Kelp canopy species and forest structure foster distinct faunal assemblages
Bibliographic record
Abstract
Abstract The biogenic structure of foundation species provides critical habitat for coastal marine taxa. In temperate ecosystems, forests of large, canopy‐forming macroalgae ( Macrocystis pyrifera , Nereocystis luetkeana ) support rich communities of fishes and macroinvertebrates. However, it is unclear how the biological assemblages of kelp forests vary with respect to the dominant canopy‐forming species, structural attributes of the kelp forest (e.g., stipe density, frond length, forest biomass, forest area), and ambient environmental conditions. To identify the main influences on the composition of kelp‐associated assemblages, we surveyed 21 kelp forests across an island archipelago in British Columbia. We found that the composition of fish and macroinvertebrate assemblages varied significantly with canopy kelp species. The assemblages of macroinvertebrates were significantly influenced by kelp species, frond length, and forest biomass, as well as some abiotic attributes (i.e., depth, hard substrate). In contrast, the pelagic–demersal and benthic fish assemblages were associated with single attributes of kelp forest structure (i.e., frond length and canopy kelp species, respectively). Our findings indicate that the biological assemblages that are associated with kelp forests, especially macroinvertebrates, may be closely tied to structural features of the canopy. By identifying the distinct assemblages fostered by kelp forests of different canopy species and structure, we can better interpret the response of marine communities to changes in coastal habitat.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".