Wildlife biodiversity provides a variety of ecosystem services and is an important indicator of overall ecosystem health. This research investigates the relationship between wildlife biodiversity and landscape characteristics in Virginia. The goal is to produce predictive models of biodiversity within the Commonwealth using environmental characteristics, including fragmentation metrics at the class- and landscape-levels, as well as other environmental variables. The 1248 12-digit watersheds in Virginia are the sampling units for the analyses, with the state stratified into the seven US Environmental Protection Agency's Level III classification. Data on wildlife alpha diversity is based on two sets of species data maintained by the Virginia Dept. of Game & Inland Fisheries (VDGIF). The first chapter provides an introduction to the issue of biodiversity conservation and the background information for this work. The second chapter describes the study using the 2001 National Land Cover Data to calculate class- and landscape-level fragmentation metrics. Best subset regression is used to determine the best predictors for wildlife biodiversity using these metrics. Final selected models range in predictive power from R2 = 0.41 to 0.73 for each of the 7 ecoregions. The third chapter analyzes the relationship between wildlife biodiversity and various environmental variables in order to determine the strength of these factors as drivers for alpha diversity. These variables are then incorporated with the fragmentation metrics in an attempt to improve the biodiversity models. The environmental variable models had R2 = 0.22 to 0.65 across the ecoregions, while R2 = 0.28 to 0.72 when the environmental and fragmentation variables are combined. The last chapter focuses on the conclusions of the studies, the limitations of the data, and the benefits of this work. Overall, our results underline the importance of using fragmentation metrics in Virginia's wildlife models. / Master of Science

I studied the behaviors, movement dynamics, habitat relationships, and population characteristics of Rocky Mountain mule deer (Odocoileus hemionus) using urban and rural winter ranges in Cache Valley, Utah , from January 1994 to February 1998. There were 2 goals to my research endeavors. The first was to assess how and why the behaviors and demographic characteristics of urban deer differed from those of rural deer. The second was to assess the scale-dependent responses to habitat and the scale-dependent patterns of habitat use by deer living in each area. To accomplish the first goal, I compared the prevalence of migration, the spatial and temporal patterns of migration, and the spatial patterns of home range use between urban and rural deer. I also compared deer reproduction and population density in each area. I then explain how behavioral and demographic dissimilarities between urban and rural deer may have corresponded to differences in their net energetic gains (NEG) on seasonal ranges. These explanations, when combined graphically, generated a time-specific hypothesis of lower NEG by urban deer on a year-round basis. To accomplish the second goal, I developed new methodologies for analyzing animal movement pathways (which represent signatures of how animals respond to habitat), and animal patterns of habitat use . These methodologies explicitly incorporated the effects of spatial scale by employing fractal geometry and information theory. The results of these analyses showed that urban and rural deer responded to their habitats in similar ways at coarse resolutions of analysis (100-600 m), but differently at fine resolutions of analysis ( 4-60 m). I argue that similarities in habitat response at coarse resolutions reflected a common movement process that allowed deer maximize use of their home ranges while minimizing energetic expenditures. With respect to patterns of habitat use, urban deer concentrated in areas with concealment vegetation , which was highly fragmented across all resolutions of analysis. Rural deer, on the other hand, dispersed throughout areas containing shrubby vegetation at fine resolutions, and south-facing slopes at coarse resolutions. Interpretation of these results is discussed in detail.

Oak wilt is a fatal disease of oaks caused by the fungus Ceratocystis fagacearum. Loss or degradation of habitat due to the disease may negatively affect the federally endangered golden-cheeked warbler (Setophaga chrysoparia). To assess the impact of oak wilt on golden-cheeked warblers, I investigated its influence on habitat selection and quality. I used remote sensing to estimate the amount of potential golden-cheeked warbler habitat currently affected by oak wilt, to predict the amount of potential habitat likely to be affected in the near future, and to assess the current probability of warbler occupancy in areas affected by oak wilt historically. I also quantified vegetative characteristics to assess overstory vegetation and regeneration in areas affected by the disease. I found proportional occupancy and territory density in unaffected areas to be, respectively, 3.5 and 1.8 times that of affected areas. Pairing success was 27% lower for territories containing oak wilt but fledging success was not affected. I estimated that 6.9% of potential golden-cheeked warbler habitat and 7.7% of the total area within my study region was affected by oak wilt in 2008. By 2018, I predicted that 13.3% of potential golden-cheeked warbler habitat and 16.0% of the study region would be affected by the disease. Using historical imagery, I found that areas affected by oak wilt in the past are less likely to be classified as current potential warbler habitat than areas never affected by the disease. I found no differences between the understory vegetation of affected and unaffected areas but that oaks were more common in the overstory than in the understory, suggesting that species composition in affected areas may shift in the years following an outbreak of the disease. My results suggest that the presence of oak wilt negatively influences habitat selection and quality for golden-cheeked warblers, likely due to reduced canopy cover in susceptible oak species. Additionally, oak wilt frequently occurs in golden-cheeked warbler habitat and will continue to spread into warbler habitat in the coming years. Future management efforts should address the threat oak wilt poses to golden-cheeked warblers by incorporating applicable preventative measures.

Ecology has been devoted to defining the content of a species’ environment. Defining the extent, or size, of a species’ environment is also pivotal to elucidating species-habitat relationships. More than a home range, this extent integrates an individual’s lifetime experiences with resources, competition, and predators. I theorised that a species’ habitat extent is identifiable from its characteristic spatial scale of habitat selection, which in turn is predicted by body size. I reviewed scale-dependent mammalian habitat selection studies and found that a characteristic scale was typically not identified, but identifiable. Of several ecological predictors tested, only body mass was a significant predictor of the relative size of a species’ characteristic habitat selection scale. Tests of existing data are confounded by differing approaches, so I empirically tested the scale-body mass hypothesis using a standardised survey of 12 sympatric terrestrial mammal species from the Canadian Rocky Mountains. For each species, support for habitat models varied across 20 scales tested. For six species, I found a characteristic selection scale, which was best predicted by species body mass in a quadratic relationship. Occurrence of large and small species was explained by habitat measured at large scales, whereas medium- sized species were explained by habitat measured at small scales. The relationship between body size and habitat selection scale is congruent with the textural-discontinuity hypothesis, and implies species’ evolutionary adaptation to landscape heterogeneity as the driver of scale-dependent habitat selection. I applied this principle to examine wolverine habitat selection, and found that anthropogenic fragmentation of the landscape influences that species’ occurrence in space at large spatial scales. Finally, I contended that the prevailing paradigm equating habitats to resources omits interspecific interactions that are key predictors of a species’ occurrences. I examined habitat selection of martens and fishers in terrestrial environments, and sea otters in marine coastal environments, and tested whether the presence of heterospecifics could explain spatial occurrence beyond landscape structure and resources. In both cases, the presence of heterospecifics explained species occurrence beyond simple resource selection. Interspecific interactions are key drivers of a species’ distribution in space; this is the spatial expression of the concepts of fundamental and realized niches. Body size interacts with landscape structure to determine the scale of a species’ response to its environment, and within this habitat extent, interspecific interactions affect the species’ pattern of occurrence and distribution. / Graduate

Despite the increases in the intensity and frequency of disturbances on coral reefs in the Red Sea over the past decade, patterns of variability in fish communities are still poorly understood. This study aims to contribute to a better understanding of how fish communities vary along multiple spatial scales (10-100’ of kilometers) and to provide a baseline for future comparisons, fundamental to assess responses to climate change and other disturbances. Coral reefs along the Saudi Arabian Red Sea coast were surveyed from 2017 to 2019. The reefs ranged from 28° N to 18 °N and were categorized according their geographical location and grouped within three regions, namely north (24-28.5°N; 12 reefs), central (20.4-22.3°N; 11 reefs), and south (18.5-21.2°N; 30 reefs). The quantification of spatial patterns was conducted based on both taxonomic- and trait-based approaches. Considering the dependence of fish communities on the benthic habitat the relationship between different attributes of the fish assemblages and coral cover was also investigated. A consistent pattern of separation between assemblages of the northern and central region from the ones in the south was observed in nearshore reefs but was not evident for offshore reefs. The southern region supported higher densities, biomass, and species richness than the other two regions. The analysis showed that transect and reef scales contributed to the greatest variation in fish communities, suggesting higher levels of variability within small spatial scales. Several parameters of the fish community (total species, total density, total biomass, total functional entities, functional richness, functional redundancy) were positively correlated to coral cover, particularly in the northern region. Responses were not consistent across the Red Sea basin, suggesting that management plans should be regionally based. This study can be helpful to design management strategies as it provides a current baseline from both taxonomic and trait perspectives for Red Sea reefs that can be used to evaluate future changes due to natural and human-based disturbances.

Cultivation of marine ecosystems began in the early Holocene and has contributed vital resources to humans over millennia. Several more recent cultivation practices, however, erode biodiversity. Emerging lines of evidence indicate that certain resource management practices may promote favourable ecological conditions. Here, I use the co-occurrence of 24 First Nations clam gardens, shellfish aquaculture farms, and unmodified clam beaches to test several hypotheses concerning the ecological implications of managing intertidal bivalve populations. To so do, in 2015 and 2016, I surveyed epifaunal (surface) and bivalve communities and quantified each intertidal sites’ abiotic conditions, including sediment characteristics and substrate composition. In 2017, I generated three-dimensional models of each site using structure-from-motion photogrammetry and measured several aspects of habitat complexity. Statistical analyses use a combination of non-parametric multivariate statistics, multivariate regression trees, and random forests to quantify the extent to which the intertidal resource cultivation structures nearshore biodiversity Chapter 1 outlines a brief history of humanity's use of marine resources, the transition from extracting to cultivating aquatic taxa, and the emergences of the northeast Pacific’s most prevalent shellfish cultivation practices: clam gardens and shellfish farms. Chapter 2 evaluates the ability of epifaunal community assessment methods to capture species diversity by conducting a paired field experiment using four assessment methods: photo-quadrat, point-intercept, random subsampling, and full-quadrat assessments. Conducting each method concurrently within multiple intertidal sites allowed me to quantify the implications of varying sampling areas, subsampling, and photo surveys on detecting species diversity, abundance, and sample- and coverage-based biodiversity metrics. Species richness, density, and sample-based rarefaction varied between methods, despite assessments occurring at the same locations, with photo-quadrats detecting the lowest estimates and full-quadrat assessments the highest. Abundance estimates were consistent among methods, supporting the use of extrapolation. Coverage-based rarefaction and extrapolation curves confirmed that these dissimilarities were due to differences between the methods, not the sample completeness. The top-performing method, random subsampling, was used to conduct Chapter 4’s surveys. Chapter 3 examines the connection between shellfish biomass and the ecological conditions clam garden and shellfish farms foster. First, I established the methodological implications of varying sediment volume on the detection of bivalve diversity, abundance, shell length, and sample- and coverage-based biodiversity metrics. Similar to Chapter 2, this examination identified the most suitable method, which I used during the 2015 and 2016 bivalve surveys. The analyses quantified several interactions between each sites’ abiotic conditions and biological communities including, the influence of substrate composition, sediment characteristics, and physical complexity on bivalve communities, and if bivalve richness and habitat complexity facilitates increases in bivalve biomass. Chapter 4 quantifies the extent to which managing intertidal bivalves enhance habitat complexity, fostering increased diversity in the epifaunal communities. This chapter combines 2015, 2016, and 2017 surveys of the sites' epifaunal communities and habitat complexity metrics, including fractal dimension at four-resolutions and linear rugosity. Clam gardens enhance fine- and broad-scale complexity, while shellfish farms primarily increase fine-scale complexity, allowing for insights into parallel and divergent community responses. Chapter 5 presents an overview of shellfish as a marine subsidy to coastal terrestrial ecosystems along the Pacific coast of North America. I identified the vectors that transport shellfish-derived nutrients into coastal terrestrial environments, including birds, mammals, and over 13,000 years of marine resource use by local people. I also examined the abundance of shellfish-derived nutrients transported, the prolonged persistence of shellfish subsidies once deposited within terrestrial ecosystems, and the ecological implications for recipient ecosystems. Chapter 6 contextualizes the preceding chapters relative to the broader literature. The objective is to provide insight into how multiple shellfish cultivation systems influence biological communities, how ecological mechanisms facilitate biotic responses, and summarize the implications for conservation planning, Indigenous resource sovereignty, and biodiversity preservation. It also explores future work, specifically the need to support efforts that pair Indigenous knowledge, and ways of knowing with Western scientific insights to address conservation challenges. / Graduate / 2022-12-13

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This dissertation is a treatise on spatially-explicit resource selection on two very different vertebrate species. The North Atlantic right whale (Eubalaena glacialis) is the most endangered large whales in the world. Ship strikes and fishing gear entanglement are impediments to recovery. The gopher tortoise (Gopherus polyphemus) is an imperiled species whose upland habitats are shrinking from urban and agricultural development. Determining spatial distribution of important resources is important for conservation strategies. Historical and modern thinking of habitat selection theory and analytical techniques are reviewed and applied to these species. Fine-scale resource selection of sea surface temperature (SST), derived from AVHRR imagery, is evaluated for right whales in the southeastern U. S. calving grounds. Aerial survey data (December-March, 1991-1998) including survey tracklines and right whale locations were entered into a Geographic Information Systems (GIS) for comparing whale use of SST to availability based on search effort. Using Monte Carlo techniques, mean and standard deviation for SSTs and latitudes of whale-sightings were compared to sampling distributions derived from available SSTs and latitudes. From these data, it was concluded that the North Atlantic right whale uses SSTs and latitudes non-randomly. Broad-scale habitat selection for gopher tortoises was evaluated from the 2003 Land Cover/Land Use map (Florida Fish and Wildlife Conservation Commission). Based on land cover and ancillary data, potential gopher tortoise habitat was developed for northeast Florida.

Using a towed underwater video camera system, benthic habitats were classified along transects in a popular offshore fishing area on the West Florida Shelf (WFS) known as “The Elbow.” Additionally, high resolution multibeam bathymetry and co-registered backscatter data were collected for the entire study area. Using these data, full coverage geologic and biotic habitat maps were developed using both unsupervised and supervised statistical classification methodologies. The unsupervised methodology used was k-means clustering, and the supervised methodology used a random forest algorithm. The two methods produced broadly similar results; however, the supervised methodology outperformed the unsupervised methodology. The results of the supervised classification demonstrated “substantial agreement” (κ>0.6) between observations and predictions for both geologic and biotic habitat, while the results of the unsupervised classification demonstrated “moderate agreement” (κ>0.4) between observations and predictions for both geologic and biotic habitat. Comparisons were made with the previously existing map for this area created by Florida Fish and Wildlife Conservation Commission’s Fish and Wildlife Research Institute (FWC-FWRI). Some features are distinguishable in both maps, but the FWC-FWRI map shows a greater extent of low relief hard bottom features than was predicted in our habitat maps. The areas predicted as low relief hard-bottom by FWC-FWRI often coincide with areas of higher uncertainty in the supervised map of geologic habitat from this study, but even when compared with ground-truth points from the towed video rather than predictions, the low relief hard bottom in FWC-FWRI’s map still corresponds to what was identified as sand in the video 73% of the time. The higher uncertainty might be a result of the presence of mixed habitats, differing morphology of hard-bottom, or the presence of sand intermixed with gravel or debris. More ground-truth samples should be taken in these areas to increase the confidence of these classifications and resolve discrepancies between the two maps. Data from the towed video system were also used to assess differences in fish communities among habitat types and to calculate habitat-specific densities for each taxa. Fish communities were found to significantly differ between soft and hard bottom habitats as well as among the hard-bottom habitats with different vertical relief (flat hard-bottom vs more steeply sloping areas). Additionally, significant differences were found between the fish communities in habitats with attached fauna such as sponges and gorgonians, and areas without attached fauna; however, attached fauna require rock to attach to and the rock habitats rarely lacked attached fauna, so this difference may just reflect the difference between fish communities in sand and rock habitats without the consideration of vertical relief. Moreover, the species driving the differences in the fish communities were identified. Fish were more likely to be present and assemblages were more species rich in more complex habitats (rockier, higher relief, presence of attached fauna). Habitat specific densities were calculated for each species, and general trends are discussed. Lastly the habitat-specific densities were extrapolated to the total area of habitat type (sand vs rock) as predicted by the supervised geologic habitat map. There is predicted to be approximately 111,000 fish (95% CI [67015, 169405]) within the study area based on this method, with ~47,000 (~43%) predicted to be within the sand habitat and ~64,000 (~57%) in the rock habitat. This demonstrates the potential of offshore rocky reefs as “critical habitats” for demersal fish in the offshore environment as rock accounts for just 4% of the study area but is expected to contain over half of the total abundance. The value of sand habitats is also shown, as due to their large area they are able to contribute substantially to the total number of fish despite sustaining comparatively low densities.

This thesis evaluates the interaction between four habitat factors vital to the gopher tortoise in Florida. Federally and state listed as threatened throughout its entire range, the gopher tortoise is vital to protect, not only for itself individually but its burrows provide an essential habitat to over 300 species making it a key stone species within its environment. Historic habitat modeling methods are reviewed for the gopher tortoise to highlight the gap on this topic. This research expanded on the methods utilized by Baskaran et al. (2006) evaluating the soil, landcover, percentage of canopy cover and the depth to water table habitat factors key to the gopher tortoise. Statistical analysis was used to establish the interactions using a regression type analysis of the presence/absence data relative to the four factors. A probability map for the study site was then computed from the results. The Analysis of Deviance results for the statistical model with land cover type as an independent variable and a 3-way interaction term for the other factors found that the land cover term was significant as an independent variable and the 3-way interaction of the other 3 habitat factors was significant. This result demonstrates that there is in fact an interaction between the habitat factors influencing the location of gopher tortoises. This finding is significant in future gopher tortoise research as it indicates that habitat factors evaluated individually may not be as important as the interactions between the factors. By understanding the interactions between the habitat factors, the FWC can work alongside other agencies to ‘increase and improve’ these key habitat areas preventing them from destruction. The map results also help pinpoint those fragmented potential habitat sites which are most at risk from full destruction and loss allowing agencies the work on protecting and expanding the suitable habitat landscape in order to ‘enhance and restore’ the gopher tortoise populations residing there, helping them to ‘maintain the gopher tortoise’s function as a keystone species’