Predicting key ectotherm population mortality in response to dynamic marine heatwaves: A Bayesian‐enhanced thermal tolerance landscape approach
Bibliographic record
Abstract
Abstract As climate change intensifies heatwaves, quantifying associated mortality within ectothermic populations is crucial for effective conservation. Thermal tolerance landscape (TTL) models are useful predictive tools that assume exponentially decreasing survival durations in individuals with increasing temperatures. This assumption has been validated through regression analyses on data from constant temperature experiments, primarily focusing on adult‐stage individuals. However, this approach does not allow for direct model validation with data from dynamic, real‐world heatwave events and overlooks early recruitment stage vulnerabilities. This study aimed to address these gaps using the blue mussel Mytilus, a foundation species forming extensive reefs along temperate coasts, as a model organism. We monitored survival rates of mussels (juveniles and adults) under constant heatwave (CHW) conditions in a laboratory experiment and under dynamic heatwave (DHW) scenarios simulated in an outdoor mesocosm experiment. Post‐heatwaves, we also assessed recruitment rates within the mesocosms. TTL models were parametrised by employing Approximate Bayesian Computation with Sequential Monte Carlo (ABC‐SMC) on each dataset separately. The parameter distributions were similar across both experiments, and the ABC‐SMC model predictions closely matched the observed survival declines, validating these models. In comparison, we found a lower predictive performance when using a Bayesian regression approach. Additionally, our best‐fit model predicted that warming across the non‐fatal DHW regimes would increase sublethal effects on mussels. The observed impact on the recruitment stage was more pronounced, with the recruitment rate following an exponential decay as sublethal effects increased. Our model projected minor (<4%) sublethal effects in adult mussels during the century's five warmest summer temperature regimes, corresponding to 0%–32% declines in recruitment rates. Our research extends the TTL model validation, demonstrates the resilience of subtidal Baltic Mytilus to future extreme heatwaves and offers an approach to predict heatwave‐induced population mortalities, applicable to other species and sensitive systems. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".