Modeling bee movement shows how a perceptual masking effect can influence flower discovery.

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 ¼ 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.

Natural variability in bee brain size and symmetry revealed by micro-CT imaging and deep learning.

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.

Nutrigonometry I: Using Right-Angle Triangles to Quantify Nutritional Trade-Offs in Performance Landscapes.

AbstractAnimals regulate their food intake to maximize the expression of fitness traits but are forced to trade off the optimal expression of some fitness traits because of differences in the nutrient requirements of each trait ("nutritional trade-offs"). Nutritional trade-offs have been experimentally uncovered using the geometric framework for nutrition (GF). However, current analytical methods to measure such responses rely on either visual inspection or complex models of vector calculations applied to multidimensional performance landscapes, making these approaches subjective or conceptually difficult, computationally expensive, and, in some cases, inaccurate. Here, we present a simple trigonometric model to measure nutritional trade-offs in multidimensional landscapes (nutrigonometry) that relies on the trigonometric relationships of right-angle triangles and thus is both conceptually and computationally easier to understand and use than previous quantitative approaches. We applied nutrigonometry to a landmark GF data set for comparison of several standard statistical models to assess model performance in finding regions in the performance landscapes. This revealed that polynomial (Bayesian) regressions can be used for precise and accurate predictions of peaks and valleys in performance landscapes, irrespective of the underlying structure of the data (i.e., individual food intakes vs. fixed diet ratios). We then identified the known nutritional trade-off between life span and reproductive rate in terms of both nutrient balance and concentration for validation of the model. This showed that nutrigonometry enables a fast, reliable, and reproducible quantification of nutritional trade-offs in multidimensional performance landscapes, thereby broadening the potential for future developments in comparative research on the evolution of animal nutrition.

The gut parasite Nosema ceranae impairs olfactory learning in bumblebees.

Pollinators are exposed to numerous parasites and pathogens when foraging on flowers. These biological stressors may affect critical cognitive abilities required for foraging. Here, we tested whether exposure to Nosema ceranae, one of the most widespread parasites of honey bees also found in wild pollinators, impacts cognition in bumblebees. We investigated different forms of olfactory learning and memory using conditioning of the proboscis extension reflex. Seven days after being exposed to parasite spores, bumblebees showed lower performance in absolute, differential and reversal learning than controls. The consistent observations across different types of olfactory learning indicate a general negative effect of N. ceranae exposure that did not specifically target particular brain areas or neural processes. We discuss the potential mechanisms by which N. ceranae impairs bumblebee cognition and the broader consequences for populations of pollinators.

A model of resource partitioning between foraging bees based on learning.

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.

Metal pollutants have additive negative effects on honey bee cognition.

Environmental pollutants can exert sublethal deleterious effects on animals. These include disruption of cognitive functions underlying crucial behaviours. While agrochemicals have been identified as a major threat to pollinators, metal pollutants, which are often found in complex mixtures, have so far been overlooked. Here, we assessed the impact of acute exposure to field-realistic concentrations of three common metal pollutants, lead, copper and arsenic, and their combinations, on honey bee appetitive learning and memory. All treatments involving single metals slowed down learning and disrupted memory retrieval at 24 h. Combinations of these metals had additive negative effects on both processes, suggesting common pathways of toxicity. Our results highlight the need to further assess the risks of metal pollution on invertebrates.

Chronic exposure to trace lead impairs honey bee learning.

Pollutants can have severe detrimental effects on insects, even at sublethal doses, damaging developmental and cognitive processes involved in crucial behaviours. Agrochemicals have been identified as important causes of pollinator declines, but the impacts of other anthropogenic compounds, such as metallic trace elements in soils and waters, have received considerably less attention. Here, we exposed colonies of the European honey bee Apis mellifera to chronic field-realistic concentrations of lead in food and demonstrated that consumption of this trace element impaired bee cognition and morphological development. Honey bees exposed to the highest of these low concentrations had reduced olfactory learning performances. These honey bees also developed smaller heads, which may have constrained their cognitive functions as we show a general relationship between head size and learning performance. Our results demonstrate that lead pollutants, even at trace levels, can have dramatic effects on honey bee cognitive abilities, potentially altering key colony functions and the pollination service.

A Non-Invasive Millimetre-Wave Radar Sensor for Automated Behavioural Tracking in Precision Farming-Application to Sheep Husbandry.

The automated quantification of the behaviour of freely moving animals is increasingly needed in applied ethology. State-of-the-art approaches often require tags to identify animals, high computational power for data collection and processing, and are sensitive to environmental conditions, which limits their large-scale utilization, for instance in genetic selection programs of animal breeding. Here we introduce a new automated tracking system based on millimetre-wave radars for real time robust and high precision monitoring of untagged animals. In contrast to conventional video tracking systems, radar tracking requires low processing power, is independent on light variations and has more accurate estimations of animal positions due to a lower misdetection rate. To validate our approach, we monitored the movements of 58 sheep in a standard indoor behavioural test used for assessing social motivation. We derived new estimators from the radar data that can be used to improve the behavioural phenotyping of the sheep. We then showed how radars can be used for movement tracking at larger spatial scales, in the field, by adjusting operating frequency and radiated electromagnetic power. Millimetre-wave radars thus hold considerable promises precision farming through high-throughput recording of the behaviour of untagged animals in different types of environments.

Quantifying Nutritional Trade-Offs across Multidimensional Performance Landscapes.

Animals make feeding decisions to simultaneously maximize fitness traits that often require different nutrients. Recent quantitative methods have been developed to characterize these nutritional trade-offs from performance landscapes on which traits are mapped on a nutrient space defined by two nutrients. This limitation constrains the broad applications of previous methods to more complex data, and a generalized framework is needed. Here, we build on previous methods and introduce a generalized vector-based approach-the vector of position approach-to study nutritional trade-offs in complex multidimensional spaces. The vector of position approach allows the estimate of performance variations across entire landscapes (peaks and valleys) and comparison of these variations between animals. Using landmark published data sets on life span and reproduction landscapes, we illustrate how our approach gives accurate quantifications of nutritional trade-offs in two- and three-dimensional spaces and can bring new insights into the underlying nutritional differences in trait expression between species. The vector of position approach provides a generalized framework for investigating nutritional differences in life-history trait expression within and between species, an essential step for the development of comparative research on the evolution of animal nutritional strategies.

Evidence of trapline foraging in honeybees.

Central-place foragers exploiting floral resources often use multi-destination routes (traplines) to maximise their foraging efficiency. Recent studies on bumblebees have showed how solitary foragers can learn traplines, minimising travel costs between multiple replenishing feeding locations. Here we demonstrate a similar routing strategy in the honeybee (Apis mellifera), a major pollinator known to recruit nestmates to discovered food resources. Individual honeybees trained to collect sucrose solution from four artificial flowers arranged within 10 m of the hive location developed repeatable visitation sequences both in the laboratory and in the field. A 10-fold increase of between-flower distances considerably intensified this routing behaviour, with bees establishing more stable and more efficient routes at larger spatial scales. In these advanced social insects, trapline foraging may complement cooperative foraging for exploiting food resources near the hive (where dance recruitment is not used) or when resources are not large enough to sustain multiple foragers at once.

Drosophila females trade off good nutrition with high-quality oviposition sites when choosing foods.

Animals, from insects to humans, select foods to regulate their acquisition of key nutrients in amounts and balances that maximise fitness. In species in which the nutrition of juveniles depends on parents, adults must make challenging foraging decisions that simultaneously address their own nutrient needs as well as those of their progeny. Here, we examined how the fruit fly Drosophila melanogaster, a species in which individuals eat and lay eggs in decaying fruits, integrate feeding decisions (individual nutrition) and oviposition decisions (offspring nutrition) when foraging. Using cafeteria assays with artificial diets varying in concentrations and ratios of protein to carbohydrates, we show that D. melanogaster females exhibit complex foraging patterns, alternating between laying eggs on high carbohydrate foods and feeding on foods with different nutrient contents depending on their own nutritional state. Although larvae showed faster development on high protein foods, both survival and learning performance were higher on balanced foods. We suggest that the apparent mismatch between the oviposition preference of females for high carbohydrate foods and the high performances of larvae on balanced foods reflects a natural situation where high carbohydrate ripened fruits gradually enrich in proteinaceous yeast as they start rotting, thereby yielding optimal nutrition for the developing larvae. Our findings that animals with rudimentary parental care uncouple feeding and egg-laying decisions in order to balance their own diet and provide a nutritionally optimal environment to their progeny reveal unsuspected levels of complexity in the nutritional ecology of parent-offspring interactions.

Collective selection of food patches in Drosophila.

The fruit fly Drosophila melanogaster has emerged as a model organism for research on social interactions. Although recent studies have described how individuals interact on foods for nutrition and reproduction, the complex dynamics by which groups initially develop and disperse have received little attention. Here we investigated the dynamics of collective foraging decisions by D. melanogaster and their variation with group size and composition. Groups of adults and larvae facing a choice between two identical, nutritionally balanced food patches distributed themselves asymmetrically, thereby exploiting one patch more than the other. The speed of the collective decisions increased with group size, as a result of flies joining foods faster. However, smaller groups exhibited more pronounced distribution asymmetries than larger ones. Using computer simulations, we show how these non-linear phenomena can emerge from social attraction towards occupied food patches, whose effects add up or compete depending on group size. Our results open new opportunities for exploring complex dynamics of nutrient selection in simple and genetically tractable groups.

Evolving nutritional strategies in the presence of competition: a geometric agent-based model.

Access to nutrients is a key factor governing development, reproduction and ultimately fitness. Within social groups, contest-competition can fundamentally affect nutrient access, potentially leading to reproductive asymmetry among individuals. Previously, agent-based models have been combined with the Geometric Framework of nutrition to provide insight into how nutrition and social interactions affect one another. Here, we expand this modelling approach by incorporating evolutionary algorithms to explore how contest-competition over nutrient acquisition might affect the evolution of animal nutritional strategies. Specifically, we model tolerance of nutrient excesses and deficits when ingesting nutritionally imbalanced foods, which we term 'nutritional latitude'; a higher degree of nutritional latitude constitutes a higher tolerance of nutritional excess and deficit. Our results indicate that a transition between two alternative strategies occurs at moderate to high levels of competition. When competition is low, individuals display a low level of nutritional latitude and regularly switch foods in search of an optimum. When food is scarce and contest-competition is intense, high nutritional latitude appears optimal, and individuals continue to consume an imbalanced food for longer periods before attempting to switch to an alternative. However, the relative balance of nutrients within available foods also strongly influences at what levels of competition, if any, transitions between these two strategies occur. Our models imply that competition combined with reproductive skew in social groups can play a role in the evolution of diet breadth. We discuss how the integration of agent-based, nutritional and evolutionary modelling may be applied in future studies to further understand the evolution of nutritional strategies across social and ecological contexts.

Recent advances in the integrative nutrition of arthropods.

In this review we highlight recent advances in four areas in which nutrition shapes the relationships between organisms: between plants and herbivores, between hosts and their microbiota, between individuals within groups and societies, and between species within food webs. We demonstrate that taking an explicitly multidimensional view of nutrition and employing the logic of the geometric framework for nutrition provide novel insights and offer a means of integration across different levels of organization, from individuals to ecosystems.

Nutritional ecology beyond the individual: a conceptual framework for integrating nutrition and social interactions.

Over recent years, modelling approaches from nutritional ecology (known as Nutritional Geometry) have been increasingly used to describe how animals and some other organisms select foods and eat them in appropriate amounts in order to maintain a balanced nutritional state maximising fitness. These nutritional strategies profoundly affect the physiology, behaviour and performance of individuals, which in turn impact their social interactions within groups and societies. Here, we present a conceptual framework to study the role of nutrition as a major ecological factor influencing the development and maintenance of social life. We first illustrate some of the mechanisms by which nutritional differences among individuals mediate social interactions in a broad range of species and ecological contexts. We then explain how studying individual- and collective-level nutrition in a common conceptual framework derived from Nutritional Geometry can bring new fundamental insights into the mechanisms and evolution of social interactions, using a combination of simulation models and manipulative experiments.

An Overlooked Consequence of Dietary Mixing: A Varied Diet Reduces Interindividual Variance in Fitness.

The determinants of diet breadth are of interest to nutritionists, ecologists, and evolutionary biologists. A recent synthesis addressing this issue found conflicting evidence for the relationship between diet breadth and mean individual fitness. Specifically, it found that while, on average, a mixed diet does increase mean fitness, in some instances, a single food provides equal (or higher) fitness than a mixed diet. Critical to ecological and evolutionary considerations of diet, however, is not only mean fitness but also variance in fitness. We combine contemporary meta-analytic methods with models of nutritional geometry to evaluate how diet affects between-individual variance in fitness within generalist consumers from a range of trophic levels. As predicted by nutritional geometry, we found that between-individual variance in fitness-related traits is higher on single-food than mixed diets. The effect was strong for longevity traits (57% higher) and reproductive traits (37%) and present but weaker for size-related traits (10%). Further, the effect became stronger as the number of available foods increased. The availability of multiple foods likely allows individuals with differing nutritional optima to customize intake, each maximizing their own fitness. Importantly, these findings may suggest that selection on traits correlated with nutritional requirements is weak in heterogeneous nutritional environments.

Radar tracking and motion-sensitive cameras on flowers reveal the development of pollinator multi-destination routes over large spatial scales.

Central place foragers, such as pollinating bees, typically develop circuits (traplines) to visit multiple foraging sites in a manner that minimizes overall travel distance. Despite being taxonomically widespread, these routing behaviours remain poorly understood due to the difficulty of tracking the foraging history of animals in the wild. Here we examine how bumblebees (Bombus terrestris) develop and optimise traplines over large spatial scales by setting up an array of five artificial flowers arranged in a regular pentagon (50 m side length) and fitted with motion-sensitive video cameras to determine the sequence of visitation. Stable traplines that linked together all the flowers in an optimal sequence were typically established after a bee made 26 foraging bouts, during which time only about 20 of the 120 possible routes were tried. Radar tracking of selected flights revealed a dramatic decrease by 80% (ca. 1500 m) of the total travel distance between the first and the last foraging bout. When a flower was removed and replaced by a more distant one, bees engaged in localised search flights, a strategy that can facilitate the discovery of a new flower and its integration into a novel optimal trapline. Based on these observations, we developed and tested an iterative improvement heuristic to capture how bees could learn and refine their routes each time a shorter route is found. Our findings suggest that complex dynamic routing problems can be solved by small-brained animals using simple learning heuristics, without the need for a cognitive map.

A simple iterative model accurately captures complex trapline formation by bumblebees across spatial scales and flower arrangements.

Pollinating bees develop foraging circuits (traplines) to visit multiple flowers in a manner that minimizes overall travel distance, a task analogous to the travelling salesman problem. We report on an in-depth exploration of an iterative improvement heuristic model of bumblebee traplining previously found to accurately replicate the establishment of stable routes by bees between flowers distributed over several hectares. The critical test for a model is its predictive power for empirical data for which the model has not been specifically developed, and here the model is shown to be consistent with observations from different research groups made at several spatial scales and using multiple configurations of flowers. We refine the model to account for the spatial search strategy of bees exploring their environment, and test several previously unexplored predictions. We find that the model predicts accurately 1) the increasing propensity of bees to optimize their foraging routes with increasing spatial scale; 2) that bees cannot establish stable optimal traplines for all spatial configurations of rewarding flowers; 3) the observed trade-off between travel distance and prioritization of high-reward sites (with a slight modification of the model); 4) the temporal pattern with which bees acquire approximate solutions to travelling salesman-like problems over several dozen foraging bouts; 5) the instability of visitation schedules in some spatial configurations of flowers; 6) the observation that in some flower arrays, bees' visitation schedules are highly individually different; 7) the searching behaviour that leads to efficient location of flowers and routes between them. Our model constitutes a robust theoretical platform to generate novel hypotheses and refine our understanding about how small-brained insects develop a representation of space and use it to navigate in complex and dynamic environments.

Environmental exposure to metallic pollution impairs honey bee brain development and cognition.

Laboratory studies show detrimental effects of metallic pollutants on invertebrate behaviour and cognition, even at low levels. Here we report a field study on Western honey bees exposed to metal and metalloid pollution through dusts, food and water at a historic mining site. We analysed more than 1000 bees from five apiaries along a gradient of contamination within 11 km of a former gold mine in Southern France. Bees collected close to the mine exhibited olfactory learning performances lower by 36% and heads smaller by 4%. Three-dimensional scans of bee brains showed that the olfactory centres of insects sampled close to the mine were also 4% smaller, indicating neurodevelopmental issues. Our study raises serious concerns about the health of honey bee populations in areas polluted with potentially harmful elements, particularly with arsenic, and illustrates how standard cognitive tests can be used for risk assessment.

Intraspecific Variability in Proteomic Profiles and Biological Activities of the Honey Bee Hemolymph.

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.