Remote sensing and GPS tracking reveal temporal shifts in habitat use in nonbreeding Black-tailed Godwits

Knowledge of the habitat requirements for migratory species throughout their full annual cycle is necessary for comprehensive species protection plans. By describing seasonal shifts of space-use patterns in a key nonbreeding area, the Senegal Delta (Mauritania, Senegal), this study addresses a significant knowledge gap in the annual cycle of the rapidly declining continental Black-tailed Godwit Limosa limosa limosa. We fitted continuous-time stochastic-process movement models with GPS location data to describe the core areas used by 22 GPS-tagged godwits over the 2022–2023 nonbreeding period. We mapped key habitat types, such as floodplain wetlands and rice fields, via supervised classification of satellite imagery. Godwits in the Senegal Delta show a distinct shift in habitat use over the nonbreeding period. The core areas of godwits in the early stages of the nonbreeding period (the wet season) were primarily in natural wetlands and fields with newly planted rice. As the rice crop matured and became too dense, godwits moved towards more recently sown rice fields. Later, as floodwaters receded and rice fields dried out, godwits abandoned rice fields and shifted towards natural wetlands with fewer invasive plants, particularly within the marshes and shallow floodplains of nature-protected areas in the lower Delta. Synthesis and applications: Our findings illustrate the shifting importance of natural and agricultural wetlands for godwits at different stages of the nonbreeding season. Protected areas in the Senegal Delta, particularly the Djoudj National Bird Sanctuary (Senegal) and Diawling National Park (Mauritania), are crucial habitats during the dry season as godwits prepare for their northward migration, whilst rice fields take a key role during the wet season. Conservation efforts should prioritize eradicating invasive plants from the Djoudj and Diawling, as well as promote agroecological management in specific rice production complexes indicated in this study.</p

Lineage Abundance Estimation for SARS-CoV-2 in Wastewater Using Transcriptome Quantification Techniques

Effectively monitoring the spread of SARS-CoV-2 mutants is essential to efforts to counter the ongoing pandemic. Predicting lineage abundance from wastewater, however, is technically challenging. We show that by sequencing SARS-CoV-2 RNA in wastewater and applying algorithms initially used for transcriptome quantification, we can estimate lineage abundance in wastewater samples. We find high variability in signal among individual samples, but the overall trends match those observed from sequencing clinical samples. Thus, while clinical sequencing remains a more sensitive technique for population surveillance, wastewater sequencing can be used to monitor trends in mutant prevalence in situations where clinical sequencing is unavailable

Traffic Control via Connected and Automated Vehicles: An Open-Road Field Experiment with 100 CAVs

The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. These "phantom jams" or "stop-and-go waves,"are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment leveraged a heterogeneous fleet of 100 longitudinally-controlled vehicles as Lagrangian traffic actuators, each of which ran a controller with the architecture described in this paper. The MegaController is a hierarchical control architecture, which consists of two main layers. The upper layer is called Speed Planner, and is a centralized optimal control algorithm. It assigns speed targets to the vehicles, conveyed through the LTE cellular network. The lower layer is a control layer, running on each vehicle. It performs local actuation by overriding the stock adaptive cruise controller, using the stock on-board sensors. The Speed Planner ingests live data feeds provided by third parties, as well as data from our own control vehicles, and uses both to perform the speed assignment. The architecture of the speed planner allows for modular use of standard control techniques, such as optimal control, model predictive control, kernel methods and others, including Deep RL, model predictive control and explicit controllers. Depending on the vehicle architecture, all onboard sensing data can be accessed by the local controllers, or only some. Control inputs vary across different automakers, with inputs ranging from torque or acceleration requests for some cars, and electronic selection of ACC set points in others. The proposed architecture allows for the combination of all possible settings proposed above. Most configurations were tested throughout the ramp up to the MegaVandertest

Traffic Control via Connected and Automated Vehicles (CAVs): An Open-Road Field Experiment with 100 CAVs

The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. Also called "phantom jams"or "stop-and-go waves,"these instabilities are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system, referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment, the MegaVanderTest (MVT), leveraged a heterogeneous fleet of 100 longitudinally controlled vehicles as Lagrangian traffic actuators, each of which ran a controller with the architecture described in this article. The MegaController is a hierarchical control architecture that consists of two main layers. The upper layer is called the Speed Planner and is a centralized optimal control algorithm. It assigns speed targets to the vehicles, conveyed through the LTE cellular network. The lower layer is a control layer, running on each vehicle. It performs local actuation by overriding the stock adaptive cruise controller, using the stock onboard sensors. The Speed Planner ingests live data feeds provided by third parties as well as data from our own control vehicles and uses both to perform the speed assignment. The architecture of the Speed Planner allows for the modular use of standard control techniques, such as optimal control, model predictive control (MPC), kernel methods, and others. The architecture of the local controller allows for the flexible implementation of local controllers. Corresponding techniques include deep reinforcement learning (RL), MPC, and explicit controllers. Depending on the vehicle architecture, all onboard sensing data can be accessed by the local controllers or only some. Likewise, control inputs vary across different automakers, with inputs ranging from torque or acceleration requests for some cars to electronic selection of adaptive cruise control (ACC) setpoints in others. The proposed architecture technically allows for the combination of all possible settings proposed previously, that is Speed Planner algorithms × local Vehicle Controller algorithms} × {full or partial sensing} × {torque or speed control}. Most configurations were tested throughout the ramp up to the MegaVandertest (MVT)

Elasmobranch conservation, challenges and management strategy in India: recommendations from a national consultative meeting

Historically, India has been projected as one of the major elasmobranch fishing nations in the world. However, management and conservation efforts are not commensurate with this trend. Along with the Wildlife (Protection) Act, 1972, several generic conservation measures are in place at the regional/local level. But India is still a long way from meeting global conservation commitments. We present here the status of elasmobranch management and conservation in India, with the specific objec-tive of identifying the gaps in the existing set-up. We also present recommendations based on a national consultative workshop held at the Central Marine Fisheries Research Institute, Kochi, in February 2020. We recommend the implementation of a National Plan of Action (NPOA-Sharks) and more in-clusive governance and policymaking for elasmobranch conservation in India