Macrophyte biomass predicts food chain length in shallow lakes
Bibliographic record
Abstract
Food chain length provides insights into how ecosystems function and respond to global change. A recent synthesis has shown that food chain length is significantly related to both ecosystem productivity and ecosystem size. Relaxation of energetic constraints on top predators has been advanced as the primary mechanism explaining the importance of ecosystem productivity whereas the significance of ecosystem size may be related to the fact that larger ecosystems provide more refuge that stabilizes intermediate predators. Given that submerged macrophytes in lakes are known to enhance both ecosystem productivity and the amount of available refuge, we hypothesized that food chain lengths, measured as the trophic positions of top predators, would be significantly related to macrophyte biomass. We tested our hypothesis by conducting a field survey of shallow lakes across a strong macrophyte but limited morphometric gradient using a hierarchical mixed effect modeling approach. We determined that macrophyte biomass was positively related to the trophic position of numerous piscivores and to food chain length across lakes. Both lake volume and macrophyte biomass were retained as fixed predictors in our model with lowest AICc, and this model explained 85% of the variation in piscivorous fish trophic positions considering both fixed and random effects (i.e., lake and species‐specific responses to lake volume). Macrophyte biomass explained 29% of the residual variation in food chain length when the effect of lake volume was removed. These results are the first to directly relate macrophyte biomass to piscivore trophic positions and food chain length. We conclude that shallow lakes with higher macrophyte biomass support longer food chains independent of ecosystem size and nutrient concentration. We suggest that loss of macrophytes largely driven by human activities reduces food chain length, with potential consequences for ecosystem function.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".