2 results
9 - Why are floral signals complex? An outline of functional hypotheses
- Edited by Sébastien Patiny, Université de Mons-Hainaut, Belgium
-
- Book:
- Evolution of Plant-Pollinator Relationships
- Published online:
- 05 January 2012
- Print publication:
- 08 December 2011, pp 279-300
-
- Chapter
- Export citation
-
Summary
Introduction
Plants produce a remarkable variety of displays to attract animals that transfer pollen. These floral displays are usually complex, broadcasting various combinations of visual, olfactory, gustatory, tactile, and thermal stimuli (Raguso 2004a). Even acoustic stimuli may be involved, as in the case of structural nectar guides used by echolocating flower-feeding bats (von Helversen and von Helversen 1999). Yet these sensorially complex advertisements likely evolved from an ancestor that primarily transmitted only chemicals, serving a defensive function (Pellmyr and Thein 1986). The subsequent amplification and elaboration of floral stimuli therefore offers an intriguing opportunity to study signal evolution. However, at present, we know surprisingly little about why floral displays consist of so many elements. This contrasts with progress in other areas: recently, researchers studying topics as diverse as sexual selection, warning displays, animal learning, and parent–offspring communication have explored the function of signal complexity (Rowe 1999; Candolin 2003; Hebets and Papaj 2005; Partan and Marler 2005).
Researchers studying plant–pollinator interactions, however, have not to date shown a comparable degree of interest in the topic of complex signals, as judged by an analysis of the research literature. An August 2010 search on the ISI Web of Science® database on journal articles published since 1995 returned only two on plant–pollinator topics containing the words “multimodal” and “signal-” in their titles, abstracts, or keywords (those articles being Raguso and Willis 2002; Kulahci et al. 2008). In comparison, the same search returned 59 articles on sexual selection topics.
22 - On optimal decision making in brains and social insect colonies
- from Part III - Action selection in social contexts
- Edited by Anil K. Seth, University of Sussex, Tony J. Prescott, University of Sheffield, Joanna J. Bryson, University of Bath
-
- Book:
- Modelling Natural Action Selection
- Published online:
- 05 November 2011
- Print publication:
- 10 November 2011, pp 500-522
-
- Chapter
- Export citation
-
Summary
Summary
The problem of how to compromise between speed and accuracy in decision making faces organisms at many levels of biological complexity. Striking parallels are evident between decision making in primate brains and collective decision making in social insect colonies: in both systems separate populations accumulate evidence for alternative choices, when one population reaches a threshold a decision is made for the corresponding alternative, and this threshold may be varied to compromise between the speed and accuracy of decision making. In primate decision making simple models of these processes have been shown, under certain parameterisations, to implement the statistically optimal procedure that minimises decision time for any given error rate. In this chapter, we adapt these same analysis techniques and apply them to new models of collective decision making in social insect colonies. We show that social insect colonies may also be able to achieve statistically optimal collective decision making in a very similar way to primate brains, via direct competition between evidence-accumulating populations. This optimality result makes testable predictions for how collective decision making in social insects should be organised. Our approach also represents the first attempt to identify a common theoretical framework for the study of decision making in diverse biological systems.
Introduction
Animals constantly make decisions. Habitat selection, mate selection, and foraging require investigation of, and choice between, alternatives that may determine an animal's reproductive success. For example, many animals invest considerable time and energy in finding a safe home (Franks et al., 2002; Hansell, 1984; Hazlett, 1981; Seeley, 1982). Similarly an animal may frequently have to deal with ambiguous sensory information in deciding whether a predator is present or not (Trimmer et al., 2008).