The Potential for Mapping Nematode Distributions for Site-specific Management

The success of site-specific nematode management depends on a grower or advisor being able to afford to make a map of an infestation that is accurate enough for management decisions. The spatial dependence of nematode infestations and correlation of soil attributes with nematode density were assessed to investigate the scale of sampling required to obtain correlated observations of density and the use of soils data to reduce the cost of sampling. Nematodes and soil were sampled on a 76.2 × 76.2-m grid in two irrigated corn (Zea mays) fields for 2 years. Nematodes of each of three species were found in 36% to 77% of the cores from a field. Spatial dependence was detected for 10 of 16 distributions, and 22% to 67% of the variation in density within a field could be attributed to spatial correlation. Density was correlated to distances of 115 to 649 m in the directions of 0, 45, 90, and 135° from the crop row, and distances varied with direction. Correlations between nematode density and soil attributes were inconsistent between species and fields. These results indicate a potential for mapping nematode infestations for site-specific management, but provide no evidence for reducing the cost of sampling by substituting soils data for nematode counts when making a map.     

Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators

Sustainable land management and land use planning require reliable information about the spatial distribution of the physical and chemical soil properties affecting both landscape processes and services. Although many studies have been conducted to identify the spatial patterns of soil property distribution on various scales and in various landscapes, only little is known about the relationships underlying the spatial distribution of soil properties in intensively used, finely structured paddy soil landscapes in the southeastern part of China. In order to provide adequate soil information for the modelling of landscape processes, such as soil water movement, nutrient leaching, soil erosion and plant growth, this study investigates to what extent cheap and readily available ancillary information derived from digital elevation models and remote sensing data can be used to support soil mapping and to indicate soil characteristics on the landscape scale. This article focuses on the spatial prediction of the total carbon and nitrogen content and of physical soil properties such as topsoil silt, sand and clay content, topsoil depth and plough pan thickness. Correlation analyses indicate that, on the one side, the distribution of C, N and silt contents is quite closely related to the NDVI of vegetated surfaces and that, on the other side, it corresponds significantly to terrain attributes such as relative elevation, elevation above nearest drainage channel and topographical wetness index. Geostatistical analyses furthermore reflect a moderately structured spatial correlation of these soil variables. The combined use of the above mentioned terrain variables and the NDVI in a multiple linear regression accounted for 29% (silt) to 41% (total C) of the variance of these soil properties. In order to select the best prediction method to accurately map soil property distribution, we compared the performance of different regionalization techniques, such as multi-linear regression, simple kriging, inverse distance to a power, ordinary kriging and regression kriging. Except for the prediction of topsoil clay content, in all cases regression kriging model “C” performed best. Compared to simple kriging, the spatial prediction was improved by up to 14% (total C), 13% (total N) and 10% (silt). Since the autocorrelation lengths of these spatially well correlated soil variables range between three and five times the soil sampling density, we consider regression kriging model “C” to be a suitable method for reducing the soil sampling density. It should help to save time and costs when doing soil mapping on the landscape scale, even in intensively used paddy soil landscapes.     

The statistical implications of autocorrelation for detection in environmental health assessment

Many environmental health and risk assessment techniques and models aim at estimating the fluctuations of selected biological endpoints through the time domain as a means of assessing changes in the environment or the probability of a particular measurement level occurring. In either case, estimates of the sample variance and mean of the sample variance are crucial to making appropriate statistical inferences. The commonly employed statistical techniques for estimating both measures presume the data were generated by a covariance stationary process. In such cases, the observations are treated as independently and identically distributed and classical statistical testing methods are applied. However, if the assumption of covariance stationarity is violated, the resulting sample variance and variance of the sample mean estimates are biased. The bias compromises statistical testing procedures by increasing the probability of detecting significance in tests of mean and variance differences. This can lead to inappropriate decisions being made about the severity of environmental damage. Accordingly, it is argued that data sets be examined for correlation in the time domain and appropriate adjustments be made to the required estimators before they are used in statistical hypothesis testing. Only then can credible and scientifically defensible decisions be made by environmental decision makers and regulators.     

Motor unit discharge rate is correlated within individuals: A case for multilevel model statistical analysis

Statistical analysis of motor unit discharge rate commonly uses the ordinary least squares based ANOVA and regression analyses or a repeated-measures ANOVA is used to account for within motor unit variance when the same motor unit is assessed multiple times. Both of these methods assume statistical independence of multiple motor units assessed within an individual. This investigation details two studies which quantify the statistical dependence of motor units within an individual. During a ramp contraction, motor unit initial discharge rate is mildly correlated within an individual (ICC: 0.11), though accounting for this effect significantly impacts regression analysis (p = 0.01). When a contraction is held at constant force and multiple observations are made on a motor unit, the motor unit discharges are more highly correlated (ICC: 0.41), even after accounting for the effects of multiple motor unit observations. A subject-level ICC of 0.01 can increase Type 1 error rate to 3.9–19.7%, depending on the number of motor units and study subjects. The increase in Type 1 error due to subject-level effects can be mitigated through the use of multilevel modeling techniques. This study details the use and benefit of multilevel models when statistically analyzing motor unit discharge data.     

Correlation and studies of habitat selection: problem,red herring or opportunity?

With the advent of new technologies, animal locations are being collected at ever finer spatio-temporal scales. We review analytical methods for dealing with correlated data in the context of resource selection, including post hoc variance inflation techniques, ‘two-stage’ approaches based on models fit to each individual, generalized estimating equations and hierarchical mixed-effects models. These methods are applicable to a wide range of correlated data problems, but can be difficult to apply and remain especially challenging for use–availability sampling designs because the correlation structure for combinations of used and available points are not likely to follow common parametric forms. We also review emerging approaches to studying habitat selection that use fine-scale temporal data to arrive at biologically based definitions of available habitat, while naturally accounting for autocorrelation by modelling animal movement between telemetry locations. Sophisticated analyses that explicitly model correlation rather than consider it a nuisance, like mixed effects and state-space models, offer potentially novel insights into the process of resource selection, but additional work is needed to make them more generally applicable to large datasets based on the use–availability designs. Until then, variance inflation techniques and two-stage approaches should offer pragmatic and flexible approaches to modelling correlated data.     

Statistical Modeling of Observational Data With Spatial Dependencies

Abstract I provide a brief introduction to the concept of spatial autocorrelation and its incorporation into regression-type models. Spatial autocorrelation occurs when the response variable is correlated with itself at other locations in the region of interest. The autocorrelation usually takes a specific form where observations close in space are more correlated than those farther apart, and the rate of decay of the correlation is a function of the distance separating 2 locations. I present 2 commonly used models: 1) geostatistical modeling in which data are collected at points in the study region and 2) conditional autoregression (lattice) models in which data are aggregated over small nonoverlapping sub-areas of the study region. I also describe incorporation of explanatory covariates, such as habitat or physico-chemical attributes. I emphasize frequentist methods, but I briefly describe Bayesian approaches. I also provide some advantages, such as obtaining correct standard errors for estimators, and disadvantages, such as requirements for larger sample sizes, of incorporating spatial autocorrelation into the modeling effort. This information can aid researchers in designing and analyzing models of the relationships between species distributions and habitat. As a result, more informative models can be developed which further aid in management of wildlife.     

Using NDVI and soil quality analysis to assess influence of agronomic management on within-plot spatial variability and factors limiting production

Crop growth and yield are the result of the efficiency of the chosen agricultural management system within the boundaries of the agro-ecological environment. Linking spatial variability in crop performance to differences in soil attributes could identify the limiting factors driving the system, since patterns of crop performance will follow the spatial variability of the underlying limiting soil attributes. The Greenseeker handheld NDVI sensor was used to determine the within-plot spatial variability of crop performance in the different management treatments of a long-term (started 1991) tillage and residue management trial. Soil quality was measured spatially in the same plots. Under zero tillage with residue removal, soil quality and crop performance followed micro-topography with higher values where elevation was lower. Under zero tillage with residue retention soil quality was high throughout the field, ensuring uniform crop growth and under conventional tillage, soil quality was intermediate. Crop performance followed the same pattern as soil moisture and the related attributes infiltration, soil structure and organic matter. Thus soil moisture is the main limiting factor of the system and it is essential for the sustainability of any management practice developed for the subtropical highlands that soil water capture and storage are optimal. Zero tillage with residue retention is therefore the practice that will result in the most sustainable management and the most stable yields for this target area.     

Soil databases and the problem of establishing regional biogeochemical trends

Regional and global environmental modeling depend on soil data for input layers or parameterization. However, randomly located observations, such as provided by agricultural databases, are not always representative of trends identified in field studies conducted under carefully controlled conditions. Many researchers lament the paucity of soil profile data in Amazônia, and suggest that given more data, regional studies would more closely approximate field research results. We assess the ability of a well‐populated regional database collected in the southwestern Brazilian Amazon to reproduce expected biogeochemical trends associated with forest clearing and pasture establishment, and explore the ramifications of relying on independently collected soil data for regional modeling. The Soteron database includes analyses of approximately 3000 soil cores collected for zoning purposes in the state of Rondônia. Pasture ages were determined from a time series of Landsat TM images classified using spectral mixture analysis. Although regional averages showed some of the temporal trends expected based on field study results (e.g. increase in pH following forest clearing), the trends were not statistically significant. Stratification by precipitation and other variables showed pasture age to be important but difficult to separate from other potential controls on soil conditions, mainly because of the reduced number of observations in each stratum. Using multiple regression, which permitted the inclusion of all potential explanatory factors and interactions, pasture age was shown to be a statistically significant predictor of soil conditions. However, the expected temporal sequence of changes documented by field chronosequence studies could not be reproduced. Properties dominated by large‐scale environmental gradients – pH, sum of base cations, aluminum saturation, and exchangeable calcium – were moderately well modeled, while those more strongly linked to dynamic spatially heterogeneous processes such as biological cycling and land management, particularly organic carbon and nitrogen, could not be modeled. Management‐induced soil changes occur at too fine a scale to be captured by most maps, and the relative changes are small compared with spatial heterogeneity caused by controls on soil development over large regions. Therefore, regardless of whether chronosequence‐derived models of biogeochemical response to land‐cover change are correct, the results of these models will not lead to spatially explicit maps that can be validated by regional reconnaissance, nor will they facilitate realistic predictions of the regional biogeochemical consequences of land‐cover change. The change from local to regional scale entails a change in the relative importance of processes controlling soil property behavior.     

Progress of ecological parasitology--parasite communities

Many of the major development in the field of parasite community ecology have been due to a switch in focus from a search for pattern to investigation of the processes that produce those patterns. This switch has been accompanied by a recognition that different processes operate at the scale of the individual host (processes determining host specificity and attributes of the niches of the parasites), within the unit of habitat (processes determining population dynamics, exchange of parasites, and transmission), and among units of habitat (processes determining colonization, extinction, or local speciation of parasites). Further developments are likely to depend upon the coordinated use of models, experimental approaches, and field observations aimed at clarifying the conditions under which the processes at each scale became particularly important.     

The generalized linear model for spatial data: assessing the effects of environmental covariates on population density in the field

With interest in spatial ecology growing, correlational field studies are likely to become increasingly important. Unfortunately, ecological field data often do not follow the assumptions of classical statistics, so techniques like the popular and powerful multiple linear regression and its variants are often unreliable, and results can be misleading. The generalized linear model (GLM) is a flexible extension of linear regression that has proved especially useful for discrete data. In this paper, the technique is adapted to accommodate spatially correlated, discrete data. Specifically, to demonstrate the approach, Japanese beetle grub [Popillia japonica Newman (Coleoptera, Scarabaeidae)] population density in the field is modeled as a function of soil organic matter content. The response variable (grub counts in small soil samples) was a spatially autocorrelated, discrete random variable. Three classes of GLMs of the association between soil organic matter content and grub density were compared: (i) regression (assuming normally distributed response variable), (ii) GLM assuming negative binomial counts, and (iii) GLM based on the assumption that the counts conformed to Taylor's power law (TPL). Because the grubs were distributed in patches rather than at random, models that explicitly accounted for the spatial autocorrelation of grub counts were constructed, and compared with models that assumed independent observations. The fitted values for the discrete GLMs [viz., (ii) and (iii)] differed noticeably from the fitted values from multiple regression; but fitted values among the negative binomial and TPL GLMs were virtually identical, regardless of whether the spatial covariance was incorporated into a model, whether a spherical or exponential variogram model was used, or whether variance function parameters were estimated over a large or small scale. However, P‐values for the overall significance of the models depended heavily on whether the GLM assumed a discrete or continuous response variable, and whether or not spatial autocorrelation in the response variable was accounted for. On average, P‐values were 45‐fold higher in the spatial GLMs than in the non‐spatial and 23‐fold higher in the discrete GLMs than in the continuous.     

Diversity and habitat relationships of hypogeous fungi. II. Factors influencing the occurrence and number of taxa

Fruit-bodies of hypogeous fungi were sampled over two seasons across 136 forested study sites representing a stratified sample of the climatic, geological and topographic features of far south-eastern mainland Australia. Two hundred and nine species, over three-quarters being undescribed, were recorded. Statistical models based on various environmental attributes measured for each site were developed for the occurrence of several common taxa. At a landscape scale, climatic factors such as mean minimum temperature of the coldest month and annual mean moisture index were important explanatory variables for most taxa examined, but the type of response varied. More locally, topographic position, soil fertility, time since last fire and micro-habitat structures such as the leaf litter layer and number of large fallen trees also influenced the distribution of taxa in different ways. A model was then developed for the number of fungal species occurring at each site. Important explanatory variables were type of substrate, topography and diversity of potential host eucalypt species. The utility of each model constructed needs evaluation by further sampling of hypogeous fungi. Possible implications of our findings for forest management are discussed. Further analyses of our existing data are also identified.     

Indirect validation of the Environmental Sensitive Area Index using soil degradation indicators: A country-scale approach

The Environmental Sensitivity Area Index (ESAI) is one of the most used frameworks to monitor land vulnerability to degradation in southern Europe, northern Africa and the Middle East. ESAI outputs were validated on the field at both local and regional scales, but a country or continental scale validation is still missing. Using non-parametric correlations and multivariate statistics, the present study contrasts the spatial distribution of the ESAI over 8100 local municipalities in Italy with 12 soil variables assessing individual soil attributes, soil degradation processes and the overall soil quality. Three supplementary variables assessing elevation, latitude and the urban–rural gradient have been also considered in the analysis. The ESAI correlated with both soil attributes (topsoil organic carbon) and degradation processes (soil contamination risk, landslide risk, grazing pressure and agricultural mechanization, considered a predisposing factor to soil compaction) varying in intensity along the elevation gradient. The approach illustrated provides an indirect evaluation of the reliability of the ESAI as a multi-domain indicator of land vulnerability to degradation in the Mediterranean region.     

Recursive Residuals for Multivariate Field Processes

Vector recursive residuals are developed for multivariate regression models on a field. A vector response variable is observed at points on a rectangular grid, together with regression variables measured at the same points. Neighbouring values of the response vector may be correlated and simple models are considered using a direct product structure for the variance matrix. Subsequent to obtaining vector recursive residuals principal component analysis is applied to obtain an evaluation of any changes that may be occurring in the regression relationship over the field. The method is then applied to the problem of detecting zones of bush fire damage and recovery from LANDSAT data.     

黄土高原小流域土壤养分的空间异质性

利用地理信息系统的空间分析功能,通过地统计学的半变异函数定量研究了黄土高原典型小流域土壤养分的空间异质性特征。结果表明;土壤有机质,全氮,有效氮,全磷和有效磷的理论模型均为球状模型,由随机因素引起的空间变异占空间总变异的比例小,其值分别为13.333%,10.938%,22.000%,9.091%和27.536%,反映5种养分具有较强的空间自相关格局,但它们的空间自相关范围具有明显的差异。土壤全氮和有效氮变程小,分别是90m和110m,有机质次之,变程是120m,而土壤全磷和有效磷的变程最大为160m,研究成果将有效地指导土壤的取样设计,以及进行土壤养分的空间内插和制图。     

Integrated management of root-knot nematode, Meloidogyne incognita infestation in tomato and grapevine

An integrated approach with the obligate bacterial parasite, Pasteuria penetrans and nematicides was assessed for the management of the root-knot nematode, Meloidogyne incognita infestation in tomato and grapevine. Seedlings of tomato cv. Co3 were transplanted into pots filled with sterilized soil and inoculated with nematodes (5000 juveniles/pot). The root powder of P. penetrans at 10 mg/pot was applied alone and in combination with carbofuran at 6 mg/pot. Application of P. penetrans along with carbofuran recorded lowest nematode infestation (107 nematodes/200 g soil) compared to control (325 nematodes/200 g soil). The rate of parasitization was 83.1% in the carbofuran and P. penetrans combination treatment as against 61.0% in the P. penetrans treatment only. The plant growth was also higher in the combination treatment compared to all other treatments. A field trial was carried out to assess the efficacy of P. penetrans and nematicides viz., carbofuran and phorate in the management of root-knot nematode, M. incognita infestation of grapevine cv. Muscat Hamburg. A nematode and P. penetrans infested grapevine field was selected and treatments either with carbofuran or phorate at 1 g a.i/vine was given. The observations were recorded at monthly interval. The results showed that the soil nematode population was reduced in nematicide treated plots. Suppression of nematodes was higher under phorate (117 nematodes/200 g soil) than under carbofuran (126.7 nematodes/200 g soil) treatment. The number of juveniles parasitized was also influenced by nematicides and spore load carried/juvenile with phorate being superior and the increase being 17.0 and 29.0% respectively over the control. The results of these experiment confirmed the compatibility of P. penetrans with nematicides and its biological control potential against the root-knot nematode.     

Temperature sensitivity of soil organic carbon decomposition increased with mean carbon residence time: Field incubation and data assimilation

Temperature sensitivity of soil organic carbon (SOC) decomposition is one of the major uncertainties in predicting climate‐carbon (C) cycle feedback. Results from previous studies are highly contradictory with old soil C decomposition being more, similarly, or less sensitive to temperature than decomposition of young fractions. The contradictory results are partly from difficulties in distinguishing old from young SOC and their changes over time in the experiments with or without isotopic techniques. In this study, we have conducted a long‐term field incubation experiment with deep soil collars (0–70 cm in depth, 10 cm in diameter of PVC tubes) for excluding root C input to examine apparent temperature sensitivity of SOC decomposition under ambient and warming treatments from 2002 to 2008. The data from the experiment were infused into a multi‐pool soil C model to estimate intrinsic temperature sensitivity of SOC decomposition and C residence times of three SOC fractions (i.e., active, slow, and passive) using a data assimilation (DA) technique. As active SOC with the short C residence time was progressively depleted in the deep soil collars under both ambient and warming treatments, the residences times of the whole SOC became longer over time. Concomitantly, the estimated apparent and intrinsic temperature sensitivity of SOC decomposition also became gradually higher over time as more than 50% of active SOC was depleted. Thus, the temperature sensitivity of soil C decomposition in deep soil collars was positively correlated with the mean C residence times. However, the regression slope of the temperature sensitivity against the residence time was lower under the warming treatment than under ambient temperature, indicating that other processes also regulated temperature sensitivity of SOC decomposition. These results indicate that old SOC decomposition is more sensitive to temperature than young components, making the old C more vulnerable to future warmer climate.     

The representation of root processes in models addressing the responses of vegetation to global change

The representation of root activity in models is here confined to considerations of applications assessing the impacts of changes in climate or atmospheric [CO₂]. Approaches to modelling roots can be classified into four major types: models in which roots are not considered, models in which there is an interplay between only selected above-ground and below-ground processes, models in which growth allocation to all parts of the plants depends on the availability and matching of the capture of external resources, and models with explicit treatments of root growth, architecture and resource capture. All models seem effective in describing the major root activities of water and nutrient uptake, because these processes are highly correlated, particularly at large scales and with slow or equilibrium dynamics. Allocation models can be effective in providing a deeper, perhaps contrary, understanding of the dynamic underpinning to observations made only above ground. The complex and explicit treatment of roots can be achieved only in small-scale highly studied systems because of the requirements for many initialized variables to run the models.     

A simulation program examining the use of ratios as raw variables in analyses of variance

An interactive, BASIC-PLUS, simulation program is described for performing a large number of multivariate analyses in accord with, or in violation of, known underlying models. Using a PDP 11/40 computer, random numbers are generated and converted into bivariate normal observations X and Y. These data are then analyzed according to three models. The three analyses are: (1) a standard analysis of variance ignoring the concomitant variable; (2) an analysis of covariance; (3) an analysis of variance on the ratio Y/X. The program can be used to examine the effect of performing multivariate analyses on data transformed into ratios. The utility of the program is the simplicity with which one can alter the underlying models and/or the mathematical relationships between X and Y. Output provides comparisons between models by accumulating rejections and other statistics on critical F-values for each model.     

Corn stover feedstock trials to support predictive modeling

To be sustainable, feedstock harvest must neither degrade soil, water, or air resources nor negatively impact productivity or subsequent crop yields. Simulation modeling will help guide the development of sustainable feedstock production practices, but not without field validation. This paper introduces field research being conducted in six states to support Sun Grant Regional Partnership modeling. Our objectives are to (1) provide a fundamental understanding of limiting factor(s) affecting corn (Zea mays L.) stover harvest, (2) develop tools (e.g., equations, models, etc.) that account for those factors, and (3) create a multivariant analysis framework to combine models for all limiting factors. Sun Grant modelers will use this information to improve regional estimates of feedstock availability. A minimum data set, including soil organic carbon (SOC), total N, pH, bulk density (BD), and soil‐test phosphorus (P), and potassium (K) concentrations, is being collected. Stover yield for three treatments (0%, 50%, and 90% removal) and concentrations of N, P, and K in the harvested stover are being quantified to assess the impact of stover harvest on soil resources. Grain yield at a moisture content of 155 g kg^?1 averaged 9.71 Mg ha^?1, matching the 2008 national average. Stover dry matter harvest rates ranged from 0 to 7 Mg ha^?1. Harvesting stover increased N–P–K removal by an average of 42, 5, and 45 kg ha^?1 compared with harvesting only grain. Replacing those three nutrients would cost $53.68 ha^?1 based on 2009 fertilizer prices. This first‐year data and that collected in subsequent years is being used to develop a residue management tool that will ultimately link multiple feedstock supplies together in a landscape vision to help develop a comprehensive carbon management plan, quantify corn stover harvest effects on soil quality, and predict regional variability in feedstock supplies.     

Assessment of soil and vegetation changes due to hydrologically driven desalinization process in an alkaline wetland,Hungary

This study investigates a soil–water–vegetation system in a drying-out alkaline sodic wetland altered by climate change and artificial drainage by evaluating the habitat pattern and the physical and chemical attributes of the upper soil. The spatial and temporal alteration of the vegetation was monitored by detailed coenological investigations and habitat mapping during a 13-year period (2002–2014) to analyse the succession trend of the habitat in the changing environment. The spatial structures of the physical and chemical attributes of the soil were surveyed by topsoil sampling along a regular network to detect the desalinization process and to reveal the discrepancies between the soil attributes and the typical habitats because anomalies between the habitat and its optimal soil properties can project a possible vegetation change in a dynamically changing sodic ecosystem. The micro-topography was investigated to detect the effect of the elevation difference on the hydrologic conditions, soil and vegetation attributes. Statistical analyses were performed to describe the characteristic pedological processes and the spatial structures of the soil parameters. An overlapping analysis was conducted to compare the soil, vegetation pattern and topography to explore the relationships in the altering soil–water–vegetation system.Rapid alterations of the habitats, species composition, and soil desalinization processes were clearly recognised. The rate of change reflects degradation beyond the natural dynamics of vegetation processes. The desalinization process was extremely rapid due to the sandy sediment. The significant changes in the vegetation and soil pattern led to the loss of diversity in the short term; annual salt pioneer swards and Puccinellia swards became highly threatened. The main driving factors in the desalinization process are water shortage caused by artificial drainage and climate change, furthermore extreme high precipitation which intensifies leaching. The degradation process can be mitigated by adequate water management because habitats have a high naturalness reflecting good regeneration potential.