Evaluating possible intraspecific variation in resistance of western hemlock (Pinaceae) to Adelges tsugae (Hemiptera: Adelgidae)

Western hemlock, Tsuga heterophylla (Rafinesque) Sargent (Pinaceae), trees growing in a clonal, commercial seed orchard on Whidbey Island, Washington, United States of America were assessed for Adelges tsugae (Annand) (Hemiptera: Adelgidae) infestation levels to determine whether there was any evidence of resistance to this herbivore. Join-count statistics revealed that A. tsugae infested trees were not clumped, but instead were randomly distributed throughout the seed orchard. Chi-square analyses suggested that there is a genetic basis for different levels of A. tsugae infestation among the genets represented in the orchard. However, since none of the genets were completely free of A. tsugae infestation, differences among genets seem to be related more to susceptibility or tolerance than complete resistance. Among the 17 genets that had five or more ramets in the seed orchard, only one had more than one ramet free of A. tsugae. That genet had four of five ramets that were free of A. tsugae. However, it is not possible to say whether that was due to chance or inherent tree characteristics that make it less susceptible to A. tsugae infestation.

Spatial and Temporal Pattern Analyses of Esca Grapevine Disease in Vineyards in France

To assess the capacity of esca to spread within vineyards of the Bordeaux region, over 8 years of annual records, containing between 1,200 and 2,300 contiguous Cabernet Sauvignon vines from 15 mature vineyards, were used for spatial statistical analyses. A group of nonparametric tests, based on join count statistics and on permutation methods, was developed to characterize the spatial structure of esca-symptomatic vines in terms of spread in any direction or within-row only. Among vineyards, a large range of spatial patterns, from random to strongly structured, associated with various prevalence rates that increased over time were observed. In four vineyards, the complex esca distribution pattern indicated different levels of clustering. By contrast, in other vineyards, only small clusters of two adjacent symptomatic vines were observed, and they were localized along rows, without enlargement over time, except in one vineyard. An analysis of spatial dependence between previously and newly symptomatic vines within k-order neighborhoods (k = 1 to 5), showed, for 5 of the 15 vineyards, that the newly symptomatic vines were located close to previously infected vines, without a favored orientation or neighbor order. All the results together suggested a limited potential for secondary local spread from neighboring symptomatic vines.

Spatial autocorrelation of communes websites: A case study of the region Stredné Považie in Slovakia

Since its origination the Internet has been the basic communication channel through which e-Government processes in our society are carried out. The basic element of such processes is an existing website. This paper is the result of long-term research during which the authors collected information on the presence or absence of websites of self-governments in Slovakia. The output can be divided into two main parts. The first one, on the basis of the acquired data, evaluates the overall trend of self-government websites between 2008-2012. The results were distributed in the form of a website devoted to communes of Slovakia, using technology enabling the graphical and cartographic interpretation of the results. In the second part, on the basis of the gathered data, the authors applied spatial autocorrelation, specifically the ‘join count statistics’ method with the subsequent application to selected phenomenon in society, in particular the occurrence of websites of a selected region of Stredné Považie in Slovakia at the same time interval. This way the authors wanted to point out the interaction of communes in the development of websites, whether through inspiration or rivalry among neighbouring communes. From this point of view, mainly in the first period under review (2008), positive spatial autocorrelation is apparent in the occurrence of communes with websites.

Spatial analysis of plant species distribution among small water bodies in an agricultural landscape

Due to their small size (0.02-1.0 ha), distinct boundaries, and conditions highly contrasting with those in the surrounding area, midfield water bodies are regarded as the so-called habitat islands. Their effective conservation calls for knowledge on their inhabitants' dispersal potential. However, direct empirical data are available for very few species only, but dispersal potential of a species may be inferred indirectly, from its distribution. The study addressed the question whether there is spatial autocorrelation in the distribution of plant species in midfield water bodies, or if the distribution is random. Spatial distribution of the midfield water bodies surveyed was analysed using the CrimeStat software, while spatial autocorrelation in distribution of 29 species was explored with the joincount.test routine of R CRAN software. Explorative spatial data analysis (ESDA) involving join-count statistics showed the presence of positive spatial autocorrelation in the distribution of ten hydro- and helophytic species. In their case, ESDA made it possible to reject the random distribution hypothesis, which opens up an avenue for exploring spatial patterns. Activities promoting the occurrence of species with limited dispersal potential should take into account their preferences in terms of shorter distances between neighbouring sites. This should make it possible to plan conservation of midfield water bodies not only as refuges, but also as stepping stone habitats facilitating migrations of wild species growing in an agricultural landscape.

Analysis of Spatiotemporal Dynamics of Virus Spread in an Australian Hop Garden by Stochastic Modeling

The dynamics of spread of Hop latent virus (HpLV), Hop mosaic virus (HpMV), and Apple mosaic virus (ApMV) in an Australian hop garden were characterized by fitting a stochastic spatiotemporal model, using the Monte Carlo Markov Chain (MCMC) method, to the changes in patterns of virus-infected plants. The model has parameters for background infection rate (from virus reservoirs outside the gardens) and local infection rate (spread from infected plants to virus-free plants). The HpLV epidemic could be explained by very short range spread within the garden, suggesting mechanical transmission through cultural operations. This is consistent with the absence of the only known aphid vector of HpLV from Australia. The HpMV epidemic could be explained by local spread coupled with background infection. This suggests that HpMV may be introduced by aphids, and subsequent localized spread may occur by aphids or mechanical means. The ApMV epidemic was also consistent with localized spread coupled with background infection, but the possibility of longer-range spread could not be rejected for this virus. Although intra-garden transmission of ApMV may occur through contact or cultural operations, these results suggest that other mechanisms could contribute to transmission of ApMV in Australian hop gardens. Join-count statistics for the patterns of infected plants supported the conclusion from the spatiotemporal modeling that short-range spread was the primary determinant of disease increase.

Measures of spatial structure in samples of genotypes for multiallelic loci

Various spatial autocorrelation statistics have been widely used both in theoretical population genetics and to study the spatial distribution of diploid genotypes in many plant and animal populations. However, previous simulation studies have considered only diallelic loci. In this paper, we use a large number of space–time simulations to characterize for the first time the parametric and statistical values of Moran's I-statistics for converted individual genotypes as well as for join- count statistics. A wide range of levels of dispersal and numbers of alleles and allele frequencies are modelled and the results reveal the different general effects of each of these factors on these statistics. We also examine the range of appropriate sampling designs and sizes for which predicted values can be interpolated for specific sampling schemes for any given population genetic field survey. Numbers of alleles and allele frequencies each affect some statistics but not others. The results indicate generally low standard deviations. The results also develop precise and efficient methods of estimating gene dispersal, based on the various autocorrelation measures of standing spatial patterns of genetic variation within populations. The results also extend these methods to loci with multiple alleles, typical of those studied through modern molecular methods.