Invasion dynamics and impact of non-native molluscs in the Laurentian Great Lakes
Bibliographic record
Abstract
The abundances of non-native species in invaded ecosystems can range from negligible to extremely high, and their effects include competition with and predation on native species, habitat alteration, and the transmission of diseases. Currently, 16 non-native mollusc species are established in the Laurentian Great Lakes, including 14 exotics introduced from other continents and two North American transplants. These species represent 52% of the diversity of all free-living non-native benthic invertebrates in the lakes. Early introductions ( Bithynia tentaculata, Pisidium moitessierianum, P. amnicum, and Valvata piscinalis ) arrived in the 19th century via solid ballast, whereas recent introductions ( Dreissena polymorpha, D. rostriformis bugensis , and Potamopyrgus antipodarum ) were primarily transported in ballast water. Most exotics originated from Eurasia (64%) and Asia (21%), with single species introductions from Europe and New Zealand. Mollusc densities vary greatly from rare (e.g., Corbicula fluminea and Radix auricularia ) to extremely high ( D. r. bugensis ). Non-native mollusc diversity is highest in shallow Lake Erie and lowest in Lake Huron. The ecological impact of exotic species is largely proportional to their population abundance, ranging from negligible (e.g., sphaeriids, C. fluminea , and Radix auricularia ) to the substantial effects from D. r. bugensis , which has transformed entire ecosystems in the four lower Great Lakes. We also examine potential future invaders (e.g., Limnoperna fortunei ) and project their likely distributions and impacts. Ongoing and projected temperature increases will likely enhance conditions for currently restricted species and increase the risk of new introductions from warmer regions, further accelerating ecosystem change.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".