Spatially and temporally variable production pathways support the Lake Erie central basin food web
Bibliographic record
Abstract
In large freshwater systems, the dominant production pathways supporting food webs are often spatiotemporally variable. We used stable isotope analysis and analysis of covariance (ANCOVA) models to investigate spatial and interannual variation in the dominant production pathways supporting fish consumers within the central basin of Lake Erie. We examined C and N stable isotope ratios of zooplankton, benthic invertebrates, and four species of fish common to nearshore areas of the central basin (yellow perch, Perca flavescens; white perch, Morone americana; rainbow smelt, Osmerus mordax; and round goby, Neogobius melanostomus) using tissue samples collected in 2017 and 2019. δ 13C values varied by location consistent with expected baseline differences in nutrient loading (13C was more enriched in the southern region) in two of six ANCOVA models. Furthermore, δ 15N values varied with individual fish size and by location in a manner consistent with spatial patterns of nutrient loading from surrounding agricultural landscapes (15N was more enriched in the northern region) and a longitudinal gradient of eutrophication, decreasing from west to east. These patterns were not exhibited by all species and did not necessarily persist across years, suggesting that additional factors (e.g., regional diet differences, river plume dynamics) also contributed to observed δ 13C and δ 15N variation. We suggest that spatiotemporal variation of stable isotope ratios should be accounted for in studies of trophic basis of production and food web structure in Lake Erie.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".