Loss of trophic complexity in saline prairie lakes as indicated by stable-isotope based community-metrics
Bibliographic record
Abstract
Variations in climate, watershed characteristics and lake-internal processes often result in a large variability of food-web complexity in lake ecosystems. Some of the largest ranges in these environmental parameters can be found in lakes across the northern Great Plains as they are characterized by extreme gradients in respect to lake morphometry and water chemistry, with individual parameters often varying over several orders of magnitude. To evaluate the effects of environmental conditions on trophic complexity in prairie lake food-webs, we analyzed carbon and nitrogen stable isotopes of fishes, zooplankton and littoral macroinvertebrates in 20 lakes across southern Saskatchewan. Our two-year study identified very diverse patterns of trophic complexity, with was predominantly associated with among-lake differences. Small but significant temporal effects were also detected, which were predominantly associated with changes in productivity. The most influential parameters related to changes in trophic complexity among lakes were salinity, complexity of fish assemblage, and indicators of productivity (e.g. nutrients, Chl a). Generally, trophic diversity, number of trophic levels, and trophic redundancy were highest in productive freshwater lakes with diverse fish communities. Surprisingly, mesosaline lakes that were characterized by very low or no predation pressure from fishes were not colonized by invertebrate predators as it is often the case in boreal systems; instead, trophic complexity was further reduced. Together, prairie lake food-webs appear to be highly sensitive to changes in salinity and the loss of piscivorous fishes, making freshwater and mesosaline lakes most vulnerable to the impacts of climate variability. This is particularly important as global circulation models predict future climate warming to have disproportionate negative impacts on hydrologic conditions in this area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".