Conclusions
Both phylogenetic and environmental effects are fundamental to
understanding the drivers and distribution of plant traits. Combining
both in a single model is challenging due to data limitations but is
possible by leveraging large scale datasets. This approach allows for
improved traits predictions compared to models that rely on either
species average or the environment in isolation and allows for robust
predictions for species and regions not included in the training data.
Across Eastern US both interspecies trait variation (driven by shifts in
species’ abundance) and intraspecific variation are key for predicting
joint trait distributions, with effects on the LES. The influence of
these components varies by species, trait, ecoregion and scale.
Our approach overcomes previous data limitations by integrating multiple
sources of biological and environmental information to create a single
integrated model. As new traits, phylogenetic, and species inventory
data is released globally, the combined approach can be extended to new
regions and unlock the potential to study patterns of intraspecific
variation for hundreds of traits-species-environment combinations. For
example, this is already possible by leveraging national forest
inventories in some European countries, Canada, New Zealand (Schelhaas
et al, 2006, Rati et al., 2018, Gills et al., 2005, Paul et al., 2021)
along with ever-growing plant traits datasets stored in TRY
(https://www.try-db.org/).
Expanding this work outside the US could contribute to further
understanding the mechanisms driving trait distributions across scales
and the link between traits, species distribution, forest assembly and
function.