Trends and patterns in the extinction risk of Australia’s birds over three decades
Bibliographic record
Abstract
Australia recently committed through the Kunming-Montreal Global Biodiversity Framework (GBF) to halt human-induced extinction of known threatened species and to reduce extinction risk of threatened species significantly by 2030. We review recent trends in extinction risk of Australian birds to provide context for current and future conservation efforts. We calculate the Red List Index (RLI) for all Australian birds as well as subsets based on geography, habitat and taxonomy. Over the period 2010 to 2020, the number of taxa reassigned to lower categories of extinction risk (n = 20; 1.5% of all taxa included) was greatly outweighed by the number moved to higher categories owing to deteriorating status (n = 93; 7%). This resulted in the steepest decadal decline in the RLI since data were first compiled in 1990. It was chiefly driven by rapid population declines in migratory shorebirds, loss of suitable habitat for species affected by wildfire in 2019–2020 and, to a lesser extent, declines in the abundance of upland rainforest birds. To a small extent, these losses were counterbalanced by improvements in status of some bird species resulting from local eradication of invasive mammals, primarily from Macquarie Island. For Australia to meet the commitments recently adopted through the GBF, conservation interventions (and hence funding) will need to be scaled up substantially. The RLI is well placed for monitoring progress towards the GBF targets and for communicating trends in the extinction risk to national avifaunas.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".