Time is a fundamental axis that shapes ecological systems. Regarding animal behavior, time, and space are linked in that the spatial characteristics of an animal’s local environment influences its temporal behavior (Kronfeld-Schor and Dayan, 2003). For example, some species make fine-scale adjustments to their temporal behavior to respond to predation risk (van der Vinne et al., 2019), competition (Kronfeld-Schor and Dayan, 2003), food availability (Owen-Smith, 2008), seasonal variability in local climatic conditions (Maloney et al., 2005), and even lunar cycles (Prugh and Golden, 2014). While temporal behavior has yet to become a major focus in animal ecology (Gaston, 2019; Kronfeld-Schor and Dayan, 2003), how animals use time as an ecological resource has inspired well-known ecological phenomenon like niche partitioning (Schoener, 1974) and predator–prey dynamics (Tambling et al., 2015). From an applied perspective, understanding if an animal can make temporal adjustments to mitigate or adapt to local environmental change remains a topic of discussion (Wolkovich et al., 2014), and is important for effective wildlife management and conservation (Levy et al., 2019).

Species that persist in human-dominated environments, like cities, require some degree of human avoidance to safely navigate these complex landscapes (Gehrt et al., 2009; Murray and St. Clair, 2015; Riley et al., 2003). In urban ecosystems, few habitat patches remain for animals to seek spatial refuge when confronted with human disturbance and/or negative interactions with other species. In these cases, temporally partitioning from these potentially dangerous interactions might be an alternative strategy. A recent global meta-analysis suggests that mammals become more nocturnal in areas with greater human disturbance (Gaynor et al., 2018). However, Frey et al., 2020 found that shifts in temporal behavior of apex predators – in response to human disturbance – caused cascading behavioral responses among mesocarnivores creating a ‘behavioral release’. These results highlight that temporal shifts toward nocturnality are not universal and may be context specific.

Of the 76 studies assessed in the Gaynor et al., 2018 global meta-analysis, only 7.8% (n = 11) assessed changes in nocturnal activity in urban areas, and all explored these changes categorically between urban and nonurban areas. Binary urban and rural categorizations generally fail to capture variation in urban development and cannot generate generalizable results that correlate to other cities (McDonnell and Pickett, 1990). Additionally, cities are unique and differ in size, land use, growth patterns, and human culture (Pacione, 2009). Variation in both spatial and temporal characteristics within and among cities could have differing effects on animal behavior. Thus, key questions remain regarding the way in which animal diel activity varies across gradients of urbanization and among differing cities. For example, the magnitude of change in diel activity patterns may be larger for more densely urbanized cities or may depend on regional variation in day and nighttime temperatures. Multicity investigations that include variation in urban intensity and regional climate can elucidate such patterns.

Gaynor et al., 2018 found that most studies in urban environments also focused on carnivore species, highlighting a gap in our understanding regarding changes in diel activity across taxa. For example, carnivores likely avoid humans in both space and time because of inimical human interactions (Clinchy et al., 2016; Kitchen et al., 2000). This may not be the case for mammals that do not regularly come in conflict with humans or do not evoke such visceral reactions by humans. Additionally, some species may be constrained by their morphology (e.g., number and type of cones and rods in their eyes) or may otherwise lack the ability to be active in differing light levels. To fully understand the variability of activity patterns and assess temporal adjustments in response to urban development, a comprehensive examination of the larger suite of urban mammals and across multiple urban environments is required.

While spatial habitat is a fundamental consideration in wildlife management and conservation, temporal habitat is often ignored (Gaston, 2019). Here, we link spatial landscape characteristics with the diel activity patterns of 8 terrestrial mammals using remote cameras deployed across 10 US cities. Our research objective was to determine which species change their diel activity across gradients of urbanization and identify what characteristics of the urban environments have the strongest association with changes in diel activity.

We found high variability in diel activity patterns within and among species and species-specific correlations between diel activity and human population density, impervious land cover, available greenspace, vegetation cover, and mean daily temperature. Our results indicate that in high-risk environments, such as cities, animals may reduce risk by modulating their temporal habitat use. Our study identifies a potential mechanism by which urban wildlife species may adapt to human-dominated environments, and provides critical insight into activity patterns of urban wildlife that will prove useful for managing these species in cities.

To quantify changes in mammal diel activity in response to urbanization, we used camera detection data for eight common urban mammal species: bobcat (Lynx rufus), coyote (Canis latrans), red fox (Vulpus vulpus), raccoon (Procyon lotor), striped skunk (Mephitis mephitis), eastern cottontail (Sylvilagus floridanus), Virginia opossum (Didelphis virginiana), and white-tailed deer (Odocoileus virginianus). Cameras were deployed in a systematic fashion across 10 US metropolitan areas as part of the Urban Wildlife Information Network (https://urbanwildlifeinfo.org/): Austin, Texas, Chicago, Illinois, Denver and Fort Collins, Colorado, Indianapolis, Indiana, Iowa City, Iowa, Orange County, California, Madison, Wisconsin, Manhattan, Kansas, and Wilmington, Delaware (Appendix 1—figure 1; see Fidino et al., 2021; Magle et al., 2019 for details).

Across 41,594 trap nights (Supplementary file 1a), we captured 79,659 total unique detection events. Total detections per species ranged from 102 to 34,931, and each species was detected in 5–10 cities at an average proportion of 0.16–0.77 sites per city (Table 1). Bobcat occurred at the lowest number of cities and proportion of sites, while raccoon occurred in all 10 cities and at the greatest proportion of sites (Table 1, see Supplementary file 1b for the proportion of sites in each city). The number of detections captured throughout the 24-hr diel period varied among species (Table 1).

Among city variation in diel activity patterns

We found that most species, on average, had a higher probability of being nocturnal (active at night or during the darkest portions of night) with the exception of bobcat and white-tailed deer (Figures 1 and 2). Most species showed variation in diel activity among cities (e.g., bobcat; Figure 1), and some species (e.g., eastern cottontail, coyote, red fox, and bobcat) exhibited profound variation in diel activity across individual sampling sites (Figure 2). For example, the predicted probability of nocturnal behavior for eastern cottontail at each sampled site ranged from 0.15 to 0.69 (see Supplementary file 1c for a full set of ranges for each species and each time category).

Figure 1

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Figure 2

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Selection for particular time categories

Of the three predator species that we analyzed (coyote, bobcat, and red fox), we found that anthropogenic and natural features were associated with variation in diel activity for only coyote and red fox (Figure 3A-C). Coyote selected for both nocturnal and crepuscular hours in areas of greater human population densities (Figure 3B), and red fox avoided nocturnal hours in areas with more available greenspace (Figure 3C). Seasonality also had an effect on both coyote and fox diel activity. Coyote selected for dawn hours (Figure 3B) and red foxes selected for dusk hours during periods of higher daily average temperatures (Figure 3C). We found no evidence that bobcats varied their diel activity across our environmental variables (Figure 3A).

Figure 3

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We found diel activity for all omnivore and herbivore species was affected by anthropogenic features. Raccoon, eastern cottontail, and white-tailed deer avoided nighttime hours in areas of greater human population density (Figure 3D, G, H), whereas Virginia opossum selected for nighttime and dusk hours in areas with greater human densities (Figure 3F). Raccoon, striped skunk, and white-tailed deer all selected for nighttime hours in areas with greater impervious land cover (Figure 3D, E, H).

Natural features were also associated with variation in diel activity for omnivore and herbivore species. As vegetation cover increased, eastern cottontails were more likely to select daytime hours (Figure 3G), whereas raccoons and white-tailed deer were more likely to select for nighttime hours and dusk (Figure 3D, H). As available greenspace increased, striped skunk were more likely to select nighttime hours (Figure 3E), whereas Virginia opossum were less likely to select nighttime and dawn hours (Figure 3F). White-tailed deer were also more likely to select nighttime and dusk hours as available greenspace increased (Figure 3H).

We found seasonality effects on all omnivore and herbivore species. Virginia opossum were more likely to avoid daytime hours as temperatures increased (Figure 3F). Daily average temperature had a positive relationship with diurnal selection for raccoons, striped skunk, eastern cottontail, and white-tailed deer (Figure 3D, E, G, H).

Probability of nocturnal activity

To further assess the probability of a species shifting toward nocturnality as a response to natural and anthropogenic features of the environment, we combined the probability of activity during night and deep night and predicted these values across each continuous covariates using the model results for each species. Coyote had a lower probability of being nocturnal in areas with lower human densities, but that probability increased significantly as human population increased (Figure 4). With a 1 standard deviation (hereafter sd) increase from the mean human population density (from 1512 to 3095 people/km²), coyotes are 18% more likely to use nighttime hours and 38% more likely with a 2 sd increase from 1512 to 4678 people/km² (Table 2). Across the available habitat gradient red fox were 21% less likely to use nighttime hours with a 1 sd increase in available greenspace from 0.41 to 0.57, and 38% less likely with a sd increase from 0.41 to 0.73 (Table 2). Note that predictor values vary because they were collected at species-specific scales and not all species were detected at the same sites.

Figure 4

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Table 2

	Available greenspace		Impervious cover		Vegetation cover		Human pop. density		Daily avg. temp
	1-Unit increase	2-Unit increase	1-Unit increase	2-Unit increase	1-Unit increase	2-Unit increase	1-Unit increase	2-Unit increase	1-Unit increase	2-Unit increase
Bobcat	1.01	1.03	1.04	1.09	1.00	1.00	1.27	1.64	0.94	0.89
	(0.72–1.58)	(0.52–2.58)	(0.74–1.75)	(0.54–3.18)	(0.57–1.77)	(0.30–3.51)	(0.87–3.09)	(0.74–10.4)	(0.59–1.24)	(0.35–1.56)
Coyote	0.99	0.97	0.94	0.89	0.97	0.94	1.18	1.38	0.95	0.89
	(0.88–1.10)	(0.76–1.22)	(0.80–1.09)	(0.64–1.19)	(0.84–1.09)	(0.71–1.18)	(1.04–1.35)	(1.05–1.80)	(0.87–1.03)	(0.74–1.06)
Red fox	0.79	0.62	0.94	0.87	1.00	0.99	1.00	1.00	0.92	0.83
	(0.66–0.93)	(0.44–0.86)	(0.75–1.14)	(0.53–1.30)	(0.89–1.13)	(0.78–1.27)	(0.86–1.17)	(0.73–1.38)	(0.82–1.01)	(0.66–1.02)
Raccoon	0.99	0.99	1.10	1.20	1.02	1.03	0.93	0.87	0.81	0.63
	(0.95–1.03)	(0.91–1.07)	(1.05–1.16)	(1.10–1.34)	(0.98–1.06)	(0.96–1.11)	(0.90–0.97)	(0.81–0.94)	(0.76–0.85)	(0.55–0.70)
Striped skunk	1.26	1.58	1.27	1.60	1.02	1.04	1.00	0.99	0.75	0.56
	(0.93–1.77)	(0.84–3.09)	(0.93–1.87)	(0.85–3.48)	(0.86–1.22)	(0.73–1.49)	(0.79–1.22)	(0.60–1.46)	(0.59–0.92)	(0.35–0.84)
Virginia opossum	0.88	0.77	1.01	1.02	1.03	1.05	1.04	1.07	1.25	1.42
	(0.81–0.96)	(0.65–0.92)	(0.91–1.12)	(0.82–1.24)	(0.96–1.10)	(0.92–1.20)	(0.97–1.11)	(0.93–1.23)	(1.14–1.36)	(1.10–1.70)
Eastern cottontail	0.99	0.99	1.03	1.05	0.93	0.87	0.90	0.81	0.57	0.31
	(0.95–1.04)	(0.90–1.07)	(0.97–1.09)	(0.94–1.18)	(0.89–0.98)	(0.78–0.96)	(0.86–0.94)	(0.75–0.89)	(0.53–0.60)	(0.27–0.34)
White-tailed deer	1.05	1.11	1.14	1.31	1.04	1.09	0.92	0.85	0.88	0.77
	(1.00–1.10)	(1.01–1.22)	(1.07–1.21)	(1.16–1.47)	(1.00–1.09)	(0.99–1.19)	(0.88–0.96)	(0.78–0.92)	(0.84–0.92)	(0.70–0.84)

White-tailed deer, eastern cottontail, and raccoon had a greater probability of being active at night where human densities were low; this probability decreased as human population increased (Figure 4). White-tailed deer were 8% less likely to use nighttime hours with a 1 sd increase in population density from 1515 to 3003 people/km², eastern cottontail were 10% less likely (from 2226 to 4633 people/km²), and raccoon were 7% less likely (from 1763 to 3789 people/km²; Table 2). With a 2 sd increase in population density (1515–4491 people/km² for white-tailed deer, 2226–7040 for eastern cottontail, and 1763–5815 for raccoon), white-tailed deer were 15% less likely to be nocturnal, eastern cottontail 19% less likely, and raccoon 13% less likely to be nocturnal (Table 2). Conversely, white-tailed deer and raccoon showed a positive relationship with increased impervious cover and nocturnality (Figure 4). White-tailed deer were 14% more likely to be active at night with a 1 sd increase in impervious cover from 0.16 to 0.31% and 31% more likely with a 2 sd increase from 0.16 to 0.45 (Table 2). Raccoons were 10% more likely to be active at night with a 1 sd increase in impervious cover and 21% more likely with an a 2 sd increase (Table 2).

Vegetation cover had a negative effect on the probability of nocturnal behavior of eastern cottontail (Figure 4). Cottontail were 7% less likely to be nocturnal when the proportion of vegetation cover increased 1 sd above the mean from 0.67 to 0.92, and 13% less likely to be nocturnal when vegetation cover increased 2 sd above the mean from 0.67 to 1 (Table 2). We also found that white-tailed deer were 5% more likely to use nighttime hours when the proportion of available greenspace increased 1 sd above the mean from 0.52 to 0.75% and 11% more likely with an increase of 2 sd from the mean from 0.52 to 0.98 (Table 2). However, Virginia opossum were 12% less likely to be nocturnal with a 1 sd increase in available greenspace from 0.34 to 0.57% and 23% less likely with an increase of 2 sd from 0.34 to 0.78 (Table 2).

Finally, we found an influence of daily average temperature (season) on eastern cottontail, raccoon, striped skunk, white-tailed deer, and Virginia opossum (Figure 4). Eastern cottontail were 43% less likely to use nighttime hours with a 1 sd increase in daily average temperature from 8.17 to 18.76°C, and 69% less likely with a 2 sd increase from 8.17 to 29.36°C (Table 2). With a 1 sd increase in temperature from 12.00 to 21.49°C, raccoon were 19% less likely to use nighttime hours, and 37% less likely with a 2 sd increase from 12.00 to 30.99°C (Table 2). Striped skunk were 25% less likely to exhibit nocturnal behavior with a 1 sd increase in daily average temperature from 15.3 to 24.62°C, and 44% less with a 2 sd increase from 15.3 to 33.94°C (Table 2). White-tailed deer were 12% less likely with a 1 sd increase from 12.01 to 22.65°C and 23% less likely with a 2 sd increase from 12.01 to 33.30°C (Table 2). Virginia opossum, however, were 25% more likely to use nighttime hours with a 1 sd increase in daily average temperature from 13.85 to 22.28°C, and 42% more likely with a 2 sd increase from 13.85 to 30.71°C (Table 2). Again, temperature ranges vary because not all species were detected at the same sites and same times.

Ecological processes act across both space and time. We have, however, only just begun to study how animals use diel time as an ecological resource to avoid risk and adapt to environmental change. We quantified the diel behavior of 8 mammal species across urban gradients in 10 US cities. Our findings indicated that mammals can modulate their use of time within the 24-hr diel period as a resource to persist in urban ecosystems. We found that nocturnal activity had the greatest response to urbanization and seasonality, and that changes in nocturnality in response to urbanization were species specific and varied among cities. Our results also illustrated the complex trade-offs that urban wildlife species must make to contend with both interspecific interactions (i.e., predation and competition) and human activity. These findings offer insight into how mammals might use time as a resource to adapt and persist in urban ecosystems.

We found that coyote had a greater probability of nocturnal behavior in areas with greater human densities. These findings are in agreement with past studies from single cities that documented increases in coyote nocturnal behavior in areas of higher human activity (Gallo et al., 2019; Grinder and Krausman, 2001a; Riley et al., 2003; Tigas et al., 2002). Notably, vehicular collisions are a major mortality factor for coyotes (Grinder and Krausman, 2001b) and coyotes have been typically persecuted by humans when they come in close contact (Dunlap, 1988; Young et al., 2019). Thus, a shift to nocturnal activity when traffic volumes are usually lower and humans are less active outdoors may be particularly important to survival in urban landscapes (Murray and St. Clair, 2015).

Red fox became less nocturnal as the proportion of local greenspace (i.e., available habitat) increased, a finding which may be explained by competition with coyote. Coyote and red fox exhibit a clear dominance hierarchy, whereby the dominant coyote negatively affects the subordinate red fox via competition and predation (Gosselink et al., 2003). Research has shown that urban coyotes occupy larger areas of greenspace (Gehrt et al., 2009). When more greenspace is available around a site, and presumably a higher probability of coyote presence, red foxes may become more diurnal to temporally avoid coyotes and reduce the risk of an interaction. Yet, when greenspace is limited, and presumably there is a lower probability of coyote presence, red foxes could be more active during nighttime hours with less risk of an interaction. These results may present further evidence that a shift in nocturnality of an apex predator or dominate species facilitates a ‘behavioral release’ in subordinate species as described by Frey et al., 2020.

In most cases, the human aspects of urban environments captured by our predictor variables had opposite effects on omnivores and herbivores. While human population densities increased nocturnal activity for coyote, it decreased nocturnal activity for white-tailed deer, eastern cottontail, and raccoon. Prey species may become more diurnal to avoid increased activity in nocturnal predators (Mills and Harris, 2020) or they may be utilizing increased human activity during daytime hours as a human-mediated shield. Prey species are known to spatially distribute themselves near human activity to act as a shield from predators (Berger, 2007; Shannon et al., 2014). In these cases, prey species may also utilize time as a human-mediated shield, exhibiting more activity at times of high human activity (daytime) in areas of high human densities. These results may seem counterintuitive given that increasing impervious cover increased the probability of nocturnal behavior exhibited by deer and raccoon (Figure 4) and selection for nighttime hours by striped skunk (Figure 3). However, a majority of impervious surfaces in the United States are roads and parking lots – places of high vehicular traffic (Frazer, 2005). Similar to coyote, vehicular collisions are a major source of mortality for these species (Glista et al., 2009). Therefore, a shift to nocturnal activity in areas with high impervious cover may be particularly important to their survival in cities and a sign of fine-scale modulation of temporal selection based on local environments.

Similarly, raccoon and white-tailed deer selected more for nighttime hours (Figure 3) in locations with high levels of vegetation cover. More vegetation equates to more protective cover. Therefore, we suggest that raccoons and white-tailed deer can use the same temporal habitat as their predators (i.e., coyote) – but with less risk – when there is more physical cover. On the other hand, eastern cottontail were more diurnal with increased vegetation (Figures 3 and 4), suggesting that more vegetation cover provides shelter from other perceived threats (i.e., humans; Gallo et al., 2019) and may allow eastern cottontail to select periods of high human activity (i.e., day). Interactions between various urban characteristics, which we did not examine in this study, should be further explored to fully understand how these characteristics jointly influence the temporal patterns of urban wildlife species.

Our results highlight the complexity of trade-offs for urban wildlife. In most cases, we found diverging activity patterns between coyote (a common urban apex predator) and subordinate or prey species in response to physical characteristics of urban environments. To persist in urban environments, it appears that urban species may have to modulate behaviors to contend with both anthropogenic risks and risk from predation or competition. Our results add to a growing body of literature that indicate species interactions in human-dominated landscapes may be better understood by explicitly considering the role humans play in those interactions (Berger, 2007; Blecha et al., 2018; Gallo et al., 2019; Magle et al., 2014).

We also found evidence that local climate, specifically temperature, regulated the diel behavior of many species. For example, white-tailed deer, eastern cottontail, and striped skunk became more diurnal as temperatures increased, presumably foraging more during the day in warmer seasons when more vegetation biomass is available. Virginia opossum showed a decrease in nocturnal behavior at lower temperatures. Given their poor thermoregulation abilities, poorly insulated fur, and cold-sensitive hands, ears, and tails (Kanda, 2005), it seems likely that Virginia opossum are morphologically constrained and thus unable to alter their diel activity patterns at colder temperatures. These results call attention to the importance of considering the impacts of morphology, physiology, and life history on a species’ capacity to adapt to environmental change. Given the interacting effects of climate change and urbanization (Stone, 2012), future research should explore how life history traits mediate temporal distributions of species activity – particularly as cities are rapidly warming (Oleson et al., 2013).

We did not find changes in diel activity for some species in response to our predictor variables. These results could be due to a lack of data on a particular species (i.e., bobcat) or because we did not sample across a large enough urban–rural gradient. Remote regions were not sampled in our study design, and some species may change their behavior at a lower level of urban intensity that we did not sample. Combining datasets from more rural and remote areas (e.g., Snapshot Serengeti Swanson et al., 2015, Snapshot USA Cove et al., 2021) could allow us to identify the level of human development that elicits changes in diel activity for potentially sensitive species. Finally, our analysis was limited to the physical characteristics of cities. Additional characteristics like chronic noise, light pollution, resource supplementation, and species interactions influence animal behaviors and should be explored in future research.

A variety of methods have been developed to study animal activity patterns and temporal behavior using time-stamped camera data (see Frey et al., 2017 and references within). However, very little work has been done to quantify changes in temporal behavior across continuous independent variables (Cox et al., 2021; Gaston, 2019). Here, we built upon Farris et al., 2015 and developed an analytical approach to quantify temporal resource selection across continuous environmental gradients. Although we have developed a new analytical tool to measure temporal selection, a theoretical context for temporal habitat selection is needed and a further understanding of disproportional selection relative to the number of hours available is a promising avenue for future animal biology research.

Temporal partitioning may facilitate human–wildlife coexistence and effectively increase available habitats for species in cities. Temporal partitioning may also limit contact between people and animals, potentially reducing negative encounters like disease transmission and attacks on people (Gaynor et al., 2018). From a management perspective, ignoring diel behavior can result in biased estimates of species abundance and patterns of habitat use and lead to misinformed conservation measures (Gaston, 2019). Additionally, recognizing plasticity in species behavior can lead to better predictions of vulnerability to anthropogenic disturbances (Gaynor et al., 2018). Therefore, we recommend that diel activity and temporal partitioning be considered in conservation and management approaches.

We have shown that mammals have significant variation in the use and selection of time throughout the diel period. Additionally, our approach allowed us – for the first time – to quantify changes in diel activity across gradients of environmental change and across multiple urban areas, revealing that changes in diel patterns are influenced by natural and human landscape characteristics. Our results highlight the need to understand how a larger proportion of the animal community responds to urbanization, and provide evidence of behavioral plasticity that allows some species to adapt to and persist in human-dominated systems. Future projections of urban growth signal that urban areas will continually encroach on wildlife habitat. Therefore, it is imperative that we consider animal behavioral responses to urbanization as we plan human spaces that can also accommodate wildlife.

All related data and R scripts have been deposited at Dryad: https://doi.org/10.5061/dryad.fxpnvx0tb.

1. 1. Gallo T
   2. Fidino M
   3. Gerber B
   4. Ahlers A
   5. Angstmann J
   6. Amaya M
   7. Concilio A
   8. Drake D
   9. Gray D
   10. Lehrer E
   11. Murray M
   12. Ryan TJ
   13. Salsbury C
   14. Sander H
   15. Stankowich T
   16. Williamson J
   17. Belaire A
   18. Simone K
   19. Magle S
   20. St Clair C

   (2022) Dryad Digital Repository

   Mammals adjust diel activity across gradients of urbanization.

   https://doi.org/10.5061/dryad.fxpnvx0tb

1. 1. Berger J

   (2007) Fear, human shields and the redistribution of prey and predators in protected areas

   Biology Letters 3:620–623.

   https://doi.org/10.1098/rsbl.2007.0415

   • PubMed
   • Google Scholar
2. 1. Blecha KA
   2. Boone RB
   3. Alldredge MW

   (2018) Hunger mediates apex predator’s risk avoidance response in wildland-urban interface

   The Journal of Animal Ecology 87:609–622.

   https://doi.org/10.1111/1365-2656.12801

   • PubMed
   • Google Scholar
3. Software

   1. Chamberlain S

   (2020)

   rnoaa: “NOAA” Weather Data from R

   R Package Version 0.9.6.
4. 1. Clinchy M
   2. Zanette LY
   3. Roberts D
   4. Suraci JP
   5. Buesching CD
   6. Newman C
   7. Macdonald DW

   (2016) Fear of the human “super predator” far exceeds the fear of large carnivores in a model mesocarnivore

   Behavioral Ecology 27:arw117.

   https://doi.org/10.1093/beheco/arw117

   • Google Scholar
5. 1. Cove MV
   2. Kays R
   3. Bontrager H
   4. Bresnan C
   5. Lasky M
   6. Frerichs T
   7. Klann R
   8. Lee TE
   9. Crockett SC
   10. Crupi AP
   11. Weiss KCB
   12. Rowe H
   13. Sprague T
   14. Schipper J
   15. Tellez C
   16. Lepczyk CA
   17. Fantle-Lepczyk JE
   18. LaPoint S
   19. Williamson J
   20. Fisher-Reid MC
   21. King SM
   22. Bebko AJ
   23. Chrysafis P
   24. Jensen AJ
   25. Jachowski DS
   26. Sands J
   27. MacCombie KA
   28. Herrera DJ
   29. van der Merwe M
   30. Knowles TW
   31. Horan RV
   32. Rentz MS
   33. Brandt LSE
   34. Nagy C
   35. Barton BT
   36. Thompson WC
   37. Maher SP
   38. Darracq AK
   39. Hess G
   40. Parsons AW
   41. Wells B
   42. Roemer GW
   43. Hernandez CJ
   44. Gompper ME
   45. Webb SL
   46. Vanek JP
   47. Lafferty DJR
   48. Bergquist AM
   49. Hubbard T
   50. Forrester T
   51. Clark D
   52. Cincotta C
   53. Favreau J
   54. Facka AN
   55. Halbur M
   56. Hammerich S
   57. Gray M
   58. Rega-Brodsky CC
   59. Durbin C
   60. Flaherty EA
   61. Brooke JM
   62. Coster SS
   63. Lathrop RG
   64. Russell K
   65. Bogan DA
   66. Cliché R
   67. Shamon H
   68. Hawkins MTR
   69. Marks SB
   70. Lonsinger RC
   71. O’Mara MT
   72. Compton JA
   73. Fowler M
   74. Barthelmess EL
   75. Andy KE
   76. Belant JL
   77. Beyer DE
   78. Kautz TM
   79. Scognamillo DG
   80. Schalk CM
   81. Leslie MS
   82. Nasrallah SL
   83. Ellison CN
   84. Ruthven C
   85. Fritts S
   86. Tleimat J
   87. Gay M
   88. Whittier CA
   89. Neiswenter SA
   90. Pelletier R
   91. DeGregorio BA
   92. Kuprewicz EK
   93. Davis ML
   94. Dykstra A
   95. Mason DS
   96. Baruzzi C
   97. Lashley MA
   98. Risch DR
   99. Price MR
   100. Allen ML
   101. Whipple LS
   102. Sperry JH
   103. Hagen RH
   104. Mortelliti A
   105. Evans BE
   106. Studds CE
   107. Sirén APK
   108. Kilborn J
   109. Sutherland C
   110. Warren P
   111. Fuller T
   112. Harris NC
   113. Carter NH
   114. Trout E
   115. Zimova M
   116. Giery ST
   117. Iannarilli F
   118. Higdon SD
   119. Revord RS
   120. Hansen CP
   121. Millspaugh JJ
   122. Zorn A
   123. Benson JF
   124. Wehr NH
   125. Solberg JN
   126. Gerber BD
   127. Burr JC
   128. Sevin J
   129. Green AM
   130. Şekercioğlu ÇH
   131. Pendergast M
   132. Barnick KA
   133. Edelman AJ
   134. Wasdin JR
   135. Romero A
   136. O’Neill BJ
   137. Schmitz N
   138. Alston JM
   139. Kuhn KM
   140. Lesmeister DB
   141. Linnell MA
   142. Appel CL
   143. Rota C
   144. Stenglein JL
   145. Anhalt-Depies C
   146. Nelson C
   147. Long RA
   148. Jo Jaspers K
   149. Remine KR
   150. Jordan MJ
   151. Davis D
   152. Hernández-Yáñez H
   153. Zhao JY
   154. McShea WJ

   (2021) SNAPSHOT USA 2019: a coordinated national camera trap survey of the United States

   Ecology 102:e03353.

   https://doi.org/10.1002/ecy.3353

   • PubMed
   • Google Scholar
6. 1. Cox DTC
   2. Gardner AS
   3. Gaston KJ

   (2021) Diel niche variation in mammals associated with expanded trait space

   Nature Communications 12:1753.

   https://doi.org/10.1038/s41467-021-22023-4

   • PubMed
   • Google Scholar
7. 1. Denwood MJ

   (2016)

   runjags: An R Package Providing Interface Utilities, Model Templates, Parallel Computing Methods and Additional Distributions for MCMC Models in JAGS

   Journal of Statistical Software 71:25.

   • Google Scholar
8. Book

   1. Dunlap T.

   (1988)

   Saving America’s Wildlife: Ecology and the American Mind, 1850-1990

   New Jersey, USA: Princeton University Press.

   • Google Scholar
9. 1. Etter DR
   2. Hollis KM
   3. Deelen TRV
   4. Ludwig DR
   5. Chelsvig JE
   6. Anchor CL
   7. Warner RE

   (2002) Survival and Movements of White-Tailed Deer in Suburban Chicago, Illinois

   The Journal of Wildlife Management 66:500.

   https://doi.org/10.2307/3803183

   • Google Scholar
10. 1. Farris ZJ
    2. Gerber BD
    3. Karpanty S
    4. Murphy A
    5. Andrianjakarivelo V
    6. Ratelolahy F
    7. Kelly MJ

    (2015) When carnivores roam: temporal patterns and overlap among M adagascar’s native and exotic carnivores

    Journal of Zoology 296:45–57.

    https://doi.org/10.1111/jzo.12216

    • Google Scholar
11. 1. Fidino MA
    2. Lehrer EW
    3. Magle SB

    (2016) Habitat Dynamics of the Virginia Opossum in a Highly Urban Landscape

    The American Midland Naturalist 175:155–167.

    https://doi.org/10.1674/0003-0031-175.2.155

    • Google Scholar
12. 1. Fidino M
    2. Barnas GR
    3. Lehrer EW
    4. Murray MH
    5. Magle SB

    (2020) Effect of Lure on Detecting Mammals with Camera Traps

    Wildlife Society Bulletin 44:543–552.

    https://doi.org/10.1002/wsb.1122

    • Google Scholar
13. 1. Fidino M
    2. Gallo T
    3. Lehrer EW
    4. Murray MH
    5. Kay CAM
    6. Sander HA
    7. MacDougall B
    8. Salsbury CM
    9. Ryan TJ
    10. Angstmann JL
    11. Amy Belaire J
    12. Dugelby B
    13. Schell CJ
    14. Stankowich T
    15. Amaya M
    16. Drake D
    17. Hursh SH
    18. Ahlers AA
    19. Williamson J
    20. Hartley LM
    21. Zellmer AJ
    22. Simon K
    23. Magle SB

    (2021) Landscape-scale differences among cities alter common species’ responses to urbanization

    Ecological Applications 31:e02253.

    https://doi.org/10.1002/eap.2253

    • PubMed
    • Google Scholar
14. 1. Frazer L

    (2005) Paving paradise: the peril of impervious surfaces

    Environmental Health Perspectives 113:A456–A462.

    https://doi.org/10.1289/ehp.113-a456

    • PubMed
    • Google Scholar
15. 1. Frey S
    2. Fisher JT
    3. Burton AC
    4. Volpe JP
    5. Rowcliffe M

    (2017) Investigating animal activity patterns and temporal niche partitioning using camera-trap data: challenges and opportunities

    Remote Sensing in Ecology and Conservation 3:123–132.

    https://doi.org/10.1002/rse2.60

    • Google Scholar
16. 1. Frey S
    2. Volpe JP
    3. Heim NA
    4. Paczkowski J
    5. Fisher JT

    (2020) Move to nocturnality not a universal trend in carnivore species on disturbed landscapes

    Oikos 129:1128–1140.

    https://doi.org/10.1111/oik.07251

    • Google Scholar
17. 1. Gallo T
    2. Fidino M
    3. Lehrer EW
    4. Magle S

    (2019) Urbanization alters predator-avoidance behaviours

    The Journal of Animal Ecology 88:793–803.

    https://doi.org/10.1111/1365-2656.12967

    • PubMed
    • Google Scholar
18. 1. Gaston KJ

    (2019) Nighttime Ecology: The “Nocturnal Problem” Revisited

    The American Naturalist 193:481–502.

    https://doi.org/10.1086/702250

    • PubMed
    • Google Scholar
19. 1. Gaynor KM
    2. Hojnowski CE
    3. Carter NH
    4. Brashares JS

    (2018) The influence of human disturbance on wildlife nocturnality

    Science (New York, N.Y.) 360:1232–1235.

    https://doi.org/10.1126/science.aar7121

    • PubMed
    • Google Scholar
20. 1. Gehrt SD
    2. Anchor C
    3. White LA

    (2009) Home Range and Landscape Use of Coyotes in a Metropolitan Landscape: Conflict or Coexistence?

    Journal of Mammalogy 90:1045–1057.

    https://doi.org/10.1644/08-MAMM-A-277.1

    • Google Scholar
21. 1. Gelman A
    2. Rubin DB

    (1992) Inference from Iterative Simulation Using Multiple Sequences

    Statistical Science 7:457–472.

    https://doi.org/10.1214/ss/1177011136

    • Google Scholar
22. Book

    1. Gelman A
    2. Hill J

    (2006) Data Analysis Using Regression and Multilevel/Hierarchical Models

    Cambridge University Press.

    https://doi.org/10.1017/CBO9780511790942

    • Google Scholar
23. 1. Glista DJ
    2. DeVault TL
    3. DeWoody JA

    (2009) A review of mitigation measures for reducing wildlife mortality on roadways

    Landscape and Urban Planning 91:1–7.

    https://doi.org/10.1016/j.landurbplan.2008.11.001

    • Google Scholar
24. 1. Gosselink TE
    2. Deelen TRV
    3. Warner RE
    4. Joselyn MG

    (2003) Temporal Habitat Partitioning and Spatial Use of Coyotes and Red Foxes in East-Central Illinois

    The Journal of Wildlife Management 67:90.

    https://doi.org/10.2307/3803065

    • Google Scholar
25. 1. Grinder MI
    2. Krausman PR

    (2001a) Home Range, Habitat Use, and Nocturnal Activity of Coyotes in an Urban Environment

    The Journal of Wildlife Management 65:887.

    https://doi.org/10.2307/3803038

    • Google Scholar
26. 1. Grinder MI
    2. Krausman PR

    (2001b) Morbidity-mortality factors and survival of an urban coyote population in Arizona

    Journal of Wildlife Diseases 37:312–317.

    https://doi.org/10.7589/0090-3558-37.2.312

    • PubMed
    • Google Scholar
27. 1. Homer CG

    (2015)

    Completion of the 2011 National Land Cover Database for the conterminous United States – representing a decade of land cover change information

    Photogramm Eng Remote Sens 81:345–353.

    • Google Scholar
28. Book

    1. Hooten MB
    2. Johnson DS
    3. McClintock BT
    4. Morales JM

    (2017) Animal Movement: Statistical Models for Telemetry Data

    CRC press.

    https://doi.org/10.1201/9781315117744

    • Google Scholar
29. 1. Hunt VM
    2. Magle SB
    3. Vargas C
    4. Brown AW
    5. Lonsdorf EV
    6. Sacerdote AB
    7. Sorley EJ
    8. Santymire RM

    (2013) Survival, abundance, and capture rate of eastern cottontail rabbits in an urban park

    Urban Ecosystems 17:547–560.

    https://doi.org/10.1007/s11252-013-0334-z

    • Google Scholar
30. 1. Kanda LL

    (2005) Winter energetics of Virginia opossums Didelphis virginiana and implications for the species’ northern distributional limit

    Ecography 28:731–744.

    https://doi.org/10.1111/j.2005.0906-7590.04173.x

    • Google Scholar
31. 1. Kitchen AM
    2. Gese EM
    3. Schauster ER

    (2000) Changes in coyote activity patterns due to reduced exposure to human persecution

    Canadian Journal of Zoology 78:853–857.

    https://doi.org/10.1139/z00-003

    • Google Scholar
32. 1. Kronfeld-Schor N
    2. Dayan T

    (2003) Partitioning of Time as an Ecological Resource

    Annual Review of Ecology, Evolution, and Systematics 34:153–181.

    https://doi.org/10.1146/annurev.ecolsys.34.011802.132435

    • Google Scholar
33. Book

    1. Kruschke JK

    (2011)

    Doing Bayesian Data Analysis (2nd ed)

    London, UK: Academic Press.

    • Google Scholar
34. 1. Levy O
    2. Dayan T
    3. Porter WP
    4. Kronfeld-Schor N

    (2019) Time and ecological resilience: can diurnal animals compensate for climate change by shifting to nocturnal activity?

    Ecological Monographs 89:e01334.

    https://doi.org/10.1002/ecm.1334

    • Google Scholar
35. 1. Magle SB
    2. Simoni LS
    3. Lehrer EW
    4. Brown JS

    (2014) Urban predator–prey association: coyote and deer distributions in the Chicago metropolitan area

    Urban Ecosystems 17:875–891.

    https://doi.org/10.1007/s11252-014-0389-5

    • Google Scholar
36. 1. Magle SB
    2. Fidino M
    3. Lehrer EW
    4. Gallo T
    5. Mulligan MP
    6. Ríos MJ
    7. Ahlers AA
    8. Angstmann J
    9. Belaire A
    10. Dugelby B
    11. Gramza A
    12. Hartley L
    13. MacDougall B
    14. Ryan T
    15. Salsbury C
    16. Sander H
    17. Schell C
    18. Simon K
    19. St Onge S
    20. Drake D

    (2019) Advancing urban wildlife research through a multi‐city collaboration

    Frontiers in Ecology and the Environment 17:232–239.

    https://doi.org/10.1002/fee.2030

    • Google Scholar
37. 1. Maloney SK
    2. Moss G
    3. Cartmell T
    4. Mitchell D

    (2005) Alteration in diel activity patterns as a thermoregulatory strategy in black wildebeest (Connochaetes gnou

    Journal of Comparative Physiology. A, Neuroethology, Sensory, Neural, and Behavioral Physiology 191:1055–1064.

    https://doi.org/10.1007/s00359-005-0030-4

    • PubMed
    • Google Scholar
38. 1. McDonnell MJ
    2. Pickett STA

    (1990) Ecosystem Structure and Function along Urban-Rural Gradients: An Unexploited Opportunity for Ecology

    Ecology 71:1232–1237.

    https://doi.org/10.2307/1938259

    • Google Scholar
39. 1. Milliren CE
    2. Evans CR
    3. Richmond TK
    4. Dunn EC

    (2018) Does an uneven sample size distribution across settings matter in cross-classified multilevel modeling? Results of a simulation study

    Health & Place 52:121–126.

    https://doi.org/10.1016/j.healthplace.2018.05.009

    • PubMed
    • Google Scholar
40. 1. Mills KL
    2. Harris NC

    (2020) Humans disrupt access to prey for large African carnivores

    eLife 9:e60690.

    https://doi.org/10.7554/eLife.60690

    • PubMed
    • Google Scholar
41. 1. Mueller MA
    2. Drake D
    3. Allen ML

    (2018) Coexistence of coyotes (Canis latrans) and red foxes (Vulpes vulpes) in an urban landscape

    PLOS ONE 13:e0190971.

    https://doi.org/10.1371/journal.pone.0190971

    • PubMed
    • Google Scholar
42. 1. Murray MH
    2. St. Clair CC

    (2015) Individual flexibility in nocturnal activity reduces risk of road mortality for an urban carnivore

    Behavioral Ecology 26:1520–1527.

    https://doi.org/10.1093/beheco/arv102

    • Google Scholar
43. 1. Oleson KW
    2. Monaghan A
    3. Wilhelmi O
    4. Barlage M
    5. Brunsell N
    6. Feddema J
    7. Hu L
    8. Steinhoff DF

    (2013) Interactions between urbanization, heat stress, and climate change

    Climatic Change 129:525–541.

    https://doi.org/10.1007/s10584-013-0936-8

    • Google Scholar
44. Book

    1. Owen-Smith N

    (2008) Effects of Temporal Variability in Resources on Foraging Behaviour In: Prins HHT

    In: Van Langevelde F, editors. Resource Ecology, Wageningen UR Frontis Series. Springer. pp. 159–181.

    https://doi.org/10.1007/978-1-4020-6850-8_14

    • Google Scholar
45. 1. Oyeyemi GM
    2. Ogunjobi EO
    3. Folorunsho AI

    (2015)

    On Performance of Shrinkage Methods – A Monte Carlo Study

    International Journal of Statistics and Applications 5:72–76.

    • Google Scholar
46. Book

    1. Pacione M

    (2009) Urban Geography: A Global Perspective

    London: Routledge.

    https://doi.org/10.4324/9780203023525

    • Google Scholar
47. 1. Page DL
    2. Schuyler PA
    3. Dupont WD
    4. Jensen RA
    5. Plummer WD
    6. Simpson JF

    (2003) Atypical lobular hyperplasia as a unilateral predictor of breast cancer risk: a retrospective cohort study

    Lancet (London, England) 361:125–129.

    https://doi.org/10.1016/S0140-6736(03)12230-1

    • PubMed
    • Google Scholar
48. 1. Prugh LR
    2. Golden CD

    (2014) Does moonlight increase predation risk? Meta-analysis reveals divergent responses of nocturnal mammals to lunar cycles

    The Journal of Animal Ecology 83:504–514.

    https://doi.org/10.1111/1365-2656.12148

    • PubMed
    • Google Scholar
49. Software

    1. R Development Core Team

    (2021) R: A language and environment for statistical computing

    R Foundation for Statistical Computing, Vienna, Austria.

    http://www.r-project.org
50. 1. Radeloff VC
    2. Helmers DP
    3. Kramer HA
    4. Mockrin MH
    5. Alexandre PM
    6. Bar-Massada A
    7. Butsic V
    8. Hawbaker TJ
    9. Martinuzzi S
    10. Syphard AD
    11. Stewart SI

    (2018) Rapid growth of the US wildland-urban interface raises wildfire risk

    PNAS 115:3314–3319.

    https://doi.org/10.1073/pnas.1718850115

    • PubMed
    • Google Scholar
51. 1. Ridout MS
    2. Linkie M

    (2009) Estimating overlap of daily activity patterns from camera trap data

    Journal of Agricultural, Biological, and Environmental Statistics 14:322–337.

    https://doi.org/10.1198/jabes.2009.08038

    • Google Scholar
52. 1. Riley SPD
    2. Sauvajot RM
    3. Fuller TK
    4. York EC
    5. Kamradt DA
    6. Bromley C
    7. Wayne RK

    (2003) Effects of Urbanization and Habitat Fragmentation on Bobcats and Coyotes in Southern California

    Conservation Biology 17:566–576.

    https://doi.org/10.1046/j.1523-1739.2003.01458.x

    • Google Scholar
53. 1. Rosatte RC

    (2000) Management of raccoons (Procyon lotor) in Ontario, Canada: Do human intervention and disease have significant impact on raccoon populations ?

    Mammalia 64:369–390.

    https://doi.org/10.1515/mamm.2000.64.4.369

    • Google Scholar
54. 1. Schoener TW

    (1974) Resource partitioning in ecological communities

    Science (New York, N.Y.) 185:27–39.

    https://doi.org/10.1126/science.185.4145.27

    • PubMed
    • Google Scholar
55. Software

    1. Schwalb-Willmann J

    (2019) getSpatialData, version 0.0.4

    R Package.

    https://github.com/16EAGLE/getSpatialData
56. 1. Shannon G
    2. Cordes LS
    3. Hardy AR
    4. Angeloni LM
    5. Crooks KR

    (2014) Behavioral responses associated with a human-mediated predator shelter

    PLOS ONE 9:e94630.

    https://doi.org/10.1371/journal.pone.0094630

    • PubMed
    • Google Scholar
57. Book

    1. Stone J

    (2012) The City and the Coming Climate: Climate Change in the Places We Live

    Cambridge University Press.

    https://doi.org/10.1017/CBO9781139061353

    • Google Scholar
58. 1. Swanson A
    2. Kosmala M
    3. Lintott C
    4. Simpson R
    5. Smith A
    6. Packer C

    (2015) Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna

    Scientific Data 2:150026.

    https://doi.org/10.1038/sdata.2015.26

    • PubMed
    • Google Scholar
59. 1. Tambling CJ
    2. Minnie L
    3. Meyer J
    4. Freeman EW
    5. Santymire RM
    6. Adendorff J
    7. Kerley GIH

    (2015) Temporal shifts in activity of prey following large predator reintroductions

    Behavioral Ecology and Sociobiology 69:1153–1161.

    https://doi.org/10.1007/s00265-015-1929-6

    • Google Scholar
60. Software

    1. Thieurmel B
    2. Elmarhraoui A

    (2019)

    suncalc: Compute Sun Position, Sunlight Phases, Moon Position and Lunar Phase

    Suncalc.
61. 1. Tigas LA
    2. Van Vuren DH
    3. Sauvajot RM

    (2002) Behavioral responses of bobcats and coyotes to habitat fragmentation and corridors in an urban environment

    Biological Conservation 108:299–306.

    https://doi.org/10.1016/S0006-3207(02)00120-9

    • Google Scholar
62. 1. Tutz G
    2. Pößnecker W
    3. Uhlmann L

    (2015) Variable selection in general multinomial logit models

    Computational Statistics & Data Analysis 82:207–222.

    https://doi.org/10.1016/j.csda.2014.09.009

    • Google Scholar
63. Book

    1. U.S. Census Bureau

    (2010)

    Population, Housing Units, Area, and Density: 2010 - County -- County Subdivision and Place More Information 2010 Census Summary File 1

    U.S. Department of Commerce.

    • Google Scholar
64. 1. van der Vinne V
    2. Tachinardi P
    3. Riede SJ
    4. Akkerman J
    5. Scheepe J
    6. Daan S
    7. Hut RA

    (2019) Maximising survival by shifting the daily timing of activity

    Ecology Letters 22:2097–2102.

    https://doi.org/10.1111/ele.13404

    • PubMed
    • Google Scholar
65. 1. van Erp S
    2. Oberski DL
    3. Mulder J

    (2019) Shrinkage priors for Bayesian penalized regression

    Journal of Mathematical Psychology 89:31–50.

    https://doi.org/10.1016/j.jmp.2018.12.004

    • Google Scholar
66. 1. Weissinger MD
    2. Theimer TC
    3. Bergman DL
    4. Deliberto TJ

    (2009) Nightly and seasonal movements seasonal home range, and focal location photo-monitoring of urban striped skunks (mephitis mephitis): implications for rabies transmission

    Journal of Wildlife Diseases 45:388–397.

    https://doi.org/10.7589/0090-3558-45.2.388

    • Google Scholar
67. 1. Wolkovich EM
    2. Cook BI
    3. McLauchlan KK
    4. Davies TJ

    (2014) Temporal ecology in the Anthropocene

    Ecology Letters 17:1365–1379.

    https://doi.org/10.1111/ele.12353

    • PubMed
    • Google Scholar
68. 1. Wright JD
    2. Burt MS
    3. Jackson VL

    (2012) Influences of an urban environment on home range and body mass of virginia opossums (Didelphis virginiana

    Northeastern Naturalist 19:77–86.

    https://doi.org/10.1656/045.019.0106

    • Google Scholar
69. 1. Young JK
    2. Hammill E
    3. Breck SW

    (2019) Interactions with humans shape coyote responses to hazing

    Scientific Reports 9:20046.

    https://doi.org/10.1038/s41598-019-56524-6

    • PubMed
    • Google Scholar

Author details

1. Travis Gallo

   1. Environmental Science and Policy, College of Science, George Mason University, Fairfax, United States
   2. Urban Wildlife Institute, Conservation and Science Department, Lincoln Park Zoo, Chicago, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing - original draft, Writing – review and editing

   For correspondence

   hgallo@gmu.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2877-9848

2. Mason Fidino

   Urban Wildlife Institute, Conservation and Science Department, Lincoln Park Zoo, Chicago, United States

   Contribution

   Conceptualization, Formal analysis, Methodology, Visualization, Writing - original draft, Writing – review and editing

   Competing interests

   No competing interests declared

3. Brian Gerber

   Department of Natural Resource Science, The University of Rhode Island, Kingston, United States

   Contribution

   Conceptualization, Methodology, Writing - original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9285-9784

4. Adam A Ahlers

   Department of Horticulture and Natural Resources, Kansas State University, Manhattan, United States

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Julia L Angstmann

   Department of Biological Sciences and Center for Urban Ecology and Sustainability, Butler University, Indianapolis, United States

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

6. Max Amaya

   Department of Biological Sciences, California State University Long Beach, Long Beach, United States

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

7. Amy L Concilio

   Department of Environmental Science and Policy, St. Edward’s University, Austin, United States

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

8. David Drake

   Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, United States

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

9. Danielle Gay

   Austin Parks and Recreation, City of Austin, Austin, United States

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

10. Elizabeth W Lehrer

    Urban Wildlife Institute, Conservation and Science Department, Lincoln Park Zoo, Chicago, United States

    Contribution

    Conceptualization, Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

11. Maureen H Murray

    Urban Wildlife Institute, Conservation and Science Department, Lincoln Park Zoo, Chicago, United States

    Contribution

    Conceptualization, Writing – review and editing

    Competing interests

    No competing interests declared

12. Travis J Ryan

    Department of Biological Sciences and Center for Urban Ecology and Sustainability, Butler University, Indianapolis, United States

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-2039-5989

13. Colleen Cassady St Clair

    Department of Biological Sciences, University of Alberta, Edmonton, Canada

    Contribution

    Conceptualization, Writing – review and editing

    Competing interests

    No competing interests declared

14. Carmen M Salsbury

    Department of Biological Sciences and Center for Urban Ecology and Sustainability, Butler University, Indianapolis, United States

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

15. Heather A Sander

    Department of Geographical and Sustainability Sciences, University of Iowa, Iowa City, United States

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

16. Theodore Stankowich

    Department of Biological Sciences, California State University Long Beach, Long Beach, United States

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

17. Jaque Williamson

    Department of Education & Conservation, Brandywine Zoo, Wilmington, United States

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

18. J Amy Belaire

    The Nature Conservancy in Texas, San Antonio, United States

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

19. Kelly Simon

    Texas Parks and Wildlife, Austin, United States

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-9694-5548

20. Seth B Magle

    Urban Wildlife Institute, Conservation and Science Department, Lincoln Park Zoo, Chicago, United States

    Contribution

    Conceptualization, Data curation, Funding acquisition, Project administration, Resources, Supervision, Writing – review and editing

    Competing interests

    No competing interests declared

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