Advertisement

Annals of Forest Science

, Volume 67, Issue 3, pp 305–305 | Cite as

Mortality of silver fir and Norway Spruce in the Western Alps — a semi-parametric approach combining size-dependent and growth-dependent mortality

  • Ghislain Vieilledent
  • Benoît Courbaud
  • Georges Kunstler
  • Jean-François Dhôte
Original Article

Abstract

Question

Tree mortality can be modeled using two complementary covariates, tree size and tree growth. Tree growth is an integrative measure of tree vitality while tree diameter is a good index of sensitivity to disturbances and can be considered as a proxy for tree age which may indicate senescence. Few mortality models integrate both covariates because classical model calibration requires large permanent plot data-sets which are rare. How then can we calibrate a multivariate mortality model including size and growth when permanent plots data are not available?

Location

To answer this question, we studied Abies alba and Picea abies mortality in the French Swiss and Italian Alps.

Method

Our study proposes an alternative semi-parametric method which includes a random sample of living and dead trees with diameter and growth measurements.

Results

We were able to calibrate a mortality model combining both size-dependent and growth-dependent mortality. We demonstrated that A. alba had a lower annual mortality rate (10%) than P. abies (18%) for low growth (< 0.2 mmyear−1). We also demonstrated that for higher diameters (DBH ≥ 70 cm), P. abies had a higher mortality rate (0.45%) than A. alba (0.32%).

Conclusion

Our results are consistent with the mechanisms of colonization-competition trade-off and of successional niche theory which may explain the coexistence of these two species in the Alps. The method we developed should be useful for forecasting tree mortality and can improve the efficiency of forest dynamics models.

Keywords

Abies alba conditional probability non-parametric model Picea abies tree mortality 

Abbreviations

DBH

Diameter at Breast Height (DBH = 1.30 m)

P. abies

Picea abies (L.) Karst. (Norway Spruce)

A. alba

Abies alba Mill. (Silver Fir)

NFI

National Forest Inventory

Mortalité du sapin pectiné et de l’épicea commmun dans les alpes occidentales — une approche semi-paramétrique combinant la mortalité dépendant de la taille et de la croissance

Résumé

Question

Il est possible de modéliser la mortalité des arbres en utilisant deux covariables complémentaires : la taille et la croissance de l’arbre. La croissance est une mesure synthétique de la vitalité alors que le diamètre est un bon indicateur de la sensibilité aux perturbations et est très fortement corrélé à l’âge de l’arbre, qui détermine la sénescence. Peu de modèles de mortalité intègrent les deux covariables, car cela nécessite, pour les approches classiques, une calibration à partir de données de placettes permanentes qui sont rares. Comment obtenir un modèle de mortalité multivarié, incluant la taille et la croissance, lorsque des données de placettes permanentes ne sont pas disponibles?

Localisation géographique

Pour répondre à cette question, nous avons étudié la mortalité du sapin pectiné (Abies alba) et de l’epicéa commmun (Picea abies) dans les Alpes suisses françaises et italiennes.

Méthode

Notre étude propose une méthode semi-parametrique alternative s’appuyant sur un échantillon d’arbres morts et vivants avec des mesures de diamètre et de croissance.

Résultats

Nous avons obtenu un modèle combinant la mortalité dépendant à la fois de la taille et de la croissance. Nous avons démontré qu’A. alba avait un taux de mortalité inférieur (10 %) à celui de P. abies (18 %) pour une faible croissance (< 0.2 mman−1). De plus, pour de larges diamètres (DBH >- 70 cm), P. abies a un taux de mortalité supérieur (0.45 %) à A. alba (0.32 %).

Conclusion

Nos résultats sont en accord avec les mécanismes de niche de succession et de compromis entre colonisation et compétition qui sont invoqués pour expliquer la coexistence des deux espéces dans les Alpes. Notre méthode devrait contribuer à améliorer la prédiction du taux de mortalité et la précision des modèles de dynamique forestière.

Mots-clés

Abies alba probabilités conditionnelles modèles non-paramétriques Picea abies mortalité des arbres 

References

  1. Ayer M., Brunk H.D., Ewing G.M., Reid W.T., and Silverman E., 1955. An empirical distribution function for sampling with incomplete information. Ann. Math. Stat. 26: 641–647.CrossRefGoogle Scholar
  2. Bigler C. and Bugmann H., 2003. Growth-dependent tree mortality models based on tree-rings. Can. J. For. Res. 33: 210–221.CrossRefGoogle Scholar
  3. Bugmann H., 1994. On the ecology of mountainous forests in a changing climate: a simulation study. Ph.D. thesis, Swiss federal institute of technology, Zürich.Google Scholar
  4. Canham C.D., Papaik M.J., and Latty E.F., 2001. Interspecific variation in susceptibility to windthrow as a function of tree size and storm severity for northern temperate tree species. Can. J. For. Res. 31: 1–10.CrossRefGoogle Scholar
  5. Clark J.S., 1996. Testing disturbance theory with long-term data: Alternative life-history solutions to the distribution of events. Am. Nat. 148: 976–996.CrossRefGoogle Scholar
  6. Coomes D.A., Duncan R.P., Allen R.B., and Truscott J., 2003. Disturbances prevent stem size-density distributions in natural forests from following scaling relationships. Ecol. Lett. 6: 980–989.CrossRefGoogle Scholar
  7. Courbaud B., de Coligny F., and Cordonnier T., 2003. Simulating radiation distribution in a heterogeneous Norway spruce forest on a slope. Agric. For. Meteorol. 116: 1–18.CrossRefGoogle Scholar
  8. Das A., Battles J., van Mantgem P.J., and Stephenson N.L., 2008. Spatial elements of mortality risk in old-growth forests. Ecology 89: 1744–1756.PubMedCrossRefGoogle Scholar
  9. Dobbertin M., 2005. Tree growth as indicator of tree vitality and of tree reaction to environmental stress: a review. Eur. J. For. Res. 124: 319–333.Google Scholar
  10. Dovčiak M., Hrivnák R., Ujházy K., and Gömöry D., 2008. Seed rain and environmental controls on invasion of Picea abies into grassland. Plant Ecol. 194: 135–148.CrossRefGoogle Scholar
  11. Eid T. and Tuhus E., 2001. Models for individual tree mortality in Norway. For. Ecol. Manag. 154: 69–84.CrossRefGoogle Scholar
  12. Fortin M., Bedard S., DeBlois J., and Meunier S., 2008. Predicting individual tree mortality in northern hardwood stands under uneven-aged management in southern Quebec, canada. Ann. For. Sci. 65: 205.CrossRefGoogle Scholar
  13. Franklin J.F., Shugart H.H., and Harmon M.E., 1987. Tree death as an ecological process. BioScience 550–556.Google Scholar
  14. Fridman J. and Valinger E., 1998. Modelling probability of snow and wind damage using tree, stand, and site characteristics from Pinus sylvestris sample plots. Scan. J. For. Res. 13: 348–356.CrossRefGoogle Scholar
  15. Gower S.T., McMurtrie R.E., and Murty D., 1996. Aboveground net primary production decline with stand age: Potential causes. Trends Ecol. Evol. 11: 378–382.PubMedCrossRefGoogle Scholar
  16. Grassi G. and Bagnaresi U., 2001. Foliar morphological and physiological plasticity in Picea abies and Abies alba saplings along a natural light gradient. Tree Physiol. 21: 959–967.PubMedGoogle Scholar
  17. Hansen E.M., Bentz B.J., Munson A.S., Vandygriff J.C., and Turner D.L., 2006. Evaluation of funnel traps for estimating tree mortality and associated population phase of spruce beetle in Utah. Can. J. For. Res. 36: 2574–2584.CrossRefGoogle Scholar
  18. Harcombe P.A., 1987. Tree life table. Bioscience 37: 557–568.CrossRefGoogle Scholar
  19. Hawkes C., 2000. Woody plant mortality algorithms: description, problems and progress. Ecol. Model. 126: 225–248.CrossRefGoogle Scholar
  20. Hubbard R.M., Bond B.J., and Ryan M.G., 1999. Evidence that hydraulic conductance limits photosynthesis in old Pinus ponderosa trees. Tree Physiol. 19: 165–172.PubMedGoogle Scholar
  21. Ihaka R. and Gentleman R., 1996. R: A Language for Data Analysis and Graphics. J. Comp. Graph. Stat. 5: 299–314.CrossRefGoogle Scholar
  22. Kobe R.K. and Coates K.D., 1997. Models of sapling mortality as a function of growth to characterize interspecific variation in shade tolerance of eight tree species of northwestern British Columbia. Can. J. For. Res. 27: 227–236.CrossRefGoogle Scholar
  23. Kobe R.K., Pacala S.W., and Silander J.A., 1995. Juvenile tree survivorship as a component of shade tolerance. Ecol. Appl. 5: 517–532.CrossRefGoogle Scholar
  24. Korzukhin M.D. and Ter-Mikaelian M.T., 1995. An individual tree-based model of competition for light. Ecol. Model. 79: 221–229.CrossRefGoogle Scholar
  25. Kunstler G., Curt T., Bouchaud M., and Lepart J., 2005. Growth, mortality, and morphological response of European beech and downy oak along a light gradient in sub-Mediterranean forest. Can. J. For. Res. 35: 1657–1668.CrossRefGoogle Scholar
  26. Lavine M., 1991. Problems in Extrapolation Illustrated with Space-Shuttle O-Ring Data. J. Am. Stat. Assoc. 86: 919–921.CrossRefGoogle Scholar
  27. Lee Y.J., 1971. Predicting mortality for even-aged stands of lodgepole pine. For. Chron. 47: 29–32.Google Scholar
  28. Lexer M.J. and Hönninger K., 2001. A modified 3D-patch model for spatially explicit simulation of vegetation composition in heteregeneous landscape. For. Ecol. Manage. 144: 43–65.CrossRefGoogle Scholar
  29. Lin J., Harcombe P.A., and Fulton M.R., 2001. Characterizing shade tolerance by the relationship between mortality and growth in tree saplings in a southeastern Texas forest. Can. J. For. Res. 31: 345–349.Google Scholar
  30. Lundstrom T., Jonas T., Stockli V., and Ammann W., 2007. Anchorage of mature conifers: Resistive turning moment, root-soil plate geometry and root growth orientation. Tree Physiol. 27: 1217–1227.PubMedGoogle Scholar
  31. MacFarlane D.W., Green E.J., Brunner A., and Burkhart H.E., 2002. Predicting survival and growth rates for individual loblolly pine trees from light capture estimates. Can. J. For. Res. 32: 1970–1983.CrossRefGoogle Scholar
  32. Monserud R.A., 1976. Simulation of forest tree mortality. For. Sci. 22: 438–444.Google Scholar
  33. Monserud R.A. and Sterba H., 1999. Modeling individual tree mortality for Austrian forest species. For. Ecol. Manage. 113: 109–123.CrossRefGoogle Scholar
  34. Moore J.A., Hamilton D.A., Xiao Y., and Byrne J., 2004. Bedrock type significantly affects individual tree mortality for various conifers in the inland Northwest, USA. Can. J. For. Res. 34: 31–42.CrossRefGoogle Scholar
  35. Muller-Landau H.C., Condit R.S., Chave J., Thomas S.C., Bohlman S.A., Bunyavejchewin S., Davies S., Foster R., Gunatilleke S., Gunatilleke N., Harms K.E., Hart T., Hubbell S.P., Itoh A., Kassim A.R., LaFrankie J.V., Lee H.S., Losos E., Makana J.R., Ohkubo T., Sukumar R., Sun I.F., Supardi N.M.N., Tan S., Thompson J., Valencia R., Munoz G.V., Wills C., Yamakura T., Chuyong G., Dattaraja H.S., Esufali S., Hall P., Hernandez C., Kenfack D., Kiratiprayoon S., Suresh H.S., Thomas D., Vallejo M.I., and Ashton P., 2006. Testing metabolic ecology theory for allometric scaling of tree size, growth and mortality in tropical forests. Ecol. Lett. 9: 575–588.PubMedCrossRefGoogle Scholar
  36. Nishimura T.B., 2006. Successional replacement mediated by frequency and severity of wind and snow disturbances in a Picea-Abies forest. J. Veg. Sci. 17: 57–64.Google Scholar
  37. Pacala S.W., Canham C., Saponara J., Silander J.A., Kobe R.K., and Ribbens E., 1996. Forest models defined by field measurements: estimation, error analysis and dynamics. Ecol. Monogr. 66: 1–43.CrossRefGoogle Scholar
  38. Pacala S.W. and Rees M., 1998. Models suggesting field experiments to test two hypotheses explaining successional diversity. Am. Nat. 152: 729–737.PubMedCrossRefGoogle Scholar
  39. Papaik M.J. and Canham C.D., 2006. Species resistance and community response to wind disturbance regimes in northern temperate forests. J. Ecol. 94: 1011–1026.CrossRefGoogle Scholar
  40. Peet R.K. and Christensen N.L., 1987. Competition and tree death. BioScience 37: 586–595.CrossRefGoogle Scholar
  41. Peltola H., Kellomaki S., Vaisanen H., and Ikonen V.P., 1999. A mechanistic model for assessing the risk of wind and snow damage to single trees and stands of Scots pine, Norway spruce, and birch. Can. J. For. Res. 29: 647–661.CrossRefGoogle Scholar
  42. Rees M., Condit R., Crawley M., Pacala S., and Tilman D., 2001. Long-term studies of vegetation dynamics. Science 293: 650–655.PubMedCrossRefGoogle Scholar
  43. Sagnard F., Pichot C., Dreyfus P., Jordano P., and Fady B., 2007. Modelling seed dispersal to predict seedling recruitment: recolonization dynamics in a plantation forest. Ecol. Model. 203: 464–474.CrossRefGoogle Scholar
  44. Schütz J.-P., 1969. Etude des phénomènes de la croissance en hauteur et en diamètre du sapin (Abies alba Mill.) et de l’épicéa (Picea abies Karst.) dans deux peuplements jardinés et une forêt vierge. Ph.D. thesis, École Polytechnique Fédérale Zurich, Zurich.Google Scholar
  45. Stokes A., Salin F., Kokutse A.D., Berthier S., Jeannin H., Mochan S., Dorren L., Kokutse N., Abd Ghani M., and Fourcaud T., 2005. Mechanical resistance of different tree species to rockfall in the French Alps. Plant Soil 278: 107–117.CrossRefGoogle Scholar
  46. Tilman D., 1994. Competition and biodiversity in spatially structured habitats. Ecology 75: 2–16.CrossRefGoogle Scholar
  47. Ulmer U., 2006. Schweizerisches Landesforstinventar LFI. Datenbankauszug der Erhebungen 1983–85 und 1993–95 vom 30. Mai 2006. Technical report, WSL, Eidg. Forschungsanstalt WSL, Birmensdorf.Google Scholar
  48. Uriarte M., Canham C.D., Thompson J., and Zimmerman J.K., 2004. A neighborhood analysis of tree growth and survival in a hurricane-driven tropical forest. Ecol. Monogr. 74: 591–614.CrossRefGoogle Scholar
  49. Valinger E. and Fridman J., 1997. Modelling probability of snow and wind damage in Scots pine stands using tree characteristics. For. Ecol. Manage. 97: 215–222.CrossRefGoogle Scholar
  50. Vieilledent G., Courbaud B., Kunstler G., Dhote J.F., and Clark J.S., 2009. Biases in the estimation of size-dependent mortality models: advantages of a semiparametric approach. Can. J. For. Res. 39: 1430–1443.CrossRefGoogle Scholar
  51. Wasser B. and Frehner M., 1996. Soins minimaux pour les forêts à fonction protectrice. Office Central Fédéral des Imprimés et du Matériel, Berne.Google Scholar
  52. Worrall J.J., Lee T.D., and Harrington T.C., 2005. Forest dynamics and agents that initiate and expand canopy gaps in Picea-Abies forests of Crawford Notch, New Hampshire, USA. J. Ecol. 93: 178–190.CrossRefGoogle Scholar
  53. Wunder J., Reineking B., Matter J.F., Bigler C., and Bugmann H., 2007. Predicting tree death for Fagus sylvatica and Abies alba using permanent plot data. J. Veg. Sci. 18: 525–534.CrossRefGoogle Scholar
  54. Wyckoff P.H. and Clark J.S., 2000. Predicting tree mortality from diameter growth: a comparison of maximum likelihood and Bayesian approaches. Can. J. For. Res. 30: 156–167.CrossRefGoogle Scholar
  55. Wyckoff P.H. and Clark J.S., 2002. The relationship between growth and mortality for seven co-occurring tree species in the southern Appalachian Mountains. J. Ecol. 90: 604–615.CrossRefGoogle Scholar
  56. Yao X.H., Titus S.J., and MacDonald S.E., 2001. A generalized logistic model of individual tree mortality for aspen, white spruce, and lodgepole pine in Alberta mixedwood forests. Can. J. For. Res. 31: 283–291.Google Scholar
  57. Zolubas P., 2003. Spruce Bark Beetle (Ips typographus L.) Risk based on individual tree parameters. In: IUFRO (Ed.), Forest insect population dynamics and host influences, Kanazawa, pp. 96–97.Google Scholar

Copyright information

© Springer S+B Media B.V. 2010

Authors and Affiliations

  • Ghislain Vieilledent
    • 1
    • 2
    • 3
  • Benoît Courbaud
    • 1
  • Georges Kunstler
    • 1
  • Jean-François Dhôte
    • 4
    • 5
  1. 1.Cemagref-Mountain Ecosystems Research UnitSaint-Martin-d’Hères CedexFrance
  2. 2.Laboratoire d’Étude des Ressources Forêt BoisAgroParisTech-UMR1092NancyFrance
  3. 3.Cirad-UPR Dynamique ForestièreMontpellier Cedex 5France
  4. 4.Laboratoire d’Étude des Ressources Forêt BoisINRA-UMR1092NancyFrance
  5. 5.ONF-Département RechercheBoulevard de ConstanceFontainebleauFrance

Personalised recommendations