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Compute trait-based interaction strengths and resident abundance matrix

Source: R/compute_interaction_strength.R
compute_interaction_strength.Rd

Computes a trait-based pairwise interaction matrix for all species (residents + invaders) using Gower distance (default) or another dissimilarity metric, with options for:

  • Kernelisation (distance, similarity, gaussian)

  • Scaling/standardisation

  • Sparsification (e.g., k-nearest neighbour graph)

Also returns Nstar, a site × resident abundance matrix from predicted values.

Usage

compute_interaction_strength(
  traits,
  predDF,
  method = "gower",
  kernel = c("distance", "similarity", "gaussian"),
  sigma = NULL,
  standardise = FALSE,
  sparsify_k = NULL
)

Arguments

traits

data.frame. Species traits, first column = species name, remaining = traits.

predDF

data.frame. Long table with columns site_id, species, and predicted abundance.

method

character. Distance metric for daisy (default "gower").

kernel

character. One of "distance", "similarity", "gaussian".

sigma

numeric. Gaussian kernel bandwidth; if NULL, set to median non-zero distance.

standardise

logical. If TRUE, scale distances to 0,1 before kernelisation.

sparsify_k

integer. If non-NULL, retain only k-nearest neighbours per species (symmetrised).

Value

A list with:

  • g_all: processed interaction matrix (distance or similarity).

  • raw_distance: unscaled Gower (or chosen) distance matrix.

  • Nstar: site × resident abundance matrix.

  • sigma: bandwidth used (if Gaussian kernel).

Details

The trait-based interaction strength matrix (g_all) is used in community assembly and invasion models to quantify potential biotic effects between species. We recommend Gower distance for mixed traits, as it is scale-free and bounded in 0,1.

The resident abundance matrix (Nstar) summarises equilibrium or expected abundances for resident species at each site, based on predictions from the fitted abundance model.

Kernelisation options:

  • "distance": return dissimilarities directly (default for transparent interpretation).

  • "similarity": convert to similarity in 0,1 by 1 - scaled_distance.

  • "gaussian": Gaussian kernel \(K = exp(-D^2 / (2\sigma^2))\), with automatic \(\sigma\) as the median non-zero distance if not supplied.