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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]

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]

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]

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]

Using unmanned aerial vehicles (UAVs) for surveys on thermostatic animals has gained prominence due to their ability to provide practical and precise dynamic censuses, contributing to developing and refining conservation strategies. However, the practical application of UAVs for animal monitoring necessitates the automation of image interpretation to enhance their effectiveness. Based on our past experiences, we present the Sichuan snub‐nosed monkey (Rhinopithecus roxellana) as a case study to illustrate the effective use of thermal cameras mounted on UAVs for monitoring monkey populations in Qinling, a region characterized by magnificent biodiversity. We used the local contrast method for a small infrared target detection algorithm to collect the total population size. Through the experimental group, we determined the average optimal grayscale threshold, while the validation group confirmed that this threshold enables automatic detection and counting of target animals in similar datasets. The precision rate obtained from the experiments ranged from 85.14% to 97.60%. Our findings reveal a negative correlation between the minimum average distance between thermal spots and the count of detected individuals, indicating higher interference in images with closer thermal spots. We propose a formula for adjusting primate population estimates based on detection rates obtained from UAV surveys. Our results demonstrate the practical application of UAV‐based thermal imagery and automated detection algorithms for primate monitoring, albeit with consideration of environmental factors and the need for data preprocessing. This study contributes to advancing the application of UAV technology in wildlife monitoring, with implications for conservation management and research. [ABSTRACT FROM AUTHOR]

Understanding subnivean life is crucial, particularly due to the major role in food webs played by small animals inhabiting this poorly known habitat. However, challenges such as remoteness and prolonged, harsh winters in the Arctic have hampered our understanding of subnivean ecology in this region. To address this problem, we present an improved autonomous, low-power system for monitoring small mammals under the snow in the Arctic. It comprises a compact camera paired with a single-board computer for video acquisition, a low-power-microcontroller-based circuit to regulate video acquisition timing, and motion detection circuits. We also introduce a novel low-power method of gathering complementary information on animal activities using passive infrared sensors. Meticulously designed to withstand extreme cold, prolonged operation periods, and the limited energy provided by batteries, the system's efficacy is demonstrated through laboratory tests and field trials in the Canadian Arctic. Notably, our system achieves a standby power consumption of approximately 60 µW, representing a seventy-fold reduction compared to previous equipment. The system recorded unique videos of animal life under the snow in the High Arctic. This system equips ecologists with enhanced capabilities to study subnivean life in the Arctic, potentially providing insights to address longstanding questions in ecology. [ABSTRACT FROM AUTHOR]

Genetic management is a critical component of threatened species conservation. Understanding spatial patterns of genetic diversity is essential for evaluating the resilience of fragmented populations to accelerating anthropogenic threats. Nowhere is this more relevant than on the Australian continent, which is experiencing an ongoing loss of biodiversity that exceeds any other developed nation. Using a proprietary genome complexity reduction‐based method (DArTSeq), we generated a data set of 3239 high quality Single Nucleotide Polymorphisms (SNPs) to investigate spatial patterns and indices of genetic diversity in the koala (Phascolarctos cinereus), a highly specialised folivorous marsupial that is experiencing rapid and widespread population declines across much of its former range. Our findings demonstrate that current management divisions across the state of New South Wales (NSW) do not fully represent the distribution of genetic diversity among extant koala populations, and that care must be taken to ensure that translocation paradigms based on these frameworks do not inadvertently restrict gene flow between populations and regions that were historically interconnected. We also recommend that koala populations should be prioritised for conservation action based on the scale and severity of the threatening processes that they are currently faced with, rather than placing too much emphasis on their perceived value (e.g., as reservoirs of potentially adaptive alleles), as our data indicate that existing genetic variation in koalas is primarily partitioned among individual animals. As such, the extirpation of koalas from any part of their range represents a potentially critical reduction of genetic diversity for this iconic Australian species. [ABSTRACT FROM AUTHOR]

Freshwater habitats support high levels of biodiversity and provide important habitat for wetland‐dependent taxa; however, impairment of aquatic connectivity through wetland loss, stream alterations and light pollution impacts species persistence and community resilience. Quantifying occupancy of these habitats to assess their use by mammals and birds can be challenging, especially for cryptic species.This study examines wetland habitat use by mammals and birds, and their spatial and temporal relationships within the Beaver Hills Biosphere in Alberta, Canada. As part of a multi‐method detection approach, camera boxes, cameras on beaver dams, and camera rafts allowed for multivariate analyses to determine temporal and spatial patterns of habitat use and species associations, while field sampling and environmental DNA (eDNA) provided data on site occupancy by a subset of semi‐aquatic mammals.From ~50,000 images, over 84 species were detected, including 52 species of birds and 25 species of mammals. Several species were spatially associated, and although most species of mammals exhibited temporal overlap, there were distinct differences, especially on beaver dams where predators such as coyotes and white‐tailed deer shared the same space. Temporally, birds were detected most often during the day and mammals at night. During new moon phases, use of beaver dams by beavers decreased dramatically, and use of camera rafts by muskrats increased.Winter field surveys allowed for a broad overview of wetland occupancy and abundance of some species, particularly by beavers and muskrats, and occasionally winter use by other semi‐aquatic mammals. Where there was limited detection of more cryptic species of semi‐aquatic mammals, eDNA analysis successfully detected American water shrew, American mink, North American river otter and northern bog lemming more often than camera and field methods.Applying a multi‐method monitoring approach for mammals and birds in wetland habitats is critical at a time when freshwater systems are experiencing dramatic declines in relative abundance of monitored wildlife populations, with many of these species underrepresented in conventional surveys. [ABSTRACT FROM AUTHOR]

Simple Summary: This study provides a description of the surveillance strategies adopted during an outbreak caused by the ST79 genotype of Coxiella burnetii, which has been endemic in the area for over a decade. It provides a detailed overview of C. burnetii infection in both wild and domestic animal populations in the Abruzzo and Molise regions. In the review of C. burnetii infections, data on the prevalence and impact of the bacterium in different animal hosts have been presented. This includes molecular findings that allow an assessment of the dynamics of infection before and after vaccination. In addition, this study proposes a novel NGS-based approach for in silico multi-spacer typing (MST) of C. burnetii. The results highlight the importance of surveillance and control measures in domestic animal populations to reduce the risk of zoonotic transmission. Coxiella burnetii is the causative agent of Q fever. The main reservoirs for this bacterium, which can lead to human infection, in our region are typically cattle, goats, and sheep. In animals, C. burnetii infection is often detected due to reproductive problems. European Member States are required to report confirmed cases annually, but the lack of uniform reporting methods makes the data rather inconsistent. The Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise is involved in official controls to identify the causes of abortions, monitor suspected or positive herds, evaluate suspected infections in pets and humans, monitor the spread in wildlife, etc. In this paper, we summarize the presence of C. burnetii over the last five years (2019–2023). Additionally, a detailed overview of C. burnetii infection in wild and domestic animals is provided. Five hundred sixty animals—including cattle; goats; sheep; wild animals, such as deer, boars, wolves, roe deer, owls, and otters; buffalo; dogs; horses; cats; and a donkey—and six human samples were tested by real-time PCR on the transposase gene IS1111 to detect C. burnetii. The MST profile was identified in some of the samples. Outbreaks of C. burnetii occurred in four herds. In one of them, it was possible to follow the outbreak from inception to eradication by evaluating the effect of vaccination on real-time PCR Ct values. A total of 116 animals tested positive for C. burnetii, including 73 goats, 42 sheep, and one bovine. None of the other samples tested positive. The strains for which the ST was performed were identified as ST79, a strain that has been present in the area for more than ten years. The effect of vaccination on the reduction of positive samples and the variation of real-time PCR Ct values was evaluated in strict correlation. [ABSTRACT FROM AUTHOR]

Simple Summary: Emerging and re-emerging infectious diseases are a significant burden on the global economy, public, and animal health. These events are predominantly zoonotic (60.3%), with the majority (75%) originating from wildlife. In Italy, there are hundreds of research, monitoring, and surveillance activities targeting wildlife-related pathogens. The objective of this review is to highlight the importance of hunters in the context of disease research and surveillance in wild populations. A total of 192 papers, 16 theses, and 94 congress abstracts were selected by applying a specific string and inclusion criteria. The contribution of hunters amounts to nearly 400,000 sampled animals. These results underscore the paradigm of One Health in wildlife health surveillance, emphasizing the crucial role of hunters in sample availability, which forms the foundation of current health surveillance activities. In Italy, there are hundreds of research, monitoring, and surveillance activities targeting emerging and re-emerging pathogens. These activities heavily rely on hunters for sample collection and early identification of morbidity/mortality events. The objective of this review is to describe and quantify the contribution of hunters in the context of disease research, monitoring, and surveillance in wild populations. A literature review and descriptive summary statistics were performed following PRISMA-2020 guidelines; articles were obtained from major scientific databases, abstracts from national and international conferences, proceedings, graduate-level theses from online library repositories, and direct contact with academic experts. The contribution of hunters in terms of sample collection for health-related activities on wildlife amounts to 400,000 sampled animals. Wild boars were involved in 158 surveillance systems/research studies, followed by red deer (71), foxes (63), and roe deer (59). The pathogens under surveillance were mainly zoonotic (Salmonella spp.), emerging (Hepatitis E virus), and/or vector-borne (West Nile virus). The temporal distribution of scientific papers followed a positive trend that reflects the growing interest in wildlife from different sectors. These results highlight how wildlife health-related efforts are a paradigm of the concept of One Health, in which the role of hunters is crucial to ensure sample availability, and it constitutes the base of much current wildlife health research, monitoring, and surveillance. [ABSTRACT FROM AUTHOR]

Background: Coxiella burnetii, the causative agent of Q fever, is a zoonotic multi‐host vector‐borne pathogen of major public health importance. Although the European Food Safety Authority has recently made the monitoring of this bacterium in wildlife a priority, the role of wild lagomorphs in the transmission and maintenance of C. burnetii is poorly understood. Aims: The aims of this study were to determine the prevalence and risk factors associated with C. burnetii circulation in European wild rabbits (Oryctolagus cuniculus) and Iberian hares (Lepus granatensis) and to assess the presence of this pathogen in ticks that feed on them in Mediterranean ecosystems in Spain, the country with the highest number of reported cases of Q fever in Europe. Methods: A total of 574 spleen samples were collected from 453 wild rabbits and 121 Iberian hares, and 513 ticks (processed in 120 pools) between the 2017/2018 and 2021/2022 hunting seasons. Results: C. burnetii DNA was detected in 103 (17.9%; 95% CI: 14.8–21.1) of the 574 wild lagomorphs tested. By species, prevalence was 16.3% (74/453; 95% CI: 12.9–19.7) in the European wild rabbit and 24.0% (29/121; 95% CI: 16.4–31.6) in the Iberian hare. At least one positive lagomorph was found on 47.9% of the 96 hunting estates sampled and in every hunting season since 2018/2019. Two risk factors associated with C. burnetii infection were as follows: outbreak of myxomatosis on the hunting estate in the month prior to sampling and high tick abundance observed by gamekeepers on the hunting estate. C. burnetii DNA was also found in 33 of the 120 (27.5%; 95% CI: 19.5–35.5) tick pools tested. The pathogen was detected in 66.7% (4/6), 29.2% (26/89) and 21.4% (3/14) of Haemaphysalis hispanica, Rhipicephalus pusillus and Hyalomma lusitanicum pools respectively. Conclusions: This study provides new epidemiological data on C. burnetii in European wild rabbits and is the first survey on this zoonotic pathogen performed in Iberian hares. Our results indicate widespread endemic circulation of C. burnetii and highlight the importance of both wild lagomorph species as natural reservoirs of this zoonotic bacterium in Mediterranean ecosystems in southern Spain, which may be of public and animal health concern. The high prevalence and wide diversity of positive tick species suggest the possible role of ticks in the epidemiological cycle of C. burnetii, with the potential risk of transmission to sympatric species, including humans. [ABSTRACT FROM AUTHOR]

Long‐term, large‐scale monitoring of wildlife populations is an integral part of conservation research and management. However, some traditional monitoring protocols lack the information needed to account for sources of measurement error in data analyses. Ignoring measurement error, such as partial availability, imperfect detection, and species misidentification, can lead to mischaracterizations of population states and processes. Accounting for measurement error is key to robust monitoring of populations, which can inform a wide variety of decisions, including harvest, habitat restoration, and determination of the legal status of species. We undertook an effort to retroactively minimize bias in a large‐scale, long‐term monitoring program for marine birds in the Salish Sea, Washington, USA, by conducting an auxiliary study to jointly estimate components of measurement error. We built a novel model in a Bayesian framework that simultaneously harnessed human observer and photographic data types to produce estimates necessary to correct for the effects of partial availability, imperfect detection, and species misidentification. Across all 31 species identified in photographs, both observers had instances of undercounting and overcounting birds but tended to undercount (observers undercounted totals across all species on 69.3%–78.9% of transects). We estimated species‐specific correction factors that can be used to correct both historical and future counts from the Salish Sea survey, which has been running since 1992. Our novel modeling framework can be applied in other multispecies monitoring contexts where minimal photographic data can be collected for the purposes of correcting for measurement error in large‐scale, long‐term datasets. [ABSTRACT FROM AUTHOR]

The ability to conduct cost‐effective wildlife monitoring at scale is rapidly increasing due to the availability of inexpensive autonomous recording units (ARUs) and automated species recognition, presenting a variety of advantages over human‐based surveys. However, estimating abundance with such data collection techniques remains challenging because most abundance models require data that are difficult for low‐cost monoaural ARUs to gather (e.g., counts of individuals, distance to individuals), especially when using the output of automated species recognition. Statistical models that do not require counting or measuring distances to target individuals in combination with low‐cost ARUs provide a promising way of obtaining abundance estimates for large‐scale wildlife monitoring projects but remain untested. We present a case study using avian field data collected in the forests of Pennsylvania during the spring of 2020 and 2021 using both traditional point counts and passive acoustic monitoring at the same locations. We tested the ability of the Royle–Nichols and time‐to‐detection models to estimate the abundance of two species from detection histories generated by applying a machine‐learning classifier to ARU‐gathered data. We compared abundance estimates from these models with estimates from the same models fit using point‐count data and to two additional models appropriate for point counts, the N‐mixture model and distance models. We found that the Royle–Nichols and time‐to‐detection models can be used with ARU data to produce abundance estimates similar to those generated by a point‐count‐based study but with greater precision. ARU‐based models produced confidence or credible intervals that were on average 31.9% (±11.9 SE) smaller than their point‐count counterpart. Our findings were consistent across two species with differing relative abundance and habitat use patterns. The higher precision of models fit using ARU data is likely due to higher cumulative detection probability, which itself may be the result of greater survey effort using ARUs and machine‐learning classifiers to sample significantly more time for focal species at any given point. Our results provide preliminary support for the use of ARUs in abundance‐based study applications, and thus may afford researchers a better understanding of habitat quality and population trends, while allowing them to make more informed conservation recommendations and actions. [ABSTRACT FROM AUTHOR]

Roads negatively impact wildlife through habitat fragmentation, loss of habitat connectivity, and wildlife-vehicle collisions, thus road mitigation structures, such as wildlife crossing structures (WCS), wildlife guards (WG), and fencing are commonly used to address this issue all over the world, including in the United States. In South Texas, such structures were built or modified along a State Highway in an effort to address road mortality for the endangered ocelot (Leopardus pardalis) and non-target wildlife species. The goal of this study was to examine temporal changes in wildlife interactions with WCS and WG during and after their construction and modification along a South Texas highway and to determine whether environmental factors influenced use of WCS. Using camera traps deployed to monitor the road mitigation structures, we compared crossing rates, repel rates, and species richness of all species that interacted with the structures, and we examined whether differential wildlife use of WCS and WG was affected by one or more structural dimensions, distance to nearby vegetation, and water presence. Crossings through WCS by wildlife decreased following the completion of construction of mitigation structures; however, repel interactions at WG increased. Overall, crossings decreased at WCS that had higher openness ratios and during periods of precipitation and higher daily temperatures, but distance to vegetation had minimal influence. These factors were shown to influence crossings of each of the five most frequently observed species differently. Lastly, the presence of pooled water at one WCS caused a decrease in crossings when the water level was highest but was not a barrier at lower water levels. By examining influences on wildlife interaction with road mitigation structures, we conclude that a variety of structures, including different WCS configurations, can be beneficial in facilitating movement and restricting entry into the right-of-way for a diversity of wildlife species beyond the target species. [ABSTRACT FROM AUTHOR]

Time series similarity matrices (informally, recurrence plots or dot-plots), are useful tools for time series data mining. They can be used to guide data exploration, and various useful features can be derived from them and then fed into downstream analytics. However, time series similarity matrices suffer from very poor scalability, taxing both time and memory requirements. In this work, we introduce novel ideas that allow us to scale the largest time series similarity matrices that can be examined by several orders of magnitude. The first idea is a novel algorithm to compute the matrices in a way that removes dependency on the subsequence length. This algorithm is so fast that it allows us to now address datasets where the memory limitations begin to dominate. Our second novel contribution is a multiscale algorithm that computes an approximation of the matrix appropriate for the limitations of the user's memory/screen-resolution, then performs a local, just-in-time recomputation of any region that the user wishes to zoom-in on. Given that this largely removes time and space barriers, human visual attention then becomes the bottleneck. We further introduce algorithms that search massive matrices with quadrillions of cells and then prioritize regions for later examination by either humans or algorithms. We will demonstrate the utility of our ideas for data exploration, segmentation, and classification in domains as diverse as astronomy, bioinformatics, entomology, and wildlife monitoring. [ABSTRACT FROM AUTHOR]

Earth's climate is rapidly changing, bringing forth questions of how domestic and wild animals will alter their behaviour in response to increasing temperatures and dryland expansion. Dwindling water availability will likely impact animal behaviour and water foraging, potentially increasing animal aggregations and interspecific contacts. These interspecific contacts are especially important for competition, predation and disease transmission among wildlife and domestic animals. In this study, we analysed interspecific wildlife and cattle contacts using two years of camera trap data at an experimental water manipulation site at a conservancy in central Kenya. We found that on average, the hourly probability of any interspecific contact was approximately 3.4 times higher at water sources versus drained water sources and 18 times higher than surrounding matrix areas, and that this relationship was amplified by dry and hot conditions. Species‐specific analyses revealed variation in the magnitude of responses across wildlife and domestic cattle, although all animals had approximately 2–3 times higher interspecific contact probability with other species at water in hot conditions versus other conditions. Notably, we observed the largest behavioural changes for relatively water‐independent species, such as giraffe, which had 3.6 times higher interspecific contact probability at water sources in hot versus other conditions. Synthesis and applications. These findings show how elevated temperatures that will become increasingly common with future climate changes can increase interspecific contacts around critical water resources. In mixed wildlife‐livestock systems, maintaining wildlife‐only water sources may be a practical management tool to mitigate human‐wildlife conflict and disease transmission at this interface, especially during dry and hot conditions. [ABSTRACT FROM AUTHOR]

Monitoring trends in wildlife communities is integral to making informed land management decisions and applying conservation strategies. Birds inhabit most niches in every environment and because of this they are widely accepted as an indicator species for environmental health. Traditionally, point counts are the common method to survey bird populations, however, passive acoustic monitoring approaches using autonomous recording units have been shown to be cost-effective alternatives to point count surveys. Advancements in automatic acoustic classification technologies, such as BirdNET, can aid in these efforts by quickly processing large volumes of acoustic recordings to identify bird species. While the utility of BirdNET has been demonstrated in several applications, there is little understanding of its effectiveness in surveying declining grassland birds. We conducted a study to evaluate the performance of BirdNET to survey grassland bird communities in Nebraska by comparing this automated approach to point count surveys. We deployed ten autonomous recording units from March through September 2022: five recorders in row-crop fields and five recorders in perennial grassland fields. During this study period, we visited each site three times to conduct point count surveys. We compared focal grassland bird species richness between point count surveys and the autonomous recording units at two different temporal scales and at six different confidence thresholds. Total species richness (focal and non-focal) for both methods was also compared at five different confidence thresholds using species accumulation curves. The results from this study demonstrate the usefulness of BirdNET at estimating long-term grassland bird species richness at default confidence scores, however, obtaining accurate abundance estimates for uncommon bird species may require validation with traditional methods. [ABSTRACT FROM AUTHOR]

This study aims to overcome the impact of complex environmental backgrounds on the recognition of wildlife in monitoring images, thereby exploring the role of a deep learning-based intelligent wildlife recognition system in biodiversity conservation. The automatic identification of wildlife images is conducted based on convolutional neural networks (CNNs). Target detection technology, based on regression algorithms, is initially employed to extract Regions of Interest (ROI) containing wildlife from images. The wildlife regions in monitoring images are detected, segmented, and converted into ROI images. A dual-channel network model based on Visual Geometry Group 16 (VGG16) is implemented to extract features from sample images. Finally, these features are input into a classifier to achieve wildlife recognition. The proposed optimized model demonstrates superior recognition performance for five wildlife species, caribou, lynx, mule deer, badger, and antelope, compared to the dual-channel network model based on VGG16. The optimized model achieves a Mean Average Precision (MAP) of 0.714, with a maximum difference of 0.145 compared to the other three network structures, affirming its effectiveness in enhancing the accuracy of automatic wildlife recognition. The model effectively addresses the issue of low recognition accuracy caused by the complexity of background information in monitoring images, achieving high-precision recognition and holding significant implications for the implementation of biodiversity conservation laws. [ABSTRACT FROM AUTHOR]

Camera traps have transformed the way we monitor wildlife and are now routinely used to address questions from a wide range of ecological and conservation aspects. Sampling design optimization and a better understanding of drivers determining the precision of detection rates (i.e. the number of detections per unit of effort) are important methodological issues. Little attention has been focused on the effect of placing more than one camera on each sampling point (hereafter, clustered design), and/or rotating (i.e. redeploying) the cameras to new placements during the sampling period.We explored the differences in the precision of detection rates between clustered vs. single camera designs when cameras remained in the same location during the study. Furthermore, the effect of keeping the placement of cameras fixed or rotating them (i.e. moving them to new locations during the sampling period), when a limited number of camera devices are available, was also evaluated. We used simulations and field data to test differences in detection rate precision for the different sampling designs. We simulated three different population distributions (random, trail‐based and aggregated) and three abundance scenarios. The simulations were validated with a field experiment focused on eight species with different behavioural traits, including artiodactyls, carnivores, lagomorphs, and birds.When a fixed number of sampling points were monitored simultaneously, clustered designs generally resulted in an increase in the precision of detection rates compared to single designs. The absolute reduction in the coefficient of variation by clustered designs was on average 0.07 units (min: 0.01, max: 0.15), which represents an average relative reduction in CV of 31% (min:6%, max:44%). An improvement in precision was also observed as a higher number of sampling points was used for all population distributions and sampling designs tested. When a fixed number of cameras were available, rotating the cameras to independent locations improved precision (an absolute reduction of 0.19 CV units) when monitoring aggregated populations, but not for random and trail‐based population distributions.Synthesis and applications: Our research provides a guideline for wildlife managers and researchers to improve the precision of camera trap detection rates and optimize resource allocation. In general, the study design should accommodate the behaviour of the target species (e.g. spatial aggregation and abundance), monitoring program logistic resources (both human and economic) and study area characteristics (e.g. accessibility and vandalism). [ABSTRACT FROM AUTHOR]

Many aerial insects are declining, yet monitoring wildlife in airspace is challenging. Aerial insectivores, which are themselves a declining guild, may be useful indicators for aerial insects. However, their use as indicators may be complicated if they differentially sample prey depending on foraging range, as predicted by central place foraging theory. We measured diet composition of purple martins (Progne subis (Linnaeus, 1758)) in Quebec by deoxyribonucleic acid (DNA) metabarcoding fecal samples collected from adults and nestlings and used GPS biologgers to measure foraging behaviour of the chick-rearing adults. Foraging range did not predict diet or diet diversity. The proportion of individuals with Diptera and Coleoptera detected in their diets was lower in developed and water habitats. Martins fed their nestlings soft-bodied insects (Diptera and Trichoptera) more often than they ate them themselves. As nestlings aged, they were fed more chitinous arthropods and less soft-bodied prey. As the proportion of different prey orders depended on habitat use and age, but not foraging distance, we argue that purple martin diet is a potential indicator of aerial insect community variation across space, but that interpretations may be complex as birds may sample different components of the aerial insect community during different life stages. [ABSTRACT FROM AUTHOR]

The development of epigenetic clocks, or the DNA methylation‐based inference of age, is an emerging tool for ageing in free ranging populations. In this study, we developed epigenetic clocks for three species of large mammals that are the focus of extensive management throughout their range in North America: white‐tailed deer, black bear and mountain goat. We quantified differential DNA methylation patterns at over 30,000 cytosine‐guanine sites (CpGs) from tissue samples of all three species (black bear n = 49; white‐tailed deer n = 47; mountain goat n = 45). We used a penalized regression model (elastic net) to build explanatory (black bear r =.95; white‐tailed deer r =.99; mountain goat r =.97) and robust (black bear Median Absolute Error or MAE = 1.33; white‐tailed deer MAE = 0.29; mountain goat MAE = 0.61) models of age or clocks. We also characterized individual CpG sites within each species that demonstrated clear differences in methylation levels between age classes and sex, which can be used to develop a suite of accessible diagnostic markers. This tool has the potential to contribute to wildlife monitoring by providing easily obtainable representations of age structure in managed populations. [ABSTRACT FROM AUTHOR]

Wildlife may represent an important source of infectious diseases for humans and other wild and domestic animals. Wild ruminants can harbour and transmit Shiga toxin-producing Escherichia coli (STEC) to humans, and some strains even carry important antimicrobial resistance. In this study, 289 livers of wild roe deer, fallow deer, red deer and chamois collected in Liguria, north-west Italy, from 2019 to 2023 were analysed. Overall, 44 STEC strains were isolated from 28 samples. The characterisation of serogroups showed the presence of O104, O113, O145 and O146 serogroups, although for 28 colonies, the serogroup could not be determined. The most prevalent Shiga toxin gene in isolated strains was Stx2, and more specifically the subtype Stx2b. The other retrieved subtypes were Stx1a, Stx1c, Stx1d and Stx2g. The isolated strains generally proved to be susceptible to the tested antimicrobials. However, multi-drug resistances against highly critical antimicrobials were found in one strain isolated from a roe deer. This study highlights the importance of wildlife monitoring in the context of a "One Health" approach. [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]

Abstract Camera traps have become important tools for the monitoring of animal populations. However, the study‐specific estimation of animal detection probabilities is key if unbiased abundance estimates of unmarked species are to be obtained. Since this process can be very time‐consuming, we developed the first semi‐automated workflow for animals of any size and shape to estimate detection probabilities and population densities. In order to obtain observation distances, a deep learning algorithm is used to create relative depth images that are calibrated with a small set of reference photos for each location, with distances then extracted for animals automatically detected by MegaDetector 4.0. Animal detection by MegaDetector was generally independent of the distance to the camera trap for 10 animal species at two different study sites. If an animal was detected both manually and automatically, the difference in the distance estimates was often minimal at a distance about 4 m from the camera trap. The difference increased approximately linearly for larger distances. Nonetheless, population density estimates based on manual and semi‐automated camera trap distance sampling workflows did not differ significantly. Our results show that a readily available software for semi‐automated distance estimation can reliably be used within a camera trap distance sampling workflow, reducing the time required for data processing, by >13‐fold. This greatly improves the accessibility of camera trap distance sampling for wildlife research and management.

Wireless image sensor networks (WISNs) are widely applied in wildlife protection as they present a better performance in remote, real-time monitoring. However, traditional WISNs suffer from the limitations of low processing capability, power consumption restrictions, and narrow transmission bandwidth, which leads to a shorter working lifetime of the monitoring system when transmitting the wildlife monitoring image with high resolution. We propose a high-efficiency distributed image compression coding method based on soft threshold iteration and quantitative perception for wildlife monitoring images to rationally assign the electricity resource. Specifically, we first utilize the histogram contrast algorithm to detect the saliency object region from the original samples and use it to generate the mask image of the wildlife region. After the mask image is obtained, the distributed image compression coding method is utilized to transmit the wildlife image, in which the saliency image region is directly transmitted as a cluster head to ensure the transmission efficiency of the wildlife region. Then the background region is assigned to the other four monitoring nodes at the same level for processing and transmission, extending the lifetime of the network. Furthermore, the soft threshold iteration algorithm is utilized to encode the image data; this is suitable for WISNs. The experimental results on our own wildlife dataset show improvements of 7.47 and 9.06% for the peak signal-tonoise ratio and 16.98 and 19.50% for the structural similarity index on the reconstructed image compared with those of the discrete cosine transform and embedded zerotree wavelets algorithms, respectively. Compared with the multihop and single-hop transmission methods, the power consumption is reduced by 29.96 and 40.84%, respectively. The results of this study indicate that the WISN technique can provide feasible solutions for the intelligent monitoring of forest biological resources. [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]

Much research has been conducted to describe the factors that determine the fecal microbiome, with diet and host phylogeny as the main drivers. The influence of diet has been described at different levels. Firstly, there are major differences in the microbiomes of herbivorous and carnivorous species and secondly the morphology of the digestive system also determines the composition and diversity of the microbiota. In this study, we aim to describe the influence of the three factors - diet, digestive system and host - on the microbiota in order to develop a model that is able to characterize host-specific metadata from an unknown fecal sample. We therefore analyzed the 16s rRNA from 525 fecal samples of 14 zoo-housed species belonging to different phylogenetic groups including herbivores, carnivores and omnivores. We found significant differences in the bacterial taxa correlated with these groups. While herbivores show positive correlations with a large number of bacterial taxa, we found fewer taxa correlating with carnivores or omnivores. We also detected considerable differences in the microbiota of the ruminant, hindgut fermenting and simple digestive system. Based on these results, we developed a logistic ensemble model, that predicts the diet and based on these findings either the herbivorous digestive system or the carnivorous host-family from a given fecal microbiota composition. This model is able to effectively discriminate herbivores, omnivores and carnivores. It also excels at predicting the herbivore-specific digestive system with 98% accuracy, further reinforcing the strong link between microbiota and digestive system morphology. Carnivorous host-family identification achieves an overall accuracy of 79%, although this performance varies between families. We provide this trained model as a tool to enable users to generate host-specific information from their microbiome data. In future research, tools such as the one presented here could lead to a combined approach of microbiome and host-specific analyses which would be a great advantage in non-invasive wildlife monitoring. [ABSTRACT FROM AUTHOR]

Technological and methodological advances in remote sensing and machine learning have created new opportunities for advancing wildlife surveys. We assembled a Community of Practice (CoP) to capitalize on these developments to explore improvements to the efficiency and effectiveness of aerial wildlife monitoring from a management perspective. The core objective of the CoP is to organize the development and testing of remote sensing and machine learning methods to improve aerial wildlife population surveys that support management decisions. Beginning in 2020, the CoP collaboratively identified the natural resource management decisions that are informed by wildlife survey data with a focus on waterbirds and marine wildlife. We surveyed our membership to establish 1) what management decisions they were using wildlife count data to inform; 2) how these count data were collected prior to the advent of remote sensing/machine learning methods; 3) the impetus for transitioning to a remote sensing/machine learning methodological framework; and 4) the challenges practitioners face in transitioning to this framework. This paper documents these findings and identifies research priorities for moving toward operational remote sensingbased wildlife surveys in service of wildlife management. [ABSTRACT FROM AUTHOR]

Simple Summary: Marine aquarium keeping is a popular hobby that fuels a global industry that still heavily relies on the sourcing of wild organisms, mostly from tropical coral reefs. The European Union (EU) is one of the largest import markets for marine ornamental fish. Despite being mandatory and already fully digital, the record-keeping of what species are imported in what numbers from which exporting countries remains blurry. The present work presents curated and consolidated data reporting the value, the exporting and importing countries, and the number of specimens, species, and families of marine ornamental fish imported to the EU between 2014 and 2021. A 24-million-euro annual trade value was recorded, and 26 million specimens were imported from more than 60 countries (mostly Indonesia, the Philippines, and Sri Lanka). A watchlist is presented to provide guidance to stakeholders on which marine ornamental fish species being traded most likely require closer monitoring due to their potential impact through trade. The European TRAde Control and Expert System (TRACES) requires some minor tunning to enable authorities to easily monitor the imports of marine ornamental fish into the EU, thus allowing an unprecedented insight into this and other trade activities targeting wildlife. Wild-caught fish from coral reefs, one of the most threatened ecosystems on the planet, continue to supply the marine aquarium trade. Despite customs and veterinary checks during imports, comprehensive data on this global industry remain scarce. This study provides consolidated data on the largest import market by value, the European Union (EU): a 24-million-euro annual trade value, detailing the main exporting and importing countries, as well as the species and families of the 26 million specimens imported between 2014 and 2021. A watchlist alert system based on the number of specimens traded, import trends, and vulnerability index according to FishBase and the IUCN Red List conservation status is presented, providing key information on which species should require closer scrutiny by authorities. While the European TRAde Control and Expert System (TRACES) electronically monitors the movement of live animals to respond quickly to biosecurity risks, one-third of marine ornamental fish imported lack species-level information. With minor adjustments, TRACES holds the potential to significantly enhance data granularity and the monitoring of wildlife trade, with marine ornamental fish being an interesting case study to validate this approach. [ABSTRACT FROM AUTHOR]

Harbour porpoises (Phocoena phocoena) are the only cetacean residents found year-round in Swedish waters and they are exposed to numerous natural and anthropogenic threats. Since the in situ monitoring of cetaceans can be difficult, invasive and often expensive, investigation of stranding patterns and examination of stranded animals can be used as a cost-effective source of data to study these elusive animals. The aim of this study was to investigate the spatiotemporal patterns of harbour porpoise stranding reports and the possible underlying causes in Swedish waters over a ten-year period (2014–2023). Additionally, the Swedish stranding network plays a key role in the collection of stranded carcasses for health and disease surveillance, and geographic coverage of the network also was analysed. When making spatial comparisons, the ten-year period was divided into two five-year blocks. Data on 854 stranded harbour porpoises were analysed from the coasts of the Skagerrak, Kattegat, and Baltic Seas. Both significant spatial and temporal patterns could be identified. Strandings peaked in July through September and hotspots occurred along most of the Swedish west coast, with the most frequent hotspots located around Öresund and especially the area around the Kullen peninsula. The spatial patterns of strandings found in this study are consistent with data on porpoise abundance, prey abundance, and gillnet fisheries' efforts. The latter is known to be one of the primary causes of porpoise mortality. Furthermore, the coverage of the Swedish stranding network increased between the two periods, likely reflecting an increased awareness of the carcass-based surveillance program, and gaps requiring network expansion efforts were identified. These results also provide baseline data to enable the continued monitoring of stranding trends, as changes may indicate changes in population distribution, size or mortality rates. [ABSTRACT FROM AUTHOR]

Enhanced animal welfare has emerged as a pivotal element in contemporary precision animal husbandry, with bovine monitoring constituting a significant facet of precision agriculture. The evolution of intelligent agriculture in recent years has significantly facilitated the integration of drone flight monitoring tools and innovative systems, leveraging deep learning to interpret bovine behavior. Smart drones, outfitted with monitoring systems, have evolved into viable solutions for wildlife protection and monitoring as well as animal husbandry. Nevertheless, challenges arise under actual and multifaceted ranch conditions, where scale alterations, unpredictable movements, and occlusions invariably influence the accurate tracking of unmanned aerial vehicles (UAVs). To address these challenges, this manuscript proposes a tracking algorithm based on deep learning, adhering to the Joint Detection Tracking (JDT) paradigm established by the CenterTrack algorithm. This algorithm is designed to satisfy the requirements of multi-objective tracking in intricate practical scenarios. In comparison with several preeminent tracking algorithms, the proposed Multi-Object Tracking (MOT) algorithm demonstrates superior performance in Multiple Object Tracking Accuracy (MOTA), Multiple Object Tracking Precision (MOTP), and IDF1. Additionally, it exhibits enhanced efficiency in managing Identity Switches (ID), False Positives (FP), and False Negatives (FN). This algorithm proficiently mitigates the inherent challenges of MOT in complex, livestock-dense scenarios. [ABSTRACT FROM AUTHOR]

Simple Summary: This study shows that automated computer vision techniques are highly effective when used to analyze images and to extract valuable information from them. We trained an algorithm with a set of 1600 images obtained from a study where wildlife approaching wild boar carcasses were monitored. This enabled the model to detect different classes of animals automatically in their natural environment with a mean average precision of 98.11%. Camera traps are becoming widely used for wildlife monitoring and management. However, manual analysis of the resulting image sets is labor-intensive, time-consuming and costly. This study shows that automated computer vision techniques can be extremely helpful in this regard, as they can rapidly and automatically extract valuable information from the images. Specific training with a set of 1600 images obtained from a study where wild animals approaching wild boar carcasses were monitored enabled the model to detect five different classes of animals automatically in their natural environment with a mean average precision of 98.11%, namely 'wild boar', 'fox', 'raccoon dog', 'deer' and 'bird'. In addition, sequences of images were automatically analyzed and the number of wild boar visits and respective group sizes were determined. This study may help to improve and speed up the monitoring of the potential spread of African swine fever virus in areas where wild boar are affected. [ABSTRACT FROM AUTHOR]

Featured Application: The research introduced in the paper develops an automatic bird species recognition system powered by feature enhancement and contrast learning, aimed at advancing ecological conservation and biological research. This system bolsters the precision of identifying bird species, aiding in the protection of endangered birds and automating the monitoring of bird populations and their migratory behaviors. It also supports in-depth behavioral and ecological research and evaluates the impact of human activities on avian life. The technology's potential applications are vast, including its use in citizen science initiatives, environmental impact assessments, educational programs, and the tourism sector, where it can provide real-time species identification, thereby enriching eco-tourism experiences and raising biodiversity awareness. Accurate bird species recognition is crucial for ecological conservation, wildlife monitoring, and biological research, yet it poses significant challenges due to the high variability within species and the subtle similarities between different species. This paper introduces an automatic bird species recognition method from images that leverages feature enhancement and contrast learning to address these challenges. Our method incorporates a multi-scale feature fusion module to comprehensively capture information from bird images across diverse scales and perspectives. Additionally, an attention feature enhancement module is integrated to address noise and occlusion within images, thus enhancing the model's robustness. Furthermore, employing a siamese network architecture allows effective learning of common features within instances of the same class and distinctions between different bird species. Evaluated on the CUB200-2011 dataset, our proposed method achieves state-of-the-art performance, surpassing existing methods with an accuracy of 91.3% and F1 score of 90.6%. Moreover, our approach showcases a notable advantage in scenarios with limited training data. When utilizing only 5% of the training data, our model still achieves a recognition accuracy of 65.2%, which is significantly higher than existing methods under similar data constraints. Notably, our model exhibits faster execution times compared to existing methods, rendering it suitable for real-time applications. [ABSTRACT FROM AUTHOR]

Passive acoustic monitoring (PAM) studies generate thousands of hours of audio, which may be used to monitor specific animal populations, conduct broad biodiversity surveys, detect threats such as poachers, and more. Machine learning classifiers for species identification are increasingly being used to process the vast amount of audio generated by bioacoustic surveys, expediting analysis and increasing the utility of PAM as a management tool. In common practice, a threshold is applied to classifier output scores, and scores above the threshold are aggregated into a detection count. The choice of threshold produces biased counts of vocalizations, which are subject to false positive/ negative rates that may vary across subsets of the dataset. In this work, we advocate for directly estimating call density: The proportion of detection windows containing the target vocalization, regardless of classifier score. We propose a validation scheme for estimating call density in a body of data and obtain, through Bayesian reasoning, probability distributions of confidence scores for both the positive and negative classes. We use these distributions to predict site-level call densities, which may be subject to distribution shifts (when the defining characteristics of the data distribution change). These methods may be applied to the outputs of any binary detection classifier operating on fixedsize audio input windows. We test our proposed methods on a real-world study of Hawaiian birds and provide simulation results leveraging existing fully annotated datasets, demonstrating robustness to variations in call density and classifier model quality. [ABSTRACT FROM AUTHOR]

Stoat (Mustela erminea) and weasel (M. nivalis) are hard to monitor as they are elusive of nature and leave few identifying marks in their surroundings. Stoat and weasel are both fully protected in Denmark and are thought to be widely distributed throughout the country. Despite this stoat and weasel were listed on the Danish Red List as Near Threatened in 2019, as their densities and population trends are unknown. Using a modified novel camera trapping device, the Double‐Mostela, a wooden box comprising a tracking tunnel and two camera traps, we attempted to obtain density estimates based on identification of individual stoats and weasels. We deployed camera traps both inside Double‐Mostela traps and externally in three different study areas in northern Zealand, Denmark, and tested commercial, American scent‐based lures to attract stoat and weasel. We obtained very low seasonal trapping rates of weasel in two study areas, but in one study area, we obtained a seasonal trapping rate of stoat larger compared to another study using the Mostela. In one study area, both species were absent. We observed no effect of scent‐based lures in attracting small mustelids compared to non‐bait traps. Potential reasons behind low capture rates of weasel and stoat are suboptimal habitat placement and timing of deployment of the Double‐Mostelas, land‐use changes over the last 200 years, predation from larger predators, as well as unintended secondary poisoning with rodenticides. Due to the scarcity of weasel and stoat captures, we were unable to make density estimates based on identification of individuals; however, we identified potential features that could be used for identification and density estimates with more captures. [ABSTRACT FROM AUTHOR]

Reliable estimates of population trends are important for prioritizing and implementing wildlife management actions. Abundance estimates are the most informative metric of population status but are difficult and costly to achieve for rare or elusive species. For bat species that are highly mobile and nocturnal, abundance estimates are usually only possible at their roosts. Although they are relatively easy to census in individual roosts, it is often unclear whether observed trends are representative of broader populations trends. Dual‐frame sampling is useful for monitoring wildlife populations that exhibit site fidelity and are highly visible but where the proportion of the population in known sites is unknown. We adapted a dual‐frame sampling approach to estimate abundance of Townsend's big‐eared bats (Corynorhinus townsendii) hibernating in a system of >800 caves at Lava Beds National Monument (LBNM) in northern California, USA, in 2013–2022. Beginning with a list of opportunistically discovered hibernacula, we expanded inference to the entire system by randomly selecting sites and stratifying survey effort according to number of bats observed during previous counts. Following 4 years of surveys, we began substituting survey‐informed estimates for some site types to improve efficiency of the monitoring effort. We estimated that a mean of 2,216 ± 112 (SE) bats hibernated at the study site annually and observed fluctuations of up to 22% around the mean population estimate and as much as 21% from the previous year. Despite annual fluctuations, we did not detect a trend in the number of hibernating bats at LBNM over the study period, underlining the challenges of detecting population trends in long‐lived species not exposed to pronounced stressors. Because our analytical approach generates reliable estimates of trend while accommodating annual variability in survey effort among years, it should be attractive to wildlife managers faced with shifting priorities and resources. [ABSTRACT FROM AUTHOR]

Research on wildlife monitoring methods is a crucial tool for the conservation of rare wildlife in China. However, the fact that rare wildlife monitoring images in field scenes are easily affected by complex scene information, poorly illuminated, obscured, and blurred limits their use. This often results in unstable recognition and low accuracy levels. To address this issue, this paper proposes a novel wildlife identification model for rare animals in Giant Panda National Park (GPNP). We redesigned the C3 module of YOLOv5 using NAMAttention and the MemoryEfficientMish activation function to decrease the weight of field scene features. Additionally, we integrated the WIoU boundary loss function to mitigate the influence of low-quality images during training, resulting in the development of the NMW-YOLOv5 model. Our model achieved 97.3% for mAP50 and 83.3% for mAP50:95 in the LoTE-Animal dataset. When comparing the model with some classical YOLO models for the purpose of conducting comparison experiments, it surpasses the current best-performing model by 1.6% for mAP50:95, showcasing a high level of recognition accuracy. In the generalization ability test, the model has a low error rate for most rare wildlife species and is generally able to identify wildlife in the wild environment of the GPNP with greater accuracy. It has been demonstrated that NMW-YOLOv5 significantly enhances wildlife recognition accuracy in field environments by eliminating irrelevant features and extracting deep, effective features. Furthermore, it exhibits strong detection and recognition capabilities for rare wildlife in GPNP field environments. This could offer a new and effective tool for rare wildlife monitoring in GPNP. [ABSTRACT FROM AUTHOR]

Accurate information on wildlife occurrence in anthropogenic landscapes is essential to develop effective conservation measures. Monitoring multiple mammal species in heterogeneous landscapes can be challenging because of the diversity of land‐uses and species behaviors. Therefore, different monitoring methods are better adapted to different contexts. We compared data obtained from camera traps with data gathered through an online survey to document the presence of mammal species in mosaic landscapes of the Garden Route Biosphere Reserve in South Africa. The survey detected the same 15 species as the camera traps as well as the additional common duiker. Both methods effectively detected most large mammal species, whereas results were less consistent for smaller species and carnivores. Combining the two methods enabled us to produce more robust estimates of species absence and confirm species presence reported by survey respondents. In less disturbed areas, respondents with good ecological knowledge were effective at monitoring mammal species, while extending the spatial coverage of the study. Bearing in mind the limitations of each method, camera traps and an online survey could complement each other if combined. Together, they can provide a more comprehensive understanding of mammal communities in anthropogenic landscapes, increasing both spatial coverage and the number of species sightings. [ABSTRACT FROM AUTHOR]

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