Given normal distributions with a single positional and variation parameter each, $p_1=\big[\mu_1, \sigma_1\big]$, $p_2=\big[\mu_2, \sigma_2\big]$, we define their Jensen-Shannon divergence as: $$ \text{JS}(p_1, p_2)=\big|\int_{\mathbb{R}}\frac{p_1+p_2}{2}\ln\big(\frac{p_1+p_2}{2}\big)-\frac{1}{2}\big(p_1\ln p_1+p_2\ln p_2\big)dx\big| $$ We write $p_1(s)=\big[\mu_1,\sigma_1/\sqrt{s}\big]$, $p_2(s)=\big[\mu_2,\sigma_2/\sqrt{s}\big]$ to signify their corresponding sampling distributions, $s>0$.

**Question**: Are there any resources that establish a relationship, ideally some identity, between the $\text{JS}(p_1, p_2)$ and $\text{JS}(p_1(s), p_2(s))$?

**EDIT** For simplicity, $1/\sqrt{s}=\lambda$, and we will refer to the distributions above in terms of their coordinates (as brackets). We will superscript the Jensen-Shannon by $\lambda$ to say that each distribution is denormalized by a factor $\lambda$. Since the Jensen-Shannon is $1$-homogenous ($f(\lambda x)=\lambda f(x)$), we can extract $\lambda$ as below, then translating by $x\mapsto x+\mu_1$,
$$
\frac{1}{\lambda}\text{JS}^{\lambda}\big([0, \sigma_1\lambda],[\mu_2-\mu_1, \sigma_2\lambda]\big)
$$
We scale by $x\mapsto x\lambda$ to eliminate the $\lambda$ factors from the $\sigma$ terms.
$$
=\text{JS}\big([0, 1], [(\mu_2-\mu_1)/\lambda, \sigma_2/\sigma_1])
$$
Of course for the original divergence, similar operations give us
$$
\text{JS}(p_1, p_2)=\text{JS}\big([0, 1], [(\mu_2-\mu_1), \sigma_2/\sigma_1]\big)
$$
So really the question becomes comparing the Jensen-Shannon divergences of two distributions, the first $[0, 1]$ and the second $[\mu_2-\mu_1, \sigma_2/\sigma_1]$, where the second Jensen-Shannon has scaled the positional argument by $1/\lambda$.

Therefore I propose we consider this question to be a multiplicative property (if any) of the position whereas the translation-invariance property is an additive property.