Near and far future conservation, land use, and land cover interactions around the wider Etosha landscape, north-central Namibia
Bibliographic record
Abstract
Recent conservation efforts have resulted in the growth of protected and conserved areas as a land use across African drylands. However, land use and land cover change (LULCC) associated with habitat fragmentation continues to be a substantive driver of biodiversity loss in multiple-use landscapes. This study highlights the significance of perspectives from diverse stakeholders in understanding LULCC in a southern African dryland where the coverage of protected and conserved areas is increasing. The study models future land cover change scenarios and assesses their alignment with the Sustainable Development Goals (SDG) Agenda 2030 and the African Union (AU) Agenda 2063. Three scenarios representing business-as-usual conditions, conservation and livestock production, and agricultural and livestock production are outlined. Under business-as-usual conditions, protected areas are conserved and built-up areas expand. However, land degradation occurs where people are concentrated around key resource areas. In a scenario focused on conservation and livestock production, conservation initiatives are strengthened, but expansion of shrublands occurs in livestock-dominated areas that are not well managed. In a scenario focused on agricultural and livestock production, farms grow but their expansion within protected areas causes human-wildlife conflicts. Desirable near and far futures — characterised by environmental integrity, human-wildlife coexistence, and an equitable, thriving wildlife-based economy — are seen as attainable through coordinated land-based activities and the implementation of community-based conservation legislation. Outputs from this study demonstrate the value of a stakeholder-led approach in tackling conservation challenges and in planning for a sustainable future for an arid region heavily reliant on land-based livelihoods.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".