Species Diversity Index Calculator (Shannon-Wiener)

Why “diversity” needs multiple indices

A simple species count tells you almost nothing about a community. Compare two forests both with 10 species. Forest A has roughly equal numbers of each species. Forest B has one dominant species making up 90% of trees, with the other 9 species barely present. Both have richness = 10, but they’re ecologically very different. Diversity indices try to capture this distinction.

There’s no single “best” diversity index. Ecologists use several because each captures a different aspect: how many species there are, how evenly distributed they are, and how likely two random individuals are to differ.

Shannon-Wiener index (H’)

The most widely cited diversity index, originally from information theory (Claude Shannon, 1948), repurposed for ecology by Robert MacArthur:

H’ = -Σ (pᵢ × ln pᵢ)

Where pᵢ is the proportion of individuals belonging to species i. Sum over all species.

Interpretation:

The natural log version (H’) is the standard. Some literature uses log₂ instead (H), giving Shannon entropy in bits. The relationship: H = H’/ln(2) ≈ H’/0.693.

Pielou’s evenness (J’)

Shannon-Wiener by itself can’t tell you whether diversity is from many species or from even distribution. Pielou’s evenness separates these:

J’ = H’ ÷ ln(S)

Where S = number of species (richness). J’ ranges from 0 to 1:

• J’ = 1.0: every species has equal abundance, which is maximum evenness
• J’ = 0.5: heavily skewed; some species dominate
• J’ = 0.0: one species accounts for all individuals (only possible in trivial case)

Most natural communities sit at J’ between 0.5 and 0.85. A J’ of 0.9+ is rare and indicates a near-perfect equitable community.

Simpson’s index comes in three forms

Simpson developed his index in 1949 to express “the probability that two randomly chosen individuals belong to different species”:

D = Σ pᵢ² (Simpson’s dominance, higher = less diverse) 1-D = 1 − Σ pᵢ² (Simpson’s diversity, higher = more diverse) 1/D = 1 ÷ Σ pᵢ² (Inverse Simpson, higher = more diverse, no upper bound)

The three forms confuse beginners. The most commonly reported in modern literature is 1 − D (Gini-Simpson), which:

• Ranges from 0 to (S-1)/S, approaching 1 for large S
• Has intuitive interpretation: “probability two random individuals are different species”
• Less sensitive to rare species than Shannon (more sensitive to dominant ones)

Comparison: Shannon vs Simpson sensitivity

Every row is worked from a full count list rather than a rounded summary, so you can paste any of them straight into the box and get the same two numbers back. The first row is the exception: the calculator needs at least two species to have anything to compare, so a single-species community is only in the table to anchor the zero end.

The last two rows are the pair worth staring at. Both have ten species. Simpson drops from 0.90 to 0.55 and Shannon drops from 2.30 to 1.10, but they are answering different questions: Simpson is telling you that two random individuals are now usually the same species, while Shannon is telling you the community has lost most of its information content.

Hill numbers, modern diversity ecology

Modern ecology increasingly uses Hill numbers (effective number of species), which unify the various indices:

• ⁰D = S (species richness, treats all species equally)
• ¹D = exp(H’) (Shannon’s exponential, weights by frequency)
• ²D = 1/D (Inverse Simpson, weights by abundance²)
• ∞D = 1/max(pᵢ) (Berger-Parker dominance reciprocal)

Hill numbers have intuitive units (“equivalent species”) and are directly comparable across studies. Lou Jost (2006, 2007) is the key reference. Most rigorous biodiversity papers now report these instead of raw Shannon or Simpson values.

Practical use: which index answers which question

Sampling effort, the hidden problem

Diversity indices depend on sample size. Larger samples find more rare species. To compare across sites, use rarefaction (random subsampling to the smallest sample size) or extrapolation methods (Chao1, ACE, iNEXT software).

A common mistake: comparing a 100-individual sample to a 1000-individual sample without standardization. The smaller sample will appear less diverse purely because of sampling effort, not real ecological difference.

Worked example

Sampling a meadow yields:

• Dandelion: 45
• Clover: 30
• Grass species A: 15
• Plantain: 8
• Wildflower X: 2

N = 100, S = 5

Proportions: 0.45, 0.30, 0.15, 0.08, 0.02

Shannon: H’ = −(0.45 × ln(0.45) + 0.30 × ln(0.30) + 0.15 × ln(0.15) + 0.08 × ln(0.08) + 0.02 × ln(0.02)) H’ = −(−0.359 − 0.361 − 0.285 − 0.202 − 0.078) H’ = 1.285, which the table above calls low diversity

Pielou: J’ = 1.285 ÷ ln(5) = 1.285 ÷ 1.609 = 0.799 (reasonably even but not perfect)

Simpson D: 0.45² + 0.30² + 0.15² + 0.08² + 0.02² = 0.203 + 0.09 + 0.023 + 0.006 + 0.0004 = 0.322 Simpson 1-D = 0.678 (moderate diversity)

Inverse Simpson (²D) = 1 ÷ 0.322 = 3.11 effective species, and exp(H’) (¹D) = 3.62 effective species. Both say the same thing in plain language: the meadow behaves like a community of three or four equally common plants, even though five were counted.

The same data gives four different numbers depending on which index you pick. All are valid, they are just different lenses.

Biodiversity applications

• Conservation prioritization: comparing relative diversity of candidate protected areas
• Pollution monitoring: declining Shannon often signals environmental degradation
• Restoration tracking: post-restoration sites should approach reference Shannon over time
• Agricultural ecology: rotational diversification effects on soil microbiome
• Marine surveys: coral reef health monitoring
• Microbiome studies: gut bacterial diversity in disease vs healthy states

How to enter your counts

Paste the raw individual counts, one per species, in any order. Commas, spaces, tabs, semicolons and line breaks all work as separators, so a column copied straight out of a spreadsheet goes in without editing.

Do not enter percentages or proportions that you have already worked out. The indices only need the ratios, so 45, 30, 15, 8, 2 and 450, 300, 150, 80, 20 give identical answers, but a list of percentages that has been rounded to whole numbers will drift slightly from the raw data.

Species with a count of zero are dropped rather than counted as present, which matters for richness. A species you looked for and did not find is not part of the community you sampled.