What Will the Future Bring for Biological Invasions on Islands? An Expert-Based Assessment
Bibliographic record
Abstract
Biological invasions are a major threat to global biodiversity with particularly strong implications for island biodiversity. Much research has been dedicated towards understanding historic and current changes in alien species distribution and impacts on islands and potential changes under future climate change. However, projections of how alien species richness and impacts on islands might develop in the future are still lacking. In the absence of reliable projections, expert-based assessments are a valuable tool to investigate the importance of different drivers and pathways and the distributions of potential impacts of future biological invasions. These insights can guide subsequent quantification efforts and inform invasive species management and policy. In this study, we performed a survey among 126 experts in invasion science ranging from scientists to managers and decision makers with a focus on island systems until the mid-21st century. The survey revealed that out of 15 drivers, six were considered important by almost all respondents (>90%). Of these, trade & transport was identified as most important at the introduction stage (99.2%) and land use/cover change as most important at the establishment (96.8%) and spread (95.2%) stage. Additionally, the experts considered that alien species were more likely to be introduced (93.7%) and spread (78.6%) as stowaways than through any other pathway. In general, respondents agreed that the impacts of alien species will increase on all types of islands, particularly on oceanic islands, followed by atolls and continental islands. Within islands, terrestrial ecosystems were assumed to be impacted more severely than marine ecosystems. Finally, the survey hints towards the potential for effective communication, scientific research and increased pro-active management of alien species on islands to reduce their future consequences. Given the major threat represented by invasive alien species on islands, these results provide crucial insights relevant for global and regional conservation efforts.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".