Trends and patterns in the extinction risk of Australia’s birds over three decades
Notice bibliographique
Résumé
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. Australia is committed to reducing extinction risk through its adoption of the Kunming-Montreal Global Biodiversity Framework.A Red List Index (RLI) for all Australian birds from 1990 to 2020 shows their extinction risk increased by 3.34%.More than 50% of the increase in extinction risk between 2010 and 2020 was caused by the 2019–2020 wildfires.The greatest increases in overall avian extinction risk were in Queensland, South Australia and New South Wales, where drought and wildfire effects were most pronounced.Red List Index trends can reflect the impact of individual threats or conservation interventions, and represent an important tool for monitoring national and global progress towards international agreements. Australia is committed to reducing extinction risk through its adoption of the Kunming-Montreal Global Biodiversity Framework. A Red List Index (RLI) for all Australian birds from 1990 to 2020 shows their extinction risk increased by 3.34%. More than 50% of the increase in extinction risk between 2010 and 2020 was caused by the 2019–2020 wildfires. The greatest increases in overall avian extinction risk were in Queensland, South Australia and New South Wales, where drought and wildfire effects were most pronounced. Red List Index trends can reflect the impact of individual threats or conservation interventions, and represent an important tool for monitoring national and global progress towards international agreements.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».