The future of sub-Saharan Africa’s biodiversity in the face of climate and societal change
Bibliographic record
Abstract
Many of the world’s most biodiverse regions are found in the poorest and second most populous continent of Africa; a continent facing exceptional challenges. Africa is projected to quadruple its population by 2100 and experience increasingly severe climate change and environmental conflict—all of which will ravage biodiversity. Here we assess conservation threats facing Africa and consider how these threats will be affected by human population growth, economic expansion, and climate change. We then evaluate the current capacity and infrastructure available to conserve the continent’s biodiversity. We consider four key questions essential for the future of African conservation: (1) how to build societal support for conservation efforts within Africa; (2) how to build Africa’s education, research, and management capacity; (3) how to finance conservation efforts; and (4) is conservation through development the appropriate approach for Africa? While the challenges are great, ways forward are clear, and we present ideas on how progress can be made. Given Africa’s current modest capacity to address its biodiversity crisis, additional international funding is required, but estimates of the cost of conserving Africa’s biodiversity are within reach. The will to act must build on the sympathy for conservation that is evident in Africa, but this will require building the education capacity within the continent. Considering Africa’s rapidly growing population and the associated huge economic needs, options other than conservation through development need to be more effectively explored. Despite the gravity of the situation, we believe that concerted effort in the coming decades can successfully curb the loss of biodiversity 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".