Ozkan pTO Method: Deng Entropy-Based Taxonomic Diversity

What Is the Ozkan pTO Method?

Ozkan (2018) introduced a novel approach to measuring taxonomic diversity using Deng entropy — a generalization of Shannon entropy rooted in Dempster-Shafer evidence theory (Dempster, 1967; Shafer, 1976).

The key idea: at each level of the taxonomic hierarchy (genus, family, order, etc.), Deng entropy measures how evenly species are distributed across groups. The product of these level-wise entropies gives a single number that captures the entire hierarchical diversity of a community.

This approach produces 8 complementary indices through a three-stage pipeline, each answering a slightly different question about the community.

library(taxdiv)

community <- c(
  Quercus_coccifera    = 25,
  Quercus_infectoria   = 18,
  Pinus_brutia         = 30,
  Pinus_nigra          = 12,
  Juniperus_excelsa    = 8,
  Juniperus_oxycedrus  = 6,
  Arbutus_andrachne    = 15,
  Styrax_officinalis   = 4,
  Cercis_siliquastrum  = 3,
  Olea_europaea        = 10
)

tax_tree <- build_tax_tree(
  species = names(community),
  Genus   = c("Quercus", "Quercus", "Pinus", "Pinus",
              "Juniperus", "Juniperus", "Arbutus", "Styrax",
              "Cercis", "Olea"),
  Family  = c("Fagaceae", "Fagaceae", "Pinaceae", "Pinaceae",
              "Cupressaceae", "Cupressaceae", "Ericaceae", "Styracaceae",
              "Fabaceae", "Oleaceae"),
  Order   = c("Fagales", "Fagales", "Pinales", "Pinales",
              "Pinales", "Pinales", "Ericales", "Ericales",
              "Fabales", "Lamiales")
)

From Shannon to Deng: Why a New Entropy?

Shannon entropy treats each species as an independent event with probability \(p_i\). But in a taxonomic hierarchy, species are grouped — two oak species share more information than an oak and a pine. Shannon cannot capture this grouping.

Deng entropy solves this through the concept of focal elements from evidence theory. At each taxonomic level, a group (e.g., “Family Fagaceae”) acts as a focal element with a mass proportional to the species it contains. The entropy accounts for both the mass distribution and the size of each focal element (how many species it contains):

\[E_d = -\sum_{i=1}^{n} m(F_i) \log_2 \frac{m(F_i)}{2^{|F_i|} - 1}\]

where \(m(F_i)\) is the mass of focal element \(F_i\) and \(|F_i|\) is the number of species it contains.

The term \(2^{|F_i|} - 1\) accounts for all possible non-empty subsets of species within the group. A genus with 3 species has \(2^3 - 1 = 7\) possible subcombinations, giving it more “evidential weight” than a single-species genus.

Deng Entropy at Each Taxonomic Level

result <- ozkan_pto(community, tax_tree)

cat("Deng entropy by taxonomic level:\n\n")
#> Deng entropy by taxonomic level:
for (i in seq_along(result$Ed_levels)) {
  level <- names(result$Ed_levels)[i]
  value <- result$Ed_levels[i]
  cat(sprintf("  %-10s Ed = %.4f\n", level, value))
}
#>   Species    Ed = 2.3026
#>   Genus      Ed = 2.5459
#>   Family     Ed = 2.5459
#>   Order      Ed = 2.9935

How to interpret:

• Species level: Equals Shannon entropy when all species are equally weighted (special case where each focal element has size 1)
• Genus level: High when species are spread across many genera. Low when most species share one genus.
• Family level: High when genera span many families. Low when the community is taxonomically narrow at the family level.
• Order level: Similar pattern at the highest taxonomic rank.

A level with Deng entropy = 0 means all species belong to a single group at that level — it contributes no taxonomic information.

The 8 Indices Explained

The Ozkan method produces 8 values organized in a 2 x 2 x 2 structure:

cat("=== All 8 Ozkan pTO indices ===\n\n")
#> === All 8 Ozkan pTO indices ===
cat("Standard (all levels):\n")
#> Standard (all levels):
cat("  uTO      =", round(result$uTO, 4), "  (unweighted diversity)\n")
#>   uTO      = 7.4895   (unweighted diversity)
cat("  TO       =", round(result$TO, 4), "  (weighted diversity)\n")
#>   TO       = 10.6675   (weighted diversity)
cat("  uTO+     =", round(result$uTO_plus, 4), "  (unweighted distance)\n")
#>   uTO+     = 8.5502   (unweighted distance)
cat("  TO+      =", round(result$TO_plus, 4), "  (weighted distance)\n\n")
#>   TO+      = 11.7283   (weighted distance)

cat("Max-informative levels:\n")
#> Max-informative levels:
cat("  uTO_max  =", round(result$uTO_max, 4), "  (unweighted, informative only)\n")
#>   uTO_max  = 7.4895   (unweighted, informative only)
cat("  TO_max   =", round(result$TO_max, 4), "  (weighted, informative only)\n")
#>   TO_max   = 10.6675   (weighted, informative only)
cat("  uTO+_max =", round(result$uTO_plus_max, 4), "  (unweighted distance, informative only)\n")
#>   uTO+_max = 8.5502   (unweighted distance, informative only)
cat("  TO+_max  =", round(result$TO_plus_max, 4), "  (weighted distance, informative only)\n")
#>   TO+_max  = 11.7283   (weighted distance, informative only)

The Three-Run Pipeline

cat("Run 1 results:\n")
#> Run 1 results:
cat("  uTO+ =", round(result$uTO_plus, 4), "\n")
#>   uTO+ = 8.5502
cat("  TO+  =", round(result$TO_plus, 4), "\n")
#>   TO+  = 11.7283

run2 <- ozkan_pto_resample(community, tax_tree, n_iter = 101, seed = 42)

cat("Run 1 (deterministic):  uTO+ =", round(run2$uTO_plus_det, 4), "\n")
#> Run 1 (deterministic):  uTO+ = 8.5502
cat("Run 2 (stochastic max): uTO+ =", round(run2$uTO_plus_max, 4), "\n")
#> Run 2 (stochastic max): uTO+ = 8.5502

plot_iteration(run2, component = "TO_plus",
               title = "Run 2: TO+ Across Iterations")

run3 <- ozkan_pto_sensitivity(community, tax_tree, run2, seed = 123)

cat("All levels:       TO+ =", round(run3$TO_plus_max, 4), "\n")
#> All levels:       TO+ = 11.7283
cat("Informative only: TO+ =", round(result$TO_plus_max, 4), "\n")
#> Informative only: TO+ = 11.7283

full <- ozkan_pto_full(community, tax_tree, n_iter = 101, seed = 42)

cat("Complete pipeline summary:\n\n")
#> Complete pipeline summary:
cat("         uTO+      TO+       uTO       TO\n")
#>          uTO+      TO+       uTO       TO
cat("Run 1:", sprintf("%9.4f %9.4f %9.4f %9.4f",
    full$run1$uTO_plus, full$run1$TO_plus,
    full$run1$uTO, full$run1$TO), "\n")
#> Run 1:    8.5502   11.7283    7.4895   10.6675
cat("Run 2:", sprintf("%9.4f %9.4f %9.4f %9.4f",
    full$run2$uTO_plus_max, full$run2$TO_plus_max,
    full$run2$uTO_max, full$run2$TO_max), "\n")
#> Run 2:    8.5502   11.7283    7.4895   10.6675
cat("Run 3:", sprintf("%9.4f %9.4f %9.4f %9.4f",
    full$run3$uTO_plus_max, full$run3$TO_plus_max,
    full$run3$uTO_max, full$run3$TO_max), "\n")
#> Run 3:    8.5502   11.7283    7.5029   10.6808

Jackknife Leave-One-Out Analysis

The jackknife procedure removes each species one at a time and recalculates all indices. This directly measures each species’ contribution:

jk <- ozkan_pto_jackknife(community, tax_tree)

cat("Jackknife results (TO+ when each species is removed):\n\n")
#> Jackknife results (TO+ when each species is removed):
jk_df <- jk$jackknife_results
for (i in seq_len(nrow(jk_df))) {
  direction <- ifelse(jk_df$TO_plus[i] > result$TO_plus, "UNHAPPY", "happy")
  cat(sprintf("  Remove %-25s -> TO+ = %.4f  [%s]\n",
              jk_df$species[i], jk_df$TO_plus[i], direction))
}
#>   Remove Quercus_coccifera         -> TO+ = 11.4820  [happy]
#>   Remove Quercus_infectoria        -> TO+ = 11.4820  [happy]
#>   Remove Pinus_brutia              -> TO+ = 11.6616  [happy]
#>   Remove Pinus_nigra               -> TO+ = 11.6616  [happy]
#>   Remove Juniperus_excelsa         -> TO+ = 11.6616  [happy]
#>   Remove Juniperus_oxycedrus       -> TO+ = 11.6616  [happy]
#>   Remove Arbutus_andrachne         -> TO+ = 11.3238  [happy]
#>   Remove Styrax_officinalis        -> TO+ = 11.3238  [happy]
#>   Remove Cercis_siliquastrum       -> TO+ = 11.2505  [happy]
#>   Remove Olea_europaea             -> TO+ = 11.2505  [happy]

cat("\nHappy species:", jk$n_happy, "\n")
#> 
#> Happy species: 10
cat("Unhappy species:", jk$n_unhappy, "\n")
#> Unhappy species: 0

• happy species: Removing them decreases diversity (they contribute positively to taxonomic structure)
• UNHAPPY species: Removing them increases diversity (they are taxonomically redundant)

Comparing Communities

degraded <- c(
  Quercus_coccifera = 40,
  Pinus_brutia      = 35,
  Juniperus_oxycedrus = 10
)

communities <- list(
  "Intact (10 spp)"  = community,
  "Degraded (3 spp)" = degraded
)

plot_radar(communities, tax_tree,
           title = "Intact vs Degraded Forest")
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_point()`).

The radar chart reveals which diversity dimensions are most affected by degradation. If abundance-weighted indices (Shannon, Simpson, TO+) drop more than presence/absence indices (AvTD, uTO+), the community has lost evenness. If both drop equally, the community has lost taxonomic breadth.

References

• Ozkan, K. (2018). A new proposed measure for estimating taxonomic diversity. Turkish Journal of Forestry, 19(4), 336-346.
• Deng, Y. (2016). Deng entropy. Chaos, Solitons & Fractals, 91, 549-553.
• Dempster, A.P. (1967). Upper and lower probabilities induced by a multivalued mapping. The Annals of Mathematical Statistics, 38(2), 325-339.
• Shafer, G. (1976). A Mathematical Theory of Evidence. Princeton University Press, Princeton, NJ.