Alpha Diversity Metrics: How to Choose the Right One
Alpha diversity is all about the variety within a single sample. Think: how many different species are there (richness)? And how evenly are those species spread out (evenness)? There are a few ways to measure this, depending on what you’re looking for. Here’s a quick breakdown:
❓“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).
- Helps estimate the total number of species, even the ones you didn’t detect directly.
- Useful if you think some species are hiding due to low counts.
- Works well if your data behaves like a Poisson distribution (typical in count-based sequencing data).
🧠 Takeaway: CHAO1 is your go-to when you’re trying to guess the true number of species, including the ones that are too rare to show up reliably.
❓“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).
- Great for comparing samples where one species might dominate more than others.
- Assumes all species are represented and sampled randomly.
🧠 Takeaway: Shannon Index gives you a more complete picture. If your sample has 10 species but one of them makes up 90% of the total, the Shannon score will reflect that imbalance.
❓“I’m mostly interested in the dominant players.”
➡ Use: Simpson Index
- Focuses more on the most abundant species.
- Rare or low-abundance species don’t have much impact here.
- Good if you want a stability-focused measure that isn’t thrown off by noise from low-read taxa.
🧠 Takeaway: Simpson Index is great when you’re interested in who’s really running the show in your community.
🧭 So…which one should I use?
Best practice is to report p-values for all metrics (e.g., CHAO1 p=0.002, Shannon p=0.042, Simpson p=0.067). But if only plotting one metric in your paper, follow these guidlines:
| Goal | Best Metric |
|---|---|
| Estimate full species count (including rare ones) | CHAO1 |
| Balance between richness and evenness | Shannon Index |
| Emphasize dominant species | Simpson Index |
| Compare across samples or studies | Use a combo! |
📈 What About Statistical Testing?
Alpha diversity metrics can be statistically compared between groups using non-parametric tests such as:
- Kruskal-Wallis Test: For comparing more than two groups.
- Wilcoxon Rank-Sum Test (Mann-Whitney U): For comparing two groups.
These tests evaluate whether diversity metrics differ significantly between conditions. Always check assumptions and consider applying corrections for multiple testing.