Given the escalating tree death rates with increasing drought disturbances, it's imperative to enhance the mortality predictions for more informed management strategies to preserve forest resources. Tree size could serve as an indicator of tree mortality under drought, as theoretical studies have demonstrated that larger trees, due to their more susceptible hydraulic structures, are more likely to die during droughts. However, empirical studies have presented inconsistent findings regarding the relationship between tree size and mortality, challenging the importance of tree size in projecting drought-induced tree mortality. The variability in this relationship has not been thoroughly quantified, particularly under increasing drought impacts, such as greater drought magnitudes (including intensity and duration), warming temperature and larger stand basal area. We compiled 52 case studies at a global scale and conducted a meta-analysis to explore how the correlation between tree size and mortality fluctuates under drought. Our results showed that: (1) The correlation coefficients of tree size-mortality varied significantly across the studies analyzed, and similar to 40 % of them reported a positive correlation, i.e., larger trees experienced higher mortality; (2) The correlation coefficients increased with drought intensity, drought duration, maximum temperature during drought, and stand basal area. Our results suggest a stronger association of tree size with tree mortality under intensified and prolonged droughts and greater basal areas. Thus, with the increasing droughts under climate change, tree size tends to play an increasing important role in projecting tree mortality. Moreover, management practices, e.g., thinning, could manipulate basal area to influence the tree size-mortality relationship.

Climate-driven forest mortality events have been extensively observed in recent decades, prompting the question of how quickly these affected forests can recover their functionality following such events. Here we assessed forest recovery in vegetation greenness (normalized difference vegetation index) and canopy water content (normalized difference infrared index) for 1,699 well-documented forest mortality events across 1,600 sites worldwide. By analysing 158,427 Landsat surface reflectance images sampled from these sites, we provided a global assessment on the time required for impacted forests to return to their pre-mortality state (recovery time). Our findings reveal a consistent decline in global forest recovery rate over the past decades indicated by both greenness and canopy water content. This decline is particularly noticeable since the 1990s. Further analysis on underlying mechanisms suggests that this reduction in global forest recovery rates is primarily associated with rising temperatures and increased water scarcity, while the escalation in the severity of forest mortality contributes only partially to this reduction. Moreover, our global-scale analysis reveals that the recovery of forest canopy water content lags significantly behind that of vegetation greenness, implying that vegetation indices based solely on greenness can overestimate post-mortality recovery rates globally. Our findings underscore the increasing vulnerability of forest ecosystems to future warming and water insufficiency, accentuating the need to prioritize forest conservation and restoration as an integral component of efforts to mitigate climate change impacts. Satellite data show declining global forest recovery from tree mortality since the 1990s, driven by warming and water scarcity. Canopy water recovers slower than greenness, stressing the need for a multifaceted approach to assessing recovery.

Drought and frost stresses play important roles in determining species distributions, especially at range margins. Understanding how stress resistance traits interact to determine vulnerability to climate change is critical. We developed a large global database of published and new measurements of drought resistance (xylem embolism resistance; P50) and frost resistance (electrolyte leakage; LT50), and investigated evolutionary trade-offs using Bayesian phylogenetic quantile regressions. Across all woody biomes, P50 ranged from -1 to -19 MPa, and LT50 from 0 to below -80°C with conifers generally more resistant than angiosperms. We found a weak trade-off between drought and frost resistance: Drought-resistant species tend to be less frost hardy, and vice versa. There are few species resistant to both stresses (e.g. junipers). Including the phylogeny reduced the strength of the relationship, reflecting the phylogenetic signal for these traits. We did not find any strong effects of LT50 on growth-related traits, but drought resistance is associated with denser wood, smaller conduits, shorter stature and lower specific leaf area. While we show a trade-off between frost and drought resistance, our study does not support the global fast-slow economics spectrum. Our results have implications for forests experiencing hotter, drier summers and potentially damaging late frosts.

The thresholds of drought duration and intensity required to provoke pulses of tree mortality across Earth's biomes remain unclear. Using globally-extensive updated databases of drought-associated tree mortality, we report substantial diversity in the types of drought events that cause tree death in different forest types. Tree-killing droughts are longer, more intense and have higher completeness (proportion of extreme drought within long-lasting droughts) in dry versus wet biomes. Mortality-inducing droughts are more intense and show higher completeness in angiosperm-dominated forests. We find a marked tendency towards long-lasting and more severe and complete droughts in recent years, particularly in more arid sites. Warming-amplified aridity is a main factor underpinning these variations. Differences in "sampling effort" across regions make it challenging to characterize the high variability in drought-induced tree mortality events. In this work we demonstrate the need to create, continuously update, and refine more extensive field-based tree mortality monitoring programs globally.

From 26 March to 29 March 2023, at the Georgia Center in Athens, GA, USA, a workshop on urban trees gathered 40 specialists from a wide array of disciplines, including tree physiology, forest ecology, arboriculture, urban tree ecology, urban forestry, soil science, ecohydrology, vegetation modelling, computer vision, and industrial design. With 28 attendees from four continents, and seven countries physically present, and a further 12 participants joining virtually, the workshop brought together an international team aiming to better understand the physiological functioning of urban trees. The Urban Trees Ecophysiology Network (UTEN) inaugural workshop sought to address the questions of how the urban environment impacts tree functioning and health, and how trees alter the microclimate of cities. Assessing tree health, performance, and survival in urban forests is a complex task that requires a deeper understanding of the physiological functioning of trees in relation to the biophysical, climatic, and social factors shaping urban environments. Confronting these complexities, the workshop brought together scientists and experts covering different aspects of biotic and abiotic stressors affecting tree health, performance, and mortality, as well as urban practitioners and managers, to encourage interdisciplinary thinking, align protocols, design/share new tools, and discuss the dissemination of findings to the public. Thus, the workshop aimed to uncover predictable patterns leading to rapid assessment of tree health and the influence of trees on the microclimate of the cities in contrasting urban environments across the globe amid climate change. The vulnerability of cities and their populations to further climate change may increase due to rapid urbanization, with 70% of Earth's population expected to live in cities by 2050 (Salbitano et al., 2016). Urban trees play a crucial role in creating liveable and sustainable communities in densely populated areas (Turner-Skoff & Cavender, 2019). They have been associated with lower air pollution (Nowak et al., 2006), improved mental health (Faber Taylor & Kuo, 2009), and lower temperatures (Baró et al., 2019; Wang et al., 2019). Particularly, their presence reduces the phenomenon of urban heat islands by providing shaded areas and actively cooling the air via transpiration (Fig. 1; Armson et al., 2012; Rahman et al., 2019), helping relieve financial strain on citizens and municipalities by reducing reliance on expensive air-cooling systems (Tsoka et al., 2021). They can also help to decrease costs associated with water pollution, runoff, and water treatment. Even though urban trees can sometimes be considered threats to infrastructure (Ossola et al., 2023), their benefits generally outweigh their risks for example by reducing road maintenance expenses and extending road lifespan through reduced UV radiation exposure (McPherson & Muchnick, 2005). On another scale, urban trees play a key role in ecosystems by providing food and habitat for birds, invertebrates, mammals, and epiphytes, thus helping with biodiversity conservation in cities (Fig. 1). They also serve the environment by mitigating climate change, actively capturing and sequestering carbon. In 2021, urban trees contributed, along with agricultural soils in the USA, to offsetting 13.1% of total gross emissions (EPA, 2023). However, trees, and therefore their benefits, are not equitably distributed (Nyelele & Kroll, 2020) and multiple studies found that urban canopy cover decreases along multiple social dimensions such as the poverty rate, minority population proportion, and low educational attainment (outlined in Riley & Gardiner, 2020). This has cascading implications for the positive services trees provide, with low-income areas presenting 15% less tree cover and being 1.5°C hotter than those areas with higher income (McDonald et al., 2021). This bias was even more extreme in the Northeastern USA, with low-income areas experiencing up to 30% less tree cover and up to 4.0°C warming relative to high-income areas. Recognizing these disparities, the network is seeking to move forward and design our projects with an eye toward equity. During breakout sessions, regional working groups were created to: (1) identify the species of interest; (2) characterize dominant stressor(s) in specific climate region; and (3) connect rapidly with local land managers to find out whether tree inventories existed and what data were already available for use. Workshop participants were divided into three climatically themed groups (Fig. 2): Hot and Wet, including scientists from Gainesville (Florida, USA) and Athens (Georgia, USA); Cool and Wet, including scientists from Montreal (Canada), Cambridge and Northampton (Massachusetts, USA), and Minneapolis–Saint Paul (Minnesota, USA); and Hot and Dry including scientists from Sydney (Australia), Tel-Aviv (Israel), Sacramento (California, USA), and Johannesburg (South Africa). Each group raised the global and regional most urgent questions and highlighted the specific needs of each region. Data collected from all cities will help tackle common questions concerning tree health and stress in the face of changing climate across biomes, as well as leveraging institutional knowledge to investigate research questions and hypotheses particular to the locality of each network node. Breakout groups echoed concepts from keynote talks highlighting the potential for public engagement. By leveraging public curiosity surrounding equipment installation and tissue collection in high-traffic public areas, research in urban environments can inspire both science communication and education. Keynotes by Jason Gordon (University of Georgia USA) and Kaisa Rissanen (Université de Québec à Montréal, Canada) highlighted the challenge of preserving scientific equipment from any human or animal-induced degradations in urban settings. To simultaneously maximize educational opportunities and discourage vandalism, workshop participants proposed installing informative panels in front of monitored trees, including a QR code for near-real-time web-based tree health information (Fig. 1). Subsequent discussions explored methods of protecting apparatus from tampering, including suggesting the use of decoy devices (Fig. 1). Keynote talks by Yakir Preisler (Harvard University, USA and ARO, Israel), Erez Feuer (Hebrew University, Israel) and Bill Miller (Licor Ltd, USA), showcased novel tools helpful for long-term monitoring of urban trees. Workshop participants decided that each city node would build and deploy a 'Trumpet' datalogger designed by Erez Feuer utilizing the Global System for Mobile (GSM) communication network for real-time data collection across multiple remote sites (Fig. 1). Upon database launch, the 'Trumpet' would be deployed on urban trees distributed to c. 30 trees per participating city, to measure air temperature, humidity, and trunk diameter variations using band dendrometers (EMS Brno Ltd, Brno, Czechia). Emphasizing the need for real-time monitoring across cities, representatives from each UTEN node agreed to monitor ecophysiological processes (e.g. sap flow, stem diameter, and water potential) and meteorological measurements (air temperature, humidity, solar radiation, and wind) using this design over the coming year(s). Participants of the workshop agreed on a platform for consistent measurement and quality control of data collection and communication from urban trees to UTEN. Thus, these data will be aggregated in a shared UTEN research database (Fig. 1), which UTEN intends to create and implement in close collaboration with municipalities for exchanging critical information on urban forest health (see arrows in Fig. 1). Following Dr Tim Rademacher's Université du Québec en Outaouais keynote talk that presented the concept of a 'witness tree', that is a tree on which different apparatus communicate their results to the general public through social media, workshop participants agreed to install one witness tree per city to enhance public and municipal engagement. Future actions will consist of selecting and establishing one witness tree per city and establishing a social media presence (X, Instagram, etc.) for it (Fig. 1). Establishing a global urban tree monitoring network is challenging in practice and will require several years of commitment to the project. However, workshop participants were eager to trial measurements and deploy sensors. The group's primary focus lies in establishing a foundational ecophysiological database, ready to benefit both the scientific community (addressing climate change responses, mechanisms, and biophysical interactions) and municipal decision-makers (in matters of species selection, cooling efficiency, treatments, etc.). While the group will encounter scientific and logistical obstacles, UTEN has the interdisciplinary expertise necessary to overcome a wide array of challenges. Future steps, including ongoing virtual meetings (which have been running monthly since December 2022), include establishing an annual in-person meeting to discuss the data collected and the future of the network, such as recruiting representatives from new nodes to aid the progressive expansion of the network into currently under-represented biogeographic and climatic regions. The integration of nodes in Europe (Clermont-Ferrand, France, and Helsinki, Finland), and South America (Bogotá, Colombia) is currently underway; however, participation of more sites in areas of the world that lack UTEN nodes (particularly in the southern hemisphere) is encouraged. Those interested in establishing a node of UTEN in their city should join the group on X at @Global_UTEN, and visit our website and complete the 'join us' form. Funding for the workshop was provided by the Georgia Forestry Commission, the Warnell School of Forestry and Natural Resources, and the Office of the Vice President for Research at the University of Georgia. YP planned and designed the UTEN workshop. MM wrote the first draft of the manuscript. WMH, DMJ, YP, RM, MB, GPJ, JG and AO contributed substantially to improving the first draft. All the other authors contributed equally to improving the final version of the manuscript. Data sharing is not applicable as no new data were generated.

Despite the abundant evidence of impairments to plant performance and survival under hotter-drought conditions, little is known about the vulnerability of reproductive organs to climate extremes. Here, by conducting a comparative analysis between flowers and leaves, we investigated how variations in key morphophysiological traits related to carbon and water economics can explain the differential vulnerabilities to heat and drought among these functionally diverse organs. Due to their lower construction costs, despite having a higher water storage capacity, flowers were more prone to turgor loss (higher turgor loss point; ΨTLP) than leaves, thus evidencing a trade-off between carbon investment and drought tolerance in reproductive organs. Importantly, the higher ΨTLP of flowers also resulted in narrow turgor safety margins (TSM). Moreover, compared to leaves, the cuticle of flowers had an overall higher thermal vulnerability, which also resulted in low leakage safety margins (LSM). As a result, the combination of low TSMs and LSMs may have negative impacts on reproduction success since they strongly influenced the time to turgor loss under simulated hotter-drought conditions. Overall, our results improve the knowledge of unexplored aspects of flower structure and function and highlight likely threats to successful plant reproduction in a warmer and drier world.

Hyper-spectral imaging has recently gained increasing attention for use in different applications, including agricultural investigation, ground tracking, remote sensing and many other. However, the high cost, large physical size and complicated operation process stop hyperspectral cameras from being employed for various applications and research fields. In this paper, we introduce a cost-efficient, compact and easy to use active illumination camera that may benefit many applications. We developed a fully functional prototype of such camera. With the hope of helping with agricultural research, we tested our camera for plant root imaging. In addition, a U-Net model for spectral reconstruction was trained by using a reference hyperspectral camera's data as ground truth and our camera's data as input. We demonstrated our camera's ability to obtain additional information over a typical RGB camera. In addition, the ability to reconstruct hyperspectral data from multi-spectral input makes our device compatible to models and algorithms developed for hyperspectral applications with no modifications required.

Vegetation greening has been suggested to be a dominant trend over recent decades, but severe pulses of tree mortality in forests after droughts and heatwaves have also been extensively reported. These observations raise the question of to what extent the observed severe pulses of tree mortality induced by climate could affect overall vegetation greenness across spatial grains and temporal extents. To address this issue, here we analyse three satellite-based datasets of detrended growing-season normalized difference vegetation index (NDVI GS ) with spatial resolutions ranging from 30 m to 8 km for 1,303 field-documented sites experiencing severe drought- or heat-induced tree-mortality events around the globe. We find that severe tree-mortality events have distinctive but localized imprints on vegetation greenness over annual timescales, which are obscured by broad-scale and long-term greening. Specifically, although anomalies in NDVI GS (ΔNDVI) are negative during tree-mortality years, this reduction diminishes at coarser spatial resolutions (that is, 250 m and 8 km). Notably, tree-mortality-induced reductions in NDVI GS (|ΔNDVI|) at 30-m resolution are negatively related to native plant species richness and forest height, whereas topographic heterogeneity is the major factor affecting ΔNDVI differences across various spatial grain sizes. Over time periods of a decade or longer, greening consistently dominates all spatial resolutions. The findings underscore the fundamental importance of spatio-temporal scales for cohesively understanding the effects of climate change on forest productivity and tree mortality under both gradual and abrupt changes.

The Walker Lane (WL) in the western Great Basin (GB) is an active plate boundary system accommodating 10%-20% of the relative tectonic motion between the Pacific and North American plates. Its neotectonic framework is structurally complex, having hundreds of faults with various strikes, rakes, and crustal blocks with vertical axis rotation. Faults slip rates are key parameters needed to quantify seismic hazard in such tectonically active plate boundaries but modeling them in complex areas like the WL and GB is challenging. We present a new modeling strategy for estimating fault slip rates in complex zones of active crustal deformation using data from GPS networks. The technique does not rely on prior estimates of slip rates from geologic studies, and only uses data on the surface trace location, dip, and rake. The iterative framework generates large numbers of block models algorithmically from the fault database to obtain many estimates of slip rates for each fault. This reduces bias from subjective choices about how discontinuous faults connect and interact to accommodate strain. Each model iteration differs slightly in block boundary configuration, but all models honor geodetic and fault data, regularization, and are kinematically self-consistent. The approach provides several advantages over bespoke models, including insensitivity to outlier data, realistic uncertainties, explicit mapping of off-fault deformation, and slip rates that are more objective and independent of geologic slip rates. Comparisons to the U.S. National Seismic Hazard Model indicate that similar to 80% of our geodetic slip rates agree with their geologic slip rates to within uncertainties. The Walker Lane (WL) is a complex zone of faults in the western Great Basin of the western United States that experiences frequent earthquakes driven by active plate tectonics. Ground networks of very sensitive GPS stations deployed over the last few decades have collected data showing where the ground deforms most quickly, and hence where earthquakes are more likely to occur. Data on how fast faults slip over time is used to inform the public about the distribution and intensity of the seismic hazard. In this study we present improved data and modeling that resolve with unprecedented detail the rates, patterns, and styles of active crustal motion, resulting in better estimates of fault slip rates in the WL. This work brings the picture of earthquake potential derived from GPS networks into sharper focus, provides new information about how plate tectonics works, and will lead to more accurate estimates of seismic hazard that can help reduce the loss of life and property from earthquakes. We estimate Walker Lane fault slip rates using a dense filtered and gridded geodetic velocity field and a robust multi-block model approach The geodetic slip rates are independent of geologic slip rates, but 80% agree with them to within uncertainties The method images off-fault deformation and vertical axis rotations providing more insight into how crustal motion drives earthquakes

Summary Turgor loss point (TLP) is an important proxy for plant drought tolerance, species habitat suitability, and drought‐induced plant mortality risk. Thus, TLP serves as a critical tool for evaluating climate change impacts on plants, making it imperative to develop high‐throughput and in situ methods to measure TLP. We developed hyperspectral pressure–volume curves (PV curves) to estimate TLP using leaf spectral reflectance. We used partial least square regression models to estimate water potential (Ψ) and relative water content (RWC) for two species, Frangula caroliniana and Magnolia grandiflora. RWC and Ψ's model for each species had R2 ≥ 0.7 and %RMSE = 7–10. We constructed PV curves with model estimates and compared the accuracy of directly measured and spectra‐predicted TLP. Our findings indicate that leaf spectral measurements are an alternative method for estimating TLP. F. caroliniana TLP's values were −1.62 ± 0.15 (means ± SD) and −1.62 ± 0.34 MPa for observed and reflectance predicted, respectively (P > 0.05), while M. grandiflora were −1.78 ± 0.34 and −1.66 ± 0.41 MPa (P > 0.05). The estimation of TLP through leaf reflectance‐based PV curves opens a broad range of possibilities for future research aimed at understanding and monitoring plant water relations on a large scale with spectral ecophysiology.

Aflatoxin contamination in peanuts (Arachis hypogaea L.) is a significant public health risk. Aflatoxin is detected postharvest after inspection of loads associated with grading at peanut buying points, leaving growers and shellers in a precarious position. Stricter limits on aflatoxin contamination could restrict the United States access to international markets. Predicting aflatoxin risk remains challenging, but improved tools could help inform postharvest storage segregation decisions and alert industry stakeholders to seasonal threats. This study aimed to develop and evaluate multiple statistical models that estimate the regional status of peanut aflatoxin contamination based on preharvest weather conditions. Our approach expanded on an existing peanut aflatoxin model for which a new geographic area and time period were tested. Weather variables served as independent variables to predict the risk of aflatoxin as the proportion of samples with greater than 20 ppb and 4 ppb aflatoxin (PGT20 [the proportion of samples with greater than 20 ppb aflatoxin] and PGT4 [the proportion of samples with greater than 4 ppb aflatoxin], respectively) across 10 counties in Georgia for 2018-2022. Best-performing models were developed through multiple linear stepwise regression explaining more than 72% and 41% of the variability in PGT20 and PGT4, respectively. Model performance further varied whether it was a year of low or high aflatoxin incidence, with temperature observed as a key influencing factor across best-performing models. This study established an adaptive approach to monitoring and managing aflatoxin risk through statistical predictive modeling, with output targeting farmers, industry, regulators, and public health officials. Future model development will aim to improve interpretation and confidence with in-season aflatoxin prediction and efficacy testing of this approach across space and time.

Collecting and analyzing hyperspectral imagery (HSI) of plant roots over time can enhance our understanding of their function, responses to environmental factors, turnover, and relationship with the rhizosphere. Current belowground red-green-blue (RGB) root imaging studies infer such functions from physical properties like root length, volume, and surface area. HSI provides a more complete spectral perspective of plants by capturing a high-resolution spectral signature of plant parts, which have extended studies beyond physical properties to include physiological properties, chemical composition, and phytopathology. Understanding crop plants’ physical, physiological, and chemical properties enables researchers to determine high-yielding, drought-resilient genotypes that can withstand climate changes and sustain future population needs. However, most HSI plant studies use cameras positioned above ground, and thus, similar belowground advances are urgently needed. One reason for the sparsity of belowground HSI studies is that root features often have limited distinguishing reflectance intensities compared to surrounding soil, potentially rendering conventional image analysis methods ineffective. Here we present HyperPRI, a novel dataset containing RGB and HSI data for in situ, non-destructive, underground plant root analysis using ML tools. HyperPRI contains images of plant roots grown in rhizoboxes for two annual crop species – peanut ( Arachis hypogaea ) and sweet corn ( Zea mays ). Drought conditions are simulated once, and the boxes are imaged and weighed on select days across two months. Along with the images, we provide hand-labeled semantic masks and imaging environment metadata. Additionally, we present baselines for root segmentation on this dataset and draw comparisons between methods that focus on spatial, spectral, and spatialspectral features to predict the pixel-wise labels. Results demonstrate that combining HyperPRI’s hyperspectral and spatial information improves semantic segmentation of target objects.

Mycotoxins and particularly aflatoxin are a concern for peanut growers, processors, and consumers. Aflatoxins are responsible for approximately 25 % of liver cancer cases worldwide, while also costing millions of dollars in lost revenue on an annual basis. Current detection techniques based on high pressure liquid chromatography (HPLC) are time, labor and cost intensive. Peanut product can be held multiple days before testing is complete, costing processors additional revenue loss. Over the past few years, The Georgia Tech Research Institute (GTRI) has been investigating the use of plant-based biogenic volatile organic compounds (bVOCs) for monitoring the status of peanut plants. Initially, GTRI utilized these bVOCs to monitor for heat/drought stress in peanut plants. They quickly saw very promising indications of the ability of bVOCs to monitor other parameters in these plants. More recently, the team has started to investigate the of use of bVOCs to monitor aflatoxin development in plants, pods, and kernels pre- and post-harvest. A field trial for detection of aflatoxin using bVOCs was conducted in August–September of 2020 where three test groups were prepared: plants treated with Aspergillus fungus; plants treated with Afla-Guard (biocontrol agent); plants not treated – acting as a control group. Plant-based bVOCs were collected from the plants before treatment, and once a week post treatment using Stir Bar Sorptive Extraction (SBSE) devices or Twisters®. Each Twister® was then analyzed via gas chromatography–mass spectrometry (GC/MS). Pods from tested plants were harvested and sent to GTRI where bVOCs were collected and analyzed using GC/MS. Several statistical analysis and machine learning techniques were applied to all the collected GC/MS data. It was found using only bVOCs, that Random Forest classification performed well for the analysis of the pod and kernel samples with an F1 score of 0.80. On the other hand, Linear Discriminate Analysis (LDA) was only able to correctly classify 50 % of plant-based samples solely on bVOCs alone, which may be due to training models developed using original labels assuming no cross contamination at the field level. These results indicate the potential for bVOC screening for aflatoxin as an important way to lower impacts to growers, shellers, and consumers.

Forest protection and afforestation have been identified as a means to partially offset anthropogenic CO2 emissions. Yet, increasingly frequent observations of drought-induced tree mortality are reported. Here, we applied a risk analysis framework for global drought-induced forest mortality by examining extreme reductions in greenness and water content of forest canopies during past mortality events as well as growth recovery of surviving individual trees following stand-scale mortality events. We defined a drought-induced mortality risk index (DMR) that explains 80% of documented tree mortality. Rising CO2 alleviated the increase of DMR with short-term drought, however, the observed DMR increases with long-term drought no matter whether considering plant responses to CO2. DMR in sites where tree mortality has been observed significantly increased since the 1980s. More than that, drought exposure threatened 0.28 billion hectares of forested areas. Our framework highlights how climate change-induced drought, especially hotter-droughts, threatens the sustainability of global forests.

Predicting soil water status remotely is appealing due to its low cost and large-scale application. During drought, plants can disconnect from the soil, causing disequilibrium between soil and plant water potentials at pre-dawn. The impact of this disequilibrium on plant drought response and recovery is not well understood, potentially complicating soil water status predictions from plant spectral reflectance. This study aimed to quantify drought-induced disequilibrium, evaluate plant responses and recovery, and determine the potential for predicting soil water status from plant spectral reflectance. Two species were tested: sweet corn (Zea mays), which disconnected from the soil during intense drought, and peanut (Arachis hypogaea), which did not. Sweet corn's hydraulic disconnection led to an extended 'hydrated' phase, but its recovery was slower than peanut's, which remained connected to the soil even at lower water potentials (-5 MPa). Leaf hyperspectral reflectance successfully predicted the soil water status of peanut consistently, but only until disequilibrium occurred in sweet corn. Our results reveal different hydraulic strategies for plants coping with extreme drought and provide the first example of using spectral reflectance to quantify rhizosphere water status, emphasizing the need for species-specific considerations in soil water status predictions from canopy reflectance.

Journal Article Causes of widespread foliar damage from the June 2021 Pacific Northwest Heat Dome: more heat than drought Get access C J Still, C J Still Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR 97331, USA Corresponding author (chris.still@oregonstate.edu) https://orcid.org/0000-0002-8295-4494 Search for other works by this author on: Oxford Academic PubMed Google Scholar A Sibley, A Sibley Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR 97331, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar D DePinte, D DePinte US Department of Agriculture, Forest Service, Pacific Northwest Region, State & Private Forestry, Forest Health Protection, Redmond, OR 97756, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar P E Busby, P E Busby Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar C A Harrington, C A Harrington US Department of Agriculture, Forest Service, Pacific Northwest Research Station, Olympia, WA 98512, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar M Schulze, M Schulze Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR 97331, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar D R Shaw, D R Shaw Department of Forest Engineering, Resources, and Management, Oregon State University, Corvallis, OR 97331, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar D Woodruff, D Woodruff US Department of Agriculture, Forest Service, Pacific Northwest Research Station, Corvallis, OR 97331, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar D E Rupp, D E Rupp Oregon Climate Change Research Institute, College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR 97331, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar C Daly, C Daly PRISM Climate Group, Northwest Alliance for Computational Science and Engineering, Oregon State University, Corvallis, OR 97331, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar ... Show more W M Hammond, W M Hammond Agronomy Department, University of Florida, Institute of Food and Agricultural Sciences, Gainesville, FL 32611, USA https://orcid.org/0000-0002-2904-810X Search for other works by this author on: Oxford Academic PubMed Google Scholar G F M Page G F M Page Biodiversity and Conservation Science, Department of Biodiversity, Conservation and Attractions, Locked Bag 104, Bentley Delivery Centre, Bentley, Western Australia 6983, AustraliaCSIRO Land and Water, Private Bag 5, Wembley, Western Australia 6913, Australia Search for other works by this author on: Oxford Academic PubMed Google Scholar Tree Physiology, Volume 43, Issue 2, February 2023, Pages 203–209, https://doi.org/10.1093/treephys/tpac143 Published: 05 January 2023 Article history Received: 08 June 2022 Accepted: 11 December 2022 Published: 05 January 2023 Corrected and typeset: 17 January 2023