Data from: An extensive suite of functional traits distinguishes wet and dry Hawaiian forests and enables prediction of species vital rates

1. The application of functional traits to predict and explain plant
species’ distributions and vital rates has been a major direction in
functional ecology for decades, yet numerous physiological traits have not
yet been incorporated into the approach. 2. Using commonly measured traits
such as leaf mass per area (LMA) and wood density (WD), and additional
traits related to water transport, gas exchange and resource economics,
including leaf vein, stomatal, and wilting traits, we tested hypotheses
for Hawaiian wet montane and lowland dry forests (MWF and LDF
respectively): (1) forests would differ in a wide range of traits as
expected from contrasting adaptation; (2) trait values would be more
convergent among dry than wet forest species due to the stronger
environmental filtering; (3) traits would be inter-correlated within
“modules” supporting given functions; (4) relative growth rate (RGR) and
mortality rate (m) would correlate with a number of specific traits, with
(5) stronger relationships when stratifying by tree size, and (6) RGR and
m can be strongly explained from trait-based models. 3. The MWF species’
traits were associated with adaptation to high soil moisture and nutrient
supply and greater shade tolerance whereas the LDF species’ traits were
associated with drought tolerance. Thus, on average, MWF species achieved
higher maximum heights than LDF species and had leaves with larger
epidermal cells, higher maximum stomatal conductance and CO2 assimilation
rate, lower vein lengths per area, higher saturated water content and
greater shrinkage when dry, lower dry matter content, higher phosphorus
concentration, lower nitrogen to phosphorus ratio, high chlorophyll to
nitrogen ratio, high carbon isotope discrimination, high stomatal
conductance to nitrogen ratio, less negative turgor loss point, and lower
WD. Functional traits were more variable in the MWF than LDF, were
correlated within modules, and predicted species’ RGR and m across
forests, with stronger relationships when stratifying by tree size. Models
based on multiple traits predicted vital rates across forests (R2 =
0.70-0.72; P < 0.01). 4. Our findings are consistent with a
powerful role of broad suites of functional traits in contributing to
forest species’ distributions, integrated plant design, and vital rates.