Department of Ecology and Resource Management / MENVSC

The field of vector-borne disease research uses multidisciplinary approaches to help understand complicated interactions. This dissertation, covers three different aspects of tick-borne disease research which all focus on exploring tick-borne diseases in the non-endemic areas of Denton, County Texas and the state of Texas with a focus on Rickettsia spp. These aspects include tick sampling, testing ticks for the presence of Rickettsia spp., and creating species distribution maps of the Rickettsia spp. Rickettsia amblyommatis and tick species Amblyomma americanum.

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Tick populations have been immigrating into northeast Tennessee from east Tennessee, Kentucky, Virginia, and North Carolina. Counties in states bordering northeast Tennessee harbor tick species associated with human illness. Human diseases transmitted by ticks include ehrlichiosis, spotted fever rickettsial group diseases, tularemia, anaplasmosis, babesiosis, Lyme disease, alpha-gal syndrome, Heartland virus, Powassan virus, and southern tick-associated rash illness (STARI). These diseases cause morbidity and mortality in human populations and may pose a high risk to individuals, wildlife, and livestock. The Cherokee National Forest covering the east Tennessee border provides a permissible environment for ticks to immigrate and thrive. Residents of northeast Tennessee frequently use the natural environment for a variety of purposes, creating exposure risk at the human-animal-environment interface. This study performed a scoping review and meta-analysis addressing topics informing epidemiological investigation of tick populations. The meta-analysis identified geography, climate, and Shannon-Wiener Diversity Index as the most significant variables associated with northeast Tennessee tick populations. Additionally, tick surveillance in northeast Tennessee counties was performed. These counties included Carter, Greene, Hancock, Hawkins, Johnson, Washington, and Unicoi. Primary tick species present in the summer included the American Dog tick (Dermacentor variabilis); the winter included the Blacklegged/Deer tick (Ixodes scapularis). Canonical correlation analysis was used to identify which environmental variables had the most influence, to what degree, and in a positive or negative direction. Altitude, total forest land, forest canopy, and fraction of surface water area were statistically significant. More altitude was correlated with more clinical cases; less total forest land, canopy, and fraction of surface water area was correlated with less clinical cases. Lastly, species distribution modeling of the invasive Asian longhorned tick was conducted. Study results indicate a low to moderate risk for tick-borne illness exposures among human populations, which is poised to increase. Species distribution modeling and clinical case data reports suggested an increasing exposure risk from improved habitat suitability. Increased risk is related to climate change and tick population growth in metropolitan areas. Finally, surveillance and control methods are summarized for integration into public health interventions.

The convergence of three young scientific disciplines (ecology, geospatial sciences, and remote sensing) has generated unique advancements in wildlife research by connecting ecological data with remote sensing data through the application of geospatial techniques. Ecological datasets may contain spatial and sampling biases. By using geospatial techniques, datasets may be useful in revealing landscape scale (e.g., statewide) trends for wildlife populations, such as population recovery and human-wildlife interactions. Specifically, black bear populations across North America vary greatly in their degree of distribution stability. The black bear population in Michigan may be considered stable or secure, whereas the population in Missouri is currently recolonizing. The focus of the research in this dissertation is to examine the ecological and anthropogenic impacts 1) on human-black bear interactions in Michigan (see Chapter 2) and 2) on black bear presence in Missouri (see Chapter 3), through the use of black bear reports provided by the public to the state wildlife agencies. By using generalized linear modeling (GLM) and maximum entropy (MaxEnt), I developed spatial distribution models of probability of occurrence/presence for the 2 study areas (Michigan and Missouri). For the Missouri study, I quantified the spatiotemporal shifts in the probability of bear presence statewide. The results from my statewide studies corroborate previous local-scale research based on rigorous data collection. Overall, human-black bear interactions (e.g., wildlife sightings, conflicts), while very dynamic, appear greatest in forested and rural areas where the preferred habitat for black bears (i.e., forest) intersects with low density anthropogenic activities. As both human and black bear populations continue to expand, it is reasonable to expect human-black bear interactions to spatiotemporally increase across both study areas. The results from my studies provide wildlife managers with information critical to management decisions such as harvest regulations and habitat conservation actions across the landscape and through time. The ability to detect and monitor ecological changes through the use of geospatial techniques can lead to insights about the stressors and drivers of population-level change, further facilitating the development of proactive causeocused management strategies.

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Small Island Developing States (SIDS) of the Caribbean are one of the world’s ‘hottest’ ‘biodiversity hotspots’. However, this biodiversity continues to be threatened by habitat loss, and now, by climate change. The research reported here investigated the potential of species distribution modelling (SDM) as a plant conservation tool within Caribbean SIDS, using Trinidad as a case study. Prior to the application of SDM, ancillary analyses including: (i) quantification and mapping of forest cover change (1969 to 2007) and deforestation rates, and (ii) assessment of the island’s vegetation community distribution and associated drivers were carried out. Community distribution and commercial importance and global/regional rarity were used to generate a list of species for assessing the potential of SDM within Trinidad. Species occurrence data were used to generate species distribution models for present climate conditions within the SDM algorithm, MaxEnt. These results were assessed through expert appraisal and concurrence with results of ecological analyses. These models were used to forecast suitable species climate space forty years into an SRES A2 future. Present and future models were then combined to produce a ‘collective change map’ which showed projected areas of species’ range expansion, contraction or stability for this group of species with respect to Trinidad’s Protected Areas (PAs) network. Despite the models being indicative rather than accurate, it was concluded that species’ climate space is likely to decrease or disappear across Trinidad. Extended beyond Trinidad into the remainder of the Caribbean region, SDM may be a crucial tool in identifying which PAs within the region (and not individual islands) will facilitate future survival of given target species. Consideration of species conservation from a regional, rather than an individual island perspective, is strongly recommended for aiding the Caribbean SIDS to adapt in response to climate change.