Eating contest between native and non-indigenous bivalve species: estimating capture efficiencies and clearance rates using natural seston
Bibliographic record
Abstract
Despite the burgeoning number of non-indigenous species (NIS) in worldwide coastal ecosystems, the quantification of their direct impacts on native communities remains largely unexplored. This is particularly true concerning feeding competition in sympatric filter-feeding bivalves. In this study, our aim was to fill a gap of knowledge on the potential trophic competition between native and non-indigenous bivalves, namely by focusing on three species that co-occur in Portuguese estuarine systems: the native cockle (Cerastoderma edule) and Portuguese oyster (Magallana angulata) and the non-indigenous Manila clam (Ruditapes philippinarum). The specific objectives were to i) estimate their capture efficiency (≈ particle retention efficiency; CE); ii) assess their clearance rates (CR); and iii) provide a science-based support for suitable management measures regarding NIS. Experiments were conducted in both field and laboratory conditions using the natural seston present in the seawater. The CE was higher for the larger size classes (8–14 μm) of particles measured (ranging from 4–14 µm), regardless of the species. While the individual CRs were not significantly different among species, the CR per gram of ash-free dry body tissue weight was significantly higher for the native cockle, suggesting that the NIS does not hold a competitive advantage in clearing suspended particles. However, the Manila clam might be limiting food sources availability to the native species since there is an overlap of their ecological niches.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".