Migratory bird stopover patterns linked to urbanization and social landscapes
Data files
Sep 08, 2025 version files 134.43 MB
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counties_df.csv
228.46 KB
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counties.shp.zip
720.18 KB
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fall_stopover_2500_v9_265_class.tif
2.52 MB
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metro_park_df.csv
22.38 MB
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README.md
6.18 KB
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spring_stopover_2500_v9_265_class.tif
2.52 MB
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tl_2019_us_cbsa.shp.zip
32.50 MB
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tl_2023_us_uac20.shp.zip
73.56 MB
Sep 08, 2025 version files 134.43 MB
-
counties_df.csv
228.46 KB
-
counties.shp.zip
720.18 KB
-
fall_stopover_2500_v9_265_class.tif
2.52 MB
-
metro_park_df.csv
22.38 MB
-
README.md
6.17 KB
-
spring_stopover_2500_v9_265_class.tif
2.52 MB
-
tl_2019_us_cbsa.shp.zip
32.50 MB
-
tl_2023_us_uac20.shp.zip
73.56 MB
Abstract
Despite global urbanization, the role of cities in macroecological processes remains understudied. Using radar estimates of migratory bird stopover across the U.S., we assessed urban landscapes' contributions to stopover and links to social demographics for 2,130 parks across 88 cities. Stopover hotspots disproportionately occurred on urban landscapes relative to land area, with nearly 50% of spring migration hotspots falling within Metropolitan Statistical Areas. The relationship between urbanization and stopover varied regionally, correlating negatively in eastern flyways and positively in western flyways. Finally, stopover was positively correlated with household income but varied considerably, with many cities showing no effect or an effect in the opposite direction. We highlight the significance of cities in a hemispheric-scale ecological process and demonstrate radar as tool for studying urban social-ecological interactions.
Dataset DOI: 10.5061/dryad.1jwstqk68
Description of the data and file structure
Data used in the study "Divergent Effects of Urbanization and Social Landscapes on Continental Migratory Bird Stopover"
All shapefiles (.shp) used in this study contain the geometry and attributes of geospatial features (e.g., points, lines, polylines, polygons). The file bundle contains the main file .shp and companion files including: .cpg, .dbf, .prj, .sbn, .sbx, .shx. Description of these file extensions is given as follows:
.shp: The main geospatial data file that contains feature geometry.
.cpg: The file specifying the codepage to identify the characterset.
.dbf: The dBASE that contains the attributes of features.
.prj: The file that contains the coordinate system and map projection information.
.sbn: The file containing the spatial index of features.
.sbx: The file containing the spatial index of features.
.shx: The file containing the index of feature geometry.
More details about these file extensions and the information they contain can be found at: https://desktop.arcgis.com/en/arcmap/latest/manage-data/shapefiles/shapefile-file-extensions.htm#:~:text=xml%E2%80%94Metadata%20for%20ArcGIS%E2%80%94stores,the%20characterset%20to%20be%20used(opens in new window).
Each .shp file can be opened and analyzed by Python, R, and many other programming languages, and open-source geospatial software such as QGIS, SAGA GIS, GRASS GIS, GeoDa, etc.
Files and variables
File: counties_df.csv
Description: .csv of all continental U.S. counties and associated features, namely their level of urbanization and mean stopover densities
Variables
- STATEFP: Numerical identifier for state in which county is located
- COUNTYF: Numerical identifier for county
- GEOID: Numerical identified for county polygon
- NAME.x: Name of county
- X2013_c: Ordinal ranking of level of urbanization (2013)
- mean_stopover_sp: Mean stopover density for spring migration season (cm2/km2)
- mean_stopover_fa: Mean stopover density for fall migration season (cm2/km2)
- flyway: Migratory flyway in which county is located
File: metro_park_df.csv
Description: .csv of urban parks across the continental U.S. with associated features, including their mean stopover densities and demographic information about residents within a 10-minute walking distance. Data derived from ParkServe database.
*Full schema available through https://www.tpl.org/park-data-downloads
Variables
- ParkID: Numerical identifier for each park
- Park_Local: Local Owner Name From Source
- Park_Size_: Park area (acres)
- Park_Size1: Park area (sq feet)
- Park_Siz_1: Park area (sq meters)
- Park_State: State in which park is located
- Park_Count: County in which park is located
- Park_Urban: City in which park is located
- Park_Zip: Zip code in which park is located
- Park_Sourc: How the park features were added to the database, either collected or created
- SUM_TOTPOP: Total population within a 10-minute walk (2022)
- SUM_KIDSVC: Total population < 20 years of age within a 10-minute walk (2022)
- SUM_YOUNGP: Total population > 20 and < 65 years of age of within a 10-minute walk (2022)
- SUM_SENIOR: Total population > 64 years of age within a 10-minute walk (2022)
- SUM_HHILOW: Total number of low income households (<75% urban area median income) within a 10-minute walk (2022)
- SUM_HHIMED: Total number of middle income households (75% - 125% urban area median income) within a 10-minute walk (2022)
- SUM_HHIHIG: Total number of high income households (>125% urban area median income) within a 10-minute walk (2022)
- SUM_WHITE_: Total White Non-Hispanic population within a 10-minute walk (2022)
- SUM_BLACK_: Total Black Non-Hispanic population within a 10-minute walk (2022)
- SUM_AMERIN: Total Native American Non-Hispanic population within a 10-minute walk (2022)
- SUM_ASIAN_: Total Asian Non-Hispanic population within a 10-minute walk (2022)
- SUM_PACIFI: Total Pacific Islander Non-Hispanic population within a 10-minute walk (2022)
- SUM_OTHRAC: Total population of 2 or more races (Non-Hispanic) within a 10-minute walk (2022)
- SUM_RACE2U: Total population of other races (Non-Hispanic) within a 10-minute walk (2022)
- SUM_HISP_S: Total Hispanic population within a 10-minute walk (2022)
- mean_stopover_sp: Mean stopover density in spring migration season
- mean_stopover_fa: Mean stopover density in fall migration season
- prop.low: Proportion of residents that are low-income within a 10-minute walk (2022)
- prop.med: Proportion of residents that are medium-income within a 10-minute walk (2022)
- prop.high: Proportion of residents that are high-income within a 10-minute walk (2022)
- GEOID: Numerical identifier for park polygon
- X2013_c: Ordinal ranking of urbanization level
- flyway: Migratory flyway in which park occurs
File: counties.shp
Description: Shapefile of all continental U.S. counties and associated features
File: tl_2019_us_cbsa.shp
Description: Shapefile of combined statistical areas across the continental U.S. derived from the United States Census Bureau
File: tl_2023_us_uac20.shp
Description: Shapefile of census urban areas across the continental U.S. derived from the United States Census Bureau
File: fall_stopover_2500_v9_265_class.tif
Description: Raster of stopover densities across the continental U.S. for the fall migration season
File: spring_stopover_2500_v9_265_class.tif
Description: Raster of stopover densities across the continental U.S. for the fall migration season
Code/software
All code for this study is available on Github [https://github.com/Migfjim1/urbanstopover].
