Nutrient cycling by fish in Lake Tanganyika
Problem statement:
Lake Tanganyika, one of the deepest and most biodiverse lakes in the world, maintains high primary productivity despite exceptionally low concentrations of nutrients. Nutrient recycling by its hyperdiverse fish community may be an important mechanism allowing for high productivity in such a low-nutrient environment. However, the relative contribution of fish to nutrient supplies remains unquantified and contextualized.
The aim of the work:
Use an existing dataset of nitrogen and phosphorus released by fish to estimate their overall contribution to nearshore nutrient availability. Analyze the data using machine learning, visualize the results, and draft a manuscript.
Method:
Data analysis and visualization with R.
Supervisor:
Benjamin Kraemer
Contact:
Benjamin Kraemer benjamin.kraemer@hydrology.uni-freiburg.de Tel. +49 (0)176 / 3014 1903
Challenge:
Students interested in writing a manuscript suitable for publication would be most welcome. Experience with (or eagerness to learn) machine learning, data analysis, and visualization in R would be an asset. Experience with (or eagerness to learn) how to use reference management software.
Language:
English