The relationship between local and regional diatom richness is mediated by the local and regional environment
Bibliographic record
Abstract
ABSTRACT Aim In this continental study, species richness at local (LSR) and regional (RSR) scales was correlated and examined as a function of stream (local) and watershed (regional) environment in an effort to elucidate what factors control diatom biodiversity across scales. Location Conterminous United States. Methods Data on diatom richness, stream conditions and watershed properties were generated by the US Geological Survey. In the present investigation, RSR was estimated as the total diatom richness in a hydrologic study unit and, together with stream and watershed characteristics, was included in stepwise multiple regressions of LSR. The unique and shared contributions of RSR, stream and watershed environment to the explained variance in LSR were determined by variance partitioning. RSR was regressed against stream and basin features averaged per study unit. Results LSR responded most strongly to variability in stream manganese concentration and RSR. Other predictors included stream discharge and iron concentration, soil organic matter content and fertilization, and proportions of open water, barren land and forest in the watershed. Variance partitioning revealed that RSR had the lowest independent contribution to explained variance in LSR. Multiple regressions identified average stream iron concentration as the most important predictor of RSR. Main conclusions Local micronutrient concentration was the major predictor of LSR, followed by RSR. Since average micronutrient supply in the region was the chief determinant of RSR, it is proposed that micronutrients had both a direct effect on LSR and an indirect effect through RSR. The same argument is extended to watershed features with an impact on stream trophic status, because of their substantial contributions to the explained variance in both LSR and RSR. Considering that the major proportion of LSR variance explained by RSR originated from the covariance of RSR with stream and watershed properties, it is concluded that the LSR–RSR relationship was mediated by the local and regional environment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".