Living time capsules: tracing evolutionary adaptation in a changing world

• Applicant: Dr. Dagmar Frisch
• Subject Area: Evolution, Anthropology ; Ecology and Biodiversity of Animals and Ecosystems, Organismic Interactions
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 461099895

The negative effects of global climate change and anthropogenic pollution on aquatic organisms and ecosystems are well documented. In the Arctic, warming and related environmental fluctuation have been particularly rapid and severe. This population-level study will use the layered sedimentary archives of Daphnia pulex and environmental variables to study its evolutionary adaptation to the effects of climate change during the past centuries in Greenland. The proposed study system will assess the rates and spectra of genomic mutations in the natural populations of a multicellular organism, and identify putative targets of selection in the light of climate change. The identity of and the genomic variation among 300 clonal Daphnia lineages will be studied from sediment layers covering the past 300 years. For this, whole genome sequencing data will be obtained from individual eggs (whole genome amplified DNA). Multivariate statistics approaches will be used to analyse the relationship between whole genome data, clonal structure/shifts and the environmental history of the lake (reconstructed from existing paleolimnological data). In the experimental approach of "resurrection ecology", cultures of clonal lineages hatched from eggs of several time periods will be established in the laboratory to represent extant and historic Daphnia populations. Experimental environments will be chosen to reflect historic and modern conditions and clonal lineages subjected to a controlled reciprocal transplant experimental design. By integrating phenotype and gene expression data, this study will test to what extent phenotypic responses are reflected in the functional genome using RNA-seq and Weighted Gene Co-Expression Network Analyses.

• DFG Programme: Research Grants