Free-ranging banded mongooses (Mungos mungo) in northeastern Botswana are infected by a novel Mycobacterium tuberculosis complex pathogen, M. mungi, which putatively infects mongooses through lesions in the skin (often the planum nasale) from an environmental reservoir. To understand the epidemiology of the yearly and highly seasonal outbreaks of M. mungi in this population of banded mongooses, researchers need to understand what factors influence banded mongoose exposure to M. mungi and banded mongoose susceptibility to M. mungi infection. Researchers have no baseline data on the behavioral ecology of this population of banded mongooses - such as home range dynamics, denning ecology, movement ecology, and foraging ecology, all of which may play a role in banded mongoose exposure to M. mungi. Further, researchers have highlighted the potential role of prolonged elevations of glucocorticoids in impairing cell-mediated immunity, which would play a significant role in determining susceptibility to a mycobacterium such as M. mungi, however, researchers have no data on the endocrinology of banded mongooses. Finally, researchers have not detected M. mungi infection in any other population of banded mongooses. Our study population has a gradient of troops (social groups) that vary from troops with extremely close association with humans in a town, to troops associated with humans at tourist lodges within the Chobe National Park, to troops with no discernible association with humans within the national park and surrounding forest reserve. Researchers have few data on how synanthropy (living with humans) affects banded mongoose behavioral ecology and no data on how synanthropy affects banded mongoose endocrinology. Researchers do not know whether or how the high level of synanthropy in this population of banded mongooses plays a role in the epidemiology of M. mungi outbreaks. Thus, we document here some aspects of banded mongoose home range dynamics, movement metrics, denning ecology and foraging behavior for our study population in northeastern Botswana. We present a novel method for screening data from global positioning system (GPS) collars for large measurement error and we present a detailed home range study. We also document the spatio-temporal dynamics of glucocorticoid production among several banded mongoose study troops across our study site, using a non-invasive assay for fecal glucocorticoid metabolites, which we validated and also present here.  We tested to see which factors, including nutritional limitation, predation risk, and reproduction (and associated competition, agonistic encounters, and predation), best explained the variation in glucocorticoid production among our study troops over several years. We found that the metrics traditionally used to screen data from GPS collars, horizontal dilution of precision (HDOP) or fix dimension (2-D or 3-D), performed poorly relative to a new screening metric that we propose, the estimated elevation error (EEE). We propose that researchers use our screening method, which combines test data and a model-averaging information-theoretic framework that uses a priori candidate models of telemetry measurement error. Although we recommend including EEE in a priori candidate models, it may not describe telemetry error in other systems as well as it did in our own. Banded mongooses in our study population formed troops of a median of 13 adults (IQR: 11 to 21 adults) and these troops used home ranges of a median of 68 ha (IQR: 39 to 134 ha) with core areas of a median of 15 ha (IQR: 9 to 28 ha). These cores (statistically-clumped space use) occurred at a median volume contour of 66 % (IQR: 58 to 71 %). Synanthropic troops showed more clumped area use than apoanthropic troops (those living away from humans). Synanthropic troops also used man-made structures for den sites in SI{81}{percent} of nights, fed from refuse sites in 13 % of foraging observations, and drank from anthropogenic water sources in 78 % of drinking observations. From our conducted adrenocorticotropic hormone challenge, we detected valid increases in fecal glucocorticoid metabolite concentrations in mongoose feces using our four tested enzyme-immunoassays. An 11-oxoetiocholanolone assay detecting 11,17-dioxoandrostanes (11,17-DOA) performed best. Using this assay, we detected expected decreases in fecal glucocorticoid metabolite concentrations 48 h after administering dexamethasone sodium phosphate. We also validated this assay using biological events as challenges, in which captive mongooses showed higher fecal glucocorticoid metabolite concentrations during reproductive activity, agonistic encounters, and depredation events. The time delay of fecal glucocorticoid metabolite excretion approximately corresponded with food transit time, at a minimum of approximately 24 h. Fecal glucocorticoid metabolite metabolism was minimal up to 8 h post-defecation. Reproduction and its associated challenges dramatically increased glucocorticoid production, which otherwise remained low and stable in a captive troop with a constant food supply and lowered predation risk. Variation in glucocorticoid production in free-ranging banded mongooses was best explained by food limitation as described by current nutritional limitation (proportion of fecal organic matter), recent rainfall (which increases soil macrofauna availability), and access to concentrated anthropogenic food resources. Habitat differences in soil macrofauna density and reproductive events also explained variation in glucocorticoid production in free-ranging mongooses, but to a much lower degree. Predation risk, as measured by canopy cover (escape from aerial predators) and group size (decreased per capita vigilance) explained very little of the variation in glucocorticoid production. In the late dry season, banded mongooses in our population may face a "perfect storm" of nutritional limitation, agonistic encounters at concentrated food resources, aggressive evictions, estrus, competition for mates, parturition, and predation pressure on pups. We suspect that this prefect storm may push glucocorticoid responses into homeostatic overload and may impair cell-mediated immunity in banded mongooses. / Ph. D.

Understanding the disease transmission dynamics in wildlife species can be difficult and can prove more complicated if the population structure of a socially living species is shaped by territoriality. Understanding the connections and movements of individuals between groups is vital to documenting how a disease may be spread. The presence of a heterogeneous landscape can further complicate attempts to describe transmission of an infectious disease. Here, I sought to understand how dispersal patterns of individual banded mongooses (Mungos mungo) could potentially influence disease transmission. Banded mongooses are small fossorial mammals that live in social groups ranging from 5 to 75 individuals and defend their territories against rival troops. The focal population of mongooses for this study lives across a complex environment in the Chobe district of northern Botswana and is faced with a novel strain of tuberculosis, Mycobacterium mungi. To infer genetic structure and individual movements between troops, I utilized microsatellite genetic markers and population genetic analyses. I found moderately strong genetic structuring (FST = 0.086) among 12 troops of banded mongooses in the study area in 2017-18. The best supported number of genetic clusters was K = 7, with a considerable amount of admixture between troops in urban areas. Compared to the average pairwise differentiation values of troops residing in natural habitats (FST = 0.102), urban troops had a lower level of differentiation (FST = 0.081), which suggests more gene flow between these troops. Among 168 mongooses genotyped, 20 were identified as being likely dispersers, with the majority moving across anthropogenic environments, suggesting that dispersal is heightened in urbanized areas. To assess whether temporal variation had an effect on genetic structure and gene flow between troops, I compared population genetic results from 5 troops in 2008 to those from the same 5 troops in 2017. Genetic differentiation was lower between troops living in urban environments than in natural environments for both 2008 and 2017. This result suggests higher gene flow across the anthropogenic landscapes at both times steps. The overall genetic structuring of the troops persisted over almost a decade, with the exception of observing more mixture and admixture in 2017 than in 2008. The effective population sizes (Ne) of troops were larger in 2008, which would indicate that genetic variability declined as time progressed. For 11 individuals confirmed to have M. mungi, an assignment test suggested that 3 mongooses were likely dispersers. This finding would contradict that of previous work, which suggested that sick banded mongooses refrained from dispersing. Sequencing of the M. mungi strains would be needed to determine whether these dispersers moved while sick or became infected after entering their new troop. These findings suggest that emphasis should be placed on closely monitoring banded mongoose troops in areas with heavy human influence. Here we see lower pairwise differentiation, higher gene flow estimates, and more frequent dispersal events. Heightened dispersal potentially can result in elevated disease transmission between troops in urban habitats. With disease transmission being the result of complex interactions between environment, host, pathogen, and time, results from this study contribute to understanding of disease transmission dynamics. / MS / Understanding how groups of the same species are connected is important for assessing how wildlife diseases spread across a landscape. For social species, connections are established by the movements of individuals between different groups; however, these can prove difficult to observe. Further complicating our ability to infer connections and movements, groups often live under different environmental conditions, which can influence movement rates. I studied banded mongooses (Mungos mungo) living in northern Botswana to assess the role of individual movement on the potential for disease transmission. Banded mongooses are small ground-dwelling mammals that live in troops of 5-75 individuals and defend group home-ranges. In Botswana, some troops are infected with a species of tuberculosis (TB, caused by the bacterium Mycobacterium mungi) that is unique to banded mongooses. Using molecular genetic tools, I estimated how genetically similar troops were to one other and estimated the rates of movement of individuals between troops. I found that troops living in urban environments tended to be more genetically similar to one other compared to troops living in natural environments within nearby Chobe National Park. I also detected more cases of individuals moving between troops in urban settings, with little evidence of movement between troops living in natural areas. These results suggest that there is more genetic exchange and a higher degree of connection between troops living in areas heavily influenced by people. With more connections between town-dwelling troops, we would expect to see higher rates of disease transmission between these urban troops, and hence should monitor their movement and health status closely. I also assessed how genetic structure and connections between banded mongoose troops changed over time by comparing results for collections of samples made in 2008 and 2017. Although more movement was detected in 2017, the overall pattern of genetic connections remained similar over the ten-year period. In particular, there was greater genetic similarity between troops in town compared to troops in natural environments in both years. Additionally, I genetically assigned TB-positive individual mongooses to their troop of origin to determine whether sick individuals moved out of their original troops. I identified three sick individuals as probable dispersers, although it is difficult with the information available to know whether they moved while infected or became ill after joining a new troop.

Effectively developing robust predictive models for forecasting infectious disease dynamics over space and time relies on successful surveillance strategies to accurately assess host infection status. We are constantly refining these models to improve our understanding of transmission and persistence dynamics in host populations but are continuously challenged with difficulties in accurately diagnosing host infection status. These challenges are especially persistent for pathogens of the Mycobacterium tuberculosis Complex (MTBC), which cause tuberculosis (TB) disease in a wide array of mammalian hosts. These challenges are further exacerbated when working with MTBC pathogens in free-ranging wildlife hosts. Although TB disease in humans is a primary concern, TB in free-ranging wildlife hosts poses a large threat to human and animal health. One recently described and novel MTBC pathogen is Mycobacterium mungi, which infects the highly social, group-living banded mongoose (Mungos mungo). M. mungi poses a large threat to human and animal health as banded mongoose hosts thrive in urbanized areas and live in close proximity to humans, but despite this threat, accurately diagnosing M. mungi infection status remains a primary challenge. Here, I develop a host response-based assay for differentiating banded mongoose with clinical M. mungi disease from individuals that are putatively healthy using transcriptional biomarkers in whole blood. To our knowledge, this is the first evaluation of host response-based transcriptional signatures to detect TB infection in unstimulated whole blood collected from a free-ranging wildlife species. I found that the expression of two genes, GBP5 and DUSP3, are significantly upregulated (GBP5, p < .05; DUSP3, p < .005) in banded mongoose with clinical M. mungi disease when compared to that of putatively healthy individuals. These results are consistent with studies of active M. tuberculosis disease in humans and support the use of host response-based assays using blood transcriptional biomarkers for diagnosing TB in free-ranging wildlife hosts. These findings are important for improving surveillance strategies for diagnosing M. mungi infection status in banded mongoose and will be essential in refining predictive models for forecasting transmission and persistence dynamics over space and time. / Creating models to predict how diseases circulate and persist within a population is dependent on our ability to accurately diagnose if a host is infected. Diagnosing infection is difficult for some diseases, including tuberculosis (TB) pathogens, which infect humans and many other mammalian species. While vast improvements have been made in diagnosing TB infection in humans, diagnosing TB in free-ranging wildlife species is a constant challenge. These challenges are further exacerbated across the different pathogen species of TB. Although TB disease in humans is a primary concern, TB in free-ranging wildlife hosts poses a large threat to human and animal health. One recently discovered TB pathogen is Mycobacterium mungi, which infects free-ranging banded mongoose (Mungos mungo). This pathogen poses a large threat to human and animals health since banded mongoose thrive in urbanized areas and live in close proximity to humans. Despite this threat, accurately diagnosing M. mungi infection in banded mongoose remains a challenge. Here, I develop a diagnostic molecular tool that uses banded mongoose blood to measure the expression of specific genes and differentiate diseased individuals from seemingly healthy individuals. To our knowledge, this is the first study that has used this specific approach for diagnosing TB in a free-ranging wildlife species. I found that the expression of two genes are significantly increased in banded mongoose with clinical M. mungi disease when compared to that of seemingly healthy individuals. These results are consistent with studies human TB disease in humans and support the use of this approach for diagnosing TB in free-ranging wildlife hosts. These findings are important for improving diagnostics for M. mungi infection in banded mongoose and will be essential in refining models for predicting how this disease circulates and persists over space and time.