Beyond bulk δ15N: Combining a suite of stable isotopic measures improves the resolution of the food webs mediating contaminant signals across space, time and communities
Bibliographic record
Abstract
Top predators are used as indicators of contaminant trends across space and time. However, signals are integrated over complex food webs, and variation in diet may confound such signals. Trophic position, assessed by bulk δ15N, is widely used to infer the variation in diet relevant to contamination, yet a single variable cannot completely describe complex food webs. Thus, we examined relationships across three aquatic systems varying from a single species to a small food web using bulk values from four isotopes and 21 amino acid-specific values. Because variation in baseline ('source') δ15N can confound estimates of trophic position , we calculated trophic position from the difference between δ15Ntrophic (δ15N for amino acids that change with trophic position) and δ15Nsource (δ15N for amino acids that do not change with trophic position). Across all three systems, variation in δ15Nsource explained over half of the variation in bulk δ15N, and stable isotope values that reflected the base of the food web (δ13C, δ18O, δ34S) predicted contaminants as well or better than δ15N—which was supported by a meta-analysis of other studies. In ospreys feeding in lakes, variation in δ15Nsource across space created a spurious relationship between ΣDDT and apparent trophic position, and masked a relationship between ΣPCB and trophic position. In a seabird guild, changes in diet over time obscured temporal variation in contaminants over five decades. In Arctic fish and invertebrates, more accurate trophic magnification factors were calculated using δ15Ntrophic-source. Thus, (1) using δ15Ntrophic-source, instead of bulk δ15N, avoided incorrect conclusions and improved accuracy of trophic magnification factors necessary to assess risk to top predators; and (2) diet assessed with multiple spatial isotopes, rather than δ15N alone, was essential to understand patterns in contaminants across space, time and biological communities. Trophic position was most important for lipophilic ‘legacy’ contaminants (ΣDDT, ΣPCB) and habitat was most important for other contaminants (ΣPBDE, ΣPFAS, mercury). We argue that the use of amino acid-specific analysis of δ15N alongside ‘non-trophic’ isotopes should be a core feature of any study that examines the influence of trophic position on chemical pollution, as required for a chemical to be added to international conventions such as the Stockholm Convention.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".