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Abiotic stresses like heat and drought pose a major threat to forest ecosystems and are anticipated to increase due to anthropogenic climate change. One mechanism by which plants cope with stress
A unique exercise facility has been developed and used to perform tipper body ergometry tests for space applications. Originally designed to simulate the muscular, cardiovascular and thermoregulatory responses to working in zero gravity, this facility may be used to conduct basic thermoregulatory investigations applicable to multiple sclerosis patients. An environmental chamber houses the tipper body ergometer and permits control of temperature, air now and humidify. The chamber is a closed system and recirculate-s air after conditioning if. A Cybex Lipper body ergometer has been mounted horizontally on the wall of the environmental chamber. In this configuration, the subject lies underneath the arm crank on a supine seat in order to turn the crank. The supine seat can be removed in order to introduce other equipment into the chamber such as a stool to allow upright arm cranking, or a treadmill to allow walk-run experiments. Physiological and environmental signals are fed into a Strawberry Tree data acquisition system while being monitored and logged using the Workbench software program. Physiological monitoring capabilities include 3-lead EKG using an H-P patient monitor, 5 site skin temperature and core temperature using YSI thermistors, and O2 consumption and CO2 production using AMFTFK Applied Electrochemistry analyzers and sensors. This comprehensive data acquisition set tip allows for calculation of various thermoregulatory indices including heat storage, evaporative heat loss, latent heat loss, and metabolic rate. The current system is capable of adding more data acquisition channels if needed. Some potential studies that could be carried out using the facility include: 1) An investigation into the efficiency of cooling various segments of the body to lower Tc 1-2 F. 2) A series of heat and mass balance studies comparing various LCG configurations.
A field research program was begun in 1969 and intensified in 1970 on the physiology and biophysical responses of second-growth Douglas fir infected with root rot fungus. A double tramway system was suspended between three 100-foot instrument towers to carry sensors for measuring the energy response from above both healthy and infected trees. Processing and analysis was completed of airborne multispectral scanner imagery collected over the Wind River research area in 1969. Likelihood ratio processing of three-channel infrared data and Euclidean distance analysis of ten-channel spectrometer data did not identify incipient root rot infection outside the training sets. In all cases infected fir was misclassified as healthy fir. It was concluded from careful examination of physiological data that Poria root rot infection has little effect on water metabolism and energy exchange. What was identified was a low-grade stress that affects respiration and metabolism over long periods of time. This led to minor changes in the external physical symptoms of Poria-infected trees which was revealed only in the shortwave reflectance data.
Measurements on foliage samples collected from several drought and salt treated plants revealed that leaf thickness decreased with increasing severity of the drought treatment and increased with increasing severity of treatment with NaCl, but remained essentially unaffected by treatment with CaCl2. Airborne data collected by multispectral scanner indicated that false color images provide selective enhancement of a diseased area. Comparison of simulated and actual aerial color and color IR photography revealed that the color renditions of the MSS simulations agreed closely with those of the actual photography.
These data support the manuscript “Acquisitive root exploration strategies help maintain higher peak sap flux rates during summer drought, but more root biomass does not”. The dataset includes processed sap flow measurements and fine-root trait data collected between 2019 and 2021 from temperate monodominant tree plots established in the 1920s to 1930s ranging in size from 0.05 to 0.8 ha at The Morton Arboretum in Lisle, IL. Sap flow was measured with ICT sap flow sensors using the heat ratio method. Fine-root traits were measured from soil cores which includes specific root length (SRL), specific root area (SRA), diameter, biomass, and length for diameter classes ≤1 mm and ≤2 mm. The package contains comma separated value (CSV) data files and associated metadata that can be viewed and analyzed using common software such as spreadsheet programs, R, and Python. These data are used to investigate how variation in fine-root traits relate to tree water use and drought response during summer drought linking belowground root traits and aboveground physiological responses.
This data set consists of PhenoCam data from the SPRUCE experiment from the beginning of whole ecosystem warming (Hanson et al. 2017) in August 2015 through March 31 of 2025 (2015-08-24 to 2025-03-31), with start- and end-of-season phenological transition dates derived through the end of autumn 2024. Digital cameras, or phenocams, installed in each SPRUCE enclosure track seasonal variation in vegetation “greenness”, a proxy for vegetation phenology and associated physiological activity. Three separate regions of interest (ROIs) were defined for each camera field of view, corresponding to different vegetation types and demarcating (1) Picea trees (vegetation type EN, for evergreen needleleaf); (2) Larix trees (vegetation type DN, for deciduous needleleaf); and (3) the mixed shrub layer (vegetation type SH). This data set consists of three sets of data files: (1) 3-day summary product files: One file for each camera and each ROI (i.e. vegetation type), characterizing vegetation color at a 3-day time step. • Contains 36 files in *.csv format inside a compressed (*.zip) file. (2) Transition date file: Estimates “greenness rising” (spring) and “greenness falling” (autumn) transition dates derived from the smoothed daily green chromatic coordinate (GCC) values, for each camera and each ROI (i.e., vegetation type). • Contains one file in *.csv format. (3) Snow flag files: Indicate days with snow on trees or snow on ground for each experimental enclosure. • Contains two files in *.csv format, one for snow on trees and one for snow on ground. This data set consists of two sets of companion files: (1) Accompanying HTML files show the 90th quantiles of the mean GCC plotted together with transition dates for each vegetation type and plot. • Contains three files in HTML format, one for each vegetation type. • One additional file in HTML format with the transition dates plotted for each vegetation type, by year. (2) R files for processing PhenoCam files and flags. • Contains five files in R file(*.R) format and the components of the phenocamr package (Version 1.1.4) used for calculating transition dates for 2015-2024. These are contained in a compressed (*.zip) file. User Note: All imagery is posted in near-real time to the PhenoCam Project web page (https://phenocam.nau.edu), where it is publicly available. Scroll to “spruce” in the Gallery or link directly to the 29 SPRUCE cameras at https://tinyurl.com/sprucecams. This data set is based on the complete camera record from SPRUCE and supersedes all previously released PhenoCam datasets (see Related Data Sets). The estimated transition dates for previously released datasets may differ slightly (in most cases, by ±3 days or less), because following standard PhenoCam processing protocols (Richardson et al. 2018, Scientific Data), smoothing and interpolation, outlier removal, and transition date estimation are always conducted using the full data record.
Previsual detection of Fomes annosus in pine plantations was studied. Detailed analyses of photographic imagery obtained over the Ann Arbor Test Site during 1969 and 1970 reveal that the Ektachrome Infrared film was superior to Ektachrome Aerographic, Infrared Aerographic, or Plus-X Aerographic films for detecting Fomes annosus damage. Of far more significance in controlling the accuracy of damage detection, however, was the experience of the photo interpreter. Ratio-processing of multispectral scanner data was investigated with data collected in June of 1970 and in June of 1972. Ratioing of the 1.5-1.8 and 1.0-1.4 micrometer channels gave good results at detecting openings in the crown canopy and adjacent infected trees. Combined level slicing of the 1.5-1.8 micrometer channel and the 1.5-1.8 to 1.0-1.4 micrometer ratio permitted separation and recognition of forest litter in the openings and stressed trees adjacent ot the openings.
The 2024 IUFRO Tree Biotechnology Conference is the biennial meeting on genomics, molecular biology, and biotechnology of forest trees, associated with the IUFRO Working Party 2.04.06. This year's meeting was held in Annapolis, MD, USA from August 4th to 8th and was hosted by Yiping Qi (University of Maryland), Edward Eisenstein (University of Maryland), Gary Coleman (University of Maryland), and Heather Coleman (Syracuse University). The conference covered seven topics over the course of five days: 1) Biological and ecological insights from OMICS, 2) Advancing technologies for targeted trait manipulation and acceptability to diverse tree species, 3) Genes, development, and physiology, 4) Translating genomics and biotechnology to practice, 5) Trees in a changing world, 6) Genetic and phenotypic diversity for breeding and genomic selection, and 7) Biotechnology for biomaterials and bioeconomy. In addition to the sessions, there were two plenary sessions, provided by John Ralph (University of Wisconsin) and Tanja Pyrhäjärvi (University of Helsinki). The meeting celebrated the second awardees of the newly created IUFRO WG 2.04.06 Award: Excellence in Forest Molecular Biology and Genomics, which was presented to Chung-Jui (C.J.) Tsai (University of Georgia). Greg Goralogia (Oregon State University) was the recipient of the associated Early Career Award. The scientific presentations at the conference highlighted cutting-edge advancements in many facets of forest biotechnology research, including applications of genomic selection in forest genetics and breeding, the use of genetic editing, tree physiology, stress response, molecular breeding, wood development, "omics" technologies, and the social and economic impacts of genetically modified (GM) trees. Scientific take homes from the meeting include the power of NMR to dissect the composition of lignin, the genomic diversity of forest trees that has enormous potential for tree improvement and the integration of systems biology with climate and geographical data. The conference attracted a mix of students (25), postdoctoral fellows (32), and scientists from academia (66) and industry (18). In all, the conference was attended by 141 registered participants, representing 20 countries that participated in 23 invited lectures (including 6 'early-career' keynotes), 27 voluntary talks and 61 poster presentations. Support for the conference was drawn from a wide variety of Academia, Industry, and Government sources, and included financial support from several tree improvement companies. Overall, the conference was a great success, providing an exceptional mix of science and social activities in a relaxed and collegial atmosphere. More information about the meeting can be found at treebiotech.org. The next meeting will be held in Stellenbosch, South Africa, in 2026, hosted jointly by Zander Myburg, Dave Drew (University of Stellenbosch,) and Sanushka Naidoo (University of Pretoria, FABI).
Results of continuing studies of forest trees subjected to varying types of stress are reported. Both greenhouse and field studies are included. Greenhouse work with tree seedlings exposed to varying levels of NaCl and CaCl2 in the soil indicated that, in the initial stages, palisade cells shrink and the amount of air space in the leaf increases. As the severity of damage increases, the cells of the spongy mesophyll shrink and flatten, and the amount of air space in the leaf decreases. Statistical analysis of foliar reflectance and associated moisture content data led to a series of regression equations for predicting foliar moisture content from reflectance data. Equations were calculated for three species, yellow birch (Betula alleghaniensis Britton), sugar maple (Acer saccharum Marsh.) and white ash (Fraxinus americana L.) having multiple correlation coefficients of 0.98, 0.94 and 0.93 respectively. Interpretation of multispectral imagery of the Ann Arbor Forestry Test Site (NASA Site 190) provided evidence that infections of Fomes annosus can be detected in the early stages. Infections of two needle cast diseases were also detected in conifer plantations in the test site. A study of automatic interpretation of multispectral scanner imagery for tree species recognition provided encouraging results.
NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “Fire Severity-Dependent Shifts in Vegetation Parameter Sensitivity: A Pre- and Post-Fire Analysis Using ELM-FATES and Explainable AI” submitted to Journal of Advances in Modeling Earth Systems (Zahura et al. 2026). The study examines vegetation physiological parameters controlling pre-fire and post-fire vegetation dynamics. To support this analysis, 73 vegetation parameters in Functionally Assembled Terrestrial Ecosystem Simulator (FATES) (Fisher et al., 2018) , which is coupled with E3SM (Energy Exascale Earth System Model) land model (ELM, ELM-FATES), were perturbed using a Sobol sequence to generate 1,024 ensemble members for two plant functional types: needleleaf evergreen extratropical trees (NEET) and C3 grass. Simulations were conducted for the pre-fire period (2016) and post-fire period (2018–2023). Burn severity was represented by modifying the Nesterov index in FATES to 75,000, 150,000, and 300,000 for low, moderate, and high severity, respectively. A no-fire scenario was also included. Simulations were performed for 16 grid cells in the American River Watershed across different burn severities and plant functional types. XGBoost (eXtreme Gradient Boosting) models were trained using the parameter ensembles and ELM-FATES-simulated outputs, including leaf area index (LAI), gross primary productivity (GPP), aboveground biomass, vegetation evaporation, transpiration, and soil evaporation. Models were trained separately for each year and burn severity, followed by SHAP (SHapley Additive exPlanations) analysis to identify changes in dominant parameters after fire disturbance. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. The data package contains the ELM-FATES simulation data. The scripts and data related to the analysis will be added later. The inputs and outputs from ELM-FATES are inside the “FATES” folder. “FATES_domain_surface” contains the domain and surface netcdfs that were used to run ELM-FATES in the study area. “FATES_parameters” contains the 1024 ensembles that were generated using Sobol sequence. “FATES_outputs” folder contains ELM-FATES simulated variables. All files are .csv and .nc (NetCDF).
The occurrence and magnitude of temporal and spatial tree water status changes in the boreal environment were studied in a floodplain forest in Alaska and in four forest types of Central Canada. Under limited water supply conditions from the rooted soil zone in early spring (freeze/thaw transition) and during summer, trees show declining water potentials. Coincidental change in tree water potential, tree transpiration and tree dielectric constant had been observed in previous studies performed in Mediterranean ecotones. If radar is sensitive to chances in tree water status as reflected through changes in dielectric constant, then radar remote sensing could be used to monitor the water status of forests. The SAR imagery is examined to determine the response of the radar backscatter to the ground based observations of the water status of forest canopies. Comparisons are made between stands and also along the large North-South gradient between sites. Data from SAR are used to examine the radar response to canopy physiological state as related to vegetation freeze/thaw and growing season length.
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Me5 Metolius-first young aged pine. This is the FLUXNET version of the carbon flux data for the site US-Me5 Metolius-first young aged pine produced by applying the standard ONEFlux (1F) software. Site Description - Previously old-growth ponderosa pine, clearcut in 1978 and allowed to regenerate naturally. Law et al (2001) Global Change Biology 7, 755-777; Law et al (2001) Agricultural and Forest Meteorology 110, 27-43; Anthoni et al (2002) Agricultural and Forest Meteorology 111, 203-222; Irvine & Law (2002) Global Change biology 8,1183-1194, Irivne et al (2004) Tree Physiology 24,753-763.
Spectral characteristics of semi-arid shrub communities were examined using Airborne Imaging Spectrometer (AIS) data collected in the tree mode on 23 May 1985. Mesic sites with relatively high vegetation density and distinct zonation patterns exhibited greater spectral signature variations than sites with more xeric shrub communities. Spectral signature patterns were not directly related to vegetation density or physiognomy, although spatial maps derived from an 8-channel maximum likelihood classification were supported by photo-interpreted surface features. In AIS data, the principal detected effect of shrub vegetation on the alluvial fans is to lower reflectance across the spectrum. These results are similar to those reported during a period of minimal physiological activity in autumn, indicating that shadows cast by vegetation canopies are an important element of soil-vegetation interaction under conditions of relatively low canopy cover.
Understanding how agroecosystems respond to environmental variability is fundamental to predicting productivity and sustainability under a changing climate. We analyzed 55 site-years of high-frequency eddy covariance observations from five agroecosystems—two perennial grasses (miscanthus and switchgrass), two annual rotation systems (maize–soybean and sorghum–soybean), and a restored native prairie—to examine ecosystem-scale carbon, water, and energy fluxes. Using an interpretable machine-learning framework with regression tree ensembles, Shapley Additive Explanations, and Accumulated Local Effects, we quantified how environmental and temporal factors regulate gross primary productivity (GPP), evapotranspiration (ET), water-use efficiency, and the Bowen ratio. Perennials exhibited stronger physiological buffering and maintained fluxes across a broader range of temperature and moisture conditions, reflecting deeper rooting and persistent canopy cover. Annuals, in contrast, showed greater short-term variability and stronger coupling to atmospheric demand, with GPP and ET declining rapidly under low humidity or soil moisture. Differences in temperature sensitivity of Bowen ratio further revealed that perennials sustained proportionally greater sensible heat flux under cool conditions, whereas annuals exhibited constrained energy exchange when evaporative demand was low. Together, these results demonstrate that crop life cycle and canopy structure are fundamental determinants of ecosystem-scale carbon–water–energy coupling. By integrating long-term flux observations with interpretable machine learning, this study identifies the environmental drivers that shape agroecosystem function and highlights how conversion from annual to perennial feedstocks can enhance climatic resilience and alter land–atmosphere energy feedbacks. These findings provide a data-driven basis for improving crop and Earth-system models and for guiding bioenergy landscape design under future climate scenarios.
During March 1988 and May 1991, the JPL airborne synthetic aperture radar, AIRSAR, collected sets of multi-temporal imagery of the Bonanza Creek Experimental Forest near Fairbanks, Alaska. These data sets consist of series of multi-polarized images collected at P-, L-, and C-bands each over a period of a few days. The AIRSAR campaigns were complemented with extensive ground measurements that included observations of both static canopy characteristics such as forest architecture as well as properties that vary on short term time scales such as canopy dielectric conditions. Observations exist for several stands of deciduous and coniferous species including white spruce (Picea glauca), black spruce (Picea mariana), and balsam poplar (Populus balsamifera). Although the duration of each campaign was fairly short, significant changes in environmental conditions caused notable variations in the physiological state of the canopies. During the 1988 campaign, environmental conditions ranged from unseasonably warm to more normal subfreezing temperatures. This permitted AIRSAR observations of frozen and thawed canopy states. During May 1991, ice jams that occurred along the river caused many stands to flood while the subsequent clearing of the river then allowed the waters to recede, leaving a snow covered ground surface. This allowed observations of several stands during both flooded and nonflooded conditions. Furthermore, the local weather varied from clear sunny days to heavy overcast days with some occurrence of rain. Measurements of leaf water potential indicated that this caused significant variations in canopy water status, allowing SAR observations of water stressed and unstressed trees. Mean backscatter from several stands is examined for the various canopy physiological states. The changes in canopy backscatter that occur as a function of environmental and physiological state are analyzed. Preliminary results of a backscatter signature modeling analysis are presented. The implications of using SAR to monitor canopy phenological state are addressed.
This dataset contains uncorrected sap velocity measurements using the heat ratio method (HRM) collected using ICT International SFM1x sensors at five urban sites in Chicago, Illinois, as part of the DOE CROCUS project. The data includes continuous monitoring of sap velocity from various tree species, including Maples (Acer spp.): Sugar Maple (Acer saccharum), Silver Maple (Acer saccharinum), and Red Maple (Acer rubrum); Oaks (Quercus spp.): Swamp White Oak (Quercus bicolor); American Elm (Ulmus americana); Honey Locust (Gleditsia triacanthos); Cottonwood (Populus deltoides); and Tree of Heaven (Ailanthus altissima) across Chicago State University (CSU), Northeastern Illinois University (NEIU), Northwestern University (NU), University of Illinois Chicago (UIC), and West Woodlawn "Blacks in Green" (BIG). These include both street trees and those in urban park locations. Measurements were collected at 15-20 minute intervals, depending on the sensor, and transmitted via Long Range Wide Area Network (LoRaWAN) protocols. The wireless data was collected by Sage Network (https://sagecontinuum.org/) nodes. The dataset includes sensor ID, Global Positioning System (GPS) coordinates, tree species (common and scientific names), tree identification number, diameter at breast height (DBH in cm), uncorrected sap velocity measurements (cm/hr) from both inner and outer probes, and Sage Node identifiers so the data can be mapped to related variables such as air quality and wind speed that were collected on the Sage nodes. All timestamps are in local Chicago time (CDT/CST). Quality control flags are provided using a 3-bit binary system indicating physical range violations (< -10 or > 60 cm/hr), step spikes (absolute difference > 36 cm/hr), and stuck sensor conditions (> 10 consecutive identical values). These are raw data, not corrected for wood anatomy or species-specific characteristics. Data is provided in comma separated (CSV) format. This dataset is part of a larger collection of CROCUS environmental monitoring data, including linked datasets from Air Quality Transmitter (AQT) sensors, Weather Transmitter (WXT) sensors, and Multi-Function Research LoRaWAN (MFR) Nodes. DOIs for the supporting data are provided as part of this data package.
Tropical ecosystems contain the world's largest biodiversity of vascular plants. Yet, our understanding of tropical functional diversity and its contribution to global diversity patterns is constrained by data availability. This discrepancy underscores an urgent need to bridge data gaps by incorporating comprehensive tropical root data into global datasets. Here, we provide a database of tropical root characteristics. This new database, TropiRoot 1.0, will be instrumental in evaluating an array of hypotheses pertaining to root functional ecology and plant biogeography, both within the tropics and relative to other global biomes. The data compilation was conducted by the TropiRoot Initiative, in partnership with the Fine-Root Ecology Database (FRED) and the Global Root Trait (GRooT) database, Colorado State University (CSU) and the Smithsonian Tropical Research Institute (STRI). Literature search and data extraction were conducted between 2020 and 2024. Literature was identified using Web of Science, Scopus, and complemented using the expert knowledge of members of TropiRoot. To provide broad environmental and geographical distributions, literature searches included root characteristics (traits) across global change drivers, natural gradients, and from different continents. We adopted FRED standardized data columns and streamlined the format to enhance accessibility for data extraction across various user groups. This optimized framework resulted in a smaller, yet comprehensive datasheet. To make the database compatible with other global root trait initiatives, column identification was standardized following the codes provided by FRED. These efforts culminated in data extracted from 104 new sources, resulting in more than 8000 rows of data (either species or community data). Most of the data in TropiRoot 1.0 include root characteristics such as root biomass, morphology, root dynamics, mass fraction, architecture, anatomy, physiology, and root chemistry. This initiative represents a 30% increase in the currently available data for tropical roots in FRED. TropiRoot 1.0 contains root characteristics from 25 different countries, where seven are located in Asia, six in South America, five in Central America and the Caribbean, four in Africa, two in North America, and 1 in Oceania. Due to the volume of data, when ancillary data were available, including soil data, these data were either extracted and included in the database or its availability was recorded in an additional column. Multiple contributors checked the entries for outliers during the collation process to ensure data quality. For text-based observations, we examined all cells to ensure that their content relates to their specific categories. For numerical observations, we ordered each numerical value from least to greatest and plotted the values, checking apparent outliers against the data in their respective sources and correcting or removing incorrect or impossible values. Some data (soil and aboveground) have different columns for the same variable presented in different units, including originally published units, but root characteristics data had units converted to match those reported in FRED. By filling a gap from global databases, TropiRoot 1.0 expands our knowledge of otherwise so far underrepresented regions and our ability to assess global trends. This advancement can be used to improve tropical forest representation in vegetation models. The data are freely available and should be cited when used.