Private protected areas exhibit greater bias towards unproductive land compared to public protected areas
Bibliographic record
Abstract
Abstract Globally, private protected areas (PPAs) have become an important tool for biodiversity conservation. While they are expanding in size and number, there is limited evidence on their potential impact on avoiding biodiversity loss, and how this impact compares to the public protected areas (public PAs). The impact of protection is measured as the actual biodiversity outcome within the area protected relative to the hypothetical outcome without protection. To maximise this positive impact, PAs need to be placed strategically on land that both harbours biodiversity and would be at risk of losing some of the biodiversity if it were not protected. We evaluate and compare the locations of PPAs and public PAs relative to random sites of similar governance type, and a range of covariates that capture biodiversity and the risk of biodiversity loss. We utilised data from a national PA database, and high-resolution data on nationally significant threatened species and indicators that capture risk of biodiversity loss at a continental scale in Australia. We find that PPAs tend to target areas of high threatened species richness. However, on average, PPAs are placed in areas that have lower risk of being cleared compared to randomly selected private land. We observe that this bias towards unproductive land is more prominent in PPAs when compared to public PAs. As nations work towards effectively conserving and managing at least 30% of the world’s lands by 2030 under the new Kunming-Montreal Global Biodiversity Framework, it becomes essential to prioritise PAs and PPAs that deliver impacts on avoiding biodiversity loss rather than solely focusing on areas that represent biodiversity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".