Spectral tests of randomness for spatial point patterns

We develop a spectral framework for testing the hypothesis of complete spatial randomness (CSR) for a spatial point pattern. Five formal tests based on the periodogram (sample spectrum) proposed in Mugglestone (1990) are considered. A simulation study is used to evaluate and compare the power of the tests against clustered, regular and inhomogeneous alternatives to CSR. A subset of the tests is shown to be uniformly more powerful than the others against the alternatives considered. The spectral tests are also compared with three widely used space-domain tests that are based on the mean nearest-neighbor distance, the reduced second-order moment function (K-function), and a bivariate Cramér-von Mises statistic. The test based on the scaled cumulative R-spectrum is more powerful than the space-domain tests for detecting clustered alternatives to CSR, especially when the number of events is small.     

A derivation of the statistical characteristics of forest fires

The analysis of large data sets concerning fires in various forested areas of the world has pointed out that burned areas can often be described by different power-law distributions for small, medium and large fires and that a scaling law for the time intervals separating successive fires is fulfilled. The attempts of deriving such statistical laws from purely theoretical arguments have not been fully successful so far, most likely because important physical and/or biological factors controlling forest fires were not taken into account. By contrast, the two-layer spatially extended forest model we propose in this paper encapsulates the main characteristics of vegetational growth and fire ignition and propagation, and supports the empirically discovered statistical laws. Since the model is fully deterministic and spatially homogeneous, the emergence of the power and scaling laws does not seem to necessarily require meteorological randomness and geophysical heterogeneity, although these factors certainly amplify the chaoticity of the fires. Moreover, the analysis suggests that the existence of different power-laws for fires of various scale might be due to the two-layer structure of the forest which allows the formation of different kinds of fires, i.e. surface, crown, and mixed fires.     

A critique of statistical aspects of ecological studies in spatial epidemiology

In this article, the mathematical assumptions of a number of commonly used ecological regression models are made explicit, critically assessed, and related to ecological bias. In particular, the role and interpretation of random effects models are examined. The modeling of spatial variability is considered and related to an underlying continuous spatial field. The examination of such a field with respect to the modeling of risk in relation to a point source highlights an inconsistency in commonly used approaches. A theme of the paper is to examine how plausible individual-level models relate to those used in practice at the aggregate level. The individual-level models acknowledge confounding, within-area variability in exposures and confounders, measurement error and data anomalies and so we can examine how the area-level versions consider these aspects. We briefly discuss designs that efficiently sample individual data and would appear to be useful in environmental settings.     

基于探索性空间分析的中国氮氧化物排放强度研究

采用全局和局部空间相关分析方法研究中国省际氮氧化物排放强度的空间自相关性和空间异质性.结果表明:2003—2011年全局自相关Moran'sⅠ值均为正数,表明省域之间氮氧化物排放强度呈现正相关的空间集聚分布,2003—2011年氮氧化物排放强度的空间聚集程度在不断加强;氮氧化物排放强度的空间异质性主要表现为氮氧化物排放强度的“冷点”区相对稳定,主要集中在东部、南部沿海和长江中游地区,“热点”区主要集中在东北、大西南、大西北和黄河中游地区;氮氧化物排放强度的空间差异与区域经济发展水平、产业结构和能源利用效率等因素密切相关,基于空间探索性数据分析方法研究我国氮氧化物排放强度的空间异质性,为国家制定差异化的区域氮氧化物减排目标和氮氧化物排放调控政策提供有益参考.     

Model-based combination of spatial information for stream networks

Evolutionary improvements in Geographic Information Systems (GIS) now routinely allow the management and mapping of spatial-temporal information. In response, the development of statistical models to combine information of different types and spatial support is of vital importance to environmental science. In this paper we develop a hierarchical spatial statistical model for environmental indicators of stream and river systems in the United States Mid-Atlantic Region by combining information from separate monitoring surveys, available contextual information on hydrologic units and remote sensing information. These models are used to estimate the indicators throughout the riverine system based on information from multiple sources and aggregate scales. The analysis is based on information underlying the Landscape Atlas of the mid-Atlantic region produced by the US Environmental Monitoring and Assessment Program (EMAP). We also combine information from two overlapping separate monitoring surveys, the EMAP Stream and River Survey and the Maryland Biological Streams Survey. We present a general framework for comparative distributional analysis based on the concept of a relative spatial distribution. As an application, the spatial model is used to predict spatial distributions and relative spatial distributions for a watershed.     

Bayesian entropy for spatial sampling design of environmental data

We develop a spatial statistical methodology to design national air pollution monitoring networks with good predictive capabilities while minimizing the cost of monitoring. The underlying complexity of atmospheric processes and the urgent need to give credible assessments of environmental risk create problems requiring new statistical methodologies to meet these challenges. In this work, we present a new method of ranking various subnetworks taking both the environmental cost and the statistical information into account. A Bayesian algorithm is introduced to obtain an optimal subnetwork using an entropy framework. The final network and accuracy of the spatial predictions is heavily dependent on the underlying model of spatial correlation. Usually the simplifying assumption of stationarity, in the sense that the spatial dependency structure does not change location, is made for spatial prediction. However, it is not uncommon to find spatial data that show strong signs of nonstationary behavior. We build upon an existing approach that creates a nonstationary covariance by a mixture of a family of stationary processes, and we propose a Bayesian method of estimating the associated parameters using the technique of Reversible Jump Markov Chain Monte Carlo. We apply these methods for spatial prediction and network design to ambient ozone data from a monitoring network in the eastern US.     

Multivariate analysis of spatial patterns: a unified approach to local and global structures

We propose a new approach to the multivariate analysis of data sets with known sampling site spatial positions. A between-sites neighbouring relationship must be derived from site positions and this relationship is introduced into the multivariate analyses through neighbouring weights (number of neighbours at each site) and through the matrix of the neighbouring graph. Eigenvector analysis methods (e.g. principal component analysis, correspondence analysis) can then be used to detect total, local and global structures. The introduction of the D-centring (centring with respect to the neighbouring weights) allows us to write a total variance decomposition into local and global components, and to propose a unified view of several methods. After a brief review of the matrix approach to this problem, we present the results obtained on both simulated and real data sets, showing how spatial structure can be detected and analysed. Freely available computer programs to perform computations and graphical displays are proposed.     

Accounting for geophysical information in geostatistical characterization of unexploded ordnance (UXO) sites

Efficient and reliable unexploded ordnance (UXO) site characterization is needed for decisions regarding future land use. There are several types of data available at UXO sites and geophysical signal maps are one of the most valuable sources of information. Incorporation of such information into site characterization requires a flexible and reliable methodology. Geostatistics allows one to account for exhaustive secondary information (i.e.,, known at every location within the field) in many different ways. Kriging and logistic regression were combined to map the probability of occurrence of at least one geophysical anomaly of interest, such as UXO, from a limited number of indicator data. Logistic regression is used to derive the trend from a geophysical signal map, and kriged residuals are added to the trend to estimate the probabilities of the presence of UXO at unsampled locations (simple kriging with varying local means or SKlm). Each location is identified for further remedial action if the estimated probability is greater than a given threshold. The technique is illustrated using a hypothetical UXO site generated by a UXO simulator, and a corresponding geophysical signal map. Indicator data are collected along two transects located within the site. Classification performances are then assessed by computing proportions of correct classification, false positive, false negative, and Kappa statistics. Two common approaches, one of which does not take any secondary information into account (ordinary indicator kriging) and a variant of common cokriging (collocated cokriging), were used for comparison purposes. Results indicate that accounting for exhaustive secondary information improves the overall characterization of UXO sites if an appropriate methodology, SKlm in this case, is used.     

A review of selection‐based tests of abiotic surrogates for species representation

Because conservation planners typically lack data on where species occur, environmental surrogates—including geophysical settings and climate types—have been used to prioritize sites within a planning area. We reviewed 622 evaluations of the effectiveness of abiotic surrogates in representing species in 19 study areas. Sites selected using abiotic surrogates represented more species than an equal number of randomly selected sites in 43% of tests (55% for plants) and on average improved on random selection of sites by about 8% (21% for plants). Environmental diversity (ED) (42% median improvement on random selection) and biotically informed clusters showed promising results and merit additional testing. We suggest 4 ways to improve performance of abiotic surrogates. First, analysts should consider a broad spectrum of candidate variables to define surrogates, including rarely used variables related to geographic separation, distance from coast, hydrology, and within‐site abiotic diversity. Second, abiotic surrogates should be defined at fine thematic resolution. Third, sites (the landscape units prioritized within a planning area) should be small enough to ensure that surrogates reflect species’ environments and to produce prioritizations that match the spatial resolution of conservation decisions. Fourth, if species inventories are available for some planning units, planners should define surrogates based on the abiotic variables that most influence species turnover in the planning area. Although species inventories increase the cost of using abiotic surrogates, a modest number of inventories could provide the data needed to select variables and evaluate surrogates. Additional tests of nonclimate abiotic surrogates are needed to evaluate the utility of conserving nature's stage as a strategy for conservation planning in the face of climate change.     

Detection and delineation of critical areas using echelons and spatial scan statistics with synoptic cellular data

Geographical surveillance for hotspot detection and delineation has become an important area of investigation both in geospatial ecosystem health and in geospatial public health. In order to find critical areas based on synoptic cellular data, geospatial ecosystem health investigations apply recently discovered echelon tools. In order to find elevated rate areas based on synoptic cellular data, geospatial public health investigations apply recently discovered spatial scan statistic tools. The purpose of this paper is to conceptualize a joint role for these together in the spirit of a cross-disciplinary cross-fertilization to accomplish more effective and efficient geographical surveillance for hotspot detection and delineation, and early warning system.     

Adding rigor to ecological network models by evaluating a set of pre-balance diagnostics: A plea for PREBAL

The widespread use of ecological network models (e.g., Ecopath, Econetwrk, and related energy budget models) has been laudable for several reasons, chief of which is providing an easy-to-use set of modeling tools that can present an ecosystem context for improved understanding and management of living marine resources (LMR). Yet the ease-of-use of these models has led to two challenges. First, the veritable explosion of the use and application of these network models has resulted in recognition that the content and use of such models has spanned a range of quality. Second, as these models and their application have become more widespread, they are increasingly being used in a LMR management context. Thus review panels and other evaluators of these models would benefit from a set of rigorous and standard criteria from which the basis for all network models and related applications for any given system (i.e., the initial, static energy budget) can be evaluated. To this end, as one suggestion for improving network models in general, here I propose a series of pre-balance (PREBAL) diagnostics. These PREBAL diagnostics can be done, now, in simple spreadsheets before any balancing or tuning is executed. Examples of these PREBAL diagnostics include biomasses, biomass ratios, vital rates, vital rate ratios, total production, and total removals (and slopes thereof) across the taxa and trophic levels in any given energy budget. I assert that there are some general ecological and fishery principles that can be used in conjunction with PREBAL diagnostics to identify issues of model structure and data quality before balancing and dynamic applications are executed. I humbly present this PREBAL information as a simple yet general approach that could be easily implemented, could be considered for further incorporation into these model packages, and as such would ultimately result in a straightforward way to evaluate (and perhaps identify areas for improving) initial conditions in food web modeling efforts.     

A model of Cellular Automata for the spatial and temporal analysis of Citrus Sudden Death with the fuzzy parameter

Citrus Sudden Death (CSD) is a disease that has affected sweet orange trees grafted on Rangpur lime in the south of Minas Gerais state and in the north of São Paulo state (Brazil). This is a worrying characteristic because the Rangpur lime responds to 85% of the Brazilian citrus rootstocks. This disease is believed to be caused by a virus transmitted by aphids (vector) of the citrus. In this work, we have adopted the Cellular Automata model (CA) to study the temporal evolution of the disease. We take into account the effect of the wind in the space covered by the aphid. To establish the relationship between the space covered by the aphid and the intensity of the wind, we have adopted the rule-based fuzzy system. Simulations were performed and compared to real data. Through the use of the mean absolute error (MAE) method, it was possible to verify a 70% of similarity between our model of data and the real one.     

基于综合气象干旱指数（CI）的干旱时空动态格局分析——以甘肃省黄土高原区为例

借助ArcGIS9．3和SPSS数据软件平台,根据甘肃省黄土高原区33个气象站1962-2010实测气象资料,利用综合气象干旱指数（CI）对甘肃省黄土高原区近50年的干旱特征进行了时空分析。首先计算了各站历年逐日的CI指数,统计近50年各站点出现的干旱过程、各时段的干旱事件,在此基础上分析了甘肃省黄土高原区历年各地区干旱发生的覆盖范围、频率和不同等级干旱发生的多年平均天数,揭示了甘肃省黄土高原区干旱发生的时空差异和动态格局。分析结果表明,（1）甘肃省黄土高原区有大范围干旱发生的年份,夏季和秋季较多分别有13、8a,冬季最少,只有3a。（2）从空间尺度来看,甘肃省黄土高原区中兰州一榆中一靖远一带和庆阳北部属于高值区,而岷县、渭源一带属于低值区;106°E以西“临洮．通渭．天水”一带和庆阳东南部是干旱变幅最大的地方。（3）从季节尺度来看,夏季干旱频率和持续日数最多,春季、秋季次之,冬季最少。     

A geostatistical approach for mapping thematic classification accuracy and evaluating the impact of inaccurate spatial data on ecological model predictions

Spatial information in the form of geographical information system coverages and remotely sensed imagery is increasingly used in ecological modeling. Examples include maps of land cover type from which ecologically relevant properties, such as biomass or leaf area index, are derived. Spatial information, however, is not error-free: acquisition and processing errors, as well as the complexity of the physical processes involved, make remotely sensed data imperfect measurements of ecological attributes. It is therefore important to first assess the accuracy of the spatial information being used and then evaluate the impact of such inaccurate information on ecological model predictions. In this paper, the role of geostatistics for mapping thematic classification accuracy through integration of abundant image-derived (soft) and sparse higher accuracy (hard) class labels is presented. Such assessment leads to local indices of map quality, which can be used for guiding additional ground surveys. Stochastic simulation is proposed for generating multiple alternative realizations (maps) of the spatial distribution of the higher accuracy class labels over the study area. All simulated realizations are consistent with the available pieces of information (hard and soft labels) up to their validated level of accuracy. The simulated alternative class label representations can be used for assessing joint spatial accuracy, i.e., classification accuracy regarding entire spatial features read from the thematic map. Such realizations can also serve as input parameters to spatially explicit ecological models; the resulting distribution of ecological responses provides a model of uncertainty regarding the ecological model prediction. A case study illustrates the generation of alternative land cover maps for a Landsat Thematic Mapper (TM) subscene, and the subsequent construction of local map quality indices. Simulated land cover maps are then input into a biogeochemical model for assessing uncertainty regarding net primary production (NPP).     

A concise review of ecological risk assessment for urban ecosystem application associated with rapid urbanization processes

ABSTRACT

Urban ecological risk (UER) caused by rapid urbanization means potential threat to urban ecosystem structure, pattern and services. The scales of ecological risk assessment (ERA) have been expanded from individual organisms to watersheds and regions. The types of stressor range from chemical to physical, biological and natural events. However, the application of ERA in urban ecosystems is relatively new. Here, we summarize the progress of urban ERA and propose an explicit framework to illumine future ERA based on UER identification, analysis, characterization, modeling, projection and early warning and management. The summary includes six urban ERA-relevant methods: weight-of-evidence (WoE), procedure for ecological tiered assessment of risks (PETAR), relative risk model (RRM), multimedia, multi-pathway, multi-receptor risk assessment (3MRA), landscape analysis and ecological models. Furthermore, we review critical cases of urban ERA in landscape ecology, soil, air, water and solid waste. Based on the Internet of Things (IoT) and cloud computing, an urban ERA management platform integrates various urban ERA methodologies that can be developed to provide better implementation strategies of UER for urban ecosystem managers and stakeholders. We develop a conceptual model of urban ERA based on the urban characteristics in China. The future applications of urban ERA include uncertainty analysis using Monte Carlo techniques on the basis of geospatial techniques and comprehensive urban ERA using nonlinear models or process models.     

The Impending Peak and Decline of Petroleum Production: an Underestimated Challenge for Conservation of Ecological Integrity

Abstract: In the last few decades petroleum has been consumed at a much faster pace than new reserves have been discovered. The point at which global oil extraction will attain a peak (“peak oil”) and begin a period of unavoidable decline is approaching. This eventuality will drive fundamental changes in the quantity and nature of energy flows through the human economic system, which probably will be accompanied by economic turmoil, political conflicts, and a high level of social tension. Besides being a geological and economic issue, peak oil is also a fundamental concern as it pertains to ecological systems and conservation because economics is a subsystem of the global ecosystem and changes in human energy‐related behaviors can lead to a broad range of effects on natural ecosystems, ranging from overuse to abandonment. As it becomes more difficult to meet energy demands, environmental considerations may be easily superseded. Given the vital importance of ecosystems and ecosystem services in a postpetroleum era, it is crucially important to wisely manage our ecosystems during the transition period to an economy based on little or no use of fossil fuels. Good policies can be formulated through awareness and understanding gained from scenario‐based assessments. Presently, most widely used global scenarios of environmental change do not incorporate resource limitation, including those of the Millennium Ecosystem Assessment and the Intergovernmental Panel on Climate Change. Considering the potential magnitude of the effects of peak oil on society and nature, the development of resource‐constrained scenarios should be addressed immediately. Ecologists and conservation biologists are in an important position to analyze the situation and provide guidance, yet the topic is noticeably absent from ecological discussions. We urge politicians, corporate chief executives, thought leaders, and citizens to consider this problem seriously because it is likely to develop into one of the key environmental issues of the 21st century.     

Combining a generic process-based productivity model and a statistical classification method to predict the presence and absence of tree species in the Pacific Northwest,U.S.A.

Although long-lived tree species experience considerable environmental variation over their life spans, their geographical distributions reflect sensitivity mainly to mean monthly climatic conditions. We introduce an approach that incorporates a physiologically based growth model to illustrate how a half-dozen tree species differ in their responses to monthly variation in four climatic-related variables: water availability, deviations from an optimum temperature, atmospheric humidity deficits, and the frequency of frost. Rather than use climatic data directly to correlate with a species’ distribution, we assess the relative constraints of each of the four variables as they affect predicted monthly photosynthesis for Douglas-fir, the most widely distributed species in the region. We apply an automated regression-tree analysis to create a suite of rules, which differentially rank the relative importance of the four climatic modifiers for each species, and provide a basis for predicting a species’ presence or absence on 3737 uniformly distributed U.S. Forest Services’ Forest Inventory and Analysis (FIA) field survey plots. Results of this generalized rule-based approach were encouraging, with weighted accuracy, which combines the correct prediction of both presence and absence on FIA survey plots, averaging 87%. A wider sampling of climatic conditions throughout the full range of a species’ distribution should improve the basis for creating rules and the possibility of predicting future shifts in the geographic distribution of species.     

A method for building spatial model of annual weed seed dispersal from experimental data and its application to simulating Bromus sterilis population dispersal

Spatial model of annual weed seed dispersal, in this article, was theoretically derived. According to the requirements of building the spatial model, we designed and done an indoor experiment of weed seed dispersal by wind. In the experiment, the seeds of Bromus sterilis were released at 100 cm height under different wind velocity conditions. Based on the experimental data, the spatial models of seed dispersal of the weed species were built, which were divided into three types according to the coefficient β < 0, β = 0, β > 0. The results showed that dispersal of annual weed seed in any direction obeyed an approximate Gaussian distribution; under the experimental conditions, spatial distribution type of weed seed dispersal changed with variation of wind velocity. Well-known Howard et al.'s model (Howard et al., 1991) of Bromus sterilis seed dispersal is an especial example of the model built in this article. The result of model analysis indicated that the distribution type described by Howard's model was similar to that of seed dispersal of the weed species at the height of 100 cm under the condition of lower wind velocity (about 2.18 m/s). Using CA simulation analysis we found that mean control agent applying to a cell with weed should have a decrease with an increase of wind velocity to prevent weed with the initial configuration from spreading, which implicated less herbicide needs spraying in every cell with weed on average when wind velocity increases.     

ToSIA—A tool for sustainability impact assessment of forest-wood-chains

Within the forest sector, the sustainability concept has evolved from a narrow focus on sustainable wood production to a much broader evaluation of environmental, social, and economic sustainability for whole value chains. A new software tool - ToSIA - has been developed for assessing sustainability impacts of Forest-Wood-Chains (FWCs). In the approach, FWCs are defined as chains of production processes (e.g. harvesting-transport-industrial processing), which are linked with products (e.g. a timber frame house). Sustainability is determined by analysing environmental, economic, and social sustainability indicators for all the production processes along the FWC. The tool calculates sustainability values as products of the relative indicator values (i.e. indicator value expressed per unit of material flow) multiplied with the material flow entering the process. Calculated sustainability values are then aggregated for the segments of the FWC or for the complete chain. The sustainability impact assessment requires carefully specified system boundaries. ToSIA uses a data-oriented approach that is very flexible in the focus of the analysis and the selection of indicators of sustainability. An example of alternative Norway spruce management systems in Southern Germany and their effects on six sustainability indicators is presented. The less intensive management system with natural regeneration and motor-manual harvesting shows higher carbon storage and slightly less energy use. It creates more employment and higher labour costs, but the average rate of accidents is also higher. ToSIA offers a transparent and consistent methodological framework to assess sustainability impacts in the forest-based sector as affected, e.g. by changes in policies, market conditions, or technology. The paper discusses strengths and limitations of the approach and provides an outlook on further development perspectives of the methodology.     

A Blueprint for the Oceans: Implications of Two National Commission Reports for Conservation Practitioners

Abstract:  In the first comprehensive reviews since 1969, two high-level reports on the status of U.S. marine ecosystems and resources have been released: that of the nongovernmental Pew Oceans Commission in 2003 and that of the congressionally mandated U.S. Commission on Ocean Policy in 2004 . Although the two commissions differed in the breadth and depth of their mandates, their findings on the state of our oceans and need for new management approaches are similar, and their major recommendations for science, governance, management and conservation, and education echo and reinforce each other. The history behind the two commissions and the structure and objectives of each remind us of the crucial roles conservation practitioners and scientists must play in the implementation of report recommendations. The reports suggest priorities for conservation practitioners to improve science, management, conservation, governance, and education to better manage and protect ocean resources and ecosystems. To redirect activities affecting the state of global marine resources, integrated research; education and outreach by terrestrial, aquatic, and marine conservation practitioners; and a clear definition of their roles in the implementation of recommendations is essential. Marine ecosystems provide a test case of society's ability to manage complexity in human and natural systems in an integrated manner, with extensive opportunities for participation by conservation practitioners from all disciplines.