Use of multi-modelling methods to inform conservation and reintroductions of pine marten Martes martes in Britain
Bibliographic record
Abstract
The successful onset of recovery of the European pine marten (Martes martes) in some parts of Britain through range expansion and, more recently translocation for reintroductions, has resulted in a strong interest in reintroduction projects throughout the country. However, the geographic scope and conservation goals of these initiatives are often local and lack consideration of how they fit within the wider context of national-scale pine marten conservation. Here, we aim to maximize conservation benefit strategically at a national level by developing a simple, transparent, and transferable framework based on landscape modelling methods and spatially explicit population viability analyses. Our new methodology has been developed specifically to inform decisions involving the spatial targeting of pine marten conservation measures. We began by applying habitat suitability and connectivity modelling at a national scale. Then, we performed spatially explicit life history simulations to assess the natural recovery of the species. This information was used to identify regions of interest for future reintroductions, and we performed subsequent simulations to assess the viability of a reintroduced population within each region. From all the regions assessed, we identified two that should be prioritized for further consideration based on our analyses of habitat suitability, connectivity and the viability of reintroduced populations. While our framework can be used to identify and prioritize regions of conservation value generally, our focus here is on the biological considerations associated with identifying suitable landscapes for pine marten reintroduction.
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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 | 0.002 | 0.006 |
| 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".