DOI: 10.5061/dryad.rfj6q57hc
title: Structural and socioeconomic features of cities predict migratory bird species richness

description: Cities are aggregates of human activities where our decisions shape the
 environment creating heterogeneity across urban centers that can have
 significant ecological effects on wildlife. Many bird species are found in
 cities during the breeding season, which implies they find sufficient
 resources in cities to support them during this energetically costly time.
 As populations of many migratory bird species are declining, knowledge of
 how they are affected by urbanization is needed. Yet, we know little about
 how the species richness of migratory birds varies across different types
 of cities. Here we ask if cities' structural and socioeconomic
 features can predict the species richness of migratory birds that
 generally select different breeding habitats during the breeding season.
 We used eBird data from census-designated urban areas in the United States
 to model the relationship between features of cities (housing density,
 median income, city age, and commuting time), environmental disturbance
 (measured by the human footprint index) and species richness by fitting
 generalized linear models to data. We show that commuting time was the
 most important factor determining species richness across cities and the
 rest of the city features were weakly associated with species richness.
 Overall species were responding to city variation in similar
 ways.  While we expected that cities with more disturbance would
 have lower species richness, our results indicate that some species are
 able to tolerate even highly disturbed cities and that cities in certain
 regions may act as a refuge to birds. This knowledge is important for our
 general understanding of cities as habitat for birds and how migratory
 birds respond to across-city variation during the breeding season.
description: # Structural and socioeconomic features of cities predict migratory bird
 species richness This
 README.txt file was generated on 2023-11-28 by Riikka P Kinnunen Data from
 Kinnunen et al. "Structural and socioeconomic features of cities
 predict migratory bird species richness". Author Information
 Principal Investigator Contact Information ``` Name: Riikka P Kinnunen
 Institution: University of Manitoba Email: rpkinnunen@gmail.com ``` File:
 city_migrants_synthesized_dryad. csv Raw data used for data analyses. Data
 was compiled from multiple sources: eBird. (2019). eBird Basic Dataset.
 Rosenberg, K. V., Dokter, A. M., Blancher, P.
 J., Sauer, J. R., Smith, A. C., Smith, P. A., Stanton, J. C., Panjabi, A.,
 Helft, L., Parr, M. and Marra, P. P. 2019. Decline of the North American
 avifauna. – Science 366: 120–124. Sheard, C., Neate-Clegg, M. H. C.,
 Alioravainen, N., Jones, S. E. I., Vincent, C., MacGregor, H. E. A.,
 Bregman, T. P., Claramunt, S. and Tobias, J. A. 2020. Ecological drivers
 of global gradients in avian dispersal inferred from wing morphology. –
 Nat. Commun. 11: 1–9. U.S. Census Bureau. 2010 Census.
 U.S. Census Bureau. TIGER/Line Shapefiles: Urban Areas. . Wildlife Conservation Society (WCS) and Center for International Earth Science Information Network (CIESIN) - Columbia University. 2005. Last of the Wild Project, Version 2, 2005 (LWP-2): Global Human Footprint Dataset (Geographic). – NASA Socioecon. Data Appl. Cent. SEDAC Palisades NY. Geographic location: USA Columns city: City name with state abbreviation. Corresponds to the city name in U.S Census data and Urban area shapefiles. city_and_state: City and state with an underscore. Corresponds to the city name in U.S Census data and Urban area shapefiles. breeding_biome: The primary breeding biomes of each species. Based on Rosenberg et al. (2019), modified from the categories used in the Avian Conservation Assessment Database that provides conservation assessment and species prioritization data for all North American bird species at global and regional scales (available from ). scientific_name: Scientific name, following eBird Taxonomy. Downloaded from the eBird Basic Dataset. common_name: Common name in English, following eBird Taxonomy. Downloaded from the eBird Basic Dataset. count_of_breeding_species: The count of species belonging to each breeding category per city. Calculated using R Statistical Software. city_long: The longitude of the city. city_lat: The latitude of the city. pop_size: Total population size. From the 2010 Census data from the U.S. Census Bureau. housing_units: The number of housing units. From the 2010 Census data from the U.S. Census Bureau. pop_density: Population density. Population per square mile. From the 2010 Census data from the U.S. Census Bureau. housing_density: Housing density. Housing units per square mile. From the 2010 Census data from the U.S. Census Bureau. median_income: Median household income. From the 2010 Census data from the U.S. Census Bureau. age: City age. We calculated the median age of a housing structure in years (2021 minus the median year when buildings were built) and used it as a proxy for city age. From the 2010 Census data from the U.S. Census Bureau. Calculated using R Statistical Software. area_sqm: Land/City area per square mile. From the 2010 Census data from the U.S. Census Bureau. total_travel_time_to_work_min: Commuting time (aggregate travel time to work in minutes). From the 2011-2015 American Community Survey 5-year estimates data from the U.S. Census Bureau. hfi_city: The mean global human footprint index (HFI) (available from ) for each of our cities. Defined by the 2010 urban area shapefile maps from the U.S. Census Bureau. The data covers many types of human influences on the environment, namely population density, land use, land cover, built-up areas, nighttime lights, roads, railroads, coastlines, and navigable rivers. Expressed as a percentage, a value of zero on the index represents area least influenced by humans and a value of 100 the most disturbed area, or area most influenced by humans.