DOI: 10.5061/dryad.c2fqz61g3
title: Coastal carbon sentinels: A decade of forest change along the eastern shore of the US signals complex climate change dynamics

description: Increased frequency and intensity of storms, saltwater intrusion, sea
 level rise, and warming temperatures are affecting forests along the
 mid-Atlantic, Southeastern, and Gulf coasts of the US. However, we still
 lack a clear understanding of how the structure of coastal forests is
 being altered by climate change drivers. Here, we used data from the
 Forest Inventory and Analyses program of the United States Forest Service
 to examine structure and biomass change in forests along the mid-Atlantic,
 Southeastern, and Gulf coasts of the US. We selected plots that have been
 resampled at low (5 m) and mid (30-50 m) elevations in coastal areas of
 states from Texas to New Jersey, allowing us to determine change in live
 trees, standing dead wood, and downed dead wood biomass (and carbon)
 stocks across a decade. We estimated forest attributes at the county level
 for each elevational class. Forest area increased by 1.9% in low elevation
 counties and by 0.3% in mid elevation counties. Live tree biomass density
 increased by 13% in low elevation counties, and by 16% in mid elevation
 counties. Standing dead biomass decreased in low elevation counties by
 9.2% and by 2.8% in mid elevation counties. On average, downed dead wood
 increased by 22% in low elevation counties and decreased by 50% in mid
 elevation counties. Changes in the stock of C in standing and downed dead
 wood (0.45 to 9.1 Tg C) are similar to soil marsh C loss (9.54 Tg C).
 Annualized growth and harvest were both higher (16% and 58% respectively)
 in mid elevation counties than low elevation counties, while annualized
 mortality was 25% higher in low elevation counties. Annualized growth in
 low elevation counties was negatively correlated to sea level rise rates,
 and positively correlated to number of storms, illustrating tradeoffs
 associated with different climate change drivers. Overall, our results
 illustrate the vulnerability of US southeastern coastal low and mid
 elevation forests to climate change and sea level rise with indications
 that the complexity and rate of change in associated ecosystem functions
 (e.g., growth, mortality, and carbon storage) within the greater social
 environment (e.g., agricultural abandonment) may increase.
description: We used data from the National Forest Inventory and Analysis
 (FIA) program which, administered by the USDA Forest Service, provides a
 comprehensive statistical inventory and associated database of forests
 across the United States. The program applies standardized techniques to
 measure forest characteristics across a national plot sampling network of
 approximately one plot per 2,428 ha, with plot locations determined using
 a hexagonal sampling framework designed to be as spatially balanced as
 possible. The plot location within each 2,428 ha hexagon was visited by
 field crews if remotely sensed data indicated it was in forest land use
 (having ≥ 10% tree canopy cover, or evidence of such cover) that was at
 least 0.4 ha in area and 37 m wide. We focused on data for the
 mid-Atlantic and Southeast coast of the US, from Texas to New Jersey. We
 selected information from forested plots located in low (~5m) and mid
 elevation (30-50 m) areas with slopes less than 15%, and had either hydric
 conditions, or were near a water feature, which are indicative of forested
 wetlands. We used the FIA methodologies to estimate forest resources
 attributes from plot level to the county level. We looked at changes in
 live trees (biomass and C), standing dead wood (SD, biomass and C), and
 downed dead wood (biomass and C, DD). The systematic FIA sample design
 further allowed for statistical population-level estimates of various
 forest attributes, such as the area of a low-elevation forest in a county,
 using an “expansion factor” assigned to each plot condition. Using a
 design-based approach to population inference, expansion factors can be
 summed across plots in a population to provide an estimate of the total
 area within that population. Similarly, the FIA sample design allows
 individual trees inventoried on plots to be scaled via an expansion factor
 to estimate the total C of trees within an area. In this case, we
 calculated the area and biomass (from standing live, standing dead, and
 downed dead) of low-elevation and mid-elevation forests in low-elevation
 and mid-elevation counties, respectively, and within each state.
 Field crews collected a wide variety of data using standardized
 protocols from each FIA plot, which covered 0.067 hectares within four
 7.31-m radius subplots arranged at the vertices and center of a triangle.
 This included the diameter, height, and species for every live and dead
 tree with a diameter at breast height (DBH) ≥ 12.7 cm. All trees with DBH
 ≥ 2.54 cm but &lt; 12.7 cm were measured in a single 2.07-m-radius
 microplot within each of the plot’s four subplots. Using the component
 ratio method, the FIA program estimates the aboveground dry biomass of
 each tree with DBH ≥ 2.54 cm in pounds. Biomass and C densities were
 calculated by scaling plot-level data to per hectare estimates for the
 counties. We estimated change in the stocks of different pools by
 subtracting time 2 from time 1. We also looked at changes in different
 size classes and decay classes (for dead wood). We used data from the two
 latest survey evaluation periods, spanning a decade of change (Table 1).
 We estimated forest biomass standing stocks and change among key
 structural components using data from 1700 plots in low elevation counties
 and 3200 plots in mid elevation counties. We estimated population level
 values for 126 low elevation counties and 179 mid elevation counties (Fig
 1). We excluded counties for which there were less than three plots in any
 survey year. To examine potential climate change
 drivers of forest dynamics we used publicly available datasets. We
 obtained sea level rise rates for 43 of the National Oceanic and
 Atmospheric Administration (NOAA) tide gauges from the Permanent Service
 for Mean Sea Level (PSMSL, Supplementary Table 1). We used the website to
 estimate rates of sea level rise from 2010-2020 to match the FIA dataset,
 given reports of accelerating rates of sea level rise in the Southeastern
 US. We calculated mean annual temperature and mean annual precipitation
 from the GridMet dataset (4 km2 spatial resolution), accessed through the
 Climate Engine portal (https://app.climateengine.org/climateEngine) for
 the period 2010-2020, to roughly match the FIA measurements. We also
 estimated change in temperature and precipitation by estimating the sen
 slope of each factor over the same time period using the Climate Engine
 portal. We used the NOAA National Hurricane Center Atlantic Hurricane
 Catalog (HURDAT2), accessed through Google Earth Engine, to count the
 number of tropical cyclones that passed through a 100 km radius buffer of
 the NOAA tide gauges for the same period. On average, low elevation
 counties were located 22.4 ± 2.6 km, while mid elevation counties were 108
 ± 5.9 km from the NOAA tide gauges.
description: # Coastal carbon sentinels: A decade of forest change along the eastern
 shore of the US signals complex climate change dynamics
 [https://doi.org/10.5061/dryad.c2fqz61g3](https://doi.org/10.5061/dryad.c2fqz61g3) ## Description of the data and file structure Data are from the Forest Inventory and Analyses Program for live trees, standing dead trees, and downed dead wood. Environmental datasets are from publicly available sources. The data are in an Excel file with two sheets. Metadata sheet has explanation of all variables and units. Data includes the data for the coastal counties included in the analyses. Data are from coastal states from Texas to New Jersey. Cells that did not have estimates available are labeled as "n/a" (not available). Lack of enough plots to estimate county or no available data led to the n/a designation.   We have also included the R code used for data management and analyses (RcodeFIAdata.txt), and the SQL code to query the FIA database (FIA_county_estimates_090524).  ## Sharing/Access information Data was derived from the following sources: * Forest Inventory and Analyses data can be accessed here: * [https://apps.fs.usda.gov/fiadb-api/evalidator](https://apps.fs.usda.gov/fiadb-api/evalidator) * Sea level rise rates can be accessed from the Permanent Service for mean sea level: * [https://psmsl.org/](https://psmsl.org/) * Data on mean annual temperature, precipitation, and the sen slope of their change can be accessed from the Climate Engine App * [https://www.climateengine.org/](https://www.climateengine.org/)