Modelling factors affecting litter mass components of pine stands

Abstract

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.

Abbreviations

GLM:

Generalized Linear Model

NDVI:

Normalized Difference Vegetation Index

NIR:

Near Infrared

RMSE:

Root Mean Squared Error

VIF:

Variance Inflation Factor

References

  1. 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.

    Article  Google Scholar 

  2. Andrzejewska, L. and K. Petrusewicz. (eds) 1975. Polish Participation in the International Biological Programme 1964–1973. Polish Academy of Sciences, Warsaw.

    Google Scholar 

  3. 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.

    Article  Google Scholar 

  4. Bray, J.R. and E. Gorham. 1964. Litter prodution of forests of the world. Adv. in Ecol. Res. 2:101–157.

    Article  Google Scholar 

  5. 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.

    Article  Google Scholar 

  6. Cseresnyés, I. and P. Csontos. 2004. Feketefenyvesek tûzveszélyességi viszonyainak elemzése McArthur modelljével. Tájökológiai Lapok 2(2):231–252.

    Google Scholar 

  7. Cseresnyés, I. and P. Csontos. 2006. Szárazsági viszonyok változása feketefenyvesekben. Tájökológiai Lapok 4(2):255–268.

    Google Scholar 

  8. 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.

  9. Crawley, M.J. 1986. The structure of plant communities, In: M.J. Crawley (ed.), Plant Ecology. Blackwell, Oxford. pp. 1–50.

  10. 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.

  11. 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.

    Article  Google Scholar 

  12. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Ellenberg, H. 1971. Integrated Experimental Ecology. Methods and Results of Ecosystem Research in the German Solling Project. Springer, Heidelberg.

    Google Scholar 

  14. 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.

    Article  Google Scholar 

  15. 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.

    Article  Google Scholar 

  16. 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.

  17. Fox, J. 1997. Applied Regression, Linear Models, and Related Methods. Sage, Beverly Hills, CA.

    Google Scholar 

  18. 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.

    Article  Google Scholar 

  19. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 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.

    Article  Google Scholar 

  21. Guisan, A. and N.E. Zimmermann. 2000. Predictive habitat distribution models in ecology. Ecol. Model. 135:147–186.

    Article  Google Scholar 

  22. 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.

  23. Jakucs, P. (ed.) 1985. Ecology of an oak forest in Hungary. Results of “Síkfõkút Project” 1. Akadémiai Kiadó, Budapest.

  24. 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.

    Article  Google Scholar 

  25. 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.

    Article  Google Scholar 

  26. 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.

  27. Legendre, P. and L. Legendre. 1998. Numerical Ecology, 2nd English ed. Elsevier, Amsterdam.

    Google Scholar 

  28. Lillesand, T.M., R.W. Kiefer and J.W. Chipman. 2003. Remote Sensing and Image Interpretation. Wiley, New York.

    Google Scholar 

  29. 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.

    Article  Google Scholar 

  30. McCullagh, P. and J.A. Nelder. 1989. Generalized Linear Models, 2nd ed. Chapman and Hall, London.

  31. Moreno, J.M. 1998. Large Forest Fires. Backhuys, Leiden.

    Google Scholar 

  32. Newbould, P.J. 1967. Methods for estimating the primary production of forest. IBP Handbook. Blackwell, Oxford.

  33. 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.

    Article  Google Scholar 

  34. 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.

    Article  Google Scholar 

  35. Orlóci, L.and N.C. Kenkel. 1985. Introduction to Data Analysis with Applications from Population and Community Ecology. International Co-operative, Fairland (Maryland).

    Google Scholar 

  36. 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.

    Article  CAS  Google Scholar 

  37. 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.

    Article  Google Scholar 

  38. Pausas, J.G. 1997. Litter fall and litter decomposition in Pinus sylvestris forests of the eastern Pyrenees. J. Veg. Sci. 8:643–650.

    Article  Google Scholar 

  39. 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.

    Article  Google Scholar 

  40. 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.

    Article  Google Scholar 

  41. Pécsi, M. 1999. (ed.) NationAl Atlas of Hungary. Cartographia, Budapest.

  42. 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.

    Google Scholar 

  43. 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.

  44. Rees, W.G. 2001. Physical Principles of Remote Sensing. Cambridge University Press, Cambridge.

    Book  Google Scholar 

  45. Rocchini, D. 2007a. Distance decay in spectral space in analysing ecosystem β-diversity. Int. J. Remote Sens. 28(11):2635–2644.

    Article  Google Scholar 

  46. Rocchini, D. 2007b. Effects of spatial and spectral resolution in estimating ecosystem α-diversity by satellite imagery. Remote Sens. Environ. 111(4):423–434.

    Article  Google Scholar 

  47. 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.

    Article  Google Scholar 

  48. 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.

  49. Rushton, S.P., S.J. Ormerod and G. Kerby. 2004. New paradigms for modelling species distributions? J. Appl. Ecol. 41:193–200.

    Article  Google Scholar 

  50. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. 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.

    Article  Google Scholar 

  52. Singh, B. 1984. Conservation and fixation of solar energy in Pinus patula plantations of Darjeeling Himalaya. Biomass 5(1):43–54.

    Article  Google Scholar 

  53. Smith, F.E. 1968. The International Biological Program and the science of ecology. Proc. Natl. Acad. Sci. USA 60(1):5–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Tamás, J. 2003. The history of Austrian pine plantations in Hungary. Acta Botanica Croatica 62(2):147–158.

    Google Scholar 

  55. Wardle, D.A., O. Zackrisson, G. Hörnberg and C. Gallet. 1997. The influence of island area on ecosystem processes. Science 227:1296–1299.

    Article  Google Scholar 

  56. Weisberg, S. 1980. Applied Linear Regression. Wiley, New York.

    Google Scholar 

  57. 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.

    Article  Google Scholar 

  58. Zólyomi, B. 1942. Die Mitteldonau-Florenscheide und das Dolomitphänomen. Botanikai Közlemények 39(5):209–231.

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to P. Csontos.

Rights and permissions

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and Permissions

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

Download citation

Keywords

  • Generalized linear models
  • Litter mass
  • NDVI
  • Needle litter
  • Pinus nigra
  • Remote sensing