Accounting for a nonlinear functional response when estimating prey dynamics using predator diet data
Bibliographic record
Abstract
Abstract Forage fish species are key in the transfer of energy from lower to upper trophic levels in marine ecosystems. Therefore, understanding their population dynamics, including population levels, is crucial for understanding productivity and the regulation of marine food webs. However, many forage fishes are poorly sampled by bottom trawl surveys, leading to poor estimates of their abundance. These estimates can be improved by using predator stomach contents as an additional sampling strategy; however, nonlinear relationships between prey abundance and predator consumption (i.e. the functional response) may bias stomach data as well. Using predator stomach contents and bottom trawl survey data, this study aimed to minimize this bias by developing a model to estimate prey dynamics and account for the predator functional response. This model was tested using a series of simulations and applied to a case study of northern sand lance Ammodytes dubius on the Grand Bank, Newfoundland, Canada. The simulations revealed that when predators consumed prey following a nonlinear functional response, our model outperformed a classical model (the model adopted by most studies) that assumed a linear functional response. In the case study, we estimated the relative abundance of sand lance from 1995 to 2018, which exhibited oscillatory dynamics with a period of approximately 7 years. Our results demonstrate that our model is capable of more accurately estimating the abundance of data‐limited prey populations, which contributes to a better understanding of food web dynamics.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".