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Paz-y-Miño C. G., Bond, A. B., Kamil, A. C., & Balda, R. P. (2004). Pinyon jays use transitive inference to predict social dominance. Nature, 430(7001), 778–781.
Abstract: Living in large, stable social groups is often considered to favour the evolution of enhanced cognitive abilities, such as recognizing group members, tracking their social status and inferring relationships among them. An individual's place in the social order can be learned through direct interactions with others, but conflicts can be time-consuming and even injurious. Because the number of possible pairwise interactions increases rapidly with group size, members of large social groups will benefit if they can make judgments about relationships on the basis of indirect evidence. Transitive reasoning should therefore be particularly important for social individuals, allowing assessment of relationships from observations of interactions among others. Although a variety of studies have suggested that transitive inference may be used in social settings, the phenomenon has not been demonstrated under controlled conditions in animals. Here we show that highly social pinyon jays (Gymnorhinus cyanocephalus) draw sophisticated inferences about their own dominance status relative to that of strangers that they have observed interacting with known individuals. These results directly demonstrate that animals use transitive inference in social settings and imply that such cognitive capabilities are widespread among social species.
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Parr, L. A., & de Waal, F. B. (1999). Visual kin recognition in chimpanzees (Vol. 399).
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Packer, C. (1977). Reciprocal altruism in Papio anubis. Nature, 265, 441–445.
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Nowak, M. A., & Sigmund, K. (1992). Tit for tat in heterogeneous populations. Nature, 355, 250–253.
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Novacek, M. J. (1992). Mammalian phylogeny: shaking the tree. Nature, 356(6365), 121–125.
Abstract: Recent palaeontological discoveries and the correspondence between molecular and morphological results provide fresh insight on the deep structure of mammalian phylogeny. This new wave of research, however, has yet to resolve some important issues.
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Nagy, M., Akos, Z., Biro, D., & Vicsek, T. (2010). Hierarchical group dynamics in pigeon flocks. Nature, 464(7290), 890–893.
Abstract: Animals that travel together in groups display a variety of fascinating motion patterns thought to be the result of delicate local interactions among group members1, 2, 3. Although the most informative way of investigating and interpreting collective movement phenomena would be afforded by the collection of high-resolution spatiotemporal data from moving individuals, such data are scarce4, 5, 6, 7 and are virtually non-existent for long-distance group motion within a natural setting because of the associated technological difficulties8. Here we present results of experiments in which track logs of homing pigeons flying in flocks of up to 10 individuals have been obtained by high-resolution lightweight GPS devices and analysed using a variety of correlation functions inspired by approaches common in statistical physics. We find a well-defined hierarchy among flock members from data concerning leading roles in pairwise interactions, defined on the basis of characteristic delay times between birds’ directional choices. The average spatial position of a pigeon within the flock strongly correlates with its place in the hierarchy, and birds respond more quickly to conspecifics perceived primarily through the left eye—both results revealing differential roles for birds that assume different positions with respect to flock-mates. From an evolutionary perspective, our results suggest that hierarchical organization of group flight may be more efficient than an egalitarian one, at least for those flock sizes that permit regular pairwise interactions among group members, during which leader–follower relationships are consistently manifested.
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Moon, C., Baldridge, M. T., Wallace, M. A., Burnham, C. - A. D., Virgin, H. W., & Stappenbeck, T. S. (2015). Vertically transmitted faecal IgA levels determine extra-chromosomal phenotypic variation. Nature, 521(7550), 90–93.
Abstract: The proliferation of genetically modified mouse models has exposed phenotypic variation between investigators and institutions that has been challenging to control1-5. In many cases, the microbiota is the presumed culprit of the variation. Current solutions to account for phenotypic variability include littermate and maternal controls or defined microbial consortia in gnotobiotic mice6,7. In conventionally raised mice, the microbiome is transmitted from the dam2,8,9. Here we show that microbially–driven dichotomous fecal IgA levels in WT mice within the same facility mimic the effects of chromosomal mutations. We observed in multiple facilities that vertically-transmissible bacteria in IgA-Low mice dominantly lowered fecal IgA levels in IgA-High mice after cohousing or fecal transplantation. In response to injury, IgA-Low mice showed increased damage that was transferable by fecal transplantation and driven by fecal IgA differences. We found that bacteria from IgA-Low mice degraded the secretory component (SC) of SIgA as well as IgA itself. These data indicate that phenotypic comparisons between mice must take into account the non-chromosomal hereditary variation between different breeders. We propose fecal IgA as one marker of microbial variability and conclude that cohousing and/or fecal transplantation enables analysis of progeny from different dams.
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McGonigle, B. (1985). Can apes learn to count? (Vol. 315).
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Maynard Smith, J., & Price, G. R. (1973). The Logic of Animal Conflict. Nature, 246, 15–18.
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Matsuzawa, T. (1985). Use of numbers by a chimpanzee. Nature, 315(6014), 57–59.
Abstract: Recent studies have examined linguistic abilities in apes. However, although human mathematical abilities seem to be derived from the same foundation as those in language, we have little evidence for mathematical abilities in apes (but for exceptions see refs 7-10). In the present study, a 5-yr-old female chimpanzee (Pan troglodytes), 'Ai', was trained to use Arabic numerals to name the number of items in a display. Ai mastered numerical naming from one to six and was able to name the number, colour and object of 300 types of samples. Although no particular sequence of describing samples was required, the chimpanzee favoured two sequences (colour/object/number and object/colour/number). The present study demonstrates that the chimpanzee was able to describe the three attributes of the sample items and spontaneously organized the 'word order'.
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