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Modelling the effect of climate-induced changes in recruitment and juvenile growth on mixed-forest dynamics: The case of montane–subalpine Pyrenean ecotones

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  • Ameztegui, Aitor
  • Coll, Lluís
  • Messier, Christian

Abstract

Most predictive models forecast significant upward displacement of forest species due to increases in temperatures, but not all the species respond in the same way to changes in climate. In temperate or mountain systems, biotic competitive interactions drive species distributions, and responses to climate change will ultimately depend upon productive and demographic processes such as growth, recruitment and mortality. We parameterized and used an individual-based, spatially explicit model of forest dynamics (SORTIE-ND) to investigate the role of species-specific differences in juvenile performance induced by climate change (juvenile growth and recruitment ability) in the dynamics of mixed forests located in the montane–subalpine ecotone of the Pyrenees. We assessed this role for two types of forests composed of three species with differing light requirements and sensitivity to climate change: (1) a mixed forest with two shade-intolerant pines (Pinus uncinata and Pinus sylvestris) and (2) a mixed forest composed by a shade-intolerant pine and a shade-tolerant fir (Abies alba). Our results show that for species with similar light requirements (i.e., both pines), small differences in sapling growth response to climate change can lead to significant differences in future species composition (an increase in P. sylvestris growth of 10% leads to an increase in its abundance from 42% to 50.3%). Conversely, in pine-fir forests, shade-tolerance results more decisive than climate-induced changes in growth in driving the future forest composition.

Suggested Citation

  • Ameztegui, Aitor & Coll, Lluís & Messier, Christian, 2015. "Modelling the effect of climate-induced changes in recruitment and juvenile growth on mixed-forest dynamics: The case of montane–subalpine Pyrenean ecotones," Ecological Modelling, Elsevier, vol. 313(C), pages 84-93.
    • Handle: RePEc:eee:ecomod:v:313:y:2015:i:c:p:84-93
    DOI: 10.1016/j.ecolmodel.2015.06.029
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    References listed on IDEAS

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    1. Seidl, Rupert & Fernandes, Paulo M. & Fonseca, Teresa F. & Gillet, François & Jönsson, Anna Maria & Merganičová, Katarína & Netherer, Sigrid & Arpaci, Alexander & Bontemps, Jean-Daniel & Bugmann, Hara, 2011. "Modelling natural disturbances in forest ecosystems: a review," Ecological Modelling, Elsevier, vol. 222(4), pages 903-924.
    2. Silvia Ferrari & Francisco Cribari-Neto, 2004. "Beta Regression for Modelling Rates and Proportions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(7), pages 799-815.
    3. Cribari-Neto, Francisco & Zeileis, Achim, 2010. "Beta Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 34(i02).
    4. Bose, Arun K. & Harvey, Brian D. & Coates, K. David & Brais, Suzanne & Bergeron, Yves, 2015. "Modelling stand development after partial harvesting in boreal mixedwoods of eastern Canada," Ecological Modelling, Elsevier, vol. 300(C), pages 123-136.
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    1. Morán-Ordóñez, Alejandra & Ameztegui, Aitor & De Cáceres, Miquel & de-Miguel, Sergio & Lefèvre, François & Brotons, Lluís & Coll, Lluís, 2020. "Future trade-offs and synergies among ecosystem services in Mediterranean forests under global change scenarios," Ecosystem Services, Elsevier, vol. 45(C).
    2. Ameztegui, Aitor & Cabon, Antoine & De Cáceres, Miquel & Coll, Lluís, 2017. "Managing stand density to enhance the adaptability of Scots pine stands to climate change: A modelling approach," Ecological Modelling, Elsevier, vol. 356(C), pages 141-150.
    3. Melo, L.C. & Schneider, R. & Fortin, M., 2018. "Estimating model- and sampling-related uncertainty in large-area growth predictions," Ecological Modelling, Elsevier, vol. 390(C), pages 62-69.

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