A Hierarchical View of Convergent Evolution in Microbial Eukaryotes<sup>1</sup>
Bibliographic record
Abstract
Distinguishing convergent evolution from other causes of similarity in organisms is necessary for reconstructing phylogenetic relationships, inferring patterns of character evolution, and investigating the forces of natural selection. In contrast to animals and land plants, the pervasiveness and adaptive significance of convergent evolution in microbes has yet to be systematically explored or articulated. Convergent evolution in microbial eukaryotes, for instance, often involves very distantly related lineages with relatively limited repertoires of morphological features. These large phylogenetic distances weaken the role of ancestral developmental programs on the subsequent evolution of morphological characters, making convergent evolution between very distantly related lineages fundamentally different from convergent evolution between closely related lineages. This suggests that examples of convergence at different levels in the phylogenetic hierarchy offer different clues about the causes and processes of macroevolutionary diversification. Accordingly (and despite opinions to the contrary), I recognize three broad and overlapping categories of phenotypic convergence-"parallel", "proximate" and "ultimate"-that represent either (1) subcellular analogues, (2) subcellular analogues to multicellular systems (and vice versa), or (3) multicellular analogues. Microbial eukaryotes living in planktonic environments, interstitial environments, and the intestinal environments of metazoan hosts provide compelling examples of ultimate convergence. After describing selected examples in microbial eukaryotes, I suggest some future directions needed to more fully understand the hierarchical structure of convergent evolution and the overall history of life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".