Nitrogen content per unit leaf area (N

Interest has surged in methods to predict continuous leaf-trait variation
from environmental factors, in order to improve ecosystem models. Coupled
carbon–nitrogen models require a method to predict N

Nitrogen (N) is an essential nutrient for primary production and plant
growth, and nitrogen content per unit leaf area (N

Site locations, climate, and leaf-trait distributions: mean annual
precipitation (MAP, mm), mean annual temperature (MAT,

Dynamic global vegetation models (DGVMs) are being extended to include
interactive carbon (C) and N cycles (Thornton et al., 2007; Xu-Ri and
Prentice, 2008; Zaehle and Friend, 2010), but there remain many open
questions about the implementation of C–N coupling (Prentice and Cowling,
2013), including the control of leaf N content, which is treated quite
differently by different models. For example, one common modelling approach
predicts photosynthetic capacity from N

Here we set out to test the predictability of N

Our analyses are based on 442 leaf measurements representing all species
found in a 100 m

Climatological data for the 27 sites were obtained from the eMAST/ANUClimate
data set
(

Mature outer-canopy leaves of each species were sampled during the growing
season using the AusPlots methodology (White et al., 2012). (Note that in
denser vegetation many species sampled are in the understorey, so their
“outer-canopy” leaves are still shaded by the overstorey. Many species thus
receive considerably reduced sunlight compared to the overstorey, implying
that the canopy-average irradiance

Values of

All statistics were performed in R3.1.3 (R Core Team, 2015). Linear
regressions were fitted using the

In a second analysis, community-mean values were calculated as simple
averages across the species in each plot, omitting the factor “N-fixer”. A
linear model was fitted to the community means of ln N

In a third analysis, N

Trait gradients were generated for ln LMA, ln N

Significant partial relationships were found for ln N

Partial residual plots for the regression of ln N

Linear regression coefficients for ln N

n/a: not applicable.

Theoretical slopes for these relationships (derived in Appendix A) are
compared with the fitted slopes in Table 1. For ln N

The proportion of leaf N allocated to Rubisco has generally been found to
decline, while the total N allocated to cell walls increases with increasing
LMA (Hikosaka and Shigeno, 2009). Figure 2 shows a strong positive partial
relationship between ln N

Partial residual plots for the linear regression of N

Fully 82 % of the variation in the community-mean value of ln
N

Linear regression coefficients for community-mean (simple average)
values of ln N

n.s.: no significance. n/a: not applicable.

Highly significant (

Trait means and regression lines for all 243 C

Linear regression coefficients for N

n/a: not applicable.

There was no significant main effect of the factor “N-fixer” and no
significant interaction between N

In total, 243 C

The variety of environments provided in this study by the long
transcontinental transect, and the number of species sampled, allowed us to
statistically separate the effects of

High N

Despite the large within-site variation in LMA found at all points along the aridity gradient, there is a significant tendency for LMA to increase with aridity, perhaps because of the resistance to dehydration conferred by stiffer leaves (Niinemets, 2001; Wright and Westoby, 2002; Harrison et al., 2010) and/or the need for leaves to avoid overheating under transient conditions of high radiation load and low transpiration rates combined with low wind speed (Leigh et al., 2012). This increase in LMA is inevitably accompanied by an increasing structural N component.

Thus, several distinct aspects of plant allocation tend to increase
N

Predicted N

In reality, however, leaf N does not consist exclusively of Rubisco and
cell-wall constituents. Leaf N includes multiple additional components,
including other photosynthetic proteins, proteins of the light-harvesting
complexes and electron transport chains, cytosolic proteins, ribosomes and
mitochondria, nucleic acids (which account for about 10–15 % of leaf N:
Chapin III and Kedrowski, 1983), and N-based defensive compounds. It is
possible that the higher N found for N-fixers resides in N-based osmolytes
(Erskine et al., 1996) or defence compounds (Gutschick, 1981). Nonetheless,
our simplifications suggest that N

By testing for acclimation along spatial gradients, the design of our study
did not allow phenotypic plasticity to be distinguished from genetic
adaptation. Phenotypic plasticity is the ability of a genotype to alter its
expressed trait values in response to environmental conditions (Bradshaw,
1965; Sultan, 2000). A part of the observed variation in trait values within
species could be due to shifts in the occurrence and frequency of different
genotypes, producing different preferred trait values. Thus, when we refer to
traits as “plastic”, this should be understood in a broad sense to allow
the possibility of a genetic component of the observed adaptive
differentiation within species. Seasonal acclimation within individual plants
can provide more direct evidence for phenotypic plasticity (Togashi et al.,
2017), whereas in this study we disregard possible seasonal variations and
instead relate trait variations to the mean annual environment. However, by
sampling all of the species present at each site and including measurements
on species at multiple sites, we could distinguish between the contribution
of plasticity sensu lato (phenotypic plasticity and/or genetic adaptation)
vs. species turnover, i.e. the progressive replacement of species with
different mean trait values, to spatial variation in the community-mean
values of a given trait. We found that

There has been a surge of interest in schemes to predict continuous trait
variation in DGVMs (e.g. Scheiter et al., 2013; Fyllas et al., 2014; van
Bodegom et al., 2014; Ali et al., 2015; Fisher et al., 2015; Meng et al.,
2015; Sakschewski et al., 2015). Some trait-based modelling approaches have
relied on empirical information on trait–trait and trait–environment
covariation, but others (e.g. Scheiter et al., 2013) have aimed to represent
the adaptive nature of trait variation explicitly. Our focus has been on
testing an explicit adaptive hypothesis for the controls of one key trait,
N

Our application of trait gradient analysis also points out a way towards
process-based treatments of functional trait diversity in next-generation
models. It is increasingly accepted that models could, and should, sample
“species” from continuous gradients of traits rather than fix the traits
associated with discrete PFTs. A hybrid approach to modelling N

Finally, we note that if our results can be corroborated more widely, this
would point to the need for a shift in the way N “limitation” is treated –
both in models and in analyses of field data. In studies of the relationship
between

We estimate optimal

Temperature-dependent reaction rates are described by the Arrhenius
equation:

Partial residual plots for the regression of ln (N

Linear regression coefficients for ln
(N

n/a: not applicable.

Species analyzed in this study can be found in Supplement S1.

Iain Colin Prentice, Ning Dong, and Andrew J. Lowe planned and designed the study; Ning Dong carried out all the field measurements and performed the data analyses. Ning Dong and Iain Colin Prentice wrote the first draft; Bradley J. Evans supported the study through provision of climate data; Ian J. Wright assisted with data interpretation, contributed with ideas throughout, and suggested important improvements to the text. Stefan Caddy-Retalic contributed important ideas to improve the text. All authors contributed to subsequent versions.

Research was funded by the Terrestrial Ecosystem Research Network (TERN)
through the AusPlots, Australian Transect Network, and eMAST facilities
(