‘Bait-resistance’ is defined as progressive decreases in bait efficacy in controlled pest species populations. Understanding the mechanisms by which bait-resistance can develop is important for the sustainable control of pests worldwide, for both wildlife conservation programs and agricultural production. Bait-resistance is influenced by both behavioural (innate and learned bait-avoidance behaviour) and physiological aspects of the target pest species (its natural diet, its body mass, the mode of action of the toxin, and the animal’s ability to biochemically break down the toxin). In this review, we summarise the scientific literature, discuss factors that can lead to innate and learned aversion to baits, as well as physiological tolerance. We address the question of whether bait avoidance or tolerance to 1080 could develop in the red fox (Vulpes vulpes), an introduced predator of significant economic and environmental importance in Australia. Sublethal poisoning has been identified as the primary cause of both bait avoidance and increased toxin-tolerance, and so, finally, we provide examples of how management actions can minimise the risk of sublethal baits in pest species populations.

Camera traps are widely used in wildlife research and monitoring, so it is imperative to understand their strengths, limitations, and potential for increasing impact. We investigated a decade of use of wildlife cameras (2012–2022) with a case study on Australian terrestrial vertebrates using a multifaceted approach. We (i) synthesised information from a literature review; (ii) conducted an online questionnaire of 132 professionals; (iii) hosted an in‐person workshop of 28 leading experts representing academia, non‐governmental organisations (NGOs), and government; and (iv) mapped camera trap usage based on all sources. We predicted that the last decade would have shown: (i) exponentially increasing sampling effort, a continuation of camera usage trends up to 2012; (ii) analytics to have shifted from naive presence/absence and capture rates towards hierarchical modelling that accounts for imperfect detection, thereby improving the quality of outputs and inferences on occupancy, abundance, and density; and (iii) broader research scales in terms of multi‐species, multi‐site and multi‐year studies. However, the results showed that the sampling effort has reached a plateau, with publication rates increasing only modestly. Users reported reaching a saturation point in terms of images that could be processed by humans and time for complex analyses and academic writing. There were strong taxonomic and geographic biases towards medium–large mammals (>500 g) in forests along Australia's southeastern coastlines, reflecting proximity to major cities. Regarding analytical choices, bias‐prone indices still accounted for ~50% of outputs and this was consistent across user groups. Multi‐species, multi‐site and multiple‐year studies were rare, largely driven by hesitancy around collaboration and data sharing. There is no widely used repository for wildlife camera images and the Atlas of Living Australia (ALA) is the dominant repository for sharing tabular occurrence records. However, the ALA is presence‐only and thus is unsuitable for creating detection histories with absences, inhibiting hierarchical modelling. Workshop discussions identified a pressing need for collaboration to enhance the efficiency, quality and scale of research and management outcomes, leading to the proposal of a Wildlife Observatory of Australia (WildObs). To encourage data standards and sharing, WildObs should (i) promote a metadata collection app; (ii) create a tagged image repository to facilitate artificial intelligence/machine learning (AI/ML) computer vision research in this space; (iii) address the image identification bottleneck via the use of AI/ML‐powered image‐processing platforms; (iv) create data commons for detection histories that are suitable for hierarchical modelling; and (v) provide capacity building and tools for hierarchical modelling. Our review highlights that while Australia's investments in monitoring biodiversity with cameras position it to be a global leader in this context, realising that potential requires a paradigm shift towards best practices for collecting, curating, sharing and analysing ‘Big Data’. Our findings and framework have broad applicability outside Australia to enhance camera usage to meet conservation and management objectives ranging from local to global scales. This review articulates a country/continental observatory approach that is also suitable for international collaborative wildlife research networks.

Context: In Western Australia, baits containing 1080 poison are widely used to control the red fox (Vulpes vulpes) for fauna conservation. Despite long-term (15–17 years) baiting programs, bait uptake by target and non-target species is largely unknown, but affects baiting efficacy. Aims: We examined bait uptake of 1080-poisoned fox baits laid according to current practice at seven riparian sites in the northern jarrah forest (of south-west Western Australia). There, intensive baiting regimes have been implemented for the protection of quokka (Setonix brachyurus) populations. Methods: Over 9 months, 299 Probait® baits were monitored regularly to determine their persistence, and, at 142 of these, Reconyx HC500 remote cameras were used to identify the species taking baits. To compare bait uptake with species presence at these sites, we calculated an activity index for each species from the number of passes of animals in front of the cameras. Key results: The species taking baits was identified for 100 of the baits monitored with cameras, and, because of multiple species taking baits, 130 bait take incidents were recorded in total. The fate of 40 of the baits was not discernible and two baits were not removed. In all, 99% of baits monitored by cameras were taken by non-target species and quokkas took 48% of them. The majority of baits (62% of the total 299 monitored) were taken before or on the first night of deployment, and 95% of baits had been taken within 7 days. With the exception of feral pigs, which took more baits than predicted from their activity index at these sites, baits were taken in proportion to the activity index of species. Foxes were present at four of the seven sites, but only one fox was observed taking a bait. Conclusions: The high level of uptake of baits by non-target animals reflects their diversity and abundance at these sites, but also significantly reduces the availability of baits to control foxes. Implications: Strategies to reduce non-target bait uptake and increase bait availability for foxes are required.

Context: Camera trapping is a widely used monitoring tool for a broad range of species across most habitat types. Camera trapping has some major advantages over other trapping methods, such as pitfall traps, because cameras can be left in the field for extended periods of time. However, there is still a need to compare traditional trapping methods with newer techniques. Aims: To compare trap rates, species richness and community composition of small mammals and reptiles by using passive, unbaited camera traps and pitfall traps. Methods: We directly compared pitfall trapping (20-L buried buckets) with downward-facing infrared-camera traps (Reconyx) to survey small reptiles and mammals at 16 sites within a forested habitat in south-western Australia. We compared species captured using each method, as well as the costs associated with each. Key results: Overall, we recorded 228 reptiles, 16 mammals and 1 frog across 640 pitfall trap-nights (38.3 animal captures per 100 trap-nights) compared to 271 reptiles and 265 mammals (for species likely to be captured in pitfall traps) across 2572 camera trap nights (20.8 animal captures per 100 trap-nights). When trap effort is taken into account, camera trapping was only 23% as efficient as pitfall trapping for small reptiles (mostly Scincidae), but was five times more efficient for surveying small mammals (Dasyuridae). Comparing only those species that were likely to be captured in pitfall traps, 13 species were recorded by camera trapping compared with 20 species recorded from pitfall trapping; however, we found significant (P < 0.001) differences in community composition between the methods. In terms of cost efficacy, camera trapping was the more expensive method for our short, 4-month survey when taking the cost of cameras into consideration. Conclusions: Applicability of camera trapping is dependent on the specific aims of the intended research. Camera trapping is beneficial where community responses to ecosystem disturbance are being tested. Live capture of small reptiles via pitfall trapping allows for positive species identification, morphological assessment, and collection of reference photos to help identify species from camera photos. Implications: As stand-alone techniques, both survey methods under-represent the available species present in a region. The use of more than one survey method improves the scope of fauna community assessments.