Genetic variation in neotropical butterflies is associated with sampling scale, species distributions, and historical forest dynamics

Abstract

Previous studies of butterfly diversification in the Neotropics have focused on Amazonia and the tropical Andes, while southern regions of the continent have received little attention. To address the gap in knowledge about the Lepidoptera of temperate South America, we analysed over 3000 specimens representing nearly 500 species from Argentina for a segment of the mitochondrial cytochrome c oxidase subunit I (COI) gene. Representing 42% of the country's butterfly fauna, collections targeted species from the Atlantic and Andean forests, and biodiversity hotspots that were previously connected but are now isolated. We assessed COI effectiveness for

Novel Pipeline for Large-Scale Comparative Population Genetics

“…Though the use of CO1 has some limitations, such as the limitations in its ability to infer deep phylogenetic relationships (Boyle & Adamowicz, 2015, Wilson, 2010), this marker was suitable for our analysis. As discussed earlier, CO1 is commonly used to identify and delineate species, and studies have also found success using it when investigating population genetic structure (Abuelmaali et al, 2021, Attiná et al, 2021, Choi et al, 2020, Froufe et al, 2014, Havel et al, 2000, Jossart et al, 2017, Liu et al, 2020, Meriam et al, 2015, Park et al, 2019, Pickett & David, 2018, Reed et al, 2006, Shum & Palumbi, 2021, Stark et al, 2021, Talbot et al, 2016, Troast et al, 2016, Xu et al, 2019). CO1 sequences of species having variability in this marker, and which are separate in CO1 profile from related species, are suitable for use in this pipeline.…”

“…However, there is evidence that there is sufficient variability in CO1 to detect major population divisions and phylogeographic history (e.g., April et al, 2012, Bernatchez & Wilson, 1998, Keogh et al, 2021, Witt & Hebert, 2000. CO1 has been used to answer questions at a variety of temporal scales, including drawing conclusions about demographic history, divergence times, and events occurring millions of years ago (Attiná et al, 2021, Choi et al, 2020, Havel et al, 2000, Jossart et al, 2017, Reed et al, 2006, Stark et al, 2021, Talbot et al, 2016. Various studies have had success using CO1 to determine population genetic structure (Abuelmaali et al, 2021, Attiná et al, 2021, Choi et al, 2020, Froufe et al, 2014, Havel et al, 2000, Jossart et al, 2017, Meriam et al, 2015, Park et al, 2019, Pickett & David, 2018, Reed et al, 2006, Shum & Palumbi, 2021, Stark et al, 2021, Talbot et al, 2016, Troast et al, 2016, Xu et al, 2019.…”

“…CO1 has been used to answer questions at a variety of temporal scales, including drawing conclusions about demographic history, divergence times, and events occurring millions of years ago (Attiná et al, 2021, Choi et al, 2020, Havel et al, 2000, Jossart et al, 2017, Reed et al, 2006, Stark et al, 2021, Talbot et al, 2016. Various studies have had success using CO1 to determine population genetic structure (Abuelmaali et al, 2021, Attiná et al, 2021, Choi et al, 2020, Froufe et al, 2014, Havel et al, 2000, Jossart et al, 2017, Meriam et al, 2015, Park et al, 2019, Pickett & David, 2018, Reed et al, 2006, Shum & Palumbi, 2021, Stark et al, 2021, Talbot et al, 2016, Troast et al, 2016, Xu et al, 2019. This success was primarily due to CO1's high rate of evolution and mutation, as well as faster coalescence rate compared to nuclear markers, which makes it useful for determining genetic differences between isolated populations (Froufe et al, 2014, Havel et al, 2000, Meriam et al, 2015, Stark et al, 2021, Xu et al, 2019.…”

“…This success was primarily due to CO1's high rate of evolution and mutation, as well as faster coalescence rate compared to nuclear markers, which makes it useful for determining genetic differences between isolated populations (Froufe et al, 2014, Havel et al, 2000, Meriam et al, 2015, Stark et al, 2021, Xu et al, 2019. CO1 has been shown to provide accurate species identification and perform well for differentiating species, though this accuracy may be reduced as the geographic distance increases (Attiná et al, 2021).…”

Comprehensive DNA barcode reference library and optimization of genetic divergence threshold facilitate the exploration of species diversity of green lacewings (Neuroptera: Chrysopidae)

“…We tested these values with the function "threshOpt". The genetic distance with the lowest cumulative error (i.e., the sum of false positive and false negative identifications) is considered to be the final optimized threshold for the "threshOpt" analysis (Brown et al, 2012;Attiná et al, 2021). The "localMinima" function will generate a scatter plot based on the matrix of genetic distance, and fit into a hump-shaped curve.…”