Relationship between demographic and genetic population size and role of the environment in a stream fish
Bibliographic record
Abstract
As wild populations become increasingly small and vulnerable, conservation managers typically must make quick decisions based on limited resources. Two crucial parameters affecting management decisions are the census population size (N) and the effective number of breeders (Nb). However, measuring N and Nb is often difficult, making it of interest to generalize one from the other. We assessed the relationship between N and Nb from 2008-2015 in twelve brook trout populations varying greatly in N (49-10032) and Nb (3-567). Most of the variability in Nb could be explained by N (R2m=0.54, p<0.001) or stream length (R2m=0.44, p<0.001) alone. The ratio Nb/N increased at small N or following an annual decrease in N (R2=0.49, p<0.01), suggesting density-dependent constraints on Nb/N (genetic compensation). We did not find any evidence for consistent differences invariability in Nb and/or Nb/N between small and large populations; however, small populations had more varying temporal variability in Nb/N ratios than large populations. Nb and Nb/N were respectively 2.5-fold and 2.3-fold more variable among populations than temporally within populations. Collectively, our results suggest that conservation resources could be saved by using N or Nb to infer the other to assess relative population sizes. However, using one variable to infer the other to monitor trends within populations is less recommended, perhaps even less so in small populations given their less predictable Nb vs. N dynamics.
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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 | 0.003 |
| 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".