Introduction
The world's forests are currently a large carbon sink (Pan et al., 2011),
helping curtail the rising of atmospheric CO2 concentrations due to
anthropogenic emissions. This present carbon sink, however, is vulnerable to
factors that can alter forest structure and function. Among such factors,
tree mortality influenced by drought has been increasingly of concern (Allen
et al., 2010). In many regions of the world, drought-influenced tree
mortality is believed to be accelerating (Peng et al., 2011; van Mantgem et
al., 2011). Whether this acceleration is related to global and regional
climate warming caused by fossil CO2 emissions is uncertain. However,
climate warming is expected to lead to a drier land surface (Sherwood and Fu,
2014) and faster developing pace and elevated intensity of drought even if it
may not directly result in a higher frequency of drought (Trenberth et al.,
2014; Cook et al., 2015). Thus, understanding and being able to predict
drought-influenced tree mortality are essential to assessing the
sustainability of the world's forests as a carbon sink if the climate system
warms as expected (Xu et al., 2013; McDowell et al., 2013a).
It is a challenge to understand and predict tree mortality in general and
drought-influenced tree mortality in particular. Drought may result in or
contribute to tree death in multiple ways. Total hydraulic disconnection
(catastrophic xylem cavitation causing complete loss of hydraulic
conductivity and leading to rapid shoot desiccation and death) is not
uncommon in shallowly rooted seedlings (e.g., Williams et al., 1997).
Although such complete hydraulic dysfunction is difficult to demonstrate in
mature plants, partial loss of hydraulic function and carbon starvation
(stomatal closure to avoid hydraulic disconnection and protect the
photosynthetic machinery resulting in reduced photosynthesis and negative
plant carbon balance) have also been advanced as agents of tree death from
drought (McDowell et al., 2008; Hoffmann et al., 2011; Anderegg et al., 2013,
2014; Nardini et al., 2013). These different mechanisms do not necessarily
operate independently (McDowell, 2011; McDowell et al., 2013b; Sevanto et al.,
2014). Furthermore, they might not be the proximate cause of death; for example,
they may simply weaken the trees by decreasing or preventing defensive
chemical production and thus predispose the trees to attacks by insects,
pathogens and fires which eventually cause death (Waring, 1987; McDowell,
2011; Pratt et al., 2014). Additionally, the literature in this area is
complicated by recent reports that techniques of hydraulic conductivity
measurement in many previous studies overestimate xylem cavitation and
increasingly so as xylem tension increases (e.g., Wheeler et al., 2013;
Rockwell et al., 2014; Cochard et al., 2015), effects which may provide
erroneous vulnerability curves and spurious evidence of xylem refilling.
Hence, other approaches relating drought-related mortality to plant water
relations may be useful.
Maintenance of plant function and short-term viability under meteorological
drought is determined by many traits, including the capacities for
restriction of water loss from shoots, efficient water transport from roots
to shoots, robust root system architecture and capacity, as well as plant
capacity to tolerate dehydration (Kozlowski and Pallardy, 2002). Drought-tolerant tree species generally possess some nexus of traits that promotes
continued carbon fixation as a drought progresses (e.g., access to deep soil
water supplies, osmotic adjustment, delayed stomatal closure, a robust
photosynthetic apparatus, maintenance of xylem function) (Hinckley et al.,
1981; Bahari et al., 1985; Abrams, 1990; Kubiske and Abrams, 1994) but at
the same time confers sufficient cellular dehydration tolerance capacity to
survive substantial water deficits (Ludlow, 1989; Martin et al., 1987).
Investigating how species with varying expression of these traits survive
over the mid to long term under natural cycles of drought, and developing
potential predictors of performance at such timescales, would be valuable in
deepening understanding of the nature of drought tolerance and modeling of
mortality under drought.
We have been monitoring the mortality of tree species at the Missouri Ozark
AmeriFlux (MOFLUX) site since 2005 and predawn leaf water potential (ψpd) since 2004 (Gu et al., 2006, 2007; Yang et al., 2009). The
different tree species monitored at the MOFLUX site exhibited a range of
drought tolerance (see the next sections). During our study period, a wide
range of precipitation regimes from abundant rain to extreme drought occurred
at the MOFLUX site, resulting in large inter-annual fluctuations in plant
water stress levels and associated tree mortality (see the next sections). In
particular, several drought events with varying drought intensity occurred
during the study period. The 2012 drought was the strongest category D4
(exceptional drought), according to the US Drought Monitor Classification
Scheme and offered a contrast to earlier, less severe droughts. The
combination of species diversity, relatively long mortality time series, and
broad range of precipitation regimes provided a rare opportunity to
investigate how drought and precipitation variability affect tree mortality,
how such impacts may be related to the physiological adaptations and drought
tolerance, and how drought-influenced tree mortality can be predicted
effectively. We explore these issues in the present paper.
Materials and methods
Study site
The study was conducted at the MOFLUX site. The
MOFLUX site, which has been operating since 2004 with a suite of
meteorological and ecological instrumentation (Gu et al., 2006, 2007; Yang et
al., 2010), is located in the University of Missouri's Baskett Wildlife
Research and Education Area (BWREA, lat. 38∘44′39′′ N, long.
92∘12′ W). BWREA is within the Ozark border region of central
Missouri. Second-growth upland oak-hickory forests constitute the major
vegetation type at the BWREA (Rochow, 1972; Pallardy et al., 1988). Major
tree species include white oak (Quercus alba L.), black oak
(Q. velutina Lam.), shagbark hickory (Carya ovata (Mill.)
K. Koch), sugar maple (Acer saccharum Marsh.), and eastern redcedar
(Juniperus virginiana L.). Although these species co-occur in MOFLUX
forests, there are differences in which species dominate in particular
locations. Ecological and physiological studies of these species (e.g.,
Fralish et al., 1978; Hinckley et al., 1981; Ginter-Whitehouse et al., 1982;
McCarthy et al., 1984; Bahari et al., 1985; Martin et al., 1985; Ni and
Pallardy 1991, 1992; Pallardy and Rhoads, 1993; Loewenstein and Pallardy,
1998; Abrams, 2003) have shown that white and black oaks and eastern redcedar
are more dominant than shagbark hickory and sugar maple in drier habitats and
exhibit adaptations promoting better function under drought conditions (e.g.,
deeper rooting, lower osmotic potentials, greater osmotic adjustment under
drought, maintenance of leaf conductance and photosynthesis to lower leaf
water potentials, greater leaf dehydration tolerance capacity). White ash
(Fraxinus americana L.) is found as a scattered tree in MOFLUX
forests and has not been studied as extensively but has been reported to
favor sites with high soil water availability (e,g., McCarthy et al., 1984;
Schlesinger, 1990; Woodcock et al., 1992).
The climate of the area is warm, humid, and continental (Critchfield, 1966),
with monthly mean temperature of -1.3 ∘C in January and
25.2 ∘C in July and an annual total precipitation average of
1083 mm (National Climatic Data Center 1981-2010 climate normals,
Columbia Regional Airport, Missouri, about 10 km to the northwest). Dominant
soils at the site are Weller silt loam (Fine, smectitic, mesic Aquertic
Chromic Hapludalf) and Clinkenbeard very flaggy clay loam (Clayey-skeletal,
mixed, superactive, mesic Typic Argiudoll) (Young et al., 2001). The
comparatively thin soils of these oak-hickory forests often exacerbate plant
water stress when droughts occur (Bahari et al., 1985; Jenkins and Pallardy,
1995).
Measurements
Meteorological measurements
Measurements of precipitation, temperature and relative humidity were made at
the top of the 30 m flux tower and used to formulate potential
meteorologically based predictors for tree mortality. Precipitation was
measured with a recording tipping bucket rain gauge (TR-525M, Texas
Electronics, USA). Data were totaled over 30 min periods. Atmospheric vapor
pressure deficit (VPD) was computed from temperature and relative humidity.
At the MOFLUX site, routine meteorological measurements are made with plenty
of redundant sensors to minimize the risk of measurement gaps.
Tree mortality monitoring
Individual trees with diameter at breast height (DBH, 1.3 m height) larger
than 9 cm were inventoried when the site was established in 2003–2004. The
inventory used 24 circular plots, each with a size of 0.08 ha. These plots
were placed at 50 m intervals away from the site flux tower along the
southeast (n= 5 plots), south (n= 5), southwest (n= 5), west
(n= 5) and northwest (n= 4) transects. The last transect had only 4
plots as it terminated in a pond. On each plot, living trees were identified
with a numbered aluminum tag, identified to species and measured for DBH. In
each subsequent year, death of tagged trees was noted and recorded during
frequent (at least monthly, during the growing season) visits to all plots.
Predawn leaf water potential (ψpd)
Since early June of 2004, measurements of ψpd have been made
periodically (weekly to biweekly) during the growing seasons. In each year,
except for 2004, the first measurements occurred in mid-May. In all years,
the last measurements took place in late October. Leaf samples were collected
before dawn for canopy and sapling individuals of common tree species at the
site. A total of 20–21 samples were obtained each day with 6–7 taken from
Quercus alba, and the rest, with at least two samples per species,
distributed among Q. velutina, Acer saccharum,
Carya ovata, Fraxinus americana L. (white ash), and
Juniperus virginiana, roughly in proportion to their relative stem
abundance in the stand. ψpd was measured with a pressure
chamber (Turner, 1981; Pallardy et al., 1991). Leaves or leaflets (both oak
species, shagbark hickory and white ash) or shoots (sugar maple and eastern
redcedar) were sampled from lower branches (< 2 m height) thus rendering
any gravitational component minimal. After excision with a razor blade,
samples were immediately placed in humidified bags in a chest cooler until
measurement promptly after sample collection was complete.
Potential predictors for tree mortality
Successful approaches to predicting drought-influenced tree mortality remain
elusive in the present and it is beneficial to explore a wide array of
possibilities (Hoffmann et al., 2011; Nardini et al., 2013). To take
advantage of our relatively long mortality data set and a broad range of
meteorological and physiological measurements, we examined a suite of
potential predictors for tree mortality. These potential predictors included
mean daily precipitation rate p¯, predawn leaf water potential
integral (PLWPI), precipitation variability index (PVI), mean effective
precipitation interval (MEPI), positive temperature anomaly integral (PTAI),
and water vapor pressure deficit integral (VPDI). Their definitions and the
rationales for applying them to study tree mortality are described below. Our
objective was not about determining which index was the best; our mortality
data set, although already rare, was still not sufficient for such a task.
Rather, we were interested in the potential of these indices as predictors of
tree mortality.
Predawn leaf water potential integral (PLWPI)
PLWPI is the area between the seasonal curve of the measured
ψpd and the zero ψpd line (Myers, 1988). The
seasonal curve is formed by linearly interpolating ψpd
measurements to days when no measurements are made so that every day in the
growing season has either a measured or interpolated ψpd. Thus
PLWPI is simply the summation of ψpd over the growing season:
PLWPI=∑i=1nψpdi, where
ψpdi is the ψpd of the ith day
in the growing season. Myers (1988) showed that PLWPI provides a link between
short-term water stress and long-term plant growth. Hanson et al. (2003) used
the soil water potential integral, which is correlated with PLWPI, to indicate
forest water stress. PLWPI is a direct, integrative measure of plant water
stress over the growing season and can be used as a relative indicator of the
overnight capacity of a plant to rehydrate leaves from soil water. PLWPI
provides an estimate similar to the “static” component of water stress
described by Tyree and Sperry (1988). Species with superior water acquisition
capacity should have relatively high (less negative) PLWPI. Hence, species
with deeper root distributions should have higher ψpd and
PLWPI than co-occurring species with shallower roots because surface soil
layers dry first. Furthermore, persistent significant loss of stem hydraulic
conductivity, if it happens, should be captured in ψpd and
PLWPI because such appreciable loss of conductivity will delay and/or prevent
overnight recovery in leaf water potential of the shoot. Although Tyree and
Sperry (1988) included this latter source of stress in the “dynamic”
component, in the case of PLWPI it is probably best considered as a component
of the “static” contribution because it contributes to lower leaf water
potentials whether or not there is any daytime flow in the soil–plant system.
Consequently, differences in ψpd and PLWPI among species in
the same environment reflect their differences in the degree of water stress
avoidance capacity. Multi-year measurements of PLWPI and mortality thus allow
interesting comparisons among levels of induced water stress, mortality and
interspecific variations in these parameters.
Precipitation variability index (PVI) and mean effective precipitation interval (MEPI)
Since long-term, continuous ψpd and PLWPI measurements are
rarely available, it is also desirable to formulate and verify potential
predictors of mortality based on routinely measured meteorological variables.
Such predictors help put particular droughts within the broader context of
precipitation regimes so that impacts of drought on tree mortality can be
effectively evaluated. In this study, we used p¯, PVI and MEPI to
quantify precipitation regimes. PVI is defined as the following:
PVI=∑i=1nRi-R¯2n,
where,
Ri=CiEi,R¯=∑i=1nRin,Ci=∑j=1ipj,Ei=ip¯.
Here, {p1, p2, …, pn} is a time series of
precipitation measurements and i=1, …, n. It is assumed that the
values of pi, i=1, …, n, are measured at regular intervals.
The intervals can be hourly, daily, or weekly, or longer, depending on the
intended use of the resultant PVI. For the purpose of this study, we assume
that the values of pi are measured daily in a unit of millimeters per day.
PVI is dimensionless. It has a value of zero for perfectly uniform
precipitation and increases as precipitation events become more sporadic.
To complement PVI, which is strictly an index of variability and does not
contain information about precipitation quantity, we used MEPI to incorporate
both precipitation variability and quantity into a single measure. MEPI is
defined as the average of all time intervals during which no precipitation
exceeding a threshold daily precipitation rate (pt) occurs. It is
based on the concept of effective precipitation (Noy-Meir, 1973; Porporato et
al., 2002). For a precipitation event to contribute effectively to relieving
plant water stress, it has to be sufficiently intense so that it can
penetrate the canopy and litter layer to wet the mineral soil. After tests
with many threshold values of pt, we found that at the MOFLUX
site, MEPI with a pt of 5 mm day-1 explained inter-annual
variations in plant mortality most effectively. We use MEPI5 to denote MEPI
with pt=5 mm day-1.
Positive temperature anomaly integral (PTAI) and water vapor pressure deficit integral (VPDI)
It has been suggested that temperature and atmospheric VPD may play elevated
roles in tree mortality influenced by global change-type droughts (Breshears
et al., 2009; Adams et al., 2009; Weiss et al., 2009; Eamus et al., 2013;
Williams et al., 2013). To complement the water availability-based potential
predictors (p¯, PLWPI, PVI and MEPI5) for tree mortality, we applied
PTAI and VPDI in an attempt to capture effects of high temperature and VPD,
respectively:
PTAI=∑i=1nmaxTi-Tci,0/48,VPDI=∑i=1nVPDi/48 .
In Eq. (6), Ti is the observed half-hourly temperature at the time step
i and Tci is the daily mean temperature climate normal of the
day during which the time step i occurs. “max” in the right-hand side
of Eq. (6) means that in any day, only those half-hourly temperatures above
the normal of that day (i.e., positive temperature anomaly) are included in
the calculation of PTAI and any negative temperature anomaly is replaced by
the value of zero in the calculation. The daily mean temperature climate
normals were computed by the US National Climatic Data Center (NCDC) with
observations of the period 1981–2010 at the Columbia Regional Airport
(less than 10 km to the northwest of the MOFLUX site). The half-hourly
temperature data were from measurements made at the top of the MOFLUX tower.
In Eq. (7), VPDi is the vapor pressure deficit in the unit of kilopascals at
i. The VPD values were half-hourly and computed from half-hourly
temperature and relative humidity observed at the top of the tower. The
division by 48 in Eqs. (6) and (7) allows PTAI and VPDI to be expressed in
units of degrees Celsius per day and kilopascals per day, respectively. The summations are over the
growing season.
Data processing and analysis
Mortality data from 2005 to 2014 and measurements of precipitation, ψpd, temperature and VPD from 2004 to 2013 were used in this study.
For precipitation, ψpd, temperature and VPD, only
growing-season measurements were used. We used days 120 and 300 as the start
and end of the growing season, respectively, based on the seasonal patterns
of leaf area index (LAI) measured at the site (data not shown). While the
onset and senescence of vegetation activities varied somewhat from year to
year (which was not found to have any direct effect on tree mortality), using
the same dates to mark the growing season for all years eliminated
uncertainties in the calculated predictors that may be caused by inter-annual
variations in the length and timing of the growing season. Persistence of
snow cover at the MOFLUX site is limited to a few weeks at most in mid-winter
and by late April all snow has long melted. We linearly interpolated
measurements of ψpd for days when predawn measurements were
not made. It was assumed that the ψpd on days 120 and 300 was
zero so that any day before the first (mid-May in every year except for 2004
and early June for 2004) or after the last measurement (late October for all
years) could be properly interpolated. This assumption was a reasonable
approximation because the first and last measurements of ψpd
were always close to zero even during years when severe drought occurred (see
the Results section).
Because meteorological and physiological measurements started in early June
2004 at the MOFLUX site, there was a measurement gap of about 40 days (days
120–160) in the growing season of 2004. We tested three strategies for
dealing with this measurement gap. In the first strategy, we filled the
measurement gap with the data from the same period of 2008 to compute the
growing-season p¯, PVI, MEPI5, PLWPI, PTAI and VPDI in 2004. This
strategy was based on the fact that both the years 2004 and 2008 were
relatively wet and the measured seasonal variations in ψpd of
these two years were broadly similar to each other (see the Results section).
The second strategy was simply to compute the potential predictors with
available data in 2004 while the third was to exclude 2004 from the analysis.
All three strategies led to similar findings in terms of how the predictors
were related to tree mortality. We reported the results based on the first
strategy.
We expressed the mortality of trees (DBH > 9 cm) in different ways based
on the specific requirements of analysis. First, we analyzed the total
mortality from 2005 to 2014 for each species within each DBH class. Each DBH
class had a width of 6.3 cm and 10 DBH classes covered all trees sampled.
For the DBH-based mortality analysis, the total mortality of each species was
expressed as a percentage relative to the total number of dead trees of all
species within each DBH class. This total mortality percentage of a species
within each DBH class was then compared to this species' relative stand
abundance in this DBH class (also a percentage number) in the 2005 forest
inventory data. This comparison indicated whether trees of a species died
proportionally or disproportionally to its stand abundance.
Second, we analyzed inter-annual variations of species mortality and their
relationships with those of p¯, PVI, MEPI5, PLWPI, PTAI and VPDI. In
this second analysis, DBH classes were not differentiated. The mortality of
each species in each year was expressed relative to either the total stem
number or the total basal area of this species in the 2005 forest inventory
data. The former was termed stem mortality and the latter basal area
mortality, all expressed as percentages.
In addition to the mortality analysis at the species level, we also analyzed
mortality at the community level. In this case, the community mortality in
each year was expressed relative to either the total stem number or the
total basal area of the whole stand in the 2005 forest inventory data. A
community PLWPI was also calculated, which was the mean of species PLWPI
weighted by species relative abundance in the stand.
Simple regressions with only two free parameters were applied for all
fittings except for two cases where three were used. This prudent use of free
parameters avoided over-fitting the limited mortality data. The Akaike
information criterion corrected for finite sample size (AICc; Anderson 2010)
was used to select for the most parsimonious model among different orders of
polynomials or simply constructed exponential types of function when
ambiguity existed as to whether a linear regression was the optimal choice. Once a
model was selected, the R2 was displayed to show the variance explained
and thus the potential of a mortality predictor. The fitting used in-house
software that has been developed over the years and supported efforts of
parameter estimation such as those of LeafWeb (leafweb.ornl.gov; Gu et al.,
2010; Sun et al., 2014)
Results
Inter-annual variations in precipitation regimes and
potential meteorologically based predictors of tree mortality
From 2004 to 2013, potential abiotic predictors of tree mortality varied
widely in association with changing precipitation regimes at the MOFLUX site
(Fig. 1). Ranked with the mean daily precipitation rate p¯ of the
growing season, the driest (wettest) and second driest (wettest) growing
seasons occurred in 2012 (2008) and 2007 (2009), respectively (Fig. 1a).
Although 2012 and 2007 did not differ much in terms of p¯, (1.4 vs.
1.6 mm day-1), both PVI (Fig. 1b) and MEPI5 (Fig. 1c) were considerably
higher in 2012 than in 2007. In addition, the PTAI (Fig. 1d) and VPDI
(Fig. 1e) were also higher in 2012 than in 2007. Thus, the growing season of
2012 had not only more variable precipitation but also higher heat stress and
atmospheric evaporative demand than that of 2007. This difference had
consequences on the tree mortality caused by the droughts of these 2 years.
The inter-annual variations in p¯, PVI, MEPI5, PTAI and VPDI were not
independent but were also not perfectly correlated either, suggesting that
these potential predictors contain independent information at least to some
degree that may be useful for relating to inter-annual variations in tree
mortality.
Seasonal, inter-annual and species variations in ψpd
Inter-annual variations in the mean daily precipitation
rate (a), precipitation variability index (b), mean effective precipitation
interval with a threshold daily precipitation rate of 5 mm day-1 (c),
positive temperature anomaly integral (d) and vapor pressure deficit integral
(e). Only growing-season data (days 120–300) are used in the calculation.
Large inter-annual variations in precipitation regimes led to contrasting
seasonal patterns in ψpd for different years and for different
species (Figs. 2, 3). For much of the growing seasons of the 2 driest
years, 2012 (Fig. 3c) and 2007 (Fig. 2d), ψpd of all species
measured was consistently below the severe water stress threshold
(-1.5 MPa) suggested by Hsiao (1973). Because ψpd generally
marks the highest leaf water potential in the diurnal cycle, the maximal
water stress that the plants at the MOFLUX site experienced during the day
(i.e., noon to early afternoon) were likely even more severe than indicated
by ψpd. If we use -2.0 MPa as a severe water stress
threshold, nearly all species had ψpd below this threshold at
some point in 2005, 2006, 2007, 2011, 2012, and 2013. During the peak of the
drought in 2012, ψpd of all species approached or declined
below -4 MPa while in 2007 the lowest value varied considerably across
species, ranging from around -2 MPa (white oak) to below -4 MPa (white
ash). In 2005 (Fig. 2b), ψpd steadily decreased initially as
the growing season progressed but the decreasing trend was interrupted by
heavy mid-season rain which rehydrated the trees. In 2006, 2011 and 2013
(Figs. 2c, 3b, and c, respectively), repeated drying and rehydrating cycles
caused fluctuations in ψpd. The years 2004, 2008, 2009 and
2010 were relatively wet and for the entire growing seasons of these years,
no species had ψpd below -1 MPa except for white ash. It is
significant to note that the recovery of ψpd after soaking
rains was prompt and complete, even during severe drought years (e.g., 2007,
Fig. 2d; 2012, Fig. 3c).
Seasonal variations in predawn leaf water potential of
different species from 2004 to 2009.
Large differences in seasonal patterns ofψpd existed among
different species under the same precipitation regimes. White ash
consistently had the lowest ψpd among all species when the
community in general had ψpd below -1 MPa. Above -1 MPa,
eastern redcedar tended to have the lowest ψpd. Therefore on
short timescales (∼ a couple of weeks), the relative positions
of different species in the cross-species variations in ψpd
were not fixed in time, depending on the level of water availability at a
particular moment. When well hydrated, all species had similar
ψpd except for eastern redcedar, whose ψpd
slightly departed from those of other species (for example, Fig. 2e for the
early growing season of 2008). Because white ash tended to have the lowest
ψpd under dry conditions but similar ψpd as
other species under wet conditions, the ψpd of white ash
fluctuated more widely than other species both within and across the years,
suggesting that it was the species least able to access deep soil water
during drought. In contrast, white oak tended to have the highest (least
negative) and least variable ψpd among all species, suggesting
that it was the species most capable of accessing deep soil water. However,
this contrast is not a complete representation of the observed patterns
because species' capacity in preserving ψpd depended on the
intensity of drought. This relativity can be most clearly seen by comparing
the seasonal variations in white oak, and to some extent black oak,
ψpd with those in other species. During most years, the oaks
had ψpd considerably higher than other species. However,
during the driest year of 2012, their ψpd was within the
variations of that of other species. In fact differences among all species
were much diminished in 2012 compared with 2007.
Seasonal variations in predawn leaf water potential of
different species from 2010 to 2013.
Species differences in ψpd were reflected and more clearly
seen in the inter-annual variations of PLWPI (Fig. 4). White ash had the
lowest PLWPI among all species for all years except for the relatively wet
year of 2009 when eastern redcedar had lower PLWPI than white ash. White
and black oaks tended to have higher PLWPI for most years. However, in the
drought of 2007 black oak PLWPI was only slightly higher than other species
exclusive of white ash, and in the exceptional drought of 2012 both white and
black oaks' PLWPIs were only slightly higher than other species. As expected
from the seasonal patterns in ψpd among species across years,
the maximal difference in PLWPI among species (max - min species PLWPI in
each year) reached a maximum at an intermediate value of community PLWPI
(Fig. 5), suggesting that the differences in ψpd and PLWPI
among species reached maximum when the site was under some intermediate
levels of water stress and diminished towards either extreme wet or extreme
dry conditions. Significantly, during the exceptional drought year of 2012
the superior capacity to keep PLWPI elevated was lost in both oak species.
Inter-annual variations in PLWPI of different species from 2004 to 2013.
The difference between the maximum and minimum PLWPI among the six species studied as a
function of the community PLWPI. The community PLWPI serves as a measure of
overall water stress of a year. This figure shows that species differences
in PLWPI reach the maximum at an intermediate level of water stress.
The relative abundance according to the 2005 forest
inventory (a) and the total relative mortality of each species from 2005 to
2014 (b) in each diameter at breast height (DBH at 1.3 m) class. The relative
abundance is expressed as a percentage of stem number of each species
relative to the total stand stem number in each DBH class. Similarly, the
relative mortality is expressed as a percentage of total mortality of each
species relative to the total stand mortality in each DBH class. Ten DBH
classes with an equal width of 6.3 cm are shown. The class with the smallest
DBH (class 1) starts with a DBH of 9 cm. This figure shows whether members of
a species die proportionally or disproportionally to its abundance in the
stand. SM, sugar maple; SH, shagbark hickory; WA, white ash; RC, eastern
redcedar; BO, black oak; WO, white oak; Other, the other species together.
Variations of tree mortality with species, DBH classes and
year
At the MOFLUX site, white oak and black oak dominated the largest DBH classes
of the stand while the abundance of other species increased in smaller DBH
classes (Fig. 6a). Over the whole mortality monitoring period (2005–2014),
the total mortality rate of a species as a percentage relative to the stand
mortality in each DBH class (Fig. 6b) differed from this species' abundance
in this given DBH class for most DBH classes and for most species (i.e.,
compare the bar lengths indicating relative abundance in Fig. 6a with the bar
lengths indicating relative mortality in Fig. 6b for the corresponding DBH
classes). For example, in DBH classes 1–6 (DBH from 9 to 47 cm), black
oak had a mortality rate relative to the stand consistently higher than its
relative abundance in the corresponding stand DBH classes as shown in the
forest inventory data, indicating that black oaks died at a
disproportionately higher rate within these DBH classes. In contrast, eastern
redcedar and shagbark hickory had relative mortality rates lower than their
relative abundances in each DBH class, suggesting that these two species had
better survivability compared with other species.
The total mortality of the 2005–2014 period shown in Fig. 6b was dominated
by exceptionally high mortality in 2013 for most species, 1 year after the
driest year of 2012. Figure 7 showed the inter-annual variations of the
yearly mortality rate expressed relative to species abundance in 2005, based
on either stem number density (Fig. 7a) or basal area (Fig. 7b). For all
species, the highest stem mortality occurred in 2013. This is also true for
the basal area mortality; the only exception was white ash which had slightly
higher basal area mortality in 2014 than in 2013, largely due to one very
large tree that died in 2014. Considering that 2013 was a year with only
modest water stress (Fig. 4), the mortality in 2014, 2 years after the
driest year of 2012, appeared to be unusually high for most species and also
for the community as a whole.
Inter-annual variations in the species mortality
expressed relative to either the stem number density (a) or the basal area
(b) of a species in 2005.
Variations of tree mortality with potential predictors with
time lag
We analyzed how mortality changed with p¯, PVI, MEPI5, PLWPI, PTAI,
and VPDI for each species as well as for the community at different lag years
(0, 1, 2 and 3 years – there were not enough data to test for more lag
years). We found that the relationships were strongest at 1-year lag in all
cases; i.e., the mortality in 1 year was best explained in terms of
variance (R2) by the p¯, PVI, MEPI5, PLWPI, PTAI and VPDI in the
previous year (all fittings in this case were linear with the same number
(i.e. Eq. 2) of parameters, making R2 comparable). This 1-year lag can be
already expected from the fact that the highest mortality occurred in 2013,
1 year after the driest year of 2012. Both at the species and community
levels, stem mortality rates decreased with an increase in the previous
year's p¯ (Figs. 8a, S1 in Supplement) and PLWPI (Figs. 8b, 9) and increased
with an increase in the previous year's PVI (Figs. 8c, S2), MEPI5 (Figs. 8d, 10), PTAI (Figs. 8e, S3) and VPDI (Figs. 8f, S4). Based on the explained variance, the most promising predictors of
tree mortality were PLWPI and MEPI5 while the performance of PVI, PTAI and
VPDI was also reasonable. The predictive capacity of p¯ was limited
because it did not contain precipitation variability information. The results
from the analyses with the basal area mortality rates showed similar patterns
and therefore are not presented here.
The mortality of the plant community as a whole expressed
relative to the stem number density of 2005 as a function of the previous
year's daily mean precipitation rate (a), PLWPI at the community level (b), precipitation variability index
(c), mean effective precipitation interval with a threshold daily
precipitation rate of 5 mm day-1 (d), positive temperature anomaly
integral (e), and vapor pressure deficit integral (f). Fittings are linear.
The species mortality expressed relative to the stem
number density of 2005 as a function of this species' PLWPI in the previous year. Fittings are linear.
The species mortality expressed relative to the stem
number density of 2005 as a function of the mean effective precipitation
integral with a threshold daily precipitation rate of 5 mm day-1
(MEPI5) in the previous year. Fittings are linear.
The mean standardized species mortality as a function of
the mean standardized PLWPI. In each
year and for each species, the standardization is done by dividing a species
mortality by the community mortality or by dividing a species PLWPI by the
absolute value of the community PLWPI. The standardized yearly values are
then averaged across the years. The standardization preserves the relative
positions of species in the PLWPI continuum. Mortality is either expressed
based on stem number density (a) or basal area (b). This figure shows that
species occupying middle positions along the relative PLWPI continuum
suffered less mortality than those at either extremes (i.e., extremely high
or low relative PLWPI).
Although we found that the relationships between mortality and predictors
appear to be strongest with a 1-year lag, this does not imply droughts do not
have longer-term impacts. For example, the unusually high mortality of tree
species in 2014 (Fig. 7) may be a result of lingering impact of the
exceptional drought of 2012 as the 2013 drought was not strong (Figs. 2–4).
When the previous 2 year's MEPI5s were used to form a composite MEPI5 in
year t [= MEPI5(t-1) + 0.5 × MEPI5(t-2)], the composite
MEPI5 explained more variance in tree mortality than the previous year's
MEPI5 alone (Fig. S5). The better performance of the composite MEPI5 suggests
a drought can affect tree mortality 2 years later.
The variations of mortality rates with the previous year's p¯, PVI,
MEPI5, PLWPI, PTAI and VPDI were clearly dominated by the impact of the
exceptional drought of 2012. However, the impact of 2012 was not the sole
determinant of the relationships. When the mortality data for 2013 and 2014
were excluded from the analysis, a linear regression still explained much of
the variance in the inter-annual variations of mortality (Fig. S6). In fact,
PTAI (Fig. S6e) and VPDI (Fig. S6f) even explained higher variances when the
“outliers” were removed, suggesting that these two predictors may work well
for moderate drought.
Mortality and drought tolerance of tree species
Within the same species, mortality decreased with an increase (i.e., becoming
less negative) in the previous year's PLWPI. However, the mortality–PLWPI
relation across species is more complicated and is not monotonic. For
example, the drought-tolerant white and black oaks generally had higher (less
negative) PLWPI than the less drought-tolerant sugar maple and shagbark
hickory, and the drought-tolerant eastern redcedar (Fig. 4), but their
mortality was also higher than the latter three species, especially after the
most severe drought year of 2012 (Fig. 7). In contrast, the less drought-tolerant white ash exhibited lower PLWPI (Fig. 4) but higher mortality than
sugar maple, shagbark hickory, and eastern redcedar (Fig. 7). These
differences across species can also be inferred from plots of annual
mortality as a function of the previous year's PLWPI (Fig. 9).
A more clear demonstration of how the mortality of a species is related to
its general capacity in regulating ψpd and PLWPI is given by
Fig. 11. Because the mortality sampling population for any particular year
was relatively small for species with less abundance in the community (e.g.,
white ash and shagbark hickory; Fig. 6a), in Fig. 11 we pooled the
decade-long data to focus on the mortality difference among species. The
pooling also allowed us to compare species in their general capacity to
regulate the dynamics of ψpd and to determine how this
capacity may be related to the risk of drought-influenced mortality. The
PLWPI of a species in a year was normalized by the absolute value of the
community PLWPI of this year and then averaged across the 10-year period.
Because the PLWPI of all species was divided by the same value in a given
year, this standardization procedure preserved their relative positions along
the PLWPI continuum and yet allowed different years of widely varying water
stress levels to be averaged. Similarly, the annual mortality of a species in
a year was normalized by the community mortality of this year and then
averaged across the 10-year period. With these normalizations, a clear convex
pattern emerged (Fig. 11): species occupying the middle of the standardized
PLWPI continuum (eastern redcedar, shagbark hickory and sugar maple) suffered
less mortality than those in the low standardized PLWPI end (white and black
oaks) or in the high standardized PLWPI end of the continuum (white ash).
Discussion
A wide range of precipitation regimes from ample seasonal moisture to
exceptional drought was observed over the 10-year tree mortality study
period at the MOFLUX site, as reflected in both precipitation variables and
PLWPI. The generally tight correlation between precipitation regimes and
time-delayed (1 year or more) inter-annual variations in the mortality of
tree species strongly supports the assertion that drought was an important
factor in death of trees during the study. Although different species had
variable degrees of water stress and drought tolerance, the mortality of all
species increased after the exceptional drought of 2012. These results are
consistent with worldwide syntheses of drought-related mortality reports
(e.g., Breshears et al., 2009; Allen et al., 2010). Furthermore, the close
relationships of mortality with various predictors, especially PLWPI, MEPI5,
PTAI and VPDI, even without considering the exceptional drought year of 2012,
indicate the apparent importance of non-extreme plant water stress in
promoting mortality (cf. Breshears et al., 2009).
Non-extreme water stress is also important for understanding species'
differences in regulating their internal water environment. Under very wet
conditions, all species are hydrated equally well while under extreme drought
conditions, any capacity of species in maintaining ψpd and
PLWPI may be overwhelmed by a lack of water even in deep soil. Only under
intermediate drought levels can any regulatory mechanism that a species might
have function effectively (Fig. 5). Thus, non-extreme water stress may be the
optimal condition for revealing differences in plant water relations across
different species.
Because we did not measure hydraulic conductivity in our species, we cannot
assert that xylem cavitation did not occur during any of the drought events.
However, two lines of evidence indicated there was no catastrophic loss of
xylem hydraulic conductivity in any species. First, mortality lagged PLWPI by
a year without the evidence of nearly immediate shoot desiccation that would
follow catastrophic hydraulic disconnection. There was some leaf scorching at
the top of the canopy during 2012, associated with the exceptional drought and
high temperatures (e.g., during July ψpd fell below -3 MPa
and average temperature was 5 ∘C above the 1980–2010 average), but
no signs of leaf desiccation (leaf curling, cracking or green hue change in
laminae). Second, during all drought events including the exceptional drought
of 2012, ψpd recovered rapidly after wetting rains. This type
of response would not be expected if catastrophic hydraulic disconnection had
occurred; rather, one would have expected little or no recovery in
ψpd. One could argue that xylem refilling and consequent
recovery of conductivity might occur with rainfall, but such refilling would
have had to be initiated when xylem water potentials were below (and
sometimes far below) those considered conducive to xylem refilling (e.g.,
Secchi and Zwieniecki, 2010; Brodersen and McElrone, 2013; Rockwell et al.,
2014). Further, as noted above, the concept of rapid refilling in xylem under
significant tensions itself is being reconsidered in the light of recent
research suggesting that conventional techniques of measuring hydraulic
conductivity overestimate xylem cavitation and increasingly so as tensions
increase (e.g., Wheeler et al., 2013; Cochard and Delzon, 2013; Rockwell et
al., 2014; Cochard et al., 2015). Hence, drought-related mortality in this
forest likely cannot be directly linked to hydraulic disconnection. Rather, a
significant but less direct role for drought as one determinant of mortality
seems more likely (McDowell et al., 2013b).
Species exhibited differences in the static component of water stress
represented by ψpd, notably with white ash having generally
lower values than other species. However, with ample rain and thus abundant
soil moisture, eastern redcedar had lower ψpd. This last
response has been observed before (Ginter-Whitehouse et al., 1982; Bahari et
al., 1985) and likely relates to the higher-resistance tracheid anatomy of
redcedar which prevents full overnight equilibration in moist soil.
Comparative analysis of water relations among species, while not arbitrary,
must necessarily be limited to the species studied. In a broader context of
previous studies in this forest, it is worth noting that another fairly
common tree species at the site (viz., black walnut (Juglans nigra
L.)) exhibits even higher ψpd than the oak species studied
here, and that white ash (heretofore not studied extensively in this forest)
exhibited responses similar to flowering dogwood (Cornus florida L.)
(Bahari et al., 1985; Ni and Pallardy, 1991; Loewenstein and Pallardy, 1998).
It must be emphasized that none of these species showed any indication of
hydraulic disconnection, as determined by failure in recovery of
ψpd after significant rainfall (e.g., Bahari et al., 1985;
Lowenstein and Pallardy, 1998). Thus, these results also suggest the
established link between PLWPI and mortality must be mediated indirectly.
PLWPI was a monotonic predictor of drought-influenced tree mortality within a
species and for the plant community as a whole; i.e., the lower it was, the
higher was the subsequent year's mortality. However, the PLWPI–mortality
relationship varied substantially across species. Our data indicated that
species with extreme (lowest or highest) positions in the continuum of PLWPI
suffered higher mortality than species with intermediate values of PLWPI. The
relationships among PLWPI, mortality and species-relative drought tolerance
also were complex. Whereas drought-tolerant oak species had both higher PLWPI
(indicating greater relative capacity to avoid low ψpd) and
higher mortality, eastern redcedar had lower PLWPI and mortality. Similarly,
while the relatively less drought-tolerant white ash had the lowest PLWPI
(indicating low capacity to avoid low ψpd) and high
mortality, sugar maple and shagbark hickory had higher PLWPI and lower
mortality. Thus, white ash's native capacity to tolerate drought may have
been exceeded during the study period, and particularly so during the 2012
drought. Elevation of oak species mortality likely has a complex explanation.
The exceptional drought of 2012 did nearly eliminate the differential
capacity of the oak species to maintain high ψpd, thus
inducing atypically high levels of water stress. While these oak species are
more important at sites with lower soil water supply capacity and demonstrate
drought-tolerant physiological attributes (e.g., Fralish et al., 1978;
Abrams, 1990, 2003), they become more susceptible to drought, and
drought-related mortality, as they age (e.g., Jenkins and Pallardy, 1995;
McCarthy et al., 2001; Voelker et al., 2008). Consistent with this assertion,
relative mortality of oaks in the present study was concentrated in the
larger (older) size classes (Fig. 6). Furthermore, as also was observed here,
previous research has shown that red oak group species such as black oak show
greater rates of mortality in drought-related mortality events than members
of the white oak group (e.g., Fan et al., 2006, 2008; Greenberg et al.,
2011).
Unless being killed outright, a tree's life terminates when sufficient
resources are either not available (Waring, 1987) or come too late to revive
vital coordinated organ activities. This death process is an end-point
response that integrates past internal and external dynamics (Hanson et al.,
2003) and is thus hard to predict mechanistically. However, tree mortality is
unique as a life-ending process due to two fundamental characteristics of
a tree's life. First, trees are sessile and thus have very limited capacity to
evade environmental stresses. Second, trees can be potentially immortal due
to their integrated biology of modular structure and meristem dormancy,
therefore, some external agent must occur to induce their death. The combination of
these two characteristics means that while it is difficult to mechanistically
predict tree mortality, the possibility exists to make robust empirical
predictions if sufficiently long, concerted observations are made on both tree
mortality and responsible external agents.
Our study demonstrates that this strategy can be particularly fruitful for
predicting drought-influenced tree mortality. PLWPI and MEPI in the growing
season of the prior year are good predictors of tree mortality in the
current year. The mean precipitation rate does not contain information about
precipitation variability while PVI lacks information about precipitation
amount. Thus, individually, they are not as good as PLWPI or MEPI in
predicting tree mortality except for mortality caused by severe droughts;
they must be used jointly to serve as predictors of tree mortality. PTAI and
VPDI apparently work better for non-extreme drought-influenced tree
mortality but may have limited capacity for a broad range of water stress
including extreme drought. Obviously these predictors will need to be tested
at multiple sites and with longer data sets before any conclusion can be
drawn with respect to which predictor(s) is the best for predicting
drought-influenced tree mortality.
Conclusions
Our study has revealed that the drought-influenced mortality of tree species
varies nonlinearly along physiologically based and meteorological drought
intensity scales, and such variations can be predicted by multiple, simply
constructed indices with a 1-year time lag. These indices include predawn
leaf water potential integral (PLWPI), mean effective precipitation interval
(MEPI), precipitation variability index (PVI), positive temperature anomaly
integral (PTAI), and vapor pressure deficit integral (VPDI). While hydraulic
disconnection in the xylem has been postulated as a mechanism for
drought-influenced tree mortality, significant but indirect effects of
drought are more likely the main cause of tree death in our study. Less
severe droughts can not only be significant promotors of tree death but also
reveal species differences in drought-tolerance capacity that might be
related to mortality risk. While species may possess different mechanisms to
regulate their internal hydraulic environment, such mechanisms can only work
under the limits imposed by the external environment; beyond these limits,
species differences in the effectiveness of regulatory mechanisms become
minimal. Severe droughts may overwhelm the capacity of even drought-tolerant
species to maintain differential levels of water potential as the soil
becomes exhausted of available water in the rooting zone, thus rendering
them more susceptible to die if predisposed by other factors such as age.
Our study also showed that the drought-influenced tree mortality is related
to the species position along the spectrum of predawn leaf water potential
with those in either ends of the spectrum being associated with elevated
risk.
Forest composition at the MOFLUX site is undergoing change today. The
disproportionally high mortality of black and white oaks and white ash in
the present study suggest that frequent droughts may cause the forest to
transition to a plant community that is composed of species occupying
intermediate positions in the continuum of predawn leaf water potential
regulation capacity. This finding may have implications for the future of
forest ecosystems in the eastern US.
Although reports of tree mortality caused by episodic drought events have
been extensive in the literature, studies based on long-term, continuous
observations such as represented by this present study have been rare (e.g.,
Breshears et al., 2009). Yet, understanding tree mortality mechanisms and
developing predictive models of tree mortality require long-term continuous
monitoring of tree mortality and environmental factors. Tree mortality caused
by episodic drought events must be investigated in the long-term background
of mortality and environmental dynamics. There is a need for increased
investment on coordinated long-term observations of tree mortality and
responsible external forcing agents in global forests.