FORMATION AND PROPAGATION OF FEEDING FRONTS IN BENTHIC MARINE INVERTEBRATES: A MODELING APPROACH
Bibliographic record
Abstract
Feeding fronts are a striking pattern of spatial distribution observed in both marine and terrestrial ecological systems. These fronts not only determine the abundance and distribution of prey populations, but on a broader scale they may also affect the structure and dynamics of entire communities. Several mechanisms leading to the formation of feeding fronts have been proposed, and chemotaxis has been suggested as an important component. Here we develop two mathematical models that show front formation can occur with simple kinesis (and without chemotaxis) in two marine invertebrates with different feeding habits: a microphagous sea star (Oreaster reticulatus) that feeds on sediments and an herbivorous sea urchin (Strongylocentrotus droebachiensis) that grazes kelp beds. We utilize a large body of detailed empirical information on movement pattern, foraging behavior, and front dynamics for each species to develop, parameterize, and evaluate our models. We found that our model predictions for the rate of advance of a front and its relationship to the density of consumers at the front were in close agreement with independently collected, empirical observations in both systems. This work shows that simple local interactions between mobile consumers and a stationary resource can result in large-scale heterogeneous patterns of abundance of both species.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".