Ice algae versus phytoplankton: resource utilization by Arctic deep sea macroinfauna revealed through isotope labelling experiments
Bibliographic record
Abstract
Climate change is expected to change future Arctic marine primary production (PP) by reducing ice algal and increasing phytoplankton contributions.As most benthic macrofauna depend on PP from the euphotic zone for food, they could be vulnerable to changes in their food supply.To investigate the differential utilization of ice algae and phytoplankton food by benthic macroinfauna, isotope labelling experiments on dual 13 C-15 N labelled ice algae and phytoplankton were carried out at 2 sites in the Canadian Arctic.After 4 d, all animals collected at North Water Polynya (NOW; 709 m) and Lancaster Sound (LS; 794 m) exhibited isotope labelling.The C:N ratio of the macrofaunal biomassspecific uptake showed that all taxa were N-limited, and the uptake of algal C and N was often decoupled.Overall, the 2 macroinfaunal communities had different responses to the food items: in LS the accumulative biomass-specific uptake of phytoplankton C and N of all fauna was higher than uptake of ice algae, whereas in NOW ice algal C was more readily utilized.When taxa were examined individually, differences in food utilization by polychaetes, bivalves and crustaceans were site-specific, with no taxa exclusively exhibiting higher rates of ice algal uptake.The dietary plasticity observed between these sites suggests that benthic macroinfauna are able to efficiently utilize both ice algae and phytoplankton as a food source, and that the replacement of ice algae with phytoplankton food may not alter faunal feeding rates or their role in benthic nutrient cycling. KEY WORDS: Climate change • Canadian Arctic Archipelago • C uptake • N uptake • Feeding experiment • Benthic • MacrofaunaArctic deep-sea sediments host diverse macroinvertebrate communities, which appear resilient to climate change mediated alterations in their food supply.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".