Abstract Aims Plants use a variety of hydraulic strategies to adapt to seasonal drought that differ by species and environmental conditions. The early-diverging Magnoliaceae family includes two closely related genera with contrasting leaf habits, Yulania (deciduous) and Michelia (evergreen), which naturally inhabit temperate and tropical regions, respectively. Here, we evaluate the hydraulic strategy of species from both genera that have been ex situ conserved in a subtropical region to determine how they respond to the novel cool–dry season climatic pattern. Methods We measured ecophysiological traits in five Michelia and five Yulania species conserved in the South China Botanical Garden in both wet and dry season conditions and monitored the whole-year sap flow for four of these species. Important Findings We found that Magnoliaceae species that have been ex situ conserved in a subtropical climate did not suffer from excessive water stress due to the mild drought conditions of the dry season and the ecophysiological adjustments the species made to avoid this stress, which differed by leaf habit. Specifically, deciduous species completely shed their leaves during the dry season, while evergreen species decreased their turgor loss points, dry mass based photosynthetic rates, stomatal conductance and specific leaf areas (SLAs) compared to wet season measurements. In comparing the two distinct leaf habits during the wet season, the leathery-leaved evergreen species had higher leaf hydraulic conductance and leaf to sapwood area ratios than the papery-leaved deciduous species, while the deciduous species had greater hydraulic conductivity calculated on both a stem and leaf area basis, dry mass based photosynthetic rates, leaf nutrients, SLAs and stomatal sizes than the evergreen species. Interestingly, species from both genera maintained similar sap flow in the wet season. Both photosynthetically active radiation and vapour pressure deficit affected the diurnal patterns of sap flow in the wet season, while only vapour pressure deficit played a dominant role in the dry season. This study reveals contrasting hydraulic strategies in Yulania and Michelia species under subtropical seasonal conditions, and suggests that these ecophysiological adjustments might be affected more by leaf habit than seasonality, thus reflecting the divergent evolution of the two closely related genera. Furthermore, we show that Magnoliaceae species that are ex situ conserved in a subtropical climate are hydraulically sound, a finding that will inform future conservation efforts of this ancient family under the threat of climatic change. hydraulic conductivity, leaf habit, leaf turgor loss point, phylogeny, photosynthesis, sap flow, stomata INTRODUCTION Plants adjust many ecophysiological traits to adapt to seasonal drought, especially hydraulic traits, which are directly related to the degree of drought tolerance among species (Domec et al. 2006; Fan et al. 2011; Meinzer et al. 2001). Under modest water stress, stomata will close to prevent unnecessary water loss and, in the process, also reduce xylem tension within the stems to avoid embolism (Arango-Velez et al. 2011; Cochard et al. 2002; Zhang et al. 2013). For most tree species, leaves are more vulnerable to drought-induced embolism than branches, implying that leaves act as safety valves to protect the plant hydraulic pathway (Bucci et al. 2012; Pivovaroff et al. 2014). Moreover, plants may reduce their overall leaf area (e.g., by shedding leaves) to protect stem functionality if the drought intensifies (Drake and Franks 2003). However, Wolfe et al. (2016) found that leaf shedding does not likely stabilizing plant water potential during typical seasonal droughts, and will generally only do so during periods of intense hydraulic stress. While deciduous species can shed their leaves during periods of drought stress, evergreen species must maintain some level of water transport to their leaves and, as such, will avoid hydraulic failure through morphological and physiological adjustments (Calkin and Pearcy 1984; Choat et al. 2005). Evergreen species also reduce their leaf area to decrease total transpiration. For instance, Eucalyptus kochii trees growing in arid conditions have a lower leaf to sapwood area ratio (AL/AS) than those growing in wet conditions (Carter and White 2009). However, evergreen leaves might also achieve lower leaf water potential at turgor loss point (Ψtlp) via osmotic adjustments in the dry season (Calkin and Pearcy 1984). As water becomes less available, active solutes will accumulate in plant cells that enable leaves to maintain cell turgor pressure under these low water potentials (Bartlett et al. 2012). Thus Ψtlp is an essential physiological determinant of plant drought tolerance within and across biomes (Bartlett et al. 2012; Maréchaux et al. 2015). Sap flow measurements are useful to explore how water use varies among individual trees or across species at the whole tree level (Granier 1985; O’Brien et al. 2004; Oren et al. 1999). Sap flows through stems when water deficits in the leaves induce greater water tension in the hydraulic pathway (Cochard et al. 1996). Thus, under seasonal drought conditions when trees induce leaf stomatal closure to conserve water by limiting transpiration, sap flow will also decrease as a result (Ježík et al. 2015; Pataki et al. 2000). Quantifying the relationships between sap flow in tree species and environmental factors (e.g., water availability and light level) is critical to assess the key limiting factors that affect species distribution and conservation (Pataki et al. 2000). Hydraulic responses to seasonal drought have been widely studied in tropical dry forests and Mediterranean woodlands (Choat et al. 2005; Nardini et al. 2014), where the hot-dry season (i.e., characterized by high temperatures and little precipitation) is very stressful for plants. Furthermore, a convergent drought vulnerability for plants was found across the global forests (Choat et al. 2012), however this excludes the subtropical forests of China, where the summer monsoon climate is characterized by contrasting hot-wet and cool-dry conditions. Hydraulic adaptations of plants in these Chinese subtropical forests likely differ from those reported in regions with hot-dry seasonal patterns. Magnoliaceae is an early-diverging angiosperm family, originating 93.5–110 million years ago, that is important for phylogenetic and evolutionary studies (Azuma et al. 2001; Kim and Suh 2013; Massoni et al. 2015). However, about half of the ~300 Magnoliaceae species alive today are threatened with extinction according to the IUCN red list due to habitat loss and over exploitation (IUCN SSC 2001). Nearly four fifths of Magnoliaceae species are currently distributed between eastern and south-eastern Asia, with the majority of species occurring in China where ex situ conservation efforts have been successful (Cicuzza et al. 2007). Unfortunately, present-day climatic change is forcing many species to shift their geographical range or even become extinct (Parmesan 2006; Thomas et al. 2004), such that studies focused on ecophysiological adaptations within botanical gardens have become increasingly important for ex situ conservation efforts (Maunder et al. 2001). Previous phylogenetic and biogeographic work found that two Magnoliaceae lineages separated into temperate (Yulania) and tropical (Michelia) areas before the Oligocene (Azuma et al. 2001; Nie et al. 2008). Although Yulania and Michelia are the most closely related lineages among the Magnoliaceae genera, the temperate-centered Yulania species are deciduous, while tropical-centered Michelia species are evergreen (Liu et al. 2016), suggesting that the two leaf habits may result from differing climatic regimes. When species originating from different climatic regions are conserved in a common subtropical location, their ecophysiological adaptations to the cool-dry season may differ between lineages, which is crucial evidence to help predict and manage the effects of climatic changes on native and ex situ conserved species (Davidson et al. 2011). To understand the ecophysiological differences between the two contrasting Magnoliaceae genera, in this study, we measured wet and dry season physiological traits in five evergreen Michelia and five deciduous Yulania species conserved in the South China Botanical Garden, and also continuously monitored sap flow in four of these species over a year. Specifically, we ask the following three questions: (1) In the dry season, when deciduous species have shed their leaves, which hydraulic traits do evergreen species adjust to maintain water balance? (2) In the wet season, do deciduous species have higher hydraulic conductivities and consume more water than evergreen species to compensate for their growth losses from the dry season leaf shedding? (3) Will the key environmental factors that influence sap flow density differ between the two seasons? We hypothesized that (a) in the dry season, evergreen species might reduce stomatal conductance, AL/AS and sap flow, but increase osmotic adjustment to release water stress compared to the wet season; (b) in the wet season, deciduous species might show higher hydraulic efficiency and higher sap flow to supply transpiration and photosynthesis due to their quick turnover strategy compared to their evergreen relatives and (c) environmental indices related to water availability, such as vapour pressure deficit, will directly drive plant hydraulic strategies in the dry season. MATERIALS AND METHODS Study site and species Experiments were conducted in the South China Botanical Garden (SCBG) (23°11′N, 113°21′E, 100 m altitude) at the Chinese Academy of Sciences in Guangzhou, China. SCBG is located in the south subtropical monsoon climatic region, where the average wet (April to September) and dry season (October to March) temperatures are 26.6°C and 17.6°C, respectively. While mean annual precipitation is ~1700 mm, 80% of this occurs in wet season. The monthly average air temperature, relative humidity (RH, %) and precipitation in Guangzhou between 1951 and 2014 were collected from the China Meteorological Data Sharing Service System (http://www.cma.gov.cn/2011qxfw/2011qsjgx). The soil water content (SWC, %) at 30 cm depth was monitored using three SM300 sensors (Delta-T Devices, Cambridge, UK) from the meteorological station in SCBG between 2013 and 2014 (supplementary Fig. S1). The Garden of Magnoliaceae at the SCBG is the largest conservation center for Magnoliaceae species in the world (Cultivated Flora of China, http://gardenflora.scbg.ac.cn/). Considering availability and accessibility, we selected five evergreen (Michelia) and five deciduous (Yulania) Magnoliaceae species to measure ecophysiological traits in both wet (August 2013) and dry seasons (January 2014) (Table 1). Two evergreen and two deciduous species from this list were also used for sap flow monitoring between April 2013 and April 2014. Three to five trees per species were selected, from which three replicate measurements per tree were made of leaf and stem hydraulic traits. All measured individuals were mature trees that had been transplanted to the SCBG Garden of Magnoliaceae between the 1960s and 1990s from the wild or other botanical gardens (Liu et al. 1997). Table 1: description of the 10 Magnoliaceae species used in this study Species Code Growth form Leaf texture Plant height (m) DBH (cm) Michelia chapensis Mc Evergreen tree Leathery 15.7 ± 0.7 36.4 ± 1.8 Michelia shiluensis Ms Evergreen tree Leathery 8.5 ± 0.2 19.2 ± 0.9 Michelia maudiae Mm Evergreen tree Leathery 10.4 ± 0.4 18.5 ± 0.6 Michelia figo Mf Evergreen shrub Leathery 4.6 ± 0.3 10.5 ± 0.5a Michelia platypetala Mp Evergreen tree Leathery 11.6 ± 0.6 14.8 ± 0.7 Yulania glabrata Yg Deciduous tree Papery 7.8 ± 0.2 17.4 ± 0.5 Yulania jigongshanensis Yj Deciduous tree Papery 6.7 ± 0.3 11.6 ± 1.2 Yulania cylindrical Yc Deciduous tree Papery 7.6 ± 0.4 13.5 ± 1.1 Yulania denudate Yd Deciduous tree Papery 6.3 ± 0.2 15.0 ± 0.7 Yulania liliiflora Yl Deciduous shrub Papery 3.7 ± 0.2 8.6 ± 0.5a Species Code Growth form Leaf texture Plant height (m) DBH (cm) Michelia chapensis Mc Evergreen tree Leathery 15.7 ± 0.7 36.4 ± 1.8 Michelia shiluensis Ms Evergreen tree Leathery 8.5 ± 0.2 19.2 ± 0.9 Michelia maudiae Mm Evergreen tree Leathery 10.4 ± 0.4 18.5 ± 0.6 Michelia figo Mf Evergreen shrub Leathery 4.6 ± 0.3 10.5 ± 0.5a Michelia platypetala Mp Evergreen tree Leathery 11.6 ± 0.6 14.8 ± 0.7 Yulania glabrata Yg Deciduous tree Papery 7.8 ± 0.2 17.4 ± 0.5 Yulania jigongshanensis Yj Deciduous tree Papery 6.7 ± 0.3 11.6 ± 1.2 Yulania cylindrical Yc Deciduous tree Papery 7.6 ± 0.4 13.5 ± 1.1 Yulania denudate Yd Deciduous tree Papery 6.3 ± 0.2 15.0 ± 0.7 Yulania liliiflora Yl Deciduous shrub Papery 3.7 ± 0.2 8.6 ± 0.5a Names in bold indicate the four species used for sap flow monitoring. Data are mean ± SEM, n = 3–5 for each species. DBH, diameter at breast height. aIndicates ground diameters for shrubs. View Large Sap flow and environment monitoring Sap flux density (JS, g H2O m−2 s−1) was recorded continuously between 17 April 2013 and 20 April 2014 at 10 min intervals all the time, with a few interruptions caused by lightning, using a self-made thermal dissipation probes based on Granier’s prototype (Granier 1985). The four selected Magnoliaceae species are diffuse-porous species (Xu 2008), meaning that vessels of these species are distributed evenly in the sapwood so that the calculation of JS could follow the classic modeling under the ideal condition. Using core samples collected with an increment borer in May 2014, sapwood areas (AS, m2) were calculated for each tree as the difference between the heartwood area and stem cross-sectional area beneath the bark. Since sapwood thickness of the sampled trees ranged between 10 and 40 mm, we used 10 mm long sensors. Each sensor consisted of two cylindrical probes (2 mm in diameter); a continuously heated upper probe and an unheated lower probe. The upper probe included a heater that was continuously supplied with constant power at 0.15 W, while the lower unheated probe served as a temperature reference (James et al. 2002). The probes were covered with a plastic box and insulated with aluminum foil to avoid direct solar heating and disturbance. Temperature differences between the probes were measured every 30 s, and 10 min means were recorded in a data logger (DL2e, Delta-T Devices, Cambridge, UK). JS along the length of the heated probe was calculated as: JS= 119×[(ΔTM−ΔT)/ΔT]1.231 (1) where ΔT is the temperature difference between the two probes, ΔTM is the maximum value of ΔT under zero-flux conditions (i.e., a period to meet such conditions needs at least 2 h with nearly zero vapor pressure deficit (VPD, kPa) and basically no changes in ΔT), and 119 and 1.231 are constant factors in the classic equation (Granier 1985). JS was calculated by manually setting ΔTM for each day using BaseLiner (v.3.0.7, C-H2O Ecology Lab, Duke University, NC, USA). For each species JS was averaged as: JS= (JS1×AS1+JS2×AS2+JS3×AS3)/ (AS1+AS2+AS3) (2) where S1, S2 and S3 are three replicate trees per species and A is the sapwood area for each of these replicate trees. The method of calculating JS for each species by weighting several trees is conventional in previous studies (Herbst et al. 2008). A SKP215 quantum sensor (Sky Instruments, Powys, UK) and an AT2&RHT2 sensor (Delta-T Devices, Cambridge, UK) were mounted on a tower adjacent to the Garden of Magnoliaceae within the SCBG for monitoring photosynthetically active radiation (PAR, µmol m−2 s−1), and air temperature (T, °C) and RH (%), respectively. Using these T and RH data, VPD was calculated as: VPD=a×exp[b×T/ (T+c)]×(1−RH) (3) where a, b and c are constants of 0.611 kPa, 17.502 (unitless) and 240.97°C, respectively (Campbell and Norman 1998). Environmental factors were monitored concurrently with sap flow, at 30 s intervals, with 10 min means continuously recorded in a data logger (DL2e, Delta-T Devices, Cambridge, UK). Stem hydraulic conductivity, leaf/sapwood area ratio and sapwood density Three to five branch stems (diameter 8–10 mm) from each sampled tree were excised using a tree pruner early in the morning. These stems were immediately recut under water to avoid embolism, leaves were sprayed with water and all stems were sealed in plastic bags with moist towels to prevent transpiration, and then rapidly transported to the laboratory. Stem segments were cut under water in ~20 cm lengths, using a razor blade to trim the ends. First, stem segments were flushed with filtered and degassed 20 mmol KCl solution (at 0.1 MPa for 10 min) to remove air embolisms. Next, a 50 cm hydraulic head was attached to one end to generate a fixed pressure with which to drive water flow in the segment. A pipette was fastened to the other end of the stem segment, and the time that it took for water flow to cross the 0.1 ml graduation mark on the pipette was recorded. These data were used to calculate hydraulic conductivity (Kh, kg m s–1 MPa–1) as the value of water flow through the segment divided by the driving pressure gradient. Sapwood specific conductivity (KS, kg m–1 s–1 MPa–1) was calculated as Kh per sapwood cross-sectional area. Leaf specific hydraulic conductivity (KL, kg m–1 s–1 MPa–1) was calculated as Kh divided by the total leaf area on the stem segment (AL). AL was determined by scanning all of the leaves on each stem with a leaf area meter (Li-3000A; Li-Cor, Lincoln, USA), and used to calculate the leaf to sapwood area ratio (AL/AS, m2 cm–2). Sapwood density (WD, g cm−3) samples were taken from the same branches used for Kh measurements. With the bark completely removed, sapwood segments were submerged in water overnight, and then the saturated volume of each sample was measured by the water displacement method. Segments were then oven dried for 72 h at 70°C and dry mass was weighed. WD is calculated as the ratio of dry mass to saturated volume in each sample. Leaf water potential, hydraulic conductance (Kleaf) and turgor loss point (Ψtlp) Predawn leaf water potential (Ψpre, MPa) was measured between 06:00 and 07:00 and midday leaf water potential (Ψmid, MPa) was measured between 12:00 and 14:00. For each species, three replicate leaves per tree were measured using a pressure chamber with a portable pressure gas cylinder (PMS, Corvallis, Oregon, USA). Kleaf (mmol m–2 s–1 MPa–1) was measured according to the high pressure method (Franks 2006). First, a single leaf was placed in the pressure chamber and the chamber pressure was increased enough to balance the pressure of the leaf (Ψ1) and remained there to equilibrate for about 5 min. Chamber pressure was then increased rapidly to Ψ2, with Δ Ψ (Ψ2-Ψ1) around 0.5 MPa. Pre-weighed Eppendorf tubes that had been stuffed with dry tissue were used to absorb exuded sap. The weight of exuded sap over the first 10 s was measured on an analytical balance (0.1 mg) as Δ W. Leaves were scanned to get leaf area (LA) with a leaf area meter (Li-3000A; Li-Cor, Lincoln, USA). Kleaf was calculated as: Kleaf= ΔW×LA–1×time–1×ΔΨ–1 (4) The bench-drying method was used to determine leaf turgor loss point (Ψtlp) (Tyree and Hammel 1972). Three to five leaves for each tree were excised and rehydrated. When the first leaf water potential reached over −0.05 MPa, the drying process was started. Leaf weight and water potential were recorded periodically until the leaf wilted. Individual leaves were then oven dried and weighed to determine leaf dry matter content (LDMC, %). Ψtlp was calculated per pressure volume models (Schulte and Hinckley 1985). Leaf gas exchange, nutrients, specific leaf area (SLA) and stomatal traits Maximum photosynthetic rate (Aarea, µmol m–2 s–1) and stomatal conductance (gs, mol m–2 s–1) of sun-exposed leaves were measured between 09:00 and 11:00 with a portable gas exchange system (Li-6400, LiCor, Lincoln, USA). Three to five leaves were measured per tree. The photosynthetic photon flux density (PPFD) in the measurement chamber was set to 1500 µmol m–2 s–1, a level that should saturate photosynthesis based on previous experiments (Liu et al. 2015). The reference chamber CO2 and leaf temperature were set to 390 ppm and 28°C, respectively. Leaves stabilized in the chamber environment for 5 min before recording a data point. Intrinsic water use efficiency (WUEi) was calculated as Aarea/gs. Between 20 and 30 leaves of each species were scanned using the leaf area meter, then oven-dried for 72 h at 70°C and dry mass weighed. Specific leaf area (SLA, cm2 g–1) was calculated by dividing leaf area by leaf dry mass. To determine leaf nutrient content, dry leaves were ground to a powder and digested with concentrated H2SO4. Kjeldahl analysis was used to measure total nitrogen content (N, %). Atomic absorption spectrum photometry (UV-6000; Metash, Shanghai, China) was used to determine total phosphorus content (P, %). Photosynthetic rate per leaf mass (Amass, nmol g−1 s−1) was calculated as Aarea × SLA. Leaf epidermal peels were prepared from fresh leaves using a sharp razor blade, then imaged on a microscope fitted with a digital camera using image analysis software (OPTPro2012 4.0, Optec XTS20, Chongqing Optec Instrument, China). Three leaves were sampled from each species, from which three epidermal peels were taken from each leaf. Three randomly-located images from each epidermal peel were analyzed. Stomatal density (SD) was calculated and guard cell width (GW) and length (GL) were measured. The stomatal pore area index (SPI, %) was calculated as SD × GL2, which reflects stomatal pore area per leaf area (Sack et al. 2003). Per Franks and Beerling (2009), the maximum diffusive conductance to water vapour (gmax), which estimates transpiration potential, can be calculated as: gmax= (d/v)×SD×amax/[(l+π/2×√(amax/π)] (5) where d (m2 s−1) is the water vapour diffusivity in air at 25°C; v (m3 mol−1) is the air molar volume at 25°C; and SD is stomatal density. amax is the maximum area of the open stomata pore, which was approximated by π∙(p/2)2, where p is the stomata pore length, estimated as GL/2 based on Franks and Beerling (2009). l is the stomata depth for fully opened stomata, estimated as GW/2; and π is the geometric constant (Franks and Beerling 2009). In Magnoliaceae species, stomata only exist on the abaxial surface of the leaf. Data analyses Data were analysed in R version 3.0.3 (R Development Core Team, 2013). Due to the lack of leaves for deciduous species in the dry season, t-tests on plant functional traits between evergreen and deciduous species were only carried out for data collected in the wet season, while differences between the wet and dry seasons were tested for evergreen species using t-tests. To quantify the relationships between JS and VPD, we partitioned the data into three categories by light level and performed boundary line analyses within each category (Chambers et al. 1985). First, JS values were filtered to remove data collected under limiting light (PAR = 0 µmol m–2 s–1) and low VPD (<0.1 kPa), conditions when empirical relationships between canopy stomatal conductance (Gs) and VPD are not well constrained (Oren et al. 1999). This enabled the resulting boundary line to give the best estimate of hydraulic limitation to water flux because the boundary line occurred during conditions that lead to the highest Gs at any given VPD. Next the relationships between JS and VPD were examined using the boundary line analysis separately for data grouped into three light levels: low light (LL; PAR < 400 µmol m–2 s–1), middle light (ML; PAR = 400–800 µmol m–2 s–1) and high light (HL; PAR = 800–1200 µmol m–2 s–1). There were no HL data in the dry season due to low PAR levels at that time. Different models to predict JS from VPD were compared, including linear, exponential, power and polynomial models, and then models with the lowest Akaike information criterion (AIC) values were selected. Due to the lack of leaves and very small JS, the relationships between JS and VPD in the two deciduous species during the dry season were not modelled. RESULTS Environmental conditions in the wet and dry seasons In the study site, air temperature and precipitation are generally higher in the wet season than the dry season, while RH and SWC do not differ between the two seasons (supplementary Fig. S1). During the experimental year, PAR and air temperature in the wet season were higher than those in the dry season (supplementary Fig. S2a and b). In the wet season, VPD was higher in the day and lower in the night than those time points in the dry season. SWC was slightly lower in the dry season compared to the wet season, yet consistently remained above 30%, indicating humid soil conditions throughout the year (supplementary Fig. S2c and d). Daily PAR, VPD and RH readings fluctuated greatly throughout the experimental year due to irregular rainfall events (supplementary Fig. S3a and b). Ecophysiological traits of evergreen and deciduous species under seasonal changes Due to the lack of leaves for the deciduous species in the dry season, we only compared ecophysiological traits between evergreen and deciduous species in the wet season (Table 2). For hydraulic traits, evergreen species had lower KS and KL, and higher AL/AS than deciduous species. Evergreen species also had marginally higher Kleaf than deciduous species (P = 0.07). There were no significant differences in WD, Ψpre, Ψmid and Ψtlp between evergreen and deciduous species. Evergreen species had significantly lower Amass, leaf N and P contents, SLA and stomatal size and higher LDMC than deciduous species (Table 2). All other leaf gas exchange and stomata traits were similar between wet season measurements in the two groups, including Aarea, gs and WUEi, as well as SD, SPI and gmax. Table 2: ecophysiological responses of the 10 Magnoliaceae species in wet and dry seasons Michelia (Ever) Yulania (Deci) Ever-deci (wet season only) Wet-dry (evergreen only) Wet (5) Dry (5) Wet (5) P of t-test P of t-test KS (kg m–1 s–1 MPa–1) 1.6 ± 0.3 1.3 ± 0.2 2.8 ± 0.4 * 0.25 KL×10–4 (kg m–1 s–1 MPa–1) 2.2 ± 0.3 2.2 ± 0.5 7.1 ± 0.6 * 0.89 Kleaf (mmol m–2 s–1 MPa–1) 4.5 ± 0.6 4.1 ± 0.5 3.3 ± 0.3 0.07 0.55 AL/AS (m2 cm–2) 0.7 ± 0.1 0.6 ± 0.0 0.4 ± 0.1 * 0.11 WD (g cm–3) 0.5 ± 0.0 0.5 ± 0.0 0.4 ± 0.0 0.68 0.46 Ψpre (MPa) −0.08 ± 0.0 −0.11 ± 0.0 −0.09 ± 0.0 0.73 0.26 Ψmid (MPa) −0.90 ± 0.08 −1.00 ± 0.17 −0.94 ± 0.15 0.83 0.63 Ψtlp (MPa) −1.46 ± 0.12 −1.88 ± 0.11 −1.40 ± 0.10 0.52 * Aarea (µmol m–2 s–1) 9.2 ± 0.5 8.6 ± 0.9 8.8 ± 1.6 0.83 0.56 Amass (nmol g–1 s–1) 107.6 ± 9.5 76.4 ± 8.2 169.0 ± 18.1 * * gs (mol m–2 s–1) 0.18 ± 0.04 0.11 ± 0.03 0.16 ± 0.04 0.64 * WUEi (µmol mol–1) 65.0 ± 5.3 75.9 ± 3.9 49.4 ± 5.0 0.16 0.68 N (%) 1.6 ± 0.2 1.6 ± 0.1 2.1 ± 0.1 * 0.94 P (%) 0.09 ± 0.01 0.10 ± 0.01 0.13 ± 0.00 * 0.38 Leaf area (cm2) 54.7 ± 13.1 47.3 ± 11.6 63.4 ± 10.4 0.21 0.68 SLA (cm2 g–1) 116.8 ± 9.3 89.8 ± 5.1 203.8 ± 11.1 * * LDMC (%) 33.0 ± 2.0 38.7 ± 2.1 25.1 ± 0.8 ** 0.08 Stomatal size (µm2) 399.9 ± 67.5 390.9 ± 65.4 597.1 ± 46.6 * 0.63 Stomatal density (mm−2) 294.9 ± 26.0 283.3 ± 16.7 256.2 ± 18.4 0.26 0.72 SPI (%) 11.2 ± 2.9 10.5 ± 2.4 15.3 ± 2.8 0.11 0.72 gmax (mol m–2 s–1) 2.5 ± 0.2 2.6 ± 0.3 2.7 ± 0.2 0.56 0.92 Michelia (Ever) Yulania (Deci) Ever-deci (wet season only) Wet-dry (evergreen only) Wet (5) Dry (5) Wet (5) P of t-test P of t-test KS (kg m–1 s–1 MPa–1) 1.6 ± 0.3 1.3 ± 0.2 2.8 ± 0.4 * 0.25 KL×10–4 (kg m–1 s–1 MPa–1) 2.2 ± 0.3 2.2 ± 0.5 7.1 ± 0.6 * 0.89 Kleaf (mmol m–2 s–1 MPa–1) 4.5 ± 0.6 4.1 ± 0.5 3.3 ± 0.3 0.07 0.55 AL/AS (m2 cm–2) 0.7 ± 0.1 0.6 ± 0.0 0.4 ± 0.1 * 0.11 WD (g cm–3) 0.5 ± 0.0 0.5 ± 0.0 0.4 ± 0.0 0.68 0.46 Ψpre (MPa) −0.08 ± 0.0 −0.11 ± 0.0 −0.09 ± 0.0 0.73 0.26 Ψmid (MPa) −0.90 ± 0.08 −1.00 ± 0.17 −0.94 ± 0.15 0.83 0.63 Ψtlp (MPa) −1.46 ± 0.12 −1.88 ± 0.11 −1.40 ± 0.10 0.52 * Aarea (µmol m–2 s–1) 9.2 ± 0.5 8.6 ± 0.9 8.8 ± 1.6 0.83 0.56 Amass (nmol g–1 s–1) 107.6 ± 9.5 76.4 ± 8.2 169.0 ± 18.1 * * gs (mol m–2 s–1) 0.18 ± 0.04 0.11 ± 0.03 0.16 ± 0.04 0.64 * WUEi (µmol mol–1) 65.0 ± 5.3 75.9 ± 3.9 49.4 ± 5.0 0.16 0.68 N (%) 1.6 ± 0.2 1.6 ± 0.1 2.1 ± 0.1 * 0.94 P (%) 0.09 ± 0.01 0.10 ± 0.01 0.13 ± 0.00 * 0.38 Leaf area (cm2) 54.7 ± 13.1 47.3 ± 11.6 63.4 ± 10.4 0.21 0.68 SLA (cm2 g–1) 116.8 ± 9.3 89.8 ± 5.1 203.8 ± 11.1 * * LDMC (%) 33.0 ± 2.0 38.7 ± 2.1 25.1 ± 0.8 ** 0.08 Stomatal size (µm2) 399.9 ± 67.5 390.9 ± 65.4 597.1 ± 46.6 * 0.63 Stomatal density (mm−2) 294.9 ± 26.0 283.3 ± 16.7 256.2 ± 18.4 0.26 0.72 SPI (%) 11.2 ± 2.9 10.5 ± 2.4 15.3 ± 2.8 0.11 0.72 gmax (mol m–2 s–1) 2.5 ± 0.2 2.6 ± 0.3 2.7 ± 0.2 0.56 0.92 Data are mean ± SEM, sample sizes (n) are given in brackets. Due to the lack of leaves for deciduous species in the dry season, t-tests on traits between evergreen and deciduous species are only carried out for the wet season data, with * and ** indicating P < 0.05 and < 0.01, respectively. KS, sapwood specific hydraulic conductivity; KL, leaf specific hydraulic conductivity; Kleaf, leaf hydraulic conductance, AL/AS, leaf to sapwood area ratio; WD, sapwood density; Ψpre, leaf water potential at predawn; Ψmid, leaf water potential at midday; Ψtlp, leaf turgor loss point; Aarea, maximum photosynthetic rate per leaf area; Amass, maximum photosynthetic rate per leaf mass; gs, stomatal conductance; WUEi, intrinsic water use efficiency; N, leaf nitrogen content; P, leaf phosphorus content; SLA, specific leaf area; LDMC, leaf dry matter content; SPI, stomatal pore area index; gmax, maximum stomatal conductance to water vapor. View Large Very few traits differed between the wet and dry season measurements of the evergreen species (Table 2). Specifically, Ψtlp, Amass, gs and SLA were significantly lower in the dry season, compared to the wet season. LDMC was marginally higher in the dry season, compared to the wet season, measurements of the evergreen species (P = 0.08). All other traits did not differ with seasonality in the evergreen species. Sap flow patterns of evergreen and deciduous species under seasonal changes For evergreen species, the dry season JS in Michelia chapensis was higher than that in the wet season, while JS in Michelia shiluensis was similar in both seasons (Fig. 1a–d; supplementary Fig. S3c and d). For deciduous species, JS was similar to evergreen species in the wet season, and it maintained its daily dynamic pattern with very low values in the dry season (Fig. 1e–h; supplementary Fig. S3e and f). JS in Yulania glabrata decreased to less than 10 g H2O m−2 s−1 between 12-Dec-2013 and 22-Mar-2014, while JS decreased to this same low level earlier in Yulania jigongshanensis, between 4-Nov-2013 and 17-Mar-2014 (supplementary Fig. S3e and f). These findings match our phenological observations of leaf longevity, where leaves of Michelia chapensis, Michelia shiluensis, Yulania glabrata and Yulania jigongshanensis persist for 12, 13.5, 6.5 and 7 months, respectively. Figure 1: View largeDownload slide daily changes in sap flux density (JS) for four species during the wet (left) and dry (right) seasons. Evergreen (Ever) and deciduous (Deci) types are labeled after the species code as in Table 1 for each panel. Data are based on typical sunny days in the wet (9–12 Aug and 14–16 Sep) and dry (16–19 Jan and 29–31 Jan) seasons. Figure 1: View largeDownload slide daily changes in sap flux density (JS) for four species during the wet (left) and dry (right) seasons. Evergreen (Ever) and deciduous (Deci) types are labeled after the species code as in Table 1 for each panel. Data are based on typical sunny days in the wet (9–12 Aug and 14–16 Sep) and dry (16–19 Jan and 29–31 Jan) seasons. In the wet season, JS in the four species was sensitive to both VPD and light levels (Fig. 2a, c, e and g). For all three of the assessed light levels, JS climaxed when VPD was around 1.5 kPa and decreased when VPD was greater than 2.0 kPa. JS also differed by light level, with higher JS in ML than LL levels, while JS was similar in ML and HL levels. In the dry season, JS in the two evergreen species also increased with VPD, but they decreased less dramatically when VPD > 2.0 kPa, than in the wet season. Moreover, they were not sensitive to light level, as JS was similar in LL and ML levels (Fig. 2b and d). Figure 2: View largeDownload slide sap flux density (JS) in relation to daytime vapor pressure deficit (VPD) for four species during the wet (left) and dry (right) seasons. Raw data, collected in 10 min intervals as in Fig. 1, are shown as grey crosses. The results of boundary line analyses selected the maximum JS at three PAR levels: low light (LL, black circles, solid thin lines), PAR < 400 µmol m−2 s−1; middle light (ML, white circles, dash lines), PAR = 400–800 µmol m−2 s−1; and high light (HL, black triangles, solid thick lines), PAR = 800–1200 µmol m–2 s–1. Due to low PAR levels, there are no HL data for the dry season. See the coefficients and Akaike information criterion (AIC) values for each curve in supplementary Table S1. Figure 2: View largeDownload slide sap flux density (JS) in relation to daytime vapor pressure deficit (VPD) for four species during the wet (left) and dry (right) seasons. Raw data, collected in 10 min intervals as in Fig. 1, are shown as grey crosses. The results of boundary line analyses selected the maximum JS at three PAR levels: low light (LL, black circles, solid thin lines), PAR < 400 µmol m−2 s−1; middle light (ML, white circles, dash lines), PAR = 400–800 µmol m−2 s−1; and high light (HL, black triangles, solid thick lines), PAR = 800–1200 µmol m–2 s–1. Due to low PAR levels, there are no HL data for the dry season. See the coefficients and Akaike information criterion (AIC) values for each curve in supplementary Table S1. DISCUSSION Ecophysiological traits of evergreen and deciduous species under seasonal changes Most of the morphological and hydraulic traits that we measured in the evergreen Magnoliaceae species did not differ between the wet and dry seasons. Only Amass, gs, Ψtlp and SLA decreased for evergreen species in the dry season, which suggests that hydraulic adjustment mainly occurred at the leaf level when the tropical Michelia species were grown in a subtropical area, with a characteristic cool-dry season. A paucity of precipitation during the dry season reduced water availability, and yet we did not see simultaneous decreases in RH, SWC or VPD. SWC remained sufficiently high as a result of the relatively high mean annual precipitation in Guangzhou (~1700 mm) and the cool dry season temperatures that yielded low VPD, which together reduced drought stress during the experiment. Therefore, these species were not exposed to extreme water stress, as is indicated by our similar dry and wet season measurements of Ψpre, a parameter that indicates soil water potential around the root zone when nighttime transpiration is lacking and the internal water storage is recharged (Bucci et al. 2004). The evergreen Magnoliaceae species had lower gs in the dry season because plants tend to close stomata to reduce transpiration, and consequently lower photosynthesis, during periods of short-term water stress (Arango-Velez et al. 2011; Cochard et al. 2002; Franks et al. 2007; Zhang et al. 2013). However, due to a sufficient soil water supply, Ψmid did not decrease, which suggests that the lower gs measured in the dry season was likely caused by low temperature stress rather than water limitation (Zhu et al. 2011). Meanwhile, leaf Ψmid remained greater than Ψtlp throughout the year because Ψmid remained fairly constant, while measurements of Ψtlp in the evergreen species strongly decreased in the dry season. The lower Ψtlp indicates that these species enhanced leaf desiccation tolerance in the dry season (Lenz et al. 2006; Maréchaux et al. 2015). Evergreen leaves tend to have lower SLA and higher LDMC in the dry season (Calkin and Pearcy 1984), which are the structural basis for lower leaf elasticity and Ψtlp through osmotic adjustments (Bartlett et al. 2012; Jacobsen et al. 2007; Scholz et al. 2012). Our findings, that the deciduous Magnoliaceae species had higher branch level hydraulic conductivity (KS and KL) with lower AL/AS and LDMC, higher Amass, leaf nutrients (N, P) and SLA, and larger stomatal size in the wet season were expected, as they acquire more resources and achieve higher assimilation during the wet portion of the year compared to the dry season, as has been reported in other species (Carter and White 2009; Chen et al. 2009; Choat et al. 2005). In addition, all ten species maintained Ψmid above Ψtlp in both seasons, indicating that they did not suffer from excessive water stress as a result of only mild drought conditions and their ecophysiological adjustments. Sap flow patterns of evergreen and deciduous species in the wet and dry seasons Contrary to our second hypothesis, the evergreen and deciduous species had similar JS values in the wet season, although deciduous species did have higher KS and KL at this time of year. These findings, however, are not completely unexpected and are consistent with previous findings (Dünisch and Morais 2002). First, KS and KL indicate the potential hydraulic conductivity, such that deciduous species might not achieve these maximum values under natural conditions. Second, the different branch architectures in evergreen and deciduous species might obscure whole-plant hydraulic differences (Givnish 2002; Meinzer et al. 2010). For example, higher AL/AS and lower KS and KL in evergreen species may result in similar canopy-level conductivities and whole-tree water consumption levels as deciduous species. Third, Ψmid and gs did not differ between evergreen and deciduous species in our study, which supports the JS pattern, but conflicts with the idea that the average canopy-level transpiration and gs values would be lower for evergreen than deciduous species (Meinzer et al. 1993). Instead, we attribute this to a sampling bias, in that our Ψmid and gs were measured on only sun-exposed leaves from the upper crown of the trees. While evergreen Michelia species have very dense crowns with most of their leaves shaded, the deciduous Yulania species have sparsely arranged branches, such that most leaves are exposed to the sun. Thus, at the whole tree level, similar JS for the two Magnoliaceae genera represent an overall balance between leaf-, branch- and tree-level water use strategies, of which the key linkages needed further investigation. The dramatic decrease in JS caused by the dry season leaf shedding of the deciduous species is natural. Indeed, it has been reported that the critical factor controlling seasonal stand transpiration is the degree of foliation present in temperate deciduous forests (Körner 1995). Moreover, under drought conditions, deciduous leaves can serve as hydraulic fuses among seasonal dry tropical forest species (Wolfe et al. 2016). JS in our evergreen species did not differ between the two seasons, despite experiencing significant decreases in gs and SLA in the dry season, which indicates that other ecophysiological regulations were at play. One possible explanation might be the specific phenology of the four species. We found that Michelia chapensis yields new leaves twice a year (i.e., in late September and early March), while Michelia shiluensis only produces new leaves once a year (i.e., in early March), and that both evergreen species bloom heavily in late February. As a consequence, the evergreen species require more water in January for flower and leaf bud growth. In contrast, the two deciduous species start to shed their leaves in the middle of September and do not grow new leaves until the middle of March. They also have very few flowers by early February (i.e., fewer than ten flowers per individual tree; H. Liu, unpublished data). Therefore, JS did not increase in the deciduous species during the dry season because the paucity of leaves and flowers at this time of year alleviates the need for significant water concentrations throughout the tree. During the wet season, PAR and VPD controlled the diurnal patterns of JS for evergreen and deciduous species in a similar way, suggesting that responses to these environmental cues is independent of leaf habit (Dünisch and Morais 2002; Pataki et al. 2000). VPD affected the shape of the diurnal patterns in JS, while PAR influenced the amount of JS, as has been reported in deciduous forests (Wullschleger et al. 2001). The effect of PAR on the relationship between JS and VPD, a trend that is reportedly related to low gs during this season, was weak in the dry season due to low overall PAR levels (<800 µmol m–2 s–1) (Williams et al. 1996). However, at the whole tree level, JS was likely prompted by more physiological processes in the evergreen species, such as flower and leaf growth. Differences between evergreen and deciduous species reflect their divergent evolution Most Magnoliaceae lineages are distributed in tropical and subtropical climates, however, the temperate-centered Yulania lineage was separated from its close relatives, the tropical-centered Michelia, before the Oligocene (Nie et al. 2008). As we show here, this long period of divergent evolution (i.e., ~40 million years) led to contrasting adaptive strategies. In the subtropical area with sufficient water availability, stems and leaves were hydraulically safe during the whole year of our study (i.e., Ψmid was never lower than Ψtlp), as is also seen in species of the dry tropical forests (Hasselquist et al. 2010), the evergreen species here did not need to strictly control for hydraulic conductivity, but instead increased investment in leaves via higher AL/AS compared with their deciduous relatives, to compensate for the shady subtropical forest environment (i.e., the natural conditions for Michelia). In contrast, the deciduous species employed high stem hydraulic conductivity (KS and KL) and leaf investment in the wet season, which allowed for greater leaf resources (i.e., high N and P content, greater stomata area), to ultimately achieve higher photosynthetic rates compared to the evergreen species (Choat et al. 2005). Overall, ex situ conservation and seasonality did not bring large ecophysiological variations, and instead leaf habit might more be decisive on how species would adapt to local environmental conditions. Furthermore, similar water consumption in evergreen and deciduous Magnoliaceae species also implies the rationality of ex situ conservation in subtropical China. CONCLUSIONS Magnoliaceae species ex situ conserved in a subtropical climate did not suffer from excessive water stress in the dry season due to their ecophysiological adjustments and only mild drought conditions during the study year. In the dry season, SWC and VPD did not significantly decrease, but precipitation, temperature and light levels were low, so that evergreen species only reduced Amass, gs, Ψtlp and SLA. Thus, we conclude that the divergent evolution between the evergreen Michelia and deciduous Yulania lineages led to different ecophysiological patterns: evergreen species have higher AL/AS, while deciduous species have higher KS, KL, Amass, leaf nutrients, SLA and stomatal size, but lower LDMC. All species showed similar sap flow patterns during the wet season. Furthermore, both PAR and VPD affected the diurnal patterns of JS in the four species over the wet season, while in the dry season, only VPD was dominant in affecting JS in evergreen species. Therefore, ecophysiological adaptations might be affected more by leaf habit than seasonality for the ex situ conserved Magnoliaceae species. These species showed good hydraulic status in the subtropical area, which provides a successful case for the ex situ conservation of this ancient family, and proves valuable for studying potential adaptations of Magnoliaceae species under climatic changes. SUPPLEMENTARY MATERIAL Supplementary material is available at Journal of Plant Ecology online. FUNDING National Natural Science Foundation of China (31670411). ACKNOWLEDGEMENTS We thank Ronghua Li, Jin Ma, Guoliang Ye and Hui Zhang from the South China Botanical Garden for their technical assistance. Conflict of interest statement. None declared. REFERENCES Arango-Velez A Zwiazek JJ Thomas BRet al. . ( 2011) Stomatal factors and vulnerability of stem xylem to cavitation in poplars. Physiol Plant 143: 154– 65. Google Scholar CrossRef Search ADS PubMed Azuma H García-Franco JG Rico-Gray Vet al. . ( 2001) Molecular phylogeny of the Magnoliaceae: the biogeography of tropical and temperate disjunctions. Am J Bot 88: 2275– 85. Google Scholar CrossRef Search ADS PubMed Bartlett MK Scoffoni C Sack L( 2012) The determinants of leaf turgor loss point and prediction of drought tolerance of species and biomes: a global meta-analysis. Ecol Lett 15: 393– 405. Google Scholar CrossRef Search ADS PubMed Bucci SJ Scholz FG Campanello PIet al. . ( 2012) Hydraulic differences along the water transport system of South American Nothofagus species: do leaves protect the stem functionality? Tree Physiol 32: 880– 93. Google Scholar CrossRef Search ADS PubMed Bucci SJ Scholz FG Goldstein Get al. . ( 2004) Processes preventing nocturnal equilibration between leaf and soil water potential in tropical savanna woody species. Tree Physiol 24: 1119– 27. Google Scholar CrossRef Search ADS PubMed Calkin HW Pearcy RW( 1984) Seasonal progressions of tissue and cell water relations parameters in evergreen and deciduous perennials. Plant Cell Environ 7: 347– 52. Campbell G Norman J( 1998) An Introduction to Environmental Biophysics . New York: Springer, 36– 51. Google Scholar CrossRef Search ADS Carter JL White DA( 2009) Plasticity in the Huber value contributes to homeostasis in leaf water relations of a mallee Eucalypt with variation to groundwater depth. Tree Physiol 29: 1407– 18. Google Scholar CrossRef Search ADS PubMed Chambers JL Hinckley TM Cox GSet al. ( 1985) Boundary-line analysis and models of leaf conductance for 4 oak-hickory forest species. For Sci 31: 437– 50. Chen J-W Zhang Q Cao K-F( 2009) Inter-species variation of photosynthetic and xylem hydraulic traits in the deciduous and evergreen Euphorbiaceae tree species from a seasonally tropical forest in south-western China. Ecol Res 24: 65– 73. Google Scholar CrossRef Search ADS Choat B Ball M Luly J Holtum JM( 2005) Hydraulic architecture of deciduous and evergreen dry rainforest tree species from north-eastern Australia. Trees 19: 305– 11. Google Scholar CrossRef Search ADS Choat B Jansen S Brodribb TJet al. . ( 2012) Global convergence in the vulnerability of forests to drought. Nature 491: 752– 5. Google Scholar PubMed Cicuzza D Newton A Oldfield S( 2007) The Red List of Magnoliaceae. Cambridge, UK: Botanic Gardens Conservation International (BGCI) and Fauna & Flora International (FFI) . Cochard H Bréda N Granier A( 1996) Whole tree hydraulic conductance and water loss regulation in Quercus during drought: evidence for stomatal control of embolism? Annales des Sciences Forestières 53: 197– 206. Google Scholar CrossRef Search ADS Cochard H Coll L Le Roux Xet al. . ( 2002) Unraveling the effects of plant hydraulics on stomatal closure during water stress in walnut. Plant Physiol 128: 282– 90. Google Scholar CrossRef Search ADS PubMed Dünisch O Morais RR( 2002) Regulation of xylem sap flow in an evergreen, a semi-deciduous, and a deciduous Meliaceae species from the Amazon. Trees 16: 404– 16. Davidson AM Jennions M Nicotra AB( 2011) Do invasive species show higher phenotypic plasticity than native species and, if so, is it adaptive? A meta-analysis. Ecol Lett 14: 419– 31. Google Scholar CrossRef Search ADS PubMed Domec JC Scholz FG Bucci SJet al. . ( 2006) Diurnal and seasonal variation in root xylem embolism in neotropical savanna woody species: impact on stomatal control of plant water status. Plant Cell Environ 29: 26– 35. Google Scholar CrossRef Search ADS PubMed Drake PL Franks PJ( 2003) Water resource partitioning, stem xylem hydraulic properties, and plant water use strategies in a seasonally dry riparian tropical rainforest. Oecologia 137: 321– 9. Google Scholar CrossRef Search ADS PubMed Fan DY Jie SL Liu CCet al. . ( 2011) The trade-off between safety and efficiency in hydraulic architecture in 31 woody species in a karst area. Tree Physiol 31: 865– 77. Google Scholar CrossRef Search ADS PubMed Franks PJ( 2006) Higher rates of leaf gas exchange are associated with higher leaf hydrodynamic pressure gradients. Plant Cell Environ 29: 584– 92. Google Scholar CrossRef Search ADS PubMed Franks PJ Beerling DJ( 2009) Maximum leaf conductance driven by CO2 effects on stomatal size and density over geologic time. Proc Natl Acad Sci USA 106: 10343– 7. Google Scholar CrossRef Search ADS PubMed Franks PJ Drake PL Froend RH( 2007) Anisohydric but isohydrodynamic: seasonally constant plant water potential gradient explained by a stomatal control mechanism incorporating variable plant hydraulic conductance. Plant Cell Environ 30: 19– 30. Google Scholar CrossRef Search ADS PubMed Givnish TJ( 2002) Adaptive significance of evergreen vs. deciduous leaves: solving the triple paradox. Silva Fennica 36: 703– 43. Google Scholar CrossRef Search ADS Granier A( 1985) A new method of sap flow measurement in tree stems. Annales Des Sciences Forestieres 42: 193– 200. Google Scholar CrossRef Search ADS Hasselquist NJ Allen MF Santiago LS( 2010) Water relations of evergreen and drought-deciduous trees along a seasonally dry tropical forest chronosequence. Oecologia 164: 881– 90. Google Scholar CrossRef Search ADS PubMed Herbst M Rosier PT Morecroft MDet al. . ( 2008) Comparative measurements of transpiration and canopy conductance in two mixed deciduous woodlands differing in structure and species composition. Tree Physiol 28: 959– 70. Google Scholar CrossRef Search ADS PubMed IUCN SSC( 2001) IUCN Red List Categories and Criteria, Version 3.1 . Gland, Switzerland and Cambridge, UK: IUCN Species Survival Commission. Jacobsen AL Pratt RB Davis SDet al. . ( 2007) Cavitation resistance and seasonal hydraulics differ among three arid Californian plant communities. Plant Cell Environ 30: 1599– 609. Google Scholar CrossRef Search ADS PubMed James SA Clearwater MJ Meinzer FCet al. . ( 2002) Heat dissipation sensors of variable length for the measurement of sap flow in trees with deep sapwood. Tree Physiol 22: 277– 83. Google Scholar CrossRef Search ADS PubMed Ježík M Blaženec M Letts MGet al. . ( 2015) Assessing seasonal drought stress response in Norway spruce (Picea abies (L.) Karst.) by monitoring stem circumference and sap flow. Ecohydrology 8: 378– 86. Google Scholar CrossRef Search ADS Körner C( 1995) Leaf Diffusive Conductances in the Major Vegetation Types of the Globe Ecophysiology of Photosynthesis . Berlin: Springer, 463– 90. Kim S Suh Y( 2013) Phylogeny of Magnoliaceae based on ten chloroplast DNA regions. J Plant Biol 56: 290– 305. Google Scholar CrossRef Search ADS Lenz TI Wright IJ Westoby M( 2006) Interrelations among pressure–volume curve traits across species and water availability gradients. Physiologia Plantarum 127: 423– 33. Google Scholar CrossRef Search ADS Liu H Lundgren MR Freckleton RPet al. . ( 2016) Uncovering the spatio-temporal drivers of species trait variances: a case study of Magnoliaceae in China. J Biogeogr 43: 1179– 91. Google Scholar CrossRef Search ADS Liu H Xu Q He Pet al. . ( 2015) Strong phylogenetic signals and phylogenetic niche conservatism in ecophysiological traits across divergent lineages of Magnoliaceae. Sci Rep 5: 12246. Google Scholar CrossRef Search ADS PubMed Liu YH Zhou RZ Zeng QW( 1997) Ex situ conservation of Magnoliaceae including its area and endangered species. J Trop Subtrop Bot 5: 1– 12. Maréchaux I Bartlett MK Sack Let al. . ( 2015) Drought tolerance as predicted by leaf water potential at turgor loss point varies strongly across species within an Amazonian forest. Funct Ecol 29: 1268– 77. Google Scholar CrossRef Search ADS Massoni J Couvreur TL Sauquet H( 2015) Five major shifts of diversification through the long evolutionary history of Magnoliidae (angiosperms). BMC Evol Biol 15: 1– 14. Google Scholar CrossRef Search ADS PubMed Maunder M Higgens S Culham A( 2001) The effectiveness of botanic garden collections in supporting plant conservation: a European case study. Biodivers Conserv 10: 383– 401. Google Scholar CrossRef Search ADS Meinzer FC Clearwater MJ Goldstein G( 2001) Water transport in trees: current perspectives, new insights and some controversies. Environ Exp Bot 45: 239– 62. Google Scholar CrossRef Search ADS PubMed Meinzer FC Goldstein G Holbrook NMet al. . ( 1993) Stomatal and environmental control of transpiration in a lowland tropical forest tree. Plant Cell Environ 16: 429– 36. Google Scholar CrossRef Search ADS Meinzer FC McCulloh KA Lachenbruch Bet al. . ( 2010) The blind men and the elephant: the impact of context and scale in evaluating conflicts between plant hydraulic safety and efficiency. Oecologia 164: 287– 96. Google Scholar CrossRef Search ADS PubMed Nardini A Gullo MAL Trifilò Pet al. . ( 2014) The challenge of the Mediterranean climate to plant hydraulics: responses and adaptations. Environ Exp Bot 103: 68– 79. Google Scholar CrossRef Search ADS Nie ZL Wen J Azuma Het al. . ( 2008) Phylogenetic and biogeographic complexity of Magnoliaceae in the Northern Hemisphere inferred from three nuclear data sets. Mol Phylogenet Evol 48: 1027– 40. Google Scholar CrossRef Search ADS PubMed O’Brien JJ Oberbauer SF Clark DB( 2004) Whole tree xylem sap flow responses to multiple environmental variables in a wet tropical forest. Plant Cell Environ 27: 551– 67. Google Scholar CrossRef Search ADS Oren R Sperry J Katul Get al. . ( 1999) Survey and synthesis of intra- and interspecific variation in stomatal sensitivity to vapour pressure deficit. Plant Cell Environ 22: 1515– 26. Google Scholar CrossRef Search ADS Parmesan C( 2006) Ecological and evolutionary responses to recent climate change. Annu Rev Ecol Evol Syst 37: 637– 69. Google Scholar CrossRef Search ADS Pataki DE Oren R Smith WK( 2000) Sap flux of co-occurring species in a western subalpine forest during seasonal soil drought. Ecology 81: 2557– 66. Google Scholar CrossRef Search ADS Pivovaroff AL Sack L Santiago LS( 2014) Coordination of stem and leaf hydraulic conductance in southern California shrubs: a test of the hydraulic segmentation hypothesis. New Phytol 203: 842– 50. Google Scholar CrossRef Search ADS PubMed R Development Core Team( 2013) R: A Language and Environment for Statistical Computing . Vienna, Austria: R Foundation for Statistical Computing. Sack L Cowan PD Jaikumar Net al. . ( 2003) The ‘hydrology’ of leaves: co-ordination of structure and function in temperate woody species. Plant Cell Environ 26: 1343– 56. Google Scholar CrossRef Search ADS Scholz FG Bucci SJ Arias Net al. . ( 2012) Osmotic and elastic adjustments in cold desert shrubs differing in rooting depth: coping with drought and subzero temperatures. Oecologia 170: 885– 97. Google Scholar CrossRef Search ADS PubMed Schulte PJ Hinckley TM( 1985) A comparison of pressure-volume curve data analysis techniques. J Exp Bot 36: 1590– 1602. Google Scholar CrossRef Search ADS Thomas CD Cameron A Green REet al. . ( 2004) Extinction risk from climate change. Nature 427: 145– 8. Google Scholar CrossRef Search ADS PubMed Tyree M Hammel H( 1972) The measurement of the turgor pressure and the water relations of plants by the pressure-bomb technique. J Exp Bot 23: 267– 82. Google Scholar CrossRef Search ADS Williams M Rastetter E Fernandes Det al. . ( 1996) Modelling the soil-plant-atmosphere continuum in a Quercus-Acer stand at Harvard Forest: the regulation of stomatal conductance by light, nitrogen and soil/plant hydraulic properties. Plant Cell Environ 19: 911– 27. Google Scholar CrossRef Search ADS Wolfe BT Sperry JS Kursar TA( 2016) Does leaf shedding protect stems from cavitation during seasonal droughts? A test of the hydraulic fuse hypothesis. New Phytol . 212:1007–18. Wullschleger SD Hanson P Todd D( 2001) Transpiration from a multi-species deciduous forest as estimated by xylem sap flow techniques. For Ecol Manag 143: 205– 13. Google Scholar CrossRef Search ADS Xu F( 2008) Anatomical Figures for Wood Idenification . Beijing: Chemical Industry Press. Zhang YJ Meinzer FC Qi JHet al. . ( 2013) Midday stomatal conductance is more related to stem rather than leaf water status in subtropical deciduous and evergreen broadleaf trees. Plant Cell Environ 36: 149– 58. Google Scholar CrossRef Search ADS PubMed Zhu GF Li X Su YHet al. . ( 2011) Seasonal fluctuations and temperature dependence in photosynthetic parameters and stomatal conductance at the leaf scale of Populus euphratica Oliv. Tree Physiol 31: 178– 95. Google Scholar CrossRef Search ADS PubMed © The Author(s) 2017. Published by Oxford University Press on behalf of the Institute of Botany, Chinese Academy of Sciences and the Botanical Society of China. All rights reserved. For permissions, please email: email@example.com
Journal of Plant Ecology – Oxford University Press
Published: Jun 1, 2018
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