taxdiv

Taxonomic diversity indices for ecological community data — in one place.
Classical measures, Clarke & Warwick distinctness, and the Deng entropy-based Ozkan (2018) method, with an interactive dashboard and publication-ready graphics.

Traditional indices such as Shannon and Simpson treat every species as equally distinct. But 10 species from 10 different families are taxonomically more diverse than 10 species from a single genus. taxdiv captures this by folding the taxonomic hierarchy into the calculation — through two complementary frameworks (Clarke & Warwick distinctness and Ozkan pTO based on Deng entropy) — and ships everything from raw Excel data to finished figures.

🖥️ Interactive Dashboard

No code required. taxdiv_explorer() launches a point-and-click Shiny app: load an Excel/CSV file, pick which index families to compute, run the analysis with a live progress bar, explore five graph types, and download results and figures.

library(taxdiv)
taxdiv_explorer()

taxdiv Explorer dashboard

⚡ Installation

# From CRAN
install.packages("taxdiv")

# Development version from GitHub
# install.packages("devtools")
devtools::install_github("mgorgoz/taxonomic-diversity-r")

🚀 Quick Start

library(taxdiv)

# Species abundances
community <- c(
  Quercus_robur      = 15,
  Pinus_nigra        = 8,
  Fagus_orientalis   = 12,
  Abies_nordmanniana = 5,
  Juniperus_excelsa  = 3
)

# Taxonomic hierarchy
tax_tree <- build_tax_tree(
  species = names(community),
  Genus   = c("Quercus", "Pinus", "Fagus", "Abies", "Juniperus"),
  Family  = c("Fagaceae", "Pinaceae", "Fagaceae", "Pinaceae", "Cupressaceae"),
  Order   = c("Fagales", "Pinales", "Fagales", "Pinales", "Pinales")
)

compare_indices(community, tax_tree)  # all 14 indices at once
ozkan_pto(community, tax_tree)        # the 8 Ozkan pTO values

From Excel — one command, all sites, automatic column detection:

data <- as.data.frame(readxl::read_excel("my_data.xlsx"))
batch_analysis(data)
#>   Site N_Species Shannon Simpson Delta Delta_star  AvTD VarTD  uTO   TO ...
#>   A1           6   1.494   0.757 1.622      2.138 2.333 0.667 2.14 3.49 ...
#>   A2           5   1.577   0.784 1.719      2.243 2.500 0.500 1.98 3.21 ...

A ready-to-use Excel template ships with the package: taxdiv_data_template.xlsx.

📊 Gallery

All seven plot functions plus the dashboard’s bar chart — examples on the bundled anatolian_trees data:

Site by index bar chart

Bar chart — compare any index across sites at a glance


Radar — compare every index across sites	Heatmap — pairwise taxonomic distance

Rarefaction — bootstrap curves for 8 indices	Funnel — 95% significance envelope (AvTD/VarTD)

Dendrogram — taxonomic hierarchy	Bubble — species contributions

Iteration — stochastic resampling trajectory (Run 2)

✨ Why taxdiv?

Feature	vegan	ape	taxdiv
Shannon / Simpson	yes	—	yes
Clarke & Warwick suite (Delta, Delta*, AvTD, VarTD)	—	partial	yes
Ozkan pTO — 8 indices (Run 1+2+3)	—	—	yes
Simulation-based significance (funnel plots)	—	—	yes
Taxonomic rarefaction with bootstrap CI	—	—	yes
Stochastic resampling + sensitivity analysis	—	—	yes
Bias-corrected Shannon (Miller-Madow, Grassberger, Chao-Shen)	—	—	yes
Excel → results in one command	—	—	yes
Interactive Shiny dashboard	—	—	yes

🧰 Features

27 exported functions across the full workflow:

Category	Functions
Classical	`shannon()` (3 bias corrections), `simpson()`
Clarke & Warwick	`delta()`, `delta_star()`, `avtd()`, `vartd()`
Ozkan pTO	`ozkan_pto()`, `pto_components()`, `deng_entropy_level()`
Ozkan pipeline	`ozkan_pto_full()`, `ozkan_pto_resample()`, `ozkan_pto_sensitivity()`, `ozkan_pto_jackknife()`
Batch / compare	`batch_analysis()`, `compare_indices()`
Simulation / rarefaction	`simulate_td()`, `rarefaction_taxonomic()`
Structure	`build_tax_tree()`, `tax_distance_matrix()`
Visualization	`plot_funnel()`, `plot_rarefaction()`, `plot_iteration()`, `plot_radar()`, `plot_heatmap()`, `plot_bubble()`, `plot_taxonomic_tree()`
Dashboard	`taxdiv_explorer()`

Every main result object has tidy print() and summary() methods (13 S3 methods total), and the three example datasets (anatolian_trees, gazi_comm, gazi_gytk) let you try everything immediately.

🔁 Excel macro equivalence

taxdiv reproduces the 8 Ozkan pTO values from the original Excel macro, with full reproducibility via a seed argument:

Excel macro	taxdiv
Run 1 — uT0+, T0+	`uTO_plus`, `TO_plus`
Run 2 — uT0, T0	`uTO`, `TO`
Run 3 — uT0+max, T0+max, uT0max, T0max	`uTO_plus_max`, `TO_plus_max`, `uTO_max`, `TO_max`

The _max variants use only informative taxonomic levels (where Deng entropy > 0), matching the macro’s Run 3 behavior.

📚 Learn more

Full documentation, tutorials, and the method theory live on the package website:

Get Started — a guided tour
Complete Workflow
Ozkan pTO Method — Deng entropy, the slicing procedure, and the 8 indices
Clarke & Warwick Distinctness
Visualization Guide
Türkçe Rehber

📦 Package status

Metric	Value
R CMD check	0 errors, 0 warnings, 0 notes
Unit tests	668 passing
Exported functions	27
S3 methods	13
Example datasets	3
Vignettes	7 (6 English + 1 Turkish)

📖 Citation

citation("taxdiv")

Gorgoz MM, Ozkan K, Negiz MG, Mert A, Ozdemir S (2026). taxdiv: Taxonomic Diversity Indices Using Deng Entropy. R package version 1.0.0. https://github.com/mgorgoz/taxonomic-diversity-r

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

Full reference list

Primary methods

Ozkan, K. (2018). A new proposed measure for estimating taxonomic diversity. Turkish Journal of Forestry, 19(4), 336–346. doi: 10.18182/tjf.441061
Ozkan, K. & Mert, A. (2022). Comparisons of Deng entropy-based taxonomic diversity measures with the other diversity measures and introduction to the new proposed (reinforced) estimators. FORESTIST, 72(2). doi: 10.5152/forestist.2021.21025
Deng, Y. (2016). Deng entropy. Chaos, Solitons & Fractals, 91, 549–553. doi: 10.1016/j.chaos.2016.08.011

Taxonomic distinctness

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. doi: 10.3354/meps129301
Clarke, K.R. & Warwick, R.M. (1998). A taxonomic distinctness index and its statistical properties. Journal of Applied Ecology, 35(4), 523–531. doi: 10.1046/j.1365-2664.1998.3540523.x
Clarke, K.R. & Warwick, R.M. (1999). The taxonomic distinctness measure of biodiversity: weighting of step lengths between hierarchical levels. Marine Ecology Progress Series, 184, 21–29. doi: 10.3354/meps184021
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. doi: 10.3354/meps216265

Classical diversity

Shannon, C.E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423. doi: 10.1002/j.1538-7305.1948.tb01338.x
Simpson, E.H. (1949). Measurement of diversity. Nature, 163, 688. doi: 10.1038/163688a0

Evidence theory & bias correction

Dempster, A.P. (1967). Upper and lower probabilities induced by a multivalued mapping. The Annals of Mathematical Statistics, 38(2), 325–339. doi: 10.1214/aoms/1177698950
Shafer, G. (1976). A Mathematical Theory of Evidence. Princeton University Press.
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. doi: 10.1023/A:1026096204727

🤝 Contributing

Contributions are welcome: