The State of Phylogenetic Analysis: Narrow Visions and Simple Answers—Examples from the Diptera (flies)
Bibliographic record
Abstract
The order Diptera is remarkably diverse, not only in species but in morphological variation in every life stage, making them excellent candidates for phylogenetic analysis. Such analysis has been hampered by methods that have severely restricted character state interpretation. Morphological-based phylogenies should be based on a deep understanding of the morphology, development and function of character states, and have extensive outgroup comparisons made to determine their polarity. Character states clearly vary in their value for determining phylogenetic relationships and this needs to be studied and utilized. Characters themselves need more explicit discussion, including how some may be developmentally or functionally related to other characters (and potentially not independent indicators of genealogical relationship). The current practice by many, of filling a matrix with poorly understood character states and highly limited outgroup comparisons, is unacceptable if the results are to be a valid reflection of the actual history of the group.Parsimony analysis is not an objective interpretation of phylogenetic relationships when all characters are treated as equal in value. Exact mathematical values applied to characters are entirely arbitrary and are generally used to produce a phylogeny that the author considers as reasonable. Mathematical appraisal of a given node is similarly inconsequential because characters do not have an intrinsic mathematical value. Bremer support, for example, provides values that have no biological reality but provide the pretence of objectivity. Cladists need to focus their attention on testing the validity of each synapomorphy proposed, as the basis for all further phylogenetic interpretation, rather than the testing of differing phylogenies through various comparative programs.Current phylogenetic analyses have come to increasingly depend on DNA sequence-based characters, in spite of their tumultuous history of inconsistent results. Until such time as sequences can be shown to produce predictive phylogenies (i.e., using Hennigian logic), independent of morphological analysis, they should be viewed with caution and certainly not as a panacea as they are commonly portrayed.The purported comprehensive analyses of phylogenetic relationships between families of Diptera by Wiegmann et al. (2011) and Lambkin et al. (2013) have serious flaws and cannot be considered as the "Periodic Table" of such relationships as originally heralded.Systematists working on Diptera have a plethora of complex and informative morphological synapomorphies in every life stage, either described or awaiting study. Many lineages have the potential of providing a wealth of evolutionary stories to share with other biologists if we produce stable phylogenies based on weighted synapomorphies and interpreted to elucidate the zoogeographic and bionomic divergence of the group. Some lineages are devoid of convincing synapomorphies and, in spite of our desires, should be recognized as being largely uninterpretable.
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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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.043 |
| Scholarly communication | 0.013 | 0.034 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".