Spring phenology, phenological response, and growing season length
Bibliographic record
Abstract
Differential phenological responsesPlant phenology is shifting as a result of global warming (IPCC, 2019).General trends include advanced spring phenology (i.e., earlier budburst and leaf-out) and delayed leaf senescence, leading to an extended leaf-on period and possibly increased growth (Peñuelas et al., 2009; IPCC, 2019;Piao et al., 2019).However, responses vary among species, e.g., those with early spring phenology-referred to here as early season species-often show more pronounced advances in spring phenology (Abu-Asab et al., 2001;Beaubien and Hamann, 2011;Shen et al., 2014) than so called late season species.As global warming progresses, these among-species differences in leaf-on time or green-cover season may increase (Morin et al., 2009;Montgomery et al., 2020), leading to an expectation of possible changes in ecosystem structure and function (Polgar et al., 2014;Primack and Gallinat, 2016).The annual development of plants in boreal and temperate regions is driven by the seasonal cycle of climatic conditions, although species-specific information about these changes is often lacking.Bud set, leaf senescence, and dormancy are induced by shorter daylength and lower temperatures in fall, while spring phenology is controlled primarily by temperatures, i.e., low chilling temperatures in fall and winter for dormancy release and high forcing temperatures for spring growth initiation (Chuine et al., 2016;Piao et al., 2019).As species chilling needs can be fulfilled long before spring arrives (see Figure 1), spring phenology is often not influenced by changes in cumulative winter chilling induced by global warming (Fu et al., 2015;Asse et al., 2018;Piao et al., 2019;Chu et al., 2021).Comparatively, early season species that need less accumulation of forcing temperatures (cumulative growing degree days or hours) to initiate spring growth are often more responsive or sensitive to rising temperatures (Abu-Asab et al., 2001;Beaubien and Hamann, 2011;Shen et al., 2014) than late season species.An early spring start is thought to help plants access resources and gain growth and competitive advantages (Polgar et al., 2014;Primack and Gallinat, 2016;Zettlemoyer et al., 2019;Montgomery et al., 2020).As early successional, exotic, and invasive species generally start growing early in spring, they are expected to benefit more from projected warming, resulting in proliferation of these species and therefore undesirable changes in ecosystems (Polgar et al., 2014;Zettlemoyer et al., 2019).However, a recent study by Chu et al. (2021) suggests that this theory is not supported by plant thermal balance in spring.Shifts in spring phenology driven by global warming are associated with changes in both timing of spring growth and forcing temperatures, with the latter more indicative of plant development and growth (Chuine et al., 2016;Man et al., 2017;Piao et al., 2019).Due to cumulative effects of spring temperatures, early season species
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".