基于GIS的城市大气TSP污染空间分析

利用GIS的空间分析功能 ,对焦作城区大气TSP的污染状况及污染源的内在关系进行了深入的分析 ,通过建立TSP污染类型划分标准和污染动态变化分类系统 ,对城区TSP污染进行了分区评价 ,并进一步对城区大气TSP污染的空间动态转化规律和趋势进行了研究     

基于市域尺度的中国化肥施用与粮食产量的时空耦合关系

化肥施用是一把双刃剑,在促进粮食产量增加、保障粮食安全的同时,导致了生态环境退化,当前亟需辨明化肥施用与粮食生产之间的时空耦合关系,以便为维护粮食安全与生态安全提供有效的对策。以中国336个地级行政区为研究单元,分析2005-2015年中国粮食产量与化肥施用量的时空格局变化特征、化肥施用量与粮食产量的时空耦合关系及其动态变化过程。研究发现：（1）2005-2015年中国化肥施用量与粮食产量均呈上升趋势,化肥施用量的区内差异呈“粮食主产区—产销平衡区—粮食主销区”递减态势;而粮食产量的区域差异总体趋于增大,且呈“粮食主产区—产销平衡区—粮食主销区”递增态势。（2）化肥施用量增幅大致呈“东—中—西”阶梯式递增的趋势,而粮食产量增幅呈明显的南北分异特征。（3）从空间分布来看,胡焕庸线以东地区以“双高区”为主,胡焕庸线以西地区以“双低区”为主,且“低效施肥区”收缩,“他因素影响区”扩张。（4）期间,粮肥耦合关系以“双增型”为主,其他类型为辅,粮肥耦合关系主要向“双增型”与“他因素影响型”转变。     

湖泊水质参数空间分析中异常值的识别与处理

异常值的存在对湖泊水质参数局部估计精度产生重要影响 ,制约了地统计学在湖泊水质研究中的应用。文章以太湖为例 ,介绍了在湖泊水质参数研究中的三种异常值识别和处理的方法。应用这些方法可以有效地降低异常值的影响水平     

安徽省近十余年市域综合实力的空间格局

在建立指标体系的基础上，对安徽省市域1991-2002年时段的经济活动进行了实证分析，刻画了20世纪90年代以来安徽省综合实力空间分布格局的动态变化：在时间纬度上，市域差异呈逐步扩大趋势，在空间结构上表现出明显的省城中心性和沿江指向性，并指出今后这种格局还在进一步变动中，市域间的经济实力悬殊加剧的趋势将不可避免。最后提出高梯度区、中梯度区立足于制造业，准低梯度区和低梯度区立足于大农业，实施“东向”战略，尽快融入“长三角”的发展思路。     

我国物流产业基本经济活动空间格局分析

基于经济基础理论,对1991～2001年间我国各省区物流产业基本经济活动部分进行了实证分析,从省际差异、东中西部差异两个方面分析了我国区域物流产业基本经济活动的省区差异:在时间维度上区域差异呈逐步扩大趋势,在空间结构上表现出较明显的沿海指向性和交通指向性,并以此分析归纳出我国物流产业基本活动的"四区(物流集聚区)一带(物流集聚带)"空间格局特征,进而得出其基本经济活动强度与区域经济发展水平、物流需求程度、科技水平、区位条件、交通等基础设施、政策及历史发展作用紧密相关的结论.     

NUTRIENT CONCENTRATION CRITERIA AND CHARACTERIZATION OF PATTERNS IN TROPHIC STATE FOR RIVERS IN HETEROGENEOUS LANDSCAPES1

ABSTRACT: A method is demonstrated for the development of nutrient concentration criteria and large scale assessment of trophic state in environmentally heterogeneous landscapes. The method uses the River Environment Classification (REC) as a spatial framework to partition rivers according to differences in processes that control the accrual and loss of algae biomass. The method is then applied to gravel bed rivers with natural flow regimes that drain hilly watersheds in New Zealand's South Island. An existing model is used to characterize trophic state (in terms of chlorophyll a as a measure of maximum biomass) using nutrient concentration, which controls the rate of biomass accrual, and flood frequency, which controls biomass loss. Variation in flood frequency was partitioned into three classes, and flow data measured at 68 sites was used to show that the classes differ with respect to flood frequency. Variation in nutrient concentration was partitioned at smaller spatial scales by subdivision of higher level classes into seven classes. The median of flood frequency in each of the three higher level classes was used as a control variable in the model to provide spatially explicit nutrient concentration criteria by setting maximum chlorophyll a to reflect a desired trophic state. The median of mean monthly soluble reactive phosphorus and soluble inorganic nitrogen measured at 68 water quality monitoring sites were then used to characterize the trophic state of each of the seven lower level classes. The method models biomass and therefore allows variation in this response variable to provide options for trophic state and the associated nutrient concentrations to achieve these. Thus it is less deterministic than using reference site water quality. The choice from among these options is a sociopolitical decision, which reflects the management objectives rather than purely technical considerations.     

Toward accurate and precise estimates of lion density

Reliable estimates of animal density are fundamental to understanding ecological processes and population dynamics. Furthermore, their accuracy is vital to conservation because wildlife authorities rely on estimates to make decisions. However, it is notoriously difficult to accurately estimate density for wide‐ranging carnivores that occur at low densities. In recent years, significant progress has been made in density estimation of Asian carnivores, but the methods have not been widely adapted to African carnivores, such as lions (Panthera leo). Although abundance indices for lions may produce poor inferences, they continue to be used to estimate density and inform management and policy. We used sighting data from a 3‐month survey and adapted a Bayesian spatially explicit capture‐recapture (SECR) model to estimate spatial lion density in the Maasai Mara National Reserve and surrounding conservancies in Kenya. Our unstructured spatial capture‐recapture sampling design incorporated search effort to explicitly estimate detection probability and density on a fine spatial scale, making our approach robust in the context of varying detection probabilities. Overall posterior mean lion density was estimated to be 17.08 (posterior SD 1.310) lions >1 year old/100 km², and the sex ratio was estimated at 2.2 females to 1 male. Our modeling framework and narrow posterior SD demonstrate that SECR methods can produce statistically rigorous and precise estimates of population parameters, and we argue that they should be favored over less reliable abundance indices. Furthermore, our approach is flexible enough to incorporate different data types, which enables robust population estimates over relatively short survey periods in a variety of systems. Trend analyses are essential to guide conservation decisions but are frequently based on surveys of differing reliability. We therefore call for a unified framework to assess lion numbers in key populations to improve management and policy decisions.     

Spatial Patterns of Breeding Success of Grizzly Bears Derived from Hierarchical Multistate Models

Conservation programs often manage populations indirectly through the landscapes in which they live. Empirically, linking reproductive success with landscape structure and anthropogenic change is a first step in understanding and managing the spatial mechanisms that affect reproduction, but this link is not sufficiently informed by data. Hierarchical multistate occupancy models can forge these links by estimating spatial patterns of reproductive success across landscapes. To illustrate, we surveyed the occurrence of grizzly bears (Ursus arctos) in the Canadian Rocky Mountains Alberta, Canada. We deployed camera traps for 6 weeks at 54 surveys sites in different types of land cover. We used hierarchical multistate occupancy models to estimate probability of detection, grizzly bear occupancy, and probability of reproductive success at each site. Grizzly bear occupancy varied among cover types and was greater in herbaceous alpine ecotones than in low‐elevation wetlands or mid‐elevation conifer forests. The conditional probability of reproductive success given grizzly bear occupancy was 30% (SE = 0.14). Grizzly bears with cubs had a higher probability of detection than grizzly bears without cubs, but sites were correctly classified as being occupied by breeding females 49% of the time based on raw data and thus would have been underestimated by half. Repeated surveys and multistate modeling reduced the probability of misclassifying sites occupied by breeders as unoccupied to <2%. The probability of breeding grizzly bear occupancy varied across the landscape. Those patches with highest probabilities of breeding occupancy—herbaceous alpine ecotones—were small and highly dispersed and are projected to shrink as treelines advance due to climate warming. Understanding spatial correlates in breeding distribution is a key requirement for species conservation in the face of climate change and can help identify priorities for landscape management and protection. Patrones Espaciales del Éxito Reproductivo de Osos Pardos, Derivados de Modelos Jerárquicos Multi‐Estado     

Estimating abundance without recaptures of marked pallid sturgeon in the Mississippi River

Abundance estimates are essential for assessing the viability of populations and the risks posed by alternative management actions. An effort to estimate abundance via a repeated mark‐recapture experiment may fail to recapture marked individuals. We devised a method for obtaining lower bounds on abundance in the absence of recaptures for both panmictic and spatially structured populations. The method assumes few enough recaptures were expected to be missed by random chance. The upper Bayesian credible limit on expected recaptures allows probabilistic statements about the minimum number of individuals present in the population. We applied this method to data from a 12‐year survey of pallid sturgeon (Scaphirhynchus albus) in the lower and middle Mississippi River (U.S.A.). None of the 241 individuals marked was recaptured in the survey. After accounting for survival and movement, our model‐averaged estimate of the total abundance of pallid sturgeon ≥3 years old in the study area had a 1%, 5%, or 25% chance of being <4,600, 7,000, or 15,000, respectively. When we assumed fish were distributed in proportion to survey catch per unit effort, the farthest downstream reach in the survey hosted at least 4.5–15 fish per river kilometer (rkm), whereas the remainder of the reaches in the lower and middle Mississippi River hosted at least 2.6–8.5 fish/rkm for all model variations examined. The lower Mississippi River had an average density of pallid sturgeon ≥3 years old of at least 3.0–9.8 fish/rkm. The choice of Bayesian prior was the largest source of uncertainty we considered but did not alter the order of magnitude of lower bounds. Nil‐recapture estimates of abundance are highly uncertain and require careful communication but can deliver insights from experiments that might otherwise be considered a failure.