Biogeosciences, 14, 3371–3385, 2017
https://doi.org/10.5194/bg-14-3371-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
On the challenges of using field spectroscopy to measure
the impact of soil type on leaf traits
Matheus H. Nunes, Matthew P. Davey, and David A. Coomes
Department of Plant Sciences, University of Cambridge, CB2 3EA, UK
Correspondence to: David A. Coomes (dac18@cam.ac.uk)
Received: 11 October 2016 – Discussion started: 4 November 2016
Revised: 16 May 2017 – Accepted: 18 May 2017 – Published: 14 July 2017
Abstract. Understanding the causes of variation in func-
tional plant traits is a central issue in ecology, particularly
in the context of global change. Spectroscopy is increasingly
used for rapid and non-destructive estimation of foliar traits,
but few studies have evaluated its accuracy when assess-
ing phenotypic variation in multiple traits. Working with 24
chemical and physical leaf traits of six European tree species
growing on strongly contrasting soil types (i.e. deep alluvium
versus nearby shallow chalk), we asked (i) whether variabil-
ity in leaf traits is greater between tree species or soil type,
and (ii) whether field spectroscopy is effective at predicting
intraspecific variation in leaf traits as well as interspecific dif-
ferences. Analysis of variance showed that interspecific dif-
ferences in traits were generally much stronger than intraspe-
cific differences related to soil type, accounting for 25 % ver-
sus 5 % of total trait variation, respectively. Structural traits,
phenolic defences and pigments were barely affected by soil
type. In contrast, foliar concentrations of rock-derived nutri-
ents did vary: P and K concentrations were lower on chalk
than alluvial soils, while Ca, Mg, B, Mn and Zn concentra-
tions were all higher, consistent with the findings of previous
ecological studies. Foliar traits were predicted from 400 to
2500 nm reflectance spectra collected by field spectroscopy
using partial least square regression, a method that is com-
monly employed in chemometrics. Pigments were best mod-
elled using reflectance data from the visible region (400–
700 nm), while all other traits were best modelled using re-
flectance data from the shortwave infrared region (1100–
2500 nm). Spectroscopy delivered accurate predictions of
species-level variation in traits. However, it was ineffective
at detecting intraspecific variation in rock-derived nutrients
(with the notable exception of P). The explanation for this
failure is that rock-derived elements do not have absorption
features in the 400–2500 nm region, and their estimation is
indirect, relying on elemental concentrations covarying with
structural traits that do have absorption features in that spec-
tral region (“constellation effects”). Since the structural traits
did not vary with soil type, it was impossible for our regres-
sion models to predict intraspecific variation in rock-derived
nutrients via constellation effects. This study demonstrates
the value of spectroscopy for rapid, non-destructive estima-
tion of foliar traits across species, but highlights problems
with predicting intraspecific variation indirectly. We discuss
the implications of these findings for mapping functional
traits by airborne imaging spectroscopy.
1 Introduction
There is currently great interest in using plant traits to un-
derstand the influence of environmental filtering and species
identity on the functioning of plant communities and to
model community responses to the environmental change
(MacGillivray et al., 1995; McGill et al., 2006; Green et al.,
2008; Funk et al., 2016). Traits vary at multiple scales within
individuals, within populations, between populations and be-
tween species (Albert et al., 2011), and analysis of this varia-
tion is key to evaluating the strength of various filtering pro-
cesses on communities growing along environmental gradi-
ents (Davey et al., 2009; Violle et al., 2012). For example,
intraspecific variation in traits may reflect differences in mi-
croclimate driven by competition, disturbance, environmen-
tal conditions and age (Funk et al., 2016), whereas interspe-
cific and intersite variation may reflect both genetic variation
and phenotypic plasticity in response to environment (Davey
et al., 2009; Sultan, 2001; Donohue et al., 2005). Despite sub-
Published by Copernicus Publications on behalf of the European Geosciences Union.
3372 M. H. Nunes et al.: Field spectroscopy of leaf traits: the challenges
stantial advances in trait-based community ecology over the
past decade (Kunin et al., 2009; Funk et al., 2016), the im-
portance of environmental filters is still debated, especially at
small scales where biotic factors may prevail over abiotic en-
vironmental constraints (Vellend, 2010). Global analyses of
leaf nitrogen, phosphorus and leaf mass per unit areas (LMA)
indicate that about half of all variation occurs within commu-
nities (Wright et al., 2004), underscoring the importance of
community-level variation in traits.
An increasing number of leaf traits are measured rou-
tinely in plant communities, and global trade-offs among
these traits are often interpreted in terms of the life his-
tories of different species (Adler et al., 2014; Pillar et al.,
2003; Aubin et al., 2009; Fry et al., 2014). In this study
we measured 24 traits which we organise into three func-
tional groups (Asner, 2014; Asner et al., 2014, 2015): (i) light
capture and growth traits include pigments, the maximum
efficiency of photosystem II (PSII), nitrogen concentration
which is closely related to protein concentration (Milton and
Dintzis, 1981), soluble C compounds and leaf water con-
tent, C isotope discrimination (δ13C), N isotope discrimina-
tion (δ15N); (ii) defence and structural traits include silicon
(Si) organic cell wall constituents (cellulose, hemicellulose
and lignin) that are associated with leaf toughness, longevity
and defence capability (Hikosaka, 2004), polyphenols that
are associated with defence against herbivores (Mithöfer and
Boland, 2012), and LMA, a primary axis of specialisation
among plants (Grime et al., 1997; Lambers and Poorter,
1992) that plays a crucial role in herbivore defence as well as
leaf longevity (Wright et al., 2004); finally, (iii) rock-derived
nutrients include phosphorus (P), which is involved in many
enzymatic, genetic and epigenetic processes (Schachtman et
al., 1998), and calcium (Ca), magnesium (Mg), potassium
(K), zinc (Zn), manganese (Mn), boron (B) and iron (Fe),
which are involved in signalling pathways and/or cofactors
of enzymes (Marschner, 2012). We recognise that leaf traits
can contribute to more than one class (e.g. LMA is related
to growth but also to defence, P is a rock-derived nutrient
also associated with growth). Many analyses of traits have
focused on interspecific variation, but there is recognition
that intraspecific variation can strongly influence species and
community responses to environmental change (e.g. Weiner,
2004; Funk et al., 2016).
There is currently great interest in using hyper-
spectroscopy as a tool for studying the chemical and struc-
tural traits of leaves, particularly because improved airborne
sensors and faster computing make it possible to map func-
tional traits from the air (Ustin et al., 2009; Asner and Mar-
tin, 2016b; Jetz et al., 2016; Asner et al., 2017). Plans to put
hyperspectral sensors into space (e.g. the German Aerospace
Center, DLR, plans to launch the Environmental Mapping
and Analysis Program, EnMAP, in 2018; Guanter et al.,
2015) will soon enable spectral response curves of vegetation
communities to be assessed at the global scale. Rapid, non-
destructive determination of leaf traits in vivo and in situ us-
ing spectroscopy reduces the need to collect large amounts of
material in the field, decreases processing time, lessens costly
chemical analyses and eliminates sampling that could itself
alter experimental conditions (Couture et al., 2013). Spec-
troscopy can provide predictions of a range of foliar traits at
the leaf and canopy scales within diverse tropical ecosystems
(Asner et al., 2011a; Doughty et al., 2011) and temperate
forests (Wessman et al., 1988; Serbin et al., 2014). However,
some traits do not have absorption features within the vis-
ible and shortwave infrared spectral range of spectrometers
conventionally used for vegetation analyses, but can be esti-
mated indirectly through their covariance with traits that do
have absorption features in the visible-to-shortwave-infrared
region (“constellation effects” sensu Dana Chadwick and As-
ner, 2016). These traits include elemental concentrations and
isotope ratios (e.g. Serbin et al., 2014). In addition, struc-
tural differences (i.e. leaf thickness, number of air water in-
terfaces, cuticle thickness and pubescence) between leaves
may have significant effects on the relationship between leaf
reflectance and traits, and can complicate interpretation of
data (Sims and Gamon, 2002; Wu et al., 2016). The abil-
ity of spectroscopy to measure intraspecific variation in mul-
tiples traits between soil types, particularly when some of
those traits are indirectly determined through constellation
effects, has not been critically evaluated.
This paper examines the drivers of leaf trait variation in
temperate woodlands growing on chalk in southern England
compared with woodlands growing on nearby alluvial soils.
Several studies have evaluated change in species composition
among British semi-natural habitats that differ markedly in
soil type (Haines-Young et al., 2003; Smart et al., 2003), but
none to our knowledge have compared within- and between-
species variation of leaf traits in this context. The alkalinity
of calcareous soils gives rise to phosphorus limitation, pre-
venting short-term responses to nitrogen addition (Grime et
al., 2000), so comparisons of chalklands with less-alkaline
soils nearby provide strong edaphic contrast. We investi-
gated 24 leaf traits on these contrasting soil types and exam-
ined the ability of reflectance spectroscopy to quantify these
leaf chemical and structural traits. We place these traits into
groups based on ordination analyses, rather than working
with pre-defined functional groups, and evaluate the func-
tional significance of these groups. Our specific questions
were as follows: (i) Is variability in leaf traits greater between
tree species or soil type? (ii) Is field spectroscopy effective at
predicting intraspecific variation in leaf traits between soil
types, as well as interspecific differences?
2 Material and methods
2.1 Field site and sampling
Leaves were collected from trees growing on deep alluvial
soils and shallow chalk soils, near Mickleham in Surrey, UK
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M. H. Nunes et al.: Field spectroscopy of leaf traits: the challenges 3373
(latitude= 51◦16′ N, longitude= 0◦19′W). The alluvial soil,
along the banks of the river Mole, was a loam of several me-
tres depth. The chalk soil was located on a steep south-facing
escarpment into which the river was cutting; the top soil was
a few centimetres deep, underlain by solid chalk (i.e. a typical
rendzina soil). The chalk soils were alkaline with an average
pH and standard deviation of 7.9± 1.0 (n= 10), whereas the
alluvial was near neutral, having a pH of 6.7± 0.2 (n= 10).
Phosphorus becomes unavailable to plants in alkaline chalk
soil (Gerke, 1992), and greater depth of loamy soil on the
alluvial surfaces must result in much greater availability of
nutrients to plants.
Across both sites, leaves were collected from 66 trees, rep-
resenting six species. The six species common to both sites
were Acer campestre (field maple), Acer pseudoplatanus
(sycamore), Corylus avellana (hazel), Crataegus monogyna
(hawthorn), Fraxinus excelsior (ash) and Sambucus nigra (el-
der). Two fully sunlit branches were selected, cut and placed
in a cool box, and subsequently transported to a laboratory
for processing within 2 h. For each branch, 10 mature leaves
were selected. Three samples of 15 leaf disks were cored
from these leaves using a 6 mm corer, wrapped in aluminium
foil and frozen in liquid N for later chemical analyses. Leaf
area was measured from fixed-height photos against a white
background analysed in ImageJ. The scanned leaves were
weighed to give hydrated mass, then dried at 70 ◦C for a min-
imum of 72 h to obtain dry mass. LMA was calculated as dry
mass per unit of fresh leaf area. Leaf water content was com-
puted as the ratio between the quantity of water (fresh weight
– dry weight) and the fresh weight. A further 22 leaf chemi-
cal traits were measured on these samples (see below).
2.2 Chemical assays
Protocols for chemical assays are adapted from those de-
veloped by the Carnegie Airborne Observatory (see http:
//spectranomics.ciw.edu). Briefly, oven dried leaves were
ground and analysed for a variety of elements and carbon
fractions. Concentration of elements (B, Ca, K, Mg, Mn, P,
Si, Fe, Zn) were determined by ashing samples in a muffle
furnace followed by digesting them in nitric acid and analysis
on an inductively coupled plasma mass spectrometry (Perkin
Elmer SCIEX, Elan DRCII, Shelton, CT, USA). Nitrogen
and carbon concentrations were determined using a Thermo
Finnigan 253 with elemental analyser using a gas chromato-
graphic separation column linked to a continuous flow iso-
tope ratio mass spectrometer. This technique also provided
foliar concentrations of the stable isotopes of N and C. Car-
bon fractions, including hemicellulose, cellulose, lignin and
soluble carbon (mainly carbohydrates, lipids, pectin and sol-
uble proteins), were determined by sequential digestion of
increasing acidity (Van Soest, 1994) in an Ankom fibre anal-
yser (Ankom Technology, Macedon, NY, USA). These car-
bon fractions are presented on an ash-free dry mass basis.
Concentrations of photosynthetic pigments (chlorophyll a,
b, anthocyanins and total carotenoids) were measured by
spectroscopy of solution derived from frozen leaf disks on
area basis. Absorbance values of the supernatant were mea-
sured at wavelengths 470, 649 and 665 nm for chlorophyll
a, b and total carotenoids determination and published equa-
tions used to calculate pigment concentrations as in Licht-
enthaler (1987). Absorbance values were also measured at
wavelengths 530 and 650 nm for anthocyanins determination
and published equations used as per Giusti et al. (1999), but
corrected for possible chlorophyll contamination as per Sims
and Gamon (2002). The maximum efficiency of photosys-
tem II (PSII) was calculated according to Genty et al. (1989)
by measuring the maximum fluorescence (Fm) and the yield
of fluorescence in the absence of an actinic (photosynthetic)
light (Fo) using a PAM fluorometer. Total phenolic concen-
tration of the upper methanol/water layer was determined
colorimetrically using the Folin–Ciocalteau method, based
on absorbance at 760 nm on a spectrophotometer and quan-
tified using tannic acid equivalents with water serving as a
blank as per Davey et al. (2007).
2.3 Leaf and canopy spectroscopy
The remaining leaves were detached from the branches, and
10 leaves were selected at random, avoiding damaged and
soft or young leaves. These leaves were laid on a matt
black surface. Reflectance within bands ranging from 400
to 2500 nm was measured using a FieldSpec 4, produced
by Analytical Spectral Devices (ASD, Boulder, Colorado,
USA). The spectrometer’s contact probe was mounted on a
clamp and firmly pushed down onto the sample, so that no
light escaped through the sides. The spectral measurements
were taken at the midpoint between the main vein and the
leaf edge, approximately halfway between the petiole and
leaf tip, with the abaxial surface pointing towards the probe.
The readings were calibrated against a Spectralon white ref-
erence every five samples. In all statistical analyses, the mean
reflectance values of the 10 measurements per branch were
used.
2.4 Statistical analyses
Analyses were performed within the R statistics framework
(R Core Team, 2014). To evaluate the correlation among
traits, Spearman rank correlation coefficient was calculated
between all trait pairs and the variables were ordered in
the figure by hierarchical clustering. Analyses of variance
(ANOVA) were used to examine the influence of species
identity and soil type on each of the 24 leaf traits. Species,
soil and soil x species terms were included in the model, and
the ratio of sum of squares of these terms versus the total sum
of squares was used as an index of species- versus site-level
variation. This partitioning of variance quantifies the varia-
tion between species and between soil types, the interaction
between soil and species and the unexplained variance (resid-
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3374 M. H. Nunes et al.: Field spectroscopy of leaf traits: the challenges
Table 1. Average, standard deviation (SD) and coefficient of variation (CV) in percentage for leaf traits of six generalist species growing
on alluvial and chalk soils. Foliar trait was statistically different between soil types with P -value< 0.05∗, < 0.01∗∗ and < 0.001∗∗∗. Note
that water content and the concentrations of defence and structure compounds are invariant of soil type, as this is key to understanding why
variation in elemental concentrations between soil types cannot be predicted indirectly by constellation effects.
Alluvial Chalk
Traits Mean±SD %CV Mean±SD %CV
Light capture and growth
N (%)∗∗∗ 2.53± 0.81 32.1 2.16± 0.73 34.0
δ15N (‰)∗∗∗ 3.43± 2.65 77.3 −3.83± 2.01 52.3
δ13C (‰) −28.2± 1.2 4.5 −28.7± 1.0 3.6
aChlorophyll a (mg m−2) 338.8± 116.0 34.2 279.6± 89.2 31.9
Chlorophyll b (mg m−2) 78.6± 27.6 35.1 64.7± 22.4 34.7
Anthocyanins (mg m−2) 423.3± 143.8 33.9 362.8± 121.6 33.5
Carotenoids (mg m−2)∗ 110.5± 40.4 36.5 88.2± 35.5 40.2
Efficiency of PSII∗∗ 0.74± 0.05 7.1 0.71± 0.06 9.8
Soluble C (%)∗∗ 73.6± 6.5 8.8 70.3± 7.5 10.6
Leaf water content (%) 59.1± 8.2 14.0 58.5± 7.9 13.5
Defence and structure
aLMA (g cm−2) 60.8± 24.0 39.4 60.6± 23.6 38.9
Phenolics (%) 83.7± 64.1 76.5 84.3± 49.7 59.0
aHemicellulose (%) 10.9± 3.2 29.8 12.5± 3.6 29.4
Cellulose (%) 10.1± 1.8 18.6 11.0± 2.1 19.3
Lignin (%) 3.9± 1.9 49.8 4.7± 3.1 64.8
aSi (%) * 0.91± 0.56 62.2 1.11± 0.79 71.5
Rock-derived nutrients
aP (%)∗∗∗ 0.20± 0.05 25.5 0.14± 0.03 26.8
K (%)∗∗∗ 0.98± 0.49 50.0 0.79± 0.50 64.4
aCa (%)∗ 1.67± 0.75 45.1 2.29± 1.24 54.1
aMg (%)∗∗∗ 0.24± 0.11 47.1 0.36± 0.15 43.8
aB (µg g−1)∗∗∗ 29.0± 8.7 30.1 34.5± 12.4 36.0
aFe (µg g−1) 122.3± 24.6 20.1 125.4± 32.0 25.5
aMn (µg g−1)∗ 84.7± 64.3 75.9 103.8± 69.5 66.9
aZn (µg g−1)∗∗∗ 22.9± 12.6 55.0 34.1± 18.7 54.9
a log-transformed prior to ANOVA.
ual variance). The residual variance comprises analytical er-
ror and various types of intraspecific variation including mi-
crosite and within-canopy variation. Where necessary, vari-
ables were log-transformed to meet assumptions of ANOVA
(see Table 1 for details). In addition, permutation-based
multivariate analysis of variance (PERMANOVA; Anderson
2001) was applied to the matrix of dissimilarity among traits
to evaluate the importance of soil type, species identity and
the soil–species interaction as a source of variation in the
24 traits simultaneously. The non-parametric permutation-
based analysis of variance (PERMANOVA) was then per-
formed on the resulting distances (10 000 permutations). An
alpha level of 0.05 was used for all significance tests, and no
effort was made to test for or address non-normal data dis-
tributions. The PERMANOVA used distance matrices calcu-
lated using the adonis function in the vegan package of R.
Leaf traits were grouped using principal component anal-
ysis (PCA) using Simca-P (2016) software (Umetrics MKS
Data Analytics Solutions, Sweden). The principal compo-
nents for the variables were obtained by the correlation ma-
trix modelling in lieu of covariance matrix modelling. We
used the unit variance scaling (van den Berg et al., 2006) to
avoid the effects of variables with high variance. The PCA
was used to obtain score scatter and loadings plots to show
the relatedness of all leaf traits in the data set. R2 and Q2
overview plots were computed from the cumulated PCA axes
1–5. R2 values denote how well a trait can be explained in
the model and Q2 denotes how well a trait can be predicted
from the data set. The traits are ranked in descending R2 or-
der of how well they correlate with the other traits in the data
set. These plots were used to evaluate whether traits clustered
into functional groups.
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M. H. Nunes et al.: Field spectroscopy of leaf traits: the challenges 3375
Partial least squares regression (PLSR) was used to evalu-
ate whether field spectroscopy can reliably predict leaf traits
(Haaland and Thomas, 1988). The spectral reflectance val-
ues of each sample were transformed into pseudo-absorption
values; that is, log [1/R]) where R is reflectance (see Bolster
et al., 1996; Gillon et al., 1999; Richardson and Reeves III,
2005; Petisco et al., 2006; Kleinebecker et al., 2009). There is
strong autocorrelation in pseudo-absorption values, so PLSR
involves dimensionality reduction, producing orthogonal un-
correlated latent vectors containing the maximum explana-
tory power in relation to the trait data (Wold et al., 2001).
The number of latent variables (nL) used in the PLSR analy-
sis was predicted by minimising the prediction residual error
sum of squares (PRESS) statistic (Chen et al., 2004; Zhao
et al., 2015). We adopted a leave-one-out cross-validation
for each PLSR model. Model accuracy and precision were
expressed by the coefficient of determination (R2) and root
mean square error (RMSE). We also standardised RMSE to
the percentage of the response range (RMSE%) by dividing
each RMSE by the maximum and minimum values of each
leaf trait, as in Feilhauer et al. (2010). RMSE andR2 were ac-
quired during both model calibration and after model valida-
tion. PLSR was conducted initially using all available wave-
lengths (i.e. 400–2500 nm), but we then evaluated whether
models based on smaller regions of the spectrum performed
any better (see Serbin et al., 2014), based on comparisons
of RMSE. The smaller regions were selected from absorp-
tion features recognised in previous papers (Curran, 1989;
Elvidge, 1990; Kokaly et al., 2009). The visible (VIS, 400–
700 nm), near infrared (NIR, 700–1500 nm) and shortwave
infrared I (SWIR I, 1500–1900 nm), shortwave infrared II
(SWIR II, 1900–2500 nm) regions, as well as combinations
of the regions (700–1100, 700–1900, 700–2500, 1100–1500,
1100–1900, 1100–2500, 1500–2500 and 400–2500 nm) were
tested and the best-supported model selected based on min-
imisation of RMSE. To evaluate the effectiveness of field
spectroscopy at measuring variation in traits related to soil
type and species identity, we partitioned variance in model-
predicted trait values using exactly the same approach as
we used with lab-measured traits (i.e. first paragraph of the
methods section).
3 Results
3.1 Soil and species controls on leaf traits
Foliar concentrations of rock-derived nutrients varied with
soil type, but few other traits varied strongly with soil. Fo-
liar concentrations of the macronutrients N, P and K were
17, 43 and 24 % higher on alluvial than on chalk soils (Ta-
ble 1). Nitrogen isotope discrimination (δ15N) varied greatly
between the two soils, from−3.8 ‰ in the chalk soil to 3.4 ‰
in the alluvial. Foliar concentrations of nutrients required in
smaller quantities (Si, Ca, Mg, B, Mn and Zn) showed the op-
Figure 1. Partitioning of variance of foliar traits between species,
soil, species–soil interaction and residual components for six gen-
eralist species found on both chalk and alluvial soils. Residual vari-
ation arises from within-site intraspecific variation, microsite vari-
ability, canopy selection and measurement error variance.
posite trend: they were higher in chalk soils (by 22, 37, 50,
19, 23 and 49 %, respectively). Fe was the only rock-derived
mineral nutrient that was unaffected by soil type. In contrast,
hemicellulose, cellulose, lignin and LMA were completely
unaffected by soil type, and pigments and traits related to wa-
ter status (δ13C and water content) varied little with soil type,
with the exception of carotenoids concentration, which was
25 % higher in alluvial soil. The efficiency of PSII showed
only a slight increase of 4 % in alluvial soil. The percent-
age contribution of soluble C was affected by soil, with an
increase in soluble C of 9 % in the alluvial soil.
Most traits varied greatly between species and that varia-
tion was far greater than the soil effects (Fig. 1). Interspecific
variation (green bars, Fig. 1) accounted for ≥ 60 % of the
variation of eight traits (in descending order Si, water con-
tent, B, soluble C, N, LMA, K and cellulose concentrations),
and ≥ 40 % of the variation of another six traits (in descend-
ing order, lignin, hemicellulose, Mg, Zn, phenolics and Fe).
Species identity exerted little or no influence on pigment con-
centrations, efficiency of PSII, δ13C, δ15N, P, Ca or Mn con-
centrations. The interactions between species and soil (blue
bars, Fig. 1) explained little variation and were significant
for δ15N, P, Mn and Zn, but for no other traits. The pigments,
efficiency of PSII and δ13C had the largest unexplained vari-
ance. PERMANOVA analyses showed that, overall, species
identity accounted for 25 % of the variation in leaf traits and
soil type accounted for 5 %, while the interaction between
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3376 M. H. Nunes et al.: Field spectroscopy of leaf traits: the challenges
Figure 2. Principal component analysis of all leaf traits (unit variance scaled) measured across all species and sites. (a) Score scatter plot
showing first and second principal components using all six species for which data exist for all 24 traits on two contrasting soil types. Colours
represent species identity: Fe is Fraxinus excelsior, Sn is Sambucus nigra, Ac=Acer campestre, Cm is Crataegus monogyna, Ca is Corylus
avellana, Ap is Acer pseudoplatanus. Samples from chalk sites are denoted by squares symbols and alluvium sites are denoted by triangles.
(b) Loadings plot showing position and correlation of all leaf traits. Traits highlighted in red denote are those with Q2 > 0.5; (c) cumulated
R2 of PCA axes 1–5 (green bars denote how well a trait can be explained in the model) and Q2 (blue bars denote how well a trait can be
predicted) values for each trait. The traits are in descending R2 order of how well they correlate with the other traits in the data set.
Biogeosciences, 14, 3371–3385, 2017 www.biogeosciences.net/14/3371/2017/
M. H. Nunes et al.: Field spectroscopy of leaf traits: the challenges 3377
Figure 3. Spectral reflectance and percentage coefficient of variation (CV) of reflectance of six generalists species for alluvial and chalk
soils. The spectral regions for each trait were selected based on the model that minimised RMSE.
species and soil accounted for virtually no variation (i.e. the
traits of different species responded similarly to soil type).
The principal component analysis (PCA) was able to dis-
tinguish species across components 1 and 2 (Fig. 2a), with
less separation of species within the same genus (i.e. A.
campestre and A. pseudoplatanus). The first two components
of PCA explain 45 % of the total variance. Separation of in-
dividuals between the soil types was weak. Growth vs. struc-
tural/defence traits were separated in its first axis and area-
based vs. concentration-based traits in its second axis. The
first two components of PCA explain 46 % of the total vari-
ance. Considering only traits that were well predicted by
PCA (i.e. had Q2 > 0.5), the first component distinguishes
the traits associated in growth (i.e. N, K and soluble car-
bon concentrations, and water content) from traits associ-
ated with leaf defence and structure (i.e. hemicellulose and
Si). The second component is chlorophyll a, chlorophyll b,
carotenoids, anthocyanins and LMA, and mainly separates
the traits that were calculated on area basis. The first compo-
nent distinguishes species relatively well, with less separa-
tion of species within the same genus (i.e. A. campestre and
A. pseudoplatanus).
3.2 Spectroscopy of leaf traits
The ability to predict leaf traits from hyperspectral re-
flectance spectra varied greatly among the 24 traits (Table 2).
The R2 values of validation data varied from 0.92 to 0.16,
with traits ranked by goodness of fit as follows (highest first):
LMA, leaf water content, Si, phenolics, carotenoids, K, B,
efficiency of PSII, N, chlorophyll a and chlorophyll b. Some
minerals, such as P, Zn and Mn, as well as δ13C and δ15N
showed lowR2. There was virtually no difference in the aver-
age reflectance curves of leaves of trees growing on chalk and
alluvial soils (Fig. 3a), but the coefficient of variation among
plants was greater on the chalk soil (Fig. 3b). Pigments were
most accurately modelled using reflectance data from the vis-
ible region of the spectra, while other traits were most ac-
curately modelled using spectral data in the 1100–2500 nm
range (Fig. 3). Efficiency of PSII and Fe were the only fo-
liar traits for which the strength of relationship was greatest
when all wavelengths between 400 and 2500 nm were used
in the model.
Some leaf traits which appeared to be predicted accu-
rately by PLSR do not have absorbance features in the 400–
2500 nm range, and were instead predicted because of their
close association with leaf traits that do have absorbance fea-
tures in that range (see correlations in Fig. 4). For instance,
Si and B do not have absorption features in the 400–2500 nm
range, but their concentrations are highly correlated to hemi-
cellulose, cellulose and lignin concentrations, and these or-
ganic polymers do have strong absorbance features in the
SWIR region. Likewise, K does not have absorption features
in the 400–2500 nm range, but K concentration is highly cor-
related to leaf water content, soluble carbon, lignin, hemi-
cellulose and cellulose, all of which have absorbance fea-
tures in the region. The importance of these constellation
effects (sensu Dana Chadwick and Asner, 2016) becomes
apparent when we examine the partitioning of variance of
PLSR-predicted trait values: several rock-derived nutrients
vary significantly with soil type when measured in leaves
(Fig. 1) but little of that variation is successfully modelled by
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3378 M. H. Nunes et al.: Field spectroscopy of leaf traits: the challenges
Table 2. Partial least squares regression (PLSR) on spectral data and leave-one-out cross-validation for 24 leaf traits of six species occurring
on both alluvial and chalk soils. The model calibration (indicated with subscript cal) and validation (indicated as subscript val) performance
was evaluated for each leaf trait by calculating the coefficient of determination (R2), root mean square error (RMSE) and the percentage root
mean square error (%) based on the given number of latent variables (nL) for each PLS model.
R2 RMSE RMSE%
Leaf trait Spectral nL Cal Val Cal Val Cal Val
range (nm)
Light capture and growth
N (%) 1100–2500 3 0.61 0.55 0.49 0.52 15.0 16.0
δ15N (‰) 1100–2500 9 0.41 0.16 3.28 4.01 23.5 28.7
δ13C (‰) 1100–2500 6 0.46 0.30 0.85 0.96 16.1 18.2
∗Chlorophyll a (mg m−2) 400–700 7 0.65 0.53 60.05 69.62 13.5 15.7
Chlorophyll b (mg m−2) 400–700 4 0.59 0.50 16.48 18.57 15.2 17.1
Anthocyanins (mg m−2) 400–700 4 0.45 0.33 99.20 110.70 18.0 20.1
Carotenoids (mg m−2) 400–700 7 0.75 0.62 19.31 23.54 11.0 13.4
Efficiency of PSII 400–2500 6 0.68 0.55 0.03 0.04 13.4 15.9
Soluble C (%) 1100–2500 4 0.54 0.46 4.76 5.15 18.1 19.6
Leaf water content (%) 1100–1500 5 0.87 0.83 2.89 3.29 9.0 10.1
Defence and structure
∗LMA (g cm−2) 1100–2500 6 0.94 0.92 1.09 1.12 6.1 6.9
Phenolics (%) 1500–1900 6 0.78 0.70 26.20 30.48 9.7 11.3
∗Hemicellulose (%) 1100–2500 4 0.44 0.35 1.28 1.30 18.4 19.8
Cellulose (%) 1100–2500 4 0.44 0.34 1.52 1.66 17.0 18.6
Lignin (%) 1100–2500 4 0.57 0.47 1.72 1.89 13.0 14.2
∗Si (%) 1100–2500 4 0.77 0.72 1.50 1.55 14.4 15.5
Rock-derived nutrients
∗P (%) 1500–2500 7 0.43 0.22 1.26 1.30 17.8 20.2
K (%) 1500–2500 7 0.70 0.61 0.27 0.31 11.9 13.6
∗Ca (%) 1500–2500 7 0.53 0.40 1.40 1.47 15.9 17.9
∗Mg (%) 1900–2500 3 0.54 0.46 1.39 1.42 15.2 16.5
∗B (µg g−1) 1500–1900 6 0.66 0.56 1.24 1.28 13.6 15.2
∗Fe (µg g−1) 700–2500 5 0.56 0.46 1.17 1.19 15.6 17.2
∗Mn (µg g−1) 1500–1900 6 0.35 0.20 1.83 1.95 20.5 22.7
∗Zn (µg g−1) 1500–1900 7 0.41 0.21 1.50 1.60 19.5 22.4
∗ Trait values were natural log-transformed for PLSR.
PLSR (Fig. 5). The explanation for this failure to model soil-
related variation correctly is that concentrations of their as-
sociated traits remain invariant of soil type (Table 1). The use
of PLSR also considerably underpredicted the importance of
soil (∼ 37 %) on the δ15N variation, presumably for similar
reasons. Some species–soil interaction effects were detected
by PLSR modelling, except for traits that showed strong in-
teraction (Mn, P and δ13C). PLSR models were better able to
detect intraspecific variation in foliar N concentrations, be-
cause much of the nitrogen is contained in proteins, which
have strong absorbance features.
4 Discussion
4.1 Patterns of variation in leaf traits
Compared with trees growing on deep alluvium, trees on thin
chalk soils had low concentrations of N, P and K macronu-
trients in their leaves, but high concentrations of several mi-
cronutrients. Similar findings have been reported for herba-
ceous species growing on chalk (Hillier et al., 1990). Phos-
phorus and several micronutrients form low-solubility com-
pounds in alkaline soils and become less available for plant
uptake (Marschner, 1995; Misra and Tyler, 2000; Tyler,
2002; Sardans and Peñuelas, 2004), while the low N concen-
trations may reflect stoichiometric constraints (Niklas et al.,
2005). The lower efficiency of PSII in the chalk soil is likely
Biogeosciences, 14, 3371–3385, 2017 www.biogeosciences.net/14/3371/2017/
M. H. Nunes et al.: Field spectroscopy of leaf traits: the challenges 3379
Figure 4. Spearman correlation rank test among leaf traits of six
species growing on both soil types. Red and black circles mean,
respectively, negative and positive correlations. Foliar traits were
organised using cluster analysis.
to be a consequence of phosphorus deficiency (dos Santos
et al., 2006). Importantly for our later discussion on indirect
estimation of traits by spectroscopy, species did not vary be-
tween soil types in their structural and defensive traits (i.e.
LMA, lignin, phenolics) despite these differences in rock-
derived nutrients. A similar lack of intraspecific change has
been found in New Zealand rainforest trees growing on allu-
vium or phosphorus-depleted marine terraces (Wright et al.,
2010) and in several other studies (Koricheva et al., 1998;
Boege and Dirzo, 2004; Fine et al., 2006).
Species had a greater influence on trait values than soils
for all traits except P, and PCA analyses demonstrated that
species with traits associated with fast growth had low con-
centrations of traits associated with defence and structure
(see Coley, 1983, 1987; Fine et al., 2006). Traits favour-
ing high photosynthetic rate and growth are usually consid-
ered advantageous in resource-rich soil environments, while
traits favouring resource conservation are considered advan-
tageous in low-resource environments (Aerts and Chapin,
1999; Westoby et al., 2002), but in this study the species were
generalists growing on both soil types. The traits most influ-
enced by species (in descending order) were Si, leaf water
content, B, soluble C, N, LMA, K, cellulose, lignin, hemi-
cellulose, magnesium, Zn, phenolics and Fe. It is interest-
ing to note that two trace elements were near the top of this
list; it is likely that strong differences in B and Si concentra-
tions between species reflect differences in ion channel ac-
tivity in roots (Ma and Yamaji, 2006). Previous studies have
also shown Si to be under strong phylogenetic control, and
Figure 5. Partitioning of variance of foliar traits between species,
soil, species–soil interaction and residual components for six gen-
eralist species found on both chalk and alluvial soils from predicted
data. Residual variation arises from within-site intraspecific vari-
ation, microsite variability, canopy selection but not measurement
error variance, and is therefore smaller than for field measurements
(Fig. 1). Predicted data were obtained from partial least square re-
gression (PLSR).
to be little affected by environmental conditions (Hodson et
al., 2005). We also found Si and B concentrations to be pos-
itively correlated, which might ameliorate the effects on B
toxicity as Si can increase B tolerance of plants (Gunes et al.,
2007). High Zn organisation at the species level corroborates
earlier analyses that showed more than 70 % of Zn variation
occurred within a family and substantial differences existed
between and within species (Broadley et al., 2007).
The patterns revealed by our variance-partitioning analy-
sis of six temperate species (Fig. 1) bear similarities to those
emerging from an analysis of 3246 species from nine tropi-
cal regions (Fig. 5 of Asner and Martin, 2016a). The tropical
analyses included a “site” term which captured variation due
to soil and geology, among other factors. They, like us, found
that taxonomic identity explained far more variation than site
for most traits. Additionally they found that foliar concentra-
tions of P and other rock-derived minerals varied strongly
with site, while nitrogen concentrations varied little; soluble
carbon, structural and defensive traits hardly varied between
sites; and pigment (in their case just chlorophyll) was the
least predictable of traits, probably because photosynthesis
is rapidly up- and downregulated in response to light in the
environment among other factors (Asner and Martin, 2011).
Similarly, δ13C is known to vary strongly with light and rel-
ative humidity (Buchmann et al., 1997; Yan et al., 2012),
www.biogeosciences.net/14/3371/2017/ Biogeosciences, 14, 3371–3385, 2017
3380 M. H. Nunes et al.: Field spectroscopy of leaf traits: the challenges
which may be why species and soil explained little of its vari-
ance in our study. These parallels between tropical and tem-
perate systems suggest broad similarities in plant responses
to soil across different regions that differ greatly in tempera-
ture.
4.2 Measuring interspecific variation in leaf traits
with field spectroscopy
The spectral regions selected by our PLSR models match
the locations of known spectral absorption features related
to proteins, starch, lignin, cellulose, hemicellulose and leaf
water content (Knipling, 1970; Curran, 1989; Elvidge, 1990;
Fourty and Baret, 1998; Kokaly et al., 2009). In the region
between 700 and 2500 of the electromagnetic spectrum, ab-
sorption features are commonly the result of overtones and
combinations of fundamental absorptions at longer wave-
lengths. The visible region was useful to predict pigment
concentrations and contributed to the predictions of the ef-
ficiency of PSII and Fe only, whereas the infrared region
was associated with the most traits. The region of impor-
tance with correlated wavelengths with nitrogen varies be-
tween 1192 nm in deciduous forest (Bolster et al., 1996) and
2490 for forage matter (Marten et al., 1983), which results
directly from nitrogen in the molecular structure. Accord-
ing to Kumar et al. (2002), three main protein absorption
features reported as important for N estimation are located
around 1680 nm, 2050 and 2170 nm. In this study, pigments
were found to influence the visible region of the spectrum
while PSII-efficiency was predicted from features across the
VSWIR range. The spectra of chlorophylls are distinct from
those of proteins because C-H bonds in their phytols tails cre-
ate a strong absorption feature not found in proteins (Katz et
al., 1966). However, pigments are tightly bound by proteins
to form photosynthetic antenna complexes that capture light
energy and transfer it to the PSI and PSII reaction centres
(Liu et al., 2004). The vibration of the bonds in the pigment–
protein complex adds additional absorption features to the
spectra of pigments and may help explain why so many
bands were involved in PSII-efficiency prediction (Porcar-
Castell et al., 2014). The 1500–1900 nm region was impor-
tant for phenolic compounds prediction, which includes the
1660 nm feature across a variety of species and phenolic
compounds (Windham et al., 1988; Kokaly and Skidmore,
2015). The primary and secondary effects of water content
on leaf reflectance are greatest in spectral bands centred at
1450, 1940, and 2500 nm (Carter and Porter, 1991), but have
also been predicted using bands between 1100 and 1230 nm
(Ustin et al., 1998; Asner et al., 2004). With respect to the
other rock-derived nutrients, Galvez-Sola et al. (2015) also
showed that near-infrared spectroscopy can constitute a fea-
sible technique to quantify several macro and micronutrients
such as N, K, Ca, Mg, Fe and Zn in different citrus leaves
with the coefficient of determination (R2) varying between
0.53 for Mn and 0.98 for Ca, whereas B showed less accurate
results with the use of spectroscopy. The regions of impor-
tance for prediction described in those studies were relatively
similar to all the mineral nutrients analysed in our study, ex-
cept for B, which had the band between 1500 and 1900 as the
best predictive region.
Some of most accurately predicted traits have no absorp-
tion features in the visible-to-near-infrared, but were instead
estimated indirectly via constellation effects. LMA is con-
sistently among the more accurately predicted traits using
spectroscopy (Asner and Martin, 2008; Serbin et al., 2014;
Chavana-Bryant et al., 2016), but is measured indirectly via
its close coupling with water content and structural traits
of leaves (Asner et al., 2011b). Silicon (Si) concentrations
were well predicted by field spectroscopy, as recently re-
ported by Smis et al. (2014). Silicon is absorbed by plants
from the soil solution in the form of silicic acid (H4SiO4),
having been translocated to the aerial parts through xylem,
and then deposited as phytoliths (Tripathi et al., 2011). Si
is closely associated with phenol- or lignin-carbohydrate
complexes (Inanaga et al., 1995), cellulose (Law and Ex-
ley, 2011), and polysaccharide and peptidoglycans (Schwarz,
1973). It seems that spectroscopy is able to predict Si concen-
trations reliably because it integrates information on several
of these foliar traits to make the predictions. Similarly, the
relative high precisions for K, Fe and B predictions may be as
strong as they are because information on several foliar traits
are integrated. Unfortunately, foliar P concentrations are not
closely predicted by spectroscopy. RNA and DNA absorb in
the ultraviolet (e.g. Tataurov et al., 2008) and phosphates in
the longwave infrared, but there are no pronounced absorp-
tion features in the VSWIR region (Homolova et al., 2013)
and covariance with other traits is weak, making constella-
tion effects unreliable. While a few spectroscopy studies have
modelled P with some success, the spectral bands chosen dif-
fer among studies (Homolova et al., 2013), suggesting that
constellation effects cannot be relied upon.
4.3 Difficulties in measuring intraspecific variation by
field spectroscopy and its implications for mapping
functional traits
Rock-derived nutrients lack absorption features in the visible
to shortwave-infrared region of the electromagnetic spectrum
so cannot be measured directly by spectroscopy. They can,
nevertheless, be estimated indirectly by virtue of the fact that
element concentrations covary with organic molecules that
do have strong absorption features (constellation effects).
This paper identifies a problem with this approach: there
were strong differences in rock-derived mineral nutrients be-
tween soil types, but we could not measure these because the
concentrations of defence and structural traits were barely
affected by soil type. We have shown many similarities be-
tween our study and those in tropical forests, demonstrating
that this problem is likely to be widespread.
Biogeosciences, 14, 3371–3385, 2017 www.biogeosciences.net/14/3371/2017/
M. H. Nunes et al.: Field spectroscopy of leaf traits: the challenges 3381
There are likely to be implications of the constellation-
effect problem for mapping functional traits using imaging
spectroscopy. Ever larger areas of earth are being mapped
with airborne spectrometers (e.g. Asner et al., 2017) and
the anticipated launch of satellite-borne sensors (e.g. En-
MAP was approved by the DLR, with launch planned for
mid-2018; Guanter et al., 2015) will soon enable vegeta-
tion and ecosystem function to be characterised at the global
scale. The effectiveness of indirect prediction of traits us-
ing constellation-effect approaches will depend critically on
whether soils act as a strong filter on tree species within
a particular region. In the Amazonian lowlands, Asner et
al. (2015) found that variation in soil P was mirrored by
changes in species composition, and that P variation among
species was correlated with changes in structural and defence
compounds: in this instance, indirect estimation should be ef-
fective (e.g. Dana Chadwick and Asner, 2016). However, in
low-diversity temperate forests, a single tree species is often
found to span many different soil types and show substan-
tial phenotypic plasticity in some traits (Oleksyninst et al.,
2002; Turnbull et al., 2016). The six species growing on both
chalk and alluvial soils in this study are a case in point. In
these low-diversity systems, it will be much more difficult to
map variation using constellation effects, for the reasons ex-
plained above. Our study confirms the power of spectroscopy
for predicting biochemical and structural plant traits, but we
urge caution in interpreting results when species range across
contrasting soil types.
5 Conclusions
Trees on thin chalk soils had lower concentrations of N, P
and K macronutrients in their leaves than trees growing on
deep alluvium, but had high concentrations of several mi-
cronutrients. Phosphorus is sequestered in insoluble forms
in alkaline soils. This shortage of plant-available phosphorus
was associated in this study with low concentrations of fo-
liar N and low efficiency of PSII, but had no effect on struc-
tural and defensive traits. Trait differences were far greater
among species than between soil types, for all traits except
foliar P. Foliar traits predicted from VSWIR reflectance spec-
tra matched the locations of known spectral absorption fea-
tures related to proteins, starch, lignin, cellulose, hemicel-
lulose and leaf water content. Some of the most accurately
predicted traits have no absorption features in the VSWIR
range, and were estimated indirectly through their covariance
with structural traits that do have absorption features in that
spectral region (constellation effects) including cell wall con-
stituents. Since these structural traits did not vary with soil
type, our models were unable to reliably predict intraspecific
variation in rock-derived nutrients via constellation effects.
Similarities between our results and those of large-scale trop-
ical studies suggest this problem is likely to be widespread.
This study demonstrates the value of spectroscopy for rapid,
non-destructive estimation of foliar traits across species, but
highlights the difficulties that can arise in detecting within-
species changes along environmental gradients.
Data availability. The data has become publicly available through
deposit to NERC’s Environmental Information Data Centre (EIDC;
Coomes et al., 2017).
Author contributions. MHN participated in the chemical analyses,
analysed the data and wrote the manuscript; MPD led the chemi-
cal analysis and contributed to the writing of the manuscript; DAC
conceived the ideas, designed the methods, supervised the collec-
tion of field data and led the writing of the manuscript. All authors
contributed critically to the drafts and gave final approval for publi-
cation.
Competing interests. The authors declare that they have no conflict
of interest.
Acknowledgements. David Coomes was supported by a grant from
NERC (NE/K016377/1) and Matheus H. Nunes is supported by
a PhD scholarship from the Conselho Nacional de Pesquisa e
Desenvolvimento (CNPq). We thank the NERC Field Spectroscopy
Facility, and particularly Alasdair MacArthur, for training and
loan of the ASD spectrometer. We thank undergraduate stu-
dents Thomas Hitchcock, Lilian Halstead, Matt Chadwick and
Connor Willmington-Holmes for helping with field work, and
Alexandra Jamieson for measuring phenolics. We are grateful to
David Burslem, University of Aberdeen, for providing access to
the carbon fractions analyser.
Edited by: Michael Bahn
Reviewed by: two anonymous referees
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