Phylogeny and classification of Odonata using targeted genomics.

Dragonflies and damselflies are a charismatic, medium-sized insect order (~6300 species) with a unique potential to approach comparative research questions. Their taxonomy and many ecological traits for a large fraction of extant species are relatively well understood. However, until now, the lack of a large-scale phylogeny based on high throughput data with the potential to connect both perspectives has precluded comparative evolutionary questions for these insects. Here, we provide an ordinal hypothesis of classification based on anchored hybrid enrichment using a total of 136 species representing 46 of the 48 families or incertae sedis, and a total of 478 target loci. Our analyses recovered the monophyly for all three suborders: Anisoptera, Anisozygoptera and Zygoptera. Although the backbone of the topology was reinforced and showed the highest support values to date, our genomic data was unable to stronglyresolve portions of the topology. In addition, a quartet sampling approach highlights the potential evolutionary scenarios that may have shaped evolutionary phylogeny (e.g., incomplete lineage sorting and introgression) of this taxon. Finally, in light of our phylogenomic reconstruction and previous morphological and molecular information we proposed an updated odonate classification and define five new families (Amanipodagrionidae fam. nov., Mesagrionidae fam. nov., Mesopodagrionidae fam. nov., Priscagrionidae fam. nov., Protolestidae fam. nov.) and reinstate another two (Rhipidolestidae stat. res., Tatocnemididae stat. res.). Additionally, we feature the problematic taxonomic groupings for examination in future studies to improve our current phylogenetic hypothesis.

Validation of Inertial Sensor to Measure Barbell Kinematics across a Spectrum of Loading Conditions.

The aim of this study was to evaluate the level of agreement in measuring back squat kinematics between an inertial measurement unit (IMU) and a 3D motion capture system (3DMOCAP). Kinematic variables included concentric peak velocity (CPV), concentric mean velocity (CMV), eccentric peak velocity (EPV), eccentric mean velocity (EMV), mean propulsive velocity (MPV), and POP-100: a proprietary variable. Sixteen resistance-trained males performed an incrementally loaded one repetition maximum (1RM) squat protocol. A series of Pearson correlations, 2 × 4 RM ANOVA, Cohen's d effect size differences, coefficient of variation (CV), and standard error of the estimate (SEE) were calculated. A large relationship existed for all variables between devices (r = 0.78-0.95). Between-device agreement for CPV worsened beyond 60% 1RM. The remaining variables were in agreement between devices with trivial effect size differences and similar CV magnitudes. These results support the use of the IMU, regardless of relative intensity, when measuring EMV, EPV, MPV, and POP-100. However, practitioners should carefully select kinematic variables of interest when using the present IMU device for velocity-based training (VBT), as certain measurements (e.g., CMV, CPV) do not possess practically acceptable reliability or accuracy. Finally, the IMU device exhibited considerable practical data collection concerns, as one participant was completely excluded and 13% of the remaining attempts displayed obvious internal error.

Quantifying the degree of bias from using county-scale data in species distribution modeling: Can increasing sample size or using county-averaged environmental data reduce distributional overprediction?

Citizen-science databases have been used to develop species distribution models (SDMs), although many taxa may be only georeferenced to county. It is tacitly assumed that SDMs built from county-scale data should be less precise than those built with more accurate localities, but the extent of the bias is currently unknown. Our aims in this study were to illustrate the effects of using county-scale data on the spatial extent and accuracy of SDMs relative to true locality data and to compare potential compensatory methods (including increased sample size and using overall county environmental averages rather than point locality environmental data). To do so, we developed SDMs in maxent with PRISM-derived BIOCLIM parameters for 283 and 230 species of odonates (dragonflies and damselflies) and butterflies, respectively, for five subsets from the OdonataCentral and Butterflies and Moths of North America citizen-science databases: (1) a true locality dataset, (2) a corresponding sister dataset of county-centroid coordinates, (3) a dataset where the average environmental conditions within each county were assigned to each record, (4) a 50/50% mix of true localities and county-centroid coordinates, and (5) a 50/50% mix of true localities and records assigned the average environmental conditions within each county. These mixtures allowed us to quantify the degree of bias from county-scale data. Models developed with county centroids overpredicted the extent of suitable habitat by 15% on average compared to true locality models, although larger sample sizes (>100 locality records) reduced this disparity. Assigning county-averaged environmental conditions did not offer consistent improvement, however. Because county-level data are of limited value for developing SDMs except for species that are widespread and well collected or that inhabit regions where small, climatically uniform counties predominate, three means of encouraging more accurate georeferencing in citizen-science databases are provided.