Deep learning on butterfly phenotypes tests evolution’s oldest mathematical model

Cuthill, Jennifer F. Hoyal; Guttenberg, Nicholas; Ledger, Sophie; Crowther, Robyn; Huertas, Blanca

doi:10.1126/sciadv.aaw4967

Cited by 50 publications

(62 citation statements)

References 39 publications

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“…There has been considerable interest in whether the H. erato and H. melpomene co-mimics have co-diverged and simultaneously converged onto the same colour pattern [88][89][90][91] or whether one species evolved towards diverse phenotypes of the other, i.e., advergence [67,[92][93][94]. Homologous genes control corresponding phenotypes [30,35,95,96], but there is no allele sharing between the melpomene and erato clade [67,68].…”

Section: Parallel Selective Sweep Signatures Between Mimetic Speciesmentioning

confidence: 99%

Selective sweeps on novel and introgressed variation shape mimicry loci in a butterfly adaptive radiation

Moest¹,

Belleghem²,

James³

et al. 2020

PLoS Biol

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Natural selection leaves distinct signatures in the genome that can reveal the targets and history of adaptive evolution. By analysing high-coverage genome sequence data from 4 major colour pattern loci sampled from nearly 600 individuals in 53 populations, we show pervasive selection on wing patterns in the Heliconius adaptive radiation. The strongest signatures correspond to loci with the greatest phenotypic effects, consistent with visual selection by predators, and are found in colour patterns with geographically restricted distributions. These recent sweeps are similar between co-mimics and indicate colour pattern turnover events despite strong stabilising selection. Using simulations, we compare sweep signatures expected under classic hard sweeps with those resulting from adaptive introgression, an important aspect of mimicry evolution in Heliconius butterflies. Simulated recipient populations show a distinct 'volcano' pattern with peaks of increased genetic diversity around the selected target, characteristic of sweeps of introgressed variation and consistent with diversity patterns found in some populations. Our genomic data reveal a surprisingly dynamic history of colour pattern selection and co-evolution in this adaptive radiation.

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Section: Parallel Selective Sweep Signatures Between Mimetic Speciesmentioning

confidence: 99%

Selective sweeps on novel and introgressed variation shape mimicry loci in a butterfly adaptive radiation

Moest¹,

Belleghem²,

James³

et al. 2020

PLoS Biol

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“…Images were obtained through the authors' collections and collections made publicly available by Cuthill et al 2019 [22] and Jiggins et al 2019 [23]. Individual genders were determined based on sexual dimorphism in the androconial region [24].…”

Section: (A) Sampling and Landmark Analysismentioning

confidence: 99%

“…H. e. hydara versus H. m. melpomene from French Guiana). As H. erato is often suggested to be the more abundant co-mimic and, thus, the model which H. melpomene mimics [21,22], the evolution of better mimetic signals in this case may reflect a melpomene populations from French Guiana [20]. The potentially recent evolutionary change of the H. e. hydara mid-forewing band phenotype from French Guiana also crosses the developmental boundary identified in the H. e. demophoon CRISPR/Cas9 KO's, which suggests the gene regulatory network that underlies this similar wing phenotype may be diverging even within the H. erato lineage (Figure 3).…”

Section: (B) Evidence Of Gene Regulatory Network Divergence Within Spmentioning

confidence: 99%

Perfect mimicry betweenHeliconiusbutterflies is constrained by genetics and development

Belleghem

Roman

Gutierrez

et al. 2020

Preprint

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Müllerian mimicry strongly exemplifies the power of natural selection. However, the exact measure of such adaptive phenotypic convergence and the possible causes of its imperfection often remain unidentified. The butterfly species Heliconius erato and Heliconius melpomene have a large diversity of co-mimicking geographic races with remarkable resemblance in melanic patterning across the midforewing that has been linked to expression patterns of the gene WntA. Recent CRISPR/Cas9 experiments have informed us on the exact areas of the wings in which WntA affects color pattern formation in both H. erato and H. melpomene, thus providing a unique comparative dataset to explore the functioning of a gene and its potential effect on phenotypic evolution. We therefore quantified wing color pattern differences in the mid-forewing region of 14 co-mimetic races of H. erato and H. melpomene and measured the extent to which mimicking races are not perfectly identical. While the relative size of the mid-forewing pattern is generally nearly identical, our results highlight the areas of the wing that prevent these species from achieving perfect mimicry and demonstrate that this mismatch can be largely explained by constraints imposed by divergence in the gene regulatory network that define wing color patterning. Divergence in the developmental architecture of a trait can thus constrain morphological evolution even between relatively closely related species.

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“…On the other hand, the paucity of empirical evidence for coevolution in Müllerian mimicry systems may simply be due to the fact that coevolution is notoriously difficult to demonstrate. Perhaps the most compelling evidence for coevolutionary convergence in Müllerian mimicry used machine learning and phylogenetic methods to demonstrate colour and pattern matching across multiple co-ocurring populations of Heliconius erato and Heliconius melpomene butterflies (Hoyal Cuthill et al, 2019). In one focussed case study, they demonstrate that the wing colour of H. erato has remained unchanged from the ancestral form while the wing colour of its H. melpomene co-mimic has changed to match that of H. erato.…”

Section: (3) Absolute Abundance and Phenotypic Inertiamentioning

confidence: 99%

Natural selection in mimicry

Anderson

Jager

2019

Biological Reviews

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Biological mimicry has served as a salient example of natural selection for over a century, providing us with a dazzling array of very different examples across many unrelated taxa. We provide a conceptual framework that brings together apparently disparate examples of mimicry in a single model for the purpose of comparing how natural selection affects models, mimics and signal receivers across different interactions. We first analyse how model–mimic resemblance likely affects the fitness of models, mimics and receivers across diverse examples. These include classic Batesian and Müllerian butterfly systems, nectarless orchids that mimic Hymenoptera or nectar‐producing plants, caterpillars that mimic inert objects unlikely to be perceived as food, plants that mimic abiotic objects like carrion or dung and aggressive mimicry where predators mimic food items of their own prey. From this, we construct a conceptual framework of the selective forces that form the basis of all mimetic interactions. These interactions between models, mimics and receivers may follow four possible evolutionary pathways in terms of the direction of selection resulting from model–mimic resemblance. Two of these pathways correspond to the selective pressures associated with what is widely regarded as Batesian and Müllerian mimicry. The other two pathways suggest mimetic interactions underpinned by distinct selective pressures that have largely remained unrecognized. Each pathway is characterized by theoretical differences in how model–mimic resemblance influences the direction of selection acting on mimics, models and signal receivers, and the potential for consequent (co)evolutionary relationships between these three protagonists. The final part of this review describes how selective forces generated through model–mimic resemblance can be opposed by the basic ecology of interacting organisms and how those forces may affect the symmetry, strength and likelihood of (co)evolution between the three protagonists within the confines of the four broad evolutionary possibilities. We provide a clear and pragmatic visualization of selection pressures that portrays how different mimicry types may evolve. This conceptual framework provides clarity on how different selective forces acting on mimics, models and receivers are likely to interact and ultimately shape the evolutionary pathways taken by mimetic interactions, as well as the constraints inherent within these interactions.

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Deep learning on butterfly phenotypes tests evolution’s oldest mathematical model

Cited by 50 publications

References 39 publications

Selective sweeps on novel and introgressed variation shape mimicry loci in a butterfly adaptive radiation

Selective sweeps on novel and introgressed variation shape mimicry loci in a butterfly adaptive radiation

Perfect mimicry betweenHeliconiusbutterflies is constrained by genetics and development

Natural selection in mimicry

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