Harvard Forest

Data Archive

HF419

Quantifying Growth and Structure along Forest Edges in the Northeastern USA 2010-2021

Related Publications

Data

• hf419-01: all edge forest plots (preview)
• hf419-02: anthropogenic edge forest plots (preview)
• hf419-03: forest growth map
• hf419-04: global forest fragmentation map

Overview

• Lead: Luca Morreale, Jonathan Thompson, Xiaojing Tang, Andrew Reinmann, Lucy Hutyra
• Investigators:
• Contact: Information Manager
• Start date: 2010
• End date: 2020
• Status: complete
• Location: Northeastern States
• Latitude: +36.0 to +49.4 degrees
• Longitude: -96.6 to -66.9 degrees
• Elevation: 0 to 2000 meter
• Datum: WGS84
• Taxa:
• Release date: 2023
• Language: English
• EML file: knb-lter-hfr.419.2
• DOI: digital object identifier
• EDI: data package
• DataONE: data package
• Related links:
• USDA FIA DataMart
• Study type: long-term measurement
• Research topic: regional studies
• LTER core area: primary production, disturbance patterns
• Keywords: aboveground production, biomass, forest dynamics, inventories, land use, stand structure
• Abstract:

  Fragmentation transforms the environment along forest edges. The prevailing narrative, driven by tropical research, suggests that edge environments increase tree mortality and structural degradation resulting in net decreases in ecosystem productivity. We show that temperate forest edges exhibit increased forest growth (basal area increment; BAI) and biomass (basal area; BA) with no change in total mortality relative to the forest interior. To assess forest edges, we analyzed more than 48,000 forest inventory plots (USDA FIA) across the north-eastern US using a quasi-experimental matching design. At forest edges adjacent to anthropogenic land covers, we report increases of 36.3% and 24.1% in forest growth and biomass, respectively. We then scale the edge impacts on growth (along anthropogenic edges only) across our study area using maps of land-cover and forest type. We find large variability in the effect of including edges on estimates of total forest growth, largely driven by differences in the prevalence of fragmentation. Estimated increases in forest growth range from a 23% increase in the agricultural-dominated western areas, a 2% increase in the least-fragmented northern regions, and a 15% increase within the metropolitan east coast. Finally, we also quantify forest fragmentation globally, at 30-m resolution, showing that temperate forests contain 52% more edge forest area than tropical forests.

  We provide two tables containing the post-matched dataset of FIA subplots, including subplot BA, BAI, and edge status. We include the associated environmental covariates, extracted from gridded raster data, and used in our matching and statistical analyses. Due to plot confidentiality restrictions we do not provide spatial locations of the FIA subplots, but we do provide unique plot identifiers that allow users to link each record to the publically-available data provided by the USDA FIA database (https://apps.fs.usda.gov/fia/datamart/). This dataset can be used to recreate our regression modelling results and regional estimates of forest growth.

  We also provide two polygon shapefile layers: EPA Level IV Ecoregion boundaries with spatially-aggregated estimates of edge and total BAI (m2 yr-1) as well as total forest area and edge forest area (forest less than 30 m from a non-forest pixel)and global terrestrial ecoregions boundaries with spatially-aggregated estimates of total forest area and edge forest area (forest less than 30 m from a non-forest pixel).

• Methods:

  The FIA collects measurements of tree size, growth, and land-use within a nested plot design across the country. Each FIA plot is composed of four individual subplots; within each subplot, the diameter at breast height (dbh) of every tree greater than 12.7cm is measured during each measurement period. Evaluating more than 48,000 plots in the USFS Northern Region sampled from 2010 to 2020 and selecting the most recent measurement cycle for each plot, we identified subplots that contained both a forest and a non-forest condition and categorized these as edges. For each subplot (168 m2 in area), we calculated two primary response variables of interest: total live tree basal area (BA) and basal area increment (BAI).

  To account for environmental controls on forest growth we included the most critical abiotic predictors of terrestrial vegetation productivity (light, water, temperature and nitrogen deposition) as covariates in the regression models. Light, water, and temperature data were drawn from spatial raster maps (0.5° resolution) as unit-less indices of relative limitation on vegetation productivity, ranging from 0 to 1(1). Nitrogen data were drawn from the 2018 NADP gridded inorganic wet nitrogen deposition product (4 km spatial resolution; kg of N ha-1)(2). Forest type classifications for each subplot are provided by the FIA and we aggregated the FIA forest types into eight broader species groups, following Thompson et al.(3)

  We performed a series of GLM regressions on our post-matched datasets, using a gamma probability distribution with an inverse link function to model the relationship of BA and BA with the suite of predictor variables. To scale the effects we quantified ecoregion forest composition by: (1) Using a 250 m resolution USFS forest type map(4), we aggregated raw forest type values to the aggregated forest type groups included in our regression models (2) We calculated the total area of each forest type group within each ecoregion, then used the average temperature, light, water, and nitrogen deposition in each ecoregion as inputs to our GLM regression models to calculate the BAI of edge and interior forest for each forest type. With the proportional area of each forest type, we calculated an area-weighted mean and then differenced the estimated edge and interior BAI to produce an expected difference of forest growth between edge and interior within each ecoregion. Finally, we combined the proportion of edge forest with the expected growth difference to quantify the estimated difference in percent increases in ecoregion BAI within each ecoregion attributable to increases of forest growth at the edge.

  We also quantified the extent of forest fragmentation throughout temperate and tropical forests worldwide at the scale of ecoregions using the Hansen Global Forest Change (v1.7) (5) dataset on Google Earth Engine (GEE). Tropical and temperate biomes were delineated in a global ecoregion map(6).

  hf419-03-forest-growth.zip

  The forest growth map is a polygon shapefile using EPA Level IV Ecoregion boundaries with ecoregion-level estimates of edge and interior forest area and growth. Projection: Albers Equal Area Conic (EPSG:102003)). GCS: NAD 1983 (EPSG:4269). US_L4CODE – Unique identifier code for the US EPA Level IV ecoregion. US_L4NAME – US EPA Level IV ecoregion name. EdgePix – Count of pixels identified as forest edge within the ecoregion (forest pixel adjacent to a pixel classified as an anthropogenic, non-forest land cover). ForPix – Count of total forest pixels within the ecoregion (includes edge pixels). EdgeBAI – Estimated total BAI for edge forest within the ecoregion (m2 yr -1). AllInt – Estimated total BAI for all forest within the ecoregion, if all forest was calculated with interior forest growth rates. (m2yr-1). TotDiff – Difference in estimated ecoregion BAI attributable to using edge-specific growth rates for edge forest pixels instead of using interior forest growth rates for all forest (m2yr-1).

  hf419-04-global-forest-frag.zip

  The global forest fragmentation map is a polygon shapefile using global terrestrial ecoregion boundaries of temperate and tropical forests, with ecoregion-level edge and interior forest area summaries. GCS:WGS 1984 (EPSG: 4326). EcoName – Global terrestrial ecoregion name. Biome – Terrestrial biome code (1 = Tropical and Subtropical Moist Broadleaf Forests, 2 = Tropical and Subtropical Dry Broadleaf Forests, 3 = Tropical and Subtropical Coniferous Forests, 4 = Temeprate Broadleaf and Mixed Forests, 5 = Temperate Conifer Forests). ForClass – Forest class identifier to differentiate between edge forest and interior forest (1 = Interior, 2 = Edge). AreaHA – Forest area in hectares (ha).

  Citations

  1. R. R. Nemani, C. D. Keeling, H. Hashimoto, W. M. Jolly, S. C. Piper, C. J. Tucker, R. B. Myneni, S. W. Running, Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science (80-. ). 300, 1560–1563 (2003)

  2. N. P. Office, National Atmospheric Deposition Program (NRSP-3). (2020)

  3. J. R. Thompson, C. D. Canham, L. Morreale, D. B. Kittredge, B. Butler, Social and biophysical variation in regional timber harvest regimes. Ecol. Appl. 27 (2017), doi:10.1002/eap.1497

  4. B. Ruefenacht, M. V Finco, M. D. Nelson, R. Czaplewski, E. H. Helmer, J. A. Blackard, G. R. Holden, A. J. Lister, D. Salajanu, D. Weyermann, K. Winterberger, Conterminous U.S. and Alaska forest type mapping using forest inventory and analysis data. Photogramm. Eng. Remote Sensing. 74, 1379–1388 (2008)

  5. M. C. Hansen, P. V Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, J. R. G. Townshend, High-resolution global maps of 21st-century forest cover change. Science (80-. ). 342, 850–853 (2013)

  6. D. M. Olson, E. Dinerstein, E. D. Wikramanayake, N. D. Burgess, G. V. N. Powell, E. C. Underwood, J. A. D’Amico, I. Itoua, H. E. Strand, J. C. Morrison, C. J. Loucks, T. F. Allnutt, T. H. Ricketts, Y. Kura, J. F. Lamoreux, W. W. Wettengel, P. Hedao, K. R. Kassem, Terrestrial ecoregions of the world: A new map of life on Earth. Bioscience. 51, 933–938 (2001)

• Organization: Harvard Forest. 324 North Main Street, Petersham, MA 01366, USA. Phone (978) 724-3302. Fax (978) 724-3595.

• Project: The Harvard Forest Long-Term Ecological Research (LTER) program examines ecological dynamics in the New England region resulting from natural disturbances, environmental change, and human impacts. (ROR).

• Funding: National Science Foundation LTER grants: DEB-8811764, DEB-9411975, DEB-0080592, DEB-0620443, DEB-1237491, DEB-1832210.

• Use: This dataset is released to the public under Creative Commons CC0 1.0 (No Rights Reserved). Please keep the dataset creators informed of any plans to use the dataset. Consultation with the original investigators is strongly encouraged. Publications and data products that make use of the dataset should include proper acknowledgement.

• License: Creative Commons Zero v1.0 Universal (CC0-1.0)

• Citation: Morreale L, Thompson J, Tang X, Reinmann A, Hutyra L. 2023. Quantifying Growth and Structure along Forest Edges in the Northeastern USA 2010-2021. Harvard Forest Data Archive: HF419 (v.2). Environmental Data Initiative: https://doi.org/10.6073/pasta/844df90f5aee5a6c604068370bc062e9.

Detailed Metadata

hf419-01: all edge forest plots

1. subp_cn: unique subplot ID that allows linking a unique subplot measurement to the publically available FIA dataset
2. edge_type: edge classification for the forested subplot (either Interior or Edge)
3. ndep_2018: atmospheric nitrogen deposition in 2018 (unit: kilogramPerHectare / missing value: NA)
4. light: light limitation (unit: dimensionless / missing value: NA)
5. water: water limitation (unit: dimensionless / missing value: NA)
6. temperature: temperature limitation (unit: dimensionless / missing value: NA)
7. bai_m2_ha: total basal area increment on the subplot, scaled by area (unit: meterSquaredPerHectarePerYear / missing value: NA)
8. live_ba_m2_ha: total living tree basal area on the subplot, scaled by area (unit: meterSquaredPerHectare / missing value: NA)
9. dead_ba_m2_ha: Total dead tree basal area on the subplot, scaled by area (unit: meterSquaredPerHectare / missing value: NA)
10. live_ntrees_ha: count of living trees on the subplot, scaled by area (unit: number / missing value: NA)
11. mean_dia_live: average diameter of live trees on the subplot (unit: centimeter / missing value: NA)

hf419-02: anthropogenic edge forest plots

1. subp_cn: unique subplot ID that allows linking a unique subplot measurement to the publically available FIA dataset
2. edge_type: edge classification for the forested subplot (either Interior or Edge)
3. ndep_2018: atmospheric nitrogen deposition in 2018 (unit: kilogramPerHectare / missing value: NA)
4. light: light limitation (unit: dimensionless / missing value: NA)
5. water: water limitation (unit: dimensionless / missing value: NA)
6. temperature: temperature limitation (unit: dimensionless / missing value: NA)
7. bai_m2_ha: total basal area increment on the subplot, scaled by area (unit: meterSquaredPerHectarePerYear / missing value: NA)
8. live_ba_m2_ha: total living tree basal area on the subplot, scaled by area (unit: meterSquaredPerHectare / missing value: NA)
9. dead_ba_m2_ha: Total dead tree basal area on the subplot, scaled by area (unit: meterSquaredPerHectare / missing value: NA)
10. live_ntrees_ha: count of living trees on the subplot, scaled by area (unit: number / missing value: NA)
11. mean_dia_live: average diameter of live trees on the subplot (unit: centimeter / missing value: NA)

hf419-03: forest growth map

• Compression: zip
• Format: Esri shapefile
• Type: vector GIS

hf419-04: global forest fragmentation map

• Compression: zip
• Format: Esri shapefile
• Type: vector GIS