Interactive effects of chemical and biological controls on food-web composition in saline prairie lakes
Bibliographic record
Abstract
Salinity is restricting habitatability for many biota in prairie lakes due to limited physiological abilities to cope with increasing osmotic stress. Yet, it remains unclear how salinity effects vary among major taxonomic groups and what role other environmental parameters play in shaping food-web composition. To answer these questions, we sampled fish, zooplankton and littoral macroinvertebrates in 20 prairie lakes (Saskatchewan, Canada) characterized by large gradients in water chemistry and lake morphometry. We showed that salinity thresholds differed among major taxonomic groups, as most fishes were absent above salinities of 2 g L-1, while littoral macroinvertebrates were ubiquitous. Zooplankton occurred over the whole salinity range, but changed taxonomic composition as salinity increased. Subsequently, the complexity of fish community (diversity) was associated with large changes in invertebrate communities. The directional changes in invertebrate communities to smaller taxa indicated that complex fish assemblages resulted in higher predation pressure. Most likely, as the complexity of fish community decreased, controls of invertebrate assemblages shifted from predation to competition and ultimately to productivity in hypersaline lakes. Surprisingly, invertebrate predators did not thrive in the absence of fishes in these systems. Furthermore, the here identified salinity threshold for fishes was too low to be a result of osmotic stress. Hence, winterkill was likely an important factor eliminating fishes in low salinity lakes that had high productivity and shallow water depth. Ultimately, while salinity was crucial, intricate combinations of chemical and biological mechanisms also played a major role in controlling the assemblages of major taxonomic groups in prairie lakes.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".