From continua to patches: examining stream community structure over large environmental gradients
Bibliographic record
Abstract
We present an approach that integrates a conceptual framework with multivariate ordination techniques and traditional parametric analyses to examine biotic and abiotic gradients in stream ecosystems. Ordinations were used to examine multivariate patterns along an environmental gradient, with individual variables used to interpret those patterns across spatial scales. The conceptual framework provides a consistent context to compare community distributions and consequently allows for hypothesis testing using ordinations. To illustrate the approach, we examined the physical template, fish and benthic macroinvertebrate communities, and algal biomass and production along a 1st- through 5th-order stream gradient in eastern Oregon. We hypothesized that longitudinal distributions of physical habitat characteristics, fishes, macroinvertebrates, and periphyton would reflect highly variable, discontinuous gradients. Multivariate patterns were determined by rotating nonparametric ordinations to a common set of variables and comparing them to conceptual models of (i) an ideal continuum, (ii) a random distribution, and (iii) discrete patches. Physical habitat and fishes reflected strong longitudinal gradients, macroinvertebrates were the most patchy, and algal biomass and production were highly variable. Distributions of individual variables from site and stream-order perspectives revealed how different factors, potentially influencing stream communities, may be continuous or patchy depending on spatial scale.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".