Landscape influences on stream fish assemblages across spatial scales in a northern Great Plains ecoregion
Bibliographic record
Abstract
We examined the relationship between multiple spatial scales of fish assemblage structure and land cover in streams of a northern Great Plains ecoregion. We used regional richness measurements, an index of biotic integrity (IBI), and nonmetric multidimensional scaling (NMS) ordination to characterize fish assemblages. These metrics were related to regional catchment landscape at two spatial scales (overall catchment, overall riparian) and then to a set of local subcatchments from within these catchments at three scales (overall subcatchment, overall riparian buffer, and reach). Relationships between catchment fish richness, IBI scores, and landscape predictors were strongest at the riparian scale, with the strongest single predictor being riparian forest (r2 = 0.63, P < 0.01). NMS ordination analysis showed clear similarities between fish species assemblages in agriculturally dominated catchments and assemblages found in smaller headwater streams. At the same time, forested catchments and catchments with larger areas exhibited similar fish species assemblages. Our results indicate that both local and regional stream fish assemblages are structured by broader-scale landscape characteristics, with land cover providing a better indication of overall available habitat volume than catchment area or stream order.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".