Biological invasions: a global assessment of geographic distributions, long‐term trends, and data gaps
Bibliographic record
Abstract
Biological invasions are one of the major drivers of biodiversity decline and have been shown to have far-reaching consequences for society and the economy. Preventing the introduction and spread of alien species represents the most effective solution to reducing their impacts on nature and human well-being. However, implementing effective solutions requires a good understanding of where the species are established and how biological invasions develop over time. Knowledge of the status and trends of biological invasions is thus key for guiding research efforts, informing stakeholders and policymakers, for targeted management efforts, and preparing for the future. However, information about the status and trends of alien species is scattered, patchy, and highly incomplete, making it difficult to assess. Published reports for individual regions and taxonomic groups are available, but large-scale overviews are scarce. A global assessment therefore requires a review of available knowledge with careful consideration of sampling and reporting biases. This paper provides a comprehensive global assessment of the status and trends of alien species for major taxonomic groups [Bacteria, Protozoa, Stramenopila, Alveolata, and Rhizaria (SAR), fungi, plants, and animals] for Intergovernmental Panel of Biodiversity and Ecosystem Services (IPBES) regions. The review provides irrefutable evidence that alien species have been introduced to all regions worldwide including Antarctica and have spread to even the most remote islands. The numbers of alien species are increasing within all taxa and across all regions, and are often even accelerating. Large knowledge gaps exist, particularly for taxonomic groups other than vascular plants and vertebrates, for regions in Africa and Central Asia, and for aquatic realms. In fact, for inconspicuous species, such as Bacteria, Protozoa, and to some degree SAR and fungi, we found records for very few species and regions. Observed status and trends are thus highly influenced by research effort. More generally, it is likely that all lists for alien species of any taxonomic group and region are incomplete. The reported species numbers therefore represent minima, and we can expect additions to all lists in the near future. We identified six key challenges which need to be addressed to reduce knowledge gaps and to improve our ability to assess trends and status of biological invasions.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".