Diverse amdoparvoviruses infection of farmed Asian badgers (Meles meles).

Amdoparvoviruses infect various carnivores, including mustelids, canids, skunks, and felids. Aleutian mink disease virus (AMDV) belongs to the prototypical species Amdoparvovirus carnivoran1. Here, we identified a novel amdoparvovirus in farmed Asian badgers (Meles meles), and we named this virus "Meles meles amdoparvovirus" (MMADV). A total of 146 clinical samples were collected from 134 individual badgers, and 30.6% (41/134) of the sampled badgers tested positive for amdoparvovirus by PCR. Viral DNA was detected in feces, blood, spleen, liver, lung, and adipose tissue from these animals. Viral sequences from eight samples were determined, five of which represented nearly full-length genome sequences (4,237-4,265 nt). Six serum samples tested positive by PCR, CIEP, and IAT, four of which had high antibody titers (> 512) against AMDV-G. Twenty-six of the 41 amdoparvovirus-positive badgers showed signs of illness, and necropsy revealed lesions in their organs. Sequence comparisons and phylogenetic analysis of the viral NS1 and VP2 genes of these badger amdoparvoviruses showed that their NS1 proteins shared 62.6%-88.8% sequence identity with known amdoparvoviruses, and they clustered phylogenetically into two related clades. The VP2 proteins shared 76.6%-97.2% identity and clustered into two clades, one of which included raccoon dog and arctic fox amdoparvovirus (RFAV), and the other of which did not include other known amdoparvoviruses. According to the NS1-protein-based criterion for parvovirus species demarcation, the MMADV isolate from farm YS should be classified as a member of a new species of the genus Amdoparvovirus. In summary, we have discovered a novel MMADV and other badger amdoparvoviruses that naturally infect Asian badgers and are possibly pathogenic in badgers.

Leishmania infantum infection in European badgers (Meles meles) from northeastern Spain: a histopathological and immunohistochemical investigation.

The European badger (Meles meles) is a common mustelid species known as a significant reservoir for various human and animal diseases. Studies investigating Leishmania infection in European badgers across Mediterranean regions have yielded inconsistent findings. In Spain, results are particularly controversial: some studies confirm the presence of Leishmania in badgers, while others do not. Our study aimed to conduct a retrospective histopathological and immunohistochemical analysis to detect Leishmania in tissues of nine European badgers from northeastern Spain, a region previously unevaluated for Leishmania infection in this species. Microscopic examination revealed lesions indicative of leishmaniosis in the lymph nodes and spleens of six badgers. In one of them, Leishmania-like structures were identified in multiple organs and confirmed via immunohistochemistry. Parasites were detected in the lymph nodes, spleen, adrenal glands, and pancreas. The parasite load was high in the adrenal glands, moderate in the lymph nodes and spleen, and low in the pancreas. No parasites were found in other examined organs. This finding represents a frequency of 11.11% (1/9) of Leishmania infection among the badgers we studied. Further investigation of wildlife and atypical reservoirs can enhance our understanding of the pathogenesis of this significant zoonotic disease.

Is the European badger a new host for Dirofilaria immitis? The first records in Greece.

Dirofilaria immitis is a ubiquitous nematode parasite with zoonotic potential, transmitted by mosquitoes, that causes heartworm disease in various animal species. Dogs are the parasite's typical final host, and wild carnivores represent the parasite's reservoir in nature. Studies on D. immitis infections in wild animals are essential to assess infection pressure for domestic animals, and until now, there has been only one infection case reported in a European badger (Meles meles). The current report describes the first two European badger cases with cardiovascular dirofilariosis in Greece. Two adult male badgers were rescued in Heraklion and Chania, Crete Island, and admitted to "ANIMA -Wildlife Rehabilitation Centre" in Athens. The detailed clinical examination revealed that the first badger suffered from severe broncho-pneumonitis while the second one displayed clinical signs associated with severe brain trauma. Blood samples were taken for haematology and biochemistry analyses during their short hospitalisation period. In addition, different routine diagnostic tests were carried out, including heartworm antigen testing (ELISA) and the modified Knott's test for microfilariae. Both badgers were positive in both tests. The animals died a few hours after their admission and the detailed necropsies followed, revealed the presence of three parasites in each animal's right heart, morphologically identified as adults of D. immitis. These findings add the European badger in the list of additional potential reservoir hosts for D. immitis and highlight the potential role of wildlife for companion animals and human health.

Shannon entropy of performance metrics to choose the best novel hybrid algorithm to predict groundwater level (case study: Tabriz plain, Iran).

Predicting groundwater level (GWL) fluctuations, which act as a reserve water reservoir, particularly in arid and semi-arid climates, is vital in water resources management and planning. Within the scope of current research, a novel hybrid algorithm is proposed for estimating GWL values in the Tabriz plain of Iran by combining the artificial neural network (ANN) algorithm with newly developed nature-inspired Coot and Honey Badger metaheuristic optimization algorithms. Various combinations of meteorological data such as temperature, evaporation, and precipitation, previous GWL values, and the month and year values of the data were used to evaluate the algorithm's success. Furthermore, the Shannon entropy of model performance was assessed according to 44 different statistical indicators, classified into two classes: accuracy and error. Hence, based on the high value of Shannon entropy, the best statistical indicator was selected. The results of the best model and the best scenario were analyzed. Results indicated that value of Shannon entropy is higher for the accuracy class than error class. Also, for accuracy and error class, respectively, Akaike information criterion (AIC) and residual sum of squares (RSS) indexes with the highest entropy value which is equal to 12.72 and 7.3 are the best indicators of both classes, and Legate-McCabe efficiency (LME) and normalized root mean square error-mean (NRMSE-Mean) indexes with the lowest entropy value which is equal to 3.7 and - 8.3 are the worst indicators of both classes. According to the evaluation best indicator results in the testing phase, the AIC indicator value for HBA-ANN, COOT-ANN, and the standalone ANN models is equal to - 344, - 332.8, and - 175.8, respectively. Furthermore, it was revealed that the proposed metaheuristic algorithms significantly improve the performance of the standalone ANN model and offer satisfactory GWL prediction results. Finally, it was concluded that the Honey Badger optimization algorithm showed superior results than the Coot optimization algorithm in GWL prediction.

Positional asphyxia: accidental death due to head-down position in a badger sett.

Death from positional asphyxia occurs when the victim is found in a position that interferes with adequate breathing. We present an unusual case of a 67-yearold man who was found deceased trapped in a badger sett. The autopsy revealed marked congestion, cyanosis and a multitude of pinpoint hemorrhages in the face, neck and upper chest. Confluent scleral and conjunctival hemorrhage were observed and numerous petechial bleedings were found in the oral mucosa and within the reflected scalp. Death was attributed to positional asphyxia due to head-down position in a badger sett.

Assessment of beneficial effects and identification of host adaptation-associated genes of Ligilactobacillus salivarius isolated from badgers.

BACKGROUND: Ligilactobacillus salivarius has been frequently isolated from the gut microbiota of humans and domesticated animals and has been studied as a candidate probiotic. Badger (Meles meles) is known as a "generalist" species that consumes complex foods and exhibits tolerance and resistance to certain pathogens, which can be partly attributed to the beneficial microbes such as L. salivarius in the gut microbiota. However, our understanding of the beneficial traits and genomic features of badger-originated L. salivarius remains elusive. RESULTS: In this study, nine L. salivarius strains were isolated from wild badgers' feces, one of which exhibited good probiotic properties. Complete genomes of the nine L. salivarius strains were generated, and comparative genomic analysis was performed with the publicly available complete genomes of L. salivarius obtained from humans and domesticated animals. The strains originating from badgers harbored a larger genome, a higher number of protein-coding sequences, and functionally annotated genes than those originating from humans and chickens. The pan-genome phylogenetic tree demonstrated that the strains originating from badgers formed a separate clade, and totally 412 gene families (12.6% of the total gene families in the pan-genome) were identified as genes gained by the last common ancestor of the badger group. The badger group harbored significantly more gene families responsible for the degradation of complex carbohydrate substrates and production of polysaccharides than strains from other hosts; many of these were acquired by gene gain events. CONCLUSIONS: A candidate probiotic and nine L. salivarius complete genomes were obtained from the badgers' gut microbiome, and several beneficial genes were identified to be specifically present in the badger-originated strains that were gained in the evolution. Our study provides novel insights into the adaptation of L. salivarius to the intestinal habitat of wild badgers and provides valuable strain and genome resources for the development of L. salivarius as a probiotic.

Humeri under external load: Mechanical implications of differing bone curvature in American otter and honey badger.

The mechanical properties of limb long bones are impacted by bone shape and especially curvature, which is therefore likely to be of adaptive value. We use finite element analysis to compare the mechanical properties of humeri of the closely related American otter and honey badger under external loads, and to analyze the significance of bone curvature. We simulate the effects generated by loads applied in directions that differ relative to the humeral longitudinal axes, and then compare the stress characteristics with a series of humerus-inspired abstracted curved structures with increasing ratio (C/R) of eccentricity C to radius of cross section R. The humeri of the two species differ in bone curvature, with C/R of 0.6201 and 0.8752, respectively. Our analysis shows that the peak and mean stress values found within the sampling line of bone models reach a minimum when the directions of loads are 105 ± 5°, and the humerus of the American otter always experienced lower stress values than those of the honey badger in the sampling line. An analysis of stress distribution in abstract curved structures showed the greatest reduction in stress when the direction of external load was equal or greater than 95°. This suggests that the variability of the direction of external loads is an important determinant of bone curvature, and should be accounted for when assessing load carrying capacity. This study provides a basis for biomechanics research and yields insight into the form-function relationship of nature's structural elements within limbs. It potentially contributes to the design of biomimetic robots while also highlighting the functional significance of humeral bone curvature in mammals.

Occurrence of Dirofilaria repens in wild carnivores in Poland.

Dirofilaria repens is an expanding vector-borne zoonotic parasite of canines and other carnivores. Sub-clinically infected dogs constitute the most important reservoir of the parasite and the source of infection for its mosquito vectors. However, occurrence of D. repens infection in wild animals may contribute to the transmission of the parasite to humans and may explain the endemicity of filariae in newly invaded regions. The aim of the current study was to determine the occurrence of D. repens in 511 blood and spleen samples from seven species of wild carnivores (wolves, red foxes, Eurasian badgers, raccoons, raccoon dogs, stone martens, and pine martens) from different regions of Poland by means of a PCR protocol targeting the 12S rDNA gene. Dirofilaria repens-positive hosts were identified in seven of fourteen voivodeships in four of the seven regions of Poland: Masovia, Lesser Poland, Pomerania and Warmia-Masuria. The highest prevalence was found in Masovia region (8%), coinciding with the highest previously recorded prevalence in dogs in Central Poland. The DNA of Dirofilaria was detected in 16 samples of three species (total prevalence 3.13%). A low and similar percentage of positive samples (1.9%, 4.2% and 4.8%) was recorded among badgers, red foxes, and wolves, respectively. Dirofilaria repens-positive hosts were identified in seven of fourteen voivodships. Based on detection in different voivodeships, D. repens-positive animals were recorded in four out of the seven regions of Poland: in Masovia, Lesser Poland, Pomerania, and Warmia-Masuria. The highest prevalence of filariae was found in Masovia region (8%), reflecting the highest previously recorded prevalence in dogs (12-50%) in Central Poland. In summary, we conducted the first comprehensive study on the epidemiology of D. repens in seven species of wild hosts in all seven regions of Poland and identified the first case of D. repens infection in Eurasian badgers in Poland and the second in Europe.

A Condition-Monitoring Method for Rolling Bearings Based on Dynamic Asynchronous Peak-Factor Ratios.

In response to issues such as the lack of capability for timely early warning and the difficulty in monitoring the status of rolling bearings, a condition-monitoring method for rolling bearings based on the Honey Badger Algorithm (HBA) for optimizing dynamic asynchronous periods is proposed. This method is founded on the peak factor and involves comparing peak factors at different periods to construct a dynamic asynchronous peak-factor-ratio-monitoring index, which is then optimized using the HBA. Simulated experiments were carried out using the XJTU-SY dataset. The results indicate that, compared to the early warning times defined by international standards, the warning times provided using this method are consistently over 33 min in advance within the test dataset. Additionally, an envelope spectrum analysis of the warning data confirms the existence of early faults. This demonstrates that the monitoring indicator developed in this paper is capable of delivering earlier and more accurate early fault warnings and condition monitoring for rolling bearings.

3D Road Lane Classification with Improved Texture Patterns and Optimized Deep Classifier.

The understanding of roads and lanes incorporates identifying the level of the road, the position and count of lanes, and ending, splitting, and merging roads and lanes in highway, rural, and urban scenarios. Even though a large amount of progress has been made recently, this kind of understanding is ahead of the accomplishments of the present perceptual methods. Nowadays, 3D lane detection has become the trending research in autonomous vehicles, which shows an exact estimation of the 3D position of the drivable lanes. This work mainly aims at proposing a new technique with Phase I (road or non-road classification) and Phase II (lane or non-lane classification) with 3D images. Phase I: Initially, the features, such as the proposed local texton XOR pattern (LTXOR), local Gabor binary pattern histogram sequence (LGBPHS), and median ternary pattern (MTP), are derived. These features are subjected to the bidirectional gated recurrent unit (BI-GRU) that detects whether the object is road or non-road. Phase II: Similar features in Phase I are further classified using the optimized BI-GRU, where the weights are chosen optimally via self-improved honey badger optimization (SI-HBO). As a result, the system can be identified, and whether it is lane-related or not. Particularly, the proposed BI-GRU + SI-HBO obtained a higher precision of 0.946 for db 1. Furthermore, the best-case accuracy for the BI-GRU + SI-HBO was 0.928, which was better compared with honey badger optimization. Finally, the development of SI-HBO was proven to be better than the others.

Hybrid Optimization Algorithm Enabled Deep Learning Approach Brain Tumor Segmentation and Classification Using MRI.

The unnatural and uncontrolled increase of brain cells is called brain tumors, leading to human health danger. Magnetic resonance imaging (MRI) is widely applied for classifying and detecting brain tumors, due to its better resolution. In general, medical specialists require more details regarding the size, type, and changes in small lesions for effective classification. The timely and exact diagnosis plays a major role in the efficient treatment of patients. Therefore, in this research, an efficient hybrid optimization algorithm is implemented for brain tumor segmentation and classification. The convolutional neural network (CNN) features are extracted to perform a better classification. The classification is performed by considering the extracted features as the input of the deep residual network (DRN), in which the training is performed using the proposed chronological Jaya honey badger algorithm (CJHBA). The proposed CJHBA is the integration of the Jaya algorithm, honey badger algorithm (HBA), and chronological concept. The performance is evaluated using the BRATS 2018 and Figshare datasets, in which the maximum accuracy, sensitivity, and specificity are attained using the BRATS dataset with values 0.9210, 0.9313, and 0.9284, respectively.

ExpHBA Deep-IoT: Exponential Honey Badger Optimized Deep Learning For Breast Cancer Detection in IoT Healthcare System.

Breast cancer (BC) is the most widely found disease among women in the world. The early detection of BC can frequently lessen the mortality rate as well as progress the probability of providing proper treatment. Hence, this paper focuses on devising the Exponential Honey Badger Optimization-based Deep Covolutional Neural Network (EHBO-based DCNN) for early identification of BC in the Internet of Things (IoT). Here, the Honey Badger Optimization (HBO) and Exponential Weighted Moving Average (EWMA) algorithms have been combined to create the EHBO. The EHBO is created to transfer the acquired medical data to the base station (BS) by choosing the best cluster heads to categorize the BC. Then, the statistical and texture features are extracted. Further, data augmentation is performed. Finally, the BC classification is done by DCNN. Thus, the observational outcome reveals that the EHBO-based DCNN algorithm attained outstanding performance concerning the testing accuracy, sensitivity, and specificity of 0.9051, 0.8971, and 0.9029, correspondingly. The accuracy of the proposed method is 7.23%, 6.62%, 5.39%, and 3.45% higher than the methods, such as multi-layer perceptron (MLP) classifier, deep learning, support vector machine (SVM), and ensemble-based classifier.

Ensemble deep honey architecture for COVID-19 prediction using CT scan and chest X-ray images.

Recently, the infectious disease COVID-19 remains to have a catastrophic effect on the lives of human beings all over the world. To combat this deadliest disease, it is essential to screen the affected people quickly and least inexpensively. Radiological examination is considered the most feasible step toward attaining this objective; however, chest X-ray (CXR) and computed tomography (CT) are the most easily accessible and inexpensive options. This paper proposes a novel ensemble deep learning-based solution to predict the COVID-19-positive patients using CXR and CT images. The main aim of the proposed model is to provide an effective COVID-19 prediction model with a robust diagnosis and increase the prediction performance. Initially, pre-processing, like image resizing and noise removal, is employed using image scaling and median filtering techniques to enhance the input data for further processing. Various data augmentation styles, such as flipping and rotation, are applied to capable the model to learn the variations during training and attain better results on a small dataset. Finally, a new ensemble deep honey architecture (EDHA) model is introduced to effectively classify the COVID-19-positive and -negative cases. EDHA combines three pre-trained architectures like ShuffleNet, SqueezeNet, and DenseNet-201, to detect the class value. Moreover, a new optimization algorithm, the honey badger algorithm (HBA), is adapted in EDHA to determine the best values for the hyper-parameters of the proposed model. The proposed EDHA is implemented in the Python platform and evaluates the performance in terms of accuracy, sensitivity, specificity, precision, f1-score, AUC, and MCC. The proposed model has utilized the publicly available CXR and CT datasets to test the solution's efficiency. As a result, the simulated outcomes showed that the proposed EDHA had achieved better performance than the existing techniques in terms of Accuracy, Sensitivity, Specificity, Precision, F1-Score, MCC, AUC, and Computation time are 99.1%, 99%, 98.6%, 99.6%, 98.9%, 99.2%, 0.98, and 820 s using the CXR dataset.

Untargeted metabolomic analysis of thoracic blood from badgers indicate changes linked to infection with bovine tuberculosis (Mycobacterium bovis): a pilot study.

INTRODUCTION: Mycobacterium bovis, the causative agent of bovine tuberculosis (bTB) in cattle, represents a major disease burden to UK cattle farming, with considerable costs associated with its control. The European badger (Meles meles) is a known wildlife reservoir for bTB and better knowledge of the epidemiology of bTB through testing wildlife is required for disease control. Current tests available for the diagnosis of bTB in badgers are limited by cost, processing time or sensitivities. MATERIALS AND METHODS: We assessed the ability of flow infusion electrospray-high-resolution mass spectrometry (FIE-HRMS) to determine potential differences between infected and non-infected badgers based on thoracic blood samples obtained from badgers found dead in Wales. Thoracic blood samples were autoclaved for handling in a containment level 2 (CL2) hazard laboratory. RESULTS: Here we show the major differences associated with with M. bovis infection were changes to folate, pyrimidine, histidine, glycerophospholipid and phosphonate metabolism. CONCLUSIONS: Our studies have indicated differences in the metabolomic signature of badgers found dead in relation to their infection status, suggesting metabolomics could hold potential for developing novel diagnostics for bTB in badgers. As well as highlighting a potential way to handle samples containing a highly pathogenic agent at CL2 for metabolomics studies.

Metabotyping the Welsh population of badgers based on thoracic fluid.

INTRODUCTION: The European badger (Meles meles) is a known wildlife reservoir for bovine tuberculosis (bTB) and a better understanding of the epidemiology of bTB in this wildlife species is required for disease control in both wild and farmed animals. Flow infusion electrospray-high-resolution mass spectrometry (FIE-HRMS) may potentially identify novel metabolite biomarkers based on which new, rapid, and sensitive point of care tests for bTB infection could be developed. OBJECTIVES: In this foundational study, we engaged on assessing the baseline metabolomic variation in the non-bTB infected badger population ("metabotyping") across Wales. METHODS: FIE-HRMS was applied on thoracic fluid samples obtained by post-mortem of bTB negative badgers (n = 285) which were part of the Welsh Government 'All Wales Badger Found Dead' study. RESULTS: Using principal component analysis and partial least squares-discriminant analyses, the major sources of variation were linked to sex, and to a much lesser extent age, as indicated by tooth wear. Within the female population, variation was seen between lactating and non-lactating individuals. No significant variation linked to the presence of bite wounds, obvious lymphatic lesions or geographical region of origin was observed. CONCLUSION: Future metabolomic work when making comparisons between bTB infected and non-infected badger samples will only need be sex-matched and could focus on males only, to avoid lactation bias.

CFHBA-PID Algorithm: Dual-Loop PID Balancing Robot Attitude Control Algorithm Based on Complementary Factor and Honey Badger Algorithm.

The PID control algorithm for balancing robot attitude control suffers from the problem of difficult parameter tuning. Previous studies have proposed using metaheuristic algorithms to tune the PID parameters. However, traditional metaheuristic algorithms are subject to the criticism of premature convergence and the possibility of falling into local optimum solutions. Therefore, the present paper proposes a CFHBA-PID algorithm for balancing robot Dual-loop PID attitude control based on Honey Badger Algorithm (HBA) and CF-ITAE. On the one hand, HBA maintains a sufficiently large population diversity throughout the search process and employs a dynamic search strategy for balanced exploration and exploitation, effectively avoiding the problems of classical intelligent optimization algorithms and serving as a global search. On the other hand, a novel complementary factor (CF) is proposed to complement integrated time absolute error (ITAE) with the overshoot amount, resulting in a new rectification indicator CF-ITAE, which balances the overshoot amount and the response time during parameter tuning. Using balancing robot as the experimental object, HBA-PID is compared with AOA-PID, WOA-PID, and PSO-PID, and the results demonstrate that HBA-PID outperforms the other three algorithms in terms of overshoot amount, stabilization time, ITAE, and convergence speed, proving that the algorithm combining HBA with PID is better than the existing mainstream algorithms. The comparative experiments using CF prove that CFHBA-PID is able to effectively control the overshoot amount in attitude control. In conclusion, the CFHBA-PID algorithm has great control and significant results when applied to the balancing robot.

Long Arm of Motorway-The Impact of Fenced Road on the Mortality of European Badgers.

We studied the impact of the new fenced and accident-safe motorway on the mortality of European badgers Meles meles on local roads in western Poland in 2010-2015. We monitored the badgers mortality on local roads of three categories: main roads, secondary roads and county roads. The study was conducted before and after the opening of the motorway in 2012. We hypothesized that the mortality of badgers is lower due to traffic concentration on motorway. Ninety two badgers were killed in collisions with vehicles on all monitored roads. Mean number of killed badgers was lowest in 2010 before the motorway opening and the highest in 2012. The mortality of badgers on regional roads was highest after the opening of the motorway due to the changes in traffic on the access roads. Within the road network, the mortality of badgers was 5.8 individuals/10 km of road per whole study period with the highest rate on main roads 8.5 individuals/10 km. The badgers mortality was highest on county roads but it was lower than expected in relation to the road network density. The highest vehicle collision risk for badgers of both sexes occurred in June. Distance to human settlements was the only environmental factor that was positively related to badger mortality on roads. We conclude that the new motorway did not reduce the mortality rate of badgers on the adjacent roads because the status of local roads has changed and now they mainly function as access roads to the motorway.

Trypanosoma (Megatrypanum) pestanai in Eurasian badgers (Meles meles) and Ixodidae ticks, Italy.

Trypanosomes are haemoflagellate protozoa transmitted by blood-feeding arthropods causing infections in a wide range of mammals, including humans. Adult badgers (Meles meles, n = 2), displaying severe paralysis, ataxia and severe ectoparasite infestation, were rescued from a peri-urban area of Bari (southern Italy). Blood samples and ectoparasites were screened for Trypanosoma spp. by the combined PCR/sequencing approach, targeting a fragment of 18S rRNA gene. Smears of haemolymph, guts and salivary glands of the alive ticks were microscopically observed. No haematological alterations, except thrombocytopenia, were found. Trypomastigotes and epimastigotes were observed in the blood smears of both badgers and Trypanosoma pestanai was molecularly identified. Out of 33 ticks (i.e. n = 31 Ixodes canisuga, n = 2 Ixodes ricinus) and two fleas (Ctenocephalides felis), 11 specimens (n = 5 I. canisuga engorged nymphs, n = 4 engorged females and n = 2 I. ricinus engorged females) tested positive only for T. pestanai DNA. All smears from ticks were negative. The present study firstly revealed T. pestanai in Ixodidae and badgers from Italy, demonstrating the occurrence of the protozoan on the peninsula. Further studies are needed to clarify the occurrence of the only known vector of this parasite, Paraceras melis flea, as well as other putative arthropods involved in the transmission of T. pestanai.

Alternative reproductive strategies provide a flexible mechanism for assuring mating success in the European badgers (Meles meles): An investigation from hormonal measures.

Selection-pressures differ with population density, but few studies investigate how this can affect reproductive physiology. European badger (Meles meles) density varies from solitary to group-living across their range, with reported mating periods throughout the entire year to specific seasonal periods. Badger reproduction is evolutionarily distinct, interrupting the direct progression from conception to gestation with delayed implantation (DI), allowing for superfecundation (SF). To establish the tactical mating flexibility afforded by DI*SF, we used cross-sectional population-level seasonal variation of circulating sex-steroids for 97 females from a high-density population. Oestradiol was highest in spring among non-parous females, then lower in summer, and remained low during following seasons, suggesting that the mating period was restricted to just spring. Oestrone was consistently higher than oestradiol; it was elevated in spring, lowest during summer, peaked in autumn, and remained elevated for pregnant females in winter. This suggests that oestrone sustains pre-implanted blastocysts throughout DI. Progesterone was low throughout, except during winter pregnancy, associated with implantation and luteal development. In contrast to multiple mating periods reported by lower-density studies, our oestradiol data suggest that, at high-density, females exhibit only one mating period (congruent with testosterone patterns in males studied previously in this same population). While additional mating periods during DI enhance fertility assurance at low-density, at high-density, we propose that when coitus is frequent, fertilisation is assured, precluding the need for further cycles and associated mating risks. This endocrinologically flexible DI*SF mating strategy likely represents a form of balancing selection, allowing badgers to succeed at a range of regional densities.

In search of the optimal enrichment program for zoo-housed animals.

Zoo-housed animals are regularly exposed to new forms of environmental enrichment to make their lives less predictable. However, providing new enrichment can have unpredictable effects. We evaluated the effectiveness of two enrichment regimes: (1) providing only new enrichments - "Novelty" and (2) only familiar enrichments - "Familiar." In this case study, enrichment efficacy was assessed using activity budget analysis and novel object tests. The subjects were two focal animals: the golden jackal (Canis aureus) and the European badger (Meles meles). Our results suggest that both assessment methods provided similar conclusions about the animal's welfare. The "Familiar" treatment significantly increased activity and decreased abnormal behavior in the jackal's activity budget, who avoided novel objects in her baseline novel object test. The Novelty treatment resulted in the growth of time the jackal spent in the moat instead of the main enclosure, and an increase in avoidance and abnormal behaviors. In contrast, the badger demonstrated a high intensity of exploratory behavior in the baseline novel object test, and both regimes led to an increase in the activity budget of the badger. We propose that a stable, but complex enrichment can be an effective robust tool to increase animal welfare, but its efficacy depends on the novelty preference of individual animals. A novel object test can be a rapid tool to assess individual animals' novelty preferences, and this information may help to choose optimal enrichment regimes for individual animals.