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Record W3119625107 · doi:10.1101/2021.01.12.426288

A simple explanation for declining temperature sensitivity with warming

2021· preprint· en· W3119625107 on OpenAlexaff
E. M. Wolkovich, Jonathan Auerbach, C. J. Chamberlain, D. M. Buonaiuto, Ailene K. Ettinger, Ignacio Morales‐Castilla, Andrew Gelman

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2021
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to elevated CO2
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsClimate changeGlobal warmingClimate sensitivitySensitivity (control systems)Global temperatureEnvironmental scienceMagnitude (astronomy)EcologyClimatologyEconometricsAtmospheric sciencesClimate modelBiologyMathematicsPhysics

Abstract

fetched live from OpenAlex

Abstract Temperature sensitivity—the magnitude of a biological response per °C—is a fundamental concept across scientific disciplines, especially biology, where temperature determines the rate of many plant, animal and ecosystem processes. Recently, a growing body of literature in global change biology has found temperature sensitivities decline as temperatures rise (Fuet al., 2015; Güsewell et al., 2017; Piao et al., 2017; Chen et al., 2019; Dai et al., 2019). Such observations have been used to suggest climate change is reshaping biological processes, with major implications for forecasts of future change. Here we present a simple alternative explanation for observed declining sensitivities: the use of linear models to estimate non-linear temperature responses. We show how linear estimates of sensitivities will appear to decline with warming for events that occur after a cumulative thermal threshold is met—a common model for many biological events. Corrections for the non-linearity of temperature response in simulated data and long-term phenological data from Europe remove the apparent decline. Our results show that rising temperatures combined with linear estimates based on calendar time produce observations of declining sensitivity—without any shift in the underlying biology. Current methods may thus undermine efforts to identify when and how warming will reshape biological processes. Significance statement Recently a growing body of literature has observed declining temperature sensitivities of plant leafout and other events with higher temperatures. Such results suggest that climate change is already reshaping fundamental biological processes. These temperature sensitivities are often estimated as the magnitude of a biological response per °C from linear regression. The underlying model for many events—that a critical threshold of warmth must be reached to trigger the event—however, is non-linear. We show that this mismatch between the statistical and biological models can produce the illusion of declining sensitivities with warming using current methods. We suggest simple alternative approaches that can better identify when and how warming will reshape biological processes.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.231
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.214
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2021
Admission routes1
Has abstractyes

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