Synergistic Impact of Climate Change and Aquatic Invasive Species on Native Fish Communities
Bibliographic record
Abstract
Climate change and invasive species are drivers of ecological disruption, fundamentally altering species distribution, community structures, and ecosystem functions. Climate change alters ecosystems by shifting temperature regimes, precipitation patterns, and seasonal cycles, influencing species distributions, ecological interactions, and survival. Invasive species threaten biodiversity by outcompeting native species for limited resources, disrupting established food-webs, and potentially leading to the decline or local extinction of native species. When combined, these stressors may amplify biodiversity loss and destabilize ecosystems through synergistic interactions. This thesis explores how climate change and invasive species interact to shape native fish communities in freshwater ecosystems. Using invasive Round Goby (Neogobius melanostomus) and two native benthic fish species, Longnose Dace (Rhinichthys cataractae) and Western Blacknose Dace (R. obtusus), in the Credit River, Ontario, Canada, I examine how thermal tolerance, feeding efficiency, and physiological responses to variable thermal environments influence competitive dynamics between invasive and native species. I test the hypothesis that Round Goby, due to its high thermal tolerance, will exhibit higher competitive performance, exerting greater competitive pressure on native fish species, which could potentially lead to significant ecological impacts. Functional-response experiments and thermal-tolerance trials demonstrate that Round Goby exhibits broader thermal tolerance and higher feeding efficiency than native species, particularly at elevated temperatures. These advantages may confer a competitive advantage over native species in warming environments, potentially contributing to shifts in local species composition and altering long-term community dynamics. The findings demonstrate how the combined effects of climate change and biological invasions can exacerbate ecological pressures, disrupting predator-prey dynamics, altering nutrient cycling, and reducing ecosystem resilience. To maintain biodiversity and ensure ecosystem stability in a warming world, a multifaceted approach that integrates habitat restoration, invasive-species management, and climate-adaptation strategies, is essential for reducing the compounded impacts of invasive species and climate change.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".