Spatio-temporal variations in conspecific density and resource availability are two of the main factors responsible for plasticity in habitat selection. Despite the need for habitat selection models that can accurately predict animal distribution given the plasticity in the selection process, no study has assessed the synergistic effects of these factors on habitat selection. We investigated density-dependent functional responses by raccoons (Procyon lotor) and striped skunks (Mephitis mephitis), two of the main hosts of the rabies virus in North America. We monitored 54 raccoons and 12 striped skunks with Global-Positioning-System collars in a landscape dominated by corn fields and forest patches. We built resource selection functions to evaluate if the selection of corn fields varied with conspecific density and corn field availability within 100% minimum convex polygons. Raccoons altered their selection of corn fields depending on both conspecific density and corn-forest edge density or corn field proportion. In areas of low corn-forest edge densities and a low corn field proportion, raccoons showed stronger selection for corn fields when few conspecifics were present. At high conspecific densities, the selection of corn fields was stronger in areas with high corn-forest edge densities and a low corn field proportion. For striped skunks, we did not detect any synergistic effect of density-dependence and functional responses. Unlike raccoons, striped skunks displayed a selection that was strongest for agricultural corridors. We show that functional responses in habitat selection can be density-dependent. In a context of infectious disease dynamics, modeling densitydependence in functional responses increases the ability to predict spatio-temporal variations in the distribution of reservoir species and thus, to delineate areas at high animal densities where the risk of disease outbreaks is relatively high. For example, the omission of density-dependence in functional responses underestimated the relative probability of raccoon occurrence in corn fields, while overestimating the relative probability of occurrence in anthropogenic areas and wetlands. Our study underscores the relevance of considering the complexity of habitat selection by all hosts of a zoonosis. Costeffective control and prevention programs used to limit disease spread can benefit from accounting for density-dependent functional responses of a multi-host disease system.

Rabies is a major issue for human and animal health in the Arctic, yet little is known about its epidemiology. In particular, there is an ongoing debate regarding how Arctic rabies persists in its primary reservoir host, the Arctic fox (Vulpes lagopus), which exists in the ecosystem at very low population densities. To shed light on the mechanisms of rabies persistence in the Arctic, we built a susceptible–exposed–infectious–recovered (SEIR) epidemiological model of rabies virus transmission in an Arctic fox population interacting with red foxes (Vulpes vulpes), a rabies host that is increasingly present in the Arctic. The model suggests that rabies cannot be maintained in resource-poor areas of the Arctic, characterized by low Arctic fox density, even in the presence of continuous reintroduction of the virus by infected Arctic foxes from neighbouring regions. However, in populations of relatively high Arctic fox density, rabies persists under conditions of higher transmission rate, prolonged infectious period and for a broad range of incubation periods. Introducing the strong cyclical dynamics of Arctic prey availability makes simulated rabies outbreaks less regular but more intense, with an onset that does not neatly track peaks in Arctic fox density. Finally, interaction between Arctic and red foxes increases the frequency and/or the intensity of rabies outbreaks in the Arctic fox population. Our work suggests that disruption of prey cycles and increasing interactions between Arctic and red foxes due to climate change and northern development may significantly change the epidemiology of rabies across the Arctic.

No abstract

Identifying ecological drivers of tick-borne pathogen spread has great value for tick-borne disease management. However, theoretical investigations into the consequences of host movement behaviour on pathogen spread dynamics in heterogeneous landscapes remain limited because spatially explicit epidemiological models that incorporate more realistic mechanisms governing host movement are rare. We built a mechanistic movement model to investigate how the interplay between multiple ecological drivers affects the risk of tick-borne pathogen spread across heterogeneous landscapes. We used the model to generate simulations of tick dispersal by migratory birds and terrestrial hosts across theoretical landscapes varying in resource aggregation, and we performed a sensitivity analysis to explore the impacts of different parameters on the infected tick spread rate, tick infection prevalence and infected tick density. Our findings highlight the importance of host movement and tick population dynamics in explaining the infected tick spread rate into new regions. Tick infection prevalence and infected tick density were driven by predictors related to the infection process and tick population dynamics, respectively. Our results suggest that control strategies aiming to reduce tick burden on tick reproduction hosts and encounter rate between immature ticks and pathogen amplification hosts will be most effective at reducing tick-borne disease risk.