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Species interactions shape biodiversity patterns, community assemblage, and the dynamics of wildlife populations. Ecological theory posits that the strength of interspecific interactions is fundamentally underpinned by the population sizes of the involved species. Nonetheless, prevalent approaches for modeling species interactions predominantly center around occupancy states. Here, we use simulations to illuminate the inadequacies of modeling species interactions solely as a function of occupancy, as is common practice in ecology. We demonstrate erroneous inference into species interactions due to error in parameter estimates when considering species occupancy alone. To address this critical issue, we propose, develop, and demonstrate an abundance‐mediated interaction framework designed explicitly for modeling species interactions involving two or more species from detection/non‐detection data. We present Markov chain Monte Carlo (MCMC) samplers tailored for diverse ecological scenarios, including intraguild predation, disease‐ or predator‐mediated competition, and trophic cascades. Illustrating the practical implications of our approach, we compare inference from modeling the interactions in a three‐species network involving coyotes (Canis latrans), fishers (Pekania pennanti), and American marten (Martes americana) in North America as a function of occupancy states and as a function of abundance. When modeling interactions as a function of abundance rather than occupancy, we uncover previously unidentified interactions. Our study emphasizes that accounting for abundance‐mediated interactions rather than simple co‐occurrence patterns can fundamentally alter our comprehension of system dynamics. Through an empirical case study and comprehensive simulations, we demonstrate the importance of accounting for abundance when modeling species interactions, and we present a statistical framework equipped with MCMC samplers to achieve this paradigm shift in ecological research. [ABSTRACT FROM AUTHOR]

Predicting animal population trajectories into the future has become a central exercise in both applied and fundamental ecology. Because demographic models classically assume population closure, they tend to provide inaccurate predictions when applied locally to interconnected subpopulations that are part of a larger metapopulation. Ideally, one should explicitly model dispersal among subpopulations, but in practice this is prevented by the difficulty of estimating dispersal rates in the wild. To forecast the local demography of connected subpopulations, we developed a new demographic model (hereafter, the two‐scale model) that disentangles two processes occurring at different spatial scales. First, at the larger scale, a closed population model describes changes in metapopulation size over time. Second, total metapopulation size is redistributed among subpopulations, using time‐varying proportionality parameters. This two‐step approach ensures that the long‐term growth of every subpopulation is constrained by the overall metapopulation growth rate. It implicitly accounts for the interconnectedness among subpopulations and avoids unrealistic trajectories. Using realistic simulations, we compared the performance of this new model with that of a classical closed population model at predicting subpopulations' trajectories over 30 years. While the classical model predicted future subpopulation sizes with an average bias of 30% and produced predictive errors sometimes >500%, the two‐scale model showed very little bias (<3%) and never produced predictive errors >20%. We also applied both models to a real dataset on European shags (Gulosus aristotelis) breeding along the Atlantic coast of France. Again, the classical model predicted highly unrealistic growths, as large as a 200‐fold increase over 30 years for some subpopulations. The two‐scale model predicted very sensible growths, never larger than a threefold increase over the 30‐year time horizon, which is more in accordance with this species' life history. This two‐scale model provides an effective solution to forecast the local demography of connected subpopulations in the absence of data on dispersal rates. In this context, it is a better alternative than closed population models and a more parsimonious option than full‐dispersal models. Because the only data required are simple counts, this model could be useful to many large‐scale wildlife monitoring programs. [ABSTRACT FROM AUTHOR]

The Himalayas, including Nepal, are a biodiversity hotspot. However, records on mammalian richness remain incomplete due to resource limitations, inadequate training, and the remote location of study areas. The unprotected forest area of the Panchthar-Ilam-Taplejung region in eastern Nepal is a vital corridor connecting India and Nepal. Using a structured methodology we aimed to increase our knowledge of mammalian diversity in this area. Camera traps were deployed throughout the Panchthar-Ilam-Taplejung area in 53 locations in winter and 54 in spring, accumulating 3014 camera trap days and generating 93,336 images, with a positive trigger rate of 29.8%. The survey revealed 17 species of medium to large-sized mammals and an additional six species of smaller unidentified mammals, including two melanic variations and two previously undocumented species. Activity patterns were calculated for species with more than five image records in both seasons. The findings contribute essential information about the Kangchenjunga Landscape, which can be used to further conservation efforts in this critical ecosystem corridor. [ABSTRACT FROM AUTHOR]

Hunting occupies a strong position in Denmark, and Danish hunters have delivered high quality data on their individual tally of bagged birds and mammals ever since it became mandatory to do so in 1941. The annual data on hunting yields of most species for more than 80 years serves to qualify the advice given by Danish wildlife biologists on hunting and wildlife management to the responsible authorities and other stakeholders. Due to overharvest or persecution many wildlife populations were heavily reduced or even driven to extinction during the last part of the nineteenth century and the first half of the twentieth century. Since then, improved legislation and management have resulted in many populations becoming re-established or increasing, and the total take by hunters increased to culminate in almost 4 million bagged mammals and birds per year around 1970. After 1970, the most distinctive pattern has been that total numbers of bagged animals has decreased, while yield in weight of the take has almost doubled over the 80-year span of data. This is because the take of smaller game has decreased due to protective measures, reduced farmland wildlife populations, and declining interest among hunters, while the take of larger species such as deer and geese has increased considerably following increasing populations resulting from improved management of hunting together with green fields in winter. The question remains as to what extent hunting and its associated disturbances and heightened shyness of wildlife keep quarry populations below the carrying capacity of the environment. This issue is particularly problematic in relation to the entire West Palearctic flyway populations of waterbirds, where lack of reliable annual bag statistics from most of the countries along the flyway prevents effective management. This challenge is further discussed in relation to societal changes in attitudes towards hunting and the future of such changes. [ABSTRACT FROM AUTHOR]

Background: The pandemic caused by SARS-CoV-2 has not only affected humans but also raised concerns about its transmission to wild animals, potentially creating natural reservoirs. Understanding these dynamics is critical for preventing future pandemics and developing control strategies. This study aims to investigate the presence of SARS-CoV-2 in wild mammals at the Belo Horizonte Zoo in Brazil, analyzing the virus's evolution and zoonotic potential. Methods: The study was conducted at the Belo Horizonte Zoo, Minas Gerais, Brazil, covering a diverse population of mammals. Oropharyngeal, rectal, and nasal swabs were collected from 47 captive animals between November 2021 and March 2023. SARS-CoV-2 presence was determined using RT-PCR, and positive samples were sequenced for phylogenetic analysis. Consensus genomes were classified using Pangolin and NextClade tools, and a maximum likelihood phylogeny was inferred using IQ-Tree. Results: Of the 47 animals tested, nine (19.1%) were positive for SARS-CoV-2. Positive samples included rectal, oropharyngeal, and nasal swabs, with the highest positivity in rectal samples. Three genomes were successfully sequenced, revealing two variants: VOC Alpha in a maned wolf (Chrysocyon brachyurus) and a fallow deer (Dama dama), and VOC Omicron in a western lowland gorilla (Gorilla gorilla gorilla). Phylogenetic analysis indicated potential human-to-animal transmission, with animal genomes clustering close to human samples from the same region. Conclusions: This study highlights the presence of SARS-CoV-2 in various wild mammal species at the Belo Horizonte Zoo, emphasizing the virus's zoonotic potential and the complexity of interspecies transmission. The detection of different variants suggests ongoing viral evolution and adaptation in new hosts. Continuous monitoring and genomic surveillance of SARS-CoV-2 in wildlife are essential for understanding its transmission dynamics and preventing future zoonotic outbreaks. These findings underscore the need for integrated public health strategies that include wildlife monitoring to mitigate the risks posed by emerging infectious diseases. [ABSTRACT FROM AUTHOR]

Background: Wild animals often suffer from infections with multiple species of parasites simultaneously. The exchange of parasites between different host species is common in nature and often involves intermediate hosts or sharing space such as pastures or watering holes. Supplementary feeding, leading to large aggregations of individuals, can have several adverse effects on wild ungulate populations, despite being a widespread management practice. One such effect is an increased risk of parasitic infections, particularly in social animals. We quantified the prevalence of selected helminths typically found in cervids, in samples of the European bison faeces, using molecular methods, and compared endoparasite species richness between supplementarily fed and nonfed European bison herds in the Białowieża Primeval Forest, NE Poland. Methods: Using the diagnostic PCR method, we analysed the faecal samples for molecular markers of nine parasite species which are typically found in cervids: moose, red deer, and roe deer. Results: All analysed samples tested positive for at least one parasite species, and the average number of parasite species per sample was 3.2. The most prevalent parasites were gastrointestinal nematodes: Ostertagia leptospicularis and Ostertagia antipini, found in 89.2% and 50.6% of the European bison faecal samples, respectively. We found significant differences in the prevalence of four parasite species between supplementarily fed and nonfed European bison herds. Co‐occurrence analysis showed that most of the associations between parasite species pairs were random. Conclusion: Management practices, such as supplementary feeding, can influence the spread of parasite infections in social mammals. This study also promotes the application of molecular methods for noninvasive parasitological monitoring of wildlife populations of endangered ruminant species sharing resources with other ungulates. [ABSTRACT FROM AUTHOR]

Accurate population monitoring is essential for effective wildlife conservation. This study compares the effectiveness of drone-based vs traditional boat-based methods for assessing an endangered Bolivian river dolphin (Inia geoffrensis boliviensis; BRD) population in Bolivia. Data were collected using high-resolution video recorded with a DJI Mavic 2 zoom drone and standardized methodologies for boat surveys. Two mixed-effects linear models incorporating Poisson and negative binomial error structures were used to compare counts obtained from drone- and boat-based surveys. Results show that drone-based surveys detected 1.15% more individuals on average than boat-based surveys. Drone counts were higher at sites with larger group sizes, leading to congruent estimates. The aerial perspective that drones offer lets researchers overcome potential challenges from boat-based surveys--for example, difficulties related to confirming individual IDs because of limited visibility due to sun glare. Transitioning from boat- to drone-based surveys offers advantages such as reduced disturbance from placement closer to the animals and improved detection rates. Ethical considerations and responsible flight practices are crucial. Standardizing methodologies and prioritizing ethical research factors are key for successful implementation. Drone-based surveys offer a promising approach to enhance wildlife monitoring and conservation practices. This study is the first of this kind for Bolivia, a national natural heritage site, and contributes to the conservation and knowledge of the BRD. [ABSTRACT FROM AUTHOR]

Effective detection techniques are important for wildlife monitoring and conservation applications and are especially helpful for species that live in complex environments, such as arboreal animals like koalas (Phascolarctos cinereus). The implementation of infrared cameras and drones has demonstrated encouraging outcomes, regardless of whether the detection was performed by human observers or automated algorithms. In the case of koala detection in eucalyptus plantations, there is a risk to spotters during forestry operations. In addition, fatigue and tedium associated with the difficult and repetitive task of checking every tree means automated detection options are particularly desirable. However, obtaining high detection rates with minimal false alarms remains a challenging task, particularly when there is low contrast between the animals and their surroundings. Koalas are also small and often partially or fully occluded by canopy, tree stems, or branches, or the background is highly complex. Biologically inspired vision systems are known for their superior ability in suppressing clutter and enhancing the contrast of dim objects of interest against their surroundings. This paper introduces a biologically inspired detection algorithm to locate koalas in eucalyptus plantations and evaluates its performance against ten other detection techniques, including both image processing and neural-network-based approaches. The nature of koala occlusion by canopy cover in these plantations was also examined using a combination of simulated and real data. The results show that the biologically inspired approach significantly outperformed the competing neural-network- and computer-vision-based approaches by over 27%. The analysis of simulated and real data shows that koala occlusion by tree stems and canopy can have a significant impact on the potential detection of koalas, with koalas being fully occluded in up to 40% of images in which koalas were known to be present. Our analysis shows the koala's heat signature is more likely to be occluded when it is close to the centre of the image (i.e., it is directly under a drone) and less likely to be occluded off the zenith. This has implications for flight considerations. This paper also describes a new accurate ground-truth dataset of aerial high-dynamic-range infrared imagery containing instances of koala heat signatures. This dataset is made publicly available to support the research community. [ABSTRACT FROM AUTHOR]

Protected areas and biological corridors play a crucial role in conserving biodiversity and promoting connectivity between habitat patches. Bhutan, known as a stronghold for tigers (Panthera tigris), has dedicated a significant portion of its land to a network of protected areas and biological corridors. However, these areas face risks from illegal activities, posing challenges for their management. To address this, the Department of Forests & Park Services in Bhutan deployed an adaptive management framework, informed by the Spatial Monitoring and Reporting Tool in 2016 in the Divisional Forest Office, Sarpang, an important corridor area outside of official protected area boundaries. This study analyses 5 years of data (2017–2021) recorded through a monitoring tool to assess changes in ranger activities and on‐ground observations. The results show improved data availability, increased patrol effort, enhanced monitoring of wildlife, and a reduction in threat detection over time. Furthermore, camera trap surveys targeting tigers reveal a potential increase in the number of individual tigers utilizing the corridor since the adoption of the tool and other management actions. These findings highlight the effectiveness of these efforts in a critical wildlife corridor. [ABSTRACT FROM AUTHOR]

This work constitutes an approach to hunting management in Uruguay. We have focused on wild boar hunting, the most widespread form of recreational hunting in this country. We explored the connections and discrepancies between the different actors based on a qualitative study conducted with conservationists, animal rights groups, nature managers, and hunters. The informality of hunting and the lack of population studies on wildlife were the main concerns raised while hunting with dogs was the most controversial issue. These results allowed us to propose some potential management actions to improve hunting management: issuing a hunting license, regulating hunting with dogs, developing collaborative wildlife monitoring, promoting new technologies within the framework of ethical hunting, and developing a realistic and effective poaching control. [ABSTRACT FROM AUTHOR]

Wild animals are recognized as significant reservoirs for various zoonotic pathogens, including antibiotic-resistant bacteria. This study aimed to investigate the presence of Staphylococcus spp. strains in fallow deer (Dama dama) inhabiting a natural preserve in Central Italy and to examine the phenotypic and genotypic antimicrobial resistance and the presence of some virulence genes among the isolates. During July and December 2022, nasal swabs were collected from 175 fallow deer, which were then analyzed through bacteriological cultures. In total, 176 Staphylococcus spp. strains were isolated and subsequently identified using MALDI-TOF mass spectrometry. S. aureus was the most abundant species with 66 (37.5%) strains, followed by S. hyicus, 34 (19.31%) strains, S. sciuri, 32 (18.18%) strains, S. chromogenes, 27 (15.34%) strains, S. xylosus, 11 (6.25%) strains, S. warneri, 5 (2.84%) strains, and S. devriesei, 1 (0.56%) strain. Antimicrobial susceptibility was assessed for each isolate via the agar disk diffusion method, testing a panel of 13 molecules belonging to 9 antimicrobial classes. The highest resistance rates were detected for penicillin (29.55%), rifampicin (22.73%), and amikacin (20.45%). Notably, intermediate susceptibility was observed for erythromycin (61.93%), enrofloxacin (28.41%), and ceftiofur (21.02%). Conversely, the strains exhibited particularly high susceptibility to amoxicillin/clavulanic acid (99.43%), cefoxitin (97.73%), and vancomycin (96.02%). Based on the results, 32 (18.18%) isolates were classified as multidrug-resistant (MDR). Two strains of S. chromogenes and one strain of S. xylosus, both resistant to penicillin, tested positive for the blaZ gene. No methicillin-resistant strains were found, and none of the isolates harbored genes associated with enterotoxin and toxic shock syndrome toxin production. This study highlights the potential role of wildlife, particularly fallow deer, as reservoirs of antibiotic-resistant Staphylococcus spp. strains. Such findings underscore the importance of monitoring wildlife for antimicrobial resistance, which could have implications for public health and veterinary medicine. [ABSTRACT FROM AUTHOR]

To effectively monitor biodiversity in streams and rivers, we need to quantify species distribution accurately.Occupancy models are useful for distinguishing between the non‐detection of a species and its actual absence. While these models can account for spatial autocorrelation, they are not suited for streams and rivers due to their unique network spatial structure.Here, I propose spatial occupancy models specifically designed for data collected on stream and river networks. I present the statistical developments and illustrate their application using data on a semi‐aquatic mammal.Overall, spatial stream network occupancy models offer a robust method for assessing biodiversity in freshwater ecosystems. [ABSTRACT FROM AUTHOR]

Efficient biodiversity surveys are critical for successful restoration monitoring and management. We studied the effect of varying sampling effort on the observed species richness of surveys of small mammals (trapping transects), bats (passive acoustic detection), and medium to large mammals (trail cameras). Field studies provided mammalian biodiversity data for 4 bottomland hardwood restoration sites in northeastern Indiana. Subsampled data were used to simulate monitoring surveys with a range of levels of effort. We then used hierarchical Bayesian nonlinear mixed models to analyze how different components of sampling effort affected observed species richness, a key monitoring outcome. We found that observed small mammal richness increased with the increased number of transects in a survey, while observed bat and medium to large mammal richness increased with the increased duration of sampling. Variation between sites was important for the observed richness of small mammals and bats but not for medium to large mammals. The key driver of richness observed in simulated surveys was related to the spatial scale at which target fauna interact with the habitat, with decreasing richness accompanied by a greater spatial scale of animal–habitat interactions. Our findings suggest taxon‐specific recommendations for efficiently quantifying the mammalian diversity of managed sites. Integr Environ Assess Manag 2024;20:1969–1981. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC) Key Points: Hierarchical subsampling was used to simulate how different levels of sampling effort affected the observed biodiversity of mammals.Observed small mammal species richness increased with number of samples (i.e., transects).For bats and medium to large mammals the duration of sampling, not number of samples, had the greatest effect on observed species richness.The spatial scale at which a species interacts with the habitat determined which component of sampling effort had the greatest effect. [ABSTRACT FROM AUTHOR]

Monitoring environmental conditions over time is essential for understanding how species regulate those conditions to satisfy their physiological needs. Variation in ultraviolet (UV) light exposure, for instance, is crucial for numerous taxa, influencing behaviour as well as essential physiological processes like vitamin D3 synthesis. Yet, despite these considerations, no commercially available data loggers exist to record UV exposures over time, preventing inquiry into this important but neglected fitness demand.To overcome this challenge, I designed the first UV data logger, custom built on the open‐source Arduino platform. This device logs UV exposures (in voltage or converted to UV index) and date‐time information for each UV reading, at user‐defined intervals. The integration of a low‐power timer into the assembly, and its weather‐resistant housing, enables long‐term use (at least several months). The cost of one device (including housing) is under £45/$55 USD.Calibration and performance tests reveal that this device is reliable under various weather conditions and produces accurate UV readings under both artificial lighting and sunlight. Both the device and its customised housing can be easily replicated and expanded upon (e.g. inclusion of additional sensors).Overall, this device represents a crucial first step in our ability to better characterise UV exposure patterns in habitats for physiological ecology studies. Exposure to UV light is adaptive for many types of organisms, both in the wild and in captivity (e.g. zoos and private hobbyists), and so I expect a broad array of scientific and public users alike to benefit from using this device. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs), commonly known as drones, show great potential in advancing the United Nations (UN) Sustainable Development Goals (SDGs), a collection of 17 worldwide targets outlined by the United Nations (UN) to tackle diverse social, economic, and environmental issues and foster sustainable development by 2030. As per the Federal Aviation Administration (FAA), UAVs can significantly improve areas of sustainability like wildlife and wildfire monitoring, precision agriculture, as well as healthcare. Furthermore, drones with communication capabilities are seen as key elements of non-terrestrial networks, and as enabling technologies for future 6G communication networks. This systematic literature review focuses on the role of UAV-enabled communications to achieve the SDGs. Our study is based on a thorough selection and analysis of scientific articles. Initially, we performed a broad search across three major databases: IEEE Xplore, Web of Science, and Scopus, yielding a set of 10,499 papers. Following a strict selection methodology (Preferred Reporting Items for Systematic Review and Meta-Analysis - PRISMA), we narrowed this set down to 28 key journal articles. Our analysis highlights six global targets that greatly benefit from UAV-enabled aerial communication. In order of impact, these are: "Sustainable Cities and Communities," where UAVs improve security and disaster management; "Life on Land," where drones help with wildlife monitoring and reforestation; "Industry, Innovation, and Infrastructure," where UAVs (Unmanned Aerial Vehicles) aid in infrastructure inspection; "No Hunger," where drones improve agricultural practices; "Good Health and Wellbeing," where UAVs assist with disease prediction and monitoring; and "Clean water and sanitation," where drones play a crucial role in efficiently monitoring water quality and distribution. In summary, this paper offers a detailed look at the applications of UAVs in achieving the SDGs through aerial communications. By identifying areas of major impact and spotting gaps and weaknesses in current research, this review article aims to guide future research and policy-making in sustainable drone applications. [ABSTRACT FROM AUTHOR]

Regular population monitoring of imperilled charismatic species such as large carnivores is critical for conservation. However, the role of monitoring in conservation is frequently diminished due to: 1) surveys being implemented in isolation, 2) limited on-ground-capacity leading to infrequent monitoring, and 3) inappropriate methods being applied. Wildlife monitoring is often resource-intensive and the utility and cost of different field protocols is rarely reported. In this study we deployed two standard field protocols aimed at collecting data on African lions within a spatial capture-recapture framework. For our first protocol, we trained Uganda Wildlife Authority rangers in search-encounter techniques, the industry gold standard for monitoring lions. The second protocol involved deploying 32 paired stations of state-of-the-art infra-red camera traps. During the search-encounter protocol, two rangers covered 2939 km in 76 days, recording 102 detections (30 individuals) in a ~ 256 km2 area. The resulting density estimates (13.91 lions/100 km2, posterior SD = 2.34) yielded acceptable precision. Conversely, 64 camera traps over 1601 trap nights yielded two usable lion detections. We argue that where wildlife tourism rangers exist, they could be a powerful addition to future lion and wildlife census attempts across the continent. Our results confirm that the current technology of store-bought infra-red camera traps is not suitable for individual identification of lions, and therefore cannot be applied to analytical models that require unambiguous individual identities. However, we encourage the continued testing and advancement of infra-red camera trap technology since in many instances, this may be preferable to white-flash camera traps, which can yield individual identities for lions. Our study also shows the immense importance of the Nile Delta for African lions in Uganda's Murchison Falls National Park, a protected area with both oil extraction and high rates of anthropogenic snaring pressure. A study on African lions in Uganda's Nile Delta shows wildlife tourism rangers, an often-forgotten group in the scientific process, deliver strong density estimates. The study also shows infrared camera traps are weak at detecting lions. [ABSTRACT FROM AUTHOR]

Tick‐borne zoonoses are an emerging health issue. The expansion of ticks is mainly driven by climatic changes but also by new approaches to the management of the natural environment, increasing the abundance of vertebrate host species and thus the potential exposure to tick bites for both humans and companion animals. In this context, a holistic approach to studying ticks' ecology is required. In the present work, we shed light on the link between environmental tick abundance (global and specific of Ixodes ricinus nymphs, as the highest zoonotic threat) and the temporal occupancy of wildlife host species retrieved from camera traps (namely, wild ruminants, mesocarnivores and wild boar). We modelled this relationship by integrating abiotic factors relevant to tick survival, such as the vegetation cover and saturation deficit, and estimated the accuracy of prediction. To collect these data, we deployed camera traps in a peri‐urban Natural Park in Northwest Italy to monitor wildlife for 1 whole year while collecting ticks in front of camera traps by dragging transects every 2 weeks. Overall, wildlife temporal occupancy showed an additive impact on tick abundance for species that are preferential hosts (deer and mesocarnivores) and a detractive impact for wild boar, which also presented a lower tick burden, particularly with regard to the tick species collected in the environment (mainly I. ricinus and Haemaphysalis punctata). Accuracy of prediction was higher for I. ricinus nymphs rather than the global model. Temporal fluctuations in the tick population were also highlighted. Wildlife temporal occupancy was not constant and varied between seasons according to feeding habits. In conclusion, we highlighted the utility of camera trap data to investigate tick ecology and acarological risk. This information is crucial in informing monitoring and prevention strategies to decrease the risk of tick bites in humans and thus zoonotic risk of tick‐borne diseases. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Surveillance of wildlife virus impacts can be passive or active. Both approaches have their strengths and weaknesses, especially regarding cost and knowledge that can be gained. Monitoring of rabbit haemorrhagic disease virus (GI.1 and GI.2) in South Australia has utilised both strategies and their methods and gained insights are discussed. Active strategies to monitor the continuing impact of rabbit haemorrhagic disease virus 2 (GI.2) on susceptible lagomorphs in countries such as the USA, Mexico, South Africa, Spain, France and Portugal are encouraged to gain critical insights into the evolution, spread and impact of this virus. Furthermore, there are lessons here for the international monitoring of diseases in wildlife, particularly where there is a risk of them becoming zoonotic. [ABSTRACT FROM AUTHOR]

Simple Summary: Accurate detection of wildlife, particularly small and hidden animals, is crucial for conservation efforts. Traditional image-based methods often struggle in complex environments. This study introduces a novel approach that combines image and text data to improve detection accuracy. By incorporating textual information about animal characteristics and leveraging a Concept Enhancement Module (CEM), our model can better understand and locate animals, even in challenging conditions. Experimental results demonstrate a significant improvement in detection accuracy, achieving an average precision of 95.8% on a challenging wildlife dataset. Compared to existing multimodal target detection algorithms, this model achieved at least a 25% improvement in AP and excelled in detecting small targets of certain species, significantly surpassing existing multimodal target detection model benchmarks. This represents a substantial improvement compared to existing state-of-the-art methods. Our multimodal approach offers a promising solution for enhancing wildlife monitoring and conservation efforts. Accurate and efficient wildlife monitoring is essential for conservation efforts. Traditional image-based methods often struggle to detect small, occluded, or camouflaged animals due to the challenges posed by complex natural environments. To overcome these limitations, an innovative multimodal target detection framework is proposed in this study, which integrates textual information from an animal knowledge base as supplementary features to enhance detection performance. First, a concept enhancement module was developed, employing a cross-attention mechanism to fuse features based on the correlation between textual and image features, thereby obtaining enhanced image features. Secondly, a feature normalization module was developed, amplifying cosine similarity and introducing learnable parameters to continuously weight and transform image features, further enhancing their expressive power in the feature space. Rigorous experimental validation on a specialized dataset provided by the research team at Northwest A&F University demonstrates that our multimodal model achieved a 0.3% improvement in precision over single-modal methods. Compared to existing multimodal target detection algorithms, this model achieved at least a 25% improvement in AP and excelled in detecting small targets of certain species, significantly surpassing existing multimodal target detection model benchmarks. This study offers a multimodal target detection model integrating textual and image information for the conservation of rare and endangered wildlife, providing strong evidence and new perspectives for research in this field. [ABSTRACT FROM AUTHOR]

Understanding what influences species and trait composition is critical for predicting changes in communities driven by landscape transformation.We explored how life‐history traits are associated with the persistence of mammal species in human‐dominated habitats within the Garden Route Biosphere Reserve, South Africa. We combined data from a camera trap study and a local ecological knowledge‐based survey in an integrated occupancy model to analyse species occurrence along a gradient of anthropogenic landscape transformation.Results confirmed that mammal occurrence in human‐modified habitats was related to specific life‐history traits. Species with more specialist diets, as well as larger body mass species were more likely to stay in protected areas. Species with slow reproductive strategies occupied more natural areas.Combining different monitoring methods enabled us to increase spatial coverage and mammal sighting numbers. This approach fostered research participation by various stakeholders, an important step for co‐designing wildlife‐friendly anthropogenic spaces.Synthesis and applications. Integrating data from a standard ecological protocol and structured participatory citizen knowledge allowed us to identify the species functional traits associated with mammal species occurrence in anthropogenic landscapes at a local scale. These results advocate for wisely combining methods and will guide conservation‐orientated land‐use planning towards the protection of natural habitats in the Garden Route Biosphere Reserve. This methodological approach will enable managers and conservationists to use data obtained from diverse protocols and should catalyse the involvement of citizens in biodiversity monitoring and conservation. [ABSTRACT FROM AUTHOR]

Accurate taxonomic species identification is essential to the study of mammals. Despite this necessity, rapid and accurate identification of cryptic, understudied, and elusive mammals remains challenging. Traditional barcoding of mitochondrial genes is standard for molecular identification but requires time-consuming wet-lab methodologies. Recent bioinformatic advancements for nanopore sequencing data offer exciting opportunities for noninvasive and field-based identification of mammals. Nanopore adaptive sampling (NAS), a polymerase chain reaction (PCR)-free method, selectively sequences regions of DNA according to user-specified reference databases. Here, we utilized NAS to enrich mammalian mitochondrial genome sequencing to identify species. Fecal DNA extractions were sequenced from 9 mammals, several collected in collaboration with Minnesota Tribal Nations, to demonstrate utility for NAS barcoding of noninvasive samples. By mapping to the entire National Center for Biotechnology Information mammalian mitochondrial reference genome database and bioinformatically analyzing highly similar matches, we successfully produced species identifications for all fecal samples. Eight of 9 species identifications matched previous PCR or animal/fecal appearance-based identifications. For the ninth species, our genetic data indicate a misidentification stemming from the original study. Our approach has a range of applications—particularly in field-based wildlife research, conservation, disease surveillance, and monitoring of wildlife trade. Of importance to Minnesota tribes is invasive species monitoring, detections, and confirmation as climate impacts cause changes in biodiversity and shifts in species distributions. The rapid assessment techniques described here will be useful as new introductions and range expansions of native and invasive species may first be detected by the presence of signs such as scat rather than direct observations and will be helpful for chronically understaffed tribal natural resources agencies. [ABSTRACT FROM AUTHOR]

Monitoring wildlife trade dynamics is an important initial step for conservation action and demand reduction campaigns to reduce illegal wildlife trade. Studies often rely on one data source to assess a species' trade, such as seizures or the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) trade data. Each database provides useful information but is often incomplete. Combining information from multiple sources helps provide a more complete understanding of trade. A recent rapid increase in demand for helmeted hornbill (Rhinoplax vigil) casques (a brightly colored, solid keratinous rostrum) led to its uplisting to critically endangered on the International Union for Conservation of Nature Red List in 2015. However, there is little current information on what factors influence trade trends and what current levels of demand are. We combined data from CITES, seizure records, and previously underused, yet abundant, art and antique auction data to examine the global trade in helmeted hornbill casque products (HHPs). Three decades of auction data revealed that 1027 individual HHPs had been auctioned since 1992; total auction sales were over US$3 million from 1992 to 2021. The number of HHPs auctioned was greatest from 2011 to 2014, just after the global art boom (2009–2011), followed by a decline in volume and price. The auction data also revealed 2 possible markets for HHPs: true antique and speculative, defined by era, price, and trade patterns. Trends in illegal trade matched those of the auction market, but legal trade remained consistently low. Combining data sources from legal, illegal, and gray markets provided an overview of the dynamics of illegal trade in an endangered species. This approach can be applied to other wildlife markets to provide a more complete understanding of trade and demand at the market level to inform future demand reduction campaigns. [ABSTRACT FROM AUTHOR]

Sport hunting is considered a multifunctional and key activity in rural Europe, and after the first COVID-19 outbreaks in 2020, it was restrained in many countries, such as Spain. This study found that during the first year of pandemics the number of hunting licences was reduced by 9%, and hunting was overall decreased but varied among types of hunting, game species and commercial and non-commercial hunting (from 17 to 95%). COVID-19 negatively impacted hunting providers and the game meat industry, and the game bag was reduced for 80% of the species from 2019 to 2020. Mobility restrictions affected mainly hunting requiring several people and 'non-local' hunters. In 2021 the game bag returned partially to pre-COVID-19 levels, but the number of licences continued a sustained negative trend. In the likely event of future lockdowns, it will be useful for policy makers to consider hunting as performing essential functions, such as species and disease control, habitat management and wildlife monitoring. [ABSTRACT FROM AUTHOR]

With increasing demands for biodiversity monitoring, the integration of camera trapping (CT) and deep learning automation holds significant promise. However, few studies have addressed the application potential of this approach in urban areas in Asia. 4064 CT images targeting 18 species of urban wildlife in South Korea were collected and used to fine-tune a pre-trained object detection model. The performance of the custom model was evaluated across three levels: animal filtering, mammal and bird classification, and species classification, to assess its applicability. A comparison with existing universal models was conducted to test the utility of the custom model. The custom model demonstrated approximately 94% and 85% accuracy in animal filtering and species classification, respectively, outperforming universal models in some aspects. In addition, recommendations regarding CT installation distances and the acquisition of nighttime data were provided. Importantly, these results have practical implications for terrestrial monitoring, especially focusing on the analysis of local species. Automating image filtering and species classification facilitates efficient analysis of large CT datasets and enables broader participation in wildlife monitoring. [ABSTRACT FROM AUTHOR]

Understanding and addressing biodiversity declines across the globe will require interdisciplinary practices that embrace multiple worldviews and weave knowledge systems.Here, we used a Two‐Eyed Seeing approach to weave Anishinaabe ecological knowledge with peer‐reviewed Western scientific literature to provide a comprehensive understanding of the drivers of a declining moose (mooz; Alces alces) population in Ontario, Canada.We interviewed 66 participants from three Anishinabek communities on the causes of moose decline and conducted a literature review of 52 Western‐science studies that focused on factors that affect moose in Ontario.Our study revealed that there was agreement among knowledge systems on the importance of climate change and disease and parasites in explaining moose population decline in Ontario. Unique perspectives were provided on the mechanisms describing climatic impacts on calf recruitment, with an emphasis on spring onset and green‐up by Western science, and winter onset and timing of the rut by Anishinaabe knowledge. Western science also focused on the effects of habitat disturbance and predation on moose, and Anishinaabe knowledge emphasised harvest pressure. Other factors identified by both knowledge systems included the impacts of roads and railways. Distinctive information offered by Anishinaabe knowledge holders included the displacement of moose from areas in response to the range expansion of white‐tailed deer (waawaashkeshi; Odocoileus virginianus) and the negative effects of contaminants introduced into the environment by mining and forestry activity.Overall, weaving knowledge systems offered a nuanced and wholistic understanding of factors affecting moose and provided different perspectives to explain interacting and cumulative effects. Our study showcases the value in weaving knowledge systems to improve understanding of ecological problems and find wholistic strategies for conservation. Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Monitoring Wildlife in their natural habitat requires direct human intervention. Some animals are scared of humans. In such situations, camera‐equipped devices are implemented to gain a clear picture of Wildlife. Objective: Current wildlife detection models detect and classify the animal from camera‐captured images, limiting the action to rescue or save them from mishaps. Also, the camera‐equipped devices are fixed at particular locations. Therefore, an efficient detection model capable of protecting the animal has potential to play an important role. Method: To this end, we present Pred‐WAR, a Convolution Neural Network (CNN)‐based image classification approach to detect and raise rescue alerts for real‐time Wildlife. In our approach, we have proposed a Mask Region‐based CNN (Mask RCNN or MRCNN) with an Automatic Mixed Precision model that is implemented on a Robot Operating System‐based mobile robot with Raspberry Pi4 to detect and raise acoustic of Lion or alarm to alert or rescue animal in real‐time. Results: Pred‐WAR obtained a mean Average Precision value of 85.47% and an F1 score of 87.73% with a precision value range between 92% to 99%, outperforming the current MRCNN model. Significance: This approach has fast computation speed and maintains accuracy that will be efficiently implemented in real‐time scenarios. [ABSTRACT FROM AUTHOR]

Indonesia has committed to implement the sustainable development goals (SDG) by 2030 including the ending trafficking of protected species and addressing the illegal wildlife demand and supply. As such, there is a need for long‐term data on wild animal trade and its contribution to the wider economy. We initiated a long‐term monitoring programme of live civet trade in wildlife markets (120 surveys, 2010–2023). Civets are traded to be kept as exotic pets and to produce civet coffee and are a proxy for other high‐profile wildlife. We recorded 2289 civets of six species, including ones with strict regulations in place. Despite the trade being illegal, and contra to Indonesia's commitments as part of the SDG to curb this trade, it remained remarkably stable over time (numbers, species, prices). As such, Indonesia is not meeting its SDG targets that are related to curbing illegal wildlife trade and illicit financial flows. [ABSTRACT FROM AUTHOR]

An important parameter in the monitoring and surveillance systems is the probability of detection. Advanced wildlife monitoring systems rely on camera traps for stationary wildlife photography and have been broadly used for estimation of population size and density. Camera encounters are collected for estimation and management of a growing population size using spatial capture models. The accuracy of the estimated population size relies on the detection probability of the individual animals, and in turn depends on observed frequency of the animal encounters with the camera traps. Therefore, optimal coverage by the camera grid is essential for reliable estimation of the population size and density. The goal of this research is implementing a spatiotemporal Bayesian machine learning model to estimate a lower bound for probability of detection of a monitoring system. To obtain an accurate estimate of population size in this study, an empirical lower bound for probability of detection is realized considering the sensitivity of the model to the augmented sample size. The monitoring system must attain a probability of detection greater than the established empirical lower bound to achieve a pertinent estimation accuracy. It was found that for stationary wildlife photography, a camera grid with a detection probability of at least 0.3 is required for accurate estimation of the population size. A notable outcome is that a moderate probability of detection or better is required to obtain a reliable estimate of the population size using spatiotemporal machine learning. As a result, the required probability of detection is recommended when designing an automated monitoring system. The number and location of cameras in the camera grid will determine the camera coverage. Consequently, camera coverage and the individual home-range verify the probability of detection. [ABSTRACT FROM AUTHOR]

The application of computational techniques to the study of zoology has greatly improved our knowledge of animal behaviour and species conservation. This study offers a thorough analysis of the ways that computational methods, such as data mining, simulation modelling, machine learning and geographic information systems (GIS) are revolutionising research in both applied and pure zoology. This review illustrates how these techniques can improve species distribution modelling, habitat suitability analysis, wildlife monitoring and behavioural investigations by looking at current case studies. More accurate forecasts have been produced by machine learning approaches, especially in the areas of species distribution and habitat suitability, which has improved the knowledge base for conservation initiatives. Through the extraction of significant patterns from large, complicated datasets, data mining has made it possible to get new insights into the vocalisations, social relationships and movement of animals. Because GIS provides precise habitat maps, landscape connectivity assessments, and spatial models of species distributions, it has proven to be an invaluable tool in ecological research. Important projections regarding the long-term effects of conservation initiatives have been made possible via simulation modelling, including individualbased models and population viability studies. The study does, however, also address the drawbacks of these computational methods, including data quality, model complexity, resource requirements and integrating disparate data sources. Notwithstanding these obstacles, zoological research seems to have a bright future because computational techniques provide effective instruments for addressing urgent ecological problems. In order to fully realise the potential of computational approaches in promoting species conservation and behavioural studies, this review emphasises the significance of ongoing innovation and multidisciplinary collaboration. [ABSTRACT FROM AUTHOR]

Context. The North American beaver (Castor canadensis) was extirpated from much of its range in the US in the 1800s due to fur trapping and change in land use. However, the species has recolonised much of its former range, including the US state of Ohio. Since 2013, the Ohio Division of Wildlife (ODOW) has monitored trends in beaver colony density via aerial surveys of 40 km × 40 km plots classified as low, medium, or high suitability based on the amount of wetland. Nonetheless, the current classification system may miss important correlates of beaver colony density. Aims. Our study aimed to (1) identify predictors of beaver colony density (number of colonies inferred from aerial counts of lodges) across Ohio, and (2) develop a model-based classification system to improve the efficacy of monitoring efforts. Methods. To predict beaver colony density in Ohio we used an aerial survey dataset of 54 plots (40 km × 40 km) collected by ODOW annually between 2013 and 2020, along with a suite of environmental, anthropogenic, and climate variables in a mixed effects modelling framework. Key results. Beaver colony density was positively associated with wetland and reclaimed surface mine areas and inversely associated with the proportion of agricultural lands. There was a negative interaction between wetland and surface mines; in general, beaver colony density increased with wetland and surface mine area. However, in plots with wetland area >1000 ha, beaver colony density was weakly negatively associated with surface mine area. Using median and interquartile ranges of model-averaged predicted beaver colony density, we developed a new classification of low, moderate and high suitability for both the survey plots and the entire state of Ohio. We found that eastern Ohio had high suitability, while the central and western parts of the state had lower suitability for C. canadensis. Conclusions. Our approach to identifying predictors for beaver colony density at broad spatial scales highlights the importance of reclaimed surface mines and wetlands for beaver populations, while the model-based habitat classification provides ODOW additional information for monitoring and beaver management decisions. Implications. Improved C. canadensis monitoring at the landscape scale using habitat classifications that consider local conditions can both improve annual survey cost-effectiveness and facilitate the sustainable management of this recovering species. [ABSTRACT FROM AUTHOR]

Wildlife monitoring is key for the effective management of natural resources. Hunting-based data (e.g., hunting bag statistics) is an important source of information to overcome the financial constraints imposed to long-term monitoring. Here, taking advantage of three decades (1989–2022) of hunting bag statistics of wild mammals in Portugal, we present the trends and discuss the issues regarding nine game species (five wild ungulates, two carnivores and two lagomorphs). Our results show that hunting bag statistics mirror the general population trends of wild mammal populations at a national scale. The data reflects the increase in number and distribution of roe deer, red deer and wild boar, and the recent expansion experienced by the Egyptian mongoose. The decline in the numbers of European wild rabbit and Iberian hare harvested are also noticeable. Hunting-based data have been used in several research contexts; however, this information is usually affected by geographical, temporal and recording bias. Portugal is no exception. Engaging the interested stakeholders is pivotal to ensure data quality. By reporting reliable and systematically collected hunting data, game managers and wildlife researchers will be able to better understand the main drivers of population dynamics, adjust hunting bags towards sustainable yield and assess the ecological and socio-economic relevance of the hunting sector. [ABSTRACT FROM AUTHOR]

Abstract Monitoring environmental conditions over time is essential for understanding how species regulate those conditions to satisfy their physiological needs. Variation in ultraviolet (UV) light exposure, for instance, is crucial for numerous taxa, influencing behaviour as well as essential physiological processes like vitamin D3 synthesis. Yet, despite these considerations, no commercially available data loggers exist to record UV exposures over time, preventing inquiry into this important but neglected fitness demand. To overcome this challenge, I designed the first UV data logger, custom built on the open‐source Arduino platform. This device logs UV exposures (in voltage or converted to UV index) and date‐time information for each UV reading, at user‐defined intervals. The integration of a low‐power timer into the assembly, and its weather‐resistant housing, enables long‐term use (at least several months). The cost of one device (including housing) is under £45/$55 USD. Calibration and performance tests reveal that this device is reliable under various weather conditions and produces accurate UV readings under both artificial lighting and sunlight. Both the device and its customised housing can be easily replicated and expanded upon (e.g. inclusion of additional sensors). Overall, this device represents a crucial first step in our ability to better characterise UV exposure patterns in habitats for physiological ecology studies. Exposure to UV light is adaptive for many types of organisms, both in the wild and in captivity (e.g. zoos and private hobbyists), and so I expect a broad array of scientific and public users alike to benefit from using this device.

While camera traps can effectively detect semi-aquatic mammal species, they are also often temporally and monetarily inefficient and have a difficult time detecting smaller bodied, elusive mammals. Recent studies have shown that extracting DNA from environmental samples can be a non-invasive, alternative method of detecting elusive species. Environmental DNA (eDNA) has not yet been used to survey American mink (Neogale vison), a cryptic and understudied North American mustelid. To help determine best survey practices for the species, we compared the effectiveness and efficiency of eDNA and camera traps in surveys for American mink. We used both methods to monitor the shoreline of seven bodies of water in northeastern Indiana from March to May 2021. We extracted DNA from filtered environmental water samples and used quantitative real-time PCR to determine the presence of mink at each site. We used Akaike's Information Criterion to rank probability of detection models with and without survey method as a covariate. We detected mink at four of the seven sites and seven of the 21 total survey weeks using camera traps (probability of detection (ρ) = 0.36). We detected mink at five sites and during five survey weeks using eDNA (ρ = 0.25). However, the highest probability of detection was obtained when both methods were combined, and data were pooled (ρ = 0.47). Survey method did not influence model fit, suggesting no difference in detectability between camera traps and eDNA. Environmental DNA was twice as expensive, but only required a little over half (58%) of the time when compared to camera trapping. We recommend ways in which an improved eDNA methodology may be more cost effective for future studies. For this study, a combination of both methods yielded the highest probability for detecting mink presence. [ABSTRACT FROM AUTHOR]

The ethical considerations and scepticism over the ecological benefits have fuelled debate about the rehabilitation of wildlife. Although there is evidence that many rehabilitated species are able to survive after rehabilitation, there is a paucity of research on the behaviour of rehabilitated pinnipeds, including grey seals (Halichoerus grypus). In this study, 14 rehabilitated grey seal juveniles were equipped with biotelemetry devices in order to conduct remote post-release monitoring and to investigate ontogeny of their movements in the Baltic Sea. The study revealed that their movements were similar to wild, non-rehabilitated grey seal pups when leaving their natal site: at first, they exhibited highly exploratory behaviour with largely transient movements, then switched into a resident movement pattern, while maximising foraging and minimising travelling time. Neither sex nor year of release, which varied in terms of rehabilitation time and body mass, had a significant effect on the ontogeny of these movements. Movements were significantly influenced by the time after release, suggesting that rehabilitated juveniles have gained experience and developed their movements over time in order to survive in the wild. [ABSTRACT FROM AUTHOR]

Simple Summary: This study addresses the growing risk of tick bites, which is becoming more prevalent due to changes in landscape, leading to an increase in wildlife that supports tick populations, raising the risk of disease transmission to humans and animals. To better understand this, we explored the ecology of ticks by examining the relationship between wildlife presence and tick abundance in two regions: an alpine hunting district and a natural park in the Apennines. We used camera traps to monitor wildlife and conducted tick sampling nearby. Additionally, we considered factors like altitude, vegetation, and climate. Our findings highlight the significant role of altitude and wildlife presence—both influenced by habitat and land management—in the global abundance of ticks in the environment. In particular, the species most impacting tick presence was roe deer. These insights could be valuable for managing natural environments, helping to reduce the risk of tick-borne diseases, and they show the utility of camera trap data, which are gathered with great detail and little disturbance of wildlife. The increase in acarological risk of tick bites is significantly driven by profound changes in landscape, which alter the density and distribution of wildlife that support tick populations. As a result of habitat shifts and land abandonment, which create environments conducive to tick proliferation, the risk of disease transmission to humans and animals is increasing. In this context, it is important to explore tick ecology by applying a comprehensive methodology. In this study, we examined the relationship between wildlife temporal occupancy and tick abundance in two distinct regions: an alpine hunting district and a natural park in the Apennines. For each sampling point, we calculated wildlife temporal occupancy from camera trap pictures and estimated ticks' abundance from dragging transects in the area immediately surrounding camera traps. In modelling the relationship between those two variables, we included abiotic factors such as saturation deficit, normalized difference vegetation index, and altitude. Results show the importance of altitude and wildlife temporal occupancy (itself related to different habitat and land management characteristics) on the ecology of questing ticks. If employed in management decisions for natural environments, such information is useful to modulate the acarological risk and thus the risk of tick-borne pathogens' transmission. [ABSTRACT FROM AUTHOR]

Introduction: Drones, or Unmanned Aerial Vehicles (UAVs), have emerged as valuable tools for wildlife monitoring, offering potentially non-intrusive observations in challenging terrains like marine ecosystems. Despite their potential, widespread use is impeded by regulatory constraints, especially in protected areas. Methods: This study aims to assess the impact of varying flight altitudes and flight approaches of two commercial drones on the behaviour of harbour seals (Phoca vitulina) and grey seals (Halichoerus grypus) and compare to existing findings for marine mammals. We conducted a comparative analysis of the response to flight altitude from 70 m gradually descending to 10 m and two different flight approaches between the DJI Phantom 4Pro and the Autel EVO II RTK drones in the Danish part of the Wadden Sea. Results: We found varying responses, where species-specific sensitivity and environmental conditions appeared to influence the behavioural reactions of the seals. Our results reveal that seal reactions to drones are complex and depend on several factors, such as flight altitude, drone model, received noise levels, approach, weather conditions, the animals' annual cycle, and geographic location. Discussion: The outcomes hold significance for refining regulatory policies governing drone flights in protected marine environments, balancing conservation efforts with technological advancements in wildlife monitoring and ensuring undisturbed observations of seals. [ABSTRACT FROM AUTHOR]

Passive acoustic monitoring can be an effective way of monitoring wildlife populations that are acoustically active but difficult to survey visually, but identifying target species calls in recordings is non-trivial. Machine learning (ML) techniques can do detection quickly but may miss calls and produce false positives, i.e., misidentify calls from other sources as being from the target species. While abundance estimation methods can address the former issue effectively, methods to deal with false positives are under-investigated. We propose an acoustic spatial capture-recapture (ASCR) method that deals with false positives by treating species identity as a latent variable. Individual-level outputs from ML techniques are treated as random variables whose distributions depend on the latent identity. This gives rise to a mixture model likelihood that we maximize to estimate call density. We compare our method to existing methods by applying it to an ASCR survey of frogs and simulated acoustic surveys of gibbons based on real gibbon acoustic data. Estimates from our method are closer to ASCR applied to the dataset without false positives than those from a widely used false positive "correction factor" method. Simulations show our method to have bias close to zero and accurate coverage probabilities and to perform substantially better than ASCR without accounting for false positives. [ABSTRACT FROM AUTHOR]

Background: Tigers, as iconic apex predators and symbols of biodiversity conservation, face numerous threats to their existence. Effective tracking and monitoring are essential for understanding and preserving these majestic creatures and their habitats. The convergence of machine learning and drone technology has emerged as transformative tools in the field of tiger tracking. Drones, or Unmanned Aerial Vehicles (UAVs), have rapidly become invaluable assets in wildlife conservation. Machine learning algorithms, with their capacity to analyze complex datasets, make predictions and automate decision-making processes, offer a novel approach to processing the massive amounts of data generated by drones, including images, sounds and sensor readings. Methods: This paper explores the historical significance of tiger tracking, the pivotal role of drones in conservation and the transformative capabilities of machine learning in wildlife monitoring. In this work, an accurate framework for tiger detection based on YOLOv8 is utilized. Result: By examining the interplay between machine learning, drone technology and tiger conservation, this paper highlights the potential for innovation and the challenges that lie ahead, promising a brighter future for these iconic creatures and their ecosystems. The fine-tuned YOLOv8 model demonstrates exceptional object detection performance, boasting a mAP50 of 0.9820 and a mAP50-95 of 0.6856, coupled with precise classification (precision 0.9646) and robust instance capture (recall 0.9580). [ABSTRACT FROM AUTHOR]

In recent years, several automated and noninvasive methods for wildlife monitoring, such as passive acoustic monitoring (PAM), have emerged. PAM consists of the use of acoustic sensors followed by sound interpretation to obtain ecological information about certain species. One challenge associated with PAM is the generation of a significant amount of data, which often requires the use of machine learning tools for automated recognition. Here, we couple PAM with BirdNET, a free-to-use sound algorithm to assess, for the first time, the precision of BirdNET in predicting three tropical songbirds and to describe their patterns of vocal activity over a year in the Brazilian Pantanal. The precision of the BirdNET method was high for all three species (ranging from 72 to 84%). We were able to describe the vocal activity patterns of two of the species, the Buff-breasted Wren (Cantorchilus leucotis) and Thrush-like Wren (Campylorhynchus turdinus). Both species presented very similar vocal activity patterns during the day, with a maximum around sunrise, and throughout the year, with peak vocal activity occurring between April and June, when food availability for insectivorous species may be high. Further research should improve our knowledge regarding the ability of coupling PAM with BirdNET for monitoring a wider range of tropical species. [ABSTRACT FROM AUTHOR]

No single studies on the Burmese Red Serows, a little known bovid to date yet considered as nationally endangered species, have been conducted in Bangladesh. As part of a systematic wildlife inventory and monitoring project, this study utilized 48 camera traps to elucidate their occurrence, activity pattern and possible sympatric association with other artiodactyls species inhabiting Baraiyadhala National Park. The study found 25 independent serow events with relative abundance index (RAI) of 0.89. The number of individuals seems low, but this is by far the country's stronghold population. The Red Serows are mostly nocturnal and showed moderate temporal activity with barking dear (∆1 = 0.59) and wild boar (∆1 = 0.62) in this area. Anthropogenic pressures due to tourists (RAI = 4.8) and poachers (RAI = 1.7) were highly evident and must have negative impact on overall wildlife of the national park. The study suggests similar studies in adjacent protected area (Hazarikhil Wildlife Sanctuary), habitat protection and restoration program, and awareness raising program targeting forest dependent communities for long term conservation of this species. [ABSTRACT FROM AUTHOR]

Insular evolution on archipelagos generates a significant proportion of global biodiversity, yet islands are among the ecosystems most sensitive to accelerating anthropogenic disturbance, introductions of non‐native species, and emerging pathogens, among other conservation challenges. The Alexander and Haida Gwaii archipelagos along North America's North Pacific Coast support a disproportionate number of endemic taxa compared to other high‐latitude terrestrial ecosystems. In this region, endemics in Canada are explicitly protected, but in the United States, endemics have been operationally ignored. We reviewed regional research on terrestrial mammals and endemics from 2000–2022 to guide wildlife management. Elevated regional endemism is due to a combination of deep and shallow temporal processes (i.e., long‐term refugial isolation vs. recent colonization). With adequate sampling, genomic analyses are well‐suited to identifying nuanced patterns of divergence and endemism, thereby facilitating a deeper understanding of regional diversity. We identified 18 mammalian endemics in Southeast Alaska, USA, at varying taxonomic scales, but research effort has significant taxonomic biases and sampling infrastructure remains inadequate. Of the 66 terrestrial and aquatic mammal species in Southeast Alaska, only 55% are represented by ≥10 archived samples over the last 2 decades. Across taxa, major spatial and temporal sampling gaps limit interpretations of wildlife responses to changing environmental conditions. The Tongass National Forest is spread across an island archipelago, and climate change is projected to have disproportionate impacts on island endemics worldwide. In this case, the United States Forest Service is not closely monitoring endemic taxa, as was required by the Tongass Land Management Plan in 1997. Our review underscores a need for increased consideration of how endemism can be incorporated into land and wildlife management across the Alexander Archipelago. Moving forward, we encourage state and federal agencies, Indigenous communities, and international collaborators to continue to partner with natural history biorepositories to ensure strategic wildlife sampling infrastructure is built and made accessible to the broader scientific community as part of the land management process. [ABSTRACT FROM AUTHOR]

Novel genomic tools can yield information important for the conservation of threatened species. Remains of hunted individuals may provide important genomic information; however, the ethics of using remains of hunted animals for conservation research, especially remains of trophy‐hunted animals, is controversial. While there are ongoing political discussions considering blanket bans on the import and use of trophy‐hunted specimens, the consequences of such bans for conservation science have not been broadly discussed. Should conservation science accept samples acquired through legal trophy hunting? We investigate the ethical and practical considerations surrounding the use of biological samples acquired through legal trophy hunting. We propose that trophy‐hunted specimens should not be banned from conservation genomic research. Specimens from hunting could contribute to science and conservation genomic monitoring of wildlife and provide information for biodiversity management. [ABSTRACT FROM AUTHOR]

To compensate for mineral deficiencies in food sources, numerous ungulate species use various methods, such as salt licking, soil ingestion, and gnawing on antlers or bones. While osteophagia, the act of gnawing on antlers or bones, is well-documented in cervid species, it has only been observed in sika deer (Cervus nippon) on Mageshima Island in southern Japan despite their widespread distribution. This study aimed to provide an initial account of the observations of osteophagia in sika deer from central Japan. One of the 15 camera traps deployed in the Hakone Nature Forest of Tamagawa University for wildlife monitoring serendipitously captured multiple instances of adult female sika deer gnawing on a four-tined antler on July 9th and 16th in 2018. These occurrences coincided with the fawning season of the species during which mineral deficiencies in the diet are prevalent, suggesting that osteophagia serves as a response to nutrient deficiencies in the diet of sika deer in central Japan. Thus, systematic surveys may yield additional evidence of this behavior in sika deer across diverse geographical areas given the absence of studies on osteophagia in sika deer conducted in other regions. Digital video images related to the article are available at http://www.momo-p.com/showdetail-e.php?movieid=momo240508cn01a. [ABSTRACT FROM AUTHOR]

• Automating wildlife monitoring with wireless 4G cameras and end-to-end data streams. • Remote monitoring of sensor performance, API handling and automated task management. • Deep learning for automated identification of focal species and human detection. • Total cost saving of >40 % through automation, AI and less regular site visits. • Enabling technologies allow scaling-up of a cost-efficient biodiversity monitoring. Modern approaches with advanced technology can automate and expand the extent and resolution of biodiversity monitoring. We present the development of an innovative system for automated wildlife monitoring in a coastal Natura 2000 nature reserve of the Netherlands with 65 wireless 4G wildlife cameras which are deployed autonomously in the field with 12 V/2A solar panels, i.e. without the need to replace batteries or manually retrieve SD cards. The cameras transmit images automatically (through a mobile network) to a sensor portal, which contains a PostgreSQL database and functionalities for automated task scheduling and data management, allowing scientists and site managers via a web interface to view images and remotely monitor sensor performance (e.g. number of uploaded files, battery status and SD card storage of cameras). The camera trap sampling design combines a grid-based sampling stratified by major habitats with the camera placement along a traditional monitoring route, and with an experimental set-up inside and outside large herbivore exclosures. This provides opportunities for studying the distribution, habitat use, activity, phenology, population structure and community composition of wildlife species and allows comparison of traditional with novel monitoring approaches. Images are transferred via application programming interfaces to external services for automated species identification and long-term data storage. A deep learning model for species identification was tested and showed promising results for identifying focal species. Furthermore, a detailed cost analysis revealed that establishment costs of the automated system are higher but the annual operating costs much lower than those for traditional camera trapping, resulting in the automated system being >40 % more cost-efficient. The developed end-to-end data pipeline demonstrates that continuous monitoring with automated wildlife camera networks is feasible and cost-efficient, with multiple benefits for extending the current monitoring methods. The system can be applied in open habitats of other nature reserves with mobile network coverage. [ABSTRACT FROM AUTHOR]

Amphibians are experiencing severe population declines, requiring targeted conservation action for the most threatened species and habitats. Unfortunately, we do not know the basic demographic traits of most species, which hinders population recovery efforts. We studied one of Madagascar's most threatened frog species, the harlequin mantella (Mantella cowanii), to confirm it is still present at historic localities and estimate annual survival and population sizes. We surveyed eleven of all thirteen known localities and were able to detect the species at eight. Using a naïve estimate of detection probability from sites with confirmed presence, we estimated 1.54 surveys (95% CI [1.10–2.37]) are needed to infer absence with 95% confidence, suggesting the three populations where we did not detect M. cowanii are now extirpated. However, we also report two new populations for the first time. Repeated annual surveys at three sites showed population sizes ranged from 13–137 adults over 3–8 years, with the most intensively surveyed site experiencing a >80% reduction in population size during 2015–2023. Annual adult survival was moderately high (0.529–0.618) and we recaptured five individuals in 2022 and one in 2023 first captured as adults in 2015, revealing the maximum lifespan of the species in nature can reach 9 years and beyond. Our results confirm M. cowanii is characterized by a slower life history pace than other Mantella species, putting it at greater extinction risk. Illegal collection for the international pet trade and continued habitat degradation are the main threats to the species. We recommend conservation efforts continue monitoring M. cowanii populations and reassess the International Union for Conservation of Nature (IUCN) Red List status because the species may be Critically Endangered rather than Endangered based on population size and trends. [ABSTRACT FROM AUTHOR]

Globally, collisions with vehicles result in millions of animal deaths every year, representing a major issue for wildlife conservation and management. Consequently, and importantly, much research has focused on understanding patterns of animal–vehicle collisions with the aim to reduce roadkill of wildlife. However, existing data on animal–vehicle collisions might also represent a novel opportunity to monitor wildlife populations. For this purpose, we compared data of >1.2 million hunter‐shot deer and >40,000 deer–vehicle collisions collected over 11 years in Denmark. We show that deer–vehicle collision data can be useful for population monitoring of roe deer (Capreolus capreolus), fallow deer (Dama dama), and red deer (Cervus elaphus). Roe deer was the most numerous species, representing 90% of observations based both on deer–vehicle collisions and on hunting bag statistics. After accounting for factors related to road infrastructure (road length and density, traffic volume), local (municipality) deer–vehicle collisions were highly correlated with hunting bag data for roe and red deer (Pearson's r > 0.7) but not fallow deer, likely due to biases in hunting bags. Moreover, we used deer–vehicle collision data to map spatiotemporal changes in the distribution of fallow and red deer, and demographic changes in all species. Combined, our results suggest that animal–vehicle collision data can be a useful tool to supplement existing methods for monitoring wildlife populations, which will be relevant for the management of these populations. We point to important shortcomings in both animal–vehicle collision and hunting bag data and provide recommendations on how to improve their accuracy in the future, to be applicable for a broader range of species. [ABSTRACT FROM AUTHOR]

Spatial overlap between wildlife and related domestic animals can lead to disease transmission, with substantial evidence for viral and bacterial spillover. Domestic and wild animals can also share potentially harmful helminth parasites, many of which have environmental transmission stages that do not require direct contact between hosts. We used camera traps, fecal sampling, and mathematical modeling to evaluate the potential for hookworm parasites to spillover from domestic dogs to wild cats in the Osa Peninsula, Costa Rica. Traditional microscopy was found to be more sensitive than DNA‐based diagnostics for parasites, though the methods were complementary. We found high hookworm (Ancylostoma spp.) prevalence in domestic dogs (74.2%, 95% CI: 67.0%–80.7%, N = 155), and considerable spatial overlap with ocelots (Leopardus pardalis) and pumas (Puma concolor), particularly on trails and dirt roads. Pumas had hookworm prevalence of 36.4% (18.6%–57.2%, N = 22), and ocelots had 27.3% (7.6%–56.5%, N = 11); however, molecular identification of these parasites was inconclusive. We developed a macroparasite transmission model to infer the likelihood of spillover, compared with separate parasite cycles, or different parasite species in each host. According to the model, spillover of hookworm from dogs would lead to a prevalence of less than 10% in wild hosts. Low presumed compatibility between wild hosts and parasites adapted to domestic species limits the prevalence that could be reached in wild species, even under potentially higher overlap. The prevalence observed was more consistent with a model that assumes hookworms in wild cats in the Osa are a cat‐specific parasite. The combination of parasitology, molecular diagnostics, and mathematical modeling used here could complement wildlife disease monitoring programs worldwide to shed light on understudied helminth–host dynamics at the domestic–wild animal interface. [ABSTRACT FROM AUTHOR]

Monitoring programs are pivotal to establishing sound management. Due to economic, logistic, and time limitations, monitoring programs often overlook differences among life‐history stages. However, species occurrence does not necessarily mean population viability, and it is unclear to what extent monitoring programs that do not consider separately adult presence and reproduction provide effective management indications. Unfortunately, collecting data on certain life stages requires high sampling effort, leading to a trade‐off between model reliability and resources needed for monitoring. We collected data on presence and reproduction of amphibians by monitoring 207 waterbodies in Lombardy (northern Italy) in 2017–2022. We then used multistate occupancy models to test whether certain environmental features, namely, pond area, hydroperiod, forest cover, shade, aquatic vegetation, and predators’ presence, differentially affected adult occurrence and breeding probabilities of multiple amphibian species. To assess optimal sampling efforts, we modeled the detection probabilities of adults and reproduction across multiple species. Finally, we identified the optimal monitoring strategy under different scenarios of resource availability, comparing adult‐only monitoring versus joint assessment of the occurrence of adults and reproduction. In many cases, the main drivers of adult occurrence and reproduction did not coincide because most investigated ecological variables affected one life stage or the other. Forest area, for instance, increased occurrence probabilities of adults of the endemic Rana latastei but showed no effect on their reproduction probabilities. Quantitative estimates of the sampling effort showed that occurrence of adults was easier to spot in 4 out of 7 species. Multicriteria decision analyses showed that when resources were scarce, monitoring adults was the optimal strategy for those 4 species. Conversely, with more resources, monitoring both adults and reproduction emerged as the best strategy for all the considered species. Integrated monitoring of adults and reproduction is essential to comprehensively identify effective conservation measures for amphibians. [ABSTRACT FROM AUTHOR]