Preventive Veterinary Medicine

Highly pathogenic avian influenza (HPAI) is among the most significant disease threats to poultry worldwide. Modelling challenges, where independent teams address a common epidemiological scenario, can support the development of response capabilities and strengthen communication between modellers and risk managers. Despite the established benefits of these exercises, they remain uncommon in animal health, and none have previously addressed avian influenza. Here Jolly Island, an artificial landscape of 9160 poultry farms with five production types, was developed and seeded with stochastic HPAI epidemics that ran their course over a three-to-four month period. From January through April 2026, 45 registered teams across five continents investigated a selected synthetic epidemic, producing probabilistic forecasts, inferring transmission drivers, and evaluating counterfactual control policies. Here the objectives, design choices, and data generation of the HPAI Modelling Challenge are described in full, covering transmission processes, surveillance system operations, and control strategy implementation, along with the selected epidemic trajectory, the narrative situation reports supplied to the teams at each phase, and the complete phase-specific datasets released to them. As the opening paper of this special issue, here is provided the foundation upon which all subsequent contributions, where teams document their approaches and analyses, are built.

Background: Bovine respiratory disease (BRD) is a major cause of antimicrobial use in cattle, raising concerns about antimicrobial resistance (AMR). This systematic review estimated pooled AMR proportions in BRD pathogens: Mannheimia haemolytica, Pasteurella multocida, and Histophilus somni, and assessed temporal and geographic trends in resistance. Methods: Five antimicrobials commonly used for BRD treatment were evaluated: penicillin G (PEN), tetracycline (TET), florfenicol (FLO), tulathromycin (TUL), and enrofloxacin (ENR). Following PRISMA guidelines, 1,097 records were screened and 24 studies met inclusion criteria. Study quality and risk of bias were assessed, and random-effects meta-analyses estimated pooled proportions of resistant isolates by antimicrobial, continent and period. Results: A total of 13,949 bacterial isolates were analysed including P. multocida (5,615), M. haemolytica (5,585), and H. somni (2,749). TET resistance was highest across all pathogens (12-30%), followed by TUL (6-11%), PEN (4-12%), FLO (0.5-1.6%) and ENR (0.2-1.1%). TET resistance in M. haemolytica and P. multocida, and FLO resistance in P. multocida were significantly more frequent in North America than in Europe (p[≤]0.05). TUL resistance increased significantly in P. multocida after 2013 (p=0.038), with similar non significant trends in M. haemolytica and H. somni. FLO resistance decreased significantly in H. somni after 2013 (p=0.013). Egger's test identified significant funnel plot asymmetry for ENR resistance in H. somni and PEN resistance in M. haemolytica, suggesting possible small study effects. Conclusion: These results offer insights into distribution of phenotypic resistance across pathogens and regions, informing BRD antimicrobial treatment guidelines and One-Health antimicrobial stewardship aimed at preserving the effectiveness of medically important antimicrobials.

The emergence of Highly Pathogenic Avian Influenza (HPAI 2.3.4.4b) in dairy herds in 2024 across 19 states in the United States of America has raised concerns regarding the potential national and global zoonotic impact. All recent modeling efforts implemented homogenous cattle-to-cattle (both intra and inter-herd) transmission models which did not capture the real-world heterogeneity in mixing of animals, individual variations in susceptibility and infectiousness and clinical incidences across pens and lactation groups. The aim of this study was to develop a heterogenous transmission model to estimate the epidemiological parameters for intra-herd HPAI transmission on Californian dairies. We developed a validated stochastic agent-based model to estimate the epidemiological parameters for intra-herd HPAI transmission on California dairies. The hierarchical agent-based model also parameterized stochastic cattle movements within-herd to simulate real dairy management practices. A cow-level SEIR transmission approach was assumed during the outbreak. A novel Bayesian Optimizer with Gaussian Process (BO-GP) was fitted to the agent-based model for validation which converged within 25-40 iterations (out of 100 per farm) with minimal loss over two distinct error metrics, namely, Poisson loss function and temporal distance metric. Our optimized simulations estimated an average R0 was around 10.7-10.8 across all farms within the first 15 days of observed outbreak on four dairy farms with a mean effective transmission rate of 4.5% per contact between susceptible and infectious cows within each pen. Our model demonstrated that the movement of cows between pens ensured localized clusters of outbreaks within the sub-herds (pen population) that prolonged the overall outbreak within farms. We estimated the total duration of infection between 14.5 and 28 days, which is higher than the estimates from the homogenous models. With an integrated hierarchical agent-based model combined with Bayesian approximation, we produced actionable insights on the epidemiology of intra-farm spread of HPAI within cow herds, thereby guiding both future model development and applied disease control strategy.

Neoehrlichia mikurensis is widely detected in tick populations across Europe, yet its host range beyond small rodents remains poorly understood. Large wild mammals are frequently exposed to tick vectors, but their role in the transmission cycle of this bacterium is unclear. Wild boars (Sus scrofa) are among the most abundant large mammals in southern Sweden and commonly inhabit tick-infested environments, making them a potentially relevant host species. In this study, spleen samples from 87 wild boars collected between 2023 and 2025 across southern and central Sweden were analysed for the presence of N. mikurensis DNA using quantitative polymerase chain reaction. N. mikurensis DNA was not detected in any of the analysed samples, despite sampling in regions where the bacterium is known to be prevalent in ticks. These results indicate that wild boars may have a limited role in the maintenance of N. mikurensis in Sweden, although the possibility of rare or transient infection cannot be excluded. Despite being based on negative findings, this study contributes to a more comprehensive understanding of the host range of N. mikurensis and highlights the importance of continued evaluation of wildlife species in the transmission dynamics of tick-borne pathogens.

Bangladesh's agricultural sector remains heavily dependent on pesticides and chemical inputs, yet limited attention has been paid to how farmers' knowledge of occupational health and environmental sustainability translates into actual practice across the full agricultural value chain. This study aimed to assess the knowledge and practices of farmers in Pabna District, Bangladesh, regarding occupational health, pesticide handling, environmentally sustainable farming, and market sanitation. A cross-sectional survey was conducted among 301 farmers in 2024 using a structured questionnaire covering socio-demographic characteristics, sustainable farming knowledge and adoption, soil testing and organic farming practices, pesticide handling and occupational health and safety behaviours, post-harvest waste management, and market sanitation conditions. Data were analysed using descriptive statistics. The majority of respondents were male (87.04%) and had no formal education (63.46%). Although most farmers reported knowledge of environmentally sustainable farming practices (97.70%) and pesticide label instructions (98.00%), corresponding safety practices were poor: only 10.63% reported wearing any personal protective equipment and 1.33% reported using a mask during pesticide application. Eye irritation was the most commonly self-reported health symptom following pesticide exposure (92.69%). Market sanitation infrastructure was similarly inadequate, with 89.00% of respondents reporting an absence of sanitary toilets. These findings indicate a consistent gap between farmers' knowledge and their actual practices across multiple domains of the agri-food system. Strengthening extension services, delivering practical hands-on occupational safety training, and investing in market sanitation infrastructure are recommended to close this gap and support safer, more sustainable farming practices in Bangladesh.

Understanding the transmission and persistence of zoonoses in wild reservoirs is crucial for outbreak prevention, demographic assessment, and conservation planning. Host demography, ecology, behaviour and within-host viral kinetics together underpin transmission dynamics but are rarely united within epidemiological models. Here we characterised the transmission dynamics of European bat lyssavirus 2 (EBLV-2) infection dynamics by uniting a long-term study (2010-2018) of the ecology of five greater mouse-eared bat (Myotis myotis) colonies in France (~2700 marked individuals) with diagnostic evidence of virus circulation. Although no lyssavirus was detected in any of the 442 saliva samples tested, seropositivity showed synchronised peaks across colonies (0-100% annually), with the highest seroprevalence in 2015-2017, but remaining low otherwise. Fitting Bayesian compartmental epidemiological models, we found evidence that shifts in EBLV-2 prevalence were primarily driven by the gregarious behaviour of populations during the mating season and age-dependent variation in transmission. The duration of maternal immunity emerged as a key, and still empirically unknown factor, which shaped whether EBLV-2 maintenance was enzootic or required an external force of infection. Lyssavirus outbreaks in M. myotis were associated with few deaths overall and had a negligible impact on population dynamics. These findings underscore the value of integrating ecological and individual-level data with mechanistic modelling to enhance our understanding and management of zoonotic pathogens in wildlife.

Trail cameras have become ubiquitous tools for ecological data collection over recent decades. Despite progress in the development of automated algorithms and artificial intelligence for image classification, our ability to process large volumes of data remain limited by the need for trained human observers to make refined judgements. We provide guidance on placement of trail cameras for observing Asian elephants (Elephas maximus) and outline a protocol for training and testing naive human observers in performing image classifications (age/sex class and group composition) that cannot yet be automated. This process can be used to develop a high-throughput workflow capable of extracting useful data from large volumes of images. Our training material consisted of 14,007 images collected from 6 trail cameras around Udawalawe National Park in Sri Lanka from 2017-2019. In the first stage, expert observers (n=3) trained a group of inexperienced participants (n=4), who engaged in an iterative process to develop a protocol document. The document was then tested on a second set of subjects (n=6) each of whom classified 350 test images in four separate sequential batches using quantitative measures of precision and accuracy. The test set was sampled from 54,435 images from an additional 25 cameras. When compared to expert observers, they achieved a fair level of precision (Fleiss' kappa = 0.247) and 82.6% accuracy. Our approach can usefully be extended to other species and contexts.

This study examined the spatial distribution of potential human encounters with dangerous large mammals in and adjacent to the Ifakara-Lupiro-Mang'ula Wildlife Management Area (ILUMA WMA) and Nyerere National Park (NNP) in southern Tanzania. Using published survey data collected over one year, this secondary analysis focused on four potentially dangerous large mammals: elephant (Loxodonta africana), buffalo (Syncerus caffer), hippopotamus (Hippopotamus amphibius) and hyena (Crocuta crocuta). Potential encounter risk was estimated using overlap indices based on co-occurrence of signs of wildlife and human activities. Potential encounter risk was found to be concentrated around readily identifiable foci within the study area and differed between species and distinguishable human livelihood activities. Potential for encountering the two most apparently important dangerous animals, elephant and buffalo, was closely associated with authorized fishing camps within the ILUMA WMA and the ecotone between substantially disturbed and largely intact natural land cover. The greatest potential for encountering hippopotamus occurred largely around two river beds, while hyena encounter potential was more closely associated with established villages and converted lands close to the western boundary of the ILUMA WMA. These new maps of hazard and risk suggest that effective human-wildlife conflict (HWC) mitigation in this WMA is unlikely to arise from any stand-alone intervention, but from targeted actions that reduce spatiotemporal overlap between people and wildlife by keeping them apart and/or protecting them against each other. Overall, this study demonstrates that mapping potential encounter risk based on spatiotemporal overlap of indirect activity indicators like tracks, spoor and signs provides a proactive and statistically powerful way to identify where hazardous encounters are most likely to occur, offering considerable advantages over retrospective assessments of much sparser observations of actual realised conflict events after they have already occurred. This new spatial risk mapping approach provides a framework for shifting management from reactive responses after conflict occurs towards proactive prevention.

Mycobacterium bovis causes tuberculosis (TB) in cattle, badgers (Meles meles), other wildlife, and humans, posing major animal and public health challenges in Britain and Ireland. Shared genotypes between cattle and badgers have been reported at regional scales, but whether this concordance holds at finer spatial scales, where control interventions operate, and whether it varies by genotype combination, remains unknown. Resolving this is critical for interpreting cross-species transmission risk and designing targeted control measures. We analyzed 1,248 M. bovis isolates from cattle and badgers (Northern Ireland, 1986-2018), including samples from a small, densely sampled intervention research area, using Multi-Locus Variable Number of Tandem Repeats Analysis (MLVA) and kernel discriminant analysis (KDA) across multiple spatial scales (25-95% density contours). We identified 36 MLVA types with four shared between species and one (MLV06) endemic to the area. Spatial concordance varied by type combination and scale: MLVA004-MLVA297 showed strong, bidirectional, scale-robust concordance; MLVA006-MLVA297 was concordant only at fine spatial scales; MLVA004-MLVA006 showed weak, asymmetric concordance; and no concordance was detected when all three types were analysed together. Our findings reveal spatial concordance between shared M. bovis genotypes varied by genotype combination and scale, indicating genotype-specific dynamics within this multi-host system.

Antimicrobial resistance (AMR) is a critical One Health challenge at the interface of agriculture, the environment and human health. In Ireland, cattle production systems generate large volumes of manure and slurry, which are routinely applied to land. However, data on the prevalence and environmental persistence of clinically important resistance phenotypes remain limited. This study investigated the occurrence, characterisation and persistence of extended-spectrum {beta}-lactamase (ESBL) and carbapenemase-producing Enterobacterales (CPE), as well as fluoroquinolone-resistant bacteria on Irish cattle farms. A total of 450 pooled bovine faecal samples were collected from farms across 25 counties, screened using selective culture, antimicrobial susceptibility testing, and MALDI-TOF MS identification. Carbapenemase activity in presumptive CPE isolates was confirmed using phenotypic and biochemical assays, including MALDI-TOF MBT STAR analysis to demonstrate carbapenem hydrolysis. In parallel, a controlled persistence study evaluated the survival of phenotypically resistant ESBL and CPE bacteria in bovine slurry under simulated seasonal conditions. Overall, 29.6% of farms were positive for ESBL and/or CPE, with ESBL only resistance most prevalent (13.7%), followed by combined ESBL and CPE phenotypes (7.5%). Fluoroquinolone resistance was frequently detected and commonly co-occurred with {beta}-lactam resistance, indicating multidrug resistance. Escherichia coli was the dominant carrier of both ESBL and carbapenemase phenotypes. Persistence experiments demonstrated sustained phenotypic resistance in slurry for up to nine months under simulated winter conditions and up to three months during simulated summer conditions, with resistance becoming undetectable following extended verification. These findings demonstrate that clinically important AMR phenotypes are embedded within Irish cattle production systems and can persist in agricultural waste streams, highlighting slurry as a significant environmental reservoir with implications for environmental dissemination and One Health surveillance.

Soil degradation is a widespread challenge that requires a broad response at the individual farm level. To ensure effectivity, the practices should be tailored to the farm context: land manager objectives and farm specific challenges. These have however been difficult to quantify. Here we demonstrate that a workable farm context can be created based on a value survey, open satellite and soil data, and published models for vegetation gross primary productivity and soil erosion. Based on the findings, despite individual differences, farmers value profitability and operational efficiency, but also biodiversity and soil health. At least the regenerative farmers surveyed also value working for the greater good more than maintaining tradition or power. In spite of wide differences in farm production orientation, we also found that each farm also had a broad variation in individual fields GPP. Most fields have a stable GPP level, which is either high or low, and that there is a 2-3-fold difference between the weakest and best producing fields indicating the potential for improving GPP by improving the growing conditions on currently weak fields. In addition, soil loss was found to be highly concentrated in critical source areas, where 10% of the field area contributed to 50% of the soil loss. Overall, open data can be linked to modelling workflows to rapidly produce a decision-making context for farmers. This facilitates benchmarking and co-learning as well as enables land managers and advisors to identify the farm context for planning effective responses to soil degradation.

The potential of alternative agricultural systems, such as organic or conservation agriculture, for biodiversity restoration has been widely investigated. However, variability of practices within each of these two categories of agricultural system has rarely been accounted for. Furthermore, these systems have most often been studied separately, isolating either organic or conservation agriculture (itself often reduced to a soil tillage reduction), and rarely analyzing these systems side by side. This study attempts to disentangle these multiple interrelationships by sampling carabid beetles in a network of 48 commercial Belgian farms including organic agriculture (OA), conservation agriculture (CA), farms combining the two approaches (OCA) and farms applying conventional practices (CVA). In each farm, we sampled carabids using pitfall and emergence traps in one winter cereal field and collected a detailed description of crop management practices. We found that community composition was primarily structured by seasonality early in the season (March, April) followed by convergence (May, June) driven by the aggregation of the most dominant species. Based on agroecosystem classification, organic certification (i.e., the absence of synthetic pesticide use) exerted a twofold stronger effect than application of conservation agriculture principles (i.e., reduced or no soil tillage) on carabid beetle community composition. Notably, OCA supported carabid assemblages that were structurally distinct from those of CA and CVA. Furthermore, carabid communities associated with OA differed significantly in composition from those of CVA. Observed and estimated species richness were also significantly higher in OCA than in CA and CVA, while OA differed from CVA only for observed richness. Beyond differences among agricultural systems, community responses were associated with specific management practices, which explained nearly a quarter of the compositional variation with significant influence of the duration since organic conversion and the frequency of synthetic pesticide application. Carabid abundances were negatively affected by synthetic pesticides and moldboard ploughing, whereas richness was more strongly related to the time since conversion to OA, cover crops, and organic amendments. Overall, these results position synthetic inputs and time since conversion as the main filters over carabid communities. Our study highlights the importance of jointly considering system level analyses and detailed management practices to better understand the complex relationships between agricultural practices and carabid communities.

Brucellosis is a globally distributed and widely under-appreciated zoonotic bacterial disease that causes infertility and abortion in livestock and febrile illness in humans, sometimes with debilitating sequelae. Brucellosis incidence estimates typically focus on acute presentations and range from 50 to 100 per 100,000 people per year for endemic settings, showing marked variation within and between countries. Estimating brucellosis incidence in low-resource settings is complicated by lack of access to and utilisation of health care and high-quality diagnostic testing for many people in rural communities. To estimate brucellosis incidence in a pastoralist population in northern Tanzania, we developed a Bayesian model that integrates multiple data sources including diagnostic test results from a hospital-based prevalence study performed in 2016-2017, test sensitivity and specificity, and data from a community-based healthcare utilisation survey in 2019-2020. We show that this population experienced a high incidence of brucellosis with wide uncertainty, with estimates of 387.6 (95% HPDI: 103.3 - 903.5) illnesses per 100,000 people per year for the total population, 199.7 (95% HPDI: 16.2 - 584.6) for individuals aged >15 years, and 632.4 (95% HPDI: 180.1 - 1379.4) for children aged [≤]15 years. These incidence estimates were among the highest in the world to date. The wide density intervals for these incidence estimates reflect uncertainty in the underlying parameter values used. Sensitivity analyses show that healthcare utilisation influenced incidence estimates the most. This result indicates that accounting for low levels of healthcare utilisation is essential to avoid under-estimating disease incidence. Our findings illustrate that brucellosis is a frequent cause of illness in this population. Increased access to treatment, prevention, and accurate diagnostic testing would greatly improve understanding of brucellosis epidemiology and the health of many people living in similar at-risk communities.

Recent zoonotic coronavirus outbreaks have sparked an interest in understanding coronaviruses circulating in animal reservoirs. While bats are recognised as major reservoirs for these viruses, coronaviruses have also been detected in a wide range of terrestrial small mammals, particularly rodents. However, coronavirus diversity in these mammals, specifically in Europe, is still poorly characterised. In this study, we detected and characterised coronaviruses in terrestrial small mammals and bats from Belgium, France, Germany, and Ireland. We screened tissue samples from rodents and shrews and environmental bat guano using pan-coronavirus real-time and conventional PCRs, with positive results confirmed by sanger sequencing. Among 2667 bat and 1152 terrestrial mammal samples, we detected 59 positive samples, 36 from bats and 23 from rodents, from which we recovered 60 coronavirus sequences, spanning both Alphacoronavirus and Betacoronavirus lineages. Through next generation sequencing we identified one Pipistrellus pygmaeus bat guano sample carrying two distinct alphacoronaviruses, representing Nyctacovirus and Pedacovirus subgenera. Among rodents, most coronavirus-positive samples originated from a zoo in Belgium, where several CoV lineages co-circulated. The identified rodent coronaviruses clustered among previously described rodent-associated lineages, some of which also include human and other mammal-derived coronavirus sequences. Consistent with previous studies, we found sarbecoviruses in horseshoe bats (particularly Rhinolophus ferrumequinum and R. hipposideros) and merbecoviruses in Plecotus auritus bats, and these viruses are distantly related to the human coronaviruses of the respective families. Similar to bat coronaviruses, some rodent coronaviruses strains seemed to be association with specific rodent species. In conclusion, our study enhances the understanding of coronaviruses in rodents and bats, revealing potential hotspots for these viruses and their implication for human and animal health. These findings underscore the necessity for continued surveillance of coronaviruses in wildlife reservoirs to mitigate future zoonotic spillover risks.

Information on Western bongo (Tragelaphus eurycerus eurycerus) across its range remains scarce and fragmented, leaving the status of populations poorly understood, limiting effective conservation planning. We conducted three camera-trap surveys across Semuliki National Park, Uganda, between October 2019 and August 2020 (90 camera locations; 11,640 camera days), during which we detected the only currently known population of Western bongo in East Africa. We assessed the status of this population using data from the final survey (March-August 2020), when bongos were detected, applying an individual identification system adapted from Eastern bongo (Tragelaphus eurycerus isaaci) and spatially explicit capture-recapture (SECR) modelling to estimate density and abundance. To reflect known aspects of bongo ecology, we incorporated sex and habitat covariates derived from remotely sensed data as predictors of density. Bongos were detected on 17 occasions, and we identified 18 individuals (11 females and 7 males), of which seven immature individuals (5 females and 2 males). The top-ranking SECR model included the tasseled cap greenness index and sex as predictors of density. The model estimated a population size of 56.49 individuals (SE = 7.69; 95% CI = 41.43-71.56), with an estimated density of 0.58 individuals km-2 (SE = 0.171; 95% CI = 0.32-1.09) within Semuliki National Park. These results provide the first quantitative baseline for this population and suggest the presence of an established breeding population, highlighting the need for structured monitoring to support its long-term conservation.

Land use change is widely recognized as a major driver of emerging zoonotic diseases. Yet, while forest plantations are expanding worldwide, their consequences on the circulation of infectious agents in wildlife remain poorly understood. We investigated how native pine plantations and broadleaved habitats shape rodent-associated infectious hazard in the generalist wood mouse (Apodemus sylvaticus), in southwestern France. Using a multi-season survey, we screened 424 wood mice for a broad range of infectious taxa and compared infection probability and infected-host abundance among pine plantation edges, broadleaved hedgerows embedded within pine plantations and broadleaved forests. Using a combination of linear mixed model and joint species distribution modelling, we tested whether infectious hazard differed between pine plantations and broadleaved forests, whether broadleaved hedgerows shifted these patterns towards those observed in broadleaved forests, and how these habitat-related patterns varied across seasons. We detected 22 potentially infectious taxa, including nine recognized zoonotic bacteria. Season strongly influenced infectious patterns, while habitat effects were taxa and season dependent. In spring, pine plantation edges increased infection probability for Bartonella grahamii and Neoerhlichia mikurensis compared to broadleaved forest, but their strong negative effect on wood mouse abundance resulted in consistently lower infected-host abundance for most infectious taxa. Broadleaved hedgerows showed distinct and season-dependent patterns with infectious profiles resembling those of pine edges in spring, but with higher infectious hazard than both other habitats for several taxa in autumn. Our results highlight that habitat effects on infectious hazard cannot be inferred from infection probability alone. Pine plantations may reduce rodent-associated zoonotic hazard through their strong negative effect on host abundance, despite increasing infection probability for some agents. Forest diversification and restoration initiatives, such as broadleaved hedgerows here, may have unforeseen epidemiological consequences, underscoring the need to integrate disease ecology into forest management and restoration to anticipate variation in zoonotic risks, while maximizing ecological benefits.

Importance: Coccidioidomycosis, also known as Valley fever, is a serious fungal disease caused by inhaling Coccidioides spores aerosolized from the soil. Despite rising public health concern, the distribution and incidence of coccidioidomycosis in the US remain poorly characterized as not all states are required to report cases, and case under-ascertainment is presumed to be high, particularly outside of the endemic region. Objective: To estimate monthly, county-level coccidioidomycosis incidence across the US while accounting for widespread under-ascertainment. Design, Setting, and Participants: We integrated multisource data, including human cases diagnosed in electronic health records, serological testing of dogs, and ecological and sociodemographic predictors, in a machine learning model estimating monthly, county-level coccidioidomycosis incidence. The model was trained on surveillance data from Arizona and California from 2013 to 2023, then applied to predict incidence in states across the contiguous US over the same time period. Main Outcomes and Measures: Incidence of coccidioidomycosis at the monthly county level. Results: The model predicted coccidioidomycosis incidence from 2013 to 2023 for 3,108 counties from all states in the contiguous US. The model identified 60 counties in non-reporting states where predicted incidence was [≥]2 cases per 100,000 population (considered moderate to high incidence). In states with mandatory reporting outside of Arizona and California, the model predicted county-level incidence rates that were 28% higher (95% bootstrap interval, 0.1%-69.8%) than those captured in national surveillance, equating to a total of 608 potentially missed cases per year (95% bootstrap interval, 340-964). Conclusions and Relevance: In this cross-sectional study, the model quantified case under-ascertainment for coccidioidomycosis relative to Arizona and California, and highlighted the potential for alternative data sources to fill in gaps in mandatory reporting. Estimating incidence while accounting for under-ascertainment and non-reporting may help efforts to improve public and physician awareness and to detect emergence in new regions.

Motion-activated camera traps are an effective tool for monitoring animals, but a single study can produce millions of images, which must be analyzed before they yield data. The burden of analyzing them has led many teams to automate the work with computer vision. These models lose accuracy on images unlike those they were trained on, however, and a general model may not use the categories a project needs. Adapting a model to a project usually requires at least one of the following: many labeled images per species, programming expertise, a graphics processing unit (GPU), or uploading images to a remote server. I describe WildObserve, a desktop application that adapts to each project without any of these requirements. SpeciesNet suggests a species for each animal that MegaDetector finds, and WildObserve keeps SpeciesNet's feature vector for each one. A project model, a logistic regression on those features, then retrains in about a second as the reviewer labels, learning the project's own species and names. From the reviewed records, and optionally the suggestions, WildObserve also fits occupancy and abundance models. Images stay on the user's computer, and every record says which model suggested its species and who confirmed it. WildObserve processed 8.1 images/s on an Apple M1 Pro laptop. On Oregon Critters, a dataset released after SpeciesNet was trained, the project model's accuracy passed that of SpeciesNet with ten labels per species (balanced accuracy 0.783 against 0.751). I also tested occupancy on 63 cameras in Malawi that SpeciesNet had not seen. With 5% of the images reviewed and the project model's suggestions counted for the rest, species' occupancy estimates differed from those of a full review by 0.11 on average. From the reviewed images alone, they differed by 0.41. WildObserve takes a project from images to species records in its own categories and on to occupancy estimates, without writing code, needing a GPU, or sending images elsewhere.

Stink bugs (Hemiptera Pentatomidae) are major agricultural pests worldwide, including both native and invasive species. In France, for instance, Halyomorpha halys has rapidly expanded its range, affecting key crops such as hazelnuts, apples, and vegetables. Meanwhile, several native, polyphagous species - Nezara viridula, Palomena prasina and, to a lesser extent, Graphosoma italicum - are re-emerging and impacting diverse crops because of changes in agricultural practices and climatic conditions. Egg parasitoids represent promising biological control agents for these pests, be it through conservation, augmentation or introduction biological control. However, their actual potential is not precisely documented due to a lack of ambitious surveys and possible taxonomic issues. This study aims to address this gap by compiling results obtained from various of egg parasitism surveys of Pentatomidae in France and implementing a large-scale DNA Barcoding approach (2000 individuals) for the emerged parasitoids. Our results indicate a high diversity of egg parasitoids, and detected some taxonomic issues and molecular inconsistencies that warrant further study. Among the three families of egg parasitoids recovered (Encyrtidae, Eupelmide, and Scelionidae), Scelionidae was the most abundant and diverse with nine species of Trissolcus (including the two adventive species, Tr. japonicus and Tr. mitsukurii) and four Telenomus species (including two unidentified to species). Scelionidae dominated the parasitoid assemblages associated with most of the Pentatomidae host species, except for H. halys for which the eupelmid Anastatus bifasciatus was the most common species.

Reliable estimates of population size are fundamental to understanding wildlife population status and informing conservation. Obtaining such estimates remains challenging for unmarked species, particularly in tropical countries where logistical and financial constraints limit intensive monitoring. The endangered dhole (Cuon alpinus), a highly social canid without natural markings, is one such species for which robust population estimates remain scarce. We estimated dhole density in the Valparai plateau, a human-dominated agroforest-forest mosaic in India's Western Ghats. We compared Spatially Explicit Capture-Recapture (SECR) models based on individual identification from fecal DNA and Space-to-Event (STE) models based on camera-trap detections. We additionally applied both approaches to individually identifiable leopards (Panthera pardus) to cross-validate STE versus SECR estimates. SECR estimated dhole density at 22 individuals per 100 sq.km, with STE estimates varying substantially depending on how the camera viewshed area was defined. STE estimates based on post hoc viewshed measurements, particularly after reducing the viewshed by 10%, were comparable to SECR estimates. In contrast, STE underestimated leopard density relative to SECR (7 versus 12 per 100 sq.km), although uncertainty intervals overlapped. Ecologically, our results indicate that human-use agroforest mosaics can support dhole densities comparable to, or exceeding, those reported from several Protected Areas, highlighting their potential importance for dhole conservation. More broadly, we argue that STE models may offer a practical and relatively inexpensive approach for baseline monitoring of dholes from camera-trap surveys, but their reliability depends strongly on accurate characterization of the effective viewshed and may vary across species and landscapes.