Community-wide consequences of variation in photoprotective physiology among prairie plants
Community-wide consequences of variation in photoprotective physiology among prairie plants
S. KOTHARI 1 2
J. CAVENDER-BARES 1 2
K. BITAN 1 2
A.S. VERHOEVEN 1 2
R. WANG 0 1
R.A. MONTGOMERY 0 1
J.A. GAMON 0 1
0 Department of Forest Resources, University of Minnesota , 115 Green Hall, 1530 Cleveland Ave. N., St. Paul, MN 55108 , USA
1 Plant Biological Sciences Program, University of Minnesota, 250 Biological Sciences Center , 1445 Gortner Avenue, Saint Paul, MN 55108 , USA
2 Department of Ecology, Evolution and Behavior, University of Minnesota , 100 Ecology, 1987 Upper Buford Circle, Saint Paul, MN 55108 , USA
Photoprotective pigments, like those involved in the xanthophyll cycle, help plants avoid oxidative damage caused by excess radiation. This study aims to characterize a spectrum of strategies used to cope with light stress by a diverse group of prairie plants at Cedar Creek Ecosystem Science Reserve (East Bethel, MN). We find that concentrations of photosynthetic and photoprotective pigments are highly correlated with one another and with other physiological traits across species and over time, and tend to be phylogenetically conserved. During a period of water limitation, plots dominated by species with constitutively low pigment concentrations showed a greater decline in mean reflectance and photochemical reflectance index, a reflectance-based indicator of photoprotective physiology, possibly due to alterations in canopy structure. Our findings suggest two contrasting strategies for withstanding light stress: (1) Using photoprotective pigments to dissipate excess energy, and (2) altering canopy structure to minimize absorbance of excess radiation. Additional key words: drought; light-use efficiency; phenology; photoinhibition; trait covariance; water-use efficiency. Received 1 July 2017, accepted 6 November 2017. +Corresponding authors; phone: (734) 502-2817, (612) 624-6337; e-mail: , e-mail: Abbreviations: Chl - total chlorophyll (a + b) concentration; CWM - community-weighted mean; DPS - xanthophyll de-epoxidation state; ETR - electron transport rate; fAPAR - fraction of absorbed photosynthetically active radiation; Fv/Fm - maximal quantum yield of PSII photochemistry; LUE - light-use efficiency; ML - maximum likelihood; NDVI - normalized difference vegetation index; NIR - near-infrared light; NPQ - nonphotochemical quenching; PC - principal component; PCA - principal component analysis; PRI - photochemical reflectance index; V+A+Z - xanthophyll pool size; VIS - visible light; WUEi - intrinsic water-use efficiency; ρn - reflectance at wavelength n; ΦPSII - effective quantum yield of PSII photochemistry. Acknowledgements: We would like to thank staff at Cedar Creek Ecosystem Science Reserve, including Kally Worm and Troy Mielke. Funding to support this study was provided by a grant from the National Science Foundation (NSF) and National Aeronautics and Space Administraton (NASA) through the Dimensions of Biodiversity program (DEB-1342872) to J. Cavender-Bares, R. Montgomery, and J. Gamon. Additional support was provided by an NSF Long-Term Ecological Research grant to Cedar Creek (DEB-1234162) and an NSF Graduate Research Fellowship to S. Kothari under Grant No. 00039202. Jon Anderson determined percent cover of species in the field, Austin Pieper assisted with HPLC and pigment extraction, and Anna K. Schweiger and Cathleen Nguyen aided in interpreting the pigment concentrations from HPLC. All members of the Cavender-Bares Lab and Cristy Portales provided useful feedback on the research and manuscript. © The Author(s). This article is published with open access at link.springer.com
Plants use a number of photoprotective mechanisms to
avoid photoinhibition and molecular damage caused by
excessive radiation, which can lower their photosynthetic
(Demmig-Adams and Adams III 1996, Ashraf
and Harris 2013)
. These mechanisms can be classified as
tolerance mechanisms, which dissipate energy or quickly
repair oxidative damage, or avoidance mechanisms, which
alter whole-leaf light capture to reduce excess energy
absorption (Steyn et al. 2002). One tolerance mechanism
of photoprotection is the xanthophyll cycle, in which the
two-step de-epoxidation of violaxanthin into zeaxanthin via
the intermediate antheraxanthin allows plants to dissipate
excess radiation through nonphotochemical quenching
(Demmig-Adams and Adams III 1996, Müller et al.
Conditions that limit the amount of energy that can be
captured by photosynthesis may force plants to dissipate
more energy through these nonphotochemical means. For
example, drought may limit photosynthesis by forcing
plants to close their stomata, causing plants to upregulate
pathways involved in NPQ, including the xanthophyll
cycle. Consequently, xanthophyll cycle activity and
xanthophyll pool sizes may be upregulated under conditions
that limit light-use efficiency (LUE), including drought.
Many other carotenoids, such as β-carotene and lutein, can
also help by de-activating reactive oxygen species
and Holzwarth 2012)
, while other pigments, such as
anthocyanins, may have other photoprotective roles, such
as screening out light
(Steyn et al. 2002)
. On the other
hand, some species have avoidance mechanisms to reduce
light interception, increase reflectance, or move
chloroplasts away from the cell surface
mechanisms that involve modification of canopy structure
have been described as “structural photoprotection”
(Valladares and Pugnaire 1999)
. In short, species may use
a variety of strategies to minimize light stress.
Lineages and functional groups of species often have
consistent differences in function that reflect their varying
. However, the
comparative ecology of photoprotection in terrestrial
plants is little-studied
(for examples, see Montgomery et
al. 2008, Savage et al. 2009, Ramírez-Valiente et al.
, leaving it unclear to what extent photoprotective
mechanisms show these patterns of structured variation
among species. Furthermore, because photoprotection is
tightly bound to other aspects of plant function, including
photosynthesis and water relations, it is important to
understand how variation across time and across species in
the use of photoprotective pigments is related to other
aspects of plant function. Do photoprotective traits
correlate with other aspects of physiology to form a
spectrum of strategies for response to light stress? Or do
they constitute an independent axis of variation?
The comparative biology of photoprotection matters
because measures of photoprotective activity are important
in characterizing LUE to model primary productivity over
(Peñuelas et al. 2011, Garbulsky et al. 2011)
Simple models of unstressed plants assume that LUE is
constant, such that net primary productivity is proportional
to absorbed photosynthetically active radiation (PAR)
(Haxeltine and Prentice 1996). This assumption is often
unrealistic – under natural conditions, plants do face stress,
frequently causing declines in LUE whose severity may
vary among sites based on their environmental conditions
and whether they are dominated by species susceptible to
(Cavender-Bares and Bazzaz 2004)
and regions vary in the functional composition and
evolutionary history of their constituent species, and
understanding the comparative physiology – including
photoprotective physiology – of these species can help
explain ecosystem-level properties. Previous studies have
revealed large variation in photoprotective strategies
among tree species in a single stand
(Gamon et al. 2005)
and between functionally distinct species that often inhabit
different niches, such as deciduous and evergreen plants
(Gamon et al. 1997, Springer et al. 2017)
. Given these
findings, characterizing the extent of variation in
photoprotective strategies among species from a single site
could help in understanding how a range of strategies can
succeed in a uniform environment.
To assess photoprotective and photosynthetic
physiology over large spatial scales or with high temporal
resolution, researchers often use nondestructive methods based
on light reflectance spectra, which can often be assessed
through remote sensing. One simple spectral index
designed to track diurnal variation in xanthophyll cycle
activity, and thus LUE, is the photochemical reflectance
index (PRI), calculated as the normalized difference
where ρn denotes the percent reflectance at a wavelength
of n nanometers
(Gamon et al. 1997)
. Since the
development of the PRI, much research has shown that
PRI is influenced not only by xanthophyll cycle activity
(“facultative” changes, sensu Gamon and Berry 2012), but
also the total pool size of xanthophylls and other pigments
(“constitutive” changes, Gamon and Berry 2012)
corresponds to total photoprotective capacity. While
constitutive changes occur more slowly, their influence is
especially dominant in comparisons across species or over
seasonal time scales
(Stylinski et al. 2002, Sims and Gamon
2002, Wong and Gamon 2015)
. In both cases, changes
associated with high photoprotection, and often low LUE –
large carotenoid pools and high xanthophyll de-epoxidation
state – cause PRI to become lower, although PRI can also be
influenced by other pigments, such as anthocyanins
(Gitelson et al. 2017a)
At scales larger than the individual leaf, spectral
characteristics of vegetation can also be influenced by the
architecture of the canopy. At low levels of canopy closure
or projected leaf area index – defned as one-sided leaf area
per unit ground area – the signature of the soil background
has a strong effect on remotely sensed spectra. Low plant
cover and canopy architectures that make soil more visible
from above can make remotely sensed PRI more
dependent on the soil background
(Barton and North
, weakening or erasing its relationship with leaf-level
pigment concentrations and LUE
(Soudani et al. 2014,
Gitelson and Gamon 2015, Gitelson et al. 2017b)
drivers like drought can influence the structure of the
canopy by causing mortality, altering phenology, and
causing plants to modify their leaf display – for example,
by leaf rolling (Kadioglu and Terzi 2007) or assuming
more vertical leaf inclinations
(Comstock and Mahall
1985, Gamon and Pearcy 1989, Joel et al. 1997)
under stressful conditions can help protect the
photosynthetic function of the plant
(Turgut and Kadioglu
1998, Pearcy et al. 2005, Nar et al. 2009)
both changes in leaf-level chemistry and canopy structure
can cause remotely measured PRI to vary over time. This
fact challenges the interpretation of remotely sensed PRI
as a measure of leaf-level LUE or photosynthetic activity
without controlling for canopy effects.
In this paper, we ask the following related questions:
How do the concentrations of xanthophyll-cycle
Materials and methods
Study site and species: We collected data from May to
October 2014 at Cedar Creek Ecosystem Science Reserve
in East Bethel, Minnesota. All measurements were
collected in the Big Biodiversity Experiment (BioDIV or
BigBio; E120), a biodiversity manipulation experiment
comprising 9 × 9 m plots seeded with 1, 2, 4, 8, or 16
tallgrass prairie species. (For this study, we ignore
plotlevel diversity to consider the 16 species in isolation.) The
16 species included belong to five families, and include
four functional groups: legumes, forbs, and C3 and C4
grasses (see the text table below). Within the species pool
selected for this study, functional groups are monophyletic
within the phylogeny, meaning that for this study,
pigments and other pigments involved in photoprotection
vary seasonally and across species within a diverse set of
Is species-level variation in photoprotective pigment
concentrations related to broader aspects of plant
What community-wide consequences arise from
species-level variation in photoprotection and associated
How are spectral indicators of photoprotective
physiology influenced by leaf-level physiology and canopy
structure at seasonal scales?
How might these patterns affect our ability to detect
changing LUE associated with xanthophyll cycle activity
using remote sensing?
functional groups and evolutionary history are closely
linked. Within the 168 plots in BioDIV, we chose a subset
of 35 plots in which to measure leaf-level physiological
parameters, including 11 monoculture plots and 24
multispecies (between 2 and 16 species) plots. Each
species was sampled in three distinct plots of varying
diversity. Monocultures were not available for all 16
species in the study. Despite regular weeding, plots often
contained species not originally planted in them
et al. 2002)
; in all plots, we only measured species planted
by design. Further details about the design of the
experiment can be found in Til
man et al. (2001
Pigment concentrations: At several points across the
season, we assessed concentrations of carotenoids and
chlorophyll (Chl) a and b in leaf tissue of individuals
belonging to each species using high-performance liquid
chromatography (n = 327 total) following previously
(Ramírez-Valiente et al. 2015)
we used a hole punch to collect disks from the youngest
fully expanded leaf of each sampled individual. These
disks were immediately wrapped in aluminum foil and
stored in a liquid nitrogen Dewar until they could be stored
in a –80°C freezer, where they were kept until analyzed
with high-performance liquid chromatography (HPLC) on
an Agilent 1200 HPLC system (Agilent Technologies Inc.,
Santa Clara, California, USA) at the University of
Minnesota. For Chl and each carotenoid pigment, we used
multiple calibration standards of known concentration to
develop calibration equations in order to convert
integrated areas of chromatogram peaks to concentrations.
We extracted anthocyanins (n = 761) from leaf disks
by shaking them for 24 h with –20°C acidified methanol
(0.1% HCl, v/v). We used a SpectraMax Plus 384 Plate
Reader (Molecular Devices, Sunnyvale, CA, USA) to
measure absorbance at 653 nm and 532 nm, the peak
absorbances of Chl and anthocyanins, respectively. About
25% of absorbance at 532 nm is due to Chl, so we used the
equation AA = A653 – 0.25 × A532 to estimate anthocyanin
absorbance (AA) from raw absorbances
. We estimated the final concentrations
using a molar extinction coefficient of 28,000 L
(Lee et al. 2005)
We used the inferred pigment concentrations, in terms
of nanograms per milligram of leaf tissue, to calculate
metrics of xanthophyll-cycle activity, following
, including the total xanthophyll cycle pool size
(V+A+Z, where V is the concentration of violaxanthin, A
is the concentration of antheraxanthin, and Z is the
concentration of zeaxanthin). We also calculated the
deepoxidation state (DPS):
We summarized the species-level variation in pigment
concentrations by performing a principal components
analysis on the correlation matrix of species mean
concentrations of total Chl, V+A+Z, neoxanthin, lutein,
β-carotene, and anthocyanins.
Gas-exchange measurements (n = 166 total) were taken
from the uppermost fully expanded leaves of selected
individuals of each species using a portable photosynthesis
system (LI-6400 XT, LI-COR, Lincoln, NE, USA). We
maintained a block temperature of 25°C and relative
humidity of 40–60%. Measurements were taken at ambient
CO2 and a PPFD of 1,500 μmol(photon) m–2 s–1 (red + 10%
blue). We adjusted for leaves that did not cover the 6 cm2
leaf chamber by using a marker to indicate the part of the
leaf that was enclosed, scanning the leaf, and using ImageJ
software to measure the area
(Schneider et al. 2012)
corrected for variation in enclosed area in all analyses.
We used gas-exchange measurements to calculate
intrinsic water-use efficiency (WUEi) as the ratio of net
photosynthetic rate [μmol(CO2) m–2 s–1] to stomatal
conductance [mol(H2O) m–2 s–1].
Chl fluorescence: We used a Hansatech FMS2 pulse
modulated Chl fluorometer to measure Chl fluorescence
parameters throughout the season (n = 590 individuals).
For each individual sampled at each sampling date, we
took a dark-acclimated and a light-acclimated
measurement on the newest fully expanded leaf. To collect
darkacclimated measurements, we attached a leaf clip to each
sampled leaf to block out sunlight for at least 30 min. After
taking each dark-acclimated measurement, we removed
the clip and allowed the leaf to acclimate to ambient
midday sunlight for at least 120 min before taking a
As a measure of the general stress level of plant leaves,
we calculated the maximal dark-acclimated quantum yield
(Fv/Fm) as (Fm – F0)/Fm. We also calculated the electron
transport rate as
ETR 0.5 0.84 ∆F/F
where ∆F/F , also notated ΦPSII, denotes the effective
quantum yield of PSII, and PPFD is the photosynthetic
photon flux density [μmol(photon) m–2 s–1]. The constant
0.5 is based on the assumption that PSI and PSII apportion
PAR equally; we treat leaf absorption as fixed when
calculating ETR, so 0.84 represents a plausible absorption
for the average leaf
(Björkman and Demmig 1987)
Predawn water potential (n = 70 total) was measured
with a Scholander pressure bomb in mid-June. Water
potential data are reported in figures as the balancing
pressure [MPa] used to counter the xylem tension, so
reported values are positive, and more positive values
represent more negative predawn water potential.
Spectral reflectance: Throughout the season, we used a
UniSpec-SC portable leaf reflectometer (PP Systems,
Amesbury, MA, US) to collect visible and near-infrared
(VIS/NIR, 310–1,100 nm) hyperspectral reflectance
spectra of multiple leaves from three individuals from each
originally seeded species in each plot we sampled (n = 632
individuals). Between plots, we used a white reference
panel (Spectralon, Labsphere, North Sutton, NH, US) to
collect a reference scan representing approximately 100%
reflectance; measurements of leaves were calibrated
against this scan. During data processing, we removed
spectra that had peak reflectance below 0.3 or appeared
clearly unlike a green leaf. At any given time point, the
spectra for leaves of the same individual were averaged,
and PRI was calculated from the average spectrum.
Every two weeks from late May through August, and
once a month in September and October, we used a
UniSpec-DC Spectral Analysis System to collect VIS/NIR
(310–1,100 nm) hyperspectral reflectance spectra at the
canopy scale in our plots, as well as in bare soil. The
instrument collected upwelling radiance and downwelling
irradiance simultaneously using two fiber optics. The
upward-pointing fiber optic measured solar irradiance with
a cosine head. The downward-pointing fiber optic had a
field-of-view restrictor to limit the field-of-view to about
15°, resulting in a spatial resolution of about 0.5 m2. We
collected a series of measurements for each plot by moving
along the northern edge of the plot and taking a scan every
0.5 m, taking care not to shadow the plot. We also
collected a white reference scan before measuring each
plot. We then calculated the reflectance at each measured
wavelength λ as
, / ,
, / ,
where L and E refer to upwelling radiance and
downwelling irradiance, respectively, and the subscripts t and p
refer to measurements over the target plot and the
calibration panel, respectively. The nominal bandwidth of
the detectors (band-to-band spacing) was 3 nm, so we used
linear interpolation to reduce the spacing to 1 nm. The
spectra for a single plot at a given sampling date were
averaged, and PRI was calculated from the plot-level
Plot-level reflectance spectra can be downloaded from
the EcoSIS Spectral Library at doi:10.21232/C2Z070.
From the plot-level spectra, we also used the mean
reflectance of bands from 350 to 1100 nm as a proxy for
albedo, which is expected to increase with the amount of
vegetation due to the high NIR reflectance of green leaf
tissue. Mean reflectance, like other features of the
reflectance spectrum, is influenced not only by the amount
of vegetation but by the way it is projected horizontally;
for example, more vertical leaf angles project less area
when viewed from above, reducing the amount of visible
Percent cover: We measured percent cover of each
species in each of our 35 plots on 19 June and 1 August.
Percent cover was evaluated in nine 0.5 × 0.5 m subplots
within each plot, positioned 1 m apart from each other
0.5 m from the plot’s northern edge. We evaluated cover
to the nearest percent below 20%, and to the nearest 5%
above that. For each subplot, bare ground was calculated
so that total cover would add up to 100%.
Climate and soil moisture: Daily precipitation and mean
temperature records were collected at the Cedar Creek
weather station, about 0.76 km away from the BioDIV
Once a month between May and September, we
measured soil moisture content at four depths using
timedomain reflectometry in 38 representative plots sampled
from the entire BioDIV experiment. We deployed a sensor
(Trime FM, IMKO GmbH, Ettlingen, Germany) with a
17 cm long probe vertically into the soil inside a PVC tube
at four depths: 3–20 cm, 20–37 cm, 80–97 cm, and 140–
157 cm. We calibrated the sensor using the same setup
with wet and dry glass beads, in accordance with
Phylogenetic tree and comparative analyses: We
created a phylogenetic tree of the species in the experiment
(Pearse and Purvis 2013)
. The tree
was built using rbcL sequences combined with a
taxonomic constraint from Phylomatic
(Webb and Donoghue
. First, a maximum likelihood (ML) estimate of the
phylogeny was created using RAxML
followed by a BEAST run to estimate branch lengths under
the ML topology
(Drummond et al. 2006)
phyloGenerator outputs an unrooted tree, we placed the
root at the known split between Poaceae and the other
species, which are all dicots. We then converted the tree
into an ultrametric chronogram using the chronos function
in the R package ape v. 4.1
(Paradis et al. 2004)
, using four
estimated ages for ancestral nodes fro
m Wikström et al.
), with additional estimates of node ages within
families drawn from
Kim et al. (2005)
Lavin et al. (2005)
for Fabaceae, and
Giussani et al. (2016)
for Poaceae. The time-calibrated, ultrametric phylogeny is
available online as a supplement to this paper (Fig. 4S).
To assess phylogenetic conservatism in pigment and
physiological traits, we calculated
Blomberg’s K (2003
for a selected set of traits. Using R packages phytools 0.6
(Revell et al. 2012)
and geiger 2.0.6
(Harmon et al. 2008)
we compared the observed K value to the distribution of
values expected under two null models: (1) A white noise
model that maintains the structure of the phylogeny but
swaps the tip labels, and (2) a simulation of Brownian
motion trait evolution over the phylogeny. Rejecting null
model (1) would suggest that evolutionary history
influences values of a trait, such that more related species are
more similar. Rejecting null model (2) suggests that trait
values cannot be described as a random walk in which
variance is proportional to evolutionary distance, a
scenario of relatively high trait conservatism (mean K = 1).
All data analysis was performed in R v. 3.3.2
Determinants of plot-level PRI: Mean plot-level PRI
increased during the early growing season, from late May
to late June, then steadily declined during the hottest, driest
part of the season, which occupied most of July (Figs. 1,2).
Plot-level PRI reached a nadir in early August, after which
it briefly peaked again following a handful of moderate
rains. Afterward, it underwent a late-season decline.
Plot-level PRI showed only a weak association with
PRI at the leaf level (Fig. 2). Instead, PRI was linked to
mean plot-level reflectance, which suggests that over
seasonal time-scales in this system, PRI was driven largely
by the amount of plant biomass, or its horizontal display.
Across sampling dates, the mean plot-level PRI was
strongly correlated with mean plot-level reflectance (r2 =
0.513; p = 0.018; n = 9). Within a single sampling date,
plot-level PRI showed significant correlations with
plotlevel reflectance from May until early July (r2 from 0.1766
to 0.2732; p<<0.01; n = 35), with no significant
relationship afterward. Also corroborating the strong link
between PRI and reflectance in the early season is the fact
that variation in both quantities across plots was correlated
with the amount of bare ground (Fig. 1S, supplement
available online). Bare soil spectra collected had a mean
PRI of –0.0975, which was lower than 97.8% of our
individual plot measurements. This indicates that an
increased signal of the soil background would lower PRI
in our system, consistent with the temporal dynamics.
Temporal dynamics of leaf-level physiology: Between
the early growing season (May/June) and July,
concentrations of all pigments declined steeply in most lineages
(Fig. 3). Because Chl concentrations declined more than
V+A+Z, the (V+A+Z)/Chl ratio increased slightly during
this period. At the same time, leaf-level PRI underwent a
slight but noticeable decline, consistent with a role in
tracking carotenoid/Chl ratios (Fig. 2C). [As expected, PRI
was also negatively correlated with DPS across species
Despite an apparent lack of precipitation and a
reduction in soil volumetric water content during July
(Fig. 1), there was surprisingly little evidence for marked
stress at the leaf level. Leaf-level PRI dipped slightly but
remained fairly constant during this period (Fig. 2C), as
did ETR and dark-acclimated Fv/Fm (Fig. 2S, supplement
available online). However, all of these traits eventually
showed late-season declines likely associated with
senescence (Fig. 2C, Fig. 2S).
Trait correlations and phylogenetic conservatism:
Across species, mean concentrations of photosynthetic and
photoprotective pigments were strongly correlated with
each other and with a suite of other physiological traits
(Fig. 4B), including traits related to water use (WUEi,
water potential) and photosynthesis (WUEi, ETR, Fv/Fm).
In a principal component analysis (PCA) of the correlation
matrix between species means of a set of pigment traits,
the first principal component (PC) explained 81.7% of the
variation, and represented a general continuum from higher
to lower constitutive pigment concentrations (Fig. 4A).
Concentrations of photosynthetic pigments also appeared
to be coordinated temporally; the early-season decline in
pigment concentrations was shown in all pigments (Fig. 3).
Both pigment traits and other physiological traits tended
to be conserved across the phylogeny (Fig. 5). Of the traits
shown in Fig. 5, only predawn water potential did not show
significant phylogenetic conservatism; for all other traits, a
white noise null model was rejected, but a Brownian motion
null model, which indicates clear conservatism, was not
Pigments and water limitation: We calculated a commu
nity weighted mean of the first PC of the PCA (Fig. 4A) by
using June percent cover data to calculate the following
∑ c PC
where cip is the cover of species i in plot p and PCi is the
value of species i along the first PC axis. As an index of
response to water limitation, we subtracted the plot-level
PRI and mean reflectance for each plot during a
particularly wet period during late June (dashed line,
Figs. 1,2) from the values during a drier period in early
August (dotted line). The resulting differences are not a
pure indicator of response to water limitation because
species undergo typical phenological changes, and we are
also not able to rule out responses to other environmental
changes. However, of all species, only L. perennis
declined significantly in percent cover between late June
and early August. Furthermore,
Wang et al. (2016)
that only in eight- and sixteen-species plots (which
included 12 of our 35 plots) did weighted mean plant
height noticeably change after late June, and in these plots,
height continued to increase into August. The fact that PŘI
and mean reflectance declined sharply as the height
remained constant or increased makes it unlikely that the
former is due purely to phenological change associated
with the life cycle of plants. Instead, it seems most
plausible that it is a stress response.
Regressions of change in plot-level PRI and mean
reflectance against the community-weighted mean of the
first PC axis showed that plots with low mean values on
the first PC axis – plots whose dominant species have
constitutively higher pigment concentrations – tended to
show a smaller decrease (or even an increase) in mean
reflectance and PRI (Fig. 6).
Concentrations of photoprotective pigments show strong
correlations, both across species and across time. This
suggests that these pigment concentrations are coordinated
as a suite of traits. Moreover, variation across species is
structured phylogenetically and by functional group, with
certain functional groups, like C4 grasses, having higher
pigment concentrations than others, like forbs. This
phylogenetic structure also corresponds with structure in
other traits; C4 species, which have high pigment
concentrations, also experience more negative predawn
water potential, and lower Fv/Fm and ETR, but higher
WUEi, mostly driven by a higher net photosynthetic rate.
Leaf-level PRI also showed modest but significant
phylogenetic conservatism; this is consistent with findings
that many parts of reflectance spectra are phylogenetically
informative and conserved, which may contribute to the
remote sensing of biodiversity
(Cavender-Bares et al.
2016, McManus et al. 2016)
The trait correlations we found suggest a strategy for
thriving in hot, dry environments, and in warmer, drier
parts of the growing season, as C4 grasses often do
(Edwards et al. 2010)
. Due to the CO2-concentrating
mechanism of their bundle-sheath cells, C4 grasses can
maintain their photosynthetic rates for some time even
after limiting stomatal conductance
reducing their susceptibility to carbon starvation and
(Taylor et al. 2014)
. High concentrations
of photoprotective pigments may also help in maintaining
photosynthetic capacity in a stressful environment with
high PAR and limited water. The ability to withstand stress
and maintain growth during the dry mid-season may permit
C4 grasses to coexist with other species through
phenological niche partitioning
(Fargione and Tilman 2005)
this context, the covariance and phylogenetic structure of
pigment concentrations makes sense as part of a
physiological strategy that allows later-maturing phenology.
Pigment concentrations were also coordinated with
each other temporally (Fig. 3). Pigment concentrations
declined in the early-to-midseason, which was surprising,
given that xanthophyll pool size has been found to increase
under mild drought in a meta-analysis
(Wujeska et al.
. The declines may have been a result of ontogenetic
changes in the developing leaves, as structural components
like lignin and cellulose expand in their share of leaf mass
(Miyazawa et al. 2003)
. Here, these ontogenetic changes
may have overwhelmed the stress response. Nevertheless,
V+A+Z declined less than other pigments, and even
increased in some lineages, consistent with the trend in the
Surprisingly, most aspects of leaf-level physiology
remained nearly constant during the July mid-season,
suggesting that most species were able to avoid adverse
effects of water limitation and light stress on
photosynthetic physiology at the leaf level (Fig. 2C, Fig. 2S).
The extent of water limitation was likely mild, so traits that
enabled tolerance or avoidance of mild stress may have
enabled species to maintain physiological function and
avoid severe damage. As expected, many traits showed
clear signatures of senescence. PRI (Fig. 2C),
darkacclimated Fv/Fm, and ETR (Fig. 2S) all showed marked
declines, indicating a breakdown in photosynthetic
capacity and reduction in LUE. These traits, which all
serve as indicators of photosynthetic downregulation, were
strongly correlated at the level of individual means (Fig.
3S, supplement available online), and PRI and ETR were
both correlated with Fv/Fm (although not with each other)
across species means (Fig. 4B), consistent with prior
(Gamon et al. 1997, Peguero-Pina et al. 2008,
Springer et al. 2017)
Given the relative stability of leaf-leaf physiology until
the late season, it is evident that temporal variation in
leaflevel PRI cannot fully explain the wide seasonal changes
in plot-level PRI. Temporal patterns at both scales
havesome similarities, including a late-season decline
most likely caused by the onset of senescence. However,
the trajectory of plot-level PRI has some features –
including a rise in the early growing season and a dip
during the dry spell in early August – that are not shown,
or not shown as clearly, in the leaf-level PRI. In these
cases, changes in PRI are perhaps better explained by
changes in total plot reflectance and soil visibility, a
conclusion reinforced by the fact that inter-plot variation
in PRI is strongly correlated with the amount of bare
ground (Fig. 1S). The temporal changes may be driven by
whole-plant growth during the early season, and by
drought response for the mid-season decline, consistent
with the findings of Gitelson et al. (2017b) in annual crops.
Studying the same plots during the same season,
et al. (2016)
found that the normalized difference
vegetation index (NDVI), a spectral index of greenness, also
declined during this mid-season drought, which they
explained as either a consequence of the drought or of
anthesis, since many species (including A. gerardii,
M. fistulosa, P. virgatum, P. purpureum, P. villosum,
S. scoparium, and S. nutans) achieve peak flowering near
that time. Flowers may decrease NDVI, depending on their
(Joel et al. 1997, Shen et al. 2009)
but their effect on PRI is unknown. It is unlikely that this
decline is caused by elevated mortality, considering the
low severity of the drought; in addition, Wang et al. (2016)
found that the only species with significant declines in
estimated cover between June and early August was
L. perennis. Although we did not directly measure species
structural responses to water limitation, it seems plausible
that adaptive changes in canopy structure are responsible
for some of this mid-season temporal variation.
Across plots and over the season, PRI appears to be
strongly affected by canopy properties, both in terms of the
amount of vegetative biomass and, potentially, the
horizontal projection of these tissues. The late May–early
June increase in plot-level PRI occurs despite an apparent
decline in leaf-level PRI during the same time. This trend
is likely due to rapid whole-plant growth in the early
growing season, which increases the fraction of absorbed
PAR (fAPAR) and overwhelms changes at the individual
leaf level. The early August decline in plot-level PRI,
which occurs without a similarly large decline in leaf-level
PRI, may be due to drought responses such as leaf rolling
or changes in leaf inclination. In this case, PRI may not
indicate efficient use of intercepted light, but rather
adaptive changes in canopy structure that allow plants to
intercept less light, reducing fAPAR.
The possibility that remotely sensed PRI integrates
both fAPAR and LUE that those species may use structural
photoprotection as may help to explain its demonstrated
success in tracking primary productivity over intervals
longer than the diurnal time-scales for which it was
(Garbulsky et al. 2011)
. However, it calls into
question the mechanistic basis of remote sensing-based
models of productivity that use PRI to measure LUE,
because temporal and spatial variation in PRI may be due
not just to variation in LUE, but also changes in canopy
(Barton and North 2001, Gitelson et al. 2017b)
Except over brief time periods, or in sites where canopy
structure remains fairly constant, as in closed-canopy
evergreen stands, canopy structure is likely to be the
dominant control on PRI.
If adaptive changes in canopy structure do drive
temporal variation in spectral characteristics, the fact that
the plots that had the greatest mid-season decline in mean
reflectance and PRI were ones dominated by species with
low pigment concentrations (Fig. 6) suggests that those
species may use structural photoprotection as an alternate
strategy to reducing light stress. Rather than maintaining
the suite of traits associated with tolerance of light stress
and water limitation that C4 grasses have, these species
may attempt to avoid light stress through changes in
canopy structure to reduce light interception, which causes
them to maintain lower leaf-level DPS and higher PRI.
Some, but not all, of these species (especially the C3
grasses and L. perennis) may also grow and flower
primarily in the cooler early season
(Wang et al. 2016)
another way of avoiding mid-season stress. The fact that
plots dominated by C4 species saw lower mid-season
declines in mean reflectance and PRI is likely due in large
part to the mid/late-season dominated phenology of these
species, rather than a direct effect of leaf-level physiology,
but leaf-level traits – including photoprotective traits – are
likely to facilitate the phenological strategies that these
species have. Together, these traits of C4 species have
consequences for whole-community properties, allowing
plots, where they predominate, to stay productive even
through water-limited parts of the growing season. On the
other hand, plots with high abundances of some
earlyseason dominants like L. perennis may face a rapid decline
From the perspective of remote sensing of physiology,
the existence of distinct strategies for photoprotection and
drought response – avoidance vs. tolerance – supports the
broader framework of “optical types,” or distinct
syndromes of linked traits that yield different signatures when
(Ustin and Gamon 2010)
. Because these
optical types correspond to functional roles, their existence
can help explain the effectiveness of remote sensing in
(Jetz et al. 2016)
and scaling up
plant physiology to whole ecosystems
and Bazzaz 2004, Ollinger et al. 2008)
. The potential for
reflectance spectroscopy to provide an integrated picture
of plant function means that it may be useful to treat
diversity in spectral properties as a kind of biodiversity
alongside functional, phylogenetic, and other
betterrecognized dimensions of biodiversity (Cavender-Bares et
To commemorate Govindjee’s 85th birthday, we
conclude with a brief note on his role in facilitating the
ecophysiology of photosynthesis. The methods and
paradigms that ecologists use to understand physiology and
fitness in the context of whole communities are limited by
the pace of advances in biochemistry and biophysics.
Although most of Govindjee’s research is not ecological,
his seminal work on topics like chlorophyll a fluorescence
and the xanthophyll cycle illuminated how plants work in
a truly integrative way. The challenge for ecologists is to
translate this physiological understanding into the
complex, multifactorial world of real, diverse plant
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