Typically, we recommend report results for all calculated metrics (e.g., “CHAO1: p=0.002, Shannon: p=0.042, Simpson: p=0.067”) as they may show different patterns depending on the underlying community changes.
❓“I want to know how many species are really there (even the rare ones).”
➡ Use: CHAO1 Index- Best when your sample has lots of low-abundance organisms (like in microbiome studies).
- Estimates the total number of species by using the pattern of rare species detection (singletons and doubletons).
- Useful when you suspect that sequencing depth limitations prevented detection of some species.
- Based on capture-recapture principles—doesn’t assume any particular statistical distribution for your data.
❓“I care about both how many species there are and how evenly they’re spread.”
➡ Use: Shannon Index- A balanced metric that considers both richness (how many) and evenness (how equal).
- Measures the “uncertainty” in predicting species identity when randomly selecting an individual.
- Sensitive to both common and rare species in the community.
- Higher values indicate greater diversity through either more species or more even distribution.
❓“I’m mostly interested in the dominant players.”
➡ Use: Simpson Index- Focuses heavily on the most abundant species in the community.
- Measures the probability that two randomly selected individuals belong to different species.
- Less sensitive to rare or low-abundance species compared to Shannon.
- Excellent for detecting changes in community dominance structure.
🧭 So…which one should I use?
Best practice is to calculate and report all three metrics, as they capture different aspects of diversity. However, if focusing on one metric for your analysis:| Goal | Best Metric | Why |
|---|---|---|
| Estimate total species richness | CHAO1 | Accounts for undetected rare species |
| Comprehensive diversity assessment | Shannon Index | Balances richness and evenness |
| Focus on dominance patterns | Simpson Index | Emphasizes abundant species |
| Compare across studies | Use combination | Different metrics reveal different patterns |
📈 What About Statistical Testing?
Alpha diversity metrics can be statistically compared between groups using non-parametric tests: Wilcoxon Rank-Sum Test (Mann-Whitney U): For comparing two groups- Tests whether the distributions of diversity values differ significantly between groups
- Appropriate when data may not be normally distributed
- Non-parametric equivalent of one-way ANOVA
- Follow with post-hoc tests if significant differences are found
- Always visualize your data first (boxplots, violin plots)
- Check for outliers that might influence results
- Consider multiple testing corrections when comparing many groups
- Report effect sizes alongside p-values when possible