As the size and extent of biological data sets grow, scientists turn to new quantitative techniques, such as network analysis, to understand biological complexity over large scales. For network analysis of microbial datasets, topological ‘co-occurrence’ networks are generated from correlative metrics, in which nodes represent observed variables and significant correlations are represented by the edges connecting them. We used an unprecedented decade-long time series of freshwater bacterioplankton molecular community fingerprints to test the following hypotheses: (1) community co-occurrence networks from this sample set are non-random, (2) seasonality explains the organization and complexity of co-occurrence networks, and (3) community richness and diversity correlate to co-occurrence network complexity.
Eutrophication, the over-enrichment of freshwaters with nutrients, is caused by complex interactions of people and ecosystems that are hard to manage. A long-term perspective shows how management can adapt to changing social and ecological realities, learning from failures and building on successes.
This material is based upon work supported by the National Science Foundation under Cooperative Agreement #DEB-0822700, NTL LTER. Any opinions, findings, conclusions, or recommendations expressed in the material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.