Occupied Patches Proportion Function
The Occupied Patches Proportion function in MetaCommunityMetrics provides a simple yet powerful metric for understanding the distribution and prevalence of species across different habitat patches within a metacommunity. By calculating the averaged, minmum and maximum proportion of patches occupied across species, this function helps ecologists assess the spatial extent of species distributions and identify potential patterns of rarity or commonness across the landscape.
This function draws on the concepts discussed by Ehrlén & Eriksson (2000) in their study on dispersal limitation and patchy occupancy in forest herbs. According to their findings, low occupancy may indicate dispersal limitation or strong competition, while high occupancy could suggest mass effects due to high dispersal rates or the ability to thrive in various conditions.
Functionality Overview
The Occupied Patches Proportion metric quantifies the averaged proportion of habitat patches in which a species is present. This information is crucial for understanding species distributions, particularly in fragmented landscapes or patchy environments where species may not occupy all available habitat. This metric can be used to identify widespread species, which occupy a large number of patches, as well as rare species, which are restricted to only a few patches.
After calculating the proportion of patches occupied for each species, the mean, minimum, and maximum proportion of patches occupied can be derived. These values are valuable indicators of ecological processes:
- Low proportion of patches occupied: May indicate dispersal limitation or strong competition among species. Such patterns could suggest that certain species struggle to colonize or persist in many patches.
- High proportion of patches occupied: May point to mass effects, where species are abundant in many patches, possibly due to high dispersal rates or the ability to thrive across a range of conditions.
The Function
MetaCommunityMetrics.prop_patches — Functionprop_patches(presence::AbstractVector, species::Union{AbstractVector, String}, patch::Union{AbstractVector, String}) -> DataFrameCalculate the proportion of patches occupied by each species and summarize the results.
This function takes three vectors: presence, species, and patch, and performs the following steps:
Arguments
presence::AbstractVector: A vector indicating the presence (1) or absence (0) of a species in a patch.species::Union{AbstractVector, String}: A vector of species names.patch::Union{AbstractVector, String}: A vector of patch identifiers.
Returns
DataFrame: A DataFrame containing the mean, minimum, and maximum proportion of patches occupied across all species.
Example
julia> using MetaCommunityMetrics
julia> df = 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
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮
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
48725 rows omitted
julia> prop_patches(df.Presence, df.Species, df.plot)
1×3 DataFrame
Row │ mean_prop_patches min_prop_patches max_prop_patches
│ Float64 Float64 Float64
─────┼───────────────────────────────────────────────────────
1 │ 0.734649 0.0833333 1.0References
- Ehrlén, J., & Eriksson, O. (2000). Dispersal Limitation and Patchy Occupancy in Forest Herbs. Ecology, 81(6), 1667-1674. https://doi.org:https://doi.org/10.1890/0012-9658(2000)081[1667:DLAPOI]2.0.CO;2