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Diel Activity Patterns of Sympatric Mesopredators in a Suburban Preserve Network

John P. Vanek1,4*, Andrew U. Rutter2,5, Timothy S. Preuss2,6, Holly P. Jones1,3, and Gary A. Glowacki2

1Department of Biological Sciences, Northern Illinois University, 1425 W Lincoln Hwy, DeKalb, Illinois, 60115, USA. 2Lake County Forest Preserve District, 1899 W Winchester Rd, Libertyville, Illinois, 60048, USA. 3Institute for the Study of the Environment, Sustainability and Energy, Northern Illinois University, 1425 W Lincoln Hwy, DeKalb, Illinois, USA. 4New York Natural Heritage Program, SUNY College of Environmental Science and Forestry, 625 Broadway, Albany, NY, 12233 5Current Affiliation AR: Kansas Department of Health and Environment, 1000 SW Jackson St, Suite 400, Topeka, KS 66612-1367. 6Current Affiliation TP: Illinois Department of Natural Resources, 28W040 State Route 58, Elgin, Illinois, 60120, USA. *Corresponding Author.

Urban Naturalist, No. 59 (2023)

Mammalian mesopredators are among the most recognizable species in urban ecosystems. Subsidized by anthropogenic resources and released from traditional sources of mortality, many mesopredators thrive in urban areas. However, patterns are not universal and surprisingly little is known about the ecology and natural history of mesopredators across different urban landscapes. Here, we describe diel activity patterns and temporal overlap of the terrestrial mesopredator community inhabiting a suburban preserve network in the suburbs of Chicago. Using nearly a decade of systematic camera trapping across 55 suburban preserves and 200+ permanent camera locations, we found strong nocturnal patterns for Northern Raccoons (Procyon lotor; 74% of detections), Striped Skunks (Mephitis mephitis; 79% of detections), and Virginia Opossums (Didelphis virginiana; 82% of detections), with less than 4% of detections for these species occurring during the day. Coyotes (Canis latrans) were less nocturnal (58% of detections), with 23% of detections occurring within twilight hours and 18% of detections during the day. In contrast, Red Foxes (Vulpes vulpes) and Domestic Cats (Felis catus) were more cathemeral. Degree of overlap was highest between Northern Raccoons, Striped Skunks, and Virginia Opossums (>89%). Our results provide quantitative assessments of diel activity periods and temporal overlap for these species in an urban ecosystem.

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Diel Activity Patterns of Sympatric Mesopredators in a Suburban Preserve Network John P. Vanek1,4*, Andrew U. Rutter2,5, Timothy S. Preuss2,6, Holly P. Jones1,3, and Gary A. Glowacki2 Abstract - Mammalian mesopredators are among the most recognizable species in urban ecosystems. Subsidized by anthropogenic resources and released from traditional sources of mortality, many mesopredators thrive in urban areas. However, patterns are not universal and surprisingly little is known about the ecology and natural history of mesopredators across different urban landscapes. Here, we describe diel activity patterns and temporal overlap of the terrestrial mesopredator community inhabiting a suburban preserve network in the suburbs of Chicago. Using nearly a decade of systematic camera trapping across 55 suburban preserves and 200+ permanent camera locations, we found strong nocturnal patterns for Northern Raccoons (Procyon lotor; 74% of detections), Striped Skunks (Mephitis mephitis; 79% of detections), and Virginia Opossums (Didelphis virginiana; 82% of detections), with less than 4% of detections for these species occurring during the day. Coyotes (Canis latrans) were less nocturnal (58% of detections), with 23% of detections occurring within twilight hours and 18% of detections during the day. In contrast, Red Foxes (Vulpes vulpes) and Domestic Cats (Felis catus) were more cathemeral. Degree of overlap was highest between Northern Raccoons, Striped Skunks, and Virginia Opossums (>89%). Our results provide quantitative assessments of diel activity periods and temporal overlap for these species in an urban ecosystem. Introduction Mesopredators are among the most recognizable species in urban ecosystems (Adams 2016). Some, such as Canis latrans Say (Coyote), Procyon lotor Linnaeus (Northern Raccoon), Didelphis virginiana Kerr (Virginia Opossum) and Mephitis mephitis Schreber (Striped Skunk), are well known urban adaptors or exploiters (Adams 2016, Feldhamer et al. 2003, Gehrt et al. 2010). Subsidized by anthropogenic resources and largely released from traditional sources of depredation (e.g., hunting pressure) and competition (e.g., due to human food sources), urban mesopredator populations can have higher survival, fecundity, body mass, and density than non-urban populations (Gehrt et al. 2010, 2011, Graser et al. 2012, Prange and Gehrt 2004, Prugh et al. 2009, Šálek et al. 2015, Wright et al. 2012). As an extreme example, Riley et al. (1998) reported Northern Raccoon densities up to 100 times that of non-urban populations, and Wright et al. (2012) found that urban Virginia Opossums weighed 34% more than non-urban opossums. Given these factors, mesopredators in urban ecosystems are often a source of human-wildlife conflict (e.g., by depredating pets or spreading disease) and can also negatively impact other species of conservation concern, such as nesting turtles (Adams 2016, Gibbons 2000, Urbanek et al. 2016). One way that urban mammals are hypothesized to persist in urban ecosystems is by avoiding humans (Bateman and Fleming 2012, Gaynor et al. 2018, Nix et al. 2018, Patten et al. 2019, Ritzel and Gallo 2020, Wang et al. 2015) which in turn may impact biotic interactions (Guiden et al. 2019). This avoidance can occur both spatially (e.g., using greenspaces within the urban matrix), temporally (i.e., being active when humans are not, such as at night), or both. For example, urban Coyotes tend to select natural habitats (e.g., urban woodlands), but may leave the safety of these undeveloped refugia to forage in densely populated neighborhoods at night (Gehrt et al. 2011, Grinder and Krausman 2001). In non-urban areas, Northern Raccoons, Virginia Opossums, and Striped Skunks are highly nocturnal (e.g., Greenwood 1982; Ryser 1995; Neiswenter et al. 2010; Lesmeister et al. 2015) Similarly, non-urban Coyotes and Vulpes vulpes Linnaeus (Red Fox) tend to be mostly nocturnal, but also exhibit crepuscular and diurnal activity, particularly in areas with minimal human disturbance (Andelt 1985, Bekoff 1977, Díaz-Ruiz et al. 2016, Kitchen et al. 2000, Travaini et al. 1993). However, while the activity periods of many mesopredator species have been described in rural or wilderness settings, there has been comparatively less work documenting the activity periods of mesopredators in urban settings, such as Striped Skunks and Virginia Opossums (Ritzel and Gallo 2020, but see Mims et al. 2022). The rise of high-quality natural history data collection, facilitated by motion-sensitive camera traps, provides a historically unparalleled opportunity to quantify and statistically analyze these traditionally anecdotal and descriptive data (Tosa et al. 2021). Here, we report on the diel activity periods of the terrestrial mesopredator community (i.e., mammalian predators between 1 and 15 kg, sensu Buskirk 1999) inhabiting an extensive preserve network in the northeastern suburbs of Chicago, Illinois (species composition initially described by Cassel 2014 and Greenspan et al. 2018). Based on nearly a decade of long-term camera trap monitoring, our objectives were to 1) describe the diel activity patterns for the terrestrial mesopredator guild and 2) quantify the degree of temporal overlap between species. Given the highly urbanized landscape, we predicted to see strong nocturnal patterns similar to that of rural populations for Northern Raccoons, Virginia Opossums, Striped Skunks, Coyotes, and Red Foxes, with the strongest nocturnality for the former three species (Gaynor et al. 2018, Grinder and Krausman 2001, Lesmeister et al. 2015, Mueller et al. 2018, Wang et al. 2015). Thus, we expected these species would maintain their natural tendencies towards nocturnality as increased diurnal activity would increase potentially negative interactions with humans. Additionally, we contextualize the activity patterns of these native species with comparisons to sympatric Felis catus Linnaeus (Domestic Cat), for which activity patterns have already been described (Vanek et al. 2021b). Material and Methods Study Area Our study took place in Lake County, Illinois, a highly suburbanized county in the Chicago Metropolitan Area (Fig. 1). Lake County has a population density ca. 600 persons/km2 with a total population >700,000 and >7,000 km of paved roads, making it one of the most densely populated counties in the US (United States Census Bureau 2017). Within this urban ecosystem, the Lake County Forest Preserve District (LCFPD) manages 55 preserves for biodiversity and outdoor recreation (e.g., hiking and bird watching but not hunting). These preserves cover ca. 10% (13,000 ha) of the county’s land area and range in size from 7 ha to >3,000 ha ( = 215.2 ha ± 182.3 SD). Plant communities within the preserve system consist mainly of forest (28%), wetland (17%), and old fields (15%). Historically dominant communities, such as prairie and savanna, are relatively uncommon (8% and 5% respectively) but are the focus of large-scale restoration efforts (Chicago Region Biodiversity Council 1999). Common invasive species include Rhamnus spp. L. (Buckthorn), Lonicera spp. L. (Honeysuckle) and Phalaris arundinacea L. (Reed Canary Grass). See Vanek (2020) for a detailed site description and landscape context. Field Methods The LCFPD initiated a long-term wildlife monitoring program in 2009 to inventory and monitor vertebrate diversity across the preserve system. The LCFPD established 232 permanent wildlife monitoring points stratified by preserve within 26 focal preserves and 29 non-focal preserves. Focal preserves were assigned a priori by local managers and were typically larger, contained more diverse plant communities, and were subject to more intense ecological restoration (Vanek 2020). Monitoring points were randomly distributed (min distance between points = 400 m) at a density of 1 point/40.5 ha in focal preserves (n = 159 points) and 1 point/81 ha in non-focal preserves (n = 74 points). Preserves were monitored on a rotating basis, with 16 focal preserves monitored in odd years and 10 focal preserves monitored in even years. Non-focal preserves were monitored every 4 years. Thus, a total of 18-21 preserves (and 82-108 points) were monitored each year. By 2013 all preserves and points had been surveyed at least once. From 2010–2018, we surveyed for mesopredators during the autumn dispersal period (when activity is typically highest) using remote camera traps at all scheduled preserves (Table 1). At each point, we set one camera (typically a Cuddeback® Ambush™, but occasionally a Bushnell® Trophy Cam™ or Leaf River® IR-3BU™ during early years of the monitoring program) along game trails, habitat edges, or natural bottlenecks (i.e., feature-based placement) to maximize detection probabilities within 100 m (typically within 50 m) of each point’s permanent GPS location. Cameras were mounted to a tree or metal t-post at a height of 0.5 m and aimed parallel to the ground or slightly downward (depending on site conditions). Cameras were set on Mondays, checked daily during the day, and removed on Fridays (thus 4 trap nights/camera location; Table 1) for a total of 5 days/4 nights per camera location per assigned year. To increase detection probabilities given our short camera deployments, we emptied 1 can (106 g) of sardines in front of each camera (5 m away) and cleared any vegetation that might block the camera’s view of the bait. Sardines have been shown to increase the detection rate of many species of mesopredators (Avrin et al. 2021). Bait was replaced as needed, and we set cameras to record 1 photo per trigger with a delay of 60 seconds. This research was approved the IACUC at Northern Illinois University (ORC# LA14-0002) and followed the ethical guidelines set forth by the American Society of Mammologists (Sikes and the Animal Care and Use Committee of the American Society of Mammalogists 2016). Activity Periods and Overlap We classified each detection from the filtered photo dataset as “diurnal”, “nocturnal”, or “crepuscular” by comparing the time the photo was taken (via each image’s metadata) to temporally and spatially explicit sunlight phases calculated with the ‘suncalc’ package (Thieurmel and Elmarhraoui 2019) in the R Statistical Computing Environment (R Core Team 2018). We classified detections as diurnal if taken during daytime (between sunrise and sunset) and as nocturnal if taken during nighttime (between the commencement of astronomical dusk and the onset of astronomical dawn). Rather than picking an arbitrary cutoff to signify the crepuscular period (e.g., 1 hour pre and post sunrise/sunset), we classified detections as crepuscular if taken during either morning twilight (i.e., between the onset of astronomical dawn and sunrise) or evening twilight (i.e., between sunset and the commencement of astronomical dusk). Sunlight phases varied throughout our sampling period. For example, on 1 Sep 2015, daytime lasted 13 hr 10 min, nighttime lasted 7 hr 01 min, and each twilight period lasted 01 hr 40 min. By the end of the sampling season (e.g., 31 Oct 2015), these time periods had shifted so that daytime lasted 10 hr 23 min, nighttime 10 hr 27 min, and each twilight period lasted 01 hr 35 min. Accounting for these differences is important in a study spanning several months (Nouvellet et al. 2012). We quantified the activity periods for each species for which sample size exceeded 50 photos using the date/time metadata from each photo and circular kernel density estimates using the overlap package in R (Ridout and Meredith 2020).This package analyzes time using a von Mises kernel density function to accurately represent the circular distribution of time of day (Ridout and Linkie 2009, Rowcliffe et al. 2014). To adjust for changing light levels from late August through the end of November, we used the sunTime function so that our activity periods were anchored relative to dawn and dusk rather than artificial clock times (Nouvellet et al. 2012, Vazquez et al. 2019) many animals engage in behaviours known to follow cyclic patterns over days (e.g. singing, diving or foraging behaviours). For example, on 1 Sept 2010, sunrise in Chicago was at 06:16 and sunset was at 13:23 pm. By October 31, sunrise had shifted by more than 1 hour to 07:21, with sunset almost an hour and a half earlier to 17:45. Ignoring these changes would lead to misleading inference. Therefore, to calibrate the sunTime function, we converted each photo’s clock time (e.g., 7:21 am) to radians (time in radians = time of day on a 0–1 scale × 2 × π), set our anchor point to Chicago’s general longitude and latitude in decimal degrees with a WGS84 projection (-88, 42), and set the time zone to “America/Chicago”. To estimate the degree of temporal overlap between species, we calculated the coefficient of overlap (Δ) using the overlapEst function in the overlap package. This coefficient ranges from 0 (no overlap) to 1 (complete overlap). We used the default bandwidth adjustment ( = 1) and the Δ 4 estimator (Ridout and Linkie 2009), which is recommended when sample sizes are > 75 (see Results). As with detection rate, we excluded all detections at the same site if they were within 30 minutes of a previous detection to avoid temporal autocorrelation of the same animal triggering a camera repeatedly. We calculated 95% confidence intervals for all overlap coefficients by generating 1,000 bootstraps in the overlap package’s bootstrap function and then extracted the confidence intervals using the bootCI function. We also analyzed Domestic Cat activity data previously reported in Vanek et al. (2021b) to provide overlap comparisons between this invasive species and native mesopredators. Results We detected all five of our target mesopredators on our camera traps: Northern Raccoon, Virginia Opossum, Coyote, Striped Skunk, and Red Fox. We also detected non-native Domestic Cats and Canis familiarus Linnaeus (Domestic Dog) (Table 2). Unlike other studies in rural Illinois (e.g., Morin et al. 2018), Domestic Dogs were rarely detected and almost always associated with humans (either leashed or a human was seen in subsequent photo captures). Thus, we did not include domestic dogs in our analyses. We also recorded photographs of 39 other vertebrates, including Mustela frenata Lichtenstein (Long-tailed Weasel) and Neovison vison Schreber (American Mink), but we excluded these smaller carnivores from our analysis due to small body masses (typically less than 1 kg). We did not detect Lynx rufus Schreber (Bobcat), Urocyon cinereoargenteus Schreber (Gray Fox), Pekania pennanti Erxleben (Fisher), or Taxidea taxus Schreber (American Badger), mesopredators which all historically occurred in Lake County but are now exceedingly rare or regionally extirpated in the Chicago metroregion (Hoffmeister 1989, Mohr 1943, Willingham 2008). Mesopredator Detections We detected at least one of the five native mesopredators at all 55 preserves and 227 monitoring points (98%) during the 9-year survey period (Table 2). The Virginia Opossum was the most photographed species (n = 11,202 photos), but after filtering photos for temporal autocorrelation, we were left with more photos of the Northern Raccoon (n = 4,238 photos). These two species represented ca. 94% of the unfiltered photo dataset, and ca. 91% of the filtered dataset. Detection rates for Northern Raccoons and Virginia Opossums were similar (119.3 and 100.6 detections/100 trap nights, respectively) and >10 times that of Coyotes and Striped Skunks, >20 times that of Domestic Cats, and >150 times that of Red Fox (Table 2). Correspondingly, Northern Raccoons and Virginia Opossums were widely distributed, and by the end of the study had been detected at nearly all preserves (98% and 96% of all preserves, respectively) and a large proportion of monitoring points (92% and 85%, respectively) (Fig. 2). In contrast to the abundant Northern racoon and Virginia Opossum, we detected Coyotes and Striped Skunks less frequently and at fewer preserves and points (Table 2). We rarely detected Red Foxes during our study (Table 1). Compared to the other native mesopredators and Domestic Cats, Red Foxes had the lowest number of photos (n = 24), lowest detection rate (0.62 detections/100 trap nights), lowest rate of naïve occupancy for both points (3.4%) and preserves (11%), and no detections in 2016 or 2017 (Table 1). However, while Red Foxes were detected at just 1 or 2 points each year, these were consistently different points, and cumulative Red Fox occupancy continues to increase (approximately) linearly over time (1 point in 2010; 5 points in 2013, 9 points in 2018). Similarly, the number of preserves with detections has increased from 1 in 2010, to 4 in 2013, and 6 in 2018. The low number of Red Fox detections precluded them from formal activity period and overlap analysis. Activity Periods and Overlap Given the number of photographs obtained for each species (Table 2), our sample sizes were sufficient to statistically characterize activity periods for Northern Racoons, Virginia Opossums, Coyotes, and Striped Skunks, but not Red Foxes. The former three species were all decidedly nocturnal with some crepuscular activity and with peaks before sunrise or after sunset (Figs. 3 and 4) and were almost never active during daylight hours. Similarly, Coyotes were mostly nocturnal with some crepuscular activity, with peaks of activity just before dawn and after dusk. However, Coyotes also exhibited some daytime activity (18% of filtered photo detections). In contrast, daytime photos represented just 3% of Northern Raccoon and Striped Skunk detections, and just 1.4% of Virginia Opossum detections (Fig. 2). Domestic Cats expressed a cathemeral activity period (i.e., not decidedly nocturnal, diurnal, or crepuscular), with 35% of detections during the day. The small number of Red Fox photos precluded a formal activity analysis, but we classified 19% of the photos as diurnal, 48% nocturnal, and 33.5% crepuscular (Fig. 2). Overlap in activity periods was highest between Striped Skunk, Northern Raccoon, and Virginia Opossum, with coefficients of overlap ranging from 0.89–0.92 (Fig. 3). Overlap between Coyotes and these three smaller species was lower, but still high, ranging from 0.74 (95% CI = 0.69–0.79) between Coyotes and Virginia Opossums to 0.81 (95% CI = 0.76–0.85) between Coyotes and Northern Raccoons. Overlap between Domestic Cats and the three smaller native mesopredators was relatively low, ranging from 0.52 (95% CI = 0.45–0.59) between cats and Virginia Opossums and to 0.58 (95% CI = 0.50–0.65) between cats and Striped Skunks (Fig. 4). While still lower than overlap with the other native mesopredators, Coyote overlap with Domestic Cats was surprisingly high at nearly 70% (Δ = 0.69, 95% CI = 0.61–0.77) (Fig. 4). Due to the small number of detections, we could not formally assess overlap between Red Fox and the other mesopredators. Discussion In this study we used 9 years of rapid camera trap surveys to quantity and assess the autumnal activity periods of terrestrial mesopredators inhabiting preserves in the suburbs of Chicago, Illinois. As expected, we found strong nocturnal patterns for all native mesopredators with a high degree of temporal overlap between Northern Raccoons, Virginia Opossums, and Striped Skunks. Coyotes, while also still exhibiting a high degree of nocturnal activity, also showed diurnal and crepuscular activity, consistent with other studies. Our use of camera trap data to elucidate diel activity periods showcases the growing utility of “next-generation natural history” (sensu Tosa et al. 2021) to generate important natural history data that can be used by managers to mitigate human-wildlife conflict and aid in the conservation of often overlooked mammalian mesopredators (Marneweck et al. 2021). While similar studies of urban activity periods typically use longer camera deployments across a fewer number of sites, our approach used shorter deployments across a greater number of sites replicated over a longer time period, potentially capturing information from a greater number of individuals across space and time. Future studies should examine changes in diel activity period across seasons, years, and by preserve characteristics (e.g., size, shape, degree of surrounding urbanization), as well as the interaction between spatial and temporal overlap of different mesopredator species. As expected, we observed strong nocturnal patterns for Northern Raccoons, Striped Skunks, and Virginia Opossums. These species are typically reported as being nocturnal (Adams 2016, Lotze and Anderson 1979, McManus 1974, Ryser 1995, Wade-Smith and Verts 1982), but there are surprisingly few studies of quantifying these patterns, especially in urban areas (but see recent studies such as Gallo et al. 2022 and Mims et al. 2022). Rather than seeing a shift to enhanced nocturnality (Nix et al. 2018), our observed proportion of Northern Raccoon, Striped Skunk, and Red Fox photos classified as nocturnal, crepuscular, and diurnal were roughly similar to those observed in a large-scale camera trap study in rural southern Illinois (Lesmeister et al. 2015). However, while the proportion of nocturnal Coyote photos was almost identical in both studies (58% in our study, 55% in Lesmeister et al. 2015), Coyotes in LCFPD preserves were much less crepuscular (23% vs 38%) and twice as diurnal (18% vs 8%) as those in rural Illinois. These results are similar to those found previously in Cook County, Chicago using radio-telemetry (Gehrt et al. 2011). Coyote behavior is quite plastic, and our findings suggest Coyotes using LCFPD preserves may be habituated to human presence (Schell et al. 2018), as hunting is prohibited and Coyotes are unlikely to suffer repercussions for simply being seen by a human in a nature preserve. In contrast, failing to avoid humans in rural areas can be maladaptive. For example, Van Deelen and Gosselink (2006) reported shooting to be the most common cause of mortality for Coyotes in rural Illinois. Our data also supports the notion that urban Coyotes may exhibit bolder behavior relative to rural Coyotes (Breck et al. 2019), and we hope managers can use these findings to better prevent or mitigate human-wildlife conflict. However, confirmation of this hypothesis would require a formal assessment of activity overlap with that of proximate human activity and should be the subject of future study. As predicted, we found extremely high rates of activity overlap (ca. 90%) between Northern Raccoons, Virginia Opossums, and Striped Skunks. Though all highly nocturnal, the main differences were in the timing of peak nocturnal activity. For example, Virginia Opossum activity peaked after sunset and then gradually declined to sunrise, while Striped Skunks and Northern Raccoons exhibited pulses of activity throughout the night. These fine-scale differences in activity might facilitate coexistence and minimize competition between these ecologically similar species (Marinho et al. 2020). Overlap between Coyotes and the three smaller mesopredators was somewhat lower (ca. 75–80%) due to the higher rate of diurnal activity for Coyotes. The high rate of overlap between Coyotes and Northern Raccoons is similar to that of forested areas in North Carolina (Chitwood et al. 2020), and supports growing evidence that Coyotes do not suppress populations of native mesopredators (Gehrt and Clark 2003, Jachowski et al. 2020, Prange and Gehrt 2007). Thus, rather than serving as a potential deterrent to turtle and ground-nesting bird predators, Coyotes might be acting as an additional source of mortality for depredation sensitive species, particularly when populations of Coyotes are artificially subsidized at high levels (Fedriani et al. 2001, Lamarre-DeJesus and Griffin 2013, Minckley 1966). Overlap between Domestic Cats and the mostly nocturnal native mesopredators was lower (ca. 50–70%), due to the cathemeral activity pattern of Domestic Cats. Feral cats typically show a crepuscular or nocturnal activity pattern, more similar to that of native mesopredators (Konecny 1987, Lavery et al. 2020, Wang and Fisher 2012). However, most cats in LCFPD preserves are presumed to be indoor-outdoor house cats or at least subsidized by humans, rather than truly feral populations as seen in Australia and some island ecosystems (Vanek et al. 2021b). Given encounter rates between predator and prey are partially dependent on temporal overlap (Allen et al. 2021), Coyotes could be potentially limiting populations of feral cats, whereas pet cats which remain closer to the safety of buildings can persist. However, the relatively high degree of overlap between Coyotes and Domestic Cats suggests that cats are not actively avoiding Coyotes, at least temporally (but see Gehrt et al. 2013). Nevertheless, the relatively lower temporal overlap between Domestic Cats and native mesopredators suggests Domestic Cats in LCFPD preserves occupy a different niche than native mesopredators, and therefore could be competing with diurnal predators (e.g., raptors) more so than otherwise ecologically similar mammalian mesopredators (as hypothesized by Vanek et al. 2021b). We used a rapid camera trap survey design that was designed as part of a long-term inventory and monitoring plan. This program incorporates not just camera trapping, but small mammal trapping, reptile and amphibian sampling, and bird surveys (Cassel 2014, Cassel et al. 2019, 2020, Vanek et al. 2021a) across numerous preserves and randomly distributed monitoring points. Using multiple survey methods can improve the number of species, individuals, and life stages detected during monitoring programs (Nichols et al. 2008), but there are often trade-offs between sampling depth and breadth based on logistical constraints. Due to the rapid nature of our camera deployments (4 nights per site), cameras were baited to increase detection probabilities. While it is possible daily camera visits to check and replace bait may have influenced activity patterns, we still captured a high degree of diurnal Coyote detections, suggesting this effect was minimal. Additionally, if bait was attracting Coyotes, this may have been responsible for the low detections of Red Foxes and complete lack of detections of Gray Foxes. However, Magle et al. (2019) found low occupancy of Red Fox in the Chicago metroregion, and Avrin et al. (2021), found bait to greatly increase detections of Gray Fox, despite high Coyote occupancy. Our results reveal that rapid, baited camera trap surveys are sufficient to detect the presence of Raccoons and Opossums, species that are important nest predators of ground-nesting birds and imperiled turtles. However, additional trap nights (via more cameras per site or leaving cameras out longer) may be necessary for formal occupancy modeling of less commonly detected species, such as stiped skunks or Domestic Cats (Kays et al. 2020, Pease et al. 2016, Vanek et al. 2021b). A Depauperate Predator Community Despite an extensive array of preserved and restored habitat, we found a depauperate mesopredator community dominated by synanthropic generalists. We failed to detect Bobcats, American Badgers, Fishers, or Gray Foxes, mesopredators that, with the exception of American Badger, persist in or are colonizing urban areas elsewhere in the country (Cove et al. 2021, Crooks 2002, LaPoint 2013, Lombardi et al. 2017, Magle et al. 2019, Young et al. 2019). Notably, though present, we rarely detected the Red Fox, a species often associated with urban areas, but may be forced out of urban greenspaces when Coyotes are present (Mueller et al. 2018). These missing species compromise 50% of the historic mesopredator community, representing a significant loss of functional diversity (e.g., the ambush-hunting and crepuscular Bobcat, semi-fossorial American Badger, and semi-arboreal Fisher). While our sampling was not exhaustive, and regular checking of bait may have deterred detection of some species (e.g., foxes), it seems likely these missing species are at least functionally if not fully extirpated in Lake County, consistent with other regional assessments (Hoffmeister 1989, Magle et al. 2019, Mohr 1943, Willingham 2008). These absences, coupled with the historic losses of large carnivores, such as C. lupus Linnaeus (Gray Wolf), Puma concolor Linnaeus (Cougar), and Ursus americanus Linnaeus (American Black Bear), suggest that ongoing restoration work in the “Chicago Wilderness” must be coupled with innovative habitat connectivity and human-dimensions work in order to restore carnivore populations in the rapidly urbanizing midwestern United States (Chicago Region Biodiversity Council 1999; Dreher 2009; Smith et al. 2014, 2016; Watkins et al. 2015). Finally, as the field of urban ecology advances, there have been calls to move beyond describing patterns and attempt to disentangle processes (Shochat et al. 2006). Understanding the processes and mechanisms dictating how and why wildlife populations thrive, perish, or merely persist in urban ecosystems is undoubtably important and will lead to better conservation and urban planning outcomes. However, documenting and describing natural history and baseline population data is also important, particularly for a rapidly developing and dynamic field such as urban ecology (Ramalho and Hobbs 2012), where species-specific effects may depend on the spatial context and overall configuration of individual cities For example, in a multi-city study, Fidino et al. (2021) found Northern Raccoon and Virginia Opossum occupancy varied as a function of housing density, but this effect could be either positive or negative depending on the housing density of each city. Thus, while advancing our understanding of ecological processes in cities is important, the description of local patterns is still imperative to plan the experimental studies needed to understand those processes, as well as to develop testable hypotheses and make effective management and conservation decisions (Bury 2006, Greene 2005). Acknowledgments This study would not have been possible without the fortitude and dedication in the field of the numerous field technicians who assisted us with camera trapping efforts over the past decade. The preservation and restoration efforts of the Lake County Forest Preserve District also deserve our acknowledgement. Their efforts to acquire and preserve wild places in Lake County have not only given wildlife space to thrive but provided us the opportunity and funding to conduct this study. Discussion with members of the Jones Lab at NIU greatly improved the manuscript, as well as a review by SL and students, along with 6 anonymous reviewers. This study was approved by the IACUC at Northern Illinois University (ORC# LA14-0002) with permits from the Illinois Department of Natural Resources, Illinois Nature Preserve Commission and the Lake County Forest Preserve District. Funding was provided by the Lake County Forest Preserve District (Grant 15) and Northern Illinois University. Literature Cited Adams, C.E. 2016. Urban Wildlife Management. Third edition. CRC Press, Boca Raton, Florida. 557 pp. Allen, M.L., M.C. Sibarani, and M. Krofel. 2021. Predicting preferred prey of Sumatran tigers Panthera tigris sumatrae via spatio-temporal overlap. Oryx 55:197–203. Andelt, W.F. 1985. Behavioral ecology of coyotes in south Texas. Wildlife Monographs 3–45. Avrin, A.C., C.E. Pekins, J.H. Sperry, and M.L. Allen. 2021. Evaluating the efficacy and decay of lures for improving carnivore detections with camera traps. Ecosphere 12:e03710. Bateman, P.W., and P.A. Fleming. 2012. Big city life: carnivores in urban environments. Journal of Zoology 287:1–23. Bekoff, M. 1977. Canis latrans. Mammalian Species 1–9. Breck, S.W., S.A. Poessel, P. Mahoney, and J.K. Young. 2019. The intrepid urban coyote: a comparison of bold and exploratory behavior in coyotes from urban and rural environments. Scientific Reports 9:2104. Bury, B.R. 2006. Natural history, field ecology, conservation biology and wildlife management: time to connect the dots. Herpetological Conservation and Biology 1:7. Buskirk, S.W. 1999. Mesocarnivores of Yellowstone. Pp. 165–187, In T.W. Clark, A.P. Curlee, S.C. Minta, and P.M. Kareiva (Eds.). Carnivores in ecosystems : the Yellowstone experience. Yale University Press, New Haven, CT, USA. Cassel, K.W. 2014. Factors influencing site occupancy of breeding birds, herptiles, mesocarnivores, and small mammals on suburban forest preserves in the Chicago Metropolitan Area. Thesis. Southern Illinois University, Carbondale, IL. 365 pp. Cassel, K.W., D.J. Morin, C.K. Nielsen, T.S. Preuss, and G.A. Glowacki. 2020. Low-intensity monitoring of small-mammal habitat associations and species interactions in an urban forest-preserve network. Wildlife Research 47. Cassel, K.W., J.P. Vanek, G.A. Glowacki, T.A. Preuss, and C.K. Nielsen. 2019. Multiscale habitat factors influence the occupancy and turnover of the Chicago region’s suburban herpetofauna. Herpetological Conservation and Biology 14:438–454. Chicago Region Biodiversity Council. 1999. Biodiversity recovery plan. Chicago, IL. Available online at Accessed February 21, 2019. Chitwood, M.C., M.A. Lashley, S.D. Higdon, C.S. DePerno, and C.E. Moorman. 2020. Raccoon vigilance and activity patterns when sympatric with coyotes. Diversity 12:341. Cove, M.V., R. Kays, H. Bontrager, C. Bresnan, M. Lasky, T. Frerichs, R. Klann, T.E. Lee Jr., S.C. Crockett, A.P. Crupi, K.C.B. Weiss, H. Rowe, T. Sprague, J. Schipper, C. Tellez, C.A. Lepczyk, J.E. Fantle-Lepczyk, S. LaPoint, J. Williamson, M.C. Fisher-Reid, S.M. King, A.J. Bebko, P. Chrysafis, A.J. Jensen, D.S. Jachowski, J. Sands, K.A. MacCombie, D.J. Herrera, M. van der Merwe, T.W. Knowles, R.V. Horan III, M.S. Rentz, L.S.E. Brandt, C. Nagy, B.T. Barton, W.C. Thompson, S.P. Maher, A.K. Darracq, G. Hess, A.W. Parsons, B. Wells, G.W. Roemer, C.J. Hernandez, M.E. Gompper, S.L. Webb, J.P. Vanek, D.J.R. Lafferty, A.M. Bergquist, T. Hubbard, T. Forrester, D. Clark, C. Cincotta, J. Favreau, A.N. Facka, M. Halbur, S. Hammerich, M. Gray, C.C. Rega-Brodsky, C. Durbin, E.A. Flaherty, J.M. Brooke, S.S. Coster, R.G. Lathrop, K. Russell, D.A. Bogan, R. Cliché, H. Shamon, M.T.R. Hawkins, S.B. Marks, R.C. Lonsinger, M.T. O’Mara, J.A. Compton, M. Fowler, E.L. Barthelmess, K.E. Andy, J.L. Belant, D.E. Beyer Jr., T.M. Kautz, D.G. Scognamillo, C.M. Schalk, M.S. Leslie, S.L. Nasrallah, C.N. Ellison, C. Ruthven, S. Fritts, J. Tleimat, M. Gay, C.A. Whittier, S.A. Neiswenter, R. Pelletier, B.A. DeGregorio, E.K. Kuprewicz, M.L. Davis, A. Dykstra, D.S. Mason, C. Baruzzi, M.A. Lashley, D.R. Risch, M.R. Price, M.L. Allen, L.S. Whipple, J.H. Sperry, R.H. Hagen, A. Mortelliti, B.E. Evans, C.E. Studds, A.P.K. Sirén, J. Kilborn, C. Sutherland, P. Warren, T. Fuller, N.C. Harris, N.H. Carter, E. Trout, M. Zimova, S.T. Giery, F. Iannarilli, S.D. Higdon, R.S. Revord, C.P. Hansen, J.J. Millspaugh, A. Zorn, J.F. Benson, N.H. Wehr, J.N. Solberg, B.D. Gerber, J.C. Burr, J. Sevin, A.M. Green, Ç.H. Şekercioğlu, M. Pendergast, K.A. Barnick, A.J. Edelman, J.R. Wasdin, A. Romero, B.J. O’Neill, N. Schmitz, J.M. Alston, K.M. Kuhn, D.B. Lesmeister, M.A. Linnell, C.L. Appel, C. Rota, J.L. Stenglein, C. Anhalt-Depies, C. Nelson, R.A. Long, K. Jo Jaspers, K.R. Remine, M.J. Jordan, D. Davis, H. Hernández-Yáñez, J.Y. Zhao, and W.J. McShea. 2021. SNAPSHOT USA 2019: A coordinated national camera trap survey of the United States. Ecology 102:e03353. Crooks, K.R. 2002. Relative sensitivities of mammalian carnivores to habitat fragmentation. Conservation Biology 16:488–502. Díaz-Ruiz, F., J. Caro, M. Delibes-Mateos, B. Arroyo, and P. Ferreras. 2016. Drivers of red fox (Vulpes vulpes) daily activity: prey availability, human disturbance or habitat structure? Journal of Zoology 298:128–138. Dreher, D. 2009. Chicago Wilderness Green Infrastructure Vision: challenges and opportunities for the built environment. Journal of Green Building 4:72–88. Fedriani, J.M., T.K. Fuller, and R.M. Sauvajot. 2001. Does availability of anthropogenic food enhance densities of omnivorous mammals? An example with coyotes in southern California. Ecography 24:325–331. Feldhamer, G.A., B.C. Thompson, and J.A. Chapman. 2003. Wild Mammals of North America: Biology, Management, and Conservation. JHU Press, Baltimore, MD. 1250 pp. Fidino, M., T. Gallo, E.W. Lehrer, M.H. Murray, C.A.M. Kay, H.A. Sander, B. MacDougall, C.M. Salsbury, T.J. Ryan, J.L. Angstmann, J. Amy Belaire, B. Dugelby, C.J. Schell, T. Stankowich, M. Amaya, D. Drake, S.H. Hursh, A.A. Ahlers, J. Williamson, L.M. Hartley, A.J. Zellmer, K. Simon, and S.B. Magle. 2021. Landscape scale differences among cities alter common species’ responses to urbanization. Ecological Applications 31:e02253. Gallo, T., M. Fidino, B. Gerber, A.A. Ahlers, J.L. Angstmann, M. Amaya, A.L. Concilio, D. Drake, D. Gay, E.W. Lehrer, M.H. Murray, T.J. Ryan, C.C. St Clair, C.M. Salsbury, H.A. Sander, T. Stankowich, J. Williamson, J.A. Belaire, K. Simon, and S.B. Magle. 2022. Mammals adjust diel activity across gradients of urbanization. Y.Y. Watanabe, C. Rutz, D. Cox, and J.T. Fisher (Eds.). eLife 11:e74756. Gaynor, K.M., C.E. Hojnowski, N.H. Carter, and J.S. Brashares. 2018. The influence of human disturbance on wildlife nocturnality. Science 360:1232–1235. Gehrt, S., J. Brown, and C. Anchor. 2011. Is the urban coyote a misanthropic synanthrope? The case from Chicago. Cities and the Environment 4:1–23. Gehrt, S.D., and W.R. Clark. 2003. Raccoons, coyotes, and reflections on the mesopredator release hypothesis. Wildlife Society Bulletin 31:836–842. Gehrt, S.D., S. Riley P.D., and B.L. Cypher (Eds.). 2010. Urban Carnivores: Ecology, Conflict, and Conservation. JHU Press, Baltimore, MD. 302 pp. Gehrt, S.D., E.C. Wilson, J.L. Brown, and C. Anchor. 2013. Population ecology of free-roaming cats and interference competition by coyotes in urban parks. M.G. Chapman (Ed.). PLoS ONE 8:e75718. Gibbons, W., J. 2000. The global decline of reptiles, deja vu amphibians. Bioscience 50:653–666. Graser, W.H., S.D. Gehrt, L.L. Hungerford, and C. Anchor. 2012. Variation in demographic patterns and population structure of raccoons across an urban landscape. The Journal of Wildlife Management 76:976–986. Greene, H. 2005. Organisms in nature as a central focus for biology. Trends in Ecology & Evolution 20:23–27. Greenspan, E., C.K. Nielsen, and K.W. Cassel. 2018. Potential distribution of coyotes (Canis latrans), Virginia opossums (Didelphis virginiana), striped skunks (Mephitis mephitis), and raccoons (Procyon lotor) in the Chicago Metropolitan Area. Urban Ecosystems 21:983–997. Greenwood, R.J. 1982. Nocturnal activity and foraging of prairie raccoons (Procyon lotor) in North Dakota. The American Midland Naturalist 107:238–243. University of Notre Dame. Grinder, M.I., and P.R. Krausman. 2001. Home range, habitat use, and nocturnal activity of coyotes in an urban environment. The Journal of Wildlife Management 65:887–898. Guiden, P.W., S.L. Bartel, N.W. Byer, A.A. Shipley, and J.L. Orrock. 2019. Predator–prey interactions in the anthropocene: Reconciling multiple aspects of novelty. Trends in Ecology & Evolution 34:616–627. Hoffmeister, D.F. 1989. Mammals of Illinois. First Edition. University of Illinois Press, Urbana. 348 pp. Jachowski, D.S., A. Butler, R.Y.Y. Eng, L. Gigliotti, S. Harris, and A. Williams. 2020. Identifying mesopredator release in multi-predator systems: a review of evidence from North America. Mammal Review 50:367–381. Kays, R., B.S. Arbogast, M. Baker-Whatton, C. Beirne, H.M. Boone, M. Bowler, S.F. Burneo, M.V. Cove, P. Ding, S. Espinosa, A. Luis Sousa Gonçalves, C.P. Hansen, P.A. Jansen, J.M. Kolowski, T.W. Knowles, M. Guimarães Moreira Lima, J. Millspaugh, W.J. McShea, K. Pacifici, A.W. Parsons, B.S. Pease, F. Rovero, F. Santos, S.G. Schuttler, D. Sheil, X. Si, M. Snider, and W.R. Spironello. 2020. An empirical evaluation of camera trap study design: how many, how long, and when? Methods in Ecology and Evolution 11:700–713. Kitchen, A.M., E.M. Gese, and E.R. Schauster. 2000. Changes in coyote activity patterns due to reduced exposure to human persecution. USDA National Wildlife Research Center - Staff Publications 78:6. Konecny, M.J. 1987. Home range and activity patterns of feral house cats in the Galápagos islands. Oikos 50:17–23. Lamarre-DeJesus, A.S., and C.R. Griffin. 2013. Use of habanero pepper powder to reduce depredation of loggerhead sea turtle nests. Chelonian Conservation and Biology 12:262–267. LaPoint, S.D. 2013. Movement ecology of fishers (Pekania pennanti) within a semi-urban landscape. Universität Konstanz, Konstanz, Germany. 163 pp. Lavery, T.H., M. Alabai, P. Holland, C. Qaqara, and N. Vatohi. 2020. Feral cat abundance, density and activity in tropical island rainforests. Wildlife Research 47:660. Lesmeister, D.B., C.K. Nielsen, E.M. Schauber, and E.C. Hellgren. 2015. Spatial and temporal structure of a mesocarnivore guild in midwestern north America. Wildlife Monographs 191:1–61. Lombardi, J.V., C.E. Comer, D.G. Scognamillo, and W.C. Conway. 2017. Coyote, fox, and bobcat response to anthropogenic and natural landscape features in a small urban area. Urban Ecosystems 20:1239–1248. Lotze, J.-H., and S. Anderson. 1979. Procyon lotor. Mammalian Species 1–8. Magle, S.B., M. Fidino, E.W. Lehrer, T. Gallo, M.P. Mulligan, M.J. Ríos, A.A. Ahlers, J. Angstmann, A. Belaire, B. Dugelby, A. Gramza, L. Hartley, B. MacDougall, T. Ryan, C. Salsbury, H. Sander, C. Schell, K. Simon, S.S. Onge, and D. Drake. 2019. Advancing urban wildlife research through a multi-city collaboration. Frontiers in Ecology and the Environment 17:232–239. Marinho, P.H., C.R. Fonseca, P. Sarmento, C. Fonseca, and E.M. Venticinque. 2020. Temporal niche overlap among mesocarnivores in a Caatinga dry forest. European Journal of Wildlife Research 66:34. Marneweck, C., A.R. Butler, L.C. Gigliotti, S.N. Harris, A.J. Jensen, M. Muthersbaugh, B.A. Newman, E.A. Saldo, K. Shute, K.L. Titus, S.W. Yu, and D.S. Jachowski. 2021. Shining the spotlight on small mammalian carnivores: Global status and threats. Biological Conservation 255:109005. McManus, J.J. 1974. Didelphis virginiana. Mammalian Species 1–6. Mims, D.M., S.A. Yasuda, and M.J. Jordan. 2022. Contrasting activity times between raccoons (Procyon lotor) and virginia opossums (Didelphis virginiana) in urban green spaces. Northwestern Naturalist 103:63–75. Society for Northwestern Vertebrate Biology. Minckley, W.L. 1966. Coyote Predation on Aquatic Turtles. Journal of Mammalogy 47:137. Mohr, C.O. 1943. Illinois furbearer distribution and income. Illinois Natural History Survey Bulletin 22:505–537. Morin, D.J., D.B. Lesmeister, C.K. Nielsen, and E.M. Schauber. 2018. The truth about cats and dogs: Landscape composition and human occupation mediate the distribution and potential impact of non-native carnivores. Global Ecology and Conservation 15:e00413. Mueller, M.A., D. Drake, and M.L. Allen. 2018. Coexistence of coyotes (Canis latrans) and red foxes (Vulpes vulpes) in an urban landscape. PLOS ONE 13:e0190971. Public Library of Science. Neiswenter, S. a., R.C. Dowler, and J.H. Young. 2010. Activity patterns of two sympatric species of skunks (Mephitis mephitis and Spilogale gracilis) in Texas. The Southwestern Naturalist 55:16–21. Nichols, J.D., L.L. Bailey, A.F. O’Connell, N.W. Talancy, E.H.C. Grant, A.T. Gilbert, E.M. Annand, T.P. Husband, and J.E. Hines. 2008. Multi-scale occupancy estimation and modelling using multiple detection methods. Journal of Applied Ecology 45:1321–1329. Nix, J.H., R.G. Howell, L.K. Hall, and B.R. McMillan. 2018. The influence of periodic increases of human activity on crepuscular and nocturnal mammals: Testing the weekend effect. Behavioural Processes 146:16–21. Nouvellet, P., G.S.A. Rasmussen, D.W. Macdonald, and F. Courchamp. 2012. Noisy clocks and silent sunrises: measurement methods of daily activity pattern. Journal of Zoology 286:179–184. Patten, M.A., J.C. Burger, and M. Mitrovich. 2019. The intersection of human disturbance and diel activity, with potential consequences on trophic interactions. PLOS ONE 14:e0226418. Pease, B.S., C.K. Nielsen, and E.J. Holzmueller. 2016. Single-camera trap survey designs miss detections: impacts on estimates of occupancy and community metrics. PLOS ONE 11:e0166689. Prange, S., and S.D. Gehrt. 2004. Changes in mesopredator-community structure in response to urbanization. Canadian Journal of Zoology 82:1804–1817. Prange, S., and S.D. Gehrt. 2007. Response of skunks to a simulated increase in coyote activity. Journal of Mammalogy 88:1040–1049. Prugh, L.R., C.J. Stoner, C.W. Epps, W.T. Bean, W.J. Ripple, A.S. Laliberte, and J.S. Brashares. 2009. The rise of the mesopredator. BioScience 59:779–791. R Core Team. 2018. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Available online at Accessed March 22, 2019. Ramalho, C.E., and R.J. Hobbs. 2012. Time for a change: dynamic urban ecology. Trends in Ecology & Evolution 27:179–188. Ridout, M., and M. Meredith. 2020. Overlap: estimates of coefficient of overlapping for animal activity patterns. Available online at Accessed March 12, 2021. Ridout, M.S., and M. Linkie. 2009. Estimating overlap of daily activity patterns from camera trap data. Journal of Agricultural, Biological, and Environmental Statistics 14:322–337. Riley, S.P.D., J. Hadidian, and D.A. Manski. 1998. Population density, survival, and rabies in raccoons in an urban national park. 76:12. Ritzel, K., and T. Gallo. 2020. Behavior change in urban mammals: a systematic review. Frontiers in Ecology and Evolution 8. Available online at Rowcliffe, J.M., R. Kays, B. Kranstauber, C. Carbone, and P.A. Jansen. 2014. Quantifying levels of animal activity using camera trap data. Methods in Ecology and Evolution 5:1170–1179. Ryser, J. 1995. Activity, movement and home range of Virginia opossum (Didelphis virginiana) in Florida. Bulletin of the Florida Museum of Natural History 38:177–194. Šálek, M., L. Drahníková, and E. Tkadlec. 2015. Changes in home range sizes and population densities of carnivore species along the natural to urban habitat gradient. Mammal Review 45:1–14. Schell, C.J., J.K. Young, E.V. Lonsdorf, R.M. Santymire, and J.M. Mateo. 2018. Parental habituation to human disturbance over time reduces fear of humans in coyote offspring. Ecology and Evolution 8:12965–12980. Shochat, E., P.S. Warren, S.H. Faeth, N.E. McIntyre, and D. Hope. 2006. From patterns to emerging processes in mechanistic urban ecology. Trends in Ecology & Evolution 21:186–191. Sikes, R.S., and the Animal Care and Use Committee of the American Society of Mammalogists. 2016. 2016 Guidelines of the American Society of Mammalogists for the use of wild mammals in research and education. Journal of Mammalogy 97:663–688. Smith, J.B., C.K. Nielsen, and E.C. Hellgren. 2014. Illinois resident attitudes toward recolonizing large carnivores. The Journal of Wildlife Management 78:930–943. Smith, J.B., C.K. Nielsen, and E.C. Hellgren. 2016. Suitable habitat for recolonizing large carnivores in the midwestern USA. Oryx 50:555–564. Thieurmel, B., and A. Elmarhraoui. 2019. Suncalc: compute sun position, sunlight phases, moon position and lunar phase. Available online at Accessed February 24, 2021. Tosa, M.I., E.H. Dziedzic, C.L. Appel, J. Urbina, A. Massey, J. Ruprecht, C.E. Eriksson, J.E. Dolliver, D.B. Lesmeister, M.G. Betts, C.A. Peres, and T. Levi. 2021. The rapid rise of next-generation natural history. Frontiers in Ecology and Evolution 9:480. Travaini, A., J.J. Aldama, and M. Delibes. 1993. Home range and activity patterns of red fox Vulpes vulpes breeding females. Acta Theriologica 38:427–434. Polish Academy of Sciences. United States Census Bureau. 2017. QuickFacts: Lake County, Illinois. Available online at Accessed December 14, 2017. Urbanek, R.E., G.A. Glowacki, and C.K. Nielsen. 2016. Effect of raccoon (Procyon lotor) reduction on Blanding’s turtle (Emydoidea blandingii) nest success. The Journal of North American Herpetology 2016:39–44. Van Deelen, T.R., and T.E. Gosselink. 2006. Coyote survival in a row-crop agricultural landscape. Canadian Journal of Zoology 84:1630–1636. Vanek, J., P. 2020. Wildlife ecology and conservation in an urban ecosystem: applications of long-term monitoring data. Dissertation. Northern Illinois University, DeKalb, IL. 173 pp. Vanek, J.P., T.S. Preuss, A.U. Rutter, H.P. Jones, and G.A. Glowacki. 2021a. Comparing two sizes of Sherman live traps using long-term data. Wildlife Society Bulletin 45:574–580. Vanek, J.P., A.U. Rutter, T.S. Preuss, H.P. Jones, and G.A. Glowacki. 2021b. Anthropogenic factors influence the occupancy of an invasive carnivore in a suburban preserve system. Urban Ecosystems 24:113–126. Vazquez, C., J.M. Rowcliffe, K. Spoelstra, and P.A. Jansen. 2019. Comparing diel activity patterns of wildlife across latitudes and seasons: Time transformations using day length. Methods in Ecology and Evolution 10:2057–2066. Wade-Smith, J., and B.J. Verts. 1982. Mephitis mephitis. Mammalian Species 173:1–7. Wang, Y., M.L. Allen, and C.C. Wilmers. 2015. Mesopredator spatial and temporal responses to large predators and human development in the Santa Cruz Mountains of California. Biological Conservation 190:23–33. Wang, Y., and D.O. Fisher. 2012. Dingoes affect activity of feral cats, but do not exclude them from the habitat of an endangered macropod. Wildlife Research 39:611. Watkins, C., L.M. Westphal, P.H. Gobster, J. Vining, A. Wali, and M. Tudor. 2015. Shared principles of restoration practice in the Chicago Wilderness region. Human Ecology Review 21:155–178. Willingham, A.N. 2008. Emerging factors associated with the decline of a gray fox population and multi-scale land cover associations of mesopredators in the Chicago metropolitan area. MSc Thesis. The Ohio State University, Columbus, OH. 167 pp. Wright, J.D., M.S. Burt, and V.L. Jackson. 2012. Influences of an urban environment on home range and body mass of virginia opossums (Didelphis virginiana). Northeastern Naturalist 19:77–86. Yang, L., S. Jin, P. Danielson, C. Homer, L. Gass, S.M. Bender, A. Case, C. Costello, J. Dewitz, J. Fry, M. Funk, B. Granneman, G.C. Liknes, M. Rigge, and G. Xian. 2018. A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies. ISPRS Journal of Photogrammetry and Remote Sensing 146:108–123. Young, J.K., J.M. Golla, D. Broman, T. Blankenship, and R. Heilbrun. 2019. Estimating density of an elusive carnivore in urban areas: use of spatially explicit capture-recapture models for city-dwelling bobcats. Urban Ecosystems 22:507–512.