🌿 ecology

The effects of fox movement and landscape heterogeneity on the spread of sarcoptic mange in urban settings

This study utilizes an agent-based model to demonstrate that red fox mange transmission in urban landscapes is accelerated by landscape heterogeneity and movement corridors, particularly when infected and susceptible foxes exhibit similar movement probabilities through fragmented habitats.

Original authors: Dimitrov, N., Gelmi-Candusso, T. A., Krkosek, M., Fortin, M.-J.

Published 2026-06-25

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CC BY 4.0

Original authors: Dimitrov, N., Gelmi-Candusso, T. A., Krkosek, M., Fortin, M.-J.

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ⚕️ This is an AI-generated explanation of a preprint that has not been peer-reviewed. It is not medical advice. Do not make health decisions based on this content. Read full disclaimer

Imagine a city not as a grid of streets for cars, but as a giant, complex maze for foxes. This paper is like a detective story trying to figure out how a contagious skin disease, called mange, spreads through a population of red foxes living in Scarborough, Ontario.

Here is the story in simple terms:

The Setting: A City of "Green" and "Gray"
Think of the city landscape as a patchwork quilt. Some squares are green and safe (parks, forests), while others are gray and scary (busy roads, concrete). Foxes don't just wander randomly; they have a map in their heads. They prefer to walk through the green squares and avoid the gray ones. This is what the scientists call "landscape heterogeneity"—the mix of different types of ground.

The Experiment: The Digital Fox Game
The researchers built a computer simulation, like a video game, to watch how foxes move and catch the disease. They tested two main rules for how the foxes behaved:

The Drunk Fox: A fox that wanders in a completely random direction, bumping into things without a plan.
The Smart Fox: A fox that knows the terrain, sticking to the "paths of least resistance" (the green patches) and avoiding the hard stuff.

They also checked if being sick changed how the fox moved. Did a sick fox wander differently than a healthy one?

The Discovery: The "Super-Highway" Effect
The results were surprising. The disease spread the fastest when the foxes acted like the "Smart Foxes" moving through a patchy, uneven landscape.

Here is the analogy: Imagine the city is a series of islands (green patches) separated by oceans of concrete. If foxes can only cross on a few narrow bridges (movement corridors), they are forced to bump into each other.

When the foxes stick to these narrow, safe bridges, they run into each other much more often than if they were free to roam anywhere.
The study found that the disease spread most quickly when the foxes followed these natural paths and when sick foxes moved just as freely as healthy ones.

The Bottom Line
The paper concludes that as cities grow and chop up nature into smaller and smaller pieces, the foxes get squeezed into fewer and fewer "highways." Because they are forced to share these narrow paths, they bump into each other more, making it much easier for the mange to jump from one fox to another.

In short: When nature is broken into small islands, the animals are forced to crowd together on the bridges, and that's where the germs spread the fastest.

Technical Summary: The Effects of Fox Movement and Landscape Heterogeneity on the Spread of Sarcoptic Mange in Urban Settings

Problem Statement
The transmission of direct-contact diseases among vertebrate hosts is significantly influenced by movement patterns across urbanized landscapes. A critical gap exists in understanding how wildlife movement is constrained by specific landcover types and how these constraints, combined with disease-altered behaviors, impact transmission dynamics. Specifically, there is a need to predict how the spread of sarcoptic mange (caused by Sarcoptes scabiei) in red fox (Vulpes vulpes) populations is affected by the interplay between landscape heterogeneity and host infection status within urban environments.

Methodology
To address this, the authors developed a Mange Transmission Model (MTM) utilizing an agent-based modeling framework. The study focused on the urban landscape of Scarborough, Ontario, Canada. The methodology involved simulating fox movement under two distinct behavioral regimes:

Random Movement: Foxes move without regard to specific landcover types.
Landcover-Based Movement: Foxes move according to the resistance of different landcover types, effectively following paths of least resistance.

The model further incorporated the infection status of individual foxes (susceptible vs. infected) to assess how disease state influences movement and subsequent transmission. The researchers compared a range of movement probability scenarios to quantify two primary metrics: the number of effective contact events and the effective reproduction number ( $R_e$ ).

Key Contributions and Results
The study demonstrates that both landscape heterogeneity and the infection status of the host are determinative factors in the spread of mange. The simulation results yielded the following specific findings:

Impact of Landscape Heterogeneity: The number of effective contact events and the effective reproduction number ( $R_e$ ) were maximized when the model accounted for landscape heterogeneity. Specifically, when foxes moved along paths of least resistance through fragmented habitats, transmission potential increased.
Impact of Infection Status: The spread of the disease was most pronounced when susceptible and infected foxes exhibited an equal probability of leaving a fragmented habitat patch. This suggests that infection status does not necessarily alter movement in a way that reduces contact; rather, the structural constraints of the landscape drive transmission when movement probabilities are uniform across health states.

Significance and Claims
The paper concludes that mange spread is likely accelerated along movement corridors within fragmented, heterogeneous urban landscapes. The authors posit that as urban expansion continues and remnant habitats are reduced, forcing animals into fewer, more constrained movement pathways, the potential for disease transmission may increase. The study underscores the imperative of integrating host movement behaviors and landscape structure into disease prediction models to better understand transmission dynamics in urban settings. The findings remain modest in scope, focusing on the mechanistic relationship between movement constraints and transmission rates rather than proposing specific management interventions or future experimental protocols.

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