Experimental designs for testing the interactive effects of temperature and light in ecology: The problem of periodicity
Bibliographic record
Abstract
Abstract Temperature and light cues interact to control many biological processes. Experiments give researchers the ability to manipulate these environmental cues independently and can be designed to robustly quantify their individual and interactive effects on any particular biological activity. Such experiments have produced important insights into the environmental controls on numerous biological processes in both plant and animal taxa across terrestrial and aquatic environments. Testing the interactive effects of multiple environmental cues, however, requires experimental treatments to be fully independent; any unmeasured experimental covariation among treatments can result in incorrect conclusions. Using a database of controlled environment experiments on the spring phenology of woody plants as a case study, we highlight how a common experimental set‐up, designed to parse the interactive effects of temperature and photoperiod on time to budburst, introduces a latent experimental covariation of these treatments by coupling photo‐ and thermoperiodicity. Using data simulations, algebraic corrections and a comparative analysis of published experiments, we demonstrate how this unmeasured experimental covariation biases statistical inference regarding the relative contribution of light and temperature cues to phenological variation. We identify this experimental covariation in more than 40% of published phenology studies that manipulate photoperiod. Our analyses demonstrate that the coupling of thermo‐ and photoperiodicity results in the overestimation of the effect of photoperiod, the underestimation of forcing effects, and misleading conclusions about their interactions on phenology. This may, in part, explain why the significance of photoperiod cues for spring phenology is currently debated in the literature. Accurate forecasting of how varying environmental conditions will impact the dynamics of biological events requires accurately quantifying cue responses. To this end, we present several options for statistical corrections and alternative experimental designs that can provide more robust estimates of the relative effects of temperature and photoperiod on phenology and many other biological processes controlled by temperature and light. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.162 | 0.322 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".