Land tenure contributions to protected area growth under alternative conservation targets in the Australian monsoon tropics
Bibliographic record
Abstract
As the global protected area (PA) network expands to meet international targets, it is important to assess whether traditional reliance on public land will suffice for projected PA growth or whether other tenures, such as Indigenous or pastoral lands, may increasingly contribute. Another consideration is whether the relative importance of different tenures varies depending on the specific goals of the PA network. We used the mammal fauna of the Australian monsoon tropics (AMT), one of the world's largest intact tropical savannas, as a case study to address these questions. We applied systematic conservation planning to identify optimal PA configurations under 2 objectives (adding to the existing PA network from any tenure vs. expanding the Indigenous protected area [IPA] network through voluntary declaration of Indigenous lands by traditional owners) and 2 species protection criteria (prioritizing currently threatened species vs. species predicted to become threatened). We calculated planning unit selection frequencies for the resulting 4 scenarios to identify high-priority areas for mammal conservation and assessed their dependence on different tenure categories. All scenarios relied heavily on Indigenous lands to achieve species representation targets, with varying contributions from pastoral land depending on the criteria prioritized. Protecting potentially threatened species required more pastoral land and Indigenous land coexisting with primary industries, whereas targets for currently threatened species were more cost-effectively met through voluntary declarations of Indigenous freehold land as IPAs. Our results highlight the potential for Indigenous lands to play a major role in achieving biodiversity conservation targets and demonstrate that land tenure requirements vary depending on conservation priorities. These findings emphasize the need to explicitly consider tenure in conservation planning to guide collaborative strategies and ensure PA growth aligns with specific biodiversity goals across diverse land management contexts.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".