Life history evolution under climate change and its influence on the population dynamics of a long‐lived plant
Bibliographic record
Abstract
Summary One of the key components of an organism's life history is the delay of reproduction until it reaches or returns to an optimal size. While we know climate can influence vital rates that shape life‐history strategies, it is also critical to understand the effects of climate change on rapid life history evolution, which might modify the influence of climate change on population dynamics. We asked how realistic changes in temperature and precipitation influence vital rates, costs of reproduction, and ultimately, evolutionarily stable ( ES ) flowering size in a long‐lived perennial plant, Orchis purpurea . We also explored how evolution of flowering size could influence population persistence under changing climate. Our approach combined model selection methods to characterize climate dependence in vital rates, stochastic integral projection models to integrate vital rates into an estimate of fitness, and adaptive dynamics to identify ES flowering sizes. Vital rates responded uniquely to seasonal temperature and precipitation, with the largest response in the size‐dependent probability of flowering. The predicted ES flowering size closely matched that observed, and responded strongly to adjusting the frequencies of extreme climate years. For example, increasing the frequency of extreme drought conditions was predicted to favour smaller reproductive sizes (and hence a shorter reproductive delay), despite observation that smaller plants were less likely to flower in dry years. This apparent discrepancy stems from a smaller payoff to delaying reproduction due to lower costs of reproduction in dry years. The model of stochastic population dynamics predicted long‐term persistence of the focal populations, even under the most extreme climate scenarios, while incorporating rapid life history evolution into predictions reduced the sensitivity of population growth to changing climate. Synthesis . Our results illustrate that long‐lived organisms can exhibit complex demographic responses to changing climate regimes. Additionally, they highlight that long‐term evolutionary responses may be in opposing directions from short‐term plastic responses to climate and emphasize the need for demographic models to integrate ecological and evolutionary influences of climate across the life cycle.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".