Login / Signup

Maladaptive plastic responses of flowering time to geothermal heating.

Johan EhrlénAlicia ValdésVigdís F HelmutsdóttirBryndís Marteinsdóttir
Published in: Ecology (2023)
Phenotypic plasticity might increase fitness if the conditions under which it evolved remain unaltered, but becomes maladaptive if the environment no longer provides reliable cues for subsequent conditions. In seasonal environments, timing of reproduction can respond plastically to spring temperature, maximizing the benefits of a long season while minimizing the exposure to unfavorable cold temperatures. However, if the relationship between early spring temperatures and later conditions changes, the optimal response might change. In geothermally heated ecosystems, the plastic response of flowering time to springtime soil temperature that has evolved in unheated areas is likely to be non-optimal, because soil temperatures are higher and decoupled from air temperatures in heated areas. We therefore expect natural selection to favor a lower plasticity and a delayed flowering in these areas. Using observational data along a natural geothermal warming gradient, we tested the hypothesis that selection on flowering time depends on soil temperature and favors later flowering on warmer soils in the perennial Cerastium fontanum. In both study years, plants growing in warmer soils began flowering earlier than plants growing in colder soils, suggesting that first flowering date responds plastically to soil temperature. In one of the two study years, selection favored earlier flowering in colder soils but later flowering in warmer soils, suggesting that the current level of plastic advance of first flowering date on warmer soils may be maladaptive in some years. Our results illustrate the advantages of using natural experiments, such as geothermal ecosystems, to examine selection in environments that recently have undergone major changes. Such knowledge is essential to understand and predict both ecological and evolutionary responses to climate warming. This article is protected by copyright. All rights reserved.
Keyphrases
  • arabidopsis thaliana
  • heavy metals
  • human health
  • climate change
  • risk assessment
  • healthcare
  • organic matter
  • machine learning
  • body composition
  • deep learning
  • artificial intelligence
  • big data