Relating stream physical habitat condition and concordance of biotic productivity across multiple taxa
Bibliographic record
Abstract
To explore the potential of assessments of stream geomorphic condition and habitat quality in evaluating lotic productivity, we investigated concordance of stream biotic productivity (aquatic macroinvertebrates, crayfish, fish, and belted kingfishers ( Ceryle alcyon )) and their physical habitat correlates in 18 streams in the Champlain Valley, Vermont, USA. Pearson correlation analysis indicated significant concordance between macroinvertebrate density and fish biomass (r = 0.76), between the density of macroinvertebrates in the orders Ephemeroptera, Plecoptera, and Trichoptera and fish biomass (r = 0.81), and between fish biomass and kingfisher brood weight (r = 0.54). We used principal component analysis followed by linear regression to investigate relationships between physical habitat condition and biotic productivity and to identify key components of physical habitat condition assessments. Our analysis supported the combined use of geomorphic and habitat assessments as a comprehensive indicator of stream physical habitat condition. We found relationships between habitat assessment scores and productivity measures of all taxa except crayfish, suggesting similar responses to physical condition across trophic levels. Our results encourage the use of additional taxa, in addition to widely used macroinvertebrate metrics, as indicators of the composite effects of physical habitat impairment in stream ecosystems.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".