# Introduction to BRCore

#### By Bolívar Aponte Rolón and Gian Maria Niccolò Benucci

#### Last modified: 18 May, 2026

## Load R libraries

    invisible(
      lapply(
        c("BRCore", "phyloseq", "tidyverse", "viridis"),
        library,
        character.only = TRUE
      )
    )

## Datasets included

The `BRCore` R package comes with a few datasets. Three 16S datasets: `switchgrass`, `mimulus`, and `bean`, from Shade and Stopnisek (2019); a 16S dataset, called `bcse`, from leaves of ten different cropping systems in the Bioenergy Crop Research Experiment from Haan et al. (2023). Here we are going to use `bcse` since it is not rarefied in contrast to the other three.

    str(bcse)
    #> Formal class 'phyloseq' [package "phyloseq"] with 5 slots
    #>   ..@ otu_table:Formal class 'otu_table' [package "phyloseq"] with 2 slots
    #>   .. .. ..@ .Data        : int [1:2861, 1:50] 0 1 0 0 27777 0 7 2 119 0 ...
    #>   .. .. .. ..- attr(*, "dimnames")=List of 2
    #>   .. .. .. .. ..$ : chr [1:2861] "OTU_427" "OTU_11" "OTU_253" "OTU_148" ...
    #>   .. .. .. .. ..$ : chr [1:50] "bcse50" "bcse69" "bcse73" "bcse191" ...
    #>   .. .. ..@ taxa_are_rows: logi TRUE
    #>   .. .. ..$ dim     : int [1:2] 2861 50
    #>   .. .. ..$ dimnames:List of 2
    #>   .. .. .. ..$ : chr [1:2861] "OTU_427" "OTU_11" "OTU_253" "OTU_148" ...
    #>   .. .. .. ..$ : chr [1:50] "bcse50" "bcse69" "bcse73" "bcse191" ...
    #>   ..@ tax_table:Formal class 'taxonomyTable' [package "phyloseq"] with 1 slot
    #>   .. .. ..@ .Data: chr [1:2861, 1:10] "OTU_427" "OTU_11" "OTU_253" "OTU_148" ...
    #>   .. .. .. ..- attr(*, "dimnames")=List of 2
    #>   .. .. .. .. ..$ : chr [1:2861] "OTU_427" "OTU_11" "OTU_253" "OTU_148" ...
    #>   .. .. .. .. ..$ : chr [1:10] "OTU_ID" "Kingdom" "Phylum" "Class" ...
    #>   .. .. ..$ dim     : int [1:2] 2861 10
    #>   .. .. ..$ dimnames:List of 2
    #>   .. .. .. ..$ : chr [1:2861] "OTU_427" "OTU_11" "OTU_253" "OTU_148" ...
    #>   .. .. .. ..$ : chr [1:10] "OTU_ID" "Kingdom" "Phylum" "Class" ...
    #>   ..@ sam_data :'data.frame':    50 obs. of  3 variables:
    #> Formal class 'sample_data' [package "phyloseq"] with 4 slots
    #>   .. .. ..@ .Data    :List of 3
    #>   .. .. .. ..$ : chr [1:50] "Leaf" "Leaf" "Leaf" "Leaf" ...
    #>   .. .. .. ..$ : chr [1:50] "Corn" "Sorghum" "Switchgrass" "Miscanthus" ...
    #>   .. .. .. ..$ : chr [1:50] "R2" "R4" "R5" "R5" ...
    #>   .. .. ..@ names    : chr [1:3] "Niche" "Crop" "Plot"
    #>   .. .. ..@ row.names: chr [1:50] "bcse50" "bcse69" "bcse73" "bcse191" ...
    #>   .. .. ..@ .S3Class : chr "data.frame"
    #>   ..@ phy_tree : NULL
    #>   ..@ refseq   :Formal class 'DNAStringSet' [package "Biostrings"] with 5 slots
    #>   .. .. ..@ pool           :Formal class 'SharedRaw_Pool' [package "XVector"] with 2 slots
    #>   .. .. .. .. ..@ xp_list                    :List of 1
    #>   .. .. .. .. .. ..$ :<externalptr>
    #>   .. .. .. .. ..@ .link_to_cached_object_list:List of 1
    #>   .. .. .. .. .. ..$ :<environment: 0x5ad933230678>
    #>   .. .. ..@ ranges         :Formal class 'GroupedIRanges' [package "XVector"] with 7 slots
    #>   .. .. .. .. ..@ group          : int [1:2861] 1 1 1 1 1 1 1 1 1 1 ...
    #>   .. .. .. .. ..@ start          : int [1:2861] 106501 2501 63001 36751 501 19251 14251 37751 3751 8501 ...
    #>   .. .. .. .. ..@ width          : int [1:2861] 250 250 250 250 250 250 250 250 250 250 ...
    #>   .. .. .. .. ..@ NAMES          : chr [1:2861] "OTU_427" "OTU_11" "OTU_253" "OTU_148" ...
    #>   .. .. .. .. ..@ elementType    : chr "ANY"
    #>   .. .. .. .. ..@ elementMetadata: NULL
    #>   .. .. .. .. ..@ metadata       : list()
    #>   .. .. ..@ elementType    : chr "DNAString"
    #>   .. .. ..@ elementMetadata: NULL
    #>   .. .. ..@ metadata       : list()

# Data Exploration and Parameter Selection for Core Identification

## Rarefaction metrics and visualization

We are not going to talk about the importance of rarefaction as it is not the goal of this vignette, but if you are interested you should read Schloss (2024a), Schloss (2024b), Hong et al. (2022), and Weiss et al. (2017) as a start.

In `BRCore`, to identify the ideal rarefaction cutoff we can plot a simple series of visuals to guide us in the decision. We can, more or less, estimate how well a DNA sample will sequence, but there are always some samples that fail to sequence (or sequence badly) and some others that will sequence “too well” for reasons that are outside our control or by chance.

In the end we will need to decide how many samples we are willing to discard and how much diversity we want to retain in our data. To do that we can use some help by plotting some diagnostics plots as shown below.

    bcse_metrics <- add_rarefaction_metrics(data = bcse)
    bcse_metrics
    #> phyloseq-class experiment-level object
    #> otu_table()   OTU Table:         [ 2861 taxa and 50 samples ]
    #> sample_data() Sample Data:       [ 50 samples by 7 sample variables ]
    #> tax_table()   Taxonomy Table:    [ 2861 taxa by 10 taxonomic ranks ]
    #> refseq()      DNAStringSet:      [ 2861 reference sequences ]

    rarefaction_plot <- plot_rarefaction_metrics(bcse_metrics)
    #> ℹ Processing 50 samples
    #>
    ℹ Generating rarefaction diagnostic plots

    ✔ Rarefaction diagnostic plots generated successfully
    #> ℹ Generating rarefaction diagnostic plots

    ✔ Generating rarefaction diagnostic plots [1s]
    print(rarefaction_plot)

Figure 1: Rarefaction metrics. a-b, histograms of samples. c, Good’s coverage per total number of sequences in a sample. d-e, log10 of sequences in a sample. f, samples ranked by sequence reads in a sample.

Note: The Good’s coverage” is a statistics used in ecology that estimates the proportion of species in a community that are represented in a sample, based on the number of species encountered and the total number of individuals sampled. Essentially, it quantifies how well a sample represents the overall diversity of a population or ecosystem. Good’s coverage is calculated as \((1-(n1/N)\), where \(n1\) is the number of unique ASV/OTUs (i.e. species) found only once, and \(N\) is the total number of sequence reads (i.e. individuals); a high percentage (e.g., >95%) means most reads are from common taxa, suggesting sufficient sampling, while low coverage indicates many rare, potentially missed, taxa.

## Rarefy the data and explore variance propagation

Originally developed by Sanders (1968) rarefaction has been adopted widely in ecology and microbiome research. It consists of randomly resampling the same number of species (ASV/OTUs) multiple times across all samples to estimate the expected number of species that would be observed if all samples were identical. It prevents samples with more individuals (e.g., more sequencing reads) from appearing more diverse simply because they were sampled more in depth.

Because rarefaction is a random process, it adds noise to the data. Each resampling event is slightly different from others due to chance. This can be problematic, especially when a very low rarefaction cutoff is selected, when, for example, we want to compare 2 treatments. With lower rarefaction cutoffs, the greater the chance you have that rarefaction iterations will differ from one another, as also shown by Cameron et al. (2021).

We developed functions that can perform and store all the rarefaction iterations to evaluate (and visualize) the noise generated in the process, as shown below:

    bcse_rarefied_list <-
      multi_rarefy(
        physeq_obj = bcse,
        depth_level = 1000,
        num_iter = 3,
        .as = "list",
        set_seed = 7642
      )
    #>
    #> ── Rarefaction iterations starting... ──────────────────────────────────────────────
    #>
    #> ── Input Validation ──
    #>
    #> ✔ Input phyloseq object is valid!
    #> ℹ Seed: 7642
    #> ℹ Input (matrix/df dim): 47 samples x 2861 taxa
    #> ℹ Rarefaction depth: 1000
    #> ℹ Iterations: 3
    #> ℹ taxa_are_rows: TRUE
    #> ℹ OTU matrix/df rownames head: bcse50, bcse69, bcse73, bcse191, bcse82, bcse102
    #> ℹ OTU matrix/df colnames head: OTU_427, OTU_11, OTU_253, OTU_148, OTU_3, OTU_78
    #> ℹ Row sums summary: Min=1193, Max=107643, Median=25209
    #>
    #> ── Rarefaction Results ──
    #>
    #> ── Sample Removal
    #> ! 3 samples removed (depth < 1000)
    #> ! Samples removed: "bcse108, bcse105, bcse110"
    #>
    #> ── Taxa Removal
    #> ✔ No taxa removed.
    #> ! Taxa are not removed across iterations to maintain consistent dimensions.
    #> Downstream analyses should handle zero-abundance taxa appropriately.
    #>
    #> ── Data Sparsity
    #> ℹ Returning list of data frames for each iteration.
    #> • Rarefied matrix (across 3 iterations):
    #>   • Min: 130570 zeros (97.1% sparsity) out of 134467 entries
    #>   • Max: 130663 zeros (97.17% sparsity) out of 134467 entries
    #>   • Avg: 130608 zeros (97.13% sparsity) out of 134467 entries
    #>
    #> ── Final Data Dimensions
    #> ✔ Output: 3 iterations with 50 unique samples
    #> • Samples per iteration:
    #>   • Min: 47
    #>   • Max: 47
    #> • Non-zero taxa per iteration:
    #>   • Min: 1046
    #>   • Max: 1098
    #>   • Avg: 1073

Let’s explore the results.

    class(bcse_rarefied_list)
    #> [1] "list"
    str(bcse_rarefied_list[[1]], list.len = 10) # Dimensions of iteration #1
    #> 'data.frame':    47 obs. of  2861 variables:
    #>  $ OTU_427  : int  0 0 0 0 0 0 0 0 0 0 ...
    #>  $ OTU_11   : int  0 0 0 0 0 0 0 0 0 0 ...
    #>  $ OTU_253  : int  0 0 0 0 0 0 0 0 0 0 ...
    #>  $ OTU_148  : int  0 1 0 11 2 0 0 0 0 78 ...
    #>  $ OTU_3    : int  261 86 651 272 249 114 192 73 96 320 ...
    #>  $ OTU_78   : int  0 0 0 0 0 0 0 0 0 0 ...
    #>  $ OTU_58   : int  0 0 0 0 0 0 0 0 0 0 ...
    #>  $ OTU_152  : int  0 0 0 0 0 0 0 0 0 0 ...
    #>  $ OTU_16   : int  2 0 0 1 1 0 0 1 0 0 ...
    #>  $ OTU_35   : int  0 0 0 0 0 0 0 0 0 0 ...
    #>   [list output truncated]

We can plot the variance (the noise we talked above) across the 3 iterations of rarefaction. We can use the `plot_variance_propagation()` function to visualize this variance in Alpha diversity across samples and iterations and compare Raw (non-rarefied) vs. Rarefied data.

    rarefaction_variance_plot <- plot_variance_propagation(
      physeq_obj = bcse,
      rarefied = bcse_rarefied_list,
      q = 0,
      group_var = "Crop",
      group_color = "Plot"
    )
    #> ✔ Input phyloseq object is valid!
    #>
    #> ── Rarefaction Variance Propagation Visualization ──────────────────────────────────
    #> ℹ Hill number order selected, q= 0
    #> ℹ Number of rarefaction iterations, n_iter= 3
    #> ℹ Comparison plot generated!
    print(rarefaction_variance_plot)

Figure 2: Alpha diversity variance propagation (variance between rarefaction iteration) across 3 iterations of rarefaction. Points represent samples. The x-axis represent the grouping variable (Crop). The y-axis represent the alpha diversity metric (q = 0, i.e. richness) calculated on the samples. The color of the points represent the ‘Plot’ variable, which is a nested variable within ‘Crop’.

Review the rarefaction and variance plots to determine the ideal sampling depth. You are looking for a “sweet spot” that maintains high diversity and samples while keeping variance across iterations low. Pay extra attention when rarefaction cutoff is quite low, as show by Cameron et al. (2021). Note this depth, as it will serve as the cutoff for the `identify_core()` step in your pipeline.

### Recreate a phyloseq object with a rarefied `otu_table()`

This is an optional step.

The `identify_core()` function accepts a phyloseq object, whether rarefied or non-rarefied. When supplying a phyloseq object with a non-rarefied `otu_table`, you must provide a list of rarefied OTU tables that result from `multi_rarefy()`. In our case, we generated `bcse_rarefied_list`.

The results from `add_rarefaction_metrics()`, `plot_rarefaction_metrics()`, and `multi_rarefy()` are meant to explore the data and inform the chosen parameters for `identify_core()`. Function `identify_core()` will calculate the distance matrix for all iterations supplied and rank OTUs based on the average distance across iterations.

If you have a phyloseq object with a rarefied `otu_table()` or if you want to use a specific rarefaction iteration generated by `multi_rarefy()`, you also have these options. The choice is up to you and depends on your preferences and the specific goals of your analysis. The `update_otu_table()` function allows for replacing the `otu_table()` with the rarefied table you prefer.

    bcse_rare_single <- update_otu_table(
      physeq_obj = bcse,
      rarefied_otus = bcse_rarefied_list,
      iteration = 1 # Speficify which iteration to use for the updated OTU table
    )
    #> ℹ Extracting iteration 1 from list of 3 iterations.
    #> ! Phyloseq object and rarefied otu_table sample names are NOT identical. Check samples removed by rarefaction below.
    #> ! 3 samples removed due to rarefaction: "bcse108, bcse105, bcse110"
    #> ℹ Building phyloseq object with 47 samples and 2861 taxa
    #> ✔ Update complete!

Let’s proceed to the core analysis.

# Core analysis

### Identify the core ASV/OTUs set

We can now identify how many core ASV/OTUs are present in the dataset. One important difference that separates `identify_core()` from other methods of identification of core taxa is that you can specify a variable (in the case of the `bcse` dataset is `Crop`) so that OTUs can be core for a level of the variable but possibly not for others. The idea stems from the assumption that the microbiome can be transient in space and time, as shown by Shade and Stopnisek (2019).

We continue with the `bcse` phyloseq object and the rarefied data `bcse_rarefied_list` throughout the vignette. See below for example using a single iteration.

    bcse_core_multi <- identify_core(
      physeq_obj = bcse,
      rarefied_list = bcse_rarefied_list,
      priority_var = "Crop",
      increase_value = 0.02,
      abundance_weight = 0,
      depth_level = 1000,
      seed = 2134
    )
    #> Seed used: 2134
    #> ✔ Input phyloseq object is valid!
    #> ℹ Using provided `rarefied_list` (3 iterations).
    #> ✔ Core prioritizing variable: Crop
    #> ℹ Ranked by Rank only
    #> ℹ Ranking OTUs based on BC dissimilarity, starting at 2026-05-18 15:17:06.698128
    #> ✔ Elbow method identified 7 core OTUs
    #> ✔ % increase method identified 30 core OTUs
    #> ✔ Analysis complete!

    # With a single iteration
    # bcse_core_single <- identify_core(
    #   physeq_obj = bcse_rare_single,
    #   priority_var = "Crop",
    #   increase_value = 0.02,
    #   seed = 2134
    # )

## How the is the core identified?

As shown by Shade and Stopnisek (2019), an interesting way to explore the abundance and occupancy inclusion thresholds used to define the core microbiome is to evaluate how well the resulting core membership captures the overarching patterns of beta-diversity found in the complete dataset. This approach treats the core microbiome as a representative subset that should preserve the main ecological relationships and sample-to-sample differences observed when using all taxa. By measuring how closely the beta-diversity patterns calculated from core taxa alone match those from the full dataset, researchers can objectively assess whether their chosen thresholds yield a meaningful and informative core that retains the essential structural information of the microbial community while reducing complexity.

To identify the optimal point where increasing the core inclusion threshold provides diminishing returns in explanatory value, we offer two automated methods.

- The first is a more stringent `elbow` approach that finds the bend in the abundance-occupancy curve where adding more taxa yields progressively smaller improvements. This method calculates the first-order difference (numerical differentiation) by assigning a score to each potential cutoff point, splitting the curve into two parts, and finding the cutoff that maximizes the difference in average rates of change between the two parts.

- The second method uses a final percent `increase` threshold in beta-diversity (recommend 2% or more), which continues adding taxa until improvements fall below this percentage.

Both methods measure improvement using Bray-Curtis similarity through the equation \(C = 1 - (BC_{core}/BC_{all})\), where \(C\) represents the contribution of ranked taxa to total similarity, \(BC_{core}\) is the Bray-Curtis similarity using only core taxa, and \(BC_{all}\) uses the complete dataset.

The cumulative explanatory value of adding each next-ranked taxon can be plotted using the `plot_identified_core()` function, with the vertical lines indicating the elbow and 2% threshold cutoff methods. These approaches eliminate the need for subjective threshold setting by automatically identifying natural breakpoints where additional taxa provide marginal returns in explanatory power.

The output is a list of 13 named items.

    str(bcse_core_multi)
    #> List of 13
    #>  $ bray_curtis_ranked : tibble [2,861 × 12] (S3: tbl_df/tbl/data.frame)
    #>   ..$ rank             : Factor w/ 2861 levels "1","10","100",..: 1 1112 2085 2196 2307 2418 2529 2640 2751 2 ...
    #>   ..$ rank_num         : num [1:2861] 1 2 3 4 5 6 7 8 9 10 ...
    #>   ..$ otu_added        : chr [1:2861] "OTU_3" "OTU_12" "OTU_21" "OTU_68" ...
    #>   ..$ MeanBC           : num [1:2861] 0.0744 0.0862 0.1019 0.1036 0.1264 ...
    #>   ..$ proportionBC     : num [1:2861] 0.12 0.139 0.164 0.167 0.203 ...
    #>   ..$ IncreaseBC       : num [1:2861] NA 1.16 1.18 1.02 1.22 ...
    #>   ..$ elbow_slope_diffs: num [1:2861] -0.000191 0.005744 0.009008 0.007127 0.010232 ...
    #>   ..$ delta_pct_max_BC : num [1:2861] NA 15.96 18.22 1.63 22 ...
    #>   ..$ is_BC_core       : logi [1:2861] TRUE TRUE TRUE TRUE TRUE TRUE ...
    #>   ..$ last_pctBC_cutoff: logi [1:2861] FALSE FALSE FALSE FALSE FALSE FALSE ...
    #>   ..$ is_elbow_core    : logi [1:2861] TRUE TRUE TRUE TRUE TRUE TRUE ...
    #>   ..$ last_elbow_cutoff: logi [1:2861] FALSE FALSE FALSE FALSE FALSE FALSE ...
    #>  $ otu_ranked         :'data.frame': 2861 obs. of  7 variables:
    #>   ..$ otu    : chr [1:2861] "OTU_3" "OTU_12" "OTU_21" "OTU_68" ...
    #>   ..$ otu_occ: num [1:2861] 1 1 1 1 1 1 1 1 1 1 ...
    #>   ..$ otu_rel: num [1:2861] 0.21728 0.01799 0.03296 0.00278 0.05897 ...
    #>   ..$ sumF   : num [1:2861] 10 10 10 10 10 10 10 10 10 10 ...
    #>   ..$ sumG   : num [1:2861] 10 10 10 10 10 10 10 10 10 10 ...
    #>   ..$ nS     : int [1:2861] 10 10 10 10 10 10 10 10 10 10 ...
    #>   ..$ rank   : num [1:2861] 2 2 2 2 2 2 2 2 2 2 ...
    #>  $ abundance_occupancy:'data.frame': 2861 obs. of  3 variables:
    #>   ..$ otu    : chr [1:2861] "OTU_427" "OTU_11" "OTU_253" "OTU_148" ...
    #>   ..$ otu_occ: num [1:2861] 0.08 0.46 0.04 0.74 1 0.32 0.3 0.06 0.96 0.04 ...
    #>   ..$ otu_rel: num [1:2861] 2.47e-05 5.55e-04 4.90e-06 4.82e-03 2.17e-01 ...
    #>  $ priority_var       : chr "Crop"
    #>  $ abundance_weight   : num 0
    #>  $ elbow              : int 7
    #>  $ bc_increase        : int 30
    #>  $ increase_value     : num 0.02
    #>  $ elbow_core         : chr [1:7] "OTU_3" "OTU_12" "OTU_21" "OTU_68" ...
    #>  $ increase_core      : chr [1:30] "OTU_3" "OTU_12" "OTU_21" "OTU_68" ...
    #>  $ otu_table          : int [1:2861, 1:50] 0 1 0 0 27777 0 7 2 119 0 ...
    #>   ..- attr(*, "dimnames")=List of 2
    #>   .. ..$ : chr [1:2861] "OTU_427" "OTU_11" "OTU_253" "OTU_148" ...
    #>   .. ..$ : chr [1:50] "bcse50" "bcse69" "bcse73" "bcse191" ...
    #>  $ metadata           :'data.frame': 50 obs. of  4 variables:
    #>   ..$ Niche    : chr [1:50] "Leaf" "Leaf" "Leaf" "Leaf" ...
    #>   ..$ Crop     : chr [1:50] "Corn" "Sorghum" "Switchgrass" "Miscanthus" ...
    #>   ..$ Plot     : chr [1:50] "R2" "R4" "R5" "R5" ...
    #>   ..$ sample_id: chr [1:50] "bcse50" "bcse69" "bcse73" "bcse191" ...
    #>  $ taxonomy           :'data.frame': 2861 obs. of  10 variables:
    #>   ..$ OTU_ID   : chr [1:2861] "OTU_427" "OTU_11" "OTU_253" "OTU_148" ...
    #>   ..$ Kingdom  : chr [1:2861] "Bacteria" "Bacteria" "Bacteria" "Bacteria" ...
    #>   ..$ Phylum   : chr [1:2861] "Myxococcota" "Actinobacteriota" "Proteobacteria" "Proteobacteria" ...
    #>   ..$ Class    : chr [1:2861] "Polyangia" "Actinobacteria" "Alphaproteobacteria" "Gammaproteobacteria" ...
    #>   ..$ Order    : chr [1:2861] "Polyangiales" "Streptomycetales" "Rhizobiales" "Enterobacterales" ...
    #>   ..$ Family   : chr [1:2861] "Polyangiaceae" "Streptomycetaceae" "Rhizobiaceae" "Enterobacteriaceae" ...
    #>   ..$ Genus    : chr [1:2861] "Aetherobacter" "Streptomyces" "Mesorhizobium" NA ...
    #>   ..$ Species  : chr [1:2861] "Uncultured_bacterium_3097" NA "Mesorhizobium_sophorae" NA ...
    #>   ..$ BestMatch: chr [1:2861] "Uncultured bacterium 3097" "Streptomyces" "Mesorhizobium sophorae" "Enterobacteriaceae" ...
    #>   ..$ Taxonomy : chr [1:2861] "OTU_427-Uncultured bacterium 3097" "OTU_11-Streptomyces" "OTU_253-Mesorhizobium sophorae" "OTU_148-Enterobacteriaceae" ...
    #>  - attr(*, "class")= chr [1:2] "identify_core_result" "list"

### Visualize the abundance-occupancy distribution and the core ASV/OTUs set

The output from `identify_core()` can be used for plotting as below:

    bcse_identified_core <- plot_identified_core(
      bray_curtis_ranked = bcse_core_multi$bray_curtis_ranked,
      elbow = bcse_core_multi$elbow,
      lastCall = bcse_core_multi$bc_increase,
      increase_value = bcse_core_multi$increase_value
    )

    # plot_identified_core() outputs a list with a dataframe and a ggplot

    print(bcse_identified_core$plot_identified_core)

Figure 3: Percent Bray-Curtis similarity between samples per ranked ASV/OTUs. Number of core ASV/OTUs identified by Elbow and Last 2% increase in Bray-Curtis similarity are shown.

### Visualize the abundance-occupancy curve

Abundance-Occupancy distributions can be plotted:

    plot_abund_occ_increase <- plot_abundance_occupancy(
      core_result = bcse_core_multi,
      core_set = "increase"
    )
    print(plot_abund_occ_increase)

Figure 4: Abundance-occupancy distribution for the ‘bcse’ dataset. The core ASV/OTUs identified by the last 2% increase method are highlighted in red.

And for the `elbow` method.

    plot_abund_occ_elbow <- plot_abundance_occupancy(
      core_result = bcse_core_multi,
      core_set = "elbow"
    )

    plot_abund_occ_elbow +
      scale_fill_manual(values = c("darkgreen", "grey"))
    #> Scale for fill is already present.
    #> Adding another scale for fill, which will replace the existing scale.

Figure 5: Abundance-occupancy distribution for the ‘bcse’ dataset. The core ASV/OTUs identified by the elbow method are highlighted in dark green.

### Visualize the core set across the variable used for detecting the core

The `plot_core_distribution()` function can be used to visualize ASV/OTU occupancy across the variable specified during core identification. The function provides three visualization methods (bar, line, and heatmap) that can be toggled via the plot_type argument. The optimal choice often depends on the number of levels in your grouping variable. For instance, bar or line plots are highly effective for up to five levels, whereas heatmaps are better suited for more complex datasets. While these are general recommendations, the core data is fully accessible in `bcse_core_multi` should you wish to develop a custom visualization.

    plot_core_dist_bar <- plot_core_distribution(
      core_result = bcse_core_multi,
      core_set = "increase",
      group_var = "Crop",
      plot_type = "bar"
    )

    print(plot_core_dist_bar)

Figure 6: Occupancy of core ASV/OTUs across the ‘Crop’ variable. Each bar represents the average occupancy of core ASV/OTUs in samples belonging to each level of the ‘Crop’ variable. A bar plot won’t work here, way too many variable levels.

    plot_core_dist_line <- plot_core_distribution(
      core_result = bcse_core_multi,
      core_set = "increase",
      group_var = "Crop",
      plot_type = "line"
    )
    print(plot_core_dist_line)

Figure 7: Occupancy of core ASV/OTUs across the ‘Crop’ variable. Each point represents the average occupancy of core ASV/OTUs in samples belonging to each level of the ‘Crop’ variable. A line plot is better than a bar plot but still not ideal for this many variable levels.

We can reorder the variable levels with the following logic before plotting.

    bcse_core_multi$metadata <- bcse_core_multi$metadata %>%
      mutate(
        Crop = recode(
          Crop,
          "Corn" = "Corn",
          "Sorghum" = "Sorghum",
          "Continuous Sorghum" = "Sorghum + cover crop",
          "Miscanthus" = "Miscanthus",
          "New Switchgrass" = "Establishing switchgrass",
          "Switchgrass" = "Mature switchgrass",
          "Early Succession" = "Successional vegetation",
          "Native Grasses" = "Native grass mix",
          "Prairie" = "Reconstructed prairie",
          "Poplar" = "Poplar"
        ),
        Crop = factor(
          Crop,
          levels = c(
            "Corn",
            "Sorghum",
            "Sorghum + cover crop",
            "Miscanthus",
            "Establishing switchgrass",
            "Mature switchgrass",
            "Successional vegetation",
            "Native grass mix",
            "Reconstructed prairie",
            "Poplar"
          )
        )
      )

    plot_core_dist_heatmap <- plot_core_distribution(
      core_result = bcse_core_multi,
      core_set = "increase",
      group_var = "Crop",
      plot_type = "heatmap"
    ) +
      viridis::scale_fill_viridis(option = "plasma", name = "Occupancy")
    #> Scale for fill is already present.
    #> Adding another scale for fill, which will replace the existing scale.

    print(plot_core_dist_heatmap)

Figure 8: Occupancy of core ASV/OTUs across the ‘Crop’ variable. Each cell represents the average occupancy of core ASV/OTUs in samples belonging to each level of the ‘Crop’ variable. A heatmap is more compact and shows well enough the average occupancy across samples in each variable level.

# Fitting Neutral Model

Neutral models in ecology assume that species (ASVs/OTUs) are functionally equivalent and that community patterns arise from random chance rather than deterministic niches or selection. As a specific case of null models (Gotelli and McGill 2006), they act as a baseline. If real-world data significantly differ from the model’s predictions, we can infer that non-random ecological forces are at play.

Building on the work of Sloan et al. (2006) and Burns et al. (2015), BRCore implements accessible functions to fit and visualize these models. Our goal is to make these powerful ecological tools easy to use without losing the conceptual rigor of the original math.

To get started, we first fit the neutral model:

    bcse_core_multi_neutral_fit <- fit_neutral_model(
      otu_table = bcse_core_multi$otu_table,
      core_set = bcse_core_multi$increase_core,
      abundance_occupancy = bcse_core_multi$abundance_occupancy
    )
    #> Waiting for profiling to be done...
    #> Warning: SNCM `stats::pbeta()` produced a warning during MLE: NaNs produced
    #> Warning: Binomial model `stats::dnorm()` produced a warning during MLE: NaNs
    #> produced
    #> Warning: Poisson model `stats::dnorm()` produced a warning during MLE: NaNs
    #> produced
    #> ℹ Neutral model fitting:
    #> • Average individuals per community (N): 40276.38
    #> • Binomial model using rounded N: 40276
    #> • Poisson model using N: 40276.38
    #> • Maximum likelihood estimation using N: 40276.38, and starting parameters: mu = 0,
    #>   sigma = 0.1
    #> Warning: SNCM `stats::pbeta()` produced a warning during MLE: NaNs produced
    #> Warning: Binomial model `stats::dnorm()` produced a warning during MLE: NaNs
    #> produced
    #> Warning: Poisson model `stats::dnorm()` produced a warning during MLE: NaNs
    #> produced
    #> ✔ Model fitting complete!

What we get is…

    str(bcse_core_multi_neutral_fit)
    #> List of 2
    #>  $ goodness_of_fit :'data.frame':    1 obs. of  24 variables:
    #>   ..$ m         : num 0.0205
    #>   ..$ m.ci      : num 0.00186
    #>   ..$ m.mle     : num 0.0205
    #>   ..$ maxLL     : num -2279
    #>   ..$ binoLL    : num -720
    #>   ..$ poisLL    : num -720
    #>   ..$ Rsqr      : num 0.528
    #>   ..$ Rsqr.bino : num -0.833
    #>   ..$ Rsqr.pois : num -0.833
    #>   ..$ RMSE      : num 0.109
    #>   ..$ RMSE.bino : num 0.215
    #>   ..$ RMSE.pois : num 0.215
    #>   ..$ AIC       : num -4554
    #>   ..$ BIC       : num -4542
    #>   ..$ AIC.bino  : num -1435
    #>   ..$ BIC.bino  : num -1423
    #>   ..$ AIC.pois  : num -1435
    #>   ..$ BIC.pois  : num -1423
    #>   ..$ N         : num 40276
    #>   ..$ Samples   : num 50
    #>   ..$ Richness  : num 2861
    #>   ..$ Detect    : num 2.48e-05
    #>   ..$ above.pred: num 0.165
    #>   ..$ below.pred: num 0.035
    #>  $ model_prediction:'data.frame':    2861 obs. of  14 variables:
    #>   ..$ otu       : chr [1:2861] "OTU_427" "OTU_11" "OTU_253" "OTU_148" ...
    #>   ..$ otu_occ   : num [1:2861] 0.08 0.46 0.04 0.74 1 0.32 0.3 0.06 0.96 0.04 ...
    #>   ..$ otu_rel   : num [1:2861] 2.47e-05 5.55e-04 4.90e-06 4.82e-03 2.17e-01 ...
    #>   ..$ membership: chr [1:2861] "Not core" "Not core" "Not core" "Not core" ...
    #>   ..$ p         : num [1:2861] 3.48e-06 6.31e-05 2.48e-06 7.98e-03 2.54e-01 ...
    #>   ..$ freq      : num [1:2861] 0.08 0.46 0.04 0.74 1 0.32 0.3 0.06 0.96 0.04 ...
    #>   ..$ freq.pred : num [1:2861] 0.00951 0.16077 0.0068 1 1 ...
    #>   ..$ pred.lwr  : num [1:2861] 0.000953 0.083916 0.000514 0.928652 0.928652 ...
    #>   ..$ pred.upr  : num [1:2861] 0.088 0.286 0.0835 1 1 ...
    #>   ..$ bino.pred : num [1:2861] 0.1306 0.9211 0.0952 1 1 ...
    #>   ..$ bino.lwr  : num [1:2861] 0.0632 0.8131 0.0405 0.9287 0.9287 ...
    #>   ..$ bino.upr  : num [1:2861] 0.251 0.969 0.208 1 1 ...
    #>   ..$ y         : chr [1:2861] NA NA NA NA ...
    #>   ..$ fit_class : chr [1:2861] "As predicted" "Above prediction" "As predicted" "Below prediction" ...
    #>  - attr(*, "class")= chr [1:2] "fit_neutral_model" "list"

### Plot the neutral model and the core set

    plot_bcse_neutral_fit <- plot_neutral_model(bcse_core_multi_neutral_fit)

    print(plot_bcse_neutral_fit)

Figure 9: Neutral model fit illustrates the neutral model of abundance-occupancy distributions for the ‘bcse’ dataset. The \(R^2\) value represents a standard coefficient of determination, calculated as \(R^2 = 1 - \frac{SS_{err}}{SS_{total}}\), providing a measure of goodness of fit. The immigration parameter (\(m\)) estimates the probability that an individual in a local sample originates from the metacommunity rather than from local dispersal. We also observe that the neutral model provides a poor fit; the \(R^2\) is notably low, and the majority of taxa (including both core and non-core ASV/OTUs) fall above the model’s prediction line. Furthermore, the estimated immigration parameter (\(m = 0.63\)) suggests that the local community is dominated by strong dispersal and mixing from the metacommunity.

Note: Interpretations of the Migration Parameter, as a rule of thumb:

- Low (\(m \approx 0–0.05\)): strong dispersal limitation; local history dominates community assembly.
- Moderate (\(m \approx 0.1–0.3\)): partial connectivity among sites; both dispersal and local structure influence community composition.
- High (\(m \geq 0.4\)): high dispersal rates and strong mixing from the regional source pool.

# Supplemental Information

It is critical to note that the core taxa identified from a single rarefaction iteration may differ from those identified through multiple iterations. To illustrate this stochasticity, if we identify the core using only the first iteration of our `bcse` phyloseq object with rarefied `otu_table()` generated above using `update_otu_table()`, we obtain the following results:

    bcse_core_single_iter1 <- identify_core(
      physeq_obj = bcse_updated_rare,
      priority_var = "Crop",
      increase_value = 0.02,
      abundance_weight = 0,
      seed = 2135
    )
    #> Seed used: 2135
    #> Error: object 'bcse_updated_rare' not found

    bcse_identified_core_iter1 <- plot_identified_core(bray_curtis_ranked = bcse_core_single_iter1$bray_curtis_ranked,
      elbow = bcse_identified_core_iter1$elbow,
      lastCall = bcse_identified_core_iter1$bc_increase,
      increase_value = bcse_identified_core_iter1$increase_value
    )
    #> Error: object 'bcse_core_single_iter1' not found
    print(bcse_identified_core_iter1$plot_idenfied_core)
    #> Error: object 'bcse_identified_core_iter1' not found

## Further Customization of `identify_core()`

While the default settings of `identify_core()` are suitable for most analyses, the function provides additional parameters to balance computational efficiency with ecological nuance. In large datasets with thousands of ASV/OTUs, ranking every single taxon can be computationally expensive. The `max_otus` parameter allows you to cap the number of taxa processed. Rare taxa (those with very low abundance) rarely contribute more than 2% to the Bray-Curtis similarity. Limiting the ranking can significantly speed up your analysis without losing the core signal. However, if set too low, you may inadvertently exclude “satellite” taxa—those that are low in abundance but highly persistent (high occupancy) across your samples.

By default, `identify_core()` prioritizes occupancy (how often a taxon appears). However, you may want to account for relative abundance (how dominant a taxon is). The `abundance_weight` argument (numeric, 0 to 1) allows you to blend these two metrics:

- abundance_weight = 0 (Default): Ranking is based strictly on occupancy and composite metrics.

- abundance_weight = 1: Ranking is based purely on mean relative abundance.

- max_otus: Limits the number of taxa processed. Useful for large datasets to improve computational efficiency.

- Intermediate Values: A value such as 0.3 creates a hybrid score, weighting occupancy at 70% and abundance at 30%.

Notably, incorporating abundance weighting will likely result in a different core set than an occupancy-only approach.

# References

Burns, Andrew R., Wesley Z. Stephens, Katie Stagaman, et al. 2015. “Contribution of Neutral Processes to the Assembly of Gut Microbial Communities in the Zebrafish over Host Development.” The ISME Journal 10: 655–64. https://doi.org/10.1038/ismej.2015.142.
Cameron, Erin S., Paul J. Schmidt, Benoit J. M. Tremblay, Monica B. Emelko, and Kirsten M. Müller. 2021. “Enhancing Diversity Analysis by Repeatedly Rarefying Next Generation Sequencing Data Describing Microbial Communities.” Scientific Reports 11: 22302. https://doi.org/10.1038/s41598-021-01636-1.
Gotelli, Nicholas J., and Brian J. McGill. 2006. “Null Versus Neutral Models: What’s the Difference?” Ecography 29: 793–800. https://doi.org/10.1111/j.2006.0906-7590.04714.x.
Haan, Natalie L., Gian M. N. Benucci, Caroline M. Fiser, Gregory Bonito, and Douglas A. Landis. 2023. “Contrasting Effects of Bioenergy Crops on Biodiversity.” Science Advances 9 (September): eadh7960. https://doi.org/10.1126/sciadv.adh7960.
Hong, Johnny, Ulas Karaoz, Perry de Valpine, and William Fithian. 2022. “To Rarefy or Not to Rarefy: Robustness and Efficiency Trade-Offs of Rarefying Microbiome Data.” Bioinformatics 38 (March): 2389–96. https://doi.org/10.1093/bioinformatics/btac127.
Sanders, Howard L. 1968. “Marine Benthic Diversity: A Comparative Study.” The American Naturalist 102 (925): 243–82.
Schloss, Patrick D. 2024a. “Rarefaction Is Currently the Best Approach to Control for Uneven Sequencing Effort in Amplicon Sequence Analyses.” mSphere 9: e00354–23. https://doi.org/10.1128/msphere.00354-23.
Schloss, Patrick D. 2024b. “Waste Not, Want Not: Revisiting the Analysis That Called into Question the Practice of Rarefaction.” mSphere 9: e00355–23. https://doi.org/10.1128/msphere.00355-23.
Shade, Ashley, and Nabeel Stopnisek. 2019. “Abundance-Occupancy Distributions to Prioritize Plant Core Microbiome Membership.” Current Opinion in Microbiology 49: 50–58. https://doi.org/10.1016/j.mib.2019.09.008.
Sloan, Warwick T., Michael Lunn, Steven Woodcock, Ian M. Head, Simon Nee, and Thomas P. Curtis. 2006. “Quantifying the Roles of Immigration and Chance in Shaping Prokaryote Community Structure.” Environmental Microbiology 8: 732–40. https://doi.org/10.1111/j.1462-2920.2005.00956.x.
Weiss, Sejin, ZhenZhen Xu, Shyamal Peddada, et al. 2017. “Normalization and Microbial Differential Abundance Strategies Depend Upon Data Characteristics.” Microbiome 5: 27. https://doi.org/10.1186/s40168-017-0237-y.