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Host - parasite interactions in hybridizing Daphnia, the role of variable environments: part 2

Subject Area Evolution, Anthropology
Term from 2011 to 2015
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 207544584
 
Final Report Year 2015

Final Report Abstract

In this collaborative DFG-SNF project we aimed to study the role of variable environments on host–parasite interactions in hybridizing host, using Daphnia (waterfleas) which are a well-established model system in this research area. In addition to previously established tools we also needed to develop new methods which were not available at the time when this project has started. Therefore, one of the biggest challenges of our project was to develop methods which would allow us to realize the aims mentioned above. Specifically, we wanted to explore the signature of hybridization and host-parasite coevolution, using old collections of formaldehyde-preserved zooplankton samples. We therefore needed to establish a DNA extraction protocol from formaldehyde-fixed zooplankton samples and a system of molecular markers which would be suitable to apply on low quality DNA. We were successful with both methodological challenges. We further established and validated a Next Generation Sequencing (NGS) protocol for our target microparasite species. Finally, we also developed a bioinformatic pipeline that corrects for multiple sources of errors generated by NGS platforms. We have successfully started to apply the molecular tools developed here on old samples. Our methods are a big break-through in the field, as they finally allow us to have a closer look into the past. In addition to development and application of these tools on field collections, we further conducted two experimental studies. In the first study we exposed Daphnia to an abiotic factor that synchronously changed with eutrophication in the lake, toxicity caused by lead (Pb). Second, we focused on the potential influence of cyanobacteria on Daphnia susceptibility. A choice of this biotic factor was triggered by an observation that was made from time-series analysis and modelling approaches performed on the long-term datasets. We believe that the methods developed here will contribute significantly to the general knowledge and importance of changing environments on evolutionary processes. The methods used in this project allow the study of past biological processes and, by extrapolating this knowledge, predicting future changes across ecosystems. Importantly, our results highlight the value of long-term data for understanding wildlife disease dynamics. Our results also contribute to the general knowledge and predictions regarding the consequences of global warming, particularly the spread of infectious diseases and biodiversity changes at the community and population levels.

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