Advertisement

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

Abstract

• 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.

Keywords

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

Notes

Acknowledgements

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

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

References

  1. Bontemps JD, Hervé JC, Leban JM, Dhôte JF (2011) Nitrogen footprint in a long-term observation of forest growth over the twentieth century. Trees 25:237–251CrossRefGoogle Scholar
  2. Cariboni J, Gatelli D, Liska R, Saltelli A (2007) The role of sensitivity analysis in ecological modelling. Ecol Model 203:167–182CrossRefGoogle Scholar
  3. Cavaignac S (2009) Les sylvoécorégions (SER) de France métropolitaine, Etude de définition. Report, French National Forest Inventory, Nogent-sur-VernissonGoogle Scholar
  4. Charru M, Seynave I, Morneau F, Bontemps JD (2010) Recent changes in forest productivity: an analysis of national forest inventory data for common beech (Fagus sylvatica L.) in north-eastern France. For Ecol Manag 260:864–874CrossRefGoogle Scholar
  5. Dhôte JF, Hervé JC (2000) Changements de productivité dans quatre forêts de chênes sessiles depuis 1930: une approche au niveau du peuplement. Ann For Sci 57:651–680CrossRefGoogle Scholar
  6. FAO, Food and Agriculture Organisation of the United Nations (2010) Global forest resource assessment 2010, country report, France. FAO Forest Department, RomeGoogle Scholar
  7. IFN, Inventaire Forestier National (2007) La forêt française - les résultats issus des campagnes d’inventaire 2005 et 2006. Report, French National Forest Inventory, Nogent-sur-VernissonGoogle Scholar
  8. IFN, Inventaire Forestier National (2010) La forêt française, Les résultats issus des campagnes d'inventaire 2005 à 2009, Les résultats pour la France. Report, French National Forest Inventory, Nogent-sur-VernissonGoogle Scholar
  9. Ingram CD, Buongiorno J (1996) Income and diversity tradeoffs from management of mixed lowland dipterocarps in Malaysia. J Trop For Sci 9:242–270Google Scholar
  10. Kaufmann E (2001) Estimation of standing timber, growth and cut. In: Brassel P, Lischke H (eds) Swiss national forest inventory: methods and models of the second assessment. WSL Swiss Federal Research Institute, Birmensdorf, pp 162–196Google Scholar
  11. Liang J (2010) Dynamics and management of Alaska boreal forest: an all-aged multi-species matrix growth model. For Ecol Manag 260:491–501CrossRefGoogle Scholar
  12. Liang J, Buongiorno J, Monserud RA (2005a) Growth and yield of all-aged Douglas fir–western hemlock forest stands: a matrix model with stand diversity effects. Can J For Res 35:2368–2381CrossRefGoogle Scholar
  13. Liang J, Buongiorno J, Monserud RA (2005b) Estimation and application of a growth and yield model for uneven-aged mixed conifer stands in California. Int For Rev 7:101–112CrossRefGoogle Scholar
  14. Lin CR, Buongiorno J (1997) Fixed versus variable-parameter matrix models of forest growth: the case of maple-birch forests. Ecol Model 99:263–274CrossRefGoogle Scholar
  15. MAAPRAT, Ministère de l’Agriculture, de l’Alimentation, de la Pêche, de la Ruralité et de l’Aménagement du Territoire (2011) Indicateurs de gestion durable des forêts françaises métropolitaines, Edition 2010. Report, French National Ministry of Agriculture, Nutrition, Fishery, Rural Area and Land-use Planning, ParisGoogle Scholar
  16. Morneau F, Duprez C, Hervé JC (2008) Les forêts mélangées en France métropolitaine. Caractérisation à partir des résultats de l’Inventaire Forestier National. Rev For Fr 60:107–120Google Scholar
  17. Nabuurs GJ, Schelhaas MJ, Pussinen A (2000) Validation of the European Forest Information Scenario Model (EFISCEN) and a projection of Finnish forests. Silva Fenn 34:167–179Google Scholar
  18. Nabuurs GJ, Pussinen A, Karjalainen T, Erhard M, Kramer K (2002) Stemwood volume increment changes in European forests due to climate change—a simulation study with the EFISCEN model. Glob Chang Biol 8:304–316CrossRefGoogle Scholar
  19. Picard N, Bar-Hen A, Guédon Y (2003) Modelling diameter class distribution with a second-order matrix model. For Ecol Manag 180:389–400CrossRefGoogle Scholar
  20. Picard N, Mortier F, Rossi V, Gourlet-Fleury S (2010a) Clustering species using a model of population dynamics and aggregation theory. Ecol Model 221:152–160CrossRefGoogle Scholar
  21. Picard N, Ouédraogo D, Bar-Hen A (2010b) Choosing classes for size projection matrix models. Ecol Model 221:2270–2279CrossRefGoogle Scholar
  22. Porté A, Bartelink HH (2002) Modelling mixed forest growth: a review of models for forest management. Ecol Model 150:141–188CrossRefGoogle Scholar
  23. Pretzsch H, Biber P, Dursky J (2002) The single tree-based stand simulator SILVA: construction, application and evaluation. For Ecol Manag 162:3–21CrossRefGoogle Scholar
  24. Pretzsch H, Grote R, Reineking B, Rötzer T, Seifert S (2008) Models for forest ecosystem management: a European perspective. Ann Bot 101:1065–1087PubMedCrossRefGoogle Scholar
  25. Robert N, Vidal C, Colin A, Hervé JC, Hamza N, Cluzeau C (2010) France. In: Tomppo E, Gschwantner T, Lawrence M, McRoberts RE (eds) National forest inventories—pathways for common reporting. Springer, Heidelberg, pp 207–221Google Scholar
  26. Salas González R, Houllier F, Lemoine B, Pierrat JC (1993) Représentativité locale des placettes d’inventaire en vue de l’estimation de variables dendrométriques de peuplement. Ann For Sci 50:469–485CrossRefGoogle Scholar
  27. Salas-González R, Houllier F, Lemoine B, Pignard G (2001) Forecasting wood resources on the basis of national forest inventory data. Application to Pinus pinaster Ait. in southwestern France. Ann For Sci 58:785–802CrossRefGoogle Scholar
  28. Saltelli A, Tarantola S, Campolongo F, Ratto M (2004) Sensitivity analysis in practice. Wiley, ChichesterGoogle Scholar
  29. Schelhaas MJ, Eggers J, Lindner M, Nabuurs GJ, Pussinen A, Päivinen R, Schuck A, Verkerk PJ, van der Werf DC, Zudin S (2007) Model documentation for the European forest information scenario model (EFISCEN 3.1.3). Report, ALTERRA / EFI, WageningenGoogle Scholar
  30. Seynave I, Gégout JC, Hervé JC, Dhôte JF, Drapier J, Bruno E, Dumé G (2005) Picea abies site index prediction by environmental factors and understorey vegetation: a two-scale approach based on survey databases. Can J For Res 35:1669–1678CrossRefGoogle Scholar
  31. Seynave I, Gégout JC, Hervé JC, Dhôte JF (2008) Is the spatial distribution of European beech (Fagus sylvatica L.) limited by its potential height growth? J Biogeogr 35:1851–1862CrossRefGoogle Scholar
  32. Solomon DS, Hosmer RA, Hayslett HT Jr (1986) A two-stage matrix model for predicting growth of forest stands in the Northeast. Can J For Res 16:521–528CrossRefGoogle Scholar
  33. Sterba H, Golser M, Moser M, Schadauer K (2000) A timber harvesting model for Austria. Comput Electron Agric 28:133–149CrossRefGoogle Scholar
  34. Thürig E, Schelhaas MJ (2006) Evaluation of a large-scale forest scenario model in heterogeneous forests: a case study for Switzerland. Can J For Res 36:671–683CrossRefGoogle Scholar
  35. Tomppo E, Gschwantner T, Lawrence M, McRoberts RE (2010) National forest inventories—pathways for common reporting. Springer, HeidelbergCrossRefGoogle Scholar
  36. UNECE, United Nations Economic Commission for Europe (2005) European forest sector outlook study, main report. United Nations, GenevaGoogle Scholar
  37. Vanclay JK (1994) Modelling forest growth and yield. Applications to mixed tropical forests. CAB International, WallingfordGoogle Scholar
  38. Wernsdörfer H, Rossi V, Cornu G, Oddou-Muratorio S, Gourlet-Fleury S (2008) Impact of uncertainty in tree mortality on the predictions of a tropical forest dynamics model. Ecol Model 218:290–306CrossRefGoogle Scholar

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

Personalised recommendations