PNC

The PNC package is an open-source, all-in-one solution for empirically testing the key assumption of phylogenetic niche conservatism (PNC) in ecological community studies. Designed for accessibility and scientific rigor, PNC streamlines the entire workflow---from integrating comprehensive, global trait databases (covering plants, birds, mammals, reptiles, amphibians, and fish) to assessing data quality, performing dimensionality reduction, and batch processing analyses.

By bridging a critical methodological gap, the PNC package enables robust, phylogenetically-informed ecology, biogeography, and conservation research---supporting more accurate predictions of species distributions, better assessments of global change risks, and stronger foundations for biodiversity conservation.

Get started with PNC to bring standardized, automated phylogenetic niche conservatism tests into your workflow!

Installation

From CRAN (recommended)

# install.packages("PNC")

From GitHub (development version)

# if (!requireNamespace("devtools", quietly = TRUE)) {install.packages("devtools")}
# devtools::install_github("biodiversity-monitoring/PNC")

Main functions of PNC and their descriptions

PNC workflow

Study case 1

library(PNC)

# Load dataset
data("BCI")
str(BCI)
data("TRY")
head(TRY)

## species level

# Extract species names
sp <- colnames(BCI$com)
sp

# Extract trait data for the specified species from the TRY database
subtraits <- extract_traits(sp, TRY, rank = "species", traits = c("LA", "LMA", "LeafN", "PlantHeight", "SeedMass", "SSD"))
head(subtraits)

# Calculate and display the coverage of the trait data
coverage(subtraits)

# Perform phylogenetic niche conservatism analysis using the pnc() function
pnc(subtraits, BCI$phy_species, methods = "lambda", pca_axes = c("PC1", "PC2"))

# Assess the robustness of the phylogenetic signal estimates
set.seed(123)
pnc_robustness(subtraits, BCI$phy_species, methods = "lambda", n_simulations = 100)

## genus level

# Extract genus names
sp2 <- unique(BCI$splist$genus)

# Extract trait data for the specified genera from the TRY database
subtraits2 <- extract_traits(sp2, TRY, rank = "genus", traits = c("LA", "LMA", "LeafN", "PlantHeight", "SeedMass", "SSD"))
head(subtraits2)

# Calculate and display the coverage of the trait data
coverage(subtraits2)

# Perform phylogenetic niche conservatism analysis
pnc(subtraits2, BCI$phy_genus, methods = "lambda", pca_axes = c("PC1", "PC2"))

# Assess the robustness of the phylogenetic signal estimates at the genus level
set.seed(123)
pnc_robustness(subtraits2, BCI$phy_genus, methods = "lambda", n_simulations = 100)

Study case 2

# Load dataset
data("HimalayanBirds")
str(HimalayanBirds)
data("AVONET")
head(AVONET)

# Extract species names
sp <- colnames(HimalayanBirds$com)
sp

# Extract trait data for the specified species from the AVONET database
subtraits <- extract_traits(sp, AVONET, rank = "species")
head(subtraits)

# Calculate and display the coverage of the trait data
coverage(subtraits)

# Perform phylogenetic niche conservatism analysis
pnc(subtraits, HimalayanBirds$phy_species, methods = "lambda", pca_axes = c("PC1", "PC2"))
set.seed(123)

# Assess the robustness of the phylogenetic signal estimates 
pnc_robustness(subtraits,
               HimalayanBirds$phy_species,
               methods = "lambda",
               pca_axes = c("PC1", "PC2"),
               n_simulations = 100)

# Perform phylogenetic niche conservatism analysis across multiple communities
compnc(com = HimalayanBirds$com, subtraits, HimalayanBirds$phy_species, methods = "lambda", pca_axes = NULL)

# Assess the robustness of phylogenetic signal estimates across multiple communities
set.seed(123)
robust_results <- compnc_robustness(HimalayanBirds$com,
                                    subtraits,
                                    HimalayanBirds$phy_species,
                                    methods = "lambda",
                                    pca_axes = c("PC1", "PC2"),
                                    n_simulations = 100)
robust_results

Reference

Condit, R., Perez, R., Aguilar, S., Lao, S., Foster, R., Hubbell, S.P. (2019). Complete data from the Barro Colorado 50-ha plot: 423617 trees, 35 years, 2019 version. https://doi.org/10.15146/5xcp-0d46

Ding, Z., Hu, H., Cadotte, M.W., Liang, J., Hu, Y., & Si, X. (2021). Elevational patterns of bird functional and phylogenetic structure in the central Himalaya. Ecography, 44(9), 1403-1417. https://doi.org/10.1111/ecog.05660

Kattge, J., Bönisch, G., Díaz, S., Lavorel, S., Prentice, I. C., Leadley, P., Tautenhahn, S., Werner, G. D. A., Aakala, T., Abedi, M., Acosta, A. T. R., Adamidis, G. C., Adamson, K., Aiba, M., Albert, C. H., Alcántara, J. M., Alcázar C, C., Aleixo, I., Ali, H., Amiaud, B., et al. (2020). TRY plant trait database -- enhanced coverage and open access. Global Change Biology, 26(1), 43-60. https://doi.org/10.1111/gcb.14904

Oliveira, B. F., São-Pedro, V. A., Santos-Barrera, G., Penone, C., & Costa, G. C. (2017). AmphiBIO, a global database for amphibian ecological traits. Scientific data, 4(1), 1-7. https://doi.org/10.1038/sdata.2017.123

Oskyrko, O., Mi, C., Meiri, S., & Du, W. (2024). ReptTraits: a comprehensive dataset of ecological traits in reptiles. Scientific Data, 11(1), 243. https://doi.org/10.1038/s41597-024-03079-5

Soria, C. D., Pacifici, M., Di Marco, M., Stephen, S. M., & Rondinini, C. (2021). COMBINE: a coalesced mammal database of intrinsic and extrinsic traits. Ecology, 102(6), e03344. https://doi.org/10.1002/ecy.3344

Thorson, J. T., Maureaud, A. A., Frelat, R., Mérigot, B., Bigman, J. S., Friedman, S. T., Palomares, M. L. D., Pinsky, M. L., Price, S. A., & Wainwright, P. (2023). Identifying direct and indirect associations among traits by merging phylogenetic comparative methods and structural equation models. Methods in Ecology and Evolution, 14(5), 1243-1255. https://doi.org/10.1111/2041-210X.14076

Tobias, J. A., Sheard, C., Pigot, A. L., Devenish, A. J. M., Yang, J., Sayol, F., Neate-Clegg, M. H. C., Alioravainen, N., Weeks, T. L., Barber, R. A., Walkden, P. A., MacGregor, H. E. A., Jones, S. E. I., Vincent, C., Phillips, A. G., Marples, N. M., Montaño-Centellas, F. A., Leandro-Silva, V., Claramunt, S., Darski, B., et al. (2022). AVONET: morphological, ecological and geographical data for all birds. Ecology Letters, 25(3), 581-597. https://doi.org/10.1111/ele.13898