Research note: Field evaluation of epiphyte recruitment (<i>Vertebrata lanosa</i>, Rhodophyta) in different microsite types on host fronds (<i>Ascophyllum nodosum</i>, Phaeophyceae)
Bibliographic record
Abstract
SUMMARY The seaweed Ascophyllum nodosum (Phaeophyceae, Fucales) often forms extensive beds in wave‐sheltered, rocky intertidal habitats on northern Atlantic shores. Since this alga is an ecosystem engineer that influences benthic biodiversity, it is important to understand the factors that regulate its performance. Epiphytism is known to affect the performance of macroalgal hosts. In this study, we investigated the effects of surface irregularities on A. nodosum fronds (wounds, branch axils, and lateral pits resulting from receptacle shedding) on the recruitment rate of its obligate epiphyte Vertebrata lanosa (=Polysiphonia lanosa, Rhodophyta, Ceramiales). For this purpose, we performed a field experiment in Nova Scotia, Canada. In June–July 2007, we created wounds on the surface of host fronds that mimicked the wounds that result from invertebrate grazing. At that time, we also mapped the position of epiphyte‐free lateral pits and branch axils on host fronds. In October 2007, after the reproductive season for V. lanosa, the percentage of microsites colonized by this epiphyte was statistically similar for wounds and lateral pits, but significantly lower for branch axils, although by a small difference (mean recruitment rates ranged between 37 and 50%). Since V. lanosa is known not to colonize smooth frond surfaces, our study suggests that the degree of epiphyte load on A. nodosum beds should largely be affected by the overall amount of surface irregularities, with little influence of the relative availability of each microsite type.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".