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Summary Plants integrate environmental stimuli to optimize photosynthesis vs water loss by controlling stomatal apertures. However, stomatal responses to temperature elevation and the underlying molecular genetic mechanisms remain less studied.We developed an approach for clamping leaf‐to‐air vapor pressure difference (VPDleaf) to fixed values, and recorded robust reversible warming‐induced stomatal opening in intact plants. We analyzed stomatal temperature responses of mutants impaired in guard cell signaling pathways for blue light, abscisic acid (ABA), CO2, and the temperature‐sensitive proteins, Phytochrome B (phyB) and EARLY‐FLOWERING‐3 (ELF3).We confirmed thatphot1‐5/phot2‐1leaves lacking blue‐light photoreceptors showed partially reduced warming‐induced stomatal opening. Furthermore, ABA‐biosynthesis, phyB, and ELF3 were not essential for the stomatal warming response. Strikingly,Arabidopsis(dicot) andBrachypodium distachyon(monocot) mutants lacking guard cell CO2sensors and signaling mechanisms, includinght1,mpk12/mpk4‐gc, andcbc1/cbc2abolished the stomatal warming response, suggesting a conserved mechanism across diverse plant lineages. Moreover, warming rapidly stimulated photosynthesis, resulting in a reduction in intercellular (CO2). Interestingly, further enhancing heat stress caused stomatal opening uncoupled from photosynthesis.We provide genetic and physiological evidence that the stomatal warming response is triggered by increased CO2assimilation and stomatal CO2sensing. Additionally, increasing heat stress functions via a distinct photosynthesis‐uncoupled stomatal opening pathway.
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High‐resolution thermal imagery reveals how interactions between crown structure and genetics shape plant temperature
Abstract Understanding interactions between environmental stress and genetic variation is crucial to predict the adaptive capacity of species to climate change. Leaf temperature is both a driver and a responsive indicator of plant physiological response to thermal stress, and methods to monitor it are needed. Foliar temperatures vary across leaf to canopy scales and are influenced by genetic factors, challenging efforts to map and model this critical variable. Thermal imagery collected using unoccupied aerial systems (UAS) offers an innovative way to measure thermal variation in plants across landscapes at leaf‐level resolutions. We used a UAS equipped with a thermal camera to assess temperature variation among genetically distinct populations of big sagebrush (Artemisia tridentata), a keystone plant species that is the focus of intensive restoration efforts throughout much of western North America. We completed flights across a growing season in a sagebrush common garden to map leaf temperature relative to subspecies and cytotype, physiological phenotypes of plants, and summer heat stress. Our objectives were to (1) determine whether leaf‐level stomatal conductance corresponds with changes in crown temperature; (2) quantify genetic (i.e., subspecies and cytotype) contributions to variation in leaf and crown temperatures; and (3) identify how crown structure, solar radiation, and subspecies‐cytotype relate to leaf‐level temperature. When considered across the whole season, stomatal conductance was negatively, non‐linearly correlated with crown‐level temperature derived from UAS. Subspecies identity best explained crown‐level temperature with no difference observed between cytotypes. However, structural phenotypes and microclimate best explained leaf‐level temperature. These results show how fine‐scale thermal mapping can decouple the contribution of genetic, phenotypic, and microclimate factors on leaf temperature dynamics. As climate‐change‐induced heat stress becomes prevalent, thermal UAS represents a promising way to track plant phenotypes that emerge from gene‐by‐environment interactions.
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- PAR ID:
- 10487326
- Publisher / Repository:
- Remote Sensing in Ecology and Conservation
- Date Published:
- Journal Name:
- Remote Sensing in Ecology and Conservation
- ISSN:
- 2056-3485
- Subject(s) / Keyword(s):
- adaptive capacity Artemisia tridentata common garden experiment leaf temperature sagebrush stomatal conductance
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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IntroductionDrought detection, spanning from early stress to severe conditions, plays a crucial role in maintaining productivity, facilitating recovery, and preventing plant mortality. While handheld thermal cameras have been widely employed to track changes in leaf water content and stomatal conductance, research on thermal image classification remains limited due mainly to low resolution and blurry images produced by handheld cameras. MethodsIn this study, we introduce a computer vision pipeline to enhance the significance of leaf-level thermal images across 27 distinct cotton genotypes cultivated in a greenhouse under progressive drought conditions. Our approach involved employing a customized software pipeline to process raw thermal images, generating leaf masks, and extracting a range of statistically relevant thermal features (e.g., min and max temperature, median value, quartiles, etc.). These features were then utilized to develop machine learning algorithms capable of assessing leaf hydration status and distinguishing between well-watered (WW) and dry-down (DD) conditions. ResultsTwo different classifiers were trained to predict the plant treatment—random forest and multilayer perceptron neural networks—finding 75% and 78% accuracy in the treatment prediction, respectively. Furthermore, we evaluated the predicted versus true labels based on classic physiological indicators of drought in plants, including volumetric soil water content, leaf water potential, and chlorophyllafluorescence, to provide more insights and possible explanations about the classification outputs. DiscussionInterestingly, mislabeled leaves mostly exhibited notable responses in fluorescence, water uptake from the soil, and/or leaf hydration status. Our findings emphasize the potential of AI-assisted thermal image analysis in enhancing the informative value of common heterogeneous datasets for drought detection. This application suggests widening the experimental settings to be used with deep learning models, designing future investigations into the genotypic variation in plant drought response and potential optimization of water management in agricultural settings.more » « less
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{"Abstract":["Premise: Extreme events are an understudied aspect of ongoing\n anthropogenic climate change that could play a disproportionate role in\n the threat that rapid environmental shifts pose to natural populations.\n Methods: We exposed plants originating from seeds that were harvested\n before (ancestors) and after (descendants) multiple extreme heat events\n from six populations across the range of Mimulus cardinalis (Phyrmaceae)\n to a short‐term heat‐wave treatment in controlled growth chamber\n environments. We assessed physiological, performance, and functional\n responses (stomatal conductance, leaf temperature deficit, photosystem II\n efficiency, relative growth rate, specific leaf area, and leaf dry matter\n content) to the heat‐wave treatment, along with evolutionary responses\n (differences between ancestors and descendants) of M. cardinalis\n populations to the recent natural extreme heat event. Results: Plants in\n the heat‐wave treatment increased their overall performance, and the\n magnitude of increase was generally greatest among trailing‐edge\n populations. Despite limited overall trait differences between ancestors\n and descendants, there was some evidence of divergent evolutionary\n responses among regions to the natural extreme heat event. However, we did\n not find evidence of adaptive evolution that affected how M. cardinalis\n populations responded to the heat‐wave treatment. Conclusions: These\n results demonstrate that many M. cardinalis populations may reside in\n environments that are below their optimum average temperature, revealing\n potential resiliency to future warming. However, limited evolutionary\n responses in M. cardinalis to the recent extreme heat wave could still\n indicate potential for future vulnerability to extreme climate events of\n increased intensity, frequency, and duration."],"TechnicalInfo":["Readme file associated with the paper Albano et al., American Journal of\n Botany (accepted October 20, 2025, to be published in February, 2026).\n Title: Range-wide responses to an extreme heat event in Mimulus cardinalis\n In this paper, we perform a resurrection experiment, exposing Mimulus\n cardinalis to a heat-wave treatment in growth chamber environments. M.\n cardinalis individuals were sourced from six populations across its range\n (two leading-edge, two range-center, two trailing-edge) and within each\n population, seeds were harvested in 2010 (ancestors) and 2017\n (descendants) which were time periods selected before and after a natural\n multi-year heat-wave event in western North America. This design allows\n for investigation of physiological, performance, and functional trait\n responses to the heat-wave treatment for each population, along with\n investigation of evolutionary responses to the natural heat-wave event\n that could affect how plants respond to the heat-wave treatment. This\n experiment addressed two main objectives: (1) quantify responses in plant\n physiological, performance, and functional traits to a heat-wave treatment\n and determine if those responses varied among populations from across the\n range of M. cardinalis, and (2) characterize differences in response to a\n heat-wave treatment between 2010 ancestors and 2017 descendants,\n indicative of an evolutionary response to the recent heat and drought\n event experienced by natural M. cardinalis populations. The script to\n analyze the data required to address these objectives can be found in\n Albano_et_al_2026_AJB_Script.R, while the data itself can be found in\n Albano_et_al_2026_AJB_Data.csv, which contains the following columns: *\n Cohort: The category of year at which an individual was harvested.\n Ancestor (2010) or Descendant (2017) * Region: The region from which an\n individual was harvested, essentially a combination of two populations\n from each region. N (leading-edge), C (range-center), or S (trailing-edge)\n * Heat: The heat-wave treatment performed on each individual. Control or\n Heat wave * Cross_ID: The cross identifier used to separate individuals\n based on their parentage through the creation of a refresher generation\n prior to the initiation of the experiment * Time1: A unitless scaled\n measure of time of day, constructed by combining individual hour, minute,\n and second variables. * Time2: The Time1 variable converted to a factor\n variable (necessary for autocorrelation models) * Date: The date on which\n physiological traits (gsw, Tdiff, and PhiPS2) were measured for each\n individual. Format: MM/DD/YYYY * Population: The population from which an\n individual was harvested. N1 (leading-edge population #1), N2\n (leading-edge 2), C1 (range-center 1), C2 (range-center 2), S1\n (trailing-edge 1), or S2 (trailing-edge 2) * gsw: Stomatal conductance (of\n water vapor) measurement from one leaf of each individual plant. Units:\n mmol m^-2 s^-1 * gsw_Pred: Predicted stomatal conductance (of water vapor)\n measurement from each control individual if it was to be exposed to the\n heat-wave treatment, based solely on the physical effects of temperature\n increase. Units: mmol m^-2 s^-1 * Tdiff: Leaf temperature at the moment\n gsw was assessed minus air temperature in the growth chamber. Units: °C *\n PhiPS2: Unitless photosystem II efficiency (ΦPSII) for each individual\n plant * leaf_RGR: The relative growth rate of each individual plant, based\n on the number of leaves present prior to and after the experiment. Units:\n leaves leaves^-1 day^-1 * SLA: The specific leaf area of one leaf on each\n individual plant. Units: cm^2 g^-1 * LDMC: The leaf dry matter content of\n one leaf on each individual plant. Units: mg g^-1 *NA values in the\n leaf_RGR, SLA, and LDMC columns represent individuals that were too small\n and/or unhealthy for this data to be collected. \\**All heat-wave plants\n ("Heat wave" in the "Heat" column) are recorded as NA\n in the gsw_Pred column because the predicted stomatal conductance increase\n measurement only applies to "Control" plants"]}more » « less
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Mencuccini, Maurizio (Ed.)Abstract Trees are arguably the most diverse and complex macro-organisms on Earth. The equally diverse functions of trees directly impact fluxes of carbon, water and energy from the land surface. A number of recent studies have shed light on the substantial within-species variability across plant traits, including aspects of leaf morphology and plant allocation of photosynthates to leaf biomass. Yet, within-tree variability in leaf traits due to microclimatic variations, leaf hydraulic coordination across traits at different physiological scales and variations in leaf traits over a growing season remain poorly studied. This knowledge gap is stymieing the fundamental understanding of what drives trait variation and covariation from tissues to trees to landscapes. Here, we present an extensive dataset measuring within-tree heterogeneity in leaf traits in California’s blue oak (Quercus douglasii) across an edaphic gradient and over the course of a growing season at an oak–grass savanna in Southern CA, USA. We found a high level of within-tree crown leaf area:sapwood area variation that was not attributable to sample height or aspect. We also found a higher level of trait integration at the tree level, rather than branch level, suggesting that trees optimize water use at the organismal level. Despite the large variance in traits within a tree crown and across trees, we did not find strong evidence for adaptive plasticity or acclimation in leaf morphological traits (e.g., changes to phenotype which increased fitness) across temporal and spatial water availability gradients. Collectively, our results highlight strong variation in drought-related physiology, but limited evidence for adaptive trait plasticity over shorter time scales.more » « less
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Ball, Marilyn (Ed.)Abstract We investigated how mangrove-island micro-elevation (i.e., habitat: center vs edge) affects tree physiology in a scrub mangrove forest of the southeastern Everglades. We measured leaf gas exchange rates of scrub Rhizophora mangle L. trees monthly during 2019, hypothesizing that CO2 assimilation (Anet) and stomatal conductance (gsw) would decline with increasing water levels and salinity, expecting more considerable differences at mangrove-island edges than centers, where physiological stress is greatest. Water levels varied between 0 and 60 cm from the soil surface, rising during the wet season (May–October) relative to the dry season (November–April). Porewater salinity ranged from 15 to 30 p.p.t., being higher at mangrove-island edges than centers. Anet maximized at 15.1 μmol m−2 s−1, and gsw was typically <0.2 mol m−2 s−1, both of which were greater in the dry than the wet season and greater at island centers than edges, with seasonal variability being roughly equal to variation between habitats. After accounting for season and habitat, water level positively affected Anet in both seasons but did not affect gsw. Our findings suggest that inundation stress (i.e., water level) is the primary driver of variation in leaf gas exchange rates of scrub mangroves in the Florida Everglades, while also constraining Anet more than gsw. The interaction between inundation stress due to permanent flooding and habitat varies with season as physiological stress is alleviated at higher-elevation mangrove-island center habitats during the dry season. Freshwater inflows during the wet season increase water levels and inundation stress at higher-elevation mangrove-island centers, but also potentially alleviate salt and sulfide stress in soils. Thus, habitat heterogeneity leads to differences in nutrient and water acquisition and use between trees growing in island centers versus edges, creating distinct physiological controls on photosynthesis, which likely affect carbon flux dynamics of scrub mangroves in the Everglades.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1002/rse2.359
