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Simulating the potential for ecological restoration of dryland forests in Mexico under different disturbance regimes

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  • Cantarello, Elena
  • Newton, Adrian C.
  • Hill, Ross A.
  • Tejedor-Garavito, Natalia
  • Williams-Linera, Guadalupe
  • López-Barrera, Fabiola
  • Manson, Robert H.
  • Golicher, Duncan J.

Abstract

Examining the potential for ecological restoration is important in areas where anthropogenic disturbance has degraded forest landscapes. However, the conditions under which restoration of degraded tropical dry forests (TDF) might be achieved in practice have not been determined in detail. In this study, we used LANDIS-II, a spatially explicit model of forest dynamics, to assess the potential for passive restoration of TDF through natural regeneration. The model was applied to two Mexican landscapes under six different disturbance regimes, focusing on the impact of fire and cattle grazing on forest cover, structure and composition. Model results identified two main findings. First, tropical dry forests are more resilient to anthropogenic disturbance than expected. Results suggested that under both a scenario of small, infrequent fires and a scenario of large, frequent fires, forest area can increase relatively rapidly. However, forest structure and composition differed markedly between these scenarios. After 400 years, the landscape becomes increasingly occupied by relatively shade-tolerant species under small, infrequent fires, but only species with both relatively high shade tolerance and high fire tolerance can thrive under conditions with large, frequent fires. Second, we demonstrated that different forms of disturbance can interact in unexpected ways. Our projections revealed that when grazing acts in combination with fire, forest cover, structure and composition vary dramatically depending on the frequency and extent of the fires. Results indicated that grazing and fire have a synergistic effect causing a reduction in forest cover greater than the sum of their individual effects. This suggests that passive landscape-scale restoration of TDF is achievable in both Mexican study areas only if grazing is reduced, and fires are carefully managed to reduce their frequency and intensity.

Suggested Citation

  • Cantarello, Elena & Newton, Adrian C. & Hill, Ross A. & Tejedor-Garavito, Natalia & Williams-Linera, Guadalupe & López-Barrera, Fabiola & Manson, Robert H. & Golicher, Duncan J., 2011. "Simulating the potential for ecological restoration of dryland forests in Mexico under different disturbance regimes," Ecological Modelling, Elsevier, vol. 222(5), pages 1112-1128.
  • Handle: RePEc:eee:ecomod:v:222:y:2011:i:5:p:1112-1128
    DOI: 10.1016/j.ecolmodel.2010.12.019
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    References listed on IDEAS

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    1. Norman Myers & Russell A. Mittermeier & Cristina G. Mittermeier & Gustavo A. B. da Fonseca & Jennifer Kent, 2000. "Biodiversity hotspots for conservation priorities," Nature, Nature, vol. 403(6772), pages 853-858, February.
    2. Scheller, Robert M. & Domingo, James B. & Sturtevant, Brian R. & Williams, Jeremy S. & Rudy, Arnold & Gustafson, Eric J. & Mladenoff, David J., 2007. "Design, development, and application of LANDIS-II, a spatial landscape simulation model with flexible temporal and spatial resolution," Ecological Modelling, Elsevier, vol. 201(3), pages 409-419.
    3. Philip Thornton & Pierre Gerber, 2010. "Climate change and the growth of the livestock sector in developing countries," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 15(2), pages 169-184, February.
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    1. Hassan Faramarzi & Seyd Mohsen Hosseini & Hamid Reza Pourghasemi & Mahdi Farnaghi, 2021. "Forest fire spatial modelling using ordered weighted averaging multi-criteria evaluation," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 67(2), pages 87-100.
    2. Cristina Martínez-Garza & Eliane Ceccon & Moisés Méndez-Toribio, 2022. "Ecological and Social Limitations for Mexican Dry Forest Restoration: A Systematic Review," Sustainability, MDPI, vol. 14(7), pages 1-21, March.
    3. Hassan Faramarzi & Seyed Mohsen Hosseini & Hamid Reza Pourghasemi & Mahdi Farnaghi, 2023. "Using machine learning techniques in multi-hazards assessment of Golestan National Park, Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(3), pages 3231-3255, July.

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