Population dynamics of house mice in Queensland grain-growing areas
Anthony Pople A E , Joe Scanlan B , Peter Cremasco B C and Julianne Farrell B DA Invasive Plant and Animal Science, Biosecurity Queensland, Department of Agriculture, Fisheries and Forestry, Queensland, Ecosciences Precinct, GPO Box 267, Brisbane, Qld 4001, Australia.
B Robert Wicks Pest Animal Research Centre, Biosecurity Queensland, Department of Agriculture, Fisheries and Forestry, 203 Tor Street, Toowoomba, Qld 4350, Australia.
C Invasive Species Branch, Department of Primary Industries, Parks, Water and Environment, PO Box 46, Kings Meadows, Tas. 7249, Australia.
D 11/301 Bridge Street, Toowoomba, Qld 4350, Australia.
E Corresponding author. Email: tony.pople@daff.qld.gov.au
Wildlife Research 40(8) 661-674 https://doi.org/10.1071/WR13154
Submitted: 11 September 2013 Accepted: 29 January 2014 Published: 27 February 2014
Abstract
Context: Irregular plagues of house mice cause high production losses in grain crops in Australia. If plagues can be forecast through broad-scale monitoring or model-based prediction, then mice can be proactively controlled by poison baiting.
Aims: To predict mouse plagues in grain crops in Queensland and assess the value of broad-scale monitoring.
Methods: Regular trapping of mice at the same sites on the Darling Downs in southern Queensland has been undertaken since 1974. This provides an index of abundance over time that can be related to rainfall, crop yield, winter temperature and past mouse abundance. Other sites have been trapped over a shorter time period elsewhere on the Darling Downs and in central Queensland, allowing a comparison of mouse population dynamics and cross-validation of models predicting mouse abundance.
Key results: On the regularly trapped 32-km transect on the Darling Downs, damaging mouse densities occur in 50% of years and a plague in 25% of years, with no detectable increase in mean monthly mouse abundance over the past 35 years. High mouse abundance on this transect is not consistently matched by high abundance in the broader area. Annual maximum mouse abundance in autumn–winter can be predicted (R2 = 57%) from spring mouse abundance and autumn–winter rainfall in the previous year. In central Queensland, mouse dynamics contrast with those on the Darling Downs and lack the distinct annual cycle, with peak abundance occurring in any month outside early spring. On average, damaging mouse densities occur in 1 in 3 years and a plague occurs in 1 in 7 years. The dynamics of mouse populations on two transects ~70 km apart were rarely synchronous. Autumn–winter rainfall can indicate mouse abundance in some seasons (R2 = ~52%).
Conclusion: Early warning of mouse plague formation in Queensland grain crops from regional models should trigger farm-based monitoring. This can be incorporated with rainfall into a simple model predicting future abundance that will determine any need for mouse control.
Implications: A model-based warning of a possible mouse plague can highlight the need for local monitoring of mouse activity, which in turn could trigger poison baiting to prevent further mouse build-up.
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