Collective motion, where large numbers of individuals move synchronously together, is achieved when individuals adopt interaction rules that determine how they respond to their neighbors' movements and positions. These rules determine how group-living animals move, make decisions, and transmit information between individuals. Nonetheless, few studies have explicitly determined these interaction rules in moving groups, and very little is known about the interaction rules of fish. Here, we identify three key rules for the social interactions of mosquitofish (Gambusia holbrooki): (i) Attraction forces are important in maintaining group cohesion, while we find only weak evidence that fish align with their neighbor's orientation; (ii) repulsion is mediated principally by changes in speed; (iii) although the positions and directions of all shoal members are highly correlated, individuals only respond to their single nearest neighbor. The last two of these rules are different from the classical models of collective animal motion, raising new questions about how fish and other animals self-organize on the move.collective animal behavior | fish shoals | group motion | self-propelled particles | self-organization C ollective motion of animal groups occurs when multiple individuals move synchronously, producing large scale "flocking" patterns (1-5). Numerous models have been developed to describe patterns of collective motion in terms of interactions between individuals (6-9). These simulation models usually assume that individuals move at a constant speed and their interactions are mediated through direction changes (1). Often these models use zonal rules, where individuals move away from neighbors at close distances and align and/or move toward neighbors at greater distances. Interactions can be with either all neighbors within some zone (7) or with a set of n nearest neighbors (10). These and other models have succeeded in reproducing qualitatively similar patterns to those seen in the collective motion of animal groups in nature.It remains unclear, however, whether the interaction rules implemented in models are the ones used by animals. Indeed, many collective motion patterns observed in nature can be simulated by models using very different interaction rules (1). We are only now beginning to accumulate evidence about which interaction rules are used. There has been recent identification of zones of repulsion and alignment in surf scoters (11). The structure of starling flocks is consistent with topological interactions between the birds (10). Homing pigeons appear to have hierarchical interactions such that birds with higher route-following fidelity act as leaders (12)(13)(14)(15). Partridge showed that lateral line and vision are both important in producing directional alignment in Gadid fish (16). Nonetheless, there remain a large number of open questions about the interactions of fish. For example, do fish adopt attraction and alignment within distinct zones as purported in most models? How many neighbors do fish ...

How different levels of biological organization interact to shape each other's function is a central question in biology. One particularly important topic in this context is how individuals' variation in behaviour shapes group-level characteristics. We investigated how fish that express different locomotory behaviour in an asocial context move collectively when in groups. First, we established that individual fish have characteristic, repeatable locomotion behaviours (i.e. median speeds, variance in speeds and median turning speeds) when tested on their own. When tested in groups of two, four or eight fish, we found individuals partly maintained their asocial median speed and median turning speed preferences, while their variance in speed preference was lost. The strength of this individuality decreased as group size increased, with individuals conforming to the speed of the group, while also decreasing the variability in their own speed. Further, individuals adopted movement characteristics that were dependent on what group size they were in. This study therefore shows the influence of social context on individual behaviour. If the results found here can be generalized across species and contexts, then although individuality is not entirely lost in groups, social conformity and group-size-dependent effects drive how individuals will adjust their behaviour in groups.

We examine changes in interaction rules, predictability, and vigilance of x-ray tetras due to external food and alarm cues.

Despite the frequency with which mixed-species groups are observed in nature, studies of collective behaviour typically focus on single-species groups. Here, we quantify and compare the patterns of interactions between three fish species, threespine sticklebacks (Gasterosteus aculeatus), ninespine sticklebacks (Pungitius pungitius) and roach (Rutilus rutilus) in both single- and mixed-species shoals in the laboratory. Pilot data confirmed that the three species form both single- and mixed-species shoals in the wild. In our laboratory study, we found that single-species groups were more polarized than mixed-species groups, while single-species groups of threespine sticklebacks and roach were more cohesive than mixed shoals of these species. Furthermore, while there was no difference between the inter-individual distances between threespine and ninespine sticklebacks within mixed-species groups, there was some evidence of segregation by species in mixed groups of threespine sticklebacks and roach. There were differences between treatments in mean pairwise transfer entropy, and in particular we identify species-differences in information use within the mixed-species groups, and, similarly, differences in responses to conspecifics and heterospecifics in mixed-species groups. We speculate that differences in the patterns of interactions between species in mixed-species groups may determine patterns of fission and fusion in such groups.

Why are mitochondria almost always inherited from one parent during sexual reproduction? Current explanations for this evolutionary mystery include conflict avoidance between the nuclear and mitochondrial genomes, clearing of deleterious mutations, and optimization of mitochondrial-nuclear coadaptation. Mathematical models, however, fail to show that uniparental inheritance can replace biparental inheritance under any existing hypothesis. Recent empirical evidence indicates that mixing two different but normal mitochondrial haplotypes within a cell (heteroplasmy) can cause cell and organism dysfunction. Using a mathematical model, we test if selection against heteroplasmy can lead to the evolution of uniparental inheritance. When we assume selection against heteroplasmy and mutations are neither advantageous nor deleterious (neutral mutations), uniparental inheritance replaces biparental inheritance for all tested parameter values. When heteroplasmy involves mutations that are advantageous or deleterious (non-neutral mutations), uniparental inheritance can still replace biparental inheritance. We show that uniparental inheritance can evolve with or without pre-existing mating types. Finally, we show that selection against heteroplasmy can explain why some organisms deviate from strict uniparental inheritance. Thus, we suggest that selection against heteroplasmy explains the evolution of uniparental inheritance.

During reproductive swarming and seasonal migration, a honeybee swarm needs to locate and move to a new, suitable nest site. While the nest-site selection process in cavity-nesting species such as the European honeybee Apis mellifera is very precise with the swarm carefully selecting a single site, open-nesting species, such as Apis florea, lack such precision. These differences in precision in the nest-site selection process are thought to arise from the differing nest-site requirements of open-and cavity-nesting species. While A. florea can nest on almost any tree, A. mellifera is constrained by the scarcity of suitable nest sites. Here we show that imprecision in the nest-site selection process allows swarms to quickly reach a decision when many nest sites are available. In contrast, a very precise nestsite selection process slows down the decision-making process when nest sites are abundant.Nest-site selection in A. florea appears to be more similar to search-space sampling than to a decision-making process. Bees appear to scout the environment for general areas in which potential nest sites are abundant. Bees involved in searching the environment for suitable nest sites are also involved in guiding the swarm once the decision to depart has been made. Generally A. florea swarms exhibit a lack of consensus in the direction indicated by dancers prior to take-off. Because of this lack of consensus a swarm of A. florea will need to determine its exact direction of travel while in flight. We show that in the absence of directional consensus a swarm of bees can still be guided towards an area containing suitable nest sites provided directional dissent is not too great and nest sites are abundant. However, if the swarm needs to move to a very specific location (a single point in space), directional dissent should be avoided, resulting in a more lengthy decision-making process prior to departure. We further show that the guidance mechanism of bee swarms, so-called 'streaking', functions both when directional dissent is present and when it is absent, making it a more general mechanism of group movement than previously thought.

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Groups of animals coordinate remarkable, coherent, movement patterns during periods of collective motion. Such movement patterns include the toroidal mills seen in fish shoals, highly aligned parallel motion like that of flocks of migrating birds, and the swarming of insects. Since the 1970’s a wide range of collective motion models have been studied that prescribe rules of interaction between individuals, and that are capable of generating emergent patterns that are visually similar to those seen in real animal group. This does not necessarily mean that real animals apply exactly the same interactions as those prescribed in models. In more recent work, researchers have sought to infer the rules of interaction of real animals directly from tracking data, by using a number of techniques, including averaging methods. In one of the simplest formulations, the averaging methods determine the mean changes in the components of the velocity of an individual over time as a function of the relative coordinates of group mates. The averaging methods can also be used to estimate other closely related quantities including the mean relative direction of motion of group mates as a function of their relative coordinates. Since these methods for extracting interaction rules and related quantities from trajectory data are relatively new, the accuracy of these methods has had limited inspection. In this paper, we examine the ability of an averaging method to reveal prescribed rules of interaction from data generated by two individual based models for collective motion. Our work suggests that an averaging method can capture the qualitative features of underlying interactions from trajectory data alone, including repulsion and attraction effects evident in changes in speed and direction of motion, and the presence of a blind zone. However, our work also illustrates that the output from a simple averaging method can be affected by emergent group level patterns of movement, and the sizes of the regions over which repulsion and attraction effects are apparent can be distorted depending on how individuals combine interactions with multiple group mates.