Find research datasets worth reusing

openCC (other)Jan 2026View details →

Tree species, diameter, and canopy class records for 4-paired plots in Black Rock Forest, NY, since 1931.

Black Rock Forest maintains eight long-term forest monitoring plots in Cornwall, NY. Four pairs of plots were established in 1931 to compare thinning treatments to nearby control plots. Four plots are approximately 0.25 acres and the other four are 0.1 acres. Tree species, diameter at breast height, height and canopy class have been measured on all stems greater than 1 inch in diameter since 1931. Plots were revisited every five years until the 1990s and annually after 1994.

openCC (other)Apr 2024View details →

Tree species, diameter, regeneration, and herbaceous cover from 218 plots in 1985 in Black Rock Forest, NY.

A stand inventory was completed in 1985 in Black Rock Forest, Cornwall, NY across 3112 acres. Trees greater than 2" in diameter at breast height (DBH) were tallied using a 10 basal area factor prism in 218 plots across 71 stands. For each tree, species, DBH, number of eight foot pieces, overall form, crown class, and any special notes were recorded. Regeneration was measured at each location by tallying all trees less than 2" DBH in a 2-m radius plot. Shrub and herbaceous cover at each location were also tallied in a 2-m radius plot.

openCC (other)Jun 2025View details →

Tree cores from three species along a natural nitrogen mineralization gradients in Michigan Lower Peninsula

Mycorrhizal fungi are understood to exhibit mutualistic relationships with trees. This study assessed this relationship via the growth of individual trees associated with different mycorrhizal communities along a gradient of N availability.

openCustomJan 2020View details →

Consequences of non-random tree species loss on litter mass loss, nutrient dynamics, carbon cycling, and decomposer communities across a terrestrial-aquatic interface at Coweeta Hydrologic Lab, Otto, NC

Although litter decomposition is a fundamental ecological process, most of our understanding comes from studies of single-species decay. Recently, litter-mixing studies have tested whether monoculture data can be applied to mixed-litter systems. These studies have mainly attempted to detect non-additive effects of litter mixing, which address potential consequences of random species loss. The focus is not on which species are lost, but the decline in diversity per se. Under global change, species loss is likely to be non-random, with some species more vulnerable to extinction than others. Under such scenarios, the effects of individual species (additivity) as well as of species interactions (non-additivity) on decomposition rates are of interest. To examine potential impacts of non-random species loss on ecosystems, we studied additive and non-additive effects of litter mixing on decomposition. A full-factorial litterbag experiment was conducted using four deciduous leaf species, from which mass loss and nitrogen content were measured. Data were analysed using a statistical approach that first looks for additive identity effects based on the presence or absence of species and then significant species interactions occurring beyond those. It partitions non-additive effects into those caused by richness and or composition.

openCC (other)Nov 2023View details →

Short-term disappearance of foliar litter of three tree species native to rain forest of Puerto Rico

Litter disappearance was examined before (1989) and after (1990) Hurricane Hugo in the Luquillo Experimental Forest, Puerto Rico using mesh litterbags containing abscised Cyrilla racemiflora or Dacryodes excelsa leaves or fresh Prestoea montana leaves. Biomass and nitrogen dynamics were compared among: i) species; ii) mid- and high-elevation forest types; iii) riparian and upland sites; and iv) among pre- and post-hurricane disturbed environments. Biomass disappearance was compared using multiple regression and negative exponential models in which the slopes were estimates of the decomposition rates subsequent to apparent leaching losses and the y-intercepts were indices of initial mass losses (leaching). C. racemiflora leaves with low nitrogen (0.39 %) and high lignin (22.1 %) content decayed at a low rate and immobilized available nitrogen. D. excelsa leaves had moderate nitrogen (0.67 %) and lignin (16.6 %) content, decayed at moderate rates, and maintained the initial nitrogen mass. P. montana foliage had high nitrogen (1.76 %) and moderate lignin (16.7 %) content and rapidly lost both mass and nitrogen. There were not significant differences in litter disappearance and nitrogen dynamics among forest types and slope positions. Initial mass loss of C. racemiflora leaves was lower in 1990 but the subsequent decomposition rate did not change. Initial mass losses and the overall decomposition rates were lower in 1990 than in 1989 for D. excelsa. D. excelsa and C. racemiflora litter immobilized nitrogen in 1990 but released 10-15% of their initial N in 1989, whereas P. montana released nitrogen in both years (25-40 %). Observed differences in litter disappearance rates between years may have been due to differences in the timing of precipitation. Foliar litter inputs during post-hurricane recovery of vegetation in Puerto Rico may serve to immobilize and conserve site nitrogen. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-00

dryad44/100

Inferring the mammal tree: Species-level sets of phylogenies for questions in ecology, evolution, and conservation

Big, time-scaled phylogenies are fundamental to connecting evolutionary processes to modern biodiversity patterns. Yet inferring reliable phylogenetic trees for thousands of species involves numerous trade-offs that have limited their utility to comparative biologists. To establish a robust evolutionary timescale for all ~6000 living species of mammals, we developed credible sets of trees that capture root-to-tip uncertainty in topology and divergence times. Our 'backbone-and-patch' approach to tree-building applies a newly assembled 31-gene supermatrix to two levels of Bayesian inference: (i) backbone relationships and ages among major lineages, using fossil node- or tip-dating; and (ii) species-level 'patch' phylogenies with non-overlapping in-groups that each correspond to one representative lineage in the backbone. Species unsampled for DNA are either excluded ('DNA-only' trees) or imputed within taxonomic constraints using branch lengths drawn from local birth-death models ('completed' trees). Joining time-scaled patches to backbones results in species-level trees of extant Mammalia with all branches estimated under the same modeling framework, thereby facilitating rate comparisons among lineages as disparate as marsupials and placentals. We compare our phylogenetic trees to previous estimates of mammal-wide phylogeny and divergence times, finding that (i) node ages are broadly concordant among studies, and (ii) recent (tip-level) rates of speciation are estimated more accurately in our study than in previous 'supertree' approaches where unresolved nodes led to branch length artifacts. Credible sets of mammalian phylogenetic history are now available for download at <a href="http://vertlife.org/phylosubsets">http://vertlife.org/phylosubsets</a>, enabling investigations of long-standing questions in comparative biology.

opencc-zeroDec 2019View details →

opencc-by-4.0Dec 2021View details →

Presence-Absence Points for Tree Species Distribution Modelling for Europe

The dataset is a collection of presence and absence points for forest tree species for Europe. Each unique combination of longitude, latitude and year was considered as an independent sample. Presence data was obtained from the harmonized tree species occurrence dataset by <a href="https://zenodo.org/record/5524611">Heisig and Hengl (2020)</a> and absence data from the <a href="https://ec.europa.eu/eurostat/web/lucas">LUCAS</a> (in-situ source) dataset. A set of 50 different forest tree species was selected from the harmonized tree species dataset and data lacking a temporal observation was overlaid with yearly forest masks derived from land cover maps produced by <a href="https://zenodo.org/record/4725429">Parente et al. (2021)</a>. We overlaid the points with the probability maps for the classes: <ul> <li>311: Broad-leaved forest,</li> <li>312: Coniferous forest,</li> <li>313: Mixed forest,</li> <li>323: Sclerophyllous forest,</li> <li>324: Transitional woodland-shrub,</li> <li>333: Sparsely vegetated area.</li> </ul> Points were included in the dataset only if the probability value extracted for at least one of the above classes was ≥ 50% for all the years considered. An additional quality flag was added to distinguish points coming from this operation and the points with original year of observation coming from source datasets. The final dataset contains 4,359,999 observations for and a total of 630 columns.  The first 8 columns of the dataset contain metadata information used to uniquely identify the points: <ul> <li>id: unique point identifier,</li> <li>year: year of observation,</li> <li>postprocess: quality flag to identify if the temporal reference of an observation comes from the original dataset or is the result of spatiotemporal overlay with forest masks,</li> <li>Tile_ID: contains the tile id from the eu_tiling_system (30 km grid),</li> <li>easting: longitude coordinates in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035),</li> <li>northing: latitude coordinates in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035),</li> <li>Atlas_class: name of the tree species according to the European Atlas of Forest Tree Species or NULL in case of absence point,</li> <li>lc1: contains original LUCAS land cover class or NULL if it's a presence point.</li> </ul> The remaining columns contain the extracted values of a series of predictor variables (temperature, precipitation, elevation, topographical information, spectral reflectance) useful for species distribution modeling applications. These points were used to model the potential and realized distribution of a series of 16 target species for the period 2000 - 2020. The approach involved training three ML models to predict probability of presence (i.e. <a href="http://link.springer.com/article/10.1023/A:1010933404324">Random Forest</a>, <a href="http://dl.acm.org/doi/abs/10.1145/2939672.2939785">XGBoost</a>, <a href="https://rss.onlinelibrary.wiley.com/doi/abs/10.2307/2344614">GLM</a>), which served as input to train a linear meta-model (i.e. <a href="http://papers.nips.cc/paper/2014/file/ede7e2b6d13a41ddf9f4bdef84fdc737-Paper.pdf">Logistic regression classifier</a>), responsible for predicting the final probability of presence for each species. The RDS file is created from a data.table object and suitable for fast reading in the R-programming environment. The CSV.GZ file contains records as a table with easting and northing in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035) and can be fed in a GIS after being unzipped. We provide RDS files for a 30km tile as an example containing raster stacks at 30m resolution of all the covariates included in the regression matrix. You can find the specific geographical location of the tile in Europe using the attached GeoPackage ("eu_tiling_system_30km"): open it in QGIS and filter by "ID". In our approach we considered both static and dynamic covariates: dynamic covariates are calculated as averages of a 4 years time window (example: 2004 contains averages from 2002 to 2006). To get the predictions for a specific year, covariates contained in the static RDS file need to be bound with the respective year. To access our predictions (probabilities and uncertainties) produced for the target species access: <ul> <li>Open Data Science Europe viewer: <a href="https://maps.opendatascience.eu">https://maps.opendatascience.eu</a></li> <li>Check the Related identifiers section of this repository to access each species individually</li> </ul> If you instead would like to know more about the creation of this dataset and the modeling: <ul> <li>watch the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li>access the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_tree.species_anv.pnv.eml">GitLab</a>)</li> </ul> A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.

opencc-by-4.0Sep 2022View details →

Xylem conductances of 24 tree species of Europe

The dataset contains the xylem conductances (ks) in kg m-2MPa-1s-1, and mmH2O mm-1s-1 as the Community Land Model 5.0 (CLM5) requires. In addition, the data set has individual records of 24 tree species describing the plant functional types broadleaf deciduous (BDT), broadleaf evergreen (BET), and needleleaf evergreen (NET) trees. The data comes from 21 references from European experiments. The tree species selected for this data set are: BDT: Acer pseudoplatanus, Betula occidentalis, Carpinus betulus, Fagus sylvatica, Fraxinus excelsior,Quercus alba, Quercus cerris, Quercus petraea, Quercus pubescens, Quercus robur, Quercus rubra, Tilia cordata, Carpinus orientalis, Quercus frainetto. BET: Quercus ilex, Quercus suber, Arbutus unedo. NET: Abies bornmulleriana, Picea abies, Pinus pinaster, Pinus sylvestris, Tsuga heterophylla, Pinus nigra, Pseudotsuga menziesii. Special consideration was taken to Pseudotsuga menziesii, which was included in the data set by selecting only the European records. The species was included in a European data set because of its importance as an introduced commercial tree species for the European continent. The individual xylem conductances were retrieved directly from tables, manuscript text, or figures. The online tool WebPlotDigitizer (https://automeris.io/WebPlotDigitizer) was used to retrieve individual records.

opencc-by-4.0Mar 2019View details →

Elevation modulates the phenotypic responses to light of four co-occurring Pyrenean forest tree species

Data on plant water potential for seedlings of four Pyrenean tree species planted along an elevation gradient. The dataset contains three files: <ol> <li>Biomass.txt: Data on plant biomass per fraction (leaf, stem and roots) 4 years after plantation. Included variables: - Piso (factor): elevational stage at which the seedling was planted. Two levels: montane (M) or subalpine (S) - Luz (factor): whether the seedling was plantes at a gap or in the understory. Two levels: gap (O) or understroy (T) - N (numeric): number of plant in that plot - Sp (factor): species. Four levels: BEPE (Betula pendula) / PISY (Pinus sylvestris) / PIUN (Pinus uncinata) / ABAL (Abies alba) - Planta (factor): code to identify uniquely each plant - File (factor): code to identify uniquely each plant - GLI (num): Global Light Index, the amount of irradiance that receives each seedling - Code (factor): code to identify uniquely each plant - PLB (numeric): total plant biomass (g) - LFB (numeric): leaf biomass (g) - STB (numeric): stem biomass (g) - RTB (numeric): root biomass (g) - LMF (numeric): leaf mass fraction (LFB/PLB) - SMF (numeric): stem mass fraction (STB/PLB) - RMF (numeric): root mass fraction (RTB/PLB) - SLA (numeric): specific leaf area - H (numeric): plant height (mm) - D (numeric): plant diameter at root collar (mm) - PB2 (numeric): total plant biomass without considering leaves (g) - SF2 (numeric): stem mass fraction without considering leaves (STB/PB2) - RF2 (numeric): root mass fraction without considering leaves (RTB/PB2)</li> <li>init_biomass.txt: for biomass at the moment of plantation - Piso (factor): elevational stage at which the seedling was planted. Two levels: montane (M) or subalpine (S) - N (numeric): number of plant - Sp (factor): species. Four levels: BEPE (Betula pendula) / PISY (Pinus sylvestris) / PIUN (Pinus uncinata) / ABAL (Abies alba) - Planta (factor): code to identify uniquely each plant - File (numeric): code to identify uniquely each plant - PB (numeric): total plant biomass (g) - LB (numeric): leaf biomass (g) - SB (numeric): stem biomass (g) - RB (numeric): root biomass (g)</li> <li>WaterPot.txt: data on plant water potential for seedlings of four Pyrenean tree species planted along an elevation gradient during a period of intense drought - Piso (factor): elevational stage at which the seedling was planted. Two levels: montane (M) or subalpine (S) - Luz (factor): whether the seedling was plantes at a gap or in the understory. Two levels: gap (O) or understroy (T) - N (numeric): number of plant  - Parcela (factor): identifier ofthe plot - Sp (factor): species. Four levels: BEPE (Betula pendula) / PISY (Pinus sylvestris) / PIUN (Pinus uncinata) / ABAL (Abies alba) - Estacion (factor): the moment for the measurement. One level: September - GLI (numeric): global light index, the ration of total irradiance received by the plant at the moment of plantation - WPt (numeric): water potential (bars)</li> </ol>

opencc-by-4.0Nov 2022View details →

Potential tree species distributions from the Last Glacial Maximum in North America

Modern tree distributions modeled under current climate and predicted to past climate. Values of '2' represent presence. The column mark is current presence, while _20000 is 20 ka, _14000 is 14 ka, _13000 is 13 ka, etc. For quick download, the .dbf for each species can be joined to the shapefile (us_can_ecosub). Alternatively, download and use the zipped folder of shapefiles (glac_shapes)..

opencc-by-4.0May 2023View details →

GlobalUsefulNativeTrees: useful tree species

The GlobalUsefulNativeTrees database (GlobUNT; <a href="https://worldagroforestry.org/output/globalusefulnativetrees">https://worldagroforestry.org/output/globalusefulnativetrees</a>) was developed after a first step of combining native distribution data across 242 countries and territories from GlobalTreeSearch (accessed 8th May 2022; Beech et al. <a href="https://doi.org/10.1080/10549811.2017.1310049">2017</a>; BGCI <a href="https://tools.bgci.org/global_tree_search.php">2022</a>) with information on ten categories of human usage documented in the World Checklist of Useful Plant Species (WCUPS; Diazgranados et al. <a href="https://knb.ecoinformatics.org/view/doi:10.5063/F1CV4G34">2020</a>). GlobUNT was described in more detail in the following publication: Kindt et al. (<a href="https://www.nature.com/articles/s41598-023-39552-1">2023</a>) GlobalUsefulNativeTrees, a database of 14,014 tree species, supports synergies between biodiversity recovery and local livelihoods in restoration. Sci Rep 13, 12640. <a href="https://doi.org/10.1038/s41598-023-39552-1">https://doi.org/10.1038/s41598-023-39552-1</a>. Version v.2023.01 of the database includes 14,014 useful tree species, representing roughly a quarter of the known tree species (as listed by GlobalTreeSearch) and a third of the plant species from WCUPS. The data set included here provides the taxonomic names for all tree species included in GlobUNT together with details on the process of standardization via the <a href="https://cran.r-project.org/package=WorldFlora">WorldFlora</a> package (Kindt <a href="https://bsapubs.onlinelibrary.wiley.com/doi/full/10.1002/aps3.11388">2020</a>). This taxonomic standardization process was completed <a href="https://www.worldagroforestry.org/output/agroforestry-species-switchboard-30">during the preparation of the third major release</a> of the <a href="https://apps.worldagroforestry.org/products/switchboard">Agroforestry Species Switchboard</a> (as a consequence, all species listed in GlobUNT are included among the 230,000+ species from the Switchboard). The development of GlobUNT was supported by the Darwin Initiative to project DAREX001 of <a href="https://www.darwininitiative.org.uk/project/DAREX001/">Developing a Global Biodiversity Standard certification for tree-planting and restoration</a> and by Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia to the <a href="https://www.worldagroforestry.org/project/provision-adequate-tree-seed-portfolio-ethiopia">Provision of Adequate Tree Seed Portfolio</a> project in Ethiopia. When using the GlobUNT species list in your work, please cite the publication (Kindt et al. (<a href="https://www.nature.com/articles/s41598-023-39552-1">2023</a>) provided above) as well as this repository using the DOI (<a href="https://zenodo.org/record/7994433">https://zenodo.org/record/7994433</a>).

opencc-by-4.0May 2023View details →

Data_Schönauer et al. (2023)_Root and branch hydraulic functioning and trait coordination across organs in drought-deciduous and evergreen tree species of a subtropical highland forest

Data used in Schönauer, M., Hietz, P., Schuldt, B., and Rewald, B. (2023). Root and branch hydraulic functioning and trait coordination across organs in drought-deciduous and evergreen tree species of a subtropical highland forest. Frontiers in plant science 14, 1127292. doi: 10.3389/fpls.2023.1127292

dryad44/100

Inferring the mammal tree: Species-level sets of phylogenies for questions in ecology, evolution, and conservation

Open the record for dataset details and reuse information.

publicDec 2019View details →

openCC (other)Dec 2021View details →

Climate Change Impacts for 14 Tree Species in Southwest Colorado

Forest management traditionally has been based on expectation of a steady climate. In the face of a changing climate, management requires projections of changes in the distribution of the climatic niche of the major species and strategies for applying the projections. We prepared climatic habitat models incorporating heatload as a topographic predictor for the 14 upland tree species of southwestern Colorado, USA, an area that has already seen substantial climate impacts. Models were trained with over 800,000 points of known presence and absence. Using 11 climate scenarios for the decade around 2060, we classified and mapped change for each species. Projected impacts are extensive. Except for the low-elevation woodland species, persistent habitat is rare. Most habitat is lost or threatened and is poorly compensated by emergent habitat. Three species may be locally extirpated. Nevertheless, strategies are described that can use the projections to apply management where it is likely to be most effective, to facilitate or assist migration, to favor species likely to be suited in the future, and to identify potential climate refugia.

openCC (other)Jun 2022View details →

Tree-ring width measurements and isotope data for riparian Populus species, Santa Clara River, 2019

This data set comprises tree-ring data collected from 114 cottonwood trees (Populus trichocarpa and Populus fremontii) within the floodplain of the Santa Clara River, CA. Tree-ring data include annual ring width measurements for all rings of each individual as well as semi-annual (earlywood and latewood) measurements of stable carbon and oxygen isotopes for pure alpha cellulose extracted from annual growth rings corresponding to calendar years 2010-2019 for a subset of 48 individuals. This data set is completed. Carbon and oxygen isotope ratios are reported using “delta” notation (i.e. δ13C and δ18O) calculated by the equation: δ13C (or δ18O) = (Rsample/Rstandard - 1) x1000 where R is the molar ratio of 13C/12C (or 18O/16O), with Rsample being that of tree ring cellulose and Rstandard that of Vienna Pee Dee Belemite (VPDB) for δ13C and Vienna Standard Mean Ocean Water (VSMOW) for δ18O. These data were used for the following publication: Williams, J., J.C. Stella, S.L. Voelker, A.M. Lambert, L. Pelletier, J.E. Drake, J.M. Friedman, D.A. Roberts, M.B. Singer. (2022). Local groundwater decline exacerbates response of dryland riparian woodlands to climatic drought. Global Change Biology.

openCC (other)Apr 2022View details →

University of Kansas Field Station: Forest demography, 1980 – 2015. On ten study plots established on three management units all live trees with a dbh > 7.5 cm (3 in) were identified to species, measured, and tagged. Trees were initially measured in 1980/1981 and re-measured in three successive time periods: 1993/95; 2002/03; and 2014/15. Trees will be measured again in 2025/26.

In 1980 researchers at the University of Kansas initiated a long-term experiment monitoring the composition of oak-hickory forest communities at the University’s field station near Lawrence, Kansas. The purpose of the study was to determine how forest species composition varied temporally across distinct habitats that varied in topography, elevation, sun exposure, management history and successional stage. Ten permanent sites were sampled approximately each decade with data collection periods of 1980/81, 1993/95, 2002/03, and 2014/15. Trees with a minimum diameter at breast height (dbh) of ≥ 7.5 cm were tagged, identified to species and measured. Trees will be measured again in 2025/26.

openCC (other)Feb 2023View details →

Leaf temperature of northeastern US tree species

Leaf temperature measurements were collected during the summer of 2020 within forested areas at the Thompson Farm Earth Systems Observatory in Durham, New Hampshire, USA. Located within the property is a registered Ameriflux site, Thompson Farm Forest (US-TFF), as well as experimental throughfall exclusion plots that are part of DroughtNet (experiment running since 2015). Leaf temperature measurements were made within the footprint of the eddy covariance flux tower as well as within both control and throughfall exclusion treatment plots. Upper canopy foliage was accessed using a bucket lift and in situ measurements made using a handheld thermal IR sensor. All data were paired with concurrent meteorological measurements from US-TFF or data from a co-located NOAA CRN station (NH Durham 2 SSW). Additionally, leaf chemical, physical, structure, and physiological traits have been measured at this site as well as canopy scale measures of structure and UAV-based spectral, thermal, and lidar imagery. Specific to this leaf temperature dataset, leaf-level light, temperature, and vpd photosynthetic response curves were measured.

openCC (other)Feb 2023View details →

Leaf angle measurements for temperate tree species in northeastern USA

Leaf angle distribution (LAD) measurements were made during the growing season in 2021 at the Harvard Forest in Petersham, MA, USA, and in 2022 at the Thompson Farm Earth Systems Observatory in Durham, NH, USA. At both sites, a level-calibrated digital angle tool was used to measure LAD in upper canopy foliage of common northeastern temperate tree species accessed using a mobile canopy lift. Additionally, at Thompson Farm, measurements were made at multiple heights to characterize differences of LAD in high, middle, and low canopy positions. Here, we have published those measurements, including a summary table of species average leaf angles and calculated parameters for fitted beta distributions. Processing scripts can be made available upon request to the authors. Additionally, leaf chemical, physical, structure, optical and physiological traits have been measured at these site as well as canopy scale measures of structure and UAV-based spectral, thermal, and lidar imagery.

openCC (other)Jul 2024View details →

Tree species identity, diameter and qualitative canopy health measurements (full, partial or dead) from 2005 to 2023 on 12 experimental oak loss plots in Black Rock Forest, NY.

Black Rock Forest established a series of 12, 0.56 ha plots in 2005 to assess impacts of the loss of tree in the genus Quercus on the forest ecosystem (entitled the Future of Oak Forests experiment). Three trunk girdling treatments, with control plots were instituted in 2008. Each plot also contained an ~10m by ~15m deer exclosure to assess the impact of herbivory post-disturbance. Trees were measured twice per year from 2008 to 2013 (except 2009 when trees were measured once) and once per year from 2014 to 2023. Data include tree species identity, diameter at breast height (DBH), canopy health (a qualitative assessment of approximate cover as full, partial or dead), presence/absence of sprouts, and location within the plot. All live trees equal to or larger than 2.5 cm DBH are included in the dataset.