Environmental specialists: their prevalence and their influence on community‐similarity analyses
Bibliographic record
Abstract
Abstract Recent research suggests that environmental or habitat specialization among plants may be less common than previously thought. These findings need to be reconciled with interpretations of community‐level similarity analyses that emphasize a strong role for specialization. Here we examine specialization within a taxonomic group that, owing to their widespread use as environmental indicators, should provide ample supportive evidence of specialization: freshwater sediment diatoms. Using an ideal, 239‐lake survey data set that reliably represents the environmental conditions among c. 9500 north‐eastern US lakes (encompassing a c. 405 000 km2 region), we show that only 29 and 14% of 401 species (those occurring in at least two lakes) exhibited narrower pH and total phosphorus niche breadths, respectively, than expected from the random occupation of lakes. These rates increase slightly using more stringent species‐inclusion criteria. Highly significant correlations between compositional and environmental resemblance matrices are shown to reflect a balance between a strong signal generated by the minority specialists, and noise generated by the majority generalists.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".