Introduction
Global change is expected to cause extensive changes in terrestrial
ecosystems, driving unprecedented redistribution of plant species and
their associated traits (Pecl et al., 2017, Diaz and Cabido, 2001).
Plant functional traits are involved in key ecosystem processes from
local community assembly (McGill et al., 2006, Sterck et al., 2011) to
global biogeochemical cycles, and these processes are interconnected
across scales (Reichstein et al., 2014, Peaucelle et al., 2019).
Relationships between traits, such as the leaf economic spectrum (LES)(Wright et al., 2005) , reveal information about biological
constraints in leaf mass allocation that impact plant ecophysiology and
have the potential to improve ecosystem models (Fisher et al, 2015).
Biotic interactions, micro-climate, and soil conditions can affect
species co-occurrence and influence local trait distributions
(Bruelheide et al., 2018, Simpson et al., 2016), and variation in
climate within species ranges can affect realized niches and drive trait
responses (Chave, 2013). Given their central role across levels of
organization, understanding how traits vary within and among species
across scales and environments is essential for conserving present and
future ecosystem function (Violle et al., 2014).
Plant traits vary geographically through a combination of interspecific
shifts in species abundances and intraspecific trait variation (Leps et
al., 2011, Laughlin et al., 2012, Valladares et al., 2014, Münzbergová
et al., 2017). Understanding how traits respond to the environment
across wide geographic areas requires approaches integrating both
sources of trait variation. This is challenging because individual level
trait data are geographically and taxonomically limited, making it hard
for traditional methods to identify the relative importance of inter-
and intraspecific variation at large scales (Henn et al., 2018). In
addition, effectively predicting broad-scale geographic patterns
requires making predictions for species not included in trait datasets
but widely distributed across the continent. Most current approaches to
understanding and predicting trait variation use community weighted mean
approaches (CWM), which circumvent these limitations by either focusing
directly on trait–environment relationships (based on direct
relationships between environment and community trait averages while
ignoring species) or by estimating traits from species averages (using
the environment only for predicting species assembly but ignoring the
effect of intraspecific variation) (Miller & Ives, 2019).
CWMs relying on direct trait-environment relationships offer the
advantage of predicting large scale community trait distributions
without requiring field surveys or estimates of species assembly.
However, by ignoring species identity (e.g., Ordonez et al., 2009) this
approach ignores known phylogenetic signals in trait variation driven by
biological, physical, and historical constraints (Wright et al., 2004,
Anderegg et al., 2018), and assumes that the environment implicitly
captures relevant changes in species distribution and abundance. Since
the information about species identity is missing from predictions,
these models cannot be used to explicitly identify the relative
contributions of inter- and intraspecific variation (but see Moles et
al. 2014) and could potentially yield worse predictions for assemblage
level trait values (when abundance data is available) unless the
environmental model fully captures relevant shifts in species abundance.
Due to these limitations, it has been suggested that predicting traits
directly from species’ average values (Swenson 2010, Clark 2016,
Wieczynski et al., 2019, Swenson 2017, Stahl et al., 2014) offers better
estimates of large-scale trait distributions. These approaches assume
that environmental drivers affect trait distributions indirectly by
shaping community structure and species abundance, implying that species
distributions are the best predictor of traits and associated ecosystem
function. These models can be used to make predictions for traits over
large areas by training on forest inventories, leveraging species
distribution models to forecast future shifts in species and
consequently trait distributions (Swenson & Weiser, 2010, Clark 2016).
Yet, this approach ignores intraspecific variation, which can be larger
than interspecific variation for broadly distributed species (Niinemets,
2015, Messier et al., 2017), and overlooks that species averages within
regional communities may diverge from their global averages (Hulshof &
Swenson, 2010).
Both these approaches contribute to our understanding of trait variation
and allow for predicting community level trait distributions without
requiring extensive field surveys of traits. However, neither approach
is designed to predict trait variation at the individual level, nor
allow for estimating intra-species variation. Also, they often fail to
account for the effect of phylogenetic signal on traits of closely
related species (but see Swenson et al., 2017), which can be potentially
important for generating more robust trait predictions for species that
are sparsely sampled (or not sampled at all) across vast geographic
areas (Blomberg et al. 2003, Swenson 2013, Swenson et al., 2014). These
limitations prevent assessment of the relative importance of inter- and
intraspecific variation on trait distributions across a continuum of
geographical scales, reducing our ability to generalize and understand
mechanisms driving trait distributions.
To address these limitations, we developed a model that combines
species, their phylogenetic relationships (from the Tree of Life;
Hinchliff et al., 2015) and environmental drivers (climate, elevation,
slope, terrain aspect) with large scale leaf trait data from the
National Ecological Observatory Network (NEON) (National Ecological
Observatory Network, 2020). While this method can only predict trait
distributions for locations with species abundance data, it makes it
possible to estimate the relative contribution of intra and interspecies
variation on trait distributions across wide geographic areas. This
allows us to address whether changes in environmental conditions have a
direct effect on the LES, within and across species. We jointly modeled
eight leaf traits: nitrogen (N%), carbon (C%), chlorophyllA (ChlA%),
chlorophyllB (ChlB%), carotenoids (Crt%), leaf mass per area (LMA, g
m-2), lignin (%) and cellulose (%). We compared this
combined model to models based on only environmental drivers or only
species and phylogeny information. We integrated the combined model with
US Forest Inventory and Analysis (FIA; USDA Forest Service, 2001, Smith
et al., 2002) and Daymet data (Thornton et al., 2018) to make trait
predictions for ~1.2 million trees across the eastern
US. We compared these predictions to the other two approaches to assess
the influence of model differences on large scale prediction, analyzed
the relative contribution of environmental factors and phylogeny to leaf
trait variation across ecoregions, and demonstrate the potential use of
this data for understanding the processes structuring ecological systems
at scale.