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Supplementary Online Material
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Local Specificity Index and permutation tests
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The local specificity index is defined as the product of prevalence and share of total count. The index is
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asymmetric by construction as it focuses on “positive” specificity over “negative” specificity. It places a
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higher importance on systematic and exclusive presence than on systematic and exclusive absence: high
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specificity to one habitat automatically entails low specificity to other habitats but the reverse is not
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true. A species uniformly distributed across many habitats has low local specificity to each of them. The
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focus on positive specificity arises from the limitations of diversity surveys: presence is easier to assess
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confidently than absence. The quantitative nature of the index makes it robust to limited contamination:
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even if a species is present in all samples due to contamination, it will be picked as specific by the index if
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its overall distribution is concentrated on a given habitat.
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Our randomization scheme reassigns samples to habitats and is similar to non-parametric association
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tests. It preserves common and rare species within a sample and therefore detects specific species
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conditionally upon the abundance distribution. Other randomizations schemes exist (Hardy 2008) but
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typically involve randomizing species abundance within samples, instead of or in addition to samples
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within habitats. Such randomization schemes do not preserve rare and abundant species and implicitly
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assume ecological equivalence of all species. When assessing the specificity of abundant species, they
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also lead to less conservative tests. Consider the extreme case depicted in Table S1.
Habitat A
Habitat B
Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 Sample 6
OTU1 100
100
100
0
0
1
0
OTU2 0
0
0
100
100
100
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Table S1: Relative abundances from a fictional dataset with 2 OTUs, 2 habitats, 2 samples per habitats and perfect association
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between OTUs and conditions.
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After randomization of both species abundances and habitats, the maximum local specificity of OTU1
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across habitats 1 and 2 is centered on 0.45 with standard deviations of 0.20. In particular, only 3 % of the
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randomized count tables have a perfect association between OTU1 and either habitat. The
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corresponding values in our randomization scheme are 0.5, 0.16 and 10 %. In particular, the specificity is
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significant under complete randomization (p = 0.03) but not under habitat switching (p = 0.10). By
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preserving rare and abundant species, our randomization scheme creates more situations where high
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abundance samples cluster in a single habitat and lead to a high value of max  ih by chance only
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whereas complete randomizations smooth out those situations. In other words, we test whether OTU1 is
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specific to habitat A, knowing that it is abundant is some samples.
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h
The decision to compare  i to the null distribution of max  ih instead of its own null distribution shifts
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the null distribution towards higher values and increases right-tail p-values (i.e. probability of being more
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specific than expected by chance). Comparing to the maximum corrects for the multiple testing
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problems, where the specificity of a species is assessed for several habitats at once, and ensures that any
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species is specific to at most one habitat.
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Implicit assumptions behind normalization
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Our index uses a double normalization of species counts. The first normalization gives the same weight
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to each sample within a habitat by changing counts to relative abundances. This normalization has
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various flavors in metagenomics studies and reflects the sampling component of sequencing, as species
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are sequenced in proportion to their relative abundances. This normalization however implicitly assumes
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species neutrality: a sample site spans only one habitat, shared by all species in that habitat, and one
ℎ
ℎ
2
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species’ gain is another’s loss. If non-competing species at a given location occupy exclusive habitats, one
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part of the bacterial community may grow whereas the rest remains unchanged. If the carrying
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capacities (proxy for contribution) of the habitats differ among samples, relative abundances are
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distorted, even though samples share a common structure. This is illustrated in Table S2. Samples 1 and
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2 correspond to the same site, made up of habitats A and B, at two different time points. The carrying
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capacity of habitat A is divided by 3 between the two time points. Although each habitat composition
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remains unchanged, the global site composition changes with time.
Habitat A (capacity = KA)
Habitat B (capacity = KB)
OTU A1 OTU A2 OTU A3 OTU B1 OTU B2 OTU B3
Sample 1 (KA = 90, KB = 30)
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30
30
10
10
10
25
25
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8.3
8.3
8.3
10
10
10
10
10
10
16.7
16.7
16.7
16.7
16.7
16.7
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Absolute counts
Sample 1
Relative abundances (%)
Sample 2 (KA = 30, KB = 30)
Absolute Counts
Sample 2
Relative abundances
Common structure: within
Habitat relative abundances (%)
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Table S2: Relative abundances from a fictional dataset with 2 samples from the same site at two time points. The habitat is
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made of two identically structured habitats (last line) but the carrying capacity of habitat A changes across time, leading to
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distorted relative abundances.
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The second normalization weights all habitats equally when computing the total count of a species. This
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implicitly assumes that all habitats have comparable bacterial loads. If not, a species can be very specific
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to a habitat while being found mostly in another. This apparent paradox, illustrated in Table S3, stems
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from the base-rate fallacy and is unavoidable in many fields, including screening tests and biomarker
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discovery. This is not a problem of unknown library sizes, as in differential abundance or differential
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expression studies, but rather of unknown bacterial load and can probably not be solved by
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normalization methods alone. It also means that local specificity is a measure of relative specificity
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rather than absolute one.
Total counts
Normalized counts
OTU1 OTU2 OTU1
OTU2
Habitat A 80
20
80
20
Habitat B
920
8
92
80
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Table S3: Raw and normalized counts from a fictional dataset with two habitats and two OTUs. OTU1 is specific to condition A
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(0.91 = 80/88) but found in equal counts (80) in conditions A and B thanks to the 10 times larger bacterial local of habitat B.
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From a pragmatic point of view, a priori information on both the habitat composition of a sampling site
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and the bacterial load of a habitat are usually unavailable. The previous assumptions are therefore
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necessary to study specificity from count data.
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New skin habitats
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The skin group from the human microbiota study is a patchwork of body sampling sites (18 sites in
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Costello et al. (2009) dataset) and is unlikely to form a coherent habitat. This is reflected in the weak
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abundance – specificity relationship and the poor fit of NCM (Fig. S7). We decided to subdivide the skin
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group into smaller groups, more likely to form environmentally coherent habitats. The groups were
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formed using hierarchical clustering of unweighted UniFrac distances and cutting the tree at the highest
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level that respects the symmetry of the human body, imposing symmetric sites (left/right) to belong to
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the same habitat. As expected, the 6 new habitats mainly regroup by spatially close by sites (hand palm
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and index finger) in addition to the enforced symmetry (left/right back of knee) with only one peculiar
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habitat: armpit and soles of feet. Kembel et al. (2012) noted that armpits and soles of feet are both dark
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and moist sampling sites; this could be the relevant environmental variables here. Repeating the analysis
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with the new skin habitats does not change the results for the previous non-skin habitats. It however
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strengthens the abundance – specificity in the new skin habitats (Fig. S1), although the relationship
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remains weaker than in other habitats. This may be evidence that in the presence of strong migration
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and less differentiated environmental conditions, as expected between skin sampling sites, the relative
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contribution of neutral processes and niche differentiation shifts towards neutral processes, as
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suggested in Gravel et al. (2006).
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We stress that the new habitats were built only from presence/absence data obtained by aggregating all
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samples from a body site. In particular, they do not explicitly incorporate any information about
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abundance or local prevalence of species. We therefore argue that the stronger abundance – specificity
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relationship is genuine and not merely an artifact of the clustering step, although the latter could
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reinforce the former.
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SOM References
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2.
Kembel, S.W., Wu, M., Eisen, J.A. & Green, J.L. (2012). Incorporating 16S gene copy number information
improves estimates of microbial diversity and abundance. PLoS Comput Biol, 8, e1002743.
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Supplementary Figures
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Figure S1: Positive relationship between local abundance – specificity in zooplankton samples after correcting counts for copy
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number variation, using either average copy number (top) in a phylum (or class for Proteobacteria) or a random copy number
1.
Hardy, O.J. (2008). Testing the spatial phylogenetic structure of local communities: statistical
performances of different null models and test statistics on a locally neutral community: Testing spatial
community phylogenetic structure. J. Ecol., 96, 914–926.
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(bottom) in the observed range of that taxon phylum (or class for Proteobacteria). The sampling distribution was a truncated
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Gaussian with mean and variance set to observed mean and variance in the phylum (or class). The sequence similarity threshold
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is 97 %.
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Figure S2: Positive relationship between local abundance and specificity in zooplakton samples using sequence similarity
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thresholds ranging from 91 to 99 %. The abundance – specificity relationship is generally stronger for higher thresholds, expect
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perhaps 99 %, but can already be observed at 91 % threshold (roughly corresponding to the family level, (Youngblut et al.
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2013)). As expected, higher thresholds reduce the abundance of the most abundant species.
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Figure S3: Positive relationship between local abundance and specificity in the human microbiome (Costello et al. 2009) using
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refined habitat. Construction of the new skin habitats is detailed in the SOM. Details of this figure are the same as for Figure 1.
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Figure S4: The abundance – specificity relationship in the human microbiome (Costello et al. 2009) is stable over time. Curves
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corresponding to different sampling days (labelled Day 1 and Day 2) are essentially identical for all habitats.
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Figure S5: Abundance of species in the human microbiome (Costello et al. 2009) is stable over time. The variations observed at
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low abundances and materialized by the distance from the diagonal are likely due to limited sequencing depth. As relative
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abundances increase, they are better estimated and more consistent across the two datasets.
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Figure S6: Positive relationship between local abundance and specificity in diverse environmental habitats from the Global
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Patterns study (Caporaso et al. 2011) using moderate subsampling (5,000 reads). This confirms that abundance – specificity
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relationship is not simply an artefact of rare species being left out when using moderate subsampling.
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Figure S7: Fit of the Neutral Community Model (Sloan et al. 2006) to the Skin group of the human microbiome (Costello et al.
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2009). The model was fitted using equation (14) of Sloan et al. (2006). NCM correctly captures the occupancy - abundance curve
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at low abundances but breaks down at high abundances, suggesting that the Skin group is partitioned into several
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environmentally coherent habitats.
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