Are wing contours good classifiers for automatic identification in Odonata? A view from the Targeted Odonata Wing Digitization (TOWD) project

In recent decades, a lack of available knowledge about the magnitude, identity and distribution of biodiversity has given way to a taxonomic impediment where species are not being described as fast as the rate of extinction. Using Machine Learning methods based on seven different algorithms (LR, CART, KNN, GNB, LDA, SVM and RFC) we have created an automatic identification approach for odonate genera, through images of wing contours.

Odonata species diversity, distributions, and status in a rare sand prairie-savanna wetscape

Inland sand areas scattered across the North American eastern deciduous forest and western tallgrass prairie ecotone are known for supporting pyrogenic early-successional vegetation and specially adapted terrestrial faunas. Many of these globally and regionally rare systems contain functionally connected wetland networks (“wetscapes”) potentially important for aquatic insects.

Growth, winter preparations and timing of emergence in temperate zone Odonata: control by a succession of larval response patterns

As warm-adapted insects of tropical origin, Odonata cope with cold periods by seasonal regulation and diapause. A model for larval-overwintering species is proposed with three response patterns related to the timing of emergence, which can be predicted from seasonal cues during the last few stadia. For emergence during the present season, there is an often time constrained pre-emergence development, accelerated by long days and higher temperatures.