NPT_continuous: Continuous Niche Classification Based on Periodic Table of...

In MultiTraits: Analyzing and Visualizing Multidimensional Plant Traits

Continuous Niche Classification Based on Periodic Table of Niches

Description

This function implements a continuous niche classification scheme based on the periodic table of niches concept. It performs a hierarchical Principal Component Analysis (PCA) approach where separate PCAs are conducted on different niche dimensions, followed by a second-level PCA to integrate results across dimensions.

Usage

NPT_continuous(data, dimension)

Arguments

data	A data frame containing species and their functional trait measurements. Each row represents a species and columns contain trait values. The data should include all traits specified in the dimension parameter.
dimension	A named list where each element represents a niche dimension (e.g., "grow", "survive", "reproductive") and contains a character vector of column names corresponding to traits associated with that dimension. Each dimension should contain multiple functionally related traits.

Details

The function implements a two-stage hierarchical PCA approach based on the methodology described in Winemiller et al. (2015) for creating continuous niche classification schemes. This approach addresses the challenge that analysis of data sets containing many functionally unrelated measures may fail to detect patterns of covariation that determine species' ecological responses to and effects on their environments.

Stage 1: Dimensional PCA Analysis

Separate Principal Component Analysis is performed on trait data for each niche dimension using the internal pca_first function. This dimensional approach ensures that all niche dimensions have an equal opportunity to influence the composite niche scheme and species ordinations. For each dimension, the function:

• Performs PCA using vegan::rda()

• Calculates variance explained by the first two principal components

• Identifies traits with highest absolute loadings on PC1 and PC2

• Extracts species scores on both principal components

Stage 2: Integration PCA

The species scores from the first two principal components of each dimensional PCA are combined into a new data matrix (with columns named as "pc1.dimension" and "pc2.dimension"). A second PCA is then performed on this matrix to create a two-dimensional continuum integrating patterns (strategies) within each of the niche dimensions. This creates a continuous ordination of species within niche space that can be used for comparative ecological analyses.

Methodological Advantages

This hierarchical PCA method prevents domination by any single type of trait or dimension. The approach allows all niche dimensions to have equal influence on the composite niche scheme, with gradients dominated by those dimensional components having greatest influence on community structure patterns.

Value

A list containing three elements:

PCA_first	A data frame summarizing the first-level PCA results for each dimension, including variance explained by PC1 and PC2 (as percentages), and the traits with highest absolute loadings on each principal component axis
PCA_second	A matrix containing species scores from the second-level PCA that integrates all niche dimensions into a unified ordination space
result	The complete second-level PCA result object from vegan::rda() containing detailed ordination results for further analysis

Note

Important Considerations:

• Missing values (NA) are automatically removed using na.omit()

• A message displays the number of rows removed due to NA values

• Each niche dimension should contain multiple functionally related traits

• Users should ensure traits are appropriately scaled/transformed before analysis

• The function uses scaling = FALSE in PCA, assuming pre-standardized data

• Column names in the data must exactly match trait names in dimensions list

References

1. Winemiller, K. O., Fitzgerald, D. B., Bower, L. M., & Pianka, E. R. (2015). Functional traits, convergent evolution, and periodic tables of niches. Ecology letters, 18(8), 737-751.

2. Yu, R., Huang, J., Xu, Y., Ding, Y., & Zang, R. (2020). Plant functional niches in forests across four climatic zones: Exploring the periodic table of niches based on plant functional traits. Frontiers in Plant Science, 11, 841.

Examples

data(PFF)
PFF[,4:21] <- log(PFF[,4:21])
traits_dimension <- list(
  grow = c("SLA","SRL","Leaf_Nmass","Root_Nmass"),
  survive = c("Height","Leaf_CN","Root_CN"),
  reproductive = c("SeedMass","FltDate","FltDur")
)
result <- NPT_continuous(data = PFF, dimension = traits_dimension)
result

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