Animals

Effective animal monitoring is essential for assessing health, behavior, and environmental interactions, particularly in research and welfare contexts. This study presents a low-cost, open-source system designed for non-invasive monitoring of budgerigars (Melopsittacus undulatus), a small parrot species frequently used in animal behavior research. The system integrates a perch-based scale for voluntary weight measurement, a temperature sensor, and a camera for image capture, all controlled by a Raspberry Pi. By leveraging fine-tuned neural networks, the system achieves automated individual recognition with high accuracy, eliminating the need for invasive tagging methods. The modular design ensures accessibility, scalability, and minimal disturbance to the animals, while the accompanying software streamlines data collection, processing including labeling, and visualization. This approach provides a comprehensive solution for continuous monitoring, offering valuable insights for research and husbandry while prioritizing animal welfare.

The recent shift in Bangladesh from tradition backyard rearing system to modern commercial layer farming has made the birds immune to infectious diseases. Pediculosis, however, continues to pose a challenge in modern production system due to its invasive nature, often going unnoticed and neglected as it is typically non-lethal, yet capable of causing significant production losses. Lice infestation is a persistent threat in poultry production; however, its implications in battery-caged commercial layer hens in Bangladesh remain insufficiently characterized. The study aimed to identify the causative louse species and evaluate its associations with clinical pathology, hematological alteration and productive performance in 30 white-feathered (15 infested + 15 non-infested) and 30 brown-feathered (15 infested + 15 non-infested) laying birds from two commercial farms in Tangail. Morphological characterization confirmed the parasite as Menacanthus stamineus, distinguished by a dorsoventrally flattened body, parabolicallly rounded head wider than long, concealed club-shaped antennae, an oblong-oval abdomen with fine setae and three pairs of short legs each bearing paired claws. Infested birds exhibited consistent clinical pathology, including pale combs, petechial hemorrhages around the vent, severe feather damage with alopecic and exudative areas and incidence of irregular and broken-shelled eggs. Production performance analysis revealed significant reduction in hen-day egg production, egg weight, and feed intake, accompanied by significantly increased feed conversion ratios. Hematological evaluation demonstrated significantly reduced hemoglobin concentration, hematocrit and erythrocyte counts in infested hens, indicating mild anemia and compromised oxygen-carrying capacity. Collectively, pediculosis was strongly associated with lice-induced self-inflicted injury and cannibalism, systemic physiological stress, impaired erythropoiesis, reduced production efficiency and compromised welfare in caged laying hens. To best of our knowledge, it was the first integrative reports from Bangladesh documenting M. stramineus infestation in battery-caged commercial layer system with concurrent evidence of hematological disruption and measurable productivity losses, underscoring its epidemiological and economic significance and urgent need for targeted, evidence-based ectoparasite control strategies.

Object detection models based on deep learning are increasingly used to reduce the manual workload associated with the processing of video recordings in behavioural research, but reproducible workflows describing how ecologists can build such models themselves remain scarce. Here, we present a step-by-step workflow for constructing a species-specific detection model and apply it as a case study to the red-legged seriema (Cariama cristata), a widely distributed but poorly studied Neotropical bird. Using 60 videos recorded with camera traps as well as non-stop recording cameras at the campus of the Universidade Federal de Vicosa (Florestal, Minas Gerais, Brazil) between 2023 and 2026, we extracted and annotated 480 images to train a YOLO26-nano detection model. On the validation set, the model achieved a precision of 0.934 and a recall of 0.786 (mAP@50 = 0.870), while performance on the independent test set was slightly lower, with a precision of 0.865 and a recall of 0.740 (mAP@50 = 0.740). When applied to unseen footage, the model processed video at roughly 4.6 times real-time speed on a standard CPU and correctly classified all four test videos for the presence or absence of seriemas. Most missed detections involved seriemas that were distant, partially out of frame, or recorded with the lower-quality non-stop cameras. These results showed that a usable detection model can be built from a comparatively small curated dataset, and the accompanying workflow and code are intended to lower the barrier for ecologists seeking to apply object detection to their own study species.

Parasitic infection is one of the common health problems of livestock in Bangladesh. Due to the country's climate, heavy monsoon rainfall, low biosecurity in farms, and high humidity, along with presence of suitable vector organisms, gastrointestinal parasitism remains widespread in cattle and other livestock. The standard method of diagnosis is microscopic examination of fecal samples, but this depends on manual observation, which is time-consuming and can lead to human error, mainly because many parasite eggs look similar to each other and samples often contain contaminants that can be mistaken for eggs or cysts. In this study, we tried to apply the YOLOv8 deep learning model for automated detection of parasitic eggs and cysts from microscopic images of livestock fecal samples. Images of clinical cases were collected, annotated, and used to train the model in Python, with batch size 16, auto optimizer, learning rate 0.01, momentum 0.937 and weight decay 0.0005. Training was done using Google Colab, and the model was evaluated using precision, recall, F1-score, mAP50, and mAP50-95. The model achieved a precision of 56%, recall of 24%, F1-score of 33.6%, mAP50 of 33%, and mAP50-95 of 22%. The relatively low recall and F1-score indicate that the model still has considerable limitations, largely due to insufficient species-specific training data and presence of image artifacts. Underrepresentation of some parasite species, such as Trichuris spp., in the dataset also caused class imbalance, which affected the model's ability to detect these species reliably. Despite these limitations, the study indicates that YOLOv8 architecture has some potential to be used for detection of parasitic eggs and cysts from microscopic images, and that further work with larger and more balanced datasets may improve performance and applicability in veterinary diagnostics. Keywords: YOLOv8, livestock parasites, deep learning, microscopic image analysis, veterinary diagnostics, Bangladesh

Robotic herding offers a promising off-animal approach to controlling the spatial distribution of grazing livestock. Nevertheless, the habituation of cattle to herding stimuli remains a critical obstacle to its practical use. This study presents the first field investigation of behavioral responses of grazing cattle to herding by a quadruped robot. We compared the herding efficacy of a drone and a quadruped robot, and tracked daily changes in responsiveness during continuous herding. Trials were conducted in a 1.1-ha pasture with Japanese Black breeding cows from June to October 2023. The quadruped robot elicited stronger avoidance responses with shorter latencies than the drone, and herding was more successful with the robot. During consecutive daily herding with the robot, behavioral responsiveness declined progressively over the initial 5 days, at a markedly slower rate than previously reported for drone herding. Following a 24-day interruption, responsiveness partially increased, but declined rapidly again over the subsequent two days. These results indicate that the quadruped robot constitutes a more persistent aversive stimulus than a drone for grazing cattle, although habituation management strategies (such as diversifying stimuli or combining sensory modalities) will be necessary for sustained control of grazing distribution.

The maned wolf (Chrysocyon brachyurus) is a near-threatened canid species that suffers from gastrointestinal (GI) disease under human care. While the cause remains poorly understood, recent studies report that altered gut microbiota is linked to GI disease in the domestic dog. The goal of this study was to describe the relationship between gut bacteria and environmental factors, GI health, short chain fatty acid (SCFA) peaks, and genetic relatedness in maned wolves. Fresh fecal samples were collected twice from wolves (first collection n = 29; second collection n = 25) housed in six facilities in the United States. Fecal alpha-1 proteinase inhibitor (FA1PI), a biomarker for GI health in canids, and SCFA peaks were quantified. Information on diet, housing group and history of GI disease or inflammation was provided by the zoological facility for each wolf. We characterized gut microbiota using 16S rRNA gene amplicon sequencing with qPCR abundance corrections. We found that dietary fiber gross energy (GE), protein GE, and number of daily produce items influenced gut microbiota. Additionally, there was a negative relationship between dietary fiber GE and Fusobacterium sp. abundance, corroborating previous findings linking fiber to altered gut microbiome composition in canids. Lastly, fiber GE was negatively correlated with FA1PI, connecting fiber to GI health in the maned wolf. Findings from this study suggest that increasing dietary fiber GE changes the gut microbiome and may improve GI health in the maned wolf, highlighting important management decisions that can be considered to maintain a healthy zoo-managed maned wolf population. IMPORTANCEFindings from this study are important because gastrointestinal disease is a common health concern for maned wolves under human care, and the causes are not well understood. The current study found that diet, especially the amount of fiber gross energy, can affect gut bacteria and may support better gastrointestinal health. Diet alterations could be a practical tool for zoos to support management of gastrointestinal health issues in maned wolves. These findings also show the importance of carefully managing diets for a near-threatened species, since better gastrointestinal health may improve overall health and well-being and support reproduction. More research is needed to determine the best amount and type of fiber, but our results provide foundational information for improving care and supporting healthy zoo-managed maned wolf populations.

While hamsters are commonly housed in groups within pet shops in Japan, small cages are often thought to restrict their physical activity, leading to arguments that larger cages should be provided. Although previous research has investigated how cage size and running wheel availability influence activity levels in individually housed hamsters, no studies to date have examined these specific effects in a social housing context. Therefore, this study investigated how cage size and the presence of a running wheel affect the activity levels of individual hamsters using the group-housing conditions with five hamsters. Video recordings were captured for 24 hours across four distinct cage environments using a camera installed directly above each cage. From these recordings, the distances traveled on both the cage floor and the running wheel were calculated for each hamster and statistically analyzed. The results revealed that overall activity levels were significantly higher in cages equipped with a running wheel than in those without. Although cage size did not yield a statistically significant difference, a marginal trend toward higher activity in larger cages was observed.

Background: Flea infestation is an important companion-animal health problem and a prime source of zoonotic exposure. However, information on cat owners awareness and preventive behavior regarding fleas and flea-borne diseases (FBDs) in Bangladesh is limited. This study assessed the knowledge, attitudes and practices (KAP) of cat owners regarding flea infestation and flea-borne diseases in selected areas of central Bangladesh. Methods: A cross-sectional survey was conducted from November 2025 to July 2026 among 238 cat owners attending veterinary clinics in Savar, Dhamrai and Manikganj Upazila. Data were collected using a pretested structured questionnaire covering respondent characteristics, flea biology and transmission, perceived risks, and flea-prevention and control practices. Descriptive statistics were used to summarize the findings, and respondents were classified according to their overall KAP scores. Results: Of the respondents, 47.1% owned cats with outdoor access and 77.7% reported that their pets had previously experienced flea infestation. Although 65.5% used flea-control treatments, awareness of zoonotic and disease-transmission risks was limited. Only 14.7% recognized that fleas can parasitize humans, while 17.6% knew that fleas can transmit infectious agents. Overall, 83.2% of respondents had poor knowledge, whereas 69.7% demonstrated moderate attitudes and 63.9% showed moderate practices. Good knowledge and good practice were uncommon, accounting for only 3.8% and 4.6% of respondents, respectively. Veterinary recommendation was the most frequently reported motivation for using flea-control products. Conclusions: Cat owners attending in the surveyed areas frequently reported flea exposure and treatment use, but substantial gaps remained in their understanding of flea biology, zoonotic potential and comprehensive control. Targeted veterinary education emphasizing routine prevention, treatment of all susceptible household animals and environmental management is needed. These findings provide regional baseline evidence and should not be interpreted as nationally representative. Keywords: Bangladesh; cat owners; ectoparasites; flea-borne diseases; flea control; knowledge, attitudes and practices; One Health.

Abstract The gayal (Bos frontalis) is an endangered semi-domesticated bovine species renowned for its high-quality beef. However, its semi-feral lifestyle, ongoing habitat fragmentation, and extensive genetic introgression from sympatric local cattle have led to dramatic population decline and severe erosion of purebred genetic integrity, posing substantial challenges to its conservation and utilization. To address the urgent demand for rapid, non-invasive, and field-compatible germplasm identification, we developed an integrated artificial intelligence (AI) framework that predicts genomic admixture composition from external morphological images. We constructed a comprehensive dataset comprising 6,245 morphological images and matched genomic sequences from 52 gayals maintained at the Yunnan Provincial Gayal Conservation Farms. Following a preliminary evaluation of nine deep learning models, five were incorporated into a anatomical segment-based multi-modal pipeline, among which Inception_V3 delivered the optimal overall performance. To enhance simultaneous extraction of local fine-grained features and global structural information, we further designed an innovative HybridInceptionViT model by integrating the multi-scale Inception module with the Vision Transformer (ViT) framework. This hybrid model significantly outperformed the baseline Inception_V3, boosting the accuracy of phenotype-derived prediction against genomic admixture estimate from 69.69% to 87.87% (absolute error <15%). This study establishes a practical, low-cost "phenotype-to-genotype" tool for rapid on-site gayal germplasm screening, offering a scalable strategy for the conservation and breeding management of endangered livestock, and holds broad application prospects for agricultural and livestock production systems.

Insect proteins represent a promising and more sustainable alternative to soybean meal in swine nutrition. However, high dietary inclusion levels often result in reduced animal performance, indicating that factors other than crude protein concentration should be considered. This study evaluated the protein quality and degradability of feed-grade defatted black soldier fly (BSF) meal in comparison with a conventional organic protein core for post-weaning piglets. Protein content, amino acid composition, reactive lysine, Maillard reaction markers, buffering capacity, and protein hydrolysability were determined. Our results showed that defatted BSF meal exhibited a protein content and amino acid profile comparable to those of the protein core but contained substantially higher levels of free amino acids. Accounting for free amino acids reduced the estimated lysine damage from 18.4% to 10.4%, highlighting the importance of considering free amino acids when determining reactive lysine. In addition, BSF meal exhibited a higher buffering capacity and lower protein degradability than the protein core. These findings provide new insights into the nutritional limitations of feed-grade BSF meal and suggest that a more accurate assessment of reactive lysine, together with dietary strategies to reduce buffering capacity, may improve its utilization in post-weaning piglet diets.

African swine fever (ASF) in wild boar poses major surveillance and control challenges, particularly in periurban and human-modified landscapes where ecological complexity, delayed detection, and heterogeneous host movements complicate out-break interpretation. In late 2025, ASF was detected in wild boar in Catalonia, Spain, creating a high-priority epidemiological scenario at the wildlife-urban interface. In this study, we analysed the 2025-2026 Catalonia outbreak using scenario-based spatial modelling with the WIMBOARD (Wild Integrated Movement Boar Outbreak and Risk Dynamics) framework. Simulations under baseline control conditions were used to interpret spread dynamics and support anticipatory surveillance. The results indicate that outbreak expansion was structured and directional rather than isotropic. Temporal outputs suggested delayed detectability between infection prevalence and mortality signals, whereas cumulative spatial risk maps, time-to-infection surfaces, and monthly infection-risk dynamics identified directional asymmetries and differentiated phases of spread. An early southward dispersal signal was consistent with initial field observations, while the north-northwest sector emerged as the most consequential expansion scenario because of its stronger functional connectivity and higher potential for regional amplification and persistence, while a later northeast-ward phase remained comparatively less influential within the 500-day simulation horizon. Monthly risk surfaces further showed that ASF spread behaved as a moving eco-epidemiological wavefront, allowing identification of shifting surveillance windows before mortality became apparent. These findings support the interpretation of the Catalonia outbreak as a landscape-mediated epidemiological process shaped by functional connectivity, wildlife behavioural adaptation, peri-urban ecological structure, and delayed detection. This study shows how spatially explicit eco-epidemiological modelling can support outbreak interpretation, surveillance prioritisation, and proactive wildlife disease management under complex field conditions.

Amphibians are among the most threatened vertebrates, yet assisted reproductive technologies (ARTs) remain underutilised in their conservation. We developed and evaluated a biologically relevant, non-lethal sperm collection protocol for Rana muscosa, a critically endangered frog in a long-term conservation breeding program. Thirteen hormone treatments were tested across six post-injection time points, and sperm quality was assessed via concentration, motility, osmolality, and pH. Generalised linear mixed models revealed that gonadotropin-releasing hormone (GnRHa) alone significantly outperformed hCG-based regimens. The 3 {micro}g/g GnRHa dose yielded the highest sperm concentration and sustained motility from 3 to 24 hours post-injection. Motility was highest under moderately acidic (pH 6.5-7.0) and hypoosmotic (75-100 mOsm/kg) conditions. To support decision-making, we applied a refined Wildlife Sperm Index (WSI) that weights motility and concentration as primary functional-yield traits, with motility given greater relative weight given its direct role in fertilisation, and treats pH and osmolality as secondary environmental-compatibility traits. Under this revised composite score, 3 {micro}g/g GnRHa achieved the highest overall WSI score, outperforming 2 {micro}g/g and 4 {micro}g/g GnRHa, which ranked second and third, respectively, despite their more favourable pH and osmolality profiles. These findings provide the first evidence-based ART protocol for R. muscosa and offer a transferable framework for optimising gamete collection, IVF, and cryopreservation in line with other amphibian species, advancing both genetic management and species recovery goals.

RationaleOxidative stress, resulting from an imbalance between the production of reactive oxygen species (ROS) and antioxidant defence mechanisms, disrupts cellular redox homeostasis and contributes to damage of biomolecules, including nucleic acids, proteins and lipids. Such disturbances can impair intracellular signalling pathways and have been implicated in the pathophysiology of female reproductive disorders. Nitroblue Tetrazolium (NBT) is a distinctive dye that assesses cellular redox activity in neutrophils through the detection of superoxide anion (O2-). However, the relationship between neutrophil-derived oxidative stress and women infertility remains to be fully elucidated. Aim of the study: We aimed to investigate the role of neutrophil oxidative stress in women infertility using a novel Nitroblue Tetrazolium (NBT) method developed in our laboratory. Materials and methodsBlood and serum specimens were obtained from a total of 100 Libyan women, comprising healthy fertile women (n = 21; controls) and infertile women (n = 79). Superoxide anion (O2-) generation in neutrophils was assessed using the novel NBT method, while malondialdehyde (MDA), a marker of lipid peroxidation, was determined using the thiobarbituric acid reactive substances (TBARS) assay. ResultsA significant increase in both NBT-reactivity levels and the percentage of NBT-positive neutrophils was observed in infertile women compared with healthy fertile controls (P < 0.0001). In addition, MDA levels were significantly higher in infertile women than in the control group, indicating enhanced lipid peroxidation. MDA levels were positively correlated with NBT-reactivity levels (r = 0.410, P = 0.0001) and the percentage of NBT-positive neutrophils (r = 0.21, P = 0.047). A significant positive correlation was also observed between NBT-reactivity levels and the percentage of NBT-positive neutrophils (r = 0.510, P = 0.0001), demonstrating a close association between neutrophil oxidative activity and lipid peroxidation in women infertility. ConclusionThe present study demonstrates the utility of a novel NBT method for detecting reactive oxygen species (ROS), a marker of oxidative stress, in neutrophils from both blood and serum specimens. The findings demonstrate increased neutrophil-derived oxidative stress in infertile women and suggest that this method may have potential as a diagnostic tool for the assessment of women infertility. ImpactThis study provides evidence that neutrophil-derived oxidative stress is significantly increased in women infertility and demonstrates the application of a novel Nitroblue Tetrazolium (NBT) assay for assessing oxidative stress in both blood and serum specimens. The significant associations between NBT-reactivity, NBT-positive neutrophils and malondialdehyde levels provide further insight into the contribution of oxidative stress to female reproductive pathophysiology. These findings support the potential utility of the novel NBT method as a simple and reliable diagnostic approach for evaluating oxidative stress in women infertility and provide a foundation for future studies investigating oxidative stress biomarkers in reproductive medicine.

The white shark Carcharodon carcharias (Linnaeus, 1758) is one of the most iconic and threatened marine predators globally and is listed as critically endangered by the IUCN in the Mediterranean Sea, where populations are primarily affected by fishing pressure and the degradation of critical habitats. Available information on Mediterranean white sharks mostly relies on fishing interactions and opportunistic surface records, thereby limiting evaluations on the species ecology. Here, we report an exceptionally rare underwater encounter with this species, documented by divers involved in an expedition aimed at removing ghost fishing gear from a World War II shipwreck in the Strait of Sicily (SoS), central Mediterranean. Given the exceptional nature of this observation, we analysed the video footage in detail with the aim of providing morphological information and novel insights into the species ecology. The footage analysis also allowed close observations on behavioural aspects and on interspecific and parasitic associations, thus providing information that is rarely achievable through surface sightings or from captured individuals, which historically represent the majority of records for this species in the region. To contextualize this observation within the SoS--an area suggested to function as a nursery and reproductive ground for the species--we also provide an updated compilation of records reported in the scientific literature over the last decade, adding 18 new records to existing datasets. This study highlights the value of shipwrecks as observation sites for rare and elusive species, and underscores the importance of non-invasive, video-based approaches in advancing knowledge of a species whose ecology in the Mediterranean remains poorly understood.

Background/ObjectivesThe integration of large language model (LLM)-based AI tools into veterinary clinical practice is rapidly increasing; however, no systematically derived taxonomy of veterinary AI query patterns has been established. This study aimed to develop and refine an exploratory taxonomy of veterinary AI query patterns across clinical stages through a structured expert-panel review process. MethodsAn exploratory cross-sectional expert panel study was conducted. 5,372 real-world query logs from a veterinary clinical AI chatbot deployed over eight months were analyzed using AI-assisted inductive coding to derive an initial taxonomy. The taxonomy was refined through literature review and subsequently reviewed by an expert panel of 38 veterinary professionals via structured online survey across five clinical stages. ResultsA taxonomy of 3 categories and 21 subtypes was established: Clinical Support Queries (Types A-H), Evidence-Based Research Queries (Types I-L), and Terminology and Drug Reference Queries (Types M-U). Type B (Differential Reasoning) had the highest overall frequency (57/188 first-choice responses), while Type D (Clinical Decision Support) was dominant immediately post-consultation (55.3%). Veterinary professionals with [&ge;]10 years of experience showed a higher frequency of Type G (Evidence Search) preference than those with <10 years of experience (18 vs. 4), while university-affiliated professionals demonstrated a distinct pattern dominated by Type G. ConclusionsTo our knowledge, no published study has previously established a veterinary-specific, clinical-stage-sensitive exploratory taxonomy of AI query patterns; this study addresses that gap. The findings provide a foundational framework for designing context-aware, stage-adaptive veterinary AI systems and benchmark evaluation tools.

This study aims to evaluate the effectiveness of curcumin and penicillin-streptomycin as diluents on changes in the structure and diversity of the chicken semen microbiome during storage. Semen was collected from Kampung chickens (native to Indonesia) and divided into five treatment groups: diluted without antibiotics or curcumin (control), and diluted with additions of 10 micromolar, 20 micromolar, and 30 micromolar curcumin, and penicillin-streptomycin, respectively. The semen was stored at 5 C for 24 hours. The composition and diversity of the semen microbiome were analyzed using 16S full-length amplicon sequencing. Analysis of the top 10 species showed that Uncultured Saccharofermentans sp. and Porphyromonas somerae served as the most dominant and stable core microbiome across all treatments. Alpha diversity analysis showed that the addition of curcumin and penicillin-streptomycin reduced microbial richness (Observed, ChaO1, ACE, and Fisher) in a dose-dependent manner, yet maintained overall diversity (Shannon and Simpson), with the penicillin-streptomycin treatment resulting in the highest species evenness (InvSimpson). Beta diversity analysis revealed extreme separation of taxonomic abundance variance in the penicillin-streptomycin group, whereas the curcumin treatment exhibited a dose-dependent pattern of microbial abundance transition. Venn diagram analysis identified 415 OTUs as the core microbiome and confirmed that curcumin acts through selective filtering that stabilizes the ecosystem without triggering the proliferation of opportunistic taxa. Penicillin-streptomycin acts more rapidly and dominantly in suppressing/killing bacterial populations, however, the addition of curcumin is able to modulate the microbial ecosystem in a more balanced manner by suppressing the growth of harmful bacteria without compromising the integrity of the chicken semen environment.

The moonfish Mene maculata (Bloch and Schneider, 1801), the sole extant species of the family Menidae, is widely distributed in the Indo-West Pacific. Although its spawning season has previously been inferred from seasonal variation in the gonadosomatic index (GSI), the histological basis for interpreting GSI as an indicator of spawning-capable status remains limited. Here we describe the gonadal structure of male and female M. maculata and evaluate how GSI and standard length relate to spawning-capable status based on germ cell development. The testis was lobular and exhibited an unrestricted spermatogonial distribution, a structure widely observed among neoteleosts, indicating that the phylogenetic distinctiveness of M. maculata was not associated with a distinctive testicular structure. In the spawning-capable female, oocytes at multiple developmental stages, from primary growth to oocyte maturation, co-occurred within the ovary, indicating asynchronous ovarian development. This finding suggests that M. maculata may be a batch spawner rather than a total spawner as previously inferred. The spawning-capable female had a GSI consistent with previously inferred spawning season estimates. By contrast, histologically examined males were spawning capable at GSI values lower than those previously associated with the inferred spawning season, suggesting that male spawning-capable status may persist beyond the period inferred from elevated GSI alone. This study provides the first histological description of reproductive biology in the phylogenetically distinctive M. maculata and establishes a histological basis for interpreting GSI as a reproductive indicator.

Livestock welfare models are developed under controlled experimental conditions but deployed across farms, breeds, management systems and label regimes, where reliability remains uncertain. We introduce the Protocol-Driven Transfer Evaluation (PDTE) framework, which treats the adaptation protocol, comprising label mapping, objective design, domain alignment, model selection, calibration and threshold policy, as the experimental variable and evaluates transfer through animal-level external validation with uncertainty quantification. We apply PDTE to a bovine welfare task involving transfer of a facial pain representation from postoperative beef cattle to dairy cows under shifts in breed, sex, production system, clinical etiology, recording environment and label fidelity. Using an author-collected Canadian Holstein and Jersey dataset with an independent eight-cow test cohort, direct source-domain transfer was weak, with sequence AUC 0.418 and cow-level AUC 0.400. PDTE identified two failure modes under weak supervision: threshold collapse, in which adaptation converges to a single prediction class, and calibration-induced collapse, in which score ranking is preserved while decision behavior deteriorates. Across protocols, objective design dominated performance. Class-balanced focal adaptation achieved stable operating behavior (sequence AUC 0.611; cow-level AUC 0.667), while a target-only model attained comparable performance without source initialization (sequence AUC 0.596; paired p = 0.984), indicating that protocol design and operating-point choices contributed more than pretraining under weak-label conditions. Animal-level uncertainty remained substantial, with a bootstrap 95% confidence interval of 0.20 to 1.00, exceeding the transfer effect. These findings show that transferability limits cannot be inferred from source-domain performance alone and require protocol-controlled, uncertainty-aware evaluation in livestock AI.

Spatial overlap of domestic animals and wildlife can have positive effects such as increased biodiversity in some areas and reduction in forest fires but it can also have negative consequences such as pathogen spillover, vector and pathogen propagation into new areas among others. Thus, understanding the interactions between wildlife and domestic animals on shared landscapes is critical for conservation and management purposes. To investigate these cattle-wildlife interactions, we chose Buck Island Ranch (BIR) in southcentral Florida. We studied spatial overlap between Domestic Cattle (Bos Taurus) and multiple wildlife species such as Wild Boar (Sus scrofa), White-tailed Deer (Odocoileus virginianus), Rabbits (Sylvilagus spp) and others in pasture, semi-pasture, and hammock habitats/vegetation types at BIR. Five management blocks were selected that had pasture and hammock habitats and within each block we established matched pairs of 100m transects. Animal dung samples were surveyed and identified in 13 pairs of transects. We used Wilcoxon signed rank test to determine differences in dung counts between the habitats and calculated Simpson diversity index to analyze the differences in diversity between the habitat types. We found that there was greater dung count of white-tailed deer in hammock habitats compared to pastures (p=0.048) and greater diversity of wildlife in hammocks than pastures (p=0.028). Cattle dung was found in high density in both hammocks and pastures. These results indicate that hammocks are areas shared between cattle and wildlife at BIR and depending on the extent of direct and indirect contacts between these animals there can be a risk for pathogen spillover. We recommend further research on local land and cattle management practices that can facilitate the co-existence of livestock and wildlife.

The common marmoset (Callithrix jacchus) is an important non-human primate model in neuroscience and biomedical research. However, existing 3D resources for this species have mainly focused on brain atlases or keypoint-based pose estimation, and reusable data resources that jointly describe the body surface, fur, articulated structure, and experimental environment remain limited. Here, we present a multimodal dataset designed to reconstruct body- environment interactions of common marmosets in three dimensions. The dataset includes a whole-body surface mesh derived from computed tomography (CT) images, fur representations based on photographic references, a rigged 3D model for pose-driven animation, synchronized behavioral videos from three individuals recorded for approximately 90 hours from eight view-points, 2D and 3D keypoint estimation data, 3D models of the experimental environment constructed from blueprint information, and rendered pseudo-egocentric views generated by integrating pose estimation results with the 3D body and environment models. Technical validation assessed the geometric agreement between the CT-derived mesh and the surface model, the accuracy of 2D and 3D keypoint estimation, the dimensional accuracy of the environment model, and the structural similarity between real and rendered images. This dataset provides a foundation for treating marmoset natural behavior not only as point trajectories but also as a three-dimensional phenomenon involving body shape and its spatial relationship with the environment, thereby enabling applications in behavioral analysis, visualization, synthetic-data generation, and future digital-twin studies.