Leaf gas-exchange models show considerable promise as paleo-CO2
proxies. They are largely mechanistic in nature, provide well-constrained
estimates even when CO2 is high, and can be applied to most
subaerial, stomata-bearing fossil leaves from C3 taxa, regardless of age
or taxonomy. Here we place additional observational and theoretical
constraints on one of these models, the “Franks” model. In order to gauge
the model's general accuracy in a way that is appropriate for fossil studies,
we estimated CO2 from 40 species of extant angiosperms, conifers, and
ferns based only on measurements that can be made directly from fossils (leaf
δ13C and stomatal density and size) and on a limited sample
size (one to three leaves per species). The mean error rate is 28 %,
which is similar to or better than the accuracy of other leading
paleo-CO2 proxies. We find that leaf temperature and photorespiration
do not strongly affect estimated CO2, although more work is warranted
on the possible influence of O2 concentration on photorespiration.
Leaves from the lowermost 1–2 m of closed-canopy forests should not be used
because the local air δ13C value is lower than the global
well-mixed value. Such leaves are not common in the fossil record but can be
identified by morphological and isotopic means.
Introduction
Leaves on terrestrial plants are well poised to record information about the
concentration of atmospheric CO2. They are in direct contact with the
atmosphere and have large surface-area-to-volume ratios, so the leaf
internal CO2 concentration is tightly coupled to atmospheric CO2
concentration. Also, leaves are specifically built for the purpose of fixing
atmospheric carbon into structural tissue and face constant selection
pressure to optimize their carbon uptake relative to water loss. As a
result, many components of the leaf system are sensitive to atmospheric
CO2, and these components feed back on one another to reach a new
equilibrium when atmospheric CO2 changes. In terms of carbon
assimilation, Farquhar and Sharkey (1982) modeled this system in its
simplest form as
An=gc(tot)×(ca-ci),
where An is the leaf CO2 assimilation rate
(µmol m-2 s-1), gc(tot) is the total
operational conductance to CO2 diffusion from the atmosphere to the site
of photosynthesis (mol m-2 s-1), ca is atmospheric
CO2 concentration (µmol mol-1 or ppm), and
ci is leaf intercellular CO2 concentration
(µmol mol-1 or ppm) (see also Von Caemmerer, 2000).
Rearranging Eq. (1) for atmospheric CO2 yields
ca=Angc(tot)×1-cica.
Equation (2) forms the basis of two leaf gas-exchange approaches for
estimating paleo-CO2 from fossils (Konrad et al., 2008, 2017; Franks
et al., 2014). In the Franks model, conductance is estimated in part from
measurements of stomatal size and density, ci/ca from
measurements of leaf δ13C along with reconstructions of coeval
air δ13C (see also Eq. 9), and An from knowledge of living
relatives and its dependency on ca (Franks et al., 2014). Following
Farquhar et al. (1980), the latter is modeled as (Franks et al., 2014;
Kowalczyk et al., 2018)
An=A0cicaca-Γ*ci0ca0ca0+2Γ*cicaca+2Γ*ci0ca0ca0-Γ*,
where Γ* is the CO2 compensation point in the absence of dark
respiration (ppm), and the subscript “0” refers to conditions at a known
CO2 concentration (typically present day). Equations (2) and (3) are
then solved iteratively until the solution for ca converges.
These gas-exchange approaches grew out of a group of paleo-CO2
proxies based on the CO2 sensitivity of stomatal density (D) or the
similar metric stomatal index (Woodward, 1987; Royer, 2001). Here, the
D–ca sensitivity is calibrated in an extant species, allowing
for paleo-CO2 inference from the same (or very similar) fossil species.
These empirical relationships typically follow a power-law function (Wynn,
2003; Franks et al., 2014; Konrad et al., 2017):
ca=1kDα,
where k and α are species-specific constants.
The related stomatal ratio proxy is simplified: D is measured in an extant
species (D0, at present-day ca0) and then the ratio of D0 to
D in a related fossil species is assumed to be linearly related to the
ratio of paleo-ca to present-day ca0 (Chaloner and McElwain,
1997; McElwain, 1998):
caca0=kD0D.
Equation (5) can be rearranged to match Eq. (4) but with α fixed at
1. Thus, paleo-CO2 estimates using the stomatal ratio proxy are based
on a one-point calibration and an assumption that α=1; observations
do not always support this assumption (e.g., α=0.43 for
Ginkgo biloba; Barclay and Wing, 2016). The scalar k was
originally set at 2 for Paleozoic and Mesozoic reconstructions so that
paleo-CO2 estimates during the Carboniferous matched that from
long-term carbon cycle models (Chaloner and McElwain, 1997). For younger
reconstructions, k is probably closer to 1 (by definition, k=1 for
present-day plants). We note that the stomatal ratio proxy was originally
conceived as providing qualitative information only about paleo-CO2
(McElwain and Chaloner, 1995, 1996; Chaloner and McElwain, 1997; McElwain,
1998) and has not been tested with dated herbaria materials or with
CO2 manipulation experiments.
At high CO2, the D–ca sensitivity saturates in many
species, leading to uncertain paleo-CO2 estimates, often with
unbounded upper limits (e.g., Smith et al., 2010; Doria et al., 2011).
Stomatal density does not respond to CO2 in all species (Woodward and
Kelly, 1995; Royer, 2001), and because D–ca relationships can be
species specific (that is, different species in the same genus with different
responses; Beerling, 2005; Haworth et al., 2010), only fossil taxa that are
still alive today should be used. The gas-exchange proxies partly address
these limitations: (1) CO2 estimates remain well-bounded – even at
high CO2 – and their precision is similar to or better than other
leading paleo-CO2 proxies (∼+35/-25 % at 95 %
confidence; Franks et al., 2014) and (2) the models are mostly mechanistic; that
is, they are explicitly driven by plant physiological principles, not just
empirical relationships measured on living plants. (3) Because the models
retain sensitivity at high CO2 and do not require that a fossil
species still be alive today, much of the paleobotanical record is open for
CO2 inference, regardless of age or taxonomy. (4) Because the
models are based on multiple inputs linked by feedbacks, they can still
perform adequately even if one or more of the inputs in a particular taxon is
not sensitive to CO2, for example stomatal density (Milligan et al.,
2019).
We note that the published uncertainties (precision) associated with the
stomatal density proxies are probably too small because they usually only
reflect uncertainty in either the calibration regression or in the measured
values of fossil stomatal density, but not both; when both sources are
propagated, errors often exceed ±30 % at 95 % confidence
(Beerling et al., 2009). Also, error rates in estimates from extant taxa
for which CO2 is known (accuracy) are usually smaller with stomatal
density proxies than with gas-exchange proxies (e.g., Barclay and Wing,
2016), but this is expected because the same taxa have been calibrated in
present-day (or near present-day) conditions. Because the gas-exchange
proxies are largely built from physiological principles, they have less
“recency” bias; that is, the gas-exchange proxies estimate present-day and
paleo-CO2 with similar certainty when the same methods are used to
determine the inputs.
Study aims and methods
Leaf gas-exchange proxies for paleo-CO2 are becoming popular (Konrad
et al., 2008, 2017; Grein et al., 2011a, b, 2013; Erdei et al., 2012;
Roth-Nebelsick et al., 2012, 2014; Franks et al., 2014; Maxbauer et al.,
2014; Montañez et al., 2016; Reichgelt et al., 2016; Tesfamichael et al.,
2017; Kowalczyk et al., 2018; Lei et al., 2018; Londoño et al., 2018;
Richey et al., 2018; Milligan et al., 2019). However, many elements in these
models remain understudied. Here we scrutinize four such elements of the
Franks et al. (2014) model and ask the following: how does the model perform across a
large number of phylogenetically diverse taxa? And how is the model affected
by temperature, photorespiration, and proximity to the forest floor? We
next describe the motivation and details of the study design (see also
Table 1 for a summary).
Attributes of datasets used to test the Franks et al. (2014)
model.
NumberMethodsElement of model testedof speciessectionNotesGeneral testing in a phylogenetically diverse set of species and with a minimal number of leaves measured per species402.1Leaves come from Panama (published by Londoño et al., 2018), Connecticut, and Puerto RicoTemperature62.2Theoretical calculations and growth-chamber experimentPhotorespirationn/a2.3Theoretical calculationsCanopy position62.4Leaves come from Panama and Connecticut
n/a: not applicable
General testing in living plants
Franks et al. (2014) tested the model on four species of field-grown trees
(three gymnosperms and one angiosperm) and one conifer grown in chambers at
480 and 1270 ppm CO2. The average error rate (absolute value of
estimated CO2 minus measured CO2, divided by measured
CO2) was 5 %. Follow-up work with three field-grown tree species
(Maxbauer et al., 2014; Kowalczyk et al., 2018), CO2 experiments on
seven tropical trees species (Londoño et al., 2018), and experiments on
two fern and one conifer species (Milligan et al., 2019) indicate somewhat
higher error rates (Fig. 1). Combined, the average error rate is 20 %
(median 13 %).
Published CO2 estimates using the Franks model for extant
plants for which the physiological inputs A0 (assimilation rate at a known
CO2 concentration) and/or gc(op)/gc(max) (ratio of
operational to maximum leaf conductance to CO2) were measured
directly. Horizontal lines are the correct CO2 concentrations.
Uncertainties in the estimates correspond to the 16th–84th percentile range.
Circles are from Londoño et al. (2018), squares from Milligan et
al. (2019), large triangle from Maxbauer et al. (2014), small triangles from
Kowalczyk et al. (2018), and diamonds from Franks et al. (2014).
In these studies, two of the key physiological inputs were measured directly
with an infrared gas analyzer: the assimilation rate at a known CO2
concentration (A0) and/or the ratio of operational to maximum stomatal
conductance to CO2 (gc(op)/gc(max), or ζ),
the latter of which is important for calculating the total leaf conductance
(gc(tot)). These two inputs cannot be directly measured on fossils;
thus, the error rates associated with Fig. 1 may not be representative for
fossil studies. Franks et al. (2014) argue that within plant functional types
growing in their natural environment, mean A0 is fairly conservative,
leading to the recommended mean A0 values in Franks et al. (2014)
(12 µmol m-2 s-1 for angiosperms, 10 for conifers, and
6 for ferns and ginkgos). Along similar lines, the mean ratio
gc(op)/gc(max) tends to be conserved across plant
functional types; Franks et al. (2014) recommend a value of 0.2, which may
correspond to the most efficient set point for stomata to control conductance
(Franks et al., 2012). This conservation of physiological function is one of
the underlying principles in the Franks model.
Here we test this assumption by estimating CO2 from 40
phylogenetically diverse species of field-grown trees. In making these
estimates, we use the recommended mean values of A0 and
gc(op)/gc(max) from Franks et al. (2014) instead of
measuring them directly (see also Montañez et al., 2016, for other ways
to infer assimilation rate from fossils). Thus, this dataset should be a more
faithful gauge for model accuracy as applied to fossils. Of the 40 species,
21 were previously published in Londoño et al. (2018), who collected
sun-adapted canopy leaves of angiosperms using a crane in Parque Nacional San
Lorenzo, Panama. To test the method in temperate forests, we collected leaves
from 11 angiosperm and 7 conifer species from Dinosaur State Park
(Rocky Hill, Connecticut), Wesleyan University (Middletown, Connecticut), and
Connecticut College (New London, Connecticut) during the summer of 2015.
Here, all trees grew in open, park-like settings; one to three sun leaves
were sampled from the lower outside crown of each tree. In January of 2015,
we also sampled sun-exposed leaves from the tree fern Cyathea arborea in El Yunque National Forest, Puerto Rico (near the Yokahú
Tower).
Stomatal size and density were measured either on untreated leaves using
epifluorescence microscopy with a 420–490 nm filter or on cleared leaves
(using 50 % household bleach or 5 % NaOH) using transmitted-light
microscopy. For most species, whole-leaf δ13C comes from Royer
and Hren (2017); the same leaves were measured for δ13C and
stomatal morphology. The UC Davis Stable Isotope Facility measured some
additional leaf samples. Atmospheric CO2 concentration (400 ppm) and
δ13Cair (-8.5 ‰) come from Mauna Loa, Hawaii
(NOAA/ESRL, 2019), which we assume are representative of the local conditions
under which we sampled (e.g., Munger and Hadley, 2017). Table S1 summarizes for
these 40 species all of the inputs needed to run the Franks model, along with
the estimated CO2 concentrations. Uncertainties in the estimates are
based on error propagation using Monte Carlo simulations (Franks et al.,
2014).
Temperature
The Franks model can be configured for any temperature. Franks et al. (2014)
recommend that the photosynthesis parameters A0 and Γ*, and the
air physical properties affecting the diffusion of CO2 into the leaf (the
ratio of CO2 diffusivity in air to the molar volume of air, or
d/v),
correspond to the mean daytime growing-season leaf temperature (more
precisely, assimilation-weighted leaf temperature). The reasoning behind this
is that (i) the assimilation-weighted leaf temperature corresponds to the
mean ci/ca derived from fossil leaf δ13C, and
(ii) both theory (Michaletz et al., 2015, 2016) and observations (Helliker
and Richter, 2008; Song et al., 2011) indicate that the control of leaf gas
exchange leads to relatively stable assimilation-weighted leaf temperatures
(∼19–25 ∘C from temperate to tropical regions) despite large
differences in air temperature. This is mostly due to the effects of
transpiration on leaf energy balance. Franks et al. (2014) chose a fixed
temperature of 25 ∘C because much of the Mesozoic and Cenozoic
correspond to climates warmer than the present day. When applying the Franks
model to known cooler paleoenvironments, improved accuracy may be achieved
with leaf-temperature-appropriate values for A0, Γ*, and d/v.
Bernacchi et al. (2003) proposed the following temperature sensitivity for
Γ* based on experiments:
Γ*=e19.02-37.83RT,
where R is the molar gas constant (8.31446×10-3 kJ K-1 mol-1) and T is leaf temperature (K). Marrero
and Mason (1972) describe the sensitivity of water vapor diffusivity to
temperature as
d=1.87×10-10T2.072P,
where P is atmospheric pressure, which we fix at 1 atmosphere. Lastly, the
temperature sensitivity of the molar volume of air follows ideal gas
principles:
v=vSTPTTSTPPPSTP,
where TSTP is 273.15 K, PSTP is 1 atmosphere, and
vSTP is the air volume at TSTP and PSTP
(0.022414 m3 mol-1).
Using Eqs. (6)–(8), we can describe how, conceptually, the sensitivities of
Γ* and d/v to leaf temperature affect estimates of CO2 from
the Franks model. We apply these relationships to a suite of 409 fossil and
extant leaves from 62 species of angiosperms, gymnosperms, and ferns. These
data come from the current study (see Sect. 2.1 and 2.4) and Londoño et
al. (2018), Kowalczyk et al. (2018), and Milligan et al. (2019).
To experimentally test more generally how the Franks model is influenced by
temperature, we grew six species of plants inside two growth chambers with
contrasting temperatures (Conviron E7/2; Winnipeg, Canada). Air temperature
was 28±0.5∘C (1σ) and 20±0.3∘C during
the day and 19±0.7∘C and 11±1.1∘C during the
night. We note that the difference in leaf temperature was probably smaller
than that in air temperature during the day (8 ∘C; see earlier
discussion). We held fixed the day length (17 h with a 30 min simulated
dawn and dusk) and CO2 concentration (500±10 ppm). Light
intensity at the heights at which we sampled leaves ranged from 100 to
400 µmol m-2 s-1. Humidity differed moderately between
chambers (76.5±1.8 % and 90.0±3.6 %). To minimize any
chamber effects, we alternated plants between chambers every 2 weeks.
Four of the species started as saplings purchased from commercial nurseries:
bare-root, 30 cm tall saplings of Acer negundo and Carpinus caroliniana, 30 cm tall saplings of Ostrya virginiana with a soil ball, and bare-root,
10 cm tall saplings of Ilex opaca. We grew
the other two species from seed: Betula lenta from a commercial
source and Quercus rubra from a single tree on Wesleyan University's
campus. All seeds were soaked in water for 24 h and then cold-stratified in
a refrigerator for 30 and 60 days, respectively.
All seeds and saplings grew in the same potting soil (Promix Bx with
Mycorise; Premier Horticulture; Quakertown, Pennsylvania, USA) and
fertilizer (Scotts all-purpose flower and vegetable fertilizer; Maryville,
Ohio, USA). They were watered to field capacity every other day, and we
discarded any excess water passing through the pots. After 3 months of
growth in the chambers, for each species–chamber pair we harvested the three
newest fully expanded leaves whose buds developed during the experiment. In
most cases, we harvested five plants per species–chamber pair; the one
exception was I. opaca, for which we were limited to three plants in the warm treatment
and two in the cool treatment.
We measured stomatal size and density on cleared leaves (using 50 %
household bleach) with transmitted-light microscopy. Whole-leaf δ13C comes from the UC Davis Stable Isotope Facility and the Light Stable
Isotope Mass Spec Lab at the University of Florida; the same leaves were
measured for δ13C and stomatal morphology. We used either a
hole punch or razor to remove two adjacent sections of leaf tissue near the
leaf centers, avoiding major veins. Because we used the same CO2 gas
cylinder (δ13C=-11.8 ‰) and laboratory space
(δ13C=-10.4 ‰) as Milligan et al. (2019), we
used their two-end-member mixing model (1/CO2 vs.
δ13C; Keeling, 1958) to calculate the δ13C of
the chamber CO2 at 500 ppm (-10.6 ‰). We used the
recommended values from Franks et al. (2014) for the physiological inputs
A0 and gc(op)/gc(max). Table S1 summarizes all of the
inputs from this experiment needed to run the Franks model, along with the
estimated CO2 concentrations. The standard errors for the inputs are
based on plant means.
To test if leaf δ13C and stomatal morphology (stomatal
density, stomatal pore length, and single guard cell width) differed between
temperature treatments across species, we implemented a mixed model in R (R
Core Team, 2016) using the lme4 (Bates et al., 2015) and lmerTest (Kuznetsova
et al., 2017) packages, with temperature and species as the two fixed
factors. To test if there was a significant difference between CO2
estimates from the two temperature treatments, we ran a Kolmogorov–Smirnov
(KS) test in R. For each species, we first estimated CO2 for each
plant in the warm and cool treatments based on simulated inputs constrained
by their means and variances. In the typical case with five plants per
chamber, this produced five CO2 estimates for the warm chamber and
the same for the cool chamber. A KS test was then used to test for a
significant temperature effect. We repeated this procedure 10 000 times,
with 10 000 associated KS tests. The fraction of tests with a
p value < 0.05 was taken as the overall p value. An advantage of this
approach is that it incorporates both within- and across-plant variation.
Photorespiration
ci/ca is estimated in the Franks model following Farquhar
et al. (1982):
Δleaf=a+(b-a)×cica,
where a is the carbon isotope fractionation due to the diffusion of CO2
in air (4.4 ‰; Farquhar et al., 1982), b is the fractionation
associated with RuBP carboxylase (30 ‰; Roeske and O'Leary, 1984),
and Δleaf is the net fractionation between air and assimilated
carbon ([δ13Cair-δ13Cleaf]/[1+δ13Cleaf/1000]).
Equation (8) can be expanded to include other effects, including
photorespiration (Farquhar et al., 1982):
Δleaf=a+(b-a)×cica-fΓ∗ca,
where f is the carbon isotope fractionation due to photorespiration.
Photorespiration occurs when the enzyme rubisco fixes O2, not
CO2 (i.e., RuBP oxygenase). One product of photorespiration is
CO2 (Jones, 1992), whose δ13C is lower than the source
substrate glycine. If this respired CO2 escapes to the atmosphere,
the δ13C of the leaf carbon becomes more positive. Thus, if
ci/ca is calculated using Eq. (8), as is common practice,
the calculation may be falsely low, leading to an underprediction of
atmospheric CO2.
Measured values for f vary from ∼9 to 15 ‰ (see compilation
in Schubert and Jahren, 2018), which is in line with theoretical predictions
(Tcherkez, 2006). At a 400 ppm atmospheric CO2 and Γ* of
40 ppm, Eq. (9) implies that ∼1 ‰ of Δleaf is
due to photorespiration, meaning that ci/ca should be ∼0.04 higher relative to Eq. (8). Here, using the suite of fossil and extant
leaves described in Sect. 2.2, we explore how the carbon isotopic
fractionation associated with photorespiration affects CO2 estimates
with the Franks model. Because ci/ca is present in both of
the fundamental equations (Eqs. 2 and 3), we solve them iteratively until
ci/ca converges.
Leaves that grow close to the forest floor
The composition of air close to the forest floor can differ considerably from
the well-mixed atmosphere. Of relevance to the Franks model, soil respiration
can lead to a locally higher CO2 concentration and lower
δ13Cair (Table 2). This effect is strongest at night, when
the forest boundary layer is thickest (e.g., Munger and Hadley, 2017), but we
focus here on daylight hours because that is when most plants take up
CO2. In wet tropical forests, which can have very high soil
respiration rates, CO2 during the day near the forest floor can be
elevated by tens of parts per million, and the δ13Cair can be
2–3 ‰ lower; in temperate forests, the deviations are smaller
(Table 2). Above ∼2 m, CO2 concentrations and air
δ13C during the daytime largely match the well-mixed
atmosphere.
Deviations in the δ13C and concentration of
CO2 close to a forest floor relative to well-mixed air above the
canopy. All measurements were made close to midday.
δ13Cair relative toCO2 relative toHeight aboveStudywell-mixed air (‰)well-mixed air (ppm)forest floor (m)Forest locationTropical forest Broadmeadow et al. (1992)-2+200.15–1Trinidad during dry seasonBuchmann et al. (1997)-2+300.70–0.75French Guiana during wet and dry seasonsHoltum and Winter (2001)n/a+500.10Panama during wet and dry seasonsLloyd et al. (1996)-3+701Brazil (Amazon Basin)Quay et al. (1989)-3+202Brazil (Amazon Basin)Sternberg et al. (1989)-2+251Panama during wet and dry seasonsTemperate forest Francey et al. (1985)-1+201TasmaniaMunger and Hadley (2017)n/a+151Massachusetts (Harvard Forest)
n/a: not applicable
As a result, leaves that grow close to the forest floor may cause the Franks
model to produce CO2 estimates higher than that of the mixed
atmosphere for at least two reasons. First, the concentration of CO2
near the forest floor is elevated; that is, the model may correctly estimate
a CO2 concentration that the user is not interested in. Second,
because the δ13Cair that a forest-floor plant experiences
is lower than the global well-mixed value, if the user chooses the well-mixed
value for model input (inferred, for example, from the δ13C of
marine carbonate; Tipple et al., 2010), then ci/ca and thus
atmospheric CO2 will be overestimated (see Eq. 2).
We sought to test how the Franks model is affected by the forest-floor
microenvironment for five tropical angiosperm species and 15 temperate
angiosperm and fern species. The tropical leaves were sampled at ∼1–2 m of height from Parque Nacional San Lorenzo, Panama. In contrast to the
canopy dataset from San Lorenzo (Sect. 2.1), these CO2 estimates
have not been previously reported. In the summer of 2015, seven fern species
were sampled at ∼0.5 m of height from Connecticut College and Wesleyan
University. Also, we used leaf vouchers from Royer et al. (2010), who sampled
eight herbaceous angiosperm species at ∼0.1–0.2 m of height from Reed
Gap, Connecticut. For all 20 species, stomatal and carbon isotopic
measurements follow the methods described in Sect. 2.1. Table S1 contains all
of the inputs needed to run the Franks model, along with the estimated
CO2 concentrations.
We also investigated if we could include the forest-floor δ13Cair effect in our estimates of atmospheric CO2. We did not
measure the CO2 concentration and δ13Cair around
our plants, so we could not directly compare our values. But, if the only
CO2 inputs close to the forest floor are from the soil and well-mixed
atmosphere, then the system can be modeled as a two-end-member mixing model
in which δ13Cair has a positive, linear relationship with
1/CO2 (Keeling, 1958). If the CO2 concentration and
δ13C of both end-members are known, the forest-floor
microenvironment should fall somewhere on the modeled line. Importantly, the
Franks model provides a second constraint on the system. Here, δ13Cair has a negative, nonlinear relationship with 1/CO2
because δ13Cair is positively related to
ci/ca and CO2. The Franks model thus provides a
second calculation for the relationship between δ13Cair and
estimated CO2 concentration. The intersection between the two curves
should be the correct δ13Cair and CO2 concentration
for the forest-floor microenvironment.
To estimate the soil CO2 end-member, we measured the
δ13C of soil organic matter collected from the A horizons of
13 soil sites at San Lorenzo and of five each at Reed Gap and Connecticut
College. For all soils, we assume a 5000 ppm CO2 concentration for a
depth that is below the zone of CO2 diffusion from the atmosphere
(∼0.3 m; Cerling, 1999; Breecker et al., 2009). The true value for wet
temperate and tropical forest soils may be somewhat less or substantially
more than 5000 ppm (Medina et al., 1986; Cerling, 1999; Hirano et al., 2003;
Hashimoto et al., 2004; Sotta et al., 2004). Because the mixing model uses
1/CO2, a much higher CO2 concentration (e.g., 10 000 ppm)
has little impact on our results.
Estimates of CO2 based on canopy leaves from 40 tree
species. Uncertainties in the estimates correspond to the 16th–84th
percentile range. Vertical line is the correct concentration (400 ppm). On
the left is an order-level vascular plant phylogeny (APW v.13; Stevens, 2001,
onwards). The number of measured species is given in parentheses.
Results and discussionGeneral testing in living plants
Estimates of CO2 across the 40 tree species sampled in the field
range from 275 to 850 ppm, with a mean of 478 ppm and median of 472 ppm
(Fig. 2); two-thirds of the estimates (a close equivalent to ±1 standard
deviation) range between 353 and 585 ppm. In 28 % of the tested species,
the estimated CO2 concentrations overlap the target
concentration (400 ppm) at 95 % confidence; for these species, the
CO2 estimates do not differ significantly from the target. There are
no strong differences across taxonomic orders or between leaves from
tropical and temperate forests. The mean error rate across the estimates is
28 % (median 24 %), which is higher than estimates that include
direct measurements of the physiological inputs A0 and
gc(op)/gc(max) (mean 20 %; median 13 %;
Fig. 1). Along similar lines, if the estimates presented in Fig. 1 are
reestimated using the values for A0 and
gc(op)/gc(max) recommended by Franks et al. (2014), the
mean error rate increases to 37 % (median 33 %). If only the
default values of A0 are used, the median error rate is 27 %, and
for only default values of gc(op)/gc(max) it is 22 %.
These results indicate that CO2 accuracy is generally improved when
A0 and/or gc(op)/gc(max) are measured. These
measurements require expensive gas-exchange equipment and are not always easy
or practical to make. Moreover, A0 and gc(op)/gc(max)
cannot be measured on fossils. Some gains in accuracy are possible by
measuring A0 and gc(op)/gc(max) on extant relatives of
the fossil species (e.g., the same genus). Absent of this, our analysis using
the recommended mean values of Franks et al. (2014) indicates an error rate,
on average, of approximately 28 %. This is comparable to or better than
other leading paleo-CO2 proxies (Franks et al., 2014).
One reliable way to improve accuracy is to estimate CO2 with multiple
species because the falsely high and falsely low estimates partly cancel each
other out. The grand mean of estimates presented in Fig. 2 (478 ppm) is
20 % from the 400 ppm target, which is less than the 28 % mean error
rate of individual estimates.
Literature compilation of the sensitivity of
gc(op)/gc(max) (ratio of operational to maximum leaf
conductance to CO2) to atmospheric CO2 concentration.
Dow et al. (2014) observed that gc(op)/gc(max) inversely
varies with CO2 in Arabidopsis thaliana, but primarily at
subambient concentrations (yellow triangles in Fig. 3). At elevated
CO2, gc(op)/gc(max) is close to 0.2, which is the
value recommended by Franks et al. (2014). Data from 11 species of
angiosperms, conifers, and ferns at present-day (or near present-day) and
elevated CO2 concentrations support the view of a limited effect at
high CO2 (Fig. 3; Franks et al., 2014; Londoño et al., 2018;
Milligan et al., 2019). More data at subambient CO2 are needed, but
for CO2 concentrations similar to or higher than the present day, we
see no strong reason to include a CO2 sensitivity in
gc(op)/gc(max). The rather low values for Cedrus deodara and many of the tropical angiosperms (<0.1) are likely due to
stress imposed by their growth-chamber environment; these
gc(op)/gc(max) values are probably not representative of
field-grown trees, which tend to be closer to 0.2 (Franks et al., 2014).
Temperature
The temperature sensitivities of the ratio of diffusivity of CO2 in
air to the molar volume of air (d/v) and the CO2 compensation point
in the absence of dark respiration (Γ*) have little effect on
estimated CO2 in the Franks model (Fig. 4). Given that
assimilation-weighted leaf temperature only varies about 7 ∘C across
plants today, the differences shown in Fig. 4, which are based on leaf
temperatures of 25 and 15 ∘C, are likely a maximum effect. As
such, we consider the use of a fixed leaf temperature (e.g., 25 ∘C)
in the model to be a defensible simplification.
Estimates of CO2 at leaf temperatures of 25 ∘C and
15 ∘C. Each symbol is an extant or fossil leaf. The difference in
estimated CO2 for any leaf is due to the theoretical effects of
temperature on gas diffusion (d/v) and the CO2 compensation point
in the absence of dark respiration (Γ*) (Eqs. 6–8).
Other inputs in the model may respond to temperature, though. In our
growth-chamber experiments for which daytime air temperatures were 28 and
20 ∘C, the effect on estimated CO2 was mixed (Fig. 5). In
five out of six species, estimated CO2 was higher in the warm
treatment, but for all species these differences were not statistically
significant (p>0.05 based on a KS
test; see Methods). Incorporating the temperature sensitivities in d/v and
Γ* had little effect (“adj” estimates in Fig. 5), as expected from
Fig. 4.
Estimates of CO2 for plants grown inside growth chambers at
daytime air temperatures of 28 and 20 ∘C. Also shown are estimates
after taking into account the temperature sensitivity of gas diffusion
(d/v) and the CO2 compensation point in the absence of dark
respiration (Γ*) (“adj”; see also Fig. 4). Dashed line is the
correct CO2 concentration (500 ppm). Uncertainties in the estimates
correspond to the 16th–84th percentile range.
None of the measured inputs – stomatal density, stomatal pore length, single
guard cell width, and leaf δ13C – were significantly affected
by temperature across all species (p>0.05 for each of the four inputs based on a mixed model; see
Sect. 2.2). These small differences probably cannot account for the
differences in estimated CO2 between temperatures. It is more likely
that some of the inputs that we did not directly measure, such as
assimilation rate (A0), the gc(op)/gc(max) ratio, or
mesophyll conductance (gm), differ from the true mean value. In the
cases for the five species for which estimated CO2 is higher in the
warm treatment, our mean A0 for the warm plants must be falsely high, or
gc(op)/gc(max) or gm is falsely low.
In summary, we see no strong reason to expand the parameterization of
temperature in the model, though more growth-chamber experiments may be
warranted. We note that in three out of the six species from the warm
treatment, the estimated CO2 cannot be distinguished from the
500 ppm target at 95 % confidence; for the cool treatment, this is true
for four of the species. Also, the across-species means of estimated
CO2 for the warm and cool treatments are reasonably close to the
target (590 and 502 ppm, respectively) and overall have a mean error rate of
25 %. This level of uncertainty is similar to our field estimates for
which
we did not measure A0 or gc(op)/gc(max) (28 %; see
Fig. 2). This too provides support for our recommendation that it is not
critical to include a broader treatment of temperature in the model.
Estimates of CO2 with and without a photorespiration effect
(f=9.1 ‰; see Eq. 10). Each symbol is an extant or fossil leaf.
Dashed line is y=x.
Photorespiration
The theoretical effects of photorespiration do not strongly impact estimates
of CO2 in the Franks model. The average effect for our 409 extant and
fossil leaves is to increase estimated CO2 by 2.2 % plus 38 ppm
(Fig. 6). At 1000 ppm, for example, estimates would increase by 60 ppm.
This calculation assumes a photorespiration fractionation (f) of
9.1 ‰, which is the value estimated for Arabidopsis thaliana (Schubert and Jahren, 2018). If a fractionation towards the upper
bound of published estimates is used instead (15 ‰), estimated
CO2 increases on average by 3.8 % plus 61 ppm. Across this range
in f, the associated uncertainty in estimated CO2 is well within
the method's overall precision (∼+35/-25 % at 95 % confidence;
Franks et al., 2014). As such, CO2 estimates made without these
photorespiration effects (i.e., using Eq. 9 instead of Eq. 10) should be
reliable, although some improvement is possible using Eq. 10 in cases in
which
f is accurately known.
We note that both f and Γ* are also affected by atmospheric
O2 concentration. Because O2 is directly responsible for
photorespiration, f should scale with O2 (or, more precisely, the
O2 : CO2 molar ratio). Unfortunately, this effect is poorly
constrained (Beerling et al., 2002; Berner et al., 2003; Porter et al.,
2017). In contrast, the theoretical effect of O2 on Γ* is
known: it is linear with an approximate slope of 2 (Farquhar et al., 1982;
see their Eq. B13). During the Phanerozoic, O2 likely ranged from
10 % to 30 %, with lows during the early Paleozoic and early Triassic
and highs during the Carboniferous to early Permian and Cretaceous (Berner,
2009; Glasspool and Scott, 2010; Arvidson et al., 2013; Mills et al., 2016;
Lenton et al., 2018). Assuming a present-day Γ* of 40 ppm (at
21 % O2), Γ* would be 60 ppm at 30 % O2 and
20 ppm at 10 % O2. Running the Franks model on our library of
409 extant and fossil leaves, we find little effect on estimated CO2:
estimates are 7.4 % higher on average at 30 % O2 than at
10 % O2 (see also McElwain et al., 2016).
Leaves that grow close to the forest floor
CO2 estimates for tropical understory leaves from five species at San
Lorenzo, Panama, are very high, ranging from 1903 to 18863 ppm (species
mean 6837 ppm). For two of the species, Londoño et al. (2018) also
analyzed canopy leaves from trees nearby, and these within-species
comparisons highlight the vast discrepancy (Ocotea sp.: 541 vs.
5737 ppm; Tontelea sp.: 622 vs. 18 863 ppm). The primary
difference in the inputs between the canopy and understory leaves is the
δ13Cleaf: Londoño et al. (2018) report a species mean
δ13Cleaf of -30.0 ‰ for the 21 canopy
species versus -35.6 ‰ for the five understory species. This
difference leads to very different mean estimates of ci/ca:
0.69 for canopy leaves versus a highly unrealistic (Diefendorf et al., 2010)
0.93 for understory leaves.
Sensitivity of estimated CO2 in the Franks model to the
δ13C of atmospheric CO2. Estimates come from leaves of
five angiosperm species that grew close to the forest floor in Parque
Nacional San Lorenzo, Panama. For each species, the step in δ13Cair between estimates is 0.5 ‰. The
dashed line is a two-end-member mixing model for CO2 between the soil
and well-mixed atmosphere. The intersection between the mixing model and the
Franks model should correspond to the CO2 concentration and
δ13Cair of the forest-floor microenvironment.
It is likely that the high CO2 estimates from understory leaves are
mostly driven by falsely high δ13Cair inputs. Following the
mixing model strategy outlined in Sect. 2.4 (and based on a soil organic
matter δ13C of -28.2 ‰ measured at San Lorenzo), we
calculate a species mean δ13Cair of -16.7 ‰
(mean of intersection points in Fig. 7). When this δ13Cair
is used to estimate CO2 with the Franks model (instead of
-8.5 ‰), the species mean drops to 699 ppm. This is somewhat
higher than the species mean from canopy leaves in the same forest (563 ppm;
red triangles in Fig. 2; Londoño et al., 2018).
Understory leaves from Connecticut temperate forests show similar but less
dramatic patterns, which we attribute to a more open canopy with stronger
atmospheric mixing. CO2 estimates for the 15 species range from 447
to 1567 ppm (mean 794 ppm). Our intersection method identifies a mean
δ13Cair of -11.2 ‰ for the Wesleyan and
Connecticut College campuses (based on a soil δ13C of
-27.6 ‰ measured at Connecticut College) and -10.3 ‰
for Reed Gap (soil δ13C=-26.4 ‰). Using these
adjusted δ13Cair, the species mean of estimated CO2
drops to 566 ppm, which is somewhat higher than the species mean from canopy
leaves in the same areas (449 ppm; blue circles in Fig. 2).
We acknowledge that this analysis is too simple. First, we did not measure
the understory CO2 concentration and δ13Cair (this
would require repeated measurements during different daytime hours over a
growing season to calculate a time-integrated value); instead, we assumed a
simple two-end-member mixing model between the soil and well-mixed
atmosphere. Second, other factors probably contribute to the differences in
estimated CO2 between canopy and understory leaves. Our predicted
δ13Cair values are too low (∼8 ‰ and
2 ‰ lower than the well-mixed atmosphere for the tropical and
temperate forests) and our estimated CO2 too high (∼100 ppm
higher than that from canopy leaves). In the lowermost 1–2 m of the canopy,
previous work suggests up to a -3 ‰ and +70 ppm deviation in
tropical forests and -1 ‰ /+20 ppm in temperate forests
(Table 1). One input that could help to resolve this discrepancy is the
assimilation rate (A0). We assumed a fixed A0 of
12 µmol m-2 s-1 for all leaves, regardless of canopy
position. Shade leaves often have lower assimilation rates than sun leaves
(Givnish, 1988). Substituting lower A0 values for understory leaves
would lower estimated CO2 roughly in proportion (Eqs. 2–3). Using
lower A0 values for shade leaves in the model is appropriate, but
determining the best value is difficult. Typical A0 values for leaves
growing at the top of the canopy in full sun are far more consistent because
photosynthesis in these leaves is usually at its maximum capacity (saturated
at full sunlight) for the prevailing atmospheric CO2 concentration.
Because the degree of shadiness near the forest floor is highly variable,
photosynthesis (A0) in these leaves will be acclimated to some fraction
of the full-sun maximum in a sun-exposed leaf, but careful thought must go
into determining what this fraction is.
We note that our mixing model strategy cannot be applied to fossils because
the global atmospheric CO2 concentration is needed (one end point for
dashed line in Fig. 7). Instead, our motivation for the analysis is to
demonstrate that (1) leaves growing in the lowermost 2 m of the canopy
should be considered with caution in the context of the Franks model, and
(2) the failure of the model is due to faulty inputs (mostly
δ13Cair), not the model itself.
In most fossil leaf deposits, shade morphotypes are comparatively rare (e.g.,
Kürschner, 1997; Wang et al., 2018) because – relative to sun leaves –
they are less durable, do not travel as far by wind, and are produced at a
slower rate (Dilcher, 1973; Roth and Dilcher, 1978; Spicer, 1980; Ferguson,
1985; Burnham et al., 1992). Our recommendation is to exclude such leaves.
There are several ways to differentiate sun vs. shade morphotypes: overall
shape (Talbert and Holch, 1957; Givnish, 1978; Kürschner, 1997; Sack et
al., 2006), shape of epidermal cells (larger and with a more undulated
outline in shade leaves; Kürschner, 1997; Dunn et al., 2015), vein
density (lower in shade leaves; Uhl and Mosbrugger, 1999; Sack and Scoffoni,
2013; Crifò et al., 2014; Londoño et al., 2018), and range in
δ13Cleaf (high when both sun and shade leaves are present,
for example in our study; Graham et al., 2014). Not all shade leaves grow
within 2 m of the forest floor, but excluding all such leaves would
eliminate the forest-floor bias.
Conclusions
The Franks model is reasonably accurate (∼28 % error rate) even
when the physiological inputs A0 (assimilation rate at a known
CO2 concentration) and gc(op)/gc(max) (ratio of
operational to maximum leaf conductance to CO2) are inferred, not
measured. Accuracy does improve when these inputs are measured (∼20 % error rate), but such measurements are not possible with fossils
and may not always be feasible with the nearest living relatives. A 28 %
error rate is broadly in line with (or better than) other leading
paleo-CO2 proxies.
Most of the possible confounding factors that we investigated appear minor.
The temperature sensitivities of d/v (related to gas diffusion) and
Γ* (CO2 compensation point in the absence of dark
respiration) have a negligible impact on estimated CO2. Our
temperature experiments in growth chambers point to larger differences in
some species, which must be related to incorrect values for inputs that were
not directly measured, such as A0, gc(op)/gc(max), and
gm (mesophyll conductance). Overall, though, we find that the
differences in estimated CO2 imparted by temperature are generally
smaller than the overall 28 % error rate.
Incorporating the covariance between CO2 concentration and
photorespiration leads to only small changes in estimated CO2.
O2 concentration affects photorespiration and may thus confound
CO2 estimates from the Franks model, but presently the effect is
poorly quantified. The effect of O2 on Γ* is better known
and imparts only small changes in estimated CO2 across a feasible
range in Phanerozoic O2 of 10 %–30 %.
Leaves from the lowermost 1–2 m of the canopy experience slightly elevated
CO2 concentrations and lower air δ13C during the
daytime relative to the well-mixed atmosphere. We find that if we use the
well-mixed air δ13C to estimate CO2 from leaves that
grew near the forest floor, estimates are too high, especially in dense
tropical canopies. When we use a two-end-member mixing model to calculate the
correct local air δ13C, the falsely high CO2 estimates
largely disappear. For fossil applications, shade leaves from the bottom of
the canopy should be avoided. Shade leaves are typically rare in the fossil
record (relative to sun leaves) and can be identified by their overall
shape, the shape of their epidermal cells, their low leaf
δ13C, and their low vein density.
Conceptually, the Franks model holds considerable promise for quantifying
paleo-CO2: it is mechanistically grounded and can be applied to most
fossil leaves. Our tests of the model's accuracy and sensitivity to
temperature and photorespiration largely uphold this promise.
Data availability
All new data are presented in the
Supplement.
The supplement related to this article is available online at: https://doi.org/10.5194/cp-15-795-2019-supplement.
Author contributions
DR, KM, MM, and LL designed and
conducted the experiments; all authors interpreted the data; DR prepared the
paper with contributions from all coauthors.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
We thank Glenn Dreyer and Peter Siver for logistical support at Connecticut
College, Shuo Wang for lab assistance, and Camilla Crifò and
Andres Baresh for collecting the tropical samples. Support for LL was
provided by the Smithsonian Tropical Research Institute, the Mark Tupper
Fellowship, the National Science Foundation (grants EAR 0824299 and OISE,
EAR, DRL 0966884), the Anders Foundation, and Gregory D. and Jennifer Walston
Johnson and the 1923 Fund.
Review statement
This paper was edited by Ed Brook and reviewed by Jennifer
Mc Elwain and one anonymous referee.
ReferencesArvidson, R. S., Mackenzie, F. T., and Guidry, M. W.: Geologic history of
seawater: A MAGic approach to carbon chemistry and ocean ventilation, Chem.
Geol., 362, 287–304, 10.1016/j.chemgeo.2013.10.012, 2013.Barclay, R. S. and Wing, S. L.: Improving the GinkgoCO2
barometer: implications for the early Cenozoic atmosphere,
Earth Planet. Sci. Lett., 439, 158–171, 10.1016/j.epsl.2016.01.012,
2016.Bates, D., Mächler, M., Bolker, B., and Walker, S.: Fitting linear
mixed-effects models using lme4, J. Stat. Softw., 67, 1–48,
10.18637/jss.v067.i01, 2015.Beerling, D. J.: Evolutionary responses of land plants to atmospheric
CO2, in: A History of Atmospheric CO2 and Its Effects on
Plants, Animals, and Ecosystems, edited by: Ehleringer, J. R., Cerling, T.
E., and Dearing, M. D., Springer, New York, 114–132, 2005.Beerling, D. J., Fox, A., and Anderson, C. W.: Quantitative uncertainty
analyses of ancient atmospheric CO2 estimates from fossil leaves,
Am. J. Sci., 309, 775–787, 10.2475/09.2009.01, 2009.Beerling, D. J., Lake, J. A., Berner, R. A., Hickey, L. J., Taylor, D. W.,
and Royer, D. L.: Carbon isotope evidence implying high O2/CO2
ratios in the Permo-Carboniferous atmosphere, Geochimica et Cosmochimica
Acta, 66, 3757–3767, 10.1016/S0016-7037(02)00901-8, 2002.Bernacchi, C. J., Pimentel, C., and Long, S. P.: In vivo temperature
response functions of parameters required to model RuBP-limited
photosynthesis, Plant Cell Environ., 26, 1419–1430, 10.1046/j.0016-8025.2003.01050.x, 2003.Berner, R. A.: Phanerozoic atmospheric oxygen: new results using the
GEOCARBSULF model, Am. J. Sci., 309, 603-606, 10.2475/07.2009.03, 2009.Berner, R. A., Beerling, D. J., Dudley, R., Robinson, J. M., and Wildman, R.
A.: Phanerozoic atmospheric oxygen, Annu. Rev. Earth Pl Sc., 31, 105–134,
10.1146/annurev.earth.31.100901.141329, 2003.Breecker, D. O., Sharp, Z. D., and McFadden, L. D.: Seasonal bias in the
formation and stable isotopic composition of pedogenic carbonate in modern
soils from central New Mexico, USA, Geol. Soc. Am. Bull.,
121, 630–640, 10.1130/B26413.1, 2009.Broadmeadow, M., Griffiths, H., Maxwell, C., and Borland, A.: The carbon
isotope ratio of plant organic material reflects temporal and spatial
variations in CO2 within tropical forest formations in Trinidad,
Oecologia, 89, 435–441, 10.1007/BF00317423,
1992.Buchmann, N., Guehl, J.-M., Barigah, T., and Ehleringer, J. R.: Interseasonal
comparison of CO2 concentrations, isotopic composition, and carbon
dynamics in an Amazonian rainforest (French Guiana), Oecologia, 110, 120–131,
doi10.1007/s004420050140, 1997.Burnham, R. J., Wing, S. L., and Parker, G. G.: The reflection of deciduous
forest communities in leaf litter: implications for autochthonous litter
assemblages from the fossil record, Paleobiology, 18,
30–49, 10.1017/S0094837300012203, 1992.
Cerling, T. E.: Stable carbon isotopes in palaeosol carbonates, Special
Publications of the International Association of Sedimentologists, 27, 43–60,
1999.Chaloner, W. G. and McElwain, J.: The fossil plant record and global climatic
change, Rev. Palaeobot. Palyno., 95, 73–82,
10.1016/S0034-6667(96)00028-0, 1997.Crifò, C., Currano, E. D., Baresch, A., and Jaramillo, C.: Variations in
angiosperm leaf vein density have implications for interpreting life form in
the fossil record, Geology, 42, 919–922, 10.1130/g35828.1, 2014.Diefendorf, A. F., Mueller, K. E., Wing, S. L., Koch, P. L., and Freeman, K.
H.: Global patterns in leaf 13C discrimination and implications for
studies of past and future climate, P. Natl. Acad.
Sci. USA, 107, 5738–5743, 10.1073/pnas.0910513107, 2010.
Dilcher, D. L.: A paleoclimatic interpretation of the Eocene floras of
southeastern North America, in: Vegetation and Vegetational History of
Northern Latin America, edited by: Graham, A., Elsevier, Amsterdam, 39–53,
1973.Doria, G., Royer, D. L., Wolfe, A. P., Fox, A., Westgate, J. A., and
Beerling, D. J.: Declining atmospheric CO2 during the late Middle
Eocene climate transition, Am. J. Sci., 311, 63–75,
10.2475/01.2011.03, 2011.Dow, G. J., Bergmann, D. C., and Berry, J. A.: An integrated model of
stomatal development and leaf physiology, New Phytol., 201, 1218–1226,
10.1111/nph.12608, 2014.Dunn, R. E., Strömberg, C. A. E., Madden, R. H., Kohn, M. J., and
Carlini, A. A.: Linked canopy, climate, and faunal change in the Cenozoic of
Patagonia, Science, 347, 258–261, 10.1126/science.1260947, 2015.Erdei, B., Utescher, T., Hably, L., Tamás, J., Roth-Nebelsick, A., and
Grein, M.: Early Oligocene continental climate of the Palaeogene Basin
(Hungary and Slovenia) and the surrounding area, Turk. J. Earth Sci., 21, 153–186,
10.3906/yer-1005-29,
2012.Farquhar, G., von Caemmerer, S., and Berry, J.: A biochemical model of
photosynthetic CO2 assimilation in leaves of C3 species, Planta,
149, 78–90, 10.1007/BF00386231, 1980.Farquhar, G. D. and Sharkey, T. D.: Stomatal conductance and photosynthesis,
Ann. Rev. Plant Physio., 33, 317–345, 10.1146/annurev.pp.33.060182.001533, 1982.Farquhar, G. D., O'Leary, M. H., and Berry, J. A.: On the relationship
between carbon isotope discrimination and the intercellular carbon dioxide
concentration in leaves, Aust. J. Plant Physiol., 9, 121–137,
10.1071/PP9820121, 1982.Ferguson, D. K.: The origin of leaf-assemblages – new light on an old
problem, Rev. Palaeobot. Palyno, 46, 117–188, 10.1016/0034-6667(85)90041-7, 1985.Francey, R., Gifford, R., Sharkey, T., and Weir, B.: Physiological influences
on carbon isotope discrimination in huon pine (Lagarostrobos franklinii), Oecologia, 66, 211–218, 10.1007/BF00379857, 1985.Franks, P. J., Leitch, I. J., Ruszala, E. M., Hetherington, A. M., and
Beerling, D. J.: Physiological framework for adaptation of stomata to
CO2 from glacial to future concentrations, Philos. T. R. Soc. B,
367, 537–546, 10.1098/rstb.2011.0270, 2012.Franks, P. J., Royer, D. L., Beerling, D. J., Van de Water, P. K., Cantrill,
D. J., Barbour, M. M., and Berry, J. A.: New constraints on atmospheric
CO2 concentration for the Phanerozoic, Geophys. Res. Lett.,
41, 4685–4694, 10.1002/2014gl060457, 2014.
Givnish, T. J.: Ecological aspects of plant morphology: leaf form in relation
to environment, Acta Biotheor., 27, 83–142, 1978.Givnish, T. J.: Adaptation to sun and shade: a whole-plant perspective,
Aust. J. Plant Physiol., 15, 63–92, 10.1071/PP9880063, 1988.Glasspool, I. J. and Scott, A. C.: Phanerozoic concentrations of atmospheric
oxygen reconstructed from sedimentary charcoal, Nat. Geosci., 3,
627–630, 10.1038/ngeo923, 2010.Graham, H. V., Patzkowsky, M. E., Wing, S. L., Parker, G. G., Fogel, M. L.,
and Freeman, K. H.: Isotopic characteristics of canopies in simulated leaf
assemblages, Geochim. Cosmochim. Ac., 144, 82–95, doi10.1016/j.gca.2014.08.032, 2014.Grein, M., Utescher, T., Wilde, V., and Roth-Nebelsick, A.: Reconstruction of
the middle Eocene climate of Messel using palaeobotanical data, Neues. Jahrb. Geol. P.-A., 260, 305–318,
10.1127/0077-7749/2011/0139, 2011a.Grein, M., Konrad, W., Wilde, V., Utescher, T., and Roth-Nebelsick, A.:
Reconstruction of atmospheric CO2 during the early Middle Eocene by
application of a gas exchange model to fossil plants from the Messel
Formation, Germany, Palaeogeogr. Palaeocl., 309,
383–391, 10.1016/j.palaeo.2011.07.008, 2011b.Grein, M., Oehm, C., Konrad, W., Utescher, T., Kunzmann, L., and
Roth-Nebelsick, A.: Atmospheric CO2 from the late Oligocene to early
Miocene based on photosynthesis data and fossil leaf characteristics,
Palaeogeogr. Palaeocl., 374, 41–51, 10.1016/j.palaeo.2012.12.025, 2013.Hashimoto, S., Tanaka, N., Suzuki, M., Inoue, A., Takizawa, H., Kosaka, I.,
Tanaka, K., Tantasirin, C., and Tangtham, N.: Soil respiration and soil
CO2 concentration in a tropical forest, Thailand, J. For. Res.-Jpn.,
9, 75–79, 10.1007/s10310-003-0046-y, 2004.Haworth, M., Heath, J., and McElwain, J. C.: Differences in the response
sensitivity of stomatal index to atmospheric CO2 among four genera of
Cupressaceae conifers, Ann. Bot.-London, 105, 411–418, 10.1093/aob/mcp309, 2010.Helliker, B. R. and Richter, S. L.: Subtropical to boreal convergence of
tree-leaf temperatures, Nature, 454, 511–514, 10.1038/nature07031, 2008.Hirano, T., Kim, H., and Tanaka, Y.: Long-term half-hourly measurement of
soil CO2 concentration and soil respiration in a temperate deciduous
forest, J. Geophys. Res., 108, 4631, 10.1029/2003JD003766, 2003.Holtum, J. and Winter, K.: Are plants growing close to the floors of tropical
forests exposed to markedly elevated concentrations of carbon dioxide?,
Aust. J. Bot., 49, 629–636, 10.1071/BT00054, 2001.
Jones, H. G.: Plants and Microclimate, Cambridge University Press, Cambridge,
1992.Keeling, C. D.: The concentration and isotopic abundances of atmospheric
carbon dioxide in rural areas, Geochim. Cosmochim. Ac., 13, 322–334,
10.1016/0016-7037(58)90033-4, 1958.Konrad, W., Roth-Nebelsick, A., and Grein, M.: Modelling of stomatal density
response to atmospheric CO2, J. Theor. Biol., 253,
638–658, 10.1016/j.jtbi.2008.03.032, 2008.Konrad, W., Katul, G., Roth-Nebelsick, A., and Grein, M.: A reduced order
model to analytically infer atmospheric CO2 concentration from
stomatal and climate data, Adv. Water Resour., 104, 145–157,
10.1016/j.advwatres.2017.03.018, 2017.Kowalczyk, J. B., Royer, D. L., Miller, I. M., Anderson, C. W., Beerling, D.
J., Franks, P. J., Grein, M., Konrad, W., Roth-Nebelsick, A., Bowring, S. A.,
Johnson, K. R., and Ramezani, J.: Multiple proxy estimates of atmospheric
CO2 from an early Paleocene rainforest, Paleoceanogr.
Paleoclimatol., 33, 1427–1438, 10.1029/2018PA003356, 2018.Kürschner, W. M.: The anatomical diversity of recent and fossil leaves of
the durmast oak (Quercus petraea Lieblein/Q. pseudocastanea
Goeppert)-implications for their use as biosensors of palaeoatmospheric
CO2 levels, Rev. Palaeobot. Palyno., 96, 1–30,
10.1016/S0034-6667(96)00051-6, 1997.Kuznetsova, A., Brockhoff, P. B., and Christensen, R. H. B.: lmerTest
package: tests in linear mixed effects models, J. Stat. Softw., 82, 1–26,
10.18637/jss.v082.i13, 2017.Lei, X., Du, Z., Du, B., Zhang, M., and Sun, B.: Middle Cretaceous
pCO2 variation in Yumen, Gansu Province and its response to the
climate events, Acta Geol. Sin.-Engl., 92, 801–813, 10.1111/1755-6724.13555, 2018.Lenton, T. M., Daines, S. J., and Mills, B. J. W.: COPSE reloaded: an
improved model of biogeochemical cycling over Phanerozoic time, Earth-Sci.
Rev., 178, 1–28, 10.1016/j.earscirev.2017.12.004, 2018.Lloyd, J., Kruijt, B., Hollinger, D. Y., Grace, J., Francey, R. J., Wong,
S.-C., Kelliher, F. M., Miranda, A. C., Farquhar, G. D., and Gash, J.:
Vegetation effects on the isotopic composition of atmospheric CO2 at
local and regional scales: theoretical aspects and a comparison between rain
forest in Amazonia and a boreal forest in Siberia, Aust. J.
Plant Physio., 23, 371–399, 10.1071/PP9960371, 1996.Londoño, L., Royer, D. L., Jaramillo, C., Escobar, J., Foster, D. A.,
Cárdenas-Rozo, A. L., and Wood, A.: Early Miocene CO2 estimates
from a Neotropical fossil assemblage exceed 400 ppm, Am. J.
Bot., 105, 1929–1937, 10.1002/ajb2.1187, 2018.Marrero, T. R. and Mason, E. A.: Gaseous diffusion coefficients, J.
Phys. Chem. Ref. Data, 1, 3–118, 10.1063/1.3253094, 1972.Maxbauer, D. P., Royer, D. L., and LePage, B. A.: High Arctic forests during
the middle Eocene supported by moderate levels of atmospheric CO2,
Geology, 42, 1027–1030, 10.1130/g36014.1, 2014.McElwain, J. C.: Do fossil plants signal palaeoatmospheric CO2
concentration in the geological past?, Philos. T. R. Soc. B, 353,
83–96, 10.1098/rstb.1998.0193, 1998.McElwain, J. C. and Chaloner, W. G.: Stomatal density and index of fossil
plants track atmospheric carbon dioxide in the Palaeozoic, Ann. Bot.,
76, 389–395, 10.1006/anbo.1995.1112, 1995.McElwain, J. C. and Chaloner, W. G.: The fossil cuticle as a skeletal record
of environmental change, Palaios, 11, 376–388, 10.2307/3515247, 1996.McElwain, J. C., Montañez, I., White, J. D., Wilson, J. P., and Yiotis,
C.: Was atmospheric CO2 capped at 1000 ppm over the past 300 million
years?, Palaeogeogr. Palaeocl., 441, 653–658,
10.1016/j.palaeo.2015.10.017, 2016.Medina, E., Montes, G., Cuevas, E., and Rokzandic, Z.: Profiles of
CO2 concentration and δ13C values in tropical rain
forests of the upper Rio Negro Basin, Venezuela, J. Trop. Ecol., 2, 207–217,
10.1017/S0266467400000821, 1986.Michaletz, S. T., Weiser, M. D., Zhou, J., Kaspari, M., Helliker, B. R., and
Enquist, B. J.: Plant thermoregulation: energetics, trait-environment
interactions, and carbon economics, Trends Ecol. Evol., 30,
714–724, 10.1016/j.tree.2015.09.006, 2015.Michaletz, S. T., Weiser, M. D., McDowell, N. G., Zhou, J., Kaspari, M.,
Helliker, B. R., and Enquist, B. J.: The energetic and carbon economic
origins of leaf thermoregulation, Nat. Plants, 2, 16129,
10.1038/nplants.2016.129, 2016.Milligan, J. N., Royer, D. L., Franks, P. J., Upchurch, G. R., and McKee, M.
L.: No evidence for a large atmospheric CO2 spike across the
Cretaceous-Paleogene boundary, Geophys. Res. Lett., 46, 3462–3472,
10.1029/2018GL081215, 2019.Mills, B. J. W., Belcher, C. M., Lenton, T. M., and Newton, R. J.: A modeling
case for high atmospheric oxygen concentrations during the Mesozoic and
Cenozoic, Geology, 44, 1023–1026, 10.1130/g38231.1, 2016.Montañez, I. P., McElwain, J. C., Poulsen, C. J., White, J. D.,
DiMichele, W. A., Wilson, J. P., Griggs, G., and Hren, M. T.: Climate,
pCO2 and terrestrial carbon cycle linkages during late Palaeozoic
glacial-interglacial cycles, Nat. Geosci., 9, 824–828, 10.1038/ngeo2822, 2016.Munger, W. and Hadley, J.: CO2 profile at Harvard Forest HEM and LPH
towers since 2009, Harvard Forest Data Archive: HF197, available at:
http://harvardforest.fas.harvard.edu:8080/exist/apps/datasets/showData.html?id=hf197
(last access: 12 April 2019), 2017.NOAA/ESRL: https://www.esrl.noaa.gov/gmd/ccgg/trends/data.html, last
access: 12 April 2019.Porter, A. S., Yiotis, C., Montañez, I. P., and McElwain, J. C.:
Evolutionary differences in Δ13C detected between spore and seed
bearing plants following exposure to a range of atmospheric
O2:CO2 ratios: implications for paleoatmosphere
reconstruction, Geochim. Cosmochim. Ac., 213, 517–533,
10.1016/j.gca.2017.07.007, 2017.Quay, P., King, S., Wilbur, D., Wofsy, S., and Rickey, J.: 13C /12C
of atmospheric CO2 in the Amazon Basin: forest and river sources,
J. Geophys. Res., 94, 18327–18336, 10.1029/JD094iD15p18327, 1989.R Core Team: R: A Language and Environment for Statistical Computing, R
Foundation for Statistical Computing, Vienna, Austria,
https://www.R-project.org/ (last access: 12 April 2019), 2016.Reichgelt, T., D'Andrea, W. J., and Fox, B. R. S.: Abrupt plant physiological
changes in southern New Zealand at the termination of the Mi-1 event reflect
shifts in hydroclimate and pCO2, Earth Planet. Sc. Lett., 455, 115–124,
10.1016/j.epsl.2016.09.026, 2016.Richey, J. D., Upchurch, G. R., Montañez, I. P., Lomax, B. H., Suarez, M.
B., Crout, N. M. J., Joeckel, R. M., Ludvigson, G. A., and Smith, J. J.:
Changes in CO2 during Ocean Anoxic Event 1d indicate similarities to
other carbon cycle perturbations, Earth Planet. Sc. Lett., 491,
172–182, 10.1016/j.epsl.2018.03.035, 2018.Roeske, C. and O'Leary, M. H.: Carbon isotope effects on enzyme-catalyzed
carboxylation of ribulose bisphosphate, Biochemistry, 23, 6275–6284,
doi10.1021/bi00320a058, 1984.Roth-Nebelsick, A., Grein, M., Utescher, T., and Konrad, W.: Stomatal pore
length change in leaves of Eotrigonobalanus furcinervis (Fagaceae)
from the Late Eocene to the Latest Oligocene and its impact on gas exchange
and CO2 reconstruction, Rev. Palaeobot. Palyno., 174,
106–112, 10.1016/j.revpalbo.2012.01.001, 2012.Roth-Nebelsick, A., Oehm, C., Grein, M., Utescher, T., Kunzmann, L.,
Friedrich, J.-P., and Konrad, W.: Stomatal density and index data of
Platanus neptuni leaf fossils and their evaluation as a CO2
proxy for the Oligocene, Rev. Palaeobot. Palyno., 206, 1–9,
10.1016/j.revpalbo.2014.03.001, 2014.
Roth, J. and Dilcher, D.: Some considerations in leaf size and leaf margin
analysis of fossil leaves, Courier Forschungsinstitut Senckenberg, 30,
165–171, 1978.Royer, D. L.: Stomatal density and stomatal index as indicators of
paleoatmospheric CO2 concentration, Rev. Palaeobot.
Palyno., 114, 1–28, 10.1016/S0034-6667(00)00074-9, 2001.Royer, D. L. and Hren, M. T.: Carbon isotopic fractionation between whole
leaves and cuticle, Palaios, 32, 199–205, 10.2110/palo.2016.073, 2017.Royer, D. L., Miller, I. M., Peppe, D. J., and Hickey, L. J.: Leaf economic
traits from fossils support a weedy habit for early angiosperms, American
J. Bot., 97, 438–445, 10.3732/ajb.0900290, 2010.Sack, L. and Scoffoni, C.: Leaf venation: structure, function, development,
evolution, ecology and applications in the past, present and future, New
Phytol., 198, 983–1000, 10.1111/nph.12253,
2013.Sack, L., Melcher, P. J., Liu, W. H., Middleton, E., and Pardee, T.: How
strong is intracanopy leaf plasticity in temperate deciduous trees?, Am.
J. Bot., 93, 829–839, 10.3732/ajb.93.6.829, 2006.Schubert, B. A. and Jahren, A. H.: Incorporating the effects of
photorespiration into terrestrial paleoclimate reconstruction, Earth-Sci.
Rev., 177, 637–642, 10.1016/j.earscirev.2017.12.008, 2018.Smith, R. Y., Greenwood, D. R., and Basinger, J. F.: Estimating
paleoatmospheric pCO2 during the Early Eocene Climatic Optimum from
stomatal frequency of Ginkgo, Okanagan Highlands, British Columbia,
Canada, Palaeogeogr. Palaeocl., 293, 120–131, 10.1016/j.palaeo.2010.05.006, 2010.Song, X., Barbour, M. M., Saurer, M., and Helliker, B. R.: Examining the
large-scale convergence of photosynthesis-weighted tree leaf temperatures
through stable oxygen isotope analysis of multiple data sets, New
Phytol., 192, 912–924, 10.1111/j.1469-8137.2011.03851.x, 2011.Sotta, E. D., Meir, P., Malhi, Y., Donato nobre, A., Hodnett, M., and Grace,
J.: Soil CO2 efflux in a tropical forest in the central Amazon,
Glob. Change Biol., 10, 601–617, 10.1111/j.1529-8817.2003.00761.x, 2004.
Spicer, R. A.: The importance of depositional sorting to the biostratigraphy
of plant megafossils, in: Biostratigraphy of Fossil Plants: Successional and
Paleoecological Analyses, edited by: Dilcher, D. and Taylor, T., Dowden,
Hutchinson, and Ross, Stroudsburg, PA, 171–183, 1980.Sternberg, L., Mulkey, S. S., and Wright, S. J.: Ecological interpretation of
leaf carbon isotope ratios: influence of respired carbon dioxide, Ecology,
70, 1317–1324, 10.2307/1938191, 1989.Stevens, P. F.: Angiosperm Phylogeny Website. Version 13,
http://www.mobot.org/MOBOT/research/APweb/ (last access: 12 April
2019), 2001.Talbert, C. M. and Holch, A. E.: A study of the lobing of sun and shade
leaves, Ecology, 38, 655–658, 10.2307/1943135,
1957.Tcherkez, G.: How large is the carbon isotope fractionation of the
photorespiratory enzyme glycine decarboxylase?, Funct. Plant Biol., 33,
911–920, 10.1071/FP06098, 2006.Tesfamichael, T., Jacobs, B., Tabor, N., Michel, L., Currano, E., Feseha, M.,
Barclay, R., Kappelman, J., and Schmitz, M.: Settling the issue of
“decoupling” between atmospheric carbon dioxide and global temperature:
[CO2]atm reconstructions across the warming Paleogene-Neogene
divide, Geology, 45, 999–1002, 10.1130/G39048.1, 2017.Tipple, B. J., Meyers, S. R., and Pagani, M.: Carbon isotope ratio of
Cenozoic CO2: a comparative evaluation of available geochemical
proxies, Paleoceanography, 25, PA3202, 10.1029/2009PA001851, 2010.Uhl, D. and Mosbrugger, V.: Leaf venation density as a climate and
environmental proxy: a critical review and new data, Palaeogeogr. Palaeocl.,
149, 15–26, 10.1016/S0031-0182(98)00189-8, 1999.
Von Caemmerer, S.: Biochemical Models of Leaf Photosynthesis, CSIRO
Publishing, Collingwood, Australia, 2000.Wang, Y., Ito, A., Huang, Y., Fukushima, T., Wakamatsu, N., and Momohara, A.:
Reconstruction of altitudinal transportation range of leaves based on
stomatal evidence: an example of the Early Pleistocene Fagus leaf
fossils from central Japan, Palaeogeogr. Palaeocl., 505, 317–325,
10.1016/j.palaeo.2018.06.011, 2018. Woodward, F. I.: Stomatal numbers are sensitive to increases in CO2
from pre-industrial levels, Nature, 327, 617–618, 10.1038/327617a0, 1987.Woodward, F. I. and Kelly, C. K.: The influence of CO2 concentration
on stomatal density, New Phytol., 131, 311–327, 10.1111/j.1469-8137.1995.tb03067.x, 1995.Wynn, J. G.: Towards a physically based model of CO2-induced stomatal
frequency response, New Phytol., 157, 391–398, 10.1046/j.1469-8137.2003.00702.x, 2003.