Stochastic phenotypic variation: Empirical results and potential use in Eurytemora research (Copepoda, Calanoida)

Dmitry Lajus, Natalia Sukhikh, Victor Alekseev

Research output


This study analyses the potential of stochastic phenotypic variation for investigating the population biology of Eurytemora. Stochastic variation is the third component of phenotypic variance, standing on equal footing with genotypic variation and phenotypic plasticity. This is a manifestation of developmental instability and usually increases under stress. In morphological traits, stochastic variation is most often studied using fluctuating asymmetry (FA) of bilateral traits. Here, using data on the FA of nine populations of three Eurytemora species from Europe and North America, we found no correlation between FA and temperature, salinity or tidal amplitude. Invasive American E. carolleeae in the Gulf of Finland (Baltic Sea) had lower FA than the same species in its native Chesapeake Bay, or than E. affinis in its native Gulf of Finland. This pattern may be caused by global warming, which brought Chesapeake Bay temperatures beyond E. carolleeae's optimal conditions, but made the Gulf of Finland a more suitable environment. Stochastic variation in life history traits is technically more difficult to study, but it may provide important information on fitness. In particular, it manifests in bet-hedging, a risk-spreading strategy beneficial in unpredictable environments. As resting eggs are common in Eurytemora, bet-hedging can be considered a genus strategy. Understanding how stochastic variation contributes to total phenotypic variance may help to interpret changes under unpredictable environmental conditions. Therefore, studies of stochastic phenotypic variation may supply information about the population biology of Eurytemora and other copepods.

Original languageEnglish
Pages (from-to)317-336
Number of pages20
Issue number3-5
Publication statusPublished - 2020

Scopus subject areas

  • Aquatic Science
  • Animal Science and Zoology

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