Journal of Urban Ecology, 2020, 1–7
doi: 10.1093/jue/juaa032
Research Article

Evaluating human–coyote encounters in an urban
landscape using citizen science2and Maximilian L. Allen3

1

Department of Forest and Wildlife Ecology, University of Wisconsin, 1630 Linden Drive, Madison, WI 53706,
USA, 2College of Natural Resources, University of Wisconsin-Stevens Point, 800 Reserve St, Stevens Point, WI
54481, USA and 3Illinois Natural History Survey, Prairie Research Institute, University of Illinois, 1816 S. Oak
Street, Champaign, IL 61820, USA*Corresponding author: E-mail: ddrake2@wisc.edu
Submitted: 7 July 2020; Received (in revised form): 3 September 2020. Accepted: 7 July 2020

Abstract
Coyotes are ubiquitous in habitats across North America, including in urban areas. Reviews of human–coyote encounters
are limited in scope and analysis and predominantly document encounters that tend to be negative, such as human–wildlife conflict, rather than benign experiences. The objective of our study was to use citizen science reports of human–coyote
interactions entered into iNaturalist to better understand the range of first person accounts of human–coyote encounters in
Madison, WI. We report 398 citizen science accounts of human–coyote encounters in the Madison area between October
2015 and March 2018. Most human–coyote encounters occurred during coyote breeding season and half of all encounters occurred in moderate development land cover. Estimated level of coyote aggressiveness varied significantly, with 90% of citizen scientists scoring estimated coyote aggression as a 0 and 7% scoring estimated aggression as a 1 on a 0–5 scale (with 0
being calm and 5 being aggressive). Our best performing model explaining the estimated distance between the human observer and a coyote (our proxy for a human–coyote encounter) included the variables distance to nearest paved road, biological season of the year relative to coyote life history, and time of day/night. We demonstrate that human–coyote interactions are regularly more benign than negative, with almost all first-hand reported human–coyote encounters being benign.
We encourage public outreach focusing on practices that can foster benign encounters when educating the public to facilitate human–coyote coexistence.
Key words: citizen science, coyote, human, iNaturalist, urban

Introduction
Due to their adaptability, omnivorous diet, and ability to successfully use a variety of habitats, coyotes (Canis latrans) haveexperienced a continent-wide range expansion (Gehrt and Riley
2010; Hody and Kays 2018). Historically confined to the southwestern region of the United States and into Mexico, coyotes
are now found throughout North America, including all ofsouthern Canada. Moreover, coyotes have colonized urban
areas and they are commonly found in human-dominated landscapes throughout their current range. Coyotes can be an

important and beneficial addition to the urban landscape because they will depredate pest or overabundant wildlife species
and their presence adds to the local biodiversity (Hudenko et al.,2010). However, these and other benefits are often overlooked
because of perceived or real human–coyote conflicts (Soulsbury
and White 2015; Poessel et al. 2017b). Human–wildlife conflictoccurs when humans determine that wildlife caused a real or
perceived negative biological, political, economic or social effect(Frank 2016).
Although seemingly increasing in frequency, coyote attacks
on pets and humans are rare (White and Gehrt 2009; Breck et al.

C The Author(s) 2020. Published by Oxford University Press.
V

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/),
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1

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David Drake,1,* Shelli Dubay

2| Journal of Urban Ecology, 2020, Vol. 6, No. 1

Methods
Study area
Our study area was located in the greater Madison, WI, area, anurban area in Dane County that is 245 km2 in area (Fig. 1).
Madison is the second largest city in Wisconsin, with a population of approximately 260 000 people (United States Census
Bureau 2020). Mean temperatures ranged from 10.4 C in winter to 20.6 C in summer with mean yearly precipitation of
87.38 cm(http://www.aos.wisc.edu/sco/clim-history/state/4700-climo.html). Our study area included the University of
Wisconsin-Madison (UW) campus, along with a mosaic of residential, commercial and semi-isolated natural areas boundedby developed roads and neighborhoods. Our study area also
encompassed several public natural areas, including the UW
Arboretum, and parks within the Madison Parks system.
Habitats consisted of upland broadleaf deciduous forests, restored tallgrass prairie, oak savanna, human-planted coniferous
forests and various wetland complexes.

iNaturalist
We established a project-specific iNaturalist page in June, 2015
dedicated to capturing citizen-science sightings of coyotes andred fox in Madison. iNaturalist is an online social network
designed to allow citizen scientists to record and share observational data of the biotic community. Over 890,000 observers
have recorded observations of over 251,000 species worldwide
since its launch in 2014 (www.inaturalist.org). We promoted the
project through the UW Urban Canid Project Facebook Page
(https://www.facebook.com/uwurbancanidproject/),
website(http://uwurbancanidproject.weebly.com/reporting-an-observa
tion.html), and local media. Each citizen-generated observation
contained information about each unique coyote sighting, including time, location, and seven optional and brief questions
about the observation. The optional questions were as follows:(i) Did you have a domestic dog with you? (ii) Did the coyote seeyou? (iii) If the coyote saw you, did it move away from you? (iv)
How close were you to the coyote (estimated in yards)? (v) On a
scale of 0 (calm) to 5 (aggressive), estimate the aggression of thecoyote, (vi) Was the coyote wearing a radio collar? (vii) Was the
coyote wearing ear tags? We included the last two questions because we were curious if ear tagged or radio collared coyotes
were seen more frequently than non-tagged or collared animals. Other than asking each iNaturalist user to complete theabove questions, instructions were minimal.
We omitted any observations from the same user that occurred within 24 hours of the previous observation to attempt toreduce the effects of observers making repeated observations of
the same animal. We did not have the ability to verify that an
iNaturalist-reported coyote was truly a coyote. However, we areconfident that iNaturalist reports were accurate for a number ofreasons. First, we screened all iNaturalist observations at least
weekly and in some instances asked for, and received, additional information from an iNaturalist user to verify the observation. Second, many iNaturalist users uploaded pictures of
their observation, and all of the uploaded pictures matched
their reported observation. Third, in many areas where
iNaturalist reports were located we also were able to document
presence of a radio-collared coyote in the same area (Mueller
et al. 2019).

Downloaded from https://academic.oup.com/jue/article/7/1/juaa032/6055886 by guest on 26 January 20212019). White and Gehrt (2009) examined coyote attacks onhumans in the United States and Canada and found that 59% of
the attacks were classified as either predatory or investigative.
Coyote attacks on children and adults were about equal, but
predatory attacks were significantly greater on children than on
adults (White and Gehrt 2009). Poessel et al. (2017b) found thatcoyote attacks on humans occurred with greater frequency inlarge urban areas in the western USA relative to other areas of
the USA and occurred in areas with greater human development and less forest cover. Wine et al. (2015) found that coyote
encounters in the Charlotte, NC, area occurred in areas with increased building density, which resulted in greater opportunity
for human–coyote encounters because of relatively higher densities of humans in a concentrated area.
Past studies of human–coyote encounters are limited inscope and analysis (White and Gehrt 2009). Of the quantitative
studies, Wine et al. (2015) found that coyote encounters occurred in areas with higher income levels because more foodsubsidies (e.g. compost and birdseed) were on the landscape relative to lower income areas. Areas of Charlotte where occupations required outdoor labor (e.g. landscapers) also experienced
higher human–coyote encounters because people spent moretime outdoors and had greater opportunities to encounter acoyote (Wine et al. 2015). Murray et al. (2015) found that coyotes
that were diseased (e.g. mange) in cities in Canada were more
likely to come closer to humans, likely because they were looking for food subsidies that could be acquired with little energy
expended. However, most accounts are based on human dimension surveys and media stories or are anecdotal (White andGehrt 2009; Poessel et al. 2013; Lu et al. 2016) and reported
accounts predominantly document encounters that tend to be
negative rather than benign experiences (Lu et al. 2016). Few
published studies document first person accounts of encounterswith coyotes.
As part of an ongoing research project live trapping andradio-collaring red foxes (Vulpes vulpes) and coyotes in
Madison, WI (the UW Urban Canid Project), we developed acitizen science page where citizen scientists could record
reports of encounters with these two urban canid species. In
this article, we are reporting only the encounters with coyotes, including time, date and location of encounter, estimated distance to coyote and estimated level of coyote
aggression, among other specifics. Our objective was to use
coyote-specific reports to better understand first person
accounts of human–coyote encounters in Madison. We
broadly define the term human–coyote encounter to include
reports of encounters with coyotes, such as an iNaturalist
user seeing a coyote in their yard, from their car window, or
while walking or jogging. Rather than iNaturalist users seeking out coyotes, we believe that the human–coyote encounters were happenstance due to the type of reported
encounters. Based on nominal media reports of human–coyote interactions in Madison, we hypothesized that the majority of human–coyote encounters would be benign. We useddistance between an observer and a coyote as a proxy for human–coyote encounters (Taylor and Knight 2003). Based on
results from Poessel et al. (2013), we predicted that human–
coyote encounters would occur most frequently during daylight hours when coyotes could be seen easier than duringnocturnal hours and during the months (December to March)that coincided with coyote breeding season when coyotes
tend to be more territorial and active (Lukasik and Alexander2011).

Human–coyote encounters in an urban landscape|3

Data variables
We included each of the variables from our iNaturalist questionnaire in statistical analyses. We estimated distance in yards
to next nearest iNaturalist observation (hereafter INAT). We
used a Geographic Information System (GIS; ESRI 2017. ArcGIS
Desktop: Release 10.4. Redlands, CA: Environmental Systems
Research Institute) to create a habitat map of our study area
based on National Land Cover Data (NLCD 2011; 30  30 m resolution; Albers Conical Equal Area projection; 1983 North
American datum), especially pertaining to human development.
Using Gosselink et al. (2003) and Mueller et al. (2019) for guidance on urban coyote habitat selection, we omitted or combined
the original 20 land cover classifications in the 2011 NLCD to arrive at the five land cover categories used in our analysis: developed open space (turf fields, non-forested parks, cemeteries;
hereafter OPEN), moderate-intensity development (20–79% impervious surface, residential neighborhoods; hereafter MODR),
high-intensity development (>80% impervious surface, industrial and commercial land; hereafter HIGH), non-developed (natural areas, including forest, grassland, emergent wetlands;
hereafter NATR) and water (open bodies of water; hereafter
WATER).
We estimated local population density (humans/km2; hereafter HUMN) data from the National Historical Geographic
Information System based on which 2011 US Census block the
coyote observation was recorded. We calculated distance inyards from nearest paved road to reported human–coyote encounter using local road data (Dane County Land Information
Office 2020; hereafter ROAD). We categorized seasons (hereafter
SEASON) based on biologically meaningful periods in the life ofcoyotes: breeding (December to March), pup-rearing (April toJuly), and non-breeding (August to November) (Bekoff and Gese

2003). We calculated time of day (hereafter TIME) based on local
sunrise and sunset times using three periods: crepuscular (sunrise [time of sunrise 6 2 h] combined with sunset [time of sunset
6 2 h]), day (began after sunrise period and lasted until sunset
period) and night (began after sunset period and lasted until
sunrise period). This approach led to uneven temporal lengthsof time periods but more accurately reflected seasonal changes
to photoperiods and coyote activity periods.

Statistical analysis
For questions with a binary response (e.g. “Did you have a domestic dog with you?”; YES or NO), we counted the number ofaffirmative or negative responses. For variables that had multiple options (e.g. number of iNaturalist observations per each of
the five NLCD land cover types), we counted the number ofanswers or observations per category. We asked each
iNaturalist observer to estimate distance between themselves
and the observed coyote based on pre-defined distance categories that were in gradations of 10 yards. We took the midpointof each observers estimated distance category as the number ofyards between the observer and coyote. We reported the distance between each iNaturalist observation and nearest paved
road based on the distance tool in ArcGIS. We used a series of v2
tests to determine if sightings varied by season, time of day,land cover, or estimated aggressiveness of coyotes. We deemed
results significant if P  0.05.
We developed 10 a priori models (Table 1) using an Akaike
information criteria (AIC) framework (Burnham and Anderson
2002) where “Estimated distance between observer and coyote(DIST)” was our response variable. Our predictor variables included summarized answers to the six optional questions askedof each person submitting an iNaturalist observation [Was a

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Figure 1: Study area in Madison, WI.

4| Journal of Urban Ecology, 2020, Vol. 6, No. 1

Table 1: A priori models explaining estimated distance between human observer and coyote (proxy for human–coyote encounter) in
Madison, WI, October 2015 to March 2018
AICc

DAIC

Wi

ROAD þ SEASON þ TIMECOLLARROAD
SEASON þ TIME
ROAD þ NLCDHUMN þ ROAD þ NLCD
SEE þ AGG þ AWAY
NULL
DOG
NLCD

3645.33652.73655.83658.53660.13661.03669.03672.23673.93675.2

0
7.3
10.5
13.2
15.7
14.8
23.7
26.9
28.6
29.9

0.96
0.03
0.005
0
0
0
0
0
0
0

dog present during the human–coyote interaction (DOG)? Did
the coyote see you (SEE)? If the coyote saw you, did it moveaway (AWAY)? Estimated aggression of the observed coyote(AGG) and Was the coyote wearing a radio collar (COLLAR) or
ear tags (TAGS)?], our five NLCD land cover categories (OPEN,
MODR, HIGH, NATR and WATER), human population density(HUMN), distance to nearest road (ROAD), biological meaningfulseason when observation occurred (SEAS) and time of day of observation (TIME). We tested for correlation between our predictor variables using the Pearson correlation function in R (V.
3.5.1, R Foundation for Statistical Computing, Vienna, Austria).
We considered two variables to be correlated if the Pearson correlation coefficient was > 0.75. Whether the observed coyote
was wearing ear tags and a radio collar were correlated, so we
randomly chose to use the response to the question about observing a coyote wearing a radio collar. Because of repeatedsampling likely due to multiple observations of the same coyote, we used the lme4 package in program R to run each of thelinear mixed effect regression models. We used the distance tothe next nearest iNaturalist observation as our random effect totry and account for variation among individual coyotes. We
modeled all other predictor variables as fixed effects. We considered the top model to be the one with the lowest AICc value
and the greatest weight. We considered any variable(s) within
our top models to be significant at the P  0.05.
Our data will be deposited in the Illinois Data Bank upon
acceptance.

Results
Our project (UW Urban Canid Project’s iNaturalist page) included reports from 1024 iNaturalist users, the majority ofwhich reported encounters with red foxes. We report 398
iNaturalist accounts of human–coyote interactions in the
Madison, WI, area between October 2015 and March 2018.
Response rate varied for the seven optional questions that each
iNaturalist observer was asked to complete. One hundred percent of iNaturalist users answered if a dog was present withthem when they encountered a coyote and provided a distanceestimate between themselves and the coyote. Ninety-nine percent (n ¼ 394) provided an estimated level of coyote aggression,and 98% (n ¼ 390) answered the question about whether the coyote moved away if it saw the iNaturalist observer. Eighty-sevenpercent (n ¼ 346) of iNaturalist users answered the question“Did the coyote see you?” Only 45% (n ¼ 179) of iNaturalist users
provided an answer to the question about seeing a radio collar
or ear tags on the coyote they encountered.

Figure 2: Number of human–coyote encounters by coyote biological season in
Madison, WI, October 2015 to March 2018.

Of those who provided an answer, 34% (n ¼ 61) noticed ear
tags and 35% (n ¼ 62) noticed a radio collar on the coyote theyobserved. The average distance from a reported human–coyote
encounter to the nearest paved road was 55 yards (693 yards). A
domestic dog was not present with the person reporting a coyote encounter 77% (n ¼ 308) of the time. The average estimateddistance between the iNaturalist observer and the observed coyote was 25 yards (625 yards; minimum distance ¼ 5 yards andmaximum distance ¼ 100 yards). Sixty-two percent (n ¼ 214) of
the time the coyote saw the observer and 55% (n ¼ 216) of thetime the coyote moved away from the observer once the observer was seen by the coyote.
The time period that human–coyote encounters occurred
was not significant (v2 ¼ 4.9, P ¼ 0.09), with 38.4% during the daytime, 35.5% during crepuscular times and 26.1% during nighttime. The season of human–coyote encounters varied
significantly (v2 ¼ 27.1, P < 0.0001), with 51% occurring during
the coyote breeding season (December to March; Fig. 2). The
land cover where human–coyote encounters occurred also varied significantly (v2 ¼ 166.8, P < 0.0001; Fig. 3), with 50% (n ¼ 200)of the reported encounters occurring in the moderate-intensity
development (MODR; 20–79% impervious surface, built environment) and 27% (n ¼ 107) occurring in non-developed (NATR; natural areas, including forest, grassland, emergent wetlands). The
estimated level of coyote aggressiveness in human–coyote
encounters varied significantly (v2 ¼ 467.4, P < 0.0001; Fig. 4),
and the vast majority of the iNaturalist users estimated the aggression level of the coyote they encountered as either 0(n ¼ 356; 90%) or 1 (n ¼ 27; 7%).
For our models determining factors driving the estimateddistance between iNaturalist observers and coyotes, one model
was clearly our top model (wi ¼ 0.96; Table 1). This model contained the variables DIST (distance between human–coyote encounter and nearest paved road; b ¼ 0.05 6 0.01 SE), SEAS
(biological season of the year; b ¼ – 4.5 6 1.38 SE), and TIME (timeof day/night human–coyote encounter occurred; mean b ¼ –5.86 6 3.75 SE).

Discussion
The objective of our study was to use our project-specificiNaturalist page to quantify first person accounts of human–
coyote encounters in Madison, WI. Most studies examining human–coyote encounters are based on human dimension surveys (Jackman and Rutberg 2015; Lu et al. 2016), second-handaccounts (Poessel et al. 2013) or media reports (Alexander andQuinn 2011). Our prediction that the majority of human–coyote

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Model

Human–coyote encounters in an urban landscapeFigure 3: Land cover types in Madison, WI, where human–coyote encounters oc-

Figure 4: Estimated coyote aggression based on human–coyote encounters in
Madison, WI, October 2015 to March 2018. Zero indicates calm and 5 indicatesaggressive.

encounters in Madison, WI, would be benign held true. Contrary
to what is reported in the peer-reviewed literature and throughmedia accounts (White and Gehrt 2009; Alexander and Quinn2011), the overwhelming majority of first-hand reported human–coyote encounters were benign, with 97% of iNaturalist
users scoring estimated coyote aggression as a 0 or 1 on a 0–5
scale. Admittedly, estimated level of coyote aggression is a subjective answer, but we suggest that the consistency with which
97% of iNaturalist users answered the question indicates thevast majority of coyote encounters were non-aggressive.
Additionally, most of the time (62%) the coyote saw theiNaturalist observer and more than half the time (55%) the coyote moved away from the iNaturalist observer after seeing thehuman. Poessel et al. (2017b) reported that between 2003 and2010, >92% of human–coyote conflicts in the Denver
Metropolitan Area involved attacks on domestic pets. However,Alexander and Quinn (2011) reported that only 24% of negative
human–coyote encounters involved people with dogs. Only 23%
of iNaturalist users in our study reported having a dog withthem when they encountered a coyote.
We predicted that human–coyote encounters would occur
most frequently during daylight hours when coyotes could beseen easier than during nocturnal hours and during the months(December to March) that coincided with coyote breeding season when coyotes tend to be more territorial and active (Lukasik
and Alexander 2011). The greatest number of reports of human–
coyote encounters in our study area occurred during the coyote
breeding season (December–March), but coyote encounterswere observed commonly during pup rearing and non-breeding
seasons. Lukasik and Alexander (2011) also found that the greatest number of sightings of coyotes in Calgary occurred during

5

the coyote breeding season and suggested that breeding season
was the time of the year coyotes were most active and therefore
presented the most opportunity to be seen. In addition, coyote
breeding season coincided with winter months, a period of theyear when foliage is absent from woody vegetation, thereby providing better sightlines. Poessel et al. (2013) reported coyote
observations in the Denver Metropolitan Area more than twice
as often on average during winter than any other time of year.
However, Weckel et al. (2010) found no seasonal effect in coyotesightings in Westchester County, NY.
Schmidt and Timm (2007) suggested seven steps of increasingly troublesome coyote behavior. The first step in the progression being an increase in observing coyotes in yards and on
streets at night and the seventh step culminating in coyotes acting aggressively towards humans in mid-day. Based on the human–coyote interactions observed in Madison, it would seemthat coyotes are demonstrating behavior equivalent to step 3 on
the Schmidt and Timm (2007) scale; that being crepuscular anddaylight observance of coyotes on streets and in parks andyards. Although appearing during daylight hours may be a reason for concern that coyotes were habituating to people,
thereby leading to possible aggressive coyote behavior, that
does not seem to be the case in our study area. Coyotes typicallyare most active during nocturnal and crepuscular hours(Mueller et al. 2019), but are obviously easiest seen during daylight times. However, other studies have found that most coyote
observations were noted during nocturnal hours (Grinder andKrausman 2001; Riley et al. 2003; Weckel et al. 2010). One explanation for coyotes often being seen during crepuscular hours inour study may be due to most observations coming during thewinter months, and as winter days become shorter, schedules
for dogwalkers and recreationists are more likely to coincidewith crepuscular coyote activity patterns (Poessel et al. 2013).
Our best performing model explaining the estimated distance between the human observer and a coyote (our proxy fora human–coyote encounter) included distance to nearest paved
road, biological season of the year relative to coyote life history,
and time of day/night. Distance to nearest paved road may have
improved sight lines for the iNaturalist observer and made iteasier to see a coyote because vegetation on each side of a roadway is oftentimes cleared so drivers have improved safety via
clear sightlines and the ability to see wildlife and other animals
coming into the roadway (Eck and McGee 2008). Mueller et al.
(2019) noted that coyotes were seen more frequently in areas ofMadison, WI, with moderate human development and less frequently in areas with natural vegetation. In suburban NY, coyotes were observed and reported by citizen scientists on
average 50% closer to forests, 36% closer to grasslands and 66%
farther from medium- to high-density development (Weckel
et al. 2010). Cover provided by forests may help reduce encounters between coyotes and people and their pets (Poessel et al.
2017a). In the Denver Metropolitan Area, coyote conflicts weremore frequent than expected in developed and open space land
cover types, possibly because of dense concentrations of people
and pets, thereby providing more chances for a human–coyote
encounter (Poessel et al. 2013, 2017a). Human–coyote conflicts
in Calgary occurred in areas of the city with dense human population, which may be associated with greater availability of anthropogenic food sources, leading to food-conditioned coyotes(Lukasik and Alexander 2011).
Poessel et al. (2017a) found that >80% of large and mediumsized cities reported human–coyote conflicts. While coyote
attacks on pets have occurred in Madison, WI in past years, ourstudy area may be unique in that human–coyote conflicts are

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curred, October 2015 to March 2018.

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6| Journal of Urban Ecology, 2020, Vol. 6, No. 1

County (WI) Public Health Department to produce a You Tube
video titled “How to Haze a Coyote”, which as of April 2020 hadreceived over 48 000 views. We also promote ways to decreasecoyote habituation of people via public talks, and using our UWUrban Canid Project’s social media and website outlets. Lastly,
we instruct people to call either 9-1-1 or the Wisconsin
Department of Natural Resources should they feel threatenedby a coyote or when a coyote is aggressive toward them, theirchildren, or their pets.

Conclusions
Coyotes are common in urban areas of North America and will
be a consistent presence arguably for as long as cities exist. As
humans, it would behoove us to figure out ways to peacefully
co-exist with urban wildlife, especially apex predators like coyotes. Our study did not elucidate the mechanisms that provide
a recipe for favorable human–coyote interactions, but it does
demonstrate that human–coyote interactions can be more benign than negative. Drake et al. (2020) posited that targetingpeople’s emotional connections to animals like coyotes will be amore effective strategy to increase support for coyotes than increasing the public’s knowledge about urban coyotes. While wewill always create opportunities to improve the public’s knowledge about urban coyotes using research-based information, we
also agree with Drake et al. (2020) and suggest that citizen science opportunities like iNaturalist have the potential to provide
an avenue to engage the public so they may report the connection they experienced seeing a coyote.

Acknowledgments
We thank Meghan Pluemer for assistance with ArcGIS. We
thank Morgan Farmer for creating Fig. 1. We thank the citizens of Madison, WI for taking time to report sightings ofcoyotes and red foxes to our iNaturalist page.
Conflict of interest statement. None declared.

References
Alexander, S. M., and Quinn, M. S. (2011) ‘Coyote (Canis Latrans)
Interactions with Humans and Pets Reported in the Canadian
Print Media (1995–2010)’, Human Dimensions of Wildlife, 16:
345–59.
Bekoff, M., and Gese, E. M. (2003) ‘Coyote (Canis Latrans)’, in G.A.
Feldhamer, B.C. Thompson, J.A. Chapman (eds.) Wild Mammalsof North America: Biology, Management, and Conservation, 2ndedn, pp. 467–481. Baltimore, MD: Johns Hopkins University
Press.
Belanche, D., Casaló, L. V., and Flavián, C. (2017) ‘Understanding
the Cognitive, Affective and Evaluative Components of Social
Urban Identity: Determinants, Measurement, and Practical
Consequences’, Journal of Environmental Psychology, 50: 138–53.
Breck, S. W. et al. (2019) ‘The Intrepid Urban Coyote: A
Comparison of Bold and Exploratory Behavior in Coyotes from
Urban and Rural Environments’, Scientific Reports, 9: 1–11.
Burnham, K., and Anderson, D. (2002) Model Selection andMulti-Model Inference. 2nd edn. New York: Springer.
Dane County Land Information Office. (2020) Road Centerlines.
<https://gis-countyofdane.opendata.arcgis.com/datasets/road-centerlines?
geometry¼-91.509%2C42.717%2C-87.337%2C43.419>.

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rare and human–coyote encounters seem to be overwhelmingly
benign relative to what is reported in the literature and media
accounts. Drake et al. (2020) found that a city’s identity shaped
support and risk perceptions for urban coyotes, and previous
studies have linked place identity with initiatives that supported the environment (Belanche et al. 2017). In a survey of
public opinion toward urban red foxes and coyotes in Madison,
WI, Nardi et al. (2020) found that the public had a more favorable attitude toward urban foxes relative to urban coyotes. In
general, 34% of the people surveyed had positive or very positive attitudes toward coyotes compared to 63% who had a positive or very positive attitude toward foxes. Regardless, lowmastery worldviews (e.g. belief that wildlife should be protected, should have rights and are an important part of the community), low perceptions of risks and high perceptions ofbenefits were related to a favorable attitude toward both urban
coyotes and urban foxes. Approximately 33% of the people surveyed had negative views towards coyotes compared to only 5%
for foxes.
The length of tenure of coyotes in an urban area tends tolead to tolerance of human–coyote encounters. Residents of anarea typically have more benign views toward urban coyotes
the longer coyotes have been present in the area (Hudenko et al.
2008; Lawrence and Krausman 2011). Zimmerman et al. (2001)
suggested that people support the presence of urban coyotes as
people get acclimated to them. Jackman and Rutberg (2015)
found growing acceptance of urban coyotes and increased opposition to lethal control, even in areas where human–coyote
conflicts have occurred. There are no known records of whencoyotes first colonized Madison, WI, but coyotes have been in
the city limits since at least the 1980s based on media reports,
so residents of our study area have a relatively lengthy coexistence with coyotes.
The use of citizen science data for research comes with inherent limitations, and iNaturalist is no exception. For example,
we experienced uneven spatial and temporal sampling, and it isimportant to understand these limitations and how they may
be affected by the life history of a target species (Mueller et al.
2019). However, the value of the data collected through ouriNaturalist project is the quality of responses we received to detailed questions about each encounter. This allowed us to examine individual human–coyote encounters, which is often a
missing component of human-wildlife interaction studies.
Although coyote habituation towards humans in our study
area seems to be increasing, the habituation does not seem tobe leading to aggressive behavior based on the iNaturalist estimates of coyote aggression. We do not have empirical evidence
to support our contention, but we suggest that the outreachefforts associated with our larger UW Urban Canid Project are
educating the general public on what to do and not do in orderto promote human–coyote co-existence. Siemer et al. (2014) described windows of opportunities following coyote attacks onchildren in New York, and resulting media coverage, at whichtime the public’s awareness about coyotes and receptiveness tocoyote-related education may change the public’s perceptions
about coyote risk and tolerance. Coyote attacks on pets in
Madison that occurred in 2009, 2011 and 2015 received considerable media attention and community town hall events were organized following the attacks. Outreach and education
regarding how to live with coyotes were disseminated at theevents. Our UW Urban Canid Project outreach efforts, on theother hand, are constant and have been an integral part of ourresearch project since we started in 2014. As part of our outreach efforts, we collaborated with the Madison and Dane

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