Understanding the relationship between a population’s probability of extinction and its carrying capacity frames conservation status assessments and guides efforts to understand and mitigate the ongoing biodiversity crisis. Despite this, our understanding of the mathematical form of this relationship remains limited. We conducted ~5 billion population viability assessments that jointly converge on a modified Gompertz curve. This pattern is consistent across >1700 distinct model populations, representing different breeding systems and widely varying rates of population growth, levels of environmental stochasticity, adult survival rate, age at first breeding, and initial population size. Analytical treatment of the underlying dynamics shows that few assumptions suffice to show that the relationship holds for any extant population subject to density-dependent growth. Finally, we discuss the implications of these results and consider the practical use of our findings by conservationists.

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 (>500g) in forests along Australia's southeastern coastlines, reflecting proximity to major cities. Regarding analytical choices, bias-prone indices still accounted for similar to 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.

Hibernation is an important strategy used by many endotherms to conserve energy and water. Global warming is changing species' phenology and hibernation patterns, but whether such changes are beneficial or harmful depends on the species' life history traits, physiology, morphology, and behavior. Mechanistic niche models can be used to make strong inferences on such responses by explicitly quantifying the consequences of changed hibernation patterns for energy and water requirements. However, they have yet to be adapted to heterothermic species. Here, we address this problem by extending the endotherm biophysical model of the NicheMapR package to encompass torpor. This model accurately predicts the energy requirements of hibernating mammals over a broad size range from microbats to bears. We then used this approach to assess the effect of climate change on a Critically Endangered hibernator, the Australian mountain pygmy possum (Burramys parvus). Specifically, we contrasted conditions for the year 2010 with two future climate-change scenarios (2or 4°C of average warming) to identify: (i) the projected changes in energy and water requirements; (ii) the advantage conferred by hibernating for the species' energy and water requirements; and (iii) the areas across southeastern Australia that could continue to support hibernation. We projected an 11%-43% reduction in hibernation hours for the mountain pygmy possum under our two climate-change scenarios. In consequence, requirements for energy increased by 4%-21%, and for water by 10%-34%. Under current conditions, hibernation reduces annual energy requirements by 44%-52% and annual water requirements by 32%-42%, but in our projections, this energetic and hydric benefit of hibernation will decline due to climate change. The total area where hibernating and not hibernating is energetically equivalent is projected to increase by 60% under 4°C warming, preventing recovery from the species' severely restricted distribution at present. Our results show that climate change will have a profound impact on the duration and patterns of hibernation, a key survival strategy, for Burramys. Our framework for analyzing changing hibernation patterns provides a new and general way to test the vulnerability and plasticity of hibernating endotherms under global change.

Expert-derived range maps are used extensively in macroecological and biogeographic analyses, yet they are subjective, taxonomically biased, and inconsistent in their treatment of species' absences. We developed a reproducible, data-driven approach to estimate species' extent of occurrence (EOO) using presence-only data and subregions of the Interim Biogeographic Regionalization for Australia (IBRA). This approach employs a Gaussian kernel density estimator calibrated for spatial coherence and ecological realism, producing maps independent of arbitrary grid structures. We applied it to 610 Australian bird species and evaluated the concordance of our data-driven EOO maps against BirdLife International expert-derived range maps. The spatial association between these two map sources, measured on a 0-1 scale, ranged from near-zero to 0.93 across species, with higher concordance among terrestrial birds. Estimates of richness using both map sources varied most at the finest spatial scale (IBRA subregions), with mean and root mean square errors at the coarsest biogeographic scale (ecoregion) being 1/3 lower than those at the subregional scale. Likewise, we demonstrated the approach's applicability across taxa by generating data-driven EOO maps for selected amphibians, mammals, reptiles, and vascular plants. Like for birds, discontinuities in the distribution of these non-avian species represented different subpopulations over Australia. Our approach minimizes the influence of sampling bias and internal discontinuities in the estimation of species' EOO, while allowing for range edge refinement and subpopulation delineation. It provides an objective and scalable alternative to expert-derived range maps, well-suited for large-scale ecological research requiring consistency in spatial precision. Given the plethora of biogeographic templates already in use, our approach is adaptable to many contexts and thus can readily support a better understanding and conservation of biodiversity at large spatial scales.

Monitoring wildlife is crucial for making informed conservation and land-management decisions. Remotely triggered cameras are widely used for this purpose, but the resulting 'big data' are laborious to process. Although artificial intelligence (AI) offers a powerful solution to this bottleneck, it has been challenging for ecologists and practitioners without substantial technical expertise to tailor current approaches to their specific use cases. Generic, online offerings also have issues of ongoing costs and data privacy. Here we present an open-source, scalable, modular, cross-platform workflow, deployed using Docker containers, which leverages deep learning for wildlife image classification. It can be run using simple command-line prompts or via a user-friendly graphical user interface (AddaxAI). It enables end-users to easily execute a full range of tasks—from animal detection and counting to species identification—on local or cloud GPU-accelerated machines. It also integrates with the widely used open-source camera-trapping software ‘Camelot’, writing AI-classification data directly to image metadata and to CSV files, ready for either expert verification or direct data analysis. The result is a user-friendly but powerful multi-platform application for wildlife-image classification and research pipelines. An example case study with Tasmanian wildlife demonstrates the utility of our classifier training and inference workflow.

Visually apparent diseases are valuable for investigating and monitoring the occurrence and prevalence of pathogens in wildlife populations through passive monitoring methods like camera trapping. Rumpwear, characterized by visible clinical signs of hair breakage and damage on the lumbosacral region, affects common brushtail possums (Trichosurus vulpecula) across Australia. However, the etiology of rumpwear remains unclear, and the spatiotemporal factors are understudied. This study investigated the epidemiology of rumpwear in common brushtail possums at Adamsfield, Tasmania (Australia), and predicted rumpwear distribution across the Tasmanian landscape. We visually classified images of rumpwear clinical signs in 6908 individual possums collected from a 3-year camera trapping network. Our results revealed that: (1) adults were twice as likely to show signs of rumpwear compared to young possums; (2) rumpwear occurrence increased with the relative activity of possums at a site; and (3) prevalence of rumpwear was seasonal, being lowest in May (3.2%-late autumn) and highest in December (27.1%-early summer). Collectively, these findings suggest that the occurrence of rumpwear may be density dependent, the putative etiological agent seems to be influenced by seasonal factors or site use. Additionally, a convolution neural network (CNN) was trained to identify rumpwear automatically based on the manually (human-expert) classified camera trap images. Applying the trained classifier to 38,589 brushtail possum images from across Tasmania, the CNN predicted that rumpwear is widespread, with an overall prevalence of 18.6%. This study provides new insights into rumpwear epidemiology and identified factors for further investigating within this host-pathogen system.

The decomposition of carrion is crucial to the functioning and nutrient cycling of ecosystems, and many species use this high-quality resource. However, the availability and reliability of carrion differs across environments. Modified landscapes, such as farms and roads, often produce a high density of carcasses, with disproportionate benefits for generalist mesoscavengers that can tolerate, or are attracted to, human presence. In this study, we placed carcasses strategically across two large islands in southern Australia and used camera traps to investigate how different scavengers locate and use carrion in forested, farmland and roadside habitats. Forest ravens, an avian generalist, were the dominant scavengers across all three habitat types but were three times more likely to discover carcasses deployed in modified landscapes. Raptors were twice as likely to discover and use carcasses in farmland habitats, indicating enhanced scavenging opportunities in these areas. The discovery rate and use of carcasses by feral cats did not differ between habitat types. In this setting, differences in carrion use between diurnal and nocturnal scavengers are potentially due to the absence of top mammalian carnivores (Tasmanian devils and quolls). Diurnal scavengers, such as forest ravens and raptors, followed expected habitat preferences, albeit with greater access to carrion due to increased persistence. However, feral cats demonstrated no preference for carcasses across habitats, likely due to the reduced landscape of fear. Overall, our study underscores the important role of native mammalian carnivores in structuring scavenger communities and further validates the value of trophic rewilding to restore key ecological functions.

We examined a zooarchaeological assemblage from Badger Island, a 12.4 km 2 landbridge island in the Furneaux Group, Bass Strait, south-eastern Australia. The accumulation consisted of Pleistocene and Holocene strata that were rich in mammal remains. Small mammal remains were accumulated by owls, whereas large mammal remains were accumulated by people and/or autochthonous mortality. The Pleistocene fauna was dominated by grassland mammals, particularly Mastacomys fuscus (Broad-toothed Rat), but these gradually declined and were largely replaced by forest–woodland dwelling mammals in the Holocene. The same pattern of faunal change has been observed on the large main island of Tasmania (∼65,000 km 2 ), suggesting changes observed at Beeton Rockshelter are representative of the region. Because all of the Furneaux Group Islands were united as one landmass in the past, the fossil fauna observed in Beeton Rockshelter is relevant to conservation-oriented mammal-restoration initiatives, which are being considered throughout the entire Furneaux Group.

We analysed fossil mammal assemblages from over 350 Late Pleistocene and Holocene sites worldwide to test whether human activities, such as agriculture, domestication and intensified land use, restructured global patterns of mammal co-occurrence. Using presence-absence data, we contrasted a novel iterative 'chase clustering' method, which is compositionally driven, against a traditional spatially constrained Ward's clustering approach. Both methods recovered continental-scale groupings in the Pleistocene, consistent with known biogeographic boundaries. Holocene land use and domestication reconfigured these historical patterns, creating novel assemblages independent of previous biogeographic constraints. Faunal turnover at the local scale varied substantially across regions, being especially pronounced in the Americas, whereas other areas showed relative stability. Even moderate expansion of domesticates altered how communities grouped, highlighting their disproportionate ecological influence. Our findings demonstrate that human-driven niche modification, beyond earlier megafaunal extinctions, profoundly reshaped mammal communities on a global scale. Recognizing these anthropogenic legacies provides essential context for anticipating how current and future human pressures might further transform biodiversity.

Modelling the spread of introduced ecosystem engineers is a conservation priority due to their potential to cause irreversible ecosystem-level changes. While existing models predict potential distributions and spread capacities, new approaches that simulate the trajectory of a species' spread over time are needed. We developed novel simulations that predict spatial and temporal spread, capturing both continuous diffusion-dispersal and occasional long-distance leaps. We focused on the introduced population of superb lyrebird Menura novaehollandiae in Tasmania, Australia. Initially introduced as an insurance population, lyrebirds have become novel bioturbators, spreading across key natural areas and becoming 'unwanted but challenging to eradicate'. Using multi-scale ecological data, our research 1) identified broad and fine-scale correlates of lyrebird occupation and 2) developed a spread simulation guided by a pattern-oriented framework. This occurrence-based modelling framework is useful when demographic data are scarce. We found that the cool, wet forests of western Tasmania with open understoreys offer well-connected habitats for lyrebird foraging and nesting. By 2023, lyrebirds had reached quasi-equilibrium within a core range in southern Tasmania and were expanding northwest, with the frontier reaching the western coast. Our model forecasts that by 2085, lyrebirds will have spread widely across suitable regions of western Tasmania. By pinpointing current and future areas of lyrebird occupation, we provide land managers with targeted locations for monitoring the effects of their expansion. Further, our area of applicability (AOA) analysis identified regions where environmental variables deviate from the training data, guiding future data collection to improve model certainty. Our findings offer an evidence-based approach for future monitoring and provide a framework for understanding the dynamics of other range-expanding species with invasive potential.

Setting camera traps along roads is often necessary for ecological research and visitor management studies, yet these locations expose cameras to theft leading to substantial data losses. Measures to minimise this risk include placing cameras away from human settlements among other things. However, the effect of this and other measures on the risk of camera-trap theft is yet to be quantified. Here, we assessed the influence of gates on roads, the frequency of vehicle and human foot traffic, distance to the nearest town, and reduced visibility, on camera-trap theft risk, using data from a four-year, geographically extensive camera-trapping study in Tasmania, Australia. The dataset covered 564 camera sites operating for 316,372 days (average of 561 camera days per unit), with 112 cumulative thefts. We used Bayesian survival modelling to determine the factors that best explained theft risk. Our results showed a high initial vulnerability to theft that gradually reduced over time, with significant predictors of reduced theft risk being: (i) lower frequencies of vehicle traffic, (ii) greater distance from the nearest town, (iii) the presence of a gate, and (iv) a temporal trend likely reflecting adaptive changes to the deployment strategies used by researchers to reduce camera visibility. The frequency of human foot traffic surprisingly did not significantly elevate theft risk. Our study evidence-based insights into factors wildlife camera users should consider to mitigate theft risk on roads and provides a robust analytical framework to identify them, with application in diverse social and ecological contexts.

AimAustralia's distinct geological history provides key insights into the diversity of its flora. While previous studies have predominantly focused on climate as the main driver of species richness, growing evidence suggests that geological factors also play an important role. This study aims to investigate the influence of Cenozoic volcanic lithologies on terrestrial vascular plant diversity in eastern Australia, disentangling the relative contributions of climate and geology to biodiversity patterns.LocationEastern Australia.TaxonTerrestrial vascular plants.MethodsWe assessed the patterns of species richness in this region by examining whether sites with Cenozoic volcanic lithologies harbour greater vascular plant diversity, using a permutational multivariate analysis of variance (PERMANOVA). We then used a supervised machine learning algorithm (decision trees) coupled with partial canonical correspondence analysis (CCA) to discriminate the environmental variables influencing species richness and site composition in 230 sites in Queensland, New South Wales, Victoria and Tasmania.ResultsSoil profile variability and terrain ruggedness, influenced by underlying volcanic lithologies, emerged as primary predictors of species richness. We found species composition, indicative of distinct ecological communities, showed greater similarity within lithological types and varied significantly across volcanic complexes at different latitudes. Notably, areas of higher species richness corresponded with a greater diversity of stratigraphic units within protected zones.Main ConclusionsOur study reveals a significant imprint of Cenozoic volcanic activity on present-day plant species richness and distribution in eastern Australia. We show that geological features, particularly lithology and site complexity, play a crucial role in shaping species richness beyond previously recognised climatic factors. These findings highlight the importance of integrating geological and edaphic factors into conservation strategies, thereby broadening our understanding of ecological dynamics, and guiding more effective biodiversity conservation.

Context Camera traps have become a crucial tool for monitoring predators and are frequently deployed with lures to boost detection. Feral cats, a problematic invasive species in Australia, are commonly monitored using camera traps with lures. Despite the additional effort required for lure deployment, it remains unclear which lures are effective for monitoring feral cats. Aims Our study aimed to address this knowledge gap and assess the impact of various lure treatments on feral cat detection and activity. We also examined the response of other predators, such as Tasmanian devils and spotted-tailed quolls, to the lures and explored how their activity influenced feral cat visitation. Methods We deployed food, olfactory and visual lures, along with a null treatment across four grids of 16 camera traps over a period of 4 months in south-eastern Tasmania. Key results We observed increased feral cat detection with food (odds ratio = 3.69, 97.5% CI = 1.04, 13.2) and visual lures (odds ratio = 5.95, CI = 1.75, 20.2), but not olfactory lures (odds ratio = 1.88, CI = 0.55, 6.51). Examining only sites where cats were detected, food (odds ratio = 3.35, CI = 1.27, 8.9), visual (odds ratio = 3.39, CI = 1.41, 8.1) and olfactory (odds ratio = 2.7, CI = 1.02, 7.1) lures all increased feral cat visitation to the camera traps. Tasmanian devil and spotted-tailed quoll activity increased 4.85-fold (CI = 2.89, 8.1) and 4.94-fold (CI = 2.92, 8.4) when using the food lure, and 4.24-fold (CI = 2.5, 7.2) and 3.49-fold (CI = 2.03, 6.0) when using the olfactory lure. Whereas a positive relationship existed between devil and cat activity overall (beta = 0.49, s.e. = 0.15, P <= 0.001), negative associations were found between devil and cat activity in the presence of food (beta = -0.36, s.e. = 0.19, P = 0.057) and olfactory (beta = -0.42, s.e. = 0.20, P = 0.026) lures. Conclusions Our findings demonstrated the effectiveness of lures in temperate environments for feral cat monitoring but highlighted potential interspecific interactions that reduce feral cat visitation. Implications We recommend a thoughtful consideration of the environment and resident species to ensure effective lure use and to minimise unintended negative influences on the capture of target species.

In applications of optically stimulated luminescence (OSL) dating to unconsolidated sediments, the burial age of a sample of grains is estimated using statistical models of the distribution of the experimentally determined equivalent doses of the grains, together with estimates of the environmental dose rate. For grains that have been vertically mixed after deposition (e.g., due to bioturbation), existing dose models may fail to appropriately account for the complexity of the mixing process, thus producing inaccurate age estimates of the original time of deposition of the 'native' grains in any particular sample (usually the quantity of most interest). Here we introduce a new dose model, the asymmetric Laplacian mixture model (ALMM), developed for vertically mixed samples with single -grain dose distributions. The approach is based on a continuous statistical mixture that models the displacement of grains in both upward and downward directions. The central dose of the native grains in each sediment sample is estimated by the ALMM, as well as the parameters associated with overdispersion of single -grain dose distributions and the (modelled) mixing process. Using Bayesian methodology, we apply the model to two series of vertically contiguous samples collected at the site of Nawarla Gabarnmang in northern Australia. Independent age estimates obtained from radiocarbon dating of charcoal fragments support the OSL ages for the native grains estimated by the ALMM. Moreover, our study includes sensitivity analyses that show the model is robust to variation in the experimental error of the OSL data, as well as a simulation study that demonstrates the model's good ability to recover the simulated central dose and its excellent coverage properties. The ALMM is introduced in the context of compound Gaussian distributions, a broadly encompassing statistical framework that includes many of the most commonly used dose models. This unifying and accessible perspective on the statistical modelling of dose distributions will support practitioners in selecting an appropriate model for samples affected by post -depositional mixing, and hopefully stimulate further theoretical developments. A new R package rstanosl is provided that fits the ALMM and other commonly used dose models using Hamiltonian Monte Carlo methods via the Stan programming language.