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Vol.:(0123456789)1 3
Photosynth Res DOI 10.1007/s11120-017-0388-5
ORIGINAL ARTICLE
Photosynthetic responses to temperature
across leaf–canopy–ecosystem scales: a 15-year study
in a Californian oak-grass savanna
Siyan Ma1 · Jessica L. Osuna2 ·
Joseph Verfaillie1 · Dennis D. Baldocchi1
Received: 9 January 2017 / Accepted: 13 April 2017 © Springer
Science+Business Media Dordrecht 2017
At the ecosystem level, photosynthetic responses to tem-perature
did follow a quadratic function on average. The optimum value of
photosynthesis occurred within a nar-row temperature range (i.e.,
optimum temperature, Topt): 20.6 ± 0.6, 18.5 ± 0.7, 19.2 ± 0.5, and
19.0 ± 0.6 °C for the oak canopy, understory grassland, entire
savanna, and open grassland, respectively. This paradigm confirms
that pho-tosynthesis response to ambient temperature changes is a
functional relationship consistent across leaf–canopy–eco-system
scales. Nevertheless, Topt can shift with variations in light
intensity, air dryness, or soil moisture. These find-ings will pave
the way to a direct determination of thermal optima and limits of
ecosystem photosynthesis, which can in turn provide a rich resource
for baseline thresholds and dynamic response functions required for
predicting global carbon balance and geographic shifts of
vegetative commu-nities in response to climate change.
Keywords Growth temperature · Temperature dependence ·
Thermal adaptation · Thermal acclimation · Net ecosystem
exchange of CO2 · Gross primary productivity
Introduction
Understanding how photosynthesis responds to variations in
temperature is critical for predicting terrestrial ecosys-tem
carbon balance and geographic vegetation distribution in response
to climate change (Lombardozzi et al. 2015; Chapin III and
Starfield 1997; Woodward 1992; Wood-ward and Lomas 2004). Yet, such
a relationship has rarely been tested at the ecosystem scale.
Hence, models on large spatial scales must rely on leaf-level
understandings (Lom-bardozzi et al. 2015; Atkin et al.
2008; Wehr et al. 2016).
Abstract Ecosystem CO2 fluxes measured with eddy-covariance
techniques provide a new opportunity to retest functional responses
of photosynthesis to abiotic factors at the ecosystem level, but
examining the effects of one factor (e.g., temperature) on
photosynthesis remains a challenge as other factors may confound
under circumstances of natu-ral experiments. In this study, we
developed a data mining framework to analyze a set of ecosystem CO2
fluxes meas-ured from three eddy-covariance towers, plus a suite of
abi-otic variables (e.g., temperature, solar radiation, air, and
soil moisture) measured simultaneously, in a Californian oak-grass
savanna from 2000 to 2015. Natural covariations of temperature and
other factors caused remarkable confound-ing effects in two
particular conditions: lower light inten-sity at lower temperatures
and drier air and soil at higher temperatures. But such confounding
effects may cancel out.
Electronic supplementary material The online version of this
article (doi:10.1007/s11120-017-0388-5) contains supplementary
material, which is available to authorized users.
* Siyan Ma [email protected]
Jessica L. Osuna [email protected]
Joseph Verfaillie [email protected]
Dennis D. Baldocchi [email protected]
1 Ecosystem Science Division, Department of Environmental
Science, Policy and Management, University of California
at Berkeley, 137 Mulford Hall # 3114, Berkeley, CA 94720,
USA
2 Atmospheric, Earth, and Energy Division, Lawrence
Livermore National Laboratory, 7000 East Avenue L-103, Livermore,
CA 94550, USA
http://orcid.org/0000-0002-6145-196Xhttp://orcid.org/0000-0003-3496-4919http://crossmark.crossref.org/dialog/?doi=10.1007/s11120-017-0388-5&domain=pdfhttp://dx.doi.org/10.1007/s11120-017-0388-5
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A common assumption is that photosynthesis-related
rela-tionships are consistent across biological hierarchical
lev-els (e.g., from chloroplast to leaf and from leaf to canopy)
(Harley and Tenhunen 1991; Farquhar et al. 1980; Berry and
Bjorkman 1980; Way and Yamori 2014). However, it is not well known
whether the assumption fits vegetative communities in natural
habitats. Filling this gap in knowl-edge requires a set of
variables measurable simultaneously across leaves, canopies, and
ecosystems. Fortunately, mod-ern technologies (e.g., portable
infrared gas analyzers and eddy-covariance towers) provide new
opportunities to cap-ture physiological information not only in
controlled envi-ronments but also in natural habitats concurrently
with a range of exposures and microclimates (Baldocchi 2008).
Temperature is one of many environmental factors that influence
photosynthesis (Farquhar et al. 1980, 2001; Reed et al.
1976; Yamori et al. 2014). Chloroplast-scale studies have
shown that photosynthesis depends on the photosyn-thetic carbon
reduction cycle and the photo-respiratory carbon oxidation cycle,
and these cycles require particu-lar levels of energy (electron
transport and ATP synthesis) (Farquhar et al. 1980, 2001;
Berry and Bjorkman 1980). Temperature can directly influence the
rate of RuBisco (ribulose-1-5-bisphosphate carboxylase/oxygenase)
activ-ity and the rate of photosynthetic electron transport
(Leun-ing 2002; Xu and Baldocchi 2003). Temperature variations also
influence photosynthetic enzymes availability, mem-brane fluidity,
and expressions of related proteins (Yamori et al. 2014).
Besides these direct effects of temperature on biochemical cycles,
temperature increases can promote photorespiration over
photosynthesis by causing the rela-tive solubility of CO2 to O2 and
the specificity to Rubisco to decrease (Way and Yamori 2014;
Bernacchi et al. 2001).
Leaf-level studies on photosynthetic response to tem-perature
rely on manipulative experiments in controllable environments by
directly measuring net photosynthetic rate (i.e., the difference
between gross photosynthesis and leaf respiration) and leaf or
growth temperature. Typically, a temperature response curve is
parabolic in shape with a vertex that represents the maximum
photosynthetic rate. The position of the vertex along the
temperature axis is known as the “optimum temperature” (Reed
et al. 1976; Berry and Bjorkman 1980; Way and Yamori 2014;
Yamori et al. 2014). As a result of thermal adaptation,
optimum temperature values can be relatively stable in certain
spe-cies or functional types; optimum temperature values can also
be quite flexible or plastic through thermal acclima-tion (Way and
Yamori 2014; Baldocchi et al. 2001; Hel-liker and Richter
2008; Yuan et al. 2011). For example, if a plant is moved to a
colder environment for a short period, its optimum temperature may
shift to a colder temperature after return to its original growth
conditions (Ghannoum and Way 2011; Way and Yamori 2014). Besides
optimum
temperature, lower and upper thermal limits of photosyn-thesis
also indicate the inherent capacities of thermal adap-tation and
acclimation of plants (Way and Yamori 2014; Yamori et al.
2014; Reed et al. 1976). Large-scale mode-ling efforts demand
such baseline information and dynamic response functions for
predicting geographic shifts of plant communities in response to
climate change (Lombardozzi et al. 2015; Woodward and Lomas
2004).
Assessing thermal adaptation and acclimation on larger scales
(e.g., ecosystems) remains a challenge (Lombar-dozzi et al.
2015). One reason is that temperature co-varies with other factors
that also influence photosynthesis under conditions of in
situ ecological experiments (Woodward and Lomas 2004; Berry and
Bjorkman 1980; Reed et al. 1976). For example, increases in
temperature are associ-ated with increased light intensity (e.g.,
sunny days or sum-mer). Stomatal conductance is sensitive to air
dryness, and air dryness is intensified by high temperature. Either
air or soil droughts can suspend photosynthesis either temporar-ily
or permanently, while droughts relate to a distribution of
precipitation in seasons. Although leaf-level studies intend to
manage or reduce confounding effects in con-trolled environmental
chambers or greenhouses (Reed et al. 1976), uncertainties in
results often arise from confound-ing effects. Technical issues and
high costs limit sample sizes. Also, an understanding of individual
species cannot precisely match that of multi-species or vegetative
com-munities. Eddy-covariance techniques afford an alternative
approach on larger spatial scales, allowing us to monitor
photosynthetic behaviors of vegetative communities auto-matically
in natural habitats. Once a tower is installed and maintained
properly, it begins to collect photosynthetic signals during the
daytime at fine time-scales (e.g., half hourly) and then
continuously over years or decades (Bal-docchi 2008; Ma et
al. 2016). Although problems such as low turbulence, rain, and
power or sensor failures may cause missing data, a tower can in
general collect more data than a traditional leaf-level experiment
can.
Having large sample sizes is a unique feature of tower-base
datasets that probably can enhance our under-standing of natural
experiments because in statistics the Law of Larger Numbers
supports the idea of achieving a “theoretical mean” for large
samples. A following-up question would be, “how large is the
large?” In the past, common sense may regard three samples as a
minimum; 15 or more samples might then already be considered as
large. Today, hundreds of thousands of samples are collected by
eddy-covariance towers worldwide, chal-lenging statistical methods
developed for dealing with experiments with small sample sizes.
Modern statisti-cal methods such as artificial neural networks,
wavelet analysis, and regression trees take advantage of large
datasets and enable more reliable predictive analytics
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(Papale and Valentini 2003; Stoy et al. 2006; Xiao
et al. 2008; Moffat et al. 2010). For basic research,
investiga-tors are also curious about functional relationships
bur-ied in “Big Data” (if we can borrow the popular term in
computation science) rather than trying to fit biological or
ecological processes into a “black box.” Mining the functional
relationships from Big Data requires a suit-able novel data mining
framework (Marr 2016; Bahga and Madisetti 2016; Boyd and Crawford
2012).
A set of ecosystem CO2 fluxes and related abiotic var-iables has
been measured over 15 years from three eddy-covariance towers
in a Californian oak-grass savanna area. Two of the towers were
installed, respectively, above and below the oak canopy to capture
clear signals of the oak canopy, and the third tower was installed
in an open grassland nearby to capture signals only from the annual
grassland, as a comparison to the grassland under the tree canopy.
The three towers allowed us to tease apart photosynthetic behaviors
of four typical vegetative communities in the study area
individually, including oak canopy, understory grassland, entire
savanna, and open grassland canopy (Ma et al. 2007, 2011).
Our study had four objectives. First, we identify potential
confounding effects caused by covariations of temperature and three
other abiotic factors (i.e., light intensity, air dryness, and soil
moisture) in the field con-ditions. Second, we test whether
canopy-level measure-ments were comparable to leaf-level
experiments under similar conditions. Third, we examine whether our
data mining framework can exact the relationships between
photosynthetic flux and temperature in a consistent way, given
different experimental scenarios. Finally, we examine average
patterns of temperature response curves for four vegetative
communities dominating the study area.
We generate four hypotheses based on our leaf-level
understandings, but we intend to retest them regard-ing the
circumstances of natural experiments over veg-etative communities.
Hypothesis I: covariations between temperature and other factors
(e.g., light intensity, air dryness, and soil moisture) would
determine natural occurrences of confounding effects. Hypothesis
II: the canopy- and leaf-level experiments would show a similar
trend in photosynthetic responses to variations in tem-perature.
Hypothesis III: variations in light intensity, air dryness, or soil
moisture influenced the magnitude of ecosystem photosynthesis, but
the pattern of temperature response curves would remain similar.
Hypothesis IV: an average temperature response curve for each
vegetative community would have a parabolic shape, which is
simi-lar to the pattern reported in the leaf-level literature.
Methods
Study sites
Oak-grass savanna is a typical ecosystem type in the foothills
of the Sierra Nevada Mountains in California, USA. Our two study
sites are located in this area, about 2 km apart: an
oak-dominated woody land (Tonzi Ranch, 38.438 N,
120.968 W) and an open annual grassland (Vaira Ranch,
38.418 N, 120.958 W). The average elevation is
177 m at the woody savanna site and 129 m at the
grassland site.
At the woody savanna site, deciduous blue oaks (Quercus
douglasii) cover about 40% of the landscape within a square
kilometer of the flux tower and are unevenly distributed in space,
with an average tree density of about 144 stem ha−1, an
average height of about 14 m, and a basal area of about
0.1 m2. Species of annual grasses and forbs under oak canopy
are similar to those in the open grass-land, including Brachypodium
distachyon, Bromus hordea‑ceous, Erodium cicutarium, Hypochaeris
glabra, Trifo‑lium dubium Sibth., Trifolium hirtum All.,
Dichelostemma volubile A., and Erodium botrys Cav. These grass
species are invasive and originally from the Mediterranean Basin
(Jackson 1985).
The sites experience a Mediterranean climate with wet, mild
winters, and dry, hot summers. Based on data collected between 1926
and 2016 from a climate station located approximately 26 km
from the study site (Camp Pardee, California, 38.258 N,
120.858 W), average annual precipitation was 546 mm;
average mean annual tem-perature was 16.6 °C; average maximum
temperature was 23.5 °C; and average minimum temperature was 9.7 °C
(http://www.wrcc.dri.edu/cgi-bin/cliMAIN.pl?ca1428).
The soil of the oak-grass savanna is an Auburn very rocky silt
loam (Lithic haploxerepts), about 0.75 m deep, overlaying
fractured rock. Additional details on the site have been reported
in previous papers (Tang et al. 2003; Xu and Baldocchi 2003,
2004).
Canopy- and ecosystem-level measurements
Two eddy-covariance towers were installed at the woody savanna
site in the spring of 2001. One was 23.5 m high, above the
tree canopy of ~10 m; the other was installed below the tree
canopy at 2 m high. The open grassland tower was installed in
the fall of 2000, also at 2 m in height (Xu and Baldocchi
2004; Ma et al. 2007). On each tower, a sonic anemometer
(Model 1352, Gill Instruments Ltd., Lymington, United Kingdom) was
installed for collecting three-dimensional wind velocities ten
times per second. An open-path infrared CO2 and water vapor
analyzer (IRGA, Li-7500, Li-cor, Lincoln, Nebraska, USA) measured
CO2
http://www.wrcc.dri.edu/cgi-bin/cliMAIN.pl?ca1428
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and water vapor concentration five times per second. These data
were recorded by on-site computers or data loggers (CR1000,
Campbell Scientific, Inc., Logan, Utah, USA). We reprocessed the
high-frequency data into half-hour average CO2 and water fluxes
with lab-made MATLAB® scripts (R2016b, The Mathworks, Inc., Natick,
Massachu-setts, USA), including spike removal, coordinate rotation,
application of standard gas laws, and corrections for vapor density
fluctuations (Webb et al. 1980). We then screened out
unreliable flux data due to low turbulence conditions, rain, and
other technical issues (e.g., power or sensor failures).
Air temperature (Tair, oC) and relative humidity were measured
using a shielded and aspirated sensor (HMP-35 A, Vaisala,
Helsinki, Finland). Water vapor deficit pres-sure (VPD, kPa) was
calculated based on measurements of Tair and relative humidity.
Volumetric soil moisture (θv, cm3 cm−3) was measured near the
soil surface (~2 cm in depth) with three frequency-domain
reflectometer probes (ML2x, Delta-T Devices, Burwell, Cambridge,
UK). Two of the soil moisture probes were installed under the tree
canopy and the third in open grassland area. Incoming
photosynthetically active radiation (PAR, μmol m−2 s−1)
was measured above the tree canopy with a quantum sen-sor
(PAR-Lite, Kipp and Zonen, Delft, Holland), and pre-cipitation (mm)
was measured with a rain gauge (Campbell Scientific Inc. Logan,
Utah, USA). These sensors were connected to data loggers (CR10X or
CR23, Campbell Sci-entific, Inc., Logan, Utah, USA) which
automatically sam-pled every 10 s and recorded half-hour
averages.
Leaf-chamber experiments
Leaf-level temperature response experiments were con-ducted in
the field on the days numbered 100, 116, 123, 128, 137, 152, 158,
and 185 starting since Jan. 1, 2008. For each day, measurements
started around 9:00 am local time and lasted 2–4 h
depending on the number of meas-urements made. Oak trees were
around 85 years with either cylindrical or round crowns. We
sampled leaves from the same area of the crown each time.
Leaf photosynthesis was measured with a 4 cm2 leaf cuvette
(LI-6400, Li-cor Inc., Lincoln, NE, USA). Once a leaf was inserted
into the chamber, the machine measured initial leaf temperature and
was set to maintain this initial temperature until stabilized
(~15 min). Leaf temperature was then reduced as far as set-up
and conditions allowed, by fitting the chamber head with a water
jacket on each side to remove heat dissipated by sleeves and either
an ice-water bath or a hot-water bath. The water was pumped through
the closed loop using a bilge pump (Super Sub Pump 500, West
Marine, Watsonville, CA, USA) fitted with a volt-age regulator.
Once steady-state conditions were again
achieved, the leaf was exposed to a saturating flash, and
photosynthesis were recorded. Then leaf temperature was increased
by 1 °C. This process was repeated at increasing temperatures until
the temperature could not be increased further. Leaf-chamber
temperatures were controlled in the range from 16 to 43 °C,
approximately ±6 °C below or above ambient air temperature. To
avoid condensation on the infrared gas analyzer mirrors, we did not
repeat the pro-cedure at lower temperatures, except a single curvet
span-ning 12 to 18 °C.
Before clamping on to a leaf, the CO2 concentration in the
cuvette was set to 380 ppm, and light intensity was set to
saturating, which was determined by photosynthetic light response
curves measured on a neighboring leaf the day before measurement of
photosynthetic temperature response curves. Control of leaf-to-air
vapor pressure dif-ference in the leaf chamber requires the flow
rate through the chamber to be adjusted depending on air
tempera-ture and transpiration rate. Because the relative humidity
of ambient air and transpiration rates at this site are low
throughout most of the summer, maintaining a constant water vapor
density in the chamber would often lead to low flow rates that
might increase chamber leakage, espe-cially in the smaller leaf
chamber of the LI-6400-49. Thus, we chose to maintain a constant
flow rate to avoid leakage problems rather than hold water vapor
density constant during each measurement.
Pheno-stages and LAI measurements
To interpret the tower-based data and match them with leaf-level
measurements correctly, we defined four pheno-stages (Pheno I, II,
and III) and leaf area index (LAI) of oak trees and annual grasses
(Fig. 1).
The dates of grass green-up, oak leaf-out, grass senes-cence,
and oak litter-fall were determined to occur when 60–70% of plant
communities showed the same phenol-ogy (Ma et al. 2007).
Pheno I was the period between grass green up and oak leaf out,
covering the autumn and winter months, while grasses grew slowly
and there were no leaves on the oak trees. Pheno II started from
the time when oak leaves came out till annual grasses were died
out. This pheno-stage was equivalent to spring when oak leaves and
grasses grew and developed rapidly. Pheno III was the period of dry
summer before rains returned. Dur-ing this period, oak leaves
stayed photosynthetically active, but photosynthetic activities
gradually decreased. In late summer, oak trees are
photosynthetically active in the early morning. Deeper roots
enabled oak trees to access ground-water, but the amount of water
was just enough to retain vital activities during the summer
(Miller et al. 2010). Thus, the growing season of grasses
included Pheno I and
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II; the growing season of oak trees included Pheno II and
III.
During the growing season of annual grassland, we clipped
grasses within a 20 × 20 cm2 square monthly at three
locations selected randomly around the flux towers. Green grass
leaves were separated from litter or senescent leaves and run
through a leaf area meter (Li-3100, Li-cor, Lincoln, Nebraska, USA)
in the lab. Oak LAI was esti-mated from images of three
upward-pointing digital cam-eras (Ryu et al. 2012). Sample
size, mean, minimum, and maximum values for each month are listed
in Supplemental Materials (S1).
A framework of data analysis
In a comparison to manipulative experiments carried out in
leaf-level studies, we visualize the surroundings of a tower
(within its footprint) as an individual “chamber” of natural
experiments. Inside the natural chamber, the state of bio-physical
conditions is relatively steady given a short period (e.g., half
hourly) when an average behavior of photosyn-thesis can be
measured. This new vision may be count-intu-itive because
tower-based data are recorded in time series by electrical data
loggers. A wavelet study confirms that autocorrelation exists on
the time-scale of 24 h (Stoy et al. 2005), supporting the
fact that temporal correlation (e.g., autocorrelation) is necessary
for subsequent time series analysis. However, the wavelet study
also suggests that a single measurement at one moment should not
correlate
with measurements a few hours before or after. Thus, we assume
that tower-based datasets record a set of depend-ent variables and
independent variables. The following is a framework for exploring
and verifying the functional relationships between a pair of
dependent and independent variables.
Defining the dependent variable—net photosynthetic flux
of ecosystems
Daytime CO2 flux (FA) measured from the tower is known as net
photosynthetic flux, a difference between photosyn-thesis and
ecosystem respiration averaged upon each half hour (Wofsy
et al. 1993). This ecosystem-level concept can be a proxy of
the leaf-level concept of net photosynthetic rate if LAI is close
to 1, which is the case of oak trees in this savanna (Ma et
al. 2011). Because FA also includes respiration of leaves, roots,
and soil microbes and may mit-igate photosynthetic signals, only
FA > 0 are used in the rest of our analyses. Daytime
and nighttime were separated with a threshold of PAR (i.e.,
5 μmol m−2 s−1).
Sorting, binning, and smoothing
A scatter plot of half-hour photosynthetic fluxes versus air
temperature did not suggest any visible pattern (Fig. 2a,
gray dots, using tree canopy as an example). One might interpret
that temperature had no effect on photosynthesis at the ecosystem
level. Or, the apparent disorder in the data
Fig. 1 Monthly leaf area index (LAI) of a oak trees and b annual
grasses shown with box plots that indicate mean (in diamond),
median (bar across the box), 95 and 5% percentiles (upper and lower
horizontal bars). Sample size, mean, mini-mum, and maximum values
of monthly LAI are also available in Supplemental Material (S1).
The bars above the box plots are three main pheno-stages including
Pheno I—the period between grass green-up and oak leaf-out, Pheno
II—the period between oak leaf-out and annual grass die-off, and
Pheno III—the period between annual grass die-out and
germination
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might suggest that temperature should not be the only fac-tor
determining individual photosynthetic fluxes on this fine
time-scale (i.e., half hour).
The former interpretation seemed acceptable after our polynomial
regression analyses failed. We then questioned whether the failure
occurred simply because the tower-based data had a large sample
size. Thus, we examined the data mining methods commonly applied in
analyzing Big Data. A key idea was to assign data into discrete
categories so that statistical characteristics could be described
within each bin but also be compared across bins. A nonparamet-ric
statistical approach—binning—is a promising method that has been
used successfully for extracting functional patterns in CO2 fluxes
and environmental factors (Falge et al. 2001; Barr et al.
2013).
We first tried binning FA with a temperature interval of 1 °C
and calculated mean, median, and quartiles within each bin
(Fig. 2a). We computed the histogram within each bin and
estimated the probability density with the Kernel Density method
(SAS 9.4, SAS Institute Inc., Cary, NC, USA). Results of both
methods indicated that the data dis-tribution of FA was skewed
within each bin, and the major-ity of flux data were located on the
positive side, indicating that photosynthetic behaviors contributed
to the majority
of signals (an example shown in Fig. 2b). When individ-ual
means (or medians) of bins together were connected across the
sampling domain of temperature, a peaked curve emerged, similar to
the pattern often found in the leaf-level literature.
This initial analysis was interesting, but the bin interval was
fixed, perhaps resulting in an uneven number of sam-ples per bin.
For example, for the tree canopy, the number of samples per bin was
approximately n = 5000, but n < 100 for extreme low or high
temperature bins. To avoid possible biases caused by uneven
numbers, we sorted the flux data by order of temperature and then
binned a pair of FA and Tair with a fixed sample size n (e.g., n =
1000) (Barr et al. 2013). Also, we calculated bin averages of
FA weighted with probability density to take skew into account,
com-paring with simple arithmetic averages (i.e., unweighted
averages) (Fig. 2c). The unweighted averages of FA were
slightly lower than the weighted value. After apply-ing a
nonparametric local regression method (LOESS), the peaked pattern
remained. Thus, the sorting–bin-ning–smoothing process did suggest
a significant pattern for the relationships between FA and Tair.
Importantly, dif-ferent computing strategies did not alter the
peaked shape. Whether the peaked shape was meaningful depended
on
Fig. 2 Panel a is a scatter plot of daytime net photosynthetic
flux of oak tree canopy (FA) against air temperature (Tair) (in
gray dots, n = 108,336), overlaid with means, medians, and
quartiles (binned by 1 °C intervals, as shown in each box plot).
Panel b is a histogram of half-hourly FA within one of the bins
(using the 23rd bin as an example), overlaid by the probability
density function estimated with the Kernel density method. Panel c
is averaged FA binned by a 1 °C
interval or by a constant number of data points (e.g., n =
1000). In addition, arithmetical bin-averaged FA are compared with
weighted bin averages of FA, while the weighting factor for each
bin is deter-mined by the probability density function, as in the
example shown in b. Smoothing curves for each group are estimated
with the LOESS method (see details in “Methods” section)
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our further comparisons with leaf-level measurements, which we
present in the “Results” section.
Confounding effects
To identify which abiotic factors might have confounded the
temperature effects on photosynthesis, we examined covariations of
light intensity, air dryness, and soil moisture with air
temperature based on half-hourly measurements over the
15 years. The probability density function of light intensity
(PAR), air dryness (VPD), and soil moisture (θv) within the domain
of air temperature (Tair) was estimated using the Kernel density
method. Higher probability den-sity values indicated greater
likelihood of co-occurrence of each confounding factor and
temperature.
Experimental scenarios
According to the range of these continuous variables in the
field, five discrete levels were designed as five experi-mental
scenarios: Low (L), Low-Moderate (LM), Moderate (M), Moderate-High
(MH), and High (H).
For the convenience of computation, the entire range of PAR
within each pheno-stage was categorized with an interval of
500 μmol m−2 s−1: L (PAR ≤500); LM (500 < PAR
≤ 1000); M (1000 < PAR ≤ 1500), MH (1500 < PAR ≤ 2000), and H
(PAR >2000). To iden-tify degrees of air dryness, we used VPD
and divided the dataset with an interval of 1 kPa: L (VPD
≤1); LM (1 < VPD ≤ 2); M (2 < VPD ≤ 3), MH (3 < VPD ≤ 4),
and H (VPD >4). Soil moisture was defined by θv with an interval
of 0.05 cm3 cm−3 (5%): L (θv ≤ 5%); LM (5% < θv ≤
10%); M (10% < θv ≤ 15%), MH (15% < θv ≤ 20%), and H (θv >
20%).
Theoretically, the total number of combinations of the
three-factor, five-level experimental scenarios is
53 = 125. Not all occurred in natural conditions
according to an analysis of frequency. Only 75 out of the 125
combinations were recorded over the 15 years. The frequencies
of the 75 combinations varied across the pheno-stages.
Verifying the tower‑derived temperature response curve
The general expression of the quadratic function is y = ax2 + bx
+ c. If a function is quadratic, its first-order differential
(
dy
dx
)
is a linear function. Thus, we resampled ΔFA
ΔTair along the response curve and applied a regression
anal-
ysis to test for linearity. The temperature at the maximum value
of photosynthetic flux, the optimum temperature, was (Topt), Topt =
−
b
2a.
We used minimum and maximum temperatures (Tmin and Tmax) to
describe the lower and upper temperature lim-its, typically when
photosynthetic flux was low, such as FA
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a decreasing trend as air temperature increased from 16 to 43 °C
(Fig. 6a). Tower-derived measurements of canopy
photosynthesis (FA_canopy) during the 8 days of the
leaf-chamber measurements used 129 samples. The smooth-ing curve
was peaked, suggesting an increasing trend in photosynthesis as
temperature increased approximately from 5 to 18 °C and a
decreasing trend with tempera-ture increasing above 18 °C
(Fig. 6b). The pattern might be more easily discernible if we
had expanded our data to include the towers during Phenos II and
III in 2008
since the sample size would increase to 5227, almost 50-fold the
number of samples from the leaf-chamber experiments.
Although the magnitudes of photosynthetic fluxes measured at the
leaf level were greater than the tower-derived canopy-level
measurements, these two data sources were comparable within the
overlaid tempera-ture range (Fig. 6c). The regression model
fitting for FA_canopy versus FA_leaf was statistically significant
(F test, p < 0.001).
Fig. 3 Variations in a light intensity (PAR), b air dryness
(VPD), and c soil moisture (θv), while net photosynthetic flux of
oak tree canopy (FA_canopy) varies with increases in air
temperature (Tair), sug-gesting that these measurable abiotic
factors mutually constrain one another in situ
Fig. 4 The probability density function of a light intensity
(PAR), b air dryness (VPD), and c soil moisture (θv) within the
domain of air temperature (Tair), estimated with the Kernel density
methods based on their half-hourly measurements from 2001 to 2015.
The color scale on the right side of each panel shows the value of
the probabil-ity
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The FA_canopy–Tair relationship under experimental
scenarios
To test how variations in light intensity, air dryness, and soil
moisture influenced the relationship between can-opy photosynthesis
and temperature, we extracted the FA_canopy–Tair relationship under
each experimental sce-nario. Unsurprisingly, canopy photosynthesis
was greater in conditions of higher light intensity, lesser air
dryness, and higher soil moisture. Most of the curves displayed in
a
peaked pattern, although the slopes of the two sides differ
(Fig. 7a, b, c).
Fig. 5 Variations in light intensity (a, PAR), air dryness (b,
VPD), and soil moisture (c, θv) during the daytime hours on days
numbered 100, 116, 123, 128, 137, 152, 158, and 185 starting since
Jan 1 in 2008, when leaf chamber experiments were carried out. The
rectan‑gles indicate the conditions controlled inside the leaf
chamber—satu-rated light intensity, relatively no stress due to air
dryness, and soil moisture levels
Fig. 6 Panel a the relationship of oak leaf photosynthesis
(FA_leaf) to temperature measured with the leaf chamber (Tleaf) (n
= 169); Panel b the relationship of tower-based oak canopy
photosynthesis (FA_canopy) to in situ air temperature (Tair)
when leaf-chamber experiments were performed (n = 129) with
saturated light intensity; Panel c FA_canopy is related to FA_leaf
positively, and the fitting model is statistically sig-nificant (p
< 0.001)
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Among the experimental scenarios, not all of the peaked shape
was symmetric, but the one for the mod-erate soil moisture level
was (Fig. 7c). Such conditions absent water stress often
occurred in the middle of spring, when light intensity was not as
low as in earlier seasons (Fig. 4a). The left-side slope
excluded effects of low light intensity at lower temperatures, and
the right-side slope eliminated effects of water stress at higher
temperatures. Interestingly, the curve extracted from the entire
sam-pling domain—the average temperature response curve—was similar
to the no-water-stress pattern (Fig. 7d–f).
Optimum temperatures
We applied the same method in extracting the average
tem-perature response curve for each vegetative community
(Fig. 8a). The average curves were all peaked, whereas the
maximum FA occurred around 20 °C. The first-order
differ-entials
(
ΔFA
ΔTair
)
were linearly related to air temperature (F
test, p < 0.0001) (Fig. 8b), verifying that the average
curve was parabolic in shape, while Topt equaled 20.6 ± 0.6, 18.5 ±
0.7, 19.2 ± 0.5, and 19.0 ± 0.6 °C for oak canopy, understory
grasses, the entire savanna, and the open
Fig. 7 Panels on the left side show the binned averages of oak
canopy photosynthesis (FA_canopy) and air temperature (Tair) under
experimental sce-narios by five levels of a light intensity (PAR),
b air dryness (VPD), and c soil moisture (θv). For each scenario,
the number of data for each bin (n) is 500. Panels on the right
side show the FA_canopy–Tair relationship (in dark triangle and
solid curve) extracted from the 15-year, tower-based measurements
during the growing season of oak trees (including pheno-stages II
and III), suggesting that the overall pattern averages out possible
confounding effects such as light intensity, air dryness, and soil
moisture; the gray triangles are the same as those for data shown
on the left side in the same row. Smoothing curves are estimated
based on averages of FA (binned by n = 1000) with the LOESS method
(see details in “Methods” section)
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grassland, respectively. Differences in these Topt values were
not statistically significant according to results of the F test,
indicating that these four vegetative communities had similar Topt
on average, which should not be surprising given that they all
belong to C3 plants and exist in basically the same location.
Besides variations in the positions of optimum tempera-ture, the
temperature response curves also showed varia-tions in the
positions of the lowest or highest temperatures that would limit
regular photosynthesis. For the vegetation types studied here, Tmin
was close to 0 °C, approximately.
Oak photosynthesis exhibited Tmax close to 40 °C, about 5 °C
higher than that of annual grasslands (Fig. 8a).
We also checked that light intensity, air dryness, or soil
moisture could cause significant changes in the values of Topt for
each vegetation type (F test, p < 0.0001). As oak canopy showed,
Topt were higher with increases in light intensity and air dryness
(Fig. 9a, b), but Topt was lower with increases in θv
(Fig. 9c). Topt could vary more than 10 °C with variations in
light intensity, air dryness, and soil moisture (Fig. 9). The
oak canopy exhibited a range larger than the annual grassland,
suggesting greater flexibility of oak photosynthesis in response to
changes in ambient temperature.
Discussion
The leaf-chamber experiment is a fundamental method that
supports the tower-based observations, although the leaf-chamber
experiments presented in this study did so partially. We were short
of data at temperatures lower than 18 °C due to condensation
problems. Also, measurements relied on a few sun-exposed leaves
belonging to three indi-vidual oak trees in the summer of 2008.
That year alone, the eddy-covariance towers measured almost 50-fold
as many samples as the leaf-chamber experiments. Thus, the towers
are more likely to capture a large population of sun-exposed and
shaded leaves distributed across a footprint several hundred meters
along the main axis of the study site (Kim et al. 2006). As a
result, the tower-based temperature response curves covered a
temperature sampling domain wider that the leaf-level experiments.
Although the eddy-covariance flux towers are also expensive and
require high-level technical support, we offer timely new methods
for analyzing flux tower data to extract information on
photo-synthetic temperature responses over ecosystems.
The flux tower is usually set up for natural experiments, but
the tower-based datasets provide both physiological and
environmental information that is richer than that histori-cally
existing. The feature of large sample sizes from the perspectives
of experiments opens up the possibility to tease apart potential
confounding factors during data analy-sis, instead of during
experiment design. Our analyses show how larger sample sizes can
average out most of confound-ing effects, whereas sub-samples under
each experimental scenarios cannot. Nevertheless, confounding
effects should be a major concern if the sample size is small. For
exam-ple, confounding effects were associated with the 8-day
leaf-chamber measurements if the light intensity inside the chamber
was not controlled at the saturated level. When we extracted
tower-based temperature response curves under different
experimental scenarios, we reduced the sample size for each
scenario, resulting in unbalanced slopes on
Fig. 8 Panel a shows the averaged relationship between
photosyn-thetic flux (FA) and air temperature (Tair) for oak
canopy, understory grassland, savanna, and open grassland, binned
by n = 1000. Panel b shows the slopes of the curves shown in Panel
a by calculating dFA/dTair along with the axis of Tair; a linear
function (statistically signifi-cant) proves that the curves shown
in Panel a are parabolic
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the left and right side of the temperature response curves.
Thus, caution on confounding effects is always needed, especially
when the entire tower-based dataset is catego-rized into
groups.
Variations in abiotic variables such as light intensity, air
dryness, and soil moisture not only can affect the magni-tude of
photosynthesis but also can shift optimum tempera-tures. However,
variations in these abiotic variables do not alter the peak shape
of the temperature response curves; background climate and
seasonality constrain the occur-rences of confounding effects. For
example, the savanna area shows two particular confounding
scenarios: (1) low light intensity confounds with lower
temperatures and (2) air and soil dryness confound with higher
temperatures, due to the Mediterranean climate type—wet winters and
springs and dry summers. In recent years, dry conditions
occur in the winters and springs more often (Ma et al.
2016). Yet the probability of occurrences of dry winters and
springs is still lower than the probability of wet winters and
springs. Thus, confounding factors and their effects on
photosynthesis depend greatly on the background climate, and we
conclude that many theoretical paradigms rarely occur
in situ.
During data analysis, we visualize the tower-based experiments
as if we perform a series of manipulative experiments numerically
(Jones 1992). This visualization impieties problems caused by
applying general climato-logical methods in synthesizing flux and
environmental variables, such as integrating flux data over a fixed
period (e.g., daily, monthly, or annually). Such a climatologi-cal
approach reduces the degree of freedom of original data and causes
loss of fine time-scale information, which
Fig. 9 Variations of optimum temperature of photosynthesis
(Topt) under experimental scenarios of light intensity (PAR), air
dryness (VPD), and soil moisture (θv) for a oak canopy, b
understory grass-land, c entire savanna, and d open grassland.
Notice that for each box plot, the mean is presented as a diamond;
the median is the bar across
the vertical rectangular box; the lower and upper edges of the
box are the 25 and 75% percentiles, respectively; the 5% percentile
is the lower bar below the box; the 95% percentile is the upper bar
above the box; and circles outside percentile bars are outliers
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reflects the fast responses of vegetation to changes in
environments (Woodward 1987). For the same reason, a few studies
may exaggerate the meaning of mean annual temperature with
ignorance of fine time-scale variations in ambient temperature
(Helliker and Richter 2008; Yuan et al. 2011; Niu et al.
2012). Thus, it is worth paying more attention to fine time-scale
variations in fluxes and ambi-ent variables to test process-based
hypotheses regarding dominated vegetative communities of
ecosystems.
The temperature response curves extracted from the entire
sampling domain have many ecological implica-tions. First, the
average position of temperature optima and limits reflects the
inherent capacity of thermal adap-tation of vegetative communities.
For example, the opti-mum temperatures for the four vegetative
communities at our study sites show the thermal characteristics of
C3 plants. We may expect differences in temperature optima and
limits are significant enough that could distinguish C3, C4, and
CAM plants or functional types occupying a variety of natural
habitats (Yamori et al. 2014; Berry and Bjorkman 1980). Thus,
by using the data mining frame-work that we proposed here, we can
further test the sta-bility of temperature response for
photosynthesis across different ecosystems or biomes.
Second, our results show that oak canopy started being
photosynthetically active in the temperature range from near 0 to
40 °C, whereas photosynthesis is inhibited in the annual grasslands
when temperatures are greater than ~35 °C. Such a 5 °C difference
in maximum temperature reflects the different thermal tolerances of
vegetative communities for performing photosynthesis and produc-ing
biomass. These temperature limits may be useful for identifying
critical thresholds of ecosystems that deter-mine the direction of
primary productivity as the global climate warms up (Woodward 1987;
Woodward et al. 2004). The lower and upper limits of
temperature for photosynthesis suggested by the tower-based
temperature response curve provide a set of quantified thresholds
of ecosystems for defining extreme climatic events.
Third, ecosystems may face serious problems if a majority of
fine time-scale ambient temperatures shifts toward the outside of
the temperature range favorable to current dominant vegetative
communities—even though mean annual temperature may vary
insignificantly—per-haps leading to species extinction in sensitive
ecosystems or regions (Chapin III and Starfield 1997; Woodward
1987). Such influences may occur gradually along with normal
climate fluctuations, but weather extremes or natural disasters may
cause sudden impacts on vegetation and ecosystem primary
productivity. More research can be carried out to clarify the
functional relationships of ecosystems in climate extremes.
Finally, this study paves the way for estimating impor-tant
parameters and response functions dynamically to feed
photosynthesis models on large spatial scales. A recent remote
sensing work reports that a dynamic temperature-related index can
improve performances of light-use effi-ciency models (Yuan
et al. 2007; Medlyn 1998; Wang et al. 2016). Tower-based
temperature response curves provide plenty of information for
extracting such indices. Further-more, our data mining framework is
suitable for mining different functional relationships, such as
light response curve or VPD response curve, and so forth. We also
see the potential of the data mining framework suitable for
exploring nighttime CO2 fluxes and following temperature response
of ecosystem respiration. To retain the focus of our study, we
decide not to extend our current efforts into those aspects, but we
expect that future research with the data mining framework will
turn tower-based datasets into an abundant data resources for
determining baseline infor-mation and response functions of
vegetative communities in ecosystems in a dynamic way.
Conclusions
This study develops a data mining framework to extract
temperature response functions for ecosystem photosyn-thesis from
eddy-covariance tower-based natural experi-ments. Our results
confirm that the temperature response of photosynthesis is a
functional relationship consistent at the ecosystem level as well.
Local background climate and seasonality constrain natural
occurrences of confound-ing effects, and most of the confounding
effects may cancel out, benefits of the feature of large sample
sizes. Thus, the tower-based data are a Big Data of ecosystem
science for exploring functional responses of ecosystems to
environ-mental factors dynamically. Our methods will turn
world-wide tower-based datasets into an abundant resource of a
determination of ecosystems thresholds and response func-tions,
which are critical for predicting terrestrial ecosystem carbon
balance and geographic vegetation distribution in responses to
climate change.
Acknowledgements This research is a member of the Ameri-Flux and
Fluxnet networks, supported in part by the Office of Sci-ence
(BReco), U.S. Department of Energy, Grant No. DE-FG02-03Reco63638
and through the Western Regional Center of the National Institute
for Global Environmental Change under Coopera-tive Agreement No.
DE-FC02-03Reco63613. Other sources of sup-port included the Kearney
Soil Science Foundation, the National Science Foundation, and the
Californian Agricultural Experiment Station. We are grateful to Dr.
Laurie Kotten and Housen Chu who commented on early versions and
Dr. Kenneth Worthy who edited the manuscript and suggested clearer
expressions in English and sci-ence. We are grateful for receiving
constructive comments from two anonymous reviewers who shared their
expertise helping us to make
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Photosynth Res
1 3
a stronger scientific contribution. We also thank the Tonzi and
Vaira families for allowing us to access their ranches for
research.
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Photosynthetic responses to temperature
across leaf–canopy–ecosystem scales: a 15-year study
in a Californian oak-grass savannaAbstract
IntroductionMethodsStudy sitesCanopy- and ecosystem-level
measurementsLeaf-chamber experimentsPheno-stages and LAI
measurementsA framework of data analysisDefining
the dependent variable—net photosynthetic flux
of ecosystems
Sorting, binning, and smoothingConfounding
effectsExperimental scenariosVerifying the tower-derived
temperature response curve
ResultsBackground climate and confounding effectsCanopy
photosynthesis during leaf-chamber experimentsThe
FA_canopy–Tair relationship under experimental
scenariosOptimum temperatures
DiscussionConclusionsAcknowledgements References