Biogeographic patterns and conservation priorities for the dung beetle tribe <scp>P</scp> hanaeini ( <scp>C</scp> oleoptera: <scp>S</scp> carabaeidae: <scp>S</scp> carabaeinae) in <scp>B</scp> olivia
Bibliographic record
Abstract
Abstract The New World Phanaeini are the best known Neotropical dung beetle tribe and a conservation priority among the Scarabaeinae, an ideal focal taxon for biodiversity research and conservation. We compiled a comprehensive distributional database for 39 phanaeine species in Bolivia and assessed patterns of species richness, body size and endemism in relation to abiotic variables and species richness and body mass of medium to large mammals across nine ecoregions. Pair‐wise linear regressions indicated that phanaeine richness, mean size and endemism are determined by different factors. In all cases mammal body mass had greater explanatory power than abiotic variables or mammal richness. Phanaeine richness was greater in ecoregions with on average smaller mammals and greater mammal richness. Mean phanaeine size increased with mean body mass of the largest herbivorous and omnivorous mammals. Endemism was greater in ecoregions with on average smaller herbivorous and omnivorous mammals. On average, smaller phanaeines had more restricted distributions than larger species; ecoregional endemism and mean body size were negatively correlated. Large phanaeines probably depend on large mammals to provide adequate food resources. Greater richness of smaller mammal species may allow for greater temporal and spatial resource partitioning and therefore greater phanaeine species richness. Low numbers of large mammal species may favour the persistence of geographically restricted phanaeine species by reducing interspecific competition with larger, more geographically widespread and presumably dominant phanaeines. Cerrado, Southwest Amazonia and Yungas are priority ecoregions for phanaeine conservation due to high total and endemic species richness.
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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 | 0.001 |
| 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".