Exploring uncertainty in population viability analysis and its implications for the conservation of a freshwater fish
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it