Bridging the Gap between Field Experiments and Machine Learning: The EC H2020 B-GOOD Project as a Case Study towards Automated Predictive Health Monitoring of Honey Bee Colonies.

Honey bee colonies have great societal and economic importance. The main challenge that beekeepers face is keeping bee colonies healthy under ever-changing environmental conditions. In the past two decades, beekeepers that manage colonies of Western honey bees (Apis mellifera) have become increasingly concerned by the presence of parasites and pathogens affecting the bees, the reduction in pollen and nectar availability, and the colonies' exposure to pesticides, among others. Hence, beekeepers need to know the health condition of their colonies and how to keep them alive and thriving, which creates a need for a new holistic data collection method to harmonize the flow of information from various sources that can be linked at the colony level for different health determinants, such as bee colony, environmental, socioeconomic, and genetic statuses. For this purpose, we have developed and implemented the B-GOOD (Giving Beekeeping Guidance by computational-assisted Decision Making) project as a case study to categorize the colony's health condition and find a Health Status Index (HSI). Using a 3-tier setup guided by work plans and standardized protocols, we have collected data from inside the colonies (amount of brood, disease load, honey harvest, etc.) and from their environment (floral resource availability). Most of the project's data was automatically collected by the BEEP Base Sensor System. This continuous stream of data served as the basis to determine and validate an algorithm to calculate the HSI using machine learning. In this article, we share our insights on this holistic methodology and also highlight the importance of using a standardized data language to increase the compatibility between different current and future studies. We argue that the combined management of big data will be an essential building block in the development of targeted guidance for beekeepers and for the future of sustainable beekeeping

Implementation of the first adaptive management plan for a European migratory waterbird population: The case of the Svalbard pink-footed goose Anser brachyrhynchus

An International Species Management Plan for the Svalbard population of the pink-footed goose was adopted under theAgreement on theConservation of African-Eurasian Migratory Waterbirds in 2012, the first case of adaptive management of a migratory waterbird population in Europe. An internationalworking group (including statutory agencies, NGO representatives and experts) agreed on objectives and actions to maintain the population in favourable conservation status, while accounting for biodiversity, economic and recreational interests. Agreements include setting a population target to reduce agricultural conflicts and avoid tundra degradation, and using hunting in some range states to maintain stable population size. As part of the adaptive management procedures, adjustment to harvest is made annually subject to population status. This has required streamlining of monitoring and assessment activities. Three years after implementation, indicators suggest the attainment of management results. Dialogue, consensus-building and engagement among stakeholders represent the major process achievements

Obsessive-compulsive disorder in adults with high-functioning autism spectrum disorder: What does self-report with the OCI-R tell us?

Little is known about the symptom profile of obsessive-compulsive disorder (OCD) in individuals who have autism spectrum disorders (ASD). It is also unknown whether self-report questionnaires are useful in measuring OCD in ASD. We sought to describe the symptom profiles of adults with ASD, OCD, and ASD + OCD using the Obsessive Compulsive Inventory-Revised (OCI-R), and to assess the utility of the OCI-R as a screening measure in a high-functioning adult ASD sample. Individuals with ASD (n = 171), OCD (n = 108), ASD + OCD (n = 54) and control participants (n = 92) completed the OCI-R. Individuals with ASD + OCD reported significantly higher levels of obsessive-compulsive symptoms than those with ASD alone. OCD symptoms were not significantly correlated with core ASD repetitive behaviors as measured on the ADI-R or ADOS-G. The OCI-R showed good psychometric properties and corresponded well with clinician diagnosis of OCD. Receiver operating characteristic analysis suggested cut-offs for OCI-R Total and Checking scores that discriminated well between ASD + versus –OCD, and fairly well between ASD-alone and OCD-alone. OCD manifests separately from ASD and is characterized by a different profile of repetitive thoughts and behaviors. The OCI-R appears to be useful as a screening tool in the ASD adult population