Abundance indices derived from fishery-independent surveys
typically exhibit much higher interannual variability than is consistent with the within-survey variance or the life history of a species. This extra variability is essentially observation noise (i.e. measurement error); it probably
reflects environmentally driven factors that affect catchability over time. Unfortunately, high observation
noise reduces the ability to detect important changes in the underlying population abundance. In our study, a
noise-reduction technique for uncorrelated observation noise that is based on autoregressive integrated moving
average (ARIMA) time series modeling is investigated. The approach is applied to 18 time series of finfish abundance, which were derived from trawl survey data from the U.S.
northeast continental shelf. Although the a priori assumption of a random-walk-plus-uncorrelated-noise model
generally yielded a smoothed result that is pleasing to the eye, we recommend that the most appropriate ARIMA model be identified for the observed time series if the smoothed
time series will be used for further analysis of the population dynamics of a species
