  Understanding what controls apparently random fluctuations in fish
recruitment is a major challenge in fisheries science. Our current inability to
anticipate recruitment failures has led to costly management actions and in
some cases complete fishery collapse. Time series observations of fish
recruitment reflect an interplay of underlying population processes,
environmental forcing, measurement error, and process error. Given that the
error component is often very strong, an important unresolved question is
whether any non-random signal can be uncovered in the annual fluctuations of
recruitment time series. Here, we address this fundamental question in an
analysis of 569 fish populations from a global database of recruitment. Using a
nonparametric time series analysis method, we find overwhelming evidence for
non-random dynamics operating in the recruitment process. Unlike previous
explorations of this topic, our approach does not require the specification of
a stock-recruitment model, and it can be used on recruitment estimates derived
from a wide-range of abundance estimation methods. The evidence for
non-randomness is robust across a wide range of fish families and abundance
estimation methods. We also find that the statistical support for
non-randomness increases consistently with the number of observations of a fish
stock. This result provides the encouraging news that with continued
observations, and the appropriate covariates, we should ultimately be able to
uncover the mechanistic drivers of fish recruitment.
