Climate change and cereal aphids: the relative effects of increasing CO2 and temperature on aphid population dynamics

Experimental evidence regarding the responses of cereal aphids to rising atmospheric CO₂ has been ambiguous. Some studies suggest increased population sizes under future CO₂ levels, others suggest decreased population sizes, and still others suggest little or no difference. Recently, Newman et al. (2003) constructed a general mathematical model of the aphid–grass interaction to investigate whether or not we should, in fact, expect a general aphid response to rising CO₂. They concluded that aphid populations are likely to be larger under future CO₂ concentrations if soil N levels are high, the aphid species' nitrogen requirement is low and the aphid species' density‐dependent response in winged morph production is weak. In that model, and in field experiments, CO₂ concentration influences aphid population dynamics through the effect it has on plant quality. However, future CO₂ concentrations are also likely to be accompanied by higher ambient temperatures, a combination that has received little focus to date. In the present paper, the Newman et al. model is used to consider the combined effects of increased CO₂ concentrations and temperature on aphid population sizes. It is concluded that, when both factors are elevated, aphid population dynamics will be more similar to current ambient conditions than expected from the results of experiments studying either factor alone. This result has important implications for future experimentation.     

Assessing the importance of a self-generated detachment process in river biofilm models

1. Epilithic biofilm biomass was measured for 14 months in two sites, located up‐ and downstream of the city of Toulouse in the Garonne River (south‐west France). Periodical sampling provided a biomass data set to compare with simulations from the model of Uehlinger, Bürher and Reichert (1996: Freshwater Biology, 36 , 249–263.), in order to evaluate the impact of hydraulic disturbance. 2. Despite differences in application conditions (e.g. river size, discharge, frequency of disturbance), the base equation satisfactorily predicted biomass between low and high water periods of the year, suggesting that the flood disturbance regime may be considered a universal mechanism controlling periphyton biomass. 3. However modelling gave no agreement with biomass dynamics during the 7‐month long low water period that the river experienced. The influence of other biomass‐regulating factors (temperature, light and soluble reactive phosphorus) on temporal biomass dynamics was weak. 4. Implementing a supplementary mechanism corresponding to a temperature‐dependent self‐generated loss because of heterotrophic processes allowed us to accurately reproduce the observed pattern: a succession of two peaks. This case study suggests that during typical summer low water periods (flow stability and favourable temperature) river biofilm modelling requires self‐generated detachment to be considered.     

Describing Ordinal Odds Ratios for Stratified r × c Tables

For an r × ctable with ordinal responses, odds ratios are commonly used to describe the relationship between the row and column variables. This article shows two types of ordinal odds ratios where local‐global odds ratios are used to compare several groups on a c‐category ordinal response and a global odds ratio is used to measure the global association between a pair of ordinal responses. When there is a stratification factor, we consider Mantel‐Haenszel (MH) type estimators of these odds ratios to summarize the association from several strata. Like the ordinary MH estimator of the common odds ratio for several 2 × 2 contingency tables, the estimators are used when the association is not expected to vary drastically among the strata. Also, the estimators are consistent under the ordinary asymptotic framework in which the number of strata is fixed and also under sparse asymptotics in which the number of strata grows with the sample size. Compared to the maximum likelihood estimators, simulations find that the MH type estimators perform better especially when each stratum has few observations. This article provides variances and covariances formulae for the local‐global odds ratios estimators and applies the bootstrap method to obtain a standard error for the global odds ratio estimator. At the end, we discuss possible ways of testing the homogeneity assumption.     

Coexistence of natural enemies in a multitrophic host–parasitoid system

Abstract.  1. This study explored the temporal and spatial aspects of coexistence over many generations in a multispecies host–parasitoid assemblage.
2. The long-term interaction between the cabbage root fly, Delia radicum (Diptera: Anthomyiidae), and two of its natural enemies, Trybliographa rapae (Hymenoptera: Fitigidae) and Aleochara bilineata (Coleoptera: Staphylinidae), in a cultivated field at Silwood Park over 19 years was explored.
3. Although time series showed that the populations were regulated, the impact of the natural enemies was highly variable. Within-year determinants showed that the spatial response of the specialist parasitoid, T. rapae , was predominantly independent of host density while A. bilineata acted simply as a randomly foraging generalist parasitoid.
4. These findings are compared and contrasted with an earlier investigation of the same system when only the first 9 years of the time series were available. This study demonstrated the potential of long-term field studies for exploring hypotheses on population regulation, persistence, and coexistence.     

Introducing the Multivariate Dale Model in Population‐Based Genetic Association Studies

Until recently, the most common parametric approaches to study the combined effects of several genetic polymorphisms located within a gene or in a small genomic region are, at the genotype level, logistic regressions and at the haplotype level, haplotype analyses. An alternative modeling approach, based on the case/control principle, is to regard exposures (e.g., genetic data such as derived from Single Nucleotide Polymorphisms – SNPs) as random and disease status as fixed and to use a marginal multivariate model that accounts for inter‐relationships between exposures. One such model is the multivariate Dale model. This model is based on multiple logistic regressions. That is why the model, applied in a case/control setting, leads to straightforward interpretations that are similar to those drawn in a classical logistic modeling framework. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Nitrogen and Phosphorus in the Finnish Energy System, 1900–2003

In producing power, humans move the nutrients nitrogen (N) and phosphorus (P) from their long‐term geological and biological stocks and release or emit them in soil, water, and the atmosphere. In Finland, peat combustion is an important driver of N and P fluxes from the environment to human economy. The flows of N and P in the Finnish energy system were quantified with partial substance flow analysis, and the driving forces of emissions of nitrogen oxides (NOx) were analyzed using the ImPACT model. In the year 2000 in Finland, 140,000 tonnes of nitrogen entered the energy system, mainly in peat and hard coal. Combustion released an estimated 66,000 tonnes of N as nitrogen oxides (NOx) and nitrous oxides (N2O) and another 74,000 tonnes as elemental N2. Most of the emissions were borne in traffic. At the same time, 6,000 tonnes of P was estimated to enter the Finnish energy system, mostly in peat and wood. Ash was mainly used in earth construction and disposed in landfills; thus negligible levels of P were recycled back to nature. During the twentieth century, fuel‐borne input of N increased 20‐fold, and of P 8‐fold. In 1900–1950, the increasing use of hard coal slowly boosted N input, whereas wood fuels were the main carrier of P. Since 1970, the fluxes have been on the rise. NOx emissions leveled off in the 1980s, though, and then declined in conjunction with improvements in combustion technologies such as NOx removal (de‐NOx) technologies in energy production and catalytic converters in cars.