Do slope orientation and sampling location determine soil biota composition?

“Evolution Canyon”is a typical Mediterranean-basin canyon with a summer dry stream at the bottom and large differences in vegetation cover and solar radiation between the north-facing slope(NFS)and the south-facing slope(SFS).It is known to act as an abiotic mediator influencing the community structure of soil fauna.The aim of this study was to determine the spatial dispersion of soil microbial and free-living nematode communities in the open sites(between shrubs)in the upper(0-10 cm)soil layer at four altitudes on both slopes.The combination of relative soil moisture availability and temperature,known to be one of the main triggers for soil biota activity,was explained by slope orientation.The above-mentioned differences were found to significantly affect microbial biomass and CO_(2)evolution resulting in temporary stress,corresponding to higher values in metabolic quotient(qCO_(2))values.These differences may represent microbial investment in energy in order to overcome stress resulting from the microclimatic differences between the two slopes.Moreover,the degree of substrate limitation(primary production due to the differences in plant cover)of microbial activity was explained by the difference in microbial functional groups.The total abundance of soil free-living nematodes was found to be 2-fold higher on the SFS than on the NFS.Thirty-nine genera,including 12 bacteria-feeders,5 fungifeeders,12 plant-parasites,and 10 omnivore-predators,were found at the study site,with 34 genera on the NFS and 29 on the SFS.The generic diversity of the bacteriafeeding nematodes was higher on the SFS than on the NFS.This study elucidates the importance of slope orientation and its effect on the structural levels of soil microbial and nematode communities.

Impact of downwind sampling location and height on inverse-Gaussian dispersion modeling: A theoretical study

In the studies of fate and transport of air emissions from animal feeding operations,Gaussian based dispersion models have been commonly used to predict downwind pollutant concentrations through forward modeling approach,or to derive emission rates and emission factors through inverse dispersion modeling approach.In the Gaussian dispersion modeling process,downwind sampling location and sampling height could generate significant impact on accuracy of the model validation,or inverse modeling results based upon field measurements.This study theoretically analyzed the impact of downwind locations and sampling height on Gaussian dispersion modeling.It was discovered that the field sampling needs to be conducted at the locations beyond the plume touching-ground distance,at a downwind distance as short as 5 m for the case scenario with zero rise of emission plume under the atmospheric stability class C,or as long as 297 m for the case scenario with 15 m rise of emission plume under the atmospheric stability class F.In order to measure the PM concentrations of the dispersion plume,the minimum sampling height at the locations within the plume touching-ground distance varied from ground level to as high as almost 14 m,whereas for the locations beyond the plume touching-ground distance,a sampling height of ground level would be acceptable.

Improved landscape sampling method for landscape character assessment

Landscape character assessment(LCA)is an effective tool for understanding people’s perceptions and preferences of landscape characteristics.Other than the assessment indicators and subjects,the reliability of photos as assessment objects is equally important for the LCA result.However,the commonly used onsite photos are mainly obtained at randomly selected locations by the researchers.We can neither know whether those photos represent the researchers’own preferences,nor,to our best knowledge,can their reliability be tested scientifically.This method is also difficult to apply in large-scale geographical areas.To address these issues,we(1)propose an improved method including the protocols of photography and the sampling of photography locations,in which the fractal principle and stratified random sampling method were combined to minimize the effects of the researchers’preferences and other factors;(2)apply the method to the Guanzhong region as an example,and obtain sampling photos and their geographical coordinates,which can be used as a data package for LCA;(3)use Fractalyse to test the sampled result and receive good validity.In conclusion,this study extends the methodological chain of the LCA and supports the application of LCA in large-scale regions.

Soil property mapping by combining spatial distance information into the Soil Land Inference Model(SoLIM)

The Soil Land Inference Model(SoLIM) was primarily proposed by Zhu et al.(Zhu A X, Band L, Vertessy R, Dutton B. 1997. Derivation of soil properties using a soil land inference model(SoLIM). Soil Sci Soc Am J. 61: 523–533.) and was based on the Third Law of Geography. Based on the assumption that the soil property value at a location of interest will be more similar to that of a given soil sample when the environmental condition at the location of interest is more similar to that at the location from which the sample was taken, SoLIM estimates the soil property value of the location of interest using the soil property values of known samples weighted by the similarity between those samples and the location of interest in terms of an attribute domain of environmental conditions. However, the current SoLIM method ignores information about the spatial distances between the location of interest and those of the sample. In this study, we proposed a new method of soil property mapping, So LIM-IDW, which incorporates spatial distance information into the SoLIM method by means of inverse distance weighting(IDW). The proposed method is based on the assumption that the soil property value at a location of interest will be more similar to that of a known sample both when the environmental conditions are more similar and when the distance between the location of interest and the sample location is shorter. Our evaluation experiments on A-horizon soil organic matter mapping in two study areas with independent evaluation samples showed that the proposed SoLIM-IDW method can obtain lower prediction errors than the original SoLIM method, multiple linear regression, geographically weighted regression, and regression-kriging with the same modeling points. Future work mainly includes the determination of optimal power parameter values and the appropriate setting of the parameter under different application contexts.