Managed forest ecosystems contribute to conservation of wildlife, and generally yield heterogeneous landscapes with patches that support different sessile organisms. Edges or boundaries between adjacent forest patches are often ecologically distinct from patch interiors and can have direct influences on community dynamics and ecosystem functioning near them. To quantify effects of edge and adjacent habitat conditions on avian metrics, I used a hierarchical multi-species occupancy model that considered individual species resource requirements to estimate community occupancy patterns, and used artificial nest surveys to model daily nest survival (DNS) using a Bayesian framework. Results indicated that adjacent forest conditions influenced bird population dynamics in focal forest stands, provided little evidence of an edge effect on avian community response patterns, and showed highest DNS in newly established forest stands. My results highlight the importance of considering type and spatial arrangement of different habitat patches for habitat planning operations on managed forest landscapes.

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I used results from camera traps set for tigers (Panthera tigris) during 2001-2011 in the Hukaung Valley Wildlife Sanctuary of northern Myanmar to assess overall biodiversity of large mammal and bird species, and to identify differences in photo rates inside and outside of the most protected core area of the Sanctuary. A total of 403 camera stations were deployed during October-July in the dry seasons of 2001-2011, 260 inside the Core area and 143 Outside. From 10,750 trap-nights I obtained 2,077 independent photos of wildlife species and 699 of domestic animals and humans, including 35 species of wild mammals (19 carnivores, 4 primates, 1 elephant, 6 even-toed ungulates, 1 pangolin, and 4 rodents) and 16 species of wild birds. Of these, 1 is considered critically endangered, 7 are endangered, 11 are vulnerable, and 5 are nearly threatened. Some species that probably occur in the Sanctuary (e.g., arboreal or semi-aquatic mammals) were not photographed, likely because of camera placement. In total, 48 wild species were photographed in the Core area vs. only 33 at locations Outside of the core area. Generally, few photos of any domestic animal species were obtained inside 9 the Core area, and no photos insurgents were obtained there, but many more photos of poachers and villagers, but also park rangers, were obtained there. Increased patrol efforts may have helped maintain species presence in the Core area, but differences in photo rates between areas were likely mostly influenced by differences in elevation, slope, density of streams, trails, and roads, and perhaps vegetative cover type. Tiger abundance is most influenced naturally by prey availability and anthropogenically by poaching. In the Hukaung Valley Wildlife Sanctuary in northern Myanmar, a major conservation area established to protect tigers, tiger presence has declined. This study was conducted to assess habitat occupancy and distribution of principal tiger prey species in the Core part of the Sanctuary by surveying for sign on 1650.9 km partitioned into 554 sampling units during November 2007 and May 2008. Using standard occupancy model in the program PRESENCE (6.2), habitat occupancy and detection probabilities were predicted and the best candidate model for each species was selected using the Akaike information criterion (AIC). By incorporating 7 environmental and 4 social covariates, the predicted habitat occupancy rates were 0.76 (SE=0.196, naïve estimate = 0.5162) for gaur (Bos gaurus), 0.91 (SE=0.03, naïve estimate = 0.7762) for sambar (Rusa unicolor), 0.57 (SE = 0.003, naïve estimate = 0.3195) for wild pigs (Sus scrofa) and 0.89 (SE = 0.001, naïve estimate = 0.7996) for muntjac (Muntiacus muntjak). Overall, shorter Euclidean distances to ranger stations and trails, decreased stream density, and broadleaved evergreen/semi-deciduous forest and relatively rare rain-fed cropland habitat occurrence positively influenced prey habitat occupancy; conversely, shorter Euclidean distances to villages, roads, and streams, higher elevations, and occurrence of mixed broadleaved and needle-leaved forest habitat 10 negatively influenced occupancy. In addition, Euclidean distance to ranger stations, trails, and roads positively affections species detections, whereas shorter Euclidean distance to villages and streams, high elevations, and high precipitation negatively affected detections. Results indicate that all four prey species were relatively well-distributed through the Sanctuary Core area. However, comparisons with tiger and prey indices of abundance elsewhere suggest that prey density is low and would not likely support many tigers.

As sensing technology develops, it plays an important role in context-aware systems. Using context information improves the user experience of ubiquitous computing. One use of sensed information is to detect a meeting in progress in an office or a conference room. In our system, sensors gather context information from an office environment and act as a presence user agent to update a presence server with context changes. These context changes can be utilized by context-aware services. The presence messaging uses the SIP for Instant Messaging and Presence Leveraging Extensions (SIMPLE) protocol and the presence information is described in eXtensible Makeup Language (XML) format. In this thesis we present a context-sensing component that recognizes meetings in a typical office environment. A context-aware system is able to use this occupancy information to infer that the room is empty, an individual is alone in the room, or a meeting is taking place in the meeting room. Context-aware services might utilize this environmental information to automatically forward a user's incoming calls to their voice mail server. This and other example applications were developed to show the usefulness of this context information. / Så som sensor tekniken utvecklas, spelar de en viktig roll i kontextmedvetna system. Genom att använda kontextuell information förbättras användarupplevelsen av 'ubiquitous computing'. Ett användningsområde för sensorinsamlad information är att upptäcka ett möte som pågår i ett kontor eller konferenslokal. I vårt system samlar sensorer information från en kontorsmiljö och uppdaterar en närvaroserver med kontextuella förändringar. Dessa förändringar kan sedan utnyttjas av kontextmedvetna tjänster. För att förmedla den närvarostatusen använder närvaroservern SIP och ’Presence Leveraging Extensions’ (SIMPLE) protokoll. Närvaro information levereras i 'eXtensible Makeup Language' (XML) format. I denna avhandling presenterar vi en kontextsensorkomponent som känner av möten i en typisk kontorsmiljö. Ett kontextmedvetet system kan använda denna komponent för att dra slutsatsen att lokalen är tom, en person är ensam i lokalen, eller ett möte äger rum i lokalen. Kontextmedvetna tjänster kan utnyttja denna information för att automatiskt vidarebefordra en användares inkommande samtal till deras röstbrevlåda. Detta och andra exempel, har utvecklats för att visa nyttan av denna kontextuella information.

The spatial distribution of a species is not random or uniform across all landscapes, nor is it independent of resource availability and risk of predation. A key aspect in the study of wildlife ecology is understanding how a species evaluates its surroundings and selects habitat that offers advantages to survival and reproductive success. In theory, an individual should select habitat that offers adequate resources to meet its biological requirements and allows it to adjust its use of resources based on a variety of abiotic and biotic habitat factors. Relationships between wildlife populations and habitat characteristics are difficult to assess, yet identification and characterization of these relationships can improve delineation of limiting habitat factors needed for effective conservation and management. The eastern spotted skunk (Spilogale putorius) is a small Mephitid (weasel family) that was once a fairly common furbearer throughout the central and southern United States, with annual range-wide harvests of over 100,000 individuals. In Virginia, the spotted skunk is classified as vulnerable, and anecdotal evidence suggests that this species has been rare or largely absent from the central and southern Appalachians over the last two decades. Limited knowledge of spotted skunk habitat associations in Virginia dictate the need for research on this species of concern. The aim of this study was to determine the associations of environmental characteristics ofwith spotted skunk habitat selection and genetic differentiation. I assessed first-order habitat selection by conducting an analysis of predicted occurrence at the landscape scale. Next, I assessed second- and third-order selection using resource utilization functions to determine habitat selection among, and within, home ranges. Further, I assessed fourth-order selection by identifying microhabitat selection and resource use at spotted skunk den site. Finally, I evaluated genetic diversity and population structure of spotted skunks in the Appalachian Mountains. I found that spotted skunk habitat is distributed in small, spatially disjunct patches and that movement, home range size, and resource selection are impacted by availability of habitat and the isolation and/or fragmentation of this suitable habitat. I found overall low genetic diversity and evidence of inbreeding within populations and geographic patterns of genetic differentiation with distinct subpopulations isolated by unsuitable landscape characteristics. Application of these results will contribute to more effective conservation of eastern spotted skunks throughout the Appalachian Mountains. / Doctor of Philosophy / Wildlife species do not occur throughout the landscape randomly or evenly, but rather choose their locations based on the availability of food, cover, and water. A key aspect in the study of wildlife ecology is understanding how a species chooses its natural surroundings and selects habitat that allows it to survive and reproduce. In theory, an animal should choose a location that offers enough resources, such as food, cover, and water to meet its biological needs and allows it to adjust based on access to these resources. By understanding the how the availability of these resources influences an animal's choice of location, wildlife managers can better determine the need for protection or management of the species in a given area. The eastern spotted skunk (Spilogale putorius) is a small, weasel-like animal that was once fairly common throughout the central and southern United States. In Virginia, the spotted skunk is a protected species, and experts suggest that it has been rare in the central and southern Appalachians for over several decades. Because there is limited knowledge of spotted skunk habitat needs in Virginia, research on this species is necessary. The goals of this study were to determine the type of habitat in which spotted skunks are choosing and how it affects the species genetic makeup. First, I determined in which general areas spotted skunks exist in Virginia. Next, I determined that the habitat for spotted skunks includes forests with ample bushy plants and rocks to hide from predators. I then examined the genetics of spotted skunks to determine relatedness among individuals and whether inbreeding was occurring. Finally, I compared the relatedness of pairs of spotted skunks to the distance between their locations and found that skunks that are located on the same mountain range are more closely related to each other than to skunks on different mountain ranges. These results will inform wildlife managers on how to provide spotted skunks with the habitat resources they need to survive and reproduce successfully, thus promoting skunk conservation.

Accelerometers deployed within smart structures produce a wealth of vibration data that can be analyzed to infer information about the types of acceleration events that are occurring within the structure. In the case of monitored smart buildings, some of these acceleration events are linked to occupant behavior, such as walking, operating machinery, closing doors, etc. The identification and classification of such events has many potential applications within a smart structure or city. Understanding occupant patterns could be beneficial for operations, retail, or HVAC management, as it could be used to monitor occupancy flow with a relatively sparse sensor network. It may also have detrimental implications in terms of cybersecurity, where such information could be mined for malicious practices if unauthorized access to the data was obtained. This work presents methods for the detection and classification of vibration events in an experimental smart building, Goodwin Hall at Virginia Tech. Goodwin Hall's 200+ accelerometer network is used to gather acceleration data, from which vibration events are automatically detected and clustered. The presence of a vibration event is detected from a raw acceleration signal with an adaptive RMS threshold method. A feature vector is then created for each extracted event as areas under regions of the FFT of the event's acceleration signal. The feature vectors are then mapped into a low-dimensional space using principal component analysis, where they are clustered with various unsupervised algorithms. These processes have shown to be successful when gathering vibration events from a single-sensor setup, but pose challenges when expanded to a multi-sensor network. Because of this, expanded applications such as a semi-supervised classifier for events detected anywhere in the building are currently still under development. This semi-supervised process, combined with the known location of each sensor would allow inferences to be drawn about the frequency of different activity types in regions of the building not captured in the labeled data. Future work intends to address these multi-sensor challenges with adjustments to the algorithm process. / Master of Science / All objects experience vibrations when they are disturbed by some force. In the case of this work, the object is complex, a classroom building, but the principle still stands. When the building is disturbed by a force it will vibrate, even if the force is small, such as a person walking down a hallway or closing a door. The vibrations caused by these 'events' are unique to the type of event, that is, footstep vibrations will be different from door vibrations. These vibrations are observed with accelerometers, and the corresponding signal is used to determine what type of event caused the vibration. First, an event is automatically detected within the signal and separated from it. Second, characteristics unique to the signal are identified, a process known as 'feature extraction.' Finally, those features are used to distinguish the event from others and to identify what had caused it based on previous experimental data. The ability to detect these events and classify them introduces many interesting applications, including any that would stem from occupant detection, including improved security or operations, retail, or HVAC management. The methods here may also be applicable to other applications, such as monitoring bridges and machinery, or for developing cutting-edge smartphone applications with the accelerometer that is built in.

Effective and efficient air transportation systems are crucial to a nation's economy and connectedness. These systems involve capital-intensive facilities and equipment and move millions of people and tonnes of freight every day. As air traffic has continued to increase, the systems necessary to ensure safe and efficient operation will continue to grow more and more complex. Hence, it is imperative that air transport analysts are equipped with the best tools to properly predict and respond to expected air transportation operations. This dissertation aims to improve on those tools currently available to air transportation analysts, while offering new ones. Specifically, this thesis will offer the following: 1) A model for predicting arrival runway occupancy times (AROT); 2) a model for predicting departure runway occupancy times (DROT); and 3) a flight planning model. This thesis will also offer an exploration of the uses of unmanned aerial vehicles for providing wireless communications services. For the predictive models of AROT and DROT, we fit hierarchical Bayesian regression models to the data, grouped by aircraft type using airport physical and aircraft operational parameters as the regressors. Recognizing that many existing air transportation models require distributions of AROT and DROT, Bayesian methods are preferred since their output are distributions that can be directly inputted into air transportation modeling programs. Additionally, we exhibit how analysts will be able to decouple AROT and DROT predictions from the traditional 4 or 5 groupings of aircraft currently in use. Lastly, for the flight planning model, we present a 2-D model using presently available wind data that provides wind-optimal flight routings. We improve over current models by allowing free-flight unconnected to pre-existing airways and by offering finer resolutions over the current 2.5 degree norm. / Ph. D.

The Virginia northern flying squirrel (Glaucomys sabrinus fuscus) and the Carolina northern flying squirrel (G. s. coloratus) are geographically isolated subspecies of the northern flying squirrel found in montane conifer-northern hardwood forests the Appalachian Mountains of the eastern United States. Both subspecies were listed under the Endangered Species Act in 1985 as endangered, and accordingly, the Virginia northern flying squirrel and the Carolina northern flying squirrel are considered high conservation priorities by state and federal agencies. Although the listing prompted work to determine the broad distribution and habitat associations of both subspecies, numerous data gaps remain, particularly with regard to habitat management and development of efficient monitoring techniques. Regional interest in restoration of red spruce (Picea rubens) forests in the central and southern Appalachian Mountains, considered to be the flying squirrels' primary habitat, increases the importance of understanding habitat selection and managers' ability to detect squirrels at multiple spatial and temporal scales. I compared two novel survey techniques (ultrasonic acoustics and camera trapping) to a traditional technique (live trapping) to determine which method had higher probability of detection (POD) and lower latency to detection (LTD, number of survey nights to initial detection) of northern flying squirrels in the region. Both novel techniques performed better than the traditional techniques with higher POD and lower LTD. I found that ultrasonic acoustics and camera trapping had similar POD, whereas LTD was significantly lower with ultrasonic acoustics versus camera trapping. Additionally, the ability to distinguish between northern flying squirrels and the parapatric southern flying squirrel (G. volans) also is possible with ultrasonic acoustics, but not with camera trapping. This ultimately makes ultrasonic acoustics the most effective and efficient method to obtain detection/non-detection data. To better inform management decisions and activities (i.e., red spruce restoration), this method should be used in conjunction with existing traditional monitoring techniques that provide demographic data such as nest boxes. I assessed habitat selection of radio-collared Virginia and Carolina northern flying squirrels at multiple spatial scales with use-availability techniques. I analyzed field data from paired telemetry and random points and determined Virginia northern flying squirrels microhabitat (within-stand habitat) selection showed preference for conifer-dominant stands with deep organic horizons, a factor that might be directly linked to food (hypogeal fungi) availability. Similar to previous studies on the Virginia northern flying squirrel on the landscape- and stand-level using Euclidean distance based analysis, Carolina northern flying squirrels also selectively preferred montane conifer forests in greater proportion than their availability on the landscape. Additionally, Carolina northern flying squirrels did not select for or against northern hardwood forests regardless of availability on the landscape. Habitat preference of both subspecies indicates that red spruce restoration activities may be important for the persistence of Appalachian northern flying squirrels into an uncertain future, as anthropogenic climate change may cause further reduction of the quality and extent of high-elevation montane conifer forests in the region. / Ph. D.