The Value of Protected Areas Ranger Service Personnel for Biodiversity Monitoring: Case Study in Paklenica National Park (Croatia)
Bibliographic record
Abstract
Abstract To preserve the long-term survival of habitats and wildlife, it is necessary to monitor their status. In protected areas, that is, biodiversity centres, rangers have excellent knowledge of specific locations and they spend a large amount of time in the field. But since rangers are not required to have a university degree, the question is whether they can be an added value in baseline survey monitoring. To investigate this issue, a case study was conducted in the Paklenica National Park (Paklenica NP), Croatia, using camera trapping survey between 2011 and 2016 at 22 camera sites. The aims of this survey were (1) to collect baseline data of mammalian diversity, (2) to analyse the regulatory effect of top predators on the trophic pyramid through top–down effects and (3) to estimate the data gathered by ranger service from the Paklenica NP. Data gathered through this study represents the first peer-reviewed list of medium- to large-sized mammals for Paklenica NP, as up till now, there is no peer-reviewed published paper of Paklenica NP mammalian diversity. Results showed that the recorded top predator–mesopredator–prey ratios were concordant with the trophic pyramid hypothesis. Also, as it was expected according to the literature, species richness indices were higher when top predators were present. On analysing the costs for conducting camera trapping survey by external and internal services, a significant difference was observed. Internal rangers’ cost was nine times lower than the external service cost. Future research run by rangers needs to be conducted in way to capture all mammalian biodiversity. Therefore, we propose camera trapping methodology for rangers in the protected areas since this may be an invaluable tool for biodiversity monitoring.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".