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I have presence-absence data for 53 wildlife species at 60 different sites. I am a bit confused as to whether a PCA would be appropriate for this sort of data. If so, is there a specific R package I need to use, and do I need to standardize the data considering it's just 0's and 1's? If not, what other data analysis options do I have? Thank you, and I appreciate any advice. I am not experienced in this topic and have confused myself looking at previous similar questions.

I have tried conducting a PCA using a couple different R packages but am unsure if this is a valid technique and if am doing it correctly.

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    $\begingroup$ Welcome to CV. Mathematically (and computationally) there's no problem, because both $0$ and $1$ are real numbers. What you need to explain is why you want to do PCA, for only then could we possibly understand what sense of "appropriate" would be meaningful in your application or what any suitable "data analysis option" might be. Please elaborate. $\endgroup$
    – whuber ♦
    Commented Aug 2, 2023 at 21:57
  • $\begingroup$ Note that PCA uses the variance/covariance matrix. The covariances on1/0 variables can be interpreted as the covariance on indicator functions, which is related to the independence gap. AFAIK it is bounded. But what to make of the transformed vectors in the eigenbasis I do not know $\endgroup$
    – Galen
    Commented Aug 2, 2023 at 22:17
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    $\begingroup$ Please do search the site. Tags pca & binary-data. The question was previously asked multiple times $\endgroup$
    – ttnphns
    Commented Aug 2, 2023 at 22:54

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You can use a PCA (it's a valid mathematical option as long as you have a matrix full of numbers, whether they're 0/1 or not), but (depending on what you want to do with the results of this analysis) you might be better off with a principal coordinates analysis (PCOA), which is like a PCA but based on distances between observations (sites, in your case); a PCOA is the same as PCA if we use Euclidean distances (i.e., $\sqrt{\sum(x_i-y_i)^2}$, but people usually use different distance metrics when they have presence/absence data, such as the Jaccard index (see e.g. this question)

If you're working in R you can search for vegan + pcoa to get links to lots of examples (vegan is the main package in R for working with community ecology data).

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