Abstract An understanding of habitat use and levels of active behaviour is foundational to wildlife behaviour, ecology, conservation and management. These variables are commonly measured by tracking individuals in space and time using biologging. In principle, camera‐trap data also contain information about both activity level and habitat use; however, because motion‐triggered cameras capture only active animals, the raw rates of capture in different habitats give us an indication of habitat use by active animals only, missing patterns of use and preference by inactive individuals. Furthermore, capture rates are readily confounded by spatially imbalanced survey effort (either by design or by accident), and by habitat‐related variation in other determinants of trap rate (animal speed and camera sensitivity). Here, we show how camera‐traps can be used to infer patterns of both activity and habitat use by deconfounding these effects. Using simulation, we show that the method is minimally biased when the key underlying assumption (spatially invariant activity pattern) is met, but can otherwise be moderately biased. It also understandably fails to provide reliable information for parts of the day when there is very little or no activity, working best for populations that are at least partly available for detection throughout the day. We demonstrate the use of the method by applying it to 7 years of springtime data on seven mammal populations from habitat‐stratified camera‐trap monitoring in Hoge Veluwe National Park, the Netherlands. The method recovered credible patterns of habitat‐specific activity levels in all species, and credible patterns of habitat use and preference in all but one strictly nocturnal species. The method uncovered hidden diurnal migration patterns between habitats, preference for resource‐rich habitat, risk avoidance during public opening hours, and spatiotemporal segregation in some species. The new method allows analysis of habitat relationships for entire communities of larger mammals without the need for trapping and tagging.

Two primary methods exist for estimating the daily travel distance by free-ranging animals: inference from animal location data recorded with biologging or wearable GNSS tags (e.g. GPS), and inference from snapshots of animal movement recorded by stationary sensors (e.g. camera traps). Formal comparisons between the two approaches, which differ in both their assumptions and in choices made during analyses, remain scarce. Here, we compare GPS- and camera-based estimates of daily travel distance at matched temporal scales under both simulated and real-world scenarios. First, using simulated trajectories, we varied the GPS sampling interval and estimation method — continuous-time movement models (CTMM) vs. straight-line displacement (SLD) — as well as camera-analysis choices (activity correction, data sparsity) to test how well each approach recovers the true distance travelled. Second, we applied the same workflow to empirical datasets with overlapping GPS telemetry and camera-trap observations from the same populations. We found that, under the simulated movement and observation scenarios considered here, daily distance travelled could be estimated with limited bias from both camera-trap and GPS data using CTMM, but not with GPS data using SLD. Accurate estimation required correction for GPS-error, sufficiently high GPS sampling frequency, activity estimation for camera-trap data and adequate camera-trap data volume to estimate instantaneous speed and activity. Using empirical data from three case studies, we found that camera trapping and GPS-based CTMM estimates were of similar magnitude with overlapping confidence intervals, while GPS-based SLD estimates were lower than the others. These findings suggest that, when appropriately implemented, both methods can yield similar and fairly unbiased estimates of daily travel distance. These results provide practical guidance for using and potentially combining GPS-based CTMM and camera trapping-based travel distances in density models such as REM (random encounter model), strengthening ecological inference and the effectiveness of wildlife monitoring and management.

Remote islands receive plastic debris from elsewhere, ranging from microplastics (>5 mm) to macroplastics, which can further breakdown into microplastics. The ingestion of microplastics by marine species has been linked to decreased fitness. Reef manta rays, Mobula alfredi , are liable to ingest microplastics due to their filter‐feeding strategy and habitat overlap with plastic hotspots. Their population is in decline due to unsustainable fishing pressures and a slow life history, with potential additional demographic pressure from plastic pollution. This study investigates the concentration and characteristics of microplastics in the top 0.5 m of the water column in reef manta rays feeding areas around the Chagos Archipelago, a large remote marine protected area that is highly contaminated by macroplastic debris. Across all samples, a mean of 1.1 microparticle/m 3 was found, the majority of which were blue and black fibers. Half of the particles were confirmed as synthetic (53.6%, n = 305 out of 569 Fourier transform infrared spectroscopy'd particles), with the main synthetic polymers being polyester (21.1%), polypropylene (8.8%) and nylon (4.6%). Egmont Atoll, an International Union for Conservation of Nature “Important Shark and Ray Area” for its importance to reef manta rays, was the most contaminated atoll around the archipelago (1.6 microparticle/m 3 ). Continued regular beach cleans in important areas for biodiversity are recommended, as well as implementing new methods to reduce local input of microplastics, such as washing machine filters, and ultimately a global continued effort to reduce plastic usage and improve its disposal.

Species conservation relies heavily on population estimates derived from capture–recapture analyses, which are liable to produce biased results if individual animals are incorrectly identified. Captive and known‐animal studies have shown that supplementing human observation with artificial intelligence (AI) has the potential to reduce these errors. However, no study has directly quantified the relationship between using AI for individual identification and the demographic estimates it produces for a threatened population in situ. We compared the demographic estimates produced by capture–recapture analyses of two distinct encounter histories constructed from the same survey data; one produced using individual identifications made by human observers alone (the ‘human‐only data set’), and one produced using AI software to aid individual identification (the ‘AI‐supplemented data set’). This approach enabled us to address two key questions: (i) does the use of artificial intelligence software for individual identification influence demographic estimates for an in situ conservation programme? (ii) How has the population of our case study species, the critically endangered Kapitia skink, responded following an extreme weather event, cyclone Fehi? We found that, without AI, human observers appeared prone to make reclassification or ‘splitting’ errors, in which a recaptured animal was wrongly assigned as a new individual. Analysis of the AI‐supplemented data set consistently produced lower estimates of population abundance over time, relative to the same analysis of the human‐only data set. This provides new evidence that wild species monitoring efforts may be prone to underestimating the extinction risk of populations if they are dependent on individual identification methodologies with high potential for human errors. Our case study species, the Kapitia skink, demonstrated a positive population trend in the period following cyclone Fehi. While promising, conservation intervention is recommended to address persistent threats. Practical implication . Supplementing human observation with AI software for individual identification could mitigate errors leading to the underestimation of extinction risk for endangered species. We encourage further development of AI software to increase its automation and accessibility and recommend that practitioners consider its use in population monitoring based on the identification of individuals in imagery.

Abstract In the United Kingdom, the management of bovine tuberculosis (bTB) challenges the coexistence of people and wildlife. Control of this cattle disease is hindered by transmission of its causative agent, Mycobacterium bovis, between cattle and badgers Meles meles. Badger culling has formed an element of bTB control policy for decades, but current government policy envisions expanding badger vaccination. Farming leaders are sceptical, citing concerns that badger vaccination would be impractical and potentially ineffective. We report on a 4‐year badger vaccination initiative in an 11 km2 area which, atypically, was initiated by local farmers, delivered by scientists and conservationists, and co‐funded by all three. Participating landholders cited controversies around culling and a desire to support neighbours as their primary reasons for adopting vaccination. The number of badgers vaccinated per km2 (5.6 km−2 in 2019) exceeded the number culled on nearby land (2.9 km−2 in 2019), and the estimated proportion vaccinated (74%, 95% confidence interval [CI] 40%–137%) exceeded the 30% threshold predicted by models to be necessary to control M. bovis. Farmers were content with how vaccination was delivered, and felt that it built trust with wildlife professionals. The percentage of badgers testing positive for M. bovis declined from 16.0% (95% CI 4.5%–36.1%) at the start of vaccination to 0% (95% CI 0%–9.7%) in the final year. With neither replication nor unvaccinated controls, this small‐scale case study does not demonstrate a causal link between badger vaccination and bTB epidemiology, but it does suggest that larger‐scale evaluation of badger vaccination would be warranted. Farmers reported that their enthusiasm for badger vaccination had increased after participating for 4 years. They considered vaccination to have been effective, and good value for money, and wished to continue with it. Synthesis and applications: Although small‐scale, this case study suggests that badger vaccination can be a technically effective and socially acceptable component of bTB control. A wider rollout of badger vaccination is more likely if it is led by the farming community, rather than by conservationists or government, and is combined with scientific monitoring. Read the free Plain Language Summary for this article on the Journal blog.

The functional stability of ecosystems depends greatly on interspecific differences in responses to environmental perturbation. However, responses to perturbation are not necessarily invariant among populations of the same species, so intraspecific variation in responses might also contribute. Such inter-population response diversity has recently been shown to occur spatially across species ranges, but we lack estimates of the extent to which individual populations across an entire community might have perturbation responses that vary through time. We assess this using 524 taxa that have been repeatedly surveyed for the effects of tropical forest logging at a focal landscape in Sabah, Malaysia. Just 39 % of taxa – all with non-significant responses to forest degradation – had invariant responses. All other taxa (61 %) showed significantly different responses to the same forest degradation gradient across surveys, with 6 % of taxa responding to forest degradation in opposite directions across multiple surveys. Individual surveys had low power (< 80 %) to determine the correct direction of response to forest degradation for one-fifth of all taxa. Recurrent rounds of logging disturbance increased the prevalence of intra-population response diversity, while uncontrollable environmental variation and/or turnover of intraspecific phenotypes generated variable responses in at least 44 % of taxa. Our results show that the responses of individual species to local environmental perturbations are remarkably flexible, likely providing an unrealised boost to the stability of disturbed habitats such as logged tropical forests.### Competing Interest StatementThe authors have declared no competing interest.

Abstract Bird colonies on islands sustain elevated productivity and biomass on adjacent reefs, through nutrient subsidies. However, the implications of this localized enhancement on higher and often more mobile trophic levels (such as sharks and rays) are unclear, as spatial trends in mobile fauna are often poorly captured by traditional underwater visual surveys. Here, we explore whether the presence of seabird colonies is associated with enhanced abundances of sharks and rays on adjacent coral reefs. We used a novel long‐range water‐landing fixed‐wing unoccupied aerial vehicle (UAV) to survey the distribution and density of sharks, rays and any additional megafauna, on and around tropical coral islands (n = 14) in the Chagos Archipelago Marine Protected Area. We developed a computer‐vision algorithm to distinguish greenery (trees and shrubs), sand and sea glitter from visible ocean to yield accurate marine megafauna density estimation. We detected elevated seabird densities over rat‐free islands, with the commonest species, sooty tern, reaching densities of 932 ± 199 per km−2 while none were observed over former coconut plantation islands. Elasmobranch density around rat‐free islands with seabird colonies was 6.7 times higher than around islands without seabird colonies (1.3 ± 0.63 vs. 0.2 ± SE 0.1 per km2). Our results are evidence that shark and ray distribution is sensitive to natural and localized nutrient subsidies. Correcting for non‐sampled regions of images increased estimated elasmobranch density by 14%, and our openly accessible computer vision algorithm makes this correction easy to implement to generate shark and ray and other wildlife densities from any aerial imagery. The water‐landing fixed‐wing long‐range UAV technology used in this study may provide cost effective monitoring opportunities in remote ocean locations.

Abstract Camera trapping has revolutionized wildlife ecology and conservation by providing automated data acquisition, leading to the accumulation of massive amounts of camera trap data worldwide. Although management and processing of camera trap‐derived Big Data are becoming increasingly solvable with the help of scalable cyber‐infrastructures, harmonization and exchange of the data remain limited, hindering its full potential. There is currently no widely accepted standard for exchanging camera trap data. The only existing proposal, “Camera Trap Metadata Standard” (CTMS), has several technical shortcomings and limited adoption. We present a new data exchange format, the Camera Trap Data Package (Camtrap DP), designed to allow users to easily exchange, harmonize and archive camera trap data at local to global scales. Camtrap DP structures camera trap data in a simple yet flexible data model consisting of three tables (Deployments, Media and Observations) that supports a wide range of camera deployment designs, classification techniques (e.g., human and AI, media‐based and event‐based) and analytical use cases, from compiling species occurrence data through distribution, occupancy and activity modeling to density estimation. The format further achieves interoperability by building upon existing standards, Frictionless Data Package in particular, which is supported by a suite of open software tools to read and validate data. Camtrap DP is the consensus of a long, in‐depth, consultation and outreach process with standard and software developers, the main existing camera trap data management platforms, major players in the field of camera trapping and the Global Biodiversity Information Facility (GBIF). Under the umbrella of the Biodiversity Information Standards (TDWG), Camtrap DP has been developed openly, collaboratively and with version control from the start. We encourage camera trapping users and developers to join the discussion and contribute to the further development and adoption of this standard.

Abstract Accurate and precise assessment of population density plays a critical role in effective wildlife management, but reliable estimates are often difficult to obtain. Camera traps have emerged as valuable noninvasive tools for studying elusive species, offering cost‐effective solutions for both marked and unmarked populations. We evaluated the consistency of badger (Meles meles) density estimates obtained from the random encounter model (REM) and camera trap distance sampling (CT‐DS) with independent estimates from spatial mark‐resight (SMR) models and quantified the bias in CT‐DS arising from animals reacting to camera traps. Six camera trap surveys were conducted in Cornwall, UK, in 2019 and 2021, and data were used to estimate badger density using the REM and CT‐DS. Four sites were included in a badger vaccination research project, providing an opportunity to mark badgers with uniquely identifiable fur clips to facilitate resighting within a SMR framework. We found consistency in the density estimates across all methods, but results had wide confidence intervals. Density estimates derived from CT‐DS tended to be higher than those from the REM and were sensitive to the exclusion of reactive sequences, resulting in a twofold decrease in the estimated density in one case. The REM tended to be the most precise method; however, where badger density was low, precision was low using all methods. Practical implication: our findings suggest animal density can be assessed from camera traps in the absence of individual identification; however, it is important to account for reactive behaviours, especially where such behaviour is prevalent. In these circumstances, we recommend utilising the REM which offers a clear methodology for addressing bias arising from reactive sequences. In addition, we emphasise the need for improved precision to ensure the effectiveness of these methods in the context of wildlife management. We offer practical considerations to facilitate the broader application of these methods, drawing upon the example of disease control through badger vaccination.

Abstract The use of camera traps to study wildlife has increased markedly in the last two decades. Camera surveys typically produce large data sets which require processing to isolate images containing the species of interest. This is time consuming and costly, particularly if there are many empty images that can result from false triggers. Computer vision technology can assist with data processing, but existing artificial intelligence algorithms are limited by the requirement of a training data set, which itself can be challenging to acquire. Furthermore, deep‐learning methods often require powerful hardware and proficient coding skills. We present Sherlock, a novel algorithm that can reduce the time required to process camera trap data by removing a large number of unwanted images. The code is adaptable, simple to use and requires minimal processing power. We tested Sherlock on 240,596 camera trap images collected from 46 cameras placed in a range of habitats on farms in Cornwall, United Kingdom, and set the parameters to find European badgers (Meles meles). The algorithm correctly classified 91.9% of badger images and removed 49.3% of the unwanted ‘empty’ images. When testing model parameters, we found that faster processing times were achieved by reducing both the number of sampled pixels and ‘bouncing’ attempts (the number of paths explored to identify a disturbance), with minimal implications for model sensitivity and specificity. When Sherlock was tested on two sites which contained no livestock in their images, its performance greatly improved and it removed 92.3% of the empty images. Although further refinements may improve its performance, Sherlock is currently an accessible, simple and useful tool for processing camera trap data.

Remote islands are disproportionately affected by plastic pollution, often originating from elsewhere, so it is important to understand its origins, to stop debris entering the ocean at their source. We investigated the origins of beached plastic drink bottles in the Chagos Archipelago, a large remote Marine Protected Area (MPA) in the Indian Ocean. We recorded the brands, countries of manufacture, types of drink, and ages of plastic bottles and their lids. The prevalent type of drink was water, with items mostly manufactured in Indonesia, China, and the Maldives. The main brands were Danone and the Coca-Cola Company. We deduced that 10 % of the items originated from ships passing the archipelago, including all the items manufactured in China. The identification of the brands creating plastic pollution in remote MPAs with high biodiversity supports extended producer responsibility, one of the proposed policy development areas of the Global Plastics Treaty.

Logged and disturbed forests are often viewed as degraded and depauperate environments compared with primary forest. However, they are dynamic ecosystems(1) that provide refugia for large amounts of biodiversity2,3, so we cannot afford to underestimate their conservation value4. Here we present empirically defined thresholds for categorizing the conservation value of logged forests, using one of the most comprehensive assessments of taxon responses to habitat degradation in any tropical forest environment. We analysed the impact of logging intensity on the individual occurrence patterns of 1,681 taxa belonging to 86 taxonomic orders and 126 functional groups in Sabah, Malaysia. Our results demonstrate the existence of two conservation-relevant thresholds. First, lightly logged forests (<29% biomass removal) retain high conservation value and a largely intact functional composition, and are therefore likely to recover their pre-logging values if allowed to undergo natural regeneration. Second, the most extreme impacts occur in heavily degraded forests with more than two-thirds (>68%) of their biomass removed, and these are likely to require more expensive measures to recover their biodiversity value. Overall, our data confirm that primary forests are irreplaceable5, but they also reinforce the message that logged forests retain considerable conservation value that should not be overlooked.

Biogeography has a critical influence on how ecological communities respond to threats and how effective conservation interventions are designed. For example, the resilience of ecological communities is linked to environmental and climatic features, and the nature of threats impacting ecosystems also varies geographically. Understanding community-level threat responses may be most accurate at fine spatial scales, however collecting detailed ecological data at such a high resolution would be prohibitively resource intensive. In this study, we aim to find the spatial scale that could best capture variation in community-level threat responses whilst keeping data collection requirements feasible. Using a database of biodiversity records with extensive global coverage, we modelled species richness and total abundance (the responses) across land-use types (reflecting threats), considering three different spatial scales: biomes, biogeographical realms, and regional biomes (the interaction between realm and biome). We then modelled data from three highly sampled biomes separately to ask how responses to threat differ between regional biomes and taxonomic group. We found strong support for regional biomes in explaining variation in species richness and total abundance compared to biomes or realms alone. Our biome case studies demonstrate that there is a high variation in magnitude and direction of threat responses across both regional biomes and taxonomic group, but all groups in tropical forest showed a consistently negative response, whilst many taxon-regional biome groups showed no clear response to threat in temperate forest and tropical grassland. Our results suggest that the taxon-regional biome unit has potential as a reasonable spatial and ecological scale for understanding how ecological communities respond to threats and designing effective conservation interventions to bend the curve on biodiversity loss.

Caecilians (Order Gymnophiona) are generalist predators of soil invertebrates, and may therefore play an important role in tropical soil ecosystems. However, their fossorial lifestyle and the associated difficulties in surveying them have caused a deficit in data for the majority of species. We applied a systematic approach and an intensive sampling strategy to an Endangered and evolutionarily distinct caecilian from the Eastern Arc Mountains, the Sagalla caecilian Boulengerula niedeni. We investigated the association between habitat type and caecilian occupancy across its entire range, the Sagalla Hill, Kenya, and explored the relationship between several variables (land use type, surface soil temperature, soil compactness and landowner prediction of caecilian presence) and its presence in different habitats. We found no significant effects of any of the investigated variables in predicting caecilian presence across the Sagalla landscape. Instead, our findings suggest that the species survives at least as well in agricultural landscapes as it does in areas with indigenous vegetation, with an estimated density of around 900 caecilians per hectare. A bimodal distribution of sizes and weights of captured specimens suggests ongoing successful breeding and recruitment. This suggests that there is a case for cautious optimism with regard to the status of B. niedeni. Our work could act as a useful pilot for further, improved caecilian surveys in the Eastern Arc Mountains and beyond, to improve our understanding and conservation of these overlooked fossorial amphibians. Keywords: amphibian, Gymnophiona, Herpelidae, soil ecology, amphibian conservation, Taita Hills

Biogeographic context, such as biome type, has a critical influence on ecological resilience, as climatic and environmental conditions impact how communities respond to anthropogenic threats. For example, land-use change causes a greater loss of biodiversity in tropical biomes compared to temperate biomes. Furthermore, the nature of threats impacting ecosystems varies geographically. Therefore, monitoring the state of biodiversity at a high spatial resolution is crucial to capture variation in threat-responses caused by biogeographical context. However such fine-scale ecological data collection could be prohibitively resource intensive. In this study, we aim to find the spatial scale that could best capture variation in community-level threat responses whilst keeping data collection requirements feasible. Using a database of biodiversity records with extensive global coverage, we modelled species richness and total abundance (the responses) across land-use types (reflecting threats), considering three different spatial scales: biomes, biogeographical realms, and regional biomes (the interaction between realm and biome). We then modelled data from three highly sampled biomes to ask how responses to threat differ between regional biomes and taxonomic group. We found strong support for regional biomes in explaining variation in species richness and total abundance compared to biomes or realms alone. Our biome case studies demonstrate that there is variation in magnitude and direction of threat responses across both regional biomes and taxonomic group, although the interpretation is limited by sampling bias in the literature. All groups in tropical forest showed a consistently negative response, whilst many taxon-regional biome groups showed no clear response to threat in temperate forest and tropical grassland. Our results provide the first empirical evidence that the taxon-regional biome unit has potential as a reasonable spatial unit for monitoring how ecological communities respond to threats and designing effective conservation interventions to bend the curve on biodiversity loss.

The European Observatory of Wildlife EOW, as part of the ENETWILD project, represents a collaborative network that has been operating since 2021 to develop and implement standardized protocols to obtain harmonized data on distribution and density of target mammal species. In so doing, the EOW aims at contributing to improving the quality of data that are available for wildlife management and risk assessment on a European scale. This report describes the activities carried out during the 2023 EOW campaign, which was joined by a total of 30 organizations who committed to collect data in 44 sites across 22 different countries. We present data on the distribution and density of three species – wild boar ( Sus scrofa ), European roe deer ( Capreolus capreolus ), and red fox ( Vulpes vulpes ) – obtained by implementing a camera trapping protocol and by fitting the random encounter model (REM) for density estimation. Camera-trap images were processed using the Agouti platform and some of its tools specifically designed for the management of camera trapping projects. This includes the use of photogrammetry to obtain parameters for the REM directly from the sequences of images. A total of 24 EOW sites were monitored in past years as well, providing multiannual density estimates and population trends and highlighting an improvement in the precision of the estimates, related to the improved study design and protocol implementation. We also describe the activities of the 2024 campaign, carried out as part of ENETWILD 2.0, where big efforts were made to expand the network, focusing on sites at risk of African Swine Fever, with wild boar/pig interactions and containing wetlands, as potential hubs for Avian Influenza. This effort resulted in the engagement of 40 participants monitoring 64 study sites (27 countries), including 28 study sites located either in infected areas or < 100km from the ASF frontline, and 25 sites with wetland habitats. Furthermore, in at least 20 sites pig farming is practised either intensively, extensively or as backyard farming. Finally, synergies were established with other international initiatives related to wildlife monitoring and disease prevention, with the aim of sharing experiences and sustaining a transnational data collection and harmonization.

Monitoring is needed to assess conservation success and improve management, but naïve or simplistic interpretation of monitoring data can lead to poor decisions. We illustrate how to counter this risk by combining decision‐support tools and quantitative counterfactual analysis. We analyzed 20 years of egg rescue for tara iti ( Sternula nereis davisae ) in Aotearoa New Zealand. Survival is lower for rescued eggs; however, only eggs perceived as imminently threatened by predators or weather are rescued, so concluding that rescue is ineffective would be biased. Equally, simply assuming all rescued eggs would have died if left in situ is likely to be simplistic. Instead, we used the monitoring data itself to estimate statistical support for a wide space of uncertain counterfactuals about decisions and fate of rescued eggs. Results suggest under past management, rescuing and leaving eggs would have led to approximately the same overall fledging rate, because of likely imperfect threat assessment and low survival of rescued eggs to fledging. Managers are currently working to improve both parameters. Our approach avoids both naïve interpretation of observed outcomes and simplistic assumptions that management is always justified, using the same data to obtain unbiased quantitative estimates of counterfactual support.

Humans can derive enormous benefit from the natural environment and the wildlife they see there, but increasing human use of natural environments may negatively impact wildlife, particularly in urban green spaces. Few studies have focused on the trade-offs between intensive human use and wildlife use of shared green spaces in urban areas. In this paper, we investigate the impacts of humans and their dogs on wildlife within an urban green space using camera trap data from Hampstead Heath, London. Spatial and temporal activity of common woodland bird and mammal species were compared between sites with low and high frequency of visits by humans and dogs. There was no significant difference in the spatial or temporal activity of wildlife species between sites with lower and higher visitation rates of humans and dogs, except with European hedgehogs (Erinaceus europaeus) which showed extended activity in the mornings and early evenings in sites with lower visitation rates. This may have implications for the survival and reproductive success of European hedgehogs. Our results suggest that adaptation to human and dog activity deserves greater study in urban green spaces, as would a broader approach to measuring possible anthropogenic effects.

In the Asian elephant (Elephas maximus), the levels of progesterone products 5α-pregnane, 3α-hydroxypregnane, and 17α-progesterone are elevated during pregnancy. Detection of a sudden decrease in blood progesterone product levels in the final days of pregnancy is considered an objective way of predicting impending parturition. Point-of-care (POC) tests eliminate the cost involved in transporting samples to an external laboratory and provide an almost instant result, facilitating decision-making for animal monitoring and management. This proof-of-concept study aims to investigate the ability of the AgPlus POC immunoassay system to measure 4-pregnen-3,20-dione in pregnant elephant serum samples and adapt the method for detection of the preparturient progesterone decrease. Frozen serum samples of two pregnant elephants (N = 82) and fresh serum samples of one pregnant elephant (N = 10) were analyzed using both the POC method and a radioimmunoassay in a reference laboratory. Statistical analysis of the data showed that there was no significant difference between the two methods for detection of the progesterone drop, indicating that the POC method can be considered appropriate for use in elephant parturition prediction. Refinement of the methodology, an increase of sample size, and temporal tandem radioimmunoassay would be required to further validate this method for use in elephant reproductive management.