The importance of metacommunity processes for long-term turnover of riffle-dwelling fish assemblages depends on spatial position within a dendritic network
Bibliographic record
Abstract
Spatial position within a dendritic network may determine environmental filters and connectivity with source of immigrants, influencing species composition and variation in metacommunities. We investigated how long-term turnover of riffle-dwelling fish assemblages is affected by niche- and dispersal-related processes. Multiple linear model selection resulted in four variables important to explain assemblage turnover: (i) habitat change, (ii) channel slope, (iii) dlink (a proxy for connectivity), and (iv) catchment area. These four variables were related to turnover in species abundance, while turnover in species occurrence was only related to habitat change. Models generated for each species indicated they were differently affected by niche- and dispersal-related processes. Our findings provide support for the hypothesis that metacommunity processes affecting temporal turnover of stream fish communities are dependent on the spatial position within a dendritic network, but also vary among species. Our study has implications for sampling design in monitoring programs, as the degree of dissimilarities in fish communities depends not only on local habitat change, but on spatial position within the drainage network and on species particularities.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".