Response to drift kelp is modified by both substratum and season: green sea urchin foraging behaviour in subsidized habitats
Bibliographic record
Abstract
The ability of organisms to detect, locate and navigate to resource patches is modified by the surrounding landscape. Green sea urchinsStrongylocentrotus droebachiensisin barren grounds exist in a food-limited state and are subject to intense competition. Rapid detection and consumption of resource patches, particularly pieces of macroalgae from adjacent algal beds, are key in determining individual growth, survival and reproductive success. Detection and movement to resource patches requires moving through a heterogeneous benthic seascape composed of rocky and sandy patches, presenting different degrees of resistance to movement. We used time-lapse photography to describe the foraging behaviour of urchins in relation to the presence of a key resource subsidy (drift kelp) and different benthic seascapes. We demonstrated that urchins could detect the presence of drift kelp in barren-ground habitats and alter their movement behaviour in response, but did not exhibit the ability to directionally navigate towards kelp in field conditions. Seascapes with increased proportions of rocky substrata facilitated increased movement in response to the presence of drift. Moreover, urchin foraging behaviour was temporally variable, with no response to the presence of drift in early spring (May). This indicates not only that interpretations of observations of urchin behaviour must take intrinsic and extrinsic seasonal dynamics into account, but that extrapolating results to explain larger-scale patterns and processes must include both spatially explicit subtidal seascapes and temporal dynamics. In many temperate and boreal regions, this indicates the need for increased subtidal benthic research in the fall and winter.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".