Effects of the spatial pattern of disturbance on the patch-occupancy dynamics of juniper–pine open woodland

Typically, studies of the disturbance effect on metapopulation dynamics are limited to understanding the effect of habitat loss although, recently, the spatial pattern of the disturbance has been shown to influence dynamics. In this study, we used a stochastic patch-dynamic model to investigate the effects of spatial disturbance patterns on the persistence of an open woodland community of Juniperus spp. and Pinus spp. First, we estimated patch-occupancy dynamics by using the coefficients that best predicted the occupancy observed in 1998 based on occupancy data from 1957. Next, we evaluated the effects of the rate and pattern of the disturbance on the extinction probability. In modeling the disturbance, we considered (1) the degree of disturbance produced by scenarios of complete destruction or degradation (with the potential for recolonization), (2) the overall rate of disturbance, and (3) the spatial autocorrelation of habitat destruction. Twenty 40-year simulations predicted a 25% increase in the number of patches, and when 50% of the habitat was removed, the impact was more pronounced after complete destruction than it was after degradation of the area. Predictions based on scenarios of complete destruction, including random, contiguous, Brownian, and autoregressive noise, demonstrated that the impact of disturbance depends upon the spatial structure of the disturbance regimen. The autocorrelated structure of the disturbance regimen had the greatest impact on patch persistence. Patch-occupancy was higher after 20 40-year simulations when habitat loss was randomly distributed than when it followed an autocorrelated patch destruction, which was simulated using autoregressive noise to produce 50% habitat destruction. In addition, while habitat loss was negatively linearly correlated with patch persistence when habitat destruction was randomly distributed, a dramatic transition shift occurred when habitat destruction was simulated following an autoregressive spatial distribution after a certain threshold of habitat destruction (40% of the actual open woodland habitat). Our study suggests that the spatial patterns of the disturbance should be considered when predicting the consequences of fragmentation and improving management strategies.     

Modelling directional spatial processes in ecological data

Distributions of species, animals or plants, terrestrial or aquatic, are influenced by numerous factors such as physical and biogeographical gradients. Dominant wind and current directions cause the appearance of gradients in physical conditions whereas biogeographical gradients can be the result of historical events (e.g. glaciations). No spatial modelling technique has been developed to this day that considers the direction of an asymmetric process controlling species distributions along a gradient or network. This paper presents a new method that can model species spatial distributions generated by a hypothesized asymmetric, directional physical process. This method is an eigenfunction-based spatial filtering technique that offers as much flexibility as the Moran's eigenvector maps (MEM) framework; it is called asymmetric eigenvector maps (AEM) modelling. Information needed to construct eigenfunctions through the AEM framework are the spatial coordinates of the sampling or experimental sites, a connexion diagram linking the sites to one another, prior information about the direction of the hypothesized asymmetric process influencing the response variable(s), and optionally, weights attached to the edges (links). To illustrate how this new method works, AEM is compared to MEM analysis through simulations and in the analysis of an ecological example where a known asymmetric forcing is present. The ecological example reanalyses the dietary habits of brook trout (Salvelinus fontinalis) sampled in 42 lakes of the Mastigouche Reserve, Québec.     

Spatial structure effects on the detection of patches boundaries using local operators

Landscapes exhibit various degrees of spatial heterogeneity according to the differential intensity and interactions among processes and disturbances that they are subjected to. The management of these spatially dynamical landscapes requires that we can accurately map them and monitor the evolution of their spatial arrangement through time. Such a mapping requires first the delineation of various spatial features present in the landscape such as patches and their boundaries. However, there are several environmental (spatial variability) as well as technical (spatial resolution) factors that impair our ability to accurately delineate patches and their boundaries as polygons. Here, we investigate how the spatial structure and spatial resolution of the data affect the accuracy of detecting patches and their boundaries over simulated landscapes and real data. Simulated landscapes consisted of two patches with parameterized spatial properties (patches’ level of spatial autocorrelation, mean value and variance) separated by a boundary of known location. Real data allowed the investigation of a more complex landscape where there is a known transition between two forest domains with unknown spatial properties. Boundary locations are defined using the lattice-wombling edge detector at various aggregation levels and the degree of patch homogeneity is determined using Getis-Ord’s G*. Results show that boundary detection using a local edge detector is greatly affected by the spatial conditions of the data, namely variance, abruptness of the spatial gradient between two patches and patches’ level of spatial autocorrelation. They also suggest that data aggregation is not a panacea for bringing out the ecological process creating the patches and that indicators derived from local measures of spatial association can be complementary tools for analysing spatial structures affecting boundary delineation.

Spatial smoothing techniques for the assessment of habitat suitability

Precise knowledge about factors influencing the habitat suitability of a certain species forms the basis for the implementation of effective programs to conserve biological diversity. Such knowledge is frequently gathered from studies relating abundance data to a set of influential variables in a regression setup. In particular, generalised linear models are used to analyse binary presence/absence data or counts of a certain species at locations within an observation area. However, one of the key assumptions of generalised linear models, the independence of observations is often violated in practice since the points at which the observations are collected are spatially aligned. In this paper, we describe a general framework for semiparametric spatial generalised linear models that allows for the routine analysis of non-normal spatially aligned regression data. The approach is utilised for the analysis of a data set of synthetic bird species in beech forests, revealing that ignorance of spatial dependence actually may lead to false conclusions in a number of situations.

Improving Predictive Models of In‐Stream Phosphorus Concentration Based on Nationally‐Available Spatial Data Coverages

Spatial data are playing an increasingly important role in watershed science and management. Large investments have been made by government agencies to provide nationally‐available spatial databases; however, their relevance and suitability for local watershed applications is largely unscrutinized. We investigated how goodness of fit and predictive accuracy of total phosphorus (TP) concentration models developed from nationally‐available spatial data could be improved by including local watershed‐specific data in the East Fork of the Little Miami River, Ohio, a 1,290 km² watershed. We also determined whether a spatial stream network (SSN) modeling approach improved on multiple linear regression (nonspatial) models. Goodness of fit and predictive accuracy were highest for the SSN model that included local covariates, and lowest for the nonspatial model developed from national data. Septic systems and point source TP loads were significant covariates in the local models. These local data not only improved the models but enabled a more explicit interpretation of the processes affecting TP concentrations than more generic national covariates. The results suggest SSN modeling greatly improves prediction and should be applied when using national covariates. Including local covariates further increases the accuracy of TP predictions throughout the studied watershed; such variables should be included in future national databases, particularly the locations of septic systems.     

Statistical matching for conservation science

The awareness of the need for robust impact evaluations in conservation is growing and statistical matching techniques are increasingly being used to assess the impacts of conservation interventions. Used appropriately matching approaches are powerful tools, but they also pose potential pitfalls. We outlined important considerations and best practice when using matching in conservation science. We identified 3 steps in a matching analysis. First, develop a clear theory of change to inform selection of treatment and controls and that accounts for real-world complexities and potential spillover effects. Second, select the appropriate covariates and matching approach. Third, assess the quality of the matching by carrying out a series of checks. The second and third steps can be repeated and should be finalized before outcomes are explored. Future conservation impact evaluations could be improved by increased planning of evaluations alongside the intervention, better integration of qualitative methods, considering spillover effects at larger spatial scales, and more publication of preanalysis plans. Implementing these improvements will require more serious engagement of conservation scientists, practitioners, and funders to mainstream robust impact evaluations into conservation. We hope this article will improve the quality of evaluations and help direct future research to continue to improve the approaches on offer.     

Research on the Variation of Population Distribution and Its Characteristics Based on Spatial Autocorrelation Method: A Case Study of Poyang Lake Region in Jiangxi Province

According to Statistical Yearbook of Jiangxi Province (2001-2006), We analyze the time-space variation of population distribution of Poyang Lake region from the two points of view.The former is quality of population, which involves culture struc-ture, occupational structure, age structure and sex structure of pop-ulation. The latter is quantity of population, which only involves the amount of population. Furthermore, we can reveal the internal relations and action mechanism of variation of population distribu-tion by analyzing the regional economic development, population urbanization, land use and ecological landscape of Poyang Lake region. It is important to provide help for region planning, ecological landscape planning and environmental protection by correct understanding the man-land relationship of natural-human ecosys-tem in Poyang Lake region.     

长江流域土地利用结构及其空间自相关分析

按照中国科学院资源环境分类系统,将WESTDC_Land_Cover_Products2.0数据集重分类为耕地、林地、草地、水域、城乡建设用地和其它未利用土地等6类,借助GIS平台研究了长江流域土地利用数量结构特点及其空间自相关特征。结果表明,长江流域的主要土地利用类型为林地、耕地和草地,三者占流域总面积的90%以上,而水域、城乡建设用地和其它未利用土地所占比例较小。各省市之间土地利用结构差异较大,与一定的空间格局相联系,并形成了土地利用结构特征不同的4个大的区域。各土地利用类型比重属性值相似的省市在长江流域全局空间内聚集分布、空间邻接,呈极显著的全局空间正自相关。其中耕地、林地、草地、城乡建设用地和其它未利用土地分布特征的空间正自相关极显著,水域分布特征的空间正自相关显著,但各种土地利用类型空间分布的重心、格局、空间自相关构成及回归机制不同。各种土地利用类型空间分布的局部空间自相关为高高(H-H)聚集或低低(L-L)聚集,高值为低值所包围或低值为高值所包围的空间孤立点(Spatial Outlier)不显著。研究表明各种土地利用类型在长江流域的空间分布具有明显的聚集性和区域特征。     

Patterns of Spatial Autocorrelation in Stream Water Chemistry

Geostatistical models are typically based on symmetric straight-line distance, which fails to represent the spatial configuration, connectivity, directionality, and relative position of sites in a stream network. Freshwater ecologists have explored spatial patterns in stream networks using hydrologic distance measures and new geostatistical methodologies have recently been developed that enable directional hydrologic distance measures to be considered. The purpose of this study was to quantify patterns of spatial correlation in stream water chemistry using three distance measures: straight-line distance, symmetric hydrologic distance, and weighted asymmetric hydrologic distance. We used a dataset collected in Maryland, USA to develop both general linear models and geostatistical models (based on the three distance measures) for acid neutralizing capacity, conductivity, pH, nitrate, sulfate, temperature, dissolved oxygen, and dissolved organic carbon. The spatial AICC methodology allowed us to fit the autocorrelation and covariate parameters simultaneously and to select the model with the most support in the data. We used the universal kriging algorithm to generate geostatistical model predictions. We found that spatial correlation exists in stream chemistry data at a relatively coarse scale and that geostatistical models consistently improved the accuracy of model predictions. More than one distance measure performed well for most chemical response variables, but straight-line distance appears to be the most suitable distance measure for regional geostatistical modeling. It may be necessary to develop new survey designs that more fully capture spatial correlation at a variety of scales to improve the use of weighted asymmetric hydrologic distance measures in regional geostatistical models.     

矿山微震时间序列的相空间构建与混沌吸引子维数确立

为了进一步挖掘矿山微震信号内蕴藏的信息,初步研究了微震信号的混沌特性。针对矿山微震信号非平稳、非线性的特点开展了以下研究:建立了一维时间序列的相空间重构模型,分析了一维和多维微震时间序列的空间相型;选取了矿山现场监测一维微震时间序列（N=2 000）,利用自相关函数求取了时间序列的延迟时间τ;建立了微震时间序列关联分维数的循环计算模型;最终确立了相应临界距离r（r=250）和相空间维数m（m=50）的值,计算求得分维数D的值为1.088 5。研究结果表明,微震一维时间序列的吸引子维数存在,同时也证明了微震时间序列具有混沌特性。该研究结果为利用混沌理论处理矿山微震数据,如初始到时精确拾取、信号特征分析与提取以及波形降噪等提供理论依据。