Progress towards the control of invasive alien species in the Cape Floristic Region’s protected areas
Bibliographic record
Abstract
Abstract This paper assesses progress towards the control of biological invasions in 18 protected areas (PAs) covering 677 584 ha in the Cape Floristic Region (CFR), and whether progress has been sufficient to achieve Target 6 of the Kunming-Montreal Global Biodiversity Framework. We used eight indicators for assessing the inputs (quality of the regulatory framework, money spent and planning coverage for species and protected areas), outputs (species and protected areas treated), and outcomes (effectiveness of species and protected area treatments) of management. The estimated money spent over 13 years (2010–2022) was ZAR 976 million, or ZAR 75 million per year. Management plans for PAs were assessed as adequate over 78.5% of the area, but only six out of 226 regulated invasive species had species-specific control plans in place. A total of 567 alien species occurred in the CFR’s PAs, 226 of which were regulated species (i.e. species that had to be controlled), 126 (55.8%) of which received some management. Spending was highly skewed, with over 60% of all funding spent on trees and shrubs in the genus Acacia . Management efforts reached 24% of the land within the CFR’s protected areas, with higher coverage in national parks (60%) than in provincial nature reserves (9%). Management effectiveness was assessed as either permanent, effective or partially effective for 29 species (20 due to biological control), and ineffective for 25; for the remainder, there was either no management or effectiveness could not be evaluated. We conclude that some progress has been made with respect to controlling invasive alien species in the CFR, but that insufficient and declining funding remains a significant barrier to effective management. To increase efficiency, it will be necessary to secure additional funding from more diversified sources, make more use of biological control and prescribed fire, and regularly monitor the outcomes of management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".