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How do interactive maternal traits and environmental factors determine offspring size in Daphnia magna?

Published online by Cambridge University Press:  10 January 2014

F. Gabsi*
Affiliation:
Institute for Environmental Research, RWTH Aachen University, Worringerweg 1, 52072 Aachen, Germany
D. S. Glazier
Affiliation:
Department of Biology, Brumbaugh Academic Center, Juniata College Huntingdon, Pennsylvania 16652, USA
M. Hammers-Wirtz
Affiliation:
Research Institute for Ecosystem Analysis and Assessment (gaiac), Kackertstrasse 10, 52072 Aachen, Germany
H. T. Ratte
Affiliation:
Institute for Environmental Research, RWTH Aachen University, Worringerweg 1, 52072 Aachen, Germany
T. G. Preuss
Affiliation:
Institute for Environmental Research, RWTH Aachen University, Worringerweg 1, 52072 Aachen, Germany
*
*Corresponding author: faten.gabsi@bio5.rwth-aachen.de
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Abstract

In this study, we investigated variation in offspring size (OS) of Daphnia magna in relation to multiple maternal traits and environmental variables. Data originated from laboratory experiments conducted at different feeding scenarios. The mother daphnids had different life-history traits and were reared under various feeding and density conditions. OS showed linear relationships with maternal traits, varying positively with maternal body size, age and brood number, and negatively with brood size and with the amount of ingested carbon. OS increased exponentially with crowding. Using stepwise multiple regression analysis, we developed an empirical model for the OS variation with the relevant maternal and environmental variables. Density dependence was considered by multiplying the resulting model by a density-effect function. We found that the ingested carbon and the maternal body size were the strongest determinants of the observed variation in the OS, whereas the brood size had the least impact on OS. Additionally, the brood number had no significant effect in determining the variability in the OS. The validity of the multivariate model was tested against an independent dataset. The model accurately predicted the OS despite several genetic and environmental differences compared with the data used for parameterization.

Type
Research Article
Copyright
© EDP Sciences, 2014

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