Integrating a seabird diet-derived recruitment index into a stock assessment model of Atlantic herring in the Northeast U.S.
Bibliographic record
Abstract
Incorporating predator-prey information in fisheries stock assessments is challenging due to the multitude, complexity, and dynamic nature of trophic interactions. However, integrating indices of abundance derived from predator diet data into assessment models as proxy abundance indices of poorly sampled age classes may have utility as an ecosystem approach that improves assessment outcomes. Here, we leveraged 34 years of observations of common terns ( Sterna hirundo ) provisioning age-1 Atlantic herring ( Clupea harengus ) to their chicks across 12 islands in the Gulf of Maine to construct an annual index of herring provisioning rates. We then integrated this index into a state-space, age-structured assessment model as an index for age-1 Atlantic herring (recruit) abundance. Our results showed that when the assessment model was fit assuming a linear relationship between the herring provisioning index and recruitment, the model produced a residual pattern indicating a non-linear response between the provisioning of Atlantic herring and their abundance in the environment. These findings suggested that Atlantic herring provisioning rates plateaued during periods of high recruitment (satiation) and remained consistent at low levels during periods of low recruitment (hyperstability). Allowing for this non-linearity in the stock assessment improved the fit to the index. Ultimately, the seabird diet index may be a useful indicator to incorporate into the Atlantic herring assessment process; however, the quantitative incorporation of this index into the assessment model is challenged by non-linear predator-prey dynamics. • We created an index of seabird-herring provisioning rates via spatiotemporal model. • We used the provisioning index as an index of age-1 fish in an assessment model. • The fitted assessment model revealed unaccounted for predator-prey dynamics. • Allowing non-linear relationship between diet index and recruitment fixed issues. • Careful integration of predator-prey indices into assessment can improve outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".