Continental relative humidity (RH) is a key climate parameter, but there is a
lack of quantitative RH proxies suitable for climate model–data comparisons.
Recently, a combination of climate chamber and natural transect calibrations
have laid the groundwork for examining the robustness of the triple oxygen
isotope composition (δ′18O and 17O-excess) of phytoliths, that can preserve in sediments, as a new proxy for past changes in RH. However,
it was recommended that besides RH, additional factors that may impact
δ′18O and 17O-excess of plant water and phytoliths be
examined. Here, the effects of grass leaf length, leaf development stage and
day–night alternations are addressed from growth chamber experiments. The
triple oxygen isotope compositions of leaf water and phytoliths of the grass
species F. arundinacea are analysed. Evolution of the leaf water δ′18O and
17O-excess along the leaf length can be modelled using a string-of-lakes approach to which an unevaporated–evaporated mixing equation must be added.
We show that for phytoliths to record this evolution, a kinetic
fractionation between leaf water and silica, increasing from the base to the
apex, must be assumed. Despite the isotope heterogeneity of leaf water along
the leaf length, the bulk leaf phytolith δ′18O and
17O-excess values can be estimated from the Craig and Gordon model and
a mean leaf water–phytolith fractionation exponent (λPhyto-LW)
of 0.521. In addition to not being leaf length dependent, δ′18O
and 17O-excess of grass phytoliths are expected to be impacted only
very slightly by the stem vs. leaf biomass ratio. Our experiment additionally
shows that because a lot of silica polymerises in grasses when the leaf
reaches senescence (58 % of leaf phytoliths in mass), RH prevailing during
the start of senescence should be considered in addition to RH prevailing
during leaf growth when interpreting the 17O-excess of grass bulk
phytoliths. Although under the study conditions 17O-excessPhyto do
not vary significantly from constant day to day–night conditions, additional
monitoring at low RH conditions should be done before drawing any
generalisable conclusions. Overall, this study strengthens the reliability
of the 17O-excess of phytoliths to be used as a proxy of RH. If future
studies show that the mean value of 0.521 used for the grass leaf
water–phytolith fractionation exponent λPhyto-LW is not
climate dependent, then grassland leaf water 17O-excess obtained from
grassland phytolith 17O-excess would inform on isotope signals of
several soil–plant-atmosphere processes.
Introduction
Recently, a combination of growth chamber and natural transect calibrations
laid the groundwork for examining the robustness of the triple oxygen
isotope composition (expressed by 17O-excess =δ′17O-0.528⋅δ′17O) of phytoliths as a new proxy for past changes in
continental atmospheric relative humidity (RH) (Alexandre et
al., 2018). Continental RH is a key climate parameter. When combined with
atmospheric temperature, it can be used to estimate the concentration of
atmospheric water vapour, one of the main components of the global water
cycle. However, global climate models have difficulties to properly capture
continental RH (Sherwood et al., 2010; Risi et
al., 2012; Fischer and Knutti, 2013), and there
is a lack of RH proxies suitable for model–data comparisons.
Phytoliths are micrometric particles of hydrous amorphous silica (SiO2(H2O)n) that form in and between living plant cells. Silica
polymerises from the sap that contains dissolved silicon (among other
nutrients) absorbed by the plant roots from the soil. Phytoliths can take
the shape of the cells they form in, which gives them morphological
taxonomic properties. Hence, phytolith morphological assemblages extracted
from buried soils, loess and sediments are commonly used for
palaeoenvironmental reconstructions (Miyabuchi
and Sugiyama, 2015; Nogué et al., 2017; Woodburn et al., 2017). In
grasses, silica, that represents several percent of the dry weight (d.w.), polymerises mainly in the leaf epidermis from
where the plant water evaporates during transpiration (Alexandre
et al., 2011; Kumar et al., 2016), and to a much lesser extent in the stem
(Webb and Longstaffe, 2000). This polymerisation is assumed to occur in
isotope equilibrium with the plant water (Alexandre
et al., 2018; Shahack-Gross et al., 1996; Webb and Longstaffe, 2000).
Variations in the 17O-excess of plant water can exceed most of the
variations identified so far in seawater, surface water, rainfall and ice (Landais
et al., 2006; Li et al., 2017; Sharp et al., 2018;
Alexandre et al., 2018). They are mainly driven by evaporative fractionation
of the transpired water in the leaves. The extent of this fractionation
partly depends on atmospheric RH (Cernusak et al., 2016; Craig and
Gordon, 1965), which changes daily, seasonally and at longer timescales of
climate change. In rainfall the 17O-excess varies slightly as it is
weakly affected by temperature (Barkan
and Luz, 2005; Uemura et al., 2010) or phase changes during air mass
transport, in contrast to the deuterium-excess (d-excess =δ2H-8.0×δ18O). In surface waters, the 17O-excess
is a powerful tool for tracing very evaporative conditions (Gázquez
et al., 2018; Surma et al., 2015, 2018). The
17O-excess of waters imprints the 17O-excess of minerals formed in
isotope equilibrium with these waters (Gázquez et al., 2017; Herwartz et
al., 2017; Passey et al., 2014; Sharp
et al., 2016, 2018).
In Alexandre et al. (2018), the 17O-excess of phytoliths from a grass
species (Festuca arundinacea) grown in a climate chamber was examined. A linear relationship with
RH was demonstrated. This relationship was close to
the one obtained for soil phytoliths collected in West Africa along a
vegetation and RH transect. This relationship allowed for the prediction of
RH with a standard error of 5.6 %. However, it was recommended that,
besides RH, additional factors that may impact the 17O-excess of bulk
phytolith samples should be examined before using this relationship as a proxy of
past RH.
In particular, in nature, the biomass of grass stem, sheath and blade, as
well as the leaf length is highly species dependent. Previous studies showed
that for grasses the water δ18O increases from stem to leaf and
from the bottom to the tip of the leaf blade (e.g. Gan et al., 2002; Helliker and
Ehleringer, 2000; Farquhar and Gan, 2003; Webb and Longstaffe, 2003;
Cernusak et al., 2016). The
17O-excess of grass water also shows variation along the leaf
(Landais et al.,
2006). This raises the question whether diversity in grass physiognomy
impacts the relationship between the 17O-excess of phytoliths and RH.
Silica precipitate throughout the grass's life, mainly, but not exclusively,
in the epidermis. The process can be either metabolically controlled or
passive, i.e. depending mainly on silica saturation during cell dehydration
when the leaf water evaporates (Kumar et al.,
2017, 2019 and references therein). The contribution of
evaporated water to the bulk leaf water may vary with transpiration
(Cernusak et al., 2016), which changes
from day to night (Caird et al., 2007) and decreases
drastically when the start of senescence occurs
(Norton et al., 2014). This raises the question
whether RH changes from day to night and from leaf growth to leaf senescence
should be considered when interpreting the 17O-excess of phytoliths as
a RH proxy.
In order to address these issues, a growth chamber experiment was set up to
explore the influence of light–dark and day–night alternations on the
17O-excess and δ′18O of leaf water and phytoliths of F. arundinacea.
Another experiment allowed us to examine the evolution of water and phytolith
17O-excess and δ′18O values along the leaf, and from young
to adult and senescent leaves of F. arundinacea. Leaf water and phytoliths were extracted
and analysed for δ18O and δ17O. Silica
concentrations were measured. Phytolith morphological assemblages that give
information on the type of tissue and cells that are silicified were
identified. The observed 17O-excess and δ′18O values of
leaf water and phytolith-forming water are compared with Craig and Gordon (1965)-derived evaporation model estimates. This comparison allows
us to assess the processes driving the 17O-excess and δ′18O
values of grass leaf water and phytoliths. Implications for the calibration
of the RH proxy are discussed.
Notations in the triple oxygen isotope system
In the triple oxygen isotope system (δ18O, δ17O)
the fractionation factors (17α and 18α) are
related by the exponent θ, where 17α=18αθ or θ=ln17α/ln18α. For the silica–water couple, and according to
the Sharp et al. (2016) empirical Eq. (10), θsilica-water
equals 0. 524 for the 5–35 ∘C temperature range. For the water
liquid–water vapour couple at equilibrium, θequil equals 0.529
for the 11–41 ∘C range (Barkan and Luz, 2005). When
evaporation occurs, a fractionation due to the vapour diffusion in air is
added to the equilibrium fractionation, as conceptualised by the Craig and
Gordon model (Craig and Gordon, 1965; Gat, 1996).
θdiff associated with this diffusion fractionation equals 0.518 (Barkan and
Luz, 2007). When RH decreases, amplitude of the fractionation governed by
θdiff increases. While θ applies to a particular well-constrained physical process, the term λ is used when several
fractionation processes occur at the same time. The overall fractionation in
the triple oxygen isotope system can be formulated as following: λ=Δ′17OA-B/Δ′18OA-B, with Δ′17OA-B=δ′17OA-δ′17OB, Δ′18OA-B=δ′18OA-δ′18OB, δ′17O=ln(δ17O+1) and δ′18O=ln(δ18O+1). δ and δ′ notations are expressed
in ‰ vs. VSMOW. In the δ′18O vs. δ′17O space, λ represents the slope of the line linking
Δ′17OA-B to Δ′18OA-B. In hydrological studies, the triple oxygen isotope composition
of water is expressed by the 17O-excess (17O-excess =δ′17O-0.528×δ′18O). In the δ′17O vs. δ′18O space, the 17O-excess depicts the δ′17O
departure from a reference line with a slope λ of 0.528. This is
the slope of the Global
Meteoric Water Line (expressed as δ′17O=-0.528×δ′18O+0.33 per meg; Luz and Barkan, 2010). The average
17O-excess of meteoric waters range from 35 to 41 per meg (Luz and
Barkan, 2010; Sharp et al., 2018). As the reference slope is close
to the liquid–vapour equilibrium exponent θequil (0.529), the
17O-excess is very convenient to highlight kinetic processes that
result from evaporation.
Material
In three growth chambers, the grass species F. arundinacea was sown and grown in commercial
potting soil in a 35 L container (53cm×35cm×22 cm, L×W×D). A total of 10 d after
germination, agar-agar was spread on the soil surface around the seedlings
to prevent any evaporation from the soil as described in Alexandre et al. (2018). Ambient RH was kept constant in the growth chamber by combining a
flow of dry air and an ultrasonic humidifier that produces vapour without any
isotope fractionation. The vapour and the soil irrigation water (IW) came
from the same source, and their triple oxygen isotope composition was similar
(-5.59 ‰ and 26 per meg for δ′18O and
17O-excess, respectively).
Experiment 1
This experiment was designed to examine the grass leaf water
and phytolith isotope signatures in different parts of the leaf and at
different stages of the leaf development. Briefly, the stages considered
were (i) young leaf, where only the end of the blade is visible as it emerges
from the sheath of the preceding leaf; (ii) adult leaf where the blade is
fully developed, the ligule is visible and the sheath is well formed; and (iii) yellow and desiccated senescent leaf.
F. arundinacea was grown for 39 d, in a climate chamber where light, air temperature
and RH were set constant at 290 µmol m-2 s-1, 20 ∘C and
73 %, respectively. On day 28, irrigation was stopped to force senescence
of the leaves. A total of 197 g of biomass was collected 11 d later.
From this biomass, young leaf (visible end of the blade), adult leaf and
senescent leaf (blade only) were separated. Adult leaves, of 24 cm length in
average, were sectioned into three parts: sheath, proximal part of the blade
(10 cm long) and apical part of the blade. Five samples resulted (Table 1).
For all samples, except the senescent leaves, 3–5 g biomass were
put in gastight glass vials and kept frozen for bulk leaf water extraction.
Senescent leaves were too dry for water extraction. The rest of the biomass
(between 10 and 70 g depending on the sample, Table 1) was dried for
phytolith extraction.
Growth chamber experiment 1: Experimental set-up, phytolith
content and triple oxygen isotope data obtained for phytoliths (Phyto), leaf
water (LW) and irrigation (IW). Growth conditions: duration: 39 d from
25 July to 2 September 2016; atmospheric temperature: 20 ∘C; leaf
temperature: 18 ∘C; atmospheric relative humidity: 73 %; no
irrigation; samples labelling: P4-75-02-09-2016.
Phyto “C” and “P” stand for phytolith concentration and proportion;
“av.” stands for weighted average. “% d.w.” stands for % dry
weight. Phytolith proportion was calculated using a mass loss correction
factor of 0.7 for senescent leaves (see text for explanation).
Samples Phytoliths (Phyto) Leaf water (LW) and irrigation water (IW) TotalPhytoPhytonδ18OSDδ17OSDδ′18OSDδ′17OSD17O-SDλnδ18Oδ17Oδ′18Oδ′17O17O-λbiomassC.P.excessexcessg% d.w.%*‰‰‰‰per meg‰‰‰‰per megYYoung leavesblade57.20.8339.220.1720.270.2138.470.1720.070.21-24370.522113.87.2013.677.17-460.525ShAdult leavessheath55.570.830335.320.0918.300.1234.710.0918.130.12-193100.52211.540.831.540.83150.538A<10proximal blade35.50.717336.240.1218.770.1435.600.1218.590.14-202230.52215.482.895.472.8910.528A>10apical blade35.92.152241.600.0221.470.0240.760.0221.250.02-27550.521112.76.6312.586.60-380.525bulk blade av.71.41.440.2620.8039.4720.58-2570.5229.094.779.044.76-190.526bulk leaf av.126.971.110038.7620.0438.0319.84-2380.5225.793.045.773.04-70.527SSenescent leavesblade12.543.0339.660.1920.510.2338.890.1920.300.23-23570.522IWIrrigation water-5.57-2.92-5.59-2.9226
* % of bulk leaf (sheath and blade).
Experiment 2a
Light triggers the opening of plant stomata with, as an
inevitable consequence, an increase in water loss through these stomata. At
night, however, stomata often do not close totally. Night transpiration is
often 5 % to 15 % of day transpiration (Caird et al.,
2007). In F. arundinacea, stomatal conductance at night can be as high as 30 % of
conductance during the day (Pitcairn et al., 1986).
Together with difference in air RH between day and night, this could affect
isotope enrichment of leaf water
(Barbour et al., 2005). This
experiment was thus designed to assess whether light–dark alternation may
impact the isotope signature of F. arundinacea leaf water.
In a growth chamber, F. arundinacea was grown for 22 d with constant light. Then, a 12 h
light–12 h dark alternation was introduced. Temperature and RH were kept
constant at 25 ∘C and 60 %, respectively. Half of the biomass was
harvested at the end of day 22 (constant light). Then the alternation period
was set up, and the second half of the biomass was harvested at the end of
day 26. In order to consider potential spatial heterogeneity, leaf blades
(both young and adult leaves) were collected from four different places in
the culture for each harvest. The eight resulting samples (Table S1 in the Supplement) were
put in gastight glass vials and kept frozen for bulk leaf water extraction.
Experiment 2b
In natural conditions, day–night alternations imply changes
in temperature and RH in addition to changes in light intensity. This
experiment was designed to assess whether over a period of several day–night
alternations, changes in RH during the night impacted the mean isotope
signature of grass leaf phytoliths.
For this experiment, the leaf water was not analysed as it only gives a
snapshot of its isotope composition. F. arundinacea was grown in two growth chambers. In
the first chamber, light, temperature and RH were kept constant (290 mmol m-2 s-1, 25 ∘C and 60 %, respectively). In the second
chamber, 12 h day–12 h night alternations were set. During the day (light 290 µmol m-2 s-1), temperature and RH were set to 25 ∘C and
60 %, respectively, whereas during the night (no light) they were set to
20 ∘C and 80 %, respectively. The leaf blades (both young and
adult leaves) were harvested after a first growth of 16 d and a second
growth of 18 d (Table S1) and dried for phytolith extraction.
MethodsPhytolith chemical extraction, counting and analysis
Phytoliths were extracted using a high-purity protocol with HCl,
H2SO4, H2O2, HNO3, KClO3 and KOH at 70 ∘C following Corbineau et al. (2013) and Alexandre et al. (2018).
Phytoliths were weighed and their mass reported to the initial leaf dry
weight (d.w.). To account for leaf mass loss during senescence, a mass loss
correction factor of 0.7, previously estimated for graminoids
(Vergutz et al., 2012), was
applied to the phytolith concentration in senescent leaves (Table 1).
Most grass phytoliths have a morphology characteristic of their cell of
origin. Phytolith morphological assemblages were thus determined to follow
the spatial evolution over time of the leaf silicification. Phytoliths
assemblages from experiment 1 were mounted on microscope slides in Canada
Balsam and counted using light microscopy at a 600× magnification. More than
200 phytoliths with a dimension greater than 5 µm and with a
characteristic morphology were counted. Phytolith types were named using the
International Code for Phytolith Nomenclature 1.0 (Madella et al., 2005) and
categorised as follows: “trapeziform short cell” and “trapeziform sinuate short
cell” coming from the short cell silicification, “elongate cylindric” and
”elongate echinate” coming from the intercoastal long cell silicification,
“acicular” produced by hair silicification and “parallelepipedal” produced by
bulliform cells silicification (Table 1, Fig. 1). These characteristic
phytoliths are commonly used for palaeoenvironmental reconstructions when
recovered from buried soils or sediments
(e.g. Woodburn et al., 2017). In addition,
thin silica particles with uncharacteristic shape and with a refractive
index too low to be accurately described using light microscopy were also
counted. Abundance of the phytolith categories are expressed in percentage of the
sum of counted particles. Three repeated counts usually give an error lower
than ±5 % (SD). The phytolith assemblages were further observed
with a scanning electron microscope (FEG-SEM, HITACHI SV6600, accelerating
voltage of 3 KV, 15∘ tilt, working distance of 14 mm and probe current of
a few picoampere to avoid charging issues), after carbon coating.
Growth chamber experiment 1: SEM pictures of phytoliths from
young, adult and senescent leaf blade: silicified trapeziform short cell (1,
2 and 3), silicified trapeziform sinuate short cell (4), undefined silicified
short cell or broken elongate cylindric long cell (5), silicified elongate
cylindric long cell (6a, 6b, 7 and 8) and silicified cell wall also reported as
silica sheets (9 and 10).
Phytoliths triple oxygen isotope analysis was performed as described in
details in Alexandre et al. (2018). The infrared (IR) laser-heating fluorination
technique (Alexandre et al., 2006; Crespin et al., 2008; Suavet et al.,
2010) was used to extract the oxygen gas (O2) after dehydration and
dehydroxylation under a flow of N2 (Chapligin et al., 2010). Then, the
O2 was passed through a -114∘C slush to refreeze gases
interfering with the mass 33 (e.g. NF). These interfering gases may be
produced during the fluorination of residual N in the line. The purified
O2 was sent to a dual-inlet mass spectrometer (ThermoQuest Finnigan
Delta Plus). The composition of the reference gas was determined through the
analyses of NBS28 for which isotope composition has been set to δ18O=9.60 ‰ vs. VSMOW, δ17O=4.99 ‰ vs. VSMOW and 17O-excess = 65 per meg. Each
analysis consisted of two runs of eight dual-inlet measurements with an
integration time of 26 s. The sample isotope compositions were
corrected on a daily basis using a quartz laboratory standard (Boulangé)
with δ18O=16.284 ‰ vs. VSMOW, δ17O=8.463 ‰ vs. VSMOW. During the measurement
period, Boulangé reproducibility (SD) was ±0.13 ‰, ±0.07 ‰ and ±11
per meg for δ18O, δ17O and 17O-excess,
respectively (n=9). For a given sample, two to three phytoliths
aliquots were analysed. Measured reproducibility ranged from 5 to 23 per meg
for 17O-excess.
Leaf water extraction and analysis
Leaf water was extracted over 6 h using a distillation line. Then a
fluorination line was used to convert water to oxygen using CoF3.
Oxygen was analysed by dual-inlet IRMS (ThermoQuest Finnigan MAT 253)
against a working O2 standard calibrated against VSMOW. The detailed
procedure was previously described in Landais et al. (2006) and Alexandre et
al. (2018). The reproducibility (two replicates) was 0.015 ‰ for δ17O, 0.010 ‰ for
δ18O and 5 per meg for 17O-excess.
Irrigation and vaporisation water analysis
The irrigation and vaporisation waters were analysed with an isotope laser
analyser (Picarro L2140-i) operated in 17O-excess mode using an
autosampler and a high-precision vaporiser as described in detail in
Alexandre et al. (2018). The reproducibility (three replicates) was 0.02 ‰, 0.01 ‰ and 10 per meg for
δ17O, δ18O and 17O-excess, respectively.
ResultsPhytolith concentration, assemblage and origin in grass leaf (experiment 1)
From young to adult and senescent blade, the phytolith content increases
sharply from 0.8 % d.w. to 1.1 % d.w. and 3.0 % d.w. (Table 1). This makes
58 % of blade phytoliths precipitating at the start of senescence. In
adult leaves, the phytolith concentration of 0.8 % d.w. and 0.7 % d.w. in the
sheaths and proximal blade increases to 2.1 % d.w. in the apical blade.
This makes apical blade phytoliths representing 52 % of adult leaf
phytoliths.
Short cell phytoliths are found in all samples, while long cell phytoliths
are absent from the adult sheath (Table S2). The ratio of long cell vs. short
and long cell phytoliths increases with phytolith concentration from young
(29 % of counted phytoliths) to adult (49 % of counted phytoliths) and
senescent (67 % of counted phytoliths) leaf blades (Fig. 2). In adult
leaves, it increases from sheath (1 % of counted phytoliths) to proximal
(19 % of counted phytoliths) and apical (59 % of counted phytoliths)
blades. Parallelepipedal bulliform and acicular hair phytoliths can be
observed but in small amounts (<2 % of counted phytoliths) in
young and senescent leaf blade samples. All phytolith assemblages contain
thin silica particles with low refractive index, difficult to count with
accuracy in light microscopy. SEM observation shows they are composed of
multi-cellular silica sheets (mostly silicified cell walls and a few
silicified stomata complexes) (Fig. 1, Table S2). Their abundance ranges
from 24 % to 18 % of counted phytoliths in young and adult blades and
increases up to 52 % of counted phytoliths in senescent leaf blades.
Because these silica sheet particles are very thin, their weight
contribution to the isotope signature of bulk phytolith assemblages is
expected to be significantly lower than their number.
Growth chamber experiment 1: Phytolith concentration vs. long
cell phytolith proportion. Error bars represent the 5 % error on counting
(refer to text for details).
Heterogeneity in the triple oxygen isotope composition of leaf water
Irrigation and leaf water (IW and LW, respectively) δ′18O,
δ′17O, and 17O-excess values obtained from experiment 1
are presented in Table 1 and Fig. 3. As expected, the lowest δ′18OLW and δ′17OLW values occur in the adult
leaf sheath. The sheath water is still 18O-enriched by 7.1 ‰
relative to the irrigation water, whereas the
difference in 17O-excess is not significant
(11 per meg). In adult leaf waters, an
evaporative fractionation trend (17O-excessLW-IW decreases and
δ′18OLW increases) occurs from the sheath to the proximal
and apical blade. Water from the young leaf blade plots close to the adult
apical blade.
Growth chamber experiment 1: leaf water and phytolith triple
oxygen isotope data and estimates.
Observed 17O-excess vs. δ′18O for leaf water (triangles)
and phytoliths (squares) in young, adult and senescent leaves (black
symbols) and along adult leaf (sheath, proximal blade, apical blade) (white
symbols) (Table 1). Error bars are displayed when larger than the symbols.
Estimated 17O-excess vs. δ′18O for bulk leaf water (Table S3)
and along the leaf length (Table S4) according to the Craig and Gordon
(C&G) model (Cernusak et
al., 2016; Farquhar et al., 2007) and the C&G model complemented with a
mixing equation.
Heterogeneity in the triple oxygen isotope composition of leaf silica
When plotted in the 17O-excess vs. δ′18O space (Fig. 3), the
triple oxygen isotope composition of phytoliths in adult leaf also show a
clear evaporative fractionation trend from the sheath to the proximal and
apical blade. λPhyto-LW decreases from 0.522 in the sheath and
proximal blade to 0.521 in the apical blade (Table S2).
Given the measurement precision, young, adult and senescent blades have
close δ′18OPhyto (38.47, 39.47, 38.89 ‰, respectively) and 17O-excessPhyto (-243,
-257 and -235 per meg, respectively) values (Table S2). The 17O-excessPhyto
value of the bulk leaf (-238 per meg) is very close to the estimate
calculated from the 17O-excessPhyto vs. RH relationship obtained
from growth chamber experiment in Alexandre et al. (2018) (-222 per meg).
Effect of light–dark and day–night alternation on the triple oxygen isotope
composition of leaf water and leaf silica
Plant water isotope data from experiment 2a where light–dark alternations
were set without changing RH, are presented in Table S1. Variations within a
given set of samples (e.g. F4-02-03-17 Day or F4-02-03-17 Night in Table S1)
are important, alerting that interpretation in term of kinetic vs. equilibrium
fractionation of small variations of δ′18OLW (<1 ‰) or 17O-excessLW (<14 per meg)
should be avoided. When considering the margins of error, the averaged
values of δ′18OLW and 17O-excessLW obtained
after the dark period are similar to the ones obtained after the light
period. It was not possible to measure the night and day transpiration flows
or the stomatal conductance during the experiment. In experiment 2b, where
temperature and RH changed with light–dark alternations, transpiration and
leaf blade phytolith concentrations did not change by more than 0.1 L d-1 and
0.2 % d.w., respectively, when light was set constant or alternates with
dark (Table S1). Differences in δ′18OPhyto and
17O-excessPhyto are lower than 1.4 ‰ and 30
per meg, respectively, but are not always in the same direction. In summary,
under the experimental set up conditions (high RH), light–dark alternation
has no obvious impact on the oxygen triple isotope composition of leaf
water and phytoliths.
DiscussionSilicification dynamics
The phytolith content and assemblages obtained from experiment 1 can be
discussed in light of previous studies investigating silica deposition in
grasses. At the cell level, silicification, which is a rapid process taking
a few hours (Kumar and Elbaum, 2017), initiates either in the
extra-membranous space or in the cell wall and proceeds centripetally until
the cell lumen is filled up (Bauer et al., 2011).
During cell lumen silicification, some cells are still viable and transfer
their content to each other before their full silicification
(Kumar and Elbaum, 2017).
Long and short cell phytoliths polymerise that way. Cell wall
silicification, not followed by complete cell lumen filling, has been also
frequently observed both in the epidermis (Kumar et al., 2017) and in the
bundle sheath parenchyma cells surrounding the veins (Motomura, 2004). Cell
wall silicification produces the multi-cellular silica sheets observed in
the phytolith samples from experiment 1.
Over the course of leaf development, short cells are the first to silicify.
This silicification is metabolically controlled
(Kumar et al., 2017, 2019 and references therein). Then, when the leaves become mature, long cell
silicification takes over (Motomura,
2004; Kumar et al., 2016). In this case, silicification is supposed to be
passive, i.e. its extent depends on silica saturation during cell
dehydration at the evaporation sites. Passive silicification applies also to
bulliform and hair cell silicification (Kumar et
al., 2017, 2019 and references therein). Increase of long vs.
long and short cell phytoliths from young to adult and senescent leaf blade,
observed in experiment 1 (Fig. 2), confirms this pattern of silicification
when the leaf develops. Cell wall silicification added to short and long
cell silicification occurs mainly when the leaf reaches senescence.
Impact of leaf length on the triple oxygen isotope compositions of grass leaf water
δ18OLW of the bulk leaf water can be estimated from the
Craig and Gordon model applied to plant leaf water by
Farquhar et al. (2007) (Table S3, adapted from spreadsheet provided in
Cernusak et al., 2016). For that purpose, the grass transpiration is
supposed to be at steady state as climatic conditions were set constant
during the 39 d of growth. We also assumed that the vapour has the same
isotope composition as the irrigation water since (i) the vaporised water
comes from the same source as the irrigation water and is not fractionated
by the vaporiser, (ii) there is no soil evaporation and (iii) transpiration
should produce a vapour with a composition similar to the one of the soil
water pumped by the roots (e.g. Welp
et al., 2008). We measured the temperature of an adult leaf of F. arundinacea grown under
conditions similar to those of experiment 1. The leaf was systematically
2 ∘C cooler than the surrounding air and no significant
temperature difference was detected between the sheath, proximal and apical
blade. Thus, the model was run for a leaf temperature of 18.4 ∘C. For estimating the δ17OLW, we used the equilibrium and
kinetic fractionation (17αeq and
17αK, respectively, Table S3) calculated according to 17αeq=18αeq0.529 and 17αk=18αk0.518.
The bulk leaf water δ′18OLW estimate is 3.35 ‰ higher than the observed value. However, the
17O-excessLW estimate is only 16 per meg lower than the observed
one (Table S3, Fig. 3). δ18OLW (and thus δ′18OLW) overestimation is common, and different
corrections have been proposed to take into account advection of less
evaporated stem water in the bulk leaf water (synthesis in
Cernusak et al., 2016). Assuming a
mixture between evaporated water and irrigation water, with the proportion
of evaporated water (E) being 0.8, brings the estimated isotope composition
of the bulk leaf water close to the observed one (differences in δ′18OLW and 17O-excessLW of 0.43 ‰
and 10 per meg, respectively) (Table S3 and Fig. 3). Our experimental setup,
where the vapour isotope composition is similar to the irrigation isotope
composition and RH is relatively high, makes the 17O-excessLW weakly sensitive to unevaporated water advection. However, this should not
be the case under natural conditions, especially at low RH.
For modelling the strong increase in δ′18OLW, concomitant
with a 17O-excessLW decrease from the irrigation water to the
sheath, proximal and apical blade of the leaf, the string-of-lakes approach
(Gat and Bowser, 1991; Helliker and Ehleringer,
2000; Farquhar and Gan, 2003) can be used. This
approach, which implies progressively 18O-enriched water segments along
the leaf, is particularly adapted to the longitudinal veinal structure of
grasses. Using the Farquhar and Gan (2003) Eqs. (2), (3) and (5), we
calculated δ18OLW and δ17OLW from 0
to 24 cm length (Table S4). In the triple oxygen isotope space, the modelled
curve (green continuous curve in Fig. 3) is characteristic of an evaporation
trend (Surma et al., 2018). The
ranges of estimated and observed δ′18OLW and
17O-excessLW values are close. However, the observed data plot
systematically on the left of the modelled curve. A mixture between
evaporated water and irrigation water must be added for the new modelled
curve (green dashed curve in Fig. 3) to fit the data. This presumes that
part of the irrigation water entering the grass circulates in the parallel
veinal structure of the blade without participating to the pool of water
successively evaporated. The contribution of evaporated water (E in
Table S4) increases from 0 at the leaf base to 1 at the apex. According to
this new modelled curve, the sheath, where stomata are few but still present
(e.g. Chaffey, 1985), already contains about 60 %
of evaporated water. This is in agreement with previous observations showing
18O enrichment in the sheath of different grass species
(Webb and Longstaffe, 2003). The
fact that the isotope signature of the young blade plots close to the
signature of the apical adult leaf blade suggests that the young leaf
proximal part was not entirely sampled. More importantly, the string-of-lakes
model implies that the δ′18OLW and 17O-excessLW
values do not evolve as a function of absolute leaf length but as a function
of distance relative to the maximum leaf length (Table S3). This makes both
the maximum 18O and 17O enrichments in the grass leaf apical part and
the isotope composition of grass bulk leaf water independent of grass leaf
length.
Impact of leaf length on the triple oxygen isotope compositions of grass leaf phytoliths
Polymerisation of silica is supposed to occur in isotope equilibrium with
the formation water, and, therefore, its isotope composition should only be
governed by temperature and the isotope composition of the leaf water
(Alexandre
et al., 2018; Dodd and Sharp, 2010; Sharp et al., 2016). From the δ′18OLW and δ′17OLW values estimated using the
string-of-lakes approach, we calculated δ′18OPhyto and
17O-excessPhyto (Table S4). We used two thermo-dependent
relationships, empirically established from diatom samples, to calculate
18αPhyto-LW, δ18OPhyto (Dodd and
Sharp, 2010) and α17OPhyto-LW assuming λPhyto-LW equals 0.524 (Sharp et al., 2016, Eq. 10).
In the 17O-excess vs. δ′18O space, the modelled phytolith
curve (green curve on Fig. 3) is above the data observed for phytoliths from
the sheath, proximal and apical blade. Changing values for 18αPhyto-LW or for leaf temperature, stomatal or boundary layer
conductance, air vapour or leaf vapour pressure in the model, do not
reconciliate observed and estimated isotope compositions. Assuming a
λPhyto-LW value of 0.522 moves the modelled curve (blue curve
on Fig. 3) lower but still above the observed data. λPhyto-LW must decrease from 0.522 to 0.520, from the base to the apex of the leaf,
for the modelled curve (red continuous curve on Fig. 3) to encompass the
observed 17O-excessPhyto values. When the mixing hypothesis
previously described for modelling the leaf water composition with length is
added, the new modelled curve (red dashed curve on Fig. 3) correctly fits the
data.
For the adult bulk leaf δ′18OPhyto and
17O-excessPhyto estimates to be nearest to the observed values, a
mean λPhyto-LW value must be set at 0.521. In this case, the
estimated δ′18OPhyto value is 3.44 ‰
higher than the observed one. The estimated 17O-excessPhyto value is similar to the observed one (10 per meg difference, Table S3,
Fig. 3). Adding a mixing process with E equals to 0.8 (as is the case for
the bulk adult leaf water estimate, refer to Sect. 6.1) brings the δ′18OPhyto estimate very close to the observed one (differences
in δ′18OPhyto and 17O-excessPhyto of
0.51 ‰ and 6 per meg, respectively) (Table S3 and Fig. 3).
The comparison between modelled and observed isotope compositions brings
insights on the factors driving δ′18OPhyto and
17O-excessPhyto in grass leaves. The λPhyto-LW value
being lower than the θsilica-water value of 0.524 calculated
after Sharp et al. (2016) implies that either the θsilica-water value previously established is overestimated or a kinetic fractionation
occurs during phytolith formation. Our modelling exercise suggests that the
amplitude of such a kinetic fractionation would increase from the base to
the apex of the leaf (λPhyto-LW decreasing regularly from
0.522 to 0.520). The proportion of short cell phytoliths for which silica
polymerisation is genetically controlled, decreases from the base to the
apex (Table S2). This would go against a kinetic fractionation being
enzymatically controlled. However, further knowledge on the mechanisms of
silica polymerisation is needed to further discuss this point.
Positions of the phytolith data on the modelled phytolith curve are not
exactly the same as positions of the leaf water data of the modelled leaf
water curve, especially for the apical part. This discrepancy suggests that
E in the apical leaf water is higher than E in the phytolith-forming water.
This can be explained assuming the following: phytolith-forming water
integrates the whole grass elongation period
(Kumar
et al., 2016, 2019; Kumar and Elbaum, 2017) while the sampled leaf water
only represents a snapshot. In grass leaf, the epidermal cells close to the
apex were produced at the base of the leaf and pushed upward during the
growth. Hence apical epidermal cells are older than the cells close to the
base, and phytoliths in these cells gather early and late phytoliths that are formed at long and short distances relative to the maximal length, with
low and high E values, respectively.
For the grass bulk leaf, despite this discrepancy along leaf length, and
assuming a mean λPhyto-LW of 0.521, δ′18OPhyto and 17O-excessPhyto record δ′18OLW and 17O-excessLW. In other terms, whatever the
grass leaf length, δ′18OPhyto and
17O-excessPhyto should be determinable from the Craig and Gordon
model complemented by an unevaporated–evaporated water mixing equation. The
main controls on δ′18OPhyto and 17O-excessPhyto
are thus the soil water and vapour isotope compositions, the difference of
temperature between leaf water and atmosphere, RH and E.
Potential impact of stem phytoliths on the triple oxygen isotope
compositions of grass phytoliths
In addition to grass leaf length, the stem vs. leaf biomass ratio can be very
heterogeneous from one grass development stage to another and from one grass
genus to another. Previous studies showed that phytoliths from grass stems
represent less than 10 % d.w. of the overall above-ground grass silica
content (e.g. Webb and Longstaffe, 2002) even in grasses with high stem
biomass such as bamboos (e.g. Ding et al., 2008). Stem phytoliths are only weakly enriched in 18O compared
to leaf phytoliths (Webb and Longstaffe, 2006). Thus,
the contribution of stem phytoliths to a soil phytolith assemblage should
slightly decrease δ′18OPhyto and increase
17O-excessPhyto average values. Assuming a
17O-excessPhyto difference between stem and bulk leaf of 200 per
meg, this would lead to a 17O-excessPhyto value for stem (10 % d.w.) and leaf (90 % d.w.) phytolith assemblage higher by 20 per meg
relative to an only leaf phytolith assemblage, which is lower than the
lowest reproducibility obtained when measuring three aliquots of phytoliths (23
per meg). We conclude that grass physiognomy should impact only very
slightly the triple oxygen isotope composition of bulk grass phytoliths.
Impact of senescence on the triple oxygen isotope composition of grass phytoliths
Our data show that 58 % of leaf phytoliths form at the end of the growth
when the leaf reaches senescence. Leaf senescence is age related or a
stress-induced developmental ageing during which transpiration decreases to
minimal level but is still efficient (Norton et
al., 2014) as epidermal conductance progressively prevails over stomatal
conductance (Smith et al., 2006).
If the cells already contain dissolved silica, epidermal evaporation, not
balanced by water input due to decreasing transpiration at the start of leaf
senescence, may lead to silica saturation and polymerisation. Thus, the bulk
phytolith assemblage of a senescent leaf will gather phytoliths formed
during both the leaf growth and start of senescence. In our experimental
conditions, where RH stays constant during both periods, the triple isotope
composition of phytoliths stays constant. However, in nature, if RH
decreases (e.g. due to seasonal decrease of precipitation in Mediterranean
and tropical areas or to colder temperatures in temperate areas) when
generalised grass leaf senescence occur, then both periods should be
considered when assessing the 17O-excessPhyto vs. RH relationship
from bulk phytolith assemblages from plant, soil or sediment.
Impact of day–night alternations on the triple oxygen isotope composition of leaf water and leaf silica
The results obtained from experiment 2a show very close isotope compositions
of leaf water during light and dark periods. The constancy of atmospheric
relative humidity and temperature, as well as the shortness of experiment 2a
(4 d with dark–light alternations after more than 2 months of constant
daylight) may have played against the closure of the stomata at dark. A
previous study on elongating leaves of F. arundinacea showed that spatial distribution of
water content within the elongation zone can stay almost constant during the
dark and light period (Schnyder and Nelson, 1988), supporting that
dark/light alternations do not always impact the stomata openness. Anyhow, a
sensitivity test shows that whatever the stomatal behaviour, change in
stomatal conductance does not impact significantly the Craig and Gordon
17O-excessLW and δ′18OLW estimates.
Experiment 2b shows that under the study high RH conditions (60 % during
the day and 80 % during the night in experiment 2b), transpiration,
silicification, δ′18OPhyto and 17O-excessPhyto
do not vary significantly from constant day to day–night conditions.
Conclusions
The data and estimates presented here contribute to a more precise
identification of the parameters to take into consideration when using the
17O-excessPhyto as a RH proxy (Alexandre et al., 2018). Neither
grass height nor grass physiognomy should significantly impact the isotope
composition of bulk grass leaf water and phytoliths. By contrast, RH
prevailing at the start of senescence should be considered in addition to RH
prevailing during leaf growth when interpreting 17O-excessPhyto.
If future studies show that the fractionation between leaf water and
phytoliths, expressed by a mean λPhyto-LW value of 0.521, is
not climate dependent, then the triple oxygen isotope composition of bulk
leaf water should be obtainable from the triple oxygen isotope composition
of grassland phytolith assemblages. The parameters driving the triple oxygen
isotope composition of both grass leaf water and phytoliths are given by the
Craig and Gordon model applied to leaves (Farquhar et al., 2007) and the
unevaporated–evaporated water mixing equation. Thus the most important
parameters are the difference between soil water and vapour isotope
compositions, the difference between leaf and atmosphere temperatures, RH
and E. Being able to record the triple oxygen isotope composition of
grassland leaf water would bring some significant insights into (i) estimating the triple oxygen isotope composition of CO2 equilibrated
with leaf water and partitioning gross fluxes of CO2 from vegetation at
the regional scale (e.g. Helliker and Ehleringer, 2000) or
(ii) estimating at the global scale the triple oxygen isotope composition of
O2 produced by the biosphere and quantifying its productivity from air
bubbles trapped in ice cores (Blunier et al., 2002).
Data availability
Data may be extracted directly from the current article
or requested from the corresponding author.
The supplement related to this article is available online at: https://doi.org/10.5194/bg-16-4613-2019-supplement.
Author contributions
AA, AL, CP, SD, CS, MP and JR designed the
experiments and carried them out. AA, AL, CP, SD, CS, MC, JCM, MP and FP did the
extractions and isotope analyses. AA prepared the paper with
contributions from all co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This study benefited from the CNRS human and technical resources allocated to the Ecotrons Research Infrastructure.
We thank Daniel Herwartz, Claudia Voigt and an anonymous reviewer for their in-depth reviews that substantially improved the
modelling approach and the paper.
Financial support
This research has been supported by INSU-LEFE and the ANR
(grant nos. ANR-17-CE01-0002 and ANR-11-INBS-0001).
Review statement
This paper was edited by Christopher Still and reviewed by Daniel Herwartz and one anonymous referee.
ReferencesAlexandre, A., Basile-Doelsch, I., Sonzogni, C., Sylvestre, F., Parron, C., Meunier, J.-D., and Colin, F.: Oxygen isotope analyses of fine silica grains using laser-extraction technique: Comparison with oxygen isotope data obtained from ion microprobe analyses and application to quartzite and silcrete cement investigation, Geochim. Cosmochim. Ac., 70, 2827–2835, 10.1016/j.gca.2006.03.003, 2006.
Alexandre, A., Bouvet, M., and Abbadie, L.: The role of savannas in the
terrestrial Si cycle: A case-study from Lamto, Ivory Coast, Global Planet.
Change, 78, 162–169, 2011.Alexandre, A., Landais, A., Vallet-Coulomb, C., Piel, C., Devidal, S., Pauchet, S., Sonzogni, C., Couapel, M., Pasturel, M., Cornuault, P., Xin, J., Mazur, J.-C., Prié, F., Bentaleb, I., Webb, E., Chalié, F., and Roy, J.: The triple oxygen isotope composition of phytoliths as a proxy of continental atmospheric humidity: insights from climate chamber and climate transect calibrations, Biogeosciences, 15, 3223–3241, 10.5194/bg-15-3223-2018, 2018.Barbour, M. M., Cernusak, L., Whitehead, D., Griffin, K., Turnbull, M. H.,
Tissue, D. T., and Farquhar, G.: Nocturnal stomatal conductance and
implications for modelling (delta oxygen 18) of leaf-respired CO2 in
temperate tree species, Funct. Plant Biol., 32, 1107–1121, 10.1071/FP05118, 2005.Barkan, E. and Luz, B.: High precision measurements of 17O/16O and 18O/16O
ratios in H2O, Rapid Commun. Mass Sp., 19, 3737–3742,
10.1002/rcm.2250, 2005.Barkan, E. and Luz, B.: Diffusivity fractionations of H216O/H217O and
H216O/H218O in air and their implications for isotope hydrology, Rapid
Commun. Mass Sp., 21, 2999–3005, 10.1002/rcm.3180, 2007.Bauer, P., Elbaum, R., and Weiss, I. M.: Calcium and silicon mineralization
in land plants: Transport, structure and function, Plant Sci., 180,
746–756, 10.1016/j.plantsci.2011.01.019, 2011.Blunier, T., Barnett, B., Bender, M. L., and Hendricks, M. B.: Biological
oxygen productivity during the last 60,000 years from triple oxygen isotope
measurements, Global Biogeochem. Cy., 16, 3–1,
10.1029/2001GB001460, 2002.Caird, M. A., Richards, J. H., and Donovan, L. A.: Nighttime Stomatal
Conductance and Transpiration in C3 and C4 Plants, Plant Physiol., 143,
4–10, 10.1104/pp.106.092940, 2007.Cernusak, L. A., Barbour, M. M., Arndt, S. K., Cheesman, A. W., English, N.
B., Feild, T. S., Helliker, B. R., Holloway-Phillips, M. M., Holtum, J. A.
M., Kahmen, A., McInerney, F. A., Munksgaard, N. C., Simonin, K. A., Song,
X., Stuart-Williams, H., West, J. B., and Farquhar, G. D.: Stable isotopes in
leaf water of terrestrial plants, Plant Cell Environ., 39, 1087–1102,
10.1111/pce.12703, 2016.
Chaffey, N. J.: Structure and Function in the Grass Ligule: Presence of
Veined and Membranous Ligules on the Same Culm of British Grasses, New
Phytol., 101, 613–621, 1985.Chapligin, B., Meyer, H., Friedrichsen, H., Marent, A., Sohns, E., and Hubberten, H.-W.: A high-performance, safer and semi-automated approach for the δ18O analysis of diatom silica and new methods for removing exchangeable oxygen, Rapid Commun. Mass Sp., 24, 2655–2664, 10.1002/rcm.4689, 2010.Corbineau, R., Reyerson, P. E., Alexandre, A., and Santos, G. M.: Towards producing pure phytolith concentrates from plants that are suitable for carbon isotopic analysis, Rev. Palaeobot. Palyno., 197, 179–185, 10.1016/j.revpalbo.2013.06.001, 2013.
Craig, H. and Gordon, L. I.: Deuterium and Oxygen 18 Variations in the Ocean
and the Marine Atmosphere, Consiglio nazionale delle richerche, Laboratorio
de geologia nucleare, Pisa, 1965.Crespin, J., Alexandre, A., Sylvestre, F., Sonzogni, C., Paillès, C., and Garreta, V.: IR laser extraction technique applied to oxygen isotope analysis of small biogenic silica samples, Anal. Chem., 80, 2372–2378, 10.1021/ac071475c, 2008.Ding, T. P., Tian, S. H., Sun, L., Wu, L. H., Zhou, J. X., and Chen, Z. Y.:
Silicon isotope fractionation between rice plants and nutrient solution and
its significance to the study of the silicon cycle, Geochim. Cosmochim.
Ac., 72, 5600–5615, 10.1016/j.gca.2008.09.006, 2008.Dodd, J. P. and Sharp, Z. D.: A laser fluorination method for oxygen isotope
analysis of biogenic silica and a new oxygen isotope calibration of modern
diatoms in freshwater environments, Geochim. Cosmochim. Ac., 74,
1381–1390, 10.1016/j.gca.2009.11.023, 2010.
Farquhar, G. D. and Gan, K. S.: On the progressive enrichment of the oxygen
isotopic composition of water along a leaf, Plant Cell Environ., 26,
801–819, 2003.Farquhar, G. D., Cernusak, L. A., and Barnes, B.: Heavy Water Fractionation
during Transpiration, Plant Physiol., 143, 11–18,
10.1104/pp.106.093278, 2007.Fischer, E. M. and Knutti, R.: Robust projections of combined humidity and
temperature extremes, Nat. Clim. Change, 3, 126–130,
10.1038/nclimate1682, 2013.Gan, K. S., Wong, S. C., Yong, J. W. H., and Farquhar, G. D.: 18O spatial
patterns of vein xylem water, leaf water, and dry matter in cotton leaves,
Plant Physiol., 130, 1008–1021, 10.1104/pp.007419, 2002.Gat, J. R.: Oxygen and Hydrogen Isotopes in the Hydrologic Cycle, Annu. Rev.
Earth Pl. Sc., 24, 225–262, 10.1146/annurev.earth.24.1.225,
1996.
Gat, J. R. and Bowser, C.: The heavy isotope enrichment of water in coupled evaporative systems, in: Stable Isotope Geochemistry: A Tribute to Samuel Epstein, edited by: Taylor Jr., H. P., O'Neil, J. R., and Kaplan, I. R., The Geochemical Society Special Publication No. 3, 159–168, 1991.Gázquez, F., Evans, N. P. and Hodell, D. A.: Precise and accurate isotope fractionation factors (α17O, α18O and αD) for water and CaSO4⚫2H2O (gypsum), Geochim. Cosmochim. Ac., 198, 259–270, 10.1016/j.gca.2016.11.001, 2017.Gázquez, F., Morellón, M., Bauska, T., Herwartz, D., Surma, J.,
Moreno, A., Staubwasser, M., Valero-Garcés, B., Delgado-Huertas, A., and
Hodell, D. A.: Triple oxygen and hydrogen isotopes of gypsum hydration water
for quantitative paleo-humidity reconstruction, Earth Planet. Sc. Lett.,
481, 177–188, 10.1016/j.epsl.2017.10.020, 2018.Helliker, B. R. and Ehleringer, J. R.: Establishing a grassland signature in
veins: 18O in the leaf water of C3 and C4 grasses, P. Natl. Acad. Sci. USA, 97, 7894–7898, 2000.Herwartz, D., Surma, J., Voigt, C., Assonov, S., and Staubwasser, M.: Triple
oxygen isotope systematics of structurally bonded water in gypsum, Geochim.
Cosmochim. Ac., 209, 254–266, 10.1016/j.gca.2017.04.026,
2017.Kumar, S. and Elbaum, R.: Interplay between silica deposition and viability
during the life span of sorghum silica cells, New Phytol., 217, 1137–1145,
10.1111/nph.14867, 2017.Kumar, S., Milstein, Y., Brami, Y., Elbaum, M., and Elbaum, R.: Mechanism of
silica deposition in sorghum silica cells, New Phytol., 213, 791–798,
10.1111/nph.14173, 2016.Kumar, S., Soukup, M., and Elbaum, R.: Silicification in Grasses: Variation
between Different Cell Types, Front. Plant Sci., 8,
10.3389/fpls.2017.00438, 2017.Kumar, S., Adiram-Filiba, N., Blum, S., Sanchez-Lopez, J. A., Tzfadia, O.,
Omid, A., Volpin, H., Heifetz, Y., Goobes, G., and Elbaum, R.: Grass silica
mineralizer (GSM1) protein precipitates silica in sorghum silica cells,
bioRxiv, 518332, 10.1101/518332, 2019.Landais, A., Barkan, E., Yakir, D., and Luz, B.: The triple isotopic
composition of oxygen in leaf water, Geochim. Cosmochim. Ac., 70,
4105–4115, 10.1016/j.gca.2006.06.1545, 2006.Li, S., Levin, N. E., Soderberg, K., Dennis, K. J., and Caylor, K. K.: Triple
oxygen isotope composition of leaf waters in Mpala, central Kenya, Earth
Planet. Sc. Lett., 468, 38–50, 10.1016/j.epsl.2017.02.015, 2017.Luz, B. and Barkan, E.: Variations of 17O/16O and 18O/16O in meteoric
waters, Geochim. Cosmochim. Ac., 74, 6276–6286,
10.1016/j.gca.2010.08.016, 2010.
Madella, M., Alexandre, A., Ball, T., and ICPN Working Group: International code for phytolith nomenclature 1.0, Ann. Bot.-London, 96, 253–260, 2005.Miyabuchi, Y. and Sugiyama, S.: 90,000-year phytolith records from caldera
rim to western foot of Aso Volcano, Japan: Implications for vegetation
history since catastrophic eruption, Quaternary Int., 397, 392–403,
10.1016/j.quaint.2015.08.015, 2015.
Motomura, H.: Silica Deposition in Relation to Ageing of Leaf Tissues in
Sasa veitchii (CarrieÁre) Rehder (Poaceae: Bambusoideae), Ann. Bot.-London, 93,
235–248, 2004.Nogué, S., Whicher, K., Baker, A. G., Bhagwat, S. A., and Willis, K. J.:
Phytolith analysis reveals the intensity of past land use change in the
Western Ghats biodiversity hotspot, Quaternary Int., 437, 82–89,
10.1016/j.quaint.2015.11.113, 2017.Norton, M. R., Lelièvre, F., and Volaire, F.: Measuring dehydration
tolerance in pasture grasses to improve drought survival, Crop Pasture Sci.,
65, 828–840, 10.1071/CP14054, 2014.Passey, B. H., Hu, H., Ji, H., Montanari, S., Li, S., Henkes, G. A., and Levin, N. E.: Triple oxygen isotopes in biogenic and sedimentary carbonates, Geochim. Cosmochim. Ac., 141, 1–25, 10.1016/j.gca.2014.06.006, 2014.Pitcairn, C. E. R., Jeffree, C. E., and Grace, J.: Influence of polishing and
abrasion on the diffusive conductance of leaf surface of Festuca arundinacea
Schreb, Plant Cell Environ., 9, 191–196,
10.1111/1365-3040.ep11611633, 1986.Risi, C., Noone, D., Worden, J., Frankenberg, C., Stiller, G., Kiefer, M., Funke, B., Walker, K., Bernath, P., Schneider, M., Wunch, D., Sherlock, V., Deutscher, N., Griffith, D., Wennberg, P. O., Strong, K., Smale, D., Mahieu, E., Barthlott, S., Hase, F., García, O., Notholt, J., Warneke, T., Toon, G., Sayres, D., Bony, S., Lee, J., Brown, D., Uemura, R., and Sturm, C.: Process-evaluation of tropospheric humidity simulated by general circulation models using water vapor isotopologues: 1. Comparison between models and observations, J. Geophys. Res., 117, D05303, 10.1029/2011JD016621, 2012.Schnyder, H. and Nelson, C. J.: Diurnal Growth of Tall Fescue Leaf Blades: I. Spatial Distribution of Growth, Deposition of Water, and Assimilate Import in the Elongation Zone, Plant Physiol., 86, 1070–1076, 10.1104/pp.86.4.1070, 1988.Shahack-Gross, R., Shemesh, A., Yakir, D., and Weiner, S.: Oxygen isotopic
composition of opaline phytoliths: Potential for terrestrial climatic
reconstruction, Geochim. Cosmochim. Ac., 60, 3949–3953,
10.1016/0016-7037(96)00237-2, 1996.Sharp, Z. D., Gibbons, J. A., Maltsev, O., Atudorei, V., Pack, A., Sengupta,
S., Shock, E. L., and Knauth, L. P.: A calibration of the triple oxygen
isotope fractionation in the SiO2–H2O system and applications to natural
samples, Geochim. Cosmochim. Ac., 186, 105–119,
10.1016/j.gca.2016.04.047, 2016.Sharp, Z. D., Wostbrock, J. A. G., and Pack, A.: Mass-dependent triple oxygen
isotope variations in terrestrial materials, Geochem. Perspect. Lett., 7,
27–31, 10.7185/geochemlet.1815, 2018.Sherwood, S. C., Ingram, W., Tsushima, Y., Satoh, M., Roberts, M., Vidale,
P. L., and O'Gorman, P. A.: Relative humidity changes in a warmer climate, J.
Geophys. Res.-Atmos., 115, D09104, 10.1029/2009JD012585, 2010.Smith, S. E., Fendenheim, D. M., and Halbrook, K.: Epidermal conductance as a
component of dehydration avoidance in Digitaria californica and Eragrostis
lehmanniana, two perennial desert grasses, J. Arid Environ., 64,
238–250, 10.1016/j.jaridenv.2005.04.012, 2006.Suavet, C., Alexandre, A., Franchi, I. A., Gattacceca, J., Sonzogni, C., Greenwood, R. C., Folco, L., and Rochette, P.: Identification of the parent bodies of micrometeorites with high-precision oxygen isotope ratios, Earth Planet. Sc. Lett., 293, 313–320, 10.1016/j.epsl.2010.02.046, 2010.Surma, J., Assonov, S., Bolourchi, M. J., and Staubwasser, M.: Triple oxygen
isotope signatures in evaporated water bodies from the Sistan Oasis, Iran,
ResearchGate, 42, 8456–8462, 10.1002/2015GL066475, 2015.Surma, J., Assonov, S., Herwartz, D., Voigt, C., and Staubwasser, M.: The
evolution of 17O-excess in surface water of the arid environment during
recharge and evaporation, Sci. Rep.-UK, 8, 4972,
10.1038/s41598-018-23151-6, 2018.Uemura, R., Barkan, E., Abe, O., and Luz, B.: Triple isotope composition of
oxygen in atmospheric water vapor, Geophys. Res. Lett., 37, L04402,
10.1029/2009GL041960, 2010.Vergutz, L., Manzoni, S., Porporato, A., Novais, R. F., and Jackson, R. B.:
Global resorption efficiencies and concentrations of carbon and nutrients in
leaves of terrestrial plants, Ecol. Monogr., 82, 205–220,
10.1890/11-0416.1, 2012.
Webb, E. A. and Longstaffe, F. J.: The oxygen isotopic compositions of
silica phytoliths and plant water in grasses: Implications for the study of
paleoclimate, Geochim. Cosmochim. Ac., 64, 767–780, 2000.Webb, E. A. and Longstaffe, F. J.: Climatic influences on the oxygen isotopic composition of biogenic silica in prairie grass, Geochim. Cosmochim. Ac., 66, 1891–1904, 2002.
Webb, E. A. and Longstaffe, F. J.: The relationship between phytolith- and
plant-water delta O-18 values in grasses, Geochim. Cosmochim. Ac., 67,
1437–1449, 10.1016/s0016-7037(02)01300-5, 2003.Webb, E. A. and Longstaffe, F. J.: Identifying the δ18O signature
of precipitation in grass cellulose and phytoliths: Refining the
paleoclimate model, Geochim. Cosmochim. Ac., 70, 2417–2426,
10.1016/j.gca.2006.02.024, 2006.Welp, L. R., Lee, X., Kim, K., Griffis, T. J., Billmark, K. A., and Baker, J.
M.: δ18O of water vapour, evapotranspiration and the sites of leaf
water evaporation in a soybean canopy, Plant Cell Environ., 31,
1214–1228, 10.1111/j.1365-3040.2008.01826.x, 2008.Woodburn, T. L., Johnson, W. C., Mason, J. A., Bozarth, S. R., and Halfen, A.
F.: Vegetation dynamics during the Pleistocene–Holocene transition in the
central Great Plains, USA, Holocene, 27, 155–163,
10.1177/0959683616652710, 2017.