Recolonization pattern of wolves in Northern Apennines, Central Italy: a Bayesian analysis using opportunistic and systematic data
Bibliographic record
Abstract
Wolves ( Canis lupus Linnaeus, 1758) have recolonized much of Europe and recently returned to the Apuan Alps (Central Italy), a partially isolated mountain chain unoccupied by wolves between 1850s and 2000s, after extirpation in early 1800s. Early stages of large carnivore recolonization often go undetected, particularly when standardized surveys are lacking. In the Apuan Alps, this left the spatiotemporal dynamics of wolf return unresolved. To reconstruct this process, we combined opportunistic playback-howling surveys (2007–2020) with camera-trap records (2011–2020), and fit a Bayesian occupancy model accounting for preferential sampling on camera-trap data. Playback surveys first confirmed reproduction in 2014; breeding units increased to four by 2018. Camera-trap occupancy rose 15-fold (2011–2018), despite early site‐selection bias. Systematic sampling (2019–2020) validated these trends, estimating occupancy at ∼0.60 and detection probabilities threefold higher than opportunistic surveys. Opportunistic data could not fully detail early recolonization and spatial mechanisms due to likely misdetection of initial reproduction. Nonetheless, combined evidence suggested a two-phase pattern consistent with other wolf recolonizations: an initial lag period followed by rapid expansion via short-distance diffusion and jump-dispersal. Although prone to biases, opportunistic records can yield insights into cryptic recolonizations, guide early management and conservation, and inform future monitoring of recolonizing large carnivores.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".