Linking organismal physiology and the landscape to predict vulnerability to climate change
Bibliographic record
Abstract
Global temperatures continue to increase at unprecedented rates, both in mean and in variance. Thus, a major challenge for scientists of the 21st century is to predict whether species will persist through these changes. One way to partly assess vulnerability to climate change is to investigate the relationships between the environment and traits that are either particularly sensitive to temperature or may confer resilience against thermal changes. In ectotherms, external temperatures dictate their physiology, thus thermal physiological traits may be key to understanding ectothermic persistence. Although population variation is integral to the evolvability of thermal physiological traits, most studies using these traits to infer vulnerability extrapolate data from one or few populations to represent the species. Furthermore, many studies also use coarse metrics of environmental temperatures which may not fully capture the variation experienced by the organism. Here, using a cold-water frog system, I demonstrate the relationships between thermal physiological traits and local environmental temperatures among populations. In my first chapter, I provide a brief overview of ectothermic physiology, environmental thermal landscapes, and the ecology of the two species of tailed frogs that I investigated. In my second chapter, I show that populations of tailed frogs vary in their critical thermal limit (CTmax) plasticity, which impacts species-level assessments of vulnerability. I also demonstrate the methodological impacts of ignoring acute responses to temperature when estimating plasticity in this trait. For my third chapter, I demonstrate relationships between CTmax and local thermal environments, including temporal and spatial variability in temperature, among populations of tailed frogs. These results show that tailed frogs have limited opportunity for behavioural avoidance of warm temperatures, and that populations of one tailed frog species show a positive relationship between CTmax and maximum stream temperature while populations of the other species does not. In my fourth chapter, I test the critical assumption that CTmax is related to fitness, specifically mortality in ecologically relevant temperatures. My results show that populations with higher estimates of CTmax experience less mortality from thermal stress in temperatures experienced in nature, demonstrating the link between CTmax and fitness. Lastly, in my fifth chapter, I return to the plasticity in CTmax results and demonstrate the relationship between this trait and local thermal environments, showing that populations experiencing greater temperature fluctuations have greater estimates of plasticity in CTmax. Overall, these results underscore the importance of sampling widely among populations when inferring vulnerability to climate changes from physiological traits. The population variation in CTmax and its plasticity that I uncovered demonstrate the differing trends in vulnerability to climate change for the two species investigated. This work also highlights the importance of quantifying local thermalscapes and highlight how similar environments can differentially shape physiological tolerance and patterns of vulnerability among populations, in turn impacting vulnerability to future warming.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".