Temporal scaling of bacterial taxa is influenced by both stochastic and deterministic ecological factors
Article first published online: 16 JAN 2008
© 2008 The Authors. Journal compilation © 2008 Society for Applied Microbiology and Blackwell Publishing Ltd
Volume 10, Issue 6, pages 1411–1418, June 2008
How to Cite
Van Der Gast, C. J., Ager, D. and Lilley, A. K. (2008), Temporal scaling of bacterial taxa is influenced by both stochastic and deterministic ecological factors. Environmental Microbiology, 10: 1411–1418. doi: 10.1111/j.1462-2920.2007.01550.x
- Issue published online: 16 JAN 2008
- Article first published online: 16 JAN 2008
- Received 8 October, 2007; accepted 19 November, 2007.
Microorganisms operate at a range of spatial and temporal scales acting as key drivers of ecosystem properties. Therefore, many key questions in microbial ecology require the consideration of both spatial and temporal scales. Spatial scaling, in particular the species–area relationship (SAR), has a long history in ecology and has recently been addressed in microbial ecology. However, the temporal analogue of the SAR, the species–time relationship, has received far less attention even in the science of general ecology. Here we focus upon the role of temporal scaling in microbial ecological patterns by coupling molecular characterization of bacterial communities in discrete island (bioreactor) systems with a macroecological approach. Our findings showed that the temporal scaling exponent (slope), and therefore taxa turnover of the bacterial taxa–time relationship decreased as selective pressure (industrial wastewater concentration) increased. Also, as the concentration of industrial wastewater increased across the bioreactors, we observed a gradual switch from stochastic community assembly to more deterministic (niche)-based considerations. The identification of broad-scale statistical patterns is particularly relevant to microbial ecology, as it is frequently difficult to identify individual species or their functions. In this study, we identify wide-reaching statistical patterns of diversity and show that they are shaped by the prevalent underlying ecological factors.