Massive economic costs of invasive bivalves in freshwater ecosystems
Bibliographic record
Abstract
Abstract Many countries lack the economic capacity to effectively manage invasive species. Yet, the direct socioeconomic impact generally much outweighs the expected costs of prevention. A distinct lack of monetary cost quantification associated with key invasive species groups impedes decision-making, and thus resource allocation, by policy makers to address invasions. Here, we synthesize published global economic costs of impacts for one key taxonomic group – freshwater bivalves – whilst explicitly considering the reliability of estimation methodologies, cost types, economic sectors and impacted regions. Although several species from this group are notorious widespread invaders, estimations of their economic costs have remained relatively sparse. Cumulative total global costs of invasive macrofouling bivalves were US$ 63.6 billion (2017 USD) across all regions and socioeconomic sectors between 1980 and 2020. Costs were heavily biased taxonomically and spatially, dominated by two families, Dreissenidae and Cyrenidae (Corbiculidae), and largely constrained to North America. The largest share of reported costs ($ 30.6 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 ($ 30.3 billion) than control or preventative measures ($ 1.7 billion). Moreover, although many impacted socioeconomic sectors lacked specification, the largest shares of costs were incurred through authorities and stakeholders ($ 26.3 billion, e.g. public and private sector interventions) and by public and social welfare ($ 11.6 billion, e.g. via power/drinking water plant and irrigation system damage). Average cost estimates over the entire period amounted to approximately $ 1.6 billion per year, most of which was incurred in North America. We thus present novel cost quantifications that offer a strong economic incentive to invest in preventative management of invasive bivalves in freshwaters. However, these costs are severely underestimated because well-documented economic impacts are lacking for most invaded countries and most invasive bivalve species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.004 |
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; both teacher heads agree on what is shown here.
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".