Landscape and Urban Planning 189 (2019) 362–371

Contents lists available at ScienceDirect

Landscape and Urban Planningjournal homepage: www.elsevier.com/locate/landurbplan

Research Paper

Using citizen science to inform urban canid management
a

b,⁎

c

Marcus A. Mueller , David Drake , Maximilian L. Allen
a
b
c

T

University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI 53706, USA
University of Wisconsin, 1630 Linden Drive, Madison, WI 53706, USA
Illinois Natural History Survey, 1816 S. Oak Street, Champaign, IL 61820, USA

ARTICLE INFOABSTRACT

Keywords:
Canis latransCitizen science
CoyoteINaturalist
Red foxVulpes vulpes

Urban wildlife populations present different scenarios for managers compared to rural populations, partly because of greater diversity in stakeholder attitudes and opinions regarding urban wildlife. Wild urban canids—especially coyotes (Canis latrans)—have been of increasing interest throughout North America in recent
years. Our objective was to evaluate the potential of using iNaturalist-generated observations of urban red foxes
and coyotes for cost-effective, customizable data collection to inform urban canid management. Our research isimportant on two fronts; first, it is a way to engage the public to make them more aware of urban canids, andsecondly, it is an attempt to empirically test if we can more efficiently and effectively track coyotes and red foxes
in urban areas. We used iNaturalist to collect over 800 community-generated locations for red foxes (Vulpes
vulpes) and coyotes in Madison, WI from 2015 and 2016. We concurrently placed radio-collars on 11 red foxes
and 11 coyotes to determine areas used in this urban ecosystem. We compared iNaturalist to radio-telemetry
locations to identify factors that led to a positive relationship between these two inherently different spatial data
sets. Greatest overlap between iNaturalist and telemetry data for both red foxes and coyotes occurred in areas
with moderate human development and there was minimal overlap in natural areas. The overlap between
iNaturalist and telemetry locations was comparable for both species, but the underlying mechanism differed by
species-specific habitat use. iNaturalist reports appeared to show where and when humans most often interactedwith red foxes and coyotes, rather than their true spatiotemporal distribution. Understanding the relationshipbetween community-generated reports and local canid distribution may inform how iNaturalist can be used as amanagement tool and allow managers to proactively monitor and manage human-wildlife interactions withurban wildlife.

1. Introduction
Urban land acreage in the continental USA has quadrupled since1945, and as of 2014 88% of Americans lived in urban areas(Nickerson, Ebel, Borchers, & Carriazo, 2017). Urban ecosystems are
inherently different than their rural counterparts, primarily due tohuman presence, but also because of increased habitat fragmentation,
increased movement barriers (i.e., roads, development), and anthropogenic sources of food and mortality (Bateman & Fleming, 2012).
Human activities are often an important driver of the distribution,
abundance, and dynamics of wildlife in urban ecosystems (Warren
et al., 2010). For example, species richness generally decreases withurbanization, but species density increases because urban ecosystems
provide resources that allow synanthropic species to persist, adapt, or
thrive (McKinney 2002, 2006, McCleery, Mooreman, & Peterson,2014).

⁎

Urban carnivores are examples of synanthropic species that present
wildlife managers with unique and challenging scenarios due to theclose proximity and interactions between carnivores and humans andtheir domestic pets (Gehrt, Riley, & Cypher, 2010). The degree of urbanization in an area determines which carnivore species exist on thelandscape as well as interspecific interactions with other sympatriccarnivores (Wang, Allen, & Wilmers, 2015, Mueller, Drake, & Allen,2018). Carnivores can be an important and positive addition to theurban landscape in that they will predate pest or overabundant wildlife
species, and their presence adds to the local biodiversity (Hundenko
et al., 2010). However, these and other positive effects are often
overlooked because of perceived or real human-carnivore conflicts(Soulsbury & White, 2015). Furthermore, the opportunity for humancarnivore interactions in urban ecosystems increases because of theincreased human density. Human-carnivore interactions often shapestakeholder (those people whom are affected by wildlife management

Corresponding author at: 1630 Linden Dr., Madison, WI 53593, USA.
E-mail addresses: ddrake2@wisc.edu (D. Drake), maxallen@illinois.edu (M.L. Allen).

https://doi.org/10.1016/j.landurbplan.2019.04.023
Received 10 May 2018; Received in revised form 28 February 2019; Accepted 25 April 20190169-2046/ © 2019 Elsevier B.V. All rights reserved.

Landscape and Urban Planning 189 (2019) 362–371

M.A. Mueller, et al.

actions) perception and experience (Curtis & Hadidian, 2010,
Hundenko, Siemer, & Decker, 2010) and in turn stakeholders often
inform management objectives (Hundenko et al., 2010). The social
impacts resulting from positive or negative human-wildlife interactions
can be a catalyst for public action and can have management implications that directly affect wildlife populations (Hundenko et al., 2010).
Public perspective and action is especially true for red foxes (Vulpes
vulpes) and coyotes (Canis latrans), two charismatic urban-adapted canids that can elicit positive and negative responses from stakeholders
interacting with them.
As habitat generalists, red foxes and coyotes have expanded theirranges in North America over the last century and are present in many
major metropolitan areas (Bekoff & Gese, 2003, Cypher, 2003, Bateman& Fleming, 2012). Foxes are often viewed favorably, but can cause
property damage through denning and are potential disease vectors(Soulsbury, Baker, Iossa, & Harris, 2010). Coyotes are generally viewed
less favorably, and concern for the health and safety of humans anddomestic pets often dictates the public’s response to urban coyotes(Soulsbury & White, 2015). Management of urban coyote populations is
primarily focused on minimizing human-coyote conflict, despite therelatively low number of coyote attacks on humans and domestic animals (Gehrt, Anchor, & White, 2009, Gehrt & Riley, 2010).
Citizen science, where members of the public contribute to scientificresearch by assisting with data collection (Dickinson, Zuckerberg, &Bonter, 2010), can be a cost-effective tool to address many of thechallenges of studying and managing urban wildlife populations(Connelly, Seimer, & Decker, 2012). Citizen science can also provide anavenue for the public to have a direct and positive experience, so they
become invested in the issue at hand. A large number of stakeholders
living in close proximity to wildlife is an ideal situation for the use of
citizen science, but the full potential of citizen science is just beginningto be understood (Bonney et al., 2009). A major benefit of citizen science—especially using the internet—is that it allows for the collectionof data across large spatial and temporal scales considered prohibitive
using traditional methods because of cost, logistics, and other barriers(Hochachka et al., 2012; Lepczyk et al., 2009). Major drawbacks andchallenges of using citizen science data include error due to variation in
observer quality and sampling bias. These shortcomings can potentiallybe remedied proactively by training participants and using standardized protocols or reactively during data analyses (Dickinson et al.,2010; Hochachka et al., 2012). Citizen science participation by landowners also facilitates data collection on private lands, a frequent logistical hurdle for urban wildlife researchers and managers (Dickinson
et al., 2010). Finally, citizen science can be used to engage stakeholders
by incorporating data into management actions, especially for conservation efforts in residential areas (Cooper, Dickinson, Phillips, &Bonney, 2007).
Access to community-generated data is becoming easier for researchers through web-based platforms like iNaturalist (www.
inaturalist.org). iNaturalist is an online social network designed toallow community scientists to record and share observational data of
the biotic community. Over 550,000 users have recorded observationsof over 210,000 species worldwide since its launch in 2008 (www.
inaturalist.org). Users share their individual observations and interact
with members of the online community. iNaturalist also allows researchers to create project-specific pages focused on collecting datawith specific goals.
As part of an ongoing research project live trapping and radio-collaring red foxes and coyotes, we developed a project-specific iNaturalist
page where community scientists could record reports of red foxes andcoyotes. Live trapping and radio-collaring and tracking urban coyotes
and red foxes is labor-intensive and costly. Using the public to identify
and report urban canid locations could be a more cost-efficient way to
track coyotes and red foxes throughout urban areas, may improve
management of human-carnivore interactions, and is a way to engage
the public in order to potentially raise awareness and tolerance forwildlife sharing the urban landscape with humans. Our objective was toevaluate the potential of using iNaturalist-generated observations ofurban red foxes and coyotes for cost-effective, customizable data collection to inform urban canid management. Specifically, our research
aimed to answer the following questions:
1. What are the characteristics of iNaturalist-generated observations ofurban canids? We hypothesized that most iNaturalist reports of red
foxes and coyotes would be in relatively close proximity to highdensities of humans and roads and during diurnal hours (Mueller
et al., 2018).
2. How accurately do iNaturalist-generated observations of red fox andcoyote distribution overlay with radio-collar location data? We hypothesized that the overlay of iNaturalist-generated observations
with radio-collar location data would be greater for red foxes thancoyotes because red foxes are more closely associated with humanpresence than coyotes (Mueller et al., 2018).
3. How do landscape and other factors differ in areas with high to low
overlap of iNaturalist-generated and radio-collar location data for
red foxes and coyotes? We hypothesized that iNaturalist-generated
reports would increase in overlap with radio-collar location data inmore open landscape that increase an observer’s ability to see redfoxes and coyotes (Ruette, Stahl, & Albaret, 2003; Lurracea,
Brussard, Jaeger, & Barrett, 2007).
2. Methods
2.1. Study area
Our study area was located in Madison, WI, an urban area in DaneCounty (Fig. 1). Madison is the second largest city in Wisconsin, with apopulation of 245,000 people. 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/4700climo.html). Our 6120 ha study area was defined by a major highway(the beltline highway) and roads for the south and west boundaries, thewest shoreline of Lake Monona and the isthmus canal for the easternboundary, and the south shore of Lake Mendota and a major road forthe northern boundary. Our study area encompassed the University of
Wisconsin-Madison (UW) campus, along with a mosaic of residential,commercial, and semi-isolated natural areas bounded by developed
roads and neighborhoods. Our study area also encompassed several
public natural areas, including the UW Lakeshore Nature Preserve, UW
Arboretum, and Owen Conservation Park, part of the Madison Parks
system. Habitats consisted of upland broadleaf deciduous forests, restored tallgrass prairie, oak savanna, human-planted coniferous forests,
and various wetland complexes.
2.2. Capture and monitoring
We live-captured adult red foxes and coyotes, using cable restraints,from November to April during 2015 and 2016. We also opportunistically trapped outside of these seasons to replace collared animals lost
due to mortality or collar failure. For full capture details see Mueller
et al. (2018). We followed all ethical capture procedures and methods
for cable restraints, and our animal handling methods were approvedby the University of Wisconsin Animal Care Use Committee (Protocol
A01559), and the Wisconsin Department of Natural Resources (WDNR)(Permit # SCP-SOD-001-2014). We fitted each animal with ear tags anda very high frequency (VHF) radio collar (Advanced Telemetry Systems,
Isanti, MN; Model # M1950 for red fox and M2220B for coyote) or
Global Positioning System (GPS) collar (Lotek Wireless Fish & Wildlife
Monitoring, Newmarket, ON; Model #G5C175C).
Once an animal was released wearing a radio-collar, we located it
2–3 times within the first 5 days to ensure it was moving and alive.
Following the initial five-day period, we attempted to locate each VHF363

Landscape and Urban Planning 189 (2019) 362–371

M.A. Mueller, et al.

Fig. 1. The top image is the study area (outlined in red) in Madison, WI. The bottom image depicts land cover classifications within our study area based on humandevelopment in Madison, WI, 2015–2016. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)collared animal via radio receiver at least once per week for the entire
duration that the radio collar functioned or the animal remained alive.
We triangulated each location based on the intersection of ≥3 telemetry bearings taken within a maximum of 15 min of each other toreduce error based on animal movement (Schmutz & White, 1990). We
also located animals using GPS readings if individuals were visuallyobserved. To ensure the accuracy of triangulations, we plotted thetelemetry bearings and estimated the location of the animal on a laptop
computer to proof locations in the field (unpublished data, RadioTracker, John Cary, University of Wisconsin, Madison, WI). We

discarded some observations based on the error ellipse, but didn't havea hard cutoff. Triangulations were all field-proofed in real time and if
the error calculation didn’t work out or had an error ellipse too large toconfidently pin an animal to a certain residential block, patch of woods,
etc., it was omitted on the spot. During the weekly location of eachanimal, we tracked them for a 5-hour period, where we located the
focal animal once per hour during that period. Weekly tracking periods
were systematically rotated around the 24-hour clock to ensure that
temporal variation in activity was captured. All GPS-collars were programmed to collect hourly locations on the same rotating schedule.
364

Landscape and Urban Planning 189 (2019) 362–371

M.A. Mueller, et al.

2.3. Community-generated locations

Statistical Computing, Vienna, Austria). We used the overlapEst function
to test if citizen reporting activity varied between red fox and coyote
observations, where we considered Δ4 > 0.80 to be strong overlap(Lynam et al., 2013). We used a Chi-square test to evaluate the proportion of iNaturalist reports in each land cover category, a Tukey HSD
test to evaluate the number of iNaturalist reports by season, and a
Welch 2-sample t-test to evaluate the estimated distance between theobserver and a coyote or red fox. All results were considered significant
at the P < 0.05 level
To create a focus area for our statistical analyses, we constructed
100% Minimum Convex Polygons (MCPs) using the adehabitatHR
package (Calenge, 2011) for each radio-collared red fox and coyote,and merged these polygons together for each species. We only included
radio locations that overlapped with the sighting data in the same time
period from our iNaturalist page (May 2015 to December 2016). The
primary reason we chose to use the MCP estimator is because it seems
to be the least affected by relatively small sample sizes compared toother home range estimators (Millspaugh et al., 2012). In addition,although MCP is limited for understanding fine-scale use and behavior,
we merged all of the individual home ranges for coyotes and red foxes
into one home range for each species because we were interested inbroader spatial use of our study area by coyotes and red foxes. There
was nominal visual difference between our 95% and 100% MCPs homeranges for each radio-collared coyote and red fox that we used to create
the merged home ranges, so we included all points in each animal’s
MCP to maximize our sample sizes.
Using ArcMap (ESRI. ArcGIS Desktop: Release 10.4. Redlands, CA:
Environmental Systems Research Institute) we overlaid a 420 m by
420 m grid over each species’ merged 100% MCPs. We chose this size
because it was approximately the size of 2–3 city blocks (depending onneighborhood) and determined it to be an appropriate resolution toprovide a practical, repeatable application for wildlife managers. We
classified these cells into four different groups based on the telemetry
locations and iNaturalist observations that fell within the cell; in essence, cell classifications indicated where the two data sets overlapped(or not) spatially. The four groups were as follows: YY, or cells that
included both iNaturalist and telemetry locations, YN, or cells that included iNaturalist locations but no telemetry locations, NY, or cells that
included no iNaturalist locations but included telemetry locations, andNN, or cells that had no iNaturalist or telemetry locations (Fig. 2).
To compare the location of iNaturalist observations to our radiotelemetry locations, we calculated the similarity, sensitivity, and specificity of the iNaturalist data (Nagy, Weckel, Toomey, Burns, & Peltz,2012). Similarity (matching classifications/total number of cells) considered a “matching classification” to be a cell where the iNaturalist andtelemetry classifications matched (i.e., YY or NN) (Nagy et al., 2012).
We calculated the sensitivity (cells containing both iNaturalist andtelemetry locations/all cells containing iNaturalist locations) to determine the percentage of “true positives”, or the probability that amember of the public observed and reported a red fox or coyote (basedon if red foxes or coyotes were observed in the cell via radio-telemetry)(Nagy et al., 2012). Finally, we calculated the specificity (cells containing no iNaturalist or telemetry locations/all cells lacking iNaturalist
locations) to determine the percentage of “true negatives”, or the
probability that a member of the public did not observe a red fox or
coyote (based on if red foxes or coyotes were not observed in the cell
via radio-telemetry) (Nagy et al., 2012).
We analyzed the factors that drove each individual cell’s classification using a suite of variables (human density, road density, percent
of our five habitat types) in an AIC modeling framework with 8 a-priori
models (Table 1). We tested for collinearity and correlation using a
Pearson’s test at the p < 0.05 level to determine that no independent
variables were significantly correlated. We fit a binomial model using
cell classification as our dependent variable. For example, in our first
analysis, we compared all cells classified as YY to all cells not classified
as YY (i.e., YN, NY, NN). We analyzed each cell classification

We used iNaturalist (www.inaturalist.org) to collect communitygenerated location data for red foxes and coyotes in our study area fromMay 2015 to December 2016. We created a custom iNaturalist project(http://www.inaturalist.org/projects/uw-urban-canid-project) by specifying our target geographic location (Madison, WI, USA) and species
of interest (red fox and coyote). We promoted the project through the
UW Urban Canid Project Facebook Page (https://www.facebook.com/
uwurbancanidproject/), website (http://uwurbancanidproject.weebly.
com/reporting-an-observation.html), and local media. Each community-generated observation had information about each uniquesighting, including the species observed, time, location, and 10 optionaland brief questions about the observation. A detailed description of how
we created the iNaturalist project can be found in Supplementary
Material 1.
We omitted any observations from the same user that occurred
within 12 h of the previous observation to attempt to reduce the effectsof observers making repeated observations of the same animal. We did
not have the ability to verify that an iNaturalist-reported red fox or
coyote was truly a red fox or coyote. However, we are relatively confident that all iNaturalist reports were accurate for a number of reasons.
First, we screened all iNaturalist observations at least weekly and insome 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 where located we were also able to document
presence of a radio-collared red fox or coyote in the same area.
2.4. Data variables
We used a Geographic Information System (GIS; ESRI. ArcGIS
Desktop: Release 10.4. Redlands, CA: Environmental Systems ResearchInstitute) to create a habitat map of our study area based on National
Land Cover Database (NLCD) from 2011 (30 × 30 m resolution; Albers
Conical Equal Area projection; 1983 North American datum), especiallypertaining to human development. Using Gosselink, Van Deelen,
Warner, and Joselyn (2003) for guidance, we omitted or combined theoriginal 20 land cover classifications in the 2011 NLCD to arrive at the 5land cover categories used in our analysis: developed open space (turffields, 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) (Fig. 1).
We used only 5 classes for ease of interpretation if wildlife managers orothers wanted to apply our NLCD analysis to another urban area. We
obtained local population density (humans/km2; hereafter HUMN) datafrom the National Historical Geographic Information System (NHGIS).
Estimates were based geographically on 2011 U.S. Census blocks using
the most current available data. We calculated road density using local
road data (Dane County Land Information Office, 2007; hereafterROAD). We categorized seasons based on biologically meaningful periods of life for each species: breeding (red fox = November-February,
coyote = December-March), pup-rearing (red fox = March-June,
coyote = April-July), and non-breeding (red fox = July-October,coyote = August-November) (Cypher, 2003, Bekoff & Gese, 2003).
2.5. Statistical analyses
To test for variation in the time of reports between red foxes andcoyotes, we converted the time of each report to radians and plotted its
kernel density distribution using the overlap package (Ridout & Linkie,2009) in the R statistical program (version 2.11.1; R Foundation for365

Landscape and Urban Planning 189 (2019) 362–371

M.A. Mueller, et al.

Fig. 2. Grid encompassing coyote (top) and red fox (bottom) 100% MCP’s in Madison, WI, 2015–2016. Grid cells were classified based on the presence (or lack) of
iNaturalist reports and radio-telemetry locations within each cell. YY indicates cells that contain both iNaturalist and telemetry locations, YN contain only iNaturalist
locations, NY contain only telemetry locations, and NN contain neither iNaturalist nor telemetry locations. (For interpretation of the references to colour in this figurelegend, the reader is referred to the web version of this article.)

individually, resulting in 8 separate statistical analyses (4 tests for eachspecies). We considered the top models to be those within < 0.90 of
the cumulative on AICc weights (w) (Burnham & Anderson, 2003). In
the event there clearly was not one top model, we used model averaging
across our top candidate models (Arnold, 2010). We evaluated the explanatory power of our independent variables based on the 90% confidence interval for each variable.

3. Results
3.1. Radio-telemetry data
We captured, marked, and radio-tracked 11 red foxes (nmale = 7,
nfemale = 4) and 11 coyotes (nmale = 7, nfemale = 4) from May 2015 to
December 2016. We collected 1312 total locations (all from VHF
366

Landscape and Urban Planning 189 (2019) 362–371

M.A. Mueller, et al.

Table 1A-priori models based on authors’ knowledge and observations of study area and study species explaining whether a red fox or coyote was observed in a 420 m by
420 m grid cell using radio telemetry, citizen science observations, both, or neither in Madison, WI, 2015–2016.
Name

Variables

Description

Human Density

HUMN

Human Habitat

MODR + HIGH

Roads
Human UseRed Fox Habitat

ROAD
ROAD + HUMN
MODR + OPEN

Coyote Habitat

NATR

Human and Red Fox
HabitatHuman and Coyote Habitat

MODR + HIGH + OPEN

Cell classification will be driven by where residential human density is the highest because more humans result in morepotential observers.
Cell classification will be driven by habitat on the landscape that is developed by humans because there are morehumans present to report canids.
Cell classification will be driven by road density because roads may increase a reporter’s ability to observe canids.
Cell classification will be driven by areas that humans use the most, including areas they occupy and travel.
Cell classification for red foxes will be driven by the amount of preferred fox habitat within a cell because foxes will use
these cells at differential rates.
Cell classification for coyotes will be driven by the amount of preferred coyote habitat within a cell because coyotes will
use these cells at differential rates.
Cell classification for foxes will be driven by the amount of human and fox habitat within each cell because the amountof each habitat may alter how frequently humans and foxes use the cell.
Cell classification for coyotes will be driven by the amount of human and coyote habitat within each cell because theamount of each habitat may alter how frequently humans and coyotes use the cell.

NATR + MODR + HIGH

collars) for red foxes ( x = 131.2 per individual, range = 2–458), and4382 total locations (2071 VHF and 2311 GPS locations) and for coyotes ( x = 393.2. range = 36 – 2311). Mean time on air for individual
red foxes was 243.6 ( ± 71.6 SE) days, and for individual coyotes was
282.3 ( ± 60.7 SE) days. We excluded 3 foxes (nmale = 2, nfemale = 1)
and 1 male coyote from further analyses because of small sample sizedue to mortality or collar failure.

Table 2
Results of cell classification for red fox and coyote target areas based on
iNaturalist and radio-telemetry locations within 420 m by 420 m grid cells in
Madison, WI, 2015–2016. YY indicates cells that contain both iNaturalist andtelemetry locations, YN contain only iNaturalist locations, NY contain only
telemetry locations, and NN do not contain either iNaturalist or telemetry locations.

Red Fox
Coyote3.2. iNaturalist responses

0.12

We collected 513 community-generated observations of red foxes
and 348 community-generated observations of coyotes by 544 uniqueiNaturalist users. Of the 861 locations, 392 (45.5%) fell within the
100% MCP focus areas (225 red fox, 167 coyote). The proportion of
iNaturalist reports occurring in each of the 5 NLCD land use types
varied for both coyotes (Χ24 = 129.08, p < 0.001) and red foxes(Χ24 = 396.98, p < 0.001) (Supplementary Material 2a).
The number of reports varied seasonally for coyotes (F2,16 = 4.851,
p = 0.023) but not for red foxes (F2,16 = 2.388, p = 0.124)(Supplementary Material 2b). We collected significantly fewer reportsof coyotes during the pup-rearing season compared to the breeding
season (p = 0.018). iNaturalist reporting activity appeared to be largely
diurnal, with peaks for both species occurring at morning crepuscular
hours. Red fox and coyote reports showed strong temporal overlap(Δ4 = 0.805) (Fig. 3). On average, observers estimated and reported
coyotes ( x = 22.99 m ± 1.43 SE) at significantly farther distances
than red foxes ( x =17.86 m ± 0.94 SE) (t606 = 3.14, p = 0.002)

0.06

0.08

0.10

75 (28)62 (16)17 (6)
34 (9)

85 (32)134 (35)

92 (34)153 (40)269
383

For our models determining factors driving YY cells for red foxes,
the model human and red fox habitat (w = 0.68) had over twice asmuch explanatory power as the next closest model (red fox habitat,w = 0.30) (Table 3). After averaging models and calculating unconditional parameter estimates, MODR and OPEN had substantial positiveeffects on the number of YY cells (Table 4).
When determining the factors driving YN cells, our top three models
were human and red fox habitat (w = 0.40), human habitat (w = 0.27),
and red fox habitat (w = 0.26) (Table 3). Using model averages andunconditional parameter estimates, none of our modeled effects were
substantial based on 90% confidence intervals.
When determining the factors driving NY cells, our top models were
human density (w = 0.46) and human use (w = 0.30) (Table 3). Using
model averaging and unconditional parameter estimates, no effects
were deemed substantial using 90% confidence intervals.
When determining the factors driving NN cells, our top models were
human and red fox habitat (w = 0.88) and red fox habitat (w = 0.12)(Table 3). After model averaging and calculating unconditional

0.04

Kernel density

Total

3.4. Red fox comparison

0.02

18:00

NN (%)

In our target areas for each canid species, we classified individual
cells based on the number of iNaturalist and telemetry points that fellwithin a particular cell (red fox = 269, coyote = 383) (Table 2, Fig. 2).
The similarity value (matching classifications/total number of cells)
was higher for red foxes (0.651) than coyotes (0.561). Sensitivity (cells
containing both iNaturalist and telemetry locations/all cells containing
iNaturalist locations) was higher for red foxes (0.815) than coyotes(0.646). Specificity (cells containing no iNaturalist or telemetry locations/all cells lacking iNaturalist locations) was higher for coyotes(0.531) than red foxes (0.520).

0.00

12:00

NY (%)

3.3. Comparison of radio-telemetry to iNaturalist data

Coyote6:00

YN (%)(Supplementary Material 2c).

Red Fox

00:00

YY (%)

24:00

Time of Day

Fig. 3. Kernel density of reports by time of day of red foxes and coyotes via
iNaturalist in Madison, WI, 2015–2016 (Δ4 = 0.805). Grey area refers to temporal overlap between red fox and coyote observations.
367

Landscape and Urban Planning 189 (2019) 362–371

M.A. Mueller, et al.

Table 3Results of a-priori modeling explaining whether a red fox was observed in a
420 m by 420 m grid cell using radio telemetry, citizen science observations,both, or neither in Madison, WI, 2015–2016. YY indicates cells that containboth iNaturalist and telemetry locations, YN contain only iNaturalist locations,
NY contain only telemetry locations, and NN do not contain either iNaturalist or
telemetry locations.

Table 5Results of a-priori modeling explaining whether a coyote was observed in a
420 m by 420 m grid cell using radio telemetry, citizen science observations,both, or neither in Madison, WI, 2015–2016. YY indicates cells that containboth iNaturalist and telemetry locations, YN contain only iNaturalist locations,
NY contain only telemetry locations, and NN do not contain either iNaturalist or
telemetry locations.

YY

YY

Model

K AICc

ΔAIC

w

Ʃw

Model

K AICc

ΔAIC

w

Ʃw

Human and red fox habitat
Red fox habitat
Human habitat
Human use
Null
Road density
Human density

3 301.53
2 303.21
2 308.37
2 319.24
0 320.42
1 320.70
1 320.82

0.00
1.68
6.84
17.71
18.89
19.18
19.29

0.68
0.30
0.02
0.00
0.00
0.00
0.00

0.68
0.98
1.00
1.00
1.00
1.00
1.00

Human and coyote habitat
Coyote habitat
Human habitat
Null
Human density
Road density
Human use

3 358.51
2 364.40
2 368.74
0 378.82
1 379.81
1 380.82
2 381.77

0.00
5.89
10.24
20.31
21.30
22.31
23.26

0.94
0.05
0.01
0.00
0.00
0.00
0.00

0.94
0.99
1.00
1.00
1.00
1.00
1.00

YN

YN

Model

K AICc

ΔAIC

w

Ʃw

Model

K AICc

ΔAIC

w

Ʃw

Human and red fox habitat
Human habitat
Red fox habitat
Road density
Human use
Null
Human density

3 120.97
2 121.79
2 121.80
1 125.14
2 127.11
0 128.81
1 129.86

0.00
0.81
0.82
4.17
6.14
7.83
8.89

0.40
0.27
0.26
0.05
0.02
0.00
0.00

0.40
0.67
0.93
0.98
1.00
1.00
1.00

Road density
Human use
Human habitat
Human and coyote habitat
Coyote habitat
Null
Human density

1 242.83
2 244.80
2 246.65
3 247.87
2 251.92
0 254.62
1 254.94

0.00
1.96
3.82
5.04
9.09
11.79
12.10

0.62
0.23
0.09
0.05
0.01
0.00
0.00

0.62
0.85
0.94
0.99
1.00
1.00
1.00

NY

NY

Model

K AICc

ΔAIC

w

Ʃw

Model

K AICc

ΔAIC

w

Ʃw

Human density
Human use
Null
Human habitat
Road density
Red fox habitat
Human and red fox habitat

1 335.76
2 336.62
0 337.62
2 339.33
1 339.57
2 339.75
3 340.58

0.00
0.87
1.86
3.58
3.81
3.99
4.82

0.46
0.30
0.15
0.08
0.07
0.06
0.03

0.46
0.76
0.91
0.99
1.00
1.00
1.00

Human and coyote habitat
Coyote habitat
Human habitat
Road density
Human use
Null
Human density

3 510.86
2 530.74
2 538.17
1 567.10
2 568.77
0 584.69
1 584.75

0.00
19.88
27.31
56.24
57.91
73.83
73.89

1.00
0.00
0.00
0.00
0.00
0.00
0.00

1.00
1.00
1.00
1.00
1.00
1.00
1.00

NN

NN

Model

K AICc

ΔAIC

w

Ʃw

Model

K AICc

ΔAIC

w

Ʃw

Human and red fox habitat
Red fox habitat
Human habitat
Human use
Road density
Human density
Null

3 310.76
2 314.73
2 322.11
2 332.49
1 342.24
1 344.42
0 347.60

0.00
3.97
11.35
21.74
31.48
33.66
36.84

0.88
0.12
0.00
0.00
0.00
0.00
0.00

0.88
1.00
1.00
1.00
1.00
1.00
1.00

Human and coyote habitat
Coyote habitat
Human habitat
Road density
Human use
Null
Human density

3 568.31
2 605.95
2 625.25
1 682.78
2 683.34
0 684.38
1 686.28

0.00
37.65
56.94
114.48115.04116.07117.97

1.00
0.00
0.00
0.00
0.00
0.00
0.00

1.00
1.00
1.00
1.00
1.00
1.00
1.00

parameter estimates, both MODR and OPEN had negative effects on thenumber of NN cells, while HIGH had a positive effect (Table 4).

Table 4
Variables that had a substantial effect on locating red foxes and coyotes in
Madison, WI, 2015–2016. YY indicates cells that contain both iNaturalist andtelemetry locations, YN contain only iNaturalist locations, NY contain only
telemetry locations, and NN do not contain either iNaturalist or telemetry locations.

3.5. Coyote comparison
For coyotes, the model human and coyote habitat (w = 0.94) was
the only model with substantial support when fitted to cells determining the factors driving YY cells (Table 5). In this model, NATR
had a substantial positive effect on the number of YY cells and HIGH
had a substantial negative effect (Table 4) based on 90% confidence
intervals.
When determining the factors driving YN cells, our top model, roaddensity (w = 0.62), had nearly three times the explanatory value of ournext closest model (human use, w = 0.23) (Table 5). After model
averaging and calculating unconditional parameter estimates, ROAD
had a substantial positive effect on the response variable (Table 4).
When determining the factors driving NY cells, our model humanand coyote habitat (w = 1.0) was the only model that had substantial
support (Table 5). In this model, NATR had a substantial positive effect

Substantial Effects (Effect Direction and Value)
Cell Classification

Red Foxes

Coyotes

YY

MODR (1.7)
OPEN (2.88)
No substantial effects
No substantial effects

NATR (0.001)
HIGH (−0.008)
ROAD (0.001)
NATR (0.009)
MODR (−0.02)
HIGH (−0.007)
NATR (−0.01)
HIGH (0.01)

YN
NY
NN

MODR (−1.79)
OPEN (−3.75)
HIGH (2.52)

368

Landscape and Urban Planning 189 (2019) 362–371

M.A. Mueller, et al.

on the number of NY cells, while MODR and HIGH had substantial
negative effects (Table 4) based on 90% confidence intervals.
When determining the factors driving NN cells, human and coyote
habitat (w = 1.00) again was the only model with substantial support(Table 5). In this model, NATR had a substantial negative effect on thenumber of NN cells, while HIGH had a substantial positive effect(Table 4).

may suggest that community-scientist portals like iNaturalist could be
more effective at recording species that frequently share the same areas
with humans.
The factors driving similarity rates (YY and NN cells) were stronger
for coyotes than for red foxes. For coyotes, our top performing modelfor YY cells had a higher proportion of preferred coyote habitat (NATR)
and a lower proportion of highly developed areas within the cell.
Classification of NN cells were driven by the same factors as YY cells,
but the direction of their effects was inversed. The trends for red foxes
were more equivocal than coyotes. For YY cells, the top performing
model suggested that the proportion of preferred land cover (MODR)
had the most substantial effect on the variation in cell classification. NN
cells was also driven by the amount of preferred land cover within thecell, but similar to coyotes, the direction of the effect was inversed. The
inverse correlation between YY and NN classifications for both coyotes
and foxes suggests that species-specific land cover selection and use ofdeveloped land covers primarily drives the accuracy of these classifications.
For YN cells (containing iNaturalist reports of coyotes, but no telemetry locations), we hypothesized that categorization would be driven
by a potential increase in the ability of an observer to see urban canids,
even if they did not frequently use the area. The top performing model
for coyotes suggested that road density best explained the variation inthese data. Roads may allow more people access to a given area,
thereby increasing the number of potential observers, or roads may
simply have better lines of sight and increase a coyote’s chances ofbeing observed in an area. Conversely, in NY cells the proportion ofnatural area within the cell was the only factor with a substantial positive effect on classification for coyotes. The classification of YN and
NY cells were again more equivocal for red foxes. Previous analyses of
red fox habitat selection suggested that red foxes displayed a high degree of individual variation in habitat selection in our study area
(Mueller et al., 2018). If cell classification is in part driven by land
cover selection, this variation may limit our ability to detect substantial
effects of our model variables. We did not specifically investigate thevariation in detectability by land cover type, but more accurate assessments of the variability in natural land covers (i.e., forest vs.
grassland) may provide more information on what factors drive theobservations of urban coyotes among different land use types. It is also
important to note that YN cells may be confounded by urban canids thatwere not wearing collars being seen and reported by community scientists. This is more likely for foxes, as we had collared individuals fromeach of the coyote packs in the study and individuals tend to use thesame areas.
All citizen science data have certain limitations, and iNaturalist is
no exception. For example, we experienced uneven sampling—both
spatially and temporally—and it is important to understand the limitations of this data collection method and how they may be affected by
the life history of a target species. In our case, the spatial sampling bias
towards developed areas did not appear to negatively impact red fox
reports in the same way it affected coyote reports. To account for the
sampling bias, citizen-science monitoring using defined transects(Burnham, Anderson, & Laake, 1980, Anderson, Marques, Shoo, &Williams, 2015) or camera traps (Foster & Harmsen, 2012, Allen,
Wittmer, Setiawan, Jaffe, & Marshall, 2016, Rich, Miller, Robinson,
McNutt, & Kelly, 2017) in otherwise poorly-sampled areas could be
used to augment the data. For more cryptic species, correction factors
or lures could be implemented in different habitats and conditions topotentially offset poor detection.
One of the keys to our success in obtaining a large amount of
iNaturalist observations was public outreach. When promoting
iNaturalist to potential users, researchers and managers may want tofocus on user rewards and incentives to increase interest and participation. This can help compensate for a general contribution to scienceoften not being enough to motivate participation (Sullivan et al., 2009,
Hochachka et al., 2012). For example, eBird uses healthy competition

4. Discussion
We compared locations of urban coyotes and red foxes collected via
telemetry to sightings reported by community scientists via iNaturalist
to evaluate the potential of using citizen science as a management tool.
We found robust similarity rates (YY and NN) for both foxes (65%) andcoyotes (56%) between the datasets, suggesting that using citizen science may be a cost-effective method for collecting data on the distribution of urban wildlife. Citizen science allows for the collection ofdata across large spatial and temporal scales (Hochachka et al., 2012;
Lepczyk et al., 2009), but applications and programs such as iNaturalist
also allow managers and researchers to collect data for specific areas or
species as well. By using social media we were able to alert community
scientists to our interest in their sightings of urban canids in Madison,
WI and collect over 800 confirmed sightings in less than two years. The
experience of many urban stakeholders is shaped by human-wildlife
conflicts (Curtis & Hadidian, 2010, Hundenko et al., 2010), but participation in citizen science programs can create positive experiences(Dickinson et al., 2010), while also collecting data that can be used bymanagers and researchers (e.g., Connelly et al., 2012). The findings
from our study suggest value in using citizen science data for managers
and researchers, but further research is needed. For example, it is important to understand where citizen science reports are common andwhere they are lacking so methodology can be changed and tested to
improve reporting data, or at the very least understand sampling biases.
There appeared to be variability in how well iNaturalist reports
reflected the areas frequently used by red foxes and coyotes determined
via radio telemetry. Our hypothesis that most iNaturalist reports would
come from areas with increased human and road density and during
diurnal hours was only partially supported. Most iNaturalist reports forboth red foxes (about 90%) and coyotes (about 80%) occurred in developed open and moderately developed areas, but not high developed
areas. Red fox and coyotes seemed to avoid high developed areas, orareas with increased human and road density (Mueller et al., 2018).
Our hypothesis that most iNaturalist reports would occur during diurnal
hours was supported, and the temporal overlap of sightings for foxes
and coyotes was high.
Our hypothesis that iNaturalist-generated observations of red foxes
would have greater overlay with radio-collar location data than forcoyotes was supported by our data. Similarity rates (YY and NN) forfoxes occurred 65% of the time and 56% of the time for coyotes.
Sensitivity—the rate at which community-generated observations
matched up with known canid locations—was higher for red foxes,
suggesting that community scientists see and report the presence of red
foxes more than coyotes. Selectivity—the rate at which communitygenerated locations match up with areas with little to no use by canids—was similar for coyotes and foxes, suggesting that even though
coyotes and foxes used different areas within the urban ecosystem,
reports from iNaturalist users assess areas of little to no use by both
species at similar rates. Similarity, sensitivity and selectivity appeared
to be reflective of human development and driven by the selection ofland cover types used by both canid species, as most reports of coyotes
and red foxes occurred in moderately developed areas. Red foxes in
Madison, WI selected for developed landscapes more so than coyotes(Mueller et al., 2018) and may have more opportunities to be observed.
Coyotes, on the other hand, are known to spatiotemporally avoid humans in urban ecosystems (Gehrt, Brown, & Anchor, 2011), possibly
limiting the potential for the public to observe and report them. This369

Landscape and Urban Planning 189 (2019) 362–371

M.A. Mueller, et al.

between birders to motivate data collection and saw marked increases
in participation since shifting its focus in this manner (Sullivan et al.,2009). It is also important to maintain interest over time, which could
allow researchers and managers to potentially observe trends in counts/
abundance of urban wildlife, incidents of human-wildlife conflicts, andchanges in the local distribution of species.

inquiry. In D. J. Decker, S. J. Riley, & W. F. Siemer (Eds.). Human dimensions of wildlife
management (pp. 122–138). (2nd ed.). Baltimore, USA: Johns Hopkins University
Press.
Conover, M. R., & Dinkins, J. B. (2012). Human dimensions of abundant wildlife management. In D. J. Decker, S. J. Riley, & W. F. Siemer (Eds.). Human dimensions ofwildlife management (pp. 177–188). (2nd ed.). Baltimore, USA: Johns Hopkins
University Press.
Cooper, C. B., Dickinson, J., Phillips, T., & Bonney, R. (2007). Citizen Science as a tool for
conservation in residential ecosystems. Ecology and Society, 12(2), 11.
Curtis, P. D., & Hadidian, J. (2010). Responding to human-carnivore conflicts in urban
areas. In S. D. Gehrt, S. P. D. Riley, & B. L. Cypher (Eds.). Urban carnivores: Ecology,
conflict, and conservation (pp. 201–211). Baltimore, USA: Johns Hopkins University
Press.
Cypher, B. L. (2003). Foxes (Vulpes species). In G. A. Feldhamer, B. C. Thompson, & J. A.
Chapman (Eds.). Wild mammals of North America: Biology, management, and conservation (pp. 511–546). (2nd ed.). Baltimore, USA: Johns Hopkins University Press.
Dane County Land Information Office. (2007). Road Centerlines and Address Ranges,
Dane County (WI): Dane County Land Information Office, Madison, WI.
Dickinson, J. L., Zuckerberg, B., & Bonter, D. N. (2010). Citizen science as an ecologicalresearch tool: Challenges and benefits. The Annual Review of Ecology, Evolution, andSystematics, 41, 149–172.
Foster, R. J., & Harmsen, B. J. (2012). A critique of density estimation from camera-trap
data. The Journal of Wildlife Management. 76, 224–236.
Gehrt, S. D., & Riley, S. P. D. (2010). Coyotes (Canis latrans). In S. D. Gehrt, S. P. D. Riley,& B. L. Cypher (Eds.). Urban carnivores: Ecology, conflict, and conservation (pp. 79–95).
Baltimore, USA: Johns Hopkins University Press.
Gehrt, S. D., Anchor, C., & White, L. A. (2009). Home range and landscape use of coyote ina metropolitan landscape: Conflict or coexistence. Journal of Mammalogy, 90(5),1045–1057.
Gehrt, S. D., Riley, S. P. D., & Cypher, B. L. (2010). Preface. In S. D. Gehrt, S. P. D. Riley, &B. L. Cypher (Eds.). Urban carnivores: Ecology, conflict, and conservation (pp. ix–x).
Baltimore, USA: Johns Hopkins University Press.
Gehrt, S. D., Brown, J. L., & Anchor, C. (2011). Is the urban coyote a Misanthropic
Synanthrope? The case from Chicago. Cities and the Environment, 4(1), 3–23.
Gosselink, T. E., Van Deelen, T. R., Warner, R. E., & Joselyn, M. G. (2003). Temporal
habitat partitioning and spatial use of coyotes and red foxes in east-central Illinois.
Journal of Wildlife Management, 67, 90–103.
Hochachka, W. M., Fink, D., Hutchinson, R. A., Sheldon, D., Wong, W., & Kelling, S.
(2012). Data-intensive science applied to broad-scale citizen science. Trends in
Ecology and Evolution, 27(2), 130–137.
Hundenko, H. W., Siemer, W. F., & Decker, D. J. (2010). Urban carnivore conservationand management: The human dimension. In S. D. Gehrt, S. P. D. Riley, & B. L. Cypher(Eds.). Urban carnivores: Ecology, conflict, and conservation (pp. 21–33). Baltimore,
USA: Johns Hopkins University Press.
Lauber, B. T., Decker, D. J., Leong, K. M., Chase, L. C., & Schulsler, T. M. (2012).
Stakeholder engagement in wildlife management. In D. J. Decker, S. J. Riley, & W. F.
Siemer (Eds.). Human dimensions of wildlife management (pp. 139–156). (2nd ed.).
Baltimore, USA: Johns Hopkins University Press.
Lepczyk, C. A., Boyle, O. D., Vargo, T. L., Gould, P., Jordan, R., Liebenberg, L., ...
Vaughan, H. (2009). Citizen science in ecology: The intersection of research andeducation. Bulletin of the Ecological Society of America, 90(3), 308–317.
Lurracea, E. S., Brussard, P. F., Jaeger, M. M., & Barrett, R. H. (2007). Coyotes, cameras,
and the assumption of equal detectability. Journal of Wildlife Management, 71,
1682–1689.
Lynam, A. J., Jenks, K. E., Tantipisanuh, N., Chutipong, W., Ngoprasert, D., Gale, G. A., ...
Cutter, P. (2013). Terrestrial activity patterns of wild cats from camera-trapping.
Raffles Bulletin of Zoology, 61(1), 407–415.
McCleery, R. A., Moorman, C. E., Wallace, M. C., & Drake, D. (2012). Managing urban
environments for wildlife. In N. J. Silvy (Ed.). The wildlife techniques manual:Management (pp. 169–191). (2nd ed.). Baltimore, USA: Johns Hopkins University
Press.
McCleery, R. A., Mooreman, C. E., & Peterson, M. N. (2014). Introduction. In R. A.
McCleery, C. E. Moorman, & M. N. Peterson (Eds.). Urban wildlife conservation (pp. 1–
10). New York, USA: Springer Publishing.
McKinney, M. L. (2002). Urbanization, biodiversity, and conservation. BioScience, 52,
883–890.
McKinney, M. L. (2006). Urbanization as a major cause of biological homogenization.
Biological Conservation, 127, 247–260.
Millspaugh, J. J., Gitzen, R. A., Belant, J. L., Kays, R. W., Keller, B. J., Kesler, D. C., Rota,
C. T., Schulz, J. H., & Bodinof, C. M. (2012). Analysis of radiotelemetry data (7th ed.).
Baltimore, USA: Johns Hopkins University Press480–501.
Mueller, M. A., Drake, D., & Allen, M. L. (2018). Coexistence of coyotes (Canis latrans) andred foxes (Vulpes vulpes) in an urban landscape. PLoS One, 13, e0190971.
Nagy, C. M., Weckel, M. E., Toomey, A., Burns, C. E., & Peltz, J. (2012). Validation of acitizen science-based model of coyote occupancy with camera traps in suburban andurban New York, USA. Wildlife Biology in Practice, 8(1), 23–35.
Nickerson, C., Ebel, R., Borchers, A., & Carriazo, F. (2017). Major uses of land in the
United States, 2007. U.S. Department of Agriculture. https://wedocs.unep.org/
bitstream/handle/20.500.11822/17914/Major_uses_of_land_in_the_United_States_2007.pdf?sequence=1.
Rich, L. N., Miller, D. A. W., Robinson, H. S., McNutt, J. W., & Kelly, M. J. (2017).
Carnivore distributions in Botswana are shaped by resource availability and intraguild species. Journal of Zoology. https://doi.org/10.1111/jzo.12470.
Ridout, M. S., & Linkie, M. (2009). Estimating overlap of daily activity patterns fromcamera trap data. Journal of Agricultural Biological and Environmental Statistics, 14,
322–337.

5. Conclusions
Stakeholder engagement—incorporating those affected by management actions into the management decision making process—iscritical for successful wildlife management (Lauber, Decker, Leong,
Chase, & Schulsler, 2012, Conover & Dinkins, 2012), and becomes increasingly complicated in urban areas with more people due to theincreased stakeholder interest in local wildlife management (McCleery,
Moorman, Wallace, & Drake, 2012). Citizen science opportunities
provided through portals like iNaturalist have the potential to provide a
valuable tool for wildlife managers in urban ecosystems to collect information for proactive management in a cost-effective manner. Involving stakeholders in data collection also provides an avenue toeducate and engage stakeholders so they are informed and vested in the
management issue at hand, and thereby in better position to support
management actions (McCleery et al., 2012).
The findings from our study suggest value in using citizen science
data for managers and researchers, but further research is needed tounderstand sampling biases. For example, it is important to understandwhere citizen science reports are common and where they are lacking.
Methodology may need to be changed and tested to improve reporting
data, or at the very least, those using citizen science data need an understanding about how sampling biases may affect management recommendations.
Acknowledgements
We thank the University of Wisconsin-Madison Graduate School andDepartment of Forest and Wildlife Ecology, and Milwaukee County
Parks for funding our research. We thank K. Lindau, H. Hovanec, M.
Torkelson, and the many other students and members of the public who
assisted with trapping and field work.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.org/10.1016/j.landurbplan.2019.04.023.
References
Allen, M. L., Wittmer, H. U., Setiawan, E., Jaffe, S., & Marshall, A. J. (2016). Scent
marking in Sunda clouded leopards (Neofelis diardi): Novel observations close a key
gap in understanding felid communication behaviours. Scientific Reports, 6, 35433.
Anderson, A. S., Marques, T. A., Shoo, L. P., & Williams, S. E. (2015). Detectability inaudio-visual surveys of tropical rainforest birds: The influence of species, weather,and habitat characteristics. PLoS One, 10(6), e0128464.
Arnold, T. W. (2010). Uninformative parameters and model selection using Akaike’s
Information Criterion. Journal of Wildlife Management, 74(6), 1175–1178.
Bateman, P. W., & Fleming, P. A. (2012). Big city life: Carnivores in urban environments.
Journal of Zoology, 287, 1–23.
Bekoff, M., & Gese, E. M. (2003). Coyote (Canis latrans). In G. A. Feldhamer, B. C.
Thompson, & J. A. Chapman (Eds.). Wild mammals of North America: Biology, management, and conservation (pp. 467–481). (2nd ed.). Baltimore, USA: Johns Hopkins
University Press.
Bonney, R., Cooper, C. B., Dickinson, J., Kelling, S., Phillips, T., Rosenberg, K. V., & Shirk,J. (2009). Citizen science: A developing tool for expanding science knowledge andscientific literacy. Bioscience, 59(11), 977–984.
Burnham, K. P., & Anderson, D. R. (2003). Model selection and multimodel inference: A
practical information-theoretic approach. Springer Science & Business Media 2003.
Burnham, K. B., Anderson, D. R., & Laake, J. L. (1980). Estimation of density from linetransect sampling of biological populations. Wildlife Monographs, 72, 3–202.
Calenge, C. (2011). Home range estimation in R: the adehabitatHR package. Office national de la classe et de la faune sauvage: Saint Benoist, Auffargis, France.
Connelly, N. A., Seimer, W. F., & Decker, D. J. (2012). Methods of human dimensions

370

Landscape and Urban Planning 189 (2019) 362–371

M.A. Mueller, et al.
Ruette, S., Stahl, P., & Albaret, M. (2003). Applying distance-sampling methods to spotlight counts of red foxes. Journal of Applied Ecology, 40, 32–43.
Soulsbury, C. D., Baker, P. J., Iossa, G., & Harris, S. (2010). Red foxes (Vulpes vulpes). In
S. D. Gehrt, S. P. D. Riley, & B. L. Cypher (Eds.). Urban carnivores: Ecology, conflict, andconservation (pp. 63–75). Baltimore, USA: Johns Hopkins University Press.
Soulsbury, C. D., & White, P. C. L. (2015). Human-wildlife interactions in urban areas: A
review of conflicts, benefits, and opportunities. Wildlife Research, 42, 541–553.
Sullivan, B. L., Wood, C. L., Iliff, M. J., Bonney, R. E., Fink, D., & Kelling, S. (2009). eBird:
A citizen-based bird observation network in the biological sciences. BiologicalConservation, 142, 2282–2292.
Schmutz, J. A., & White, G. C. (1990). Error in telemetry studies: Effects of animal
movement on triangulation. The Journal of Wildlife Management, 53(3), 506–510.
Wang, Y., Allen, M. L., & Wilmers, C. W. (2015). Mesopredator spatial and temporal
responses to large predators and human development in the Santa Cruz Mountains ofCalifornia. Biological Conservation, 190, 23–33.
Warren, P. S., Harlan, S. L., Boone, C., Lerman, S., Shochat, E., & Kinzig, A. (2010). Urban
ecology and human social organizations. In K. J. Gaston (Ed.). Urban ecology (pp.
172–201). New York, USA: Cambridge University Press.

371