Too Hot to Handle: A Meta‐Analytical Review of the Thermal Tolerance and Adaptive Capacity of North American Sturgeon
Bibliographic record
Abstract
Understanding how ectotherms may fare with rising global temperatures and more frequent heatwaves is especially concerning for species already considered at-risk, such as long-lived, late-maturing sturgeon. There have been concerted efforts to collect data on the movement behavior and thermal physiology of North American sturgeon to enhance conservation efforts; thus, we sought to synthesize these data to understand how sturgeon respond to thermal stress and what capacity they have to acclimate and adapt to warming. Here, we combined a systematic literature review and meta-analysis, integrating field-based observations (distribution and spawning) and laboratory-based experiments (survival, activity, growth, metabolism, and upper thermal limits) for large-scale insights to understand the vulnerability of North American sturgeon to rising global temperatures. We summarized the preferred thermal habitat and thermal limits of sturgeon in their natural environment and using meta-analytical techniques, quantified the effect of prolonged temperature change on sturgeon whole-animal physiology and acute upper thermal limits. While acclimation did not have significant effects on physiological rates or survival overall, there were positive trends of activity and metabolism in young-of-the-year sturgeons, likely offset by negative trends of survival in early life. Notably, North American sturgeon have a greater capacity for thermal tolerance plasticity than other fishes, increasing upper thermal limits by 0.56°C per 1°C change in acclimation temperature. But with limited laboratory-based studies, more research is needed to understand if this is a sturgeon trait, or perhaps that of basal fishes in general. Importantly, with these data gaps, the fate of sturgeon remains uncertain as climate change intensifies, and physiological impacts across life stages likely limit ecological success.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.017 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".