Kerth, G. (2010). Group decision-making in animal societies. In P. Kappeler (Ed.), Animal Behaviour: Evolution and Mechanisms (pp. 241–265). Springer Berlin Heidelberg.
Abstract: Individuals need to coordinate their activities to benefit from group living. Thus group decisions are essential for societies, especially if group members cooperate with each other. Models show that shared (democratic) decisions outperform unshared (despotic) decisions, even if individuals disagree about actions. This is surprising as in most other contexts, differences in individual preferences lead to sex-, age-, or kin-specific behaviour. Empirical studies testing the predictions of the theoretical models have only recently begun to emerge. This applies particularly to group decisions in fission-fusion societies, where individuals can avoid decisions that are not in their interest. After outlining the basic ideas and theoretical models on group decision-making I focus on the available empirical studies. Originally most of the relevant studies have been on social insects and fish but recently an increasing number of studies on mammals and birds have been published, including some that deal with wild long-lived animals living in complex societies. This includes societies where group members have different interests, as in most mammals, and which have been less studied compared to eusocial insects that normally have no conflict among their colony members about what to do. I investigate whether the same decision rules apply in societies with conflict and without conflict, and outline open questions that remain to be studied. The chapter concludes with a synthesis on what is known about group decision-making in animals and an outlook on what I think should be done to answer the open questions.
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Parrish, J. K., & Viscido, S. V. (2005). Traffic rules of fish schools: A review of agent-based approaches. In C. K. Hemelrijk (Ed.), Self-organisation and the evolution of social behaviour. (pp. 50–80). Cambridge: Cambridge University Press.
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Kruska, D. (1988). Mammalian domestication and its effect on brain structure and behavior. In H. J. Jerison, & I. Jerison (Eds.), Intelligence and Evolutionary Biology. New York: Springer-Verlag.
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Clutton-Brock, J. (1995). Origins of the dog: domestication and early history. In J. A. Serpell (Ed.), The Domestic Dog: Its Evolution, Behaviour and Interactions with People. Cambridge: Cambridge University Press.
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Van Horik, J., Clayton, N., & Emery, N. (2012). Convergent evolution of cognition in Corvids, Apes and other animals. In J. Vonk, & T. Shackelford (Eds.), Oxford Handbook of Comparative Evolutionary Psychology. New York: Oxford University Press.
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Zeder, M. A. (2011). Pathways to animal domestication. In A. Damania, & P. Gepts (Eds.), Harlan II: Biodiversity in Agriculture: Domestication, Evolution, and Sustainability. Davis: University of California.
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Amodio, P., Boeckle, M., Schnell, A. K., Ostojic, L., Fiorito, G., & Clayton, N. S. (2018). Grow Smart and Die Young: Why Did Cephalopods Evolve Intelligence? Trends. Ecol. Evol., .
Abstract: Intelligence in large-brained vertebrates might have evolved through independent, yet similar processes based on comparable socioecological pressures and slow life histories. This convergent evolutionary route, however, cannot explain why cephalopods developed large brains and flexible behavioural repertoires: cephalopods have fast life histories and live in simple social environments. Here, we suggest that the loss of the external shell in cephalopods (i) caused a dramatic increase in predatory pressure, which in turn prevented the emergence of slow life histories, and (ii) allowed the exploitation of novel challenging niches, thus favouring the emergence of intelligence. By highlighting convergent and divergent aspects between cephalopods and large-brained vertebrates we illustrate how the evolution of intelligence might not be constrained to a single evolutionary route.
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Hofmeester, T. R., Cromsigt, J. P. G. M., Odden, J., Andrén, H., Kindberg, J., & Linnell, J. D. C. (2019). Framing pictures: A conceptual framework to identify and correct for biases in detection probability of camera traps enabling multi-species comparison. Ecol Evol, .
Abstract: Abstract Obtaining reliable species observations is of great importance in animal ecology and wildlife conservation. An increasing number of studies use camera traps (CTs) to study wildlife communities, and an increasing effort is made to make better use and reuse of the large amounts of data that are produced. It is in these circumstances that it becomes paramount to correct for the species- and study-specific variation in imperfect detection within CTs. We reviewed the literature and used our own experience to compile a list of factors that affect CT detection of animals. We did this within a conceptual framework of six distinct scales separating out the influences of (a) animal characteristics, (b) CT specifications, (c) CT set-up protocols, and (d) environmental variables. We identified 40 factors that can potentially influence the detection of animals by CTs at these six scales. Many of these factors were related to only a few overarching parameters. Most of the animal characteristics scale with body mass and diet type, and most environmental characteristics differ with season or latitude such that remote sensing products like NDVI could be used as a proxy index to capture this variation. Factors that influence detection at the microsite and camera scales are probably the most important in determining CT detection of animals. The type of study and specific research question will determine which factors should be corrected. Corrections can be done by directly adjusting the CT metric of interest or by using covariates in a statistical framework. Our conceptual framework can be used to design better CT studies and help when analyzing CT data. Furthermore, it provides an overview of which factors should be reported in CT studies to make them repeatable, comparable, and their data reusable. This should greatly improve the possibilities for global scale analyses of (reused) CT data.
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Byrne R.W. (1994). The evolution of intelligence. In P.J.B. Slater and T.R. Halliday (Ed.), Behaviour and Evolution (pp. 223–265). Cambridge,UK: Cambridge University Press.
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