Leonid Asipov1
L-Data Research LTD,
Israel
pdf version
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, L-Data, Israel (www.ldata.co.il).
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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