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Pathways of degradation in rangelands in Northern Tanzania show their loss of resistance, but potential for recovery.

Joris H WiethaseRob CritchlowCharles FoleyLara FoleyElliot J KinseyBrenda G BergmanBoniface OsujakiZawadi MbwamboPaul Baran KirwayKelly R RedekerSusan E HartleyColin M Beale
Published in: Scientific reports (2023)
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.
Keyphrases
  • climate change
  • machine learning
  • human health
  • deep learning
  • endothelial cells
  • artificial intelligence
  • cross sectional
  • social support
  • data analysis