Nestedness patterns and the dual nature of community reassembly in <scp>C</scp>alifornia streams: a multivariate permutation‐based approach
Bibliographic record
Abstract
Abstract Many factors contribute to the nonrandom processes of extinctions and invasions that are changing the structure of ecological communities worldwide. These factors include the attributes of the species, their abiotic environment, and the interactions and feedbacks between them. The relative importance of these factors has been difficult to quantify. We used nested subset theory and a novel permutation‐based extension of gradient analysis to disentangle the direct and indirect pathways by which these factors affect the metacommunity structure of freshwater fishes inhabiting the streams tributary to the San Francisco Bay. Our analyses provide quantitative measures of how species and stream attributes may influence extinction vulnerability and invasion risk, highlight the need for considering the multiple interacting drivers of community change concurrently, and indicate that the ongoing disassembly and assembly of Bay Area freshwater fish communities are not fully symmetric processes. Fish communities are being taken apart and put back together in only partially analogous trajectories of extinction and invasion for which no single explanatory hypothesis is sufficient. Our study thereby contributes to the forecasting of continued community change and its effects on the functioning of freshwater ecosystems.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".