GPS collar data and social-ecological feature data for examining the movement of coyotes in Los Angeles, California (2019-2021)

How societal, ecological, and infrastructural attributes interact to influence wildlife movement is uncertain. We explored whether neighborhood socioeconomic status and environmental quality were associated with coyote (Canis latrans) movement patterns in Los Angeles, California, and assessed the performance of integrated social-ecological movement models. Herein are 1) the raw GPS data for 20 coyotes collared between 2019-2021, and 2) the social-ecological feature rasters used for data analyses.

Methods
GPS collar data: Beginning in October 2019, 20 coyotes were outfitted with GPS collars (Ecotone, solar powered, GPS/GSM/UHF), with collars remaining active between 1-23 months. Six females and 14 males were collared.

Raster data: All geospatial covariates were rasterized and converted to 30m2 spatial resolution using ArcGIS Pro v 3.1.1 (ESRI 2023).

Ecological covariates: (1) normalized difference vegetation index (NDVI) from spring 2021 (Landsat 8); (2) distance to rivers and streams (CDFW, 2020); (3) distance to lakes (California State Geoportal, 2021); and (4) distance to green spaces, including (a) Los Angeles and San Bernardino county parks (County of Los Angeles), (b) golf courses, and (c) cemeteries (City of Los Angeles, 2023). Distance to green spaces was considered separately from NDVI since in arid regions green spaces will not always have a notable vegetation greenness signature. 

Linear infrastructure covariates: (1) road density (data.gov); (2) distance to storm and flood channels and drains (County of Los Angeles, 2023); and (3) distance to railways (California Rail Network, 2022). 

Societal covariates: (1) human population density (Census.gov); (2) building density (Dao, 2020); (3) development intensity (NLCD, 2022); (4) median income (County of Los Angeles, 2023; Southern California Association of Governments); and (5) pollution burden percentile (Cal Enviro Screen 4.0). Development intensity was reclassified as 0=no data, 1=undeveloped land cover classes, 2=developed: open space, 3=developed: low intensity, 4=developed: med intensity, and 5=developed: high intensity. Cal Enviro Screen provides a pollution burden index that is calculated from 13 metrics related to drinking water characteristics, groundwater quality, air quality, soil pollutants, and hazardous waste.