Sensory Biology as a Risk Factor for Invasion Success and Native Fish Decline
Bibliographic record
Abstract
Abstract Native freshwater fish populations are among the world's most threatened taxa due to the combined effects of habitat degradation and invasive alien species. Habitat degradation negatively impacts native species, whereas invasive species tend to possess adaptations, such as thermal and salinity tolerance, that are more suited to the degraded environment. Sensory ecology may also be a contributing factor. Most threatened native species are visual feeders, whereas invasive species found in degraded systems often have nonvisual specializations. Behavioral and distributional characteristics of the invasive Western Mosquitofish Gambusia affinis and the New Zealand native Inanga Galaxias maculatus illustrate the potential for sensory biology to influence foraging success, distribution, and species interaction between degraded and clear habitats. Behavioral trials measured the change in feeding rate in clear (0 NTU) and turbid (100 NTU) water over 30 min for Inanga and Western Mosquitofish feeding on brine shrimp Artemia salina nauplii. These experiments showed that Western Mosquitofish maintained similar feeding rates between clear and turbid water, whereas the native species exhibited a marked decline in feeding efficiency in turbid water. Across a strong natural turbidity boundary, the alien species was found to dominate turbid habitat less than 1 m from clear water, where both species were found. Accounting for sensory biology as a potential contributing factor in the establishment of invasive fish species in degraded habitat may help to identify invasive species risk and to shape strategies for rehabilitating native species.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".