Mechanistic macroecology: exploring the drivers of latitudinal variation in terrestrial body size in a General Ecosystem Model
Bibliographic record
Abstract
ABSTRACT Many mechanisms have been hypothesized to explain Bergmann’s rule - the correlation of body size with latitude. However, it is not feasible to assess the contribution of hypothesised mechanisms by experimental manipulation or statistical correlation. Here, we evaluate two of the principal hypothesised mechanisms, related to thermoregulation and resource availability, using structured experiments in a mechanistic global ecosystem model. We simulated the broad structure of assemblages and ecosystems using the Madingley model, a mechanistic General Ecosystem Model (GEM). We compared emergent modelled biogeographic patterns in body mass to empirical patterns for mammals and birds. We then explored the relative contribution of thermoregulation and resource availability to body mass clines by manipulating the model’s environmental gradients. Madingley produces body size gradients that are in broad agreement with empirical estimates. Thermoregulation and resource availability were both important controls on body mass for endotherms, but only temperature for ectotherms. Our results suggest that seasonality explains animal body mass patterns through a complex set of mechanisms. Process-based GEMs generate broadly realistic biogeographic body mass patterns. Ecologists can use them in novel ways: to explore causality, or for generating and testing hypotheses for large-scale, emergent ecological patterns. At the same time, macroecological patterns are useful for evaluating mechanistic models. Iteratively developing GEMs, and evaluating them against macroecological patterns, could generate new insights into the complex causes of such patterns.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".