Litter mass represents a key factor in the process of carbon sequestration. Pine plantations are known to accumulate high amounts of litter, which may act as real carbon sink only if it persists for long time. Thus, predicting litter mass by means of robust and straightforward models which convey information from several ecological predictors become crucial in this framework. The aim of this paper was to test for relationships between environmental predictors and pine litter mass (total branch, needle and cone) by Generalized Linear Models, exploring the contribution of different environmental variables in describing patterns of pine litter mass. Different predictors accounting for seasonality, spatial and geomorphological variability, pine stand properties, remotely sensed derived biomass were taken into account. Considering total litter mass, observed vs. predicted values showed a statistically highly significant relation (p < 0.001) by retaining four variables: elevation, latitude, stand age and season. Similar results were achieved for the needle litter mass, which represented anyway the largest fraction of the litter. Regarding branch litter mass, only stand age appeared to be a significant variable. For cone litter mass, no variables were statistically significant in explaining its variance. Potential ecological background processes responsible for the correlations between variables are discussed.
Generalized Linear Model
Normalized Difference Vegetation Index
Root Mean Squared Error
Variance Inflation Factor
Ågren, G.I. and M.F. Knecht. 2001. Simulation of soil carbon and nutrient development under Pinus sylvestris and Pinus contorta. Forest Ecol. Manag. 141:117–129.
Andrzejewska, L. and K. Petrusewicz. (eds) 1975. Polish Participation in the International Biological Programme 1964–1973. Polish Academy of Sciences, Warsaw.
Berg, B. and V. Meentemeyer. 2001. Litter fall in some European coniferous forests as dependent on climate: a synthesis. Can. J. Forest Res. 31(2):292–301.
Bray, J.R. and E. Gorham. 1964. Litter prodution of forests of the world. Adv. in Ecol. Res. 2:101–157.
Coppin, P., I. Jonckheere, K. Nackaerts, B. Muys and E. Lambin. 2004. Digital change detection methods in ecosystem monitoring: a review. Int J. Remote Sens. 25: 1565–1596.
Cseresnyés, I. and P. Csontos. 2004. Feketefenyvesek tûzveszélyességi viszonyainak elemzése McArthur modelljével. Tájökológiai Lapok 2(2):231–252.
Cseresnyés, I. and P. Csontos. 2006. Szárazsági viszonyok változása feketefenyvesekben. Tájökológiai Lapok 4(2):255–268.
Cseresnyés, I., P. Csontos and E. Bózsing 2006 Stand age influence on litter mass of Pinus nigra plantations on dolomite hills in Hungary. Can. J. Bot. 84(3):363–370.
Crawley, M.J. 1986. The structure of plant communities, In: M.J. Crawley (ed.), Plant Ecology. Blackwell, Oxford. pp. 1–50.
Das, A.K. and P.S. Ramakrishnan. 1985. Litter dynamics in khasi pine (Pinus kesiyaRoyle ex Gordon) of North-Estern India. Forest Ecol. Manag. 10(1–2):135–153.
De Santo, A.V., F.A. Rutigliano, B. Berg, A. Fioretto, G. Puppi and A. Alfani. 2002. Fungal mycelium and decomposition of needle litter in three contrasting coniferous forests. Acta Oecol. 23(4):247–259.
Dewar, R.C. and M.G.R. Cannell. 1992. Carbon sequestration in the trees, products and soils of forest plantations - an analysis using UK examples. Tree Physiology 11(1):49–71.
Ellenberg, H. 1971. Integrated Experimental Ecology. Methods and Results of Ecosystem Research in the German Solling Project. Springer, Heidelberg.
Engler, R., A. Guisan and L. Rechsteiner. 2004. An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. J. Appl. Ecol. 41:263–274.
Filcheva, E., M. Noustorova, S. Gentcheva-Kostadinova and M.J. Haigh. 2000. Organic accumulation and microbial action in surface coal-mine spoils, Pernik, Bulgaria. Ecol. Eng. 15(1–2):1–15.
Fioretto, A., A. Musacchio, G. Andolfi and A.V. De Santo. 1998. Decomposition dynamics of litters of various pine species in a corsican pine forest. Soil Biol. Biochem. 30(6): 721–727.
Fox, J. 1997. Applied Regression, Linear Models, and Related Methods. Sage, Beverly Hills, CA.
García-Plé, C., P. Vanrell and M. Morey. 1995. Litter fall and decomposition in a Pinus halepensis forest on Mallorca. J. Veg. Sci. 6:7–22.
Gower, S.T., R.E. McMurtrie and D. Murty. 1996. Aboveground net primary production decline with stand age: potential causes. Trends Ecol. Evol. 11(9):378–382.
Guisan, A., J. Edwards and T. Hastie. 2002. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol. Model. 157:89–100.
Guisan, A. and N.E. Zimmermann. 2000. Predictive habitat distribution models in ecology. Ecol. Model. 135:147–186.
Hastie, T.J. and D. Pregibon. 1992. Generalized linear models, In: J.M. Chambers and T.J. Hastie (eds.) Statistical Models in S. Wadsworth and Brooks/Cole, California, pp. 195–248.
Jakucs, P. (ed.) 1985. Ecology of an oak forest in Hungary. Results of “Síkfõkút Project” 1. Akadémiai Kiadó, Budapest.
Kavvadias, V.A., D. Alifragis, A. Tsiontsis, G. Brofas and G. Stamatelos. 2001. Litterfall, litter accumulation and litter decomposition rates in four forest ecosystems in northern Greece. Forest Ecol. Manag. 144:113–127.
Koetz, B., F. Baret, H. Poilve and J. Hill. 2005. Use of coupled canopy structure dynamic and radiative transfer models to estimate biophysical canopy characteristics. Remote Sens. Environ. 95:115–124.
Law, B.E., P.E. Thornton, J. Irvine, P.M. Anthoni and S. Van Tuyl. 2001. Carbon storage and fluxes in ponderosa pine forests at different developmental stages. Global Change Biol. 7(7):755–777.
Legendre, P. and L. Legendre. 1998. Numerical Ecology, 2nd English ed. Elsevier, Amsterdam.
Lillesand, T.M., R.W. Kiefer and J.W. Chipman. 2003. Remote Sensing and Image Interpretation. Wiley, New York.
Makinen, H. and F. Colin. 1999. Predicting the number, death, and self-pruning of branches in Scots pine. Can. J. Forest Res. 29(8):1225–1236.
McCullagh, P. and J.A. Nelder. 1989. Generalized Linear Models, 2nd ed. Chapman and Hall, London.
Moreno, J.M. 1998. Large Forest Fires. Backhuys, Leiden.
Newbould, P.J. 1967. Methods for estimating the primary production of forest. IBP Handbook. Blackwell, Oxford.
Noble, I.R., G.A.V. Bary and A.M. Gill 1980. McArthur’s fire-danger meters expressed as equations, Australian J. Ecol. 5:201–203.
Ohlemüller, R., P. Bannister, K.J.M. Dickinson, S. Walker, B.J. Anderson and J.B. Wilson. 2004. Correlates of vascular plant species richness in fragmented indigenous forests: assessing the role of local and regional factors. Community Ecol. 5:45–54.
Orlóci, L.and N.C. Kenkel. 1985. Introduction to Data Analysis with Applications from Population and Community Ecology. International Co-operative, Fairland (Maryland).
Osono, T., Y. Ono and H. Takeda. 2003. Fungal ingrowth on forest floor and decomposing needle litter of Chamaecyparis obtusa in relation to resource availability and moisture condition. Soil Biol. Biochem. 35(11):1423–1431.
Paul, K.I., P.J. Polglase, J.G. Nyakuengama and P.K. Khanna. 2002. Change in soil carbon following afforestation. Forest Ecol. Manag. 168(1–3):241–257.
Pausas, J.G. 1997. Litter fall and litter decomposition in Pinus sylvestris forests of the eastern Pyrenees. J. Veg. Sci. 8:643–650.
Pausas, J.G., J. Carreras, A. Ferrè and X. Font. 2003. Coarse-scale plant species richness in relation to environmental heterogeneity. J. Veg. Sci. 14:661–668.
Peichl, M. and M.A. Arain. 2006. Above- and belowground ecosystem biomass and carbon pools in an age-sequence of temperate pine plantation forests. Agr. Forest Meteorol. 140:51–63.
Pécsi, M. 1999. (ed.) NationAl Atlas of Hungary. Cartographia, Budapest.
Pócs, T. 1999. Studies on the cryptogamic vegetation of loess cliff, I. Orographic desert in the Carpathian Basin (in Hungarian). Kitaibelia 4(1):143–156.
R Development Core Team. 2007. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.
Rees, W.G. 2001. Physical Principles of Remote Sensing. Cambridge University Press, Cambridge.
Rocchini, D. 2007a. Distance decay in spectral space in analysing ecosystem β-diversity. Int. J. Remote Sens. 28(11):2635–2644.
Rocchini, D. 2007b. Effects of spatial and spectral resolution in estimating ecosystem α-diversity by satellite imagery. Remote Sens. Environ. 111(4):423–434.
Rocchini, D., C. Ricotta and A. Chiarucci. 2007. Using satellite imagery to assess plant species richness: The role of multispectral systems. Appl. Veg. Sci. 10:325–332.
Roig, S., M. del Río, I. Canellas and G. Montero. 2005. Litter fall in Mediterranean Pinus pinaster Ait. stands under different thinning regimes. Forest Ecol. Manag. 206(1–3):179–190.
Rushton, S.P., S.J. Ormerod and G. Kerby. 2004. New paradigms for modelling species distributions? J. Appl. Ecol. 41:193–200.
Schlesinger, W.H. and J. Lichter. 2001. Limited carbon storage in soil and litter of experimental forest plots under increased atmospheric CO2. Nature 411:466–469.
Schulze, E.D., J. Lloyd, F.M. Kelliher, C. Wirth, C. Rebmann, B. Luhker, M. Mund, A. Knohl, I.M. Milyukova, W. Schulze, W. Ziegler, A.B. Varlagin, A.F. Sogachev, R. Valentini, S. Dore, S. Grigoriev, O. Kolle, M.I. Panfyorov, N. Tchebakova and N.N. Vygodskaya. 1999. Productivity of forests in the Eurosiberian boreal region and their potential to act as a carbon sink – a synthesis. Global Change Biol. 5(6):703–722.
Singh, B. 1984. Conservation and fixation of solar energy in Pinus patula plantations of Darjeeling Himalaya. Biomass 5(1):43–54.
Smith, F.E. 1968. The International Biological Program and the science of ecology. Proc. Natl. Acad. Sci. USA 60(1):5–11.
Tamás, J. 2003. The history of Austrian pine plantations in Hungary. Acta Botanica Croatica 62(2):147–158.
Wardle, D.A., O. Zackrisson, G. Hörnberg and C. Gallet. 1997. The influence of island area on ecosystem processes. Science 227:1296–1299.
Weisberg, S. 1980. Applied Linear Regression. Wiley, New York.
Wienand, K.T. and D.S. William. 1995. Long-term phosphorus fertilization effects on the litter dynamics of an age sequence of Pinus elliottii plantations in the southern Cape of South Africa. Forest Ecol. Manag. 75:135–146.
Zólyomi, B. 1942. Die Mitteldonau-Florenscheide und das Dolomitphänomen. Botanikai Közlemények 39(5):209–231.
About this article
Cite this article
Csontos, P., Rocchini, D. & Bacaro, G. Modelling factors affecting litter mass components of pine stands. COMMUNITY ECOLOGY 8, 247–255 (2007). https://doi.org/10.1556/ComEc.8.2007.2.11
- Generalized linear models
- Litter mass
- Needle litter
- Pinus nigra
- Remote sensing