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Lianas and soil nutrients predict fine-scale distribution of above-ground biomass in a tropical moist forest

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Author(s)

Alicia Ledo, Janine B. Illian, Stefan A. Schnitzer, S. Joseph Wright, James W. Dalling, David F. R. P. Burslem

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Abstract

1.  Prediction of carbon dynamics in response to global climate change requires an understanding of the processes that govern the distribution of carbon stocks. Above ground biomass (AGB) in tropical forests is regulated by variation in soil fertility, climate, species composition and topography at regional scales, but the drivers of fine-scale variation in tropical forest AGB are poorly understood. The factors that control the growth and mortality of individual trees may be obscured by the low resolution of studies at regional scales.
2.  In this paper, we evaluate the effects of soil nutrients, topography and liana abundance on the fine-scale spatial distribution of AGB and density of trees for a lowland tropical moist forest in Panama using additive regression models.
3.  Areas with larger values of AGB were negatively associated with the presence of lianas, which may reflect competition with lianas and/or the association of lianas with disturbed or open canopy patches within forests. AGB was positively associated with soils possessing higher pH and K concentrations, reflecting the importance of below-ground resource availability on AGB independently of stem density.
4.  Synthesis: Our results shed new light the factors that influence fine-scale tree AGB and carbon stocks in tropical forests: liana abundance is the strongest predictor, having a negative impact on tree AGB. The availability of soil nutrients was also revealed as an important driver of fine-scale spatial variation in tree AGB.
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Details

Original languageEnglish
Pages (from-to)1819-1828
Number of pages10
JournalJournal of Ecology
Volume104
Issue number6
Early online date11 Aug 2016
DOIs
Publication statusPublished - Nov 2016

    Research areas

  • Above-ground biomass spatial distribution, Carbon dynamics, Carbon stocks, INLA approach, Liana, Resource competition, Soil nutrients, Spatial statistics

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