Introduction pathways of economically costly invasive alien species
Bibliographic record
Abstract
Abstract Introduction pathways play a pivotal role in the success of Invasive Alien Species (IAS)—the subset of alien species that have a negative environmental and/or socio-economic impact. Pathways refer to the fundamental processes that leads to the introduction of a species from one geographical location to another—marking the beginning of all alien species invasions. Increased knowledge of pathways is essential to help reduce the number of introductions and impacts of IAS and ultimately improve their management . Here we use the InvaCost database, a comprehensive repository on the global monetary impacts of IAS, combined with pathway data classified using the Convention on Biological Diversity (CBD) hierarchical classification and compiled from CABI Invasive Species Compendium, the Global Invasive Species Database (GISD) and the published literature to address five key points. Data were available for 478 individual IAS. For these, we found that both the total and annual average cost per species introduced through the ‘Stowaway’ (US$144.9bn; US$89.4m) and ‘Contaminant’ pathways (US$99.3bn; US$158.0m) were higher than species introduced primarily through the ‘Escape’ (US$87.4bn; US$25.4m) and ‘Release’ pathways (US$64.2bn; US$16.4m). Second, the recorded costs (both total and average) of species introduced unintentionally was higher than that from species introduced intentionally. Third, insects and mammals, respectively, accounted for the greatest proportion of the total cost of species introduced unintentionally and intentionally respectively, at least of the available records; ‘Stowaway’ had the highest recorded costs in Asia, Central America, North America and Diverse/Unspecified regions. Fourthly, the total cost of a species in a given location is not related to the year of first record of introduction, but time gaps might blur the true pattern. Finally, the total and average cost of IAS were not related to their number of introduction pathways. Although our findings are directly limited by the available data, they provide important material which can contribute to pathway priority measures, notably by complementing studies on pathways associated with ecologically harmful IAS. They also highlight the crucial need to fill the remaining data gaps—something that will be critical in prioritising limited management budgets to combat the current acceleration of species 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".