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Bridging the gap between ecophysiological and genetic knowledge to assess the adaptive potential of European beech

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  • Kramer, K.
  • Buiteveld, J.
  • Forstreuter, M.
  • Geburek, T.
  • Leonardi, S.
  • Menozzi, P.
  • Povillon, F.
  • Schelhaas, M.J.
  • Teissier du Cros, E.
  • Vendramin, G.G.
  • van der Werf, D.C.

Abstract

In this study we aimed to combine knowledge of the ecophysiology and genetics of European beech to assess the potential of this species to adapt to environmental change. Therefore, we performed field and experimental studies on the genetic and ecophysiological functioning of beech. This information was integrated through a coupled genetic–ecophysiological model for individual trees that was parameterized with information derived from our own studies or from the literature. Using the model, we evaluated the adaptive response of beech stands in two ways: firstly, through sensitivity analyses (of initial genetic diversity, pollen dispersal distance, heritability of selected phenotypic traits, and forest management, representing disturbances) and secondly, through the evaluation of the responses of phenotypic traits and their genetic diversity to four management regimes applied to 10 study plots distributed over Western Europe. The model results indicate that the interval between recruitment events strongly affects the rate of adaptive response, because selection is most severe during the early stages of forest development. Forest management regimes largely determine recruitment intervals and thereby the potential for adaptive responses. Forest management regimes also determine the number of mother trees that contribute to the next generation and thereby the genetic variation that is maintained. Consequently, undisturbed forests maintain the largest amount of genetic variation, as recruitment intervals approach the longevity of trees and many mother trees contribute to the next generation. However, undisturbed forests have the slowest adaptive response, for the same reasons.

Suggested Citation

  • Kramer, K. & Buiteveld, J. & Forstreuter, M. & Geburek, T. & Leonardi, S. & Menozzi, P. & Povillon, F. & Schelhaas, M.J. & Teissier du Cros, E. & Vendramin, G.G. & van der Werf, D.C., 2008. "Bridging the gap between ecophysiological and genetic knowledge to assess the adaptive potential of European beech," Ecological Modelling, Elsevier, vol. 216(3), pages 333-353.
  • Handle: RePEc:eee:ecomod:v:216:y:2008:i:3:p:333-353
    DOI: 10.1016/j.ecolmodel.2008.05.004
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    References listed on IDEAS

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    1. Arii, Ken & Caspersen, John P. & Jones, Trevor A. & Thomas, Sean C., 2008. "A selection harvesting algorithm for use in spatially explicit individual-based forest simulation models," Ecological Modelling, Elsevier, vol. 211(3), pages 251-266.
    2. Duursma, R.A. & Marshall, J.D. & Robinson, A.P. & Pangle, R.E., 2007. "Description and test of a simple process-based model of forest growth for mixed-species stands," Ecological Modelling, Elsevier, vol. 203(3), pages 297-311.
    3. Austin, Mike, 2007. "Species distribution models and ecological theory: A critical assessment and some possible new approaches," Ecological Modelling, Elsevier, vol. 200(1), pages 1-19.
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    1. Vacchiano, Giorgio & Ascoli, Davide & Berzaghi, Fabio & Lucas-Borja, Manuel Esteban & Caignard, Thomas & Collalti, Alessio & Mairota, Paola & Palaghianu, Ciprian & Reyer, Christopher P.O. & Sanders, T, 2018. "Reproducing reproduction: How to simulate mast seeding in forest models," Ecological Modelling, Elsevier, vol. 376(C), pages 40-53.
    2. Pretzsch, Hans & Forrester, David I. & Rötzer, Thomas, 2015. "Representation of species mixing in forest growth models. A review and perspective," Ecological Modelling, Elsevier, vol. 313(C), pages 276-292.

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