Can we predict ecosystem functioning using tightly linked functional gene diversity?
- Subject Areas
- Ecology, Microbiology
- biodiversity and ecosystem functioning, nitrogen fixation, functional genes, prediction, marine sediments
- © 2017 Roger et al.
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2017. Can we predict ecosystem functioning using tightly linked functional gene diversity? PeerJ Preprints 5:e2958v1 https://doi.org/10.7287/peerj.preprints.2958v1
Biodiversity generally affects ecosystem processes, but to what extent detailed knowledge about specific aspects of biodiversity helps us understand and predict specific ecosystem functions is not well-known. We hypothesised that information about functional gene abundance and diversity would provide a better way of predicting a particular function catalysed by that gene product, than would a more generic descriptor of species diversity. For our purposes, we used marine benthic nitrogen-fixing bacteria as a model system. We first used published observational data to relate nitrogen fixation to the abundance of the gene nifH, which encodes enzymes involved in the fixation of atmospheric nitrogen gas. We then used the fitted model, which explained 37% of the variance, to predict nitrogen fixation in other sediment samples for which nitrogen fixation and nifH abundance was determined. The model provided no predictive power for benthic nitrogen fixation on independent data. Additional information on the diversity of the general bacterial community, and the nitrogen fixing community in particular did not improve predictions. It was also not possible to predict nitrogen fixation based on the abundance of particular gene variants or bacterial taxa. Our results demonstrate that process rates can be intrinsically difficult to predict based on community metrics even when the community data and the process are tightly coupled.
This is the first version of the preprint.