A novel modelling framework to explicitly simulate predator interaction with poison baits
C. Pacioni
A B G , D. S. L. Ramsey A , Nathan H. Schumaker C , Tracey Kreplins D and M. S. Kennedy E F
A Arthur Rylah Institute for Environmental Research, Department of Environment, Land, Water and Planning, 123 Brown Street, Heidelberg, Vic. 3084, Australia.
B Murdoch University, South Street, Murdoch, WA 6150, Australia.
C Department of Fisheries and Wildlife, Oregon State University, Corvallis, OR 97331, USA.
D Department of Primary Industries and Regional Development, 75 York Road, Northam, WA 6401, Australia.
E Department of Primary Industries and Regional Development, 3 Baron-Hay Court, South Perth, WA 6151, Australia.
F Present address: Department of Agriculture and Fisheries, 203 Tor Street, Toowoomba, Qld 4350, Australia.
G Corresponding author. Email: carlo.pacioni@gmail.com
Wildlife Research - https://doi.org/10.1071/WR19193
Submitted: 10 October 2019 Accepted: 18 June 2020 Published online: 9 September 2020
Abstract
Context: Management of human–wildlife conflicts is of critical importance for both wildlife conservation and agricultural production. Population models are commonly used to simulate population dynamics and their responses to management actions. However, it is essential that this class of models captures the drivers and mechanisms necessary to reliably forecast future system dynamics.
Aims: We aimed to develop a flexible modelling framework with the capacity to explicitly simulate individual interactions with baits (with or without the presence of other management tools), for which parameter estimates from field data are available. We also intended for the model to potentially accommodate multi-species interaction and avoidance behaviours.
Methods: We expanded an existing spatially explicit, individual-based model to directly simulate bait deployment, animal movements and bait consumption. We demonstrated the utility of this model using a case study from Western Australia where we considered two possible exclusion-fence scenarios, namely, the completion of a landscape-scale and smaller-scale fences. Within each of these proposed cells, using data obtained from a camera-trap study, we evaluated the performance of two levels of baiting to control wild dogs (Canis familiaris), in contrast with the option of no control.
Results: The present study represents a substantial step forward in accurately modelling predator dynamics. When applying our model to the case study, for example, it was straightforward to investigate whether outcomes were sensitive to the bait-encounter probability. We could further explore interactions between baiting regimes and different fence designs and demonstrate how wild dog eradication could be achieved in the smaller cell under the more intense control scenarios. In contrast, the landscape-scale fence had only minor effects unless it was implemented as a preventive measure in an area where wild dogs were not already established.
Conclusions: The new component of the model presented here provides fine-scale control of single components of individual–bait interactions.
Implications: The effect of management actions (e.g. lures) that affect this process can be easily investigated. Multi-species modelling and avoidance behaviours can readily be implemented, making the present study widely relevant for a range of contexts such as multi-species competition or non-target bait uptake.
Additional keywords: conservation biology, introduced species, pest control, population dynamics, population modelling, species interactions, wildlife management.
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