Synchrony in adult survival is remarkably strong among common temperate songbirds across France.

Synchronous variation in demographic parameters across species increases the risk of simultaneous local extinction, which lowers the probability of subsequent recolonization. Synchrony therefore tends to destabilize meta-populations and meta-communities. Quantifying interspecific synchrony in demographic parameters, like abundance, survival, or reproduction, is thus a way to indirectly assess the stability of meta-populations and meta-communities. Moreover, it is particularly informative to identify environmental drivers of interspecific synchrony because those drivers are important across species. Using a Bayesian hierarchical multisite multispecies mark-recapture model, we investigated temporal interspecific synchrony in annual adult apparent survival for 16 common songbird species across France for the period 2001-2016. Annual adult survival was largely synchronous among species (73%, 95% credible interval [47%-94%] of the variation among years was common to all species), despite species differing in ecological niche and life history. This result was robust to different model formulations, uneven species sample sizes, and removing the long-term trend in survival. Synchrony was also shared across migratory strategies, which suggests that environmental forcing during the 4-month temperate breeding season has a large-scale, interspecific impact on songbird survival. However, the strong interspecific synchrony was not easily explained by a set of candidate weather variables we defined a priori. Spring weather variables explained only 1.4% [0.01%-5.5%] of synchrony, while the contribution of large-scale winter weather indices may have been stronger but uncertain, accounting for 12% [0.3%-37%] of synchrony. Future research could jointly model interspecific variation and covariation in breeding success, age-dependent survival, and age-dependent dispersal to understand when interspecific synchrony in abundance emerges and destabilizes meta-communities.

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.

Real-time monitoring of honeybee colony daily activity and bee loss rates can highlight the risk posed by a pesticide.

Information on honeybee foraging performance and especially bee loss rates at the colony level are crucial for evaluating the magnitude of effects due to pesticide exposure, thereby ensuring that protection goals for honeybee colonies are met (i.e. threshold of acceptable effects). However, current methods for monitoring honeybee foraging activity and mortality are very approximate (visual records) or are time-limited and mostly based on single cohort analysis. We therefore assess the potential of bee counters, that enable a colony-level and continuous monitoring of bee flight activity and mortality, in pesticide risk assessment. After assessing the background activity and bee loss rates, we exposed colonies to two concentrations of sulfoxaflor (a neurotoxic insecticide) in sugar syrup: a concentration that was considered to be field realistic (0.59 µg/ml) and a higher concentration (2.36 µg/ml) representing a worst-case exposure scenario. We did not find any effect of the field-realistic concentration on flight activity and bee loss rates. However, a two-fold decrease in daily flight activity and a 10-fold increase in daily bee losses were detected in colonies exposed to the highest sulfoxaflor concentration as compared to before exposure. When compared to the theoretical trigger values associated with the specific protection goal of 7 % colony-size reduction, the observed fold changes in daily bee losses were often found to be at risk for colonies. In conclusion, the real-time and colony-level monitoring of bee loss rates, combined with threshold values indicating at which levels bee loss rates threaten the colony, have great potential for improving regulatory pesticide risk assessments for honeybees under field conditions.

Lack of definition of mathematical terms in ecology: The case of the sigmoid class of functions in macro-ecology.

Defining mathematical terms and objects is a constant issue in ecology; often definitions are absent, erroneous, or imprecise. Through a bibliographic prospection, we show that this problem appears in macro-ecology (biogeography and community ecology) where the lack of definition for the sigmoid class of functions results in difficulties of interpretation and communication. In order to solve this problem and to help harmonize papers that use sigmoid functions in ecology, herein we propose a comprehensive definition of these mathematical objects. In addition, to facilitate their use, we classified the functions often used in the ecological literature, specifying the constraints on the parameters for the function to be defined and the curve shape to be sigmoidal. Finally, we interpreted the different properties of the functions induced by the definition through ecological hypotheses in order to support and explain the interest of such functions in ecology and more precisely in biogeography.