Larger animals tend to have larger brains and smaller animals tend to have smaller ones. However, some species do not fit the pattern that would be expected based on their body size. This variation between species can also apply to individual brain regions. This may be due to evolutionary forces shaping the brain when favouring particular behaviours. However, it is difficult to directly link changes in species behaviour and variations in brain structure.

One way to understand the impact of evolutionary adaptations is to study species that have developed new behaviours and compare them to related ones that lack such a behaviour. An opportunity to do this lies in the ability of several species of fish to produce and sense electric fields in water. While this system is not found in most fish, it has evolved multiple times independently in distantly-related lineages. Schumacher and Carlson examined whether differences in the size of brains and individual regions between species were associated with the evolution of electric field generation and sensing.

Micro-computed tomography, or μCT, scans of the brains of multiple fish species revealed that the species that can produce electricity – also known as ‘electrogenic’ species’ – have more similar brain structures to each other than to their close relatives that lack this ability. The brain regions involved in producing and detecting electrical charges were larger in these electrogenic fish. This similarity was apparent despite variations in how total brain size has evolved with body size across species.

These results demonstrate how evolutionary forces acting on particular behaviours can lead to predictable changes in brain structure. Understanding how and why brains evolve will allow researchers to better predict how species’ brains and behaviours may adapt as human activities alter their environments.

Brains are composed of multiple regions that vary widely in size across vertebrates and are associated with particular functions and behaviors (Striedter, 2005; Striedter and Northcutt, 2020). Much of the variation in brain region sizes is attributed to the allometric scaling of each region with total brain size (concerted evolution), which may result from conservation and constraint in developmental neurogenesis (Finlay and Darlington, 1995; Striedter, 2005). Seemingly disproportionately enlarged regions can have larger allometric slopes by extending the timing of neurogenesis for late developing brain regions such as the cortex and cerebellum (Finlay and Darlington, 1995). However, changes in brain region sizes independent of total brain size, or mosaic shifts, have also been observed in several taxa and are hypothesized to reflect selection on traits associated with those regions (Barton and Harvey, 2000; Striedter, 2005). Mosaic shifts in fine-scale brain regions and circuits are well accepted and have been linked to changes in behavior (Carlson et al., 2011; Vélez et al., 2017; Vélez et al., 2019; Gutiérrez-Ibáñez et al., 2014; Moore and DeVoogd, 2017; DeCasien and Higham, 2019; Krebs, 1990), but the scale at which selection can act to alter brain region sizes remains unclear. There may potentially be more flexibility for mosaic changes in nuclei or circuits dedicated to specific functions compared to major brain regions that serve multiple functions and may be subject to greater developmental and phylogenetic constraints.

Most studies looking at the scaling of major brain regions instead find evidence of concerted evolution (Finlay and Darlington, 1995; Striedter, 2005; Yopak et al., 2010). There is some evidence of mosaic evolution at these scales (Hoops et al., 2017; Sukhum et al., 2018), but the drivers and selective pressures necessary for mosaic evolution to overcome constraints, whether developmental or due to functional interconnectivity, remain unclear. Further, this is difficult to test without repeated evolution of the same phenotypes.

Mosaic brain evolution of major brain regions is hypothesized to occur more frequently at larger taxonomic scales and alongside behavioral innovations that open new niches since mosaic shifts are more likely to contribute to major differences in brain function (Striedter, 2005). In dragon lizards, mosaic brain evolution is associated with ecomorph (species similar in morphology and behavior inhabiting the same ecological niche; Hoops et al., 2017), but as many different behavioral and sensory changes occur alongside ecomorph development, it is difficult to identify specific selective pressures favoring the observed ecomorph brain structure. Weakly electric fishes are excellent for testing whether mosaic brain evolution occurs with behavioral novelty: these fishes evolved behaviorally novel active electrosensory systems with several neural innovations, which likely resulted in strong selection for electrosensory processing capabilities (Carlson and Arnegard, 2011). Further, multiple lineages independently evolved similar electrosensory systems (Crampton, 2019).

Previous studies found that African weakly electric fishes (Mormyroidea) evolved extremely large brains along with mosaic increases in the sizes of the cerebellum and hindbrain relative to other non-electric osteoglossiforms (Sukhum et al., 2018; Sukhum et al., 2016). These mosaic increases occurred alongside the evolution of an active electrosensory system (electrogenesis + electroreception), but since this is only a single lineage, it is impossible to determine whether these mosaic shifts are associated with the evolution of this electrosensory system or with other phenotypes that differentiate mormyroids from their closest living relatives. Further, one non-electric osteoglossiform, Xenomystus nigri, is electroreceptive, and there is no evidence that it has experienced mosaic brain evolution compared to other non-electric osteoglossiforms.

Electrogenesis and electroreception have evolved multiple times, but sparingly, across vertebrates—at least six times and at least two times, respectively, within ray-finned fishes (Actinopterygii; Figure 1). However, the degree to which electrosensory systems have evolved, with respect to their component parts and how they are utilized, varies across these different independent origins. For example, some lineages produce strong electrical discharges while others produce weak electrical discharges, some lineages have evolved multiple types of electroreceptors while others have evolved just one, and the usage and developmental origins of electrogenesis differ across lineages (Crampton, 2019). Here, we investigated another lineage of fishes, otophysans, which includes taxa that evolved similar electrosensory systems independent of mormyroids, but to varying degrees with respect to their components and utilization, to determine whether these mosaic shifts are found repeatedly alongside the evolution of active electrosensing. Although osteoglossiform and otophysan lineages have convergently evolved similar electrosensory systems (Crampton, 2019), there are some distinctive differences in the degree of electrosensory usage, electroreceptor type, and electrical discharge type that could indicate differential selective pressures on the brains of these species. These differences allowed us to assess how multiple aspects of electrosensory systems relate to mosaic brain evolution.

Figure 1

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To investigate how electrosensory systems relate to structural brain variation, we combined published osteoglossiform data for electrogenic mormyroids, electroreceptive Xenomystus, and non-electrosensory outgroup species (Sukhum et al., 2018) with otophysan data for two additional electrogenic lineages (Gymnotiformes and Synodontis Siluriformes), electroreceptive (but not electrogenic) siluriforms, and non-electrosensory Characiformes and Cypriniformes (outgroup otophysans). Using micro-computed tomography (μCT) scans, we measured total brain and brain region volumes for 15 electrogenic, 3 electroreceptive, and 4 non-electrosensory otophysan species. Combined with the published osteoglossiform data, this yielded a dataset of 32 species (Figure 2, Figure 2—video 1). We measured the volumes of seven distinct brain regions (olfactory bulbs, OB; telencephalon, TEL; hindbrain, HB; optic tectum, OT; torus semicircularis, TS; cerebellum, CB; and rest of brain, RoB) to determine patterns of major brain region scaling across taxa. Rest of brain includes thalamus, hypothalamus, and additional midbrain structures excluding optic tectum and torus semicircularis (see ‘Brain region delimitation’).

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Electrosensory system evolution is not associated with shifts in total brain size scaling

A recent study found a steeper brain–body allometric relationship for osteoglossiforms compared to other actinopterygians, but not for seven other focal ray-finned fish orders, which may, in part, be driven by the highly speciose mormyroids (Tsuboi, 2021). To determine if the evolution of an electrosensory system is associated with extreme encephalization or shifts in brain–body allometric relationships, we combined our data with published brain and body mass data across ray-finned fishes (Tsuboi, 2021; Tsuboi et al., 2018), which resulted in a combined dataset of 870 species across 46 orders, with phylogenetic data from a previously assembled time-calibrated phylogeny (Rabosky et al., 2018). We used Bayesian reversible-jump bivariate multiregime Ornstein–Uhlenbeck modeling (OUrjMCMC; Uyeda et al., 2017) to identify shifts in both y-intercept and slope of brain–body allometric relationships. This approach allows shifts to be identified without assuming their location a priori.

We identified eight allometric shifts across actinopterygians, three in lineages with at least three descendants: osteoglossiforms, and two shifts within percomorphs (Figure 3A). The first (hereafter referred to as percomorph grade A) included Lophiiformes, Tetraodontiforms, Acanthuriformes, some descendants from Scorpaeniformes, and some descendants from Perciformes. The second (hereafter referred to as percomorph grade B) included Gasterosteiformes, some descendants from Scorpaeniformes, and some descendants from Perciformes. Shifts were also detected for Gymnothorax meleagris, Synodontis multipunctatus, Arothron nigropunctatus, and a mormyroid lineage containing Isichthys henryi + Brienomyrus brachyistius. All other taxa best fit the ancestral allometry. However, it is worthwhile to note that additional shifts may be present within actinopterygians. There are more than 20,000 known actinopterygians while we only have data for 870 species. Further, multiple single species were identified as having increased brain–body allometries; however, it remains unclear if these single species have particularly enlarged brains compared to their closest relatives or if a more speciose lineage with enlarged brains would be identified with additional sampling of close relatives.

Figure 3

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Figure 3—source data 1

Ornstein–Uhlenbeck modeling (OUrjMCMC) and phylogenetic generalized least squares (PGLS) fitted brain–body allometries for each grade.

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Figure 3—source data 2

Results (p-values) of phylogenetically corrected pairwise post-hoc tests with a Bonferroni correction for an analysis of covariance (ANCOVA) comparing phylogenetic generalized least squares (PGLS) relationships of brain size against body for each identified grade.

Differences in intercept are below the diagonal and differences in slope are above the diagonal. Significant differences are shown in bold.

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Figure 3—source data 3

Model selection results for phylogenetic generalized least squares (PGLS) relationships systematically collapsing each putative grade to its ancestral grade.

The best-fit model is shown in bold.

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Figure 3—source data 4

Ornstein–Uhlenbeck modeling (OUrjMCMC) estimated marginal likelihoods and model selection results for shifts in brain–body allometries associated with electrosensory phenotypes.

The best-fit model is shown in bold.

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For shifts in lineages (i.e., for each grade) containing at least three descendants, we tested the putative grades in a phylogenetic generalized least squares (PGLS) framework for shifts in both slope and y-intercept using a phylogenetically corrected analysis of covariance (ANCOVA) and phylogenetically corrected pairwise post-hoc testing with a Bonferroni correction. We found that all of the grades differ significantly in slope (p<0.05) while only osteoglossiforms relative to the ancestral grade and relative to percomorph grades A and B differ in y-intercept (p<0.05; Figure 3B, Figure 3—source data 2). However, only osteoglossiforms had an increased slope relative to the ancestral grade while the slope for percomorph grade A decreased relative to the ancestral grade with a further decrease in percomorph grade B. To confirm shifts across all putative grades, we fit the following PGLS models: all the identified shifts, each grade collapsed in turn to its ancestral grade, and a single allometric relationship across all taxa. Collapsing S. petricola to the ancestral grade was the best-fit model (ΔAIC > 3) with the model containing all putative shifts as second best (ΔAIC > 6; Figure 3—source data 3).

To explicitly address whether shifts in the allometric relationship are associated with the evolution of electrosensory phenotypes, we ran OUrjMCMC models with fixed shifts for lineages that evolved ampullary electroreceptors, lineages that evolved tuberous electroreceptors, and lineages that evolved electrogenesis in addition to the following null hypotheses: a fixed shift at the branch leading to osteoglossiforms as found previously (Tsuboi, 2021), only shifts in the allometric relationship for intercept but not slope, and a single allometric relationship across all taxa. We found that the model with eight shifts provided the best fit to the data (2ln(BF)>28; Figure 3—source data 4). In addition, when forcing a global slope across all taxa, we found no shifts in intercept with a posterior probability >0.1 despite all parameters having estimated sample sizes >1000. Taken together, these results suggest that fishes can evolve an active electrosensory system without evolving a brain as large as that of mormyroids.

Electrogenic species have similar structural brain variation

To determine how brain structure varies in association with electrosensory phenotype, we used regional measurements for all taxa and ran a phylogenetically corrected principal components analysis (pPCA). Considering the phylogenetic relationships among otophysans are still debated (Crampton, 2019; Hughes et al., 2018; Rabosky et al., 2013), our goal was only to account for relatedness to the best of our ability, not to propose a resolved phylogeny. We found that electrogenic species cluster distinctly from both electroreceptive and non-electrosensory species, which overlap considerably (Figure 4A). All electroreceptive lineages (mormyroids, Xenomystus, gymnotiforms, and siluriforms) have evolved ampullary electroreceptors, which detect relatively low-frequency electrical information, while only gymnotiforms and mormyroids have evolved additional tuberous electroreceptors that broaden the frequency range of detectable signals (Crampton, 2019). We find that electrogenic species with both electroreceptor types cluster distinctly from electrogenic fishes with only ampullary electroreceptors (Synodontis siluriforms). The first principal component (PC1) explained 92.02% of the variation in brain region volumes and is strongly correlated with total brain volume (ρ = –0.99, p<10^–16). PC2 explained 4.81% of the total variation, which is 60.3% of the variation in region volumes not explained by total brain volume. Whereas all of the brain regions loaded in the same direction for PC1, cerebellum, torus semicircularis, and hindbrain loaded negatively on PC2 while the remaining regions (telencephalon, rest of brain, optic tectum, and olfactory bulbs) loaded positively. This suggests that concerted brain evolution explains the most variation in region volumes as seen in PC1, but that mosaic brain evolution could be contributing to the observed variation in brain region volumes as seen in PC2.

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Phylogenetically corrected principal components analysis (pPCA) loadings for each non-normalized brain region.

OB, olfactory bulbs; TEL, telencephalon; HB, hindbrain; OT, optic tectum; TS, torus semicircularis; CB, cerebellum; RoB, rest of brain.

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Phylogenetically corrected principal components analysis (pPCA) phylogenetic generalized least squares (PGLS) model selection results for non-normalized data (N = 31).

PC1 was correlated with total brain volume, thus total brain volume was removed from all PC1 models. The best-fit model for each PC is shown in bold.

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Figure 4—source data 3

Phylogenetic flexible discriminant analysis (pFDA) results table showing the regional coefficients for each discriminant axis and the means for each electrosensory phenotype group along each axis: electrogenic + tuberous and ampullary electroreceptors (E + A + T), electrogenic + only ampullary electroreceptors (E + A), and non-electric (Not E).

OB, olfactory bulbs; TEL, telencephalon; HB, hindbrain; OT, optic tectum; TS, torus semicircularis; CB, cerebellum; RoB, rest of brain.

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To ensure that differences in size ranges between regions were not biasing the results, we z-score-normalized the region volumes, reran the pPCA, and found nearly identical results (Figure 4—figure supplement 1). As there are multiple approaches to multivariate clustering analyses, we also ran a phylogenetic flexible discriminant analysis (pFDA) to investigate whether convergence in brain structure across electrosensory phenotypes persists irrespective of method. Again, we find distinct clustering between electrogenic species with both electroreceptor types, electrogenic species with only ampullary electroreceptors, and non-electric species, indicating three distinct electrosensory-associated cerebrotypes (Clark et al., 2001). The resulting three discriminant functions (i.e., number of electrosensory phenotype groups – 1) of the observed data accurately predicted electrosensory phenotype from the residuals of brain characters for all 32 species, further suggesting a relationship between electrosensory phenotypes and the observed brain region volume variation (Figure 4—figure supplement 2, Figure 4—source data 3). As all three clustering approaches demonstrated the same conclusions, we proceeded with the non-normalized pPCA.

To assess the relative importance of electrosensory phenotypes in explaining the axes of brain structural variation (PCs 1–4), we ran candidate models that considered body mass, total brain volume, presence or absence of electrogenesis, and electroreceptor type (tuberous and ampullary vs. only ampullary vs. none). Models that only consider allometric scaling with body mass and total brain volume would be consistent with concerted evolution while models that also consider either one or both electrosensory phenotypes would be in line with mosaic brain evolution since more than just allometric scaling explains the observed variation in brain region volumes. Since PC1 strongly correlates with brain size, we removed total brain volume as a variable from all PC1 models.

We found that the model that considers the electrogenesis phenotype better explained PC1, but is statistically indistinguishable from the concerted model (Figure 4—source data 2), which further supports the role of concerted evolution in determining the sizes of individual brain regions. The model that considers electroreceptor type better explained PC2 (Figure 4—source data 2), which supports our hypothesis that the electrosensory system is related to mosaic evolutionary changes in brain region scaling. PC3 explains 1.68% of the total variation (21.1% of the variation not explained by total brain volume) and largely reflects the variation between olfactory bulbs and optic tectum with no separation between electrosensory phenotypes (Figure 4B). The model that considers electroreceptor type better predicts PC3 but is statistically indistinguishable from the concerted model, suggesting that this axis of brain variation likely evolved concertedly with brain size (Figure 4—source data 2). PC4 explains 0.54% of the total variation (6.8% of the variation not explained by total brain volume), largely reflects the variation between torus semicircularis and cerebellum, and is better predicted by the model that considers both electrosensory phenotypes (Figure 4B, Figure 4—source data 2). Taken together, these results highlight the importance of both concerted and mosaic brain evolution in producing the observed variation in brain region volumes.

Mosaic shifts in electrogenic species relative to non-electric species

To directly test for mosaic shifts associated with electrosensory phenotypes, we fit PGLS regressions for each brain region against total brain size for species with each electrosensory phenotype and used an ANCOVA to test for significant differences in both slope and y-intercept of the PGLS relationships for each brain region. Considering the debate on how best to assess patterns of brain region scaling (Finlay and Darlington, 1995; Yopak et al., 2010), we also determined PGLS relationships for each brain region against total brain volume minus the focal brain region (remaining brain volume).

Since electroreceptive-only species and non-electrosensory species overlapped considerably in the pPCA, we combined all non-electric taxa and performed a phylogenetically corrected ANCOVA with phylogenetically corrected pairwise post-hoc testing to compare electrogenic taxa with both tuberous and ampullary electroreceptors (mormyroids and gymnotiforms), electrogenic taxa with only ampullary electroreceptors (Synodontis siluriforms), and non-electric taxa (Figure 5, Figure 5—figure supplement 1, Figure 5—source data 1, Figure 5—source data 2, Figure 5—source data 3, Figure 5—source data 4, Figure 5—source data 5, Figure 5—source data 6). We found a significant increase in y-intercept in cerebellum for all electrogenic species relative to non-electric species (p<0.05). For torus semicircularis, we found a significant increase in y-intercept in electrogenic + ampullary-only taxa relative to non-electric taxa (p<0.05) and a further increase in y-intercept in electrogenic + ampullary + tuberous taxa (p<10^–4). We found a significant increase in y-intercept in hindbrain for electrogenic + ampullary + tuberous taxa relative to non-electric taxa (p<0.01) with electrogenic + ampullary-only taxa being intermediate (p>0.05). These results were the same for both regressions against total brain volume (Figure 5, Figure 5—source data 1, Figure 5—source data 2) and regressions against remaining brain volume (Figure 5—figure supplement 1, Figure 5—source data 4, Figure 5—source data 5, Figure 5—source data 6).

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Results of an analysis of covariance (ANCOVA) comparing phylogenetic generalized least squares (PGLS) relationships of region volume against total brain volume for each electrosensory phenotype: slope p, intercept p, and Pagel’s lambda (λ).

Significant differences are shown in bold. OB, olfactory bulbs; TEL, telencephalon; HB, hindbrain; OT, optic tectum; TS, torus semicircularis; CB, cerebellum; RoB, rest of brain.

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Figure 5—source data 2

Results (p-values) of pairwise post-hoc tests with a Bonferroni correction for an analysis of covariance (ANCOVA) comparing phylogenetic generalized least squares (PGLS) relationships of region volume against total brain volume for each electrosensory phenotype: electrogenic + tuberous and ampullary electroreceptors (E + A + T), electrogenic + only ampullary electroreceptors (E + A), and non-electric (Not E).

Differences in intercept are below the diagonal, and differences in slope are above the diagonal for each brain region. Significant differences are shown in bold. Post-hoc tests were not performed when the ANCOVA revealed no significant differences (indicated by ‘n.s.’). OB, olfactory bulbs; TEL, telencephalon; HB, hindbrain; OT, optic tectum; TS, torus semicircularis; CB, cerebellum; RoB, rest of brain.

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Figure 5—source data 3

Estimated effect sizes (Cohen’s d) of each contrast for an analysis of covariance (ANCOVA) comparing phylogenetic generalized least squares (PGLS) relationships of region volume against total brain volume for each electrosensory phenotype: electrogenic + tuberous and ampullary electroreceptors (E + A + T), electrogenic + only ampullary electroreceptors (E + A), and non-electric (Not E).

Post-hoc tests were not performed when the ANCOVA revealed no significant differences (indicated by ‘n.s.’). OB, olfactory bulbs; TEL, telencephalon; HB, hindbrain; OT, optic tectum; TS, torus semicircularis; CB, cerebellum; RoB, rest of brain.

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Figure 5—source data 4

Results of an analysis of covariance (ANCOVA) comparing phylogenetic generalized least squares (PGLS) relationships of region volume against total brain–region volume for each electrosensory phenotype: slope p, intercept p, and Pagel’s lambda (λ).

Significant differences are shown in bold. OB, olfactory bulbs; TEL, telencephalon; HB, hindbrain; OT, optic tectum; TS, torus semicircularis; CB, cerebellum; RoB, rest of brain.

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Figure 5—source data 5

Results (p-values) of pairwise post-hoc tests with a Bonferroni correction for an analysis of covariance (ANCOVA) comparing phylogenetic generalized least squares (PGLS) relationships of region volume against total brain–region volume for each electrosensory phenotype: electrogenic + tuberous and ampullary electroreceptors (E + A + T), electrogenic + only ampullary electroreceptors (E + A), and non-electric (Not E).

Differences in intercept are below the diagonal, and differences in slope are above the diagonal for each brain region. Significant differences are shown in bold. Post-hoc tests were not performed when the ANCOVA revealed no significant differences (indicated by ‘n.s.’). OB, olfactory bulbs; TEL, telencephalon; HB, hindbrain; OT, optic tectum; TS, torus semicircularis; CB, cerebellum; RoB, rest of brain.

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Figure 5—source data 6

Estimated effect sizes (Cohen’s d) of each contrast for an analysis of covariance (ANCOVA) comparing phylogenetic generalized least squares (PGLS) relationships of region volume against total brain–region volume for each electrosensory phenotype: electrogenic + tuberous and ampullary electroreceptors (E + A + T), electrogenic + only ampullary electroreceptors (E + A), and non-electric (Not E).

Post-hoc tests were not performed when the ANCOVA revealed no significant differences (indicated by ‘n.s.’). OB, olfactory bulbs; TEL, telencephalon; HB, hindbrain; OT, optic tectum; TS, torus semicircularis; CB, cerebellum; RoB, rest of brain.

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There were significant decreases in y-intercept in olfactory bulbs and rest of brain for electrogenic + ampullary + tuberous taxa relative to both electrogenic + ampullary-only and non-electric taxa when regressed against total brain volume (p_OB<0.05, p_RoB<0.05). When regressed against remaining brain volume, we found similar results for olfactory bulbs (p<0.05), but we only found a significant decrease in y-intercept for electrogenic + ampullary + tuberous taxa relative to non-electric taxa in rest of brain (p<10^–4). For optic tectum, we found a significant decrease in y-intercept in electrogenic + ampullary + tuberous taxa relative to non-electric taxa (p<10^–4) with electrogenic + ampullary-only taxa as intermediate (p>0.05) when regressed against total brain volume. When regressed against remaining brain volume, we found a significant decrease in optic tectum y-intercept for electrogenic + ampullary + tuberous taxa relative to both electrogenic + ampullary-only and non-electric taxa (p<0.05). There were no significant differences in y-intercept for telencephalon when regressed against either total brain volume or remaining brain volume (p>0.05).

These results show that there are similar mosaic shifts in lineages that independently evolved electrogenesis regardless of analysis method. We also found a significant increase in slope in cerebellum and a significant decrease in slope in optic tectum for electrogenic + ampullary-only species relative to non-electric species when regressed against total brain volume (p_CB<0.05, p_OT<0.05), which may be related to the reduced species sampling and brain size distribution of electrogenic + ampullary-only species (N = 4) relative to non-electric (N=10) and electrogenic + ampullary + tuberous species (N = 18). When regressed against remaining brain volume, we found a significant decrease in slope in cerebellum for non-electric taxa relative to both electrogenic + ampullary + tuberous taxa and electrogenic + ampullary-only taxa, which may be related to the obvious non-electric outlier Pantodon buchholzi. We did not find a significant difference in optic tectum slope when regressed against remaining brain volume (p>0.1). All other slope comparisons were nonsignificant for both analyses (p>0.1).

To assess how different brain regions covary with respect to electrosensory phenotype, we fit PGLS regressions for each region-by-region comparison and performed phylogenetically corrected ANCOVAs with phylogenetically corrected pairwise post-hoc testing for the same electrosensory phenotype groups (Figure 6). For electrogenic + ampullary + tuberous taxa relative to non-electric taxa, we found significant differences in y-intercept for olfactory bulbs against telencephalon (p<0.05); rest of brain against cerebellum (p<10^–3); telencephalon against olfactory bulbs (p<0.01), hindbrain (p<0.01), and cerebellum (p<0.01); hindbrain against optic tectum (p<10^–4) and telencephalon (p<10^–3); torus against hindbrain (p<0.01); and cerebellum against olfactory bulbs (p<10^–3), optic tectum (p<10^–4), and rest of brain (p<10^–4). For electrogenic + ampullary + tuberous taxa relative to both electrogenic + ampullary-only and non-electric taxa, we found significant differences in y-intercept for olfactory bulbs against hindbrain (p<0.01), torus (p<0.01), and cerebellum (p<0.05); optic tectum against hindbrain (p<0.01), torus (p<10^–3), and cerebellum (p<0.05); rest of brain against hindbrain (p<0.01) and torus (p<10^–4); telencephalon against torus (p<0.01); hindbrain against olfactory bulbs (p<0.05) and rest of brain (p<0.01); and torus against olfactory bulbs (p<0.01), optic tectum (p<10^–3), rest of brain (p<10^–4), and telencephalon (p<0.05). For both electrogenic + ampullary + tuberous taxa and electrogenic + ampullary-only relative to non-electric taxa, we found significant differences in y-intercept for cerebellum against telencephalon (p<0.05) and in slope for rest of brain against optic tectum (p<0.05); telencephalon against optic tectum (p<0.05); hindbrain against optic tectum (p<0.01); torus against optic tectum (p<0.01); and cerebellum against optic tectum (p<0.01) and telencephalon (p<0.05). All remaining comparisons for y-intercept and slope were nonsignificant (p>0.05).

Figure 6

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Figure 6—source data 1

Matrix of region-by-region analysis of covariance (ANCOVA) results comparing phylogenetic generalized least squares (PGLS) relationships for each electrosensory phenotype: slope p (S), intercept p (I), and Pagel’s lambda (L).

Columns indicate the region on the y-axis, and rows indicate the region on the x-axis. Significant differences are shown in bold. OB, olfactory bulbs; TEL, telencephalon; HB, hindbrain; OT, optic tectum; TS, torus semicircularis; CB, cerebellum; RoB, rest of brain.

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Figure 6—source data 2

Matrix of region-by-region analysis of covariance (ANCOVA) results comparing phylogenetic generalized least squares (PGLS) relationships for each electrosensory phenotype: electrogenic + tuberous and ampullary electroreceptors (E + A + T), electrogenic + only ampullary electroreceptors (E + A), and non-electric (Not E).

Columns indicate the region on the y-axis, and rows indicate the region on the x-axis. Numbers in each matrix cell are the results (p-values) of pairwise post-hoc tests with a Bonferroni correction. Differences in intercept are below the diagonal, and differences in slope are above the diagonal for each inset. Significant differences are shown in bold. Post-hoc tests were not performed when the ANCOVA revealed no significant differences (indicated by ‘n.s.’). OB, olfactory bulbs; TEL, telencephalon; HB, hindbrain; OT, optic tectum; TS, torus semicircularis; CB, cerebellum; RoB, rest of brain.

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Figure 6—source data 3

Matrix of region-by-region analysis of covariance (ANCOVA) results comparing phylogenetic generalized least squares (PGLS) relationships for each electrosensory phenotype: electrogenic + tuberous and ampullary electroreceptors (E + A + T), electrogenic + only ampullary electroreceptors (E + A), and non-electric (Not E).

Columns indicate the region on the y-axis, and rows indicate the region on the x-axis. Numbers in each matrix cell are the estimated effect sizes (Cohen’s d) of each contrast. Post-hoc tests were not performed when the ANCOVA revealed no significant differences (indicated by ‘n.s.’). OB, olfactory bulbs; TEL, telencephalon; HB, hindbrain; OT, optic tectum; TS, torus semicircularis; CB, cerebellum; RoB, rest of brain.

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Taken together, these results suggest that there are two covarying brain structure groupings, with olfactory bulbs, optic tectum, telencephalon, and rest of brain falling into one group and hindbrain, torus, and cerebellum falling into the other group as all comparisons of group 1 regions against group 2 regions have significant mosaic shifts between electrosensory phenotypes. However, this pattern is strongest for tuberous receptor taxa relative to non-electric taxa. These findings mirror the pPCA results, in which these taxa were separated primarily by PC2, for which olfactory bulbs, optic tectum, telencephalon, and rest of brain loaded positively, whereas hindbrain, torus, and cerebellum all loaded negatively (Figure 4). Differences in slope were only found in regions where there are obvious outliers in the non-electric fishes (cerebellum: P. buchholzi; optic tectum: Microglanis iheringi), suggesting that these species might have mosaic shifts relative to other non-electric osteoglossiforms and otophysans, respectively, but additional studies are needed to investigate this possibility further. We did not find any slope differences in the olfactory bulb comparisons, which also have an obvious outlier (M. iheringi); however, there is more variability in olfactory bulbs compared to the other brain regions, which may decrease the influence of outliers.

Lineage-specific mosaic shifts within electrosensory phenotypes

To determine if there are lineage-specific differences within electrosensory phenotypes, we performed a phylogenetically corrected ANCOVA of each brain region between mormyroids and gymnotiforms (Figure 7A, Figure 7—figure supplement 1A, Figure 7—source data 1, Figure 7—source data 2) and between non-electric osteoglossiforms and otophysans (Figure 7, Figure 7—figure supplement 1B, Figure 7—source data 1, Figure 7—source data 2). When regressed against total brain volume, we found a shallower slope and smaller y-intercept for mormyroids in olfactory bulbs (p_slope<10^–4, p_intercept<0.05) and rest of brain (p_slope<10^–2, p_intercept<0.05), shallower slope for mormyroids in torus semicircularis (p<10^–2), and steeper slope for mormyroids in cerebellum (p<0.05). We found similar results when regressed against remaining brain volume, but we did not find a significant difference in cerebellum slope between mormyroids and gymnotiforms (p>0.05). All remaining comparisons for y-intercept and slope were nonsignificant for both analyses (p>0.05). For the region-by-region comparisons, we did not find the same groupings as across electrosensory phenotypes (Figure 7—figure supplement 2, Figure 7—source data 3), further suggesting that the observed differences in brain structure are related to evolving electrosensory systems. We did find significant differences in y-intercept and/or slope in most olfactory bulb comparisons (p<0.05), except olfactory bulbs and torus comparisons and optic tectum against olfactory bulbs (p>0.05). We also found significant differences in y-intercept for the telencephalon and torus versus rest of brain comparisons (p<0.05) and in slope for some comparisons between optic tectum, hindbrain, torus, and cerebellum (p<0.05). These differences are likely contributing to the secondary clustering between mormyroids and gymnotiforms in the pPCA (Figure 4A) and suggest that there are more nuanced distinctions in brain structure between these two lineages. For non-electric fishes, we found that osteoglossiforms have a significantly larger y-intercept for telencephalon (p_TEL<10^–2), but no differences in either slope or y-intercept in the other brain regions (p>0.1) when regressed against both total brain volume (Figure 7B, Figure 7—source data 1) and remaining brain volume (Figure 7—figure supplement 1B, Figure 7—source data 2). We found that osteoglossiforms also have significantly larger y-intercepts for telencephalon against all other regions (p<0.05) and for torus against cerebellum (p<0.05), but no differences in either slope or y-intercept in the other comparisons (p>0.05; Figure 7—figure supplement 3, Figure 7—source data 4).

Figure 7 with 4 supplements see all

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Figure 7—source data 1

Lineage analysis of covariance (ANCOVA) results comparing phylogenetic generalized least squares (PGLS) relationships of region volume against total brain volume for each lineage: slope p, intercept p, Pagel’s lambda (λ), Cohen’s d.

Significant differences are shown in bold. OB, olfactory bulbs; TEL, telencephalon; HB, hindbrain; OT, optic tectum; TS, torus semicircularis; CB, cerebellum; RoB, rest of brain.

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Figure 7—source data 2

Lineage analysis of covariance (ANCOVA) results comparing phylogenetic generalized least squares (PGLS) relationships of region volume against total brain–region volume for each lineage: slope p, intercept p, Pagel’s lambda (λ), Cohen’s d.

Significant differences are shown in bold. OB, olfactory bulbs; TEL, telencephalon; HB, hindbrain; OT, optic tectum; TS, torus semicircularis; CB, cerebellum; RoB, rest of brain.

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Figure 7—source data 3

Matrix of region-by-region analysis of covariance (ANCOVA) results comparing phylogenetic generalized least squares (PGLS) relationships for mormyroids vs. gymnotiforms: slope p (S), intercept p (I), Pagel’s lambda (L), and Cohen’s d (D).

Columns indicate the region on the y-axis, and rows indicate the region on the x-axis. Significant differences are shown in bold. OB, olfactory bulbs; TEL, telencephalon; HB, hindbrain; OT, optic tectum; TS, torus semicircularis; CB, cerebellum; RoB, rest of brain.

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Figure 7—source data 4

Matrix of region-by-region analysis of covariance (ANCOVA) results comparing phylogenetic generalized least squares (PGLS) relationships for non-electric osteoglossiforms vs. non-electric otophysans: slope p (S), intercept p (I), Pagel’s lambda (L), and Cohen’s d (D).

Columns indicate the region on the y-axis, and rows indicate the region on the x-axis. Significant differences are shown in bold. OB, olfactory bulbs; TEL, telencephalon; HB, hindbrain; OT, optic tectum; TS, torus semicircularis; CB, cerebellum; RoB, rest of brain.

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Figure 7—source data 5

Matrix of region-by-region analysis of covariance (ANCOVA) results comparing phylogenetic generalized least squares (PGLS) relationships for wave vs. pulse gymnotiforms: slope p (S), intercept p (I), Pagel’s lambda (L), and Cohen’s d (D).

Columns indicate the region on the y-axis, and rows indicate the region on the x-axis. Significant differences are shown in bold. OB, olfactory bulbs; TEL, telencephalon; HB, hindbrain; OT, optic tectum; TS, torus semicircularis; CB, cerebellum; RoB, rest of brain.

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Some gymnotiforms produce continuous electrical discharges with variable frequency (wave-type), while others produce discrete electric discharges separated by variable periods of stasis (pulse-type). To assess whether there are mosaic shifts associated with the evolution of electrical discharge type, we performed a phylogenetically corrected ANCOVA between wave-type and pulse-type gymnotiforms (Figure 7C, Figure 7—figure supplement 1C, Figure 7—source data 1, Figure 7—source data 2). We found significant increases in slope for wave gymnotiforms in olfactory bulbs against hindbrain and cerebellum and in telencephalon against hindbrain and cerebellum (p<0.05; Figure 7—figure supplement 4, Figure 7—source data 5). However, we found no differences in either y-intercept or slope of any region against total brain volume between wave and pulse gymnotiforms and only a significant increase in slope in telencephalon against remaining brain volume for wave gymnotiforms (p<0.05), suggesting that the transition between discharge types did not relate to brain structure at this scale. It was not possible to directly test this in mormyroids since there is only one wave-type mormyroid species (Gymnarchus niloticus) that is sister to the family of all pulse-type mormyroids. However, the electrosensory system of the wave-type mormyroid is similar to that of gymnotiforms (Bell and Maler, 2005), and the wave mormyroid tends to be more similar to gymnotiforms in both brain region residuals (Figure 7A) and the pPCA (Figure 4). After excluding the wave mormyroid, we found the same regional differences associated with all mormyroids, along with a significant increase in the cerebellum y-intercept in pulse mormyroids relative to gymnotiforms (p<0.05), suggesting that the extraordinarily enlarged cerebellum of some mormyroid species is the result of both a steeper allometric relationship and a mosaic shift in the ancestor of pulse mormyroids.

We used osteoglossiform and otophysan fishes to test whether mosaic shifts in brain region volumes are associated with the convergent evolution of behaviorally novel active electrosensory systems. Although the mosaic shifts previously found in mormyroids were hypothesized to be related to the evolution of electrogenesis, it remained unknown if these patterns would be found in other electrogenic lineages. The brain scaling patterns of electrogenic versus non-electric osteoglossiforms and otophysans are strikingly similar despite the considerable phylogenetic distance between them, revealing distinct electrosensory associated cerebrotypes (Figure 8). Further, these electrosensory cerebrotypes converged independent of the variable brain–body allometric relationships of osteoglossiforms and otophysans (Figure 3).

Figure 8

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Gymnotiforms have the most similar electrosensory system to mormyroids in terms of electrosensory structures, neural processing, and behavioral usage (Hopkins, 1995). In both of these lineages, we found mosaic increases in cerebellum, hindbrain, and torus semicircularis compared to their non-electric relatives, which suggests that these evolutionary shifts in brain structure largely reflect their coevolution with electrogenesis and tuberous electroreceptor phenotypes. First-order electrosensory processing takes place in the electrosensory lateral line lobe (ELL) of the hindbrain, which projects to the torus for further processing of electrocommunication and electrolocation signals (Baker et al., 2013; Bell and Maler, 2005; Metzen and Chacron, 2021). Electrosensory information projects from the torus both directly and indirectly to areas of the cerebellum with large, reciprocal connections between cerebellum, torus, and the ELL of both mormyroids and gymnotiforms. Multiple areas of the mormyroid cerebellum show responses to electrosensory stimuli (Russell and Bell, 1978). More generally, cerebellum is also known to be involved in predicting the sensory consequences of motor movements and subsequent error detection, nonmotor functions, and learning (Hull, 2020; Popa and Ebner, 2019; Strick et al., 2009). The overwhelming evidence of feedback circuits between initial electrosensory processing regions and cerebellum in both mormyroids and gymnotiforms suggests that the cerebellum may also be involved in processing electrosensory information in addition to electromotor control (Bell and Maler, 2005; Paulin, 1993). The hindbrain is also involved in generating electromotor output (Caputi et al., 2005), which suggests that both electrosensory processing and electromotor control are related to evolutionary changes in relative region sizes.

Electrogenic Synodontis, which only have ampullary electroreceptors, have significant mosaic increases in cerebellum and torus semicircularis relative to non-electric fishes. Synodontis electrical discharges are likely detectable by their ampullary electroreceptors (Hagedorn et al., 1990; Zupanc and Bullock, 2005) and involved in electrocommunication (Albert and Crampton, 2006; Boyle et al., 2014), which could relate to enlargement of these regions relative to electroreceptive but non-electric species. We found that Synodontis were intermediate between electrogenic fishes with tuberous receptors and non-electric fishes in torus and hindbrain, although the difference in hindbrain was not significant despite the hindbrain’s role in generating electromotor output in synodontids (Hagedorn et al., 1990; Kéver et al., 2020). The hindbrain also contains the facial and vagal lobes, which are enlarged in siluriforms and cypriniforms (Striedter, 2005), potentially obscuring a relationship with electrogenesis, and further highlighting the complexity of gross-scale brain region evolution.

The addition of tuberous electroreceptors increases the range of detectable signals and total electrosensory input to the brain relative to only ampullary electroreceptors (Crampton, 2019), and more subregions of the torus and hindbrain are devoted to electrosensory processing in tuberous electroreceptor species (Bell and Maler, 2005). The enlarged torus in Synodontis may also relate to acoustic communication. Some Synodontis species produce swim bladder sounds (Boyle et al., 2014), and the torus is involved in auditory processing (Fay and Edds-Walton, 2008). This is also true for mormyroids, as they are known to have specialized hearing, and some species produce acoustic signals (Ladich and Winkler, 2017). However, gymnotiforms are not known to produce acoustic signals, and all otophysans possess accessory structures that improve hearing (Ladich and Winkler, 2017). Additionally, M. iheringi and several Corydoras species are also known to utilize acoustic communication (Kaatz et al., 2010), but we did not find an enlarged torus in these taxa.

Interestingly, all electrogenic fishes have a significant mosaic increase in cerebellum regardless of electroreceptor type. Relative brain region sizes of electroreceptive only species are largely consistent with non-electrosensory species, which suggests that the evolution of electrogenesis strongly relates to structural brain composition. However, two chondrichthyan lineages have independently evolved electrogenesis, Torpediniformes and Rajidae (Bennett, 1971), with no evidence of mosaic shifts in cerebellar size in these taxa (Mull et al., 2020; Yopak et al., 2010; Figure 5—figure supplement 2). It is possible this is because chondrichthyan cerebellums are already massively enlarged compared to their closest relatives, agnathans, which arguably lack a proper cerebellum (Striedter and Northcutt, 2020). Unfortunately, the brain structure of other independently evolved electrogenic lineages, such as Malapterurus catfishes, Astroscopus stargazers, and Uranoscopus stargazers, remain unknown. The addition of these lineages could better elucidate the relationship between the evolution of electrogenesis and structural brain composition, especially as stargazers are the only electrogenic fishes that lack electroreceptors of any type.

Alternatively, it is possible the enlarged cerebellum in Synodontis is unrelated to the evolution of electrogenesis. Like Synodontis, Rajidae produce sporadic discharges likely used for electrocommunication, while mormyroids and gymnotiforms produce near continuous discharges (Bennett, 1971; Crampton, 2019). Considering we find a further enlargement in the cerebellum of pulse mormyroids which produce electrical discharges at varying and complex timing intervals, it is possible that the specific usage and complexities of electrical discharges may relate to the degree of cerebellar enlargement. We do not find any differences in cerebellar volume of wave and pulse gymnotiforms, but pulse gymnotiforms produce discharges at regular intervals like wave-type fishes and unlike the irregularly discharging pulse mormyroids (Caputi et al., 2005). Further research on the usage of sporadic electrical discharges and the related electrosensory pathways are needed to better elucidate the relationship between electrogenesis and cerebellar enlargement. In particular, further research on Synodontis species is greatly warranted as electrogenesis in these species appears evolutionarily labile (i.e., some species produce continuous discharges, some produce sporadic discharges, and one species produced no discharges under experimental conditions), yet only 13 of 200+ species have been investigated for electrogenesis, and very little is known about electrosensory processing in these species (Baron et al., 1994; Baron, 2002; Boyle et al., 2014; Hagedorn et al., 1990). We also found a shift in the brain–body allometry of S. multipunctatus; however, we are unable to determine whether this is associated with any specific electrosensory phenotypes without additional research into more synodontid species. Our results only further highlight the value of these species in understanding the relationship between electrogenesis and brain evolution.

Electrosensory information also projects to the telencephalon (Bell and Maler, 2005), but we do not find a mosaic increase in any electrosensory taxa relative to non-electric taxa. This is likely because the telencephalon is involved in higher-order sensory integration across many different sensory modalities (Striedter and Northcutt, 2020) while sensory systems mostly remain segregated in the lower-order processing of hindbrain and torus (Meek and Nieuwenhuys, 1998). Surprisingly, we find that non-electric osteoglossiforms have a mosaic increase in telencephalon relative to non-electric otophysans. The telencephalon of osteoglossiforms is highly differentiated, more so than other teleosts (Meek and Nieuwenhuys, 1998), which suggests that osteoglossiforms evolved enlarged telencephalons relative to Elopomorpha, followed by a relative decrease in mormyroids alongside the evolution of electrogenesis and mosaic increases in other brain regions. Further research is needed to determine why osteoglossiforms have enlarged telencephalons.

Optic tectum is also involved in sensorimotor integration, particularly with respect to the electrosensory, lateral line, and visual systems, in addition to being the primary target of visual input to the brain (Meek and Nieuwenhuys, 1998). Yet we find that tuberous receptor fishes have a mosaic decrease in optic tectum relative to non-electric fishes while electrogenic + ampullary-only fishes are intermediate. Additionally, tuberous receptor fishes have a mosaic decrease in olfactory bulbs relative to fishes lacking tuberous receptors, which together could indicate decreased reliance on visual and olfactory systems. Gymnotiforms are thought to have poor vision (Takiyama et al., 2015), and different mormyroid lineages specialize to varying degrees in visual versus electrocommunication systems (Stevens et al., 2013). We do find a mosaic decrease in rest of brain in electrogenic taxa with tuberous electroreceptors relative to fishes lacking tuberous receptors and a mosaic decrease in rest of brain in mormyroids relative to gymnotiforms that could reflect a trade-off in one or more of the subregions that comprise the rest of brain, but since we combined these subregions, we are unable to speculate about their evolution.

Here, we assume that increased brain region volume corresponds to increased neuron number, which increases processing power and reflects behavioral changes that natural selection can act upon in one lineage with respect to another. However, increases in absolute regional volume can result from increased neuron number, neuron size, glia number, or any combination thereof (Herculano-Houzel, 2012; Marhounová et al., 2019). Previous studies found that neuron number and size tend to scale with brain size, but this scaling varies across both lineages and brain regions, with some regions having more neurons than expected given total brain size (Barton, 2012; Herculano-Houzel et al., 2014; Kverková, 2022; Marhounová et al., 2019). These findings suggest that volume measures could over- or underrepresent neuron number, and future studies should investigate the neuronal composition of these regions to better investigate how absolute region volumes and processing capabilities have changed.

Changes in relative regional volumes can result from any of the aforementioned mechanisms in the focal region, but they can also result from changes in other regions that cause a shift in the relative proportion of any given region. In particular, we want to emphasize that all of our identified mosaic shifts are relative to total brain size and to other lineages. An increase in the size of one region necessitates a decrease in one or more of the other brain regions since all regional measurements are relative to total brain size. For example, an increase in the number of neurons in the cerebellum of mormyroids would lead to an increase in absolute cerebellar volume. Even if the neuron number, size, and glial content of the telencephalon remained constant in all osteoglossiforms and thus no changes in absolute telencephalon volume occurred, mormyroid telencephalon size would have necessarily decreased relative to total brain size since total brain size has increased with the addition of more cerebellar neurons. Thus, the relative proportion of telencephalon may have decreased in mormyroids relative to other osteoglossiforms due solely to increases in the absolute sizes of cerebellum, hindbrain, and torus. However, it is quite difficult to distinguish this scenario from the possibility that absolute telencephalon size has also changed in some manner, especially when other factors such as body size differ as well. Further, the patterns of regional scaling in one lineage are relative to others, and may reflect differences in relative investment across lineages.

Due to the complex evolutionary histories of different brain regions, subregions, and total brain size, we are unable to make any claims regarding the evolution of absolute region sizes at this phylogenetic scale. Even commonly used ‘reference’ brain regions such as our ‘rest of brain’ have subregions like the lateral hypothalamic regions and preglomerular complex that are known to vary tremendously in size across teleosts, and the preglomerular complex is extensive in mormyroids in particular (Wullimann, 2020; Wullimann and Northcutt, 1990). Regardless of the specific differences in absolute region volumes, we still find overwhelming evidence of relative differences in the volume of individual regions that, although the mechanism is currently unknown, are likely still reflecting biologically meaningful differences in relative neural processing investment across these fishes.

Evidence of mosaic evolution at smaller subregional, nuclei, and circuit levels is readily available (Carlson et al., 2011; Vélez et al., 2017; Gutiérrez-Ibáñez et al., 2014; Moore and DeVoogd, 2017; DeCasien and Higham, 2019; Krebs, 1990), but rarely have mosaic shifts been observed at the level of major brain regions and even less so alongside convergence in behavioral evolution. Our findings support the hypothesis that mosaic brain evolution occurs more readily under substantial selective pressure that favors a greater expansion of a particular brain region than allometric scaling can accommodate without incurring a substantial energetic cost (Striedter and Northcutt, 2020). Although not necessarily exclusive to electrosensing or sensory systems in general, we suspect the evolution of this novel sensory system provided such a strong selective pressure. Indeed, our clearest example of gross-scale mosaic evolution occurs alongside evolutionary changes in both the sensory and motor system, and we suggest looking towards other instances of behavioral novelty for additional potential examples of gross-scale mosaic brain evolution.

However, the evolutionary pressures on brain structure are multifaceted. As tasks require further integration of different areas of the brain, selective pressure favoring one trait could instead lead to coordinated selection favoring the expansion of all regions (Avin et al., 2021; Striedter and Northcutt, 2020). Indeed, we find that multiple regions covary with electrosensory phenotypes (Figure 6). Not only are there evolutionary forces acting on brain region scaling to consider, but also the evolutionary forces acting upon total brain size. After constraint on total brain size was added in a bare-bones model of brain region evolutionary dynamics, the probability of mosaic evolution increased under most tested scenarios (Avin et al., 2021). The decoupling of brain–body allometries has been reported in birds and mammals, whereas actinopterygians are consistently found to have more constrained allometric relationships, whether through explicit constraints or strong stabilizing selection (Tsuboi, 2021; Tsuboi et al., 2018). Thus, it is entirely possible that the interactions of both region size and total brain size scaling may increase the likelihood of gross-scale mosaic brain evolution in fishes relative to birds and mammals. This may reflect differences in evolutionary strategies to enlarge brain size in conjunction with indeterminant and determinant growth, respectively. However, more research is needed on the evolution of brain–body and region–brain allometries in other lineages with indeterminant growth.

Our results also highlight the importance of considering differences in allometric slope and relaxing the assumption of shared allometric relationships for major taxonomic groups. With brain–body allometries, average slopes across major vertebrate taxonomic levels (class to genus) are relatively constant (Tsuboi et al., 2018); however, when not a priori defining grades based on strict taxonomic-level distinctions, significant differences in the allometric relationships of various groups at different taxonomic levels are readily detected (Ksepka et al., 2020; Smaers et al., 2021; Figure 3). Indeed, when we allow only intercept to vary between grades while assuming parallel slopes, we no longer detect any reliable grade shifts within actinopterygians. Even when allowing slope to vary, two of our identified shifts were undetectable when assuming the brain–body allometry is constant within orders despite analyzing much of the same data (Tsuboi, 2021).

Like previous work in other lineages (Finlay and Darlington, 1995; Yopak et al., 2010), we also find differences in slope across region–brain allometries at varying taxonomic levels, indicating that the dramatic differences in observed region volumes can result from both mosaic shifts between lineages and evolutionary changes in total brain size within a lineage (Figure 5—source data 1, Figure 7—source data 1). As interspecific (evolutionary) allometries are an emergent property of developmental (within individuals) and static (within species) allometries (Pélabon et al., 2014; Tsuboi, 2021), it is difficult to assess the mechanism leading to these differences in slope across species without further research into evolutionary changes in developmental and static allometries. Previous work considering the effect of static allometries on evolutionary allometries found that evolutionary changes in both the static slope and intercept are contributing to the steeper evolutionary slope found across osteoglossiforms (Tsuboi, 2021). Steeper static slopes may indicate a higher rate of brain growth to body growth in adult stages while larger static intercepts might reflect increased brain mass at the transition between embryonic and juvenile growth phases in fishes (Oikawa et al., 1992; Oikawa and Itazawa, 1984; Tsuboi, 2021). Previous work in marsupials did not find any mechanistic links between regional neurogenesis timing or growth rate and static or evolutionary region–brain allometric differences in either slope or intercept despite finding extensive heterochronic differences between species (Carlisle et al., 2017). This finding suggests that the intraspecific mechanisms resulting in these interspecific scaling differences may differ across species, but additional research is needed to determine whether there are shared intraspecific mechanisms resulting in interspecific differences in slope versus intercept in other lineages, especially in those with extensive adult neurogenesis.

Different lineages can independently evolve the same phenotype via the same mechanism (parallel evolution) or different mechanisms (convergent evolution). Given the phylogenetic distance between osteoglossiforms and otophysans, it would be more remarkable to find that the different electrosensory systems and mosaic shifts in brain region volumes evolved in parallel rather than by convergence. Given that the mechanism of these regional increases remains unknown, we argue for a more conservative assumption of convergent evolution for electrosensory cerebrotypes. This is supported by the fact that the cerebellar subregion that has expanded the most in mormyroids is the valvula cerebelli while in gymnotiforms, it is the corpus cerebelli (Meek and Nieuwenhuys, 1998). Additionally, the torus is laminar in gymnotiforms while there are distinct nuclei in the non-laminar torus of mormyroids (Bell and Maler, 2005), and we find evidence of a steeper brain–body allometric relationship for osteoglossiforms that appears unrelated to the evolution of an electrosensory system in mormyroids. Interestingly, we found a subsequent decrease in the brain–body allometry of two small-brained sister mormyroids (I. henryi and B. brachyistius), which further suggests the steeper brain–body allometry of osteoglossiforms is unrelated to electrosensory capabilities. Further research is needed into osteoglossiforms as a whole to determine why this lineage has an increased brain–body allometry. Regardless of whether the mechanism of relative regional scaling evolution is convergent or parallel, we provide evidence of repeated, independent mosaic evolution of major brain regions in association with a convergent behavioral novelty. These findings demonstrate that evolutionary changes in gross-scale brain structure are surprisingly predictable alongside the evolution of active electrosensory systems, even when the underlying brain–body allometry differs. More broadly, these findings suggest that mosaic brain evolution may occur alongside the evolution of behavioral novelty and could reflect a degree of predictability in brain evolution with behavioral evolution, especially when constraint in brain–body allometries are strong.

Brain measurement data is located in Supplementary File 1. Brain mass data is located in Supplementary File 2. All analysis code and phylogenetic trees are available in Dryad. The raw micro-computed tomography scans are too large to post (multiple TBs), but are available upon request. To request raw otophysan and/or osteoglossiform scans, contact the corresponding author. We ask that those who want access to the scan data send us an external hard drive, which we will upload all the data to and then return.

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Decision letter after peer review:

Thank you for submitting your article "Convergent mosaic brain evolution is associated with the evolution of novel electrosensory systems in teleost fishes" for consideration by eLife. Your article has been reviewed by 4 peer reviewers, including Catherine Emily Carr as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by a Reviewing Editor and Ronald Calabrese as the Senior Editor.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

Your reviewers have reached a consensus that your manuscript. would be suitable for eLife if additional analyses were conducted. We have identified 3 major additions to your analyses that should substantially strengthen your conclusions.

1. Identify where and when changes in slope and intercept have occurred along the teleost phylogeny (e.g. Smaers et al. 2021 or Ksepka et al. 2020). It is our opinion that this analysis would be possible with the addition of the Tsuboi data on body and brain size, since 32 species are not enough. We understand that merging the two datasets could be difficult given the different methods of measurement, but there should be enough overlap in the species measured that it can be tested if the two data sets are too different.

2. Use a discriminant analysis to show more formal convergence between the brains of electrogenic fishes.

3. Finally, we suggest a reanalysis of how the cerebellar, toral, alar brainstem hypertrophy impacts the supposed reduction in the volumes of other brain regions. You report significant decreases in electrogenic clades in the size of the olfactory bulb, rest of the brain, and optic tectum. This may be an artifact that results from including the cerebellum and other enlarged areas in the dependent variable.

Reviewer #1 (Recommendations for the authors):

The authors achieved their aims, and in general the results support their conclusion, except for a concern about the analysis of significant decreases in other brain areas.

Reviewer #2 (Recommendations for the authors):

1) The authors present in all the figures all the individual used but analyses were done with the mean for each species (which is correct). I think that the mean, not individuals should be included in all figures. I think this would help to better see the different groups as the combination of shapes and colors becomes a little crowded. Also, given that many individuals were used for most species, the measuring error could be included in the PGLS. See Tsuboi 2021 for an example of this.

2) In the case of testing for differences in the relative size of the brain I think it would be important to include the additional data mentioned above and perform an analysis similar to that found in Smaers et al., 2021 (Science), where the point in the phylogeny where changes in allometric scaling between brain and body size happen are determined. This would allow to determine if the evolution of electroreceptors or electrogenesis coincides with grade shits in the brain-body allometry. Another important analysis would be to determine where changes in the evolutionary rate of brain and body size occur. This because of the unique allometric scaling brain and body in mormyrids (Tsuboid 2021) mentioned above, is possible that this change can be explained by a change in body size not brain size.

3) I think Figure 4—figure supplement 2 should be included in the main manuscript as I think that it better shows the differences in cerebellum size and other structures between electrogenic and non electrogenic species. Also, the graphs of cerebellum size vs torus semicircular or hindbrain support the co evolution of this structures. Additionally, I don't think that the graph above and below the diagonal are necessary, as they are redundant and only lead to confusion.

4) Has the volumetric measurement been validated in sectioned brains? It seems to me that the borders shown in figure 7 are very rough and it would be important to know at which degree they actually capture the structures that they intent. This seems particularly relevant for the optic tectum and torus semicircularis.

5) I think is important for the authors to report the loadings of the principal component analysis. The strength and variation in the loading of the principal size axis (PC1) have been usually used as a way to evaluate how much a structure has been influenced by mosaic evolution. My guess is that in this case the loadings of the cerebellum in PC1 are not as strong as other structures.

6) Line 192: are non phylogenetic ANCOVAs been used in this case? This should never be the case. I understand that the lack of post-hoc analysis in a phylogenetic contex is frustrating but non-phylogenetically corrected ANCOVAS are not the solution. See https://smaerslab.com/gls-ancova/ for an implementation of a phylogenetic ANCOVA.

7) Why is figure 6 included? Have the electric discharges for these 3 species not been reported before? Even if this is the case, it doesn’t seem necessary as a main figure in the manuscript, especially at the end. Perhaps as a supplementary material would be better.

8) The use of residuals in figure 2 is not ideal in this case as the different slope of mormyrids makes it so that the small mormyrids have small residuals while the larger ones have large ones.

Reviewer #3 (Recommendations for the authors):

Line 45: perhaps explain more clearly that mosaic evolution is well accepted for small cell groups but less clear for major brain regions (which may be "carved up" differently in different species to generate mosaic patterns).

Line 54: it would be good to define "ecomorph" or just describe what you mean without using the word. "Ecomorph brain structure" is especially unclear unless you already know…

Line 72: It is unclear in this sentence whether the "to varying degrees" applies to the independent evolution (e.g., some features evolved independently, some not) or to the similarity (the systems evolved independently but are only partially similar).

Figure 1: it might be good to mention that the relative position of Mormyrus tapirus and B. brachyistius has flipped relative to Sukhum et al., 2018.

Figure 3: It might be good to mention somewhere that your PCA analyses yield what others have called "cerebrotypes"; I think Iwaniuk was one of the earliest to use this approach.

Line 153: Is it justified to write "electrosensory cerebellum"? E.g., in the synodontids, do we know that their cerebellum processes electrosensory information? In general, I'd be cautious in assigning any of your studied regions to a particular sensory modality, since you lack the resolution and functional data to do so.

– Lines 197-200: I found these sentences to contain the most important data, but they got lost a bit amongst all the other results. Might it help to construct an additional figure in which you show at which point in the various lineages you hypothesize what kinds of changes in the brain? I could have used such a figure to get a clearer picture of your main results.

Figure 4: I like this figure, though it is busy. It took me a while to understand your complicated legends and inserts, but it all makes sense. As noted in my "public review" it's hard to know how to interpret these data because dramatic increases in the size of the cerebellum would force at least some other regions to appear as if they'd shrunk, but I still like the figure.

– Line 228: given your finding that the allometric slope of the cerebellum increased in a particular lineage, does that set up the cerebellum to become enlarged through "concerted evolution" as species within this lineage increase in absolute brain size. I.e., have you identified a case where a mosaic evolutionary change leads to shifts in brain region proportions through concerted evolution? In short, I would have liked to see you make more of your findings on changes in intercepts vs. slopes; this is a pretty novel aspect of your findings, which warrants additional scrutiny (I think).

Line 298: Why do you think the Synodontids don't show an enlarged hindbrain relative to other catfishes? Could it be that the hindbrain of catfishes is already pretty enlarged because of their vagal and facial lobes, making it difficult for changes in other hindbrain regions to stand out?

– Line 339: Here is a good example of how your failure to appreciate how increases in the proportional size of one region force decreases in others sets you up to make (potentially) unwarranted claims. I bet mormyroids didn't really reduce the size of their telencephalon, relative to the non-electrogenic relatives (I say that in part because I know that the telencephalon of at least some mormyroids is remarkably well differentiated); it's just that their cerebellum is so enlarged that the telencephalon has become proportionately smaller. I think those are important distinctions to make.

– Line 354: Again, the shifts in proportional brain region size need not involve any changes in neuron number or size. They could simply arise from changes in the size of other brain regions.

– Line 409: The point about electrogenic Synodontids not being monophyletic is kind of buried in the text; how does it affect your interpretations/conclusions?

– Line 422: I was very confused initially by your description of how you delineated brain regions. You make it sound as if you determine region volumes only on the basis of the various planes you describe. That would yield very approximate and misleading volumes. Instead, as I gathered from reading the Sorkhum et al., 2018, paper, you use those planes to mark some boundaries between regions, but you actually trace the outlines of brain regions to get your estimates. As far as I can tell, you are using the brain's external and internal surfaces together with your "planes" to delineate the brain regions. This should be clarified right up front (don't wait until lines 537-539 and 555). I know it may seem obvious to you, but I spent quite a bit of time wondering how you could possibly get the images on the right side of Figure 7 from the various planes you illustrate in the rest of the figure. Call me dumb, but I bet I wouldn't be the only one confused :-

Reviewer #4 (Recommendations for the authors):

I have a few recommendations for the authors that I think will strengthen the manuscript as a whole.

One of the weaknesses I highlighted was the lack of sufficient background information for non-specialists. Addressing this minor weakness will increase the impact of the work by broadening readership. I think the same can be said of the Discussion. The last few sentence of the last paragraph are good, but I think could be expanded a bit more. It might help if the authors answered some broader questions here. For example, is this a pattern that might be specific to electrosensory systems? Do you expect to see correlated evolution like this outside of sensory systems? I should clarify that I do not think this needs to be extensive, but a broader approach would again improve readership and impact.

It might also be worth trying some alternative, multivariate approaches. Cluster analysis can be quite useful for these kinds of studies, as demonstrated by several comparative neuroanatomical papers, including several on bony fishes. Although I am unaware of a phylogeny-informed cluster analysis algorithm, comparing the cluster analysis with the phylogeny can be informative to determine the extent to which convergent brain forms have evolved. This could help strengthen the authors' case by presenting the data in a slightly more interpretable way than the PCA or scatterplots of brain region allometry. I will emphasize that I do not consider it necessary to use such an approach, but worth trying out to see if it strengthens the conclusions further.

Also, in relation to the statistics, ensure that within the Results you are clear that when significant differences in intercepts were found, there were no differences in slopes. This is not consistent throughout the results and some readers will take issue if they think everything is based solely on intercepts with no testing for different slopes.

I also suggest that the authors check out the BMC Biology paper by Avin et al. (https://link.springer.com/article/10.1186/s12915-021-01024-1). It is a novel approach to testing the concerted vs. mosaic models that Schumacher and Carlson might want to discuss in the context of their findings.

Last, I recommend incorporating supplemental Figure 4 into the manuscript proper. It was not only interesting, it strongly supported many of the authors' conclusions.

Essential revisions:

Your reviewers have reached a consensus that your manuscript. would be suitable for eLife if additional analyses were conducted. We have identified 3 major additions to your analyses that should substantially strengthen your conclusions.

1. Identify where and when changes in slope and intercept have occurred along the teleost phylogeny (e.g. Smaers et al. 2021 or Ksepka et al. 2020). It is our opinion that this analysis would be possible with the addition of the Tsuboi data on body and brain size, since 32 species are not enough. We understand that merging the two datasets could be difficult given the different methods of measurement, but there should be enough overlap in the species measured that it can be tested if the two data sets are too different.

We thank the reviewers for this suggestion. Between the Tsuboi data and our data, we had a total of 870 species with brain size, body size, and sequence data. We found that shifts in the brain body allometry are not associated with the evolution of electrosensory phenotypes, but we did identify a shift in both slope and intercept in the branch leading to osteoglossiforms and at two other locations within actinopterygians. Future research is needed to assess why and how osteoglossiforms were able to evolve this shift, but a shifted allometry is not necessary to evolve an electrosensory system nor mosaic shifts in major brain regions. Instead, we find similar shifts in the region-brain allometries of electrosensory species relative to non-electrosensory species despite differences in brain-body scaling between electrosensory taxa. We have made substantial additions to the manuscript with this analysis, but see in particular methods (lines 778-826), results (lines 110-153), discussion (lines 513-528, 530-538, 569-575), and figure 3 and its source data files.

2. Use a discriminant analysis to show more formal convergence between the brains of electrogenic fishes.

We thank the reviewers for this suggestion. We performed a phylogenetic flexible discriminant analysis and found perfect separation between our electrosensory phenotypes, which is consistent with our pPCA results. We added this analysis to the methods (lines 854-866), results (lines 177-186), and as Figure 4 —figure supplements 2 and Figure 4—source data 3.

3. Finally, we suggest a reanalysis of how the cerebellar, toral, alar brainstem hypertrophy impacts the supposed reduction in the volumes of other brain regions. You report significant decreases in electrogenic clades in the size of the olfactory bulb, rest of the brain, and optic tectum. This may be an artifact that results from including the cerebellum and other enlarged areas in the dependent variable.

We thank the reviewers for this perspective and apologize for the lack of clarity regarding this point. We have added additional analyses of the region x region comparisons and moved this figure to the main text (Figure 6 and associated source data files, Figure 7—figure supplements 2-4, Figure 7—source data 3-5). As pointed out by a reviewer below, regressing each region against rest of brain does help to address this point, but also assumes that there have been no evolutionary changes in any of the other regions that make up rest of brain. However, we know this is not true as the lateral hypothalamic regions and preglomerular complex are known to vary substantially across teleosts, and mormyroids are known to have extensive preglomerular complexes (Wulliman 2020, Wulliman and Northcutt 1990). Unfortunately, we do not have enough species to reconstruct ancestral absolute region volumes to address how these observed mosaic shifts correspond to directional changes in absolute region volumes across this phylogenetic scale. However, the reviewers have brought up good points about how mosaic shifts can result from a relative increase in one region, which also appears as a decrease in one or more other regions due to the use of relative measures. However, we argue that this is not necessarily an artifact as this decrease could be reflected in any number of other region volumes, and thus finding relative decreases associated with the evolution of electrogenesis can still be informative regarding relative investment in different brain regions and potential functional trade-offs, even in the absence of changes in absolute volumes. Future studies will be needed to determine whether there are any changes in absolute region volumes, and their direction. We have incorporated substantial changes to the text to better clarify these points throughout, and incorporate the region x region analyses and combine them with the region x total brain volume results to address this point. In particular, see lines 261-295, 307-317, 330-331, 473-499.

Reviewer #1 (Recommendations for the authors):

The authors achieved their aims, and in general the results support their conclusion, except for a concern about the analysis of significant decreases in other brain areas.

We thank the reviewer for identifying this miscommunication. We have added additional region x region analyses to help address this point. We have also clarified that all of our identified mosaic shifts are relative to other lineages and do not necessarily reflect changes in absolute region sizes without additional work to directly address evolutionary changes in absolute region sizes. We have incorporated these changes throughout but see in particular lines 261-295, 307-317, 330-331, 473-499 and figure 6. See also our detailed response to essential revision 3.

Reviewer #2 (Recommendations for the authors):

1) The authors present in all the figures all the individual used but analyses were done with the mean for each species (which is correct). I think that the mean, not individuals should be included in all figures. I think this would help to better see the different groups as the combination of shapes and colors becomes a little crowded. Also, given that many individuals were used for most species, the measuring error could be included in the PGLS. See Tsuboi 2021 for an example of this.

We thank the reviewer for this suggestion. We agree that some of the plots are too crowded and switched to plotting means instead of individuals for all region by region plots (figure 6, figure 7—figure supplement 2-4). We agree that it is important to incorporate measurement error in analyses where possible, and we did attempt to include this. Unfortunately, we did not have the statistical power to incorporate measurement error in these analyses with our sample size considering there is error in both the x and the y variables as well as in the relationship between them (see Ives et al. 2007). We did include measurement error in all OUrjMCMC analyses with 870 species (lines 794-797).

2) In the case of testing for differences in the relative size of the brain I think it would be important to include the additional data mentioned above and perform an analysis similar to that found in Smaers et al., 2021 (Science), where the point in the phylogeny where changes in allometric scaling between brain and body size happen are determined. This would allow to determine if the evolution of electroreceptors or electrogenesis coincides with grade shits in the brain-body allometry. Another important analysis would be to determine where changes in the evolutionary rate of brain and body size occur. This because of the unique allometric scaling brain and body in mormyrids (Tsuboid 2021) mentioned above, is possible that this change can be explained by a change in body size not brain size.

We thank the reviewer for this suggestion. We directly tested the hypotheses that grade shifts are associated with the evolution of electrosensory phenotypes and found that this is not supported as well as the unconstrained shifts found from fitting across all of the data (lines 143-153, figure 3—source data 4). See also our detailed response to essential revision 1. However, we do not feel comfortable making claims about the relative magnitude of changes in brain size vs body size evolution as done in Smaers et al. 2021 because unlike birds and mammals, fish have indeterminate growth. The brain and body sizes of fish that have been studied do not necessarily reflect the full range of possible adult brain and body sizes for many species. Additionally, there is likely a taxonomic bias in which some species better represent the full range of body sizes due to the ease with which some groups can be collected relative to others, in particular with respect to deep sea and pelagic fishes. For these reasons, we are unable to make any claims about the relationship of brain size evolution to body size evolution.

3) I think Figure 4—figure supplement 2 should be included in the main manuscript as I think that it better shows the differences in cerebellum size and other structures between electrogenic and non electrogenic species. Also, the graphs of cerebellum size vs torus semicircular or hindbrain support the co evolution of this structures. Additionally, I don't think that the graph above and below the diagonal are necessary, as they are redundant and only lead to confusion.

We thank the reviewer for this suggestion. We have moved this figure to the main text (Figure 6) and included discussion of their results. See in particular lines 261-295. However, we disagree that the graphs above and below the diagonal are redundant as which region is the dependent versus independent variable differs, and since we are regressing the regions against each other, this distinction makes a difference.

4) Has the volumetric measurement been validated in sectioned brains? It seems to me that the borders shown in figure 7 are very rough and it would be important to know at which degree they actually capture the structures that they intent. This seems particularly relevant for the optic tectum and torus semicircularis.

We thank the reviewer for this comment. We did not perform any histology on these brains, but we did use previous histological studies to inform our region boundaries. We have updated the text to include this (line 629). There are three methods commonly used to collect region volume measurements in studies like this: histology, ellipsoid modeling, and intact imaging techniques such as micro-computed tomography and micro-magnetic resonance imaging. Unfortunately, they all have flaws and require defining a boundary between regions. A couple studies, including one in fish, have directly compared these three methods and advocate for the use of intact imaging techniques over both histology and ellipsoid modeling as histology is prone to shrinkage, especially in the telencephalon, and many brain regions are clearly not ellipsoids (Ullman et al. 2010, White and Brown 2015). We agree that some of our boundaries are rough and do include some brain tissue from outside of that region, in particular with the torus semicircularis (see discussion of this point in lines 733-744). However, these overestimations, and thus underestimations of other regions (of rest of brain in the case of torus semicircularis), are relatively small and more importantly, consistent across all of the taxa in this study. As we are interested in the relative patterns of region volumes across taxa and not the absolute region volumes, a consistent overestimation of torus semicircularis, or any other region, does not alter these patterns.

5) I think is important for the authors to report the loadings of the principal component analysis. The strength and variation in the loading of the principal size axis (PC1) have been usually used as a way to evaluate how much a structure has been influenced by mosaic evolution. My guess is that in this case the loadings of the cerebellum in PC1 are not as strong as other structures.

We thank the reviewer for this suggestion. We have added the loadings as Figure 4—source data 1. We find that the loadings on PC1 are comparable for all brain regions.

6) Line 192: are non phylogenetic ANCOVAs been used in this case? This should never be the case. I understand that the lack of post-hoc analysis in a phylogenetic contex is frustrating but non-phylogenetically corrected ANCOVAS are not the solution. See https://smaerslab.com/gls-ancova/ for an implementation of a phylogenetic ANCOVA.

We thank the reviewer for identifying this miscommunication. We corrected for phylogeny in each of our ANCOVAs in the same manner as recommended by Smaers and Rohlf (2016, Evolution) and others, and we apologize for the lack of clarity in the results. We have edited the text throughout the methods and results and figure 5 and 7 legends to clarify that all ANCOVAs were phylogenetically corrected. All post-hoc analyses were also performed on the PGLS relationships following Mull et al. 2020. We have edited the text to clarify that all post-hoc analyses have a phylogenetic correction as well.

7) Why is figure 6 included? Have the electric discharges for these 3 species not been reported before? Even if this is the case, it doesn’t seem necessary as a main figure in the manuscript, especially at the end. Perhaps as a supplementary material would be better.

8) The use of residuals in figure 2 is not ideal in this case as the different slope of mormyrids makes it so that the small mormyrids have small residuals while the larger ones have large ones.

We thank the reviewer for this suggestion. Unfortunately, synodontids are woefully understudied—only 13 out of 200+ species have been investigated for electrogenesis despite the first finding 30+ years ago. Electrical discharges had not previously been reported from the species in this study, but we have moved this figure from the main text to figure 2—figure supplement 1 as this was not the primary goal of this study.

Reviewer #3 (Recommendations for the authors):

Line 45: perhaps explain more clearly that mosaic evolution is well accepted for small cell groups but less clear for major brain regions (which may be "carved up" differently in different species to generate mosaic patterns).

We thank the reviewer for this suggestion and have better clarified this distinction throughout, but see in particular lines 42-48.

Line 54: it would be good to define "ecomorph" or just describe what you mean without using the word. "Ecomorph brain structure" is especially unclear unless you already know…

We thank the reviewer for identifying this point of confusion. We defined ecomorph in line 58.

Line 72: It is unclear in this sentence whether the "to varying degrees" applies to the independent evolution (e.g., some features evolved independently, some not) or to the similarity (the systems evolved independently but are only partially similar).

We thank the reviewer for identifying this point of confusion. We have clarified that different lineages evolved electrosensory phenotypes independently from mormyroids, and that which lineages have which electrosensory phenotypes varies in line 83.

Figure 1: it might be good to mention that the relative position of Mormyrus tapirus and B. brachyistius has flipped relative to Sukhum et al., 2018.

We thank the reviewer for this comment. We added this to the methods (line 842). We would like to note that in this study we used genetic data from 6 genes while the tree in Sukhum et al. 2018 was made from only one gene.

Figure 3: It might be good to mention somewhere that your PCA analyses yield what others have called "cerebrotypes"; I think Iwaniuk was one of the earliest to use this approach.

We thank the reviewer for this suggestion and have incorporated the use of cerebrotype throughout but in particular with line 182 and figure 4.

Line 153: Is it justified to write "electrosensory cerebellum"? E.g., in the synodontids, do we know that their cerebellum processes electrosensory information? In general, I'd be cautious in assigning any of your studied regions to a particular sensory modality, since you lack the resolution and functional data to do so.

We thank the reviewer for this suggestion. We removed electrosensory associated from line 170.

– Lines 197-200: I found these sentences to contain the most important data, but they got lost a bit amongst all the other results. Might it help to construct an additional figure in which you show at which point in the various lineages you hypothesize what kinds of changes in the brain? I could have used such a figure to get a clearer picture of your main results.

We thank the reviewer for this suggestion. We added an additional figure (figure 8) summarizing our main findings.

Figure 4: I like this figure, though it is busy. It took me a while to understand your complicated legends and inserts, but it all makes sense. As noted in my "public review" it's hard to know how to interpret these data because dramatic increases in the size of the cerebellum would force at least some other regions to appear as if they'd shrunk, but I still like the figure.

We thank the reviewer for this comment. We agree that the figure is complex. However, we argue that these results demonstrate the relative differences in region scaling without necessarily demonstrating how absolute region volumes have changed without further research into more taxa and quantifying neuron numbers. See also our detailed response to essential revision 3.

– Line 228: given your finding that the allometric slope of the cerebellum increased in a particular lineage, does that set up the cerebellum to become enlarged through "concerted evolution" as species within this lineage increase in absolute brain size. I.e., have you identified a case where a mosaic evolutionary change leads to shifts in brain region proportions through concerted evolution? In short, I would have liked to see you make more of your findings on changes in intercepts vs. slopes; this is a pretty novel aspect of your findings, which warrants additional scrutiny (I think).

We thank the reviewer for this comment. We have added further discussion on evolutionary changes in intercept vs changes in slope (lines 543-559), but both are likely contributing to the extreme enlargement of the cerebellum in mormyroids. However, it is not currently possible to identify the order in which these changes occurred without further research into how evolution of developmental and intraspecific allometric relationships contribute to differences in interspecific allometries. Much recent work (e.g. Ksepka et al. 2020, Smaers et al. 2021) has really highlighted the importance of considering differences in slope between lineages.

Line 298: Why do you think the Synodontids don't show an enlarged hindbrain relative to other catfishes? Could it be that the hindbrain of catfishes is already pretty enlarged because of their vagal and facial lobes, making it difficult for changes in other hindbrain regions to stand out?

We thank the reviewer for this comment. We agree that the enlarged facial and vagal lobes could potentially be obscuring a relationship with hindbrain size and electrogenesis in Synodontis (lines 384-390). This could also be related to the evolution of tuberous electroreceptors which substantially increase the amount of electrosensory input to the brain relative to ampullary electroreceptors (lines 391-394).

– Line 339: Here is a good example of how your failure to appreciate how increases in the proportional size of one region force decreases in others sets you up to make (potentially) unwarranted claims. I bet mormyroids didn't really reduce the size of their telencephalon, relative to the non-electrogenic relatives (I say that in part because I know that the telencephalon of at least some mormyroids is remarkably well differentiated); it's just that their cerebellum is so enlarged that the telencephalon has become proportionately smaller. I think those are important distinctions to make.

We thank the reviewer for identifying this miscommunication. We have clarified that this relative decrease in the telencephalon of mormyroids is relative to non-electric outgroups, but doesn’t necessarily reflect a change in absolute region size (lines 444-447, 478-487).

– Line 354: Again, the shifts in proportional brain region size need not involve any changes in neuron number or size. They could simply arise from changes in the size of other brain regions.

We thank the reviewer for this comment. We have added this point to the discussion and better clarified the distinction between relative and absolute changes in region scaling (lines 473-489).

Line 409: The point about electrogenic Synodontids not being monophyletic is kind of buried in the text; how does it affect your interpretations/conclusions?

We apologize for the miscommunication and lack of clarity regarding this point. We meant to communicate that of the 200+ species of Synodontis, only 13 species total have been tested for electrogenesis. 12 of those species produced electrical discharges under experimental conditions. These 12 species do not come from a single clade and instead are broadly distributed across the Synodontis phylogeny. Thus, it remains unknown how electrogenesis has evolved in Synodontis with respect to the number of origins and losses. As the three species we were able to test in this study produced electrical discharges, we stand by our finding that mosaic shifts in TS (but to a lesser degree than tuberous receptor fishes) and CB are associated with electrogenesis. However, more research is needed to determine how these regions vary across other Synodontis species who may or may not produce electrical discharges. Given that 1 of the tested species did not produce discharges under experimental conditions, we argue that these fishes would be a good model to study the intermediate relationships between evolutionary changes in brain structure and the evolution of electrogenesis. We have clarified this point in lines (612-620) and also added more on electrogenic catfishes in general to the discussion (lines 425-437).

– Line 422: I was very confused initially by your description of how you delineated brain regions. You make it sound as if you determine region volumes only on the basis of the various planes you describe. That would yield very approximate and misleading volumes. Instead, as I gathered from reading the Sorkhum et al., 2018, paper, you use those planes to mark some boundaries between regions, but you actually trace the outlines of brain regions to get your estimates. As far as I can tell, you are using the brain's external and internal surfaces together with your "planes" to delineate the brain regions. This should be clarified right up front (don't wait until lines 537-539 and 555). I know it may seem obvious to you, but I spent quite a bit of time wondering how you could possibly get the images on the right side of Figure 7 from the various planes you illustrate in the rest of the figure. Call me dumb, but I bet I wouldn't be the only one confused :-

We thank the reviewer for identifying this point of confusion. We adjusted the text to clarify up front that we did indeed use the external and internal surfaces of the brain in addition to our defined planes when manually tracing brain regions to estimate their volumes (lines 631-636).

Reviewer #4 (Recommendations for the authors):

I have a few recommendations for the authors that I think will strengthen the manuscript as a whole.

One of the weaknesses I highlighted was the lack of sufficient background information for non-specialists. Addressing this minor weakness will increase the impact of the work by broadening readership. I think the same can be said of the Discussion. The last few sentence of the last paragraph are good, but I think could be expanded a bit more. It might help if the authors answered some broader questions here. For example, is this a pattern that might be specific to electrosensory systems? Do you expect to see correlated evolution like this outside of sensory systems? I should clarify that I do not think this needs to be extensive, but a broader approach would again improve readership and impact.

We thank the reviewer for this comment. We have added further broadened the discussion to address these points (lines 500-512).

It might also be worth trying some alternative, multivariate approaches. Cluster analysis can be quite useful for these kinds of studies, as demonstrated by several comparative neuroanatomical papers, including several on bony fishes. Although I am unaware of a phylogeny-informed cluster analysis algorithm, comparing the cluster analysis with the phylogeny can be informative to determine the extent to which convergent brain forms have evolved. This could help strengthen the authors' case by presenting the data in a slightly more interpretable way than the PCA or scatterplots of brain region allometry. I will emphasize that I do not consider it necessary to use such an approach, but worth trying out to see if it strengthens the conclusions further.

We thank the reviewer for this suggestion. We added a phylogenetic flexible discriminant analysis and found perfect separation between the electrosensory phenotypes. We added this analysis to the methods (lines 854-866), results (lines 177-186), and as figure 4 —figure supplements 2 and figure 4—source data 3. See also our detailed response to essential revision 2.

Also, in relation to the statistics, ensure that within the Results you are clear that when significant differences in intercepts were found, there were no differences in slopes. This is not consistent throughout the results and some readers will take issue if they think everything is based solely on intercepts with no testing for different slopes.

We thank the reviewer for identifying the miscommunication. We have clarified where slope differences were nonsignificant throughout but see lines 259, 306, 318 in particular.

I also suggest that the authors check out the BMC Biology paper by Avin et al. (https://link.springer.com/article/10.1186/s12915-021-01024-1). It is a novel approach to testing the concerted vs. mosaic models that Schumacher and Carlson might want to discuss in the context of their findings.

We thank the reviewer for recommending this paper. We agree that their findings fit nicely with ours and have added discussion of these points in lines 513-528.

Last, I recommend incorporating supplemental Figure 4 into the manuscript proper. It was not only interesting, it strongly supported many of the authors' conclusions.

We thank the reviewer for this suggestion. We have moved this figure to the main text (figure 6).

Author details

1. Erika L Schumacher

   Department of Biology, Washington University, St Louis, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2492-1117

2. Bruce A Carlson

   Department of Biology, Washington University, St Louis, United States

   Contribution

   Conceptualization, Writing - review and editing

   For correspondence

   carlson.bruce@wustl.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2151-0443

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