When do predator exclusion fences work best? A spatially explicit modelling approach
C. Pacioni
A B E * , M. S. Kennedy C D * and D. S. L. Ramsey A
A Arthur Rylah Institute for Environmental Research, Department of Environment, Land, Water and Planning, 123 Brown Street, Heidelberg, Vic. 3084, Australia.
B School of Veterinary and Life Sciences, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia.
C Department of Primary Industries and Regional Development, 3 Baron-Hay Court, South Perth, WA 6151, Australia.
D Present address: Department of Agriculture and Fisheries, 203 Tor Street, Toowoomba, Qld 4350, Australia.
E Corresponding author. Email: carlo.pacioni@gmail.com
Wildlife Research 48(3) 209-217 https://doi.org/10.1071/WR19192
Submitted: 14 October 2019 Accepted: 16 August 2020 Published: 27 November 2020
Abstract
Context: Exclusion fences are increasingly used to prevent interactions between predators (introduced and native) and assets such as endangered species or livestock. However, challenges remain in identifying when exclusion fences are an optimal investment and the intended outcome is likely to be achieved. The level of association with complementary methods of control that is needed is also unclear.
Aims: We aimed to quantify the interactions among factors that affect fencing efficiency, including the size of the fenced area, the fence permeability, the initial density of the predator population, and its survival of complementary control methods.
Methods: Using a spatially explicit, individual-based model, we simulated wild dog (dingo) populations as a proxy for describing predator dynamics inside a fenced area under different management practices and fence designs. We then fit a generalised linear model to the model outcomes to assess the effects of the four factors mentioned above.
Key results: Lethal control had a strong effect on wild dog density when the survival of control was lower than 0.5. Fences generally had an effect on wild dog density only when their permeability was lower than ~1% and their effect was most noticeable when the initial density was very low (<2 dogs per 100 km2), or when survival of control was very low (<0.5). Conversely, when the initial density was very high (~12 dogs per 100 km2), a fence with a low permeability (<1.5%) caused the paradoxical effect that wild dog density could be higher than that obtained with a more permeable fence. Wild dog eradication was possible only when survival of control was 0.25 or lower, except when either initial density or fence permeability were extremely low (<2 dogs per 100 km2 and <0.1% respectively).
Conclusions: Our results demonstrated that large exclusion fences can be an effective aid in managing predator populations. We recommend that this tool should be used as a preventive measure before predators establish a population inside the area targeted for exclusion, in tandem with lethal control, or when an initial marked reduction of predator density can be achieved. We also demonstrated that eradication can be achieved only when a narrow combination of parameters is met.
Implications: Land managers should carefully evaluate when and at what scale control tools should be deployed to control wild dog populations. Landscape application of exclusion fences faces the challenge of high maintenance to ensure low permeability, coupled with very high sustained suppression of wild dog density, which are unlikely to be feasible options in the long term. Conversely, the same control techniques could provide efficient asset protection at a smaller scale where fence maintenance and sufficient control effort can be sustained.
Keywords: dingo, HexSim, livestock predation, livestock attacks, pest control, population modelling, wild dog, wildlife management, cluster fence, cell fence.
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