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  Reliability of vegetation resilience estimates depends on biomass density

Smith, T., Boers, N. (2023): Reliability of vegetation resilience estimates depends on biomass density. - Nature Ecology & Evolution, 7, 1799-1808.
https://doi.org/10.1038/s41559-023-02194-7

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 Creators:
Smith, Taylor1, Author
Boers, Niklas2, Author              
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1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Concerns have been raised that the resilience of vegetated ecosystems may be negatively impacted by ongoing anthropogenic climate and land-use change at the global scale. Several recent studies present global vegetation resilience trends based on satellite data using diverse methodological set-ups. Here, upon a systematic comparison of data sets, spatial and temporal pre-processing, and resilience estimation methods, we propose a methodology that avoids different biases present in previous results. Nevertheless, we find that resilience estimation using optical satellite vegetation data is broadly problematic in dense tropical and high-latitude boreal forests, regardless of the vegetation index chosen. However, for wide parts of the mid-latitudes—especially with low biomass density—resilience can be reliably estimated using several optical vegetation indices. We infer a spatially consistent global pattern of resilience gain and loss across vegetation indices, with more regions facing declining resilience, especially in Africa, Australia and central Asia.

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Language(s): eng - English
 Dates: 2023-08-082023-09-142023-11-01
 Publication Status: Finally published
 Pages: 20
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1038/s41559-023-02194-7
MDB-ID: pending
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: FutureLab - Artificial Intelligence in the Anthropocene
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Tipping Elements
OATYPE: Hybrid Open Access
 Degree: -

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Title: Nature Ecology & Evolution
Source Genre: Journal, SCI, Scopus
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Pages: - Volume / Issue: 7 Sequence Number: - Start / End Page: 1799 - 1808 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/202002112
Publisher: Nature