Animals in groups touch each other, move in paths that cross, and interact in complex ways. Current video tracking methods sometimes switch identities of unmarked individuals during these interactions. These errors propagate and result in random assignments after a few minutes unless manually corrected. We present idTracker, a multitracking algorithm that extracts a characteristic fingerprint from each animal in a video recording of a group. It then uses these fingerprints to identify every individual throughout the video. Tracking by identification prevents propagation of errors, and the correct identities can be maintained indefinitely. idTracker distinguishes animals even when humans cannot, such as for size-matched siblings, and reidentifies animals after they temporarily disappear from view or across different videos. It is robust, easy to use and general. We tested it on fish (Danio rerio and Oryzias latipes), flies (Drosophila melanogaster), ants (Messor structor) and mice (Mus musculus).

According to the classic results of Galton and Condorcet, as well as in modern decision-making models, accuracy in groups increases with group size. However, these studies do not consider the naturally occurring situation in which individuals dynamically re-evaluate their decision with a possible change of opinion. The dynamics of re-evaluation in groups are very different to individual re-evaluation because individuals influence the group and the group influences the individual. We find that individual accuracy in a group is higher when individuals re-evaluate because all members have more access to social information, while in single decisions, those deciding first have less. This improvement is smaller in large groups as in this case errors can cascade across the members of the group before re-evaluation can correct them. The net result is a maximal accuracy at a small group size. We also analyzed the case in which individuals are influenced only by a small number of the other individuals. In this case, cascading errors affect the interacting subgroups but are very unlikely to reach the whole group. This results in a local optimum at a small group size but also an optimum at a very large size. We thus

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BackgroundAnimals can show very different behaviors even in isogenic populations, but the underlying mechanisms to generate this variability remain elusive. We use the zebrafish (Danio rerio) as a model to test the influence of histone modifications on behavior.ResultsWe find that laboratory and isogenic zebrafish larvae show consistent individual behaviors when swimming freely in identical wells or in reaction to stimuli. This behavioral inter-individual variability is reduced when we impair the histone deacetylation pathway. Individuals with high levels of histone H4 acetylation, and specifically H4K12, behave similarly to the average of the population, but those with low levels deviate from it. More precisely, we find a set of genomic regions whose histone H4 acetylation is reduced with the distance between the individual and the average population behavior. We find evidence that this modulation depends on a complex of Yin-yang 1 (YY1) and histone deacetylase 1 (HDAC1) that binds to and deacetylates these regions. These changes are not only maintained at the transcriptional level but also amplified, as most target regions are located near genes encoding transcription factors.ConclusionsWe suggest that stochasticity in the histone deacetylation pathway participates in the generation of genetic-independent behavioral inter-individual variability.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1428-y) contains supplementary material, which is available to authorized users.

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