Ecological and socioeconomic impacts of invasive alien species in island ecosystems
Bibliographic record
Abstract
Minimizing the impact of invasive alien species (IAS) on islands and elsewhere requires researchers to provide cogent information on the environmental and socioeconomic consequences of IAS to the public and policy makers. Unfortunately, this information has not been readily available owing to a paucity of scientific research and the failure of the scientific community to make their findings readily available to decision makers. This review explores the vulnerability of islands to biological invasion, reports on environmental and socioeconomic impacts of IAS on islands and provides guidance and information on technical resources that can help minimize the effects of IAS in island ecosystems. This assessment is intended to provide a holistic perspective on island-IAS dynamics, enable biologists and social scientists to identify information gaps that warrant further research and serve as a primer for policy makers seeking to minimize the impact of IAS on island systems. Case studies have been selected to reflect the most scientifically-reliable information on the impacts of IAS on islands. Sufficient evidence has emerged to conclude that IAS are the most significant drivers of population declines and species extinctions in island ecosystems worldwide. Clearly, IAS can also have significant socioeconomic impacts directly (for example human health) and indirectly through their effects on ecosystem goods and services. These impacts are manifest at all ecological levels and affect the poorest, as well as richest, island nations. The measures needed to prevent and minimize the impacts of IAS on island ecosystems are generally known. However, many island nations and territories lack the scientific and technical information, infrastructure and human and financial resources necessary to adequately address the problems caused by IAS. Because every nation is an exporter and importer of goods and services, every nation is also a facilitator and victim of the invasion of alien species. Wealthy nations therefore need to help raise the capacity of island nations and territories to minimize the spread and impact of IAS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".