Transpiration Adaptation Time Periods to Changing

Transpiration Adaptation Time Periods to Changing
Ambient CO2 Concentrations.
Leonid Asipov1
L-Data Research LTD,
Israel
Abstract
CO2 response curves are a common experimental procedure for assessing plants
photosynthetic and water consumption properties. The measurements are performed on a
leaf patch confined in a sealed chamber, with controlled temperature, illumination, humidity
and ambient CO2 concentration (Ca). The response curves are obtained from measurements
with stepwise increases in the Ca. In order to keep experiment uniformity and reduce the
measurement noise, the data sampling following every Ca alteration, has to take place after
the parameters have reached their steady state. However, in the existing literature we
have found no report suggesting the optimal time intervals sufficient for transpiration to
reach it. Few studies reported usage of coefficient-of-variation (COV) to determine the
steady-state point. The reported COV threshold was 2-5% per minute. In this study we’ve
verified whether the COV method is accurate for steady state determination, and have
estimated experimentally the optimal time intervals should be used for CO2 response curves
of Tomato and Arabidopsis plants. We’ve found a considerable difference between curves
with short (four to five minutes) and longer (25-40) constant time lags between the
sampled points, in both plants. Our conclusions are that using COV to determine steady
state of parameters can lead to inaccuracy especially in parameters with slow response,
such as the transpiration rate. The preferred strategy, keeping uniformity between different
experiments, is to define a constant adaptation time lag, for all the curves, which optimally
found to be 20-40 minutes for both, Arabidopsis and Tomato plants.
Introduction
Portable gas-exchange systems (such as Licor LI6400, Ciras 1, 2, 3) are a convenient
experimental tool for measurement of carbon assimilation and transpiration of plant leaf
patches. The devices confine a leaf in a chamber with controlled PAR, humidity, air
temperature and ambient CO2 concentration (Ca), while measuring the CO2 assimilation (A)
and the transpiration (T) rates. Due to our ability to alter the conditions influencing the
chamber-confined leaf, such systems are useful for comparisons of physiological parameters
between different plants or between leaves of the same plant.
A common example for the discussed above experiments are measurement of
photosynthesis and transpiration at rising Ca. Environmental conditions such as the
Photosynthetically-Active-Radiation (PAR) levels often affect the leaves of a plant
differentially (Chen et al, 2008) which greatly affects the results of the CO2 response curve.
Acclimation to the chamber conditions, before the start of the measurement might diminish
the differences caused by variable conditions beforehand. Optimal acclimation periods might
change with plant species and have to be determined experimentally.
While the response curve is measured, Ca is consequentially changed from lower to higher
levels. To prevent noise, the data should be sampled at photosynthesis and transpiration
steady state. In the literature we have encountered two approaches for steady state
determination. One is using predetermined time lags between each Ca change and the other
is based upon Coefficient of Variation (COV) calculations. The Ca is changes when COV gets
below certain threshold.
The goal of this paper is to determine the optimal adaptation time periods needed for
transpiration to reach steady state between the CO2 alterations, and to verify the accuracy
of COV calculations for steady state determination.
Materials and Methods
Literature searches were made using the keywords A/Ci curve and CO2 response curve, in
Google Scholar and Science-Direct database.
Gas Exchange measurements
For the measurements we’ve used Li-Cor 6400 portable photosynthesis system (Open
System
Version 4.0, and 5.3 Li-Cor Biosciences Inc. Lincoln, NE).
Fig 2 A and B: Blue-Red LED chamber was used. It is a chamber with a built in blue and
red light emitters, which were set to 700 and 300µE (Fig 2B and 2A, respectively). Since the
leaf was clumped inside the chamber, the device was set to automatic data logging every
two minutes. The CO2 concentrations were manually changed as shown in the figures. The
experiment took place in an environmentally controlled room with constant temperature and
artificial illumination (which does not affect the measured leaf inside the chamber).
Fig 3B: Flourimeter chamber was used, at 600µE PAR levels. The leaf was clamped and
adapted to the chamber environment (380 µL L-1 CO2) for 25 minutes. Afterwards, the two
curves were measured sequentially on the same leaf (first the five minute curve and then
an adaptation period of 25 minutes at 380 µL L-1 CO2) and then the 25 minute curve was
measured. The experiment took place in a controlled environment.
Fig 3A: The experimental setup was similar to Fig3, with the change of PAR to 180µE and
the intervals to four and 40 minutes.
Table 2: The experiments were with similar setup to Fig2 and Fig3.
Plants
Tomato:
Greenhouse grown wt Solanum lycopersicum from Ailsa Claig strain of approximately 3
months old (Fig2, Table 3‘’) and younger one month old plants from the same strain were
used in experiments described in Fig3 and Table2’.
In both cases young leaves in the upper half of the plant were taken for measurements.
Arabidopsis:
One month old, controlled room grown, wt Arabidopsis Thaliana from Columbia strain was
used for experiments which results are shown in Fig2, and Table2.
Data analysis software:
The experimental data was analyzed using Data-Light 0.1 visual data analysis platform,
LData, Israel (www.ldata.simpliscience.com).
Used calculations
Coefficient of variation (COV) was calculated using the following formula:
COV = (Standard Deviationminute / Meanminute) * 100
Since our sampling was usually longer than a minute, the COV was calculated per sampling
period and divided by the time interval.
Abbreviations
Ca – Ambient CO2 concentration
Ci– Intercellular CO2 concentration
T – Transpiration rate
COV – coefficient of variation
A – CO2 assimilation rate
PAR– Photosynthetically-Active-Radiation
Results
In order to find the commonly used time lags between the samples, we’ve looked in
reported A-Ci curves. At most, the actual time durations were not specified. Few papers
mentioned time durations in the range of five to 20 minutes, and few have used COV
calculations to determine the steady state point following Ca alterations. The reported COV
threshold was two to five percent per minute.
1. Did not mention
neither COV nor
specific time period
2.
After
COV <
2%
3. After
COV <
5%
4. 15-20
min
interval
5. 5 min
interval
6. 10 min
interval
Horst et al., 2008
Araya et al., 2006
Chen et al., 2005
Morgan et al., 2004
Keutgen et al., 2005
Pimentel et al., 2007
Kosobryukhov et al.,
2000
Barrett and Gifford, 2005
Lopes and Araus, 2006
Antonelli et al., 2007
Habermann et al.,2003
Youssef and Awad, 2008
Manter
and
Kerrigan,
2004
Ribeiro
et al.,
2003
Steduto et
al., 2000
Sunflower
Flexas et al.,
2007
Citrus limon
Wang et al.,
2007
Prunus persica
Fig 1 and Table 1: Distribution of published papers considering information
supplied about adaptation periods between the sampled points of the Ca
response curve.
Coefficient of variation (two to five percent) was used in some of the studies to determine
photosynthesis and transpiration steady state (Table 1). The definition for coefficient of
variation is the ratio of the standard deviation to the mean at give time range, and
according To LI6400’s user’s manual , is calculated per one minute. The COV of
transpiration after Ca changes can get below 2% per minute long before reaching steady
state (Fig 2B).
Fig 2: Tomato transpiration response times and the COV following changes in
ambient CO2 concentration.
To demonstrate the consequence of time interval durations (at each point of A-Ci curve
measurement) on the transpiration, we compare 5-25 and 4-40 minute time intervals ,
presented as Transpiration-Ca curves of wt Arabidopsis Thaliana, Columbia strain (Fig 2A)
and wt Tomato - Solanum lycopersicum Ailsa Claig strain (Fig2B).
Fig 2: Transpiration versus ambient CO2 concentration (T-Ca) curves.
A. Arabidopsis T-Ca with time interval duration of 4 min (black) and 40 min
(Gray).
B. Tomato T-Ca with time interval duration of 5 min (black) and 24 min (Gray).
The difference between the results of the CO2 response curves with shorter and longer time
lags (Fig2), indicate that on shorter ones, transpiration does not manage to reach its
steady state. Most of the reviewed papers talk about the Ci parameter which is dependent
on CO2 assimilation rate, transpiration rate and leaf temperature (von Caemmerer and
Farquhar, 1981). In such case, longer adaptation lags, might have had an impact on the
research findings. The impact of longer adaptation periods on the Ci parameter is
demonstrated in Fig 1A.
In the following table, we summarize the adaptation time periods observed after typical Ca
alterations at two different illumination levels, on two individual plants from each species:
Arabidopsis and Tomato. The calculated COV is the average during the whole period until
steady state is reached, however similarly to Fig 2B, it gets to its peak following the Ca
change and then gradually decreases.
Plant PAR
[µE]
CO2
Change
380-80
µL L-1
CO2
Change
80 - 380
µL L-1
CO2
Change
80-650
µL L-1
CO2
Change
650-80
µL L-1
Time to
transpiration
steady state
[minutes]
Average
Transpiration
COV during
the period of
reaching
steady state
[ % /
minute]
`Arabidopsis 500 * 29 1.34 +/-
0.58
`Arabidopsis 500 * 33 1.1 +/- 0.6
``
Arabidopsis
180 * 31 1.3 +/- 1
``
Arabidopsis
180 * 36 3.8 +/- 1.7
` Tomato 500 * 13 2.3 +/- 0.3
` Tomato 500 * 18 3.7 +/- 1.3
` Tomato 190 * 39 1 +/-0.54
` Tomato 190 * 21 3.6 +/- 1.2
`` Tomato 190 * 22 3.2 +/- 2.4
`` Tomato 190 * 21 2.5 +/- 1.2
`` Tomato 190 * 12 3.3 +/- 1.4
`` Tomato 190 * 19 3.6 +/- 2.8
Table 2: Time durations until steady state and average COV and during the
period, at two different PAR levels and few common Ca alterations. Two individual
plants of each species were used (marked with tags). The Ca alterations are
shown with asterisks.
Discussion and Conclusions
The optimal adaptation time periods to Ca changes for Tomato and Arabidopsis are 20-40
minutes. Since the COV becomes more and more gradual as the parameters approach
steady state, we expect it to be lower than 2% before the actual steady state point (Fig 2).
COV of less than 2% cannot be used at most cases, since at lower COV values the variation
of the measured parameter becomes similar to the spontaneous noise.
We suggest that using COV to determine steady state may be inaccurate for slowly
changing parameters such as the transpiration rate.
The importance of reaching steady state for all the measured parameters is in minimization
of noise, and maximization of significance. If steady state is not reached (Fig3), the
physiological parameters are influenced by the rate of response to the environment
changes, which may depend upon many unmeasured factors which may be beyond the
scope of the experiment. If COV is used to determine steady state, and it is not actually
reached, we would expect differences in the time periods given to different leaves to adapt,
which possibly leads to great inaccuracy of the results.
For results significance and reduction of noise, the data has to be sampled after steady
state is reached, preferably at constant time periods between the points. This way, even if
the steady state is not reached, different experiments are comparable because leaves were
given the same time to adapt to the Ca change.
Usage of the optimal time lags of 20-40 minutes between the sampled points is suggested
for highest accuracy.
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