山东赤松种群的空间分布型及强度

利用分布类型和聚集强度指数对山东主产区赤松种群的空间分布型及强度进行了初步研究,结果表明：鲁东丘陵区的赤松种群生林一般表现为负二项分布和奈曼分布,鲁中南山地丘陵区的人工林则呈现均匀分布;在赤松种群不同发育阶段,出现了由幼苗→幼树→大中树扩散态势的一般规律,但鲁东丘陵区次生林由于受人为、虫害等因素的影响,其分布类型和聚散态势呈明显的多样化,鲁中南山地丘陵区人工林则表现为聚集→扩散的独特态势。     

抚育间伐对栓皮栎种群空间分布格局的影响

抚育间伐是一种重要的改善林木生长条件的经营措施,对林分结构和动态具有重要影响。为研究抚育间伐对林木种群空间结构与格局影响的内在机制,以间伐和未间伐的栓皮栎人工林为研究对象,通过设置2个100 m×100 m样地并进行每木定位和调查,在采用径级结构代替年龄结构方法将栓皮栎种群划分为幼树(2 cm≤DBH<5 cm)、小树(5 cm≤DBH<13 cm)和大树(DBH≥13 cm)3个生长阶段的基础上,分别采用Ripley’s K函数衍生的g(r)函数和双变量g12(r)函数,对栓皮栎种群空间分布点格局及不同生长阶段栓皮栎种群之间的关联性进行了研究。结果表明,间伐和未间伐样地栓皮栎种群空间分布点格局分别在0-16 m和0-33 m距离尺度内呈聚集分布,而分别在大于16 m和33 m距离尺度内呈随机分布;间伐和未间伐样地栓皮栎幼树、小树和大树的株数比分别为8?741?699和261?1134?683,且间伐样地栓皮栎幼树、小树和大树种群分别在0-14、1-16、0-6 m距离尺度内呈现均匀或聚集分布,而在其他距离尺度上表现为随机分布;栓皮栎幼树、小树和大树之间仅在间伐样地0-6 m距离尺度内呈现一定的相关性,而在未间伐样地更大的距离尺度内有显著的关联性,如栓皮栎幼树和大树之间在6-38 m距离尺度上呈显著正相关。因此,抚育间伐一定程度上使得栓皮栎种群在更大距离尺度上呈现出随机分布状态,并弱化了不同生长阶段的林木栓皮栎种群的关联性,这调整了栓皮栎种群空间竞争关系,有利于大径级林木个体的培育。该研究可以为开展抚育间伐对林木种群的影响的研究提供理论依据,也可以为制定科学合理的抚育技术措施提供参考。     

地统计学在土壤重金属研究中的应用及展望

从采矿学与地质学研究中发展起来的地统计学是应用数理统计学的一个分支。与传统的统计学相比，地统计学可应用于土壤重金属研究中，能探索土壤重金属的空间分布特征及其变异规律。地统计学的基础理论与方法主要包括：区域化变量、半方差函数、克立格空间插值技术。半方差函数可以用来描述研究土壤重金属分布的空间相关性；而克立格插值可以对未采样区土壤重金属的含量进行无偏最优估计。在对地统计学理论进行简要阐述的基础上，回顾了近些年在土壤重金属研究的采样设计、空间结构分析、空间插值等方面的应用，并就其应用前景作了展望。     

A nonparametric procedure for analyzing repeated measures of spatially correlated data

Many agricultural, biological, and environmental studies involve detecting temporal changes of a response variable, based on data observed at sampling sites in a spatial region and repeatedly over several time points. That is, data are repeated measures over time and are potentially correlated across space. The traditional repeated-measures analysis allows for time dependence but assumes that the observations at different sampling sites are mutually independent, which may not be suitable for field data that are correlated across space. In this paper, a nonparametric large-sample inference procedure is developed to assess the time effects while accounting for the spatial dependence using a block bootstrap. For illustration, the methodology is applied to describe the population changes of root-lesion nematodes over time in a production field in Wisconsin.     

Simple Spatial Modeling Tool for Prioritizing Prescribed Burning Activities at the Landscape Scale

Abstract:   Resources for prescribed fire are frequently insufficient to manage public lands for all conservation and resource management objectives, necessitating prioritization of the application of fire across the landscape within any given year. Defining tradeoffs when applying prescribed fire to large landscapes is problematic not only because of the complexity of weighing competing management objectives at the landscape scale, but also because of the difficult nature of independently applying need-to-burn criteria to large areas. We present a case study of a simple modeling process implemented at Eglin Air Force Base in the Florida Panhandle (U.S.A.) to prioritize the application of prescribed fire. In a workshop setting, managers and biologists identified key conservation criteria and landscape management objectives that drive the application of prescribed fire. Remote sensing and other spatial data were developed to directly or indirectly represent all these criteria. Using geographic information system software, managers and biologists weighted each criterion according to its relative contribution to overall burn prioritization, and individual values for the criterion were scored according to how they influence the need to burn. Subsequently, this process has been validated and modified through ecological monitoring. This modeling process has also been applied to the 77,400-ha Blackwater River State Forest, public land adjacent to Eglin Air Force Base, demonstrating its applicability to lands with varying management priorities. The advantages of this model-based approach for prioritizing prescribed fire include the reliance on accessible, inexpensive software, the development of spatially explicit management objectives, the ease of transferability, and clearly stated assumptions about management that may be tested and reviewed through monitoring and public comment.     

Assessing Land-Use Impacts on Natural Resources

/ Much information is available on changes that occur in natural resources from both spatially-explicit data on environmental conditions and models of the interactions of these conditions and resources with human activities. The strategy for assessing land-use impacts on natural resources developed in this paper provides a framework for using relevant data and models to address questions of how management practices can promote both use and protection of resources. This assessment strategy integrates spatially explicit environmental data using geographic information systems (GIS) with computer models that simulate changes in land cover in response to land-use impacts. The computer models also simulate susceptibility of species to changes in habitat suitability and landscape patterns. The approach is applied to management of limestone barrens on the Oak Ridge Reservation in East Tennessee. Potential limestone barrens habitats are identified by overlaying appropriate soils, geology, slope, and land-use/land-cover conditions. Their validity is tested against known sites containing rare species that occur in these habitats. The location of habitats at risk in the aftermath of human activities is determined by using an available area model that identifies the size and proximity of sites that particular types of species can no longer use as habitat. The resulting risk map can be used in land management planning. The approach uses readily available in situ and remotely sensed data and is applicable to a wide range of locations and land-use scenarios. This approach can be refined based on needs identified by land managers and on the sensitivity of the results to the resolution of available resource information.KEY WORDS: Land management; Assessment; Habitat characterization; Limestone barrens; Ecological modeling; Geographic information systems     

ECONOMIC AND WATER QUALITY EFFECTS OF VARIABLE AND UNIFORM APPLICATION OF NITROGEN1

ABSTRACT: Nitrogen (N) fertilizer rates for achieving optimum crop yields often vary within a field due to spatial variability in soil moisture and nitrogen content and other crop growth factors. When there is substantial within-field variability in these factors, uniform application of N (UAN) may not be economically efficient in terms of maximizing net return because N is likely to be over-applied in some areas and under-applied in other areas of the field. In addition, over-application can adversely affect water quality. A sample of fields in a Midwestern agricultural watershed is used to test for statistically significant differences in N application rates, crop yields, surface and ground water quality and net returns between UAN and variable application of N (VAN) for four cropping systems. Profitability and water quality benefits of VAN are sensitive to the distribution of soil types within a field. Water quality effects and profitability of UAN and VAN vary with cropping systems. VAN is not uniformly superior to UAN in terms of increasing net returns and improving water quality for the farming systems and watershed evaluated in this study.     

REGRESSION MODELS OF HERBICIDE CONCENTRATIONS IN OUTFLOW FROM RESERVOIRS IN THE MIDWESTERN USA, 1992–19931

ABSTRACT: Reservoirs are used to store water for public water supply, flood control, irrigation, recreation, hydropower, and wildlife habitat, but also often store undesirable substances such as herbicides. The outflow from 76 reservoirs in the midwestern USA, was sampled four times in 1992 and four times in 1993. At least one herbicide was detected in 82.6 percent of all samples, and atrazine was detected in 82.1 percent of all samples. Herbicide properties; topography, land use, herbicide use, and soil type in the contributing drainage area; residence time of water in reservoirs; and timing of inflow, release, and rainfall all can affect the concentration of herbicides in reservoirs. A GIS was used to quantify characteristics of land use, agricultural chemical use, climatic conditions, topographic character, and soil type by reservoir drainage basins. Multiple linear and logistic regression equations were used to model mean herbicide concentrations in reservoir outflow as a function of these characteristics. Results demonstrate a strong association between mean herbicide concentrations in reservoir outflow and herbicide use rates within associated drainage basins. Results also demonstrate the importance of including soils and basin hydrologic characteristics in models used to estimate mean herbicide concentrations.     

NONLINEAR MODELING AND PREDICTION OF A RIVER FLOW SYSTEM1

ABSTRACT: Model estimation and prediction of a river flow system are investigated using nonlinear system identification techniques. We demonstrate how the dynamics of the system, rainfall, and river flow can be modeled using NARMAX (Nonlinear Autoregressive Moving Average with eXogenuous input) models. The parameters of the model are estimated using an orthogonal least squares algorithm with intelligent structure detection. The identification of the nonlinear model is described to represent the relationship between local rainfall and river flow at Enoree station (inputs) and river flow at Whitmire (output) for a river flow system in South Carolina.     

VERIFICATION OF THE RHEA-OROGRAPHIC-PRECIPITATION MODEL1

ABSTRACT: Observed April 1 snowpack accumulations within and near the Gunnison River basin in southwestern Colorado are compared with simulations from the Rhea-orographic-precipitation model to determine if the model simulates reliable magnitudes and temporal and spatial variability in winter precipitation for the basin. Twenty simulations of the Rhea model were performed using‘optimal’parameter sets determined for 10-kilometer (km) grids (10-km by 10-km grid cells) through stochastic calibration. Comparisons of Rhea-model simulations of winter precipitation with April 1 snowpack accumulations at 32 snowcourse stations were performed for the years 1972–1990. For most stations and most years the Rhea model reliably simulates the temporal and spatial variability in April 1 snowpack accumulations. However, in general, the Rhea-model underestimates April 1 snowpack accumulations in the Gunnison River basin area, and the underestimation is greatest for locations that receive the largest amount of snow. A significant portion of the error in Rhea-model simulations is due to the calibration of the Rhea model using gauge-catch precipitation measurements which can be as much as 50 percent below actual snowfall accumulations. Additional error in the Rhea-model simulations is a result of the comparison of gridded precipitation values to observed values measured at points.