Angler Perception and Population Dynamics of the Northern Snakehead (Channa argus) in the Potomac River & Tributaries

Our research sought to address the extent to which the northern snakehead (Channa argus), an invasive fish species, represents a threat to the Potomac River ecosystem. The first goal of our research was to survey the perceptions and opinions of recreational anglers on the effects of the snakehead population in the Potomac River ecosystem. To determine angler perceptions, we created and administered 113 surveys from June – September 2014 at recreational boat ramps along the Potomac River. Our surveys were designed to expand information collected during previous surveys conducted by the U.S. Fish and Wildlife Service. Our results indicated recreational anglers perceive that abundances and catch rates of target species, specifically largemouth bass, have declined since snakehead became established in the river. The second goal of our research was to determine the genetic diversity and potential of the snakehead population to expand in the Potomac River. We hypothesized that the effective genetic population size would be much less than the census size of the snakehead population in the Potomac River. We collected tissue samples (fin clippings) from 79 snakehead collected in a recreational tournament held between Fort Washington and Wilson’s Landing, MD on the Potomac River and from electrofishing sampling conducted by the Maryland Department of Natural Resources in Pomonkey Creek, a tributary of the Potomac River. DNA was extracted from the tissue samples and scored for 12 microsatellite markers, which had previously been identified for Potomac River snakehead. Microsatellite allele frequency data were recorded and analyzed in the software programs GenAlEx and NeEstimator to estimate heterozygosity and effective genetic population size. Resampling simulations indicated that the number of microsatellites and the number of fish analyzed provided sufficient precision. Simulations indicated that the effective population size estimate would expect to stabilize for samples > 70 individual snakehead. Based on a sample of 79 fish scored for 12 microsatellites, we calculated an Ne of 15.3 individuals. This is substantially smaller than both the sample size and estimated population size. We conclude that genetic diversity in the snakehead population in the Potomac River is low because the population has yet to recover from a genetic bottleneck associated with a founder effect due to their recent introduction into the system.

CONCURRENT LOCALIZATION AND MAPPING WITH SONAR SENSORS AND CONSIDERATION OF VEHICLE MOTION

Simultaneous Localization and Mapping (SLAM) is a procedure used to determine the location of a mobile vehicle in an unknown environment, while constructing a map of the unknown environment at the same time. Mobile platforms, which make use of SLAM algorithms, have industrial applications in autonomous maintenance, such as the inspection of flaws and defects in oil pipelines and storage tanks. A typical SLAM consists of four main components, namely, experimental setup (data gathering), vehicle pose estimation, feature extraction, and filtering. Feature extraction is the process of realizing significant features from the unknown environment such as corners, edges, walls, and interior features. In this work, an original feature extraction algorithm specific to distance measurements obtained through SONAR sensor data is presented. This algorithm has been constructed by combining the SONAR Salient Feature Extraction Algorithm and the Triangulation Hough Based Fusion with point-in-polygon detection. The reconstructed maps obtained through simulations and experimental data with the fusion algorithm are compared to the maps obtained with existing feature extraction algorithms. Based on the results obtained, it is suggested that the proposed algorithm can be employed as an option for data obtained from SONAR sensors in environment, where other forms of sensing are not viable. The algorithm fusion for feature extraction requires the vehicle pose estimation as an input, which is obtained from a vehicle pose estimation model. For the vehicle pose estimation, the author uses sensor integration to estimate the pose of the mobile vehicle. Different combinations of these sensors are studied (e.g., encoder, gyroscope, or encoder and gyroscope). The different sensor fusion techniques for the pose estimation are experimentally studied and compared. The vehicle pose estimation model, which produces the least amount of error, is used to generate inputs for the feature extraction algorithm fusion. In the experimental studies, two different environmental configurations are used, one without interior features and another one with two interior features. Numerical and experimental findings are discussed. Finally, the SLAM algorithm is implemented along with the algorithms for feature extraction and vehicle pose estimation. Three different cases are experimentally studied, with the floor of the environment intentionally altered to induce slipping. Results obtained for implementations with and without SLAM are compared and discussed. The present work represents a step towards the realization of autonomous inspection platforms for performing concurrent localization and mapping in harsh environments.