Long-Range Transport of Sulfur from Northeast Asia to Chengshantu, Shandong Peninsula: Measurement and Simulation

Observations of air pollutants were conducted at Chengshantu in January 1996 to clarify the extent of trans-boundary pollution from the Asian continent. A nested air quality prediction modeling system (NAQPMS), which included parameters on emission, transport, diffusion, deposition and transformation of sulfur oxides, was performed to compile sulfur concentrations over the observation site. The model calculation reproduced the observed variations of sulfur oxides and sulfate well, although the model calculations could not reproduce the extensively low sulfate concentrations. Using nesting improves the ability of the modeling system to capture peak episodes seen in the observations. Calculation of the origins of deposited sulfur in each country with the NAQPMS shows that the long-range transport of sulfur is very serious. For Japan, 49% of sulfur deposition was from other countries during the campaign period.     

广州市农业表层土壤镉和铅多尺度空间结构

基于广州市2005年641个表层农业土壤样点数据,使用地统计学和多尺度嵌套模型,探讨了土壤Cd和Pb在多粒度尺度上的空间结构并进行插值估计。研究发现,随着空间粒度由粗到细,土壤Cd和Pb空间相关性依次减弱,半方差函数对重金属空间变异的代表性依次增强。多尺度嵌套模型插值效果略优于单尺度的普通克里格法,前者插值结果显示:广州农业土壤Cd和Pb含量普遍未超过国家土壤环境质量二级标准,Cd超标区主要位于中心城区的菜地,少量位于从化区、南沙区以及花都区与白云区交界。     

Application of agro-ecological concepts and hierarchy theory in the design of databases for spatial and temporal characterisation of land and soil

A hierarchial system for integrating information on soil, climate, landscape and land use at various scales, is discussed. A series of nested data bases at various scales (1:5 million and larger) are prepared, according to the concepts of agro-ecological stratification in combination with the tenets of Hierarchy Theory. Each polygon at each scale is treated as a unique area with its own environmental envelope of integrated information. Transposition between scales and the scientific integrity of the data is accomplished through the nested databases, and by carefully matching the “grain” of the data to the scale of each database.

The nested databases are “matched” with a collection of crop growth models and other procedures of data analyses, each of which is specific to a general scale of application, and compatible to one of the available databases. The crop growth models are arranged in increasing detail, as reflected in the structure of the subroutines and time steps, and describe a variety of purposes from estimating yield potential to defining yield variability and production risk. The system is supported by a geographic information system.     

基于改进SWAT模型的多水源灌区节水潜力尺度效应

灌溉节水潜力是指导和评价灌区节水改造的重要参数,其分析需要考虑尺度效应。该研究以湖北漳河灌区杨树垱流域为背景,采用改进SWAT(Soil and Water Assessment Tool)模型构建灌区分布式水文模型,并利用实测径流、蒸发蒸腾量及灌溉用水量校正及验证模型。以子流域嵌套方式将研究区划分为6个尺度,基于现状情景,设置增加塘堰汇流面积、提高渠系水利用系数及采用水稻节水灌溉模式3种节水情景。基于改进SWAT模型不同情景的模拟结果,定义并计算分析不同节水情景下传统的及考虑回归水重复利用的2种节水潜力及其随尺度变化规律。结果表明,改进SWAT模型具有良好的模拟效果(R20.80,纳什效果系数大于0.80)。在增加塘堰汇流面积情景下,由于不同尺度塘堰供水比例的变化,2种节水潜力均随着尺度的增大呈现先增加再降低继而趋于稳定的规律;在提高渠系水利用系数情景下,由于漳河水库在不同尺度的供水比例的变化,2种节水潜力均随尺度增大逐渐降低并趋于稳定;在水稻节水灌溉模式情景下,由于不同尺度水稻节水灌溉面积占比的变化,2种节水潜力均随尺度增大呈现先减小再增大继而趋于稳定的规律。3种节水情景下的2种节水潜力的大小关系,主要由节水措施实施前后不同尺度灌溉用水量变化及灌溉回归水重复利用量变化共同决定。研究可为灌区水管理及节水改造工程布局提供决策依据和指导方向。     

不同尺度下田间土壤水分和混合电导率空间变异性与套合结构模型

应用地统计学原理和方法 ,分析了夏玉米田间 90m90m(L区 ) ,内套一个 30m 30m(M区) ,再内套一个 10m 10m(S区 ) 3个尺度下土壤含水量和电导率的空间变异性。结果表明 ,各尺度间土壤含水量的标准差和变异系数比较接近而电导率的差别较大。在L和M尺度下土壤含水量在 3个置信度下呈正态或对数正态分布 ,在S尺度下只在 0.01置信度下呈对数正态分布 ;在L尺度下土壤电导率仅在信度为 0.05和0.01时呈对数正态分布 ,在M尺度下仅在信度为 0.01时正态或对数正态分布 ,较高信度时呈对数正态分布 ,在S尺度下不呈正态或对数正态分布。各尺度下它们的空间结构可用球状模型描述 ,其变程分别约为 2.2～19.8m(土壤含水量 )和 2.5～17.2m(电导率 ) ,不同尺度间土壤含水量和电导率的空间变异结构函数差别较大 ,用套合结构模型能更好地描述不同尺度下土壤含水量和电导率的空间变异性。     

The behaviour of soil process models of ammonia volatilization at contrasting spatial scales

Process models are commonly used in soil science to obtain predictions at a spatial scale that is different from the scale at which the model was developed, or the scale at which information on model inputs is available. When this happens, the model and its inputs require aggregation or disaggregation to the application scale, and this is a complex problem. Furthermore, the validity of the aggregated model predictions depends on whether the model describes the key processes that determine the process outcome at the target scale. Different models may therefore be required at different spatial scales. In this paper we develop a diagnostic framework which allows us to judge whether a model is appropriate for use at one or more spatial scales both with respect to the prediction of variations at those scale and in the requirement for disaggregation of the inputs. We show that spatially nested analysis of the covariance of predictions with measured process outcomes is an efficient way to do this. This is applied to models of the processes that lead to ammonia volatilization from soil after the application of urea. We identify the component correlations at different scales of a nested scheme as the diagnostic with which to evaluate model behaviour. These correlations show how well the model emulates components of spatial variation of the target process at the scales of the sampling scheme. Aggregate correlations were identified as the most pertinent to evaluate models for prediction at particular scales since they measure how well aggregated predictions at some scale correlate with aggregated values of the measured outcome. There are two circumstances under which models are used to make predictions. In the first case only the model is used to predict, and the most useful diagnostic is the concordance aggregate correlation. In the second case model predictions are assimilated with observations which should correct bias in the prediction, and errors in the variance; the aggregate correlations would be the most suitable diagnostic.     

A Stepwise-Inference-Based Optimization System for Supporting Remediation of Petroleum-Contaminated Sites

Groundwater contamination by leakage and spill of petroleum hydrocarbons from underground storage tanks has been a major environmental concern. Among various remediation alternatives, the vacuum-enhanced free product recovery (VFPR) is an important technology to extract light nonaqueous-phase liquids (LNAPLs) from subsurface. However, efficient design of a VFPR system was challenging to practitioners, since the process of hydrocarbon removal is costly and time consuming. To address such a problem, an integrated study system for optimizing the VFPR process was developed through coupling a numerical modeling system, a multivariate regression technique and nonlinear optimization model into a general framework. A two-dimensional multiphase flow simulation system was provided for modeling VFPR processes. An iterative stepwise-inference regression (ISIR) method was advanced for establishing a linkage between remediation actions and system responses. A nonlinear optimization model embedded with ISIR was then established for generating desired operating conditions. The results from a case study demonstrated that the established optimization model could effectively analyze tradeoffs between various environmental and economical considerations, and provide effective decision supports for site remediation practices. Compared with the conventional stepwise-cluster analysis method, the proposed ISIR method was more efficient and reliable in approximating relationships between remediation actions and system responses, and could significantly enhance the robustness of optimization solutions.     

Multiscale sources of spatial variation in soil. II. A non-Brownian fractal model and its application in soil survey

Stochastic fractals (for example, the fractional Brownian noises) model the often observed power law relation between the variance of a soil property and the length of transect sampled, but fail to account for abrupt changes of the mean (i.e. soil boundaries), for second-order stationarity and for the non self-similarity of variations at different scales that are observed in real data. This paper proposes a non-Brownian, nested model to account for situations where differences of soil have been caused by superimposed, independently acting soil-forming processes having different weights and acting at separate, discrete scales. The model is explained and theoretical examples of the semivariograms and confidence limits that arise from it are given. The model is applied to three sets of experimental data and is found to give a remarkably good fit where lateral mixing of soil has been negligible and soil boundaries are sharp. The implications for efficient mapping in situations where the soil results from a number of superimposed, independent causes are discussed.     

Non-homogeneity of variance components from spatially nested sampling of the soil

Spatially nested sampling and the associated nested analysis of variance by spatial scale is a well-established methodology for the exploratory investigation of soil variation over multiple, disparate scales. The variance components that can be estimated this way can be accumulated to approximate the variogram. This allows us to identify the important scales of variation, and the general form of the spatial dependence, in order to plan more detailed sampling by design-based or model-based methods. Implicit in the standard analyses of nested sample data is the assumption of homogeneity in the variance, i.e. that all variations from sub-station means at some scale represent a random variable of uniform variance. If this assumption fails then the comparable assumption of stationarity in the variance, which is an important assumption in geostatistics, will also be implausible. However, data from nested sampling may be analysed with a linear mixed model in which the variance components are parameters which can be estimated by residual maximum likelihood (REML). Within this framework it is possible to propose an alternative variance parameterization in which the variance depends on some auxiliary variable, and so is not generally homogeneous. In this paper we demonstrate this approach, using data from nested sampling of chemical and biogeochemical soil properties across a region in central England, and use land use as our auxiliary variable to model non-homogeneous variance components. We show how the REML analysis allows us to make inferences about the need for a non-homogeneous model. Variances of soil pH and cation exchange capacity at different scales differ between these land uses, but a homogeneous variance model is preferable to such non-homogeneous models for the variance of soil urease activity at standard concentrations of urea.     

面向知识图谱构建的水产动物疾病诊治命名实体识别

疾病诊治是水产动物健康养殖工程的重要支撑，知识图谱是水产动物疾病诊治知识表示及应用的有效手段，命名实体识别是构建水产动物疾病诊治知识图谱的关键。针对一词多义、实体嵌套等导致的水产动物疾病诊治命名实体识别准确率不高的问题，该研究提出了融合BERT（Bidirectional Encoder Representations from Transformers）与CaBiLSTM （Cascade Bi-directional Long Short-Term Memory）的实体识别模型。首先，建立水产动物疾病诊治专用语料库，并利用语料库中的数据对设计的模型进行训练；其次，采用"分层思想"设计CaBiLSTM模型进行嵌套实体识别，用降维的内层实体特征提升外层实体的辨析度，并引入BERT模型增添实体位置信息；最后，为验证所提出方法的有效性进行对比试验。试验结果表明，提出的融合BERT与CaBiLSTM模型对水产动物疾病诊治命名实体识别准确率、召回率、F1值分别达到93.07%、92.85%、92.96%。研究表明，该模型能够有效解决水产动物疾病诊治命名实体识别过程中由于一词多义、实体嵌套等导致的识别准确率不高问题，可提高水产动物疾病诊治知识图谱的构建质量，促进水产健康养殖工程发展。     

Non-Stationary Dependence Structures for Spatial Extremes

Max-stable processes are natural models for spatial extremes because they provide suitable asymptotic approximations to the distribution of maxima of random fields. In the recent past, several parametric families of stationary max-stable models have been developed, and fitted to various types of data. However, a recurrent problem is the modeling of non-stationarity. In this paper, we develop non-stationary max-stable dependence structures in which covariates can be easily incorporated. Inference is performed using pairwise likelihoods, and its performance is assessed by an extensive simulation study based on a non-stationary locally isotropic extremal t model. Evidence that unknown parameters are well estimated is provided, and estimation of spatial return level curves is discussed. The methodology is demonstrated with temperature maxima recorded over a complex topography. Models are shown to satisfactorily capture extremal dependence.     

Modelling nesting habitat as a conservation tool for the Eurasian black vulture (Aegypius monachus) in Dadia Nature Reserve, northeastern Greece

The Eurasian black vulture (Aegypius monachus) is a globally endangered species, vulnerable in Europe and endangered in Greece. We modelled its nesting preferences in Dadia reserve, northeastern Greece using logistic models at multi-scale level combined by Bayesian statistics. Compared with the random sites, the vultures nested at trees with greater diameter (DBH), lower height, and lower total number of trees around the nest tree, steeper slopes and greater distance from forest roads. Our results indicate that conservation and management guidelines for black vultures must aim: (1) to preserve old, isolated mature trees in the reserve, (2) to maintain the zones of strict protection as vulture sanctuary, including most of the suitable nesting habitat, (3) to protect the apparently suitable but still unused habitat in the rest of the reserve from disturbance and logging, (4) to monitor any changes in habitats and use our models to evaluate and predict their effect on vulture nesting in the reserve.     

Extension of landscape-based population viability models to ecoregional scales for conservation planning

Landscape-based population models are potentially valuable tools in facilitating conservation planning and actions at large scales. However, such models have rarely been applied at ecoregional scales. We extended landscape-based population models to ecoregional scales for three species of concern in the Central Hardwoods Bird Conservation Region and compared model projections against long-term trend data from the North American Breeding Bird Survey. We used a spatially-explicit demographic model and structured the regional population into ecological subsections on the basis of habitat, landscape patterns, and demographic rates to assess species viability. Our model projections were within 2% of the Breeding Bird Survey trends over the last 40 years for each species. Wood thrush (Hylocichla mustelina) populations remained relatively stable over the simulation and worm-eating warbler (Helmitheros vermivorus) abundance increased throughout most of the time period until reaching carrying capacity. In contrast, the prairie warbler (Dendroica discolor) population steadily declined by 0.59% annually. The combination of habitat and demographic modeling allowed us to create models that address processes driving these populations at all scales, which is critical to understanding how regional populations respond to landscape processes such as habitat loss and fragmentation. Therefore, because it is spatially explicit and directly addresses population growth and viability, this approach provides a valuable foundation to planning conservation strategies, offering the ability to identify the most salient risks to viability and explore ways to address them.     

Soil respiration across scales: The importance of a model–data integration framework for data interpretation

Soil respiration represents the second largest CO₂ flux of the terrestrial biosphere and amounts 10 times higher than the current rate of fossil‐fuel combustion. Thus, even a small change in soil respiration could significantly intensify—or mitigate—current atmospheric increases of CO₂, with potential feedbacks to climate change. Consequently, to understand future dynamics of the earth system, it is mandatory to precisely understand the response of soil respiration to changing environmental factors. Among those changing factors, temperature is the one with clearest and most certain future trend. The relationship of soil respiration to environmental factors and particular temperature has been tackled at a variety of scales—from laboratory via field to global scale, with each of the approaches having its particular problems, where results from different scales are sometimes contradictory. Here, we give an overview of modeling approaches to soil respiration, discuss the dependencies of soil respiration on various environmental factors, and summarize the most important pitfalls to consider when analyzing soil respiration at the respective scale. We then introduce a model–data synthesis framework that should be able to reconcile and finally integrate apparently contradictory results from the various scales.     

Nesting habitat selection by bitterns Botaurus stellaris in Britain and the implications for wetland management

The rarity and decline of the bittern Botaurus stellaris in Britain has prompted large-scale wetland restoration and more recently, wetland creation projects. In order to guide such habitat management, we investigated whether any large or fine-scale features within British wetland sites best described the selection of female bittern nesting positions. Birds nested in continuous vegetation (usually Phragmites dominated) that was on average 100 m at its narrowest width. When compared with random locations, nests had less scrub and more vegetated open water edge in their wider vicinity and were immediately surrounded by smaller percentage coverage of non-Phragmites species and thicker Phragmites stems. Of most importance, female bitterns nested at points where deeper water was maintained into the driest part of the season, perhaps using the presence of water tolerant plant species as an indication of this. The results can be used to aid the design of new wetland sites to take into account the needs of nesting female bittern.     

Modelling the rhizosphere: a review of methods for 'upscaling' to the whole-plant scale

The rhizosphere is a dynamic region where multiple interacting processes in the roots and surrounding soil take place, with dimensions set by the distance to which the zone of root influence spreads into the soil. Its complexity is such that some form of mathematical modelling is essential for understanding which of the various processes operating are important, and a minimal model of the rhizosphere must provide information on (a) the spatiotemporal concentration changes of mobile solutes in the root‐influenced soil, and (b) the cumulative uptake of solutes per unit length of root over time. Both are unique for a given set of parameters and initial conditions and hence the model is fully deterministic. ‘Up‐scaling’ to uptake by whole plants by integrating individual fluxes requires a measure of the growth and senescence of the root system. Root architecture models are increasingly successful in providing this. The spatio‐temporal scales of the rhizosphere and roots are sufficiently different that they can be treated separately, and this greatly simplifies modelling. The minimal model has been successfully applied to the more‐mobile nutrients in soil, such as nitrate or potassium, but much less successfully to less‐soluble nutrients such as phosphorus, because other, undescribed processes become important. These include transfers from unavailable forms, heterogeneity of resource distribution, root competition, water redistribution and adaptive processes. Incorporating such processes into models can disrupt independent scaling. In general, scaling from the scale of the individual root to that of the whole plant does not pose insuperable problems. Paradoxically, the major challenge in introducing more complexity is that experimental corroboration of the model is required at the individual root scale.     

Shift in nesting ground of the long-legged buzzard (Buteo rufinus) in Judea, Israel – An effect of habitat change

Until the 1980s, at least 31 pairs of long-legged buzzards (Buteo rufinus) nested along the streams of the Judean Mountains in Israel, mostly on rocky cliffs, which – according to existing literature – is the common nesting style of this bird. During the past 40 years, however, nesting in these areas has substantially decreased, with many pairs of buzzards now nesting on trees in the Judean Foothills.We suggest that the geographical shift in nesting area, and with it the dramatic change from nesting on cliffs to nesting on trees, is probably due to the increase in land cover (as a result of afforestation, expansion of human settlements and recovery of the Mediterranean chaparral) that has taken place in the Judean Mountains during the last four decades. Buzzards forage in open habitats and the change in land cover has hindered their ability to locate prey. Since there are no cliffs appropriate for nesting in the Judean Foothills, the buzzards were thus forced to adapt to a new style of nesting. This hypothesis is further supported by our observations that within their new nesting grounds in the Judean Foothills, breeding success was significantly related to the area of the open habitat within the territory.These findings have important scientific and ecological implications. We recommend that foresters should take into consideration the effect of afforestation on open-landscape raptors, and while planning new forests will consider also their needs, particularly leaving large open swaths of land as foraging grounds for such raptors.     

Multi-scale Modeling of Animal Movement and General Behavior Data Using Hidden Markov Models with Hierarchical Structures

Hidden Markov models (HMMs) are commonly used to model animal movement data and infer aspects of animal behavior. An HMM assumes that each data point from a time series of observations stems from one of N possible states. The states are loosely connected to behavioral modes that manifest themselves at the temporal resolution at which observations are made. Due to advances in tag technology and tracking with digital video recordings, data can be collected at increasingly fine temporal resolutions. Yet, inferences at time scales cruder than those at which data are collected and, which correspond to larger-scale behavioral processes, are not yet answered via HMMs. We include additional hierarchical structures to the basic HMM framework, incorporating multiple Markov chains at various time scales. The hierarchically structured HMMs allow for behavioral inferences at multiple time scales and can also serve as a means to avoid coarsening data. Our proposed framework is one of the first that models animal behavior simultaneously at multiple time scales, opening new possibilities in the area of animal movement and behavior modeling. We illustrate the application of hierarchically structured HMMs in two real-data examples: (i) vertical movements of harbor porpoises observed in the field, and (ii) garter snake movement data collected as part of an experimental design. Supplementary materials accompanying this paper appear online.     

Nesting habitat of the glossy black-cockatoo in central New South Wales

The reliance of cockatoos upon large hollows for nesting makes them vulnerable to any reduction in this resource. Species inhabiting forests managed for timber production are at risk because logging has the capacity to reduce hollow abundance significantly. In this study I identified the factors significantly associated with the selection of nest trees and nest sites by glossy black-cockatoos. Glossy black-cockatoos nested in vertical spouts in large trees. Nests were more likely to be located relatively high in senescent trees or standing dead trees (snags), factors most likely related to hollow suitability. As the density of potential nest hollows increased within a site, so to did the relative probability of it being used for nesting. The number of potential hollows serves as an index for the availability of suitable hollows. The persistence of glossy black-cockatoos within the study area will require careful management of nesting resources. Maximising the available hollow resource within known nesting areas is desirable. This is most efficiently achieved by facilitating the recruitment of large trees. This will require the exclusion of logging from nesting areas. Snags are a critical nesting resource. Information on snag dynamics is required and active intervention, such as the erection of nest boxes, may be necessary if unsustainable snag loss is occurring.