Back

Combined salinity and acidity stressors alter Daphnia magna population growth and structure under prolonged absence of photoperiod

Khudr, M. S.; Purkiss, S. A.; Hager, R.

2019-12-12 zoology
10.1101/2019.12.11.872630 bioRxiv
Show abstract

Although natural and anthropogenic influences affect freshwater ecosystems globally at unprecedented levels, the effects of co-occurring physico-chemical stress on zooplankton phenotypic plasticity under extreme conditions remain understudied.We exposed a laboratory-raised clonal population of Daphnia magna to different stress levels of acidity and salinity undergoing complete constant light over 30 days. Overall, population size and age structure at day 10 considerably differed between specific stress contexts. All populations expanded compared to the starting population on day 1. On day 30, overall, population size increased but showed significant differences between treatment groups. Surprisingly, Daphnia performed better under combined stress of salinity and acidity than under acidity alone as the extra salinity in the medium may have counterbalanced sodium loss caused by lower pH. Our results reveal a considerable degree of differential reproductive and ontogenetic plasticity in response to combined stressors under disrupted photoperiod. Exposure to constant light led to increased population size, which may be a result of supercharged ion regulation that enables zooplankton to survive better under specific levels of extreme environmental change and adverse chemical stress. Our findings merit further molecular investigation of phenotypic plasticity of the congeners across severe combined stress conditions.Competing Interest StatementThe authors have declared no competing interest.View Full Text

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.