Carbon pathways to zooplankton: insights from the combined use of stable isotope and fatty acid biomarkers
Bibliographic record
Abstract
Summary 1. Numerous studies have quantified the relative contribution of terrestrial‐ and phytoplankton‐derived carbon sources to zooplankton secondary production in lakes. However, few investigated the pathways along which allochthonous and autochthonous carbon (C) was actually conveyed to consumers. 2. We suggest that the combined use of fatty acid and stable isotope biomarkers could solve this issue. We conducted a field study on two oligotrophic lakes, in which primary production increased significantly between 2002 and 2004. We used modelling to estimate the contribution of terrestrial‐ and phytoplankton‐derived C to particulate organic C (POC) and zooplankton production from their δ13C values in 2002 and 2004. 3. According to the isotope model, phytoplankton‐derived C accounted for a major part of the POC pool in both lakes and supported more Daphnia sp. production in 2004 than in 2002. Fatty acid data revealed that increased contribution of algal‐C to Daphnia production, although common between both lakes, was achieved through C pathways that were different. In one lake, Daphnia grazed more intensively on phytoplankton, whereas in the other there was greater grazing on bacteria. In the latter case, the increased primary production resulted in greater release of algal‐derived dissolved organic C (DOC), which may have supported extra bacterial and eventually Daphnia, production. 4. This is the first study illustrating that the combination of fatty acid and stable isotope biomarkers could further our understanding of the factors controlling the relative magnitude of food webs pathways conveying organic matter to zooplankton.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".