perturbations decrease as an ecosystem approaches a tipping point for a large-scale collapse. We show that the recovery trajectory depends on: (1) the resilience of the ecosystem at large scale, (2) the dispersal rate of organisms, and (3) the scale of the perturbation. In addition, we show that recovery of natural disturbances in a heterogeneous environment can potentially function as an indicator of resilience of a large-scale ecosystem. Our analyses reveal fundamental differences between large-scale weak and local-scale strong perturbations, leading to an overview of opportunities and limitations of the use of local disturbance-recovery experiments. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10021-017-0154-8) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6954009 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-69540092020-01-23 Slow Recovery from Local Disturbances as an Indicator for Loss of Ecosystem Resilience van de Leemput, Ingrid A. Dakos, Vasilis Scheffer, Marten van Nes, Egbert H. Ecosystems Article A range of indicators have been proposed for identifying the elevated risk of critical transitions in ecosystems. Most indicators are based on the idea that critical slowing down can be inferred from changes in statistical properties of natural fluctuations and spatial patterns. However, identifying these signals in nature has remained challenging. An alternative approach is to infer changes in resilience from differences in standardized experimental perturbations. However, system-wide experimental perturbations are rarely feasible. Here we evaluate the potential to infer the risk of large-scale systemic transitions from local experimental or natural perturbations. We use models of spatially explicit landscapes to illustrate how recovery rates upon small-scale perturbations decrease as an ecosystem approaches a tipping point for a large-scale collapse. We show that the recovery trajectory depends on: (1) the resilience of the ecosystem at large scale, (2) the dispersal rate of organisms, and (3) the scale of the perturbation. In addition, we show that recovery of natural disturbances in a heterogeneous environment can potentially function as an indicator of resilience of a large-scale ecosystem. Our analyses reveal fundamental differences between large-scale weak and local-scale strong perturbations, leading to an overview of opportunities and limitations of the use of local disturbance-recovery experiments. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10021-017-0154-8) contains supplementary material, which is available to authorized users. Springer US 2017-06-02 2018 /pmc/articles/PMC6954009/ /pubmed/31983890 http://dx.doi.org/10.1007/s10021-017-0154-8 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Article van de Leemput, Ingrid A. Dakos, Vasilis Scheffer, Marten van Nes, Egbert H. Slow Recovery from Local Disturbances as an Indicator for Loss of Ecosystem Resilience |
title | Slow Recovery from Local Disturbances as an Indicator for Loss of Ecosystem Resilience |
title_full | Slow Recovery from Local Disturbances as an Indicator for Loss of Ecosystem Resilience |
title_fullStr | Slow Recovery from Local Disturbances as an Indicator for Loss of Ecosystem Resilience |
title_full_unstemmed | Slow Recovery from Local Disturbances as an Indicator for Loss of Ecosystem Resilience |
title_short | Slow Recovery from Local Disturbances as an Indicator for Loss of Ecosystem Resilience |
title_sort | slow recovery from local disturbances as an indicator for loss of ecosystem resilience |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6954009/ https://www.ncbi.nlm.nih.gov/pubmed/31983890 http://dx.doi.org/10.1007/s10021-017-0154-8 |
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