Title:	Taxonomic Diversity Indices Using Deng Entropy
Version:	0.1.0
Description:	Calculates taxonomic diversity indices for ecological community data using Deng entropy framework and classical approaches (Shannon, Simpson, Clarke & Warwick). Provides functions for computing taxonomic distinctness, average taxonomic distinctness (AvTD/Delta+), variation in taxonomic distinctness (VarTD/Lambda+), and Deng entropy-based measures that incorporate taxonomic hierarchy information. Includes tools for constructing taxonomic trees and computing pairwise taxonomic distances.
Imports:	stats, rlang
License:	MIT + file LICENSE
Encoding:	UTF-8
Language:	en-US
LazyData:	true
RoxygenNote:	7.3.3
Depends:	R (≥ 4.1.0)
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown, ggplot2
Config/testthat/edition:	3
VignetteBuilder:	knitr
URL:	https://github.com/mgorgoz/taxonomic-diversity-r, https://mgorgoz.github.io/taxonomic-diversity-r/
BugReports:	https://github.com/mgorgoz/taxonomic-diversity-r/issues
NeedsCompilation:	no
Packaged:	2026-03-28 21:33:27 UTC; muratgorgoz
Author:	Muhammet Murat Gorgoz [aut, cre], Kursad Ozkan [aut], Mehmet Guvenc Negiz [aut]
Maintainer:	Muhammet Murat Gorgoz <muratgorgoz350@gmail.com>
Repository:	CRAN
Date/Publication:	2026-04-01 08:50:02 UTC

taxdiv: Taxonomic Diversity Indices Using Deng Entropy

Description

Calculates taxonomic diversity indices for ecological community data using Deng entropy framework and classical approaches (Shannon, Simpson, Clarke & Warwick). Provides functions for computing taxonomic distinctness, average taxonomic distinctness (AvTD/Delta+), variation in taxonomic distinctness (VarTD/Lambda+), and Deng entropy-based measures that incorporate taxonomic hierarchy information. Includes tools for constructing taxonomic trees and computing pairwise taxonomic distances.

Author(s)

Maintainer: Muhammet Murat Gorgoz muratgorgoz350@gmail.com (ORCID)

Authors:

• Kursad Ozkan (ORCID)

• Mehmet Guvenc Negiz (ORCID)

See Also

Useful links:

• https://github.com/mgorgoz/taxonomic-diversity-r

• https://mgorgoz.github.io/taxonomic-diversity-r/

• Report bugs at https://github.com/mgorgoz/taxonomic-diversity-r/issues

Anatolian Forest Trees: Multi-Site Species Data

Description

A data frame containing 20 tree species from Anatolian forests, distributed across three sample plots with varying community compositions. Species abundances follow the Westhoff & van der Maarel (1973) scale (1–9). Taxonomic classification includes seven ranks from species to kingdom.

Usage

anatolian_trees

Format

A data frame with 33 rows and 9 columns:

Site

Sample plot name (character)

Species

Binomial species name with underscore separator (character)

Genus

Genus (character)

Family

Family (character)

Order

Order (character)

Class

Class (character)

Phylum

Phylum / Division (character)

Kingdom

Kingdom (character)

Abundance

Westhoff abundance value, integer 1–9 (numeric)

Details

The three sites represent different forest types:

Karisik_Orman

Mixed forest – both conifers and broadleaves (12 species)

Yaprakli_Orman

Broadleaf-dominated forest (13 species)

Konifer_Orman

Conifer-dominated forest (8 species)

This dataset can be used directly with batch_analysis for multi-site analysis:

batch_analysis(anatolian_trees)

To extract a single community for use with ozkan_pto or compare_indices:

site1 <- anatolian_trees[anatolian_trees$Site == "Karisik_Orman", ]
community <- setNames(site1$Abundance, site1$Species)
tax_tree  <- site1[, c("Species", "Genus", "Family", "Order",
                        "Class", "Phylum", "Kingdom")]
ozkan_pto(community, tax_tree)

References

Westhoff, V. & van der Maarel, E. (1973). The Braun-Blanquet approach. In: R.H. Whittaker (ed.), Ordination and classification of communities. Handbook of Vegetation Science 5, 617–726.

See Also

batch_analysis for multi-site analysis, gazi_comm and gazi_gytk for a single-community example.

Examples

data(anatolian_trees)
head(anatolian_trees)

# Multi-site analysis
batch_analysis(anatolian_trees)

# Single site extraction
site1 <- anatolian_trees[anatolian_trees$Site == "Karisik_Orman", ]
comm  <- setNames(site1$Abundance, site1$Species)
tax   <- site1[, c("Species", "Genus", "Family", "Order",
                    "Class", "Phylum", "Kingdom")]
ozkan_pto(comm, tax)

Average Taxonomic Distinctness (Delta+)

Description

Calculates the average taxonomic distinctness (AvTD, Delta+) based on Clarke & Warwick (1998). This is a presence/absence-based measure of the average taxonomic distance between all pairs of species.

Usage

avtd(species, tax_tree, weights = NULL)

Arguments

Value

A numeric value representing the average taxonomic distinctness (Delta+).

References

Clarke, K.R. & Warwick, R.M. (1998). A taxonomic distinctness index and its statistical properties. Journal of Applied Ecology, 35, 523-531.

Examples

tax <- data.frame(
  Species = c("Quercus_robur", "Pinus_nigra", "Fagus_orientalis",
              "Abies_nordmanniana"),
  Genus = c("Quercus", "Pinus", "Fagus", "Abies"),
  Family = c("Fagaceae", "Pinaceae", "Fagaceae", "Pinaceae"),
  Order = c("Fagales", "Pinales", "Fagales", "Pinales"),
  stringsAsFactors = FALSE
)

spp <- c("Quercus_robur", "Pinus_nigra", "Fagus_orientalis")
avtd(spp, tax)

Batch Analysis from a Single Data Frame

Description

Computes all diversity indices for one or more sample sites from a single data frame (e.g., imported from Excel). The function automatically detects the site column, taxonomic columns, and abundance column, splits the data by site, and returns a summary data frame with species count and 14 diversity indices per site.

Usage

batch_analysis(
  data,
  site_column = NULL,
  tax_columns = NULL,
  abundance_column = "Abundance",
  correction = c("none", "miller_madow", "grassberger", "chao_shen"),
  parallel = FALSE,
  n_cores = NULL
)

Arguments

Details

When no site column is present (or all values are identical), the entire data set is treated as a single community.

The function calculates the following indices per site:

• Shannon: Shannon-Wiener entropy (shannon)

• Simpson: Gini-Simpson index (simpson)

• Delta: Clarke & Warwick taxonomic diversity (delta)

• Delta_star: Clarke & Warwick taxonomic distinctness (delta_star)

• AvTD: Average taxonomic distinctness (avtd)

• VarTD: Variation in taxonomic distinctness (vartd)

• uTO: Unweighted taxonomic diversity (Ozkan pTO, all levels)

• TO: Weighted taxonomic diversity (Ozkan pTO, all levels)

• uTO_plus: Unweighted taxonomic distance (Ozkan pTO, all levels)

• TO_plus: Weighted taxonomic distance (Ozkan pTO, all levels)

• uTO_max: Unweighted taxonomic diversity (informative levels only)

• TO_max: Weighted taxonomic diversity (informative levels only)

• uTO_plus_max: Unweighted taxonomic distance (informative levels only)

• TO_plus_max: Weighted taxonomic distance (informative levels only)

Value

A data frame with one row per site and columns: Site, N_Species, Shannon, Simpson, Delta, Delta_star, AvTD, VarTD, uTO, TO, uTO_plus, TO_plus, uTO_max, TO_max, uTO_plus_max, TO_plus_max.

See Also

compare_indices for analysis with pre-built community vectors, build_tax_tree for building taxonomic trees manually.

Examples

# Single-site data (no Site column)
df <- data.frame(
  Species   = c("sp1", "sp2", "sp3", "sp4"),
  Genus     = c("G1", "G1", "G2", "G2"),
  Family    = c("F1", "F1", "F1", "F2"),
  Order     = c("O1", "O1", "O1", "O1"),
  Abundance = c(10, 20, 15, 5),
  stringsAsFactors = FALSE
)
batch_analysis(df)

# Multi-site data (with Site column)
df2 <- data.frame(
  Site      = c("A", "A", "A", "B", "B", "B"),
  Species   = c("sp1", "sp2", "sp3", "sp1", "sp3", "sp4"),
  Genus     = c("G1", "G1", "G2", "G1", "G2", "G2"),
  Family    = c("F1", "F1", "F1", "F1", "F1", "F2"),
  Order     = c("O1", "O1", "O1", "O1", "O1", "O1"),
  Abundance = c(10, 20, 15, 5, 25, 10),
  stringsAsFactors = FALSE
)
batch_analysis(df2)

Build a Taxonomic Tree from Species Data

Description

Creates a taxonomic hierarchy data frame from species classification information. This is a convenience function for constructing the tax_tree input required by other functions in the package.

Usage

build_tax_tree(species, ...)

Arguments

Value

A data frame with species as the first column and taxonomic ranks as subsequent columns.

Examples

tree <- build_tax_tree(
  species = c("Quercus_robur", "Pinus_nigra", "Fagus_orientalis"),
  Genus   = c("Quercus", "Pinus", "Fagus"),
  Family  = c("Fagaceae", "Pinaceae", "Fagaceae"),
  Order   = c("Fagales", "Pinales", "Fagales")
)

Compare All Diversity Indices Side by Side

Description

Computes all available diversity indices for one or more communities and returns them in a single data frame. Optionally produces a grouped bar plot for visual comparison.

Usage

compare_indices(
  communities,
  tax_tree,
  correction = c("none", "miller_madow", "grassberger", "chao_shen"),
  plot = FALSE
)

Arguments

Details

The function calculates the following indices:

• Shannon: Shannon-Wiener entropy (shannon)

• Simpson: Gini-Simpson index (simpson)

• Delta: Clarke & Warwick taxonomic diversity (delta)

• Delta_star: Clarke & Warwick taxonomic distinctness (delta_star)

• AvTD: Average taxonomic distinctness (avtd)

• VarTD: Variation in taxonomic distinctness (vartd)

• uTO: Unweighted taxonomic diversity (Ozkan pTO, all levels)

• TO: Weighted taxonomic diversity (Ozkan pTO, all levels)

• uTO_plus: Unweighted taxonomic distance (Ozkan pTO, all levels)

• TO_plus: Weighted taxonomic distance (Ozkan pTO, all levels)

• uTO_max: Unweighted taxonomic diversity (informative levels)

• TO_max: Weighted taxonomic diversity (informative levels)

• uTO_plus_max: Unweighted taxonomic distance (informative levels)

• TO_plus_max: Weighted taxonomic distance (informative levels)

Value

If plot = FALSE, a data frame with communities as rows and indices as columns. If plot = TRUE, a list with two elements:

table

The data frame of index values.

plot

A ggplot object showing a grouped bar chart.

See Also

shannon, simpson, delta, delta_star, avtd, vartd, ozkan_pto, pto_components

Examples

tax <- build_tax_tree(
  species = c("sp1", "sp2", "sp3", "sp4"),
  Genus   = c("G1", "G1", "G2", "G2"),
  Family  = c("F1", "F1", "F1", "F2"),
  Order   = c("O1", "O1", "O1", "O1")
)

# Single community
comm <- c(sp1 = 10, sp2 = 20, sp3 = 15, sp4 = 5)
compare_indices(comm, tax)

# Multiple communities
comm_list <- list(
  Site_A = c(sp1 = 10, sp2 = 20, sp3 = 15, sp4 = 5),
  Site_B = c(sp1 = 5, sp2 = 5, sp3 = 5, sp4 = 5)
)
compare_indices(comm_list, tax)

Taxonomic Diversity Index (Delta)

Description

Calculates the taxonomic diversity index (Delta) from Warwick & Clarke (1995). This is the average weighted path length between every pair of individuals, including same-species pairs (weighted 0).

Usage

delta(community, tax_tree, weights = NULL)

Arguments

Details

\Delta = \frac{\sum\sum_{i<j} w_{ij} x_i x_j + \sum_i 0 \cdot x_i(x_i-1)/2}{\sum\sum_{i<j} x_i x_j + \sum_i x_i(x_i-1)/2}

Value

A numeric value representing taxonomic diversity (Delta).

References

Warwick, R.M. & Clarke, K.R. (1995). New 'biodiversity' measures reveal a decrease in taxonomic distinctness with increasing stress. Marine Ecology Progress Series, 129, 301-305.

See Also

delta_star() for taxonomic distinctness (excluding same-species), avtd() for presence/absence-based AvTD, ozkan_pto() for Deng entropy-based alternative.

Examples

comm <- c(sp1 = 5, sp2 = 3, sp3 = 3, sp4 = 1, sp5 = 3)
tax <- data.frame(
  Species = paste0("sp", 1:5),
  Genus = c("G1", "G1", "G2", "G2", "G2"),
  Family = c("F1", "F1", "F1", "F2", "F2"),
  stringsAsFactors = FALSE
)
delta(comm, tax)

Taxonomic Distinctness (Delta*)

Description

Calculates the taxonomic distinctness (Delta*) from Warwick & Clarke (1995). This is the average weighted path length between individuals of different species only.

Usage

delta_star(community, tax_tree, weights = NULL)

Arguments

Details

\Delta^* = \frac{\sum\sum_{i<j} w_{ij} x_i x_j} {\sum\sum_{i<j} x_i x_j}

Value

A numeric value representing taxonomic distinctness (Delta*).

References

Warwick, R.M. & Clarke, K.R. (1995). New 'biodiversity' measures reveal a decrease in taxonomic distinctness with increasing stress. Marine Ecology Progress Series, 129, 301-305.

See Also

delta() for taxonomic diversity (including same-species), avtd() and vartd() for presence/absence measures.

Examples

comm <- c(sp1 = 5, sp2 = 3, sp3 = 3, sp4 = 1, sp5 = 3)
tax <- data.frame(
  Species = paste0("sp", 1:5),
  Genus = c("G1", "G1", "G2", "G2", "G2"),
  Family = c("F1", "F1", "F1", "F2", "F2"),
  stringsAsFactors = FALSE
)
delta_star(comm, tax)

Calculate Deng Entropy at a Single Taxonomic Level

Description

Computes the Deng entropy (Ed) for a given set of group proportions at a specific taxonomic level. This is the core entropy calculation from Deng (2016), which generalizes Shannon entropy through the Dempster-Shafer evidence theory framework.

Usage

deng_entropy_level(
  abundances,
  group_sizes = NULL,
  correction = c("none", "miller_madow", "grassberger", "chao_shen")
)

Arguments

Details

The Deng entropy is calculated as:

E_d = -\sum_{i} m(F_i) \ln \frac{m(F_i)}{2^{|F_i|} - 1}

At species level, each focal element has cardinality 1, so Deng entropy reduces to Shannon entropy:

E_d^S = H = -\sum_i p_i \ln p_i

At higher levels (genus, family, etc.), |F_i| equals the number of species within each group, and the mass function is the normalized proportion of total abundance in each group.

Bias correction is only meaningful at the species level where Deng entropy equals Shannon entropy. At higher taxonomic levels the mass function has a different structure and bias-correction formulas do not apply.

Value

A numeric value representing the Deng entropy at that level.

References

Deng, Y. (2016). Deng entropy. Chaos, Solitons & Fractals, 91, 549-553.

See Also

ozkan_pto() which uses this function internally, shannon() for classical Shannon entropy and bias corrections.

Examples

# Shannon entropy (species level, |Fi| = 1 for all)
deng_entropy_level(c(4, 2, 3, 1, 2, 3, 2, 2))

# With bias correction at species level
deng_entropy_level(c(4, 2, 3, 1, 2), correction = "chao_shen")

# Deng entropy at genus level with group sizes
deng_entropy_level(c(9, 3, 7), group_sizes = c(3, 2, 3))

Example Community Vector: 8 Anatolian Tree Species

Description

A named numeric vector of species abundances for a single forest community with 8 Anatolian tree species. Abundance values follow the Westhoff & van der Maarel (1973) scale (1–9). This vector mirrors the hypothetical example in Ozkan (2018).

Usage

gazi_comm

Format

A named numeric vector with 8 elements. Names are species binomials (underscore-separated); values are integer abundances (1–4).

Details

The species include 3 genera from Pinaceae, 2 from Fagaceae, 1 each from Cupressaceae and Betulaceae, spanning 2 orders (Pinales, Fagales).

Pair with gazi_gytk for analysis:

ozkan_pto(gazi_comm, gazi_gytk)
compare_indices(gazi_comm, gazi_gytk)

References

Ozkan, K. (2018). A new proposed measure for estimating taxonomic diversity. Turkish Journal of Forestry, 19(4), 336–346.

See Also

gazi_gytk for the matching taxonomy, anatolian_trees for a multi-site dataset.

Examples

data(gazi_comm)
data(gazi_gytk)

# Ozkan pTO
ozkan_pto(gazi_comm, gazi_gytk)

# All indices at once
compare_indices(gazi_comm, gazi_gytk)

Example Taxonomy: 8 Anatolian Tree Species

Description

A data frame containing the taxonomic hierarchy for the 8 species in gazi_comm, with 3 taxonomic ranks (Genus, Family, Order). This compact taxonomy table is designed for quick demonstrations and unit testing.

Usage

gazi_gytk

Format

A data frame with 8 rows and 4 columns:

Species

Binomial species name (character)

Genus

Genus (character)

Family

Family (character)

Order

Order (character)

Details

The taxonomy represents:

• 8 genera: Pinus, Cedrus, Quercus, Fagus, Juniperus, Carpinus

• 4 families: Pinaceae (3 spp), Fagaceae (3 spp), Cupressaceae (1), Betulaceae (1)

• 2 orders: Pinales (4 spp), Fagales (4 spp)

References

Ozkan, K. (2018). A new proposed measure for estimating taxonomic diversity. Turkish Journal of Forestry, 19(4), 336–346.

See Also

gazi_comm for the matching community vector, build_tax_tree for building custom taxonomies.

Examples

data(gazi_gytk)
gazi_gytk

# Compute taxonomic distance matrix
tax_distance_matrix(gazi_gytk)

Calculate Ozkan's Taxonomic Diversity Index (pTO)

Description

Computes the four components of the Deng entropy-based taxonomic diversity measure proposed by Ozkan (2018): weighted/unweighted taxonomic diversity (TO, uTO) and weighted/unweighted taxonomic distance (TO+, uTO+).

Usage

ozkan_pto(community, tax_tree, max_level = NULL)

Arguments

Details

The method uses the slicing procedure from Ozkan (2018). At each slice (nk = 0, 1, ..., n_s), species with abundance <= nk are removed. The surviving species receive EQUAL weight (1/count) — abundance information enters indirectly through which species survive each slice.

Deng entropy at each taxonomic level is computed using these equal proportions, where the mass function m(Fi) = count_in_group / total_count and |Fi| = number of species in that taxonomic group.

The core product formula at each slice is:

\prod_{i=1}^{L} \left( w_i \left( \frac{(e^{E_d^S})^2} {e^{E_d^i}} + 1 \right) \right)

where E_d^S is the Deng entropy at species level and E_d^i is the Deng entropy at level i, computed using presence/absence (equal weight) proportions.

pTO+ (taxonomic distance) uses only the nk=0 slice:

pT_O^+ = \ln \prod_{i=1}^{L} \left( w_i \left( \frac{(e^{E_d^S})^2}{e^{E_d^i}} + 1 \right) \right)

pTO (taxonomic diversity) aggregates across all slices:

pT_O = \ln \left( \frac{\sum_{k=0}^{n_s} (n_s - n_k) \prod_{i=1}^{L} \left( w_i \left( \frac{(e^{E_d^S})^2} {e^{E_d^i}} + 1 \right) \right)}{n_s + \sum n_k} \right)

Value

A named list with components:

uTO

Unweighted taxonomic diversity (all levels)

TO

Weighted taxonomic diversity (all levels)

uTO_plus

Unweighted taxonomic distance (all levels)

TO_plus

Weighted taxonomic distance (all levels)

uTO_max

Unweighted taxonomic diversity (max informative level)

TO_max

Weighted taxonomic diversity (max informative level)

uTO_plus_max

Unweighted taxonomic distance (max informative level)

TO_plus_max

Weighted taxonomic distance (max informative level)

Ed_levels

Deng entropy at each taxonomic level (nk=0 slice)

max_informative_level

Integer: highest level with Ed > 0

References

Ozkan, K. (2018). A new proposed measure for estimating taxonomic diversity. Turkish Journal of Forestry, 19(4), 336-346. DOI: 10.18182/tjf.441061

Deng, Y. (2016). Deng entropy. Chaos, Solitons & Fractals, 91, 549-553.

See Also

deng_entropy_level() for the core Deng entropy calculation, pto_components() for a convenience wrapper returning a named vector, delta() and avtd() for Clarke & Warwick alternatives.

Examples

# Simple example with 5 species
comm <- c(sp1 = 4, sp2 = 2, sp3 = 3, sp4 = 1, sp5 = 2)
tax <- data.frame(
  Species = paste0("sp", 1:5),
  Genus   = c("G1", "G1", "G1", "G2", "G2"),
  Family  = c("F1", "F1", "F1", "F1", "F1"),
  stringsAsFactors = FALSE
)
ozkan_pto(comm, tax)

# With auto max-level detection
ozkan_pto(comm, tax, max_level = "auto")

Full Ozkan pTO Pipeline (Islem 1 + 2 + 3)

Description

Runs the complete Ozkan taxonomic diversity analysis pipeline: jackknife (Islem 1), stochastic resampling (Islem 2), and sensitivity analysis (Islem 3), returning the maximum values across all three runs. This is equivalent to running all three steps in the Excel macro sequentially.

Usage

ozkan_pto_full(community, tax_tree, n_iter = 101L, seed = NULL)

Arguments

Details

This function implements the full Excel macro pipeline in a single call:

1. Islem 1: Leave-one-out jackknife to identify contributing (happy) vs non-contributing (unhappy) species, plus deterministic pTO calculation.

2. Islem 2: Stochastic resampling – unhappy species are always included, happy species get 50\

3. Islem 3: Sensitivity analysis – unhappy species get P = (S-1)/S, happy species get a data-driven probability.

4. Final result: Maximum values across all three runs.

Value

A named list with components:

uTO_plus

Final maximum uTO+ across all 3 runs

TO_plus

Final maximum TO+ across all 3 runs

uTO

Final maximum uTO across all 3 runs

TO

Final maximum TO across all 3 runs

run1

Deterministic pTO result (from ozkan_pto())

run2

Full Run 2 result (from ozkan_pto_resample())

run3

Full Run 3 result (from ozkan_pto_sensitivity())

jackknife

Jackknife result with species classifications

References

Ozkan, K. (2018). A new proposed measure for estimating taxonomic diversity. Turkish Journal of Forestry, 19(4), 336-346. DOI: 10.18182/tjf.441061

See Also

ozkan_pto() for deterministic calculation only, ozkan_pto_resample() for Run 2 only, ozkan_pto_sensitivity() for Run 3 only, ozkan_pto_jackknife() for jackknife only.

Examples

comm <- c(sp1 = 4, sp2 = 2, sp3 = 3, sp4 = 1, sp5 = 2)
tax <- data.frame(
  Species = paste0("sp", 1:5),
  Genus   = c("G1", "G1", "G1", "G2", "G2"),
  Family  = c("F1", "F1", "F1", "F1", "F1"),
  stringsAsFactors = FALSE
)
set.seed(42)
result <- ozkan_pto_full(comm, tax, n_iter = 101)
result$uTO_plus  # Final maximum uTO+
result$TO_plus   # Final maximum TO+

Jackknife Analysis for Ozkan's pTO Index (Islem 1 / Run 1)

Description

Implements the leave-one-out jackknife procedure from the Ozkan Excel macro (Islem 1). Removes each species one at a time, recalculates pTO, and identifies "happy" (contributing) and "unhappy" (non-contributing) species. A species is "happy" if its removal decreases the pTO index, indicating it positively contributes to the community's taxonomic diversity.

Usage

ozkan_pto_jackknife(community, tax_tree, component = "uTO_plus")

Arguments

Details

The jackknife procedure follows the Excel macro's Islem 1 logic:

1. Compute pTO for the full community.

2. For each species i, remove it and compute pTO for the remaining community (leave-one-out).

3. Compare each leave-one-out result against the full-community value.

4. If removing species i DECREASES the specified component (pTO becomes smaller), species i is classified as "happy" (contributing).

5. If removing species i does NOT decrease the component, species i is classified as "unhappy" (non-contributing).

The happy/unhappy classification is used by ozkan_pto_resample() (Islem 2) and ozkan_pto_sensitivity() (Islem 3) to apply different resampling probabilities to each species category.

Value

A named list with components:

full_result

The ozkan_pto() result for the full community

jackknife_results

Data frame with leave-one-out results per species

species_status

Named logical vector: TRUE = happy (contributing), FALSE = unhappy (non-contributing)

n_happy

Number of happy species

n_unhappy

Number of unhappy species

References

Ozkan, K. (2018). A new proposed measure for estimating taxonomic diversity. Turkish Journal of Forestry, 19(4), 336-346. DOI: 10.18182/tjf.441061

See Also

ozkan_pto() for the core calculation, ozkan_pto_resample() for Run 2, ozkan_pto_full() for the full 3-run pipeline.

Examples

comm <- c(sp1 = 4, sp2 = 2, sp3 = 3, sp4 = 1, sp5 = 2)
tax <- data.frame(
  Species = paste0("sp", 1:5),
  Genus   = c("G1", "G1", "G1", "G2", "G2"),
  Family  = c("F1", "F1", "F1", "F1", "F1"),
  stringsAsFactors = FALSE
)
jk <- ozkan_pto_jackknife(comm, tax)
jk$species_status   # Which species are happy (contributing)?
jk$n_happy           # How many happy species?

Stochastic Resampling of Ozkan's pTO Index (Islem 2 / Run 2)

Description

Implements the stochastic resampling procedure from Ozkan's Excel macro (Islem 2). First performs a jackknife (Islem 1) to identify "happy" (contributing) and "unhappy" (non-contributing) species, then runs stochastic resampling where unhappy species are always included and happy species are randomly included with 50\

Usage

ozkan_pto_resample(community, tax_tree, n_iter = 101L, seed = NULL)

Arguments

Details

The algorithm follows the Excel macro's Islem 1 + Islem 2 logic:

1. Run jackknife (ozkan_pto_jackknife()) to classify each species as happy or unhappy.

2. Iteration 1: Use the original community (deterministic).

3. Iterations 2..n_iter: For each species:

   • Unhappy species (AA = 0): always included with original abundance.

   • Happy species (AA > 0): randomly included (50\ or excluded. Uses RANDBETWEEN(0,1) * abundance.

4. Return the maximum of each component across all iterations.

Value

A named list with components:

uTO_plus_max

Maximum unweighted taxonomic distance across iterations

TO_plus_max

Maximum weighted taxonomic distance across iterations

uTO_max

Maximum unweighted taxonomic diversity across iterations

TO_max

Maximum weighted taxonomic diversity across iterations

uTO_plus_det

Deterministic uTO+ (first iteration, same as ozkan_pto())

TO_plus_det

Deterministic TO+ (first iteration)

uTO_det

Deterministic uTO (first iteration)

TO_det

Deterministic TO (first iteration)

n_iter

Number of iterations performed

species_status

Named logical vector from jackknife (TRUE = happy)

jackknife

Full jackknife result from ozkan_pto_jackknife()

n_positive

Number of iterations with positive uTO+

iteration_results

Data frame with all iteration results

References

Ozkan, K. (2018). A new proposed measure for estimating taxonomic diversity. Turkish Journal of Forestry, 19(4), 336-346. DOI: 10.18182/tjf.441061

See Also

ozkan_pto_jackknife() for the jackknife step, ozkan_pto_sensitivity() for Run 3, ozkan_pto_full() for the full pipeline.

Examples

comm <- c(sp1 = 4, sp2 = 2, sp3 = 3, sp4 = 1, sp5 = 2)
tax <- data.frame(
  Species = paste0("sp", 1:5),
  Genus   = c("G1", "G1", "G1", "G2", "G2"),
  Family  = c("F1", "F1", "F1", "F1", "F1"),
  stringsAsFactors = FALSE
)
set.seed(42)
result <- ozkan_pto_resample(comm, tax, n_iter = 101)
result$species_status  # Happy/unhappy classification

Sensitivity Analysis of Ozkan's pTO Index (Islem 3 / Run 3)

Description

Implements the sensitivity analysis procedure from Ozkan's Excel macro (Islem 3). Uses the jackknife results from Run 2 to apply species-specific inclusion probabilities: unhappy species get P = (S-1)/S, happy species get a data-driven probability derived from Run 2 iteration results.

Usage

ozkan_pto_sensitivity(
  community,
  tax_tree,
  run2_result,
  n_iter = NULL,
  seed = NULL
)

Arguments

Details

The algorithm follows the Excel macro's Islem 3 logic:

For each species, the inclusion probability depends on its jackknife classification from Islem 1:

• Unhappy species (AA = 0, non-contributing): Included with probability (S-1)/S, where S is total species count. In the Excel formula: IF(RANDBETWEEN(1, S) > 1, H2, 0).

• Happy species (AA > 0, contributing): Included with probability derived from Run 2 results. In the Excel formula: IF(L25 >= RANDBETWEEN(0, K22), H2, 0), where L25 is a summary score from Run 2 and K22 is the iteration count.

The happy species probability is computed as:

P_{happy} = \frac{\max(0, N_{positive} - S) + 1}{N_{iter} + 1}

where N_{positive} is the number of Run 2 iterations that produced a positive uTO+ value and S is the species count.

The maximum pTO across all three runs (Run 1, 2, 3) is the final result.

Value

A named list with components:

uTO_plus_max

Maximum uTO+ across Run 1, 2, and 3

TO_plus_max

Maximum TO+ across all runs

uTO_max

Maximum uTO across all runs

TO_max

Maximum TO across all runs

run3_uTO_plus_max

Maximum uTO+ from Run 3 only

run3_TO_plus_max

Maximum TO+ from Run 3 only

run3_uTO_max

Maximum uTO from Run 3 only

run3_TO_max

Maximum TO from Run 3 only

n_iter

Number of iterations performed

species_probs

Named numeric vector of inclusion probabilities

prob_happy

Probability used for happy species

prob_unhappy

Probability used for unhappy species

iteration_results

Data frame with all Run 3 iteration results

References

Ozkan, K. (2018). A new proposed measure for estimating taxonomic diversity. Turkish Journal of Forestry, 19(4), 336-346. DOI: 10.18182/tjf.441061

See Also

ozkan_pto_resample() for Run 2, ozkan_pto_full() for the full pipeline.

Examples

comm <- c(sp1 = 4, sp2 = 2, sp3 = 3, sp4 = 1, sp5 = 2)
tax <- data.frame(
  Species = paste0("sp", 1:5),
  Genus   = c("G1", "G1", "G1", "G2", "G2"),
  Family  = c("F1", "F1", "F1", "F1", "F1"),
  stringsAsFactors = FALSE
)
set.seed(42)
run2 <- ozkan_pto_resample(comm, tax, n_iter = 101)
ozkan_pto_sensitivity(comm, tax, run2, n_iter = 101)

Bubble Chart of Species Contributions to Diversity

Description

Creates a bubble chart showing each species' abundance (x-axis), average taxonomic distance to other species (y-axis), and relative contribution to the community (bubble size). Species that are both abundant and taxonomically distant from others contribute most to overall taxonomic diversity.

Usage

plot_bubble(community, tax_tree, color_by = NULL, title = NULL)

Arguments

Details

For each species i, the average taxonomic distance is calculated as:

\bar{\omega}_i = \frac{1}{S-1} \sum_{j \neq i} \omega_{ij}

where \omega_{ij} is the pairwise taxonomic distance and S is the number of species. Bubble size represents the product of relative abundance and average distance, indicating each species' contribution to overall taxonomic diversity.

Value

A ggplot object.

See Also

tax_distance_matrix, compare_indices

Examples

comm <- c(sp1 = 25, sp2 = 18, sp3 = 30, sp4 = 12, sp5 = 8)
tax <- build_tax_tree(
  species = paste0("sp", 1:5),
  Genus   = c("G1", "G1", "G2", "G2", "G3"),
  Family  = c("F1", "F1", "F1", "F2", "F2"),
  Order   = c("O1", "O1", "O1", "O1", "O1")
)
plot_bubble(comm, tax)

Funnel Plot for AvTD/VarTD

Description

Produces a Clarke & Warwick style funnel plot showing expected confidence limits for Average Taxonomic Distinctness (AvTD) and/or Variation in Taxonomic Distinctness (VarTD) as a function of species richness. Observed site values can be overlaid to assess whether they fall within or outside the expected range.

Usage

plot_funnel(
  sim_result,
  observed = NULL,
  index = c("avtd", "vartd"),
  title = NULL,
  point_labels = TRUE,
  mean_color = "darkblue",
  ci_color = "steelblue"
)

Arguments

Details

The funnel shape arises because small samples (low S) have greater random variation in AvTD/VarTD, producing wider confidence bands. As S approaches the full species pool, the band narrows.

Observed points falling below the lower confidence limit suggest the community has lower taxonomic breadth than expected by chance, potentially indicating environmental stress or biotic homogenisation.

Requires the ggplot2 package.

Value

A ggplot object.

References

Clarke, K.R. & Warwick, R.M. (1998). A taxonomic distinctness index and its statistical properties. Journal of Applied Ecology, 35, 523-531.

See Also

simulate_td() for generating the simulation, avtd() and vartd() for the underlying calculations.

Examples

tax <- data.frame(
  Species = paste0("sp", 1:10),
  Genus   = rep(c("G1", "G2", "G3", "G4", "G5"), each = 2),
  Family  = rep(c("F1", "F1", "F2", "F2", "F3"), each = 2),
  Order   = rep(c("O1", "O1", "O2", "O2", "O2"), each = 2),
  stringsAsFactors = FALSE
)
sim <- simulate_td(tax, n_sim = 99, seed = 42)

# Basic funnel plot
plot_funnel(sim)

# With observed sites
obs <- data.frame(
  site  = c("Site_A", "Site_B"),
  s     = c(5, 8),
  value = c(2.5, 1.8)
)
plot_funnel(sim, observed = obs)

Plot Taxonomic Distance Heatmap

Description

Visualizes the pairwise taxonomic distance matrix as a colored heatmap using ggplot2. Species are ordered by hierarchical clustering so that taxonomically similar species appear adjacent.

Usage

plot_heatmap(
  tax_tree,
  label_size = 3,
  title = NULL,
  low_color = "white",
  high_color = "#B22222"
)

Arguments

Details

The heatmap displays the full symmetric distance matrix computed by tax_distance_matrix. The diagonal (self-distance = 0) appears in the lowest color. Species are reordered using hierarchical clustering (UPGMA) to reveal taxonomic groupings visually.

Value

A ggplot object.

See Also

tax_distance_matrix, plot_taxonomic_tree

Examples

tax <- build_tax_tree(
  species = c("sp1", "sp2", "sp3", "sp4"),
  Genus   = c("G1", "G1", "G2", "G2"),
  Family  = c("F1", "F1", "F1", "F2")
)
plot_heatmap(tax)

Plot pTO Iteration Results from Run 2 or Run 3

Description

Visualizes the iteration-by-iteration pTO values from stochastic resampling (Run 2) or sensitivity analysis (Run 3). Each iteration's value is shown as a point, with the deterministic (Run 1) value displayed as a horizontal reference line.

Usage

plot_iteration(resample_result, component = "TO", title = NULL)

Arguments

Details

The plot includes three visual elements:

• Grey points: Individual iteration values

• Red dashed line: Deterministic (Run 1) value

• Blue dashed line: Maximum value across all iterations

This helps assess how stochastic species removal affects the pTO index and whether the maximum exceeds the deterministic value.

Value

A ggplot object showing iteration values as points, the deterministic value as a dashed red line, and the maximum value as a dashed blue line.

See Also

ozkan_pto_resample, ozkan_pto_sensitivity

Examples

comm <- c(sp1 = 10, sp2 = 20, sp3 = 15, sp4 = 5)
tax <- build_tax_tree(
  species = paste0("sp", 1:4),
  Genus = c("G1", "G1", "G2", "G2"),
  Family = c("F1", "F1", "F1", "F2")
)
res <- ozkan_pto_resample(comm, tax, n_iter = 101, seed = 42)
plot_iteration(res, component = "TO")

Radar (Spider) Chart for Multi-Community Index Comparison

Description

Creates a radar chart comparing diversity indices across multiple communities. Each axis represents a different index, and each community is drawn as a colored polygon. Values are normalized to 0-1 scale so that indices with different magnitudes can be compared visually.

Usage

plot_radar(communities, tax_tree, indices = NULL, title = NULL)

Arguments

Details

Each index value is normalized using min-max scaling across the communities being compared:

x_{norm} = \frac{x - x_{min}}{x_{max} - x_{min}}

If all communities have the same value for an index (i.e., x_{max} = x_{min}), the normalized value is set to 0.5.

The radar chart is built using polar coordinates in ggplot2. Each community appears as a colored polygon overlay, making it easy to spot which community scores higher on which indices.

Value

A ggplot object.

See Also

compare_indices for tabular comparison

Examples

tax <- build_tax_tree(
  species = c("sp1", "sp2", "sp3", "sp4"),
  Genus   = c("G1", "G1", "G2", "G2"),
  Family  = c("F1", "F1", "F1", "F2")
)
comms <- list(
  Site_A = c(sp1 = 10, sp2 = 20, sp3 = 15, sp4 = 5),
  Site_B = c(sp1 = 5, sp2 = 5, sp3 = 5, sp4 = 5)
)
plot_radar(comms, tax)

Plot Taxonomic Rarefaction Curve

Description

Visualises a rarefaction curve with confidence intervals using ggplot2. Accepts output from rarefaction_taxonomic().

Usage

plot_rarefaction(
  rare_result,
  title = NULL,
  xlab = "Sample Size (individuals)",
  ylab = NULL,
  ci_color = "steelblue",
  line_color = "darkblue"
)

Arguments

Details

The plot shows the mean diversity value at each sample size as a solid line, surrounded by a shaded ribbon representing the bootstrap confidence interval. A vertical dashed line marks the total sample size (full data).

Requires the ggplot2 package.

Value

A ggplot object.

See Also

rarefaction_taxonomic() for computing the rarefaction curve.

Examples

comm <- c(sp1 = 10, sp2 = 5, sp3 = 8, sp4 = 2, sp5 = 3)
tax <- data.frame(
  Species = paste0("sp", 1:5),
  Genus   = c("G1", "G1", "G2", "G2", "G3"),
  Family  = c("F1", "F1", "F1", "F2", "F2"),
  stringsAsFactors = FALSE
)
rare <- rarefaction_taxonomic(comm, tax, index = "shannon", n_boot = 50)
plot_rarefaction(rare)

Plot Taxonomic Tree as a Dendrogram

Description

Visualizes a taxonomic hierarchy as a dendrogram (tree diagram) using ggplot2. The function converts the taxonomic distance matrix into a hierarchical clustering object and renders it as a horizontal dendrogram with species labels colored by a chosen taxonomic rank.

Usage

plot_taxonomic_tree(
  tax_tree,
  community = NULL,
  color_by = NULL,
  label_size = 3,
  title = NULL
)

Arguments

Details

The dendrogram is constructed from the pairwise taxonomic distance matrix (computed via tax_distance_matrix) using hierarchical clustering (hclust with method = "average"). Branch heights reflect taxonomic distance: species in the same genus merge at the lowest level, while species in different orders merge at the highest level.

When a community vector is provided, species labels are annotated with abundance values in parentheses, e.g., "Quercus_coccifera (25)".

This function requires the ggplot2 package. If ggplot2 is not installed, the function will stop with an informative error message.

The clustering method used is UPGMA (Unweighted Pair Group Method with Arithmetic Mean), which is standard for taxonomic classification trees where branch lengths represent average distances between groups.

Value

A ggplot object that can be further customized with ggplot2 functions (e.g., + theme(), + labs()).

References

Clarke, K.R. & Warwick, R.M. (1998). A taxonomic distinctness index and its statistical properties. Journal of Applied Ecology, 35, 523–531.

See Also

build_tax_tree for creating the taxonomy input, tax_distance_matrix for the underlying distance calculation.

Examples

# Build a simple taxonomic tree
tax <- build_tax_tree(
  species = c("Quercus_robur", "Quercus_petraea", "Pinus_nigra",
              "Pinus_brutia", "Juniperus_excelsa"),
  Genus   = c("Quercus", "Quercus", "Pinus", "Pinus", "Juniperus"),
  Family  = c("Fagaceae", "Fagaceae", "Pinaceae", "Pinaceae", "Cupressaceae"),
  Order   = c("Fagales", "Fagales", "Pinales", "Pinales", "Pinales")
)

# Basic dendrogram
plot_taxonomic_tree(tax)

# Color by Family, with abundance info
comm <- c(Quercus_robur = 25, Quercus_petraea = 18,
          Pinus_nigra = 30, Pinus_brutia = 12,
          Juniperus_excelsa = 8)
plot_taxonomic_tree(tax, community = comm, color_by = "Family")

Calculate All Eight pTO Components (Convenience Wrapper)

Description

Returns a named numeric vector with all eight Ozkan (2018) components: four using all taxonomic levels and four using only the informative levels (max version), matching the Excel macro's Run 1+2+3 output.

Usage

pto_components(community, tax_tree)

Arguments

Value

A named numeric vector with eight elements: uTO, TO, uTO_plus, TO_plus, uTO_max, TO_max, uTO_plus_max, TO_plus_max.

See Also

ozkan_pto() for the full result including per-level entropy.

Examples

comm <- c(sp1 = 4, sp2 = 2, sp3 = 3, sp4 = 1, sp5 = 2)
tax <- data.frame(
  Species = paste0("sp", 1:5),
  Genus   = c("G1", "G1", "G1", "G2", "G2"),
  Family  = c("F1", "F1", "F1", "F1", "F1"),
  stringsAsFactors = FALSE
)
pto_components(comm, tax)

Taxonomic Diversity Rarefaction

Description

Computes rarefaction curves for taxonomic diversity indices by subsampling individuals from the community at increasing sample sizes. Uses bootstrap resampling to estimate expected diversity and confidence intervals at each sample size.

Usage

rarefaction_taxonomic(
  community,
  tax_tree,
  index = c("shannon", "simpson", "species", "uTO", "TO", "uTO_plus", "TO_plus", "avtd"),
  steps = 20,
  n_boot = 100,
  ci = 0.95,
  seed = NULL,
  correction = c("none", "miller_madow", "grassberger", "chao_shen"),
  parallel = FALSE,
  n_cores = NULL
)

Arguments

Details

The algorithm works as follows:

1. Expand the abundance vector into an individual-level vector (e.g., c(sp1=3, sp2=2) becomes c("sp1","sp1","sp1","sp2","sp2")).

2. For each sample size (from min to total N), draw n_boot random subsamples without replacement.

3. For each subsample, count species abundances and compute the chosen diversity index.

4. Return the mean and confidence interval at each step.

Value

A data frame with columns:

sample_size

Number of individuals in the subsample

mean

Mean index value across bootstrap replicates

lower

Lower bound of the confidence interval

upper

Upper bound of the confidence interval

sd

Standard deviation across replicates

References

Gotelli, N.J. & Colwell, R.K. (2001). Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecology Letters, 4, 379-391.

Ozkan, K. (2018). A new proposed measure for estimating taxonomic diversity. Turkish Journal of Forestry, 19(4), 336-346.

See Also

plot_rarefaction() for visualising the rarefaction curve, ozkan_pto() for full pTO calculation, shannon() and simpson() for classical indices, avtd() for average taxonomic distinctness.

Examples

comm <- c(sp1 = 10, sp2 = 5, sp3 = 8, sp4 = 2, sp5 = 3)
tax <- data.frame(
  Species = paste0("sp", 1:5),
  Genus   = c("G1", "G1", "G2", "G2", "G3"),
  Family  = c("F1", "F1", "F1", "F2", "F2"),
  stringsAsFactors = FALSE
)
rarefaction_taxonomic(comm, tax, index = "shannon", n_boot = 50)

Shannon Diversity Index

Description

Calculates the Shannon-Wiener diversity index (H') for a community, optionally applying a bias correction for small samples.

Usage

shannon(
  community,
  base = exp(1),
  correction = c("none", "miller_madow", "grassberger", "chao_shen")
)

Arguments

Details

The naive (MLE) Shannon index is calculated as:

H' = -\sum_{i=1}^{S} p_i \ln(p_i)

where p_i = n_i / N is the proportion of species i, N is the total number of individuals, and S is the number of species observed.

The MLE estimator has a known negative bias that is significant for small samples. Three bias-correction methods are available:

Miller-Madow (1955): Adds a first-order bias correction term:

H_{MM} = H_{MLE} + \frac{S_{obs} - 1}{2N}

Grassberger (2003): Uses the digamma function instead of the logarithm:

H_G = \ln(N) - \frac{1}{N} \sum_i n_i \psi(n_i)

where \psi is the digamma function.

Chao-Shen (2003): Applies a Good-Turing coverage correction with Horvitz-Thompson weighting:

\hat{C} = 1 - f_1 / N

H_{CS} = -\sum_i \frac{\hat{p}_i \ln \hat{p}_i}{1 - (1 - \hat{p}_i)^N}

where \hat{p}_i = \hat{C} \cdot n_i / N and f_1 is the number of singletons.

Bias corrections require integer abundance counts. A warning is issued if non-integer values are detected with correction != "none".

Value

A numeric value representing the Shannon diversity index.

References

Miller, G.A. & Madow, W.G. (1954). On the maximum likelihood estimate of the Shannon-Wiener index of diversity. AFCRC-TR-54-75.

Grassberger, P. (2003). Entropy estimates from insufficient samplings. arXiv:physics/0307138.

Chao, A. & Shen, T.-J. (2003). Nonparametric estimation of Shannon's index of diversity when there are unseen species in sample. Environmental and Ecological Statistics, 10, 429-443.

See Also

simpson() for Simpson diversity, deng_entropy_level() for Deng entropy (a generalization of Shannon).

Examples

comm <- c(10, 5, 8, 3, 12)
shannon(comm)
shannon(comm, correction = "miller_madow")
shannon(comm, correction = "grassberger")
shannon(comm, correction = "chao_shen")

Simpson Diversity Index

Description

Calculates the Simpson diversity index (1 - D) for a community.

Usage

simpson(community, type = c("gini_simpson", "inverse", "dominance"))

Arguments

Details

Simpson's dominance index D is calculated as:

D = \sum_{i=1}^{S} p_i^2

The Gini-Simpson index is 1 - D and the inverse Simpson is 1/D.

Value

A numeric value representing the Simpson index.

See Also

shannon() for Shannon diversity.

Examples

comm <- c(10, 5, 8, 3, 12)
simpson(comm)
simpson(comm, type = "inverse")

Simulate Expected AvTD/VarTD Under Random Sampling

Description

Generates the null distribution of Average Taxonomic Distinctness (AvTD) and/or Variation in Taxonomic Distinctness (VarTD) by randomly drawing species subsets from a regional species pool. Used to construct funnel plots for statistical testing (Clarke & Warwick 1998, 2001).

Usage

simulate_td(
  tax_tree,
  s_range = NULL,
  n_sim = 999L,
  index = c("both", "avtd", "vartd"),
  weights = NULL,
  ci = 0.95,
  seed = NULL,
  parallel = FALSE,
  n_cores = NULL
)

Arguments

Details

For each value of S in s_range, n_sim random subsets of S species are drawn (without replacement) from the full species pool in tax_tree. AvTD and/or VarTD are computed for each random subset. The mean and percentile-based confidence limits are recorded.

The resulting object can be passed to plot_funnel() to produce the classic Clarke & Warwick funnel plot.

Value

A data frame with class "td_simulation" containing columns:

s

Species richness (number of species drawn)

mean_avtd

Mean simulated AvTD (if index includes avtd)

lower_avtd

Lower CI bound for AvTD

upper_avtd

Upper CI bound for AvTD

mean_vartd

Mean simulated VarTD (if index includes vartd)

lower_vartd

Lower CI bound for VarTD

upper_vartd

Upper CI bound for VarTD

Attributes: ci, index, n_sim, pool_size.

References

Clarke, K.R. & Warwick, R.M. (1998). A taxonomic distinctness index and its statistical properties. Journal of Applied Ecology, 35, 523-531.

Clarke, K.R. & Warwick, R.M. (2001). A further biodiversity index applicable to species lists: variation in taxonomic distinctness. Marine Ecology Progress Series, 216, 265-278.

See Also

plot_funnel() for visualisation, avtd() and vartd() for the underlying calculations.

Examples

tax <- data.frame(
  Species = paste0("sp", 1:10),
  Genus   = rep(c("G1", "G2", "G3", "G4", "G5"), each = 2),
  Family  = rep(c("F1", "F1", "F2", "F2", "F3"), each = 2),
  Order   = rep(c("O1", "O1", "O2", "O2", "O2"), each = 2),
  stringsAsFactors = FALSE
)
sim <- simulate_td(tax, n_sim = 99, seed = 42)
sim

Compute Taxonomic Distance Matrix

Description

Calculates pairwise taxonomic distances between species based on their positions in a taxonomic hierarchy. Distance is computed as the weighted proportion of taxonomic levels at which two species diverge.

Usage

tax_distance_matrix(tax_tree, species = NULL, weights = NULL)

Arguments

Value

A symmetric matrix of taxonomic distances between species. With default equal step weights (1, 2, 3, ...), values range from 0 (same species) to the number of taxonomic levels (maximum distance when no common ancestor is found at any level).

Examples

tax <- data.frame(
  Species = c("Quercus_robur", "Pinus_nigra", "Fagus_orientalis"),
  Genus = c("Quercus", "Pinus", "Fagus"),
  Family = c("Fagaceae", "Pinaceae", "Fagaceae"),
  Order = c("Fagales", "Pinales", "Fagales"),
  stringsAsFactors = FALSE
)

tax_distance_matrix(tax)

Variation in Taxonomic Distinctness (Lambda+)

Description

Calculates the variation in taxonomic distinctness (VarTD, Lambda+) based on Clarke & Warwick (2001).

Usage

vartd(species, tax_tree, weights = NULL)

Arguments

Value

A numeric value representing the variation in taxonomic distinctness (Lambda+).

References

Clarke, K.R. & Warwick, R.M. (2001). A further biodiversity index applicable to species lists: variation in taxonomic distinctness. Marine Ecology Progress Series, 216, 265-278.

Examples

tax <- data.frame(
  Species = c("Quercus_robur", "Pinus_nigra", "Fagus_orientalis",
              "Abies_nordmanniana"),
  Genus = c("Quercus", "Pinus", "Fagus", "Abies"),
  Family = c("Fagaceae", "Pinaceae", "Fagaceae", "Pinaceae"),
  Order = c("Fagales", "Pinales", "Fagales", "Pinales"),
  stringsAsFactors = FALSE
)

spp <- c("Quercus_robur", "Pinus_nigra", "Fagus_orientalis")
vartd(spp, tax)