Influence of temperature on pea aphid Acyrthosiphon pisum (Hemiptera: Aphididae) resistance to natural enemy attack

The ability to resist or avoid natural enemy attack is a critically important insect life history trait, yet little is understood of how these traits may be affected by temperature. This study investigated how different genotypes of the pea aphid Acyrthosiphon pisum Harris, a pest of leguminous crops, varied in resistance to three different natural enemies (a fungal pathogen, two species of parasitoid wasp and a coccinellid beetle), and whether expression of resistance was influenced by temperature. Substantial clonal variation in resistance to the three natural enemies was found. Temperature influenced the number of aphids succumbing to the fungal pathogen Erynia neoaphidis Remaudiere & Hermebert, with resistance increasing at higher temperatures (18 vs. 28degreesC). A temperature difference of 5degreesC (18 vs. 23degreesC) did not affect the ability of A. pisum to resist attack by the parasitoids Aphidius ervi Haliday and A. eadyi Stary Gonzalez & Hall. Escape behaviour from foraging coccinellid beetles (Hippodamia convergens Guerin-Meneville) was not directly influenced by aphid clone or temperature (16 vs. 21degreesC). However, there were significant interactions between clone and temperature (while most clones did not respond to temperature, one was less likely to escape at 16degreesC), and between aphid clone and ladybird presence (some clones showed greater changes in escape behaviour in response to the presence of foraging coccinellids than others). Therefore, while larger temperature differences may alter interactions between Acyrthosiphon pisum and an entomopathogen, there is little evidence to suggest that smaller changes in temperature will alter pea aphid-natural enemy interactions.

Nocturnal ranging behaviour of urban hedgehogs, Erinaceus europaeus, in relation to risk and reward

Urban areas have both positive and negative influences on wildlife. For terrestrial mammals, one of the principle problems is the risk associated with moving through the environment whilst foraging. In this study, we examined nocturnal patterns of movement of urban-dwelling hedgehogs (Erinaceus europaeus) in relation to (i) the risks posed by predators and motor vehicles and (ii) nightly weather patterns. Hedgehogs preferentially utilised the gardens of semi-detached and terraced houses. However, females, but not males, avoided the larger back gardens of detached houses, which contain more of the habitat features selected by badgers. This difference in the avoidance of predation risk is probably associated with sex differences in breeding behaviour. Differences in nightly movement patterns were consistent with strategies associated with mating behaviour and the accumulation of fat reserves for hibernation. Hedgehogs also exhibited differences in behaviour associated with the risks posed by humans; they avoided actively foraging near roads and road verges, but did not avoid crossing roads per se. They were, however, significantly more active after midnight when there was a marked reduction in vehicle and foot traffic. In particular, responses to increased temperature, which is associated with increased abundance of invertebrate prey, were only observed after midnight. This variation in the timing of bouts of activity would reduce the risks associated with human activities. There were also profound differences in both area ranged and activity with chronological year which warrant further investigation.

Accumulation of anticoagulant rodenticides in a non-target insectivore, the European hedgehog (Erinaceus europaeus)

Studies on exposure of non-targets to anticoagulant rodenticides have largely focussed on predatory birds and mammals; insectivores have rarely been studied. We investigated the exposure of 120 European hedgehogs (Erinaceus europaeus) from throughout Britain to first- and second-generation anticoagulant rodenticides (FGARs and SGARs) using high performance liquid chromatography coupled with fluorescence detection (HPLC) and liquid-chromatography mass spectrometry (LCMS). The proportion of hedgehogs with liver SGAR concentrations detected by HPLC was 3-13% per compound, 23% overall. LCMS identified much higher prevalence for difenacoum and bromadiolone, mainly because of greater ability to detect low level contamination. The overall proportion of hedgehogs with LCMS-detected residues was 57.5% (SGARs alone) and 66.7% (FGARs and SGARs combined); 27 (22.5%) hedgehogs contained >1 rodenticide. Exposure of insectivores and predators to anticoagulant rodenticides appears to be similar. The greater sensitivity of LCMS suggests that hitherto exposure of non-targets is likely to have been under-estimated using HPLC techniques.

Sparse kernel density estimation technique based on zero-norm constraint

A sparse kernel density estimator is derived based on the zero-norm constraint, in which the zero-norm of the kernel weights is incorporated to enhance model sparsity. The classical Parzen window estimate is adopted as the desired response for density estimation, and an approximate function of the zero-norm is used for achieving mathemtical tractability and algorithmic efficiency. Under the mild condition of the positive definite design matrix, the kernel weights of the proposed density estimator based on the zero-norm approximation can be obtained using the multiplicative nonnegative quadratic programming algorithm. Using the -optimality based selection algorithm as the preprocessing to select a small significant subset design matrix, the proposed zero-norm based approach offers an effective means for constructing very sparse kernel density estimates with excellent generalisation performance.

Particle swarm optimization aided orthogonal forward regression for unified data modeling

We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density function estimation. A particle swarm optimization (PSO) aided orthogonal forward regression (OFR) algorithm based on leave-one-out (LOO) criteria is developed to construct parsimonious radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines the center vector and diagonal covariance matrix of one RBF node by minimizing the LOO statistics. For regression applications, the LOO criterion is chosen to be the LOO mean square error, while the LOO misclassification rate is adopted in two-class classification applications. By adopting the Parzen window estimate as the desired response, the unsupervised density estimation problem is transformed into a constrained regression problem. This PSO aided OFR algorithm for tunable-node RBF networks is capable of constructing very parsimonious RBF models that generalize well, and our analysis and experimental results demonstrate that the algorithm is computationally even simpler than the efficient regularization assisted orthogonal least square algorithm based on LOO criteria for selecting fixed-node RBF models. Another significant advantage of the proposed learning procedure is that it does not have learning hyperparameters that have to be tuned using costly cross validation. The effectiveness of the proposed PSO aided OFR construction procedure is illustrated using several examples taken from regression and classification, as well as density estimation applications.

Probability density estimation with tunable kernels using orthogonal forward regression

A generalized or tunable-kernel model is proposed for probability density function estimation based on an orthogonal forward regression procedure. Each stage of the density estimation process determines a tunable kernel, namely, its center vector and diagonal covariance matrix, by minimizing a leave-one-out test criterion. The kernel mixing weights of the constructed sparse density estimate are finally updated using the multiplicative nonnegative quadratic programming algorithm to ensure the nonnegative and unity constraints, and this weight-updating process additionally has the desired ability to further reduce the model size. The proposed tunable-kernel model has advantages, in terms of model generalization capability and model sparsity, over the standard fixed-kernel model that restricts kernel centers to the training data points and employs a single common kernel variance for every kernel. On the other hand, it does not optimize all the model parameters together and thus avoids the problems of high-dimensional ill-conditioned nonlinear optimization associated with the conventional finite mixture model. Several examples are included to demonstrate the ability of the proposed novel tunable-kernel model to effectively construct a very compact density estimate accurately.

Regression based D-optimality experimental design for sparse kernel density estimation

This paper derives an efficient algorithm for constructing sparse kernel density (SKD) estimates. The algorithm first selects a very small subset of significant kernels using an orthogonal forward regression (OFR) procedure based on the D-optimality experimental design criterion. The weights of the resulting sparse kernel model are then calculated using a modified multiplicative nonnegative quadratic programming algorithm. Unlike most of the SKD estimators, the proposed D-optimality regression approach is an unsupervised construction algorithm and it does not require an empirical desired response for the kernel selection task. The strength of the D-optimality OFR is owing to the fact that the algorithm automatically selects a small subset of the most significant kernels related to the largest eigenvalues of the kernel design matrix, which counts for the most energy of the kernel training data, and this also guarantees the most accurate kernel weight estimate. The proposed method is also computationally attractive, in comparison with many existing SKD construction algorithms. Extensive numerical investigation demonstrates the ability of this regression-based approach to efficiently construct a very sparse kernel density estimate with excellent test accuracy, and our results show that the proposed method compares favourably with other existing sparse methods, in terms of test accuracy, model sparsity and complexity, for constructing kernel density estimates.

Radial basis function classifier construction using particle swarm optimisation aided orthogonal forward regression

We develop a particle swarm optimisation (PSO) aided orthogonal forward regression (OFR) approach for constructing radial basis function (RBF) classifiers with tunable nodes. At each stage of the OFR construction process, the centre vector and diagonal covariance matrix of one RBF node is determined efficiently by minimising the leave-one-out (LOO) misclassification rate (MR) using a PSO algorithm. Compared with the state-of-the-art regularisation assisted orthogonal least square algorithm based on the LOO MR for selecting fixednode RBF classifiers, the proposed PSO aided OFR algorithm for constructing tunable-node RBF classifiers offers significant advantages in terms of better generalisation performance and smaller model size as well as imposes lower computational complexity in classifier construction process. Moreover, the proposed algorithm does not have any hyperparameter that requires costly tuning based on cross validation.

Structures of Pd(CN)2and Pt(CN)2: intrinsically nanocrystalline materials?

Analysis and modeling of X-ray and neutron Bragg and total diffraction data show that the compounds referred to in the literature as “Pd(CN)2”and“Pt(CN)2” are nanocrystalline materials containing of small sheets of vertex-sharing square-planar M(CN)4 units, layered in a disordered manner with an intersheet separation of 3.44 A at 300 K. The small size of the crystallites means that the sheets’ edges form a significant fraction of each material. The Pd(CN)2 nanocrystallites studied using total neutron diffraction are terminated by water and the Pt(CN)2 nanocrystallites by ammonia, in place of half of the terminal cyanide groups, thus maintaining charge neutrality. The neutron samples contain sheets of approximate dimensions 30 A x 30 A. For sheets of the size we describe, our structural models predict compositions of Pd(CN)2-xH2O and Pt(CN)2-yNH3 (x = y = 0.29). These values are in good agreement with those obtained from total neutron diffraction and thermal analysis, and are also supported by infrared and Raman spectroscopy measurements. It is also possible to prepare related compounds Pd(CN)2-pNH3 and Pt(CN)2-qH2O, in which the terminating groups are exchanged. Additional samples showing sheet sizes in the range 10 A x 10 A (y = 0.67) to 80 A x 80 A (p = q = 0.12), as determined by X-ray diffraction, have been prepared. The related mixed-metal phase, Pd1/2Pt1/2(CN)2-qH2O(q = 0.50), is also nanocrystalline (sheet size 15 A x 15 A). In all cases, the interiors of the sheets are isostructural with those found in Ni(CN)2. Removal of the final traces of water or ammonia by heating results in decomposition of the compounds to Pd and Pt metal, or in the case of the mixed-metal cyanide, the alloy, Pd1/2Pt1/2, making it impossible to prepare the simple cyanides, Pd(CN)2, Pt(CN)2 or Pd1/2Pt1/2(CN)2, by this method.

The role of management and landscape context in the restoration of grassland phytophagous beetles

1. Declines in area and quality of species-rich mesotrophic and calcareous grasslands have occurred all across Europe.While the European Union has promoted schemes to restore these grasslands, the emphasis for management has remained largely focused on plants. Here we focus on restoration of the phytophagous beetles of these grasslands. Although local management, particularly that which promotes the establishment of host plants, is key to restoration success, dispersal limitation is also likely to be an important limiting factor during the restoration of phytophagous beetle assemblages. 2. Using a 3-year multi-site experiment, we investigated how restoration success of phytophagous beetles was affected by hay-spreading management (intended to introduce target plant species), success in restoration of the plant communities and the landscape context within which restoration was attempted. 3. Restoration success of the plants was greatest where green hay spreading had been used to introduce seeds into restoration sites. Beetle restoration success increased over time, although hayspreading had no direct effect. However, restoration success of the beetles was positively correlated with restoration success of the plants. 4. Overall restoration success of the phytophagous beetles was positively correlated with the proportion of species-rich grassland in the landscape, as was the restoration success of the polyphagous beetles. Restoration success for beetles capable of flight and those showing oligophagous host plant specialism were also positively correlated with connectivity to species-rich grasslands. There was no indication that beetles not capable of flight showed greater dependence on landscape scale factors than flying species. 5. Synthesis and applications. Increasing the similarity of the plant community at restoration sites to target species-rich grasslands will promote restoration success for the phytophagous beetles. However, landscape context is also important, with restoration being approximately twice as successful in those landscapes containing high as opposed to low proportions of species-rich grassland. By targeting grassland restoration within landscapes containing high proportions of species-rich grassland, dispersal limitation problems associated with restoration for invertebrate assemblages are more likely to be overcome.