A balanced measure of conservation outcomes: tracking woody vegetation loss and protection in Queensland, Australia
Bibliographic record
Abstract
Abstract Quantifying changes in loss of native vegetation cover relative to its coverage in protected areas (PAs) offers a valuable means of evaluating conservation performance, yet this approach is rarely applied in practice. Here, we assessed historic and recent remnant woody vegetation loss and protection across Queensland, Australia’s most biodiverse state. By comparing the proportion of remnant woody vegetation loss with the extent of formally PAs across 13 bioregions and 116 subregions, we generate a balanced evaluation of conservation progress and apply a risk classification framework. We found that Queensland has lost 21.4% of its original woody vegetation since European colonisation, with only 9.6% currently protected. Importantly, one-fifth of this loss occurred between 2000 and 2018, despite protection more than doubling during the same period. Our results show that protection gains were concentrated in low-risk regions, while areas with considerable historical loss, such as the Brigalow Belt and Mulga Lands, continued to experience clearing and low protection gains. By 2018, 44% of subregions fell into high or very high-risk categories, while only 15% were classified as not at risk. The risk status for most subregions remained unchanged, which suggests that limited conservation progress has been achieved in high-pressure landscapes. Our findings highlight the limitations of using protected area expansion alone as a metric of conservation progress. A balance-sheet approach tracking both protection and loss can provide a more robust framework for evaluating conservation outcomes. This type of balance-sheet framework can support more effective conservation planning, including restoration and stricter land-clearing controls. Our results offer practical insights for aligning national actions with global biodiversity targets, such as those outlined in the Kunming-Montreal Global Biodiversity Framework.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".