Data file for the paper Does functional trait diversity predict above-ground biomass and
productivity of tropical forests? Testing three alternative hypotheses by Finegan et al.,
published in Journal of Ecology
This file contains the field data from permanent sample plot studies at tropical moist and wet
forest sites in Brasil, Bolivia and Costa Rica that were used in this paper.
Each row represents a plot and the first columns identify the country where it is located and the
human disturbance regime (OG – undisturbed old-growth plots; logged, plots with selective
logging of timber species, logged and treated, plots with selective logging and post-logging
silvicultural treatment). There are a total of 62 plots which have pre-disturbance data. 21
undisturbed control plots have data for biomass increments.
The following columns contain the values of the biomass response variables and community
functional properties used as predictor variables in the published analysis: an explanation of the
abbreviations follows (readers should refer to the methods section of the paper for further
details):
AGBi – initial whole-plot above ground biomass
∆AGBsurv – biomass increment due to trees that survived the measurement periods analysed
∆AGBrec – biomass increment due to trees recruited during the measurement periods analysed
Several columns with community weighted mean trait values (CWM) follow, t0 indicates that
these are values for the first measurement of each plot. SLA, specific leaf area, cm2/g; LDMC, leaf
dry matter content, mg/g; LTS, leaf tensile strength (called force to tear in our manuscript, in
N/mm), N, P, leaf nitrogen and phosphorous contents, in mg/g; N:P, leaf nitrogen:phosphorous
ratio, unitless; WSG, wood specific gravity, unitless; Hmax, species adult height in m.
The column titled “species forming 75% t0”, contains the number of species used in the calculation
of CWM trait values following the biomass ratio hypothesis.
Plot values of four functional diversity indices then follow, as described in our article: Fric,
functional richness; Feve, functional evenness; Fdiv, functional divergence and Fdis, functional
dispersion. Functional diversity indices were calculated using values of four traits: WSG, Hmax, N
and SLA.