  We present a data structure that in a dynamic graph of treedepth at most $d$,
which is modified over time by edge insertions and deletions, maintains an
optimum-height elimination forest. The data structure achieves worst-case
update time $2^{{\cal O}(d^2)}$, which matches the best known parameter
dependency in the running time of a static fpt algorithm for computing the
treedepth of a graph. This improves a result of Dvo\v{r}\'ak et al. [ESA 2014],
who for the same problem achieved update time $f(d)$ for some non-elementary
(i.e. tower-exponential) function $f$. As a by-product, we improve known upper
bounds on the sizes of minimal obstructions for having treedepth $d$ from
doubly-exponential in $d$ to $d^{{\cal O}(d)}$.
  As applications, we design new fully dynamic parameterized data structures
for detecting long paths and cycles in general graphs. More precisely, for a
fixed parameter $k$ and a dynamic graph $G$, modified over time by edge
insertions and deletions, our data structures maintain answers to the following
queries:
  - Does $G$ contain a simple path on $k$ vertices?
  - Does $G$ contain a simple cycle on at least $k$ vertices?
  In the first case, the data structure achieves amortized update time
$2^{{\cal O}(k^2)}$. In the second case, the amortized update time is $2^{{\cal
O}(k^4)} + {\cal O}(k \log n)$. In both cases we assume access to a dictionary
on the edges of $G$.
