Background Bovine tuberculosis (TB) is a burden to cattle farming in Great Britain. Poor biosecurity has been identified as contributing to the epidemic. Methods We conducted a systematic review of epidemiological studies published in the scientific literature between 1921 and 2024 that measured the association between farm biosecurity and cattle TB. Eligible studies controlled for confounding factors and reported statistically significant association/s between biosecurity and TB (p < 0.05) and/or an effect ratio/s of more than 3. Biosecurity uptake in England was assessed using official Disease Report Forms (DRFs) from 4074 TB incidents occurring in 2018 and 2019. Results Thirty-three papers with 116 effect estimates met the inclusion criteria and were grouped according to a five-point biosecurity plan. There was consistent evidence for TB risk being reduced by reducing contact with neighbouring herds and preventing cattle at higher TB risk from entering herds. The evidence for the effectiveness of measures for reducing contact between badgers and cattle was inconsistent. The DRF data showed a low uptake of biosecurity to reduce contact between badgers and cattle. Limitations All the studies identified were retrospective. Biosecurity was measured using different instruments, for example, questionnaires. Conclusions There is analytical epidemiological evidence supporting guidance for improving biosecurity, but there are some limitations. Further research is needed to identify the most effective wildlife-focused measures.

In parts of Europe, the European badger Meles meles acts as a wildlife host for Mycobacterium bovis, the causative agent of bovine tuberculosis (bTB). Options for reducing the risk of transmission of M. bovis from badgers to cattle include biosecurity measures, culling, and vaccination of badgers using the BCG vaccine. The use of vaccination as a control measure for tuberculosis (TB) in badgers has increased in recent years, with large-scale deployment in Ireland, and increasing use in England alongside a gradual phasing out of badger culling. Here we review evidence relating to the use of badger vaccination as a tool for controlling TB in badgers and cattle. Vaccination reduces the severity and progression of TB in badgers experimentally infected with M. bovis in laboratory studies, and significantly decreases the likelihood of naturally-acquired infection in free-living badgers in field trials. Modelling studies evaluating different strategies for controlling TB in badgers predict that badger vaccination will reduce TB prevalence in badger populations and lead to corresponding reductions in cattle herd disease incidence. While large scale field trials have not been undertaken to quantify the level of impact of badger vaccination on cattle bTB incidence in the UK, field studies in Ireland suggest that in some situations badger vaccination can result in beneficial disease outcomes in cattle which are comparable to those from badger culling. Attitudes to badger vaccination vary among stakeholder groups. Although members of the public are relatively positive about the benefits of vaccination, farmers are generally negative, due to concerns about practicality and effectiveness, along with a view that badger populations need to be controlled. The evidence published to date indicates that badger vaccination has the potential to contribute to the control of TB prevalence in wild badger populations and to form part of a wider strategy for controlling bTB. Future research should focus on investigating the effect of badger vaccination on bTB in cattle, along with understanding the impacts of vaccination in badgers in a broader range of ecological settings. Further understanding of the drivers of negative attitudes towards vaccination will nonetheless be crucial for incentivising and increasing the deployment of badger vaccination.

Understanding animal movements at different spatial scales presents a significant challenge as their patterns can vary widely from daily foraging behaviours to broader migration or territorial movements. This challenge is of general interest because it impacts the ability to manage wildlife populations effectively. In this study, we conduct diffusion analysis based on European badger (Meles meles) movement data obtained from three different regions in the UK (Gloucestershire, Cornwall, and Northern Ireland) and fit a generalised linear mixed-effects model to examine the relationship between variables. We also feature a novel application of extended dynamic mode decomposition (EDMD) to uncover patterns relating to badger social organisation. By applying our approach to these different populations, we were able to assess its performance across a range of badger densities. A key result was that in some areas, EDMD clusters matched observed group home ranges, whilst in others, discrepancies likely arose because of population management interventions, such as badger culling. The methods presented offer a promising approach for studying territoriality and the impacts of management strategies on animal movement behaviour.

Bovine tuberculosis (bTB), caused by Mycobacterium bovis, is the most significant infectious disease of UK cattle. Badgers (Meles meles) can also be infected by M. bovis and may act as a source of infection for cattle. Management of infection risks could be supported by the availability of sensitive blood tests for bTB in badgers. We explored whether metabolomic changes in peripheral blood could be used for the diagnosis of bTB in wild, naturally infected badgers. Flow infusion electrospray - high-resolution mass spectrometry (FIE-HRMS) was used to assess the peripheral blood of trapped wild badgers (n = 148) for metabolite changes linked to bTB infection status. Infection status was established by culture, Dual Path Platform (DPP) and interferon (IFN-γ) blood tests or mycobacterial culture. Data were analysed by multivariate and receiver operating characteristic (ROC) curve analyses. Metabolite shifts were identified in badger blood samples associated with their corresponding infection status. Biochemical pathway analyses suggested that sphingolipid and glycerophospholipid metabolism was enriched in bTB badgers. However, some individual metabolites changes appeared to differ depending on badger sex. Our data shows that metabolomic assessment of peripheral blood can indicate bTB infection status. However, larger sample sizes are required to establish if our metabolites have potential as diagnostic biomarkers.

Pathogens rarely mix freely throughout host populations, and the presence of barriers to transmission can be detected as patterns of increased genetic isolation among pathogen isolates. Despite the importance of transmission patterns in host societies, and the risk of epizootics from wildlife disease systems, barriers to open pathogen transmission are poorly understood in wild hosts. We tested the influence of host kinship and social structure on genetic divergence among strains of Mycobacterium bovis, the causative agent of bovine tuberculosis (bTB), in a wild badger population. We measured genetic distances between M. bovis isolates from badger hosts that varied in their own genetic similarity (a proxy for kinship) and in their social group affiliations. Using jack-knifing analyses to control for pseudoreplication, we found that genetic distances between pathogen isolates decreased with increasing kinship of host dyads, but only when hosts shared the same social group. Our findings suggest that the open transmission of bTB in wild hosts is constrained by a combination of social and kin structure, in particular the sharing of similar pathogen strains among kin within social groups. We discuss the implications of these transmission structures for the understanding and management of wildlife diseases.

Theory predicts that high population density leads to more strongly connected spatial and social networks, but how local density drives individuals' positions within their networks is unclear. This gap reduces our ability to understand and predict density-dependent processes. Here we show that density drives greater network connectedness at the scale of individuals within wild animal populations. Across 36 datasets of spatial and social behaviour in >58,000 individual animals, spanning 30 species of fish, reptiles, birds, mammals and insects, 80% of systems exhibit strong positive relationships between local density and network centrality. However, >80% of relationships are nonlinear and 75% are shallower at higher values, indicating saturating trends that probably emerge as a result of demographic and behavioural processes that counteract density's effects. These are stronger and less saturating in spatial compared with social networks, as individuals become disproportionately spatially connected rather than socially connected at higher densities. Consequently, ecological processes that depend on spatial connections are probably more density dependent than those involving social interactions. These findings suggest fundamental scaling rules governing animal social dynamics, which could help to predict network structures in novel systems.

Aminopeptidase N (APN) is a transmembrane protein that mediates the attachment of the spike protein of several clinically important coronaviruses (CoVs) responsible for respiratory and intestinal diseases in animals and humans. To assess the potential for APN-mediated viral tropism, we characterized APN receptor distribution in the respiratory and intestinal tissues of various artiodactyls (cervids, bovids, camelids and suids) and carnivores (canids, felids, mustelids and phocids) using immunohistochemistry. In the lungs, APN expression was limited to artiodactyls, with strong expression in the bronchiolar epithelium and weaker expression in pneumocytes. Nasal turbinate and tracheal samples, where available, showed stronger APN expression in artiodactyls over carnivores. APN was consistently detected on the microvilli of enterocytes in the small intestine across multiple taxa, while the presence in the colon was more variable. Of the animals examined, pig and alpaca consistently expressed the most abundant APN in the upper and lower respiratory tract. In silico evaluation of APN orthologue sequences from humans, artiodactyls and carnivores identified distinct evolutionary relationships. Further in silico binding predictions for alpaca alphacoronavirus and human coronavirus 229E with cognate and heterologous alpaca and human APN revealed substantial overlapping binding footprints with high conservation of amino acid residues, suggesting an evolutionary divergence and subsequent adaptation of a 229E-like or ancestral virus within a non-human animal host. This combined anatomical and in silico approach enhances understanding of host susceptibility, tissue tropism and viral transmission mechanisms in APN-dependent CoVs and has the potential to inform future strategies for disease modelling, surveillance and control.

Bovine tuberculosis (bTB) is a major disease of cattle in the UK, placing a significant economic burden on the taxpayer. The causative agent, Mycobacterium bovis, has a wide host range, including the European badger (Meles meles). While badgers have been implicated in the transmission and maintenance of infection in cattle in areas of endemic disease, their role at the edge of the endemic area is poorly understood. Here we present data on the prevalence of infection in badgers collected along the southern edge of England’s bTB epidemic. Stakeholders across five counties (Oxfordshire, Berkshire, Buckinghamshire, Hampshire, and East Sussex) submitted found-dead badgers for post-mortem examination and testing by bacterial culture. The overall prevalence, as confirmed by whole genome sequencing, was 6.5% (28/428), ranging between 1.1% (1/88) in Hampshire and 13.0% (14/108) in Oxfordshire. The commonest M. bovis clade in badgers was B6-62, which was predominant in 4/5 counties. B6-62 was also the commonest clade found in cattle and was detected in all counties except East Sussex where, although absent from the cattle population, it was detected in local badgers. This study highlights the co-incidence of infection in badgers and cattle in parts of the southern edge area consistent with localised clustering of infection in both species.

One of the main aims of wildlife disease ecology is to identify how disease dynamics vary in space and time and as a function of population density. However, monitoring spatiotemporal and density-dependent disease dynamics in the wild is challenging because the observation process is error-prone, which means that individuals, their disease status, and their spatial locations are unobservable, or only imperfectly observed. In this paper, we develop a novel spatially-explicit capture-recapture (SCR) model motivated by an SCR data set on European badgers (Meles meles), naturally infected with bovine tuberculosis (Mycobacterium bovis, TB). Our model accounts for the observation process of individuals as a function of their latent activity centers, and for their imperfectly observed disease status and its effect on demographic rates and behavior. This framework has the advantage of simultaneously modeling population demographics and disease dynamics within a spatial context. It can therefore generate estimates of critical parameters such as population size; local and global density by disease status and hence spatially-explicit disease prevalence; disease transmission probabilities as functions of local or global population density; and demographic rates as functions of disease status. Our findings suggest that infected badgers have lower survival probability but larger home range areas than uninfected badgers, and that the data do not provide strong evidence that density has a non-zero effect on disease transmission. We also present a simulation study, considering different scenarios of disease transmission within the population, and our findings highlight the importance of accounting for spatial variation in disease transmission and individual disease status when these affect demographic rates. Collectively these results show our new model enables a better understanding of how wildlife disease dynamics are linked to population demographics within a spatiotemporal context.

Pathogen whole-genome sequencing (WGS) has been used to track the transmission of infectious diseases in extraordinary detail, especially for pathogens that undergo fast and steady evolution, as is the case with many RNA viruses. However, for other pathogens evolution is less predictable, making interpretation of these data to inform our understanding of their epidemiology more challenging and the value of densely collected pathogen genome data uncertain. Here, we assess the utility of WGS for one such pathogen, in the "who-infected-whom" identification problem. We study samples from hosts (130 cattle, 111 badgers) with confirmed infection of M. bovis (causing bovine Tuberculosis), which has an estimated clock rate as slow as ∼0.1-1 variations per year. For each potential pathway between hosts, we calculate the relative likelihood that such a transmission event occurred. This is informed by an epidemiological model of transmission, and host life history data. By including WGS data, we shrink the number of plausible pathways significantly, relative to those deemed likely on the basis of life history data alone. Despite our uncertainty relating to the evolution of M. bovis, the WGS data are therefore a valuable adjunct to epidemiological investigations, especially for wildlife species whose life history data are sparse.

Marking free-living wild mammals may be desirable during field studies and management interventions; however, doing so presents practical challenges. In the context of disease management interventions, different approaches such as vaccination and culling may be deployed in adjacent areas resulting in a need to identify previously vaccinated individuals to avoid losses of vaccination benefits. Badgers ( Meles meles ) have been identified as a wildlife reservoir of bovine tuberculosis in several countries. In England, the primary means of controlling disease in badger populations has been culling, although policy also includes the use of badger vaccination. Vaccination and culling can therefore increasingly take place in adjacent areas. The current means of marking vaccinated badgers is to apply a fur clip; however, the performance of this method has not been assessed. In this study, we assessed the field performance of livestock markers not previously trialled on badgers. We also assessed the performance of fur clips in terms of (a) how likely they are to be detected on recapture and (b) their detectability using remote cameras. None of the livestock markers trialled persisted well on badger fur. Detectability of fur clips on re-captured badgers fell to 50% in adult badgers by approximately 3 months from application. In cubs, detectability fell to less than 50% within 3 weeks of application. We suggest it is highly likely that, if vaccination and culling were carried out in adjacent areas and fur clipping was the primary means of determining vaccination status, a proportion of recently vaccinated badgers would be removed, particularly cubs. This has important implications for disease control, and we suggest options for minimising such losses.

Abstract Background Studies describing the movement of free-ranging animals often use remotely collected global positioning system (GPS) data. However, such data typically only include intermittent positional information, with a sampling frequency that is constrained by battery life, producing sub-sampling effects that have the potential to bias interpretation. GPS-enhanced ‘dead-reckoning’ of animal movements is an alternative approach that utilises combined information from GPS devices, tri-axial accelerometers, and tri-axial magnetometers. Continuous detailed information of animal movement, activity and habitat selection can then be inferred from finer-scale GPS-enhanced dead-reckoning. It is also a useful technique to reveal the minutiae of an animal’s movements such as path tortuosity. However, examples of studies using these approaches on terrestrial species are limited. Methods Collars equipped with GPS, tri-axial accelerometer, and tri-axial magnetometer loggers were deployed on European badgers, Meles meles, to collect data on geo-position, acceleration and magnetic compass heading, respectively. This enabled us to compare GPS data with calculated GPS-enhanced dead-reckoned data. We also examined space use, distances travelled, speed of travel, and path tortuosity in relation to habitat type. Results Nightly distances travelled were 2.2 times greater when calculated using GPS-enhanced dead-reckoned data than when calculated using GPS data alone. The use of dead-reckoned data reduced Kernel Density Estimates (KDE) of animal ranges to approximately half the size (0.21 km2) estimated using GPS data (0.46 km2). In contrast, Minimum Convex Polygon (MCP) methods showed that use of dead reckoned data yielded larger estimates of animal ranges than use of GPS-only data (0.35 and 0.27 km2, respectively). Analyses indicated that longer periods of activity were associated with greater travel distances and increased activity-related energy expenditure. Badgers also moved greater distances when they travelled at faster speeds and when the routes that they took were less tortuous. Nightly activity-related energy expenditure was not related to average travel speed or average ambient temperature but was positively related to the length of time individuals spent outside the sett (burrow). Badger activity varied with habitat type, with greater distance, speed, track tortuosity, and activity undertaken within woodland areas. Analyses of the effects of varying GPS sampling rate indicate that assessments of distance travelled depend on the sampling interval and the tortuosity of the animal’s track. Where animal paths change direction rapidly, it becomes more important to use dead-reckoned data rather than GPS data alone to determine space use and distances. Conclusions This study demonstrates the efficacy of GPS-enhanced dead-reckoning to collect high-resolution data on animal movements, activity, and locations and thereby identify subtle differences amongst individuals. This work also shows how the temporal resolution of position fixes plays a key role in the estimation of various movement metrics, such as travel speed and track tortuosity.

Bovine tuberculosis (bTB) has significant socio-economic and welfare impacts on the cattle industry in parts of the world. In the United Kingdom and Ireland, disease control is complicated by the presence of infection in wildlife, principally the European badger. Control strategies tend to be applied to whole populations, but better identification of key sources of transmission, whether individuals or groups, could help inform more efficient approaches. Mechanistic transmission models can be used to better understand key epidemiological drivers of disease spread and identify high-risk individuals and groups if they can be adequately fitted to observed data. However, this is a significant challenge, especially within wildlife populations, because monitoring relies on imperfect diagnostic test information, and even under systematic surveillance efforts (such as capture-mark-recapture sampling) epidemiological events are only partially observed. To this end we develop a stochastic compartmental model of bTB transmission, and fit this to individual-level data from a unique > 40-year longitudinal study of 2,391 badgers using a recently developed individual forward filtering backward sampling algorithm. Modelling challenges are further compounded by spatio-temporal meta-population structures and age-dependent mortality. We develop a novel estimator for the individual effective reproduction number that provides quantitative evidence for the presence of superspreader badgers, despite the population-level effective reproduction number being less than one. We also infer measures of the hidden burden of infection in the host population through time; the relative likelihoods of competing routes of transmission; effective and realised infectious periods; and longitudinal measures of diagnostic test performance. This modelling framework provides an efficient and generalisable way to fit state-space models to individual-level data in wildlife populations, which allows identification of high-risk individuals and exploration of important epidemiological questions about bTB and other wildlife diseases.

Wild animals commonly harbour infectious diseases with risk of spillover to humans and livestock. Bovine tuberculosis (bTB) is one such disease, posing significant socio-economic and welfare threats to the cattle industry worldwide. Identifying superspreaders, those individuals most responsible for onward transmission of infection, is critical for disease management. In practice, superspreaders are hard to identify because monitoring relies on imperfect surveillance and imperfect diagnostic tests, hence key epidemiological events, including transmission, are only partially observed. To infer the hidden dynamics of disease spread in wildlife, we fitted an individual-level stochastic spatial meta-population model of bTB transmission to data from a longitudinal study of the European badger ( Meles meles ). We develop a novel estimator for the individual effective reproduction number, providing quantitative evidence for the presence of superspreader badgers, despite the population-level effective reproduction number being less than one. The efficiency of bTB control in badgers could be substantially increased by targeting interventions at the relatively small proportion of individuals responsible for most onward transmission. Our modelling framework provides a flexible, efficient and generalisable means of fitting state-space models to individual-level data, to identify high-risk individuals and explore important epidemiological questions about bTB and other diseases of wildlife, livestock and humans.

In the UK and Republic of Ireland, the European badger (Meles meles) is considered the most significant wildlife reservoir of the bacterium Mycobacterium bovis, the cause of bovine tuberculosis (bTB). To expand options for bTB surveillance and disease control, the Animal and Plant Health Agency developed a bespoke physical restraint cage to facilitate collection of a small blood sample from a restrained, conscious badger in the field. A key step, prior to pursuing operational deployment of the novel restraint cage, was an assessment of the relative welfare impacts of the approach. We used an established welfare assessment model to elicit expert opinion during two workshops to compare the impacts of the restraint cage approach with the only current alternative for obtaining blood samples from badgers in the field, which involves administration of a general anaesthetic. Eleven panellists participated in the workshops, comprising experts in the fields of wildlife biology, animal welfare science, badger capture and sampling, and veterinary science. Both approaches were assessed to have negative welfare impacts, although in neither case were overall welfare scores higher than intermediate, never exceeding 5-6 out of a possible 8. Based on our assessments, the restraint cage approach is no worse for welfare compared to using general anaesthesia and possibly has a lower overall negative impact on badger welfare. Our results can be used to integrate consideration of badger welfare alongside other factors, including financial cost and efficiency, when selecting a field method for blood sampling free-living badgers.

The social environment experienced by livestock can have implications for their health, welfare, and quently, their productivity. Changes in the dairy industry have led to larger herd sizes and altered management of cows, which has impacted their social environment. Studies have shown that mixing of animals can lead social instability of groups and expansion of herds can lead to high stocking densities resulting in social stress negative effects on production. Yet few studies have assessed the putative impact of positive cow-cow teractions, such as proximity to preferred herd mates and engaging in grooming, on milk production and health. To address this, we used cattle proximity as a proxy for affiliative interactions between cows in dairy herds in south-west England over one week study periods. We created proximity networks of dairy and measured cow-cow associations according to milk yield, somatic cell count (SCC; an indicator of mastitis), parity (number of lactations in the cow's lifetime), and lactation stage (grouped by days in milk for current lactation). We then assessed associations between social factors and production and health measures (milk and SCC). In all three herds, cows interacted more with cows in the same parity, suggesting early social bonding may be evident later in life and that grouping animals in terms of parity might encourage affiliative interactions. Cows did not associate according to milk yield, SCC, or lactation stage. There was no significant association between milk production or SCC and the total time spent in social contact with other cows, the mean time with the four closest herd mates, or the number of closest herd mates of the same parity. We suggest that further research on positive social environments for dairy cattle is warranted in the interests of improving welfare enabling a more robust assessment of the putative effects on production and health parameters

High population density should drive individuals to more frequently share space and interact, producing better-connected spatial and social networks. Despite this widely-held assumption, it remains unconfirmed how local density generally drives individuals' positions within wild animal networks. We analysed 34 datasets of simultaneous spatial and social behaviour in >55,000 individual animals, spanning 28 species of fish, reptiles, birds, mammals, and insects. >80% of systems exhibited strongly positive relationships between local density and network centrality, providing broad empirical evidence that local density increases connectedness at the individual level. However, >75% of density-connectedness relationships were nonlinear, and density's importance declined at higher values in >70% of systems, signifying saturating effects. Density's effect was much stronger and less saturating for spatial than social networks, suggesting population density drives individuals to become disproportionately spatially connected rather than socially. These findings reveal fundamental trends underlying societal structuring, with widespread behavioural, ecological, and evolutionary implications. ### Competing Interest Statement The authors have declared no competing interest.

Abstract Bayesian approaches to the modelling of ecological systems are increasingly popular, but there are competing methods for formal model comparisons. Here, we focus on the task of performing multimodel inference through estimating posterior model weights, which encompasses uncertainties in the choice of competing model structure into the inference outputs. Model‐based approaches such as reversible‐jump Markov chain Monte Carlo (RJ‐MCMC) are flexible and allow multimodel inference, but can be complex to implement and optimise, and so we translate a model‐based approach for ecological applications using Importance Sampling to estimate the marginal likelihood of the data given a particular model. This approach allows for model comparison through the estimation of Bayes' Factors or interpretable posterior model probabilities, yielding model weights that facilitate multimodel inference through Bayesian model averaging. We demonstrate Importance Sampling with two case study investigations in animal demography: censused analysis of banded mongoose (Mungos mungo) survival where missing data are uncommon, and capture–mark–recapture analysis of European badger (Meles meles) survival where data are commonly missing. We compare outcomes of the model comparison using the Importance Sampling approach to those obtained through single‐model inference approaches using Deviance information criteria and the Watanabe–Akaike information criteria. The results of the Importance Sampling method aligns with RJ‐MCMC model comparisons while often being more straightforward to fit and optimise, particularly if the competing models are non‐nested.

Natural cases of zooanthroponotic transmission of SARS-CoV-2 to animals have been reported during the COVID-19 pandemic, including to free-ranging white-tailed deer (Odocoileus virginianus) in North America and farmed American mink (Neovison vison) on multiple continents. To understand the potential for angiotensin-converting enzyme 2 (ACE2)-mediated viral tropism we characterised the distribution of ACE2 receptors in the respiratory and intestinal tissues of a selection of wild and semi-domesticated mammals including artiodactyls (cervids, bovids, camelids, suids and hippopotamus), mustelid and phocid species using immunohistochemistry. Expression of the ACE2 receptor was detected in the bronchial or bronchiolar epithelium of several European and Asiatic deer species, Bactrian camel (Camelus bactrianus), European badger (Meles meles), stoat (Mustela erminea), hippopotamus (Hippopotamus amphibious), harbor seal (Phoca vitulina), and hooded seal (Cystophora cristata). Further receptor mapping in the nasal turbinates and trachea revealed sparse ACE2 receptor expression in the mucosal epithelial cells and occasional occurrence in the submucosal glandular epithelium of Western roe deer (Capreolus capreolus), moose (Alces alces alces), and alpaca (Vicunga pacos). Only the European badger and stoat expressed high levels of ACE2 receptor in the nasal mucosal epithelium, which could suggest high susceptibility to ACE2-mediated respiratory infection. Expression of ACE2 receptor in the intestinal cells was ubiquitous across multiple taxa examined. Our results demonstrate the potential for ACE2-mediated viral infection in a selection of wild mammals and highlight the intra-taxon variability of ACE2 receptor expression, which might influence host susceptibility and infection.

Genome-wide homozygosity, caused for example by inbreeding, is expected to have deleterious effects on survival and/or reproduction. Evolutionary theory predicts that any fitness costs are likely to be detected in late life because natural selection will filter out negative impacts on younger individuals with greater reproductive value. Here we infer associations between multi-locus homozygosity (MLH), sex, disease and age-dependent mortality risks using Bayesian analysis of the life histories of wild European badgers Meles meles in a population naturally infected with Mycobacterium bovis (the causative agent of bovine tuberculosis [bTB]). We find important effects of MLH on all parameters of the Gompertz-Makeham mortality hazard function, but particularly in later life. Our findings confirm the predicted association between genomic homozygosity and actuarial senescence. Increased homozygosity is particularly associated with an earlier onset, and greater rates of actuarial senescence, regardless of sex. The association between homozygosity and actuarial senescence is further amplified among badgers putatively infected with bTB. These results recommend further investigation into the ecological and behavioural processes that result in genome-wide homozygosity, and focused work on whether homozygosity is harmful or beneficial during early life-stages.