Wetland microhabitats support distinct communities of aquatic macroinvertebrates
Bibliographic record
Abstract
The drivers of aquatic macroinvertebrate distribution in Prairie Pothole Region wetlands are not as well understood as in other aquatic ecosystems (e.g. rivers or lakes). We collected aquatic macroinvertebrates from 35 fishless prairie pothole wetlands in Alberta, including two habitat zones: the emergent zone and the open-water zone. Within each zone, we collected a vegetation sample and a water column sample, thus capturing four distinct microhabitats. We tested for community differences among these microhabitats with nested ANOVAs, looking at macroinvertebrate abundance, taxa richness, and evenness. We also visualized trends in community composition among the microhabitats with nonmetric multidimensional scaling ordination. Interestingly, we observed no difference in macroinvertebrate communities between the open-water and the emergent habitat zones. However, we found significant differences in richness and evenness between water column and vegetation sample types nested within habitat zones. Additionally, we observed high taxonomic turnover between sample types. Our results emphasize the importance of within-zone microhabitats in structuring aquatic macroinvertebrate communities in prairie pothole wetlands, and the relative insignificance of emergent and open-water habitat zone distinctions. Future analyses of macroinvertebrates in wetlands should sample both the vegetation and the water column, regardless of habitat zone, to prevent biased surveys of macroinvertebrate communities.
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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".