CONTROL DE CALIDAD DE LOS DATOS

The neutral model presented two reasonable fits to the observed data (Red Tarns: r2 = 0.55 and TekapoB Tarn 2 r2 =0.65), with Nmvalues similar to other bacterial communities (Keymer et al., 2009,

Drakare & Liess, 2010, Östman et al., 2010). Under strict neutral processes, the abundance (pi) of a

given taxon should not deviate from the predicted neutral model shown in Figure 3-16. However, it is clear that not all taxa follow the predictions of the neutral model exactly and the TekapoA and TekapoB data does not appear to fit the model as convincingly as other bacterial communities presented in the literature (Sloan et al., 2006, Drakare & Liess, 2010, Östman et al., 2010, Ayarza & Erijman, 2011). These results may indicate a limited role of neutral immigration in structuring these bacterial communities. The majority of the deviations were caused by rarer taxa being more widely distributed than predicted and it is difficult to determine if this was caused by limitations of the molecular method (i.e., ARISA not detecting bacteria with abundances < 1%). Interestingly, datasets

with the highest RA values also exhibited the highest r2 values and appeared to fit the neutral model better (Red Tarns and TekapoB, Tarn 2), indicating that the regional invariance approach appeared to support the neutral model analyses.

The r2 values generated from the neutral model were markedly lower than the values presented in Östman et al. (2010), which varied between explaining 63 and 98% of the variation with an average of 85%. One possible explanation for this discrepancy may be methodological, as tRFLP was utilised in their investigations. ARISA is known to be more sensitive for estimating taxon richness and detecting rarer taxa than tRFLP (Danovaro et al., 2006, Okubo & Sugiyama, 2009). Consequently, as tRFLP likely detected lower richness, it may have led to the neutral model fitting their data better as there would have been fewer variables to predict and if rarer taxa were not detected using tRFLP, this would have further biased the results. Indeed, Drakare & Liess (2010) tested the neutral model of Sloan et al. (2006) using ARISA and explained 68.5% of the diversity of aquatic bacterial

communities, which is more similar to the r2 values observed in this chapter. Furthermore, Drakare & Liess’ (2010) study also identified that the taxonomic resolution used to study bacterial communities influenced the results of the neutral model. For example, when the model was tested using total bacterial diversity (ARISA) a reasonable fit to the model was observed (68.5%). However, when ARISA was carried out using cyanobacteria specific primers (i.e., ARISA targeting only cyanobacteria) the model could not fit the data. A recent study by Logares et al. (2013) tested the neutral model using 454-sequencing data generated from Antarctic and Scandinavian lake communities. In this instance the neutral model did not perform as well (explaining 18-50% of the variation and failing to fit one dataset), with most of the deviations being caused by rarer taxa occupying more sites than predicted. This highlights the importance of taxonomic resolution when studying bacterial communities and suggests not all functional groups are regulated by the same ecological processes.

When using the neutral models such as Sloan et al. (2006), the parameters (e.g., Nm)of the model

are estimated iteratively by minimising the sum of squares of errors and this approach has been criticised as ‘curve-fitting’ and difficult to reject (Bell, 2005, Gotelli & McGill, 2006, McGill et al., 2006). Furthermore, a good fit of bacterial abundance distribution to a theoretical model does not distinguish ecological process from pattern (Alonso et al., 2006, Dumbrell et al., 2010). In addition, other ecological or environmental factors could still produce a good fit to the neutral model (Keymer et al., 2009). That being said, neutral models like the one used in this investigation are one of a handful in microbial ecology making testable predictions (e.g., estimating the number of sites a given taxon should occupy), which fit the data presented here relatively well. And like any good theoretical model, it provides us with useful information when the model fails to explain the majority of the variation (e.g., indicating that species sorting or other niche processes may be more important in regulating different members of a bacterial assemblage). However, it is apparent that neutral

models, and the predictions based on ecological equivalence, do not explain all of the variation observed in this investigation. It is important to remember species sorting and neutral processes are not mutually exclusive, so it is highly likely niche and neutral processes both play an important role in structuring bacterial communities but maybe at different spatio-temporal levels (Langenheder & Szekely, 2011, Lee et al., 2013). Future work should continue to consider regional diversity (i.e., source pool diversity) as well as local diversity when studying bacterial assemblages.

The investigation of artificial randomly-assembled communities (ARC) provided an additional, more liberal assessment of the possible role of stochastic assembly among these bacterial communities. Here, the observed local bacterial communities (OLC) were significantly different to the ARC in all datasets, providing evidence that the OLC were not simply random assembled communities from a diverse source pool. However, the ‘overlap’ between the OLC and ARC varied between the datasets, whereby the Red Tarns and TekapoB datasets had the highest level of overlap between the two ARCs and OLCs. Interestingly, nearly 50% of the samples collected from TekapoB Tarn 3 were found within the ARC data-cloud. This suggests that once an adequate number of samples are collected from a given environment (in this case it was 62 samples from a 3900 m2 tarn), one has a 50% chance of explaining what other local communities would resemble simply by randomly sub-sampling the source pool community. This approach may have useful applications in microbial ecology, firstly, as an additional null model that complements more conservative neutral models (such as the Sloan et al. (2006) neutral model used here, for example). Secondly, the ARC approach could be used as an informative predictor of what other local communities would resemble after an environment has been sufficiently sampled. For example, if one collects numerous samples from a continuous

environment, the random sub-sampling approach could be used to predict the likelihood of bacterial community composition in other sites. This approach may allow microbial ecologists to determine the number of samples needed to be collected from a given environment in order to predict the composition of other local communities simply by using the ARCs with high confidence levels (e.g., 70% of additional OLCs fall within the ARC data-cloud).