Phenological Responses of Plants and Pollinators to Human-altered Climates
Bibliographic record
Abstract
The timing of plant and animal life cycle events are changing in response to human-altered climates. These shifts in phenological event dates can be detected through time and across space. For example, shifts through time are usually detected in response to global climate alterations, such as increases in average spring temperature since the Industrial Revolution. In contrast, shifts across space are usually detected in response to local-scale climate alterations, such as increases in temperature in urban compared to rural areas. Phenological responses to altered climates can also be studied through experimentation by directly manipulating the relevant environmental cues. Despite the relative ease of phenological measurement, how we interpret phenological shifts and changes to species interactions is not always straightforward. Careful consideration of the metadata, such as the underlying sampling and pooling decisions made across spatial and temporal scales and across levels of biological organization, is often required. In addition to scale-dependency, a lack of knowledge of the cues driving phenological responses further complicates the interpretation of phenological shifts. In my thesis, I address certain knowledge gaps and issues relevant to phenological sampling, scaling, mechanism, and synchrony between interacting species. I do this through the re-analysis of a long-term dataset on plant phenology and through observational and manipulative studies of Cercis canadensis, the eastern redbud tree, and some of its bee pollinators. In Chapter 2, I show how sampling decisions across space and time can affect phenological patterns and our ability to attribute those patterns to climate change. In Chapter 3, I show that the flowering phenology of C. canadensis within the City of Toronto is highly variable in space. This variability correlates with microclimate and may result in greater temporal overlap with certain C. canadensis floral visitors. In Chapter 4, I experimentally manipulate temperature cues and show that cue-response differences can lead to changes in temporal overlap between C. canadensis flowering onset and the emergence of a pollinator, Osmia lignaria. Through the use of novel sampling and experimental approaches, my thesis addresses knowledge gaps in phenological scaling and mechanism and improves our understanding of how plants and pollinators respond to human-altered climates.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".