What this entity is
Canonical name: PERMANOVA (permutational multivariate analysis of variance). 1
Known aliases: permutational multivariate analysis of variance. 1
Stable external identifier: Not reported in the source document. 1
PERMANOVA is a method used in this corpus to test whether environmental, spatial, phylogenetic, and other sample-group variables explain multivariate community or gene-module composition. 1
Use in SSO subsurface community ecology
PERMANOVA applied to 37 sediment core segments found that hydrogeological zone explained 27.5% of community variance, with F = 4.05 and p = 0.0001. 1
In the same analysis, well identity explained 19.2% of community variance but was not significant, with F = 0.80 and p = 0.979. 1
The report interprets these results as evidence that depth and saturated-zone plume intersection have stronger effects on sediment community composition than horizontal well identity, although the plume explanation remains a hypothesis because direct SSO geochemistry was unavailable. 1
A separate PERMANOVA of groundwater communities found that well identity explained 49.9% of variance with p = 0.001, filter size explained 10.1% with p = 0.001, depth within the saturated zone explained 2.5% with p = 0.430, and sampling date explained 0.8% with p = 0.998. 1
These groundwater results support persistent well-level spatial structure over the 9-day sampling interval, but they do not establish long-term or seasonal stability. 1
Use in long-term soil warming
In the Harvard Forest warming study, PERMANOVA on Bray–Curtis genus distances refined the SSO findings by showing that treatment explained 7.6% of variance (p = 0.069), whereas soil horizon explained 30.6% (p = 0.0002) and the treatment × horizon four-cell factor explained 41% (p = 0.0002). 2 This supports the broader conclusion that environmental context can structure community composition, while indicating that the warming response was strongly horizon-specific rather than a uniform treatment effect. 2
The detailed analysis is documented in Harvard Forest Long-Term Warming — DNA vs RNA Functional Response. 2
Use in lignin-enrichment community ecology
In a 21-sample lignin-enrichment experiment, Bray–Curtis PERMANOVA supported and substantially strengthened the evidence that treatment restructures bacterial communities: across seven groups, treatment explained 97.9% of variance (F = 111.35, p = 0.001), while the Base-versus-Round-1 model explained 0.992 of variance (F = 394.42, p = 0.008). 3 The Round-2 factorial model refined this result by separating prior history from current conditions: Round-1 history explained 58.9% (F = 14.31, p = 0.002), whereas current carbon source explained 32.7% (F = 4.85, p = 0.018). 3
The same analysis cautions that PERMANOVA signal is not exclusively a difference in group centroids: PERMDISP, a test of multivariate dispersion, was significant for 16S (p = 0.0004) and ITS (p = 0.0001), indicating that heterogeneous within-group dispersion also contributed. 3 This qualifies, rather than contradicts, the strong treatment and history effects, particularly because fungal history was not statistically detectable (R² = 0.142, p = 0.090). 3
The detailed analysis is documented in Lignin Enrichment and Ecological Memory in Microbial Communities. 3
Use in prophage-module ecology
In the prophage analysis, PERMANOVA on Bray-Curtis module composition across a 1,773-species subsample found significant effects for genome-size quartile, environment, and family-level phylogeny, each with p = 0.01; genome size was dominant (F = 212.99), followed by environment (F = 30.04) and phylogeny (F = 6.17). 4 This supports the existing environmental-context findings while refining them by showing that environment explained prophage-module composition beyond host phylogeny, although genome size remained the strongest reported predictor. 4
The detailed analysis is documented in Prophage Gene Modules and Terminase-Defined Lineages Across Bacterial Phylogeny and Environmental Gradients. 4
Use in Pseudomonas carbon-pathway ecology
A PERMANOVA-like permutation test of 62-pathway carbon profiles across 54 free-living and plant-associated Pseudomonas species found a significant association with isolation environment after 999 permutations (p = 0.006); between-group mean distance was 2.054 versus within-group mean distance of 1.890. 5 This supports the existing evidence that environmental context structures multivariate composition, while refining it by showing that the ecological signal was modest: a four-class Random Forest achieved balanced accuracy of 0.408 +/- 0.169 against a 0.250 chance baseline. 5
Unlike the stronger treatment, horizon, and prophage-module effects above, the Pseudomonas result indicates that carbon profiles alone are insufficient for fine-grained environment discrimination; the dominant axis instead separated Pseudomonas s.s. from Pseudomonas_E. 5 The detailed analysis is documented in Carbon Source Utilization Predicts Ecology and Lifestyle in Pseudomonas. 5
Related pages
The method is documented in the source summary SSO Subsurface Community Ecology — Spatial Structure, Functional Gradients, and Hydrogeological Drivers. 1
The lignin results support condition-specific-fitness, where community composition is interpreted in relation to carbon regime, and ecological-memory, where Round-1 history is distinguished from current conditions. 3
The results contribute to ecotype-environment-gene-content, which addresses environment-linked microbial community structure. 1
The results also inform multi-omics-integration, because geochemistry and metagenomics were identified as needed to validate the 16S-based interpretations. 1
The prophage application also informs phage-defense-syndromes-and-arms-race, because PERMANOVA identified environment-linked prophage-module composition beyond phylogeny. 4
The Pseudomonas application informs environment-embedding-geography by testing environmental separation in pathway-profile space and metabolic-model-gapfilling by evaluating GapMind-derived carbon capabilities. 5