Prediction of ecological impact of two alien gobiids in habitat structures of differing complexity
Bibliographic record
Abstract
Rapid expansion of round goby (Neogobius melanostomus) and western tubenose goby (Proterorhinus semilunaris) beyond their native ranges calls for urgent management action, particularly in localities with high introduction possibility of the species. We used a comparative functional response (FR) approach to forecast invader ecological impact. We compared their foraging efficiency in three habitat structures: sand, coarse gravel, and coarse gravel with an artificial plant. Both Neogobius melanostomus and Proterorhinus semilunaris showed type II FR in all habitats. Interspecific comparison showed similar handling times in both the gravel and gravel with artificial plant habitats, suggesting similar per capita impact. Intraspecific comparison showed significantly lower handling times of both predators on gravel substrates. Therefore, their ecological impact in such an environment could be higher. Neogobius melanostomus showed higher attack rates on sandy substrates compared with Proterorhinus semilunaris, while no significant differences were observed on other substrates. Our results highlight the importance of interacting factors in management of ecosystems with multiple invaders, as the elimination of invasive Neogobius melanostomus may lead to utilization of the empty niche by alien Proterorhinus semilunaris with similar ecological impact.
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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 | 0.001 |
| 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 | 0.000 | 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".