Wiethase, J.H. orcid.org/0000-0003-2008-1613, Critchlow, R., Foley, C. et al. (9 more authors) (2023) Pathways of degradation in rangelands in Northern Tanzania show their loss of resistance, but potential for recovery. Scientific Reports, 13 (1). 2417.
Abstract
Semiarid rangelands are identified as at high risk of degradation due to anthropogenic pressure and climate change. Through tracking timelines of degradation we aimed to identify whether degradation results from a loss of resistance to environmental shocks, or loss of recovery, both of which are important prerequisites for restoration. Here we combined extensive field surveys with remote sensing data to explore whether long-term changes in grazing potential demonstrate loss of resistance (ability to maintain function despite pressure) or loss of recovery (ability to recover following shocks). To monitor degradation, we created a bare ground index: a measure of grazeable vegetation cover visible in satellite imagery, allowing for machine learning based image classification. We found that locations that ended up the most degraded tended to decline in condition more during years of widespread degradation but maintained their recovery potential. These results suggest that resilience in rangelands is lost through declines in resistance, rather than loss of recovery potential. We show that the long-term rate of degradation correlates negatively with rainfall and positively with human population and livestock density, and conclude that sensitive land and grazing management could enable restoration of degraded landscapes, given their retained ability to recover.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Computational models; Environmental impact; Grassland ecology; Machine learning; Restoration ecology |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Biosciences (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 28 Feb 2023 12:04 |
Last Modified: | 28 Feb 2023 12:04 |
Status: | Published |
Publisher: | Springer Science and Business Media LLC |
Refereed: | Yes |
Identification Number: | 10.1038/s41598-023-29358-6 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:196835 |