Control tools : Requirements of tools to control feral cats

In 2008, the background document to the ‘Threat abatement plan for predation by feral cats’ (DEWHA 2008) considered the main control techniques for feral cats as trapping, shooting and exclusion fencing. Baiting was recognised as the most cost-effective method for broad-scale control, but was not commonly employed on the Australian mainland, although it had been used successfully in island eradications (Campbell et al. 2011). A sausage bait using 1080, Eradicat, had recently been developed and employed in Western Australia (Algar and Burrows 2004), but there were concerns over its application to the eastern states where native species are less tolerant of 1080 (Johnston et al. 2011). Development of an effective, humane cat-specific toxin and bait was seen as a high priority for feral cat management in Australia (DEWHA 2008). There has been progress on this front with development of the Curiosity bait using PAPP as a toxin (Johnston et al. 2011, Johnston et al. 2012) and other toxin delivery methods (Read 2010, Read et al. 2014). There have also been further applications of Eradicat, including on the mainland (Algar et al. 2013), and other control methods, and there is a better understanding of cat ecology and impacts, which will help improve strategies for their control. A review of control techniques and their application is thus timely

Demography of feral camels in central Australia and its relevance to population control

Since their release over 100 years ago, camels have spread across central Australia and increased in number. Increasingly, they are being seen as a pest, with observed impacts from overgrazing and damage to infrastructure such as fences. Irregular aerial surveys since 1983 and an interview-based survey in 1966 suggest that camels have been increasing at close to their maximum rate. A comparison of three models of population growth fitted to these, albeit limited, data suggests that the Northern Territory population has indeed been growing at an annual exponential rate of r = 0.074, or 8% per year, with little evidence of a density-dependent brake. A stage-structured model using life history data from a central Australian camel population suggests that this rate approximates the theoretical maximum. Elasticity analysis indicates that adult survival is by far the biggest influence on rate of increase and that a 9% reduction in survival from 96% is needed to stop the population growing. In contrast, at least 70% of mature females need to be sterilised to have a similar effect. In a benign environment, a population of large mammals such as camels is expected to grow exponentially until close to carrying capacity. This will frustrate control programs, because an ever-increasing number of animals will need to be removed for zero growth the longer that culling or harvesting effort is delayed. A population projection for 2008 suggests ~10 500 animals need to be harvested across the Northern Territory. Current harvests are well short of this. The ability of commercial harvesting to control camel populations in central Australia will depend on the value of animals, access to animals and the presence of alternative species to harvest when camels are at low density

Effectiveness of commercial harvesting in controlling feral-pig populations

Context. The feral pig (Sus scrofa) is a widespread pest species in Australia and its populations are commonly controlled to reduce damage to agriculture and the environment. Feral pigs are also a resource and harvested for commercial export as game meat. Although many other control techniques are used, commercial harvesting of feral pigs is often encouraged by land managers, because it carries little or no cost and is widely perceived to control populations. Aims. To use feral-pig harvesting records, density data and simple harvest models to examine the effectiveness of commercial harvesting to reduce feral-pig populations. Methods. The present study examined commercial harvest off-take on six sites (246-657 km2) in southern Queensland, and 20 large blocks (~2-6000 km2) throughout Queensland. The harvest off-take for each site was divided by monthly or average annual population size, determined by aerial survey, to calculate monthly and annual harvest rates.Asimple harvest model assuming logistic population growth was used to determine the likely effectiveness of harvesting. Key results. Commercial harvest rates were generally low (50%) in long-term population size were isolated occurrences and not maintained across sites and years. High harvest rates were observed only at low densities. Although these harvest rates may be sufficiently high to hold populations at low densities, the population is likely to escape this entrapment following a flush in food supply or a reduction in harvest effort. Implications. Our results demonstrated that, at current harvest rates, commercial harvesting is ineffective for the landscape-scale control of feral-pig populations. Unless harvest rates can be significantly increased, commercial harvesting should be used as a supplement to, rather than as a substitute for, other damage-control techniques

Impacts on nontarget avian species from aerial meat baiting for feral pigs

Bait containing sodium fluoroacetate (1080) is widely used for the routine control of feral pigs in Australia. In Queensland, meat baits are popular in western and northern pastoral areas where they are readily accepted by feral pigs and can be distributed aerially. Field studies have indicated some levels of interference and consumption of baits by nontarget species and, based on toxicity data and the 1080 content of baits, many nontarget species (particularly birds and varanids) are potentially at risk through primary poisoning. While occasional deaths of species have been recorded, it remains unclear whether the level of mortality is sufficient to threaten the viability or ecological function of species. A series of field trials at Culgoa National Park in south-western Queensland was conducted to determine the effect of broadscale aerial baiting (1.7 baits per km2) on the density of nontarget avian species that may consume baits. Counts of susceptible bird species were conducted prior to and following aerial baiting, and on three nearby unbaited properties, in May and November 2011, and May 2012. A sample of baits was monitored with remote cameras in the November 2011 and May 2012 trials. Over the three baiting campaigns, there was no evidence of a population-level decline among the seven avian nontarget species that were monitored. Thirty per cent and 15% of baits monitored by remote cameras in the November 2011 and May 2012 trials were sampled by birds, varanids or other reptiles. These results support the continued use of 1080 meat baits for feral pig management in western Queensland and similar environs

Drones vs helicopters for broad-scale animal surveys considerations for future use

Effective monitoring is key for effective wildlife management. Aerial surveys are a proven method for monitoring medium/large-sized mammals (e.g. macropods, feral pigs) in Australia's rangelands. However, conventional aircraft are noisy, expensive, and considered an occupational safety risk for biologists. UAS (unmanned aerial systems, or drones) may offer potential safety and efficiency gains, but need to be assessed against the current best-practice techniques. We tested the ability of a long-range, fixed-wing drone (300m agl, 65-93 km h-1 , thermal and colour imaging) to survey macropod populations and validated the results against those from conventional helicopter surveys (61m agl, 93 km h-1 , human observers). Four, 80-km long transects at Roma in southwestern Queensland were surveyed and the outputs analysed using line-transect distance sampling methods. The drone was able to survey over half (56%) of the 320 km transects, and over 448 km of survey flights in total. However, the drone technique was unable to distinguish between macropod species, recorded <13% of the macropod density observed during the helicopter survey, and required more flight and data processing time. Long-range drones clearly have potential for landscape-scale wildlife monitoring but results must match or exceed the conventional techniques. Future UAS applications to wildlife monitoring require a proven ability to identify animals, a similar or greater detection probability than conventional techniques, an efficient means of data collation/analysis, and comparable costs to current-best practice survey methods. We discuss the issues for potential users to consider to ensure that new survey technologies can be used to optimal benefit

Home ranges of rusa deer (Cervus timorensis) in a subtropical peri-urban environment in South East Queensland

Wild rusa deer (Cervus timorensis) are increasing in numbers and distribution in peri-urban eastern Australia. To effectively manage rusa deer, land managers need to know the extent of their movements to determine the appropriate scale of control through trapping and shooting. We found that in a subtropical peri-urban environment in South East Queensland, four rusa deer (three male, one female) with GPS collars annually ranged over areas of <400 ha with core areas of ~100 ha over a period of 10–17 months. Our limited data indicated their relatively small home ranges varied little in size and location from season-to-season, suggesting that these deer can be effectively managed at the local level

Home ranges of rusa deer (Cervus timorensis) in a subtropical peri-urban environment in South East Queensland

Wild rusa deer (Cervus timorensis) are increasing in numbers and distribution in peri-urban eastern Australia. To effectively manage rusa deer, land managers need to know the extent of their movements to determine the appropriate scale of control through trapping and shooting. We found that in a subtropical peri-urban environment in South East Queensland, four rusa deer (three male, one female) with GPS collars annually ranged over areas of <400 ha with core areas of ~100 ha over a period of 10–17 months. Our limited data indicated their relatively small home ranges varied little in size and location from season-to-season, suggesting that these deer can be effectively managed at the local level

Population dynamics of house mice in Queensland grain-growing areas

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.Onaverage, 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

Dancing to a different tune: changing reproductive seasonality in an introduced chital deer population

Male and female reproductive behaviour is typically synchronised. In species such as those in the family Cervidae, reproductive timing is often cued by photoperiod, although in females, it can be dependent on body condition. When a species is introduced to a novel environment, the environment changes, or responses of the sexes to such cues differ, asynchronous reproductive behaviour between males and females may occur. We investigated the seasonality of reproductive behaviour in introduced chital deer in northern Queensland by examining male antler phase in relation to female conception rates. We then analysed the influence of different variables likely to affect the timing of male and female reproductive physiology. The lowest percentage of chital in hard antler in any 1 month in this study was 35% (Fig. 1), but the average value was closer to 50%, thus there was a seasonal peak in antler phase linked with photoperiod. Females conceived at any time of year, but were strongly influenced by the amount of rainfall 3 months prior to conception. This resulted in varying conception peaks year-to-year that often did not correspond to the male’s peak in hard antler. In this system, a proportion of males and females were physiologically and behaviourally ready to mate at any time of the year. We predict that differences in the timing of the peaks between the males and females will lead to increased reproductive skew (variation in reproductive success among individual males). This pattern may select for different mating strategies or physiological mechanisms to increase reproductive success.Fig. 1The average percentage of male chital deer in hard antler by month from 2014 to 2019 in north Queensland. Values above the bars indicate the total number of males that were sampled in each month and the error bars indicate the standard error. In the month with the lowest % males in hard antler in the entire study (November, 2017), 35% of males were in hard antle