Rarefaction Curve Calculator

Enter your Total Individuals, Species Abundance Data, and Subsample Size, then choose your Diversity Method and Confidence Interval to calculate your Expected Species Count, Observed Species, Sampling Efficiency, and Confidence Bounds.

Total count of all individuals in your sample

Comma-separated list of abundance counts for each species

Size of subsample for rarefaction calculation

Results

Expected Species Count

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Observed Species Count

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Sampling Efficiency

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Lower Confidence Bound

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Upper Confidence Bound

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Rarefaction Curve

Results Table

Frequently Asked Questions

What is rarefaction and why is it important?

Rarefaction is a statistical technique used to estimate species richness by standardizing samples to the same size. It corrects for bias in species counts due to unequal sampling effort and helps determine if sufficient sampling has been conducted.

How do I interpret the rarefaction curve?

A steep curve indicates many new species are still being discovered, suggesting more sampling is needed. A flattening curve suggests most species have been found and sampling effort may be adequate for the community.

What format should I use for species abundance data?

Enter the abundance count for each species separated by commas. For example: 25,18,15,12,10,8,7,6,5,4 where each number represents how many individuals of that species were observed.

What's the difference between richness, Shannon, and Simpson diversity?

Species richness counts the number of different species. Shannon diversity considers both richness and evenness of species distribution. Simpson diversity emphasizes dominant species and is less sensitive to rare species.

How should I choose the subsample size for rarefaction?

Choose a subsample size smaller than your total sample size, typically the size of your smallest sample if comparing multiple communities. This allows standardized comparison across different sampling efforts.

What do the confidence intervals tell me?

Confidence intervals show the range of uncertainty around the expected species count. Wider intervals indicate greater uncertainty, often due to small sample sizes or highly uneven species distributions.

When is rarefaction analysis not appropriate?

Rarefaction may not be suitable when samples have very different ecological conditions, when rare species are of particular interest, or when dealing with molecular data where low-abundance sequences may be artifacts.

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