A new perspective: Atlantic herring (Clupea harengus) as a case study for time series analysis and historical data
Bibliographic record
Abstract
This thesis endeavors to develop methods for the historical analysis of a specific species and location to begin understanding fishery patterns and change over time. The main goal was to develop statistical methods to address historical data and provide long-term information on fishery trends and potential relationships between the fishery and outside influences. The Atlantic herring (Clupea harengus) fishery was investigated for underlying patterns and the possible impact of outside variables and events from 1870 to 2007. In the Gulf of Maine, Atlantic herring (Clupea harengus) provide critical forage for many economically valuable species, while supporting a major New England fishery. Extensive research and stock assessments conducted on herring since the 1960s have focused on recent patterns of distribution, abundance, and other fishery characteristics. This work has often neglected longer-term patterns or changes and the long history of anthropogenic influence and exploitation. Further, the current management strategy for herring may be insufficient and herring ecology is not fully understood. Specific questions remain on stock structure and the viability of inshore populations, in addition to the possibly major changes in herring abundance and distribution suggested by historical documents. Due to these questions and their ecological and economic importance, herring are an interesting case study for the investigation of historical data and the application of time series analysis (TSA). Here, TSA was used to explore long-term herring fishery data and the possible influence of anthropogenic events and natural drivers from 1871 to the present (2007). Historical information on Atlantic herring and oceanographic features was compiled from many sources across New England and in St. Andrews Bay, Canada. For herring, the information was aggregated into a time series by total pounds per year for Maine and the Canadian Bay of Fundy. In addition, a time series was built for sea surface temperature (SST) and surface salinity at St. Andrews Biological Station (SABS) in Canada. Finally, a timeline constructed from the qualitative historical text summarized potentially influential socioeconomic and industry events by year. An initial visual comparison explored possible correlation between fluctuations in the herring time series and events in the time line. Viable events were found to explain many of the visually identified fluctuations. Once time series were constructed, TSA was used to model the underlying patterns of the herring fishery and oceanographic data. More specifically, auto-regressive-integrated-moving-average (ARIMA) models were applied. These models were then used to interpolate the missing years for complete time series, and ARIMA models were run again on these complete data sets. The final model for the Maine herring fishery was an ARIMA(1,1,0), meaning that the pounds in one year was explained, at least in part, by pounds the year before. For Canada, the model was an ARIMA(0,1,1), indicating that the pounds were more explained by the conservation of noise, or error, from the year previously. The models developed were then used to begin examining the impact of the events from the qualitative timeline and oceanographic features (SST and salinity) on the fishery time series. Intervention analysis detected outliers, called interventions, representing years of unexpected change in the herring time series. These years were compared to the qualitative time line to determine a possible explanatory event. Such events were speculated for the majority of interventions found. Finally, cross-correlation analysis compared the herring time series with the SABS SST and salinity time series for possible cause-and-effect relationships. The analysis found no significant relationships between the series. This study demonstrated the potential of TSA and historical data, including the qualitative literature, to better understand fisheries over the long term. TSA is a useful tool for applying historical data to study ecosystems in their entirety, from historical fisheries to today, rather than isolated in time or context. Results can broaden the temporal and ecosystem perspective in which fishery statistics are examined, and methodologies can be refined and expanded in the future. However, as used here, TSA addresses only catch statistics, not abundance or other population parameters. These methods should be used in conjunction with traditional statistical approaches and to inform stock assessment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".