Environmental Management

, Volume 55, Issue 1, pp 159–170 | Cite as

Understanding Human–Coyote Encounters in Urban Ecosystems Using Citizen Science Data: What Do Socioeconomics Tell Us?

  • Stuart Wine
  • Sara A. GagnéEmail author
  • Ross K. Meentemeyer


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 highly-urbanized Mecklenburg County, North Carolina, USA using 707 public reports of coyote sightings, high-resolution land cover, US Census data, and an autologistic multi-model 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.


Autologistic regression Crowdsourcing Human–wildlife conflict Invasion Multimodel inference Species distribution model Urban wildlife 



We sincerely thank Mecklenburg County residents who reported coyote sightings for their time and effort and Division of Nature Preserves and Natural Resources staff for access to the resulting dataset. Thanks also to John Vogler whose help with data manipulation and processing was absolutely invaluable to our efforts. Finally, we thank two anonymous reviewers for their helpful comments on an earlier version of this paper. This work was supported by the University of North Carolina at Charlotte.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Stuart Wine
    • 1
  • Sara A. Gagné
    • 1
    Email author
  • Ross K. Meentemeyer
    • 2
  1. 1.Department of Geography and Earth SciencesUniversity of North Carolina at CharlotteCharlotteUSA
  2. 2.Department of Forestry and Environmental ResourcesNorth Carolina State UniversityRaleighUSA

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