All forests are not equal: population demographics and denning behaviour of a recovering small carnivore in human modified landscapes
Bibliographic record
Abstract
Landscapes occupied by recovering carnivore populations in Europe are highly modified by human activity. It is unclear how recovering predators will adapt and sustain populations in highly altered landscapes, with most existing research focused on large carnivores. To address this we contrast population demographics and denning behaviour of a small carnivore, the pine marten Martes martes , in a semi‐natural wooded landscape and a human‐modified landscape with limited forest cover composed of conifer plantation, using radio‐telemetry on 20 free‐ranging individuals in Northern Ireland. In the semi‐natural landscape, martens selected old growth, native forest making almost exclusive use of arboreal dens in living trees and standing deadwood. Martens persisted in the human‐modified landscape but with lower population density and recruitment, with a male‐biased sex ratio. In the human‐modified landscape martens denned in marginal habitats such as scrub, heath and property boundaries, while making use of subterranean or man‐made structures for dens in response to a lack of above ground denning opportunities. We demonstrate landscape change‐induced differences in behaviour and population structure in a recovering carnivore. The results highlight the importance of evaluating the availability of denning sites in carnivore conservation and provide valuable management considerations, key to mitigating human–wildlife conflict as carnivores continue to recover and recolonise Europe.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".