About

Our faculty are engaged and dedicated educators, advisors, and mentors and have been honored with numerous university-wide and national teaching awards. Our classes emphasize the latest research coupled with cutting-edge technology and practices making our graduates among the most competitive candidates in the country for natural resource professions. Our curricula include everything from protected lands management and urban forestry, to industrial forestry operations and ecology. Small class sizes and faculty dedicated to teaching afford FREC students the chance to get to know their professors personally. Wide varieties of academic and professional opportunities are available through research, student organizations, and public outreach programs organized by the faculty.

Research topics

• Physical geography
• Mathematics
• Geography
• Economics
• Environmental science
• Forestry
• Statistics

Selected publications

• Global Wood Density Database v.2 (GWDD v.2)

  DIGITAL.CSIC (Spanish National Research Council (CSIC)) · 2026-01-15 · 1 citations

  datasetOpen access

  The Global Wood Density Database v.2 The Global Wood Density Database v.2 (GWDD v.2) is a collection of 109,626 taxonomically standardized wood density records and 15,093 additional bark density records. Data include measurements at different levels of aggregation (individual, species) and both georeferenced records and values from the literature. For a full description of the database, please see the corresponding manuscript. (Fischer et al. 2026. Beyond species means – the intraspecific contribution to global wood density variation. New Phytol. https://doi.org/10.1111/nph.70860). It includes and supersedes the GWDD v.1 (Zanne et al. 2009, https://doi.org/10.5061/dryad.234). When using the GWDD v.2 in your work, please cite Fischer et al. 2026 as well as this repository using the corresponding DOI (10.5281/zenodo.16919509). If you would like to report an issue or suggest improvements for future updates of the GWDD, please do so on github: https://github.com/fischer-fjd/GWDD/issues Aggregated wood density data We provide pre-aggregated wood density data, with wood density estimates at species, binomial species and genus level. Wood density estimates are derived from hierarchical (random effects) models and provided both as simple species mean values (wsg_est) and as species mean values for trunks (wsg_est_trunk) and branches (wsg_est_branch) separately. In addition, we provide raw wood density means (wsg_raw), but we do not recommend using them for practical purposes due to outliers for poorly sampled species. gwddagg_v2.x_species: pre-aggregated wood density values for 17,261 species, including infraspecific epithets; comprises 16,828 taxonomically resolved species well as 433 values with uncertain taxonomic status gwddagg_v2.x_binomial: pre-aggregated wood density values for 16,905 binomial species gwddagg_v2.x_genus: pre-aggregated wood density values for 3,198 genera Raw wood density data In addition, we also provide the underlying raw wood density database. This collection contains one metadata file and raw data files in .csv format. Since special characters (e.g., in the references) can be distorted by operating systems when reading in .csv files, we also provide all data in .RData format, which can be loaded into R with the load() function. columns_gwdd_v2.x: metadata for all the columns included in the GWDD v.2 gwdd_v2.x: the GWDD v.2, including all 109,626 wood density records gwdd_v2.x_withbark: the GWDD v.2, including all 109,626 wood density records and 15,093 additional bark density records

  PublisherDOI

• Beyond species means – the intraspecific contribution to global wood density variation

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-08-28

  preprintOpen access

  Abstract Wood density is central for estimating vegetation carbon storage and a plant functional trait of great ecological and evolutionary importance. However, the global extent of wood density variation is unclear, especially at the intraspecific level. We assembled the most comprehensive wood density collection to date (GWDD v.2), including 109,626 records from 16,829 plant species across woody life forms and biomes. Using the GWDD v.2, we explored the sources of variation in wood density within individuals, within species, and across environmental gradients. Intraspecific variation accounted for up to 15% of overall wood density variation (sd = 0.068 g cm -3 ). Sapwood densities varied 50% less than heartwood densities, and branchwood densities varied 30% less than trunkwood densities. Individuals in extreme environments (dry, hot, acidic soils) had higher wood density than conspecifics elsewhere (+0.02 g cm -3 , ∼4% of the mean). Intraspecific environmental effects strongly tracked interspecific patterns (r = 0.83) but were only 20–30% as large and varied considerably among taxa. Individual plant wood density was difficult to predict (RMSE > 0.08 g cm -3 ; single-measurement R 2 = 0.59). We recommend (i) systematic within-species sampling for local applications, and (ii) expanded taxonomic coverage combined with integrative models for robust estimates across ecological scales.

  PublisherDOI

• A global map of wood density

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-08-28 · 2 citations

  preprintOpen access

  Abstract Wood density influences how quickly woody plants grow, how long they live and how much carbon they store, yet its global variation remains poorly mapped. Here we combined 109,626 wood density measurements from 16,829 species with 300,949 vegetation plots to produce a km-scale map of community-weighted wood density for every woody biome. Our model led to a prediction accuracy 32–51 % higher than previous global products, and a 1.8–3.7-fold wider wood density range (0.28–1.00 g cm −3 ; global mean: 0.57 g cm −3 ) than previously assumed. Spatial cross-validation showed low bias (±2.5 % of the mean), and uncertainties decreased from 20% in poorly sampled drylands and boreal regions to 5% in data-rich temperate forests. Mean annual temperature was the best predictor of community-weighted mean wood density, increasing by 0.01 g cm −3 for every 1°C change. We deliver a low-bias, high-resolution wood density layer for Earth system models, together with spatially explicit error maps. This study represents a major step forward for carbon accounting and trait-based forecasts of vegetation change.

  PublisherOA PDFDOI

• Estimating County level Timber Volume in Virginia Using Small Area Estimation

  2021

  Senior authorCorresponding
  • Environmental science
  • Forestry
  • Geography

  Accurate estimates of forest components such as, biomass, composition, or health are important for forest management and policy decisions. The USDA Forest Service Forest Inventory and Analysis (FIA) program serves as a national survey system to assess such forest characteristics within the United States (US). When making estimates at the county level or smaller spatial scale with FIA plot data, the accuracy in estimation of forest components drops. Here we use NAIP data in conjunction with FIA data in order to improve upon county level timber volume estimation precision in Virginia using Small area estimation (SAE).

  Publisher

• GAPS IN SAMPLING AND LIMITATIONS TO TREE BIOMASS ESTIMATION: A REVIEW OF PAST SAMPLING EFFORTS OVER THE PAST 50 YEARS

  2015-01-01

  review

  Tree biomass models are widely used but differ due to variation in the quality and quantity of data used in their development. We reviewed over 250 biomass studies and categorized them by species, location, sampled diameter distribution, and sample size. Overall, less than half of the tree species in Forest Inventory and Analysis database (FIADB) are without a published biomass model and most of the sampled trees are less than 13 inches diameter at breast height (d.b.h.). Although some species are well represented with biomass sampled, most focus on the aboveground components and as a result, there are important spatial gaps in their sampling as there was general divergence between the observed and sampled biomass centroids. In addition, most studies we analyzed did not sample trees of poor form or vigor, which means the models may not be representative of the larger population. Currently, this information is being used to address existing biomass sampling gaps in order to develop more robust prediction models.

  Publisher

• Advancing individual tree biomass prediction: assessment and alternatives to the component ratio method

  2015-01-01 · 3 citations

  article

  Prediction of forest biomass and carbon is becoming important issues in the United States. However, estimating forest biomass and carbon is difficult and relies on empirically-derived regression equations. Based on recent findings from a national gap analysis and comprehensive assessment of the USDA Forest Service Forest Inventory and Analysis (USFS-FIA) component ratio method (CRM) for estimating both biomass and carbon using historical individual tree biomass data, alternative approaches for predicting forest biomass and carbon were evaluated. The different CRM approaches tested included: 1) development and use of a unified stem taper equation to estimate stem volume; 2) updated model forms and parameters for predicting biomass components; and 3) comparison of alternative wood density values. Overall, these modifications show the potential to improve estimates of forest biomass and carbon, but additional testing is required before implementation.

  Publisher

• Estimating Changes in Residential Water Demand for Voluntary and Mandatory Water-Use Restrictions Implemented during the 2002 Virginia Drought

  2005-01-01

  articleSenior author
  Publisher

Frequent coauthors

• Jereme Frank

  US Forest Service

  2 shared

• David W. MacFarlane

  Michigan State University

  2 shared

• James A. Westfall

  Northern Research Station

  2 shared

• Aaron R. Weiskittel

  University of Maine

  2 shared

• Tom Fox

  1 shared

• Christiana E. Hilmer

  San Diego State University

  1 shared

• David L.R. Affleck

  University of Montana

  1 shared

• D.R.B. Bosch

  1 shared