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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
Article

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 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.

Keywords

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

Notes

Acknowledgments

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.

References

  1. Andelt WF, Andelt SH (1981) Habitat use by coyotes in southeastern Nebraska. J Wildlife Manage 45:1001–1005CrossRefGoogle Scholar
  2. Andelt WF, Mahan BR (1980) Behavior of an urban coyote. Am Midl Nat 103:399–400CrossRefGoogle Scholar
  3. Arthur LM (1981) Coyote control: the public response. J Range Manage 34:14–15CrossRefGoogle Scholar
  4. Atkinson KT, Shackleton DM (1991) Coyote, Canis latrans, ecology in a rural-urban environment. Can Field Nat 105:49–54Google Scholar
  5. Atwood TC, Weeks HP, Gehring TM (2004) Spatial ecology of coyotes along a suburban-to-rural gradient. J Wildlife Manage 68:1000–1009CrossRefGoogle Scholar
  6. Baker RO, Timm RM (1998) Management of conflicts between urban coyotes and humans in southern California. In: Proceedings of the eighteenth vertebrate pest conference 1998, Paper 1Google Scholar
  7. Bekoff M, Gese EM (2003) Coyote (Canis latrans). Paper 224, USDA National Wildlife Research CenterGoogle Scholar
  8. Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer-Verlag, New YorkGoogle Scholar
  9. Clarke LW, Jenerette GD, Davila A (2013) The luxury of vegetation and the legacy of tree biodiversity in Los Angeles, CA. Landscape Urban Plan 116:48–59CrossRefGoogle Scholar
  10. Cohn JP (2008) Citizen science: can volunteers do real research? Bioscience 58:192–197CrossRefGoogle Scholar
  11. Delaney DG, Sperling CD, Adams CS, Leung B (2008) Marine invasive species: validation of citizen science and implications for national monitoring networks. Biol Invasions 10:117–128CrossRefGoogle Scholar
  12. Dickinson JL, Zuckerberg B, Bonter DN (2010) Citizen science as an ecological research tool: challenges and benefits. Annu Rev Ecol Evol Syst 41:149–172CrossRefGoogle Scholar
  13. Engel SR, Voshell JR (2002) Volunteer biological monitoring: can it accurately assess the ecological condition of streams? Am Entomol 48:164–177CrossRefGoogle Scholar
  14. ESRI (2012) ArcGIS 10.1 software. ESRI, RedlandsGoogle Scholar
  15. Estes JA et al (2011) Trophic downgrading of planet Earth. Science 333:301–306CrossRefGoogle Scholar
  16. Fedriani JM, Fuller TK, Sauvajot RM (2001) Does availability of anthropogenic food enhance densities of omnivorous mammals? An example with coyotes in southern California. Ecography 24:325–331CrossRefGoogle Scholar
  17. Finkler H, Terkel J (2012) The contribution of cat owners’ attitudes and behaviours to the free-roaming cat overpopulation in Tel Aviv, Israel. Prev Vet Med 104:125–135CrossRefGoogle Scholar
  18. Galloway AWE, Tudor MT, Vander Haegen WM (2006) The reliability of citizen science: a case study of Oregon white oak stand surveys. Wildlife Soc B 34:1425–1429CrossRefGoogle Scholar
  19. Gehrt SD (2006) Urban coyote ecology and management. Bulletin 929, Ohio State University ExtensionGoogle Scholar
  20. Gehrt SD, Anchor C, White LA (2009) Home range and landscape use of coyotes in a metropolitan landscape: conflict or coexistence? J Mammal 90:1045–1057CrossRefGoogle Scholar
  21. Gese EM, Morey PS, Gehrt SD (2012) Influence of the urban matrix on space use of coyotes in the Chicago metropolitan area. J Ethol 30:413–425CrossRefGoogle Scholar
  22. Gibeau ML (1998) Use of urban habitats by coyotes in the vicinity of Banff Alberta. Urban Ecosyst 2:129–139CrossRefGoogle Scholar
  23. Gompper ME (2002) Top carnivores in the suburbs? Ecological and conservation issues raised by colonization of north-eastern North America by coyotes. Bioscience 52:185–190CrossRefGoogle Scholar
  24. Gosselink TE, Van Deelen TR, Warner RE, Joselyn MG (2003) Temporal habitat partitioning and spatial use of coyotes and red foxes in east-central Illinois. J Wildlife Manage 67:90–103CrossRefGoogle Scholar
  25. Grinder MI, Krausman PR (2001) Home range, habitat use, and nocturnal activity of coyotes in an urban environment. J Wildlife Manage 65:887–898CrossRefGoogle Scholar
  26. Grubbs SE, Krausman PR (2009a) Use of urban landscape by coyotes. Southwest Nat 54:1–12CrossRefGoogle Scholar
  27. Grubbs SE, Krausman PR (2009b) Observations of coyote-cat interactions. J Wildlife Manage 73:683–685CrossRefGoogle Scholar
  28. Kellert SR (1985) Public perceptions of predators, particularly the wolf and coyote. Biol Conserv 31:167–189CrossRefGoogle Scholar
  29. Lawrence SE, Krausman PR (2011) Reactions of the public to urban coyotes (Canis latrans). Southwest Nat 56:404–409CrossRefGoogle Scholar
  30. McClure MF, Smith NS, Shaw WW (1995) Diets of coyotes near the boundary of Saguaro National Monument and Tucson, Arizona. Southwest Nat 40:101–104Google Scholar
  31. McIvor DE, Conover MR (1994) Perceptions of farmers and non-farmers toward management of problem wildlife. Wildlife Soc B 22:212–219Google Scholar
  32. McKinney ML (2006) Urbanization as a major cause of biotic homogenization. Biol Conserv 127:247–260CrossRefGoogle Scholar
  33. Meentemeyer RK, Tang WW, Dorning MA, Vogler JB, Cunniffe NJ, Shoemaker DA (2013) FUTURES: multilevel simulations of emerging urban-rural landscape structure using a stochastic patch-growing algorithm. Ann Assoc Am Geogr 103:785–807CrossRefGoogle Scholar
  34. Messmer TA, Brunson MW, Reiter D, Hewitt DG (1999) United States public attitudes regarding predators and their management to enhance avian recruitment. Wildlife Soc B 27:75–85Google Scholar
  35. Morey PS, Gese EM, Gehrt S (2007) Spatial and temporal variation in the diet of coyotes in the Chicago metropolitan area. Am Midl Nat 158:147–161CrossRefGoogle Scholar
  36. Newman C, Buesching CD, Macdonald DW (2003) Validating mammal monitoring methods and assessing the performance of volunteers in wildlife conservation—”Sed quis custodiet ipsos custodies?”. Biol Conserv 113:189–197CrossRefGoogle Scholar
  37. Pham TTH, Apparicio P, Seguin AM, Landry S, Gagnon M (2012) Spatial distribution of vegetation in Montreal: an uneven distribution or environmental inequity? Landscape Urban Plan 107:214–224CrossRefGoogle Scholar
  38. Prugh LR, Stoner CJ, Epps CW, Bean WT, Ripple WJ, Laliberte AS, Brashares JS (2009) The rise of the mesopredator. Bioscience 59:779–791CrossRefGoogle Scholar
  39. Quinn T (1995) Using public sighting information to investigate coyote use of urban habitat. J Wildlife Manage 59:238–245CrossRefGoogle Scholar
  40. Quinn T (1997a) Coyote (Canis latrans) food habits in three urban habitat types of western Washington. Northwest Sci 71:1–5Google Scholar
  41. Quinn T (1997b) Coyote (Canis latrans) habitat selection in urban areas of western Washington via analysis of routine movements. Northwest Sci 71:289–297Google Scholar
  42. Riley SPD, Sauvajot RM, Fuller TK, York EC, Kamradt DA, Bromley C, Wayne RK (2003) Effects of urbanization and habitat fragmentation on bobcats and coyotes in southern California. Conserv Biol 17:566–576CrossRefGoogle Scholar
  43. See L et al (2013) Comparing the quality of crowdsourced data contributed by expert and non-experts. PLoS ONE 8(7):e69958CrossRefGoogle Scholar
  44. Shargo ES (1988) Home range, movements, and activity patterns of coyotes (Canis latrans) in Los Angeles suburbs. Dissertation, University of CaliforniaGoogle Scholar
  45. Singh KK, Vogler JB, Shoemaker DA, Meentemeyer RK (2012) Lidar-Landsat data fusion for large-area assessment of urban land cover: balancing spatial resolution, data volume and mapping accuracy. ISPRS J Photogramm 74:110–121CrossRefGoogle Scholar
  46. Smith AC, Koper N, Francis CM, Fahrig L (2009) Confronting collinearity: comparing methods for disentangling the effects of habitat loss and fragmentation. Landscape Ecol 24:1271–1285CrossRefGoogle Scholar
  47. Soulé ME, Bolger DT, Alberts AC, Wrights J, Sorice M, Hill S (1988) Reconstructed dynamics of rapid extinctions of chaparral-requiring birds in urban habitat islands. Conserv Biol 2:75–92CrossRefGoogle Scholar
  48. Team RC (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. http://www.R-project.org/. Accessed Dec 2013
  49. Timm RM, Baker RO, Bennett JR, Coolahan CC (2004) Coyote attacks: an increasing suburban problem. In: Proceedings of the twenty-first vertebrate pest conference 2004, paper 1Google Scholar
  50. Way JG, Ortega IM, Auger PJ, Strauss EG (2002) Box-trapping eastern coyotes in southeastern Massachusetts. Wildlife Soc B 30:695–702Google Scholar
  51. Way JG, Ortega IM, Strauss EG (2004) Movement and activity patterns of eastern coyotes in a coastal, suburban environment. Northeast Nat 11:237–254CrossRefGoogle Scholar
  52. Weckel ME, Mack D, Nagy C, Christie R, Wincorn A (2010) Using citizen science to map human-coyote interaction in suburban New York, USA. J Wildlife Manage 74:1163–1171CrossRefGoogle Scholar

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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