Submitted:
14 January 2026
Posted:
14 January 2026
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Abstract
Keywords:
1. Introduction
2. Molecular Sequence Evidence
2.1. Methodological Errors: Long-Branch Attraction (LBA)
2.1.1. Heterogeneity-Induced LBA
- Compositional heterogeneity arises from differences in base or amino acid usage across species, influenced by factors such as GC/AT bias or amino acid biosynthetic cost [14,15,16]. This can lead to convergent composition between distantly related taxa, potentially resulting in false groupings [14,15,17,18].
- Substitutional heterogeneity refers to variation in substitution rates and patterns (i.e., the probabilities of substitution between amino acids are not uniform) across sites [19,20,21]. Such heterogeneity can lead to saturation at rapidly evolving sites, causing the retained signal to reflect amino acid preferences rather than true phylogenetic relationships, and thereby introducing systematic errors due to convergent substitutions, also contributing to LBA [18,19,21].
2.1.2. Outgroup-Induced LBA
2.2. Biological Errors: Tree Incongruence and Gene Loss
2.2.1. “Observed” Factors: Observable Signals Within the Dataset
2.2.2. “Unobserved” Factors: Hidden Biases Beyond the Dataset
3. Rare Molecular Event Evidence
3.1. Fusion-with-Mixing in Nuclear Genomes: Anti-Convergent and Irreversible
3.2. Transfer of Mitochondrial Genes to the Nucleus: Irreversible but Lacking Anti-Convergence
4. Morphological and Embryological Evidence
4.1. Impact of Evolutionary Models on Criteria for Structural Complexity
4.1.1. Nervous Systems and Choanocytes: Independent Origins Disrupt Complexity-Based Inference
4.1.2. Germ Layers, Body Axes, and Symmetry: Secondary Loss Obscures Evolutionary History
4.2. Reassessment of Evidence and Its Implications for Morphological Complexity
4.2.1. Epithelial Tissue: Structural Complexity
4.2.2. Neuron-like and Muscle-like Functions: Physiological Complexity
5. Paleontological Evidence
5.1. Evidence Based on Constraining the Earliest Emergence
5.1.1. Traditional Fossil Evidence
- Characters criterion – whether the diagnostic traits used to assign a fossil to a particular group are well defined and phylogenetically informative;
- Diagnosis criterion – whether those characters are demonstrably present in the fossil;
- Age criterion – whether geochronological dating is available.
5.1.2. Molecular Fossil Evidence
5.2. Evidence Based on Reconstructing Evolutionary Trajectories
- The common ancestor of metazoans may already have been cylindrically symmetric [71], a body plan superficially similar to the hydroid form; moreover, skeletal biomineralization was widespread in the Ediacaran–Cambrian interval [126]. This raises the possibility that dinomischids represent either the metazoan ancestor itself or a case of convergence.
- The earliest dinomischid fossils postdate known ctenophores, whereas some controversial taxa such as Eoandromeda octobrachiata are older but exhibit more derived traits [102]. This paradox suggests both the potential existence of undiscovered basal lineages and the possibility that dinomischids were derived from ctenophores rather than ancestral to them.
- Because soft-bodied ctenophores have an exceptionally poor preservation potential, their role in early metazoan evolution remains obscure. This taphonomic gap further complicates phylogenetic interpretation. Overall, the evolutionary significance of dinomischid fossils remains ambiguous, and the signal they provide is limited.
6. Discussion
- explaining the causes of discrepancies among different disciplines;
- evaluating the resolving power of each methodological approach;
- exploring possible ways to integrate different lines of evidence; and
- assessing the overall tendencies of current evidence.
6.1. Why Are the Discrepancies Among Disciplines so Large?
- Accumulation of confounding evolutionary events. Over long evolutionary periods, extensive convergence and reversals inevitably occur, especially among lineages that have evolved independently since their divergence. This effect is especially striking in morphology, where even highly complex structures such as nervous systems have evolved independently and converged, while sponges appear to have undergone remarkably extensive secondary loss of characters. In molecular phylogenetics, such phenomena are even more common, giving rise to LBA and tree incongruence. In paleontology, the effect is smaller but still notable: traditional fossils may differ so greatly from crown groups that their relationships become obscure, while molecular fossils may be difficult to interpret due to uncertainties in biomarker specificity.
- Loss of evolutionary information. Although convergence can be viewed as a consequence of information loss, the mechanisms are distinct enough to warrant separate consideration. As seen in molecular sequences, saturation can erase phylogenetic signal, reducing sites to random noise. Large-scale gene or character loss across many taxa can similarly obliterate information, affecting both molecular and morphological analyses. In some cases, information loss arises directly from methodological constraints—for instance, molecular fossils preserve only chemical composition and concentration. Even traditional fossils, which appear to “fix” morphological characters in time, have effectively lost all molecular information. Unlike missing fossils, this kind of information loss affects entire clades or lineages, leaving no isolated remnants and thus representing a complete, irreversible loss of data.
- Destruction of physical evidence. This issue is specific to paleontology: over geological time, physical fossils may be lost or damaged through physicochemical processes. In contrast to information loss, the discovery of a single well-preserved fossil can sometimes recover evolutionary information through comparison. Nevertheless, fossil destruction likely occurs more frequently than true information loss. Although molecular sequence loss in fossils might mechanistically resemble physicochemical damage, its lineage-wide and irreversible nature justifies treating it as information loss rather than physical destruction.
- Asynchrony among disciplines. Evolutionary changes detected by one discipline may have little or no direct effect on others. For example, multiple minor-effect genetic changes may leave morphology largely unchanged, while a single regulatory mutation could drastically alter morphology but have little impact on sequence-based trees. Over long periods, this asynchronous accumulation amplifies discrepancies among data types. Notably, although the amplifying effect of time is not explicitly addressed, the issue of asynchrony among different lines of evidence had already been recognized [140].
6.2. How Far Back in Time Can Different Methods Reliably Resolve Evolutionary Relationships?
6.2.1. Factors extending the Resolution Limit
- Reducing the impact of confounding evolutionary events. This can be achieved when (I) such events are mechanistically improbable, (II) traits are insensitive to minor mutations, or (III) large data volumes dilute their effects.
- Preservation of evolutionary information. Facilitated when (I) evolutionary rates are low, avoiding saturation; (II) multiple coordinated changes are required for loss, making complete erasure unlikely; or (III) large datasets buffer against random loss.
- Ease of evidence acquisition. This includes (I) data readily obtainable from extant taxa (e.g., molecular sequences) and (II) ancient evidence that is both widespread and preservable (e.g., molecular fossils).
6.2.2. The Most Promising Approach: Highly Anti-Convergence and Highly Irreversible Marginal Instances (HACHIMI)
- Resistance to confounding events. By definition, such events are inherently robust to convergence and reversal. For example, in FWM, convergence would require similar chromosomal fusions involving entire linkage groups, followed by an intermixing of gene order between them, while reversal would necessitate implausibly precise re-separation of merged genes [40]; other potential alternative pathways are likewise highly improbable. Moreover, analyses of this type do not depend on exact sequences or gene order, making them insensitive to small-scale changes.
- Resistance to information loss. The rarity of these events prevents saturation and secondary loss, and some (e.g., FWM) require multiple concurrent changes to alter a single character, providing strong resistance to data loss.
- Accessibility of evidence. While not inherent to the definition, HACHIMI-type events are typically more readily detectable than traditional fossils, which are the main exception.
6.2.3. Resolution Limits of Other Approaches
- Molecular sequence evidence. High rates of convergence and reversal generate severe conflicts (e.g., LBA), mitigated only partially by large datasets. Saturation and gene loss are common, and although sequences are easy to obtain from extant taxa, the method’s Resolution Limit remains shallow.
- Embryological and morphological evidence. While convergence and reversal occur, high-resolution imaging and molecular tools now enable detailed identification and differentiation of these traits (e.g., the nervous system of ctenophores). Because most morphological traits are polygenic, true loss is rare, and residual evidence can often be detected. However, data collection is labor-intensive and prone to incompleteness. Thus, despite a potentially deeper Resolution Limit, accumulation of high-quality data is slow and low-quality noise remains common.
- Traditional fossil evidence. Traditional fossils preserve limited, coarse-grained morphological information and rarely allow assessment of development or molecular traits. Crucially, specimens are rare. Consequently, their Resolution Limit is extremely shallow.
- Molecular fossil evidence. Biomarkers may be produced by multiple taxa, and specificity is difficult to confirm. Apart from chemical composition, nearly all information is lost. Despite their abundance and ease of sampling, molecular fossils have a very limited resolution capacity.
6.3. How Can Different Approaches be Integrated?
- Molecular sequences – encoding divergence times and gene evolutionary histories;
- HACHIMIs – rare events offering high-resolution support for specific nodes;
- Embryology and morphology – preserving phenotypic evolutionary trajectories;
- Paleontology – providing direct records of lineage existence and transitions.
6.4. Which Hypothesis Is Currently Better Supported?
- HACHIMI evidence (FWM) supports the Ctenophora-sister hypothesis.
- Morphological and embryological evidence: recent high-quality studies tend to be neutral or mildly support the Ctenophora-sister hypothesis, whereas older, less-resolved evidence often supports the Porifera-sister hypothesis (Figure 2c). Overall, Porifera-sister evidence remains quantitatively more abundant, but Ctenophora-sister evidence exhibits greater methodological rigor and data reliability, indicating a trend increasingly favoring the latter.
- Molecular sequence evidence remains highly contentious but currently leans weakly toward the Porifera-sister hypothesis.
- Paleontological evidence (both traditional and molecular) is too fragmentary and uncertain to strongly support either side.
7. Conclusion
Funding
Data Availability Statement
Acknowledgements
Conflicts of Interest
Appendix A
| Year | Long-Branch Attraction (LBA) | Tree Incongruence | |||
|---|---|---|---|---|---|
| Model Complexity | Data Simplification | Outgroup Choice | “Observed” Factors | “Unobserved” Factors | |
| 2008 | First EST-based phylogeny supporting the Ctenophora-sister hypothesis [3] | ||||
| 2013 | First ctenophore whole-genome data further supporting Ctenophora-sister [12] | ||||
| 2015 | Site-heterogeneous models (e.g., CAT) show better fit than site-homogeneous models [6,7] |
Data excluding nuclear proteins mostly support Ctenophora-sister [6] | Homogeneous models show no effect of outgroup choice [6] | Correcting underestimation of gene loss restores Porifera-sister topology from Ryan et al.’s dataset [7] | |
| Excluding highly conserved proteins (e.g., ribosomal genes) exacerbates LBA [7] | Under CAT models, closely related outgroups recover Porifera-sister [7] | ||||
| 2017 | First use of amino acid recoding recovers Porifera-sister [23] | ||||
| 2020 | Widespread gene loss, especially in controversial lineages [37] | ||||
| 2021 | CAT models found to be overfitted; simpler site-heterogeneous models support Ctenophora-sister [8] | Amino acid recoding introduces biases by reducing information content [8] | Outgroup choice has no effect under simple site-heterogeneous models [8] | ||
| 2022 | Tree incongruence lower under Porifera-sister than Ctenophora-sister, mainly due to incomplete lineage sorting (ILS) [9] | ||||
| 2025 | Used only genes with consistent signals across concatenation and consensus analyses, recovering Porifera-sister [28] | ||||
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| Type | Detail | ||
|---|---|---|---|
| Mechanism | Mitigating Factors | Mainly Affected Aspects1 | |
| Confounding Events | convergence, reversal |
low conv. & rev. potential, robust to micro-variation, extensive dataset |
molecular sequence***, morphology & embryology*, traditional fossil*, molecular fossil**** |
| Information Loss | saturation effect, gene loss, information reduction |
low evolutionary rate, multi-step loss required, extensive dataset |
molecular sequence***, morphology & embryology*, traditional fossil**, molecular fossil**** |
| Evidence Destruction | physicochemical destruction | extant, broad & stable record |
traditional fossil****, molecular fossil* |
| Rank & Type | Evaluation Dimension | Remark | |||
|---|---|---|---|---|---|
| Confounding Events | Information Loss | Accessibility | |||
| No.1 | HACHIMI | very rare | very rare | (very) easy1 | most promising |
| No.2 | Morph. & Embryo. | distinguishable | distinguishable | easy | currently underestimated |
| No.32 | Molecular Sequence | frequent | frequent | very easy | requires error correction |
| No.4 | Traditional Fossil | hard to discern | significant | very hard | — |
| No.5 | Molecular Fossil | indistinguishable | almost all | easy | — |
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