Predicting the global economic costs of biological invasions by tetrapods
Bibliographic record
Abstract
Globalisation has accelerated rates of biological invasions worldwide, leading to widespread environmental perturbations that often translate into rapidly expanding socio-economic costs. Although such monetary costs can be estimated from the observed effects of invasions, the pathways that lead invasive species to become economically impactful remain poorly understood. Here, we implement the first global-scale test of the hypothesis that adaptive traits that influence demographic resilience predict economic costs, using invasive terrestrial vertebrates as models given their well-catalogued impacts and characteristics. Our results reveal that total global costs of invasive tetrapods are conservatively in the tens of billions of dollars, with the vast majority due to damage costs from invasive mammals. These monetary impacts are predicted by longevity, female maturation age, diet and invasion pathway traits, although the directionality in the association between impacts and these drivers varied across classes. Alarmingly, costs remain unknown for >90 % of recorded established alien tetrapods worldwide, and across the majority of invaded countries. These huge socio-economic costs demonstrate the necessity of mitigating tetrapod invasions and filling knowledge gaps. Effective identification of traits predictive of costs among and within these groups can facilitate the prioritisation of resources to efficiently target the most damaging existing and emerging invasive tetrapod species. • Global economic costs of invasive tetrapods conservatively sum to >US$55 billion. • Costs are predicted by species traits — longevity, female maturation age, diet and invasion pathway. • Directionality of predictions can differ between ecto- and endothermic invaders. • Significant discrepancies exist between databases documenting ecological and economic impacts. • Profiling of life history traits can help to identify and mitigate future costs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".