Entrevista a Noemí Agustina, responsable de Prensa y Nuevas

I predicted that the smaller ponds would contain less even bacterial communities (evenness hypothesis) based on the empirical data presented in Woodcock et al. (2007). I provide no support for this hypothesis. In fact, when a significant interaction between community evenness and pond size was identified, the opposite was the case; smaller ponds contained more evenly distributed bacterial communities. This result was also supported by the 16S sequencing data, which indicated ARISA is relatively sensitive at detecting differences in bacterial community evenness. Indeed, additional regression analysis carried out between the Gini coefficient data generated from ARISA and 16S rRNA sequencing confirmed this conclusion and identified a significant correlation between the two approaches (P=0.01, r2=0.28; OLS regression; Pearson’s r=0.52). In general, more diverse communities are expected to exhibit higher levels of community unevenness, as ecological communities are normally dominated by a few abundant taxa and lots of rare taxa (it must be stressed that the exact relationship between evenness and richness is still not completely

understood, even among communities of macroorganisms, and remains a relatively controversial topic; see Soininen et al. (2012)); a relationship that has been empirically observed in numerous macroorganism communities (Stirling & Wilsey, 2001, Wilsey et al., 2005, Zhang et al., 2012). However, the results from the 16S sequence data did not support this, and the more diverse communities were far more evenly distributed, a result that supports a recent study by Pholchan et al. (2013). The results from this investigation show a highly significant relationship between

community evenness and taxon richness, indicating that, like some macroorganism communities, evenness and richness are intricately related (Stirling & Wilsey, 2001, Zhang et al., 2012).

4.10.3Taxon-time relationship

The TTR, which predicts an increase in observed taxon richness over time, was tested and a significant TTR was observed for all of the ponds studied in this investigation, with the power law model producing a significant and convincing fit to the data in all cases. The ω values from the power law model (ω=0.14-0.25) were very similar to those reported in previous work. For example, Wells et al. (2011) indentified a highly significant positive TTR within activated sludge communities using tRFLP, with a ω value of 0.209. Van der Gast et al. (2008) also identified a positive TTR among

bacterial communities associated with bioreactors in a wastewater treatment plant using DGGE, with ω values ranging from 0.16 to 0.51. Here, van der Gast and colleagues’ demonstrated the slope of the TTR (i.e., the ω value) responded in a predictable manner to different starting concentrations of industrial wastewater, indicating that ω values reduced with increased environmental stress. Finally, Kim et al. (2013) used 454 pyrosequencing of 16S rRNA genes to investigate the TTR identifying a ω value of 0.5. These studies investigated the dynamic and diverse bacterial communities associated with bioreactors and activated sludge wastewater treatment plants, which may explain why, on

average, higher values of ω were observed. Although the TTR has received little attention in aquatic microbiology, there is growing evidence that the TTR occurs at various ecological scales and that the power law model generates accurate descriptions of the TTR. Observing bacterial communities over different temporal, as well as spatial, scales is continuing to be recognised as an important facet of microbial ecology (Jones et al., 2012). Consequently, the TTR and the power law model may prove to be a useful tool in explaining, and predicting, bacterial taxa turnover through time. In the macrobial world the ω exponent of the TTR is known to be negatively correlated with the spatial scale of observation (White et al., 2006, Wells et al., 2011). However, although the 550 L ponds did appear to exhibit lower ω values, no significant relationship between ω values and pond size was identified in this study. Contrary to the cumulative richness data, absolute taxon richness did not show any temporal trends and remained relatively stable over time, supporting the results of van der Gast et al. (2008) and agreeing with observations of macroorganism communities (Brown et al., 2001).

The pond communities investigated here exhibited strong temporal patterns, with bacterial communities becoming more dissimilar to each other over time; a result which supports the conclusions of previous research (Wells et al., 2011, Cabrol et al., 2012, Portillo et al., 2012). Communities sampled during the same week were also more similar to each other than to

communities from different weeks and surprisingly similar temporal successional trajectories were identified for the 550, 150 and 23L ponds. Jones et al. (2012) presented temporal ARISA and

sequencing data from lakes of various sizes. In their study, spatial variation at a single time point was far smaller than variation through time, and temporal variation was observed of time periods < 48 hours. It is therefore becoming increasingly apparent that temporal and spatial variations both need to be incorporated into microbial studies. The incorporation of temporal sampling is a difficult issue (Redford & Fierer, 2009, Lauber et al., 2013). For example, it has taken microbial ecologists a relatively long time to consistently collect adequate replicate samples across small spatial scales (Prosser, 2010). Will temporal replicates also need to be considered when sampling bodies of water and if so, how would this be carried out? These are important questions that are worthy of

discussion and future research. That being said, I show here that c. 20% of bacterial taxa detected in Week 1 communities were still present in Week 83 communities, although the exact percentage seemed to vary depending on pond size, with the 550 L ponds retaining a higher percentage of taxa over time. While clear temporal variation in community structure was observed, a significant number of taxa remained within the ponds during the course of the experiment. Further research in aquatic systems should investigate the temporal turnover of bacterial taxa over longer and shorter periods of time. This will help to elucidate some of the issues related to temporal replication.