Dispersal-Niche Continuum Index (DNCI) Functions

The DNCI_multigroup() function in MetaCommunityMetrics is adapted from the DNCI_multigroup() function in the R package DNCImper (https://github.com/Corentin-Gibert-Paleontology/DNCImper), authored by Corentin Gibert, Gilles Escarguel, Annika Vilmi, Jianjun Wang, Aurelien Jamoneau, and Maxime Lopez, and licensed under GPL-3. The underlying methods are described in Gibert and Escarguel (2019) and Vilmi et al. (2021). This function was adapted from R to Julia in August 2024 and is redistributed here under GPL-3 in accordance with the terms of the original license. Modifications include: (1) empty sites and singletons (species occupying only one site at a given time) are permitted, whereas the original implementation does not allow them; and (2) a new output column has been added to flag five edge cases where permutation will fail, which are common when simulated data are used.

The function quantifies the balance between dispersal and niche processes within a metacommunity, providing insight into community structure and the relative influence of these two key ecological drivers. Vilmi et al. (2021) developed DNCI based on the PER-SIMPER method introduced by Gibert and Escarguel (2019) to compare observed community composition against three null model scenarios: (1) a niche assembly model that randomizes species identities while maintaining site-level species richness, (2) a dispersal assembly model that randomizes spatial locations while maintaining species-level occurrence frequencies, and (3) a combined model that maintains both constraints. PER-SIMPER uses the SIMPER analysis (Clarke 1993) to generate profiles of species contributions to average between-group dissimilarity for both the observed data and the community matrices permuted by the three null models, where dissimilarity is averaged across all site pairs. PER-SIMPER provides qualitative analysis of similarity between the observed SIMPER profile and null model PER-SIMPER profiles, while DNCI quantifies these similarities to calculate the relative importance of dispersal and niche processes.

Functionality Overview

Unlike the other metrics in this package, DNCI analysis operates on only one time point at a time. Positive DNCI values suggest niche processes dominate community assembly, while negative DNCI values suggest dispersal limitation is more influential at a given time point. DNCI values that do not differ significantly from zero suggest equal contributions from both processes at a given time point.

Before calculating the DNCI, sites must be assigned to groups, as the DNCI relies on analyzing community composition across site groups. This package provides DNCI_create_groups() to perform the grouping suggested by Vilmi et al. (2021) for all time points, and DNCI_plot_groups() to visualize the groups at a given time point—neither of which is available in the R implementation. However, using DNCI_create_groups() is optional; users can perform their own grouping as long as it follows the grouping criteria suggested by Vilmi et al. (2021): (1) at least 2 groups; (2) a minimum of 5 sites per group; and (3) the difference in the number of taxa and sites between any two groups, relative to the larger group, does not exceed 40% and 30%, respectively. Grouping that does not meet these criteria may bias the DNCI value.

The Functions

MetaCommunityMetrics.DNCI_create_groups — Function
DNCI_create_groups(time::AbstractVector, latitude::Vector{Float64}, longitude::Vector{Float64}, site::AbstractVector, species::AbstractVector, presence::AbstractVector) -> Dict{Int, DataFrame}

This function creates groupings of sites for each unique time step in a dataset which can then used for calculating DNCI. Only presence-absence data can be used.

Arguments

  • time::AbstractVector: Vector or single value representing sampling dates. Can be strings, integers, or any other type.
  • latitude::Vector: A vector indicating the latitude of each site.
  • longitude::Vector: A vector indicating the longitude of each site.
  • site::AbstractVector: A vector indicating the spatial location of each site. At least 10 sites are required for clustering.
  • species::AbstractVector: A vector indicating the species present at each site.
  • presence::AbstractVector: A vector indicating the presence (1) or absence (0) of species at each site.

Returns

  • Dict{Int, DataFrame}: A dictionary where each key represents a unique time point from the input data, with the corresponding value being a DataFrame for that time step. Each DataFrame contains the following columns:
    • Time
    • Latitude
    • Longitude
    • Site
    • Species
    • Presence
    • Group (indicating the assigned groups).

Details

  • This function performs hierarchical clustering (complete linkage) on the geographical coordinates (latitude and longitude) of sampling sites at each time point separately and processes all time points in a single execution. A minimum of 10 sites is required to proceed with grouping; if fewer are present at a given time step, the grouping is returned as missing. Otherwise, the number of clusters k is initialized as the largest integer no greater than the total number of sites divided by 5 (with a minimum of 2). Sites are then assigned to k groups based on their geographical proximity.
  • This function incorporates checks and adjustments to ensure the following conditions are met:
    • At least 2 groups
    • A minimum of 5 sites per group
    • The difference in the number of taxa and sites between any two groups, relative to the larger group, does not exceed 40% and 30%, respectively
    These conditions are critical for calculating an unbiased DNCI value. If the conditions are not met, the function first attempts to fix the groupings iteratively: if the smallest group contains only one site, that site is merged into the nearest neighboring group; otherwise, the nearest site from any other group is reassigned into the smallest group based on its distance to the smallest group's centroid. If the conditions are still not met after this adjustment, k is decremented by 1 and the process repeats, until either the conditions are satisfied or k falls below 2 (in which case the grouping is returned as missing).
  • Empty sites are allowed.

Example

julia> using MetaCommunityMetrics, Pipe, DataFrames

julia> df = load_sample_data()
53352×12 DataFrame
   Row │ Year   Month  Day    Sampling_date_order  plot   Species  Abundance  Presence  Latitude  Longitude  standardized_temperature  standardized_precipitation 
       │ Int64  Int64  Int64  Int64                Int64  String3  Int64      Int64     Float64   Float64    Float64                 Float64                  
───────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
     1 │  2010      1     16                    1      1  BA               0         0      35.0     -110.0                0.829467              -1.4024
     2 │  2010      1     16                    1      2  BA               0         0      35.0     -109.5               -1.12294               -0.0519895
     3 │  2010      1     16                    1      4  BA               0         0      35.0     -108.5               -0.409808              -0.803663
     4 │  2010      1     16                    1      8  BA               0         0      35.5     -109.5               -1.35913               -0.646369
     5 │  2010      1     16                    1      9  BA               0         0      35.5     -109.0                0.0822                 1.09485
   ⋮   │   ⋮      ⋮      ⋮             ⋮             ⋮       ⋮         ⋮         ⋮         ⋮          ⋮                ⋮                        ⋮
 53348 │  2023      3     21                  117      9  SH               0         0      35.5     -109.0               -0.571565              -0.836345
 53349 │  2023      3     21                  117     10  SH               0         0      35.5     -108.5               -2.33729               -0.398522
 53350 │  2023      3     21                  117     12  SH               1         1      35.5     -107.5                0.547169               1.03257
 53351 │  2023      3     21                  117     16  SH               0         0      36.0     -108.5               -0.815015               0.95971
 53352 │  2023      3     21                  117     23  SH               0         0      36.5     -108.0                0.48949               -1.59416
                                                                                                                                            53342 rows omitted
                                                                                          
julia> grouping_result = DNCI_create_groups(df.Sampling_date_order, df.Latitude, df.Longitude, df.plot, df.Species, df.Presence)
Warning: Group count fell below 2 at time 10, which is not permissible for DNCI analysis. Groups assigned as missing.
Warning: Group count fell below 2 at time 14, which is not permissible for DNCI analysis. Groups assigned as missing.
Warning: Group count fell below 2 at time 76, which is not permissible for DNCI analysis. Groups assigned as missing.
Warning: Group count fell below 2 at time 89, which is not permissible for DNCI analysis. Groups assigned as missing.
Warning: Group count fell below 2 at time 99, which is not permissible for DNCI analysis. Groups assigned as missing.
Dict{Int64, DataFrames.DataFrame} with 117 entries:
  5   => 456×7 DataFrame…
  56  => 456×7 DataFrame…
  35  => 456×7 DataFrame…
  55  => 456×7 DataFrame…
  110 => 456×7 DataFrame…
  114 => 456×7 DataFrame…
  60  => 456×7 DataFrame…
  30  => 456×7 DataFrame…
  32  => 456×7 DataFrame…
  6   => 456×7 DataFrame…
  67  => 456×7 DataFrame…
  45  => 456×7 DataFrame…
  117 => 456×7 DataFrame…
  73  => 456×7 DataFrame…
  ⋮   => ⋮

julia> grouping_result[10]      
456×7 DataFrame
 Row │ Time   Latitude  Longitude  Site   Species  Presence  Group   
     │ Int64  Float64   Float64    Int64  String3  Int64     Missing 
─────┼───────────────────────────────────────────────────────────────
   1 │    10      35.0     -110.0      1  BA              0  missing 
   2 │    10      35.0     -109.5      2  BA              0  missing 
   3 │    10      35.0     -108.5      4  BA              0  missing 
   4 │    10      35.5     -109.5      8  BA              0  missing 
   5 │    10      35.5     -109.0      9  BA              0  missing 
  ⋮  │   ⋮       ⋮          ⋮        ⋮       ⋮        ⋮         ⋮
 452 │    10      35.5     -110.0      7  SH              0  missing 
 453 │    10      35.5     -108.5     10  SH              0  missing 
 454 │    10      36.0     -108.5     16  SH              0  missing 
 455 │    10      36.5     -108.0     23  SH              0  missing 
 456 │    10      36.5     -107.5     24  SH              0  missing 
                                                     446 rows omitted      
 
julia> grouping_result[60]                                                      
456×7 DataFrame
 Row │ Time   Latitude  Longitude  Site   Species  Presence  Group  
     │ Int64  Float64   Float64    Int64  String3  Int64     Int64? 
─────┼──────────────────────────────────────────────────────────────
   1 │    60      35.0     -108.5      4  BA              0       1
   2 │    60      35.0     -108.0      5  BA              1       1
   3 │    60      35.0     -107.5      6  BA              0       1
   4 │    60      35.5     -110.0      7  BA              0       2
   5 │    60      35.5     -108.0     11  BA              0       1
  ⋮  │   ⋮       ⋮          ⋮        ⋮       ⋮        ⋮        ⋮
 452 │    60      35.5     -109.0      9  SH              0       2
 453 │    60      35.5     -108.5     10  SH              0       1
 454 │    60      35.5     -107.5     12  SH              1       1
 455 │    60      36.0     -108.5     16  SH              0       4
 456 │    60      36.5     -108.0     23  SH              0       4
                                                    446 rows omitted
source
MetaCommunityMetrics.DNCI_plot_groups — Function
DNCI_plot_groups(latitude::Vector{Float64}, longitude::Vector{Float64}, group::AbstractVector, output_file="groups.svg") -> String

Visualizes grouping results by generating an SVG image displaying the geographic coordinates and cluster assignments of sampling sites.

Arguments

  • latitude::Vector{Float64}: A vector of latitude coordinates of the sampling sites.
  • longitude::Vector{Float64}: A vector of longitude coordinates of the sampling sites.
  • group::AbstractVector: A vector indicating the group assignments for each data point.
  • output_file::String="clusters.svg": The filename for the output SVG visualization. Default is "groups.svg".

Returns

  • String: The path to the created SVG file.

Details

  • The functions provides visualization for one time point per function call.
  • The function generates a standalone SVG file that can be viewed in any web browser or image viewer.
  • Each group is assigned a unique color, and sampling sites are plotted based on their geographic coordinates.
  • The visualization includes a legend identifying each group.

Example

julia> using MetaCommunityMetrics, Pipe, DataFrames

julia> df = load_sample_data()
53352×12 DataFrame
   Row │ Year   Month  Day    Sampling_date_order  plot   Species  Abundance  Presence  Latitude  Longitude  standardized_temperature  standardized_precipitation 
       │ Int64  Int64  Int64  Int64                Int64  String3  Int64      Int64     Float64   Float64    Float64                 Float64                  
───────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
     1 │  2010      1     16                    1      1  BA               0         0      35.0     -110.0                0.829467              -1.4024
     2 │  2010      1     16                    1      2  BA               0         0      35.0     -109.5               -1.12294               -0.0519895
     3 │  2010      1     16                    1      4  BA               0         0      35.0     -108.5               -0.409808              -0.803663
     4 │  2010      1     16                    1      8  BA               0         0      35.5     -109.5               -1.35913               -0.646369
     5 │  2010      1     16                    1      9  BA               0         0      35.5     -109.0                0.0822                 1.09485
   ⋮   │   ⋮      ⋮      ⋮             ⋮             ⋮       ⋮         ⋮         ⋮         ⋮          ⋮                ⋮                        ⋮
 53348 │  2023      3     21                  117      9  SH               0         0      35.5     -109.0               -0.571565              -0.836345
 53349 │  2023      3     21                  117     10  SH               0         0      35.5     -108.5               -2.33729               -0.398522
 53350 │  2023      3     21                  117     12  SH               1         1      35.5     -107.5                0.547169               1.03257
 53351 │  2023      3     21                  117     16  SH               0         0      36.0     -108.5               -0.815015               0.95971
 53352 │  2023      3     21                  117     23  SH               0         0      36.5     -108.0                0.48949               -1.59416
                                                                                                                                            53342 rows omitted
                                                                                          
julia> grouping_result = DNCI_create_groups(df.Sampling_date_order, df.Latitude, df.Longitude, df.plot, df.Species, df.Presence)
Warning: Group count fell below 2 at time 10, which is not permissible for DNCI analysis. Groups assigned as missing.
Warning: Group count fell below 2 at time 14, which is not permissible for DNCI analysis. Groups assigned as missing.
Warning: Group count fell below 2 at time 76, which is not permissible for DNCI analysis. Groups assigned as missing.
Warning: Group count fell below 2 at time 89, which is not permissible for DNCI analysis. Groups assigned as missing.
Warning: Group count fell below 2 at time 99, which is not permissible for DNCI analysis. Groups assigned as missing.
Dict{Int64, DataFrames.DataFrame} with 117 entries:
  5   => 456×7 DataFrame…
  56  => 456×7 DataFrame…
  35  => 456×7 DataFrame…
  55  => 456×7 DataFrame…
  110 => 456×7 DataFrame…
  114 => 456×7 DataFrame…
  60  => 456×7 DataFrame…
  30  => 456×7 DataFrame…
  32  => 456×7 DataFrame…
  6   => 456×7 DataFrame…
  67  => 456×7 DataFrame…
  45  => 456×7 DataFrame…
  117 => 456×7 DataFrame…
  73  => 456×7 DataFrame…
  ⋮   => ⋮

julia> grouping_result[60]                                                      
456×7 DataFrame
 Row │ Time   Latitude  Longitude  Site   Species  Presence  Group  
     │ Int64  Float64   Float64    Int64  String3  Int64     Int64? 
─────┼──────────────────────────────────────────────────────────────
   1 │    60      35.0     -108.5      4  BA              0       1
   2 │    60      35.0     -108.0      5  BA              1       1
   3 │    60      35.0     -107.5      6  BA              0       1
   4 │    60      35.5     -110.0      7  BA              0       2
   5 │    60      35.5     -108.0     11  BA              0       1
  ⋮  │   ⋮       ⋮          ⋮        ⋮       ⋮        ⋮        ⋮
 452 │    60      35.5     -109.0      9  SH              0       2
 453 │    60      35.5     -108.5     10  SH              0       1
 454 │    60      35.5     -107.5     12  SH              1       1
 455 │    60      36.0     -108.5     16  SH              0       4
 456 │    60      36.5     -108.0     23  SH              0       4
                                                    446 rows omitted

julia> DNCI_plot_groups(grouping_result[60].Latitude, grouping_result[60].Longitude, grouping_result[60].Group; output_file="groups.svg")
source

This plot shows the clustering result for time step 1 based on geographic coordinates: Cluster Plot

MetaCommunityMetrics.DNCI_multigroup — Function
DNCI_multigroup(comm::Matrix, groups::Vector, Nperm::Int=1000; Nperm_count::Bool=true) -> DataFrame

Calculates the dispersal-niche continuum index (DNCI) for a metacommunity, a metric proposed by Vilmi et al. (2021). The DNCI quantifies the balance between dispersal and niche processes within a metacommunity, providing insight into community structure and the relative influence of these two key ecological drivers.

Arguments

  • comm::Matrix: A presence-absence data matrix where rows represent observations (e.g., sites) and columns represent species.
  • groups::Vector: A vector indicating the group membership for each row in the comm matrix. You can use the DNCI_create_groups function to generate the group membership.
  • Nperm::Int=1000: The number of permutations for significance testing. Default is 1000.
  • Nperm_count::Bool=true: A flag indicating whether the number of permutations is printed. Default is true.

Returns The returned DataFrame will have the following columns:

  • Group1: The first group in the pair.
  • Group2: The second group in the pair.
  • DNCI: The dispersal-niche continuum index, calculated as the difference between the mean deviation of the observed species contribution profile from the dispersal null model and the mean deviation from the niche null model, both standardized relative to a quasi-swap null model (which preserves both row and column sums). A DNCI value significantly below zero indicates that dispersal processes are the dominant drivers of community composition, while a value significantly above zero suggests that niche processes play a primary role. If the DNCI is not significantly different from zero, dispersal and niche processes contribute equally to spatial variations in community composition.
  • CI_lower: The lower bound of the 95% confidence interval for the DNCI.
  • CI_upper: The upper bound of the 95% confidence interval for the DNCI.
  • Status: A string indicating how the DNCI is calculated. It is mainly used to flag edge cases as follows:
    • normal indicates that the DNCI is calculated as normal.
    • empty_community indicates no species existed at any sites in a given group pair, DNCI, CI_lower, and CI_upper are returned as NaN.
    • only_one_species_exists indicates that only one species existed in a given group pair, which is not possible to calculate relative species contribution to overall dissimilarity. DNCI, CI_lower, and CI_upper are returned as NaN.
    • quasi_swap_permutation_not_possible indicates that the quasi-swap permutation (a matrix permutation algorithm that preserves row and column sums) is not possible due to extreme matrix constraints that prevent any rearrangement of species across sites. DNCI, CI_lower, and CI_upper are returned as NaN.
    • one_way_to_quasi_swap indicates that only one arrangement is possible under quasi-swap constraints, preventing generation of a null distribution. DNCI, CI_lower, and CI_upper are returned as NaN.
    • inadequate_variation_quasi_swap indicates that quasi-swap permutations generated insufficient variation (coefficient of variation <1%) for reliable statistical inference. DNCI, CI_lower, and CI_upper are returned as NaN.

Details

  • The function calculates the DNCI for each pair of groups in the input data. See the DNCI column description above for interpretation.
  • Different from the original implementation, empty sites and singletons (species that only occupy one site at a given time) are allowed, and a new Status column has been added to flag five edge cases where the DNCI calculation will fail, which are common when simulated data are used.
  • This function is an adaptation of DNCI_multigroup() from the R package DNCImper (https://github.com/Corentin-Gibert-Paleontology/DNCImper), authored by Corentin Gibert, Gilles Escarguel, Annika Vilmi, Jianjun Wang, Aurelien Jamoneau, and Maxime Lopez, and licensed under GPL-3. First adapted from R to Julia in August 2024.
  • Before calculating the DNCI, sites must be assigned to groups, as the DNCI relies on analyzing community composition across site groups. This package provides DNCI_create_groups() to perform the grouping suggested by Vilmi et al. (2021) for all time points, and DNCI_plot_groups() to visualize the groups at a given time point—neither of which is available in the R implementation. However, the use of DNCI_create_groups() is optional; users can perform their own grouping as long as it fulfills the group requirements suggested by Vilmi et al. (2021):
    • At least 2 groups
    • A minimum of 5 sites per group
    • The difference in the number of taxa and sites between any two groups, relative to the larger group, does not exceed 40% and 30%, respectively.

Example

julia> using MetaCommunityMetrics, Pipe, DataFrames, Random

julia> df = load_sample_data()
53352×12 DataFrame
   Row │ Year   Month  Day    Sampling_date_order  plot   Species  Abundance  Presence  Latitude  Longitude  standardized_temperature  standardized_precipitation 
       │ Int64  Int64  Int64  Int64                Int64  String3  Int64      Int64     Float64   Float64    Float64                 Float64                  
───────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
     1 │  2010      1     16                    1      1  BA               0         0      35.0     -110.0                0.829467              -1.4024
     2 │  2010      1     16                    1      2  BA               0         0      35.0     -109.5               -1.12294               -0.0519895
     3 │  2010      1     16                    1      4  BA               0         0      35.0     -108.5               -0.409808              -0.803663
     4 │  2010      1     16                    1      8  BA               0         0      35.5     -109.5               -1.35913               -0.646369
     5 │  2010      1     16                    1      9  BA               0         0      35.5     -109.0                0.0822                 1.09485
   ⋮   │   ⋮      ⋮      ⋮             ⋮             ⋮       ⋮         ⋮         ⋮         ⋮          ⋮                ⋮                        ⋮
 53348 │  2023      3     21                  117      9  SH               0         0      35.5     -109.0               -0.571565              -0.836345
 53349 │  2023      3     21                  117     10  SH               0         0      35.5     -108.5               -2.33729               -0.398522
 53350 │  2023      3     21                  117     12  SH               1         1      35.5     -107.5                0.547169               1.03257
 53351 │  2023      3     21                  117     16  SH               0         0      36.0     -108.5               -0.815015               0.95971
 53352 │  2023      3     21                  117     23  SH               0         0      36.5     -108.0                0.48949               -1.59416
                                                                                                                                            53342 rows omitted
                                                                                          
julia> grouping_result = DNCI_create_groups(df.Sampling_date_order, df.Latitude, df.Longitude, df.plot, df.Species, df.Presence)
Warning: Group count fell below 2 at time 10, which is not permissible for DNCI analysis. Groups assigned as missing.
Warning: Group count fell below 2 at time 14, which is not permissible for DNCI analysis. Groups assigned as missing.
Warning: Group count fell below 2 at time 76, which is not permissible for DNCI analysis. Groups assigned as missing.
Warning: Group count fell below 2 at time 89, which is not permissible for DNCI analysis. Groups assigned as missing.
Warning: Group count fell below 2 at time 99, which is not permissible for DNCI analysis. Groups assigned as missing.
Dict{Int64, DataFrames.DataFrame} with 117 entries:
  5   => 456×7 DataFrame…
  56  => 456×7 DataFrame…
  35  => 456×7 DataFrame…
  55  => 456×7 DataFrame…
  110 => 456×7 DataFrame…
  114 => 456×7 DataFrame…
  60  => 456×7 DataFrame…
  30  => 456×7 DataFrame…
  32  => 456×7 DataFrame…
  6   => 456×7 DataFrame…
  67  => 456×7 DataFrame…
  45  => 456×7 DataFrame…
  117 => 456×7 DataFrame…
  73  => 456×7 DataFrame…
  ⋮   => ⋮

julia> grouping_result[60]
456×7 DataFrame
 Row │ Time   Latitude  Longitude  Site   Species  Presence  Group  
     │ Int64  Float64   Float64    Int64  String3  Int64     Int64? 
─────┼──────────────────────────────────────────────────────────────
   1 │    60      35.0     -108.5      4  BA              0       1
   2 │    60      35.0     -108.0      5  BA              1       1
   3 │    60      35.0     -107.5      6  BA              0       1
   4 │    60      35.5     -110.0      7  BA              0       2
   5 │    60      35.5     -108.0     11  BA              0       1
  ⋮  │   ⋮       ⋮          ⋮        ⋮       ⋮        ⋮        ⋮
 452 │    60      35.5     -109.0      9  SH              0       2
 453 │    60      35.5     -108.5     10  SH              0       1
 454 │    60      35.5     -107.5     12  SH              1       1
 455 │    60      36.0     -108.5     16  SH              0       4
 456 │    60      36.5     -108.0     23  SH              0       4
                                                    446 rows omitted

julia> group_df = @pipe df |>
                filter(row -> row[:Sampling_date_order] == 60, _) |>
                select(_, [:plot, :Species, :Presence]) |>
                innerjoin(_, grouping_result[60], on = [:plot => :Site, :Species], makeunique = true)|>
                select(_, [:plot, :Species, :Presence, :Group]) |>
                unstack(_, :Species, :Presence, fill=0)
24×21 DataFrame
 Row │ plot   Group   BA     DM     DO     DS     NA     OL     OT     PB     PE     PF     PH     PL     PM     PP     RF     RM     RO     SF     SH    
     │ Int64  Int64?  Int64  Int64  Int64  Int64  Int64  Int64  Int64  Int64  Int64  Int64  Int64  Int64  Int64  Int64  Int64  Int64  Int64  Int64  Int64 
─────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
   1 │     4       1      0      1      1      0      0      1      1      0      0      0      0      0      0      0      0      1      0      1      0
   2 │     5       1      1      1      1      0      0      0      1      0      1      0      0      0      0      1      0      1      0      0      0
   3 │     6       1      0      1      1      0      0      0      0      0      1      0      0      0      0      1      0      1      0      0      0
   4 │     7       2      0      1      1      0      0      1      1      0      0      0      0      0      0      1      0      0      0      0      1
   5 │    11       1      0      1      1      0      0      0      1      0      0      0      0      0      0      1      0      0      0      0      0
  ⋮  │   ⋮      ⋮       ⋮      ⋮      ⋮      ⋮      ⋮      ⋮      ⋮      ⋮      ⋮      ⋮      ⋮      ⋮      ⋮      ⋮      ⋮      ⋮      ⋮      ⋮      ⋮
  20 │     9       2      0      1      0      0      0      1      1      0      1      0      0      0      0      0      0      0      0      0      0
  21 │    10       1      0      0      0      0      0      0      0      0      1      0      0      0      0      1      0      0      0      0      0
  22 │    12       1      0      0      0      0      1      0      1      0      1      0      0      0      1      0      0      1      0      0      1
  23 │    16       4      0      0      1      0      0      0      1      0      1      0      0      0      0      0      0      1      0      0      0
  24 │    23       4      0      1      0      0      0      0      0      1      0      0      0      0      1      0      0      0      0      0      0
                                                                                                                                           14 rows omitted
                                                                                                                                          
julia> comm= @pipe group_df |>
                  select(_, Not([:plot,:Group])) |>
                  Matrix(_)
24×19 Matrix{Int64}:
 0  1  1  0  0  1  1  0  0  0  0  0  0  0  0  1  0  1  0
 1  1  1  0  0  0  1  0  1  0  0  0  0  1  0  1  0  0  0
 0  1  1  0  0  0  0  0  1  0  0  0  0  1  0  1  0  0  0
 0  1  1  0  0  1  1  0  0  0  0  0  0  1  0  0  0  0  1
 0  1  1  0  0  0  1  0  0  0  0  0  0  1  0  0  0  0  0
 0  1  1  0  0  0  0  1  1  0  0  0  0  0  0  1  0  0  0
 1  1  1  0  0  0  0  0  0  0  0  0  0  1  0  1  0  0  0
 ⋮              ⋮              ⋮              ⋮        
 0  0  0  0  0  1  0  1  0  0  0  0  0  0  0  0  0  0  0
 0  0  0  0  0  0  1  0  1  0  0  1  1  0  0  0  0  0  0
 0  1  0  0  0  1  1  0  1  0  0  0  0  0  0  0  0  0  0
 0  0  0  0  0  0  0  0  1  0  0  0  0  1  0  0  0  0  0
 0  0  0  0  1  0  1  0  1  0  0  0  1  0  0  1  0  0  1
 0  0  1  0  0  0  1  0  1  0  0  0  0  0  0  1  0  0  0
 0  1  0  0  0  0  0  1  0  0  0  0  1  0  0  0  0  0  0

julia> Random.seed!(1234) 

julia> DNCI_result = DNCI_multigroup(comm, group_df.Group, 1000; Nperm_count = false)
6×6 DataFrame
 Row │ group1  group2  DNCI      CI_lower  CI_upper   status 
     │ Int64   Int64   Float64   Float64   Float64    String 
─────┼───────────────────────────────────────────────────────
   1 │      1       2  -3.41127  -4.49801  -2.32453   normal
   2 │      1       3  -2.44866  -3.47842  -1.41891   normal
   3 │      1       4  -2.3671   -3.59558  -1.13862   normal
   4 │      2       3  -2.65022  -3.79488  -1.50556   normal
   5 │      2       4  -3.0168   -4.23428  -1.79932   normal
   6 │      3       4  -1.83521  -2.81466  -0.855765  normal
source

References

  1. Clarke, K. R. (1993). Non‐parametric multivariate analyses of changes in community structure. Australian journal of ecology, 18(1), 117-143. https://doi.org:https://doi.org/10.1111/j.1442-9993.1993.tb00438.x
  2. Gibert, C., & Escarguel, G. (2019). PER‐SIMPER—A new tool for inferring community assembly processes from taxon occurrences. Global Ecology and Biogeography, 28(3), 374-385. https://doi.org:https://doi.org/10.1111/geb.12859
  3. Vilmi, A., Gibert, C., Escarguel, G., Happonen, K., Heino, J., Jamoneau, A., ... & Wang, J. (2021). Dispersal–niche continuum index: a new quantitative metric for assessing the relative importance of dispersal versus niche processes in community assembly. Ecography, 44(3), 370-379. https://doi.org:https://doi.org/10.1111/ecog.05356