Modelling roe deer (<i>Capreolus capreolus</i>) in a gradient of forest fragmentation: behavioural plasticity and choice of cover
Bibliographic record
Abstract
The ability of a species to exhibit behavioural plasticity to environmental conditions has consequences for its success in fragmented landscapes. The roe deer, Capreolus capreolus (L., 1758), is one of the foremost examples of behavioural flexibility among ungulates. This species has increased rapidly in range from its original forest-mosaic habitat into open agricultural plains. Open-land roe deer populations show distinct differences in spatial and social behaviour, including larger group sizes, compared with forest-living roe deer populations. This is traditionally viewed as an antipredator strategy. The presence of strong behavioural plasticity in species response to landscape structure suggests that this should also be a concern in models attempting to describe effects of landscape change on species distribution. To date the implications of behavioural plasticity for modelling species' response to environmental conditions has received little attention. We used an individual-based model of roe deer to evaluate the consequences of behavioural plasticity for predictions made regarding population response to woodland fragmentation. The inclusion of a flexible behavioural strategy, where increased group size could buffer lack of woodland cover, resulted in significantly higher estimates of population size, population persistence, and the ability of the population to cope with fragmentation. This clearly demonstrates that behavioural plasticity in species response to landscape structure may affect our ability to accurately predict the effects of landscape change and should be a concern to modellers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".