Investigating Climate Change and Its Impact on Tree Senescence at Queen’s Biological Station
Bibliographic record
Abstract
Vegetation phenology tracks the seasonal progression of plant activity, acting as a key regulator of ecological dynamics and biosphere-climate interactions. The rate of phenology is primarily sensitive to temperature and precipitation, and changes in these environmental factors significantly influence the timing of life cycle events in plants. Climate change-driven shifts in environmental factors disrupt cues that regulate leaf senescence in trees, potentially influencing tree health and mortality. Given the strong correlation between climatic factors and plant phenology, monitoring senescence is crucial to understanding changes in tree life cycles in response to climate change. Monitoring phenology has been commonly applied using remote sensing techniques to examine the progression of vegetation phenology and is significantly advancing tree senescence research. This project examines the timing and rate of tree senescence and its response to climate change at the Queen's University Biological Station (QUBS). Images of trees at QUBS are collected on the Phenocam Network, situated above a forest canopy, capturing images every 30 minutes since 2008. From each acquired time series image, red, green, and blue (RGB) colour channel information was extracted, specifically Red Chromatic Coordinate (RCC) and Green Chromatic Coordinate (GCC) values, to quantify the redness and greenness of pixels in an image. By coupling these colour-based metrics with climate data, this study aims to quantify the effects of climate change on the rate of leaf senescence and its implications for tree mortality. The results from this research may reveal that over a 16-year period, climate change, driven by warmer temperatures and altered precipitation patterns, is accelerating leaf senescence in tree species. Findings explain strong correlations between climate variables and the timing of senescence within tree species. While trees may accelerate senescence as a defense against stressors, early senescence can harm tree health, ecosystem dynamics, and environmental processes, particularly with climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".