Demographic Consequences of Phenological Shifts in Response to Climate Change
Bibliographic record
Abstract
When a phenological shift affects a demographic vital rate such as survival or reproduction, the altered vital rate may or may not have population-level consequences. We review the evidence that climate change affects populations by shifting species’ phenologies, emphasizing the importance of demographic life-history theory. We find many examples of phenological shifts having both positive and negative consequences for vital rates. Yet, few studies link phenological shifts to changes in vital rates known to drive population dynamics, especially in plants. When this link is made, results are largely consistent with life-history theory: Phenological shifts have population-level consequences when they affect survival in longer-lived organisms and reproduction in shorter-lived organisms. However, there are just as many cases in which demographic mechanisms buffer population growth from phenologically induced changes in vital rates. We provide recommendations for future research aiming to understand the complex relationships among climate, phenology, and demography, which will help to elucidate the extent to which phenological shifts actually alter population persistence.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".