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.

Managing geese with recreational hunters?

As many goose populations across the northern Hemisphere continue to rise, the role of hunters to manage these populations is increasingly being considered. We studied recreational goose hunters in Denmark to assess their behavioural and motivational characteristics, willingness to alter their hunting effort, as well as their ability to act as stewards of a rapidly increasing goose population. We identified several behavioural characteristics that typify effective goose hunting practices. We suggest a degree of specialization is necessary to increase goose harvests, as well as mitigating animal welfare issues (e.g. wounding). However, the majority of Danish goose hunters can be considered to be casual participants in this form of hunting. This poses a challenge for wildlife managers wishing to engage recreational hunters to manage highly dynamic wildlife populations, such as geese. If recreational hunters are to be used as a management tool, wildlife managers and hunting organizations will need to consider how best to facilitate skill development, hunting practices and socially legitimate hunting ethics to foster the stewardship role of hunting. We conclude that it is incumbent on wildlife managers to recognize and deal with both internal factors (e.g. skill development) and external influences (e.g. animal welfare concerns). In doing so, potential tensions in the multi-functionality of hunting can be alleviated, maintain hunting as a legitimate and accepted recreational past-time and management tool.

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 the Agreement on the Conservation of African-Eurasian Migratory Waterbirds in 2012, the first case of adaptive management of a migratory waterbird population in Europe. An international working 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.