Spatial distribution of Gasterophilus pecorum (Diptera) eggs in the desert steppe of the Kalamaili Nature Reserve (Xinjiang, China)

Background The dominant Gasterophilus species in the desert steppe (Xinjiang, China) Gasterophilus pecorum poses a serious threat to the reintroduced Przewalski’s horses. We investigated the distribution pattern of G. pecorum eggs in June 2017. Methods Two sampling methods, transect and grid, were used, and the results were analyzed via geostatistics by semivariance. The nest quadrat was used to determine the optimal quadrat size. Results Eggs were found in 99 quadrats (63.1%) and 187 clusters (1.5%) of Stipa caucasica on the steppe. The mean oviposition count of a cluster was 3.8 ± 1.6. Three-eggs is the mode of which females oviposit on each ovigerous S. caucasica (22.0%). Semivariogram analysis revealed that the distance of spatial dependence for eggs was 921 m, 1233 m and 1097 m for transect 1, transect 2 and grid methods, respectively, while spatial continuity was 62%, 77% and 57.0% for transect 1, transect 2 and grid, respectively. The eggs showed a patchy, aggregated distribution pattern. This suggested the spherical model is most applicable. The proportion of ovigerous S. caucasica was significantly correlated with the distance from water resources (r = − 0.382, p = 0). Conclusion Our findings indicated that diversification of G. pecorum oviposition was a new adaptative strategy for its survival in the desert steppe ecological niche. This made it more efficient at infecting hosts in the local environment. Areas surrounding water resources, especially around the drinking paths of equids (500 m radius surrounding the water), were concentrated epidemic areas. It is suggested that more attention to be paid to the ecological characteristics of G. pecorum in order to develop control measures that would reduce the infection risk for Przewalski’s horses.

Spatially compounded surge events: an example from hurricanes Matthew and Florence

The simultaneous rise of tropical-cyclone-induced flood waters across a large hazard management domain can stretch rescue and recovery efforts. Here we present a means to quantify the connectedness of maximum surge during a storm with geospatial statistics. Tide gauges throughout the extensive estuaries and barrier islands of North Carolina deployed and operating during hurricanes Matthew (n=82) and Florence (n=123) are used to compare the spatial compounding of surge for these two disasters. Moran's I showed the occurrence of maximum storm tide was more clustered for Matthew compared to Florence, and a semivariogram analysis produced a spatial range of similarly timed storm tide that was 4 times as large for Matthew than Florence. A more limited data set of fluvial flooding and precipitation in eastern North Carolina showed a consistent result – multivariate flood sources associated with Matthew were more concentrated in time as compared to Florence. Although Matthew and Florence were equally intense, they had very different tracks and speeds which influenced the timing of surge along the coast.

Spatially Compounded Surge Events: An Example from Hurricanes Matthew and Florence

The simultaneous rise of tropical cyclone induced flood waters across a large hazard management domain can stretch rescue and recovery efforts. Here we present a means to quantify the connectedness of maximum surge during a storm with geospatial statistics. Tide gauges throughout the extensive estuaries and barrier islands of North Carolina deployed and operating during Hurricanes Matthew (n = 82) and Florence (n = 123) are used to compare the spatial compounding of surge for these two disasters. Moran's I showed the occurrence of maximum storm tide was more clustered for Matthew compared to Florence, and a semivariogram analysis produced a spatial range of similarly timed storm tide that was four times as large for Matthew than Florence. A more limited data set of fluvial flooding and precipitation in eastern North Carolina showed a consistent result – multivariate flood sources associated with Matthew were more concentrated in time as compared to Florence. Although Matthew and Florence were equally intense, they had very different tracks and speeds, which influenced the timing of surge along the coast. We hope this method could be used for other landfalling tropical cyclones to better understand the drivers that lead to spatially compounded surge events.

Spatiotemporal Distribution of Cattle Dung Patches in a Subtropical Soybean-Beef System under Different Grazing Intensities in Winter

Cattle dung distribution in pastoral ecosystems is uneven and affects nutrient availability to plants. Thus, identifying its spatiotemporal patterns is crucial to understanding the mechanisms underlying the system functioning. We aimed to characterize the spatiotemporal distribution of dung patches in mixed black oat (Avena strigosa Schreb.) and Italian ryegrass (Lolium multiflorum Lam.) pastures grazed at different intensities (sward heights of 0.1, 0.2, 0.3 and 0.4 m) in the winter stocking period of an integrated soybean-beef system in southern Brazil. All dung patches were located and georeferenced every 20 days. Dung distribution was analyzed using Thiessen polygons and semivariogram analysis. The spatial pattern of dung deposition was virtually similar over time but created distinct patterns in paddocks managed at different grazing intensities. Dung patch density was greater close to attraction points, resting and socialization areas regardless of grazing intensity. Lighter grazing intensities presented stronger spatial patterns with increased dung density in those areas, but those patterns weakened with increasing grazing intensity. Dung patches covered 0.4%, 0.9%, 1.1% and 1.5% of the area in paddocks managed at 0.4, 0.3, 0.2 and 0.1 m sward heights, respectively. Geostatistics proved useful for identifying spatial patterns in integrated crop-livestock systems and will potentially support further investigations.

Spatial and spatiotemporal analysis of Meloidogyne hapla and Pratylenchus penetrans populations in commercial potato fields in New York, USA

Summary Meloidogyne hapla and Pratylenchus penetrans are important plant-parasitic nematodes affecting potato in New York and the Northeastern United States, yet little is known of their spatial patterns and spatiotemporal dynamics. Spatial patterns of M. hapla and Pratylenchus spp. were quantified using semivariogram analysis and Spatial Analysis by Distance IndicEs (SADIE). Nematode populations were assessed within each of three commercial potato fields in 2016 and 2017, with fields sampled on two occasions in-season. Semivariogram analysis and ordinary kriging indicated initial population densities to be spatially dependent over an average range of 110 m for M. hapla and 147 m for Pratylenchus spp. SADIE indicated Pratylenchus spp. to be significantly aggregated in nearly all fields (10 of 12 samplings, to 2.113). Meloidogyne hapla populations were aggregated in only three of 12 samplings ( to 1.738). Spatiotemporal analysis using the association function of SADIE indicated a strong and significant association between initial and final population densities of M. hapla and Pratylenchus spp. within fields. This information is fundamental for the development of enhanced sampling protocols for estimation of plant-parasitic nematodes and evaluating the feasibility of site-specific nematicide application in New York potato fields.

Changes in the Transmission Dynamic of Chikungunya Virus in Southeastern Senegal

In Senegal, chikungunya virus (CHIKV) is maintained in a sylvatic cycle and causes sporadic cases or small outbreaks in rural areas. However, little is known about the influence of the environment on its transmission. To address the question, 120 villages were randomly selected in the Kedougou region of southeastern Senegal. In each selected village, 10 persons by randomly selected household were sampled and tested for specific anti-CHIKV IgG antibodies by ELISA. We investigated the association of CHIKV seroprevalence with environmental variables using logistic regression analysis and the spatial correlation of village seroprevalence based on semivariogram analysis. Fifty-four percent (51%–57%) of individuals sampled during the survey tested positive for CHIKV-specific IgG. CHIKV seroprevalence was significantly higher in populations living close to forested areas (Normalized Difference Vegetation Index (NDVI), Odds Ratio (OR) = 1.90 (1.42–2.57)), and was negatively associated with population density (OR = 0.76 (0.69–0.84)). In contrast, in gold mining sites where population density was >400 people per km2, seroprevalence peaked significantly among adults (46% (27%–67%)) compared to all other individuals (20% (12%–31%)). However, traditional gold mining activities significantly modify the transmission dynamic of CHIKV, leading to a potential increase of the risk of human exposition in the region.

BARIUM AND LEAD LEVELS IN SITES FOR DISPOSAL OF OIL WELL WASTE

ABSTRACT Indiscriminate disposal of waste from the oil industry has led to contamination of soils and water sources and toxic effects in the biota. Thus, this study aimed to assess barium (Ba) and lead (Pb) spatial variability in disposal sites for oil well drilling and prospecting wastes. The soil in the study area is classified as Red Latosol, and the area is located in Santa Maria do Oeste, Paraná State (Brazil). Barium and Pb spatial variabilities were determined using a 76-point sampling grid. The levels of metals were determined in soil samples collected at each sampling point, at the depth ranges of 0.0-0.3, 0.3-0.6, 0.6-0.9 and 0.9-1.2 m. Data were mapped using geostatistical interpolators for spatial dependence determination, modeling and validation of semivariograms, and respective interpolation. Thirty Ba and Pb high concentration samples were selected for leaching and solubilization assays. As semivariogram analysis showed spatial dependence, mapping by ordinary kriging was performed for both metals. The high levels found for both metals arose from well drilling activities, as no direct relationship was found between such levels with geology and genesis of the local rock. The concentrations of the metals were higher than the research values considering the Agricultural / AP-Max scenario, therefore, the study area was characterized as class 4 (contaminated). Although these metals have low solubility, i.e. low contamination risk to subsurface waters, they may pose contamination risks to water bodies by means of soil runoff.

Study the spatial variability of the noise levels inside two commercial poultry housing with different adiabatic evaporative cooling systems

The objective of the present work was to determinate the map the spatial distribution of noise levels inside two commercial poultry housing having different adiabatic evaporative cooling systems, during the life cycle of birds. The noise level was assessed by a digital sound level meter. The data were measured manually at each point in six predetermined sections, totaling 36 points. Spatial distribution maps of noise were generated for the inside of each animal facility, using geostatistics technique through semivariogram analysis and interpolation by ordinary kriging. It concludes that the birds were, in general, subjected to noise levels above 62.0 dBA and during clean and disinfect (decontamination period) the sheds were at approximately 35.0 dBA. The spatial profile of the noise level to the productive environment provide for the attainment of more detailed information about the studied system.

A Stochastic Approach for Performance Prediction of Aircraft Engine Components Under Manufacturing Uncertainty

Efficient modeling of uncertainty introduced by the manufacturing process is critical in the design of the components of the aircraft engines. In this study, a stochastic approach is presented to efficiently account for the geometric uncertainty, associated with the manufacturing process, in the accurate performance prediction of aircraft engine components. A semivariogram analysis procedure is proposed in this approach to quantify spatial variability of the uncertain geometric parameters based on the manufactured specimens. Karhunen-Loeve expansion is utilized to create a set of correlated random variables from the uncertainty data obtained by variogram analysis. The detailed model of the component is created accounting for the uncertainties quantified by these correlated random variables. A stochastic upscaling method is then utilized to form a simplified model that can represent this detailed model with high accuracy under uncertainties. Specifically, a parametric model generation process is developed to represent the detailed model using Bezier curves and the uncertainties are upscaled to the parameters of this parametric representation. The modal frequency-based reliability analysis of a turbine blade example is used to demonstrate the efficacy of the proposed approach. The application results show that the proposed method effectively captures the geometric uncertainties introduced by manufacturing while providing accurate predictions under uncertainties.