Dinarippiger gen. nov. (Tettigoniidae: Bradyporinae: Ephippigerini), a new saddle bush-cricket genus for Ephippiger discoidalis Fieber, 1853 from the Dinaric karst

A new genus of the tribe Ephippigerini, Dinarippiger Skejo, Kasalo, Fontana et Tvrtković gen. nov., is described based on the characters of occiput coloration, tegmina coloration, cerci and pronotum shape. The new genus is morphologically intermediate between the genera Ephippiger Berthold, 1827 and Uromenus Bolívar, 1878, and presently includes only Dalmatian Saddle Bush Cricket, Dinarippiger discoidalis (Fieber, 1853) comb. nov., hitherto known as Ephippiger discoidalis Fieber, 1853. The species inhabits NE Italy (mainly Carso Triestino), SW Slovenia, Croatia, Bosnia & Herzegovina, and Montenegro, i.e., islands and karst habitats along the eastern Adriatic coast, with isolated findings in Albania and Italy. Its prominent variation in size and coloration has already produced many synonyms (= limbata Fischer, 1853, = limbata var. major Krauss, 1879, = limbata var. minor Krauss, 1879, = selenophora Fieber, 1853, = sphacophila Krauss, 1879), which may suggest that what is currently regarded as a single species could represent a complex of distinct species with restricted distributions. This study also presents an annotated distribution map and a bioacoustic analysis of D. discoidalis comb. nov. Further research, especially adopting molecular methods, is necessary to assess possible cryptic diversity within the genus Dinarippiger gen. nov. and elucidate its evolutionary history

A solution to dependency: using multilevel analysis to accommodate nested data

In neuroscience, experimental designs in which multiple observations are collected from a single research object (for example, multiple neurons from one animal) are common: 53% of 314 reviewed papers from five renowned journals included this type of data. These so-called 'nested designs' yield data that cannot be considered to be independent, and so violate the independency assumption of conventional statistical methods such as the t test. Ignoring this dependency results in a probability of incorrectly concluding that an effect is statistically significant that is far higher (up to 80%) than the nominal level (usually set at 5%). We discuss the factors affecting the type I error rate and the statistical power in nested data, methods that accommodate dependency between observations and ways to determine the optimal study design when data are nested. Notably, optimization of experimental designs nearly always concerns collection of more truly independent observations, rather than more observations from one research object. © 2014 Nature America, Inc. All rights reserved