Economic costs of invasive bivalves in freshwater ecosystems
Bibliographic record
Abstract
Abstract Aim To assess spatio‐temporal and taxonomic patterns of available information on the costs of invasive freshwater bivalves, as well as to identify knowledge gaps. Location Global. Time period 1980–2020. Taxon studied Bivalvia. Methods We synthesize published global economic costs of impacts from freshwater bivalves using the InvaCost database and associated R package, explicitly considering the reliability of estimation methodologies, cost types, economic sectors and impacted regions. Results Cumulative total global costs of invasive macrofouling bivalves were $ 63.7 billion (2017 US$) across all regions and socio‐economic sectors between 1980 and 2020. Costs were heavily biased taxonomically and spatially, dominated by two families, Dreissenidae and Cyrenidae (Corbiculidae), and largely reported in North America. The greatest share of reported costs ($ 31.5 billion) did not make the distinction between damage and management. However, of those that did, damages and resource losses were one order of magnitude higher ($ 30.5 billion) than control or preventative measures ($ 1.7 billion). Moreover, although many impacted socio‐economic sectors lacked specification, the largest shares of costs were incurred by authorities and stakeholders ($ 27.7 billion, e.g., public and private sector interventions) and through impacts on public and social welfare ($ 10.1 billion, e.g., via power/drinking water plant and irrigation system damage) in North America. Average cost estimates over the entire period amounted to approximately $ 1.6 billion per year, most of which was incurred in North America. Main conclusions Our results highlight the burgeoning economic threat caused by invasive freshwater bivalves, offering a strong economic incentive to invest in preventative management such as biosecurity and rapid response eradications. Even if the damages and resource losses are severely understated because economic impacts are lacking for most invaded countries and invasive bivalve species, these impacts are substantial and likely growing.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".