Bray-Curtis dissimilarity on relative abundance data is the Manhattan distance (aka L₁ distance)

Warning: super-technical post ahead, but I have made this point in oral discussions at least a few times, so I thought I would write it up. It is a trivial algebraic manipulation, but because “ℓ₁ norm” sounds too mathy for ecologists while “Bray-curtis” sounds too ecological and ad-hoc for mathematically minded people, it’s good to see that it’s the same thing on normalized data.

Assuming you have two feature vectors, Xᵢ, Xⱼ, if they have been normalized to sum to 1, then the Bray-Curtis dissimilarity is just their ℓ₁ distance, aka Manhattan distance (times ½, which is a natural normalization so that the result is between zero and one).

This is the Wikipedia definition of the Bray-Curtis dissimiliarity (there are a few other, equivalent, definitions around, but we’ll use this one):

BC = 1 – 2 Cᵢⱼ/(Sᵢ + Sⱼ), where Cᵢⱼ =Σₖmin(Xᵢₖ, Xⱼₖ)  and Sᵢ = ΣₖXᵢₖ.

While the Manhattan distance is D₁ = Σₖ|Xᵢₖ – Xⱼₖ|

We are assuming that they sum to 1, so Sᵢ=Sⱼ=1. Thus,

BC = 1 – Σₖmin(Xᵢₖ, Xⱼₖ)

Now, this still does not look like the Manhattan distance (D₁, above). But for any a and b ≥0, it holds that

min(a,b) = (a + b)/2 – |a – b|/2

(this is easiest to see graphically:  start at the midpoint, (a+b)/2 and subtract half of the difference, |a-b|/2).

Thus, BC = 1 -Σₖmin(Xᵢₖ, Xⱼₖ) = 1 – Σₖ{(Xᵢₖ + Xⱼₖ)/2 – |Xᵢₖ + Xⱼₖ|/2}

Because, we assumed that the vectors were normalized,  Σₖ(Xᵢₖ + Xⱼₖ)/2 =(ΣₖXᵢₖ +ΣₖXⱼₖ)/2 = 1, so

BC = 1 – 1 + Σₖ|Xᵢₖ + Xⱼₖ|/2 = D₁/2.


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