Quantification of fish littoral carbon use and trophic position using stable isotopes: an empirical comparison of equations using freshwater lakes
Bibliographic record
Abstract
Stable isotope analysis is a widely used to quantify the flow of energy, nutrients, and biomass through aquatic food webs, where values of carbon (13C) and nitrogen (15N) have been used to estimate littoral carbon use (LCU) and trophic position (TP) in lentic ecosystems, respectively. Standardizing stable isotope values with these metrics allows for comparison across different systems and time scales; however, several equations to quantify LCU and TP have been introduced with little guidance on the bias and limitations of each equation, or how and when different equations should be used. Here, we provide recommendations on the appropriate use of LCU and TP equations in freshwater ecosystems based on our analysis of case studies using five fish species common to the study lakes. We address three common challenges of ecosystem variability, namely (1) temporal, (2) spatial, and (3) differing rates of tissue turnover between study and baseline organisms. We begin with a relatively simple case study (Parry Sound) then explored challenges around spatial (Lake Erie) and temporal (Canoe Lake) variation, and the use of multiple fish tissue types (Canoe Lake). We found LCU and TP estimates to be highly variable and dependent on the equation used. High percentages of individual fish exhibited unrealistic LCU values, and the equation of LCU used had a large effect on calculated TP values. We found relative littoral carbon use (LCUR) produces the most consistent TP estimate, avoiding extreme values while reflecting the natural variability of the system. We propose a framework for calculating LCU and TP, allowing researchers to produce the least amount of bias relative to the known feeding ecology of study species while making estimates comparable across systems. Based on our case studies, we have developed a decision tree to guide researchers in estimating LCU and TP in freshwater 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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".