Toward a More Dynamic Metabolic Theory of Ecology to Predict Climate Change Effects on Biological Systems
Bibliographic record
Abstract
AbstractThe metabolic theory of ecology (MTE) aims to link biophysical constraints on individual metabolic rates to the emergence of patterns at the population and ecosystem scales. Because MTE links temperature's kinetic effects on individual metabolism to ecological processes at higher levels of organization, it holds great potential to mechanistically predict how complex ecological systems respond to warming and increased temperature fluctuations under climate change. To scale up from individuals to ecosystems, applications of classical MTE implicitly assume that focusing on steady-state dynamics and averaging temperature responses across individuals and populations adequately capture the dominant attributes of biological systems. However, in the context of climate change, frequent perturbations from steady state and rapid changes in thermal performance curves via plasticity and evolution are almost guaranteed. Here, we explain how some of the assumptions made when applying MTE's simplest canonical expression can lead to blind spots in understanding how temperature change affects biological systems and how this presents an opportunity for formal expansion of the theory. We review existing advances in this direction and provide a decision tree for identifying when dynamic modifications to classical MTE are needed for certain research questions. We conclude with empirical and theoretical challenges to be addressed in a more dynamic MTE for understanding biological change in an increasingly uncertain world.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".