Exaptation Traits for Megafaunal Mutualisms as a Factor in Plant Domestication
Bibliographic record
Abstract
Megafaunal extinctions are recurring events that cause evolutionary ripples, as cascades of secondary extinctions and shifting selective pressures reshape ecosystems. Megafaunal browsers and grazers are major ecosystem engineers, they: keep woody vegetation suppressed; are nitrogen cyclers; and serve as seed dispersers. Most angiosperms possess sets of physiological traits that allow for the fixation of mutualisms with megafauna; some of these traits appear to serve as exaptation (preadaptation) features for farming. As an easily recognized example, fleshy fruits are, an exaptation to agriculture, as they evolved to recruit a non-human disperser. We hypothesize that the traits of rapid annual growth, self-compatibility, heavy investment in reproduction, high plasticity (wide reaction norms), and rapid evolvability were part of an adaptive syndrome for megafaunal seed dispersal. We review the evolutionary importance that megafauna had for crop and weed progenitors and discuss possible ramifications of their extinction on: (1) seed dispersal; (2) population dynamics; and (3) habitat loss. Humans replaced some of the ecological services that had been lost as a result of late Quaternary extinctions and drove rapid evolutionary change resulting in domestication.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".