Advancing spatial analysis of invasive species movement data to improve monitoring, control programs and decision making: feral cat home range as a case study

Cameron Wilson; Matthew Gentle; Bronwyn Fancourt

doi:10.1071/PC24031

RESEARCH ARTICLE (Open Access)

Next Contents Vol 30(5)

Advancing spatial analysis of invasive species movement data to improve monitoring, control programs and decision making: feral cat home range as a case study

Cameron Wilson

^A ^B ^* , Matthew Gentle ^B ^C and Bronwyn Fancourt ^A ^B ^D

+ Author Affiliations

- Author Affiliations

^A School of Environmental and Rural Science, University of New England, Armidale, NSW, Australia.

^B Pest Animal Research Centre, Biosecurity Queensland, Department of Agriculture and Fisheries, Toowoomba, Qld, Australia.

^C School of Sciences, University of Southern Queensland, Toowoomba, Qld, Australia.

^D Queensland Parks and Wildlife Service, Department of Environment, Science and Innovation, Toowoomba, Qld, Australia.

^* Correspondence to: cameron.wilson@daf.qld.gov.au

Handling Editor: Mike Calver

Pacific Conservation Biology 30, PC24031 https://doi.org/10.1071/PC24031

Submitted: 1 May 2024 Accepted: 27 August 2024 Published: 18 September 2024

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Context

Many invasive animals are typically active across large areas, making monitoring and control programs expensive. To be efficacious, monitoring devices and control tools need to be strategically located to maximise the probability of encounter. This requires an understanding of how the target species uses the landscape, through identifying key habitat or landscape features that are preferred and used disproportionately more frequently by the species. Spatial analysis of animal movements can help identify high use areas.

Aims

The variability introduced by different range calculation methods can lead to uncertainty in subsequent habitat analyses. We aimed to determine which method is superior for accurate delineation of core areas for feral cats.

Methods

We analysed spatial data from 35 collared feral cats across four Australian study sites between 2016 and 2019, and compared the core areas generated using seven commonly used home range estimation methods.

Key results

We found that the α-hull method provided a higher precision of polygon placement, resulting in lower Type I and II errors and higher conformity to landscape features than other methods. The α-hull used a single default parameter and required no subjective input, making it a more objective, superior method.

Conclusions

We recommend that the α-hull method be used to define core activity areas for feral cats, enabling more robust habitat analysis, and identification of key habitat and landscape features to strategically target for monitoring and control programs.

Implications

This strategic approach could significantly improve cost efficiencies, particularly where existing management is widely dispersed, and core activity areas are clumped.

Keywords: core area, Felis catus, feral cat, GPS collar, home range, kernel, LoCoH, MCP, α-hull.

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