A model-based meta-analysis for estimating species-specific wood density and identifying potential sources of variation

Kiona Ogle, Sharmila Pathikonda, Karla Sartor, Jeremy W. Lichstein, Jeanne L.D. Osnas, Stephen W. Pacala

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Plant functional traits are important determinants of survival and fitness, and wood density (WD) is a key trait linked to mechanical stability, growth rates and drought- and shade-tolerance strategies. Thus, rigorous WD estimates are necessary to identify factors affecting tree performance. We obtained 1766 records of WD from the literature for 141 tree species in the United States. We implemented a hierarchical Bayesian (HB) meta-analysis that incorporated sample size, variance, covariate (e.g. moisture content and latewood proportion) and methodological information to obtain standardized estimates of WD for 305 U.S. tree species. The HB framework allowed 'borrowing of strength' between species such that WD estimates for data-poor species were informed by data-rich species via taxonomic or phylogenetic relationships. After accounting for important covariates and sampling effects, evaluation of the residual variation revealed the potential importance of environmental factors and evolutionary history. Differential variation in WD between species within genera and between genera within orders suggested that WD is relatively conserved in some genera and orders, but not in others. WD also varied between studies (or sites) indicating the potential influence of edaphic, topographic, or population factors on intraspecific variation in WD. Synthesis. Our hierarchical Bayesian approach overcomes many of the limitations of traditional meta-analyses, and the incorporation of phylogenetic or taxonomic information facilitates estimates of trait values for data-poor species. We provide relatively well-constrained WD estimates for 305 tree species, which may be useful for tree growth and forest models, and the uncertainties associated with the estimates may inform future sampling campaigns. Our hierarchical Bayesian approach overcomes many of the limitations of traditional meta-analyses, and the incorporation of phylogenetic or taxonomic information facilitates estimates of trait values for data-poor species. We provide relatively well-constrained wood density estimates for 305 tree species, which may be useful for tree growth and forest models, and the uncertainties associated with the estimates may inform future sampling campaigns.

Original languageEnglish (US)
Pages (from-to)194-208
Number of pages15
JournalJournal of Ecology
Volume102
Issue number1
DOIs
StatePublished - Jan 1 2014

Fingerprint

wood density
meta-analysis
phylogenetics
tree growth
phylogeny
sampling
uncertainty
shade tolerance
latewood
intraspecific variation
drought tolerance
moisture content
fitness
environmental factor
drought
water content

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Ecology
  • Plant Science

Keywords

  • Borrowing of strength
  • Evolutionary history
  • Hierarchical Bayesian model
  • Meta-analysis
  • Plant development and life-history traits
  • Plant functional traits
  • Wood density
  • Wood specific gravity

Cite this

Ogle, Kiona ; Pathikonda, Sharmila ; Sartor, Karla ; Lichstein, Jeremy W. ; Osnas, Jeanne L.D. ; Pacala, Stephen W. / A model-based meta-analysis for estimating species-specific wood density and identifying potential sources of variation. In: Journal of Ecology. 2014 ; Vol. 102, No. 1. pp. 194-208.
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A model-based meta-analysis for estimating species-specific wood density and identifying potential sources of variation. / Ogle, Kiona; Pathikonda, Sharmila; Sartor, Karla; Lichstein, Jeremy W.; Osnas, Jeanne L.D.; Pacala, Stephen W.

In: Journal of Ecology, Vol. 102, No. 1, 01.01.2014, p. 194-208.

Research output: Contribution to journalArticle

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AB - Plant functional traits are important determinants of survival and fitness, and wood density (WD) is a key trait linked to mechanical stability, growth rates and drought- and shade-tolerance strategies. Thus, rigorous WD estimates are necessary to identify factors affecting tree performance. We obtained 1766 records of WD from the literature for 141 tree species in the United States. We implemented a hierarchical Bayesian (HB) meta-analysis that incorporated sample size, variance, covariate (e.g. moisture content and latewood proportion) and methodological information to obtain standardized estimates of WD for 305 U.S. tree species. The HB framework allowed 'borrowing of strength' between species such that WD estimates for data-poor species were informed by data-rich species via taxonomic or phylogenetic relationships. After accounting for important covariates and sampling effects, evaluation of the residual variation revealed the potential importance of environmental factors and evolutionary history. Differential variation in WD between species within genera and between genera within orders suggested that WD is relatively conserved in some genera and orders, but not in others. WD also varied between studies (or sites) indicating the potential influence of edaphic, topographic, or population factors on intraspecific variation in WD. Synthesis. Our hierarchical Bayesian approach overcomes many of the limitations of traditional meta-analyses, and the incorporation of phylogenetic or taxonomic information facilitates estimates of trait values for data-poor species. We provide relatively well-constrained WD estimates for 305 tree species, which may be useful for tree growth and forest models, and the uncertainties associated with the estimates may inform future sampling campaigns. Our hierarchical Bayesian approach overcomes many of the limitations of traditional meta-analyses, and the incorporation of phylogenetic or taxonomic information facilitates estimates of trait values for data-poor species. We provide relatively well-constrained wood density estimates for 305 tree species, which may be useful for tree growth and forest models, and the uncertainties associated with the estimates may inform future sampling campaigns.

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