Fish community structure varies by location and presence of artificial islands: a case study in Hamilton Harbour, Lake Ontario
Bibliographic record
Abstract
Abstract Artificial islands and reefs have been used in the Laurentian Great Lakes for over 40 years as a means of improving aquatic habitat; but research on their efficacy has primarily focused on their ability to increase the abundance of specific sportfish, top predators, or other keystone species. To understand the importance of islands in structuring the whole fish community, we took a holistic approach and analysed the effect of islands, location, and the interaction effect between the two in structuring fish communities in Hamilton Harbour, Lake Ontario using a 30-year electrofishing dataset. The effect of islands varied by location within the harbour, with some species showing a preference for islands in some locations while avoiding them in others. Island communities also tended to have significantly different species compositions, with higher index of biotic integrity scores and species richness, greater numbers of pollution intolerant fishes, centrarchids, and fewer generalist species. However, these results paled in comparison to the level of inter-annual variation in the fish community of the harbour, which has changed markedly over the 30-year time span. Taken together, our results highlight that while island creation can influence the fish community, the type and magnitude of effect will vary based on their distance to other suitable habitats (i.e., location within the system) and the design of the island itself. Further, the noted inter-annual variability emphasizes the importance of considering long time scales (> 10–20 years) when exploring fish community responses to habitat creation. Collectively, these results will help the design of more effective management strategies for restoring fish communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".