Foraging ecology of native pumpkinseed (<i>Lepomis gibbosus</i>) following the invasion of zebra mussels (<i>Dreissena polymorpha</i>)
Bibliographic record
Abstract
Invasive species are a major concern both biologically and economically because of the rates at which they spread and their alterations to ecosystems. Zebra mussels (Dreissena polymorpha) are of concern in many regions owing to expenses related to industrial equipment damage and control education efforts. However, their greatest cost may be their ecological impact across North America. We tested pumpkinseed (Lepomis gibbosus) from three populations in Ontario (Canada) to determine if there was variation across populations or sampling periods in the consumption of zebra mussels. Using a combination of stomach content and stable isotope analyses, we found that pumpkinseed diet consisted of primarily zebra mussels across all populations and sampling periods, including a minimum of 54% in all lakes and sampling periods based on stable isotope mixing models. The current resource use patterns of pumpkinseed in our study populations indicate that there has been a shift from their pre-invasion diet of benthic littoral invertebrates such as gastropods. The large-scale consumption of invasive mussels by native fishes may have important implications for natural biological control of these mussels, decreasing overall mussel abundance and rate of spread. We discuss our results in the context of their implications for the overall health and dynamics of aquatic ecosystems.
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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".