Examining the effects of climate change and species invasions on Ontario walleye populations: can walleye beat the heat?
Bibliographic record
Abstract
Abstract Aim The combined effects of multiple environmental stressors continue to threaten global biodiversity, yet predicting how biotic interactions between native and invasive species may change across a landscape in a multiple stressor environment is relatively understudied. We aim to identify how the invasion of smallmouth bass (Micropterus dolomeiu) may influence native walleye (Sander vitreus) populations across Ontario lakes at the landscape scale in a changing climate. Location Ontario, Canada. Methods Using a database that included the abundance and occurrence of over 130 fish species, lake chemistry and lake morphology for 722 lakes, a redundancy analysis was conducted to identify environmental conditions preferred by walleye and smallmouth bass. Multiple linear regression models were then developed to identify the relationship between walleye and multiple stressors (including climate change and biotic interactions with invasive species). Using future scenarios of climate change, we were then able to project future walleye–smallmouth bass co‐occurrences. Results Smallmouth bass were found to prefer different environmental conditions than walleye; however, when walleye and smallmouth bass were found in the same lakes, walleye abundance was reduced almost threefold. Multiple regression models further suggested that there are fewer walleye in lakes with smallmouth bass. Subsequently, we predicted that under future scenarios of climate change the overlapping co‐occurrence of walleye and smallmouth bass may increase by 86–332% by the year 2070. Main conclusions We illustrate the importance of including multiple environmental stressors in statistical models when attempting to understand how native species will be impacted by invasive species and climate change. While independently climate change is anticipated to lower walleye abundances across Ontario, this change is expected to be exacerbated by invasions of warmwater predators.
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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 | 0.001 |
| 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) | 0.002 | 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 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".