Global economic costs of herpetofauna invasions
Bibliographic record
Abstract
Abstract Biological invasions by amphibian and reptile species (i.e. herpetofauna) are numerous and widespread and have caused severe impacts on ecosystems, the economy and human health. However, there remains no synthesised assessment of the economic costs of these invasions. Therefore, using the most comprehensive database on the economic costs of invasive alien species worldwide (InvaCost), we analyse the costs caused by invasive alien herpetofauna according to taxonomic, geographic, sectoral and temporal dimensions, as well as the types of these costs. The cost of invasive herpetofauna totaled 17.0 billion US$ between 1986 and 2020, divided between 6.3 billion US$ for amphibians, 10.4 billion US$ for reptiles and 334 million US$ for mixed classes. However, these costs were associated predominantly with only two species (brown tree snake Boiga irregularis and American bullfrog Lithobates catesbeianus), with 10.3 and 6.0 billion US$ in costs, respectively. Costs for the remaining 19 reported species were relatively minor (< 0.6 billion US$), and they were entirely unavailable for over 94% of known invasive herpetofauna worldwide. Also, costs were positively correlated with research effort, suggesting research biases towards well-known taxa. So far, costs have been dominated by predictions and extrapolations (79%), and thus empirical observations for impact were relatively scarce. The activity sector most affected by amphibians was authorities-stakeholders through management (> 99%), while for reptiles, impact was reported mostly through damages to mixed sectors (65%). Geographically, Oceania and Pacific Islands recorded 63% of total costs, followed by Europe (35%) and North America (2%). Cost reports have generally increased over time. A greater effort in studying the costs of invasive herpetofauna is necessary for a more complete understanding of invasion impacts of these species. We emphasise the need for greater control and prevention policies concerning the spread of current and future invasive herpetofauna.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".