Climate Change Impacts on the Phenology of Laurentian Great Lakes Fishes
Bibliographic record
Abstract
Freshwater ecosystems around the world are increasingly impacted by climate change, yet there remains a lack of long-term empirical data on how these changes are manifesting. In the Laurentian Great Lakes, a globally significant freshwater system, fish and their habitats are expected to be affected by warming water temperatures and increasing risks of species invasions. Despite these projections, relatively few studies have documented whether such shifts are already occurring. Our objective was to assess how climate change has influenced the community and migration phenology of native and non-native fishes that use coastal wetlands in the Great Lakes. To do so, we analyzed local summer water temperatures and a 27-year dataset (1997-2023) comprising arrivals of 16 fish species intercepted at the Cootes Paradise Marsh Fishway, a common carp (Cyprinus carpio) exclusion barrier at the western end of Lake Ontario. Over the study period, we found that mean summer water temperatures increased by over 1°C, consistent with broader global warming trends. Using non-metric multidimensional scaling, we observed a unidirectional shift in fish community structure over time, rather than cyclical fluctuations or stabilization, indicating sustained ecological change. Analyses on phenology revealed that first, peak, and last arrival dates occurred earlier over time, while the duration of presence at the Fishway decreased for both native and non-native species. These results provide evidence that climate change is already altering the community and phenology of fishes in Great Lakes wetlands. More broadly, our findings contribute to the growing body of literature showing that climate-driven phenological shifts are reshaping freshwater ecosystems globally, underscoring the need for adaptive, climate-informed conservation and management strategies.
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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 | 0.001 |
| 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".