Bees do not use nearest-neighbour rules for optimization of multi-location routes

Choosing the best way: how wild common marmosets travel to efficiently exploit resources

While foraging, animals have to find potential food sites, remember these sites, and plan the best navigation route. To deal with problems associated with foraging for multiple and patchy resources, primates may employ heuristic strategies to improve foraging success. Until now, no study has attempted to investigate experimentally the use of such strategies by a primate in a context involving foraging in large-scale space. Thus, we carried out an experimental field study that aimed to test if wild common marmosets (Callithrix jacchus) employ heuristic strategies to efficiently navigate through multiple feeding sites distributed in a large-scale space. In our experiment, we arranged four feeding platforms in a trapezoid configuration with up to 60 possible routes and observe marmosets' decisions under two experimental conditions. In experimental condition I, all platforms contained the same amount of food; in experimental condition II, the platforms had different amounts of food. According to the number and arrangement of the platforms, we tested two heuristic strategies: the Nearest Neighbor Rule and the Gravity Rule. Our results revealed that wild common marmosets prefer to use routes consistent with a heuristic strategy more than expected by chance, regardless of food distribution. The findings also demonstrate that common marmosets seem to integrate different factors such as distance and quantity of food across multiple sites distributed over a large-scale space, employing a combination of heuristic strategies to select the most efficient routes available. In summary, our findings confirm our expectations and provide important insights into the spatial cognition of these small neotropical primates.

A Sublethal Concentration of Sulfoxaflor Has Minimal Impact on Buff-Tailed Bumblebee (Bombus terrestris) Locomotor Behaviour under Aversive Conditioning

Pesticide exposure has been cited as a key threat to insect pollinators. Notably, a diverse range of potential sublethal effects have been reported in bee species, with a particular focus on effects due to exposure to neonicotinoid insecticides. Here, a purpose-built thermal-visual arena was used in a series of pilot experiments to assess the potential impact of approximate sublethal concentrations of the next generation sulfoximine insecticide sulfoxaflor (5 and 50 ppb) and the neonicotinoid insecticides thiacloprid (500 ppb) and thiamethoxam (10 ppb), on the walking trajectory, navigation and learning abilities of the buff-tailed bumblebee (Bombus terrestris audax) when subjected to an aversive conditioning task. The results suggest that only thiamethoxam prevents forager bees from improving in key training parameters (speed and distanced travelled) within the thermal visual arena. Power law analyses further revealed that a speed-curvature power law, previously reported as being present in the walking trajectories of bumblebees, is potentially disrupted under thiamethoxam (10 ppb) exposure, but not under sulfoxaflor or thiacloprid exposure. The pilot assay described provides a novel tool with which to identify subtle sublethal pesticide impacts, and their potential causes, on forager bees, that current ecotoxicological tests are not designed to assess.

Mild movement sequence repetition in five primate species and evidence for a taxonomic divide in cognitive mechanisms

When animals forage, they face complex multi-destination routing problems. Traplining behaviour-the repeated use of the same route-can be used to study how spatial memory might evolve to cope with complex routing problems in ecologically distinct taxa. We analyzed experimental data from multi-destination foraging arrays for five species, two cercopithecine monkeys (vervets, Chlorocebus pygerythrus, and Japanese macaques, Macaca fuscata) and three strepsirrhines (fat-tailed dwarf lemurs, Cheirogaleus medius, grey mouse lemurs, Microcebus murinus, and aye-ayes, Daubentonia madagascariensis). These species all developed relatively efficient route formations within the arrays but appeared to rely on variable cognitive mechanisms. We found a strong reliance on heuristics in cercopithecoid species, with initial routes that began near optimal and did not improve with experience. In strepsirrhines, we found greater support for reinforcement learning of location-based decisions, such that routes improved with experience. Further, we found evidence of repeated sequences of site visitation in all species, supporting previous suggestions that primates form traplines. However, the recursive use of routes was weak, differing from the strategies seen in well-known traplining animals. Differences between strepsirrhine and cercopithecine strategies may be the result of either ecological or phylogenetic trends, and we discuss future possibilities for disentangling the two.

The contribution of plant spatial arrangement to bumble bee flower constancy

Floral constancy of foraging bees influences plant reproduction. Constancy as observed in nature arises from at least four distinct mechanisms frequently confounded in the literature: context-independent preferences for particular plant species, preferential visitation to the same species as the previous plant visited (simple constancy), the spatial arrangement of plants, and the relative abundances of co-flowering species. To disentangle these mechanisms, we followed individual bee flight paths within patches where all flowering plants were mapped, and we used step selection models to estimate how each mechanism influences the probability of selecting any particular plant given the available plants in a multi-species community. We found that simple constancy was positive: bees preferred to visit the same species sequentially. In addition, bees preferred to travel short distances and maintain their direction of travel between plants. After accounting for distance, we found no significant effect of site-level plant relative abundances on bee foraging choices. To explore the importance of the spatial arrangement of plants for bee foraging choices, we compared our full model containing all parameters to one with spatial arrangement removed. Due to bees' tendency to select nearby plants, combined with strong intraspecific plant clumping, spatial arrangement was responsible for about 50% of the total observed constancy. Our results suggest that floral constancy may be overestimated in studies that do not account for the spatial arrangement of plants, especially in systems with intraspecific plant clumping. Plant spatial patterns at within-site scales are important for pollinator foraging behavior and pollination success.

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.

Patch selection by bumble bees navigating discontinuous landscapes

Pollen and nectar resources are unevenly distributed over space and bees must make routing decisions when navigating patchy resources. Determining the patch selection process used by bees is crucial to understanding bee foraging over discontinuous landscapes. To elucidate this process, we developed four distinct probability models of bee movement where the size and the distance to the patch determined the attractiveness of a patch. A field experiment with a center patch and four peripheral patches of two distinct sizes and distances from the center was set up in two configurations. Empirical transition probabilities from the center to each peripheral patch were obtained at two sites and two years. The best model was identified by comparing observed and predicted transition probabilities, where predicted values were obtained by incorporating the spatial dimensions of the field experiment into each model's mathematical expression. Bumble bees used both patch size and isolation distance when selecting a patch and could assess the total amount of resources available in a patch. Bumble bees prefer large, nearby patches. This information will facilitate the development of a predictive framework to the study of bee movement and of models that predict the movement of genetically engineered pollen in bee-pollinated crops.

Landscape structure affects the sunflower visiting frequency of insect pollinators

Mass-flowering crop monocultures, like sunflower, cannot harbour a permanent pollinator community. Their pollination is best secured if both managed honey bees and wild pollinators are present in the agricultural landscape. Semi-natural habitats are known to be the main foraging and nesting areas of wild pollinators, thus benefiting their populations, whereas crops flowering simultaneously may competitively dilute pollinator densities. In our study we asked how landscape structure affects major pollinator groups’ visiting frequency on 36 focal sunflower fields, hypothesising that herbaceous semi-natural (hSNH) and sunflower patches in the landscape neighbourhood will have a scale-dependent effect. We found that an increasing area and/or dispersion of hSNH areas enhanced the visitation of all pollinator groups. These positive effects were scale-dependent and corresponded well with the foraging ranges of the observed bee pollinators. In contrast, an increasing edge density of neighbouring sunflower fields resulted in considerably lower visiting frequencies of wild bees. Our results clearly indicate that the pollination of sunflower is dependent on the composition and configuration of the agricultural landscape. We conclude that an optimization of the pollination can be achieved if sufficient amount of hSNH areas with good dispersion are provided and mass flowering crops do not over-dominate the agricultural landscape.

Some theoretical notes on spatial discounting

Spatial discounting is a largely underexplored area of decision-making research, both theoretically and empirically, especially when compared to intertemporal choice, which has received significant attention in psychology and animal behaviour. Spatial decision problems seem to share some of the same features of a temporal decision problem (namely, the risk of reward objects disappearing and the opportunity cost of waiting), but there are several additional factors that affect the appropriate discount function for distant rewards. These include more significant opportunity costs, changes in the distances to all the other available opportunities, the post-reward costs of getting back home, the complex energetics associated with locomotion and all the additional risks faced by travelling itself. This paper organises and explores these factors and suggests some normative models that should predict the adaptive behaviour of animals and humans.

From Paths to Routes: A Method for Path Classification

Many animals establish, learn and optimize routes between locations to commute efficiently. One step in understanding route following is defining measures of similarities between the paths taken by the animals. Paths have commonly been compared by using several descriptors (e.g., the speed, distance traveled, or the amount of meandering) or were visually classified into categories by the experimenters. However, similar quantities obtained from such descriptors do not guarantee similar paths, and qualitative classification by experimenters is prone to observer biases. Here we propose a novel method to classify paths based on their similarity with different distance functions and clustering algorithms based on the trajectories of bumblebees flying through a cluttered environment. We established a method based on two distance functions (Dynamic Time Warping and Fréchet Distance). For all combinations of trajectories, the distance was calculated with each measure. Based on these distance values, we grouped similar trajectories by applying the Monte Carlo Reference-Based Consensus Clustering algorithm. Our procedure provides new options for trajectory analysis based on path similarities in a variety of experimental paradigms.

Spatio-temporal dynamics of landscape use by the bumblebee Bombus pauloensis (Hymenoptera: Apidae) and its relationship with pollen provisioning

Understanding how bees use resources at a landscape scale is essential for developing meaningful management plans that sustain populations and the pollination services they provide. Bumblebees are important pollinators for many wild and cultivated plants, and have experienced steep population declines worldwide. Bee foraging behavior can be influenced by resource availability and bees’ lifecycle stage. To better understand these relationships, we studied the habitat selection of Bombus pauloensis by tracking 17 queen bumblebees with radio telemetry in blueberry fields in Entre Ríos province, Argentina. To evaluate land use and floral resources used by bumblebees, we tracked bees before and after nest establishment and estimated home ranges using minimum convex polygons and kernel density methods. We also classified the pollen on their bodies to identify the floral resources they used from the floral species available at that time. We characterized land use for each bee as the relative proportion of GPS points inside of each land use. Bumblebees differed markedly in their movement behavior in relation to pre and post nest establishment. Bees moved over larger areas, and mostly within blueberry fields, before nest establishment. In contrast, after establishing the nest, the bees preferred the edges near forest plantations and they changed the nutritional resources to prefer wild floral species. Our study is the first to track queen bumblebee movements in an agricultural setting and relate movement changes across time and space with pollen resource availability. This study provides insight into the way bumblebee queens use different habitat elements at crucial periods in their lifecycle, showing the importance of mass flowering crops like blueberry in the first stages of queen’s lifecycle, and how diversified landscapes help support bee populations as their needs changes during different phases of their lifecycle.

Bumblebees learn foraging routes through exploitation-exploration cycles

How animals explore and acquire knowledge from the environment is a key question in movement ecology. For pollinators that feed on multiple small replenishing nectar resources, the challenge is to learn efficient foraging routes while dynamically acquiring spatial information about new resource locations. Here, we use the behavioural mapping t-Stochastic Neighbouring Embedding algorithm and Shannon entropy to statistically analyse previously published sampling patterns of bumblebees feeding on artificial flowers in the field. We show that bumblebees modulate foraging excursions into distinctive behavioural strategies, characterizing the trade-off dynamics between (i) visiting and exploiting flowers close to the nest, (ii) searching for new routes and resources, and (iii) exploiting learned flower visitation sequences. Experienced bees combine these behavioural strategies even after they find an optimal route minimizing travel distances between flowers. This behavioural variability may help balancing energy costs-benefits and facilitate rapid adaptation to changing environments and the integration of more profitable resources in their routes.

The Central Complex as a Potential Substrate for Vector Based Navigation

Insects use path integration (PI) to maintain a home vector, but can also store and recall vector-memories that take them from home to a food location, and even allow them to take novel shortcuts between food locations. The neural circuit of the Central Complex (a brain area that receives compass and optic flow information) forms a plausible substrate for these behaviors. A recent model, grounded in neurophysiological and neuroanatomical data, can account for PI during outbound exploratory routes and the control of steering to return home. Here, we show that minor, hypothetical but neurally plausible, extensions of this model can additionally explain how insects could store and recall PI vectors to follow food-ward paths, take shortcuts, search at the feeder and re-calibrate their vector-memories with experience. In addition, a simple assumption about how one of multiple vector-memories might be chosen at any point in time can produce the development and maintenance of efficient routes between multiple locations, as observed in bees. The central complex circuitry is therefore well-suited to allow for a rich vector-based navigational repertoire.

A spatial network analysis of resource partitioning between bumblebees foraging on artificial flowers in a flight cage

BACKGROUND

Individual bees exhibit complex movement patterns to efficiently exploit small areas within larger plant populations. How such individual spatial behaviours scale up to the collective level, when several foragers visit a common area, has remained challenging to investigate, both because of the low resolution of field movement data and the limited power of the statistical descriptors to analyse them. To tackle these issues we video recorded all flower visits (N = 6205), and every interaction on flowers (N = 628), involving foragers from a bumblebee (Bombus terrestris) colony in a large outdoor flight cage (880 m2), containing ten artificial flowers, collected on five consecutive days, and analysed bee movements using networks statistics.

RESULTS

Bee-flower visitation networks were significantly more modular than expected by chance, indicating that foragers minimized overlaps in their patterns of flower visits. Resource partitioning emerged from differences in foraging experience among bees, and from outcomes of their interactions on flowers. Less experienced foragers showed lower activity and were more faithful to some flowers, whereas more experienced foragers explored the flower array more extensively. Furthermore, bees avoided returning to flowers from which they had recently been displaced by a nestmate, suggesting that bees integrate memories of past interactions into their foraging decisions.

CONCLUSION

Our observations, under high levels of competition in a flight cage, suggest that the continuous turnover of foragers observed in colonies can led to efficient resource partitioning among bees in natural conditions.

Navigation strategies in three nocturnal lemur species: diet predicts heuristic use and degree of exploratory behavior

Humans generally solve multi-destination routes with simple rules-of-thumb. Animals may do the same, but strong evidence is limited to a few species. We examined whether strepsirrhines, who diverged from haplorhines more than 58 mya, would demonstrate the use of three heuristics used by humans and supported in vervets, the nearest neighbor rule, the convex hull, and a cluster strategy, when solving a multi-destination route. We hypothesized that the evolution of these strategies may depend on a species' dietary specialization. Three nocturnal lemur species were tested on an experimental array at the Duke Lemur Center. Frugivorous fat-tailed dwarf lemurs (Cheirogaleus medius) were expected to follow paths most consistent with distance-saving navigational heuristics because fruit trees are stationary targets. Gray mouse lemurs (Microcebus murinus) and aye-ayes (Daubentonia madagascariensis), which rely on more mobile and ephemeral foods, were expected to use fewer paths consistent with these heuristics and be more exploratory. Our data supported all of these hypotheses. Dwarf lemurs used paths consistent with all three heuristics, took the shortest paths, and were the least exploratory. Mouse lemurs were quite exploratory but sometimes used paths consistent with heuristics. Aye-ayes showed no evidence of heuristic use and were the most exploratory. Distinguishable patterns of inter- and intra-individual variation in ability to solve the route, speed, and behavior occurred in each species. This research suggests that these simple navigational heuristics are not part of a readily available set of cognitive tools inherited by all primates but instead evolve due to need in each lineage.

Vervet monkey (Chlorocebus pygerythrus) behavior in a multi-destination route: Evidence for planning ahead when heuristics fail

Animal paths are analogous to intractable mathematical problems like the Traveling Salesman Problem (TSP) and the shortest path problem (SPP). Both the TSP and SPP require an individual to find the shortest path through multiple targets but the TSP demands a return to the start, while the SPP does not. Vervet monkeys are very efficient in solving TSPs but this species is a multiple central place forager that does not always return to the same sleeping site and thus theoretically should be selected to find solutions to SPPs rather than TSPs. We examined path choice by wild vervets in an SPP experimental array where the shortest paths usually differed from those consistent with common heuristic strategies, the nearest-neighbor rule (NNR-go to the closest resource that has not been visited), and the convex hull (put a mental loop around sites, adding inner targets in order of distance from the edge)-an efficient strategy for TSPs but not SPPs. In addition, humans solving SPPs use an initial segment strategy (ISS-choose the straightest path at the beginning, only turning when necessary) and we looked at vervet paths consistent with this strategy. In 615 trials by single foragers, paths usually conformed to the NNR and rarely the slightly more efficient convex hull, supporting that vervets may be selected to solve SPPs. Further, like humans solving SPPs, vervets showed a tendency to use the ISS. Paths consistent with heuristics dropped off sharply, and use of the shortest path increased, when heuristics led to longer paths showing trade-offs in efficiency versus cognitive load. Two individuals out of 17, found the shortest path most often, showing inter-individual variation in path planning. Given support for the NNR and the ISS, we propose a new rule-of-thumb termed the "region heuristic" that vervets may apply in multi-destination routes.

Continuous Radar Tracking Illustrates the Development of Multi-destination Routes of Bumblebees

Animals that visit multiple foraging sites face a problem, analogous to the Travelling Salesman Problem, of finding an efficient route. We explored bumblebees' route development on an array of five artificial flowers in which minimising travel distances between individual feeders conflicted with minimising overall distance. No previous study of bee spatial navigation has been able to follow animals' movement during learning; we tracked bumblebee foragers continuously, using harmonic radar, and examined the process of route formation in detail for a small number of selected individuals. On our array, bees did not settle on visit sequences that gave the shortest overall path, but prioritised movements to nearby feeders. Nonetheless, flight distance and duration reduced with experience. This increased efficiency was attributable mainly to experienced bees reducing exploration beyond the feeder array and flights becoming straighter with experience, rather than improvements in the sequence of feeder visits. Flight paths of all legs of a flight stabilised at similar rates, whereas the first few feeder visits became fixed early while bees continued to experiment with the order of later visits. Stabilising early sections of a route and prioritising travel between nearby destinations may reduce the search space, allowing rapid adoption of efficient routes.

A lightweight, inexpensive robotic system for insect vision

Designing hardware for miniaturized robotics which mimics the capabilities of flying insects is of interest, because they share similar constraints (i.e. small size, low weight, and low energy consumption). Research in this area aims to enable robots with similarly efficient flight and cognitive abilities. Visual processing is important to flying insects' impressive flight capabilities, but currently, embodiment of insect-like visual systems is limited by the hardware systems available. Suitable hardware is either prohibitively expensive, difficult to reproduce, cannot accurately simulate insect vision characteristics, and/or is too heavy for small robotic platforms. These limitations hamper the development of platforms for embodiment which in turn hampers the progress on understanding of how biological systems fundamentally work. To address this gap, this paper proposes an inexpensive, lightweight robotic system for modelling insect vision. The system is mounted and tested on a robotic platform for mobile applications, and then the camera and insect vision models are evaluated. We analyse the potential of the system for use in embodiment of higher-level visual processes (i.e. motion detection) and also for development of navigation based on vision for robotics in general. Optic flow from sample camera data is calculated and compared to a perfect, simulated bee world showing an excellent resemblance.

Inter-individual variability in the foraging behaviour of traplining bumblebees

Workers of social insects, such as bees, ants and wasps, show some degree of inter-individual variability in decision-making, learning and memory. Whether these natural cognitive differences translate into distinct adaptive behavioural strategies is virtually unknown. Here we examined variability in the movement patterns of bumblebee foragers establishing routes between artificial flowers. We recorded all flower visitation sequences performed by 29 bees tested for 20 consecutive foraging bouts in three experimental arrays, each characterised by a unique spatial configuration of artificial flowers and three-dimensional landmarks. All bees started to develop efficient routes as they accumulated foraging experience in each array, and showed consistent inter-individual differences in their levels of route fidelity and foraging performance, as measured by travel speed and the frequency of revisits to flowers. While the tendency of bees to repeat the same route was influenced by their colony origin, foraging performance was correlated to body size. The largest foragers travelled faster and made less revisits to empty flowers. We discuss the possible adaptive value of such inter-individual variability within the forager caste for optimisation of colony-level foraging performances in social pollinators.

Bumblebee flight performance in cluttered environments: effects of obstacle orientation, body size and acceleration

Locomotion through structurally complex environments is fundamental to the life history of most flying animals, and the costs associated with movement through clutter have important consequences for the ecology and evolution of volant taxa. However, few studies have directly investigated how flying animals navigate through cluttered environments, or examined which aspects of flight performance are most critical for this challenging task. Here, we examined how body size, acceleration and obstacle orientation affect the flight of bumblebees in an artificial, cluttered environment. Non-steady flight performance is often predicted to decrease with body size, as a result of a presumed reduction in acceleration capacity, but few empirical tests of this hypothesis have been performed in flying animals. We found that increased body size is associated with impaired flight performance (specifically transit time) in cluttered environments, but not with decreased peak accelerations. In addition, previous studies have shown that flying insects can produce higher accelerations along the lateral body axis, suggesting that if maneuvering is constrained by acceleration capacity, insects should perform better when maneuvering around objects laterally rather than vertically. Our data show that bumblebees do generate higher accelerations in the lateral direction, but we found no difference in their ability to pass through obstacle courses requiring lateral versus vertical maneuvering. In sum, our results suggest that acceleration capacity is not a primary determinant of flight performance in clutter, as is often assumed. Rather than being driven by the scaling of acceleration, we show that the reduced flight performance of larger bees in cluttered environments is driven by the allometry of both path sinuosity and mean flight speed. Specifically, differences in collision-avoidance behavior underlie much of the variation in flight performance across body size, with larger bees negotiating obstacles more cautiously. Thus, our results show that cluttered environments challenge the flight capacity of insects, but in surprising ways that emphasize the importance of behavioral and ecological context for understanding flight performance in complex environments.

Signatures of a globally optimal searching strategy in the three-dimensional foraging flights of bumblebees

Simulated annealing is a powerful stochastic search algorithm for locating a global maximum that is hidden among many poorer local maxima in a search space. It is frequently implemented in computers working on complex optimization problems but until now has not been directly observed in nature as a searching strategy adopted by foraging animals. We analysed high-speed video recordings of the three-dimensional searching flights of bumblebees (Bombus terrestris) made in the presence of large or small artificial flowers within a 0.5 m³ enclosed arena. Analyses of the three-dimensional flight patterns in both conditions reveal signatures of simulated annealing searches. After leaving a flower, bees tend to scan back-and forth past that flower before making prospecting flights (loops), whose length increases over time. The search pattern becomes gradually more expansive and culminates when another rewarding flower is found. Bees then scan back and forth in the vicinity of the newly discovered flower and the process repeats. This looping search pattern, in which flight step lengths are typically power-law distributed, provides a relatively simple yet highly efficient strategy for pollinators such as bees to find best quality resources in complex environments made of multiple ephemeral feeding sites with nutritionally variable rewards.

Monitoring Flower Visitation Networks and Interactions between Pairs of Bumble Bees in a Large Outdoor Flight Cage

Pollinators, such as bees, often develop multi-location routes (traplines) to exploit subsets of flower patches within larger plant populations. How individuals establish such foraging areas in the presence of other foragers is poorly explored. Here we investigated the foraging patterns of pairs of bumble bees (Bombus terrestris) released sequentially into an 880m2 outdoor flight cage containing 10 feeding stations (artificial flowers). Using motion-sensitive video cameras mounted on flowers, we mapped the flower visitation networks of both foragers, quantified their interactions and compared their foraging success over an entire day. Overall, bees that were released first (residents) travelled 37% faster and collected 77% more nectar, thereby reaching a net energy intake rate 64% higher than bees released second (newcomers). However, this prior-experience advantage decreased as newcomers became familiar with the spatial configuration of the flower array. When both bees visited the same flower simultaneously, the most frequent outcome was for the resident to evict the newcomer. On the rare occasions when newcomers evicted residents, the two bees increased their frequency of return visits to that flower. These competitive interactions led to a significant (if only partial) spatial overlap between the foraging patterns of pairs of bees. While newcomers may initially use social cues (such as olfactory footprints) to exploit flowers used by residents, either because such cues indicate higher rewards and/or safety from predation, residents may attempt to preserve their monopoly over familiar resources through exploitation and interference. We discuss how these interactions may favour spatial partitioning, thereby maximising the foraging efficiency of individuals and colonies.

Exploring miniature insect brains using micro-CT scanning techniques

The capacity to explore soft tissue structures in detail is important in understanding animal physiology and how this determines features such as movement, behaviour and the impact of trauma on regular function. Here we use advances in micro-computed tomography (micro-CT) technology to explore the brain of an important insect pollinator and model organism, the bumblebee (Bombus terrestris). Here we present a method for accurate imaging and exploration of insect brains that keeps brain tissue free from trauma and in its natural stereo-geometry, and showcase our 3D reconstructions and analyses of 19 individual brains at high resolution. Development of this protocol allows relatively rapid and cost effective brain reconstructions, making it an accessible methodology to the wider scientific community. The protocol describes the necessary steps for sample preparation, tissue staining, micro-CT scanning and 3D reconstruction, followed by a method for image analysis using the freeware SPIERS. These image analysis methods describe how to virtually extract key composite structures from the insect brain, and we demonstrate the application and precision of this method by calculating structural volumes and investigating the allometric relationships between bumblebee brain structures.

Vervet monkeys use paths consistent with context-specific spatial movement heuristics

Animal foraging routes are analogous to the computationally demanding "traveling salesman problem" (TSP), where individuals must find the shortest path among several locations before returning to the start. Humans approximate solutions to TSPs using simple heuristics or "rules of thumb," but our knowledge of how other animals solve multidestination routing problems is incomplete. Most nonhuman primate species have shown limited ability to route plan. However, captive vervets were shown to solve a TSP for six sites. These results were consistent with either planning three steps ahead or a risk-avoidance strategy. I investigated how wild vervet monkeys (Chlorocebus pygerythrus) solved a path problem with six, equally rewarding food sites; where site arrangement allowed assessment of whether vervets found the shortest route and/or used paths consistent with one of three simple heuristics to navigate. Single vervets took the shortest possible path in fewer than half of the trials, usually in ways consistent with the most efficient heuristic (the convex hull). When in competition, vervets' paths were consistent with different, more efficient heuristics dependent on their dominance rank (a cluster strategy for dominants and the nearest neighbor rule for subordinates). These results suggest that, like humans, vervets may solve multidestination routing problems by applying simple, adaptive, context-specific "rules of thumb." The heuristics that were consistent with vervet paths in this study are the same as some of those asserted to be used by humans. These spatial movement strategies may have common evolutionary roots and be part of a universal mental navigational toolkit. Alternatively, they may have emerged through convergent evolution as the optimal way to solve multidestination routing problems.

Bumblebee Homing: The Fine Structure of Head Turning Movements

Changes in flight direction in flying insects are largely due to roll, yaw and pitch rotations of their body. Head orientation is stabilized for most of the time by counter rotation. Here, we use high-speed video to analyse head- and body-movements of the bumblebee Bombus terrestris while approaching and departing from a food source located between three landmarks in an indoor flight-arena. The flight paths consist of almost straight flight segments that are interspersed with rapid turns. These short and fast yaw turns ("saccades") are usually accompanied by even faster head yaw turns that change gaze direction. Since a large part of image rotation is thereby reduced to brief instants of time, this behavioural pattern facilitates depth perception from visual motion parallax during the intersaccadic intervals. The detailed analysis of the fine structure of the bees' head turning movements shows that the time course of single head saccades is very stereotypical. We find a consistent relationship between the duration, peak velocity and amplitude of saccadic head movements, which in its main characteristics resembles the so-called "saccadic main sequence" in humans. The fact that bumblebee head saccades are highly stereotyped as in humans, may hint at a common principle, where fast and precise motor control is used to reliably reduce the time during which the retinal images moves.

Landscape fragmentation and pollinator movement within agricultural environments: a modelling framework for exploring foraging and movement ecology

Pollinator decline has been linked to landscape change, through both habitat fragmentation and the loss of habitat suitable for the pollinators to live within. One method for exploring why landscape change should affect pollinator populations is to combine individual-level behavioural ecological techniques with larger-scale landscape ecology. A modelling framework is described that uses spatially-explicit individual-based models to explore the effects of individual behavioural rules within a landscape. The technique described gives a simple method for exploring the effects of the removal of wild corridors, and the creation of wild set-aside fields: interventions that are common to many national agricultural policies. The effects of these manipulations on central-place nesting pollinators are varied, and depend upon the behavioural rules that the pollinators are using to move through the environment. The value of this modelling framework is discussed, and future directions for exploration are identified.

Unravelling the mechanisms of trapline foraging in bees

Trapline foraging (repeated sequential visits to a series of feeding locations) is a taxonomically widespread but poorly understood behavior. Investigating these routing strategies in the field is particularly difficult, as it requires extensive tracking of animal movements to retrace their complete foraging history. In a recent study, we used harmonic radar and motion-triggered video cameras to track bumblebees foraging between artificial flowers in a large open field. We describe how all bees gradually developed a near optimal trapline to link all flowers and have identified a simple learning heuristic capable of replicating this optimisation behavior. Our results provide new perspectives to clarify the sequence of decisions made by pollinating insects during trapline foraging, and explore how spatial memory is organized in their small brains.   "I have always regretted that I did not mark the bees by attaching bits of cotton wool or eiderdown to them with rubber, because this would have made it much easier to follow their paths." Charles Darwin(1.)

Behavioural syndromes and social insects: personality at multiple levels

Animal personalities or behavioural syndromes are consistent and/or correlated behaviours across two or more situations within a population. Social insect biologists have measured consistent individual variation in behaviour within and across colonies for decades. The goal of this review is to illustrate the ways in which both the study of social insects and of behavioural syndromes has overlapped, and to highlight ways in which both fields can move forward through the synergy of knowledge from each. Here we, (i) review work to date on behavioural syndromes (though not always referred to as such) in social insects, and discuss mechanisms and fitness effects of maintaining individual behavioural variation within and between colonies; (ii) summarise approaches and principles from studies of behavioural syndromes, such as trade-offs, feedback, and statistical methods developed specifically to study behavioural consistencies and correlations, and discuss how they might be applied specifically to the study of social insects; (iii) discuss how the study of social insects can enhance our understanding of behavioural syndromes-research in behavioural syndromes is beginning to explore the role of sociality in maintaining or developing behavioural types, and work on social insects can provide new insights in this area; and (iv) suggest future directions for study, with an emphasis on examining behavioural types at multiple levels of organisation (genes, individuals, colonies, or groups of individuals).

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.

Pollinating bees, along with bats, hummingbirds, rodents and primates, typically develop circuits (traplines) to visit multiple foraging sites in an efficient stable sequence. The question of how animals encode and process spatial information to develop these impressive foraging patterns remains poorly understood. Previously we showed that an iterative improvement heuristic model of bumblebee traplining can replicate the establishment of stable routes by bees between flowers distributed over several hectares. Here we tested the model against a variety of datasets with different configurations of flowers and found it to give good agreements with all these observations. We have thus shown how these complex dynamic routing problems can be solved by small-brained bees using simple learning heuristics and without acquiring a ‘map-like’ memory. The proposed heuristic shows how bees develop optimal routes simply by following multi-segment journeys composed of learnt flight routines (local vectors), each pointing towards target locations (flowers) and coupled to a visual context (landmarks or panoramas). Such a decentralized representation of space relying on learnt sensorimotor routines is akin to ‘route-based’ navigation as described in desert ants, where spatial information is thought to be processed by separate, potentially modular, guidance systems.

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

Automated tracking of bumblebees and computer simulations reveal how bees locate a series of flowers and optimize their routes to visit them all.

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.

Many food resources, such as flowers refilling with nectar or fruits ripening on a tree, replenish over time, so animals that depend on them need to develop strategies to reduce the energy they use during foraging. Here we placed five artificial flowers in a field and set out to examine how bumblebees optimize their foraging routes between distant locations. We tracked the flight paths of individual bees with harmonic radar and recorded all their visits to flowers with motion-sensitive video cameras. This dataset allowed us to study how bees gradually discover flowers, learn their exact position in the landscape, and then find the shortest route to collect nectar from each flower in turn. Using computer simulations, we show that the level of optimisation performance shown by bees can be replicated by a simple learning algorithm that could be implemented in a bee brain. We postulate that this mechanism allows bumblebees to optimise their foraging routes in more complex natural conditions, where the number and productivity of flowers vary.

Spatial reorientation by geometry in bumblebees

Human and non-human animals are capable of using basic geometric information to reorient in an environment. Geometric information includes metric properties associated with spatial surfaces (e.g., short vs. long wall) and left-right directionality or 'sense' (e.g. a long wall to the left of a short wall). However, it remains unclear whether geometric information is encoded by explicitly computing the layout of surface geometry or by matching images of the environment. View-based spatial encoding is generally thought to hold for insect navigation and, very recently, evidence for navigation by geometry has been reported in ants but only in a condition which does not allow the animals to use features located far from the goal. In this study we tested the spatial reorientation abilities of bumblebees (Bombus terrestris). After spatial disorientation, by passive rotation both clockwise and anticlockwise, bumblebees had to find one of the four exit holes located in the corners of a rectangular enclosure. Bumblebees systematically confused geometrically equivalent exit corners (i.e. corners with the same geometric arrangement of metric properties and sense, for example a short wall to the left of a long wall). However, when one wall of the enclosure was a different colour, bumblebees appeared to combine this featural information (either near or far from the goal) with geometric information to find the correct exit corner. Our results show that bumblebees are able to use both geometric and featural information to reorient themselves, even when features are located far from the goal.