Influence of lake volume on trophic position, carbon use, and resource partitioning in fish across a narrow range of ecosystem size
Bibliographic record
Abstract
Lake size is an important factor governing seasonal variation in limnological phenomena, origin of nutrient sources, species interactions, cross-habitat linkages, and trophic pathways, all having complex influences on food web structure and function. Lake size effects are most clearly demonstrated across very wide gradients in surface area or volume. This approach incorporates several complicating and collinear elements such as changing fish assemblages and species richness, and therefore, incorporates additional but unaccounted shifts in food web structure and function. A comparison across a finer lake size gradient where fish assemblages and species richness change little or not at all is needed in order to understand the direct influence of lake size. Here, we quantified spring (May) and summer (August) food web metrics (trophic position, littoral carbon use, and resource partitioning) in six fish species across six lakes (volume = 8.7 x106 m3 to 814.5x106 m3) located in Algonquin Provincial Park (Ontario, Canada) using carbon (d13C), nitrogen (d15N), and sulfur stable isotopes (d34S) . Lake volume was the most important factor describing trophic position and littoral carbon use for all species across all six lakes, except cisco (Coregonus artedi) d34S. Relationships between food web metrics and lake size were not as strong as previous studies that looked at a wider range of lake sizes, and trophic position and littoral carbon use exhibited negative and positive relationships with lake size, respectively, contradictory to previous studies. Even though lake size was still the best predictor of feeding in these species, other lake characteristics, including amount of habitat (littoral:limnetic volume ratio) and nutrients (total phosphorus), were also significant; therefore, comparisons of similar sized lakes should use established relationships for a wider range of lake size with caution.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".