LepTraits 1.0 A globally comprehensive dataset of butterfly traits

Shirey, Vaughn; Larsen, Elise A.; Doherty, Andra; Kim, Clifford A.; Al-Sulaiman, Faisal T.; Hinolan, Jomar D.; Itliong, Micael Gabriel A.; Naive, Mark Arcebal K.; Ku, Minji; Belitz, Michael W.; Jeschke, Grace R.; Barve, Vijay; Lamas, Gerardo; Kawahara, Akito Y.; Guralnick, Robert; Pierce, Naomi E.; Lohman, David J.; Ries, Leslie

doi:10.1038/s41597-022-01473-5

Cited by 25 publications

(16 citation statements)

References 29 publications

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“…Modifications of our model for correlating gene family sizes with diet breadth while taking into account phylogenetic relatedness can be used in future studies to correlate gene family size with other quantitative variables, such as habitat type or other aspects of life history. For butterfly and moth species, databases are growing for this type of trait information, such as the European and Maghreb Butterfly Trait Database (Middleton-Welling et al 2020) and LepTraits (Shirey et al 2022).…”

Section: Where Do We Go Now?mentioning

confidence: 99%

Genome-wide gene birth-death dynamics are associated with diet breadth variation in Lepidoptera

Dort

Bijl

Wahlberg

et al. 2023

Preprint

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Comparative analyses of gene birth-death dynamics (GBDD) have the potential to reveal gene families that played an important role in the evolution of morphological, behavioral, or physiological variation. Here, we used whole genomes of 30 species of butterflies and moths to identify GBDD among the Lepidoptera that are associated with specialist or generalist feeding strategies. Our work advances this field by using a uniform set of annotated proteins for all genomes, investigating associations while correcting for phylogeny, and assessing all gene familes rather than a priori subsets. We discovered that the sizes of several important gene families (e.g., those associated with pesticide resistance, xenobiotic detoxification, and/or protein digestion) are significantly correlated with diet breadth. We also found 12 gene families showing significant shifts in GBDD at the butterfly (Papilionoidea) crown node, the most notable of which was a family of pheromone receptors that underwent a contraction potentially linked with a shift to visual-based mate recognition. Our findings highlight the importance of uniform annotations, phylogenetic corrections and unbiased gene family analyses in generating a list of candidate genes that warrant further exploration.

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Section: Where Do We Go Now?mentioning

confidence: 99%

Genome-wide gene birth-death dynamics are associated with diet breadth variation in Lepidoptera

Dort

Bijl

Wahlberg

et al. 2023

Preprint

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“…We analyzed metadata associated with 6,146 butterfly specimens from six families that were subjected to sequence capture for several phylogenetic studies undertaken as part of ButterflyNet (Espeland et al, 2018;Toussaint et al, 2018;Toussaint et al, 2019;Braby et al, 2020;Carvalho et al, 2020;Valencia-Montoya et al, 2021;Toussaint et al, 2021a;Toussaint et al, 2021b;Kawahara et al, 2022). This NSF-funded collaborative network aims to infer the phylogeny of butterflies and aggregate data on species distributions (Pinkert et al, 2022) and traits (Shirey et al, 2022;butterflynet.org). The phylogenomic component of the project used two sequence capture probe sets.…”

Section: Samplesmentioning

confidence: 99%

Predictors of sequence capture in a large-scale anchored phylogenomics project

et al. 2022

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Next-generation sequencing (NGS) technologies have revolutionized phylogenomics by decreasing the cost and time required to generate sequence data from multiple markers or whole genomes. Further, the fragmented DNA of biological specimens collected decades ago can be sequenced with NGS, reducing the need for collecting fresh specimens. Sequence capture, also known as anchored hybrid enrichment, is a method to produce reduced representation libraries for NGS sequencing. The technique uses single-stranded oligonucleotide probes that hybridize with pre-selected regions of the genome that are sequenced via NGS, culminating in a dataset of numerous orthologous loci from multiple taxa. Phylogenetic analyses using these sequences have the potential to resolve deep and shallow phylogenetic relationships. Identifying the factors that affect sequence capture success could save time, money, and valuable specimens that might be destructively sampled despite low likelihood of sequencing success. We investigated the impacts of specimen age, preservation method, and DNA concentration on sequence capture (number of captured sequences and sequence quality) while accounting for taxonomy and extracted tissue type in a large-scale butterfly phylogenomics project. This project used two probe sets to extract 391 loci or a subset of 13 loci from over 6,000 butterfly specimens. We found that sequence capture is a resilient method capable of amplifying loci in samples of varying age (0–111 years), preservation method (alcohol, papered, pinned), and DNA concentration (0.020 ng/μl - 316 ng/ul). Regression analyses demonstrate that sequence capture is positively correlated with DNA concentration. However, sequence capture and DNA concentration are negatively correlated with sample age and preservation method. Our findings suggest that sequence capture projects should prioritize the use of alcohol-preserved samples younger than 20 years old when available. In the absence of such specimens, dried samples of any age can yield sequence data, albeit with returns that diminish with increasing age.

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“…In addition, some of the methods for building NJ trees allow for missing distances between pairs of species (Criscuolo & Gascuel, 2008). The flexibility provided in both these steps, calculating distances and building trees, will help circumvent the many gaps that most trait databases have, particularly for taxa less well studied than birds (e.g., Pekar et al 2022; Shirey et al 2022).…”

Section: Discussionmentioning

confidence: 99%

Calculating functional diversity metrics using neighbor-joining trees

Cardoso

Guillerme

Mammola

et al. 2022

Preprint

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The study of functional diversity (FD) provides ways to understand phenomena as complex as community assembly or the dynamics of biodiversity change under multiple pressures. Different frameworks are used to quantify FD, either based on dissimilarity matrices (e.g., Rao entropy, functional dendrograms) or multidimensional spaces (e.g. convex hulls, kernel-density hypervolumes). While the first does not enable the measurement of FD within a richness/divergence/regularity framework, or results in the distortion of the functional space, the latter does not allow for comparisons with phylogenetic diversity (PD) measures and can be extremely sensitive to outliers. We propose the use of neighbor-joining trees (NJ) to represent and quantify functional diversity in a way that combines the strengths of current FD frameworks without many of their weaknesses. Our proposal is also uniquely suited for studies that compare FD with PD, as both share the use of trees (NJ or others) and the same mathematical principles. We test the ability of this novel framework to represent the initial functional distances between species with minimal functional space distortion and sensitivity to outliers. The results using NJ are compared with conventional functional dendrograms, convex hulls, and kernel-density hypervolumes using both simulated and empirical datasets. Using NJ we demonstrate that it is possible to combine much of the flexibility provided by multidimensional spaces with the simplicity of tree-based representations. Moreover, the method is directly comparable with PD measures, and enables quantification of the richness, divergence and regularity of the functional space.

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LepTraits 1.0 A globally comprehensive dataset of butterfly traits

Cited by 25 publications

References 29 publications

Genome-wide gene birth-death dynamics are associated with diet breadth variation in Lepidoptera

Genome-wide gene birth-death dynamics are associated with diet breadth variation in Lepidoptera

Predictors of sequence capture in a large-scale anchored phylogenomics project

Calculating functional diversity metrics using neighbor-joining trees

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