Geographical patterns of micro-organismal community structure: are diatoms ubiquitously distributed across boreal streams?
Article first published online: 10 SEP 2009
© 2009 The Authors
Volume 119, Issue 1, pages 129–137, January 2010
How to Cite
Heino, J., Bini, L. M., Karjalainen, S. M., Mykrä, H., Soininen, J., Vieira, L. C. G. and Diniz-Filho, J. A. F. (2010), Geographical patterns of micro-organismal community structure: are diatoms ubiquitously distributed across boreal streams?. Oikos, 119: 129–137. doi: 10.1111/j.1600-0706.2009.17778.x
- Issue published online: 23 DEC 2009
- Article first published online: 10 SEP 2009
- Manuscript Accepted 8 June 2009
A topic under intensive study in community ecology and biogeography is the degree to which microscopic, as well as macroscopic organisms, show spatially-structured variation in community characteristics. In general, unicellular microscopic organisms are regarded as ubiquitously distributed and, therefore, without a clear biogeographic signal. This view was summarized 75 years ago by Baas-Becking, who stated “everything is everywhere, but, the environment selects”. Within the context of metacommunity theory, this hypothesis is congruent with the species sorting model. By using a broad-scale dataset on stream diatom communities and environmental predictor variables across most of Finland, our main aim was to test this hypothesis. Patterns of spatial autocorrelation were evaluated by Moran's I based correlograms, whereas partial regression analysis and partial redundancy analysis were used to quantify the relative importance of environmental and spatial factors on total species richness and on community composition, respectively. Significant patterns of spatial autocorrelation were found for all environmental variables, which also varied widely. Our main results were clear-cut. In general, pure spatial effects clearly overcame those of environmental effects, with the former explaining much more variation in species richness and community composition. Most likely, missing environmental variables cannot explain the higher predictive power of spatial variables, because we measured key factors that have previously been found to be the most important variables (e.g. pH, conductivity, colour, phosphorus, nitrogen) shaping the structure of diatom communities. Therefore, our results provided only limited support for the Baas-Becking hypothesis and the species sorting perspective of metacommunity theory.