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20 results for “alpine butterfly”
Asynchronous life cycles contribute to reproductive isolation between two Alpine butterflies
<p>Data from: Asynchronous life cycles contribute to reproductive isolation between two Alpine butterflies</p> <p><strong>Abstract</strong></p> <p>Geographic isolation often leads to the emergence of distinct genetic lineages that are at least partially reproductively isolated. Zones of secondary contact between such lineages are natural experiments that allow investigating how reproductive isolation evolves and co-existence is maintained. While temporal isolation through allochrony has been suggested to promote reproductive isolation in sympatry, its potential for isolation upon secondary contact is far less understood. Sampling two contact zones of a pair of mainly allopatric Alpine butterflies over several years and taking advantage of museum samples, we show that the contact zones have remained geographically stable over several decades. Furthermore, they seem to be maintained by the asynchronous life cycles of the two butterflies, with one reaching adulthood primarily in even and the other primarily in odd years. Genomic inferences document that allochrony is leaky and that gene flow from allopatric sites scales with the degree of geographic isolation. Overall, we show that allochrony has the potential to contribute to the maintenance of secondary contact zones of lineages that diverged in allopatry.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p>Morphology contains the following files:</p> <p>wing_morpho.R<br> R scripts for data transformation of wing shape</p> <p>genital_morpho.R<br> R scripts for data transformation of genital morphology</p> <p><br> Models_used.R:<br> R scripts used to produce the statistical analyses.</p> <p>genital_morpho_master_with_pca.txt<br> Phenotypic data for genital morphology</p> <p>wing_contemporary_morpho_master_with_pca.txt<br> Phenotypic data for contemporary wing patterns</p> <p>wing_historic_morpho_master_with_pca.txt<br> Phenotypic data for wing patterns from museum samples</p> <p>The text files contains the following information:</p> <p>ID = Individual ID<br> genotyped_allopatric = was the individual genotyped<br> latitude<br> longitude<br> DATE = Date of collection<br> DAY = Day of collection<br> MONTH = Month of collection<br> YEAR = Year of collection<br> SPOT = Collection site<br> boxplotID = ID to reproduce boxplot order as used in the paper<br> colory = color code to plot<br> cycle = year cycle (2018/19 or 2020/21)<br> yeartype = even or odd year<br> genital_x_LM1 = linear measure of genital landmark 1 along the x axis<br> genital_y_LM1 = linear measure of genital landmark 1 along the y axis<br> genital_x_LM2 = linear measure of genital landmark 2 along the x axis <br> genital_y_LM2 = linear measure of genital landmark 2 along the y axis <br> genital_x_LM3 = linear measure of genital landmark 3 along the x axis <br> genital_y_LM3 = linear measure of genital landmark 3 along the y axis <br> genital_x_LM4 = linear measure of genital landmark 4 along the x axis <br> genital_y_LM4 = linear measure of genital landmark 4 along the y axis <br> genital_x_LM5 = linear measure of genital landmark 5 along the x axis <br> genital_y_LM5 = linear measure of genital landmark 5 along the y axis <br> v_t1 = length relationship between v and t1<br> v_t2 = length relationship between v and t2 <br> v_t3 = length relationship between v and t3 <br> t3_t1 = length relationship between t3_t1 <br> t3_t2 = length relationship between t3_t2 <br> t2_t1 = length relationship between t2_t1 <br> v_tg = length relationship between v and tg <br> PC1.x = PC1 axis for unprojected morphospace<br> PC2.x = PC2 axis for unprojected morphospace <br> PC3.x = PC3 axis for unprojected morphospace <br> PC4.x = PC4 axis for unprojected morphospace <br> PC5.x = PC5 axis for unprojected morphospace <br> PC6.x = PC6 axis for unprojected morphospace <br> PC7.x = PC7 axis for unprojected morphospace <br> PC1.y = PC1 axis for projected morphospace <br> PC2.y = PC2 axis for projected morphospace <br> PC3.y = PC3 axis for projected morphospace <br> PC4.y = PC4 axis for projected morphospace <br> PC5.y = PC5 axis for projected morphospace <br> PC6.y = PC6 axis for projected morphospace <br> PC7.y = PC7 axis for projected morphospace</p> <p> </p> <p><br> wing_ProcCoord1 = Procrustes coordinate 1<br> wing_ProcCoord2 = Procrustes coordinate 2<br> wing_ProcCoord3 = Procrustes coordinate 3<br> wing_ProcCoord4 = Procrustes coordinate 4<br> wing_ProcCoord5 = Procrustes coordinate 5<br> wing_ProcCoord6 = Procrustes coordinate 6<br> wing_ProcCoord7 = Procrustes coordinate 7<br> wing_ProcCoord8 = Procrustes coordinate 8<br> wing_ProcCoord9 = Procrustes coordinate 9<br> wing_ProcCoord10 = Procrustes coordinate 10<br> wing_ProcCoord11 = Procrustes coordinate 11<br> wing_ProcCoord12 = Procrustes coordinate 12<br> wing_ProcCoord13 = Procrustes coordinate 13<br> wing_ProcCoord14 = Procrustes coordinate 14<br> wing_ProcCoord15 = Procrustes coordinate 15<br> wing_ProcCoord16 = Procrustes coordinate 16<br> wing_ProcCoord17 = Procrustes coordinate 17<br> wing_ProcCoord18 = Procrustes coordinate 18<br> wing_ProcCoord19 = Procrustes coordinate 19<br> wing_ProcCoord20 = Procrustes coordinate 20<br> wing_ProcCoord21 = Procrustes coordinate 21<br> wing_ProcCoord22 = Procrustes coordinate 22<br> wing_ProcCoord23 = Procrustes coordinate 23<br> wing_ProcCoord24 = Procrustes coordinate 24<br> wing_ProcCoord25 = Procrustes coordinate 25<br> wing_ProcCoord26 = Procrustes coordinate 26<br> wing_ProcCoord27 = Procrustes coordinate 27<br> wing_ProcCoord28 = Procrustes coordinate 28<br> wing_ProcCoord29 = Procrustes coordinate 29<br> wing_ProcCoord30 = Procrustes coordinate 30<br> wing_ProcCoord31 = Procrustes coordinate 31<br> wing_ProcCoord32 = Procrustes coordinate 32<br> wing_ProcCoord33 = Procrustes coordinate 33<br> wing_ProcCoord34 = Procrustes coordinate 34<br> wing_ProcCoord35 = Procrustes coordinate 35<br> wing_ProcCoord36 = Procrustes coordinate 36<br> wing_ProcCoord37 = Procrustes coordinate 37<br> wing_ProcCoord38 = Procrustes coordinate 38<br> wing_ProcCoord39 = Procrustes coordinate 39<br> wing_ProcCoord40 = Procrustes coordinate 40<br> wing_ProcCoord41 = Procrustes coordinate 41<br> wing_ProcCoord42 = Procrustes coordinate 42<br> wing_ProcCoord43 = Procrustes coordinate 43<br> wing_ProcCoord44 = Procrustes coordinate 44<br> wing_ProcCoord45 = Procrustes coordinate 45<br> wing_ProcCoord46 = Procrustes coordinate 46<br> wing_ProcCoord47 = Procrustes coordinate 47<br> wing_ProcCoord48 = Procrustes coordinate 48<br> wing_ProcCoord49 = Procrustes coordinate 49<br> wing_ProcCoord50 = Procrustes coordinate 50<br> wing_ProcCoord51 = Procrustes coordinate 51<br> wing_ProcCoord52 = Procrustes coordinate 52<br> wing_ProcCoord53 = Procrustes coordinate 53<br> wing_ProcCoord54 = Procrustes coordinate 54<br> PC1.x = PC1 unprojected<br> PC2.x = PC2 unprojected<br> PC3.x = PC3 unprojected<br> PC4.x = PC4 unprojected<br> PC5.x = PC5 unprojected<br> PC6.x = PC6 unprojected<br> PC7.x = PC7 unprojected<br> PC8.x = PC8 unprojected<br> PC9.x = PC9 unprojected<br> PC10.x = PC10 unprojected<br> PC11.x = PC11 unprojected<br> PC12.x = PC12 unprojected<br> PC13.x = PC13 unprojected<br> PC14.x = PC14 unprojected<br> PC15.x = PC15 unprojected<br> PC16.x = PC16 unprojected<br> PC17.x = PC17 unprojected<br> PC18.x = PC18 unprojected<br> PC19.x = PC19 unprojected<br> PC20.x = PC20 unprojected<br> PC21.x = PC21 unprojected<br> PC22.x = PC22 unprojected<br> PC23.x = PC23 unprojected<br> PC24.x = PC24 unprojected<br> PC25.x = PC25 unprojected<br> PC26.x = PC26 unprojected<br> PC27.x = PC27 unprojected<br> PC28.x = PC28 unprojected<br> PC29.x = PC29 unprojected<br> PC30.x = PC30 unprojected<br> PC31.x = PC31 unprojected<br> PC32.x = PC32 unprojected<br> PC33.x = PC33 unprojected<br> PC34.x = PC34 unprojected<br> PC35.x = PC35 unprojected<br> PC36 = PC36 unprojected<br> PC37 = PC37 unprojected<br> PC38 = PC38 unprojected<br> PC39 = PC39 unprojected<br> PC40 = PC40 unprojected<br> PC41 = PC41 unprojected<br> PC42 = PC42 unprojected<br> PC43 = PC43 unprojected<br> PC44 = PC44 unprojected<br> PC45 = PC45 unprojected<br> PC46 = PC46 unprojected<br> PC47 = PC47 unprojected<br> PC48 = PC48 unprojected<br> PC49 = PC49 unprojected<br> PC50 = PC50 unprojected<br> PC51 = PC51 unprojected<br> PC52 = PC52 unprojected<br> PC53 = PC53 unprojected<br> PC54 = PC54 unprojected<br> PC1.y = PC1 projected<br> PC2.y = PC2 projected<br> PC3.y = PC3 projected<br> PC4.y = PC4 projected<br> PC5.y = PC5 projected<br> PC6.y = PC6 projected<br> PC7.y = PC7 projected<br> PC8.y = PC8 projected<br> PC9.y = PC9 projected<br> PC10.y = PC10 projected<br> PC11.y = PC11 projected<br> PC12.y = PC12 projected<br> PC13.y = PC13 projected<br> PC14.y = PC14 projected<br> PC15.y = PC15 projected<br> PC16.y = PC16 projected<br> PC17.y = PC17 projected<br> PC18.y = PC18 projected<br> PC19.y = PC19 projected<br> PC20.y = PC20 projected<br> PC21.y = PC21 projected<br> PC22.y = PC22 projected<br> PC23.y = PC23 projected<br> PC24.y = PC24 projected<br> PC25.y = PC25 projected<br> PC26.y = PC26 projected<br> PC27.y = PC27 projected<br> PC28.y = PC28 projected<br> PC29.y = PC29 projected<br> PC30.y = PC30 projected<br> PC31.y = PC31 projected<br> PC32.y = PC32 projected<br> PC33.y = PC33 projected<br> PC34.y = PC34 projected<br> PC35.y = PC35 projected</p> <p> </p> <p> </p> <p> </p> <p>Genomics contains the following files (Genomic data is available from NCBI BioProject: PRJNA1019795):</p> <p>all_euryale_calls.vcf.gz<br> The unfiltered VCF file</p> <p>euryale_V2.sh<br> Shell script for the genomic data analysis</p> <p>introgress.R<br> R script for running Introgress</p> <p>introgress_all_east2.txt<br> Output of Introgress for the Eastern contact zone</p> <p>introgress_all_west2.txt<br> Output of Introgress for the Western contact zone</p> <p>Admixture_output.txt<br> Output of Admixture assuming either 2 or 3 genomic clusters (K) with the respective population and ID</p> <p>Outliers2BombyxMori.txt<br> BLAST summary of outlier regions against Bombyx Mori</p> <p>Outliers2ManjolaJurtina.txt<br> BLAST summary of outlier regions against Manjola jurtina</p> <p>Outliers2ParargeAegeria.txt<br> BLAST summary of outlier regions against Pararge aegeria</p> <p> </p>
Alpine butterflies want to fly high: Species and communities shift upwards faster than their host plants
<p>Despite sometimes strong co-dependencies of insect herbivores and plants, responses of individual taxa to accelerating climate change are typically studied in isolation. Thereby, biotic interactions that potentially limit species in tracking their preferred climatic niches are ignored. Here, we chose butterflies as a prominent representative of herbivorous insects to investigate the impacts of temperature changes and their larval host plant distributions along a 1.4 km elevational gradient in the German Alps. Following a sampling protocol of 2009, we re-visited 33 grassland plots in 2019 over an entire growing season. We quantified changes in butterfly abundance and richness by repeated transect walks on each plot and disentangled the direct and indirect effects of locally assessed temperature, site management, and larval and adult food resource availability on these patterns. Additionally, we determined elevational range shifts of butterflies and host plants at both the community and species level. Comparing the two sampled years (2009, 2019), we found a severe decline in butterfly abundance and a clear upward shift of butterflies along the elevational gradient. We detected shifts in the peak of species richness, community composition and at the species level, whereby mountainous species shifted particularly strongly. In contrast, host plants showed barely any change, neither concerning species richness, nor individual species shifts. Further, temperature and host plant richness were the main drivers of butterfly richness, with change in temperature explaining best the change of richness over time. We conclude that host plants are not yet hindering butterfly species and communities from shifting upwards. However, the mismatch between butterfly and host plant shifts might become a problem for this very close plant-herbivore relationship, especially towards higher elevations, if butterflies fail to adapt to new host plants. Further, our results support the value of conserving traditional extensive pasture use as a promoter of host plants and thereby butterfly richness.</p>
Demographic fluctuations lead to rapid and cyclic shifts in genetic structure among populations of an alpine butterfly, Parnassius smintheus
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Alpine butterflies want to fly high: Species and communities shift upwards faster than their host plants
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Data from: Cold adaptation across the elevation gradient in an alpine butterfly species complex
<p><span><span>1. Temperature acts as a major factor on the timing of activity and behaviour in butterflies, and it might represent a key driver of butterfly diversification along elevation gradients. Under this hypothesis, local adaptation should be found along the elevation gradient, with butterflies from high elevation populations able to remain active at lower ambient temperature than those from low elevation. </span></span></p> <p><span><span>2. We recorded the warming-up rate and the thoracic temperature at take-off of 123 individuals of the Alpine butterfly species complex <i>Coenonympha arcania - C. macromma - C. gardetta</i> in controlled conditions. </span></span></p> <p><span><span>3. Warming-up rate increased with elevation within <i>C. arcania</i>: high elevation males of <i>C. arcania</i> were able to warm-up more quickly, as compared to low elevation ones.</span></span></p> <p><span><span>4. High elevation <i>C. gardetta</i> had a darker underwing pattern than low elevation ones. This high-elevation species was significantly smaller (lower weight and wing surface) than the two other species, and had a faster warming up rate.</span></span></p> <p><span><span>5. Our results suggest that the ability to warm-up quickly and to take-flight at a high body temperature evolved adaptively in the high-altitude <i>C. gardetta</i>, and that low temperature at high altitude may explain the absence there of <i>C. arcania</i>, while the hybrid nature of <i>C. macromma</i> is probably the explanation of its elevation overlap with both other species, and its local replacement of <i>C. gardetta</i>.</span></span></p>
Dataset 2 for Large‐ and small‐scale geographic structures affecting genetic patterns across populations of an Alpine butterfly
<p>Understanding factors influencing patterns of genetic diversity and the population genetic structure of species is of particular importance in the current era of global climate change and habitat loss. These factors include the evolutionary history of a species as well as heterogeneity in the environment it occupies, which in turn can change across time. Most studies investigating spatio-temporal genetic patterns have focused on patterns across wide geographical areas rather than local variation, but the latter can nevertheless be important particularly in topographically complex areas. Here we consider these issues in the Sooty Copper butterfly (<i>Lycaena tityrus</i>) from the European Alps, using genome-wide SNPs identified through RADseq. We found strong genetic differentiation within the Alps with four genetic clusters, indicating western, central, and eastern refuges, and a strong reduction of genetic diversity from west to east. This reduction in diversity may suggest that the southwestern refuge was the largest one in comparison to other refuges. Also, the high genetic diversity in the West may result from (1) admixture of different western refuges, (2) more recent demographic changes, or (3) introgression of lowland <i>L. tityrus</i> populations. At small spatial scales, populations were structured by several landscape features and especially by high mountain ridges and large river valleys. We detected 36 outlier loci likely under altitudinal selection, including several loci related to membranes and cellular processes. We suggest that efforts to preserve alpine <i>L. tityrus </i>should focus on the genetically diverse populations in the western Alps, and that the dolomite populations should be treated as genetically distinct management units, since they appear to be currently more threatened than others. This study demonstrates the usefulness of SNP-based approaches for understanding patterns of genetic diversity, gene flow and selection in a region that is expected to be particularly vulnerable to climate change.</p>
Phylogenomics reveal extensive phylogenetic discordance due to incomplete lineage sorting following the rapid radiation of alpine butterflies (Papilionidae: Parnassius)
<p><span><span>In rapid radiation, the earliest components of evolutionary divergence are often difficult to resolve, which were always driven by the characteristics of taxa and the limitations of alternative analytical methods</span>. </span><span>The origin and radiation of the alpine butterfly <em>Parnassius</em>, a high-altitude mountainous insect group, can be attributed to the uplift of the Qinghai-Tibet Plateau (QTP). Despite detailed phylogenetic analyses of the genus, deep phylogenetic relationships among the major subgenera remain recalcitrant. In this study, 102 individuals from 10 representative <em>Parnassius</em> species were sampled to resolve the phylogenetic relationships among subgenera based on nuclear and mitochondrial genome data sets. Gene-tree/species-tree conflicts were detected by concatenation and multispecies coalescent (MSC) approaches. We recovered a well-supported species tree, despite these conflicts, and detected considerable phylogenetic discordance among genomic regions. The main explanation for the topological discordance among subgenera was extensive incomplete lineage sorting (ILS), whereas introgression events were not prominent. The origin and explosive radiation of <em>Parnassius</em> (i.e., rapid succession of speciation events) in the late Miocene associated with environmental events on the plateau led to short internal branches, thereby increasing ILS and topological conflicts, especially among closely related subgenera. Our results also suggested that MSC approaches (SNAPP and SVDquartets) are accurate and superior to the concatenation approach; in particular, SVDquartets can explicitly accommodate gene-tree/species-tree conflicts caused by high ILS and demonstrate strong robustness. Lastly, we explored the phylogenomic data by testing multiple sources of phylogenomic conflict to clarify the strengths and limitations of different approaches, while considering phylogenetic signal variation in mitochondrial loci. We anticipate that the phylogeny described here will be the backbone of future evolutionary studies of the genus and will provide insight into phylogenetic discordance due to rapid radiation.</span></p>
Dataset 1 for Large- and small-scale geographic structures affect genetic patterns across populations of an Alpine butterfly
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Dataset 2 for Large‐ and small‐scale geographic structures affecting genetic patterns across populations of an Alpine butterfly
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Phylogenomics reveal extensive phylogenetic discordance due to incomplete lineage sorting following the rapid radiation of alpine butterflies (Papilionidae: Parnassius)
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Cold adaptation across the elevation gradient in an alpine butterfly species complex
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Spatial variation in early-winter snow cover determines local dynamics in a network of alpine butterfly populations
<p>Snow cover is an extremely variable but critical component of alpine environments. We use long term population data on multiple small populations of the alpine butterfly <i>Parnassius smintheus</i>, combined with high-resolution satellite imagery of meadows, to show a strong link between fine-scale spatial and temporal variation in early-winter snow cover and annual change in butterfly population size, accounting for up to 80 percent of the variation in annual population change. Snow cover in early winter for each meadow is the best predictor of annual adult population change, despite being estimated for a relatively short time-window in late November. We identify a means by which subpopulation response to a local, short-term weather variable can be assessed over a large spatial extent, but also at a resolution relevant to the biology and local dynamics of this alpine species.</p>
Data from: Pivotal effect of early-winter temperatures and snowfall on population growth of alpine Parnassius smintheus butterflies
Geographic range shifts in species' distributions, due to climate change, imply altered dynamics at both their northern and southern range limits, or at upper and lower elevational limits. There is therefore a need to identify specific weather or climate variable(s), and life stages or cohorts on which they act, and how these affect population growth. Identifying such variables permits prediction of population increase or decline under a changing climate, and shifts in a species' geographic range. For relatively well studied groups, such as butterflies, geographic range shifts are well documented, but weather variables and mechanisms causing those shifts are not well known. The Holarctic butterfly genus Parnassius (Papilionidae) inhabits northern and alpine environments subject to variable and extreme weather. As such, Parnassius species are vulnerable not only to long-term changes in average conditions but especially to short-term extreme weather events. We use population growth estimates for the alpine butterfly, Parnassius smintheus, from 21 populations in the Rocky Mountains of Canada, over a 20-year interval, combined with techniques of machine learning (randomForests) and parametric modeling to identify the important weather variables determining population growth. We do this to determine the seasons and life-stages of P. smintheus most affected by climate change. Extreme minimum and maximum temperatures in November, in combination with November snowfall, affect annual population growth most, more so than do mean temperatures in November, and more so than weather at any other time of year. Populations decline both in years with low extreme minimum temperatures in November, and especially in years with high extreme maximum temperatures in November, indicating that overwintering eggs are particularly vulnerable to early-winter weather. Snowfall ameliorates the negative effects of extreme temperatures, particularly for extreme warm events. Results provide insight into biological mechanisms by which over-wintering eggs might be affected by early winter weather. Short-term extreme weather in November, acting on a single pivotal life-stage (egg) is a far better predictor of population change of alpine Parnassius smintheus butterflies than is the general index of climate, the Pacific Decadal Oscillation (PDO).
Data from: Genetic, morphological and ecological variation across a sharp hybrid zone between two alpine butterflies species
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Data from: A range-wide genetic bottleneck overwhelms landscape heterogeneity and local abundance in shaping genetic patterns of an alpine butterfly (Lepidoptera: Pieridae: Colias behrii)
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Data from: Pivotal effect of early-winter temperatures and snowfall on population growth of alpine Parnassius smintheus butterflies
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Spatial variation in early-winter snow cover determines local dynamics in a network of alpine butterfly populations
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Data from: Speciation with gene flow: evidence from a complex of alpine butterflies (Coenonympha, Satyridae)
Until complete reproductive isolation is achieved, the extent of differentiation between two diverging lineages is the result of a dynamic equilibrium between genetic isolation and mixing. This is especially true for hybrid taxa, for which the degree of isolation in regard to their parental species is decisive in their capacity to rise as a new and stable entity. In this work, we explored the past and current patterns of hybridization and divergence within a complex of closely related butterflies in the genus Coenonympha in which two alpine species, C. darwiniana and C. macromma, have been shown to result from hybridization between the also alpine C. gardetta and the lowland C. arcania. By testing alternative scenarios of divergence among species, we show that gene flow has been uninterrupted throughout the speciation process, although leading to different degrees of current genetic isolation between species in contact zones depending on the pair considered. Nonetheless, at broader geographic scale, analyses reveal a clear genetic differentiation between hybrid lineages and their parental species, pointing out to an advanced stage of the hybrid speciation process. Finally, the positive correlation observed between ecological divergence and genetic isolation among these butterflies suggests a potential role for ecological drivers during their speciation processes.
Anthropogenic and natural barriers affect genetic connectivity in an Alpine butterfly
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Data from: Speciation with gene flow: evidence from a complex of alpine butterflies (Coenonympha, Satyridae)
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