Spatial phylogenetics of butterflies in relation to environmental drivers and angiosperm diversity across North America

Local climate change velocities and evolutionary history explain multidirectional range shifts in a North American butterfly assemblage

Species are often expected to shift their distributions either poleward or upslope to evade warming climates and colonise new suitable climatic niches. However, from 18-years of fixed transect monitoring data on 88 species of butterfly in the midwestern United States, we show that butterflies are shifting their centroids in all directions, except towards regions that are warming the fastest (southeast). Butterflies shifted their centroids at a mean rate of 4.87 km year-1. The rate of centroid shift was significantly associated with local climate change velocity (temperature by precipitation interaction), but not with mean climate change velocity throughout the species' ranges. Species tended to shift their centroids at a faster rate towards regions that are warming at slower velocities but increasing in precipitation velocity. Surprisingly, species' thermal niche breadth (range of climates butterflies experience throughout their distribution) and wingspan (often used as metric for dispersal capability) were not correlated with the rate at which species shifted their ranges. We observed high phylogenetic signal in the direction species shifted their centroids. However, we found no phylogenetic signal in the rate species shifted their centroids, suggesting less conserved processes determine the rate of range shift than the direction species shift their ranges. This research shows important signatures of multidirectional range shifts (latitudinal and longitudinal) and uniquely shows that local climate change velocities are more important in driving range shifts than the mean climate change velocity throughout a species' entire range.

Rising minimum temperatures contribute to 50 years of occupancy decline among cold-adapted Arctic and boreal butterflies in North America

Global climate change has been identified as a potential driver of observed insect declines, yet in many regions, there are critical data gaps that make it difficult to assess how communities are responding to climate change. Poleward regions are of particular interest because warming is most rapid while biodiversity data are most sparse. Building on recent advances in occupancy modeling of presence-only data, we reconstructed 50 years (1970-2019) of butterfly occupancy trends in response to rising minimum temperatures in one of the most under-sampled regions of North America. Among 90 modeled species, we found that cold-adapted species are far more often in decline compared with their warm-adapted, more southernly distributed counterparts. Furthermore, in a post hoc analysis using species' traits, we find that species' range-wide average annual temperature is the only consistent predictor of occupancy changes. Species with warmer ranges were most likely to be increasing in occupancy. This trend results in the majority of butterflies increasing in occupancy probability over the last 50 years. Our results provide the first look at macroscale butterfly biodiversity shifts in high-latitude North America. These results highlight the potential of leveraging the wealth of presence-only data, the most abundant source of biodiversity data, for inferring changes in species distributions.

Predicting and optimizing reactive oxygen species metabolism in Punica granatum L. through machine learning: role of exogenous GABA on antioxidant enzyme activity under drought and salinity stress

BACKGROUND

Drought and salinity stress have been proposed as the main environmental factors threatening food security, as they adversely affect crops' agricultural productivity. As a potential solution, the application of plant growth regulators to enhance drought and salinity tolerance has gained considerable attention. γ-aminobutyric acid (GABA) is a four-carbon non-protein amino acid that accumulates in plants as a response to stressful conditions. This study focused on a comparative assessment of several machine learning (ML) regression models, including radial basis function, generalized regression neural network (GRNN), random forest (RF), and support vector regression (SVR) to develop predictive models for assessing the effect of different concentrations of GABA (0, 10, 20, and 40 mM) on various physio-biochemical traits during periods of drought, salinity, and combined stress conditions. The physio-biochemical traits included antioxidant enzyme activities (superoxide dismutase, SOD; peroxidase, POD; catalase, CAT; and ascorbate peroxidase, APX), protein content, malondialdehyde (MDA) levels, and hydrogen peroxide (H2O2) levels. The non‑dominated sorting genetic algorithm‑II (NSGA‑II) was employed for optimizing the superior prediction model.

RESULTS

The GRNN model outperformed the other ML algorithms and was therefore selected for optimization by NSGA-II. The GRNN-NSGA-II model revealed that treatment with GABA at concentrations of 20.90 mM and 20.54 mM, under combined drought and salinity stress conditions at 20.86 and 20.72 days post-treatment, respectively, could result in the maximum values for protein content (by 0.80 and 0.69), APX activity (by 50.63 and 51.51), SOD activity (by 0.54 and 0.53), POD activity (by 1.53 and 1.72), CAT activity (by 4.42 and 5.66), as well as lower MDA levels (by 0.12 and 0.15) and H2O2 levels (by 0.44 and 0.55), respectively, in the 'Atabaki' and 'Rabab' cultivars.

CONCLUSIONS

This study demonstrates that the GRNN-NSGA-II model, as an advanced ML algorithm with a strong predictive ability for outcomes in combined stressful environmental conditions, provides valuable insights into the significant factors influencing such multifactorial processes.

Description of Uniphylus gen. nov., a new genus of Carcharodini (Lepidoptera, Hesperiidae, Pyrginae) for Staphylus evemerus Godman & Salvin, 1896

Staphylus evemerus Godman & Salvin, 1896 is a species with a unique set of morphological characters within Carcharodini. Also, mitochondrial cytochrome oxidase subunit 1 (COI) sequences analysis demonstrated a large genetic distance with other related genera of the tribe. Therefore, this paper aims to describe a new genus for this species, which is named as Uniphylus gen. nov. Besides the morphological redescription of the male of Uniphylus evemerus (Godman & Salvin, 1896) new comb., the description of the female is provided for the first time, as well as an updated distributional map with all records known so far for this species.

Global determinants of insect mitochondrial genetic diversity

Understanding global patterns of genetic diversity is essential for describing, monitoring, and preserving life on Earth. To date, efforts to map macrogenetic patterns have been restricted to vertebrates, which comprise only a small fraction of Earth's biodiversity. Here, we construct a global map of predicted insect mitochondrial genetic diversity from cytochrome c oxidase subunit 1 sequences, derived from open data. We calculate the mitochondrial genetic diversity mean and genetic diversity evenness of insect assemblages across the globe, identify their environmental correlates, and make predictions of mitochondrial genetic diversity levels in unsampled areas based on environmental data. Using a large single-locus genetic dataset of over 2 million globally distributed and georeferenced mtDNA sequences, we find that mitochondrial genetic diversity evenness follows a quadratic latitudinal gradient peaking in the subtropics. Both mitochondrial genetic diversity mean and evenness positively correlate with seasonally hot temperatures, as well as climate stability since the last glacial maximum. Our models explain 27.9% and 24.0% of the observed variation in mitochondrial genetic diversity mean and evenness in insects, respectively, making an important step towards understanding global biodiversity patterns in the most diverse animal taxon.

Diversity of Palaearctic Dragonflies and Damselflies (Odonata)

More than 1.2 million distribution records were used to create species distribution models for 402 Palaearctic species of dragonflies and damselflies. On the basis of these diversity maps of total, lentic and lotic diversity for the whole of the Palaearctic (excluding China and the Himalayan region) are presented. These maps show a clear pattern of decreasing diversity longitudinally, with species numbers dropping in the eastern half of Europe and remaining low throughout a large part of Russia, then increasing again towards Russia’s Far East and Korea. There are clear differences in diversity patterns of lentic and lotic species, with lentic species being dominant in colder and more arid areas. Areas with a high diversity of species assessed as threatened on the IUCN red list are largely restricted to the Mediterranean, Southwest Asia, and Japan, with clear hotspots found in the Levant and the southern half of Japan. The diversity at species, generic, and family level is higher in the south of Japan than in areas at a similar latitude in the western Mediterranean. This is likely to be the result of the more humid climate of Japan resulting in a higher diversity of freshwater habitats and the stronger impact of the glacial periods in the Western Palaearctic in combination with the Sahara, preventing tropical African lineages dispersing northwards.

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

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.

LepTraits 1.0 A globally comprehensive dataset of butterfly traits

Here, we present the largest, global dataset of Lepidopteran traits, focusing initially on butterflies (ca. 12,500 species records). These traits are derived from field guides, taxonomic treatments, and other literature resources. We present traits on wing size, phenology,voltinism, diapause/overwintering stage, hostplant associations, and habitat affinities (canopy, edge, moisture, and disturbance). This dataset will facilitate comparative research on butterfly ecology and evolution and our goal is to inspire future research collaboration and the continued development of this dataset.

Measurement(s)	Wingspan • Habitat Affinity • oviposition • voltinism • phenology • hostplant association
Technology Type(s)	natural language processing
Sample Characteristic - Organism	Lepidoptera
Sample Characteristic - Location	Global