Data from: Mountaintop removal mining alters stream salamander population dynamics
Bibliographic record
Abstract
Aim: Population dynamics are often tightly linked to the condition of the landscape. Focusing on a landscape impacted by mountaintop removal coal mining (MTR), we ask the following questions: (1) How does MTR influence vital rates including occupancy, colonization and persistence probabilities, and conditional abundance of stream salamander species and life stages? (2) Do species and life stages respond similar to MTR mining or is there significant variation among species and life stages? Location: Freshwater and terrestrial habitats in Central Appalachia (South‐eastern Kentucky, USA). Methods: We conducted salamander counts for three consecutive years in 23 headwater stream reaches in forested or previously mined landscapes. We used a hierarchical, N‐mixture model with dynamic occupancy to calculate species‐ and life stage‐specific occupancy, colonization and persistence rates, and abundance given occupancy. We examined the coefficients of the hierarchical priors to determine population variation among species and life stages. Results: Over 3 years, reference sites had greater salamander abundances and were occupied at a much higher rate than streams impacted by MTR. At sites impacted by MTR mining, most salamander species and life stages exhibited reduced initial occupancy, colonization rates, persistence rates and conditional abundance relative to reference stream reaches. Furthermore, the rates in MTR sites showed low variance, reinforcing that species and life stages were responding similar to MTR. Main conclusions: Salamander populations in landscapes modified by MTR mining exhibited significantly reduced vital rates compared to reference sites. Yet, similarity in responses across species suggests that management or restoration may benefit the entire salamander assemblage. For example, reforestation could reduce landscape resistance, repair altered hydrologic regimes and allow for higher rates of colonization and persistence in streams impacted by MTR.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".