Relationship of habitat variability to population size in a stream fish
Bibliographic record
Abstract
The relationship between habitat variability and population size in fragmented habitats is poorly understood, yet might have important evolutionary consequences. For instance, fragmentation could (1) shift habitat characteristics, and by extension, selective regimes, in a consistent direction as populations and the fragments they occupy are reduced in size (directional hypothesis): or (2) increase variability in habitats among similarly sized populations as fragment size decreases (variable hypothesis). We investigated these alternatives based on multiyear habitat, demographic, and genetic data from 19 fragmented populations of a stream fish varying in census size (N) and effective number of breeders (N(b)). Mean habitat parameters were significantly related to N and N(b), but the forms of the relationships varied, and there was no evidence of consistent directional differences in habitat parameters from small to large population size. Small populations exhibited a wider range of variances in habitat parameters than large populations, and to a lesser extent, small populations also had greater variability in mean habitat parameters, possibly signaling more diverse selective regimes. These results suggest that many different environments are associated with small population size in nature, counter to the frequently cited assumption that small populations tend to occur only in marginal environments. In addition to well-documented demographic and genetic stochasticity operating within small populations, our work raises the possibility that small populations exhibit more variable and potentially less predictable evolutionary responses to future environmental change.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| 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".