Our study utilized 66 motion-triggered cameras deployed annually for three-month periods, for three consecutive years, to study the wildlife community within residential properties of a human-impacted landscape in New York state. Three-month deployment periods allowed detection of both transient and resident individuals, although we could not distinguish between these categories. Passerine birds and some small mammals (e.g. shrews, voles) known to be present in the properties were not readily detectable and excluded from analyses. Other small mammals, such as white-footed mice, known to be ubiquitous in this region (LoGiudice et al. 2003) were infrequently detected and thus not fully modeled for occupancy.
From our imagery alone, we identified 18 mammal and 6 non-passerine bird species. A 2018 study by Linske et al. (2018) that utilized camera traps to measure biodiversity in residential Connecticut yards, identified 12 (non-domestic) mammal species as well as wild turkey and birds of prey. Our cameras captured all mammal species identified in Linske et al. (2018), as well as black rats, fishers, gray foxes, woodchucks, red squirrels, southern flying squirrels and white-footed mice. We detected additional species likely due to our longer duration of camera deployment (17,820 camera trap days compared to 2,240 camera trap days). A 2019 study by Stark et al. (2019), which used camera traps to study predator communities, identified six carnivore and one marsupial species (raccoons, coyotes, black bears, red foxes, striped skunks, bobcats, and opossums) in four preserves in Westchester County, NY and Morris County, NJ. Based on detection and reported relative abundance indices (RAIs), gray foxes, raccoons, red foxes, opossums, and striped skunks were more abundant in our study, while American black bears and coyotes were more abundant in preserves. However, our cameras were deployed between late September and early March, corresponding with the American black bear’s hibernation period. Bobcat RAIs between the two studies were comparable. The detection and higher RAIs for most species found in our study provides evidence that moderately developed landscapes (i.e., residential yards) can contain a higher abundance of mammals than natural areas. Results from additional studies (Linkse et al. 2018; Hansen et al. 2020), which utilized camera-traps in residential and natural areas, have found similar results, suggesting that there might be features of residential landscapes that benefit some wildlife species.
Our full species-richness model estimated a maximum of 33 species present, based on our 2018–2019 data. The difference between the number of species estimated by our species-richness model and the number of species directly detected by our cameras highlights the importance of accounting for imperfect detection when using motion-triggered cameras to study biodiversity. We did not detect any strong spatial patterns when modeling estimated distributions of species with naïve occupancies > 0.10, which may reflect homogeneity between our camera clusters at broader spatial scales (~ 1 km).
Eight species that meet the basic requirements for detection by our cameras (body weight > 100g, ground-dwelling) and have been identified in Dutchess County, NY (Ueda K, 2021) were not captured by our cameras: muskrats (Ondatra zibethicus), American beavers (Castor canadensis), North American river otters (Lontra canadensis) North American porcupines (Erethizon dorsatum), American minks (Neovison vison) long-tailed weasels (Mustela frenata) snowshoe hares (Lepus americanus), and short-tailed weasels (Mustela erminea). These species might occur at densities low enough that we failed to detect them with our cameras, or they might not be present in residential communities, owing to habitat specialization. For example, muskrats, beavers, otters, and minks require aquatic habitat.
A second goal of our study was to examine how individual species respond to anthropogenic landscape features. White-tailed deer, Virginia opossums, red foxes, eastern gray squirrels, and raccoons were detected in every one of our study neighborhoods. Their presence at all study locations prevented us from using covariates within site-occupancy models to understand the varying influences of anthropogenic landscape features. These species may thrive in human-impacted areas due to human-subsidized resources, or lower predation/hunting risk. While both ground-dwelling and large enough to be captured by cameras, bobcats and fishers were rarely detected in our neighborhoods. Two additional studies that utilized cameras in residential yards and natural locations in North Carolina (Kays and Parsons 2014; Hansen et al. 2020), also failed to detect bobcats in residential yards, only finding them in less human-impacted locations. Infrequent detection of bobcats in residential areas suggests they may be unable to thrive in such fragmented landscapes. Additionally, as obligate carnivores, due to their diet, bobcats may be unable to exploit supplemental food resources (Anderson and Lovallo 2003) found in residential yards.
We were able to create site-occupancy models for 13 species. Based on our results, gray foxes, red squirrels, striped skunks, and eastern cottontails were more likely to be present in neighborhoods with more structures and road coverage. Similar results for gray foxes were found in Kays and Parsons (2014). Conversely, Kays and Parsons (2014) found a negative relationship between eastern cottontail occupancy and nonhabitat. Coyotes, woodchucks, wild turkeys, and chipmunks are more likely to be present in neighborhoods with less coverage by structures and roads. Previous studies have found that coyotes have adapted to urban and suburban conditions in some areas (Gehrt et al. 2011, Parsons et al. 2018a) but are relatively rare in others (Kays and Parsons, 2014). While woodchucks are generally considered adaptable to urban habitats (Lehrer et al. 2012), the timing of our camera deployment, corresponding with woodchuck’s hibernation, could have influenced our results. Our models for chipmunks might have low accuracy because their small body size and torpor period. Indeed, another small mammal, the white-footed mouse, was detected so infrequently that occupancy models could not be constructed, although this species is among the most widespread and abundant of all vertebrates in such landscapes (LoGiudice et al. 2003, 2008).
Surprisingly, neighborhoods with more habitat that is forest had lower occupancy for coyotes, eastern chipmunks, eastern cottontails, gray foxes, woodchucks, striped skunks, and wild turkeys. These species might be avoiding residential yards when more forested habitat outside of yards is available. Alternatively, these species might be attracted to supplemental food in yards that have less forest.
The average distance between cameras and the nearest house positively affected occupancy for eastern cottontails, woodchucks, striped skunks, and gray foxes, potentially suggesting aversion to humans. Similarly, Gallo et al. (2019) hypothesized that eastern cottontails limit their activity patterns to early and late hours to avoid human activity. Additionally, Lehrer et al. (2012) concluded that woodchucks were not strongly habituated to humans. Occupancy was negatively related to the average distance between the cameras and the nearest house for wild turkeys and coyotes, indicating these species are less human-averse. In the Chicago Metropolitan Area, human-exploited resources, such as garbage, can comprise up to 25% of coyote’s diet during seasons with less prey availability (Morey et al.2007), which may explain why coyotes were more likely to occupy areas close to human homes. Finally, occupancy was positively related to the average distance between the camera and the nearest road for chipmunks, woodchucks, striped skunks, and wild turkeys, indicating road avoidance. Occupancy of eastern cottontails and gray foxes, however, was negatively related to the average distance to the nearest road.
Chipmunks, eastern cottontails, woodchucks, red squirrels, striped skunks, and wild turkeys were all less likely to be detected when more cameras were located along game trails. These species might avoid game trails to limit detection by predators. Finally, fencing negatively influenced detection for wild turkeys and woodchucks, indicating we were less likely to detect each species in properties with more fencing. Fencing, however, had a positive relationship with detection for eastern cottontails, which was also found in Kays and Parsons (2014). Fenced yards potentially create a safer environment for eastern cottontails by excluding predators.
Interpretations of species occupancy and diversity patterns in residential areas should consider the broader landscape context encompassing the study sites. The regions of Dutchess County, NY in which we conducted our study are largely forested (median 46.3% of the land area), with medians of 30.3% and 24.2% in herbaceous vegetation (including lawns and agriculture) and non-habitat, respectively. Forest connectivity was high (median clumpiness = 0.95), and discrete forest patches were uncommon (median upper bound of discrete patches = 33) (Table 1). Prior research in this region, focusing on changes in vertebrate community structure as the size of forest patches varied, found that small mammals and deer occupied all size ranges of forest patches but other mammal species were more likely to be detected in larger patches (LoGiudice et al. 2003; LoGiudice et al. 2008). Hence, population responses to land use change, particularly abundance and presence/absence may depend on local landscape structure. (Jackson and Fahrig 2014, Moll 2020).
Together, these results allow us to describe a biodiversity baseline for residential properties in the US Northeast and contribute to understanding how anthropogenic landscape features affect biodiversity. Understanding biodiversity in residential areas is particularly important because the rate at which natural landscapes are changing (Venter er al., 2016; Williams et al. 2020). Of course, our findings are based only on data collected within Dutchess County, NY and the response of species occupancy could vary in different geographic areas. Additionally, our cameras were deployed over autumn and winter, so our results may not generalize over all seasons, especially for species that hibernate or undergo periods of torpor during colder months. Finally, as is true of all camera trap studies, motion-triggered cameras are not able to detect small and volant animals. In the future it could be useful to combine camera trapping data with other methods to detect small and non-ground dwelling species to get a more complete picture of overall biodiversity. As landscapes continue to change, it will be important to continue to use camera traps to document biodiversity changes in residential areas.