A comparison of the functional ecology of visual vs. nonvisual foraging in two planktivorous marine fishes
Bibliographic record
Abstract
Some visually foraging planktivorous fish will facultatively engage in nonvisual foraging when opportunities arise or necessity dictates. Yet, little is known about the ecology of nonvisual foraging. We examined the roles of prey size, fish size, and prey density on the nonvisual foraging of walleye pollock, Theragra chalcogramma (40100 mm total length), and sablefish, Anoplopoma fimbria (6689 mm), in the laboratory. Both species were size selective, disproportionately consuming large prey just as they do during visual foraging. Large prey were encountered more often, presumably because they were more easily detected by the fish's lateral-line system. When foraging visually, larger fish consumed more prey, but during nonvisual foraging, there was no foraging advantage to greater fish size. Unlike visual detection distances, lateral-line detection distances may not increase appreciably with fish size. Lastly, prey density influenced nonvisual prey consumption. Walleye pollock were characterized by a type I functional response, whereas sablefish were characterized by a type II functional response. Models of planktivore foraging typically assume negligible foraging by particulate feeders below their visual foraging thresholds. On the basis of this study and field data, we suggest that foraging models for particulate feeders, such as juvenile walleye pollock and sablefish, should account for nonvisual size-selective foraging.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".