Flow permanence affects aquatic macroinvertebrate diversity and community structure in three headwater streams in a forested catchment
Bibliographic record
Abstract
Drying can be a common disturbance affecting macroinvertebrate communities in headwater streams. Whether intermittent and ephemeral streams have a lower diversity and (or) unique assemblage structure relative to physically similar and nearby perennial streams is still debated. We investigated changes in the diversity and assemblage composition of aquatic macroinvertebrates occupying debris dams in three headwater streams with a gradient of flow permanence (perennial, intermittent, and ephemeral) during a dry period in the austral summer of 2007 and a wet period in the spring of 2008. In the dry period, mean taxon richness and abundance in debris dams were lower in the intermittent and ephemeral streams than in the perennial stream, and the length of time without connected surface flow appeared to produce different patterns in community composition. However, during the wet period, mean taxon richness, abundance, and community composition of macroinvertebrates were very similar among the three streams. Hierarchical Bayesian modeling showed evidence for a strong effect of permanence on taxon richness, abundance, and evenness within debris dams. Taxa from the perennial stream were extremely efficient at colonizing seasonally dry nearby streams. Differences in assemblage structure between these temporary and permanent headwater streams may only arise seasonally and also appear related to flow permanence.
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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 | 0.001 | 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".