Seascapes and foraging success: Movement and resource discovery by a benthic marine herbivore
Bibliographic record
Abstract
Abstract Spatially concentrated resources result in patch‐based foraging, wherein the detection and choice of patches as well as the process of locating and exploiting resource patches involve moving through an explicit landscape composed of both resources and barriers to movement. An understanding of behavioral responses to resources and barriers is key to interpreting observed ecological patterns. We examined the process of resource discovery in the context of a heterogeneous seascape using sea urchins and drift kelp in urchin barrens as a model system. Under field conditions, we manipulated both the presence of a highly valuable resource (drift kelp) and a barrier to movement (sandy substratum) to test the interacting influence of these two factors on the process of resource discovery in barren grounds by urchins. We removed all foraging urchins (Strongylocentrotus droebachiensis) from replicate areas and monitored urchin recolonization and kelp consumption. We tested two hypotheses: (1) unstable substratum is a barrier to urchin movement and (2) the movement behavior of sea urchins is modified by the presence of drift kelp. Very few urchins were found on sand, sand was a permeable barrier to urchin movement, and the permeability of this barrier varied between sites. In general, partial recolonization occurred strikingly rapidly, but sand slowed the consumption of drift kelp by limiting the number of urchins. Differences in the permeability of sand barriers between sites could be driven by differences in the size structure of urchin populations, indicating size‐specific environmental effects on foraging behavior. We demonstrate the influence of patchy seascapes in modulating grazing intensity in barren grounds through modifications of foraging behavior. Behavioral processes modified by environmental barriers play an important role in determining grazing pressure, the existence of refuges for new algal recruits, and ultimately the dynamics of urchin‐algal interactions in barren grounds.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".