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Influences of Forest Structure, Climate and Species Composition on Tree Mortality across the Eastern US

Few studies have quantified regional variation in tree mortality, or explored whether species compositional changes or within-species variation are responsible for regional patterns, despite the fact that mortality has direct effects on the dynamics of woody biomass, species composition, stand struc...

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Detalles Bibliográficos
Autores principales: Lines, Emily R., Coomes, David A., Purves, Drew W.
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2954149/
https://www.ncbi.nlm.nih.gov/pubmed/20967250
http://dx.doi.org/10.1371/journal.pone.0013212
Descripción
Sumario:Few studies have quantified regional variation in tree mortality, or explored whether species compositional changes or within-species variation are responsible for regional patterns, despite the fact that mortality has direct effects on the dynamics of woody biomass, species composition, stand structure, wood production and forest response to climate change. Using Bayesian analysis of over 430,000 tree records from a large eastern US forest database we characterised tree mortality as a function of climate, soils, species and size (stem diameter). We found (1) mortality is U-shaped vs. stem diameter for all 21 species examined; (2) mortality is hump-shaped vs. plot basal area for most species; (3) geographical variation in mortality is substantial, and correlated with several environmental factors; and (4) individual species vary substantially from the combined average in the nature and magnitude of their mortality responses to environmental variation. Regional variation in mortality is therefore the product of variation in species composition combined with highly varied mortality-environment correlations within species. The results imply that variation in mortality is a crucial part of variation in the forest carbon cycle, such that including this variation in models of the global carbon cycle could significantly narrow uncertainty in climate change predictions.