Bray-Curtis Dissimilarity
Also known as: Bray-Curtis index, Sorensen-Bray-Curtis, percentage difference
Bray-Curtis dissimilarity is a quantitative measure of compositional difference between two samples, widely used in ecology and community analysis. Introduced by John Bray and John T. Curtis in 1957 for comparing forest communities, this index ranges from 0 (identical composition) to 1 (completely different). It is sensitive to abundance differences and is particularly effective for abundance data such as species counts, microbial populations, or preference intensities.
Key highlights
- Intuitive interpretation for ecological and compositional data; ranges from 0 to 1
- Sensitive to both presence and abundance; emphasizes dominant features
- Robust and stable with different community sizes
- Widely adopted in ecology, microbiology, and environmental science with extensive validation
Intuition
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How it works
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When to use it
Bray-Curtis dissimilarity is ideal for comparing compositional data such as species abundance, microbial community profiles, or preference distributions. It works well when relative abundance is more important than absolute counts and when you have positive-valued data. Use it in ecology, microbiology, community analysis, and decision-making with intensity preferences. It is sensitive to dominant species or attributes, which can be an advantage or disadvantage depending on context.
Strengths & limitations
- Intuitive interpretation for ecological and compositional data; ranges from 0 to 1
- Sensitive to both presence and abundance; emphasizes dominant features
- Robust and stable with different community sizes
- Widely adopted in ecology, microbiology, and environmental science with extensive validation
- Not invariant to transformation; scaling affects the result unless data is normalized first
- Sensitive to rare species or attributes; not suitable when rare elements should not influence distance
- Assumes positive, count-like data; negative values are not interpretable
- Does not account for phylogenetic or hierarchical relationships between attributes
Common pitfalls
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Applications
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Frequently asked
How does Bray-Curtis dissimilarity differ from Euclidean distance?
Euclidean distance measures spatial separation in absolute terms; Bray-Curtis measures compositional difference as a proportion of total abundance. Bray-Curtis is bounded [0,1] and is normalized by total abundance, making it more suitable for compositional and count data.
Can Bray-Curtis be used with presence-absence data?
While it works with binary data, presence-absence data is better analyzed with Sorensen-Dice or Jaccard indices, which are designed for binary similarity. Bray-Curtis is optimized for abundance data.
Does data normalization matter for Bray-Curtis dissimilarity?
Yes. Bray-Curtis is not invariant to scaling, so if samples have different total abundances due to sampling effort differences, normalization (e.g., to relative proportions) is recommended before comparison.
What does a Bray-Curtis value of 0.5 mean?
A Bray-Curtis value of 0.5 indicates that 50% of the total abundance is shared or overlaps between the two samples. The remaining 50% is unique or different between them, representing moderate compositional similarity.
Sources
- 1.Bray, J. R., & Curtis, J. T. (1957). An ordination of the upland forest communities of southern Wisconsin. Ecological Monographs, 27(4), 325-349.
- 2.Sorensen, T. (1948). A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons. Biologiske Skrifter, 5, 1-34.
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ScholarGate. (2026, June 3). Bray-Curtis Dissimilarity. ScholarGate. https://scholargate.app/decision-making/bray-curtis-dissimilarity