DOI: 10.5061/dryad.280gb5mxh
title: Data From: Spatiotemporal synchrony of climate and fire across North America (1750-1880)

description: After a policy of aggressive fire suppression in most of North America
 during the 20th century, increasing aridity has driven widespread,
 synchronous fire occurrence in recent decades. A lack of historical
 (pre-1880) fire records limits our ability to understand long-term
 continental fire-climate dynamics. The goal of this study is to use
 tree-ring reconstructions to determine the relationships between
 spatio-temporal patterns in historical climate and widespread fire
 occurrence in North America, and whether they are stable through
 time.  We applied regionalization methods to tree-ring
 reconstructions of historical summer soil moisture and annual fire
 occurrence to independently identify broad- and fine-scale climate and
 fire regions based on common inter-annual variability. We then tested
 whether the regions were stable through time and for spatial
 correspondence between the climate and fire regions. Last, we used
 correlation analysis to quantify the strength of the fire-climate
 associations through time. We found that broad-scale historical patterns
 in climate and fire have strong spatial coherence. Although climate and
 fire regions vary over time, large core areas of the regions were stable.
 The association between climate and fire varied through time and was
 strongest in western North America, likely due to a combination of
 factors, such as the magnitude of drought frequency and severity, as well
 as varying use of fire by human communities. The historical perspective
 gained through tree-ring reconstructions of climate and fire patterns and
 their association suggests that the recent climate-driven synchrony of
 fire across large areas in recent decades is not unprecedented, will
 likely continue into the future, and may exhibit similar spatial patterns.
  
description: Our study area includes North America between 20°N and 60°N
 (Figure 1). We used this latitudinal range because outside of this area
 there are few fire history sites with sufficient data prior to 1880
 CE (Margolis et al. 2022). The fire data reconstructed from tree rings are
 located in forested regions; consequently, our study may not be
 representative of fire regimes in non-forest vegetation.
 We analyzed records of fire occurrence for the period 1750-1880
 from NAFSN (Margolis et al. 2022), using tree-level records of the year of
 fire occurrence from 1,159 sites. The start year of the analysis, 1750,
 was chosen to optimize the longest possible period with the broadest
 geographic coverage of sites that were continuously recording fire.
 Continuously recorded fire data are necessary to prevent biases in the
 cluster analysis related to decreasing sample depth associated with
 tree-ring sample decay (Swetnam et al. 1999). The analysis period ended in
 the year 1880 due to the strong influence of fire exclusion from changing
 land use, disrupted Indigenous burning, and fire suppression (Swetnam et
 al. 2016), although the timing, cause, and level of fire exclusion varies
 across the study area (Margolis et al. 2022). We used
 the <em>burnr </em>package in R (v0.6.1, Malevich et al. 2018)
 and applied two filters to the site-level fire-scar data to include fire
 records that were: 1) replicated within each site
 (&gt; 2 trees scarred per site) and 2) recorded by
 10% or more of trees within each site. The minimum-tree filter has a
 larger influence on small sites with few trees, while the percent-scarred
 filter reduces the influence of large sites (e.g., &gt; 20 trees; Falk
 et al. 2007). This combination of filters is commonly used in fire history
 analyses (e.g., Swetnam and Baisan 1996) to minimize the effects of
 varying sample depth among sites and reduce the contribution of small
 fires that may obscure the signal of widespread synchronous fires. To
 normalize for the higher density of NAFSN sites in the West, we aggregated
 fire data from the sites into hexels with a diameter of 100 km (hexel area
 8660 km<sup>2</sup>; Figure 1) and produced a time series of
 percent of sites scarred in each hexel per year. To
 represent historical climate (1750-1880), we used an existing tree-ring
 reconstruction of soil moisture as a proxy for fuel aridity (Williams et
 al. 2022a). Summer soil moisture (June-August; JJA) was reconstructed by
 relating climate-sensitive tree-ring widths to modern gridded soil
 moisture products using point-by-point regression, producing annual grids
 (°1 latitude-longitude).<em> </em>Anomalies were calculated as
 standardized (<em>z</em>-scored) values relative to the
 1901-2000 mean at each point.
description: # Data From: Spatiotemporal synchrony of climate and fire across North
 America (1750-1880)
 [https://doi.org/10.5061/dryad.280gb5mxh](https://doi.org/10.5061/dryad.280gb5mxh) Primary data are the occurrence of wildfire based on tree ring fire scars (NAFSN_Fire-Binary_1750-1880_10pct_2min.csv), derived from the North American Fire Scar Network (NAFSN, Margolis et al. 2022). Soil moisture anomalies (SMz_recon.nc) derived from tree rings were derived using methods from Williams et al. 2022. Also included are equal area hexagons (NAM_hexels.shp) used in analyses and a shapefile of North America (NAM.shp). A basemap is provided from Nasa's BlueMarble dataset. Metadata relating to the fire scar sites used in this analysis (NAFSS_Master_Metadata_eqm.csv) and the entire NAFSN as of 2/1/2024 (NAFSS_Master_Metadata.csv) for recreating figure 1. Code for recreating analyses and figures are also included. ## Description of the data and file structure Fire scar data represents the binary occurrence of fire (2 - fire scar; 1 - recording year) from approximately 1200 sites distributed across North America. Each column represents a single site, each row represents a unique year. The metadata relating spatial locations and other properties of dataset are referenced by NAFSS_Master_Metadata_eqm.csv and matched by a key ID. A full description of the metadata and the NAFSN can be found in *Margolis et al. 2022.* A breakdown of the metadata columns is as follows: file_name – Name of the File impd_filename  - Name of the file in IMPD in_file – Is the FHX object in a master file? TRUE/FALSE contr_year - contribution year data_source - source of data         site_name - name of site site_code - site ID contributor - Name of contributor to NAFSN         Longitude - Degrees west               Latitude - Degrees north first_year - first year of recording data      last_year - last year of recording data      first_scar - year of first scar            last_scar - year of last scar             n_trees - total trees          tree_species - tree species sampled following USFS four letter code               area_sampled_ha - area sampled in hectares         ref_ID - reference ID Soil moisture anomalies are in a self-describing netcdf format derived from methods in *Williams et al. 2022*. Values are standard deviation anomalies relative to 1921-2000 between the years 1499-2010. ## Sharing/Access information Data was derived from the following sources: * [Margolis et al. 2022 NAFSN ](https://doi.org/10.1002/ecs2.4159) * [Williams et al. 2022 SMz ](https://doi.org/10.1038/s41558-022-01290-z) ## Code/Software The R code provided is designed to read these files in from a specified folder location, "GEB_DATA/". A provided RProject file should help with organization. The code are numbered sequentially in order of dependencies. For example, run GEB_01_Data, first. All other files (GEB_02, etc.) should be run in sequential order.