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Grazing and aridity reduce perennial grass abundance in semi-arid rangelands – Insights from a trait-based dynamic vegetation model

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Listed:
  • Pfeiffer, Mirjam
  • Langan, Liam
  • Linstädter, Anja
  • Martens, Carola
  • Gaillard, Camille
  • Ruppert, Jan C.
  • Higgins, Steven I.
  • Mudongo, Edwin I.
  • Scheiter, Simon

Abstract

Semi-arid tropical rangelands substantially contribute to livelihoods of subsistence farmers, but are threatened by undesired vegetation shifts due to climate change and overgrazing. Grazing-induced shifts of the grass community composition are often associated with rangeland degradation. To identify sustainable management strategies, a process-based understanding of grass functional diversity and rangeland dynamics is required. We present a new scheme for aDGVM2, a dynamic vegetation model for tropical ecosystems, that distinguishes annual and perennial grasses based on trait trade-offs to improve the representation of rangeland communities. Additionally, the model includes a new scheme that describes selective grazing and grazing effects on grass-layer composition. We tested the new model version for various grazing intensities along a precipitation gradient in South Africa. Mean annual precipitation below 500 mm constrained rangeland productivity and carrying capacity. Increasing grazing intensity reduced rangeland productivity and increased annual grass abundance. Heavy grazing resulted in annual grass dominance. Livestock preferred perennial over annual grasses at low grazing intensities at all except the two driest sites; preference switched to annual grasses at intermediate intensities, and became non-discriminating at high grazing intensities. Rangeland recovery after removal of grazers required 2–15 years. We conclude that management intervention reducing or eliminating grazing pressure during and after stress years is crucial to allow rangeland recovery and avoid permanent degradation.

Suggested Citation

  • Pfeiffer, Mirjam & Langan, Liam & Linstädter, Anja & Martens, Carola & Gaillard, Camille & Ruppert, Jan C. & Higgins, Steven I. & Mudongo, Edwin I. & Scheiter, Simon, 2019. "Grazing and aridity reduce perennial grass abundance in semi-arid rangelands – Insights from a trait-based dynamic vegetation model," Ecological Modelling, Elsevier, vol. 395(C), pages 11-22.
  • Handle: RePEc:eee:ecomod:v:395:y:2019:i:c:p:11-22
    DOI: 10.1016/j.ecolmodel.2018.12.013
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    References listed on IDEAS

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    1. Müller, Birgit & Schulze, Jule & Kreuer, David & Linstädter, Anja & Frank, Karin, 2015. "How to avoid unsustainable side effects of managing climate risk in drylands — The supplementary feeding controversy," Agricultural Systems, Elsevier, vol. 139(C), pages 153-165.
    2. Steven I. Higgins & Simon Scheiter, 2012. "Atmospheric CO2 forces abrupt vegetation shifts locally, but not globally," Nature, Nature, vol. 488(7410), pages 209-212, August.
    3. Muller, Birgit & Frank, Karin & Wissel, Christian, 2007. "Relevance of rest periods in non-equilibrium rangeland systems - A modelling analysis," Agricultural Systems, Elsevier, vol. 92(1-3), pages 295-317, January.
    4. Scheiter, Simon & Savadogo, Patrice, 2016. "Ecosystem management can mitigate vegetation shifts induced by climate change in West Africa," Ecological Modelling, Elsevier, vol. 332(C), pages 19-27.
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    Cited by:

    1. Qiuan Zhu & Huai Chen & Changhui Peng & Jinxun Liu & Shilong Piao & Jin-Sheng He & Shiping Wang & Xinquan Zhao & Jiang Zhang & Xiuqin Fang & Jiaxin Jin & Qi-En Yang & Liliang Ren & Yanfen Wang, 2023. "An early warning signal for grassland degradation on the Qinghai-Tibetan Plateau," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    2. Cisneros-Pineda, Alfredo & Aadland, David & Tschirhart, John, 2020. "Impacts of cattle, hunting, and natural gas development in a rangeland ecosystem," Ecological Modelling, Elsevier, vol. 431(C).
    3. Caixia Hou & Mengmeng Zhang & Mengmeng Wang & Hanliang Fu & Mengjie Zhang, 2021. "Factors Influencing Grazing Behavior by Using the Consciousness-Context-Behavior Theory—A Case Study from Yanchi County, China," Land, MDPI, vol. 10(11), pages 1-16, October.

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