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Animals have evolved complex foraging strategies to obtain a nutritionally balanced diet and associated fitness benefits. Recent research combining state-space models of nutritional geometry with agent-based models (ABMs), show how nutrient targeted foraging behavior can also influence animal social interactions, ultimately affecting collective dynamics and group structures. Here we demonstrate how social network analyses can be integrated into such a modeling framework and provide a practical analytical tool to compare experimental results with theory. We illustrate our approach by examining the case of nutritionally mediated dominance hierarchies. First we show how nutritionally explicit ABMs that simulate the emergence of dominance hierarchies can be used to generate social networks. Importantly the structural properties of our simulated networks bear similarities to dominance networks of real animals (where conflicts are not always directly related to nutrition). Finally, we demonstrate how metrics from social network analyses can be used to predict the fitness of agents in these simulated competitive environments. Our results highlight the potential importance of nutritional mechanisms in shaping dominance interactions in a wide range of social and ecological contexts. Nutrition likely influences social interactions in many species, and yet a theoretical framework for exploring these effects is currently lacking. Combining social network analyses with computational models from nutritional ecology may bridge this divide, representing a pragmatic approach for generating theoretical predictions for nutritional experiments. [ABSTRACT FROM AUTHOR]

Recent developments allowed establishing virtual‐reality (VR) setups to study multiple aspects of visual learning in honey bees under controlled experimental conditions. Here, we adopted a VR environment to investigate the visual learning in the buff‐tailed bumble bee Bombus terrestris. Based on responses to appetitive and aversive reinforcements used for conditioning, we show that bumble bees had the proper appetitive motivation to engage in the VR experiments and that they learned efficiently elemental color discriminations. In doing so, they reduced the latency to make a choice, increased the proportion of direct paths toward the virtual stimuli and walked faster toward them. Performance in a short‐term retention test showed that bumble bees chose and fixated longer on the correct stimulus in the absence of reinforcement. Body size and weight, although variable across individuals, did not affect cognitive performances and had a mild impact on motor performances. Overall, we show that bumble bees are suitable experimental subjects for experiments on visual learning under VR conditions, which opens important perspectives for invasive studies on the neural and molecular bases of such learning given the robustness of these insects and the accessibility of their brain. [ABSTRACT FROM AUTHOR]

Analysing large numbers of brain samples can reveal minor, but statistically and biologically relevant variations in brain morphology that provide critical insights into animal behaviour, ecology and evolution. So far, however, such analyses have required extensive manual effort, which considerably limits the scope for comparative research. Here we used micro-CT imaging and deep learning to perform automated analyses of 3D image data from 187 honey bee and bumblebee brains. We revealed strong inter-individual variations in total brain size that are consistent across colonies and species, and may underpin behavioural variability central to complex social organisations. In addition, the bumblebee dataset showed a significant level of lateralization in optic and antennal lobes, providing a potential explanation for reported variations in visual and olfactory learning. Our fast, robust and user-friendly approach holds considerable promises for carrying out large-scale quantitative neuroanatomical comparisons across a wider range of animals. Ultimately, this will help address fundamental unresolved questions related to the evolution of animal brains and cognition. Author summary: Bees, despite their small brains, possess a rich behavioural repertoire and show significant variations among individuals. In social bees this variability is key to the division of labour that maintains their complex social organizations and has been linked to the maturation of specific brain areas as a result of development and foraging experience. This makes bees an ideal model for understanding insect cognitive functions and the neural mechanisms that underlie them. However, due to the scarcity of comparative data, the relationship between brain neuro-architecture and behavioural variance remains unclear. To address this problem, we developed an AI-based approach for automated analysis of three-dimensional brain images and analysed an unprecedentedly large dataset of honey bee and bumblebee brains. Through this process, we were able to identify previously undescribed anatomical features that correlate with known behaviours, supporting recent evidence of lateralized behaviour in foraging and pollination. Our method is opensource, easily accessible online, user-friendly, fast, accurate, and robust to different species, enabling large-scale comparative analyses across the animal kingdom. This includes investigating the impact of external stressors such as environmental pollution and climate change on cognitive development, helping us understand the mechanisms underlying the cognitive abilities of animals and the implications for their survival and adaptation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Applied Ecology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of American Naturalist is the property of University of Chicago and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Simple Summary: Insect hemolymph is equivalent to blood in higher vertebrates. It is the main site for immune responses, mediates nutrient transportation to organs and tissues, and has antimicrobial and antioxidant properties. Hemolymph can thus provide information about the health status of an insect. Here we report intraspecific variation in hemolymph properties of Western honey bees Apis mellifera sampled in four locations providing different diets across Egypt. Bees that had access to a rich and varied diet had higher protein concentrations and levels of biological activities in their hemolymph than bees that were only fed sucrose solution. This suggests hemolymph analyses could be used as a powerful indicator for monitoring bee populations, with the aim of improving their health and pollination efficiency. Pollinator declines have raised major concerns for the maintenance of biodiversity and food security, calling for a better understanding of environmental factors that affect their health. Here we used hemolymph analysis to monitor the health status of Western honey bees Apis mellifera. We evaluated the intraspecific proteomic variations and key biological activities of the hemolymph of bees collected from four Egyptian localities characterized by different food diversities and abundances. Overall, the lowest protein concentrations and the weakest biological activities (cytotoxicity, antimicrobial and antioxidant properties) were recorded in the hemolymph of bees artificially fed sucrose solution and no pollen. By contrast, the highest protein concentrations and biological activities were recorded in bees that had the opportunity to feed on various natural resources. While future studies should expand comparisons to honey bee populations exposed to more different diets and localities, our results suggest hemolymph samples can be used as reliable indicators of bee nutrition. [ABSTRACT FROM AUTHOR]

Understanding how pollinators move across space is key to understanding plant mating patterns. Bees are typically assumed to search for flowers randomly or using simple movement rules, so that the probability of discovering a flower should primarily depend on its distance to the nest. However, experimental work shows this is not always the case. Here, we explored the influence of flower size and density on their probability of being discovered by bees by developing a movement model of central place foraging bees, based on experimental data collected on bumblebees. Our model produces realistic bee trajectories by taking into account the autocorrelation of the bee's angular speed, the attraction to the nest (homing), and a gaussian noise. Simulations revealed a « masking effect » that reduces the detection of flowers close to another, with potential far reaching consequences on plant-pollinator interactions. At the plant level, flowers distant to the nest were more often discovered by bees in low density environments. At the bee colony level, foragers found more flowers when they were small and at medium densities. Our results indicate that the processes of search and discovery of resources are potentially more complex than usually assumed, and question the importance of resource distribution and abundance on bee foraging success and plant pollination. Author summary: Understanding how pollinators move in space is key to understand plant reproduction and its consequences on terrestrial ecosystems. Current models assume simple movement rules that predict flowers are more likely to be visited—and hence pollinated—the closer they are to the pollinators' nest. Here we developed an explicit movement model that incorporates realistic features of bumblebee behaviour, and calibrated it with experimental data collected in naturalistic conditions. Our model shows that the probability to visit a flower does not only depend on its position, but also on the position of other flowers around that may mask it from the forager. This perceptual masking effect means that pollination efficiency depends on the density and spatial arrangement of flowers around the pollinators' nest, often in counter-intuitive ways. Taking these effects into account may be key for improving practical actions in precision pollination and pollinator conservation. [ABSTRACT FROM AUTHOR]

"What a Bee Knows: Exploring the Thoughts, Memories, and Personalities of Bees" by Stephen Buchmann is a book that delves into the fascinating world of bees. Unlike other bee books, this one is written by a pollination ecologist who provides a unique perspective on bees and their interactions with their environment. The author takes readers on a journey through recent discoveries in bee behavior and cognition, highlighting the diverse nature of bees and their intriguing habits. The book also touches on the decline of bees and the importance of protecting these vital pollinators. [Extracted from the article]

A social insect colony can be seen as a superorganism in which nutrient collection and regulation is achieved through the collective action of workers. Here we investigated how workers of the yellow-legged hornet Vespa velutina, an invasive predator of honeybees and other insects in Europe, distribute the incoming food within their nests. Food transfer among colonies differing in composition of developmental stage and abundance of different castes was compared by feeding workers with protein and carbohydrate solutions labelled with non-radioactive heavy metals (rubidium and caesium) and measuring the heavy metal tracers’ occurrence in adults and larvae in the nests after 24h. Caesium labelled sucrose was more abundant in the adults (workers, males and the queen), while rubidium labelled proteins were more abundant in the larvae. The lightest larvae received more protein than the others. The lightest workers received more carbohydrate, and the largest workers more protein. Even if this work is exploratory, our results considerably improve our knowledge of hornet biology by showing how food distribution patterns may vary throughout the development cycle of colonies. They also provide new perspectives to understand how specific nutrients are distributed in nests, and may help develop Trojan horses biological control strategies based on food distribution. [ABSTRACT FROM AUTHOR]

Central place foraging pollinators tend to develop multi-destination routes (traplines) to exploit patchily distributed plant resources. While the formation of traplines by individual pollinators has been studied in detail, how populations of foragers use resources in a common area is an open question, difficult to address experimentally. We explored conditions for the emergence of resource partitioning among traplining bees using agent-based models built from experimental data of bumblebees foraging on artificial flowers. In the models, bees learn to develop routes as a consequence of feedback loops that change their probabilities of moving between flowers. While a positive reinforcement of movements leading to rewarding flowers is sufficient for the emergence of resource partitioning when flowers are evenly distributed, the addition of a negative reinforcement of movements leading to unrewarding flowers is necessary when flowers are patchily distributed. In environments with more complex spatial structures, the negative experiences of individual bees on flowers favour spatial segregation and efficient collective foraging. Our study fills a major gap in modelling pollinator behaviour and constitutes a unique tool to guide future experimental programs. Author summary: Pollinating animals, like bees, face the challenge of maximising their returns on plant resources while minimising their foraging costs. Observations show bees establish idiosyncratic foraging routes (traplines) to visit familiar plants using short paths. This is an effective strategy for collecting pollen and nectar that are dispersed and renewable resources. Intriguingly, different bees seem to establish non-overlapping traplines, which aids in partitioning resources. So far, however, how bees establish these foraging strategies is a mystery. It seems unfeasible for them to be able to negotiate with competing foragers. Here we present a simple computational model derived from empirical observations suggesting bees can develop efficient routes between flowers while minimizing spatial overlaps with competitors based only on their history of reinforcement in a floral array. In the model, bees learn to return to flowers where they found nectar and avoid flowers that were found empty. Numerical simulations of our model predict the emergence of resource partitioning between pairs of bees under various conditions. This suggests a simple strategy to promote efficient foraging among competing agents on a renewable resource that could apply to many different pollinating animals. [ABSTRACT FROM AUTHOR]

Social groups take a myriad of forms, reflecting the countless different ways in which animals can interact and associate (Wilson, 2000). The Joint Special Feature in I Methods in Ecology and Evolution i and the I Journal of Animal Ecology i is a celebration of research by animal social network scientists, introducing novel methods and questions pertaining to Animal Social Network Analysis (ASNA). ANIMAL NETWORKS UNDER DIFFERENT ENVIRONMENTS Social structure represents the most plastic aspect of animal societies as individuals can, through social interactions, regulate conflicts (Aureli & de Waal, 2000), create affiliative bonds (De Waal & Roosmalen, 1979), cooperate (Seyfarth & Cheney, 2012), transmit information and learn (Hoppitt & Laland, 2013). [Extracted from the article]

Understanding how animal movements change across space and time is a fundamental question in ecology. While classical analyses of trajectories give insightful descriptors of spatial patterns, a satisfying method for assessing the temporal succession of such patterns is lacking.Network analyses are increasingly used to capture properties of complex animal trajectories in simple graphical metrics. Here, building on this approach, we introduce a method that incorporates time into movement network analyses based on temporal sequences of network motifs.We illustrate our method using four example trajectories (bumblebee, black kite, roe deer, wolf) collected with different technologies (harmonic radar, platform terminal transmitter, global positioning system). First, we transformed each trajectory into a spatial network by defining the animal's coordinates as nodes and movements in between as edges. Second, we extracted temporal sequences of network motifs from each movement network and compared the resulting behavioural profiles to topological features of the original trajectory. Finally, we compared each sequence of motifs with simulated Brownian and Lévy random motions to statistically determine differences between trajectories and classical movement models.Our analysis of the temporal sequences of network motifs in individual movement networks revealed successions of spatial patterns corresponding to changes in behavioural modes that can be attributed to specific spatio‐temporal events of each animal trajectory. Future applications of our method to multi‐layered movement and social network analysis yield considerable promises for extending the study of complex movement patterns at the population level. [ABSTRACT FROM AUTHOR]