The faunal Ponto-Caspianization of European waterways
Bibliographic record
Abstract
Abstract As alien invasive species are a key driver of biodiversity loss, understanding patterns of rapidly changing global species compositions depends upon knowledge of biological invasion dynamics and trends. The Ponto-Caspian region is among the most notable donor regions for aquatic invasive species in Europe. Using macroinvertebrate time series collected over 52 years (1968–2020) at 265 sites across 11 European countries, we examined the occurrences, invasion rates and interspecific interactions of freshwater Ponto-Caspian fauna, as well as biotic homogenization trends in European waterways. According to our data, Ponto-Caspian macroinvertebrates increased from two species in 1972 to 29 species in 2012 and we found a greater richness of invading Ponto-Caspian macroinvertebrates in datasets collected closer to the source region. This may reflect dispersal lag times, indicating that more distant sites will be increasingly invaded in the future. The pioneering Ponto-Caspian species that arrived first were often bivalves (46.5% of cases), particularly Dreissena polymorpha, followed secondarily by amphipods (83.8%; primarily Chelicorophium curvispinum and Dikerogammarus villosus). With increasing invasions, the time between consecutively-appearing invaders decreased six-fold, indicating potential facilitations that improved invasion success of subsequent taxa. We detected a negative relationship between the abundance trends of the first and second Ponto-Caspian macroinvertebrate species, suggesting that interactions between these species are asymmetrical, with the initial invader facilitating the subsequent invader at the potential cost of its own population growth rate. Lastly, we found that macroinvertebrate communities became increasingly similar over time. European invasion rates from the Ponto-Caspian region suggest a high potential for future invasions from this region, with more hotspots and donor regions likely emerging.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".