Phenological niches and the future of invaded ecosystems with climate change
Bibliographic record
Abstract
In recent years, research in invasion biology has focused increasing attention on understanding the role of phenology in shaping plant invasions. Multiple studies have found non-native species that tend to flower distinctly early or late in the growing season, advance more with warming or have shifted earlier with climate change compared with native species. This growing body of literature has focused on patterns of phenological differences, but there is a need now for mechanistic studies of how phenology contributes to invasions. To do this, however, requires understanding how phenology fits within complex functional trait relationships. Towards this goal, we review recent literature linking phenology with other functional traits, and discuss the role of phenology in mediating how plants experience disturbance and stress-via climate, herbivory and competition-across the growing season. Because climate change may alter the timing and severity of stress and disturbance in many systems, it could provide novel opportunities for invasion-depending upon the dominant climate controller of the system, the projected climate change, and the traits of native and non-native species. Based on our current understanding of plant phenological and growth strategies-especially rapid growing, early-flowering species versus later-flowering species that make slower-return investments in growth-we project optimal periods for invasions across three distinct systems under current climate change scenarios. Research on plant invasions and phenology within this predictive framework would provide a more rigorous test of what drives invader success, while at the same time testing basic plant ecological theory. Additionally, extensions could provide the basis to model how ecosystem processes may shift in the future with continued 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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".