Macroecological patterns in coarse-grained microbial communities

Reviewer #1 (Public Review):

Shoemaker and Grilli analyze publicly available sequencing data to quantify how the microbial diversity of ecosystems changes with the taxonomic scale considered (e.g., diversity of genera vs diversity of families). This study builds directly on Grilli's 2020 paper which used this data to show that for many different microbial species, the distribution of abundances of the species across sampling sites belongs to a simple one-parameter family of gamma distributions. In this work, they show that the gamma distribution also describes the distribution of abundances of higher taxonomic levels. The distribution now requires two parameters, but the second parameter can be approximately derived by treating the distributions of lower-level taxonomic units as being independent. The difference between the species-level result and the result at higher taxonomic levels suggests that in some sense microbial species are ecologically meaningful units.

While the higher-level taxon abundance distributions can be well-approximated assuming independence of the constituent species, this approach substantially underestimates variation in community richness and diversity among sampling sites. Much of this extra variability appears to be driven by variability in sample size across sites. It is not clear to me how much this variation in sample size is itself due to variation in sampling effort versus variation in overall microbial densities. This variation in sample size also produces correlations between taxon richness at lower and higher taxonomic levels. For instance, sites with large samples are likely to have both many species within a genus and many genera. The authors also consider taxon diversity (Shannon index, i.e. entropy), which is constructed from frequencies and is therefore less sensitive to sample size. In this case, correlations between diversity across taxonomic scales instead appear to depend on the idiosyncratic correlations among species abundances.

This paper's results are presented in a fairly terse manner, even when they are describing summary statistics that require a lot of thought to interpret. I don't think it would make sense to try to understand it without having first worked through the 2020 paper. But everyone interested in a general understanding of microbial ecology should read the 2020 paper, and once one has done that, this paper is worth reading as well simply for seeing how the major pattern in that paper shifts as one moves up in the taxonomic scale.

Reviewer #2 (Public Review):

Summary
In this research advance, the authors purport to show that the unified neutral theory of biodiversity (UNTB) is not a suitable null model for exploring the relationship between macroecological quantities, and additionally that the stochastic logistic growth model (SLM) is a viable replacement. They do this by citing other studies where UNTB was unable to capture individual macroecological quantities and then demonstrating SLM's strength at modeling macroecological diversity metrics. They extend this analysis to show SLM's modeling capability at multiple scales of coarse-graining. Finally, the authors conduct a similar analysis to Madi et al. (2020) by investigating the relationship between diversity measures within a group and across coarse-grained groups (e.g. genera diversity in one family compared to diversity of families). The authors show that choosing SLM as a null model reveals some previously reported relationships to be no longer "novel", in the sense that the patterns can be adequately captured by the null model.

Strengths
1. The authors make a strong argument that UNTB is not a good null model of macroecological observables and especially relationships between them. The authors convincingly argue that a SLM is a better null since the gamma distribution it predicts is a better description of the empirical Abundance Fluctuation Distributions (AFD).
2. The authors show that the gamma distribution predicted by SLM is a good fit for the AFD's at many different scales of coarse-graining, not just the OTU level as was previously demonstrated.
3. The authors convincingly demonstrate how SLM can be used to test the relevance of interactions to macroecological relationships.

Weaknesses
1. Use of the word "predict" with the SLM in this advance is confusing, and to this reviewer seems to make a stronger claim than shown by the authors. For example, in their abstract, the authors state "We found that measures of biodiversity at a given scale can be consistently predicted using predictions (sic) derived from a minimal model of ecology." This appears to imply that a minimal model predicted the behavior of a system when in reality it accurately described the data it was trained on. This potential for confusion extends throughout the text and obscures what was actually achieved.
2. More generally, to my mind the presentation in the manuscript could benefit from a clearer delineation between the question of "what patterns are explainable by a noninteracting model vs require interactions" (which could be assessed with no reference to SLM, but by a simple randomization test), and whether specifically, SLM is a good null model / better than UNTB.

Overall Impact
The authors achieve their aims, even though the text is at times dense. The use of SLM as a non-interacting null model for macroecological quantities and relationships is well supported by the text, and SLM should be used as a null model for these types of phenomena going forward.