MultiTraits (version 0.5.0)

TN: Generate Plant Trait Network

Description

This function creates a network graph from a plant trait correlation matrix, applying thresholds for correlation strength and significance.

Usage

TN(traits_matrix, rThres = 0.2, pThres = 0.05, method = "pearson")

Value

Returns an igraph object representing the trait network.

Arguments

traits_matrix

A numeric matrix where each column represents a plant trait and each row represents a sample.

rThres

Numeric, threshold for correlation coefficient, default is 0.2. Correlations with absolute values below this threshold are set to zero.

pThres

Numeric, threshold for p-value, default is 0.05. Only correlations with p-values below this threshold are included in the network.

method

Character, specifies the correlation method to use: "pearson" (default) or "spearman".

Details

The function performs the following steps:

1. Calculates Pearson correlation coefficients and p-values for the trait matrix.

2. Applies correlation coefficient and p-value thresholds to filter relationships.

3. Constructs an unweighted undirected graph from the filtered correlation matrix.

4. Removes self-loops and isolated nodes from the graph.

5. Adds correlation coefficients as edge attributes.

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, method = "pearson")
Tn_result