Temperature‐dependent shifts in phenology contribute to the success of exotic species with climate change
Bibliographic record
Abstract
PREMISE OF THE STUDY: The study of how phenology may contribute to the assembly of plant communities has a long history in ecology. Climate change has brought renewed interest in this area, with many studies examining how phenology may contribute to the success of exotic species. In particular, there is increasing evidence that exotic species occupy unique phenological niches and track climate change more closely than native species. METHODS: Here, we use long-term records of species’ first flowering dates from fi ve northern hemisphere temperate sites (Chinnor, UK and in the United States, Concord, Massachusetts; Fargo, North Dakota; Konza Prairie, Kansas; and Washington,D.C.) to examine whether invaders have distinct phenologies. Using a broad phylogenetic framework, we tested for differences between exotic and native species in mean annual flowering time, phenological changes in response to temperature and precipitation,and longer-term shifts in first flowering dates during recent pronounced climate change (“flowering time shifts”). KEY RESULTS: Across North American sites, exotic species have shifted flowering with climate change while native species, on average, have not. In the three mesic systems, exotic species exhibited higher tracking of interannual variation in temperature,such that flowering advances more with warming, than native species. Across the two grassland systems, however, exotic species differed from native species primarily in responses to precipitation and soil moisture, not temperature. CONCLUSIONS: Our findings provide cross-site support for the role of phenology and climate change in explaining species’ invasions.Further, they support recent evidence that exotic species may be important drivers of extended growing seasons observed with climate change in North America.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".