The role of humans in the diversification of a threatened island raptor

Background  

Anthropogenic habitat modifications have led to the extinction of many species and have favoured the expansion of others. Nonetheless, the possible role of humans as a diversifying force in vertebrate evolution has rarely been considered, especially for species with long generation times. We examine the influence that humans have had on the colonization and phenotypic and genetic differentiation of an insular population of a long-lived raptor species, the Egyptian vulture (Neophron percnopterus).     

Farms,pastures and woodlands: the fine‐scale distribution of Palearctic Culicoides spp. biting midges along an agro‐ecological gradient

The spatial epidemiology of Bluetongue virus (BTV) at the landscape level relates to the fine‐scale distribution and dispersal capacities of its vectors, midges belonging to the genus Culicoides Latreille (Diptera: Ceratopogonidae). Although many previous researches have carried out Culicoides sampling on farms, little is known of the fine‐scale distribution of Culicoides in the landscape immediately surrounding farms. The aim of this study was to gain a better understanding of Culicoides populations at increasing distances from typical dairy farms in north‐west Europe, through the use of eight Onderstepoort‐type black‐light traps positioned along linear transects departing from farms, going through pastures and entering woodlands. A total of 16 902 Culicoides were collected in autumn 2008 and spring 2009. The majority were females, of which more than 97% were recognized as potential vectors. In pastures, we found decreasing numbers of female Culicoides as a function of the distance to the farm. This pattern was modelled by leptokurtic models, with parameters depending on season and species. By contrast, the low number of male Culicoides caught were homogeneously distributed along the transects. When transects entered woodlands, we found a higher abundance of Culicoides than expected considering the distance of the sampling sites to the farm, although this varied according to species.     

A Bayesian Developmental Approach to Robotic Goal-Based Imitation Learning

A fundamental challenge in robotics today is building robots that can learn new skills by observing humans and imitating human actions. We propose a new Bayesian approach to robotic learning by imitation inspired by the developmental hypothesis that children use self-experience to bootstrap the process of intention recognition and goal-based imitation. Our approach allows an autonomous agent to: (i) learn probabilistic models of actions through self-discovery and experience, (ii) utilize these learned models for inferring the goals of human actions, and (iii) perform goal-based imitation for robotic learning and human-robot collaboration. Such an approach allows a robot to leverage its increasing repertoire of learned behaviors to interpret increasingly complex human actions and use the inferred goals for imitation, even when the robot has very different actuators from humans. We demonstrate our approach using two different scenarios: (i) a simulated robot that learns human-like gaze following behavior, and (ii) a robot that learns to imitate human actions in a tabletop organization task. In both cases, the agent learns a probabilistic model of its own actions, and uses this model for goal inference and goal-based imitation. We also show that the robotic agent can use its probabilistic model to seek human assistance when it recognizes that its inferred actions are too uncertain, risky, or impossible to perform, thereby opening the door to human-robot collaboration.