Highly nested snail and clam assemblages in boreal lake littorals: Roles of isolation, area, and habitat suitability
Bibliographic record
Abstract
:Many ecological assemblages show a nested subset pattern of species distribution, i.e., common species occur in all assemblages, whereas rare species tend to occur in progressively more diverse assemblages. We examined the determinants of nestedness and assemblage composition of lake-dwelling snails and clams in a boreal landscape using nestedness calculator, rank correlation, and canonical correspondence analysis (CCA). Both snail and clam assemblages were highly nested, and the nested subset pattern correlated with an index of isolation and habitat suitability (mainly water chemistry) for snails and with habitat suitability for clams. Habitat suitability and isolation were themselves highly correlated, thus obscuring the detection of their relative importance to nestedness. Yet, it appears that nestedness in this molluscan fauna is due mainly to nested tolerance of abiotic factors. Partial CCA showed that isolation and habitat suitability were almost equally important correlates of species composition for snails, whereas lake area was the key factor related to clam assemblage composition, followed by habitat suitability. It thus appears that while the degree of nestedness in species composition of lake-dwelling clams and snails may be highly correlated with a single variable, the overall pattern of species composition among lakes requires multiple explanatory variables.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".