Prevalence and spatial distribution characteristics of human echinococcosis in China

BackgroundEchinococcosis is a zoonotic parasitic disease caused by larval stages of cestodes belonging to the genus Echinococcus. The infection affects people’s health and safety as well as agropastoral sector. In China, human echinococcosis is a major public health burden, especially in western China. Echinococcosis affects people health as well as agricultural and pastoral economy. Therefore, it is important to understand the prevalence status and spatial distribution of human echinococcosis in order to advance our knowledge of basic information for prevention and control measures reinforcement.MethodsReport data on echinococcosis were collected in 370 counties in China in 2018 and were used to assess prevalence and spatial distribution. SPSS 21.0 was used to obtain the prevalence rate for CE and AE. For statistical analyses and mapping, all data were processed using SPSS 21.0 and ArcGIS 10.4, respectively. Chi-square test and Exact probability method were used to assess spatial autocorrelation and spatial clustering.ResultsA total of 47,278 cases of echinococcosis were recorded in 2018 in 370 endemic counties in China. The prevalence rate of human echinococcosis was 10.57 per 10,000. Analysis of the disease prevalence showed obvious spatial positive autocorrelation in globle spatial autocorrelation with two aggregation modes in local spatial autocorrelation, namely high-high and low-high aggregation areas. The high-high gathering areas were mainly concentrated in northern Tibet, western Qinghai, and Ganzi in the Tibetan Autonomous Region and in Sichuan. The low-high clusters were concentrated in Gamba, Kangma and Yadong counties of Tibet. In addition, spatial scanning analysis revealed two spatial clusters. One type of spatial clusters included 71 counties in Tibet Autonomous Region, 22 counties in Qinghai, 11 counties in Sichuan, three counties in Xinjiang Uygur Autonomous Region, two counties in Yunnan, and one county in Gansu. In the second category, six types of spatial clusters were observed in the counties of Xinjiang Uygur Autonomous Region, and the Qinghai, Gansu, and Sichuan Provinces.ConclusionThis study showed a serious prevalence of human echinococcosis with obvious spatial aggregation of the disease prevalence in China. The Qinghai-Tibet Plateau is the "hot spot" area of human echinococcosis in China. Findings from this study indicate that there is an urgent need of joint strategies to strengthen efforts for the prevention and control of echinococcosis in China, especially in the Qinghai-Tibet Plateau.     

The state of the leprosy epidemic in Yunnan,China 2011–2020: A spatial and spatiotemporal analysis,highlighting areas for intervention

BackgroundDespite public health efforts to reduce the leprosy burden in Yunnan, China, leprosy remains an important public health problem in some specific areas. We analyzed the epidemiological characteristics and spatial distribution of leprosy in Yunnan, China, and provide data to guide disease prevention and control efforts.Methodology/principal findingsThe surveillance data of newly detected leprosy cases in Yunnan, China, during 2011–2020 were extracted from the LEPROSY MANAGEMANT INFORMATION SYSTEM IN CHINA (LEPMIS), and spatial distribution analysis, spatial autocorrelation analysis, and spatiotemporal scanning were performed with ArcGIS 10.6.1, GeoDa 1.8.8, and SaTScan 9.4.3 software, respectively. A total of 1907 newly detected leprosy cases were reported in Yunnan, China, during 2011–2020. The new case detection rate (NCDR) decreased from 0.62 in 2011 to 0.25 in 2020, with an annual incidence of 0.41/100,000 population. The proportions of multibacillary (MB) cases, cases in female patients, cases causing grade 2 physical disability (G2D), and cases in pediatric patients were 67.07%, 33.93%, 17.99%, and 2.83%, respectively. The number of counties with an incidence above 1/100,000 population decreased from 30 in 2011 to 8 in 2020. The Moran’s I of leprosy in Yunnan, China, during 2011–2020 ranged from 0.076 to 0.260, indicating the presence of spatial clusters. Local spatial autocorrelation (LSA) analysis showed that high-high cluster areas (hot spots) were mainly distributed in the southeastern, northern, and northwestern regions. Spatiotemporal scanning showed three clusters with high NCDRs. The probably primary clusters, occurring during January 1, 2011–December 31, 2015, covered 11 counties in the southeastern region (RR = 5.046515, LRR = 271.749664, P = 0.000).ConclusionThe number of leprosy cases in Yunnan decreased overall, although some high-NCDR regions remained. Geographic information system (GIS) analysis coupled with spatial analysis indicated regions with leprosy clusters. Continuous leprosy prevention and control strategies in Yunnan Province should be established, and interventions in high-risk regions should be prioritized and further strengthened.     

南四湖湿地景观格局脆弱度的时空分异特征

选取南四湖湿地1985、2000年Landsat 5 TM及2015年Landsat 8 OLI遥感影像为数据源,利用敏感度指数和适应度指数构建景观脆弱度指数,结合半变异函数和空间自相关等空间统计学方法,探究南四湖湿地景观格局脆弱度的适宜研究尺度及该尺度下脆弱度的时空分异特征.结果表明:1 km"1 km等间距网格能消除随机因素对各景观指数值的影响,为适宜研究尺度.1985—2015年,南四湖湿地整体景观格局脆弱度呈恶化趋势,较高、高脆弱度范围随时间显著扩张.脆弱度空间异质性上升,受非结构性因素的影响增强,由空间自相关引起的空间变异略有减弱.脆弱度全局空间正相关性较强,呈空间集聚模式,且集聚现象日趋明显;局域自相关以空间局部聚集为主,高-高区的显著性水平最强,低-低区的显著性随时间增强.气温、降水等自然因素影响湖区景观格局脆弱度的空间分布,社会经济活动、政策体制等人为因素是脆弱度恶化的主要原因.     

Distribution of multiple myeloma in India: Heterogeneity in incidence across age,sex and geography

BackgroundThis study aimed to investigate the distribution of multiple myeloma (MM) in India and provide a comprehensive narrative about its incidence, including differential patterns across age, sex and geography.MethodsMM cases diagnosed during 2012-14 were obtained from 27 populations based cancer registries in India by consulting the latest National Cancer Registry Programme reports. Crude (CR) and age-specific (ASR) rates of MM incidence were determined. Age-adjusted rates (AARs) were estimated by standardizing the CR values using age-specific weights recommended for LMIC countries (including India) for men and women separately, along with the corresponding 95% confidence interval (95% CI) measures.ResultsAltogether, 1916 MM cases (male/female: 1123/793) were documented (i.e. 1.19% of all cancers, 95% CI: 1.14–1.24%). Overall CR of MM in India was 1.27 (95% CI: 1.20–1.35)/ 100,000 in men and 0.95 (95% CI: 0.89–1.02)/ 100,000 in women, while the corresponding AARs were 1.13 (95% CI: 1.07–1.20) and 0.81 (95% CI: 0.75 – 0.88) per 100,000 respectively. The ASR values increased steadily with age. Most cases belonged to the 60–69 yrs bracket. However, regional and sex-specific differences in MM profile were observed. MM incidence was highest in the Southern and Northern zones, and least in the Northeast. The Northern and Central zones had higher proportion of MM in the 50–59 yrs age group, whereas Eastern zone had higher proportion of cases aged 70 yrs and above.ConclusionIncidence of MM in India is presented. Marked variations in MM incidence were noted with respect to age, sex and geography.     

Accurate influenza forecasts using type-specific incidence data for small geographic units

Influenza incidence forecasting is used to facilitate better health system planning and could potentially be used to allow at-risk individuals to modify their behavior during a severe seasonal influenza epidemic or a novel respiratory pandemic. For example, the US Centers for Disease Control and Prevention (CDC) runs an annual competition to forecast influenza-like illness (ILI) at the regional and national levels in the US, based on a standard discretized incidence scale. Here, we use a suite of forecasting models to analyze type-specific incidence at the smaller spatial scale of clusters of nearby counties. We used data from point-of-care (POC) diagnostic machines over three seasons, in 10 clusters, capturing: 57 counties; 1,061,891 total specimens; and 173,909 specimens positive for Influenza A. Total specimens were closely correlated with comparable CDC ILI data. Mechanistic models were substantially more accurate when forecasting influenza A positive POC data than total specimen POC data, especially at longer lead times. Also, models that fit subpopulations of the cluster (individual counties) separately were better able to forecast clusters than were models that directly fit to aggregated cluster data. Public health authorities may wish to consider developing forecasting pipelines for type-specific POC data in addition to ILI data. Simple mechanistic models will likely improve forecast accuracy when applied at small spatial scales to pathogen-specific data before being scaled to larger geographical units and broader syndromic data. Highly local forecasts may enable new public health messaging to encourage at-risk individuals to temporarily reduce their social mixing during seasonal peaks and guide public health intervention policy during potentially severe novel influenza pandemics.     

Locational Determinants of Emissions from Pollution-Intensive Firms in Urban Areas

Industrial pollution has remained as one of the most daunting challenges for many regions around the world. Characterizing the determinants of industrial pollution should provide important management implications. Unfortunately, due to the absence of high-quality data, rather few studies have systematically examined the locational determinants using a geographical approach. This paper aimed to fill the gap by accessing the pollution source census dataset, which recorded the quantity of discharged wastes (waste water and solid waste) from 717 pollution-intensive firms within Huzhou City, China. Spatial exploratory analysis was applied to analyze the spatial dependency and local clusters of waste emissions. Results demonstrated that waste emissions presented significantly positive autocorrelation in space. The high-high hotspots generally concentrated towards the city boundary, while the low-low clusters approached the Taihu Lake. Their locational determinants were identified by spatial regression. In particular, firms near the city boundary and county road were prone to discharge more wastes. Lower waste emissions were more likely to be observed from firms with high proximity to freight transfer stations or the Taihu Lake. Dense populous districts saw more likelihood of solid waste emissions. Firms in the neighborhood of rivers exhibited higher waste water emissions. Besides, the control variables (firm size, ownership, operation time and industrial type) also exerted significant influence. The present methodology can be applicable to other areas, and further inform the industrial pollution control practices. Our study advanced the knowledge of determinants of emissions from pollution-intensive firms in urban areas.     

Examining determinants of geographic variation in colorectal cancer mortality in North Carolina: A spatial analysis approach

PurposeA recent study using national data from 2000 to 2009 identified colorectal cancer (CRC) mortality “hotspots” in 11 counties of North Carolina (NC). In this study, we used more recent, state-specific data to investigate the county-level determinants of geographic variation in NC through a geospatial analytic approach.MethodUsing NC CRC mortality data from 2003 to 2013, we first conducted clustering analysis to confirm spatial dependence. Spatial economic models were then used to incorporate spatial structure to estimate the association between determinants and CRC mortality. We included county-level data on socio-demographic characteristics, access and quality of healthcare, behavioral risk factors (CRC screening, obesity, and cigarette smoking), and urbanicity. Due to correlation among screening, obesity and quality of healthcare, we combined these factors to form a cumulative risk group variable in the analysis.ResultsWe confirmed the existence of spatial dependence and identified clusters of elevated CRC mortality rates in NC counties. Using a spatial lag model, we found significant interaction effect between CRC risk groups and socioeconomic deprivation. Higher CRC mortality rates were also associated with rural counties with large towns compared to urban counties.ConclusionOur findings depicted a spatial diffusion process of CRC mortality rates across NC counties, demonstrated intertwined effects between SES deprivation and behavioral risks in shaping CRC mortality at area-level, and identified counties with high CRC mortality that were also deprived in multiple factors. These results suggest interventions to reduce geographic variation in CRC mortality should develop multifaceted strategies and work through shared resources in neighboring areas.     

基于生态系统服务价值变化的环境与经济协调发展研究——以陕西省为例

为探讨生态环境与经济发展间的协调程度,选取2000、2005、2010和2015年4期的陕西省107个县（区）数据,利用单位面积生态系统服务价值和人均GDP构建环境经济协调度指数。结合空间自相关分析和空间杜宾模型等方法,分析了环境经济协调度指数的时空演变特征及驱动因素。研究表明：（1）从时间上看,陕西省环境经济协调度指数整体呈现缓慢下降趋势;从空间上看,大部分县（区）已处于基本协调和中度协调状态,并形成了陕北地区优于陕南地区,陕南地区优于关中地区的空间格局。（2）陕西省环境经济协调度指数具有显著的空间正相关性,高高集聚和低低集聚效果比较明显。（3）退耕还林工程、城镇化水平、产业结构、降水量是提升协调度指数的关键因素,气温则是制约协调度发展的约束要素。未来应因地制宜实施差异化的发展战略,以实现环境与经济协调发展。     

Incidence and mortality of myeloid malignancies in children,adolescents and Young adults in Brazil: A population-based study

BackgroundMyeloid malignancies (MM) are heterogeneous when it comes to incidence rates and pathogenesis. These variation rates are important to generate hypotheses on causal aetiology. This study aimed to describe incidence and mortality patterns of MM among children, adolescents and young adults (cAYA) in Brazil and to evaluate trends in incidence and mortality rate overtime.MethodsData were extracted from a dataset of 15 Population-based Cancer Registries located in five Brazilian geographical regions and calculated by age-specific, crude, and age-standardized incidence (ASR) and mortality rates per million persons. Joinpoint regression analyses were performed for trends evaluations, regionally. Annual Percent Change (APC) and Average Annual Percent Change (AAPC) were also estimated.ResultsThe overall ASR for incidence and mortality of MM in Brazil was 14.57 and 8.83 per million, respectively. The AML (non-APL AML and APL) incidence rate is 8.18 per million, whereas other MM subtypes altogether have an incidence rate of 2.62 per million, and not otherwise specified (NOS) is 3.70 per million. The analysis of incidence trends (AAPC) showed a significant decline in Manaus (-5.6%) and São Paulo (-4.7%), and a significant increase was observed in Fortaleza (5.8%). Mortality trends steadily declined in all registries, with significant declines occurring in Goiânia (-1.5%), Belo Horizonte (-2.3%), São Paulo (-2.5%), Curitiba (-2.8%) and Porto Alegre (-4.1%).ConclusionOur findings showed differences in the incidence and mortality rates of MM in cAYA in Brazil, geographically. Infants-AML have the highest incidence within the cAYA population (17.42 per million). There was a substantial decrease in mortality rate observed, which was interpreted as an improvement in MM recognition and therapeutic approach.     

三江平原农田土壤跳虫多尺度空间自相关性

土壤动物多尺度空间自相关性特征是土壤动物空间异质性和空间共存格局研究的重要基础。以土壤跳虫为研究对象,以中国科学院三江平原沼泽湿地生态实验站为研究区,于2011年8月和10月,选取具30 a以上耕作历史的旱地为实验样地,以5 m为步长设置一个50 m×50 m的实验样地,采用地统计中的全局Moran's I指数和局部Moran's I指数相结合的方法,研究土壤跳虫多尺度空间自相关性特征。全局空间自相关结果表明,土壤跳虫群落总密度和多数跳虫种类存在显著的多尺度空间自相关特征,多数为显著的正的空间自相关性,形成明显的空间集群。局部空间自相关结果表明,土壤跳虫群落总密度和所有跳虫种类均存在显著的局部空间自相关性,在局部地区形成"高—高"和/或"低—低"的空间集群,并和"高—低"和/或"低—高"的空间孤立区相伴发生,即形成"斑块"和"孔隙"相间分布的水平镶嵌格局;这种镶嵌格局具一定的时间动态特征,从夏季到秋季斑块大小和空间分布范围略有变化。研究表明,土壤跳虫具有明显的多尺度空间自相关性;空间集群是土壤跳虫的常见空间分布格局,在样地内形成"斑块"和"孔隙"相间分布的水平镶嵌格局,这种格局具年内时间变异性。     

湖北省潜江市生态系统服务功能价值空间特征

为了揭示生态系统服务功能价值在县级尺度上空间分布状况与特征,运用探索性空间数据分析(ESDA)方法,在已有研究基础上,结合RS/GIS技术,计算潜江市2009年生态系统服务功能价值,分析各样点生态系统服务功能价值强度的全局空间自相关性和局部空间自相关性,并对各样点生态系统服务功能价值强度进行了半变异函数分析、模型模拟和空间插值.结果表明,2009年,潜江市VES总和为190344.45万元.市域空间上生态系统服务功能价值强度呈现明显的全局空间正相关关系,但相关性随距离增加而下降.局部空间自相关模式表现出一个明显的“热核”区及两个明显的“冷极”区.半变异函数分析得到C0=0.005,Co+C=0.447,a=1.68(km),表明数据具有很强的空间自相关性.通过克吕格插值发现生态系统服务功能价值空间分布表现为以西南一东北方向为主轴的高值—低值核心扩散形态.     

Carbon emission accounting and spatial distribution of industrial entities in Beijing—Combining nighttime light data and urban functional areas

Quantifying current carbon emissions their fine scale spatial distribution is necessary to improve carbon emission management, requirements, and emission reduction strategies of key industries. This study established an entity-level model to estimate carbon emissions by combining geographic information of points of interest (POIs) and nighttime light data from Beijing in 2018. The model accounted for the carbon emissions of Beijing's key entities and industries and simulated their spatial distribution. The results showed a good fit between the carbon emissions of the entities and nighttime light brightness values. The 130-m resolution of the urban carbon emission distribution data had a higher spatial simulation accuracy than that of the 1-km Open-Data inventory for anthropogenic carbon dioxide (ODIAC) data. Through the lens of urban functional areas, the average value of carbon emissions was highest in commercial areas and lowest in public management and service areas, at 78,840.11 tC/km² and 6844.79 tC/km², respectively. In terms of the industrial sector, the transportation industry had the highest carbon emissions, with a total of 31.86 Mt., while non-metal mining and oil and gas extraction had almost no energy consumption, with total carbon emissions of 1.38 Mt. The spatial clustering results showed that the distribution of carbon emissions in Beijing had a significant positive spatial correlation; forming high-high aggregation clusters dominated by the city center and major business districts and a low-low aggregation clusters dominated by the city's suburban areas. The simulation model clearly reflected the fine scale characteristics of carbon emissions, in terms of their quantity and spatial distribution. Results obtained in this study can aid relevant departments to formulate appropriate strategies for collectively guiding industrial enterprises towards carbon neutrality.     

赣江上游流域景观生态风险的时空分异——从生产-生活-生态空间的视角

以赣江上游流域为研究对象,利用1995年、2005年、2015年的3期遥感影像,分析1995—2015年赣江上游流域的土地利用演变特征;基于景观扰动指数与景观脆弱指数构建土地利用生态风险模型;采用半变异分析、自相关分析等方法,研究了赣江上游流域土地利用风险时空变化与驱动力。结果表明:赣江上游流域城乡生活空间面积显著增加,从三生空间看,农业生产空间面积显著减少,土地利用类型由生态、生产空间向城乡生活空间转化,土地利用程度提高。1995—2015年期间土地利用生态风险逐渐上升,高-高型聚类多分布于章贡区、南康区、信丰县等附近,低-低型聚类多分布于安远县、全南县等附近,高风险区、较高面积逐渐扩大,低风险区、较低风险区面积逐渐缩小,引起土地利用类型变化的因素主要为总人口、公路里程、城镇化率等人为因素。研究结果可为区域生态安全格局构建、土地利用的优化布局提供借鉴。     

川西农牧交错带“三生”空间冲突演变特征分析及模拟预测——以阿坝州四县为例

川西农牧交错带处于我国第一、第二阶梯的交替地带,是西部生态脆弱典型区,地势起伏显著,地形及生态环境复杂,积极开展川西农牧交错带"三生"空间冲突（生活、生产、生态）研究,将有助于川西农牧交错带生态安全保障与社会经济协调良性发展。研究区域为阿坝藏族羌族自治州四县（马尔康、理县、黑水、松潘）,以2005-2020年土地利用数据为基础,通过CLUE-S软件模拟了未来的土地利用模式;将研究区域"三生"空间分为生活生产空间、生态生产空间、生产生态空间、生态空间4种类型,并建立空间冲突测算模型,对研究区域2005-2025年四期空间冲突水平进行了测算。结果表明：（1）2005-2025年四县域面积所占比重最大的是生态生产空间,之后依次为生态空间、生产生态空间、生活生产空间。生活生产、生产生态空间面积连续上升,仅生态生产空间面积减少,生态空间面积以波动变化为主。（2）2005-2025年该区域以中等空间冲突为主。（3）2005-2025年,总体而言空间冲突值的高-高聚集区主要位于四县域的农牧交错带、生活生产空间的交叉区域且表现明显,高-高聚集区在松潘县和马尔康县的农牧交错带有逐渐扩大的趋势,黑水县和理县集聚区面积先减少后轻微扩张;空间冲突值的低-低集聚区主要位于以生态生产空间为主与生态空间的交叉区域;集聚不显著的区域以生态空间为主。（4）整体来看,川西农牧交错区域"三生"空间中生态生产空间冲突强度最高。     

Local multiplicity adjustment for the spatial scan statistic using the Gumbel distribution

The spatial scan statistic is an important and widely used tool for cluster detection. It is based on the simultaneous evaluation of the statistical significance of the maximum likelihood ratio test statistic over a large collection of potential clusters. In most cluster detection problems, there is variation in the extent of local multiplicity across the study region. For example, using a fixed maximum geographic radius for clusters, urban areas typically have many overlapping potential clusters, whereas rural areas have relatively few. The spatial scan statistic does not account for local multiplicity variation. We describe a previously proposed local multiplicity adjustment based on a nested Bonferroni correction and propose a novel adjustment based on a Gumbel distribution approximation to the distribution of a local scan statistic. We compare the performance of all three statistics in terms of power and a novel unbiased cluster detection criterion. These methods are then applied to the well-known New York leukemia dataset and a Wisconsin breast cancer incidence dataset.     

Incidence and Geographical Variation of Amyotrophic Lateral Sclerosis (ALS) in Southern Germany – Completeness of the ALS Registry Swabia

Objective of this paper was to investigate the incidence, potential geographical clusters and the completeness of the amyotrophic lateral sclerosis (ALS) registry in Southern Germany (Swabia).Age-standardized incidence rates (ASR) and ratios (SIR) as well as 95% confidence intervals (CI) were estimated at county level. Capture-recapture (CARE) procedures were applied taking data source dependency into account to estimate the quality of case ascertainment in the ALS registry Swabia. We identified 438 ALS cases (53% men, 47% women) in the target population of about 8.4 Mio inhabitants. The gender ratio (men∶women) was 1.1∶1. The mean age at onset of ALS was 63.8 (SD = 11.9) years for men and 66.0 (12.2) for women. The age distribution peaked in the age group 70–74 years. The ASR of ALS was 2.5 per 100,000 person years (PY; 95% CI: 2.3–2.7). The mean SIR was 1.1 per 100,000 PY (95% CI: 1.0–1.2). High SIR suggesting geographical clusters were observed in two counties (Göppingen and Bodenseekreis), but the variation was not statistically significant (p-values = 0.2 and 0.5). The percentage of CARE estimated missing cases was 18.9% in the registry yielding an ASR of 3.1 per 100,000 PY. The high coverage of the CARE estimated completeness of the ALS registry Swabia indicates excellent quality for future projects. Regional variations have to be investigated further.     

大比例尺土壤保持服务制图分级方法研究

科学地编制生态系统服务空间分布图,有助于识别生态系统优先保护热点及空缺,能够为国土安全规划提供基础信息。但由于缺乏统一科学的生态系统服务分级方法,生态系统服务分布图的展示效果和制图效率都受到限制。不恰当的分级制图会误导决策者对生态系统服务空间分布特征的判断,增加生态保护决策的不确定性。以构建生态服务制图分级标准为目标,对我国6个典型县区的土壤保持服务数据展开1：25万比例尺制图分级案例研究。采用分级精度、面积均衡性、极差一致性、空间自相关一致性4个分级质量评价指标评价并比较了目前地图编制中常用的自然断点法、几何间隔法、累积比例法、等差法、分位数法5种分级算法的分级制图效果。结果表明,6个县区有相似的土壤保持服务分布特征：低值区覆盖面积大,高值区覆盖面积小。各县区土壤保持服务制图最优分级方法有差异：延庆区、丰满区最优分级方法为自然断点法;永靖县、天山区、安宁市最优分级方法为累积比例法;富阳区最优分级方法为几何间隔法。各分级方法中,累积比例法在6个县区综合制图效果最优或接近最优,适用性最好,能够较好的刻画各县区的土壤保持服务空间分布特征。本文提出的分级评价方法及其结果,可为生态系统土壤保持服务制图规范化和自动化提供科学依据。     

Spatiotemporal Transmission Dynamics of Hemorrhagic Fever with Renal Syndrome in China, 2005–2012

BackgroundHemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease caused by many serotypes of hantaviruses. In China, HFRS has been recognized as a severe public health problem with 90% of the total reported cases in the world. This study describes the spatiotemporal dynamics of HFRS cases in China and identifies the regions, time, and populations at highest risk, which could help the planning and implementation of key preventative measures.MethodsData on all reported HFRS cases at the county level from January 2005 to December 2012 were collected from Chinese Center for Disease Control and Prevention. Geographic Information System-based spatiotemporal analyses including Local Indicators of Spatial Association and Kulldorff''s space-time scan statistic were performed to detect local high-risk space-time clusters of HFRS in China. In addition, cases from high-risk and low-risk counties were compared to identify significant demographic differences.ResultsA total of 100,868 cases were reported during 2005–2012 in mainland China. There were significant variations in the spatiotemporal dynamics of HFRS. HFRS cases occurred most frequently in June, November, and December. There was a significant positive spatial autocorrelation of HFRS incidence during the study periods, with Moran''s I values ranging from 0.46 to 0.56 (P<0.05). Several distinct HFRS cluster areas were identified, mainly concentrated in northeastern, central, and eastern of China. Compared with cases from low-risk areas, a higher proportion of cases were younger, non-farmer, and floating residents in high-risk counties.ConclusionsThis study identified significant space-time clusters of HFRS in China during 2005–2012 indicating that preventative strategies for HFRS should be particularly focused on the northeastern, central, and eastern of China to achieve the most cost-effective outcomes.     

Biased parasitoid sex ratios: Wolbachia,functional traits,local and landscape effects

Adult sex ratio (ASR) is a key demographic parameter, being essential for the survival and dynamics of populations. Biased ASRs are adaptations to the environment on different scales, resulting from different mechanisms such as inbreeding, mating behaviour, resource limitations, endosymbionts such as Wolbachia, and changes in density or spatial distribution. Parasitoid ASRs are known to be strongly biased, but little information is available on how they are affected by large-scale variables such as landscape composition or fragmentation. We examined whether landscape scale variables affect the ASR of several parasitoid species belonging to the same tritrophic gall inducer community. We examined the effects of various explanatory variables on parasitoid ASR: the ovipositor length (a species level functional trait), resource amount (gall size) and density (local scale) as well as habitat amount, land use and landscape history (landscape scale). We controlled for the incidence and prevalence of Wolbachia infections. We found that parasitoid ASR is best explained by and positively correlated with ovipositor length and gall diameter. The interaction of functional traits with habitat availability also significantly explained parasitoid ASRs. Our results support the hypothesis that large-scale environmental characteristics affect parasitoid ASRs in addition to intrinsic and local characteristics.     

汾河流域生态敏感性综合评价及时空演变特征

基于格网尺度,在定量构建景观生态风险敏感性、水土流失敏感性、生物多样性敏感性的基础上,对汾河流域综合生态敏感性进行评价,采用空间自相关分析与圈层分析方法,揭示其时空演变特征。结果表明：2000-2018年研究区重度、极度敏感区面积减少,生态环境得到改善,生态敏感性空间分异与流域地形分布具有一致性;生态敏感性具有显著的空间正相关关系,在空间上呈连片连带式分布,具有明显的聚集性特征,随着高高、低低聚集网格数量的减少,生态敏感性空间结构逐渐趋于离散化;结合圈层分布,可将西南象限9-13圈层的吕梁山区与东南象限9-13圈层的太岳山区识别为流域生态环境治理的重点区域,此类区域地形以山地丘陵为主,体现了地形因素对流域生态敏感性空间分布的影响;研究区生态敏感性变化是生态本底与人类胁迫共同作用的结果。