Abstract The coyote (Canis latrans) has dramatically
expanded its range to include the cities and suburbs of the
western US and those of the Eastern Seaboard. Highly
adaptable, this newcomer’s success causes conflicts with
residents, necessitating research to understand the distribution
of coyotes in urban landscapes. Citizen science can
be a powerful approach toward this aim. However, to date,
the few studies that have used publicly reported coyote
sighting data have lacked an in-depth consideration of
human socioeconomic variables, which we suggest are an
important source of overlooked variation in data that
describe the simultaneous occurrence of coyotes and
humans. We explored the relative importance of socioeconomic
variables compared to those describing coyote
habitat in predicting human–coyote encounters in highlyurbanized
Mecklenburg County, North Carolina, USA
using 707 public reports of coyote sightings, high-resolution
land cover, US Census data, and an autologistic multimodel
inference approach. Three of the four socioeconomic
variables which we hypothesized would have an
important influence on encounter probability, namely
building density, household income, and occupation, had
effects at least as large as or larger than coyote habitat
variables. Our results indicate that the consideration of
readily available socioeconomic variables in the analysis of
citizen science data improves the prediction of species
distributions by providing insight into the effects of
important factors for which data are often lacking, such as
resource availability for coyotes on private property and
observer experience. Managers should take advantage of
citizen scientists in human-dominated landscapes to monitor
coyotes in order to understand their interactions with
humans.