Annals of Forest Science

, Volume 66, Issue 1, pp 105–105 | Cite as

Why and where do adult trees die in a young secondary temperate forest? The role of neighbourhood

  • José Miguel OlanoEmail author
  • Nere Amaia Laskurain
  • Adrián Escudero
  • Marcelino De La Cruz
Original Article


  • • The density and identity of tree neighbourhood is a key factor to explain tree mortality in forests, especially during the stem exclusion phase.

  • • To understand this process, we built a logistic model for mortality in a spatially explicit context, including tree and neighbourhood predictors. Additionally, we used this model to build mortality risk frequency distributions. Finally, we tested this model against a random mortality model to predict the spatial pattern of the forest.

  • • Annual mortality rate was high for pedunculate oak (Quercus robur, 6.99%), moderate for birch (Betula celtiberica, 2.19%) and Pyrenean oak (Q. pyrenaica, 1.58%) and low for beech (Fagus sylvatica, 0.26%). Mortality risk models for pedunculate oak and birch included stem diameter, tree height, canopy position and neighbourhood. Mortality was affected by the specific nature of the neighbourhood showing a clear competitive hierarchy: beech > pedunculate oak > birch. Models based on random mortality and logistic regression model were able to predict the spatial pattern of survivors although logistic regression predictions were more accurate.

  • • Our study highlights how simple models such as the random mortality one may obscure much more complex spatial interactions.


inhomogeneous Ripley’s K logistic model temperate mixed forest succession tree mortality 

Pourquoi et où les arbres adultes meurent dans une jeune forêt tempérée? Le rôle du voisinage


  • • La densité et l’identité des arbres du voisinage sont un facteur clé pour expliquer la mortalité d’arbres dans les peuplements forestiers, surtout pendant la phase d’exclusion du tronc.

  • • Pour comprendre ce processus, nous avons construit un modèle logistique pour la mortalité dans un contexte spatialement explicite, en incluant l’arbre et des prédicteurs de voisinage. De plus, nous avons utilisé ce modèle pour construire des distributions de fréquence de risque de mortalité. Finalement, nous avons évalué ce modèle par rapport à un modèle de mortalité aléatoire pour prédire la structure spatiale de la forêt.

  • • Le taux de mortalité annuelle était élevé pour le chêne pédonculé (Quercus robur, 6.99 %), modéré pour le bouleau (Betula celtiberica, 2.19 %) et le chêne tauzin (Q. pyrenaica, 1.58 %) et faible pour le hêtre (Fagus sylvatica, 0,26 %). Les modèles de risque de mortalité pour le chêne pédonculé et le bouleau intégraient le diamètre du tronc, la hauteur de l’arbre, la position du houppier et le voisinage. La mortalité a été affectée par la nature spécifique du voisinage montrant une hiérarchie claire dans l’aptitude compétitive : hêtre > chêne pédonculé > bouleau. Les modèles basés sur une mortalité aléatoire et le modèle logistique ont été capables de prédire la répartition spatiale des survivants bien que les prédictions du modèle logistique étaient plus précises.

  • • Notre étude montre comment des modèles simples basés sur une mortalité aléatoire peuvent obscurcir des interactions spatiales beaucoup plus complexes.


Ripley’s K non homogènes modèle logistique forêt tempérée mixte succession mortalité des arbres 


  1. Aakala T., Kuuluvainen T., De Grandpré L., and Gauthier S., 2007. Trees dying standing in the northeastern boreal old-growth forests of Quebec: spatial patterns, rates and temporal variation. Can. J. For. Res. 37: 50–61.CrossRefGoogle Scholar
  2. Baddeley A. and Turner R., 2005. Spatstat: an R package for analyzing spatial point patterns. J. Stat. Soft. 12: 1–42.Google Scholar
  3. Baddeley A. and Turner R., 2006. Modelling Spatial Point Patterns in R. In: Baddeley A., Gregori P., Mateu J., Stoica R., and Stoyan D. (Eds.), Case studies in spatial point process modelling, Springer, Heidelberg, pp. 23–74.CrossRefGoogle Scholar
  4. Baddeley A., Moller J., and Waagepetersen R., 2000. Non- and semiparametric estimation of interaction in inhomogeneous point patterns. Stat. Neer. 54: 329–350.CrossRefGoogle Scholar
  5. Batista J.L.F. and Maguire D.A., 1998. Modelling the spatial structure of tropical forests. For. Ecol. Manage. 110: 293–314.CrossRefGoogle Scholar
  6. Bigler C. and Bugmann H., 2004. Predicting the time of tree death using dendrochronological data. Ecol. Appl. 14: 902–914.CrossRefGoogle Scholar
  7. 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
  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. De la Cruz M., 2006. Introducción al análisis de datos mapeados o algunas de las (muchas) cosas que puedo hacer si tengo coordenadas. Ecosistemas 15: 19–39.Google Scholar
  10. De la Cruz M., 2008. ECESPA: Functions for spatial point pattern analysis. R package version 1.1-0. Scholar
  11. De la Cruz M., Romao R., and Escudero A. Where do seedlings die? A spatio-temporal analysis of early mortality in a semi-arid gypsophyte. Ecography, DOI: 10.1111/j.0906-7590.2008.05299.x.Google Scholar
  12. Dobbertin M., Baltensweiler A., and Rigling D., 2001. Tree mortality in an unmanaged mountain pine (Pinus mugo var. uncinata) stand in the Swiss National Park impacted by root rot fungi. For. Ecol. Manage. 145: 79–89.CrossRefGoogle Scholar
  13. Drobyshev I., Linderson H., and Sonesson K., 2007. Temporal mortality pattern of pedunculate oaks in southern Sweden. Dendrochronologia 24: 97–108.CrossRefGoogle Scholar
  14. Drobyshev I., Niklasson M., Linderson H., Sonesson K., Karlsson M., Nilsson S.G., and Lanner J., 2008. Lifespan and mortality of old oaks — combining empirical and modelling approaches to support their management. in Southern Sweden. Ann. For. Sci. 65: 401.CrossRefGoogle Scholar
  15. Garcia D. and Zamora R., 1993. Persistence, multiple demographic strategies and conservation in long-lived Mediterranean plants. J. Veg. Sci. 14: 921–926.CrossRefGoogle Scholar
  16. Goreaud F. and Pelissier R., 1999. On explicit formulas of edge effect correction for Ripley’s K-function. J. Veg. Sci. 10: 433–438.CrossRefGoogle Scholar
  17. Grimm V. and Railsback S.F., 2005. Individual-based Modeling and Ecology. Princeton University Press, 480 p.Google Scholar
  18. Harper J.L., 1977. Population biology of plants. Academic Press, 892 p.Google Scholar
  19. Hawkes C., 2000. Woody plant mortality algorithms: description, problems and progress. Ecol. Model. 126: 225–228.CrossRefGoogle Scholar
  20. He F.L. and Duncan R.P., 2000. Density dependent effects of tree survival in an old-growth fir forest. J. Ecol. 88: 676–688.CrossRefGoogle Scholar
  21. Herrera J., Laskurain N.A., Escudero A., Loidi J., and Olano J.M., 2001. Sucesión secundaria en un abedular-hayedo en el Parque Natural de Urkiola (Bizkaia) mediante dendrocronología. Lazaroa 22: 59–66.Google Scholar
  22. Hood S. and Bentz B., 2007. Predicting postfire Douglas-fir beetle attacks and tree mortality in the Northern Rocky Mountains. Can. J. For. Res. 37: 1058–1069CrossRefGoogle Scholar
  23. Hosmer D.W. and Lemeshaw S., 2000. Applied logistic regression. 2nd ed. John Wiley & Sons, New York. Blackwell Publishing, 392 p.CrossRefGoogle Scholar
  24. Hubbell S.P., Ahumada J.A., Condit R., and Foster R.B., 2001. Local neighborhood effects on long-term survival of individual trees in a neotropical forest. Ecol. Res. 16: 859–875.CrossRefGoogle Scholar
  25. Kenkel N.C., 1988. Pattern of self-thinning in Jack Pine: testing the random mortality hypothesis. Ecology 69: 1017–1024.CrossRefGoogle Scholar
  26. Laskurain N.A., 2008. Dinámica espacio-temporal de un bosque secundario en el Parque Natural de Urkiola. Tesis Doctoral, Leioa, 180 p.Google Scholar
  27. Legendre P. and Legendre L., 1998. Numerical ecology. Elsevier, Amsterdam, 853 p.Google Scholar
  28. LePage P.T., Canham C.D., Coates K.D., and Bartemucci P., 2000. Seed abundance versus substrate limitation of seedling recruitment in northern temperate forests of British Columbia. Can. J. For. Res. 30: 415–427.CrossRefGoogle Scholar
  29. Leuschner C., Hertel D., Coners H., and Büttner V., 2001. Root competition between beech and oak: a hypothesis. Oecologia 126: 276–284.CrossRefGoogle Scholar
  30. Liu D.S., Kelly M., Gong P., and Guo Q., 2007. Characterizing spatial-temporal tree mortality patterns associated with a new forest disease. For. Ecol. Manage. 253: 220–231.CrossRefGoogle Scholar
  31. Lorimer C.G., 1983. Tests of age independent competition indices for individual trees in natural hardwood stands. For. Ecol. Manage. 6: 343–360CrossRefGoogle Scholar
  32. Lugo A.E. and Scatena F.N., 1996. Background and catastrophic mortality in tropical moist, wet, and rain forests. Biotropica 28, 585–599.CrossRefGoogle Scholar
  33. Luis J.F.S. and Fonseca T.F., 2004. The allometric model in the stand density management of Pinus pinaster Ait. in Portugal. Ann. For. Sci. 61: 807–814.CrossRefGoogle Scholar
  34. Monserud R.A. and Sterba H., 1999. Modeling individual tree mortality for Austrian forest species. For. Ecol. Manage. 113: 109–123.CrossRefGoogle Scholar
  35. Olano J.M. and Palmer M.W., 2003. Stand dynamics of an Appalachian old-growth forest during a severe drought episode. For. Ecol. Manage. 174: 139–148.CrossRefGoogle Scholar
  36. Olano J.M., Caballero I., Laskurain N.A., Loidi J., and Escudero A., 2002. Seed bank spatial patterning in a birch to beech transition forest. J. Veg. Sci. 13: 775–784.CrossRefGoogle Scholar
  37. Oliver C.D. and Larson B.C., 1996. Forest stand dynamics. John Wiley and Sons, Inc., 520 p.Google Scholar
  38. Ozenda P., 1985. La végétation de la chaine alpine dans l’espace montagnard européen. Ed. Masson, 344 p.Google Scholar
  39. Paluch J.G. and Bartkowicz L.E., 2004. Spatial interactions between Scots Pine (Pinus sylvestris L.) common oak (Quercus robur L.) and silver birch (Betula pendula Roth.) as investigated in stratified stands in mesotrophic site conditions. For. Ecol. Manage. 192: 229–240.CrossRefGoogle Scholar
  40. Peet R.K. and Christensen N.L., 1987. Competition and tree death. Bioscience 37: 586–595.CrossRefGoogle Scholar
  41. Piovesan G., Di Filippo A., Alessandrini A., Biondi F., and Schirone B., 2005. Structure, dynamics and dendroecology of an old-growth Fagus forest in the Apennines. J. Veg. Sci. 16: 13–28.Google Scholar
  42. Reggelbrugge J.C. and Conard S.G., 1993. Modeling tree mortality following wildfire in Pinus ponderosa forests of the central Sierra Nevada of California. Int. J. Wildl. Fire 3: 139–148.CrossRefGoogle Scholar
  43. R Development Core Team, 2007. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.Google Scholar
  44. Ripley B.D., 1988. Statistical inference for spatial processes. Cambridge University Press, 148 p.Google Scholar
  45. Sterner R.W., Ribic C.A., and Schatz G.E., 1986. Testing for life historical changes in spatial patterns of four tropical tree species. J. Ecol. 74: 621–633.CrossRefGoogle Scholar
  46. Suarez M.L., Ghermandi L., and Kitzberger T., 2004. Factors predisposing episodic drought-induced tree mortality in Nothofagus site, climatic sensitivity and growth trends. J. Ecol. 92: 954–966.CrossRefGoogle Scholar
  47. 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
  48. Wiegand T., Gunatilleke S., and Gunatilleke N., 2007. Species associations in an heterogeneous Sri Lankan Dipterocarp forest. Am. Nat. 107: 77–95.CrossRefGoogle Scholar
  49. Wolf A., Møller P.F., Bradshaw R.H.W., and Bigle J., 2004. Storm damage and long-term mortality in a semi-natural, temperate deciduous forest. For. Ecol. Manage. 188: 197–210.CrossRefGoogle Scholar
  50. Woods K.D., 2000. Dynamics in late successional hemlock-hardwood forests over three decades. Ecology 81: 110–126.Google Scholar
  51. 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
  52. 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
  53. Yao X., 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
  54. Zens M.S. and Peart D.R., 2003. Dealing with death data: individual hazards, mortality and bias. Trends Ecol. Evol. 18: 366–373.CrossRefGoogle Scholar
  55. Zhao D., Borders B., and Wilson M., 2004. Individual-tree diameter growth and mortality models for bottomland mixed-species hardwood stands in the lower Mississippi alluvial valley. For. Ecol. Manage. 199: 307–322.Google Scholar

Copyright information

© Springer S+B Media B.V. 2009

Authors and Affiliations

  • José Miguel Olano
    • 1
    Email author
  • Nere Amaia Laskurain
    • 2
  • Adrián Escudero
    • 3
  • Marcelino De La Cruz
    • 4
  1. 1.Área de Botánica, Departamento de Ciencias AgroforestalesEscuela de Ingenierías AgrariasSoriaSpain
  2. 2.Laboratorio de Botánica, Departamento de Biología Vegetal y Ecología, Facultad de CienciasUPV/EHUBilbaoSpain
  3. 3.Área de Biodiversidad y Conservación, ESCETUniversidad Rey Juan CarlosMóstolesSpain
  4. 4.Departamento de Biología Vegetal, E. U. I. T. AgrícolaUniversidad Politécnica de MadridMadridSpain

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