Click beetles (Elateridae) identify conservation units in Oriental and European beech forests
Bibliographic record
Abstract
Abstract Beech trees form major parts of lowland temperate forests in the Western Palearctic. To protect biodiversity, many beech forests have been designated as World Heritage Sites or established as networks of beech forest reserves. However, the insect communities within these protected forests have not been well explored. In this study, elaterids (Elateridae, Coleoptera) in 26 beech forests, from France in the west to Iran in the east, were sample to identify conservation clusters and hotspots of biodiversity. Sampling was mostly carried out using window traps and all specimens were identified to the species level. A total of 118 species were identified including one previously unreported species. Community composition analyses that focused on rare species identified five clusters comprising distinct communities: (i) the Hyrcanian Forest in Iran, (ii) the Lesser Caucasus in Türkiye, Georgia and Armenia, (iii) the Greater Caucasus in Georgia, (iv) the Pyrenees and (v) a cluster made up of forests from Central Europe, the Balkan region and the Carpathians. After controlling for sampling effort (individuals), the highest richness was found in the Caucasus region. The proportion of endemics was highest in the Oriental beech forests of the Caucasus and in Hyrcanian forests. These findings highlight the unique biodiversity of beech forests and support calls for intensified conservation actions in beech forests, particularly in the Caucasus and Hyrcanian regions, which should be prioritized for conservation efforts, due to their unique fauna. Implications for insect conservation Our study underscores the importance of protecting beech forests, especially in the Caucasus and Hyrcanian regions, as they host unique and endemic insect species critical for biodiversity conservation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".