Environmental modulation of reproductive activity of the invasive mussel <i>Limnoperna fortunei</i>: implications for antifouling strategies
Bibliographic record
Abstract
Abstract Limnoperna fortunei (Dunker, 1857) (Bivalvia) invaded Argentina through the Río de la Plata estuary around 1990 and is presently established throughout five South American countries as a dominant component of the benthic fauna and a major nuisance for industry and power plants. Between 1997 and 2006 we monitored the reproductive activity of L. fortunei through weekly measurements of planktonic larvae in six South American water bodies: Río de la Plata estuary, Paraná and Carapachay rivers, Salto Grande, Itaipú and Embalse de Río Tercero reservoirs. Mean larval densities varied between 4000 and 7000 individuals m−3; except in the reservoirs of Itaipú (450 ind. m−3) and Salto Grande (869 ind. m−3), where the mussel was first recorded shortly before our surveys, and upstream dispersal is limited (Salto Grande). In all cases, reproductive output decreases during the winter. At four of the six sites surveyed larval densities were comparatively high for 8.8–10.2 months per year. A lower food supply is possibly responsible for the shorter reproductive period of 5.9 months at Embalse. At Salto Grande, there is a well‐defined mid‐summer drop in larval numbers, coinciding with blooms of cyanobacteria. We propose that, in addition to temperature, two major factors may regulate the reproductive activity of L. fortunei: (i) the availability of food; and (ii) blooms of toxic cyanobacteria, significantly shortening the otherwise very long reproductive period. This information is important for the design of antifouling programmes involving the use of molluscicides, and has potential for reduced biocide use. These results provide supporting evidence for some fundamental ecological theories of invasions discussed here.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".