Annals of Forest Science

, Volume 69, Issue 7, pp 829–844 | Cite as

Large-scale dynamics of a heterogeneous forest resource are driven jointly by geographically varying growth conditions, tree species composition and stand structure

  • Holger WernsdörferEmail author
  • Antoine Colin
  • Jean-Daniel Bontemps
  • Hélène Chevalier
  • Gérôme Pignard
  • Sylvain Caurla
  • Jean-Michel Leban
  • Jean-Christophe Hervé
  • Meriem Fournier
Original Paper


• Context

Forest resource projections are required as part of an appropriate framework for sustainable forest management. Suitable large-scale projection models are usually based on national forest inventory (NFI) data. However, sound projections are difficult to make for heterogeneous resources as they vary greatly with respect to the factors that are assumed to drive forest dynamics on a large spatial scale, e.g. geographically varying growth conditions (here represented by NFI regions), tree species composition (here broadleaf-dominated, conifer-dominated and broadleaf-conifer mixed stands) and stand structure (here high forest, coppice forest and high-coppice forest mixture).

• Question and objective

Our question was how does the variance of forest dynamics parameters (i.e. growth, felling and mortality, and recruitment processes) and that of 20-year forest resource projections partition between these factors (NFI region, tree species composition and stand structure), including their interactions. Our objective was to capitalise on the suitability of an existing multi-strata, diameter class matrix model for the purposes of making projections for the highly heterogeneous French forest resource.

• Methods

The model was newly calibrated for the entire territory of metropolitan France based on most recent NFI data, i.e. for years 2006–2008. The forest resource was divided into strata by crossing the factors NFI region, tree species composition and stand structure. The variance partitioning of the parameters and projections was assessed based on a model sensitivity analysis.

• Results

Growth, felling and mortality varied mainly with NFI region and species composition. Recruitment varied mainly with NFI region and stand structure. All three factors caused variations in resource projections, but with unequal intensities. Factor impacts included first order and interaction effects.

• Conclusions

We found, by considering both first order and interaction effects, that NFI region, species composition and stand structure are ecologically relevant factors that jointly drive the dynamics of a heterogeneous forest resource. Their impacts, in our study, varied depending on the forest dynamics process under consideration. Recruitment would appear to have a particularly great impact on resource changes over time.


Forest resource Forest dynamics Stratification Matrix model Tree diameter class National forest inventory 



We are particularly grateful to Nicolas Picard, Sylvie Gourlet-Fleury, Frédéric Mortier and Dakis-Yaoba Ouédraogo at the French CIRAD (Centre de Coopération Internationale en Recherche Agronomique pour le Développement) for valuable discussions on forest dynamics modelling. Moreover, we thank two reviewers for constructive and helpful comments on an earlier manuscript.


Funding was provided by the French General Directorate for Education and Research DGER (Direction Générale de l'Enseignement et de la Recherche).


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Copyright information

© INRA / Springer-Verlag France 2012

Authors and Affiliations

  • Holger Wernsdörfer
    • 1
    • 2
    Email author
  • Antoine Colin
    • 3
  • Jean-Daniel Bontemps
    • 1
    • 2
  • Hélène Chevalier
    • 3
  • Gérôme Pignard
    • 4
  • Sylvain Caurla
    • 5
    • 6
  • Jean-Michel Leban
    • 7
  • Jean-Christophe Hervé
    • 3
  • Meriem Fournier
    • 1
    • 2
  1. 1.AgroParisTech, UMR1092, Laboratoire d’Etude des Ressources Foret Bois (LERFoB), ENGREFNancyFrance
  2. 2.INRA, UMR1092, Laboratoire d’Etude des Ressources Foret Bois (LERFoB), Centre INRA de NancyChampenouxFrance
  3. 3.IGNNogent-sur-VernissonFrance
  4. 4.Direction Départementale des Territoires et de la Mer de l’HéraultMission des Systèmes d’Information (MSI)MontpellierFrance
  5. 5.AgroParisTech, UMR356, Laboratoire d’Economie Forestiere (LEF), ENGREFNancyFrance
  6. 6.INRA, UMR356, Laboratoire d’Economie Forestiere (LEF)NancyFrance
  7. 7.ENSTIB, LERMaB, Université de LorraineEpinalFrance

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