Multiple targets of the Global Biodiversity Framework must be addressed to manage invasive alien species in protected areas
Bibliographic record
Abstract
The Kunming-Montreal Global Biodiversity Framework (GBF) sets out ambitious global targets to reduce biodiversity loss by 2030 and will determine the conservation agenda for the next decade. Invasive alien species are a major driver of biodiversity loss in terrestrial and marine ecosystems; and a key focus of the GBF is therefore to reduce their introduction by 50% through pathway management as well as eradicating or controlling established alien species in priority sites (Target 6). Protected areas are among the most important priority sites for the management of biological invasions. However, delivery of Target 6 for protected areas entails coordination with other GBF targets especially in relation to rapidly evolving pathways such as increasing international and domestic tourism (Target 15), progressive encroachment of urban areas (Target 12), development of intensive agriculture/aquaculture systems in buffer zones (Target 10), species rafting on marine plastic (Target 7), and growing risk from range-shifting species under climate change (Target 8). The management of established invasive alien species requires effective spatial planning (Target 1) to prioritise the limited human and financial resources available to manage biological invasions including recognising those protected areas facing the greatest immediate and future threat, identifying the species that pose the greatest risk to threatened species (Target 4) and/or Nature’s Contributions to People (Target 11), and obtaining the necessary finance required to effectively control priority species (Target 19). The goal of expanding protected areas to cover 30% of land, water, and seas (Target 3) will need to avoid the inclusion of areas already harbouring invasive alien species. Addressing biological invasions must be an inclusive process (Target 22) undertaken over multiple years that involves the sharing of knowledge and data (Target 21). Decision-makers, protected area managers, researchers, and representative of local communities should all be involved in the regular prioritisation, implementation, and review of management activities. Consequently, the effective management of biological invasions to halt biodiversity loss by 2030 will not be realised by having an exclusive focus on achieving Target 6; it will also require that substantial progress is made with most GBF Targets. Elucidating the interconnectedness of different GBF Targets in relation to their direct or indirect role in the effective management of biological invasions reveals opportunities for a more integrated approach to biodiversity conservation. The inclusion of the multiple GBF targets in strategies to address invasive alien species is the step change needed to reduce the magnitude of this threat to biodiversity by 2030.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".