Description of hydrogeologic heterogeneity and evaluation of radionuclide transport at an underground nuclear test

Realistic models of contaminant transport in groundwater demand detailed characterization of the spatial distribution of subsurface hydraulic properties, while at the same time programmatic constraints may limit collection of pertinent hydraulic data. Fortunately, alternate forms of data can be used to improve characterization of spatial variability. We utilize a methodology that augments sparse hydraulic information (hard data) with more widely available hydrogeologic information to generate equiprobable maps of hydrogeologic properties that incorporate patterns of connected permeable zones. Geophysical and lithologic logs are used to identify hydrogeologic categories and to condition stochastic simulations using Sequential Indicator Simulation (SIS). The resulting maps are populated with hydraulic conductivity values using field data and Sequential Gaussian Simulation (SGS). Maps of subsurface hydrogeologic heterogeneity are generated for the purpose of examining groundwater flow and transport processes at the Faultless underground nuclear test, Central Nevada Test Area (CNTA), through large-scale, three-dimensional numerical modeling. The maps provide the basis for simulation of groundwater flow, while transport of radionuclides from the nuclear cavity is modeled using particle tracking methods. Sensitivity analyses focus on model parameters that are most likely to reduce the long travel times observed in the base case. The methods employed in this study have improved our understanding of the spatial distribution of preferential flowpaths at this site and provided the critical foundation on which to build models of groundwater flow and transport. The results emphasize that the impacts of uncertainty in hydraulic and chemical parameters are dependent on the radioactive decay of specific species, with rapid decay magnifying the effects of parameters that change travel time.     

Incorporating spatial autocorrelation into cellular automata model: An application to the dynamics of Chinese tamarisk (Tamarix chinensis Lour.)

Spatial autocorrelation (SAC) is frequently encountered in most spatial data in ecology. Cellular automata (CA) models have been widely used to simulate complex spatial phenomena. However, little has been done to examine the impact of incorporating SAC into CA models. Using image-derived maps of Chinese tamarisk (Tamarix chinensis Lour.), CA models based on ordinary logistic regression (OLCA model) and autologistic regression (ALCA model) were developed to simulate landscape dynamics of T. chinensis. In this study, significant positive SAC was detected in residuals of ordinary logistic models, whereas non-significant SAC was found in autologistic models. All autologistic models obtained lower Akaike's information criterion corrected for small sample size (AICc) values than the best ordinary logistic models. Although the performance of ALCA models only satisfied the minimum requirement, ALCA models showed considerable improvement upon OLCA models. Our results suggested that the incorporation of the autocovariate term not only accounted for SAC in model residuals but also provided more accurate estimates of regression coefficients. The study also found that the neglect of SAC might affect the statistical inference on underlying mechanisms driving landscape changes and obtain false ecological conclusions and management recommendations. The ALCA model is statistically sound when coping with spatially structured data, and the adoption of the ALCA model in future landscape transition simulations may provide more precise probability maps on landscape transition, better model performance and more reasonable mechanisms that are responsible for landscape changes.     

The effect of species response form on species distribution model prediction and inference

Ecological theory and current evidence support the validity of various species response curves according to a variety of environmental gradients. Various methods have been developed for building species distribution models but it is not well known how these methods perform under various assumptions about the form of the underlying species response. It is also not well known how spatial correlation in species occurrence affects model performance. These effects were investigated by applying an environmental envelope method (BIOCLIM) and three regression-based methods: logistic regression (LR), generalized additive modelling (GAM), and classification and regression tree (CART) to simulated species occurrence data. Each simulated species was constructed as a sum of responses with varying weights. Three basic species response curves were assumed: Gaussian (bell-shaped), Beta (skew) and linear. The two non-linear responses conform to standard ecological niche theory. All three responses were applied in turn to three simulated environmental variables, each with varying degrees of spatial autocorrelation. GAM produced the most consistent model performance over all forms of simulated species response. BIOCLIM and CART were inclined to underrate the performance of variables with a linear response. BIOCLIM was less sensitive to data density. LR was susceptible to model misspecification. The use of a linear function in LR underestimated the performance of variables with non-linear species response and contributed to increased spatial autocorrelation in model residuals. Omission of important environmental variables with non-linear species response also contributed to increased spatial autocorrelation in model residuals. Adding a spatial autocovariate term to the LR model (autologistic model) reduced the spatial autocorrelation and improved model performance, but did not correct the misidentification of the dominant environmental determinant. This is to be expected since the autologistic approach was designed primarily for prediction and not for inference. Given that various forms of species response to environmental determinants arise commonly in nature: (1) higher order functions should always be tested when applying LR in modelling species distribution; (2) spatial autocorrelation in species distribution model residuals can indicate that environmental determinants with non-linear response are missing from the model; and (3) deficiencies in LR model performance due to model misspecification can be addressed by adding a spatial autocovariate to the model, but care should be taken when interpreting the coefficients of the model parameters.     

Pyric Herbivory: Rewilding Landscapes through the Recoupling of Fire and Grazing

Abstract:  Our understanding of fire and grazing is largely based on small-scale experimental studies in which treatments are uniformly applied to experimental units that are considered homogenous. Any discussion of an interaction between fire and grazing is usually based on a statistical approach that ignores the spatial and temporal interactions on complex landscapes. We propose a new focus on the ecological interaction of fire and grazing in which each disturbance is spatially and temporally dependent on the other and results in a landscape where disturbance is best described as a shifting mosaic (a landscape with patches that vary with time since disturbance) that is critical to ecological structure and function of many ecosystems. We call this spatiotemporal interaction pyric herbivory (literal interpretation means grazing driven by fire). Pyric herbivory is the spatial and temporal interaction of fire and grazing, where positive and negative feedbacks promote a shifting pattern of disturbance across the landscape. We present data we collected from the Tallgrass Prairie Preserve in the southern Great Plains of North America that demonstrates that the interaction between free-roaming bison ( Bison bison ) and random fires promotes heterogeneity and provides the foundation for biological diversity and ecosystem function of North American and African grasslands. This study is different from other studies of fire and grazing because the fires we examined were random and grazing animals were free to roam and select from burned and unburned patches. For ecosystems across the globe with a long history of fire and grazing, pyric herbivory with any grazing herbivore is likely more effective at restoring evolutionary disturbance patterns than a focus on restoring any large vertebrate while ignoring the interaction with fire and other disturbances .     

珠三角区域PM2.5时空变异特征

珠三角区域PM_2.5污染严重,以 2012年9月─2013年8月62个大气监测站的PM_2.5联网数据为基础,采用地统计学方法定性、定量分析了该区域ρ(PM_2.5)的时空变异特征. 定性分析结果表明,基底效应在0.12~0.30之间,相应ρ(PM_2.5)变异属于以结构性变异为主的Ⅰ、Ⅱ类,对应的空间自相关程度为强、较强,说明珠三角区域的ρ(PM_2.5)分布差异主要由区域结构所致. 定量分析结果表明:①空间自相关距离受气象因素影响,随方向和时间在51~85 km之间变化,东西方向的影响距离(75~85 km)最大. ②ρ(PM_2.5)在南北方向的变异幅度指数(0.34~0.70)和变异速度指数〔0.14~0.38 μg/(m3·km)〕在各方向中均为最大;而东北─西南方向的2个指标则均为最小,其中变异幅度指数为0.25~0.42,变异速度指数在0.13~0.34 μg/(m3·km)之间,即南北方向的ρ(PM_2.5)变化大于其他方向.③综合异质指数介于0.14~0.54之间,说明ρ(PM_2.5)总体保持在中等异质水平. 鉴于珠三角区域ρ(PM_2.5)的空间变异特征,在进行监测站布设时,矩形网格相较于方形网格更适合于对该区域地理空间进行划分,其中网格的长为东西方向平均空间自相关距离(78 km)的2倍,宽为南北方向平均空间自相关距离(56 km)的2倍.      

基于空间计量模型的河南省用水效率影响因素分析

在水资源总量约束下,提高水资源利用效率是保障缺水地区城镇化、工业化与经济社会发展的必然途径。论文以河南省地级市2000—2013年面板数据为基础,运用空间计量模型,从水资源禀赋、水资源开发利用程度、经济发展水平、城镇化水平、产业结构、用水结构、农业现代化水平、信息化水平等方面入手,对河南省用水效率的影响因素进行分析。结果表明：1）河南省地级市用水效率存在不可忽视的空间自相关特征,时空固定形式下的空间杜宾模型可以实现其最优模拟;2）河南省各地级市用水效率明显受邻近地级市用水效率的影响,其空间溢出弹性系数达到0.31;3）水资源禀赋、水资源开发利用程度对河南省各地级市用水效率产生显著的负向影响,但其空间溢出效应不明显;4）经济发展水平、城镇化发展水平、产业结构优化水平对河南省各地级市用水效率产生显著的正向影响,而且除工业化水平外,空间溢出效应十分明显;5）用水结构变化、农业现代化、信息化水平与河南省用水效率的相关关系总体不明显,河南省水资源可持续利用与全国“四化”协调发展示范区建设的关系有待进一步协调。     

Using an individual-based model to quantify scale transition in demographic rate functions: Deaths in a coral reef fish

Scientifically informed population management requires quantitatively accurate demographic rate functions that apply at the spatial scale at which populations are actually managed, but practical constraints confine most field measurements of such functions to small study plots. This paper employs an individual-based population growth model to extrapolate the death rate function in a well-studied coral reef fish, the bridled goby Coryphopterus glaucofraenum, from the scale of coral reef “cells” at which it was measured to the larger scale of an entire coral reef. Density dependence in the whole-reef function actually proves stronger than in the local function because high goby density occasionally arises in local patches with few refuges from predators, producing very high mortality there. This IBM-based approach extends the reach of scale transition theory by examining considerably more realistic models than standard analytical methods can presently handle.     

Allometric scaling of resource acquisition by ruminants in dynamic and heterogeneous environments

We present a mechanistic formulation of the intake response of ruminants to vegetation biomass based solely on physiological and morphological parameters that scale allometrically with the animal's body mass. The model is applied to describe herbivore-vegetation interactions in dynamic and heterogeneous landscapes with low quality but abundant “tall grass” and high quality but sparsely available “short grass”, under two conditions: “uncoupled” (such that the effect of food intake on vegetation biomass can be neglected), or “coupled” (such that the vegetation biomass is determined by herbivore feeding). The results show that under uncoupled conditions, the minimum acceptance (proportion of vegetation consumed by the herbivore) at which the herbivore can leave its current patch without reducing its intake rate is when it has depleted the current patch by the energetic cost required to travel to another patch. The maximum acceptance at which the herbivore should leave its patch is when it has depleted the current patch by the cumulative energetic cost of traveling, handling, cropping, and digesting. Under coupled conditions, the optimal acceptance equals half the relative growth rate of the vegetation. Analytical solutions are obtained for equilibrium values for utilization of the vegetation, and for the densities of vegetation and ruminants, expressed in physiological and morphological herbivore parameters.     

Abolition of set-aside schemes, associated impacts on habitat structure and modelling of potential effects of cross-farm regulation

In intensively farmed regions, habitat fragmentation represents a major pressure on biodiversity. Depending on its spatial setting, set-aside land can increase size and connectivity of habitats and thus counteract fragmentation. In 2008, the EU-wide set-aside obligation was suspended and a large proportion of set-aside land was re-cultivated. With Denmark as case we apply an indicator to measure the effect of set-aside land on spatial structure of semi-natural habitats in term of habitat size and connectivity. Furthermore, we model effects of a hypothetical spatial regulation, where set-aside land with the greatest benefit for habitat structure is retained as uncultivated, while set-aside land with the least effect is re-cultivated. The model is applied to individual farms and to farm agglomerations of increasing sizes, enabling us to explore potential effects of cross-farm regulation. The novelty of our approach is the application of observed land-uses changes for modelling a hypothetical regulation working on a range of spatial scales. Results show that after abolition of set-aside schemes the effect of set-aside land on habitat structure was more than halved. Modelled spatial regulation considerably reduces impacts. Effects increase with increasing size of farm agglomerations. However, marginal benefits become negligible at agglomeration sizes over 36 km².     

Importance of Accounting for Detection Heterogeneity When Estimating Abundance: the Case of French Wolves

Abstract: Assessing conservation strategies requires reliable estimates of abundance. Because detecting all individuals is most often impossible in free‐ranging populations, estimation procedures have to account for a <1 detection probability. Capture–recapture methods allow biologists to cope with this issue of detectability. Nevertheless, capture–recapture models for open populations are built on the assumption that all individuals share the same detection probability, although detection heterogeneity among individuals has led to underestimating abundance of closed populations. We developed multievent capture–recapture models for an open population and proposed an associated estimator of population size that both account for individual detection heterogeneity (IDH). We considered a two‐class mixture model with weakly and highly detectable individuals to account for IDH. In a noninvasive capture–recapture study of wolves we based on genotypes identified in feces and hairs, we found a large underestimation of population size (27% on average) occurred when IDH was ignored.