Environmental filtering of crustacean zooplankton communities in fishless boreal lakes: expectations and exceptions
Bibliographic record
Abstract
To test the paradigm that large-bodied zooplankton species dominate in fishless lakes, we evaluated crustacean zooplankton composition of 53 fishless boreal lakes across a broad geographic scale (north-west Saskatchewan, Canada) in relation to contemporary environmental factors. Lake productivity, acid–base status and intensity of invertebrate predation were the three major environmental filters of crustacean zooplankton composition of the survey lakes, while lake morphometry and drainage basin characteristics likely had indirect effects by influencing the variations in above environmental factors. The traditionally expected dominance of large-bodied zooplankton species in fishless conditions only occurred in well-buffered shallow lakes with higher productivity and intense invertebrate predation. Daphnia pulex was dominant in more eutrophic lakes, while higher abundances of Holopedium gibberum were favored by relatively mesotrophic conditions. In exception to the traditional view, small-bodied species dominated in 57% of survey lakes as influenced by interactions of biotic and abiotic factors. Bosmina longirostris and cyclopoid copepods dominated less productive, deeper lakes with low invertebrate predation. Predominance of Leptodiaptomus minutus was supported under unfavorable environmental conditions for other species, i.e. poor buffering capacity and/or low calcium concentration. The present study demonstrates that exceptions to the generally expected species composition in fishless lakes can be caused by interactions among abiotic and biotic factors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".