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. 2012 Sep;6(9):1653-64.
doi: 10.1038/ismej.2012.22. Epub 2012 Mar 29.

Stochastic and deterministic assembly processes in subsurface microbial communities

James C Stegen  1 , Xueju Lin, Allan E Konopka, James K Fredrickson
Affiliations

Stochastic and deterministic assembly processes in subsurface microbial communities

"V体育ios版" James C Stegen et al. ISME J. 2012 Sep.

Abstract

A major goal of microbial community ecology is to understand the forces that structure community composition. Deterministic selection by specific environmental factors is sometimes important, but in other cases stochastic or ecologically neutral processes dominate. Lacking is a unified conceptual framework aiming to understand why deterministic processes dominate in some contexts but not others. Here we work toward such a framework. By testing predictions derived from general ecological theory we aim to uncover factors that govern the relative influences of deterministic and stochastic processes. We couple spatiotemporal data on subsurface microbial communities and environmental parameters with metrics and null models of within and between community phylogenetic composition. Testing for phylogenetic signal in organismal niches showed that more closely related taxa have more similar habitat associations. Community phylogenetic analyses further showed that ecologically similar taxa coexist to a greater degree than expected by chance. Environmental filtering thus deterministically governs subsurface microbial community composition. More importantly, the influence of deterministic environmental filtering relative to stochastic factors was maximized at both ends of an environmental variation gradient. A stronger role of stochastic factors was, however, supported through analyses of phylogenetic temporal turnover. Although phylogenetic turnover was on average faster than expected, most pairwise comparisons were not themselves significantly non-random VSports手机版. The relative influence of deterministic environmental filtering over community dynamics was elevated, however, in the most temporally and spatially variable environments. Our results point to general rules governing the relative influences of stochastic and deterministic processes across micro- and macro-organisms. .

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Figures

Figure 1
Figure 1
Summary of methods characterizing within community phylogenetic composition. After estimating a phylogeny that includes all OTUs across all sampled communities, only the OTUs within a given community are retained (observed OTUs shown as gray circles; numbers are OTU identities). In the example, there are two observed OTUs in the community and the phylogenetic distance between them is two branch length units (sum of gray branches). Abundances are not shown, but are assumed equal so that MNTDobs is 2 units. To generate a null expectation for MNTD the observed OTUs and their relative abundances are randomly placed on the phylogeny (red circles; note that OTU identities are the same, only their positions on the phylogeny have changed). MNTD is then re-calculated, providing one null MNTD value (MNTDnull). Red branches connect the two (randomly placed) OTUs and their sum is 8, so MNTDnull=8 units. The randomization is repeated (blue circles), providing a second MNTDnull value (4 units), and so on until 999 randomizations are completed. This provides a distribution of MNTDnull values to which MNTDobs is compared (Step 3). In the example, MNTDobs (vertical dashed line) is much smaller than the average MNTDnull value. The equation for the NTI is given under Step 3 and measures the difference between MNTDobs and mean MNTDnull (given as formula image) in units of standard deviations, where standard deviation is measured on the MNTDnull distribution. Repeating Steps 1–3 across all communities provides the NTI distribution in Step 4 (actual distribution shown in Figure 4). An NTI distribution with a mean greater (less) than zero indicates niche-based processes cause OTUs to be, on average, more closely (distantly) related than expected under random community assembly.
Figure 2
Figure 2
Summary of methods for characterizing turnover in community phylogenetic composition between a given pair of communities. After estimating a phylogeny that includes all OTUs across all sampled communities, only the OTUs within a given pair of communities (k and m) are retained (observed OTUs shown as gray circles; numbers are OTU identities). In the example there are two OTUs in each community. The mean of (gray) branch lengths connecting each OTU in community k with its closest relative in community m (and vice versa) gives βMNTDobs=8 branch length units (abundances assumed equal). To generate a null expectation for βMNTD the observed OTUs are randomly placed on the phylogeny (red circles) and βMNTD is re-calculated, always using minimum phylogenetic distances (red branches) connecting OTUs between the two communities. In the example OTU1 and OTU4 are closest relatives (4 units apart) and OTU3 and OTU2 are closest relatives (2 units apart). Taking the mean gives βMNTDnull=3 units. The randomization is repeated (blue circles), providing a second MNTDnull value (8 units, using blue branches connecting closest relatives between communities), and so on until 999 randomizations are completed. This provides a distribution of βMNTDnull values to which βMNTDobs is compared. In the example, βMNTDobs (vertical dashed line) is larger than the average βMNTDnull value. βNTI measures the difference between βMNTDobs and mean βMNTDnull (given as formula image) in units of standard deviations (equation shown in Step 3). Repeating Steps 1–3 across all pairwise comparisons provides the βNTI distribution in Step 4 (actual distribution shown in Figure 4). A βNTI distribution with a mean greater (less) than zero indicates that niche-based processes cause phylogenetic turnover between communities to be, on average, greater (less) than expected under random community assembly.
Figure 3
Figure 3
Median habitat differences between pairs of OTUs as a function of between OTU phylogenetic distances. Medians are taken within phylogenetic distance bins. Black and gray data points represent differences in OTU mean depth and mean river elevation, respectively. The vertical dotted line and the transition from solid to dashed curves indicates the point at which the curve fit becomes non-monotonic, thereby approximating the phylogenetic distance threshold where phylogenetic signal is lost (∼13% of the maximum phylogenetic distance across the entire tree).
Figure 4
Figure 4
Kernal density estimates for distributions of abundance weighted nearest taxon index (NTI) and its between-community analog (βNTI). Each observation is the number of null model standard deviations the observed value is from the mean of its associated null distribution. Positive (negative) NTI values indicate shorter (longer) nearest taxon distances within a community than expected by chance. Positive (negative) βNTI values indicate greater (less) than expected turnover in phylogenetic composition than expected given the observed degree of taxonomic turnover. For both metrics, individual values below −2 or above +2 are statistically significant, as indicated by the dashed black lines. The dashed gray line is centered on zero, the expectation under neutral community assembly.
Figure 5
Figure 5
(a–c) Beta Mean Nearest Taxon Distance (βMNTD) or (d–f) Beta Nearest Taxon Index (βNTI) as a function of difference in Columbian River water elevation. Each panel shows data for the shallow sampling depth (10 m) across one of the three well clusters (see panel headers). Gray squares represent all comparisons from a given well cluster, and solid gray lines show best-fit regression models (linear, exponential or logarithmic). Solid black squares show results from comparing a sample in each well from mid-May to later sampling dates until early-July, and solid black lines are best-fit regression models to those data. Only significant regressions are shown. Horizontal dashed lines in (d–f) indicate βNTI values of −2 and +2, the values beyond which an individual βNTI value is considered statistically significant.
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