Spatial ecology, biodiversity, and abiotic determinants of Congo’s bai ecosystem
Why this work is in the frame
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Bibliographic record
Abstract
Canopy gaps are foundational features of rainforest biodiversity and successional processes. The bais of Central Africa are among the world’s largest natural forest clearings and thought to be critically important islands of open-canopy habitat in an ocean of closed-canopy rainforest. However, while frequently denoted as a conservation priority, there are no published studies on the abundance or distribution of bais across the landscape, nor on their biodiversity patterns, limiting our understanding of their ecological contribution to Congolese rainforests. We combined remote sensing and field surveys to quantify the abundance, spatial distribution, shape, size, biodiversity, and soil properties of bais in Odzala-Kokoua National Park (OKNP), Republic of the Congo (hereafter, Congo). We related bais spatial distribution to variation in hydrology and topography, compared plant community composition and 3D structure between bais and other open ecosystems, quantified animal diversity from camera traps, and measured soil moisture content in different bai types. We found bais to be more numerous than previously thought (we mapped 2,176 bais in OKNP), but their predominantly small size (80.7% of bais were <1ha), highly clustered distribution, and restriction to areas of low topographic position makes them a rare riparian habitat type. We documented low plant community and structural similarity between bais types and with other open ecosystems, and identified significant differences in soil moisture between bai and open ecosystem types. Our results demonstrate that two distinct bais types can be differentiated based on their plant and animal communities, soil properties, and vegetation structure. Taken together, our findings provide insights into how bais relate to other types of forest clearings and on their overall importance to Congolese rainforest ecosystems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.055 |
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 it