It’s about time! Leaf minimum conductance determines time to reach critical thresholds for leaf dehydration in a seasonal tropical forest

It’s about time! Leaf minimum conductance determines time to reach critical thresholds for leaf dehydration in a seasonal tropical forest | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results It’s about time! Leaf minimum conductance determines time to reach critical thresholds for leaf dehydration in a seasonal tropical forest View ORCID Profile Ron Sunny , View ORCID Profile Malavika Venu , View ORCID Profile Bandaru Peddiraju , View ORCID Profile Souparna Chakrabarty , View ORCID Profile Deepak Barua doi: https://doi.org/10.1101/2025.07.07.663429 Ron Sunny 1 Department of Biology, Indian Institute of Science Education and Research , Pune, India 411008 2 National Center for Biological Science , Bengaluru, Karnataka 560097, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ron Sunny For correspondence: ronsunny1512{at}gmail.com dbarua{at}iiserpune.ac.in Malavika Venu 1 Department of Biology, Indian Institute of Science Education and Research , Pune, India 411008 3 University of Illinois Urbana-Champaign , IL 61820, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Malavika Venu Bandaru Peddiraju 1 Department of Biology, Indian Institute of Science Education and Research , Pune, India 411008 2 National Center for Biological Science , Bengaluru, Karnataka 560097, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Bandaru Peddiraju Souparna Chakrabarty 1 Department of Biology, Indian Institute of Science Education and Research , Pune, India 411008 4 Memorial Sloan Kettering Cancer Center , New York, NY 10065, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Souparna Chakrabarty Deepak Barua 1 Department of Biology, Indian Institute of Science Education and Research , Pune, India 411008 Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Deepak Barua For correspondence: ronsunny1512{at}gmail.com dbarua{at}iiserpune.ac.in Abstract Full Text Info/History Metrics Supplementary material Preview PDF Summary Leaf minimum conductance ( g min ) is important in determining plant responses to drought. However, we do not understand how g min is related to drought tolerance across species, and how important it is in determining the time to reach critical dehydration levels. In 18 coexisting species from a seasonal tropical forest we quantified g min to test relationships with early, moderate and severe dehydration thresholds associated with tolerance to turgor loss, breakdown of structural integrity, and disruption of cellular function, respectively. We quantified other functional and hydraulic traits to determine the major axes of trait variation in these species. Variation in g min was the primary determinant of the time to reach critical levels of dehydration, and was unrelated to thresholds for early and severe dehydration. Surprisingly, g min was negatively related to maximum stomatal conductance, but unrelated to other functional and hydraulic traits. These results highlight the importance of avoiding dehydration via minimizing g min , and suggest that avoidance, tolerance to early, and severe dehydration represent independent strategies for coping with drought. This would allow coexisting species to balance opportunities for carbon gain with costs of physiological breakdown when faced with varying intensities and durations of drought. Introduction Leaf water status is central to determining multiple aspects of leaf function. Increased leaf dehydration during drought causes the breakdown of vital physiological processes with important implications for carbon assimilation, growth, and mortality ( Choat et al ., 2018 ; Powers et al ., 2020 ; McDowell et al ., 2022 ). Determining the key physiological processes ( Blackman et al ., 2010 ; Klein et al ., 2014 ; Bartlett et al ., 2016 ; Trueba et al ., 2019 ), the order in which these are disrupted ( Bartlett et al ., 2016 ; Trueba et al ., 2019 ), and how these differ across species has provided a better mechanistic understanding of species responses to drought. However, a comprehensive understanding of the diverse processes that drive responses to drought is still lacking ( Mantova et al ., 2022 ), and the ability to predict drought-induced reductions in function, and associated changes in growth, productivity, and mortality remains challenging ( Trugman et al ., 2021 ). The dynamics of leaf water loss during drought, which determine the time taken to reach critical levels of dehydration has been highlighted as an important aspect of species responses for which we lack essential information ( Hammond & Adams, 2019 ; Brodribb et al ., 2020 ; Machado et al ., 2021 ; Blackman et al ., 2023 ). In this study, we examined leaf water loss during dehydration, and quantified leaf minimum conductance in coexisting evergreen and deciduous species from a seasonally dry tropical forest, and tested how residual water loss from leaves during drought is related to tolerance to early, moderate and severe dehydration. Over the last couple of decades, a large body of work has identified the key physiological processes that are disrupted with increasing dehydration in leaves, and this has allowed a better understanding of the underlying mechanisms, and the functional consequences of exposure to drought. During the early stages of leaf dehydration, loss of turgor and stomatal closure results in the cessation of carbon assimilation ( Klein et al ., 2014 ; Bartlett et al ., 2016 ). With increasing water loss there is increased leaf shrinkage, breakdown in the structural integrity of leaves, and the loss of conductance of the extra-xylem mesophyll pathways ( Scoffoni et al ., 2014 ; Johnson et al ., 2018 ). The reduced water potential in the xylem vessels leads to increased embolism and disruption of leaf hydraulic conductance, and ultimately the loss of rehydration capacity ( Venturas et al ., 2017 ; John et al ., 2018 ). More recently, studies have shown that with severe dehydration there is loss of cellular function and viability which ultimately results in cell death and leaf necrosis ( Trueba et al ., 2019 ; Mantova et al ., 2022 ). The sequence of breakdown of these physiological processes with increasing dehydration is typically correlated across species; i.e., species tolerant to mild dehydration are usually also tolerant to moderate and severe dehydration. This suggests coordination due to correlated selection or underlying mechanistic linkages ( Bartlett et al ., 2016 ; Trueba et al ., 2019 ; Jin et al ., 2023 ; Ziegler et al ., 2024 ). The leaf water status that results in the breakdown of key physiological process, e.g. the relative water content (RWC) at loss of turgor, represents important species-specific thresholds that are related to drought tolerance ( Anderegg et al ., 2016 ; Torres-Ruiz et al ., 2024 ). Thus, quantification of species thresholds has allowed assessment of vulnerabilities of species, forests and ecosystems to drought ( Maherali et al ., 2004 ; Bartlett et al ., 2016 ). The substantial progress in identifying the important physiological processes that are disrupted with increasing dehydration is in contrast to the general lack of studies that have examined the dynamics of water loss during drought. Plants continue to lose water even after complete stomatal closure ( Schreiber & Riederer, 1996 ; Schuster et al ., 2017 ). This residual water loss, leaf minimum conductance ( g min ), occurs via the cuticle, or through leaky stomata ( Duursma et al ., 2019 ). The magnitude of water loss via g min can be substantial, and can vary considerably across species ( Schreiber & Riederer, 1996 ; Schuster et al ., 2017 ; Duursma et al ., 2019 ; Ochoa et al ., 2024 ). Such estimates of g min are important for quantifying water loss, water use efficiency, and gas exchange across scales from leaves to ecosystems ( De Kauwe et al ., 2020 ; Lanning et al ., 2020 ; Ochoa et al ., 2024 ). There is growing evidence to suggest that g min is important in species responses to drought, and may determine the time to reach critical levels of dehydration ( Gleason et al ., 2014 ; Blackman et al ., 2016 , 2019 , 2023 ; Martin-StPaul et al ., 2017 ; De Kauwe et al ., 2020 ; Machado et al ., 2021 ; Wang et al ., 2024 ; Ziegler et al ., 2024 ). However, our understanding of how important g min is in determining the time species take to reach critical levels of dehydration, how it is related to other hydraulic traits that contribute to drought tolerance, and the determinants of variation in g min across species is still incomplete ( Choat et al ., 2018 ; Brodribb et al ., 2020 ; Blackman et al ., 2023 ; Ochoa et al ., 2024 ). Physiological thresholds like the water potential that results in a 50 % disruption of hydraulic conductance are widely used as indices of drought tolerance, and species that can maintain function at lower levels of dehydration are assumed to be more tolerant ( Bartlett et al ., 2016 ; Trueba et al ., 2019 ; Jin et al ., 2023 ; Ziegler et al ., 2024 ). However, in solely using such physiological thresholds as a measure of drought tolerance, an implicit assumption being made is that differences in water loss between species are negligible. Avoiding dehydration by minimizing water loss can represent an alternate strategy to cope with drought ( Martin-StPaul et al ., 2017 ), and the rates of residual water loss, and differences between species can be substantial ( Schreiber & Riederer, 1996 ; Schuster et al ., 2017 ; Duursma et al ., 2019 ). Thus, overall tolerance to drought may be better represented by measures that incorporate both species thresholds and the rates of water loss ( Blackman et al., 2016 ). The time taken by species when exposed to drought to reach critical thresholds that result in impairment of function represents such an integrated measure. Results from studies that have examined how g min is related to thresholds for hydraulic dysfunction suggest these may not be related ( Petek-Petrik et al ., 2023 ; Waite et al ., 2024 ), or may even be positively related to each other ( Martin-StPaul et al ., 2017 ; Ziegler et al ., 2024 ). Thus, species with high drought tolerance may also have high g min , indicative of a tradeoff between tolerance (thresholds) and avoidance (water loss via g min ) ( Martin-StPaul et al ., 2017 ). As a consequence, the rank order of species drought tolerance based on thresholds, may not reflect the ranks based on the time taken by species to reach these physiological thresholds. To understand how leaf minimum conductance ( g min ) is related to critical physiological thresholds, we quantified relative water content (RWC) based thresholds for early, moderate and severe dehydration. We selected the RWC that resulted in turgor loss, which is mechanistically linked to stomatal closure ( Bartlett et al ., 2016 ; Martin-StPaul et al ., 2017 ), as the threshold for early dehydration. Turgor loss point thresholds vary widely ( Maréchaux et al ., 2015 ), and are related to species responses to drought ( McGregor et al ., 2021 ), and species distributions across gradients of water availability ( Baltzer et al ., 2008 ). We used the RWC that caused significant leaf shrinkage, an indicator of the loss of leaf structural integrity and disruption in leaf hydraulic conductance ( Scoffoni et al ., 2014 , 2017 ) as a threshold related to moderate dehydration. Leaf shrinkage is associated with the breakdown of the extra-xylem hydraulic pathways, and the loss of cell-cell connectivity ( Sancho-Knapik et al ., 2011 ; Scoffoni et al ., 2014 ; Buckley, 2015 ). Finally, we used the leaf RWC that results in the breakdown in photosystem-II (PSII) function as a threshold for severe dehydration. Recent studies have suggested that the tolerance of leaf photochemistry to dehydration is a promising trait to assess plant drought tolerance ( Fortunel et al ., 2023 ). The breakdown of PSII function is directly linked to cell viability, cellular death and leaf necrosis ( Mantova et al ., 2022 ). Unlike turgor loss and shrinkage, which represent thresholds from which leaves can potentially recover, disruption of PSII function represents irreversible damage that ultimately leads to cell death ( Mantova et al ., 2021 ). We used leaf dehydration assays to quantify leaf minimum conductance ( g min ) in 18 coexisting evergreen and deciduous trees from a seasonally dry tropical forest in peninsular India. To determine tolerance to different intensities of water stress from early to severe dehydration, we quantified the RWC based thresholds for loss of turgor, breakdown of structural integrity, and disruption of cellular function in leaves of these species. In the course of the leaf dehydration assays we quantified the time taken by species to reach these critical physiological breakpoints. This allowed us to test how time to reach thresholds was related to g min and the thresholds themselves. Additionally, we quantified stomatal traits, key leaf functional traits, and other hydraulic traits to test how these are related to leaf minimum conductance ( g min ). Finally, we used a principal component analysis to determine the major axes of variation and hence strategies that emerge from the traits examined. Materials and Methods Site description, species selection and sample collection The Northern Western Ghats region in peninsular India is highly seasonal, and most of the mean annual precipitation of around 2266 mm occurs during the monsoon months between June and October ( New et al ., 2002 ). The long dry season with average monthly rainfall less than 100 mm extends from November to May. The landscape is topographically diverse with valleys carved out by the Bhima river and its tributaries. The top of the valleys consist of flattened ridges with low soil depth and high light availability, and are characterized by vegetation of low stature and a higher percentage of deciduous species (open forests). In contrast, the bottom of the valleys that have greater soil depth and low light availability in the understorey, are dominated by tall statured evergreen species (closed forests). The transition vegetation in the slopes of the valleys are intermediate between the closed and open forests. Evergreen and deciduous leaf habits that represent distinct strategies to cope with the seasonally varying water availability are both common in our study site ( Sunny et al ., 2025 ). Unlike deciduous species that shed their leaves in the dry season, evergreen species typically minimize water loss and maximize drought tolerance to maintain their canopy throughout the year ( Álvarez-Yépiz et al ., 2017 ). We selected 18 angiosperm tree species (11 evergreen and 7 deciduous) based on their dominance in a seasonally dry tropical forest near Nigdale, Maharashtra, India. The cumulative basal area of these 18 species is greater than 80 % of the total basal area in these forests (Jazeera et al ., 2016). Mature individuals of each species were selected, and upper canopy sun-exposed branches of around 1 m in length were collected using a combination of tree-climbers and telescopic tree pruners. Leaf dehydration assay and quantification of leaf minimum conductance For 6 replicate individuals of each species, 2 nd or 3 rd order branches containing fully expanded and mature leaves were collected between March and May of 2017. These branches were placed in a darkened plastic bag, and the bags were sealed with moist paper towels to keep the air in the bag water-saturated for transportation to the laboratory. On the same day in the laboratory, the leaves were cut underwater, placed in beakers with petioles immersed in water, and put in a sealed plastic bag in the dark for overnight rehydration (> 12 h). The next morning, saturated fresh weight (SFW) was quantified for one mature leaf from each individual. The leaf was then kept upright and allowed to dry in the laboratory on a drying rack at low irradiance. A table fan set at low speed was used to minimize boundary layer conductance throughout the assay. Temperature and humidity was monitored using a custom-built Arduino-based data logger and sensors. The average relative humidity in the room was 38.1 ± 7.0 %, and the average temperature was 27.1 ± 2.1 °C. After the initial saturated fresh weight measurement, the leaf was weighed after the 1 st , 2 nd , 3 rd , 6 th , 9 th and 13 th hour, and subsequently, after every 12 hours of drying. The assay continued till the dark-adapted fluorescence measurements (described in the next section) reached zero, indicating a complete loss of cellular function. The leaves were subsequently oven-dried for 72 hours at 70 °C, and the dry weight (DW) quantified. The relative water content (RWC) corresponding to every time point ( t ) for a given leaf was calculated as: where FW( t ) corresponds to the leaf weight at time t . An exponential decay model was fit to RWC as a function of time, and this was used to determine the time at which a leaf lost 50 % of its RWC (Time RWC50 ). Leaf minimum conductance ( g min ) was determined using protocols described by Sack et al. (2003) and Sack and Scoffoni (2010) . Briefly, this was quantified as the rate of water loss from the leaf after stomatal closure, divided by the vapour pressure deficit (VPD) and normalized by the double-sided leaf area. The first hour of drying was excluded, to account for complete stomatal closure, and the slope of the subsequent linear portion of the leaf drying curve was used to quantify g min . Leaf area was measured using a desktop scanner and ImageJ (version 1.47v; National Institutes of Health, Maryland, US). Leaf shrinkage The same leaf used for the above assay was used to quantify leaf thickness and shrinkage using a digital micrometer (±0.002 mm, Mitutoyo) during dehydration ( Scoffoni et al ., 2014 ). The initial thickness was measured for the water saturated leaf before the initiation of drying, and then subsequently after the 1 st , 2 nd , 3 rd , 6 th , 9 th and 13 th hour of drying, and subsequently, after every 12 hours. The final leaf thickness was measured for the oven-dried leaves. Leaf thickness was normalized by the initial thickness measured for the water-saturated leaf to calculate relative thickness. An exponential decay model was fit to the pooled data from all individuals of a species for change in relative thickness as a function of leaf RWC. The leaf RWC that corresponded to a 50 % decrease in relative leaf thickness was determined (RWC shrink50 ), and used as a measure of leaf tolerance to shrinkage during dehydration. The time taken to lose 50 % leaf thickness during dehydration (Time shrink50 ) was determined for each species by fitting a three-parameter exponential decay model to relative leaf thickness as a function of time. Chlorophyll a fluorescence The maximum quantum yield of photosystem II (PSII) was quantified as the ratio of dark-adapted variable fluorescence to maximum fluorescence ( F v /F m ), where F v = F m − F 0 , and F m and F 0 are the maximum and basal fluorescence, respectively. These Chlorophyll a fluorescence measurements were made on the same leaves used for the previous assays. Leaves were dark-adapted for 20 minutes, and fluorescence measurements made on the adaxial side, towards the center of the leaf avoiding the midrib, with a PAM 2500 fluorometer (Walz, Effeltrich, Germany). The maximum quantum yield of PSII is an index of leaf photochemical performance, and 50 % loss of PSII function is indicative of irreversible breakdown of the photosynthetic machinery and cellular function. A five-parameter logistic model was fit to the F v /F m response to RWC, and the RWC that resulted in the initial 5 % decrease (RWC flbrk ), and the 50 % decrease (RWC fl50 ) in F v /F m was determined. The time taken to lose 5 % and 50 % of PSII function during dehydration (Time flbrk and Time fl50 ) were determined for each species by fitting a five-parameter logistic model for F v /F m as a function of time. Pressure-volume curves to determine tolerance to turgor loss A second bench drying assay for generating leaf pressure-volume curves was carried out during November and December in 2017 and 2018. Sun-exposed, upper canopy leaves were collected from five individuals of every species, rehydrated overnight as described above, and subjected to drying. The leaves were weighed, and water potential (PMS pressure chamber, Model 1515D) quantified at intervals of 0.2 to 0.3 MPa till a water potential of −3 MPa was reached. The leaves were subsequently oven-dried at 70 °C for 72 hours to determine the dry weight, and the RWC estimated (as described above). The pressure-volume curves were used to estimate water potential at the turgor loss point ( Ψ TLP ), RWC at the turgor loss point (RWC TLP ), modulus of elasticity ( e ), and capacitance (CFT), using the protocol described by Sack et al. (2011). We used RWC TLP for each species generated from the pressure-volume curves to estimate the time to loss of turgidity (Time TLP ) from the species-level exponential decay model corresponding to loss of leaf RWC as a function of time, obtained from the assay described above. Stomatal traits Six mature and sun-exposed leaves were collected from five of the same individuals of each study species. Nail polish varnish imprints were obtained from the central portion of the abaxial side taking care to avoid the midrib and major veins. Stomatal density, guard cell length, and pore length were quantified from images of the imprints using ImageJ ( Schneider et al ., 2012 ). Stomatal density (StomDen) was estimated as the number of stomata in a 20 × magnified field of view, and stomatal pore length (Pore size) and guard cell length (GCL) measured from 40 × magnified images. The maximum stomatal conductance ( g wmax ) for water was calculated using the following equation ( Franks & Farquhar, 2001 ; Franks et al ., 2009 ): where d is the diffusivity of water vapour in air (2.49 × 10 -5 m 2 ·s -1 ), amax is the maximum stomatal pore area (m −2 ) calculated as an ellipse of area π × (0.5 × pore length) × (0.25 × pore length), v is molar volume of air (0.0224 m 3 ·mol −1 ), and l is the depth of the stomatal pore which was approximated as 0.25 × Guard cell length. We could not obtain proper peels for two of the 18 study species. For Callicarpa tomentosa , stomatal peels were patchy due to the presence of leaf hair, and this allowed estimation of dimensions for individual stomata, but not stomatal density. For Mallotus phillipensis , neither stomatal dimensions nor density could be quantified, but data from a published study ( Gangadhara, 2016 ) were used for our analyses. Leaf functional traits A minimum of five mature and sun-exposed leaves were collected from each of the same six individuals of every species for quantifying leaf area, leaf dry matter content (LDMC) and leaf mass per area (LMA) ( Pérez-Harguindeguy et al ., 2013 ). Leaves were rehydrated overnight, as described above. Saturated fresh weight and leaf area were quantified for the rehydrated leaves, and dry weight after oven drying at 70 °C for 72 hours. LDMC was calculated as leaf dry weight per saturated fresh weight (g·g -1 ), and LMA as leaf dry weight per leaf area (g·m -2 ). In-situ leaf water status and phenology To measure the leaf water status of the species in the driest time of the year, sun-exposed upper canopy leaves were collected at the end of the summer between 1230 and 1500 hours in May 2018. Leaf RWC was quantified for five or more mature leaves from 6 replicate individuals of each species. The leaves were individually stored in previously weighed, darkened, and sealed plastic bags for transport to the laboratory and was weighed within three hours of collection. The leaves were then rehydrated overnight by immersing the petioles in a beaker of water. The saturated fresh weight (SFW) of the rehydrated leaves was measured the following day, and the leaves were oven-dried at 70 °C for 72 hours before measurement of their dry weight (DW). RWC was calculated as in equation (1) to estimate the in-situ minimum relative water content (RWC min ). Midday leaf water potential at the driest time of the year ( Ψ min ) was measured at the end of the dry season in April and May of 2019 for 12 of the 18 study species. Ψ min for the remaining 5 species were quantified in April and May of 2024. We were unable to determine Ψ min for Ficus racemosa due to the non-availability of healthy leaves during the dry season. Five mature leaves were collected from three replicate individuals of each species between 1230 and 1500 hours from the sun-exposed upper canopy. Water potential was measured immediately after collection using a portable pressure chamber (Model 1515D, PMS Instruments Co., Albany, OR, USA). In addition to the discrete evergreen and deciduous leaf habit categories, we examined the relationship of g min and physiological thresholds to quantitative measures of leaf phenology. Woody species in these seasonal forests vary in how much of their canopy they lose in the dry season, and also in the duration that they remain leafless ( Chakrabarty et al ., 2021 ). The total canopy of individuals was scored by direct visual observation in a semi-quantitative manner on a scale from 0 to 100 % in steps of 10 %. A score of zero represented the complete absence of leaves, while a score of 100 represented a full canopy. Observations were conducted monthly for four years between 2014 and 2017. The average canopy maintained by individuals over the year was estimated, and 100 − average canopy, a measure of the average canopy loss (ACL), was used as a quantitative measure of deciduousness. Data Analysis We used the ‘drc’ package ( Ritz & Streibig, 2005 ) in R (version 4.1.1) for fitting logistic functions to the dehydration responses, and quantification of the corresponding thresholds (RWC TLP , RWC shrink50 , RWC flbrk , RWC fl50 ), and the time to reach the thresholds (Time RWC50 , Time shrink50 , Time TLP , Time flbrk , Time fl50 ). All measured and derived traits were tested for normality using the Shapiro-Wilk test and transformed when necessary. We examined variation in traits using nested analysis of variance, with species nested within leaf habit. Bivariate trait relationships were assessed using Spearman’s rank correlation analysis. Multivariate trait dimensions were assessed using a principal component analysis (PCA) with the ‘factoextra’ package ( Kassambara & Mundt, 2020 ) in R (version 4.1.1). We could not include stomatal traits, or RWC min in the PCA analysis as we did not have data for all of the study species. All analyses were conducted using R (version 4.1.1). View this table: View inline View popup Download powerpoint Table 1: The traits examined, abbreviations and units. Results There was large variation in the rate of water loss from leaves of the 18 study species, and the time to reach an RWC of 50 % varied more than 10-fold, ranging from around 2 to 21 hours ( Fig. 1 , Table S3). Deciduous species had higher rates of residual water loss, and reached 50 % RWC faster than evergreen species ( Fig. 1 , Table S3). Leaf minimum conductance ( g min ) varied more than six-fold ranging from 1.21 to 7.41 mmol·m -2 ·s -1 , and was higher for deciduous than evergreen species ( Fig. 1 inset, Table S3). The residual loss of water was not constant, and rates of water loss decreased with decreasing RWC. Methods to quantify g min typically make measurements during the early hours of drying ( Sack & Scoffoni, 2010 ; Slot et al ., 2021 ), but for some species, like Memecylon umbellatun , there was still considerable water loss even on the third day of drying. To understand how g min was related to water loss over longer periods of time, we examined how our estimates of g min was related to time to reach RWC of 50 % (Time RWC50 ). There was a significant negative relationship between g min and Time RWC50 , indicating that species with higher residual water loss early during drying reached an RWC of 50 % sooner (Fig. S6). Download figure Open in new tab Figure 1: Loss of water quantified as change in RWC during leaf dehydration for the 18 study species (evergreen − green, deciduous − yellow). Dashed lines indicate the range of time taken to reach 50 % RWC for species with the highest and lowest rates of water loss. The inset shows box and whisker plots for leaf minimum conductance ( g min ) for the evergreen (E) and deciduous (D) species. ANOVA results for differences between leaf habit (evergreen and deciduous), and across species nested within leaf habit are depicted by *** for P < 0.01. The RWC-based thresholds for early, moderate and severe dehydration differed across species ( Fig. 2 ). The leaf RWC that resulted in loss of turgor (RWC TLP ) ranged from 95 to 78.6 % across species ( Fig. 2a, d , Table S3). The water potential at turgor loss ( Ψ TLP ) ranged from −0.59 to - 2.51 MPa MPa (Table S3), and was not related to RWC TLP (r = 0.30, p = 0.21). The loss of leaf structural integrity (RWC shrink50 ) occurred at a lower RWC than turgor loss (for all but one of the species examined), and ranged from 90. 4 to 70.8 % ( Fig. 2b, d , Table S3). Breakdown of PSII function, indicative of loss of cellular function and viability, occurred at much lower leaf RWC for all species ( Fig. 2c, d , Table S3). The RWC that resulted the initial breakdown of PSII function (RWC flbrk ) ranged from 65.5 to 26.5 %, and for 50 % loss of PSII function (RWC fl50 ) from 33.3 to 7.6 %. RWC flbrk was closely related to RWC fl50 (Fig. S7) and for all further analysis we use RWC flbrk as the threshold for PSII breakdown. Importantly, we found no evidence of any relationships between species thresholds for loss of turgor, breakdown of structural integrity, and loss of cellular function and viability (Fig. S8). Additionally, there were no significant relationships between all three thresholds and g min (Fig. S8). Download figure Open in new tab Figure 2: Pressure-volume curves, leaf thickness, and Photosystem II (PSII) function during leaf dehydration for the study species (evergreen − green, deciduous − yellow). These assays were used to determine the leaf RWC thresholds for: (a) turgor loss (RWC TLP ); (b) loss of structural integrity (RWC shrink50 ); and, (c) breakdown of PSII function (RWC flbrk ). Dashed lines indicates species with minimum and maximum threshold values. (d) Box-plots for these physiological thresholds, and minimum leaf RWC (RWC min ). ANOVA results for differences between leaf habit (evergreen and deciduous), and across species nested within leaf habit are depicted by *** for P < 0.01, * for P < 0.1 and ‘ns’ for not significant. The RWC threshold for turgor loss (RWC TLP ) was lower for the deciduous than for the evergreen species, while in contrast, the threshold for breakdown in PSII function (RWC flbrk ) was lower for the evergreen than for the deciduous species ( Fig 2d ). There was no difference between evergreen and deciduous species in the RWC thresholds for loss of structural integrity (RWC shrink50 ) ( Fig 2d ). The minimum leaf RWC (RWC min ) for these species in their natural habitats at the driest time of the year differed across species, and was lower for the evergreen compared to the deciduous species ( Fig. 2d , Table S3). RWC min ranged from around to 97 to 69 % ( Fig. 2d , Table S3), and for 10 of the 18 study species RWC min was lower than RWC TLP . RWC min was also lower than RWC shrink50 for 3 species, but higher than RWC flbrk for all species. The sequence of events with respect to time ( Fig. 3 ) was similar to the sequence with respect to RWC ( Fig. 2d ). Turgor loss occurred rapidly, typically within 1 to 2 hours since the initiation of dehydration, and this did not differ between evergreen and deciduous species ( Fig. 3 ). There was large variation across species in the time to 50 % shrinkage (Time shrink50 ) which ranged from 40 minutes to 15 hours, and in the time to loss PSII function (Time flbrk ) which ranged from 2 hours to more than 36 hours. Leaves of deciduous species reached critical thresholds for loss of structural integrity and loss of PSII function earlier than the evergreen species ( Fig. 3 , Table S3). Download figure Open in new tab Figure 3: Time taken to reach the critical thresholds (RWC TLP , RWC shrink50 , and RWC flbrk ) for the 18 study species (yellow – deciduous, green – evergreen). ANOVA results for differences across leaf habit (evergreen and deciduous), and across species nested within leaf habit are depicted by *** for P < 0.01, * for P < 0.1 and ‘ns’ for not significant. The time required for species to reach all three RWC thresholds was negatively related to g min ( Fig. 4a, c, e ).The results for relationships between the time to reach these critical thresholds and species thresholds themselves were mixed: while significant for loss of structural integrity ( Fig. 4d ), this was not significant for turgor loss and breakdown of PSII function ( Fig. 4b, f ). Download figure Open in new tab Figure 4: Relationship of the time taken to reach the thresholds with g min and the thresholds themselves for the 18 study species (yellow – deciduous, green – evergreen). (a, b) time to turgor loss (log Time TLP ); (c, d) Time to loss of structural integrity (Time shrink50 ); (e, f) Time to breakdown of PSII function (Time flbrk ). Results for Spearman’s correlation coefficient (r) are shown, and solid lines represent type II regression fits for significant relationships ( P < 0.01). Leaf minimum conductance was positively related to stomatal density, and maximum stomatal conductance, but not related to pore size (Fig. S9). None of the other hydraulic, or leaf functional traits examined were related to g min (Fig. S10). Finally, for the in situ parameters examined for these species in the field, g min was positively related to the average canopy loss of species, but unrelated to the minimum RWC or minimum water potentials experienced by species in their natural environments during the driest time of the year (Fig. S10). Results from the PCA showed that the first three PC dimensions explained more than 70 % of the total variation, with 31.5%, 26.4 %, and 12.7 % explained by the first, second and third PC axes, respectively ( Fig. 5 , Table S11). PC1 was primarily associated with rates of water loss and time to reach thresholds. PC1 was negatively correlated with time to loss of PSII function (Time flbrk ) and structural integrity (Time shrink50 ), and positively correlated with g min ( Fig. 5 , Table S12). This axis also had significant, but lower contributions from average canopy loss (ACL), the RWC threshold for structural integrity (RWC shrink50 ), time to turgor loss (Time TLP ), and leaf mass per area (LMA). PC2 was primarily associated with species thresholds for early dehydration, and was positively related leaf capacitance (CFT) and time to turgor loss (Time TLP ), and negatively related to leaf modulus of elasticity ( E ), the RWC threshold for turgor loss (RWC TLP ), and leaf dry matter content (LDMC). Finally, PC3 was positively correlated with tolerance to severe dehydration (RWC flbrk ) and leaf size (LA), and negatively correlated with water potential at turgor Loss ( Ψ TLP ). Download figure Open in new tab Figure 5: Principal component (PC) analysis of the leaf traits examined for the study species. Variable correlation plots for: (a) PC axes 1 and 2; and, (b) PC axes 2 and 3. The colour for the trait vectors represent the percent contributions to dimensions examined. Trait abbreviations and details are provided in Table 1. Discussion We report significant variation in leaf minimum conductance ( g min ) across 18 coexisting trees from a seasonally dry tropical forest. Leaf minimum conductance varied 6-fold, and was greater for deciduous than evergreen species. Critical physiological thresholds indicative of tolerance to early, moderate and severe dehydration also differed across species, but were not related to each other, or to g min . Importantly, we found that g min was the primary determinant of the time species took to reach these physiological thresholds. Results from a PCA analysis revealed three major independent axes of variation in hydraulic traits: The first represented variation in traits related to g min and the time to reach critical thresholds; the second, tolerance to mild and moderate dehydration; and, the third, tolerance to severe dehydration. Surprisingly, g min was negatively related to maximum stomatal conductance, suggesting that species can concurrently maximize conductance and minimize water loss via leaf minimum conductance. Tropical species were poorly represented in previous global analysis of g min ( Schuster et al ., 2017 ; Duursma et al ., 2019 ), but there has been a recent increase in studies reporting g min for tropical trees ( Machado et al ., 2021 ; Slot et al ., 2021 ; Levionnois et al ., 2021 ; Loram-Lourenço et al ., 2022 ; Manzi et al ., 2022 ; Ziegler et al ., 2024 ; Wittemann et al ., 2024 ; Middleby et al ., 2024 ; Boisseaux et al ., 2025 ). We collated this, and previous data for g min of tropical species (> 200 species from > 8 sites) to contextualize our results. The range of g min observed across our 18 species was similar to what was reported from a seasonal tropical forest in Brazil ( Machado et al ., 2021 ), but lower than the overall range across the tropics (Fig. S13). The higher g min in deciduous than in evergreen species in our site is consistent with differences reported in most other tropical sites. While residual water loss represents a small fraction of the maximum potential leaf conductance, integrated over the cumulative leaf surface area for an individual, and over time of exposure to drought, the observed differences in g min are likely to be significant and important. Minimizing water loss with reduced g min may be particularly relevant for seasonally dry tropical forests like our study site, where species have to endure a harsh and prolonged dry season, and in future climates with higher temperatures and increased vapour pressure deficits ( Hammond et al ., 2022 ; Song et al ., 2025 ). As expected, loss of turgor and breakdown of structural integrity occurred at mild to moderate levels of dehydration, while breakdown of PSII function occurred with more severe dehydration at lower leaf RWC. The RWC threshold for turgor loss (RWC TLP ) was lower for deciduous than evergreen species, indicating that deciduous species could sustain turgor at lower RWC levels and were more tolerant to mild dehydration. This would allow the more acquisitive deciduous species to continue carbon assimilation during early dehydration. In contrast, deciduous species had higher RWC thresholds for breakdown in PSII function (RWC flbrk ). Thus, evergreen species that maintained most of their canopy through the dry season were more tolerant to severe dehydration levels that result in loss of cellular function and viability. While these physiological thresholds differed across species, these were not related to each other. Species that were more tolerant to mild dehydration were not necessarily the most tolerant to loss of structural integrity, or cell function and viability. This suggests lack of underlying mechanistic linkages, physiological constraints, or correlated selection for these traits in these species, unlike what has been reported for other hydraulic traits ( Bartlett et al ., 2016 ; Trueba et al ., 2019 ). The time taken by species to reach critical levels of dehydration integrates species-specific thresholds and rates of residual water loss. While turgor loss happened rapidly, there was large variation across species in the time to loss of structural integrity, and loss of PSII function. Interestingly, the time to these critical thresholds was primarily determined by g min . Thus, species with higher g min reached critical thresholds for turgor loss, structural integrity loss, and PSII function breakdown earlier. Given that minimizing water loss and increasing physiological tolerance to dehydration can represent distinct, independent strategies to deal with drought, our results highlight the importance of moving beyond the current safety margin metrics to assess vulnerability of species to drought ( Volaire, 2018 ; Trugman et al ., 2021 ; McDowell et al ., 2022 ). Such current safety margin metrics are exclusively based on physiological thresholds, whereas using integrated measures such as ‘time to physiological threshold’ may better capture species vulnerabilities. Not surprisingly, the traditional threshold-based safety margins (RWC TLP − RWC flbrk ) were not correlated with the time-based safety margin (Time TLP − Time flbrk ) for our study species (Fig. S10, S14). Our study, along with a growing body of literature ( Blackman et al ., 2016 , 2023 ; Petek-Petrik et al ., 2023 ; Ziegler et al ., 2024 ; Burlett et al ., 2025 ), calls attention to the need for such integrated measures of plant drought sensitivity. Congruent with the lack of relationships between thresholds, and between thresholds and g min , the principle components analyses of trait combinations in these species showed multiple orthogonal axes of variation in hydraulic traits in our study species. The primary axis (PC1) captured variation associated with traits related to rates of residual water loss. This axis represented a spectrum with conservative evergreen species with low g min and high LMA that were able to maintain structural integrity and cellular function for longer durations during dehydration at one end, and acquisitive deciduous species with high g min and low LMA that lost structural integrity and cellular function earlier during dehydration at the other extreme. The second axis (PC2) represented a spectrum of variation in capacitance, LDMC, and tolerance to mild and moderate dehydration. Finally, the third axis (PC3) primarily represented variation in tolerance to severe dehydration, leaf area, and in situ minimum RWC experienced by species in their natural environment. Overall, our results suggest that these traits can evolve independently to allow a wider diversity of adaptive strategies to cope with the environmental conditions that vary in the severity of drought ( Martin-StPaul et al ., 2017 ; Blackman et al ., 2019 ). In coexisting species, this would allow the fine tuning of strategies to balance opportunities for carbon gain with costs of potential breakdown in physiological function under varying intensities and durations of drought. The lack of coordination across measures of tolerance has important implications for ecologists and modelers, for extrapolating drought responses for a wide range of conditions based on a smaller number of traits ( Bartlett et al ., 2016 ; Kim et al ., 2024 ). A positive relationship of g min with stomatal size and density may be expected if residual water loss occurs via incompletely closed or leaky stomata ( Muchow & Sinclair, 1989 ; Ochoa et al ., 2024 ). In addition to the mechanistic insights that such a relationship provides, this has important implications because increased stomatal size and density can drive higher stomatal conductance and enable greater carbon assimilation ( Franks et al ., 2009 ). A recent study found positive relationships between g min and both stomatal density and carbon assimilation ( Machado et al ., 2021 ). This suggests a tradeoff where increased assimilation facilitated by higher conductance comes at the cost of greater water loss from leaky stomata. However, only one other study ( Muchow & Sinclair, 1989 ) has reported a positive relationships between g min and stomatal density, and the majority of others that have examined this report no relationships (summarized in Ochoa et al ., 2024 ). Surprisingly, we observed a significant negative relationships between g min and stomatal density, and g min and maximum potential stomatal conductance. Thus, species with low residual water loss had higher stomatal densities and maximum stomatal conductance. This is congruent with results from causal analyses of stomatal traits and g min by Ochoa et al. (2025), and confirm their inference that residual water loss may evolve independently of stomatal traits allowing species to simultaneously increase maximum stomatal conductance and minimize residual water loss. The lack of a tradeoff with maximum stomatal conductance, and no clear relationships with other hydraulic and functional traits left open the question of why species maintained high residual water loss when they could minimize g min at no apparent cost. One possible explanation could lie in the potential benefit that g min can provide for maintaining safe operating leaf temperatures during drought. Droughts are frequently accompanied by high temperatures (IPCC 2021), and in the seasonal forests in our study site the hottest time of the year coincides with the late dry season (Sastry & Barua, 2018, Javad et al. 2025 ). Limited water availability and closed stomata may limit the ability of leaves to effectively cool themselves under these conditions ( Evans et al ., 2025 ). Recent studies have shown that the relative contribution of cuticular versus stomatal conductance increases dramatically at higher temperatures ( Slot et al ., 2021 ; Zailaa et al ., 2024 ; Fernandes et al ., 2025 ; Garen & Michaletz, 2025 ), and this may be important in cooling leaves exposed to extreme temperatures during drought. The temperature dependence of g min indicates that the importance of residual water loss in driving impairment of leaf and plant function will be more dominant in future climates with hotter and more intense droughts. Conclusion These results go beyond recent studies which suggest that residual water loss from leaves is important in determining the time to ultimate hydraulic failure and mortality ( Blackman et al ., 2016 ; Ziegler et al ., 2024 ; Burlett et al ., 2025 ). We show that g min is central in determining the time taken to reach physiological thresholds that impair leaf function across a wide range of leaf water status from mild to severe dehydration. Differences in g min and time to critical physiological thresholds in coexisting species can result in differential carbon assimilation, growth and survival in the face of varying intensities and duration of drought. Such g min driven differences in plant performance are likely to be even more important in future climates with hotter climate change-type droughts. The independence of g min with tolerance to mild and severe tolerance further emphasizes the importance of quantifying leaf minimum conductance in addition to other traits that contribute to drought tolerance to comprehensively assess vulnerability of species to drought. Competing interests Authors declare no competing interests. Author contributions RS and DB designed the study. RS collected the data with help from MV for the bench drying assays, and BP for quantification of stomatal traits. SC collected the phenology data. RS analyzed the data with help from DB. RS and DB wrote the initial drafts of the manuscript with input from MV, BP and SC. Data availability The data that supports the findings of this study are available in the supplementary material of this article. Acknowledgements The authors wish to thank Kalu and Ganpath for assistance in field work; Dhrubojyoti Patra and Omkar Khache for help with sample collection and processing; Dr. Narendra Kadoo for providing the pressure chamber; Akhil Javad for help with the leaf economic budget model analysis; Indian Institute of Science Education and Research (IISER), Pune for intra-mural funds and support for Ron Sunny (Integrated PhD fellowship). References ↵ Álvarez-Yépiz JC , Búrquez A , Martínez-Yrízar A , Teece M , Yépez EA , Dovciak M . 2017 . 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You are going to email the following It’s about time! Leaf minimum conductance determines time to reach critical thresholds for leaf dehydration in a seasonal tropical forest Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share It’s about time! Leaf minimum conductance determines time to reach critical thresholds for leaf dehydration in a seasonal tropical forest Ron Sunny , Malavika Venu , Bandaru Peddiraju , Souparna Chakrabarty , Deepak Barua bioRxiv 2025.07.07.663429; doi: https://doi.org/10.1101/2025.07.07.663429 Share This Article: Copy Citation Tools It’s about time! Leaf minimum conductance determines time to reach critical thresholds for leaf dehydration in a seasonal tropical forest Ron Sunny , Malavika Venu , Bandaru Peddiraju , Souparna Chakrabarty , Deepak Barua bioRxiv 2025.07.07.663429; doi: https://doi.org/10.1101/2025.07.07.663429 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Ecology Subject Areas All Articles Animal Behavior and Cognition (7617) Biochemistry (17633) Bioengineering (13856) Bioinformatics (41841) Biophysics (21399) Cancer Biology (18529) Cell Biology (25422) Clinical Trials (138) Developmental Biology (13352) Ecology (19860) Epidemiology (2067) Evolutionary Biology (24281) Genetics (15582) Genomics (22461) Immunology (17700) Microbiology (40295) Molecular Biology (17140) Neuroscience (88413) Paleontology (666) Pathology (2823) Pharmacology and Toxicology (4813) Physiology (7632) Plant Biology (15107) Scientific Communication and Education (2042) Synthetic Biology (4284) Systems Biology (9808) Zoology (2267)