The neglected importance of managing biological invasions for sustainable development
Bibliographic record
Abstract
Abstract Biological invasions have substantial and rising social‐ecological impacts threatening human livelihoods and communities and hampering progress towards a just and equitable world. Currently, biological invasions are not adequately recognised and included in the UN Agenda 2030. Using a literature review conducted in Web of Science, we highlight the bias in available literature of biological invasions related to the UN Agenda 2030 and its Sustainable Development Goals. We find abundant scientific literature towards environmental and biodiversity related sustainability targets while other especially provisioning targets are less well represented. Subsequently, we discuss the risks of neglecting biological invasions within sustainable development and how invasive alien species can have changing and adverse effects through time counteracting the intended benefits at the time of introduction. Finally, we provide key recommendations for action at the international scale to ensure that biological invasions are adequately considered in sustainable development. Those recommendations include (1) acknowledgement of biological invasions as a key threat to sustainable development, (2) a call for stronger multilateral exchange under the umbrella of an adequately financed coordinating body and (3) appropriate implementation and resource provisioning for international monitoring, data infrastructure, data exchange and use of adequate indicators of biological invasions to streamline decision making based on a solid evidence base. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".