average_interlayer_clustering_coefficient: Average Interlayer Clustering Coefficient for Plant Trait...

In MultiTraits: Analyzing and Visualizing Multidimensional Plant Traits

Average Interlayer Clustering Coefficient for Plant Trait Multilayer Networks

Description

Computes the Average Interlayer Clustering Coefficient (AICC) for a plant trait multilayer network (PTMN). The AICC is the mean interlayer clustering coefficient across all nodes in the network, capturing the overall prevalence of tightly knit cross-layer modules. High AICC values indicate that integrated, modular cross-layer structures are prevalent, reflecting strong phenotypic integration and modularity, which can allow flexible responses to multidimensional environmental pressures.

Usage

average_interlayer_clustering_coefficient(data)

Arguments

data	A PTMN object created by the PTMN() function, containing the multilayer network structure with nodes representing plant traits and edges representing trait relationships within and across functional layers.

Details

Calculate Average Interlayer Clustering Coefficient

The Average Interlayer Clustering Coefficient is calculated as:

AICC = \frac{1}{n} \sum_{i=1}^{n} ICC_i

where n is the total number of nodes and ICC_i denotes the interlayer clustering coefficient of node v_i. This parameter is particularly useful for understanding multidimensional plant adaptations and coordinated responses across different plant organs and functional systems.

In the context of plant ecology, higher AICC values suggest that plants have evolved integrated trait networks that facilitate coordinated responses to environmental challenges. For example, non-native woody species have been shown to exhibit higher AICC values compared to native species, potentially contributing to their invasion success.

Value

A numeric value representing the average interlayer clustering coefficient. Values range from 0 to 1, where higher values indicate stronger cross-layer integration and more tightly connected functional 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")
average_interlayer_clustering_coefficient(graph)

## End(Not run)

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