Model evaluation
We built the phylogeny-only, environment-only, and combined (phylogeny
and environment) models using leaf trait data from NEON and evaluated
their explanatory power using the Bayesian R2 of the
predicted values for 88 out-of-sample test trees. For all 8 leaf traits,
the combined model explained the largest amount of variance in the
held-out test data (average R2 across the 8 traits =
0.64), substantially outperforming both the environment-only (average
R2 = 0.35) and phylogeny-only (average
R2 = 0.52) models (Figure 1, Figure S.2). A
hierarchical clustering of model residuals supported two major trait
classes (Figure S.3): traits mainly involved in photosynthesis (Croft et
al., 2017) and traits involved in leaf structure. The combined model had
the highest performance for predicting LMA (R2 = 0.81)
and the lowest performance for predicting ChlA (R2 =
0.51). Uncertainty in predictions was accurately estimated across all
traits and for all models (Figure S.4), with the combined model showing
mean 95% coverage values ranging from 94.3% to 98.8%. The importance
of different environmental drivers varied among traits, supporting an
important role of climate in driving leaf economics in local communities
(Ordoñez et al. 2009). Precipitation and temperature were mainly
important for traits involved in photosynthesis (N%, ChlA, ChlB and
Carotenoids), generally having a positive effect on their concentration
(except for ChlB, Figure S.5). Net radiation showed a negative effect on
N%, while vapor pressure generally had a negative effect on pigments
but a positive effect on traits associated with leaf toughness and
durability (cellulose, lignin and C%). Elevation was the most important
topographic predictor and the strongest environmental driver of LMA
(Figure S.5), consistent with previous studies (Reich & Oleksyn, 2004,
Hedin 2004, Poorter et al., 2009, Kitajima et al., 2016). The joint
model structure also captured the strong correlation among LMA and N%
characteristic of the leaf economics spectrum (Reich et al, 1997, Wright
et al., 2004).
Role of species and
environment in predicting traits at the continental scale
We used variance partitioning on the Bayesian R2 from
out-of-sample test data to explore the relative contributions of inter-
and intraspecific trait variation at continental scale (Supplement 4).
On average, interspecific variation (pure phylogenetic and species
effect) accounted for 25% of the total explained variation across the 8
traits, intraspecific variation (pure environment effect) accounted for
13%, and joint phylogenetic-environment effects accounted for 23%. The
relative importance of inter- and intraspecific effects varied widely
among traits. Species and phylogeny explained most of the variation in
structural traits (e.g. LMA, C% and lignin%). For these traits, often
used in large scale distribution studies, species distributions may
contain more information about traits than direct predictions from the
environment, as previously suggested by CWM models (Clark 2016, Yang &
Swenson, 2018). These results are aligned with previous studies
suggesting that the relative extent of intra-species variation among
communities is negatively related to spatial extent (Siefert, et al.
2015) and that at continental and global scales, patterns of leaf traits
are mainly driven by leaf economic strategies at the species level
(Wright et al., 2005). In contrast, the intraspecific component of the
model accounted for as much or more of the variance than the
interspecific one for pigments.
This difference between structural and photosynthesis traits may be
explained by being driven by different kinds of tradeoffs. Structural
traits are more affected by leaf lifespan and toughness, which varies
widely across species (Wright et al. 2004, Kitajima et al. 2012, Osnas
et al. 2018, Lichstein et al. 2021), whereas photosynthetic traits are
less variable among species due to the fundamental need for all species
to maximize carbon gain (subject to ecological conditions and tradeoffs;
Wright et al. 2004; Maire et al, 2015). Our analysis only quantifies
intraspecific variation from the upper (sun-lit) canopy. Although this
is a common practice (Pérez-Harguindeguy et al. 2013), it likely results
in underrepresenting intraspecific variation by ignoring leaf
variability across the light gradient, a major source of intraspecific
variation (Osnas et al., 2018). Furthermore, 62% of species in our
analysis were only sampled within a single NEON site, thus representing
only a small fraction of species’ true environmental ranges. Thus, our
analysis likely underestimates the true level of intraspecific trait
variation.