The minimum spanning tree (MST) problem is one of the most basic optimization problems on metric spaces and graphs. We study the problem of computing a $(1+ε)$-approximation to the MST of an $n$-point metric space of doubling dimension $\mathrm{ddim}$. In doubling metrics, previous deterministic algorithms incur a running time with dependence $ε^{-O(\mathrm{ddim})}$. We give a deterministic algorithm that computes a $(1+ε)$-approximate MST in time $$ O_{\mathrm{ddim}}\left(n\log n+ \min\left\{n^2,\frac{n}ε\log(1/ε)\right\}\right) $$ in the standard real RAM model with a unit-cost exact-distance oracle. For bounded doubling dimension, this improves the previous dependence on $ε$ to essentially linear in $ε^{-1}$. We complement this algorithm with a lower bound of $$ Ω\!\left(n\log n+ \min\left\{n^2,\frac{n}ε\log(1/ε)\right\}\right) $$ on running time in the same model, already for metrics of doubling dimension at most seven, showing that our running time is essentially tight. For points in $\mathbb R^d$ with $d = O(1)$, exploiting Euclidean geometry allows us to further improve the dependence on $ε$. We obtain a $(1+ε)$-approximate MST in expected time $$ O_d\left( n \log n + \min\left\{n^2, \frac{n}{ε^{1 - β_d}} \log^{1-β_d}(1/ε) \right\} \right) $$ for some $β_d=Θ(1/d)$, improving the accuracy-dependent term $nε^{-2}\log^2(1/ε)$ of Arya and Mount [SODA'16].

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