Towards adaptive fire management for biodiversity conservation: Experience in South African National Parks
Bibliographic record
Abstract
This paper reviews the experience gained in three South African national parks (Kruger, Table Mountain and Bontebok) with regard to the adaptive management of fire for the conservation of biodiversity. In the Kruger National Park, adaptive approaches have evolved over the past 15 years, beginning initially as a form of ‘informed trial and error’, but progressing towards active adaptive management in which landscape-scale, experimental burning treatments are being applied in order to learn. In the process, significant advances in understanding regarding the role and management of fire have been made. Attempts have been made to transfer the approaches developed in Kruger National Park to the other two national parks. However, little progress has been made to date, both because of a failure to provide an agreed context for the introduction of adaptive approaches, and because (in the case of Bontebok National Park) too little time has passed to be able to make an assessment. Fire management interventions, ultimately, will manifest themselves in terms of biodiversity outcomes, but definite links between fire interventions and biodiversity outcomes have yet to be made.Conservation implications: Significant challenges face the managers of fire-prone and fire adapted ecosystems, where the attainment of ecosystem goals may require approaches (like encouraging high-intensity fires at hot and dry times of the year) that threaten societal goals related to safety. In addition, approaches to fire management have focused on encouraging particular fire patterns in the absence of a sound understanding of their ecological outcomes. Adaptive management offers a framework for addressing these issues, but will require higher levels of agreement, monitoring and assessment than have been the case to date.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".