Abstracts (first author)


Drawing a natural fitness landscape in space and time: temporal adaptation of soil bacterial communities

Author(s): Kraemer SA, Kassen R


Microbial communities are fundamental for ecosystem function, yet only little is known about their in situ ecology and evolution. How are such communities structured in space and time and which environmental factors drive their adaptation? We obtained soil bacterial isolates from three sites in a spatially structured design every month for eight months, representing a complete growth season. In a fully factorial transplant experiment we measured fitness of all isolates in media mirroring the environmental conditions in the soil sample they were isolated from (their “home” soil) and compared it to their growth rates in media representing soil at different spatial and temporal distances, thus not only describing the spatial fitness landscape for each isolate but also how the shape of this landscape changes over time. In comparison to growth under “home” soil conditions, growth rates were steadily declining in media representing future soil conditions, indicating temporal adaption of isolates. Moreover, fitness increased in media representing past conditions, providing evidence for past selection for successful growth. These findings were unaffected by limiting our analysis to isolates with vigorous growth rates or to soil obtained from different geographical sites. Spatial structuring, either at large (kilometer) or small (meter) scales, did not significantly influence bacterial fitness, indicating a large role of dispersal in soil bacterial biogeography at these scales. Lastly, we correlated environmental factors such as nutrient ion availability, mean temperature and pH with the obtained fitness landscapes to deduce key factors influencing bacterial temporal adaptation in nature.



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XIV Congress of the European Society for Evolutionary Biology

Organization Team
Department of Animal Biology (DBA)
Faculty of Sciences of the University of Lisbon
P-1749-016 Lisbon


Computational Biology & Population Genomics Group