Exploring uncertainty in population viability analysis and its implications for the conservation of a freshwater fish
Bibliographic record
Abstract
Abstract A spatially explicit metapopulation viability model was created within RAMAS‐GIS to address questions related to the conservation and management of a freshwater species at risk ( Notropis anogenus ). Population viability analysis was conducted to evaluate extinction risk and sensitivity analyses were undertaken to identify the most important spatial and non‐spatial parameters influencing extinction and decline. As biodiversity offsets are increasingly used to compensate for habitat loss, the population model was also used to explore the effectiveness of four potential offsetting mechanisms. In particular, this study addressed whether the impact of habitat loss on a species at risk could be compensated by: (i) increasing habitat elsewhere; (ii) increasing vital rates; (iii) increasing abundance; and (iv) increasing connectivity. Results suggest that extinction risk is low for this metapopulation and that the risk of extinction was most sensitive to vital rates. Compensating habitat loss with habitat gain, the most straightforward approach explored, was by far the most effective type of compensation. Increasing vital rates was the second most promising approach. Although increasing abundance and increasing connectivity could not be categorically ruled out, their effectiveness was much more limited. Overall, this study provided insight into the influence of spatial and non‐spatial parameters on abundance, patch occupancy, and extinction risk of an aquatic species. This approach can be applied to a wide variety of species to evaluate the effect of ecosystem perturbations and inform management options.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".