Recovery of dung beetle biodiversity and traits in a regenerating rainforest: a case study from Costa Rica's Osa Peninsula
Bibliographic record
Abstract
Abstract Dung beetles are frequently used to assess tropical biodiversity patterns and recovery in human‐modified forests. We conducted a comprehensive dung beetle survey (coprophagous and necrophagous communities) within five habitat types, across a land‐use gradient, in the ecologically biodiverse Osa Peninsula, located in Costa Rica's south Pacific. In addition to assessing species richness, abundance, and biomass, we also assessed community level traits and species‐specific responses using a generalised joint attribute modelling approach. We found that under favourable conditions (40–50 years of regeneration, close proximity to contiguous old‐growth forest and control of poaching), secondary rainforest recovered similar levels of species richness, and key traits of old‐growth forest dung beetle communities. However, at the community‐level, dung beetle abundance, richness, biomass, and diversity varied between habitat types of different anthropogenic disturbance and land‐use. Generally, the carrion beetle community did not recover as well as the dung beetle community and the abundance of dung beetles was a third lower in naturally regenerating secondary forest compared with old growth. Regenerating secondary growth and plantation forests showed community compositions similar to old growth forests, while open and fragmented habitats had degraded and impoverished levels of dung beetle biodiversity. Overall, the levels of dung‐beetle biodiversity detected are encouraging for naturally regenerating secondary forest, suggesting a high potential value of these areas to buffer the pressure of deforestation and habitat alteration on remaining old‐growth tropical forests.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".