MetaCommunityMetrics.jl
A collection of tools and utilities for analyzing meta-communities in Julia.
Welcome to the documentation for MetaCommunityMetrics. Here you can find guides and reference material on how to use the functions.
An Overview
This package is a comprehensive toolkit designed to characterize the spatiotemporal structure and dynamics of a metacommunity—a network of communities linked by the dispersal of multiple, interacting species, each with unique niche breadths. It includes functions to calculate a range of specific metrics, which have been previously implemented in R and proven valuable for metacommunity analysis.
However, they come with high computational costs, especially for large species community datasets. To address this issue, MetaCommunityMetrics.jl was developed in Julia, a programming language known for its efficiency in handling computationally intensive tasks. This implementation significantly improves the efficiency of calculating these metrics, making it a powerful tool for metacommunity analysis.
These metrics include:
- Beta diversity decompositions in space/time: total diversity, species replacement (turnover), and richness differences for both presence-absence and abundance data
- Dispersal-niche continuum index to evaluate the degree to which communities are influenced by dispersal processes and niche breadth
- Niche overlap indices to determine the extent of niche sharing among species within the metacommunity
- The proportion of habitat patches occupied by each species
- The variability of community composition across different spatial and temporal scales
Getting Started
Installation
To install MetaCommunityMetrics, use the following command:
using Pkg
Pkg.add("MetaCommunityMetrics")
using MetaCommunityMetricsFunction Documentation
Accessing the Sample Data for exploring the functions
This package utilizes rodent data from the Portal Project, a long-term study of a Chihuahuan desert ecosystem (available at https://github.com/weecology/portalr), as sample data for testing and benchmarking functions between Julia and R. The original rodent abundance data were collected monthly across 24 plots, recording a total of 24 species. For the purposes of this package, the data are filtered for the period from 2010 to 2023, transformed into long format, and species that were not recorded during this period are removed. Additionally, plots with no observations at a given time step are excluded. A new column, Sampling_date_order, was created to sequentially number the sampling dates, starting at 1 and continuing through 117, to facilitate analysis. The locations of the plots (Latitude and Longitude) were simulated for testing the DNCI functions. The scripts to download and wrangle the data can be found here:
To assess the sample data, use the following command:
using MetaCommunityMetrics
load_sample_data()Example
julia> using MetaCommunityMetrics
julia> load_sample_data()
48735×10 DataFrame
Row │ Year Month Day Sampling_date_order plot Species Abundance Presence Latitude Longitude
│ Int64 Int64 Int64 Int64 Int64 String3 Int64 Int64 Float64 Float64
───────┼────────────────────────────────────────────────────────────────────────────────────────────────────
1 │ 2010 1 16 1 1 BA 0 0 35.0 -110.0
2 │ 2010 1 16 1 2 BA 0 0 35.0 -109.5
3 │ 2010 1 16 1 8 BA 0 0 35.5 -109.5
4 │ 2010 1 16 1 9 BA 0 0 35.5 -109.0
5 │ 2010 1 16 1 11 BA 0 0 35.5 -108.0
6 │ 2010 1 16 1 14 BA 0 0 36.0 -109.5
7 │ 2010 1 16 1 17 BA 0 0 36.0 -108.0
8 │ 2010 1 16 1 22 BA 0 0 36.5 -108.5
9 │ 2010 1 16 1 6 BA 0 0 35.0 -107.5
10 │ 2010 1 16 1 13 BA 0 0 36.0 -110.0
11 │ 2010 1 16 1 15 BA 0 0 36.0 -109.0
12 │ 2010 1 16 1 20 BA 0 0 36.5 -109.5
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮
48725 │ 2023 3 21 117 15 SH 1 1 36.0 -109.0
48726 │ 2023 3 21 117 19 SH 0 0 36.5 -110.0
48727 │ 2023 3 21 117 20 SH 0 0 36.5 -109.5
48728 │ 2023 3 21 117 21 SH 0 0 36.5 -109.0
48729 │ 2023 3 21 117 22 SH 0 0 36.5 -108.5
48730 │ 2023 3 21 117 1 SH 0 0 35.0 -110.0
48731 │ 2023 3 21 117 9 SH 0 0 35.5 -109.0
48732 │ 2023 3 21 117 10 SH 0 0 35.5 -108.5
48733 │ 2023 3 21 117 12 SH 1 1 35.5 -107.5
48734 │ 2023 3 21 117 16 SH 0 0 36.0 -108.5
48735 │ 2023 3 21 117 23 SH 0 0 36.5 -108.0
48712 rows omitted