Information theory and the neuropeptidergic regulation of seasonal reproduction in mammals and birds

Tyler J. Stevenson, Gregory F. Ball

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)

Abstract

Seasonal breeding in the temperate zone is a dramatic example of a naturally occurring change in physiology and behaviour. Cues that predict periods of environmental amelioration favourable for breeding must be processed by the brain so that the appropriate responses in reproductive physiology can be implemented. The neural integration of several environmental cues converges on discrete hypothalamic neurons in order to regulate reproductive physiology. Gonadotrophin-releasing hormone-1 (GnRH1) and Kisspeptin (Kiss1) neurons in avian and mammalian species, respectively, show marked variation in expression that is positively associated with breeding state. We applied the constancy/contingency model of predictability to investigate how GnRH1 and Kiss1 integrate different environmental cues to regulate reproduction. We show that variation in GnRH1 from a highly seasonal avian species exhibits a predictive change that is primarily based on contingency information. Opportunistic species have low measures of predictability and exhibit a greater contribution of constancy information that is sex-dependent. In hamsters, Kiss1 exhibited a predictive change in expression that was predominantly contingency information and is anatomically localized. The model applied here provides a framework for studies geared towards determining the impact of variation in climate patterns to reproductive success in vertebrate species.
Original languageEnglish
Pages (from-to)2477-2485
Number of pages9
JournalProceedings of the Royal Society of London. B, Biological Sciences
Volume278
Issue number1717
Early online date5 Jan 2011
DOIs
Publication statusPublished - 22 Aug 2011

Keywords

  • photoperiod
  • gonadotrophin-releasing hormone-1
  • luteinizing hormone releasing hormone
  • kisspeptin
  • starling
  • hamster

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