Detecting Unusual Temporal Patterns in Fisheries Time Series Data

Abstract: Long-term sampling of fisheries data is an important source of information for making inferences about the temporal dynamics of populations that support ecologically and economically important fisheries. For example, time series of catch-per-effort data are often examined for the presence of long-term trends. However, it is also of interest to know whether certain sampled locations are exhibiting temporal patterns that deviate from the overall pattern exhibited across all sampled locations. Patterns at these “unusual” sites may be the result of site-specific abiotic (e.g., habitat) or biotic (e.g., the presence of an invasive species) factors that cause these sites to respond differently to natural or anthropogenic drivers of population dynamics or to management actions. We present a Bayesian model selection approach that allows for detection of unique sites—locations that display temporal patterns with documentable inconsistencies relative to the overall global average temporal pattern. We applied this modeling approach to long-term gill-net data collected from a fixed-site, standardized sampling program for Yellow Perch Perca flavescens in Oneida Lake, New York, but the approach is also relevant to shorter time series data. We used this approach to identify six sites with distinct temporal patterns that differed from the lakewide trend, and we describe the magnitude of the difference between these patterns and the lakewide average trend. Detection of unique sites may be informative for management decisions related to prioritizing rehabilitation or restoration efforts, stocking, or determining fishable areas and for further understanding changes in ecosystem dynamics.