In 2021, 17-year periodical cicadas (Magicicada spp.) in Brood X emerged throughout much of the eastern United States. Due to their abundance and lack of predator avoidance during their short six-week period on the above-ground landscape, many species of animals consume cicadas and in turn have short-term habitat use and population responses. The response of bats to periodical cicadas is largely unknown. Using a before-after-control-impact study design, we deployed ultrasonic acoustic detectors during the summers of 2020–2022 within and just outside the range of the cicada emergence along the Potomac River corridor of Maryland and Virginia. For total bat activity, there was significantly more acoustic activity within the range of the cicada emergence the year during and the year following emergence than where emergence did not occur. Additionally, for all individual species and phonic groups, bat activity increased the year during and year after the emergence within the range of periodical cicadas compared to the year prior to emergence. Our study demonstrates that periodical cicadas may serve as a resource pulse that can cause an increase in bat activity and suggests at least a short-term local to regional population increase from immigration the year during and from recruitment the year following. Bats of the mid-Atlantic have been negatively affected by numerous stressors including white-nose syndrome, wind turbine development, and land use change. One species suffering widespread population decline over the last decade in the mid-Atlantic has been the little brown bat (Myotis lucifugus). Understanding the space use and resource selection of remaining populations is critical for management and species recovery. Nevertheless, there are difficulties with studying the movement of insectivorous bats due to their small size and high vagility. Traditional foraging studies of Myotis spp. have used fixed station telemetry and associated multi-azimuthal observations to estimate the location of individuals. However, single azimuth observations are often unable to be included in these studies leading to more potential location estimates missing from movement models. During the summer of 2022, I radio-tagged little brown bats from a maternity colony along the Potomac River in Maryland and collected bearings on nine individuals using fixed station telemetry from sunset until bats returned to the maternity roost. Location estimates were obtained from both single and multi-azimuthal observations. I fit movement models for each individual and used auto-correlated kernel density estimation (AKDE) to estimate space use. I also assessed second and third order habitat selection of individual bats. The average 95% AKDE for males was 889 ha ± 424.6 and 699.3 ha ± 129 for adult females. I found bats had higher predicted use of habitat that was closer in distance to water and wetlands and further from open habitat at the second order and closer to water and open habitat at the third order of resource selection. Habitat associations were similar to previous little brown bat resource selection studies. However, estimates of space use were larger than those using other movement models. This study was novel in that it incorporated single azimuth observations and periodicity into models of space use, thereby increasing sample size and offering a new framework for future telemetry studies. These estimates of space use can be used by land managers to protect the habitat of this imperiled species. / Master of Science / In 2021, 17-year periodical cicadas (Magicicada spp.) in Brood X emerged throughout much of the eastern United States in high abundance. As an abundant food resource, many animals consume periodical cicadas while they are above ground during the summers. This has allowed for short-term population increases and changes in reproduction in some species. However, the response of bats to periodical cicadas is largely unknown. To study this, I deployed ultrasonic acoustic detectors that recorded six species/species groups of bats during the summers of 2020–2022 within and just outside the range of the cicada emergence along the Potomac River Corridor, Maryland, and Virginia. I observed significantly more total bat acoustic activity within the range of the cicada during the emergence year and the year following than where emergence did not occur. Also, for all individual species and species groups, bat activity increased in the year during and after the emergence within the range of periodical cicadas compared to the year before emergence. My study demonstrates that periodical cicadas cause an increase in bat activity and may suggest a population increase in the areas where the emergence has occurred. Bats of the mid-Atlantic have experienced population declines due to numerous stressors including white-nose syndrome caused by the fungus Pseudogymnoascus destructans, wind turbine development, and habitat fragmentation. One species that has been heavily affected by white-nose syndrome is the little brown bat (Myotis lucifugus). To help conserve the species, understanding their home range and their resource needs is critical. However, little brown bats are small and agile fliers which poses difficulties for tracking them. Traditional foraging studies of species in this genus have used telemetry to obtain triangulations and location estimates of individuals but often leave out data collected from a single bearing as it is difficult to calculate the true location of the animal. During the summer of 2022, I radio-tagged little brown bats from a maternity colony along the Potomac River in Maryland and collected bearings on nine individuals using fixed station telemetry from sunset until bats returned to the maternity roost. We estimated locations from both triangulations and single bearings. I calculated movement models for each individual and fit them to an auto-correlated kernel density estimator (AKDE) of space use. The average 95% auto-correlated kernel density estimates of space use (AKDE) for males was 889 ha ± 424.6 and 699.3 ha ± 129 for adult females. I also assessed the habitat selection of individuals within their home range and across the nearby landscape. I found that bats preferred to use areas closer to water and wetlands and preferred to use areas closer to open habitat within their home range but preferred to use areas further from open habitat across the landscape. Habitat associations were similar to previous little brown bat resource selection studies. However, estimates of space use were larger than those using other movement models. My study was novel in that it incorporated single azimuth observations and periodicity into models of space use, thereby increasing sample size and offering a new framework for future telemetry studies. These estimates of space use can be used by land managers to protect the habitat of this imperiled species.

We consider time-of-arrival based robust geolocation in harsh line-of-sight/non-line-of-sight environments. Herein, we assume the probability density function (PDF) of the measurement error to be completely unknown and develop an iterative algorithm for robust position estimation. The iterative algorithm alternates between a PDF estimation step, which approximates the exact measurement error PDF (albeit unknown) under the current parameter estimate via adaptive kernel density estimation, and a parameter estimation step, which resolves a position estimate from the approximate log-likelihood function via a quasi-Newton method. Unless the convergence condition is satisfied, the resolved position estimate is then used to refine the PDF estimation in the next iteration. We also present the best achievable geolocation accuracy in terms of the Cramér-Rao lower bound. Various simulations have been conducted in both real-world and simulated scenarios. When the number of received range measurements is large, the new proposed position estimator attains the performance of the maximum likelihood estimator (MLE). When the number of range measurements is small, it deviates from the MLE, but still outperforms several salient robust estimators in terms of geolocation accuracy, which comes at the cost of higher computational complexity.