Annals of Forest Science

, 74:26

Quantifying competition in white spruce (Picea glauca) plantations

  • Alexa Bérubé-Deschênes
  • Tony Franceschini
  • Robert Schneider
Original Paper

Abstract

Key message

In mixed forest plantations in sub-boreal forests with high levels of natural regeneration ingrowth, competition must be quantified differently for each species, with distant-independent indices working better for the planted species and distant-dependent indices for ingrown balsam fir. Although broadleaved competition hinders growth of coniferous species more than coniferous competition, the differentiation between clades is not important enough to improve growth predictions.

Context

The use of ecosystem-based forest management has changed how forest stands are tended. This shift in the management paradigm has led to a higher tolerance in natural ingrowth regeneration in plantations. The correct way of quantifying competition must thus be assessed to develop growth simulators.

Aims

An individual tree relative basal area increment (RBAI) growth model for white spruce, balsam fir and other coniferous and broadleaved species was calibrated.

Methods

Using data obtained from 94 sample plots in 48 white spruce plantations from Eastern Quebec, we considered both linear and nonlinear models of RBAI as a function of site index, tree size and tree competition. The tested distance-dependent and distance-independent indices were also discriminated according to competitor clade (conifers or broadleaves).

Results

The best competition index for balsam fir was distance-dependent whereas a distant-independent one was retained for the other species groups. Moreover, broadleaved competitors had stronger effect on RBAI for white spruce growth when compared to coniferous competitors.

Conclusion

Competition must be quantified depending on if the species is planted or ingrown. However, dividing competition into clades (i.e. coniferous versus broadleaved) is not necessary, at least in the present study.

Keywords

Inter- and intra-specific competition Tree growth Distance-independent and distance dependant competition indices Modelling 

References

  1. Barrette M, Leblanc M, Thiffault N et al (2014) Issues and solutions for intensive plantation silviculture in a context of ecosystem management. For Chron 90:748–762. doi:10.5558/tfc2014-147 CrossRefGoogle Scholar
  2. Boivin F, Paquette A, Papaik MJ et al (2010) Do position and species identity of neighbours matter in 8–15-year-old post harvest mesic stands in the boreal mixedwood? For Ecol Manag 260:1124–1131. doi:10.1016/j.foreco.2010.06.037 CrossRefGoogle Scholar
  3. Boucher Y, Arseneault D, Sirois L (2006) Logging-induced change (1930-2002) of a preindustrial landscape at the northern range limit of northern hardwoods, eastern Canada. Can J For Res 36:505–517CrossRefGoogle Scholar
  4. Boucher Y, Arseneault D, Sirois L, Blais L (2009) Logging pattern and landscape changes over the last century at the boreal and deciduous forest transition in Eastern Canada. Landsc Ecol 24:171–184. doi:10.1007/s10980-008-9294-8 CrossRefGoogle Scholar
  5. Burns RM, Honkala BH (1990) Silvics of North America. Volume 1. Conifers. Agriculture Handbook (Washington) no. 654Google Scholar
  6. Canham CD, Papaik MJ, Uriarte M et al (2006) Neighborhood analyses of canopy tree competition along environmental gradients in New England forests. Ecol Appl 16:540–554CrossRefPubMedGoogle Scholar
  7. Comeau PG, Wang JR, Letchford T (2003) Influences of paper birch competition on growth of understory white spruce and subalpine fir following spacing. Can J For Res 33:1962–1973CrossRefGoogle Scholar
  8. Daniels RF, Burkhart HE, Clason TR (1986) A comparison of competition measures for predicting growth of loblolly pine trees. Can J For Res 16:1230–1237. doi:10.1139/x86-218 CrossRefGoogle Scholar
  9. Del Degan B (2010) Projet de loi 57 sur l’aménagement durable du territoire forestier. Synthèse des études d’impacts et analyse critique. Del Degan, Massé, Québec, Québec. 16 p. https://www.mffp.gouv.qc.ca/publications/forets/gestion/synthese-etudes-impacts.pdf accessed on Oct. 17th 2016
  10. Dieler J, Pretzsch H (2013) Morphological plasticity of European beech (Fagus sylvatica L.) in pure and mixed-species stands. For Ecol Manag 295:97–108. doi:10.1016/j.foreco.2012.12.049 CrossRefGoogle Scholar
  11. Duursma RA, Mäkelä A, Reid DEB et al (2010) Self-shading affects allometric scaling in trees. Funct Ecol 24:723–730. doi:10.1111/j.1365-2435.2010.01690.x CrossRefGoogle Scholar
  12. Fortin M, Langevin L (2010) ARTÉMIS-2009: un modèle de croissance basé sur une approche par tiges individuelles pour les forêts du Québec. Mémoire de recherche forestière no. 156. Ministère des Ressources naturelles et de la Faune, Direction de la recherche forestière. Gouvernement du Québec, Québec, CanadaGoogle Scholar
  13. Fortin M, Bédard S, Deblois J (2009) SaMARE: un modèle par tiges individuelles destiné à la prévision de la croissance des érablière de structure inéquienne du Québec méridional. Mémoire de recherche forestière no. 155. Ministère des Ressources naturelles et de la Faune, Direction de la recherche forestière. Gouvernement du Québec, Québec, CanadaGoogle Scholar
  14. Fortin M, Tremblay S, Schneider R (2014) Evaluating a single tree-based growth model for even-aged stands against the maximum size–density relationship: some insights from balsam fir stands in Quebec. Canada For Chron 90:503–515CrossRefGoogle Scholar
  15. Franceschini T, Schneider R (2014) Influence of shade tolerance and development stage on the allometry of ten temperate tree species. Oecologia:1–11. doi:10.1007/s00442-014-3050-3
  16. Gagné L, Lavoie L (2014) Rendement des jeunes forêts et potentiel d’éclaircie commerciale dans la forêt publique et la forêt privée du Bas-Saint-Laurent. Conférence Régionale des Élus du Bas-Saint-Laurent (CRÉBSL), Québec, CanadaGoogle Scholar
  17. Gagné, Lavoie, Binot (2012) Croissance et propriétés mécaniques du bois après éclaircie commerciale dans une plantation d’épinette blanche (Picea glauca) âgée de 32 ans. Can J For Res 42:291–302. doi:10.1139/x11-181 CrossRefGoogle Scholar
  18. Gagné L, Sirois L, Lavoie L (2016) Comparaison du volume et de la valeur des bois résineux issus d’éclaircies par le bas et par dégagement d’arbres-élites dans l’Est du Canada. Can J For Res 46:1320–1329CrossRefGoogle Scholar
  19. Gander W, Golub GH, Strebel R (1994) Fitting of circles and ellipses—least squares solutions. British J. Math. Computer Sci 4:33–60Google Scholar
  20. Gauthier S, Vaillancourt M-A, Leduc A et al (2008) Aménagement écosystémique en forêt Boréale. Presse de l’Université du Québec, PUQGoogle Scholar
  21. Getzin S, Dean C, He F et al (2006) Spatial patterns and competition of tree species in a Douglas-fir chronosequence on Vancouver Island. Ecography 29:671–682CrossRefGoogle Scholar
  22. Glover G, Hool J (1979) A basal area ratio predictor of loblolly pine plantation mortality. For. Sci. 25(2): 275–282Google Scholar
  23. Goudiaby V, Brais S, Berninger F, Schneider R (2012) Vertical patterns in specific volume increment along stems of dominant jack pine (Pinus banksiana) and black spruce (Picea mariana) after thinning. Can J For Res 42:733–748. doi:10.1139/x2012-029 CrossRefGoogle Scholar
  24. Groot A, Adhikary S, Sharma M et al (2014) Effect of species composition on the production rate and efficiency of young Picea glaucaPopulus tremuloides forests. For Ecol Manag 315:1–11CrossRefGoogle Scholar
  25. Hegyi F (1974) A simulation model for managing jack-pine stands. Growth models for tree and stand simulation 30: 74-90.Google Scholar
  26. Humbert L, Gagnon D, Kneeshaw D, Messier C (2007) A shade tolerance index for common understory species of northeastern North America. Ecol Indic 7:195–207. doi:10.1016/j.ecolind.2005.12.002 CrossRefGoogle Scholar
  27. Larocque GR (2002) Examining different concepts for the development of a distance-dependent competition model for red pine diameter growth using long-term stand data differing in initial stand density. For Sci 48:24–34Google Scholar
  28. Larocque GR, Marshall PL (1993) Evaluating the impact of competition using relative growth rate in red pine (Pinus resinosa Ait.) stands. For Ecol Manag 58:65–83. doi:10.1016/0378-1127(93)90132-7 CrossRefGoogle Scholar
  29. Martin GL, Ek AR (1984) A comparison of competition measures and growth models for predicting plantation red pine diameter and height growth. For. Sci. 30(3): 731-743Google Scholar
  30. Martin E, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd 96:226–231Google Scholar
  31. Millet J (2012) L’architecture des arbres des régions tempérées. Multimondes, QuébecGoogle Scholar
  32. Ministère des Forêts, de la Faune et des Parcs (2013) Loi sur l’aménagement durable du territoire forestier. Gouvernement du Québec, QuébecGoogle Scholar
  33. Nyland RD (2003) Even- to uneven-aged: the challenges of conversion. For Ecol Manag 172:291–300. doi:10.1016/S0378-1127(01)00797-6 CrossRefGoogle Scholar
  34. Othmani A, Piboule A, Krebs M, et al (2011) Towards automated and operational forest inventories with T-Lidar, In 11th International Conference on LiDAR Applications for Assessing Forest Ecosystems (SilviLaser 2011). Hobart, AustraliaGoogle Scholar
  35. Pinheiro JC, Bates DM (2000) Mixed-effects models in S and S-PLUS. Springer, New YorkCrossRefGoogle Scholar
  36. Pinheiro J, Bates D, DebRoy S, et al (2015) nlme: linear and nonlinear mixed effects models. https://cran.r-project.org/web/packages/nlme/index.html accessed on Oct. 17th 2016
  37. Prégent G, Picher G, Auger I (2010) Tarif de cubage, tables de rendement et modèles de croissance pour les plantations d’épinette blanche au Québec. Mémoire de recherche forestière no. 176. Ministère des Forêts, de la Faune et des Parcs, Direction de la recherche forestière. Gouvernement du Québec, Québec, CanadaGoogle Scholar
  38. Pretzsch H (2009) Forest dynamics, growth and yield, springer. Springer Berlin HeidelbergGoogle Scholar
  39. Pretzsch H, Bielak K, Block J et al (2013) Productivity of mixed versus pure stands of oak (Quercus petraea (Matt.) Liebl. And Quercus robur L.) and European beech (Fagus sylvatica L.) along an ecological gradient. Eur J For Res 132:263–280. doi:10.1007/s10342-012-0673-y CrossRefGoogle Scholar
  40. Prévosto B (2005) Les indices de compétition en foresterie: exemples d’utilisation, intérêts et limites. Revue forestière française 5:413–430CrossRefGoogle Scholar
  41. R Development Core Team (2011) R: a language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. https://www.R-project.org/accessed on October 17th 2016
  42. Reineke LH (1933) Perfecting a stand-density index for even-aged forests. J Agric Res 46:627–638Google Scholar
  43. Renka RJ, Gebhardt A, Eglen S, et al (2013) tripack: triangulation of irregularly spaced data. R package version 1.3–6. https://CRAN.R-project.org/package=tripack accessed on Oct. 17th 2016
  44. Robitaille A, Saucier J-P (1998) Paysages régionaux du Québec méridional. Direction de la gestion des stocks forestiers et Direction des relations publiques, Ministère des ressources Naturelles du Québec, Les publications du Québec. QuébecGoogle Scholar
  45. Schneider R, Franceschini T, Fortin M et al (2016) Growth and yield models for predicting tree and stand productivity. In: Ecological forest management handbook. Taylor & Francis Group/CRC Press. Taylor & Francis Group/CRC Press, Boca Raton, FloridaGoogle Scholar
  46. Schütz JP (2001) Opportunities and strategies of transforming regular forests to irregular forests. For Ecol Manag 151:87–94CrossRefGoogle Scholar
  47. Schütz JP (2002) Silvicultural tools to develop irregular and diverse forest structures. Forestry 75:329–337CrossRefGoogle Scholar
  48. Simard SW, Sachs DL, Vyse A, Blevins LL (2004) Paper birch competitive effects vary with conifer tree species and stand age in interior British Columbia forests: implications for reforestation policy and practice. For Ecol Manag 198:55–74CrossRefGoogle Scholar
  49. Simard SW, Hagerman SM, Sachs DL et al (2005) Conifer growth, Armillaria ostoyae root disease, and plant diversity responses to broadleaf competition reduction in mixed forests of southern interior British Columbia. Can J For Res 35:843–859CrossRefGoogle Scholar
  50. Spurr SH (1962) A measure of point density. For. Sci. 8(1): 85-96Google Scholar
  51. Valladares F, Niinemets Ü (2008) Shade tolerance, a key plant feature of complex nature and consequences. Annu Rev Ecol Evol Syst 39:237–257. doi:10.1146/annurev.ecolsys.39.110707.173506 CrossRefGoogle Scholar
  52. Valladares F, Pearcy RW (1998) The functional ecology of shoot architecture in sun and shade plants of Heteromeles arbutifolia M. Roem., a Californian chaparral shrub. Oecologia 114:1–10CrossRefPubMedGoogle Scholar
  53. Voronoï G (1908) Nouvelles applications des paramètres continus à la théorie des formes quadratiques. Deuxième mémoire Recherches sur les parallélloèdres primitifs Journal für die reine und angewandte Mathematik 134:198–287Google Scholar
  54. Wang JR, Comeau P, Kimmins JP (1995) Simulation of mixedwood management of aspen and white spruce in northeastern British Columbia. Water Air Soil Pollut 82:171–178. doi:10.1007/BF01182831 CrossRefGoogle Scholar
  55. Weiskittel AR, Hann DW, Kershaw Jr, JA, Vanclay JK (2011) Forest growth and yield modeling. John Wiley & Sons. West SussexGoogle Scholar
  56. Wykoff WR, Crookston NL, Stage, AR (1982) User's guide to the stand prognosis model. General technical report INT-133. S Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station.Google Scholar

Copyright information

© INRA and Springer-Verlag France 2017

Authors and Affiliations

  • Alexa Bérubé-Deschênes
    • 1
  • Tony Franceschini
    • 1
  • Robert Schneider
    • 1
  1. 1.Chaire de Recherche sur la Forêt Habitée, Département de Biologie, Chimie et GéographieUniversité du Québec à Rimouski (UQAR)RimouskiCanada

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