Long-term variation in isotopic baselines and implications for estimating consumer trophic niches
Bibliographic record
Abstract
Carbon and nitrogen stable isotope ratios are increasingly used to study long-term food web change. Temporal variation at the base of the food web may impact the accuracy of trophic niche estimates, but data describing interannual baseline variation are limited. We quantified baseline variation over a 23-year period in a north-temperate lake and used a simulation model to examine how this variation might affect consumer trophic niche estimates. Interannual variation in C and N stable isotope ratios was significant for both benthic and pelagic primary consumer baselines. Long-term linear trends and shorter-term autoregressive patterns were apparent in the data. There were no correlations among benthic and pelagic C and N baselines. Simulations demonstrated that error in estimated fish trophic niches, but not bias, increased substantially when sampling of baselines was incomplete. Accurate trophic niche estimates depended more on accurate estimation of baseline time series than on accurate estimation of growth and turnover rates. These results highlight the importance of previous and continued efforts to constrain bias and error in long-term stable isotope food web studies.
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 | 0.007 | 0.024 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".