Thermal performance of ecosystems: Modeling how physiological responses to temperature scale up in communities
Bibliographic record
Abstract
Understanding how ecosystems respond to their environmental temperature is a major challenge. Thermodynamic constraints on species' metabolic rates are expected to affect ecosystem characteristics, but species interactions and interspecific variation in physiological thermal response curves (TRC) may obscure ecosystem-level responses to temperature. As a result, macroecological patterns related to temperature are still poorly understood. We investigate how physiological TRC scale up to ecosystem-level thermal responses by modifying the Tangled Nature (TaNa) model, a stochastic network model of ecology and evolution. We include new parameterizations that make reproduction, death, and mutation temperature-dependent. We find that ecosystem survival probability depends on how the minimum fitness required for species survival varies with temperature. The thermal response of ecosystem survival probability is the only ecosystem property that is sensitive to interspecific variation in TRC. Species richness scales up directly from the TRC of mutation rate, and average species population sizes are inversely related to mutation rate, with Species Abundance Distributions (SADs) exhibiting more rare species in warmer temperatures. Interactions between species are also inversely related to mutation, with positive interactions occurring more frequently in colder temperatures. The abundance of surviving ecosystems is not sensitive to temperature. This work helps clarify the specific relationships between physiological responses to temperature and ecosystem-level repercussions when species are interacting and adapting to their thermal environments.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".