Modeling Wolf, Canis lupus, Recolonization Dynamics to Plan Conservation Actions Ahead: Will the “Big Bad Wolves” Howl Again in Slavonia, Croatia?
Bibliographic record
Abstract
A century ago, wolves ranged throughout Croatia but were eradicated from Slavonia—a region that could serve as a crucial corridor connecting the Carpathian and Dinaric–Balkan wolf populations. Such a corridor would promote genetic exchange and help maintain ecosystem stability. Recent wolf sightings in Slavonia indicate that natural recolonization may be possible. Understanding how this process unfolds under different management scenarios is essential for minimizing conflicts and supporting successful recolonization. In this study, we modeled wolf population dynamics in Slavonia and surrounding areas using 11 scenarios, grouped into three categories: adverse events, increased carrying capacity, and population supplementation. These scenarios encompassed various management strategies, including a baseline scenario and others designed to address system uncertainties. Our results show that scenarios involving corridor construction and wolf translocation have the lowest probability of extinction. In contrast, adverse events carry a high risk of extinction, and simply expanding suitable habitats is not enough to ensure population viability. These findings underscore the importance of integrated conservation strategies that combine habitat corridors, population management, and conflict mitigation. Long-term planning is critical, as differences in outcomes become more pronounced over time. Connectivity with stable neighboring populations is vital for the long-term survival of wolves in the region. Future research should investigate whether protected areas alone are sufficient to sustain wolves as apex predators or if large-scale ecosystem restoration—including trophic rewilding—is necessary for successful recolonization.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".