Certain recent advances in statistical methodology have promising potential for fruitful use in general biology and the fisheries sciences. This paper reviews and discusses some of the relevant themes, including accurate modelling via focused model selection techniques, dynamic goodness-of-fit testing of processes evolving over time, finding break points for phenomena experiencing regime shifts, prediction uncertainty, and optimal combination of information across diverse sources via confidence distributions. The methods are illustrated for the Hjort liver quality index time series. Its roots lie in the classic study by Hjort in 1914, where liver quality of the Northeast Arctic cod (Gadus morhua) for 1880–1912 was reported on and studied, along with related factors, making it one of the first teleost time series ever published. The series has been extended both backwards and forwards in time, to 1859–2012, due to comprehensive archival and calibration efforts of Kjesbu et al. in 2014, yielding one of the longest time series of marine science. Our study offers a detailed examination of this series and how it relates to and interacts with associated factors, including winter temperatures, length distribution parameters, cod mortality, and a certain index related to availability of food. We identify certain mild nonstationary aspects of the time series, show that there is a regime shift around 1990 in the ways the liver series interacts with winter temperatures, and demonstrate that mortality and food availability play important roles.
An Efficient Prediction Model for Water Discharge in Schoharie Creek, NY