Variable temperature effects of Open Top Chambers at polar and alpine sites explained by irradiance and snow depth
Bibliographic record
Abstract
Environmental manipulation studies are integral to determining biological consequences of climate warming. Open Top Chambers (OTCs) have been widely used to assess summer warming effects on terrestrial biota, with their effects during other seasons normally being given less attention even though chambers are often deployed year-round. In addition, their effects on temperature extremes and freeze-thaw events are poorly documented. To provide robust documentation of the microclimatic influences of OTCs throughout the year, we analysed temperature data from 20 studies distributed across polar and alpine regions. The effects of OTCs on mean temperature showed a large range (-0.9 to 2.1 °C) throughout the year, but did not differ significantly between studies. Increases in mean monthly and diurnal temperature were strongly related (R(2) = 0.70) with irradiance, indicating that PAR can be used to predict the mean warming effect of OTCs. Deeper snow trapped in OTCs also induced higher temperatures at soil/vegetation level. OTC-induced changes in the frequency of freeze-thaw events included an increase in autumn and decreases in spring and summer. Frequency of high-temperature events in OTCs increased in spring, summer and autumn compared with non-manipulated control plots. Frequency of low-temperature events was reduced by deeper snow accumulation and higher mean temperatures. The strong interactions identified between aspects of ambient environmental conditions and effects of OTCs suggest that a detailed knowledge of snow depth, temperature and irradiance levels enables us to predict how OTCs will modify the microclimate at a particular site and season. Such predictive power allows a better mechanistic understanding of observed biotic response to experimental warming studies and for more informed design of future experiments. However, a need remains to quantify OTC effects on water availability and wind speed (affecting, for example, drying rates and water stress) in combination with microclimate measurements at organism level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".