Role of grazing by sea urchins Strongylocentrotus droebachiensis in regulating the invasive alga Codium fragile ssp. tomentosoides in Nova Scotia
Bibliographic record
Abstract
To assess the potential of herbivory in regulating the invasive green alga Codium fragile ssp.tomentosoides, field and laboratory experiments were conducted with the green sea urchin Strongylocentrotus droebachiensis in Nova Scotia, Canada.In the field, urchins at different densities (0, 50 and 100 urchins m -2 ) were caged on boulders covered with a canopy of either the kelp Laminaria longicruris, Codium, or a mixture of both species for 13 wk.In the treatment with Laminaria only, ∼90% of the canopy was removed within 34 and 75 d in cages with 100 and 50 urchins m -2 , respectively.In contrast, Codium cover decreased by ∼20% at both levels of urchin density in the treatment with Codium only, and did not differ significantly from the control (no urchins) at the end of the experiment.In the mixed canopy treatment, urchins showed a preference for Laminaria, consuming 90% of kelp cover within 39 and 54 d (at 100 and 50 urchins m -2 , respectively), while Codium cover increased gradually.Urchins grazed turf-forming red algae in all treatments, although in treatments with Laminaria, intensive grazing of turf only occurred once kelp was completely consumed.In the laboratory, urchins fed single diets of Laminaria or Codium for 8 wk had similar grazing rates (∼0.20 dry weight g urchin -1 d -1 ), while urchins fed a mixed diet consumed 2 times more kelp (0.15 g urchin -1 d -1 ) than Codium (0.08 g urchin -1 d -1 ).These experimental results indicate that urchins prefer kelp but will consume Codium when other algal foods are not available.We predict that urchin aggregations encountering mixed stands of kelp and Codium will initially graze the kelp and turf algae, creating patches of Codium that ultimately will be consumed as well.
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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) | 0.000 | 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".