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Large carnivores play many vital biological, economic, and conservation roles, however, their biological traits (low population densities, cryptic behavior) make them difficult to monitor. Pumas have been particularly difficult to study because the lack of distinctive markings on their coats prevents individual identification, precluding mark-recapture and other similar analyses. Further, compared to temperate areas, research on the interspecific interactions of Central American felids is particularly lacking. I used single- and multi-season, single-species occupancy models and two-species co-occurrence models to analyze camera trapping and habitat data collected at eight study sites across Belize. Puma occupancy was positively influenced by jaguar trap success, understory density, canopy cover, and human trap success, and negatively influenced by stream density. Jaguar trap success was the best predictor of where pumas occurred, while prey species were not found to influence puma occupancy. Mean occupancy was 0.740 (0.013) and ranged from 0.587 (0.042) to 0.924 (0.030). Over time, puma occupancy rates were generally high (> 0.90) and stable. Puma occupancy was higher in logged areas, suggesting that current levels of natural resource extraction at those sites were not detrimental to the species. Co-occurrence modeling showed little evidence for interactions between the carnivores, suggesting that jaguars may be acting as an umbrella species and that conservation efforts directed at jaguars are likely to benefit the other carnivores, including pumas. Overall, these findings are positive for puma conservation, but human-induced land use change is expanding and further monitoring will give us insight into how pumas respond to human encroachment. / Master of Science

Mandated by the Sikes Act of 1960, natural resource managers work to manage the habitats and wildlife that are found on military installations in the United States and Territories. At Camp Edwards Military Training Reservation (hereby abbreviated to Camp Edwards), (Bourne, MA), such wildlife includes the state-protected eastern box turtle (Terrapene carolina carolina) and the declining prairie warbler (Setophaga discolor), which both occupy pine barrens. In 2020, natural resource managers at Camp Edwards noticed that eastern box turtles were being infected by myiasis, which occurs when flesh flies deposit larvae into the living tissue of a vertebrate host. In the literature, it has been documented that several ectothermic hosts respond to disease or parasite infection through a phenomenon referred to as ‘behavioral fever’ by moving to warmer locations to raise their internal temperature. Behavioral fever may clear the infection faster because higher body temperatures can induce parasite mortality or prevent secondary infections. However, it is unclear if myiasis induces behavioral fever in eastern box turtles or impacts other aspects of their behavior, such as habitat use. In Chapter 1, I compare behavior and habitat characteristics of myiasis infected and noninfected eastern box turtles at Camp Edwards. I radio-tracked 48 turtles weekly from May to August 2022. Upon capture, I recorded their infection status, shell surface temperature, and capture location habitat characteristics: understory vegetation, basal area, and canopy closure. I used generalized linear models and linear models to compare body condition indexes, shell temperatures, habitat use, and movement distances between infection statuses, sexes, and age classes. I found that myiasis infection had no significant effect on any variable other than shell surface temperature, which suggests infected turtles may be exhibiting behavioral fever. A second species of great concern at Camp Edwards are prairie warblers. Prairie warblers occupy early successional forests, which means that habitat management could have a direct impact on the distribution and abundance of this species. Despite declining populations regionally, prairie warbler populations at Camp Edwards have increased in the last few years. In Chapter 2, I analyze the effect of management projects (i.e., prescribed fire and mechanical projects) on prairie warbler colonization, extinction, and detection probabilities at Camp Edwards. I found that colonization was significantly predicted by the number of years since management and the proportion of the following vegetation cover types at a site: grassland, disturbed land, pitch pine – oak forest, and pitch pine – scrub oak community. I also found that extinction was significantly predicted by the proportion of pitch pine – scrub oak community at a site. Lastly, I found that detection probability was significantly predicted by the year of observation and the proportion of the following vegetation cover types: grassland, pitch pine – oak forest, and pitch pine – scrub oak community. These results can help managers predict how prairie warbler populations respond to management projects at Camp Edwards.

Intelligent vehicles require perception systems with high performances. Usually, perception system consists of multiple sensors, such as cameras, 2D/3D lidars or radars. The works presented in this Ph.D thesis concern several topics on cameras and lidar based perception for understanding dynamic scenes in urban environments. The works are composed of four parts.In the first part, a stereo vision based visual odometry is proposed by comparing several different approaches of image feature detection and feature points association. After a comprehensive comparison, a suitable feature detector and a feature points association approach is selected to achieve better performance of stereo visual odometry. In the second part, independent moving objects are detected and segmented by the results of visual odometry and U-disparity image. Then, spatial features are extracted by a kernel-PCA method and classifiers are trained based on these spatial features to recognize different types of common moving objects e.g. pedestrians, vehicles and cyclists. In the third part, an extrinsic calibration method between a 2D lidar and a stereoscopic system is proposed. This method solves the problem of extrinsic calibration by placing a common calibration chessboard in front of the stereoscopic system and 2D lidar, and by considering the geometric relationship between the cameras of the stereoscopic system. This calibration method integrates also sensor noise models and Mahalanobis distance optimization for more robustness. At last, dynamic occupancy grid mapping is proposed by 3D reconstruction of the environment, obtained from stereovision and Lidar data separately and then conjointly. An improved occupancy grid map is obtained by estimating the pitch angle between ground plane and the stereoscopic system. The moving object detection and recognition results (from the first and second parts) are incorporated into the occupancy grid map to augment the semantic meanings. All the proposed and developed methods are tested and evaluated with simulation and real data acquired by the experimental platform "intelligent vehicle SetCar" of IRTES-SET laboratory.

Les systèmes de perception, qui sont à la base du concept du véhicule intelligent, doivent répondre à des critères de performance à plusieurs niveaux afin d’assurer des fonctions d’aide à la conduite et/ou de conduite autonome. Aujourd’hui, la majorité des systèmes de perception pour véhicules intelligents sont basés sur la combinaison de données issues de plusieurs capteurs (caméras, lidars, radars, etc.). Les travaux de cette thèse concernent le développement d’un système de perception à base d’un capteur de vision stéréoscopique et d’un capteur lidar pour l’analyse de scènes dynamiques en environnement urbain. Les travaux présentés sont divisés en quatre parties.La première partie présente une méthode d’odométrie visuelle basée sur la stéréovision, avec une comparaison de différents détecteurs de primitives et différentes méthodes d’association de ces primitives. Un couple de détecteur et de méthode d’association de primitives a été sélectionné sur la base d’évaluation de performances à base de plusieurs critères. Dans la deuxième partie, les objets en mouvement sont détectés et segmentés en utilisant les résultats d’odométrie visuelle et l’image U-disparité. Ensuite, des primitives spatiales sont extraites avec une méthode basée sur la technique KPCA et des classifieurs sont enfin entrainés pour reconnaitre les objets en mouvement (piétons, cyclistes, véhicules). La troisième partie est consacrée au calibrage extrinsèque d’un capteur stéréoscopique et d’un Lidar. La méthode de calibrage proposée, qui utilise une mire plane, est basée sur l’exploitation d’une relation géométrique entre les caméras du capteur stéréoscopique. Pour une meilleure robustesse, cette méthode intègre un modèle de bruit capteur et un processus d’optimisation basé sur la distance de Mahalanobis. La dernière partie de cette thèse présente une méthode de construction d’une grille d’occupation dynamique en utilisant la reconstruction 3D de l’environnement, obtenue des données de stéréovision et Lidar de manière séparée puis conjointement. Pour une meilleure précision, l’angle entre le plan de la chaussée et le capteur stéréoscopique est estimé. Les résultats de détection et de reconnaissance (issus des première et deuxième parties) sont incorporés dans la grille d’occupation pour lui associer des connaissances sémantiques. Toutes les méthodes présentées dans cette thèse sont testées et évaluées avec la simulation et avec de données réelles acquises avec la plateforme expérimentale véhicule intelligent SetCar” du laboratoire IRTES-SET. / Intelligent vehicles require perception systems with high performances. Usually, perception system consists of multiple sensors, such as cameras, 2D/3D lidars or radars. The works presented in this Ph.D thesis concern several topics on cameras and lidar based perception for understanding dynamic scenes in urban environments. The works are composed of four parts.In the first part, a stereo vision based visual odometry is proposed by comparing several different approaches of image feature detection and feature points association. After a comprehensive comparison, a suitable feature detector and a feature points association approach is selected to achieve better performance of stereo visual odometry. In the second part, independent moving objects are detected and segmented by the results of visual odometry and U-disparity image. Then, spatial features are extracted by a kernel-PCA method and classifiers are trained based on these spatial features to recognize different types of common moving objects e.g. pedestrians, vehicles and cyclists. In the third part, an extrinsic calibration method between a 2D lidar and a stereoscopic system is proposed. This method solves the problem of extrinsic calibration by placing a common calibration chessboard in front of the stereoscopic system and 2D lidar, and by considering the geometric relationship between the cameras of the stereoscopic system. This calibration method integrates also sensor noise models and Mahalanobis distance optimization for more robustness. At last, dynamic occupancy grid mapping is proposed by 3D reconstruction of the environment, obtained from stereovision and Lidar data separately and then conjointly. An improved occupancy grid map is obtained by estimating the pitch angle between ground plane and the stereoscopic system. The moving object detection and recognition results (from the first and second parts) are incorporated into the occupancy grid map to augment the semantic meanings. All the proposed and developed methods are tested and evaluated with simulation and real data acquired by the experimental platform “intelligent vehicle SetCar” of IRTES-SET laboratory.