ROLE OF SURFACE WOUNDS AND BROWN ALGAL EPIPHYTES IN THE COLONIZATION OF <i>ASCOPHYLLUM NODOSUM</i> (PHAEOPHYCEAE) FRONDS BY <i>VERTEBRATA LANOSA</i> (RHODOPHYTA)<sup>1</sup>
Bibliographic record
Abstract
Ascophyllum nodosum (L.) Le Jol. forms extensive beds in wave-sheltered, rocky intertidal habitats on the northwestern Atlantic coast. This fucoid seaweed is host to an obligate red algal epiphyte, Vertebrata lanosa (L.) T. A. Chr. [=Polysiphonia lanosa (L.) Tandy], and two facultative brown algal epiphytes, Elachista fucicola (Velley) Aresch. and Pylaiella littoralis (L.) Kjellm. Although V. lanosa can occur throughout most of the length of host fronds, it largely predominates in midfrond segments. The two brown algal epiphytes are restricted to distal segments. Through field experiments conducted in Nova Scotia, Canada, we tested the hypothesis that surface wounds are required for the colonization of distal segments of host fronds by V. lanosa. Distal tissues normally have a smooth surface because of their young age (A. nodosum fronds grow apically). By creating small wounds that mimicked grazing wounds distributed elsewhere on host fronds, we demonstrated that V. lanosa can colonize distal frond segments during the growth and reproductive season (summer and autumn). Approximately half of the artificial wounds were colonized by V. lanosa during this time. The experimental exclusion of both brown algal epiphytes from distal frond segments did not affect colonization by V. lanosa. Thus, we conclude that the absence of surface irregularities on distal segments of host fronds, specifically small wounds, is the main factor explaining the absence of V. lanosa there. We propose that further experimental work clarifying epiphyte distribution in host beds will enhance our ability to understand the functional role of epiphytes in intertidal ecosystems.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".