crosslayer_module_assoc: Calculate Module Interlayer Association (MIA) for Plant Trait...

In MultiTraits: Analyzing and Visualizing Multidimensional Plant Traits

Calculate Module Interlayer Association (MIA) for Plant Trait Multilayer Networks

Description

This function computes the Module Interlayer Association (MIA), a key topological parameter for Plant Trait Multilayer Networks (PTMNs). MIA quantifies the proportion of interlayer edges within each network module, reflecting the degree of cross-layer integration within modules. High MIA values suggest the formation of tightly connected cross-layer modules that facilitate coordinated adaptation across different plant organs and functional systems.

Usage

crosslayer_module_assoc(data)

Arguments

data	A data frame containing the PTMN edge information with columns: node.from, node.to, layer.from, and layer.to. This is typically the output from the PTMN function.

Details

The function uses the Girvan-Newman algorithm to detect network modules, then calculates the proportion of interlayer edges within each module that spans multiple layers. The MIA is computed as the average of interlayer association values across all cross-layer modules. This metric is particularly useful for understanding how plant traits coordinate across different organs (roots, stems, leaves) and functional systems in response to environmental pressures.

In the context of plant invasion ecology, higher MIA values have been associated with greater invasion success, as they may indicate more efficient coordination among functional systems.

Value

A numeric value representing the average Module Interlayer Association (MIA). Returns NA if no interlayer modules are found. Values range from 0 to 1, where higher values indicate stronger cross-layer integration within modules.

See Also

PTMN for constructing plant trait multilayer networks

Examples

## Not run: 
data(forest_invader_tree)
data(forest_invader_traits)
traits <- forest_invader_traits[, 6:73]
layers <- list(
  shoot_dynamics = c("LeafDuration", "LeafFall50", "LeafRate_max",
                     "Chl_shade50", "LAgain", "FallDuration",
                     "LeafOut", "Chl_sun50", "EmergeDuration",
                     "LeafTurnover"),
  leaf_structure = c("PA_leaf", "Mass_leaf", "Lifespan_leaf",
                     "Thick_leaf", "SLA", "Lobe", "LDMC",
                     "Stomate_size", "Stomate_index"),
  leaf_metabolism = c("J_max", "Vc_max", "Asat_area", "CC_mass",
                      "LSP", "AQY", "CC_area", "Rd_area",
                      "Asat_mass", "WUE", "Rd_mass", "PNUE"),
  leaf_chemistry = c("N_area", "Chl_area", "DNA", "Phenolics",
                     "Cellulose", "N_mass", "N_litter", "Chl_ab",
                     "Chl_mass", "N_res", "C_litter", "C_area",
                     "C_mass", "Ash", "Lignin", "Solubles",
                     "Decomp_leaf", "Hemi"),
  root = c("NPP_root", "SS_root", "SRL", "RTD", "RDMC",
           "NSC_root", "Decomp_root", "Starch_root",
           "C_root", "N_root", "Lignin_root"),
  stem = c("Latewood_diam", "Metaxylem_diam", "Earlywood_diam",
           "NSC_stem", "Vessel_freq", "SS_stem", "Cond_stem",
           "Starch_stem")
)
graph <- PTMN(traits, layers_list = layers, method = "pearson")
crosslayer_module_assoc(graph)

## End(Not run)

MultiTraits documentation built on March 22, 2026, 9:06 a.m.