Community Ecology

, Volume 8, Issue 2, pp 247–255 | Cite as

Modelling factors affecting litter mass components of pine stands

  • P. CsontosEmail author
  • D. Rocchini
  • G. Bacaro


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 models Litter mass NDVI Needle litter Pinus nigra Remote sensing 



Generalized Linear Model


Normalized Difference Vegetation Index


Near Infrared


Root Mean Squared Error


Variance Inflation Factor


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Authors and Affiliations

  1. 1.MTA-ELTE Research Group for Theoretical Biology and EcologyL. Eötvös UniversityBudapestHungary
  2. 2.Dipartimento di Scienze Ambientali “G. Sarfatti”Università di SienaSienaItaly
  3. 3.TerraData environmetrics, Dipartimento di Scienze Ambientali “G. Sarfatti”Università di SienaSienaItaly

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