MultiTraits (version 0.5.0)

TN_plot: Plot Trait Network Graph

Description

This function visualizes the trait network graph generated by the TN function.

Usage

TN_plot(graph, style = 1, vertex.size = 20, vertex.label.cex = 0.6)

Value

An object of class igraph. This function generates a visualization of the trait network graph. When style = 1, it displays a community structure plot. When style = 2, it displays a circular layout plot where vertex colors represent community membership, edge thickness represents correlation strength, and edge color represents the sign of the correlation (black for positive, red for negative).

Arguments

graph

An igraph object representing the trait network.

style

A numeric value that determines the plotting style (default is 1).

vertex.size

Numeric value for the size of vertices in the plot (default is 20).

vertex.label.cex

Numeric value for the scaling factor of vertex labels (default is 0.6).

Details

The function uses the cluster_edge_betweenness algorithm to identify communities in the graph and assigns community membership to vertices. It offers two plotting styles:

• Style 1: Plots the community structure.

• Style 2: Plots the graph in a circular layout with vertex colors representing communities. The vertex size and label size can be customized using vertex.size and vertex.label.cex parameters respectively.

References

1. He, N., Li, Y., Liu, C., et al. (2020). Plant trait networks: improved resolution of the dimensionality of adaptation. Trends in Ecology & Evolution, 35(10), 908-918. https://doi.org/10.1016/j.tree.2020.06.003

2. Li, Y., Liu, C., Sack, L., Xu, L., Li, M., Zhang, J., & He, N. (2022). Leaf trait network architecture shifts with species‐richness and climate across forests at continental scale. Ecology Letters, 25(6), 1442-1457. https://doi.org/10.1111/ele.14009

Examples

data(PFF)
PFF_traits <- PFF[, c("Height", "Leaf_area","LDMC","SLA","SRL","SeedMass","FltDate",
                      "FltDur","Leaf_Cmass","Leaf_Nmass","Leaf_CN","Leaf_Pmass",
                      "Leaf_NP","Leaf_CP","Root_Cmass","Root_Nmass","Root_CN")]
PFF_traits <- na.omit(PFF_traits)
head(PFF_traits)
Tn_result <- TN(traits_matrix = PFF_traits, rThres = 0.2, pThres = 0.05)
TN_plot(Tn_result, style = 1, vertex.size = 20, vertex.label.cex = 0.6)
TN_plot(Tn_result, style = 2, vertex.size = 20, vertex.label.cex = 0.6)