THE ROLE OF ENVIRONMENTAL AND SPATIAL PROCESSES IN STRUCTURING LAKE COMMUNITIES FROM BACTERIA TO FISH
Bibliographic record
Abstract
We assessed the relative roles of local environmental conditions and dispersal on community structure in a landscape of lakes for the major trophic groups. We use taxonomic presence-absence and abundance data for bacteria, phytoplankton, zooplankton, and fish from 18 lakes in southern Quebec, Canada. The question of interest was whether communities composed of organisms with more limited dispersal abilities, because of size and life history (zooplankton and fish) would show a different effect of lake distribution than communities composed of good dispersers (bacteria and phytoplankton). We examine the variation in structure attributable to local environmental (i.e., lake chemical and physical variables) vs. dispersal predictors (i.e., overland and watercourse distances between lakes) using variation partitioning techniques. Overall, we show that less motile species (crustacean zooplankton and fish) are better predicted by spatial factors than by local environmental ones. Furthermore, we show that for zooplankton abundances, both overland and watercourse dispersal pathways are equally strong, though they may select for different components of the community, while for fish, only watercourses are relevant dispersal pathways. These results suggest that crustacean zooplankton and fish are more constrained by dispersal and therefore more likely to operate as a metacommunity than are bacteria and phytoplankton within this studied landscape.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".