Reproductive losses due to climate change‐induced earlier flowering are not the primary threat to plant population viability in a perennial herb
Bibliographic record
Abstract
Abstract Despite a global footprint of shifts in flowering phenology in response to climate change, the reproductive consequences of these shifts are poorly understood. Furthermore, it is unknown whether altered flowering times affect plant population viability. We examine whether climate change‐induced earlier flowering has consequences for population persistence by incorporating reproductive losses from frost damage (a risk of early flowering) into population models of a subalpine sunflower (Helianthella quinquenervis). Using long‐term demographic data for three populations that span the species’ elevation range (8–15 years, depending on the population), we first examine how snowmelt date affects plant vital rates. To verify vital rate responses to snowmelt date experimentally, we manipulate snowmelt date with a snow removal experiment at one population. Finally, we construct stochastic population projection models and Life Table Response Experiments for each population. We find that populations decline (λs < 1) as snowmelt dates become earlier. Frost damage to flower buds, a consequence of climate change‐induced earlier flowering, does not contribute strongly to population declines. Instead, we find evidence that negative effects on survival, likely due to increased drought risk during longer growing seasons, drive projected population declines under earlier snowmelt dates. Synthesis. Shifts in flowering phenology are a conspicuous and important aspect of biological responses to climate change, but here we show that the phenology of reproductive events can be unreliable measures of threats to population persistence, even when earlier flowering is associated with substantial reproductive losses. Evidence for shifts in reproductive phenology, along with scarcer evidence that these shifts actually influence reproductive success, are valuable but can paint an incomplete and even misleading picture of plant population responses to climate change.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".