Variation in Effects of Climate Change on Salmonid Demography: Extent, Scale, and Underlying Mechanisms
Bibliographic record
Abstract
Predicting species responses to climate change is an increasingly important objective in ecological research and natural resource management. However, heterogeneity in demography, life history, and habitat characteristics across multiple spatial scales can generate substantial diversity in population responses, complicating species-level assessments. Therefore, for widely distributed species consisting of many fragmented populations, understanding the mechanisms that underlie population variation can improve predictions of climate impacts across the species range. Using salmonid fishes as a model system, my thesis investigates variation in population responses to climate change at global, regional, and local scales. First, through a global meta-analysis of 156 studies of 23 species, I demonstrated that population responses to temperature and precipitation exhibit significant spatial, temporal, and biological patterns that broadly align with predictions based on salmonid thermal limits. Importantly, I showed that salmonid populations at low latitudes and elevations tend to be most negatively impacted by rising temperatures. Subsequently, I analyzed mark-recapture and stream temperature data collected during field surveys in Cape Race (Newfoundland, Canada) since 2010 to characterize local-scale variation in demography, climate impacts, and thermal regimes among eleven populations of brook trout (Salvelinus fontinalis) separated by <5 km. I showed that variation in recruitment, growth, and demographic relationships combined to generate diverse population dynamics that stabilized brook trout abundance across Cape Race, and that thermal regimes driven by groundwater inputs contributed to population diversity. Finally, using population-specific demographic and life history data, I built eco-genetic models that simulated responses to future climate warming across Cape Race brook trout populations, which emphasized the role of life history evolution and thermal habitat variation in determining population persistence. Together, my thesis shows that large-scale gradients in latitude and elevation structure salmonid responses to climate change, but substantial fine-scale variation is embedded within these trends due to heterogeneity in habitat characteristics, human impacts, and eco-evolutionary dynamics experienced by populations. Similar research frameworks that employ diverse methodologies and integrate data across scales will be crucial for understanding the complex impacts of climate change on salmonids and other freshwater fish populations, and should inform the conservation of a wide range of species.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".