Temperature extremes from a leaf perspective: Micro- vs macro-climate predictors of dwarf-shrub thermal tolerance limits
Bibliographic record
Abstract
Despite their broad climatic and geographic ranges and dominant ecosystem roles across boreal, alpine and arctic vegetation zones; dwarf shrubs can be sensitive to climatic changes, in particular shifting thermal regimes. But questions remain as to the macro-or micro-climatic conditions we should focus upon when considering these changing plant-climate relationships. As a case study highlighting how microclimate insights may contribute at scales from the field through to land-surface models, in the DURIN project we explore how thermal microclimate measures at plant- and leaf-levels can be used to better inform models regarding the thermal tolerance limits of leaves. At high-latitudes, increasing summer heat extremes and aseasonal freezing events associated with changing snowpack dynamics, will expose dwarf-shrubs to potentially stressful conditions for photosynthesis and carbon gain. We explore thermal damage by quantifying temperature at which there is a 50% decline in the maximum quantum yield of photosystem II (FV/FM), for a range of dwarf shrub species growing across habitats and bioclimatic zones. We then compare the extent of photosystem damage to various estimates of temperature extremes as derived from plant-level climate sourced from TOMST loggers, FLIR imagery, and leaf-level thermocouples deployed in dwarf-shrub and non-dwarf-shrub plots. The time-series of fine-scale characterization of dwarf-shrub microclimates in-situ will be correlated with downscaled microclimate estimates from NicheMapR, along with nearby weather station data to highlight the discrepancies between macro- and microclimates. These comparisons allow us to additionally ask fundamental questions about the ways in which we should assess thermal tolerance taking into greater consideration methods for quantifying heat stress. Classical assays of photosynthetic thermal tolerance limits have focused on singular and short exposure times to temperature stress, but increasingly the field is moving towards providing more biologically meaningful insights into thermal tolerance exposures, enabling us to better define and experimentally impose thermal stress events. An emerging discussion surrounds the use of the thermal death time framework, where cumulative thermal stress is applied, e.g. a range of exposure times at varying temperatures. Our work will help develop protocols for the thermal death time framework which requires a more nuanced understanding of thermal stress events at plant and leaf-level, integrating our micro-and macro-climate insights.
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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 | 0.000 |
| 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".