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Keywords:

  • diurnal cycle;
  • flow conducting area;
  • flow imaging;
  • hydraulic conductivity;
  • Münch;
  • NMR;
  • pressure-flow hypothesis

ABSTRACT

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES

We used dedicated magnetic resonance imaging (MRI) equipment and methods to study phloem and xylem transport in large potted plants. Quantitative flow profiles were obtained on a per-pixel basis, giving parameter maps of velocity, flow-conducting area and volume flow (flux). The diurnal xylem and phloem flow dynamics in poplar, castor bean, tomato and tobacco were compared. In poplar, clear diurnal differences in phloem flow profile were found, but phloem flux remained constant. In tomato, only small diurnal differences in flow profile were observed. In castor bean and tobacco, phloem flow remained unchanged. In all plants, xylem flow profiles showed large diurnal variation. Decreases in xylem flux were accompanied by a decrease in velocity and flow-conducting area. The diurnal changes in flow-conducting area of phloem and xylem could not be explained by pressure-dependent elastic changes in conduit diameter. The phloem to xylem flux ratio reflects what fraction of xylem water is used for phloem transport (Münch’s counterflow). This ratio was large at night for poplar (0.19), castor bean (0.37) and tobacco (0.55), but low in tomato (0.04). The differences in phloem flow velocity between the four species, as well as within a diurnal cycle, were remarkably small (0.25–0.40 mm s−1). We hypothesize that upper and lower bounds for phloem flow velocity may exist: when phloem flow velocity is too high, parietal organelles may be stripped away from sieve tube walls; when sap flow is too slow or is highly variable, phloem-borne signalling could become unpredictable.


INTRODUCTION

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES

The ability to internally distribute water, nutrients and carbohydrates is essential for the functioning of higher plants. Long-distance transport in plants occurs along two parallel pathways, the xylem and the phloem. The xylem is responsible for the transport of water and nutrients from the soil to the leaves, whereas the phloem is responsible for the transport of photosynthates, amino acids and electrolytes from source leaves to the rest of the plant. According to the most widely accepted (but not undisputed) views, the flow of water in both systems is driven by pressure differences. The cohesion-tension (CT) theory, which was first proposed by Dixon & Joly (1894), states that the pressure difference between root and leaves, which is the driving force of xylem transport, is generated by surface tension at the evaporating surfaces of the leaf. The negative pressure (tension) is transmitted through the continuous water column in the rigid xylem vessels, from the leaves to the root apices and throughout all parts of the apoplast of the plant (Tyree 1997). In the phloem, the Münch pressure-flow hypothesis (Münch 1930) states that a pressure gradient is actively maintained between ‘source’ leaves and ‘sink’ tissues by the loading of solutes in the leaves, and unloading of solutes in sink tissues. At the source, the hydrostatic pressure increases as osmotically active solutes such as sucrose are secreted into the phloem sieve tubes (Ziegler 1975; van Bel 2003). At the sinks, the opposite occurs when solutes are removed from the phloem sap. The resulting osmotically generated pressure gradient drives the phloem sap from source to sink.

Although both the CT theory and the pressure-flow hypothesis have been around for many decades, it has always remained a problem to test the validity of both theories in living plants. Xylem and phloem tissues are deeply embedded within the plant, and both are extremely sensitive to experimental manipulation. The xylem is especially sensitive to cutting and puncturing because it operates under tension and will easily become embolized (Wei, Steudle & Tyree 1999). The phloem is even more sensitive than the xylem because it consists of living cells that are known to invoke a range of immediate defence reactions following all but the most subtle invasive manipulations (Knoblauch et al. 2001; van Bel 2003). These sensitivities have for a long time made it very difficult to characterize long-distance transport in the living plant. However, over the last two decades, novel techniques have opened up completely new approaches to study the processes governing long-distance transport. New molecular techniques have made it possible to identify a great number of carriers, pumps and (water) channels that are involved in the transport of solutes and water over the plasma membrane of sieve tubes (Patrick et al. 2001; Lalonde et al. 2003). The development of the pressure probe has made it possible to study the pressure gradients that are driving phloem and xylem flow, for instance, by measuring the turgor pressures of cells and sieve tubes (Steudle 1990; Tomos & Leigh 1999; Gould, Minchin & Thorpe 2004). The xylem pressure probe has sparked a heated debate regarding the driving forces of xylem flow and validity of the CT theory (Zimmermann et al. 1993; Tyree 1997; Meinzer, Clearwater & Goldstein 2001).

While the knowledge about the forces and processes that are driving xylem and especially phloem transport is increasing rapidly, the knowledge about the dynamic behaviour of phloem and xylem sap flow in intact plants remains rather limited. This is mainly caused by the fact that it is difficult and often impossible to non-invasively measure xylem and phloem sap flows using the methods that are currently available. Heat-based methods have been used to estimate xylem flow (for a review see Smith & Allen 1996), and radioisotope labelling techniques have been used to estimate flow velocities and mass flows in the xylem and the phloem (e.g. 14C: MacRobbie 1971; 11C: Minchin & Thorpe 2003). Each of these approaches has disadvantages that limit their usefulness. They are often not completely non-invasive, as some heat-based methods require probes to be inserted into the plant stem, and some radioactive labelling techniques require the destruction of plant samples. In addition, the interpretation of the results can become a problem because parameters such as size, anatomy, tissue heterogeneity and exchange between tissues can influence measurements as well as the calibration of the measurements. An issue specific to radioisotope labelling is that not the movement of water is measured, but the movement of labelled carbon, which may not yield the same results. Finally, neither approach provides spatial information about the position of the flowing xylem or phloem sap in the plant stem.

A more recently developed method that has seen a lot of progress over the last 10 years is flow imaging by means of nuclear magnetic resonance imaging (abbreviated to NMR imaging, MR imaging or MRI; for a recent review of applications of NMR in the plant sciences see Köckenberger 2001). The first NMR methods to measure xylem transport, although without imaging, were developed as early as 1984 by Van As and Schaafsma (Van As & Schaafsma 1984). Callaghan, Eccles & Xia (1988) introduced an NMR method to measure flow in combination with imaging, termed q-space flow imaging. Using a slightly modified version of this method, Köckenberger et al. (1997) were the first to measure xylem and phloem transport in an intact 6-day-old castor bean seedling, growing in the dark. The approach used by Köckenberger et al. was very time-consuming with a measurement time of 4.5 h. A more rapid flow-imaging method called FLASH was developed by Rokitta, Zimmermann & Haase (1999), which was shown to be capable of measuring phloem and xylem flow in 40-day-old castor bean plants, placed horizontally in a high-field superconducting magnet (Peuke et al. 2001). However, the short measurement times (between 4 and 7 min) did come at a price. The method yielded a quantitative average velocity per pixel, but did not give information about the velocity profile, nor did it allow the volume flow to be quantified in absolute units. Furthermore, spatial resolution was reduced to speed up the imaging part of the NMR sequence.

At the same time, a q-space flow-imaging method was developed that allowed the flow profile of every pixel in an image to be recorded quantitatively, with a relatively high spatial resolution and while keeping measurement times down to 15–30 min. This was done by combining flow encoding with a rapid (turbo) NMR imaging scheme (Scheenen et al. 2000a, 2001). The quantitative flow profiles allowed the following parameters to be calculated on a per-pixel basis, without making any assumptions about the flow profile per pixel or the number and diameter of vessels per pixel: the amount of stationary water, the flow-conducting area, the average velocity of the flowing water and the volume flow (flux). The q-space flow-imaging approach was used in combination with a low-field MRI set-up to visualize and quantify xylem flow in tomato (Scheenen et al. 2000a), in stem pieces of chrysanthemum (Scheenen et al. 2000b) and in large cucumber plants (Scheenen et al. 2002). While in the last study the authors were able to visualize phloem sap movement, they were not yet able to quantify phloem flow in the same manner as was demonstrated for xylem flow.

Phloem transport in plants is particularly difficult to measure, even by means of NMR flow imaging. The slow flow velocities and the very small flowing volumes in the presence of large amounts of stationary water make it difficult to distinguish the slowly flowing phloem sap from freely diffusing water. In this paper, we present a q-space flow-imaging approach that was optimized to measure phloem transport as well as xylem transport in a variety of plants, in terms of flow profile, average linear velocity, flow-conducting area and average volume flow. In addition, we present modifications that have made it possible for our NMR imaging hardware to accept large potted plants and allow them to be placed upright inside the magnet. In order to demonstrate the potential of our flow-imaging methods, measurements were taken on large plants of four species (poplar, tomato, castor bean and tobacco), each over a period of several days. The combination of dedicated NMR equipment and NMR flow-imaging methods allowed us to routinely measure and quantify, for the first time to our knowledge, phloem as well as xylem transport in plants up to a size of 2 m and over a period of up to a week. The results of these measurements are discussed with regard to the flow characteristics and diurnal dynamics of phloem and xylem flow, and the cycling of water between phloem and xylem (Münch’s counterflow).

MATERIALS AND METHODS

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES

NMR imaging set-up

The homebuilt NMR system was based on a 0.72 T electromagnet with a 10 cm air gap (Fig. 1), controlled by a Bruker Avance 200 unit (Bruker, Karlsruhe, Germany). In order to provide access for (potted) plants, a shielded gradient system with a plan parallel geometry was used, with a maximum gradient strength of 1 T m−1 in the X, Y and Z direction (Resonance Instruments Ltd, Witney, UK). The 50 mm air gap between the two gradient plates provided free access to the center of the magnet, from the front and back of the gradient set, as well as from above and below. Because a resistive magnet is inherently sensitive to temperature fluctuations, the magnetic field was stabilized by means of a homebuilt external 19F lock unit.

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Figure 1. (a) Schematic overview of the nuclear magnetic resonance imaging set-up. (b) Enlarged view of the radio frequency (RF) coil assembly, which was mounted around the stem and fixed to a support before the plant was placed in the imager. The open build of the electromagnet and gradient set provides easy access for potted plants up to a size of 2 m.

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A solenoid radio frequency (RF) coil for induction and detection of the NMR signal was custom-made for every plant, providing a high filling factor and an optimal signal to noise ratio (SNR), despite the small sample size and relatively low magnetic field. Firstly, a loosely fitting mould with a diameter of 10 mm was put around the stem of the plant. The RF coil was then constructed by wrapping nine turns of 0.5 mm copper wire around the mould. The finished coil was connected to a compact, homebuilt tuning circuit, electromagnetically shielded by means of aluminum foil and copper tape, and fixed to a rod next to the plant (Fig. 1b). The assembly of pot, plant and coil was then inserted, upright, into the magnet, with the RF assembly in the center of the magnet, between the two gradient plates.

NMR flow imaging

The spatial resolution that can be obtained using NMR imaging is limited when compared with optical microscopy. Here, the highest spatial resolution that was obtained in a flow-imaging measurement was ∼100 × 200 µm at a slice thickness of 3 mm. Although it is possible to obtain higher spatial resolutions, it will in general not be high enough to resolve individual sieve tubes or xylem vessels. As a consequence, pixels that contain flowing water will always contain a significant amount of stationary water. When conduits are very small, as is the case in phloem tissue, the relative amount of flowing water per pixel can be as small as a few percent. The greatest challenge in measuring phloem water transport therefore is to distinguish a small amount of very slowly moving water from a (very) large amount of stationary water that is exhibiting random movement as a result of Brownian motion (e.g. see Fig. 2b).

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Figure 2. A total propagator of phloem flow in poplar (a), composed of a summation of the propagators of all pixels in the phloem flow mask (inset). Making use of the fact that the flow profile of stationary water is symmetrical around zero, the flow profile of flowing and freely diffusing stationary water was calculated (b). When calibrated with the signal from the reference tube, the area under the graph becomes a measure of the amount of flowing water.

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In NMR flow imaging, a flow measuring sequence can be said to consist of three steps: excitation, flow encoding and image data acquisition. Flow encoding generally involves two pulsed field gradients (PFG), separated by a flow-labelling period Δ. The two PFGs provide sensitivity to displacement in an NMR flow measurement. Here, in order to be able to discern flowing water from randomly diffusing water, Δ was varied from 15 ms for fast-flowing xylem water, to 200 ms for slow-moving phloem water. Linear displacement was measured by stepping the amplitude (G) of the PFGs –Gmax to +Gmax and sampling q-space completely, as described previously by Scheenen et al. (2000a). After Fourier transformation of the signal as a function of gradient amplitude, the complete distribution of displacements (i.e. flow profile) within Δ in the direction of the gradient, which was chosen to be the flow direction, was obtained for every pixel of an image. Such a displacement distribution is called a propagator (Kärger & Heink 1983).

Depending on the purpose of a measurement, a pulsed field gradient – spin echo – turbo spin echo sequence (PFG-SE-TSE) or a stimulated-echo version of the same sequence (PFG-STE-TSE) was used. The latter sequence is especially well suited for measuring very slow flow because it allows the use of long flow-labelling times (Scheenen et al. 2001). The PFG-SE-TSE sequence was used to measure xylem water transport, the PFG-STE-TSE sequence to measure phloem water transport. In both cases, rapid image encoding was performed using a Turbo Spin Echo sequence with a turbo factor of 16, thus acquiring and making efficient use of 16 echoes upon every excitation.

The fact that the displacement distribution of stationary water is symmetrical around zero was used to separate the stationary from the flowing water. The signal in the non-flow direction was mirrored around the Y-axis and subtracted from the signal in the flow direction, to produce the displacement distribution of the flowing and the stationary water (Fig. 2, The displacement distribution is here presented as a velocity distribution). Because the signal amplitude is proportional to proton density, the integral of the graph provides a measure for the amount of water. The average velocity of the flowing water was then calculated by taking the amplitude weighted average of the velocity distribution. Using this approach, the following flow characteristics were extracted in a model-free fashion, as described by Scheenen et al. (2000b): total amount of water per pixel, amount of stationary water per pixel, amount of flowing water (or flow-conducting area) per pixel, average linear velocity per pixel (including the direction of flow) and volume flow per pixel. The NMR signal intensity of a reference tube was used for calibration. The only assumption and requirement involved in this method of quantification is that there be no flow in two directions, within the same pixel or ensemble of pixels. Spatial resolution was carefully chosen to meet this condition.

In NMR, signal is lost as a result of two relaxation processes, T2 and T1. These relaxation times relate to tissue water content and cell size or vessel diameter. A more detailed explanation of the meaning of T1 and T2 in MRI of biological tissues is outside the scope if this paper, but can be found elsewhere (Belton & Ratcliffe 1985; Callaghan, Clark & Forde 1994; Köckenberger 2001; van der Weerd et al. 2002). In a flow measurement, a significant amount of signal can be lost between excitation and signal acquisition. The amount of signal that is lost depends on the physicochemical properties of the subject, the length of the flow-labelling time, and on the relaxation mechanism that the NMR signal is subjected to during flow labelling. In a PFG-SE-TSE flow measurement, the signal is subjected to T2 relaxation, whereas during a PFG-STE-TSE flow measurement, signal is mainly lost as a result of T1 relaxation (Scheenen et al. 2001). If the relaxation rate of a reference tube that is used for calibration is not equal to the relaxation rate of the subject to be measured, quantification errors may occur. Therefore, differences in the relaxation rates of reference tubes and subjects were compensated for using estimates based on separately measured (flow insensitive) T1 and T2 parameter maps (for details see further discussion).

Flow measurements were processed in a number of ways. Quantitative xylem flow maps were obtained by analysing singular xylem flow measurements on a per-pixel basis. Quantitative images of phloem flow were obtained by averaging three or more consecutive flow measurements. For both xylem and phloem flow measurements, ‘total propagators’ were constructed by summing the propagators of all flow-containing pixels into a one-dimensional total propagator. Flow-containing pixels were identified as such on the basis of previously calculated quantitative flow maps. In this case, the spatial information was not used to calculate flow maps, but to discard pixels that only contained stationary water.

A single flow measurement typically took between 15 and 30 min (32 G-steps). To produce spatially resolved phloem flow maps, three or more measurements were averaged, thus increasing measurement time. Data analysis was performed in IDL (Research Systems Inc., Boulder, CO, USA), using homebuilt processing and fitting routines.

T1 and T2 measurements

T2 imaging was done using a multispin-echo imaging pulse sequence (Donker et al. 1997; Edzes, van Dusschoten & Van As 1998), a repetition time (TR) of 5000 ms, a spin-echo time (TE) of 4.5 ms and a spectral bandwidth of 50 kHz. An image matrix of 128 × 128 pixels was acquired per echo, and 128 echoes were acquired per echo train. T1 imaging was performed by recording a series of 17 separate amplitude images at increasing TR (from 67 up to 10 000 ms), using a TSE imaging pulse sequence with slice selection (Scheenen et al. 2000a), a turbo factor of 4 and an effective TE of 4.5 ms, a spectral bandwidth of 50 kHz, and eight dummy scans. In both measurement types, two acquisitions were averaged to improve image quality. The in-plane resolution typically was ∼100 × 100 µm at a slice thickness of 3 mm.

The acquired NMR data sets were processed using home-made routines written in IDL (Research Systems Inc.). The data sets were fitted on a per-pixel basis, using a monoexponential decay function (van der Weerd et al. 2000), yielding quantitative maps of either amplitude, 1/T1 and T1; or amplitude, 1/T2 and T2 (Donker et al. 1997; Edzes et al. 1998).

Plant material

Grey poplar (Populus tremula×Populus alba, INRA clone 717 1B4), castor bean (Ricinus communis), tomato (Lycopersicon esculentum cv. Counter) and tobacco (Nicotiana tabacum cv. Petit Havana SR1) plants were obtained from various academic sources. The poplar, castor bean and tobacco plants were grown on commercial potting soil (Naturado potting soil; Naturado Bodemvoeding BV, Veenendaal, the Netherlands), while the tomato plants were grown on a perlite substrate (Agra-perlite No. 1, grading 0.6–1.5 mm) with a computer-controlled drip irrigation system. Before the experiments, the plants were transferred to a climate chamber (22 °C, 50% relative humidity (RH), 16 h photoperiod 150–200 µmol s−1 m−2 photosynthetically active radiation (PAR); 20 °C, 50% RH, 8 h dark period) and grown for a period of at least 3 weeks. Two weeks before measurements, the plants were repotted to custom-made rectangular pots, made to fit the NMR set-up. The 1.50-meter-tall poplar plants were transferred to 10 L pots filled with a mixture of 75% commercial potting soil (Naturado Bodemvoeding BV) and 25% perlite, supplemented with 30 g of Osmocote slow-release fertilizer (Scotts International PBG, Heerlen, the Netherlands). The smaller (approx. 90 cm tall) castor bean and tobacco plants were transferred to 7 L pots filled with the same soil mixture. Before transferring the tomato plants to hydroponic culture, the perlite substrate was carefully washed from the roots. The tomato plants were then placed in 10 L containers with continuously aerated growth medium. In order to keep the tomato plants in a vegetative state, all flowers and side shoots were removed during the course of cultivation.

Before measurements, the plants were fitted with an RF coil and placed in the NMR set-up. The plants were then left to acclimate for a minimum of 2 d before the actual measurements commenced. In the NMR set-up, the plants were subjected to the following environmental conditions. Poplar, tomato and tobacco: day period 14 h, 25–27 °C, 29–45% RH, 200 µmol photons s−1 m−2 PAR; night period 10 h, 22 °C, 24–40% RH. Castor bean: day period 16 h, 25 °C, 28% RH, 200 µmol photons s−1 m−2 PAR; night period 8 h, 22 °C, 24% RH.

RESULTS

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES

In order to demonstrate the potential of our NMR imaging set-up and flow-imaging methods, series of phloem and xylem flow measurements were carried out on large, fully differentiated plants (and a small tree). Firstly, we will present the results of a typical phloem and xylem flow measurement carried out on poplar in full detail. Secondly, we will compare the results of phloem and xylem flow measurements collected during long-term experiments on poplar, castor bean, tomato and tobacco.

Flow measurements poplar

By analysing the PFG-SE-TSE (xylem flow) measurements and the PFG-STE-TSE (phloem flow) measurements as described elsewhere (Scheenen et al. 2000b), we were able to construct flow maps of phloem transport as well as xylem transport. Using the reference tubes for calibration, the flow measurements were processed to yield quantitative flow maps representing the amount of stationary water per pixel (Fig. 3a), the amount of flowing water, here presented as the flow-conducting area per pixel (Fig. 3b), the average linear velocity of the flowing water (Fig. 3c), and the average volume flow per pixel (Fig. 3d). The position and shape of the phloem and xylem flow maps correspond closely with the phloem and xylem regions that are visible in the anatomical reference provided by the amplitude, T1 and T2 maps (Fig. 4).

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Figure 3. Overlaid flow parameter maps, calculated from a single pulsed field gradient – spin echo – turbo spin echo (PFG-SE-TSE) flow measurement (blue) showing upward-flowing xylem sap, and five averaged pulsed field gradient – stimulated-echo – turbo spin echo (PFG-STE-TSE) flow measurements (red) of downward-flowing phloem sap. Using the reference tubes for calibration, the flow measurements were quantified to give parameter maps representing the amount of stationary water per pixel (a), the flow-conducting area per pixel (b), the average linear velocity of the flowing water (c), and the average volume flow per pixel (d). For the PFG-SE-TSE experiment, the following parameters were used: image matrix 128 × 128, field of view 12.5 mm, repetition time 2.5 s, turbo factor 32, 2 averages, labelling time Δ 50 ms, δ 3 ms, Gmax 0.307 T m−1; and for the PFG-STE-TSE experiments, image matrix 64 × 64, field of view 12.5 mm, repetition time 2.5 s, turbo factor 16, 2 averages, labelling time Δ 200 ms, δ 2.5 ms, Gmax 0.230 T m−1.

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Figure 4. A comparison of the anatomical reference provided by parameter maps depicting water content (a), T2 (b) and T1 (c), with the parameter maps of xylem and phloem volume flow (d), which were both plotted over an image depicting water content. Phloem flow was flowing in a downward direction and is shown in red, whereas xylem was flowing in the opposite direction and is shown in blue. The phloem flow ring appears to correspond closely with the bright, narrow ring visible in image b and c. Experimental parameters of the T1 measurement: field of view 12.5 mm, image matrix 128 × 128, longest repetition time 10 s, 2 averages; experimental parameters T2 measurement: field of view 12.5 mm, image matrix 128 × 128, repetition time 5 s, echo time 4.5 ms, 2 averages.

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In this study, the quantitative phloem and xylem flow maps were used primarily to identify and make masks of all pixels containing water moving in an upward (xylem) or downward direction (phloem; inset Fig. 2a). By adding the signal of all pixels within the mask, a total propagator was obtained (Fig. 2a), containing the flow information of all pixels within the flow mask. The total propagators were analysed in the same manner as was carried out with the pixel propagators in imaging mode. Making use of the fact that the velocity distribution of stationary water is symmetrical around zero (no net displacement), the signal in the non-flow direction (here: the right hand side) was mirrored around the Y-axis and subtracted from the signal in the flow direction to produce the velocity distribution of the flowing and the stationary water (Fig. 2b). The resulting flow profiles were then again used to calculate the flow-conducting area, the average velocity of the flowing water, and by taking the integral of the propagator of the flowing water, the total volume flow.

Diurnal dynamics of phloem and xylem transport

During experiments lasting one and a half to three day–night cycles, repetitive phloem and xylem flow measurements were taken on the stem of large, fully differentiated and rapidly growing poplar, castor bean, tomato and tobacco plants. For every plant quantitative phloem and xylem flow maps were calculated and subsequently used to make xylem and phloem masks. These masks were then employed to calculate the total flow profiles of all flowing phloem and xylem water in the plant stems, as well as the information that is contained in these flow profiles: the amount of flowing water, the average linear velocity and total volume flow.

Phloem transport

Poplar showed clear differences in day and night phloem flow profile, in contrast to the day and night phloem flow profiles that were recorded for castor bean, tomato and tobacco (Fig. 5a–d). At night, the average linear velocity in poplar decreased from 0.34 ± 0.03 mm s−1 to 0.24 ± 0.02 mm s−1 (Figs 6a and 7b). Interestingly, the decrease in flow velocity coincided with a clear increase in the flow-conducting area (Fig. 7a), countering the decrease in flow velocity. As a result, the total phloem volume flow of poplar during the day and night period remained virtually unchanged (Fig. 6a). During the day, an average volume flow of 0.87 ± 0.14 mm3 s−1 was measured, at night, a volume flow of 0.91 ± 0.09 mm3 s−1 (Fig. 7c).

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Figure 5. The average day- and night-time phloem flow profiles in poplar (a), castor bean (b), tomato (c) and tobacco (d). Phloem flow masks for each plant are shown in the top left corner; the flow profile of stationary water within the flow mask is not shown. Open symbols: average day profiles; closed symbols: average night profiles. Each flow profile represents the mean ± SD of 8–30 individual flow profiles measured in the course of 2 to 4 d.

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Figure 6. The volume flow and average linear velocity of phloem water in poplar (a), castor bean (b), tomato (c) and tobacco (d), measured over the course of 2 to 4 d. Black and white bars indicate day and night. Closed symbols: volume flow; open symbols: average linear velocity.

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Figure 7. The average total flow-conducting area (a), average linear flow velocity (b) and average total volume flow (c) in the phloem of poplar, castor bean, tomato and tobacco. Daytime averages are shown in black, night-time averages in grey. Each bar represents the mean ± SD of 8–30 individual measurements.

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In tomato, no significant differences between the day and night phloem flow profiles were observed (Fig. 5c), although the average linear flow velocity did appear to show a small decrease in the dark (Fig. 6c). The average linear velocity was 0.40 ± 0.04 mm s−1 during the day, and 0.35 ± 0.05 mm s−1 at night (Fig. 7b). The average flow-conducting area appeared to decrease at night as well, with values of 0.29 ± 0.04 mm2 during the day and 0.23 ± 0.04 mm2 at night (Fig. 7a). The insignificant but consistent differences in flow velocity and flow-conducting area resulted in slight differences between day and night-time volume flow. During the day, a total volume flow of 0.12 ± 0.02 mm3 s−1 was measured, versus 0.08 ± 0.02 mm3 s−1 at night (Fig. 7c). In contrast, the phloem flow characteristics of castor bean and tobacco did not exhibit any day/night differences, neither with regard to volume flow, nor with regard to average flow velocity and amount of flowing water.

The average phloem sap volume flows, as well as the average flow-conducting areas that were measured in the four plant species, showed large differences that roughly corresponded with the differences in the size of the plants that were used (Fig. 7a & c). Despite the fact that the plants were of different species and different sizes, the differences in average linear phloem flow velocities between the plants were remarkably small, ranging from 0.40 mm s−1 in tomato to 0.25 mm s−1 in castor bean (during daytime; Fig. 7b).

Xylem transport

The diurnal xylem transport in the four plant species exhibited large day/night differences, with regard to the average day and night-time flow profiles (Fig. 8), as well as with regard to the diurnal differences in average flow velocities and volume flows (Figs 9 & 10). In all four plant species, the xylem volume flows at night were greatly reduced. In poplar, 17.9 ± 0.90 mm3 s−1 xylem water was transported on average during the day, against 4.66 ± 0.52 mm3 s−1 at night, a reduction of 74%. In castor bean, these values were 3.71 ± 0.46 mm3 s−1 during the day and 0.63 ± 0.50 mm3 s−1 at night (−83%), in tomato 8.00 ± 0.92 mm3 s−1 during the day and 2.26 ± 0.52 mm3 s−1 at night (−72%), and in tobacco 1.14 ± 0.27 mm3 s−1 during the day and 0.18 ± 0.06 mm3 s−1 at night (−84%; Fig. 10c). Interestingly, these reductions were caused not only by a decrease in the average linear velocities (Fig. 10b), but also by a decrease in the flow-conducting areas (Fig. 10a). In poplar and tomato, the decrease in flow-conducting area was 28 and 25%, respectively, whereas in castor bean and tobacco, the flow-conducting area was decreased even further, with, respectively, 46 and 53%.

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Figure 8. The average day- and night-time xylem flow profiles in poplar (a), castor bean (b), tomato (c) and tobacco (d). Xylem flow masks for each plant are shown in the top left corner; the flow profile of stationary water within the flow mask is not shown. Open symbols: average day profiles; closed symbols: average night profiles. Each flow profile represents the mean ± SD of 10–25 individual flow profiles measured in the course of 2 to 4 d.

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Figure 9. The volume flow and average linear velocity of xylem water in poplar (a), castor bean (b), tomato (c) and tobacco (d), measured over the course of 2 to 4 d. Black and white bars indicate day and night. Closed symbols: volume flow; open symbols: average linear velocity.

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Figure 10. The average total flow-conducting area (a), average linear flow velocity (b) and average total volume flow (c) in the xylem of poplar, castor bean, tomato and tobacco. Daytime averages are shown in black, night-time averages in grey. Each bar represents the mean ± SD of 8–30 individual measurements.

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The highest average linear xylem flow velocity was measured in tomato (5.10 ± 0.54 mm s−1, daytime), whereas the lowest average linear xylem flow velocity in the light was found in poplar (1.60 ± 0.09 mm s−1, daytime; Fig. 10b). However, the poplar plant did have the largest flow-conducting area (11.2 mm2) and transported the largest volume flow of xylem water of the four plants in this study.

Münch’s counterflow

The water that is moving down in the phloem must, under normal conditions, first have been transported upward through the xylem. The phloem to xylem volume flow ratio then reflects, in the absence of terminal sinks such as fruit, what fraction of the flowing water in the xylem is replacing water that is exported from the source leaves by way of the phloem, in a process that has previously been called recirculation (Pate et al. 1985) or Münch’s counterflow (Tanner & Beevers 1990). During the night, the phloem to xylem volume flow ratio was surprisingly large: 0.19 in poplar, 0.37 in castor bean, and even as high as 0.55 in tobacco (Fig. 11). The phloem/xylem volume flow ratio at night in tomato was much lower, 0.04. During the day, when the contribution of transpiration to the xylem volume flows were much higher, the phloem to xylem ratios dropped to 0.10 in tobacco, 0.07 in castor bean, 0.05 in poplar and a mere 0.02 in tomato.

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Figure 11. The phloem to xylem ratio in poplar, castor bean, tomato and tobacco. The phloem to xylem ratio indicates what part of the xylem volume flow is recirculated by means of the phloem. Daytime ratios are shown in black, night-time ratios in grey.

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DISCUSSION

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES

NMR set-up and flow-imaging methods

NMR imagers that are used for plant work often have been developed for other purposes, such as medical imaging. As a result, many machines consist of horizontal bore superconducting magnets with cylindrical imaging gradients. For plant work this is not an ideal set-up. In such a system, plants have to be placed horizontally instead of vertically, and fitting the shoot or roots of a plant through the narrow cylindrical bore of a gradient set can be stressful and damaging for the plant. In the current study, we used an NMR imager consisting of an electromagnet with an open structure. The imaging gradient set in the centre of the magnet was made up of two flat plates instead of the more conventional cylindrical gradient sets. This configuration allowed us to easily place large plants, up to a size of 2 m, upright in the NMR magnet (Fig. 1). Placement of plants could be undertaken without causing much stress to the subject, other than the stress that is caused by moving and handling it. Potentially stressful actions like mounting an RF coil (Fig. 1b) or, when necessary, the removal of a branch or leaf could be undertaken well in advance. In this study, the plants were left to acclimate to the conditions in the magnet for a minimum of 2 d before commencing measurements, although this may not have been necessary because the plants were growing vigorously and showed uninhibited water uptake within a few hours after they were placed in the magnet.

Measuring phloem flow by means of NMR flow imaging has, from a technical point of view, for a long time remained a very challenging enterprise. Köckenberger et al. (1997) were the first to use NMR flow imaging to measure phloem flow in an intact castor bean seedling grown in the dark. In order to measure flow, they used a ‘difference propagator’ technique that yielded flow profiles that were, as far as the general principle is concerned, comparable to the flow profiles presented in the current study. In order to quantify these (distinctly non-rectangular) flow profiles, the difference propagators were fitted with a rectangular function. This is a valid approach when the following conditions are met: the flow within vessels is laminar; only one vessel is present per pixel, or more vessels are within one pixel but all have the same diameter; and vessels are never only partially within one pixel and partially within another. Under these conditions a flow profile would always have a rectangular shape (broadened by diffusion). However, in practice the recorded flow profiles were not rectangular, but looked like the flow profiles presented in this paper (Figs 2, 5 & 8), showing a large contribution of water moving at lower speeds and a smaller contribution of fast moving water. Fitting a flow profile like this with a rectangular function may yield a correct value for the total volume flow, but the average velocity of the phloem sap would be overestimated. In addition, during data processing, information with regard to the amount of stationary water per pixel is lost.

A more rapid flow-imaging method called FLASH was developed by Rokitta et al. (1999), which was later shown to be capable of measuring phloem and xylem flow in 40-day-old castor bean plants, placed horizontally in a high-field (7 T) superconducting magnet (Peuke et al. 2001). In this approach the spatial resolution was reduced in conjunction with a faster flow-encoding method in order to shorten the measurement time. While the method was quantitative in that it yielded an overall average velocity per pixel, it did not yield information about the velocity profile that gave rise to a particular average velocity, nor did it allow the volume flow to be quantified in absolute units because the flow-conducting area per pixel was not known.

The NMR flow measurement methods presented in the current paper are especially useful in that they provide, for the first time to our knowledge, a means to record quantitative flow profiles (propagators) of flowing phloem water, while at the same time providing a good spatial resolution at acceptable measurement times. The quantitative flow profiles are unique in that they make it possible to calculate the flow-conducting area per pixel, the average flow velocity per pixel, the amount of stationary water per pixel, and the volume flow per pixel, quantitatively and without the necessity to make any assumptions regarding the flow profile (Scheenen et al. 2000b). As a result of the more favourable anatomy of xylem tissue (large vessels, large amounts of flowing water, and relatively high flow velocities), the SNR of a single PFG-SE-TSE xylem flow measurement was already sufficient to construct quantitative flow maps with a good spatial resolution (Figs 3 & 4). To measure phloem transport, a more elaborate strategy had to be employed. It was usually necessary to lower the spatial resolution of PFG-STE-TSE phloem flow measurements, take more averages per individual measurement, and average three or more individual PFG-STE-TSE phloem flow measurements to raise the SNR sufficiently to allow the measurement to be evaluated on a per-pixel basis and calculate the quantitative phloem flow maps shown in Figs 3 and 4. This approach typically required a minimum of 45 min effective measurement time. Alternatively, the information originating from all flow-containing pixels in an image was summed into a one-dimensional flow profile (Fig. 2), making it possible to analyse measurements individually and lowering effective measurement time to 15–30 min. The combination of the NMR imaging set-up and NMR flow-imaging methodology presented in the current paper thus allowed us to routinely measure quantitative flow profiles of both phloem and xylem flow, in a variety of large plants up to a size of 2 m. The ability to measure phloem flow has already found further application in a study of the effects of cold girdling on phloem mass flow in castor bean (Peuke, Windt & Van As 2006).

How fast does phloem sap flow?

We recorded an average phloem flow velocity of 0.25 ± 0.03 mm s−1 in castor bean during the day (Fig. 7b). This value is in good agreement with the phloem flow velocities reported in earlier studies. Hall et al. used 14C carbon labelling to measure phloem flow in 6-week-old castor bean plants, reporting an average velocity of 0.233 mm s−1 (Hall, Baker & Milburn 1971). Grimmer (1999) used 11C carbon labelling and found phloem flow velocities between 0.387 and 0.41 mm s−1. Köckenberger et al. (1997) found an average phloem flow velocity of 0.58 mm s−1 in 6-day-old castor bean seedlings that were grown in the dark, but because of the reasons discussed earlier, this value will most likely be an overestimation. Peuke et al. (2001) used an NMR flow-imaging method (FLASH) to measure phloem flow velocity, and reported a flow velocity of 0.25 mm s−1, averaged over all plants in the experiment.

Remarkably, the flow velocities we measured in poplar, castor bean, tomato and tobacco were all within the same range, irrespective of plant size and species. The highest average linear velocity that was measured was 0.44 mm s−1 in tomato; the lowest recorded value was 0.25 mm s−1 in castor bean (Fig. 7b). The phloem flow velocities in a wide range of plants, as far as these are available from literature, are roughly within the same range as well. In castor bean, phloem flow velocities with values between 0.233 (Hall et al. 1971) and 0.58 mm s−1 were found (Köckenberger et al. 1997). Mortimer (1965) investigated phloem transport in sugar beet petioles using 14C labelling and found phloem flow velocities between 0.139 and 0.417 mm s−1. In soybean, Fisher (1978) measured phloem flow velocities between 0.13 and 0.15 mm s−1. Hartt (1967) reported flow velocities between 0.17 and 0.22 mm s−1 in sugar cane. Taking into account that these flow velocities were measured in different species and using different techniques, the differences are remarkably small. As far as we are aware, only one example – measured in a monocot, in contrast to the studies mentioned before – is an exception. Passioura & Ashford (1974) measured phloem flow velocities of 0.2 up to 1.7 mm s−1 in wheat. However, these values were found in root systems that were pruned to produce exceptionally high flow rates.

It is widely accepted that phloem sap flow is governed by the solute consumption of the terminal and axial sinks, if the sources are strong enough to meet the demand (Thompson 2006). Local volume flow would thus depend on phloem solute content, and sink consumption downstream. The local sap flow velocity would then be determined by the number and the diameters of the phloem conduits that conduct this volume of flow. It would seem fair to assume that in different species, the number and diameter of the phloem conduits in a cross section of stem show considerable variation. The balance between source strength and sink demand may be different as well. This could easily result in large differences in flow velocity. Then why are the average phloem flow velocities that have been measured so far this similar?

One might speculate that the phloem is scaled and regulated to maintain a constant and relatively slow flow of sap. A limitation of sap flow velocity may be necessary, for instance, because too fast a flow would threaten to dislocate parietal proteins or organelles that are present in the sieve tube (Ehlers, Knoblauch & van Bel 2000; van Bel 2003). It is also conceivable that the flow velocity should not be too low, or too variable. If the flow velocity would be too low, for instance, during times of low sink demand, then phloem-borne molecular signals might take too long to arrive at their destinations. When phloem sap flow would be highly variable, then the transit time of molecular signals might become unpredictable.

Diurnal variation in phloem and xylem flow rates

As would be expected, xylem flow in the four plant species exhibited clear diurnal dynamics with regard to flow profile, average volume flow and average linear velocity (Figs 8–10). Interestingly, the decrease in volume flow at night was not only associated with a decrease in average linear velocity, but, for all four plant species, also with a decrease in flow-conducting area (Fig. 10a). The reductions in flow-conducting area were surprisingly large, with values in the order of 50% for castor bean and tobacco, and in the order of 25% for poplar and tomato. It is well known that plant stems can exhibit a diurnal pattern of micro expansion and contraction. However, a contraction of the stem (and perhaps of xylem vessels) would be expected during the day when xylem pressures are most negative, not during the night. Therefore, the reduction in flow-conducting area must have been caused by a reduction in the number of flow-conducting xylem vessels. The slowest moving water will be found in the vessels with the smallest diameters. It is likely that, under conditions of low transpiration, the flowing water in these vessels will be the first to become indistinguishable from stationary water.

A number of early studies have demonstrated that phloem bulk transport is sensitive to changes in plant water status. Hall & Milburn (1973) observed that phloem exudation in castor bean decreased upon the application of water stress, and increased again when the water stress was lifted. Peel & Weatherly (1962) measured diurnal sap exudation rates in rooted willow cuttings. They measured an increase in sap exudation rate in the dark relative to that in the light, showing that not only water stress, but also more subtle differences in plant water status as caused by the differences in day/night transpiration rate, can cause differences in phloem exudation rate. Peuke et al. (2001), in contrast, did not find significant differences between day and night phloem flow velocities that were measured by means of NMR imaging in 35–40-day-old castor bean plants.

In our study, we did not observe significant differences between the day and night-time phloem volume flow rates in any of the plants (Fig. 7c). In tobacco, castor bean and tomato, the day and night flow profiles were virtually identical (Fig. 5), although in tomato the flow-conducting area, average flow velocity and volume flow appeared to be slightly (but not significantly) higher during the day than during the night period (Fig. 7). Poplar was the only plant in which the day- and night-time phloem flow profiles were clearly different (Fig. 5). At night, the phloem flow-conducting area was significantly larger than during the day (Fig. 7a). The increase in flow-conducting area at night was compensated for by a significant decrease in the average flow velocity, so that the resulting volume flow at night remained almost unchanged (Fig. 7c). The increase in flow-conducting area at night could have been caused by an increase in the number of flow-conducting sieve tubes, or by an increase in sieve tube diameter.

Trees exhibit a diurnal pattern of trunk shrinkage and expansion. Up to 50% of this diameter variation was shown to originate from shrinkage and swelling of phloem and bark tissue (Sevanto et al. 2002). In Monterey pine, measurements were taken on trees from which the bark was removed, leaving the phloem tissue uncovered. In this condition, the daily change in thickness of the phloem layer was about 6%. In castor bean, Kallarackal & Milburn (1985) reported that the diameter of stems decreased when phloem turgor was released, by cutting the phloem above or below the site where stem diameter was measured. These reports demonstrate that the phloem can expand and contract. However, in the current study we observed an increase of flow-conducting area in polar, from 2.6 mm2 during the day, to 3.8 mm2 at night. This is an increase of 46%. According to Thompson & Holbrook 2003, the change in cross-sectional sieve tube area as a function of pressure is given by the following formula:

  • image

Here, a is the flow-conducting area, p pressure, and ɛ the drained pore elastic modulus. If for sieve tubes an elastic modulus of 30 MPa is assumed (Holtta et al. 2006), then it can be calculated that for this large an increase in flow-conducting area, a change in turgor pressure of about 11 MPa would be required. This pressure difference is much larger than would normally be expected in the phloem. We conclude that the diurnal change in flow-conducting area cannot be explained by shrinkage and expansion of sieve tubes, but is probably caused by a change in the number of flow-conducting conduits.

It is known that diurnal sucrose export rates can vary considerably, within a diurnal cycle as well as between plants. Grodzinski, Jiao & Leonardos (1998) used 14CO2 labelling to determine the daytime carbon export rate in 21 plant species. At ambient CO2, they found linear relationships between photosynthesis, sugar synthesis and concurrent export. At high CO2, when more sugars were assimilated, the relationships between photosynthesis and export rate and between sugar synthesis and export rate were weaker, probably because the phloem export capacity became limiting and sugars and starch were accumulated in the leaf.

Grimmer & Komor (1999) measured the phloem transport rate in castor bean plants grown under elevated and normal CO2 conditions by pulse labelling with 11CO2. During the daytime period, they did not find a higher velocity or sucrose concentration in the sieve tube sap from plants under elevated CO2, despite the fact that these plants were assimilating more carbon. The carbon balance of the source leaves indicated that the carbon export rate during the day was the same for both CO2 conditions. However, large differences showed up at night. The carbon export rate of plants grown at normal CO2 declined to approximately half the daytime rate, while the carbon export in plants grown under elevated CO2 remained high. Furthermore, the plants grown at elevated CO2 accumulated starch in the leaves, in contrast to the plants grown at normal CO2. It was concluded, based on these and other findings, that the carbon export rate is at its upper limit in a series of ambient and experimental conditions (Komor 2000). Under ambient CO2 conditions, plants probably operate at or near their maximum carbon export rate during the day period, and at a lower carbon export rate at night, when the carbon pools in the leaf are slowly drained.

Because sucrose is the main osmoticum driving the phloem transport stream (van Bel 2003), the phloem bulk flow might be expected to slow down concurrently with a decline in carbon export at night. This, however, was not observed in the current study. The average phloem volume flows in poplar, castor bean, tomato and tobacco showed small fluctuations throughout the diurnal cycle (Fig. 6), but averaged over a whole day or night period the differences were insignificant (Fig. 7). In the current study, the carbon export rate and sucrose content of the phloem sap were not measured, so we cannot conclude directly that the phloem flow was kept constant independently from declining carbon export rates. However, our results correspond nicely with the observations published by Peuke et al. (2001), who found that sucrose concentrations in the phloem sap dropped by 12% at night, while the day/night phloem flow velocities remained unchanged.

These results support the idea that phloem flow velocities in plants are conservative in nature, and that phloem sap flow may be regulated to remain constant, regardless of changes in apoplastic water potential, source strength, or sink solute consumption. One might speculate that phloem volume flow is regulated by modulating phloem solute content. This might be achieved at the sources or the terminal sinks, but also along the length of transport phloem, where a rigorously regulated process of release and retrieval of photosynthates has been shown to take place (Aloni, Wyse & Griffith 1986; Minchin & Thorpe 1987; van Bel 2003).

Münch’s counterflow

Water flowing upward in the xylem is replenishing water that is used by three main processes: transpiration, growth and phloem mass flow. The latter process is known as recirculation (Pate et al. 1985) or Münch’s counterflow (Tanner & Beevers 1990, 2001) It is commonly estimated that the contribution of Münch’s counterflow to the total xylem volume flow in transpiring plants is very low or even negligible (e.g. Jeschke et al. 1996). Our results show that during the day the phloem to xylem ratios indeed were low (although not completely negligible), with a maximum value of 0.10 (10%) in tobacco. However, during the night, the phloem to xylem ratio increased to much higher values. In tobacco and castor bean values of 0.54 and 0.37 were measured, respectively, implying that in tobacco 54% and in castor bean 37% of the xylem bulk flow at night is actually generated and recirculated by the phloem.

The recycling of xylem water by way of the phloem was measured for the first time by Köckenberger et al. (1997), in a 6-day-old castor bean seedling grown in the dark. In the stem of the seedling, just below the cotyledons, they measured a xylem volume flow of 38 µL h−1 (5 µL h−1 of which was estimated to be used for growth) and a phloem volume flow of 17 µL h−1, giving a phloem to xylem ratio of 0.45 – a value close to the one we measured in a full-grown castor bean plant during the dark period (Fig. 11). This is surprising because a seedling, in contrast to a fully developed plant, is not expected to transpire much. In the seedling, the phloem to xylem ratio would therefore be expected to be higher than in the full-grown plant. Jeschke et al. (1996) modelled water flows in full-grown 44–53-day-old castor bean plants. They estimated that in a 9 d period, only 1% of water was transported downward in the phloem compared with the upward directed transpiration stream in the xylem. Even though no distinction was made between day- or night-time transport, this estimate appears to be too low.

Tanner & Beevers (2001) investigated the question whether transpiration is required to supply the plant with minerals. They did so by growing sunflower plants under two conditions, one where mineral nutrition was only supplied during the 12 h day period, and one where it was only supplied during the 12 h night period while the plants grew under conditions of near 100% relative humidity. They did not find any difference in the growth rates of both groups of plants, indicating that plants were able to take up and distribute minerals in the absence of transpiration. Making a number of assumptions, they estimated that the contribution of Münch’s counterflow would have been 400 mL out of a total volume of 1985 mL, giving a phloem to xylem ratio of 0.20. This estimate compares well with the phloem to xylem ratios that we present in the current paper.

We conclude that throughout the day, and especially at night, a significant percentage of xylem water is not transpired but recirculated by means of the phloem. Münch’s counterflow may thus play a significant role in maintaining xylem circulation at night.

ACKNOWLEDGMENTS

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES

We wish to thank the unknown reviewers of this manuscript for their suggestions and thoughtful reviews. We also thank Dr A. Peuke (University of Freiburg, chair of Tree Physiology) for critically reading the manuscript and providing us with small poplar plants and castor bean seeds, and Dr M. Bots (Department of Experimental Botany, University of Nijmegen) for providing the tobacco plants. This research was supported by the Dutch Technology Foundation (STW), Applied Science Division of the Netherlands Organization for Scientific Research (NWO), project number WBI 4803.

REFERENCES

  1. Top of page
  2. ABSTRACT
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. ACKNOWLEDGMENTS
  8. REFERENCES
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