Identifying opportunities for transboundary conservation in Africa
Bibliographic record
Abstract
The conservation of natural and cultural resources shared between countries is a significant challenge that can be addressed through the establishment of transboundary conservation areas (TBCAs). TBCAs enable countries to harmonize cross-border governance and management, increase protected area (PA) coverage, and strengthen relationships between neighbouring countries and communities. In Africa, many ecosystems and species ranges span multiple countries, making TBCAs a crucial tool for biodiversity conservation. However, there is a lack of research on where TBCAs can be established or need to be established. To address this gap, we conducted a study to identify opportunities for establishing TBCAs in Africa. We first compiled an up-to-date list of existing TBCAs on the continent. Then, we identified potential TBCAs by identifying protected areas next to country borders that are adjacent to other protected areas in a neighbouring country. We also evaluated the functional connectivity between these PA pairs and prioritized potential TBCAs based on size, connectivity, and ease of establishment. We identified 27 existing TBCAs and 8,481 potential TBCAs in Africa composed of various possible combinations of 2,326 individual PAs. Our results provide a baseline of existing TBCAs and offer a better understanding of where transboundary conservation might be established or strengthened. We also highlight areas where future transboundary conservation efforts could safeguard PA connectivity. This information can guide policy and decision-making processes towards promoting conservation and sustainable use of natural and cultural resources shared between countries in Africa.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".