Warm and arid regions of the world are hotspots of superorganism complexity
Bibliographic record
Abstract
Biologists have long been fascinated by the processes that give rise to phenotypic complexity of organisms, yet whether there exist geographical hotspots of phenotypic complexity remains poorly explored. Phenotypic complexity can be readily observed in ant colonies, which are superorganisms with morphologically differentiated queen and worker castes analogous to the germline and soma of multicellular organisms. Several ant species have evolved 'worker polymorphism', where workers in a single colony show quantifiable differences in size and head-to-body scaling. Here, we use 256 754 occurrence points from 8990 ant species to investigate the geography of worker polymorphism. We show that arid regions of the world are the hotspots of superorganism complexity. Tropical savannahs and deserts, which are typically species-poor relative to tropical or even temperate forests, harbour the highest densities of polymorphic ants. We discuss the possible adaptive advantages that worker polymorphism provides in arid environments. Our work may provide a window into the environmental conditions that promote the emergence of highly complex phenotypes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".