Benthic mats offer a potential subsidy to pelagic consumers in tundra pond food webs
Bibliographic record
Abstract
We quantified the potential nutritional contribution of benthic mats to pelagic consumers in tundra ponds using three approaches. (1) We compared benthic and pelagic habitats based on their algal biomass and community composition and assessed the dietary quality in terms of fatty acid (FA) concentrations in both habitats. The algal community compositions differed significantly between habitats. Both benthic and pelagic habitats contained saturated and unsaturated FAs, but on average there were more FAs relative to carbon in the pelagic habitat (44 ± 29 µg mg C −1 ) than the benthic habitat (23 ± 18 µg mg C −1 ) across all studied ponds. (2) We quantified the contribution of benthic mats to the whole‐lake FA pool and found that benthic mats dominate (on average > 90%) the basal FA resource within tundra ponds. (3) Using a series of feeding experiments, we found that Daphnia survival and FA concentrations increased when a benthic mat slurry supplemented low concentrations of phytoplankton. However, Daphnia could not survive solely on benthic mats, and nor did benthic mats increase adult Daphnia survival when mats were offered as coherent pieces. The extent to which pelagic consumers could be supported by benthic production challenges the view that benthic habitats are functionally separate from the pelagic food webs within northern lake ecosystems.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".