Intra-specific differences in allometric equations for aboveground biomass of eastern Mediterranean Pinus brutia
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Biomass prediction is important when dealing for instance with carbon sequestration, wildfire modeling, or bioenergy supply. Although allometric models based on destructive sampling provide accurate estimates, alternative species-specific equations often yield considerably different biomass predictions. An important source of intra-specific variability remains unexplained.
The aims of the study were to inspect and assess intra-specific differences in aboveground biomass of Pinus brutia Ten. and to fill the gap in knowledge on biomass prediction for this species.
Two hundred one trees between 2.3 and 55.8 cm in diameter at breast height were sampled throughout the eastern- and southernmost natural distribution area of P. brutia, in Middle East, where it forms different stand structures. Allometric equations were fitted separately for two countries. The differences in biomass prediction at tree, stand, and forest level were analyzed. The effect of stand structure and past forest management was discussed.
Between-country differences in total aboveground biomass were not large. However, differences in biomass stock were large when tree components were analyzed separately. Trees had higher stem biomass and lower crown biomass in dense even-aged stands than in more uneven-aged and sparse stands.
Biomass and carbon predictions could be improved by taking into account stand structure in biomass models.
KeywordsAllometry Biomass allocation Allometric models Carbon sequestration Biomass prediction Pine Stand structure
Data collection was supported by Agencia Española de Cooperación Internacional para el Desarrollo (AECID) and Fundación Biodiversidad. The authors wish to thank the Ministries of Agriculture of the Governments of Lebanon and Syria as well as the Forest Sciences Centre of Catalonia (CTFC) for their precious collaboration.
- Bragg DC, Guldin JM (2010) Estimating long-term carbon sequestration patterns in even- and uneven-aged southern pine stands. In: Jain TB, Graham RT, Sandquist J (eds) Integrated management of carbon sequestration and biomass utilization opportunities in a changing climate: Proceedings of the 2009 National Silviculture Workshop; 2009 June 15–18; Boise, ID. Proceedings RMRS-P-61. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins, pp 111–123Google Scholar
- Chave J, Andalo C, Brown S, Cairns MA, Chambers JQ, Eamus D, Fölster H, Fromard F, Higuchi N, Kira T, Lescure JP, Nelson BW, Ogawa H, Puig H, Riéra B, Yamakura T (2005) Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145:87–99PubMedCrossRefGoogle Scholar
- Crow TR (1978) Common regressions to estimate tree biomass in tropical stands. For Sci 24:110–114Google Scholar
- de-Miguel S, Mehtätalo L, Shater Z, Kraid B, Pukkala T (2012a) Evaluating marginal and conditional predictions of taper models in the absence of calibration data. Can J For Res 42:1383–1394Google Scholar
- de-Miguel S, Pukkala T, Assaf N, Bonet JA (2012b) Even-aged or uneven-aged modelling approach? A case for Pinus brutia. Ann For Sci 69:455–465Google Scholar
- de-Miguel S, Pukkala T, Shater Z, Assaf N, Kraid B, Palahí M (2010) Models for simulating the development of even-aged Pinus brutia stands in Middle East. Forest Syst 19:449–457Google Scholar
- del Río M, Barbeito I, Bravo-Oviedo A, Calama R, Cañellas I, Herrero C, Bravo F (2008) Carbon sequestration in Mediterranean pines forests. In: Bravo F, LeMay V, Jandl R, von Gadow K (eds) Managing forest ecosystems: the challenge of climate change. Springer, Berlin, pp 221–246, 338 pCrossRefGoogle Scholar
- Durkaya A, Durkaya B, Ünsal A (2009) Predicting the above-ground biomass of calabrian pine (Pinus brutia Ten.) stands in Turkey. Afr J Biotechnol 8:2483–2488Google Scholar
- Feldpausch TR, Banin L, Phillips OL, Baker TR, Lewis SL, Quesada CA, Affum-Baffoe K, Arets EJMM, Berry NJ, Bird M, Brondizio ES, de Camargo P, Chave J, Djagbletey G, Domingues TF, Drescher M, Fearnside PM, França MB, Fyllas NM, Lopez-Gonzalez G, Hladik A, Higuchi N, Hunter MO, Iida Y, Salim KA, Kassim AR, Keller M, Kemp J, King DA, Lovett JC, Marimon BS, Marimon-Junior BH, Lenza E, Marshall AR, Metcalfe DJ, Mitchard ETA, Moran EF, Nelson BW, Nilus R, Nogueira EM, Palace M, Patiño S, Peh KSH, Raventos MT, Reitsma JM, Saiz G, Schrodt F, Sonké B, Taedoumg HE, Tan S, White L, Wöll H, Lloyd J (2011) Height–diameter allometry for tropical forest trees. Biogeosciences 8:1081–1106CrossRefGoogle Scholar
- Gray KL, Reinhardt ED (2003) Analysis of Algorithms for predicting canopy fuel. In: Proceedings of the Second International Wildland Fire Ecology and Fire Management Congress and Fifth Symposium on Fire and Forest Meteorology, November 16–20, 2003, Orlando, FL. American Meteorological Society, p 5.8Google Scholar
- Henry M, Picard N, Trotta C, Manlay RJ, Valentini R, Bernoux M, Saint-André L (2011) Estimating tree biomass of Sub-Saharan African forests: a review of available allometric equations. Silva Fenn 45:477–569Google Scholar
- Intergovernmental Panel on Climate Change (IPCC) (2006) IPCC guidelines for national greenhouse gas inventories, prepared by the National Greenhouse Gas Inventories Programme, Eggleston HS, Buendia L, Miwa K, Ngara T, Tanabe K (eds). IGES, JapanGoogle Scholar
- Isik F, Isik K, Lee SJ (1999) Genetic variation in Pinus brutia Ten. in Turkey: I. growth, biomass and stem quality traits. For Genet 6:89–99Google Scholar
- Jenkins JC, Chojnacky DC, Heath LS, Birdsey RA (2003) National-scale biomass estimators for United States tree species. For Sci 49:12–35Google Scholar
- Küçük Ö, Bilgili E (2007) Crown fuel load for young calabrian pine (Pinus brutia Ten.) trees. J For Fac Kastamonu Uni-Kastamonu 7:180–189Google Scholar
- Marklund LG (1987) Biomass functions for Norway spruce (Picea abies (L.) Karst.) in Sweden. Sveriges lantbruksuniversitet Rapporter–Skog 43:1–127Google Scholar
- Marklund LG (1988) Biomassafunktioner för tall, gran och björk i Sverige. Sveriges lantbruksuniversitet Rapporter–Skog 45:1–73Google Scholar
- Montero G, Ruiz-Peinado R, Muñóz M (2005) Producción de biomasa y fijación de CO2 por los bosques españoles. Monografías INIA nO 13Google Scholar
- Návar J (2010) Measurement and assessment methods of forest aboveground biomass: a literature review and the challenges ahead. In: Momba M, Bux F (eds) Biomass. Sciyo, Croatia, pp 27–64Google Scholar
- Palumets YK (1988) Distribution of Norway spruce phytomass fractions as a function of age and climatic factors. Soviet Forest Sci 2:34–40Google Scholar
- Pukkala T, Karsikko J, Kolström T (1992) A spatial model for the diameter of thickest branch of Scots pine. Silva Fenn 26:219–230Google Scholar
- R Development Core Team (2011) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. ISBN 3-900051-07-0. http://www.R-project.org/
- Repola J (2009) Biomass equations for Scots pine and Norway spruce in Finland. Silva Fenn 43:625–647Google Scholar
- Snowdon P, Eamus D, Gibbons P, Khanna PK, Keith H, Raison RJ, Kirschbaum MUF (2000) Synthesis of allometrics, review of root biomass and design of future woody biomass sampling strategies. National Carbon Accounting System Technical Report 17. Australian Greenhouse Office, Canberra, 114 pGoogle Scholar
- Tinker D, Stakes GK, Arcano RM (2010) Allometric equation development, biomass, and aboveground productivity in Ponderosa pine forests, Black Hills, Wyoming. West J Appl For 25:112–119Google Scholar
- Zianis D, Muukkonen P, Mäkipää R, Mencuccini M (2005) Biomass and stem volume equations for tree species in Europe. Silva Fenn Monographs 4, 63 pGoogle Scholar