scholarly journals Unsupervised machine learning reveals mimicry complexes in bumblebees occur along a perceptual continuum

2019 ◽  
Vol 286 (1910) ◽  
pp. 20191501 ◽  
Author(s):  
Briana D. Ezray ◽  
Drew C. Wham ◽  
Carrie E. Hill ◽  
Heather M. Hines
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Colour Pattern ◽  
Unsupervised Machine Learning ◽  
Transition Zones ◽  
Müllerian Mimicry ◽  
Active Selection ◽  
Mullerian Mimicry ◽  
Colour Patterns ◽  
Patterns And Processes

Müllerian mimicry theory states that frequency-dependent selection should favour geographical convergence of harmful species onto a shared colour pattern. As such, mimetic patterns are commonly circumscribed into discrete mimicry complexes, each containing a predominant phenotype. Outside a few examples in butterflies, the location of transition zones between mimicry complexes and the factors driving mimicry zones has rarely been examined. To infer the patterns and processes of Müllerian mimicry, we integrate large-scale data on the geographical distribution of colour patterns of social bumblebees across the contiguous United States and use these to quantify colour pattern mimicry using an innovative, unsupervised machine-learning approach based on computer vision. Our data suggest that bumblebees exhibit geographically clustered, but sometimes imperfect colour patterns, and that mimicry patterns gradually transition spatially rather than exhibit discrete boundaries. Additionally, examination of colour pattern transition zones of three comimicking, polymorphic species, where active selection is driving phenotype frequencies, revealed that their transition zones differ in location within a broad region of poor mimicry. Potential factors influencing mimicry transition zone dynamics are discussed.

scholarly journals Müllerian mimicry in bumble bees is a transient continuum

2019 ◽  
Author(s):  
Briana D. Ezray ◽  
Drew C. Wham ◽  
Carrie Hill ◽  
Heather M. Hines
Keyword(s):  
United States ◽  
Large Scale ◽  
The United States ◽  
Bumble Bees ◽  
Colour Pattern ◽  
Transition Zones ◽  
Müllerian Mimicry ◽  
Active Selection ◽  
Mullerian Mimicry ◽  
Colour Patterns

AbstractMüllerian mimicry theory states that frequency dependent selection should favour geographic convergence of harmful species onto a shared colour pattern. As such, mimetic patterns are commonly circumscribed into discrete mimicry complexes each containing a predominant phenotype. Outside a few examples in butterflies, the location of transition zones between mimicry complexes and the factors driving them has rarely been examined. To infer the patterns and processes of Müllerian mimicry, we integrate large-scale data on the geographic distribution of colour patterns of all social bumble bees across the contiguous United States and use these to quantify colour pattern mimicry using an innovative machine learning approach based on computer vision and image recognition. Our data suggests that bumble bees exhibit a manifold of similar, but imperfect colour patterns, that continuously transition across the United States, supporting the idea that mimicry is not discrete. We propose that bumble bees are mimicking a perceptual colour pattern average that is evolutionarily transient. We examine three comimicking polymorphic species,Bombus flavifrons, B. melanopygus,andB. bifarius, where active selection is driving colour pattern frequencies and determine that their colour pattern transition zones differ in location and breadth within a broad region of poor mimicry. Furthermore, we explore factors driving these differences such as mimicry selection dynamics and climate.

scholarly journals Locomotor mimicry in Heliconius butterflies: contrast analyses of flight morphology and kinematics

1999 ◽  
Vol 354 (1380) ◽  
pp. 203-214 ◽  
Author(s):  
Robert B. Srygley
Keyword(s):  
Wing Length ◽  
Beat Frequency ◽  
Warning Signal ◽  
Wing Shape ◽  
The Body ◽  
Colour Pattern ◽  
Müllerian Mimicry ◽  
Flight Morphology ◽  
Mullerian Mimicry ◽  
Colour Patterns

Müllerian mimicry is a mutualism involving the evolutionary convergence of colour patterns of prey on a warning signal to predators. Behavioural mimicry presumably adds complexity to the signal and makes it more difficult for Batesian mimics to parasitize it. To date, no one has quantified behavioural mimicry in Müllerian mimicry groups. However, morphological similarities among members of mimicry groups suggested that pitching oscillations of the body and wing–beat frequency (WBF) might converge with colour pattern. I compared the morphology and kinematics of four Heliconius species, which comprised two mimicry pairs. Because the mimics arose from two distinct lineages, the relative contributions of mimicry and phylogeny to variation in the species' morphologies and kinematics were examined. The positions of the centre of body mass and centre of wing mass and wing shape diverged among species within lineages, and converged among species within mimicry groups. WBF converged within mimicry groups, and it was coupled with body pitching frequency. However, body–pitching frequency was too variable to distinguish mimicry groups. Convergence in WBF may be due, at least in part, to biomechanical consequences of similarities in wing length, wing shape or the centre of wing mass among co–mimics. Nevertheless, convergence in WBF among passion–vine butterflies serves as the first evidence of behavioural mimicry in a mutualistic context.

scholarly journals Uncovering the effects of Müllerian mimicry on the evolution of conspicuousness in colour patterns

2019 ◽  
Author(s):  
Ombeline Sculfort ◽  
Ludovic Maisonneuve ◽  
Marianne Elias ◽  
Thomas G. Aubier ◽  
Violaine Llaurens
Keyword(s):  
Predation Risk ◽  
Prey Species ◽  
Colour Pattern ◽  
Warning Coloration ◽  
Local Composition ◽  
Müllerian Mimicry ◽  
In The Wild ◽  
Mullerian Mimicry ◽  
Colour Patterns ◽  
Number Of Individuals

AbstractThe conspicuousness of colour pattern in defended species associates with a high detectability by predators, making its evolution puzzling. Müllerian mimicry, the convergence of warning coloration among defended prey species, is pervasive in communities of conspicuous prey, and mimicry switches, with mutant individuals having the same colour pattern as other co-mimetic species, may often associate with changes in conspicuousness. Yet, the implication of mimicry for the evolution of conspicuousness has not been considered. Here, we build a model describing the population dynamics of conspicuous defended prey to explore the invasion conditions of mutants that differ from other individuals by their conspicuousness. We assume that predation risk depends not only on the number of individuals sharing a given colour pattern within the population but also on the presence of co-mimetic species. We compare the evolutionary fates of mutant colour patterns (1) that are similar to the ancestral colour pattern and thus belong to the same mimicry ring (assemblage of co-mimetic species), or (2) that are different from the ancestral colour pattern and thus potentially belong to a distinct mimicry ring. Our analytical derivations show that (1) less conspicuous colour patterns are more likely to be selected within mimicry ring, and that (2) a mimicry switch lowering predation risk can promote the invasion of a more conspicuous colour pattern. We thus highlight that the variation in conspicuousness observed in the wild results not only from the characteristics of the colour pattern (detectability, salience) but also from the local composition of mimetic communities.

scholarly journals A Deep and Scalable Unsupervised Machine Learning System for Cyber-Attack Detection in Large-Scale Smart Grids

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 80778-80788 ◽  
Author(s):  
Hadis Karimipour ◽  
Ali Dehghantanha ◽  
Reza M. Parizi ◽  
Kim-Kwang Raymond Choo ◽  
Henry Leung
Keyword(s):  
Machine Learning ◽  
Smart Grids ◽  
Large Scale ◽  
Attack Detection ◽  
Learning System ◽  
Cyber Attack ◽  
Unsupervised Machine Learning

scholarly journals Mimics here and there, but not everywhere: Müllerian mimicry in Ceroglossus ground beetles?

2016 ◽  
Vol 12 (9) ◽  
pp. 20160429 ◽  
Author(s):  
Carlos P. Muñoz-Ramírez ◽  
Pierre-Paul Bitton ◽  
Stéphanie M. Doucet ◽  
Lacey L. Knowles
Keyword(s):  
Phylogenetic Signal ◽  
Ground Beetle ◽  
Intraspecific Diversity ◽  
Apparent Lack ◽  
Müllerian Mimicry ◽  
Geographical Regions ◽  
Mullerian Mimicry ◽  
Colour Patterns ◽  
Apparent Colour ◽  
Colour Morphs

The ground beetle genus Ceroglossus contains co-distributed species that show pronounced intraspecific diversity in the form of geographical colour morphs. While colour morphs among different species appear to match in some geographical regions, in others, there is little apparent colour matching. Mimicry is a potential explanation for covariation in colour patterns, but it is not clear whether the degree of sympatric colour matching is higher than expected by chance given the obvious mismatches among morphs in some regions. Here, we used reflectance spectrometry to quantify elytral coloration from the perspective of an avian predator to test whether colour similarity between species is, indeed, higher in sympatry. After finding no significant phylogenetic signal in the colour data, analyses showed strong statistical support for sympatric colour similarity between species despite the apparent lack of colour matching in some areas. We hypothesize Müllerian mimicry as the responsible mechanism for sympatric colour similarity in Ceroglossus and discuss potential explanations and future directions to elucidate why mimicry has not developed similar levels of interspecific colour resemblance across space.

scholarly journals Unsupervised learning of sequence-specific aggregation behavior for a model copolymer

Soft Matter ◽  
2021 ◽  
Author(s):  
Antonia Statt ◽  
Devon C Kleeblatt ◽  
Wesley F. Reinhart
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Large Scale ◽  
Materials Science ◽  
Aggregation Behavior ◽  
Unsupervised Machine Learning ◽  
Computational Materials Science ◽  
Learning Scheme ◽  
Local Environments ◽  
Computational Materials

We apply a recently developed unsupervised machine learning scheme for local environments [Reinhart, Computational Materials Science, 2021, 196, 110511] to characterize large-scale, disordered aggregates formed by sequence-defined macromolecules. This method...

Evolution of Metastable Structures in Bimetallic Catalysts from Microscopy and Machine-Learning Molecular Dynamics

2020 ◽  
Author(s):  
Jin Soo Lim ◽  
Jonathan Vandermause ◽  
Matthijs A. van Spronsen ◽  
Albert Musaelian ◽  
Christopher R. O’Connor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Large Scale ◽  
Materials Science ◽  
Complete Characterization ◽  
Layer By Layer ◽  
Surface Restructuring ◽  
Metastable Structures ◽  
Mechanistic Investigation ◽  
Underlying Mechanisms

Restructuring of interface plays a crucial role in materials science and heterogeneous catalysis. Bimetallic systems, in particular, often adopt very different composition and morphology at surfaces compared to the bulk. For the first time, we reveal a detailed atomistic picture of the long-timescale restructuring of Pd deposited on Ag, using microscopy, spectroscopy, and novel simulation methods. Encapsulation of Pd by Ag always precedes layer-by-layer dissolution of Pd, resulting in significant Ag migration out of the surface and extensive vacancy pits. These metastable structures are of vital catalytic importance, as Ag-encapsulated Pd remains much more accessible to reactants than bulk-dissolved Pd. The underlying mechanisms are uncovered by performing fast and large-scale machine-learning molecular dynamics, followed by our newly developed method for complete characterization of atomic surface restructuring events. Our approach is broadly applicable to other multimetallic systems of interest and enables the previously impractical mechanistic investigation of restructuring dynamics.

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