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:

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❓“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.

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❓“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.

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❓“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.

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🧭 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:
GoalBest Metric
Estimate full species count (including rare ones)CHAO1
Balance between richness and evennessShannon Index
Emphasize dominant speciesSimpson Index
Compare across samples or studiesUse a combo!

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📈 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.