Grazing preference and isotopic contributions of kelp to Zostera marina mesograzers
Bibliographic record
Abstract
In seagrass food webs, small invertebrate mesograzers often exert top-down control on algal epiphytes growing on seagrass blades, which in turn releases the seagrass from competition for light and nutrients. Yet, nearshore habitat boundaries are permeable, and allochthonous subsidies can provide alternative food sources to in-situ production in seagrass meadows, which may in turn alter mesograzer-epiphyte interactions. We examined the contribution of allochthonous kelp (Nereocystis luetkeana), autochthonous epiphytic macroalgal (Smithora naiadum), Ulva lactuca, and seagrass production to mesograzer diets in a subtidal Zostera marina (eelgrass) meadow. In both choice feeding experiments and isotopic analysis, mesograzer diets revealed a preference for allochthonous N. luetkeana over Z. marina, S. naiadum, and U. lactuca. Notably, Idotea resecata showed an ~20x greater consumption rate for N. luetkeana in feeding experiments over other macrophytes. In the meadow, we found a positive relationship between epiphytic S. naiadum and gammarid amphipod biomass suggesting weak top-down control on the S. naiadum biomass. Epiphyte biomass may be driven by bottom-up factors such as environmental conditions, or the availability and preference of allochthonous kelp, though further work is needed to disentangle these interactions. Additionally, we found that gammarid and caprellid amphipod biomass were positively influenced by adjacency to kelp at seagrass meadow edges. Our findings suggest that N. luetkeana kelp subsidies are important to the diets of mesograzers in Z. marina meadows. Spatial planning and management of marine areas should consider trophic linkages between kelp and eelgrass habitats as a critical seascape feature if the goal is to conserve nearshore food web structure and function.
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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) | 0.001 | 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".