Tree aboveground biomass, basal area, atmospheric deposition, and climate data for sugar maple (Acer saccharum), paper birch (Betula papyrifera), tuliptree (Liriodendron tulipifera), red spruce (Picea rubens), ponderosa pine (Pinus ponderosa), cottonwood (Populus deltoides), quaking aspen (Populus tremuloides), and black cherry (Prunus serotina) measured in United States Department of Agriculture Forest Inventory Analysis plots from 1997-2022.

Abstract These data (TreeData_2024_10_11.csv) were developed by E&S Environmental, with funding provided by the U.S. Environmental Protection Agency, to develop an initial set of tree growth and survival response models using Bayesian methods. US-EPA ORD and a team of science advisors at USFS, NPS, USGS, Syracuse University, University of Minnesota, Virginia Tech, and RTI provided input on the development of these data to explore candidate explanatory predictor variables for estimating tree growth and survival. These data were then provided to NPS to serve as the basis for expanded Bayesian model development and analyses in collaboration with Virginia Tech. These data were used for analyses in Lofton et al., "Contrasting effects of short- versus long-term responses to nitrogen deposition in U.S. tree species". Code to run the analyses associated with Lofton et al. is provided on GitHub (https://github.com/melofton/nps_ndep_remeasure) and published in Zenodo. All variables included in the dataset along with their units and descriptions are provided in TreeData_Dictionary.xlsx. Methods USDA Forest Inventory Analysis (FIA) data Tree growth, survival, basal area, and aboveground tree carbon data were obtained from or calculated from data within the USDA FIA database (https://apps.fs.usda.gov/fia/datamart/datamart.html), accessed on 21 Nov 2022. The FIA program regularly collects several tree and site variables from permanent plots across the United States as part of its Nationwide Forest Inventory (NFI) effort (https://research.fs.usda.gov/programs/nfi). NFI plots are selected using a probabilistic sampling design, with approximately one plot for every 6000 acres of forest, and the sampling effort includes plots of all management and ownership types (Burrill et al., 2025). On each plot, tree height, diameter, and survival status for all trees at least 12.7 cm in diameter are collected following NFI field protocols (Burrill et al., 2025). Aboveground biomass is calculated as a function of tree diameter and height and is subsequently converted to aboveground carbon by applying species-specific carbon fractions to aboveground biomass values (Burrill et al., 2025). Plots are resampled approximately every 5-10 years. This dataset includes NFI data from 07 Apr 1997 to 23 Mar 2022 for eight focal tree species—sugar maple (Acer saccharum), paper birch (Betula papyrifera), tuliptree (Liriodendron tulipifera), red spruce (Picea rubens), ponderosa pine (Pinus ponderosa), cottonwood (Populus deltoides), quaking aspen (Populus tremuloides), and black cherry (Prunus serotina). Atmospheric deposition and climate data Nitrogen and sulfur deposition data over NFI plots during the study period were obtained from U.S. National Atmospheric Deposition Program’s National Trends Network (NTN; https://nadp.slh.wisc.edu/networks/national-trends-network/) and Total Deposition Science Committee (TDEP ver. 2022.02; https://nadp.slh.wisc.edu/committees/tdep/). Specifically, from 1985-1999, NTN interpolated wet deposition and applied the dry:wet ratios from TDEP (a 3-yr average obtained using data from 2000-2002) were used to obtain total deposition. From 2000-2021, only TDEP data were used, which included total N and S deposition (wet plus dry deposition of both oxidized and reduced N and S). Climate data (including various measures of solar radiation, air temperature, precipitation, vapor pressure deficit, and various climate indices) were retrieved for NFI plots during the study period from ClimateNA v7.40 (Wang et al., 2016). ClimateNA is a software application that downscales gridded (4 x 4 km) monthly climate data from the Parameter-elevation Regressions on Independent Slopes Model (PRISM; Daly et al., 2008). References Burrill, E. A., Christensen, G. A., Conkling, B. L., DoTommaso, A. M., Kralicek, K. M., Lepine, L. C., Perry, C. J., Pugh, S. A., Turner, J. A., & Walker, D. M. (2025). The Forest Inventory and Analysis Database, FIADB user guides, volume: database description (version 9.4), nationwide forest inventory (NFI). https://research.fs.usda.gov/understory/forest-inventory-and-analysis-database-user-guide-nfi Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor, G. H., Curtis, J., & Pasteris, P. P. (2008). Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal of Climatology, 28(15), 2031–2064. https://doi.org/10.1002/joc.1688 Wang, T., Hamann, A., Spittlehouse, D., & Carroll, C. (2016). Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America. PLOS ONE, 11(6), e0156720. https://doi.org/10.1371/journal.pone.0156720