Search Results (497)

Babesia microti is a protozoan that infects red blood cells, causing hemolytic anemia and flu-like symptoms in humans. Understanding co-infections is crucial for the better diagnosis, treatment, and management of tick-borne diseases. This study examined the prevalence of Babesia microti co-infection with other prevalent tick-borne pathogens in Pennsylvania. The dataset acquired from the Dr. Jane Huffman Wildlife Genetics Institute included passive surveillance data from Ixodes spp. from 2021 to 2023. Submitted ticks were screened for tick-borne pathogens using species-specific TaqMan qPCR. Of the 793 B. microti-positive ticks pulled for analysis, 65.0% were co-infected with other pathogens (n = 516). Notably, 60.9% of the B. microti-positive ticks were co-infected with Borrelia burgdorferi, 10.2% with Anaplasma phagocytophilum Ap-ha, and 7.5% carried a triple co-infection with B. burgdorferi and A. phagocytophilum Ap-ha. The rates of B. microti infection and its co-infections are on the rise, with patterns observed in Pennsylvania and other regions of the USA. While other studies have collected both nymphal and adult ticks to screen for co-infections in Pennsylvania, our study stood out as a unique contribution to the field by focusing exclusively on B. microti-positive ticks. The continued monitoring of tick-borne co-infections is vital to prevent misdiagnosis and ensure effective treatment regimens. Full article

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. Full article

Conservation managers increasingly employ reinforcement techniques to bolster declining populations by reintroducing non-wild individuals born in captivity into natural habitats, but success rates remain modest. In this study, the success is evaluated of reinforcement efforts using satellite tracking and field observation data collected between 2010 and 2021. It focuses on 13 non-wild individuals, as follows: seven red-crowned cranes Grus japonensis, two white-naped cranes Antigone vipio, and four demoiselle cranes Anthropoides virgo, as well as five wild individuals including two red-crowned cranes and three white-naped cranes. The assessment criteria included survival, movement, and reproduction, utilizing a comprehensive scoring method. The scoring process indicates that more timely field observation records and the movement pattern scoring combining models and trajectories can improve the accuracy of estimation. From the results, although wild individuals generally achieve higher scores across these metrics, statistical differences were not significant possibly due to limited sample size. Notably, non-wild individuals frequently displayed residence, nomadic, or abnormal migration. In addition, field observations underscored the benefits of pairing non-wild individuals with their wild counterparts to enhance migration success. So in order to enhance migration success, it is advisable to release non-wild individuals approaching sexual maturity in proximity to wild subadult flocks during the breeding or summering periods. Additionally, during the overwintering phase, these individuals should be released in areas where wild populations are concentrated. Full article

While mangrove ecosystems are rich in biodiversity, they are increasingly impacted by climate change and urban pollutants. The current study provides first insights into the emergence of potentially pathogenic yeasts in Hong Kong’s mangroves. Sediment and water samples were collected from ten urban and rural mangroves sites. Initial CHROMagar^TM Candida Plus screening, representing the first application of this differential medium for water and soil samples collected from a non-clinical environment, enabled the rapid, preliminary phenotypic identification of yeast isolates from mangroves. Subsequent molecular profiling (ITS and/or 28S nrDNA sequencing) and antifungal drug susceptibility tests were conducted to further elucidate yeast diversity and drug resistance. A diversity of yeasts, including 45 isolates of 18 distinct species across 13 genera/clades, was isolated from sediments and waters from Hong Kong mangroves. Molecular profiling revealed a dominance of the Candida/Lodderomyces clade (44.4%), a group of notorious opportunistic pathogens. The findings also reveal a rich biodiversity of non-Candida/Lodderomyces yeasts in mangroves, including the first reported presence of Apiotrichum domesticum and Crinitomyces flavificans. A potentially novel Yamadazyma species was also discovered. Remarkably, 14.3% of the ubiquitous Candida parapsilosis isolates displayed resistance to multiple antifungal drugs, suggesting that mangroves may be reservoirs of multi-drug resistance. Wildlife, especially migratory birds, may disseminate these hidden threats. With significant knowledge gaps regarding the environmental origins, drug resistance, and public health impacts of pathogenic yeasts, urgent surveillance is needed from a One Health perspective. This study provides an early warning that unrestrained urbanization can unleash resistant pathogens from coastal ecosystems globally. It underscores the necessity for enhanced surveillance studies and interdisciplinary collaboration between clinicians, ornithologists, and environmental microbiologists to effectively monitor and manage this environmental health risk, ensuring the maintenance of ‘One Health’. Full article

Severe habitat loss and fragmentation due to extensive road development have escalated wildlife–vehicle collisions (WVCs) as one of the major causes of wildlife mortality. This study, spanning 9 years from 2009 to 2017, presents comprehensive WVC data in South Korea, including species composition, seasonal and regional patterns, and road factors influencing WVCs, aiming to analyze their impact and propose effective mitigation strategies. We collected WVC data with road variables for 9 years from 4561 km of nationwide monitoring road sections and analyzed the data to understand the relationship between WVCs and road characteristics, as well as species-specific patterns. A nationwide survey identified 13,606 WVCs involving 143 terrestrial vertebrate species, and patterns and models of the top seven mammal species (raccoon dog (Nyctereutes procyonoides), Siberian chipmunk (Eutamias sibiricus), Siberian weasel (Mustela sibirica), water deer (Hydropotes inermis), red squirrel (Sciurus vulgaris), Korean hare (Lepus coreanus), and leopard cat (Prionailurus bengalensis)) were presented. Patterns revealed declines in WVCs overall, except for water deer. Although spatial differences in WVCs seemed linked more to wildlife habitats, certain road features correlated both positively or negatively with WVC frequency, highlighting complexities in the effectiveness of preventative measures. For effective mitigation and prevention of WVCs, comprehensive strategies considering species traits, seasonality, and road types should be implemented Full article

Connectivity between protected areas is necessary to prevent habitat fragmentation. Biodiverse countries like Tanzania craft legislation to promote habitat connectivity via the creation of ecological corridors, but their viability for wildlife often remains unknown. We therefore develop a scalable and replicable approach to assess and monitor multispecies corridor viability using geospatial modeling and field data. We apply and test the approach in the Makuyuni study area: an unprotected ecological corridor connecting Tarangire National Park to Essmingor mountain, Makuyuni Wildlife Park and Mto Wa Mbu Game Controlled Area. We analyzed the viability of Makuyuni as an ecological corridor by creating and validating a geospatial least-cost corridor model with field observations of wildlife and livestock. We created the model from publicly available spatial datasets augmented with manual digitization of pastoral homesteads (bomas). The least-cost corridor model identified two likely pathways for wildlife, confirmed and validated with field observations. Locations with low least-cost values were significantly correlated with more wildlife observations (Spearman’s rho = −0.448, p = 0.002). Our findings suggest that Makuyuni is a viable ecological corridor threatened by development and land use change. Our methodology presents a replicable approach for both monitoring Makuyuni and assessing corridor viability more generally. The incorporation of manually digitized homesteads (bomas) and field-based livestock observations makes corridor assessment more robust by taking into account pastoral land uses that are often missing in land cover maps. Integration of geospatial analysis and field observations is key for the robust identification of corridors for habitat connectivity. Full article

The epidemic of avian influenza outbreaks, especially high-pathogenicity avian influenza (HPAI), which causes respiratory disease and death, is a disaster in poultry. The outbreak of HPAI in 2014–2015 caused the loss of 60 million chickens and turkeys. The most recent HPAI outbreak, ongoing since 2021, has led to the loss of over 50 million chickens so far in the US and Canada. Farm biosecurity management practices have been used to prevent the spread of the virus. However, existing practices related to controlling the transmission of the virus through wild birds, especially waterfowl, are limited. For instance, ducks were considered hosts of avian influenza viruses in many past outbreaks. The objectives of this study were to develop a machine vision framework for tracking wild birds and test the performance of deep learning models in the detection of wild birds on poultry farms. A deep learning framework based on computer vision was designed and applied to the monitoring of wild birds. A night vision camera was used to collect data on wild bird near poultry farms. In the data, there were two main wild birds: the gadwall and brown thrasher. More than 6000 pictures were extracted through random video selection and applied in the training and testing processes. An overall precision of 0.95 ([email protected]) was reached by the model. The model is capable of automatic and real-time detection of wild birds. Missed detection mainly came from occlusion because the wild birds tended to hide in grass. Future research could be focused on applying the model to alert to the risk of wild birds and combining it with unmanned aerial vehicles to drive out detected wild birds. Full article

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. Full article

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. Full article

There is a need to reduce human–wildlife conflicts in the area around Liupanshan Nature Reserve in Ningxia Hui Autonomous Region, China. This study investigated the financial losses caused by wild boar and their causes. A questionnaire investigation (n = 135) and a field test were conducted, which included 108 sample lines and 97 infrared cameras. A principal component analysis and generalised linear model was used to analyse the importance of the effect of the factors on wild boar damage. Based on an estimate of 17,049 wild boars in the study area, we found that in the agricultural land owned by the residents, the boar density of each county and distance from the village to the nature reserve were the most significant factors that affected crop damage. Then, financial losses in spring, summer, and autumn had a moderate effect on financial loss, and the crop type had the lowest effect. We recommend reducing the wild boar population by increasing leisure hunting and the number of leopards. Additionally, a focus on farmland protection is a practical way to prevent wild boar invasions. Meanwhile, it is also necessary to conduct long-term monitoring of wild boar population status and manage the relationship between the government, research teams, and local people to more efficiently and comprehensively reduce conflicts between humans and wild boars. Full article

Antimicrobial resistance (AMR) surveillance in fecal Escherichia coli isolates from wildlife is crucial for monitoring the spread of this microorganism in the environment and for developing effective AMR control strategies. Wildlife can act as carriers of AMR bacteria and spread them to other wildlife, domestic animals, and humans; thus, they have public health implications. A total of 128 Escherichia coli isolates were obtained from 66 of 217 fecal samples obtained from different wild animals using media without antibiotic supplementation. Antibiograms were performed for 17 antibiotics to determine the phenotypic resistance profile in these isolates. Extended-spectrum β-lactamase (ESBL) production was tested using the double-disc synergy test, and 29 E. coli strains were selected for whole genome sequencing. In total, 22.1% of the wild animals tested carried multidrug-resistant E. coli isolates, and 0.93% (2/217) of these wild animals carried E. coli isolates with ESBL-encoding genes (bla_CTX-M-65, bla_CTX-M-55, and bla_EC-1982). The E. coli isolates showed the highest resistance rates to ampicillin and were fully susceptible to amikacin, meropenem, ertapenem, and imipenem. Multiple resistance and virulence genes were detected, as well as different plasmids. The relatively high frequency of multidrug-resistant E. coli isolates in wildlife, with some of them being ESBL producers, raises some concern regarding the potential transmission of antibiotic-resistant bacteria among these animals. Gaining insights into antibiotic resistance patterns in wildlife can be vital in shaping conservation initiatives and developing effective strategies for responsible antibiotic use. Full article

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19, has been found to infect various domestic and wild animal species. In this study, convenience serum samples from 575 bison, 180 elk, and 147 samples from various wildlife species collected between 2020 and 2023 from several regions in the United States were analyzed for the presence of SARS-CoV-2-specific antibodies. Two commercial ELISA assays based on the inhibition of the SARS-CoV-2 receptor-binding domain (sVNT) or the nucleocapsid protein (N-ELISA) of SARS-CoV-2 were used. Positive samples from the sVNT were additionally evaluated using a conventional virus neutralization test (VNT). Our results indicated that 1.2% of bison, 2.2% of elk, and 4.1% of the other wildlife species serum samples were seropositive in the sVNT, whereas 4.2% of bison, 3.3% of elk, and 1.4% of the other captive wildlife species serum samples tested positive by the N-ELISA. Among the sVNT serum samples, two samples from bison, one sample from elk, and five serum samples from other wildlife species (one cheetah, one gorilla, two lions, and one hippopotamus) had neutralizing antibody titers in the VNT, indicating these species are susceptible to SARS-CoV-2 infection. These findings highlight the importance of broad surveillance efforts for the effective monitoring of SARS-CoV-2 in non-human hosts. Full article

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. Full article

This research aimed to apply a risk management methodology to multiple surface water sources in urban and rural areas of a developing country. The applied methodology enabled the identification of hazards, classification, and the prioritization of risks at 21 collection points in the rural area and 9 collection points in the urban area. Both rural and urban areas exhibited common events with a high-risk level, such as human access (100% in urban areas and 90% in rural areas), climatic events, and inadequate collection structures (100% of points in both urban and rural areas). However, rural areas presented specific risks associated with animal husbandry (70% of points with high risk), a lack of monitoring, limited infrastructure (30% of points with high risk), and wildlife, including birds and worms (50% of points with high risk in rural areas and 10% in urban points). On the other hand, urban areas faced challenges related to vandalism and sabotage (high risk in 40% of points). Understanding these similarities and differences permits integrated risk management among the various stakeholders who can contribute to risk management within a watershed. Full article

Urban natural parks represent a remarkable concept that evokes the coexistence of human habitation with a wild environment, and the associated interactions between human and natural territories. In this context, urban noise infringes upon the natural soundscape, leading to various consequences for both realms. This study seeks to characterize the impact of anthropic noise levels on biodiversity in the urban natural Văcărești Park (Bucharest, Romania), utilizing on-site measurements and software simulation techniques. The study seeks to develop a method for evaluating integrative strategies to mitigate the impact of traffic noise on wildlife in an urban wild park, without addressing the specific effects of noise on the perception and communication of individual species. By calibrating field measurements with laboratory results, a more reliable data set will be used to identify areas where the biophonic environment is impacted by anthropogenic noise. Since human-generated noise in an urban natural park predominantly originates from road traffic and industrial sites, managing traffic noise and its propagation pathways could substantially improve the park’s soundscape. Additionally, this study will apply software simulations for noise reduction strategies, such as vegetation planting and earthen embankments, to obtain suitable solutions and propose plausible and effective actions to authorities for improving the biophonic environment. This research could also serve as the basis for long-term monitoring, allowing for the assessment of the evolution and impact of implemented measures over time. Full article