Effects of harvesting and stubble management on abundance of pest rodents (Mus musculus) in a conservation agriculture system

BACKGROUND: The shift to more environmentally sensitive agricultural practices over the last several decades has changed farmland landscapes worldwide. Changes including no-till and retaining high biomass mulch has been coincident with an increase in rodent pests in South Africa, India, South America and Europe, indicating a possible conflict between conservation agriculture (CA) and rodent pest management. Research on effects of various crop management practices associated with CA on pest rodent population dynamics is needed to anticipate and develop CA-relevant management strategies. RESULTS: During the Australian 2020–2021 mouse plague, farmers used postharvest stubble management practices, including flattening and/or cutting, to reduce stubble cover in paddocks to lessen habitat suitability for pest house mice. We used this opportunity to assess the effects of both harvest and stubble management on the movement and abundance of mice in paddocks using mouse trapping and radio tracking. We found that most tracked mice remained resident in paddocks throughout harvest, and that mouse population abundance was generally unaffected by stubble management. CONCLUSION: Recent conversions to CA practices have changed how pest house mice use cropped land. Management practices that reduce postharvest habitat complexity do not appear to reduce the attractiveness of paddocks to mice, and further research into new management strategies in addition to toxic bait use is required as part of an integrated pest management approach.</p

Background food influences rate of encounter and efficacy of rodenticides in wild house mice

Baiting is widely used in wildlife management for various purposes, including lethal control, fertility control, disease and parasite control, and conditioned aversion programs for many invasive vertebrate species. The efficacy of baiting programs relies on the likelihood that target animals will encounter the bait, consume it and receive an appropriate dose of the active ingredient. However, there has been little focus on encounter rate of toxic baits combined with behavioural aversion, which are likely to be significant factors affecting efficacy. Following optimal foraging theory, the likelihood of an animal encountering and consuming a toxic grain bait should increase in proportion to its availability relative to background food quantity if it is neither more or less detectable or palatable. Furthermore, the probability of consuming toxic baits might also be influenced by bait aversion following ingestion of a non-lethal dose of toxin. Using a model system of wild house mice (Mus musculus L.) in mouse-proof enclosures in Australia, we manipulated background food, applied zinc phosphide (Zn3P2) baits and measured mouse mortality. When background food was scarce, mouse mortality was high, whereas an increasing abundance of background food led to reduced mortality. A scenario modelling random encounters and including bait aversion explained 78% of the variation in observed mortality outcomes and achieved a closer fit to the data than modelling random encounters alone. Mortality rates were predicted to be higher with a higher strength bait, which would overcome behavioural aversion. Ensuring that animals locate and consume a lethal dose of toxic bait is a critical factor for successful bait delivery and efficacy. This is particularly significant in toxic baiting programs, where sublethal doses can make animals feel sick, leading to a negative association with the bait, and the development of aversion. Synthesis and applications: Our findings explain why some toxic baiting programs might fail. To achieve successful control, efforts should be directed at reducing the availability of background food to increase the probability of encounter and uptake of toxic baits. It is important to measure and understand the role of background food on toxic baiting programs to explain variable outcomes and inform strategies for successful bait delivery.</p

Target SNP sequencing coverage from hybridization sequence capture.

Each curve represents a different sample (N = 317), and the observed proportion of targeted SNPs (3,651 total) that had coverage equal to or greater than the value indicated on the x-axis.</p

Comparison of pairwise kinship values, estimated using PC-Relate for the same samples (N = 47) using both the GigaMUGA genotyping (x-axis) and custom hybridization capture sequencing SNP datasets (y-axis).

Each point represents a unique dyad, colored by the corresponding degree of kinship.</p

Estimated genotyping cost comparison between array genotyping and custom hybridization capture sequencing panel.

Estimated genotyping cost comparison between array genotyping and custom hybridization capture sequencing panel.</p