Increasing variability in resource supply over time disrupts plant–pollinator interactions
Bibliographic record
Abstract
Abstract Insect–plant interactions are key determinants of plant and insect fitness, providing important ecosystem services around the world—including the Arctic region. Recently, it has been suggested that climate warming causes rifts between flower and pollinator phenology. To what extent the progression of pollinators matches the availability of flowers in the Arctic season is poorly known. In this study, we aimed to characterize the community phenology of flowers and insects in a rapidly changing Arctic environment from a descriptive and functional perspective. To this end, we inferred changes in resource availability from both a plant and an insect point of view, by connecting resource and consumer species through a metaweb of all the plant–insect interactions ever observed at a site. Specifically, we: (1) characterized species‐specific phenology among plants and insects at two High‐Arctic sites—Cambridge Bay in Nunavut, Canada, and Zackenberg in Northeast Greenland; (2) quantified competition for flowers using sticky flower mimics; (3) used information on plant–pollinator interactions to quantify supply and demand for pollinator services versus flower resources during the summer; and (4) compared patterns observed within a focal summer at each site to patterns of long‐term change at Zackenberg, using a 25‐year time series of plant flowering and insect phenology. Within summers, we found evidence of a general mismatch between supply and demand. Over the 25‐year time series, the number of weeks per summer when resource supply fell below a standardized threshold increased significantly over time. In addition, variation in resource availability increased significantly over years. We suggest that the number of resource‐poor weeks per year is increasing and becoming less predictable in the High Arctic. This will have important implications for plant pollination, pollinator fitness, and the future of the Arctic ecosystem, as both plants and their pollinators are faced with widening resource gaps.
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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 | 0.000 |
| 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) | 0.001 | 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".