Tree Physiology 34, 757–765
doi:10.1093/treephys/tpu051
Research paper
How significant is nocturnal sap flow?
Michael A. Forster1,2,3
1ICT
International, Armidale, New South Wales, Australia; 2School of Agriculture and Food Sciences, The University of Queensland, St Lucia, Queensland, Australia;
author ([email protected])
3Corresponding
Received January 12, 2014; accepted May 16, 2014; published online July 1, 2014; handling Editor Nathan Phillips
Nocturnal sap flow (Qn) has been found to occur across many taxa, seasons and biomes. There is no general understanding
as to how much Qn occurs and whether it is a significant contribution to total daily sap flow (Q). A synthesis of the literature
and unpublished data was made to determine how significant is Qn, as a proportion of Q (%Qn), across seasons, biomes,
phylogenetic groups and different thermometric sap flow methods. A total of 98 species were analysed to find that %Qn, on
average, was 12.03% with the highest average dataset of 69.00%. There was significantly less %Qn in winter than in other
temperate seasons, and significantly less %Qn in the wet season than in the dry season. The equatorial and tropical biomes
had significantly higher %Qn than the warm temperate and nemoral biomes. The heat ratio method (HRM) and the thermal
dissipation (TDP) method had significantly higher %Qn than the heat balance method. Additional analysis between HRM and
TDP found HRM to have significantly higher %Qn in winter, wet season and various biomes. In all but one out of 246 cases Qn
occurred, demonstrating that Qn is significant and needs to be carefully considered in sap flow and related studies.
Keywords: atmospheric evaporative demand, biome, hydraulic redistribution, night-time, nocturnal transpiration, vapour
pressure deficit.
Introduction
Nocturnal sap flow (Qn) is the night-time movement of fluid
within the sapwood of a plant’s root, stem or branch. Qn has
largely been regarded as insignificant and most sap flow
research has not unreasonably focused on day-time patterns.
Moreover, in earlier research it was believed that Qn should be
equal to zero. Thus, all sap flow values were ‘corrected’ for zero
Qn, which led to the underestimation of total flow (Daley and
Phillips 2006). Increasingly, there has been a greater awareness that nocturnal sap flow can in fact have a significant contribution to total daily sap flow (Dawson et al. 2007, Pfautsch
et al. 2011). To date, most research has been on demonstrating
that Qn does indeed occur, yet it is still not widely understood
how significant Qn is as a proportion of total daily Q.
The significance of Qn to total sap flow has been defined in a
number of ways: (i) as a percentage of maximum day-time rate
(e.g., Rosado et al. 2012); (ii) nocturnal rates as a ­percentage
of dry, day-time summer rates measured at noon (e.g., Dawson
et al. 2007); (iii) Qn as a proportion of diurnal sap flow (e.g.,
Pfautsch et al. 2011); and (iv) Qn as a proportion of total daily
sap flow (e.g., Phillips et al. 2010). As a percentage of maximum day-time rate, values >70% have been reported (Rosado
et al. 2012). Night-time rates of sap velocity were as high as
25% for a range of species (Dawson et al. 2007), and Qn as
a proportion of total daily flow has been reported as high as
39% in Quercus ilex (Barbeta et al. 2012). These examples
highlight that nocturnal sap flow is non-trivial and needs to be
explicitly considered in any water-use study.
Causally, Qn has been related to either nocturnal stomatal conductance or stem refilling. For example, Qn has been positively
correlated with environmental driving factors such as vapour
pressure deficit (VPD; Bucci et al. 2004, Dawson et al. 2007,
Mitchell et al. 2009, Zeppel et al. 2010, Pfautsch et al. 2011,
Rosado et al. 2012, Doronila and Forster 2014), or a combination of VPD and wind speed (Green et al. 1989, Benyon 1999,
Daley and Phillips 2006, Phillips et al. 2010). Such observations led to the suggestion that Qn is a passive process where
© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected]
758 Forster
stomatal opening is responding to environmental driving factors and thus causing Qn (Dawson et al. 2007). Rosado et al.
(2012), however, contended that there can be significant stomatal control over Qn. Qn has been correlated with stem refilling
(Daley and Phillips 2006), particularly in drought-stressed trees
where stomata have been demonstrated to be closed at night
(Caspari et al. 1993, Pfautsch and Adams 2012).
Regardless of the causal force, Qn is now recognized to
be important to overall plant function with significant consequences. For example, a functional approach found that Qn
was related to higher leaf nitrogen concentration, growth and
shade intolerance (Marks and Lechowicz 2007). Qn has been
studied within an elevated atmospheric CO2 context (Zeppel
et al. 2011) and has also been causally correlated with circadian rhythms (Resco de Dios et al. 2013). Qn is known to
affect disequilibrium in the pre-dawn soil–plant water potential relationship, which has consequences for field methodologies measuring drought stress in plants (Donovan et al. 1999,
2001, 2003, Kavanagh et al. 2007). Ecosystem water use, as
measured by the eddy flux technique, is now known to have
errors due to the inability to accurately measure nocturnal
water use (Goulden et al. 1996, Fisher et al. 2007, Moore et al.
2008). Due to the growing acceptance that Qn is significant,
a synthesis of data to date is timely. However, all research to
date has focused on empirical studies of one or a few cooccurring species at a single location, typically over a season
or two of measurements. These data have improved knowledge of Qn; however, their conclusions are limited. Therefore, a
synthesis of data from across numerous species at numerous
locations around the globe is needed.
Sap flow is measured in plants (primarily woody stemmed
species) via thermometric methods (Smith and Allen 1996,
Vandegehuchte and Steppe 2013). Each method has advantages and limitations, and to date no one technique has been
found to be universal (Smith and Allen 1996, Vandegehuchte
and Steppe 2013). One major limitation of various thermometric methods, besides the heat ratio method (HRM) and
the heat field deformation (HFD) method, is the inability, or
unreliability, of measuring low or reverse rates of sap movement (Burgess et al. 2001). The ability to accurately measure
low and reverse sap flow is critically important in studies of
Qn where low rates of flow are a given (Daley and Phillips
2006). Additionally, studies of hydraulic redistribution in
stems have found that, during the night, sap flow can actually travel downwards along the trunk or roots (Burgess and
Bleby 2006, Nadezhdina et al. 2009, Bleby et al. 2010) or
as foliar uptake from the atmosphere that can then rehydrate
stems and soils (Eller et al. 2013). Not all sap flow methods
can measure reverse sap flow, and those techniques that can
measure reverse flow may, on average, have a lower Qn, but
this hypothesis has yet to be tested. Therefore, it is unknown
whether reported patterns of Q, and the resulting conclusion
Tree Physiology Volume 34, 2014
on how significant is Qn, are influenced by the chosen method
of measurement.
With current technology, it is only practical to determine
sap flow behaviour in trees and shrubs. Measuring sap flow
in plants such as herbs and grasses is difficult. However,
lysimetry and porometry studies suggest that nocturnal
behaviour in shrubs, herbs and grasses is in a similar range
to trees. For example, night-time transpiration was 8% in wellwatered wheat (Rawson and Clarke 1988), between 3 and
10.8% in tomato (Caird et al. 2007a) and as high as 44.3%
in Arabidopsis (Christman et al. 2009). Nocturnal stomatal
conductance can be just as high in herbaceous dicots and
shrubs as deciduous trees, yet in perennial grasses is lower
(Snyder et al. 2003, Caird et al. 2007b). A plant’s photosynthetic anatomy may also affect Qn. For example, Crassulacean
acid metabolism (CAM) plants photosynthesize at night with
open stomata (Neales et al. 1968) and, presumably, nocturnal
sap flow rates. Species with the ability to switch between C3
and CAM photosynthetic pathways, such as Clusia minor, show
high nocturnal sap flow when in the CAM mode (Herrera et al.
2008).
This study provides general patterns in Qn at a temporal
and spatial macro scale. This study explicitly focuses only on
sap flow at night and does not include other measurements
of nocturnal water movement, such as stomatal conductance,
eddy flux, lysimeters or Bowen ratio. Although these measurement techniques provide important insights into nocturnal plant
water use, there has, to date, not been a summary of the large
amount of data from sap flow measurements. The published
literature and other datasets were compiled to summarize and
compare global patterns of Qn, as a proportion of total daily sap
flow (%Qn), across seasons, biomes and phylogenetic groups.
Several hypotheses were tested based on the premise that
%Qn, on a macro scale, is primarily driven by atmospheric evaporative demand. Specifically, %Qn will be significantly higher in
summer than in other temperate seasons, dry season %Qn will
be higher than that in the wet season and continental and equatorial biomes will have significantly higher %Qn than temperate
biomes.
Materials and methods
Data were compiled in four ways: database search, cited literature, direct contact with authors and researchers, and
unpublished data from the author’s own research. The Web
of Science database was accessed in early July 2013, and the
following search terms were queried: ‘sap flow AND nocturnal’ and ‘sap flow AND night time’. A total of 110 results were
found from both searches, of which 77 were unique. Within
these research papers, the cited literature was searched to
find any additional papers that were missed from the database
search. Conference proceedings were also included in the
Nocturnal sap flow 759
literature search. Although there are many research projects
that measured nocturnal sap flow, this study was limited to
those published records and research projects that explicitly
studied and reported nocturnal sap flow (see Table S1 available as Supplementary Data at Tree Physiology Online). Qn data
from published sources were taken from the text, table or figures. If not explicitly provided, data were mined where possible from figures. If neither of these options were possible,
the ­corresponding author was contacted directly for access to
original data.
Additional data were provided through contacts known to
be researching Qn or through the author’s own unpublished
data (see Table S1 available as Supplementary Data at Tree
Physiology Online). In cases where original datasets were provided, night-time was defined as periods where solar radiation
was <2 W m−2. If solar radiation data were not provided, sunset
and sunrise times were found for the location of the measured
plant. A conservative approach was taken where night-time
was defined as 1 h after sunset to 1 h before sunrise. All Qn
data were only taken from 24-h periods without rain following
Pfautsch and Adams (2012). Additionally, only species with a
C3 photosynthetic pathway were considered.
The primary dependent variable in this study was Qn as a
proportion of total daily sap flow (%Qn). Temperate and tropical seasons were defined by the source author or in the published study. Where it was unclear or not explicitly mentioned,
a season was not assigned to the data. Where original datasets
were provided, at least 3 weeks of data in the middle of each
season were analysed. Definition of seasons is provided in
Table 1. Biome was defined following Walter’s scheme (Walter
and Box 1976) as these biomes are delineated by atmospheric
evaporative demand. Biome definitions are detailed in Table 1.
Only three records from a boreal biome were available and
came from the study by Hogg and Hurdle (1997). These data
were merged with the nemoral biome for statistical purposes
as the study site described by Hogg and Hurdle (1997) is
similar in definition to the nemoral biome definition of Walter’s
scheme. A phylogenetic group was defined as the occurrence
of five or more records, from a summer or dry season period,
of a single or group of species in a common genus. Sap flow
methods that were used in the studies are defined in Table 1.
Data analysis
When three or more groups were compared, Hochberg’s GT2
method (Hochberg 1974) was employed due to uneven sample
sizes (Sokal and Rohlf 2001). Comparison of two groups with
sample sizes >20 was carried out with a two-tailed Student’s
t-test with Satterthwaite’s method for uneven variances (Sokal
and Rohlf 2001). Comparison of two groups with sample sizes
<20 was performed with the non-parametric Kolmogorov–
Smirnov test (Sokal and Rohlf 2001). Groups were checked for
normality and were log or log + 1 transformed where necessary.
Table 1. ​Outline of the seasons, biomes and sap flow methods used in
the studies. In published sources, seasons were defined by the author
and unpublished data were defined by definitions in this table. Where
it was unclear data were not assigned to a season. Biomes are derived
from Walter’s scheme (Walter and Box 1976). References for sap
flow methods are as follows. CHPM: Swanson and Whitfield (1981);
HB: Sakuratani (1981), Čermák et al. (2004); HFD: Nadezhdina et al.
(1998, 2012); HRM: Burgess et al. (2001); and TDP: Granier (1985,
1987). General descriptions of the methods are also provided in Smith
and Allen (1996) and Vandegehuchte and Steppe (2013).
Group
Season
​ ​Spring
​ ​Summer
​ ​Autumn
​ ​Winter
​ ​Dry season
​ ​Wet season
Biome
​ ​Equatorial
Definition
Northern hemisphere: March, April, May
Southern hemisphere: September, October,
November
Northern hemisphere: June, July, August
Southern hemisphere: December, January,
February
Northern hemisphere: September, October,
November
Southern hemisphere: March, April, May
Northern hemisphere: December, January,
February
Southern hemisphere: June, July, August
Variable, defined by study author or data owner
Variable, defined by study author or data owner
Lacking seasonality; high rainfall; e.g., tropical
rainforest
​ ​Tropical
Distinct wet/dry season; high rainfall; e.g.,
savannah
​ ​Continental
Hot summer, cold winter; low rainfall; e.g., arid
​ ​Mediterranean
Hot, dry summer; cold, wet winter; e.g.,
chaparral, mallee
​ ​Warm temperate Warm to hot summer, cold winter; equitable
rainfall; e.g., temperate evergreen forest
​ ​Nemoral
Warm summer, freezing winter with snow; e.g.,
temperate deciduous forest
Sap flow method
​ ​CHPM
Compensation heat pulse method
​ ​HB
Heat balance method, includes stem HB and
trunk segment HB methods
​ ​HFD
Heat field deformation method
​ ​HRM
Heat ratio method
​ ​TDP
Thermal dissipation probes, also known as heat
dissipation, thermal dissipation or the Granier
technique
Results
A total of 98 species and 246 datasets were found for data
analysis (see Table S1 available as Supplementary Data at Tree
Physiology Online). Nine of the 246 datasets were data from a
single 24-h period, whereas all the other datasets were from
three or more 24-h periods. Data were recorded from all continents except Africa and Antarctica.
When all data were considered, %Qn was 12.03%. There was
only one record of a plant showing zero %Qn over a measurement period. The highest average value over a measurement
Tree Physiology Online at http://www.treephys.oxfordjournals.org
760 Forster
period was 69.00%. Figure 1a shows the frequency distribution of data and Figure 1b is a box plot of descriptive statistics.
Figure 1 shows that most %Qn data are <20% and there are
several outliers. Table 2 summarizes the mean, maximum and
standard deviation for seasons, biomes, phylogenetic groups
and sap flow methods.
There was only a significant difference between autumn
and winter seasons (Figure 2) where autumn had significantly higher %Qn than winter (mean significant difference
(MSD) = −0.0522, P < 0.05). The dry season also had significantly higher %Qn than the wet season (t = 3.739, df = 48,
P < 0.001).
The equatorial biome had significantly higher %Qn than
the Mediterranean (MSD = −0.0403, P < 0.05), warm temperate (MSD = −0.1867, P < 0.05) and nemoral biomes
(MSD = −0.0990, P < 0.05). The tropical biome had significantly
higher %Qn than the warm temperate (MSD = −0.1525, P < 0.05)
and nemoral biomes (MSD = −0.0641, P < 0.05). Figure 3
shows that there were no further significant differences between
biomes.
The phylogenetic group of Picea had significantly higher
%Qn than the group Acer (MSD = −0.0837, P < 0.05). Figure 4
shows that there were no further significant differences
between genera.
The HRM and the thermal dissipation (TDP) method had
significantly higher %Qn than the heat balance (HB) method
(MSD = −0.1045, P < 0.05; and MSD = −0.0693, P < 0.05).
There were no other significant differences (Figure 5). Figure
6a shows the mean and 95% confidence interval (CI) for the
comparison in the summer season between HB, HRM and
TDP. The HRM and TDP had significantly higher %Qn than HB
(MSD = −0.1193, P < 0.05; and MSD = −0.0035, P < 0.05).
Figure 6b shows the mean and 95% CI for the comparison in
the nemoral biome between HB, HRM and TDP. The HRM and
TDP had significantly higher %Qn than HB (MSD = −0.1744,
P < 0.05; and MSD = −0.1874, P < 0.05).
Figure 6c summarizes data for various seasons in a comparison between HRM and TDP. The HRM was significantly
higher in %Qn than TDP in winter (D = 0.600, P = 0.047)
but there was no significant difference in spring (D = 0.209,
P = 0.893) or autumn (D = 0.333, P = 0.469). The HRM
also had significantly higher %Qn than TDP during the wet
season (Figure 6c; D = 0.717, P = 0.025) but not the dry
season (D = 0.333, P = 0.328). Figure 6d shows that HRM
Table 2. ​Summary statistics for the groups that were later compared
in the statistical analyses in this study. %Qn, nocturnal sap flow as a
proportion of total daily sap flow; n, number of samples; maximum,
the maximum average value record from a study; SD, sample standard
deviation. See Table 1 for definitions of biome and sap flow method
groups.
All data
Seasons
​ ​Spring
​ ​Summer
​ ​Autumn
​ ​Winter
​ ​Dry season
​ ​Wet season
Biome
​ ​Equatorial
​ ​Tropical
​ ​Continental
​ ​Mediterranean
​ ​Warm temperate
​ ​Nemoral
Genus
​ ​Acacia
​ ​Acer
​ ​Betula
​ ​Eucalyptus
​ ​Picea
​ ​Quercus
Sap flow method
​ ​CHPM
​ ​HB
​ ​HFD
​ ​HRM
​ ​TDP
%Qn
n
Maximum
SD
12.03
246
69.00
9.33
10.25
8.78
11.66
7.56
18.84
12.77
30
86
29
20
32
22
18.35
29.70
25.37
20.71
34.00
22.00
4.73
5.72
5.74
6.21
6.14
5.51
29.10
16.54
15.25
10.56
7.60
10.53
14
49
7
25
77
73
69.00
34.00
27.56
22.60
18.80
29.70
23.18
6.78
6.11
5.99
3.97
6.43
14.96
4.11
12.11
10.27
14.60
7.89
5
8
8
28
9
7
26.12
9.14
29.70
33.27
19.44
17.40
6.94
2.36
9.02
6.85
4.23
4.98
9.41
8.17
13.38
13.05
12.66
15
33
7
83
107
22.00
29.70
19.19
34.00
69.00
5.49
7.80
4.32
6.72
11.66
Figure 1. ​Descriptive statistics for all of the %Qn data collated in this study. (a) Frequency distribution (n, number of observations) of %Qn; (b) a
boxplot of median, 25th and 75th percentiles, error bars indicating the 10th and 90th percentiles, and outliers.
Tree Physiology Volume 34, 2014
Nocturnal sap flow 761
Figure 2. ​Mean and 95% CIs for (a) temperate seasons and (b) tropical seasons. Overlapping CIs indicate non-significant relationships according
to the GT2 statistical method.
Figure 3. ​Mean and 95% CIs for various biomes recorded in this
study. Overlapping CIs indicate non-significant relationships according to the GT2 statistical method. A detailed description of biomes is
provided in Table 1.
Figure 5. ​Mean and 95% CIs for common thermometric sap flow methods. Overlapping CIs indicate non-significant relationships according
to the GT2 statistical method. A detailed description of sap flow methods is provided in Table 1.
was significantly higher than TDP in the tropical (t = 2.015,
df = 41, P = 0.050) and warm temperate biomes (t = 3.711,
df = 29, P = 0.001).
Discussion
Figure 4. ​​Mean and 95% CIs for various phylogenetic groups recorded
in this study. Overlapping CIs indicate non-significant relationships
according to the GT2 statistical method.
According to this study, Qn occurs in all species and on most
nights. For Qn not to occur is exceptional. Out of 246 data
records, there was only one instance where there was no Qn,
and this was due to the method employed to measure sap
flow (see below). There is now little doubt that Qn contributes
significantly to Q and therefore it is critically important that it is
measured. In studies that aim to calculate hydrological budgets
of individual plants, plots, communities or catchments, and for
modelling purposes, it is vitally important that Qn is accurately
quantified and considered.
Atmospheric evaporative demand appears to be a significant
factor driving Qn. Previous studies have quantified this by finding a significant correlation between VPD, or a combination
of VPD and wind speed, with Qn (Green et al. 1989, Benyon
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762 Forster
Figure 6. ​Mean and 95% CIs for analyses of the HB, HRM and TDP sap flow methods. (a and b) HB, HRM and TDP were only compared within summer and nemoral biomes. (c) HRM vs TDP within seasons and (d) biomes (which had sufficient sample size) only. See text for statistical significance.
1999, Dawson et al. 2007, Moore et al. 2008, Mitchell et al.
2009, Phillips et al. 2010, Zeppel et al. 2010, Buckley et al.
2011, Pfautsch et al. 2011, Rosado et al. 2012, Doronila and
Forster 2014). This assumption was supported here with %Qn
being significantly higher in the dry season over the wet season, and significantly less in more temperate biomes. The trend
among temperate seasons was not as clear, with %Qn only
being significantly higher in autumn over winter. Why summer
did not have significantly higher %Qn than the other seasons
is not apparent. One possibility for this result may be that the
length of the night-time period during autumn is longer than
summer and therefore there is more time for Qn to accumulate.
Nevertheless, in the tropics, where day length is approximately
the same all year round, there was a significantly higher %Qn
during the dry season when evaporative demand is generally
higher (Specht and Specht 1999). The equatorial and tropical biomes also had the highest %Qn, which possibly lends
further support to the hypothesis that atmospheric evaporative
demand is an important causal factor driving Qn. This finding
may have significant ramifications for plant physiology given
climate change scenarios. For example, it is predicted that
there will be an increase in atmospheric evaporative demand
and greater aridity over the 21st century (Dai 2011). Increased
atmospheric evaporative demand has been linked to an
increase in tree and forest mortality (Eamus et al. 2013). How
an increase in night-time evaporative demand impacts on plant
health is not well understood, but given that this study found
that %Qn can be as high as 69% it is possibly significant. One
approach to determine the impact that an increase in evaporative demand can have on species is to determine their optimal
Tree Physiology Volume 34, 2014
VPD for sap flow (Doronila and Forster 2014). Species that have
a higher optimal VPD for sap flow may be far better equipped to
survive if climate change scenarios ­eventuate.
There are differences in %Qn depending on the chosen thermometric sap flow method. This study was not an explicit test of
the accuracy of sap flow methods to measure Qn; rather it highlights that there is a difference between methods. It has been
appreciated for some time that the CHPM will underestimate Qn
(Becker 1998, Burgess et al. 2000, Bleby et al. 2004). %Qn for
CHPM was not significantly different from the other techniques
in this study; however, this was due to the small sample size
in the statistical analysis. There was only one instance where
an entire dataset in a study showed zero %Qn, and this was
measured with CHPM during a winter period. An HRM sensor on
the same tree over the same period showed 4.62%Qn, lending
support to the fact that CHPM is problematic at low rates of sap
flow. Caution should also be taken in regard to the HFD results
due to low sample size in this study, although the HFD technique is reported to be accurate at low flows (Vandegehuchte
and Steppe 2013).
The HB technique showed significantly lower %Qn than the
HRM and TDP techniques. The data from this study could not
explicitly determine why night-time sap flow was lower when
measured with the HB technique. If %Qn is significantly lower,
then the measurement of either day- or night-time sap flow
would relatively bias one technique over another. That is, if daytime values are considered high flow and night-time as low or
reverse flow, then a certain technique may be more accurate
at measuring high vs low or reverse flow. Due to issues involving thermal disequilibrium within HB sensors and the heated
Nocturnal sap flow 763
stem segment (which assume measurements are conducted
under thermal equilibrium), there can be an overestimation of
sap flow at high flow (Grime and Sinclair 1999) and as much
as 150% error at low flow (Steppe et al. 2005). The HRM technique is reported to be very accurate at low and moderate flows
(Vandegehuchte and Steppe 2013); however, under certain
circumstances related to poor installation of sensor needles, it
is inaccurate at extremely high flows (Bleby et al. 2008). The
accurate measurements of low flow, coupled with measurements of a ‘midday depression’, may lead to overall measurements showing higher night-time values relative to day-time
values. However, in this study all of the datasets derived from
the HRM technique did not show a midday depression [only
one from Resco de Dios et al. (2013) had a midday depression
(personal communication with authors) and this dataset was
not included in the analysis]. Therefore, based on the available
evidence, HRM measured day- and night-time sap flow accurately. A laboratory and field assessment of the TDP technique
found large errors at low flows, most likely associated with calibration issues and the correct measurement of zero flow conditions (Steppe et al. 2010).
A significant feature of nocturnal sap flow is the movement
of sap in a direction that is not from soil, plant and atmosphere.
Known as reverse flow, hydraulic lift or hydraulic redistribution,
its measurement is becoming more common across many different species and biomes (e.g., Brooks et al. 2002, Scholz
et al. 2002, Oliveira et al. 2005, Nadezhdina et al. 2009,
2010, David et al. 2013). It is critical that any study explicitly examining nocturnal sap flow, or any study that aims to
accurately characterize vegetation water use, has the ability
to measure reverse and even radial movement of sap flow. To
this end, measurement techniques should have the ability to
measure reverse and positive sap movement, and have multiple measurement points along the radial profile (Čermák and
Nadezhdina 1998, Burgess et al. 2000, Nadezhdina et al.
2002, Burgess and Bleby 2006).
The HB, HRM and TDP techniques all require a period of
zero flow in order to calculate sap flow from raw heat velocity or input data (Smith and Allen 1996, Grime and Sinclair
1999, Vandegehuchte and Steppe 2013). In practice, zero flow
is assumed to occur at night or at some designated period
during the night such as 4 am (Regalado and Ritter 2007).
As seen in this study, nocturnal sap flow occurs in all species and on most nights. Therefore, there cannot be an a priori
assumption that night-time represents zero flow and therefore
this period can be used for the correct calculation of sap flow
data. The only way to determine true zero flow conditions is
by cutting the stem below and above the measurement zone
(Vandegehuchte and Steppe 2013). This appears to be a common method in studies employing HRM but less so with HB
and TDP. True zero flow is determined in the HFD method by
K-value without any cuttings (Nadezhdina et al. 2012). In some
instances, it is not possible to cut the sample plant, for example in studies in national reserves or heritage trees. Zero flow
in these instances is determined via the ‘saturation technique’
where a night-time, or preferably several subsequent nights,
of near-zero VPD (atmospheric saturation) and a saturated
soil profile is needed (e.g., Pfautsch et al. 2011). Although the
saturation technique is not without flaws, it is a much better
approach than just assuming zero flow occurs at night.
Conclusion
Qn is common and widespread in species from all biomes and
seasons. On average, ~10% of total daily sap flow occurs during the night. At a large spatial and temporal scale, Qn is possibly
being driven by atmospheric evaporative demand, but this conclusion is not definitive. Phylogenetic independent contrasts are
needed to determine the cause and evolutionary significance of
Qn. Results from various studies differ according to the technology employed to measure sap flow. Although this study did not
determine the accuracy of various techniques, the HRM technique
measures significantly higher %Qn than the HB and TDP techniques. The causes for this difference need further investigation.
Supplementary data
Supplementary data are available at Tree Physiology online.
Acknowledgments
The author thanks Mila Bristow, Brooke Howell, Kathleen Kavanagh,
Álvaro López-Bernal, Christian Marks, Sebastian Pfautsch, Victor
Resco de Dios, Emma Steggles and Jie Li for access to original or
unpublished data. He also thanks Naděžda Naděždina, Christian
Marks and two anonymous reviewers for very helpful comments.
Conflict of interest
The author is an employee of ICT International, a manufacturer
of sap flow instruments.
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