PTMN_metrics: Calculate Plant Trait Multilayer Network (PTMN) Metrics

In MultiTraits: Analyzing and Visualizing Multidimensional Plant Traits

Calculate Plant Trait Multilayer Network (PTMN) Metrics

Description

This function computes comprehensive network metrics for plant trait multilayer networks, including both node-level and global network properties. It quantifies the structural characteristics and connectivity patterns that reveal coordinated adaptation mechanisms across multiple plant organs and functional systems.

Usage

PTMN_metrics(graph)

Arguments

graph

A multilayer network object created by the PTMN function, representing trait relationships across different plant organs or functional systems.

Details

The PTMN framework addresses limitations of traditional single-layer plant trait networks by capturing trait relationships across multiple plant organs and functional systems. These metrics quantify network topology using specially designed parameters that facilitate identification of hub traits and key cross-layer functional modules, essential for understanding coordinated adaptation of plant traits.

Node-level metrics help identify traits that serve as integration points across different plant systems, while global metrics characterize the overall network structure and connectivity patterns. Higher interlayer connectivity may indicate greater functional integration and potentially enhanced adaptive capacity.

Value

A list containing two data frames:

node

Node-level metrics including:

• node: Name of the trait node

• layer: Layer (organ/functional system) containing the node

• interlayer_degree: Number of connections a node has across layers

• interlayer_closeness: Measure of how close a node is to all other nodes across layers

• interlayer_clustering_coefficient: Local clustering coefficient considering interlayer connections

global

Global network metrics including:

• Interlayer_edge_density: Proportion of possible interlayer connections that are realized

• Average_interlayer_path_length: Average shortest path length between nodes across layers

• Average_interlayer_clustering_coefficient: Network-wide clustering coefficient

• Module_interlayer_association: Degree of modular organization across layers

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")
PTMN_metrics(graph)

## End(Not run)

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