Package 'tidyttmoment'

Calculate functional diversity indices from tidy data

Description

Wraps the fundiversity package to calculate functional diversity indices (FRic, FDiv, FEve, FDis, Rao's Q) directly from a tidy long-format data frame. Automatically handles parallelization if a future::plan() is set.

Usage

tidy_calc_diversity(
  df,
  comm_names,
  sp_names,
  trait_names,
  trait_value,
  weight,
  index = c("FRic", "FDiv", "FEve", "FDis", "RaoQ")
)

Arguments

df	A data frame containing the trait and community data.
comm_names	Unquoted column name containing the community names/IDs.
sp_names	Unquoted column name containing the species names/IDs.
trait_names	Unquoted column name containing the trait IDs.
trait_value	Unquoted column name containing the actual trait values.
weight	Unquoted column name containing the variable used to weight the trait values (e.g. abundance).
index	Character vector of indices to compute. Options are "FRic", "FDiv", "FEve", "FDis", "RaoQ". Default is to compute all.

Value

A tibble with community coordinates and the selected functional diversity indices.

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Calculate community weighted mean, variance, skewness, and kurtosis

Description

Calculates the community weighted mean, variance, skewness, and excess kurtosis of a given trait based on the moments described in Wieczynski et al. (2019).

Usage

tidy_calc_moment(df, trait_names, comm_names, trait_value, weight)

Arguments

df	A data frame containing the trait and community data.
trait_names	Unquoted column name containing the trait IDs.
comm_names	Unquoted column name containing the community names/IDs.
trait_value	Unquoted column name containing the actual trait values.
weight	Unquoted column name containing the variable used to weight the trait values (e.g. abundance).

Value

Returns a tibble with the columns comm, trait, cwm (mean), cwv (variance), cws (skewness), and cwk (excess kurtosis).

References

Enquist, B. J., Norberg, J., Bonser, S. P., Violle, C., Webb, C. T., Henderson, A., ... & Savage, V. M. (2015). Scaling from traits to ecosystems. Advances in Ecological Research, 52, 249-318.

Metcalfe, R. J., Ozturk, M., & Pouteau, R. (2020). Using functional traits to model annual plant community dynamics. Ecology.

Šímová, I., Violle, C., Kraft, N. J., Storch, D., Svenning, J. C., Gallagher, R. V., ... & Enquist, B. J. (2018). Spatial patterns and climate relationships of major plant traits in the New World differ between woody and herbaceous species. Global Ecology and Biogeography, 27(8), 895-916.

Wieczynski, D. J., Boyle, B., Buzzard, V., Duran, S. M., Henderson, A. N., Hulshof, C. M., ... & Savage, V. M. (2019). Climate shapes and shifts functional biodiversity in forests worldwide. Proceedings of the National Academy of Sciences, 116(2), 587-592.

Examples

df <- data.frame(trait = c("height", "height", "weight", "weight"),
                 trait_value = c(5, 10, 15, 12),
                 abundancia = c(1, 2, 1, 3),
                 comm = c("A", "A", "B", "B"))
tidy_calc_moment(df,
trait_names = trait,
comm_names = comm,
trait_value = trait_value,
weight = abundancia)

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Calculate functional rarity indices from tidy data

Description

Wraps the funrar package to calculate functional rarity indices (Distinctiveness and Uniqueness) directly from a tidy long-format data frame.

Usage

tidy_calc_rarity(df, comm_names, sp_names, trait_names, trait_value, weight)

Arguments

df	A data frame containing the trait and community data.
comm_names	Unquoted column name containing the community names/IDs.
sp_names	Unquoted column name containing the species names/IDs.
trait_names	Unquoted column name containing the trait IDs.
trait_value	Unquoted column name containing the actual trait values.
weight	Unquoted column name containing the variable used to weight the trait values (e.g. abundance).

Value

A list of tibbles containing distinctiveness and uniqueness metrics.

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Convert tidy long data to community and trait matrices

Description

Helper function to pivot a tidy long-format data frame into a site-by-species abundance matrix and a species-by-trait matrix, which are required for indices like functional diversity and rarity.

Usage

tidy_to_matrices(df, comm_names, sp_names, trait_names, trait_value, weight)

Arguments

df	A data frame containing the trait and community data.
comm_names	Unquoted column name containing the community names/IDs.
sp_names	Unquoted column name containing the species names/IDs.
trait_names	Unquoted column name containing the trait IDs.
trait_value	Unquoted column name containing the actual trait values.
weight	Unquoted column name containing the variable used to weight (e.g., abundance).

Value

A list containing site_species (a site by species matrix) and species_traits (a species by trait matrix).

Examples

df <- data.frame(
  comm = c("A", "A", "B", "B"),
  species = c("sp1", "sp2", "sp1", "sp3"),
  trait = c("height", "height", "height", "height"),
  trait_value = c(5, 10, 5, 15),
  abundance = c(1, 2, 1, 3)
)
tidy_to_matrices(df, comm, species, trait, trait_value, abundance)

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