Using key and critical biodiversity areas to identify gaps in the protected area network in the Limpopo Province, South Africa
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The Kunming‐Montreal Global Biodiversity Framework (KMGBF) commits signatories to expand the global protection of land and sea by 30% in 2030. Additionally, in South Africa, a local target set in 2016 aims to conserve 16% of terrestrial areas using protected areas within a two‐decade time frame. Concurrently, it is crucial to recognize and prioritize sites where biodiversity must be protected immediately. This recognition has given rise to global Key Biodiversity Areas (KBAs) and South Africa's Critical Biodiversity Areas (CBAs). KBAs are sites of significance for the global persistence of biodiversity. In South Africa, CBAs delineate primarily or partially natural areas needing management. Despite their significance, an assessment of KBAs and CBAs in South Africa's Limpopo province, specifically the Vhembe District, is lacking. Employing GIS techniques, our evaluation focused on the coverage, size, and distribution of protected areas in the Vhembe District. Our analysis revealed that protected areas cover an impressive 38% of the Vhembe District. Critical Biodiversity Areas cover 9465 km 2 (36%) of the region. Alarmingly, 70% (6809 km 2 ) of these CBA sites lack protection. Additionally, KBAs cover 30% of the region, with 39% of sites covering approximately 3273 km 2 and laying outside the protected area network, rendering them entirely unprotected. Sluggish protected areas establishment rates and a deficiency in the strategic targeting of significant sites have resulted in over 10,000 km 2 of land warranting protection, particularly along the Soutpansberg Mountain Range. Moreover, South Africa's national target, established in 2016, which aims to protect a mere 16% of terrestrial areas by 2036, falls short of the global KMGBF target, reinforcing the urgency for an update in national policy and embracing other conservation methods. These findings suggest that, despite the commendable 38% protection of the district, setting a precedent for the rest of the country, there is a crucial need for municipalities, districts, and provinces to draw insights from the shortfalls of the Vhembe District.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it