FoxNet: An individual-based model framework to support management of an invasive predator, the red fox

Invasive predators are a key driver of biodiversity decline, and effective predator management is an important conservation issue globally. The red fox (Vulpes vulpes) poses a significant threat to wildlife, livestock and human health across Eurasia, North America and Australia. Despite worldwide investment in red fox management, decision makers still lack flexible tools for predicting control efficacy. We have developed FoxNet, a spatially explicit, individual-based model (IBM) framework that can be customised to predict red fox population dynamics, including responses to control and landscape productivity. High-resolution models can be run across northern and southern hemisphere landscapes. We present four case-study models to verify FoxNet outputs, explore key sensitivities and demonstrate the framework's utility as a management planning tool. FoxNet models were largely successful in reproducing the demographic structure of two red fox populations in highly contrasting landscapes. They also accurately generated the relationship between home-range size and fox-family density for home-range sizes between 1.0 and 9.6 km(2), and captured the rapid decline and seasonally driven recovery of a red fox population following poison-baiting. An exploration of alternative poison-baiting scenarios for a conservation reserve predicted that current management suppresses red fox density by similar to 70% and showed that frequent baiting is required to combat recolonisation. Baiting at higher densities or establishing a baited buffer would further reduce red fox density. Predictions were sensitive to home-range and litter size assumptions, illustrating the value of region-specific data on red fox movement and biology. Synthesis and applications. We have developed a versatile individual-based model framework to guide management of the red fox, a globally significant invasive predator. Our framework, FoxNet, can be customised to generate realistic predictions of red fox population dynamics in diverse landscapes, making it immediately applicable to the design and optimisation of predator control programmes at scales relevant to management. Future extensions could explore competitor and prey responses to red fox control and the effects of habitat disturbance on predator population dynamics.