Unexpected Diversity in Regenerating Sites Stresses the Importance of Baselines: A Case Study With Bats (Order Chiroptera) on the Osa Peninsula, Costa Rica
Bibliographic record
Abstract
Introduction: Background and Research Aims: Assessing biodiversity recovery is key to determine whether the objectives of habitat restoration for conservation are met. Many restoration initiatives use cross-sectional comparisons of wildlife communities to infer restoration impact instead of longitudinal assessments from a baseline state. Using an indicator of biodiversity in the neotropics— bats— we demonstrate how assessing community diversity and composition in an area targeted for restoration prior to implementation, and when compared to surrounding intact forest, provides the groundwork to track changes in the community post-restoration. Methods We assessed bat communities by 1) using mist-net surveys to identify species in the family Phyllostomidae (leaf-nosed bats), and 2) conducting acoustic surveys to identify non-phyllostomid species (aerial insectivores). Results For both groups, we found that areas targeted for restoration had similar diversity as the surrounding forest, but the two habitat types differed in community composition. Phyllostomids were captured at higher rates in forest, but aerial insectivores were detected at higher rates in restoration habitat. Conclusion Our baseline assessment revealed unexpected diversity in areas targeted for restoration. The presence of all trophic groups in restoration habitat suggests that bats provide key ecosystem services in the restoration process, such as through seed dispersal, pollination and insect pest control. Implications for Conservation: Conducting a baseline survey of bats in areas targeted for restoration demonstrated that the community was not species poor at the baseline and was different from the surrounding forest, allowing us to better track restoration success and the effects of different restoration treatments.
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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 | 0.001 | 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 | 0.003 | 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) | 0.001 | 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".