Tree-killing bark beetles are the most economically important insects in conifer forests worldwide. However, despite N200 years of research, the drivers of population eruptions and crashes are still not fully understood and the existing knowledge is thus insufficient to face the challenges posed by the Anthropocene. We critically analyze potential biotic and abiotic drivers of population dynamics of an exemplary species, the European spruce bark beetle (ESBB) (Ips typographus) and present a multivariate approach that integrates the many drivers governing this bark beetle system. We call for hypothesis-driven, large-scale collaborative research efforts to improve our understanding of the population dynamics of this and other bark beetle pests. Our approach can serve as a blueprint for tackling other eruptive forest insects.

No abstract

1. The extinction of species is a non-random process, and understanding why some species are more likely to go extinct than others is critical for conservation efforts. Functional trait-based approaches offer a promising tool to achieve this goal. In forests, deadwood-dependent (saproxylic) beetles comprise a major part of threatened species, but analyses of their extinction risk have been hindered by the availability of suitable morphological traits.2. To better understand the mechanisms underlying extinction in insects, we investigated the relationships between morphological features and the extinction risk of saproxylic beetles. Specifically, we hypothesised that species darker in colour, with a larger and rounder body, a lower mobility, lower sensory perception and more robust mandibles are at higher risk.3. We first developed a protocol for morphological trait measurements and present a database of 37 traits for 1,157 European saproxylic beetle species. Based on 13 selected, independent traits characterising aspects of colour, body shape, locomotion, sensory perception and foraging, we used a proportional-odds multiple linear mixed-effects model to model the German Red List categories of 744 species as an ordinal index of extinction risk.4. Six out of 13 traits correlated significantly with extinction risk. Larger species as well as species with a broad and round body had a higher extinction risk than small, slimThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

No abstract

This publication provides the first comprehensive DNA barcode data set for the Neuropterida of Central Europe, including 80 of the 102 species (78%) recorded from Bavaria (Germany) and three other species from nearby regions (Austria, France and the UK). Although the 286 specimens analyzed had a heterogeneous conservation history (60% dried; 30% in 80% EtOH; 10% fresh specimens in 95% EtOH), 237 (83%) generated a DNA barcode. Eleven species (13%) shared a BIN, but three of these taxa could be discriminated through barcodes. Four pairs of closely allied species shared barcodes including Chrysoperla pallida Henry et al., 2002 and C. lucasina Lacroix, 1912; Wesmaelius concinnus (Stephens, 1836) and W. quadrifasciatus (Reuter, 1894); Hemerobius handschini Tjeder, 1957 and H. nitidulus Fabricius, 1777; and H. atrifrons McLachlan, 1868 and H. contumax Tjeder, 1932. Further studies are needed to test the possible synonymy of these species pairs or to determine if other genetic markers permit their discrimination. Our data highlight five cases of potential cryptic diversity within Bavarian Neuropterida: Nineta flava (Scopoli, 1763), Sympherobius pygmaeus (Rambur, 1842), Sisyra nigra (Retzius, 1783), Semidalis aleyrodiformis (Stephens, 1836) and Coniopteryx pygmaea Enderlein, 1906 are each split into two or three BINs. The present DNA barcode library not only allows the identification of adult and larval stages, but also provides valuable information for alpha-taxonomy, and for ecological and evolutionary research.

No abstract

No abstract

No abstract