Phenological shifts of native and invasive species under climate change: insights from the <i>Boechera–Lythrum</i> model
Bibliographic record
Abstract
Warmer and drier climates have shifted phenologies of many species. However, the magnitude and direction of phenological shifts vary widely among taxa, and it is often unclear when shifts are adaptive or how they affect long-term viability. Here, we model evolution of flowering phenology based on our long-term research of two species exhibiting opposite shifts in floral phenology: Lythrum salicaria, which is invasive in North America, and the sparse Rocky Mountain native Boechera stricta Genetic constraints are similar in both species, but differences in the timing of environmental conditions that favour growth lead to opposite phenological shifts under climate change. As temperatures increase, selection is predicted to favour earlier flowering in native B. stricta while reducing population viability, even if populations adapt rapidly to changing environmental conditions. By contrast, warming is predicted to favour delayed flowering in both native and introduced L. salicaria populations while increasing long-term viability. Relaxed selection from natural enemies in invasive L. salicaria is predicted to have little effect on flowering time but a large effect on reproductive fitness. Our approach highlights the importance of understanding ecological and genetic constraints to predict the ecological consequences of evolutionary responses to climate change on contemporary timescales.This article is part of the themed issue 'Human influences on evolution, and the ecological and societal consequences'.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".