Abstract
Concerns over cat homelessness, over-taxed animal shelters, public health risks, and environmental impacts has raised attention on urban-cat populations. To truly understand cat population dynamics, the collective population of owned cats, unowned cats, and cats in the shelter system must be considered simultaneously because each subpopulation contributes differently to the overall population of cats in a community (e.g., differences in neuter rates, differences in impacts on wildlife) and cats move among categories through human interventions (e.g., adoption, abandonment). To assess this complex socio-ecological system, we developed a multistate matrix model of cats in urban areas that include owned cats, unowned cats (free-roaming and feral), and cats that move through the shelter system. Our model requires three inputs—location, number of human dwellings, and urban area—to provide testable predictions of cat abundance for any city in North America. Model-predicted population size of unowned cats in seven Canadian cities were not significantly different than published estimates (p = 0.23). Model-predicted proportions of sterile feral cats did not match observed sterile cat proportions for six USA cities (p = 0.001). Using a case study from Guelph, Ontario, Canada, we compared model-predicted to empirical estimates of cat abundance in each subpopulation and used perturbation analysis to calculate relative sensitivity of vital rates to cat abundance to demonstrate how management or mismanagement in one portion of the population could have repercussions across all portions of the network. Our study provides a general framework to consider cat population abundance in urban areas and, with refinement that includes city-specific parameter estimates and modeling, could provide a better understanding of population dynamics of cats in our communities.
Highlights
Concerns over the population density of free-roaming domestic cats (Felis catus) in urban areas is a global issue centered on the issues of cat welfare [1,2,3], impacts on wildlife populations [4,5,6], and risks to public health [1, 7, 8]
Our objectives were to (i) develop a multistate matrix population model for owned cats, unowned cats, and cats that move between these two categories via the shelter system applicable to any city in North America using simple inputs available in national census data sets, (ii) assess model fit by comparing model-derived population abundance predictions of urban areas where cats, and unowned cats especially, have been estimated, and (iii) determine the sensitivities of population abundance to demographic vital rates and transition across the entire cat population network for Guelph, Ontario, Canada where previous cat population estimates [23, 26] and shelter statistics [27] are available
State 3 and 4 are collectively unowned cats but free-roaming cats comprised stray, abandoned, or lost cats that had been previously socialized to people and were candidates to be surrendered to an animal shelter system or adopted by citizens off the street whereas strict feral cats are neither socialized to people nor candidates to be adopted (Fig 1B)
Summary
Concerns over the population density of free-roaming domestic cats (Felis catus) in urban areas is a global issue centered on the issues of cat welfare [1,2,3], impacts on wildlife populations [4,5,6], and risks to public health [1, 7, 8]. Central to understanding the impacts of cats, and a first step in developing humane and cost-effective management strategies, is quantifying population abundance and identifying the factors that drive population dynamics. The debate around cat management centers on the use and effectiveness of lethal and non-lethal control measures to address concerns over the free-roaming cat population density [1, 9, 10]. While the objectives of such control measures are rarely stated explicitly, owing to the variety of stakeholder views on the role, rights, and impact of cats, generally public expectations relate to issues of ethical population control, the cost and effectiveness of implementing these controls, and the likelihood of achieving a specified management objective [11, 12]
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