Pea Plants Show Risk Sensitivity

Efrat Dener, Alex Kacelnik, Hagai Shemesh

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

Sensitivity to variability in resources has been documented in humans, primates, birds, and social insects, but the fit between empirical results and the predictions of risk sensitivity theory (RST), which aims to explain this sensitivity in adaptive terms, is weak [1]. RST predicts that agents should switch between risk proneness and risk aversion depending on state and circumstances, especially according to the richness of the least variable option [2]. Unrealistic assumptions about agents’ information processing mechanisms and poor knowledge of the extent to which variability imposes specific selection in nature are strong candidates to explain the gap between theory and data. RST's rationale also applies to plants, where it has not hitherto been tested. Given the differences between animals’ and plants’ information processing mechanisms, such tests should help unravel the conflicts between theory and data. Measuring root growth allocation by split-root pea plants, we show that they favor variability when mean nutrient levels are low and the opposite when they are high, supporting the most widespread RST prediction. However, the combination of non-linear effects of nitrogen availability at local and systemic levels may explain some of these effects as a consequence of mechanisms not necessarily evolved to cope with variance [3, 4]. This resembles animal examples in which properties of perception and learning cause risk sensitivity even though they are not risk adaptations [5].

Original languageEnglish
Pages (from-to)1763-1767
Number of pages5
JournalCurrent Biology
Volume26
Issue number13
DOIs
StatePublished - 11 Jul 2016

Keywords

  • energy budget rule
  • phenotypic plasticity
  • plant behavior
  • risk sensitivity
  • roots foraging

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology (all)
  • Agricultural and Biological Sciences (all)

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