# Measures of overlap

EcologicalNetworks.overlapFunction
overlap(N::T; dims=dims::Union{Nothing,Integer}=nothing) where {T <: BipartiteNetwork}

Returns the overlap graph for a bipartite network. The dims keyword argument can be 1 (default; overlap between top-level species) or 2 (overlap between bottom-level species). See the documentation for ?overlap for the output format.

source
overlap(N::T; dims::Union{Nothing,Integer}=nothing) where {T <: UnipartiteNetwork}

Returns the overlap graph for a unipartite network. The dims keyword argument can be 1 (overlap based on preys) or 2 (overlap based on predators), or nothing (default; overlap based on both predators and preys). The overlap is returned as a vector of named tuples, with elements pair (a tuple of species names), and overlap (the number of shared interactors). The ordering within the pair of species is unimportant, since overlap graphs are symetrical.

source
EcologicalNetworks.AJSFunction
AJS(N::T; dims::Union{Nothing,Integer}=nothing) where {T <: UnipartiteNetwork}

Additive Jaccard Similarity for pairs of species in the network. AJS varies between 0 (no common species) to 1 (same profiles). This function can be used to measure AJS based on only successors or predecessors, using the dims argument.

Note that this function uses all direct preys and predators to measure the similarity (and so does not go beyond the immediate neighbors).

References

Gao, P., Kupfer, J.A., 2015. Uncovering food web structure using a novel trophic similarity measure. Ecological Informatics 30, 110–118. https://doi.org/10.1016/j.ecoinf.2015.09.013

source
EcologicalNetworks.EAJSFunction
EAJS(N::T; dims::Union{Nothing,Integer}=nothing) where {T <: UnipartiteNetwork}

Extended Additive Jaccard Similarity for pairs of species in the network. AJS varies between 0 (no common species) to 1 (same profiles). This function can be used to measure AJS based on only successors or predecessors, using the dims argument.

Note that this function counts all interactions up to a distance of 50 to define the neighbourhood of a species. This should be more than sufficient for most ecological networks.

References

Gao, P., Kupfer, J.A., 2015. Uncovering food web structure using a novel trophic similarity measure. Ecological Informatics 30, 110–118. https://doi.org/10.1016/j.ecoinf.2015.09.013

source