Mechanisms and management of acaricide resistance for Tetranychus urticae in agroecosystems

Journal of Pest Science - The two-spotted spider mite, Tetranychus urticae Koch, is a constant threat to sustainable production of numerous economically important crops globally. Management of T....     

Kernel Intensity Estimation of 2-Dimensional Spatial Poisson Point Processes From k-Tree Sampling

To estimate the spatial intensity (density) of plants and animals, ecologists often sample populations by prespecifing a spatial array of points, then measuring the distance from each point to the k nearest organisms, a so-called k-tree sampling method. A variety of ad hoc methods are available for estimating intensity from k-tree sampling data, but they assume that two distinct points of the array do not share nearest neighbors. However, nearest neighbors are likely to be shared when the population intensity is low, as it is in our application. The purpose of this paper is twofold: (a) to derive and use for estimation the likelihood function for a k-tree sample under an inhomogeneous Poisson point-process model and (b) to estimate spatial intensity when nearest neighbors are shared. We derive the likelihood function for an inhomogeneous Poisson point-process with intensity λ(x,y) and propose a likelihood-based, kernel-smoothed estimator \(\hat{\lambda}(x,y)\) . Performance of the method for k=1 is tested on four types of simulated populations: two homogeneous populations with low and high intensity, a population simulated from a bivariate normal distribution of intensity, and a “cliff” population in which the region is divided into high- and low-intensity subregions. The method correctly detected spatial variation in intensity across different subregions of the simulated populations. Application to 1-tree samples of carnivorous pitcher plants populations in four New England peat bogs suggests that the method adequately captures empirical patterns of spatial intensity. However, our method suffers from two evident sources of bias. First, like other kernel smoothers, it underestimates peaks and overestimates valleys. Second, it has positive bias analogous to that of the MLE for the rate parameter of exponential random variables.     

A mechanism of extreme growth and reliable signaling in sexually selected ornaments and weapons

Many male animals wield ornaments or weapons of exaggerated proportions. We propose that increased cellular sensitivity to signaling through the insulin/insulin-like growth factor (IGF) pathway may be responsible for the extreme growth of these structures. We document how rhinoceros beetle horns, a sexually selected weapon, are more sensitive to nutrition and more responsive to perturbation of the insulin/IGF pathway than other body structures. We then illustrate how enhanced sensitivity to insulin/IGF signaling in a growing ornament or weapon would cause heightened condition sensitivity and increased variability in expression among individuals--critical properties of reliable signals of male quality. The possibility that reliable signaling arises as a by-product of the growth mechanism may explain why trait exaggeration has evolved so many different times in the context of sexual selection.     

Computerized pattern recognition: a new technique for the analysis of chemical communication

Computerized pattern recognition techniques can be applied to the study of complex chemical communication systems. Analysis of high resolution gas chromatographic concentration patterns of the major volatile components of the scent marks of a South American primate, Saguinus fuscicollis, demonstrates that the concentration patterns can be used to predict the gender and subspecies of unknown donors.