Redefining the damselfly families: a comprehensive molecular phylogeny of Zygoptera (Odonata)

An extensive molecular phylogenetic reconstruction of the suborder Zygoptera of the Odonata is presented, based on mitochondrial (16S, COI) and nuclear (28S) data of 59% of the 310 genera recognized and all (suspected) families except the monotypic Hemiphlebiidae. A partial reclassification is proposed, incorporating morphological characters. Many traditional families are recovered as monophyletic, but reorganization of the superfamily Coenagrionoidea into three families is proposed: Isostictidae, Platycnemididae and Coenagrionidae. Archboldargia Lieftinck, Hylaeargia Lieftinck, Palaiargia Förster, Papuargia Lieftinck and Onychargia Selys are transferred from Coenagrionidae to Platycnemididae, and Leptocnemis Selys, Oreocnemis Pinhey and Thaumatagrion Lieftinck from Platycnemididae to Coenagrionidae. Each geographically well‐defined clade of Platycnemididae is recognized as a subfamily, and thus Disparoneurinae (i.e. Old World ‘Protoneuridae’) is incorporated, Calicnemiinae is restricted, and Allocnemidinae (type genus: Allocnemis Selys) subfam.n ., Idiocnemidinae (type genus: Idiocnemis Selys) subfam.n . and Onychargiinae (type genus: Onychargia Selys) subfam.n . and Coperini trib.n . (type genus: Copera Kirby) are described. Half of Coenagrionidae belongs to a well‐supported clade incorporating Coenagrion Kirby and the potential subfamilies Agriocnemidinae, Ischnurinae and Pseudagrioninae. The remainder is less well defined, but includes the Pseudostigmatidae and New World Protoneuridae that, with Argiinae and Teinobasinae, may prove valid subfamilies with further evidence. Ninety‐two per cent of the genera formerly included in the polyphyletic Amphipterygidae and Megapodagrionidae were studied. Pentaphlebiidae, Rimanellidae and Devadattidae fam.n . (type genus: Devadatta Kirby) are separated from Amphipterygidae, and Argiolestidae, Heteragrionidae, Hypolestidae, Philogeniidae, Philosinidae and Thaumatoneuridae from Megapodagrionidae. Eight further groups formerly placed in the latter are identified, but are retained as incertae sedis; the validity of Lestoideidae, Philogangidae and Pseudolestidae is confirmed. For some families (e.g. Calopterygidae, Chlorocyphidae) a further subdivision is possible; Protostictinae subfam.n . (type genus: Protosticta Selys) is introduced in Platystictidae. Numerous new combinations are proposed in the Supporting Information. Many long‐established families lack strong morphological apomorphies. In particular, venation is incongruent with molecular results, stressing the need to review fossil Odonata taxonomy: once defined by the reduction of the anal vein, Protoneuridae dissolves completely into six clades from five families.     

Distinction of strains of bean common mosaic virus and blackeye cowpea mosaic virus using antibodies to N- and C- or N-terminal peptide domains of coat proteins

Earlier attempts to discriminate serologically strains NL1, NL3 and NY15 of bean common mosaic virus (BCMV) and strain W of blackeye cowpea mosaic virus (B1CMV) had been unsuccessful. Antibodies directed towards N- and C-, or N-terminal peptide regions of the coat proteins of the above strains enabled the distinction between B1CMV-W, BCMV-NY15 and BCMV-NL3 in electroblot immunoassay and in ELISA. The distinction was better with antibodies directed towards N-termini than with those to N- and C-termini. Strain NL1 of BCMV cross-reacted with both B1CMV-W and BCMV-NY15, but not with BCMV-NL3. Taxonomic implications of these findings are discussed.     

Multiplication and distribution of iris severe mosaic virus in secondarily-infected iris bulbs after stress induced by heat and wounding

In freshly-lifted iris bulbs infected with iris severe mosaic virus (ISMV), virus was not always detected in the basal plate and rarely in bulb scale tissue. After exposing the bulbs to stress (wounding or high-temperature treatment) the sensitivity of virus detection was enhanced. The improved detection of viral antigen after local stress (wounding) coincided with an increase of viral RNA synthesis. When general stress (high-temperature treatment) was applied, the virus could be reliably detected in the basal plate, and usually in vascular bundles and surrounding tissue. Virus was detected in the upper part of the bulb scale when such tissues were detached from the basal plate. Thus, virus must have been present in the scales in localised spots, albeit at a very low concentration, and multiplication is likely to be the main factor involved in the improved sensitivity of viral detection. The distribution of ISMV in the bulb after local or general stress treatment is discussed.     

Origin-related, environmental, sex, and age determinants of immunocompetence, susceptibility to ectoparasites, and disease symptoms in the barn owl

Knowledge of the role of origin‐related, environmental, sex, and age factors on host defence mechanisms is important to understand variation in parasite intensity. Because alternative components of parasite defence may be differently sensitive to various factors, they may not necessarily covary. Many components should therefore be considered to tackle the evolution of host–parasite interactions. In a population of barn owls (Tyto alba), we investigated the role of origin‐related, environmental (i.e. year, season, nest of rearing, and body condition), sex, and age factors on 12 traits linked to immune responses [humoral immune responses towards sheep red blood cells (SRBC), human serum albumin (HSA) and toxoid toxin TT, T‐cell mediated immune response towards the mitogen phytohemagglutinin (PHA)], susceptibility to ectoparasites (number and fecundity of Carnus haemapterus, number of Ixodes ricinus), and disease symptoms (size of the bursa of Fabricius and spleen, proportion of proteins that are immunoglobulins, haematocrit and blood concentration in leucocytes). Cross‐fostering experiments allowed us to detect a heritable component of variation in only four out of nine immune and parasitic parameters (i.e. SRBC‐ and HSA‐responses, haematocrit, and number of C. haemapterus). However, because nestlings were not always cross‐fostered just after hatching, the finding that 44% of the immune and parasitic parameters were heritable is probably an overestimation. These experiments also showed that five out of these nine parameters were sensitive to the nest environment (i.e. SRBC‐ and PHA‐responses, number of C. haemapterus, haematocrit and blood concentration in leucocytes). Female nestlings were more infested by the blood‐sucking fly C. haemapterus than their male nestmates, and their blood was less concentrated in leucocytes. The effect of year, season, age (i.e. reflecting the degree of maturation of the immune system), brood size, position in the within‐brood age hierarchy, and body mass strongly differed between the 12 parameters. Different components of host defence mechanisms are therefore not equally heritable and sensitive to environmental, sex, and age factors, potentially explaining why most of these components did not covary. © 2007 The Linnean Society of London, Biological Journal of the Linnean Society, 2007, 90 , 703–718.     

Evaluation of enteric methane prediction equations for dairy cows used in whole farm models

The importance of evaluating greenhouse gas (GHG) emissions from dairy cows within the whole farm setting is being realized as more important than evaluating these emissions in isolation. Current whole farm models aimed at evaluating GHG emissions make use of simple regression equations to predict enteric methane (CH₄) production. The objective of the current paper is to evaluate the performance of nine CH₄ prediction equations that are currently being used in whole farm GHG models. Data used to evaluate the prediction equations came from a collection of individual (IND) and treatment averaged (TRT) data. Equations were compared based on mean square prediction error (MSPE) and concordance correlation coefficient (CCC) analysis. In general, predictions were poor, with root MSPE (as a percentage of observed mean) values ranging from 20.2 to 52.5 for the IND database and from 24.0 to 38.2 for the TRT database and CCC values ranging from 0.009 to 0.493 for the IND database and from 0.000 to 0.271 for the TRT database. Overall, the equations of Moe & Tyrrell and IPCC Tier II performed best on the IND dataset, and the equations of Moe & Tyrrell and Kirchgeßner et al., performed best on the TRT dataset. Results show that the simple more generalized equations performed worse than those that attempted to represent important aspects of diet composition, but in general significant amounts of bias and deviation of the regression slope from unity existed for all equations. The low prediction accuracy of CH₄ equations in whole farm models may introduce substantial error into inventories of GHG emissions and lead to incorrect mitigation recommendations.     

Effects of climate change on productivity of cereals and legumes; model evaluation of observed year-to-year variability of the CO2 response

The effect of elevated [CO₂] on the productivity of spring wheat, winter wheat and faba bean was studied in experiments in climatized crop enclosures in the Wageningen Rhizolab in 1991–93. Simulation models for crop growth were used to explore possible causes for the observed differences in the CO₂ response. Measurements of the canopy gas exchange (CO₂ and water vapour) were made continuously from emergence until harvest. At an external [CO₂] of 700 μmol mol^?1 Maximum Canopy CO₂ Exchange Rate (CCERmax) at canopy closure was stimulated by 51% for spring wheat and by 71% for faba bean. At the end of the growing season, above ground biomass increase at 700 μmol mol^?1 was 58% (faba bean), 35% (spring wheat) and 19% (winter wheat) and the harvest index did not change. For model exploration, weather data sets for the period 1975-88 and 1991–93 were used, assuming adequate water supply and [CO₂] at 350 and 700 μmol mol^?1. For spring wheat the simulated responses (35–50%) were at the upper end of the experimental results. In agreement with experiments, simulations showed smaller responses for winter wheat and larger responses for faba bean. Further model explorations showed that this differential effect in the CO₂ response may not be primarily due to fundamental physiological differences between the crops, but may be at least partly due to differences in the daily air temperatures during comparable stages of growth of these crops. Simulations also showed that variations between years in CO₂ response can be largely explained by differences in weather conditions (especially temperature) between growing seasons.