Variability Metrics Functions

The variability metrics functions in MetaCommunityMetrics are designed to capture changes in dispersal and density-dependent biotic interactions by investigating temporal variability and synchrony across spatial scales and organizational levels within a metacommunity. These functions are based on the work of Wang et al. (2019), which provides a framework for quantifying variability at different scales and contexts within a community.

An Overview

In MetaCommunityMetrics, the CV_meta function is directly adapted from the R function var.partition in Wang et al. (2019).

The function provides four metrics that are designed to quantify variability at different scales and contexts within a metacommunity:

  • Local-scale average species variability (CV_s_l)
  • Regional-scale average species variability (CV_s_r)
  • Local-scale average community variability (CV_c_l)
  • Regional-scale community variability (CV_c_r)

The Function

MetaCommunityMetrics.CV_meta — Function
CV_meta(abundance::AbstractVector, time::AbstractVector, site::AbstractVector, species::AbstractVector) -> DataFrame

Calculates coefficients of variation (CV) for species and community biomass at both local and regional scales within a metacommunity.

Arguments

  • abundance::AbstractVector: Vector representing the abundance of species.
  • time::AbstractVector: Vector representing sampling dates.
  • site::AbstractVector: Vector representing site names or IDs.
  • species::AbstractVector: Vector representing species names or IDs.

Returns

  • DataFrame: A DataFrame containing the following columns:
    • CV_s_l: Local-scale average species variability.
    • CV_s_r: Regional-scale average species variability.
    • CV_c_l: Local-scale average community variability.
    • CV_c_r: Regional-scale community variability.

Example

julia> using MetaCommunityMetrics, Pipe

julia> df = @pipe load_sample_data()
53352×12 DataFrame
   Row │ Year   Month  Day    Sampling_date_order  plot   Species  Abundance  Presence  Latitude  Longitude  normalized_temperature  normalized_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> CV_summary_df = CV_meta(df.Abundance, df.Sampling_date_order, df.plot, df.Species)
1×4 DataFrame
 Row │ CV_s_l   CV_s_r    CV_c_l    CV_c_r   
     │ Float64  Float64   Float64   Float64  
─────┼───────────────────────────────────────
   1 │ 1.48859  0.944937  0.718266  0.580183
source

References

  • Wang, S., Lamy, T., Hallett, L. M. & Loreau, M. Stability and synchrony across ecological hierarchies in heterogeneous metacommunities: linking theory to data. Ecography 42, 1200-1211 (2019). https://doi.org:https://doi.org/10.1111/ecog.04290