Influence of phytodetrital quality on macroinfaunal community structure and epifaunal response
Bibliographic record
Abstract
In order to evaluate the influence of food quality on deep-sea macroinfaunal and mega-epifaunal communities, we deployed experimental food pulses at 890 m depth in Barkley Canyon, NE Pacific.Pulses of 2 microalgal species (i.e.Chaetoceros calcitrans and Nannochloropsis oculata) differed in nitrogen, carotenoid, and lipid content (i.e.quality).After 8 mo, we sampled the enriched patches and controls with push cores.The community structure between the 2 treatments differed in the deeper sediment layers (5-10 cm).Despite no significant differences in the surface sediments (0-5 cm) among the 2 enrichment patches and the control, the macroinfaunal community from sediments enriched with N. oculata separated in ordination space.These results suggest that the experimental duration exceeded the appropriate time-frame to detect initial colonizers at the surface sediments.Analyses of functional traits, including migration, colonization, foraging, and food selectivity, separated communities in ordination space among algal treatments and control, although not significantly.Based on fixed camera images of each treatment over the course of the experiment, we observed more epifaunal Brachyura visits (between 50 and 40 more) to a patch enriched with N. oculata than to a patch enriched with C. calcitrans or the procedural control patch during the first 2 wk of the experiment.The visible area of 1 enrichment patch per algal treatment disappeared (> 90%) ~1.5 mo after deployment.Our study points to influences of food quality, at the algal class level, on macroinfaunal community structure and function, epifaunal disturbance, and sediment reworking.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".