Counting the Capital's cats: Estimating drivers of abundance of free-roaming cats with a novel hierarchical model

Free-roaming cats are a conservation concern in many areas but identifying their impacts and developing mitigation strategies requires a robust understanding of their distribution and density patterns. Urban and residential areas may be especially relevant in this process because free-roaming cats are abundant in these anthropogenic landscapes. Here, we estimate the occupancy and density of free-roaming cats in Washington D.C. and relate these metrics to known landscape and social factors. We conducted an extended camera trap survey of public and private spaces across D.C. and analyzed data collected from 1483 camera deployments from 2018 to 2020. We estimated citywide cat distribution by fitting hierarchical occupancy models and further estimated cat abundance using a novel random thinning spatial capture-recapture model that allows for the use of photos that can and cannot be identified to individual. Within this model, we utilized individual covariates that provided identity exclusions between photos of unidentifiable cats with inconsistent coat patterns, thus increasing the precision of abundance estimates. This combined model also allowed for unbiased estimation of density when animals cannot be identified to individual at the same rate as for free-roaming cats whose identifiability depended on their coat characteristics. Cat occupancy and abundance declined with increasing distance from residential areas, an effect that was more pronounced in wealthier neighborhoods. There was noteworthy absence of cats detected in larger public spaces and forests. Realized densities ranged from 0.02 to 1.75 cats/ha in sampled areas, resulting in a district-wide estimate of ~7296 free-roaming cats. Ninety percent of cat detections lacked collars and nearly 35% of known individuals were ear-tipped, indicative of district Trap-Neuter-Return (TNR) programs. These results suggest that we mainly sampled and estimated the unowned cat subpopulation, such that indoor/outdoor housecats were not well represented. The precise estimation of cat population densities is difficult due to the varied behavior of subpopulations within free-roaming cat populations (housecats, stray and feral cats), but our methods provide a first step in establishing citywide baselines to inform data-driven management plans for free-roaming cats in urban environments.

Spatial and temporal overlap of domestic cats (Felis catus) and native urban wildlife

Free-roaming domestic cats (Felis catus) are known to pose threats to ecosystem health via transmission of zoonotic diseases and predation of native wildlife. Likewise, free-roaming cats are also susceptible to predation or disease transmission from native wildlife. Physical interactions are required for many of these risks to be manifested, necessitating spatial and temporal overlap between cats and wildlife species. Therefore, knowledge of the location and extent of shared habitat and activity periods would benefit management programs. We used data from a 3-year camera trap survey to model species-specific occupancy and identify landscape variables that contribute to the distribution of free-roaming domestic cats and eight native mammal species in Washington, DC. (USA). Our analysis includes five species that are common prey items of domestic cats, and three species that are potential disease vectors or are otherwise known to be a risk to cats. We then predicted the probability of occupancy and estimated the probability of spatial overlap between cats and each native wildlife species at multiple scales. We also used kernel density estimations to calculate temporal overlap between cats and each native wildlife species. Across spatial scales, occupancy for potential disease vector species was generally positively correlated with canopy cover and open water. Prey species were also generally positively correlated with canopy cover, but displayed negative associations with human population density and inconsistent associations with average per capita income. Domestic cat occupancy was negatively correlated with natural habitat characteristics and positively correlated with human population density. Predicted spatial overlap between domestic cats and native wildlife was greatest for potential disease vector species. Temporal overlap was high (>0.50) between cats and all but two native wildlife species, indicating that temporal overlap is probable wherever species overlap spatially. Our findings indicate that the risk to and from domestic cats varies across urban landscapes, but primarily arises from human activities. As such, humans are implicated in the negative outcomes that result from cats interacting with wildlife. Data-driven management to reduce such interactions can aid in cat population management, biodiversity conservation, and public health campaigns.