Testing the role of ecological selection on colour pattern variation in the butterfly Parnassius clodius.
TL;DR: It is suggested that elements of butterfly wing phenotypes respond independently to different sources of selection and that thermoregulation is an important driver of phenotypic differentiation in Parnassian butterflies.
Abstract: Colour pattern has served as an important phenotype in understanding the process of natural selection, particularly in brightly coloured and variable species like butterflies. However, different selective forces operate on aspects of colour pattern, for example by favouring warning colours in eyespots or alternatively favoring investment in thermoregulatory properties of melanin. Additionally, genetic drift influences colour phenotypes, especially in populations undergoing population size change. Here, we investigated the relative roles of genetic drift and ecological selection in generating the phenotypic diversity of the butterfly Parnassius clodius. Genome-wide patterns of single nucleotide polymorphism data show that P. clodius forms three population clusters, which experienced a period of population expansion following the last glacial maximum and have since remained relatively stable in size. After correcting for relatedness, morphological variation is best explained by climatic predictor variables, suggesting ecological selection generates trait variability. Solar radiation and precipitation are both negatively correlated with increasing total melanin in both sexes, supporting a thermoregulatory function of melanin. Similarly, wing size traits are significantly larger in warmer habitats for both sexes, supporting a Converse Bergmann Rule pattern. Bright red coloration is negatively correlated with temperature seasonality and solar radiation in males, and weakly associated with insectivorous avian predators in univariate models, providing mixed evidence that selection is linked to warning coloration and predator avoidance. Together, these results suggest that elements of butterfly wing phenotypes respond independently to different sources of selection and that thermoregulation is an important driver of phenotypic differentiation in Parnassian butterflies.
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TL;DR: The Nebria ingens complex (Coleoptera: Carabidae), a water‐affiliated ground beetle lineage, is used to test how drainage basins are linked to their observed population structure and it is found that the major drainage systems of the Sierra Nevada Mountains in California best explain the population structure of the N. ingen complex.
Abstract: The evolutionary histories of alpine species are often directly associated with responses to glaciation. Deep divergence among populations and complex patterns of genetic variation have been inferred as consequences of persistence within glacier boundaries (i.e., on nunataks), while shallow divergence and limited genetic variation are assumed to result from expansion from large refugia at the edge of ice shields (i.e., massifs de refuge). However, for some species, dependence on specific microhabitats could profoundly influence their spatial and demographic response to glaciation, and such a simple dichotomy may obscure the localization of actual refugia. In this study, we use the Nebria ingens complex (Coleoptera: Carabidae), a water-affiliated ground beetle lineage, to test how drainage basins are linked to their observed population structure. By analysing mitochondrial COI gene sequences and genome-wide single nucleotide polymorphisms, we find that the major drainage systems of the Sierra Nevada Mountains in California best explain the population structure of the N. ingens complex. In addition, we find that an intermediate morphotype within the N. ingens complex is the product of historical hybridization of N. riversi and N. ingens in the San Joaquin basin during glaciation. This study highlights the importance of considering ecological preferences in how species respond to climate fluctuations and provides an explanation for discordances that are often observed in comparative phylogeographical studies.
6 citations
Cites background from "Testing the role of ecological sele..."
...A few other studies have shown population expansion during glaciation followed by postglacial population declines in alpine and subalpine species (Galbreath et al., 2009; Huang et al., 2016; Zaman et al., 2019)....
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TL;DR: It is shown that novel white alleles in Neodiprion lecontei (pine sawfly) larvae spread via selection rather than drift, and bottom-up selection via host plants may drive divergence among populations as white larvae were disproportionately abundant on a pine species with a reduced carotenoid content relative to other pine hosts.
Abstract: Our understanding of how novel color traits evolve in aposematic taxa is based largely on studies of reproductive stages and organisms with endogenously produced pigmentation. In these systems, genetic drift is often required for novel alleles to overcome strong purifying selection stemming from frequency-dependent predation and positive assortative mating. Here we show that the importance of these mechanisms can differ if selective processes are considered in larval stage instead. By integrating population genomic data, predation experiments and phenotypic measurements of larvae and their host plants, we show that novel white alleles in Neodiprion lecontei (pine sawfly) larvae spread via selection rather than drift. The cost of being rare was not offset by an enhanced aposematic display or immune function. Instead, bottom-up selection via host plants may drive divergence among populations as white larvae were disproportionately abundant on a pine species with a reduced carotenoid content relative to other pine hosts.
6 citations
TL;DR: In this article , the authors integrate data from field surveys, predation experiments, population genomics, and phenotypic correlations to explain the origin and maintenance of geographic variation in a diet-based larval pigmentation trait in the redheaded pine sawfly (Neodiprion lecontei), a pine feeding hymenopteran.
Abstract: Our understanding of how novel warning color traits evolve in natural populations is largely based on studies of reproductive stages and organisms with endogenously produced pigmentation. In these systems, genetic drift is often required for novel alleles to overcome strong purifying selection stemming from frequency‐dependent predation and positive assortative mating. Here, we integrate data from field surveys, predation experiments, population genomics, and phenotypic correlations to explain the origin and maintenance of geographic variation in a diet‐based larval pigmentation trait in the redheaded pine sawfly (Neodiprion lecontei), a pine‐feeding hymenopteran. Although our experiments confirm that N. lecontei larvae are indeed aposematic—and therefore likely to experience frequency‐dependent predation—our genomic data do not support a historical demographic scenario that would have facilitated the spread of an initially deleterious allele via drift. Additionally, significantly elevated differentiation at a known color locus suggests that geographic variation in larval color is currently maintained by selection. Together, these data suggest that the novel white morph likely spread via selection. However, white body color does not enhance aposematic displays, nor is it correlated with enhanced chemical defense or immune function. Instead, the derived white‐bodied morph is disproportionately abundant on a pine species with a reduced carotenoid content relative to other pine hosts, suggesting that bottom‐up selection via host plants may have driven divergence among populations. Overall, our results suggest that life stage and pigment source can have a substantial impact on the evolution of novel warning signals, highlighting the need to investigate diverse aposematic taxa to develop a comprehensive understanding of color variation in nature.
4 citations
TL;DR: A large‐scale comparative phylogeographical study on three Argynnini butterfly species that have similar life histories, but differ in ecological generalism and dispersal abilities shows that the species share similar colonization histories, and that the same ecological factors covary with genetic differentiation within these species to some extent.
Abstract: Understanding which factors and processes are associated with genetic differentiation within and among species remains a major goal in evolutionary biology. To explore differences and similarities in genetic structure and its association with geographical and climatic factors in sympatric sister species, we conducted a large‐scale (>32° latitude and >36° longitude) comparative phylogeographical study on three Argynnini butterfly species (Speyeria aglaja, Fabriciana adippe and F. niobe) that have similar life histories, but differ in ecological generalism and dispersal abilities. Analyses of nuclear (ddRAD‐sequencing derived SNP markers) and mitochondrial (COI sequences) data revealed differences between species in genetic structure and how genetic differentiation was associated with climatic factors (temperature, solar radiation, precipitation, wind speed). Geographical proximity accounted for much of the variation in nuclear and mitochondrial structure and evolutionary relationships in F. adippe and F. niobe, but only explained the pattern observed in the nuclear data in S. aglaja, for which mitonuclear discordance was documented. In all species, Iberian and Balkan individuals formed genetic clusters, suggesting isolation in glacial refugia and limited postglacial expansion. Solar radiation and precipitation were associated with the genetic structure on a regional scale in all species, but the specific combinations of environmental and geographical factors linked to variation within species were unique, pointing to species‐specific responses to common environments. Our findings show that the species share similar colonization histories, and that the same ecological factors, such as niche breadth and dispersal capacity, covary with genetic differentiation within these species to some extent, thereby highlighting the importance of comparative phylogeographical studies in sympatric sister species.
4 citations
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TL;DR: The main innovations of the new version of the Arlequin program include enhanced outputs in XML format, the possibility to embed graphics displaying computation results directly into output files, and the implementation of a new method to detect loci under selection from genome scans.
Abstract: We present here a new version of the Arlequin program available under three different forms: a Windows graphical version (Winarl35), a console version of Arlequin (arlecore), and a specific console version to compute summary statistics (arlsumstat). The command-line versions run under both Linux and Windows. The main innovations of the new version include enhanced outputs in XML format, the possibility to embed graphics displaying computation results directly into output files, and the implementation of a new method to detect loci under selection from genome scans. Command-line versions are designed to handle large series of files, and arlsumstat can be used to generate summary statistics from simulated data sets within an Approximate Bayesian Computation framework.
13,581 citations
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...5 (Excoffier & Lischer, 2010)....
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TL;DR: In this paper, the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data was introduced, which is a general-purpose machine learning method with a simple and precise mathematical formulation.
Abstract: The availability of detailed environmental data, together with inexpensive and powerful computers, has fueled a rapid increase in predictive modeling of species environmental requirements and geographic distributions. For some species, detailed presence/absence occurrence data are available, allowing the use of a variety of standard statistical techniques. However, absence data are not available for most species. In this paper, we introduce the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data. Maxent is a general-purpose machine learning method with a simple and precise mathematical formulation, and it has a number of aspects that make it well-suited for species distribution modeling. In order to investigate the efficacy of the method, here we perform a continental-scale case study using two Neotropical mammals: a lowland species of sloth, Bradypus variegatus, and a small montane murid rodent, Microryzomys minutus. We compared Maxent predictions with those of a commonly used presence-only modeling method, the Genetic Algorithm for Rule-Set Prediction (GARP). We made predictions on 10 random subsets of the occurrence records for both species, and then used the remaining localities for testing. Both algorithms provided reasonable estimates of the species’ range, far superior to the shaded outline maps available in field guides. All models were significantly better than random in both binomial tests of omission and receiver operating characteristic (ROC) analyses. The area under the ROC curve (AUC) was almost always higher for Maxent, indicating better discrimination of suitable versus unsuitable areas for the species. The Maxent modeling approach can be used in its present form for many applications with presence-only datasets, and merits further research and development. © 2005 Elsevier B.V. All rights reserved.
13,120 citations
TL;DR: In this paper, the authors created a new dataset of spatially interpolated monthly climate data for global land areas at a very high spatial resolution (approximately 1 km2), including monthly temperature (minimum, maximum and average), precipitation, solar radiation, vapour pressure and wind speed, aggregated across a target temporal range of 1970-2000, using data from between 9000 and 60,000 weather stations.
Abstract: We created a new dataset of spatially interpolated monthly climate data for global land areas at a very high spatial resolution (approximately 1 km2). We included monthly temperature (minimum, maximum and average), precipitation, solar radiation, vapour pressure and wind speed, aggregated across a target temporal range of 1970–2000, using data from between 9000 and 60 000 weather stations. Weather station data were interpolated using thin-plate splines with covariates including elevation, distance to the coast and three satellite-derived covariates: maximum and minimum land surface temperature as well as cloud cover, obtained with the MODIS satellite platform. Interpolation was done for 23 regions of varying size depending on station density. Satellite data improved prediction accuracy for temperature variables 5–15% (0.07–0.17 °C), particularly for areas with a low station density, although prediction error remained high in such regions for all climate variables. Contributions of satellite covariates were mostly negligible for the other variables, although their importance varied by region. In contrast to the common approach to use a single model formulation for the entire world, we constructed the final product by selecting the best performing model for each region and variable. Global cross-validation correlations were ≥ 0.99 for temperature and humidity, 0.86 for precipitation and 0.76 for wind speed. The fact that most of our climate surface estimates were only marginally improved by use of satellite covariates highlights the importance having a dense, high-quality network of climate station data.
7,558 citations
"Testing the role of ecological sele..." refers methods in this paper
...Global climate variables were obtained from the worldclim data set v.2.0 (Fick & Hijmans, 2017) and used to generate 19 bioclimatic variable using the dismo package (Hijmans, Phillips, Leathwick, & Elith, 2017), while mean, maximum, and minimum values of solar radiation from 1991–2010 were obtained…...
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TL;DR: The package adegenet for the R software is dedicated to the multivariate analysis of genetic markers by implementing formal classes and functions to manipulate and analyse genetic markers.
Abstract: Summary: The package adegenet for the R software is dedicated to the multivariate analysis of genetic markers. It extends the ade4 package of multivariate methods by implementing formal classes and functions to manipulate and analyse genetic markers. Data can be imported from common population genetics software and exported to other software and R packages. adegenet also implements standard population genetics tools along with more original approaches for spatial genetics and hybridization. Availability: Stable version is available from CRAN: http://cran. r-project.org/mirrors.html. Development version is available from adegenet website: http://adegenet.r-forge.r-project.org/. Both versions can be installed directly from R. adegenet is distributed under the GNU General Public Licence (v.2). Contact: jombart@biomserv.univ-lyon1.fr Supplementary information: Supplementary data are available at Bioinformatics online.
5,690 citations
"Testing the role of ecological sele..." refers methods in this paper
...The PCA was im‐ plemented in the R package adegenet (Jombart, 2008), with the number of significant eigenvectors assessed using the Tracy‐Widom test....
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TL;DR: BEAST 2 now has a fully developed package management system that allows third party developers to write additional functionality that can be directly installed to the BEAST 2 analysis platform via a package manager without requiring a new software release of the platform.
Abstract: We present a new open source, extensible and flexible software platform for Bayesian evolutionary analysis called BEAST 2. This software platform is a re-design of the popular BEAST 1 platform to correct structural deficiencies that became evident as the BEAST 1 software evolved. Key among those deficiencies was the lack of post-deployment extensibility. BEAST 2 now has a fully developed package management system that allows third party developers to write additional functionality that can be directly installed to the BEAST 2 analysis platform via a package manager without requiring a new software release of the platform. This package architecture is showcased with a number of recently published new models encompassing birth-death-sampling tree priors, phylodynamics and model averaging for substitution models and site partitioning. A second major improvement is the ability to read/write the entire state of the MCMC chain to/from disk allowing it to be easily shared between multiple instances of the BEAST software. This facilitates checkpointing and better support for multi-processor and high-end computing extensions. Finally, the functionality in new packages can be easily added to the user interface (BEAUti 2) by a simple XML template-based mechanism because BEAST 2 has been re-designed to provide greater integration between the analysis engine and the user interface so that, for example BEAST and BEAUti use exactly the same XML file format.
5,183 citations
"Testing the role of ecological sele..." refers methods in this paper
...4 (Bouckaert et al., 2014), which uses Bayesian inference and the Markov chain Monte Carlo (MCMC) procedure to derive the pos‐ terior distribution of local rates and times....
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