您现在的位置: 首页> 研究主题> 生态学研究

生态学研究

生态学研究的相关文献在1981年到2022年内共计172篇,主要集中在普通生物学、预防医学、卫生学、植物学 等领域,其中期刊论文159篇、会议论文2篇、专利文献74861篇;相关期刊126种,包括西北人口、上海师范大学学报(自然科学版)、资源与生态学报(英文版)等; 相关会议2种,包括江西省动物学会第七次会员代表大会暨学术研讨会、第八届中国林业青年学术年会等;生态学研究的相关文献由355位作者贡献,包括陈雄新、俞顺章、张继红等。

生态学研究—发文量

期刊论文>

论文:159 占比:0.21%

会议论文>

论文:2 占比:0.00%

专利文献>

论文:74861 占比:99.79%

总计:75022篇

生态学研究—发文趋势图

生态学研究

-研究学者

  • 陈雄新
  • 俞顺章
  • 张继红
  • 张莹
  • 方建光
  • 杜美荣
  • 汤明
  • 王巍
  • 王洪法
  • 高亚平
  • 期刊论文
  • 会议论文
  • 专利文献

搜索

排序:

年份

    • 翁晓东; 底明晓; 卢萍; 陆远鸿; 戴年华
    • 摘要: 白鹤是一种珍稀濒危鹤类,是我国Ⅰ级重点保护物种,全球数量仅3500—4000只。白鹤繁殖于俄罗斯北部,迁徙停歇于我国松辽平原。鄱阳湖湿地是白鹤的重要越冬地,越冬种群约占全球数量的95%以上。20世纪80年代以来,国内外开展了大量该物种的生态学研究。本文梳理归纳了白鹤的繁殖、停歇、越冬等重要生活史过程中的生态学研究进展,并对今后的相关研究提出了展望,以期为白鹤的保护和管理工作提供基础性资料。
    • 赵哲; 王海涛; 姜宝法
    • 摘要: 近年来,环境监测、疾病监测等各种监测网络不断健全,监测系统成为开展生态学研究的重要数据来源.监测数据类型包括了横断面数据、时间序列数据和面板数据,涉及暴露、结局和混杂3个维度.针对该数据的信息属性和结构特点,相关统计学方法逐渐发展完善,出现了一些新的方法、模型.基于数据的时空属性,本文对监测数据在生态学研究中常用模型的原理、适用条件及优劣进行了综述.%In recent years,with the improvement of various surveillance network,surveillance system has become an important data source for ecological study.Different data types,including cross-sectional data,time series data and panel data,containing abundant information involving exposure,outcome and confoundings.Gradually,some new statistical methods have been developed or improved for the special structural characteristics of surveillance data.In this paper,we summarized the principles of these models,preconditions,as well as their advantages and limitations.
    • 牛越; 陈仁杰; 林之靖; 阚海东
    • 摘要: 目的 探讨大气PM2.5暴露对北京、上海和广州3个城市5家医院每日急诊和门诊人次的影响.方法 采用方便抽样的方法,选取北京、上海和广州3个城市的5家医院(北京医院和中日友好医院、上海交通大学医学院附属新华医院和广州医科大学附属第三医院荔湾医院和附属第一医院)为研究现场,收集2013年1月1日至2015年12月31日医院的急诊和门诊资料,以及城市的空气污染监测资料和气象资料.采用广义相加模型分析PM2.5暴露对医院每日急诊和门诊人次的影响,同时采用Meta分析获得合并效应值.结果 2013-2015年,5家医院急诊和门诊量分别为1 378 501和18 139 779人次.北京医院、中日友好医院、上海新华医院、广州医学院荔湾医院和附属第一医院的PM2.5日均浓度分别为(81.8±68.7)、(83.2±69.7)、(54.4±34.1)、(43.5±24.8)μg/m3.PM2.5暴露对每日急诊和门诊人次的影响存在滞后效应,滞后0~1 d的影响最大,PM2.5浓度每升高10 μg/m3,每日急诊和门诊就诊的ER(95%CI)值分别为0.56%(0.14%,0.99%)和0.63%(0.07%,1.19%);分别调整O3、NO2、SO2和CO后,PM2.5浓度每升高10 μg/m3,每日急诊就诊的ER(95%CI)值分别为0.50%(0.10%,0.90%)、0.34%(-0.02%,0.69%)、0.36%(0.02%,0.69%)和0.56%(0.10%,1.03%),每日门诊就诊的ER(95%CI)值分别为0.65%(0.08%,1.21%)、0.29%(-0.17%,0.75%)、0.48%(-0.06%,1.03%)和0.48%(-0.02%, 0.99%).结论 PM2.5暴露可增加北京、上海和广州3个城市5家医院的每日急诊和门诊人次.%Objective To explore the effect of fine particulate matters (PM2.5) exposure on emergency visits and outpatient visits of 5 hospitals in Beijing, Shanghai and Guangzhou from 2013 to 2015. Methods Using convenient sampling method, 5 general hospitals in Beijing, Shanghai and Guangzhou were selected which included Beijing hospital, China-Japan friendship hospital, Xinhua hospital affiliated to Shanghai jiaotong University School of Medicine, the liwan hospital of the third affiliated hospital and the first affiliated hospital of Guangzhou Medical University. The emergency and outpatient data, air pollution monitoring data and meteorological data were collected from January 1, 2013 to December 31, 2015. A generalized additive model was used to analyze the effect of PM2.5exposure on daily hospital emergency and outpatient visits, and Meta analysis was used to obtain the combined effect value. Results The number of emergency and outpatient visits of 5 hospitals was 1 378 501 and 18 139 779 in total, respectively. The mean± SD of PM2.5exposures in Beijing hospital, China-Japan friendship hospital, Xinhua hospital affiliated to Shanghai jiaotong University School of Medicine, the liwan hospital of the third affiliated hospital and the first affiliated hospital of Guangzhou Medical University were (81.8±68.7), (83.2±69.7), (54.4±34.1), (43.5± 24.8) μg/m3, respectively. Results of single pollutant model analysis showed that 0-1 day lag concentrations of PM2.5had the largest effect on emergency visits and outpatient visits. For a 10 μg/m3increase of PM2.5 concentration, excess risk (ER) (95%CI) of emergency and outpatient visits was 0.56% (0.14%, 0.99%) and 0.63% (0.07%, 1.19%), respectively. After adjusting for O3, NO2, SO2, and CO, for a 10 μg/m3increase of PM2.5concentrations, the ER (95%CI) of emergency visits was 0.50% (0.10%, 0.90%), 0.34% (-0.02%, 0.69%), 0.36% (0.02%, 0.69%) and 0.56% (0.10%, 1.03%), respectively and the ER (95% CI ) of outpatient visits was 0.65% (0.08%, 1.21%), 0.29% (-0.17%, 0.75%), 0.48% (-0.06%, 1.03%) and 0.48% (-0.02%, 0.99%), respectively. Conclusion Our results suggested that PM2.5exposure can increase emergency and outpatient visits of 5 hospitals in Beijing, Shanghai and Guangzhou.
    • 段俊; 苏虹; 罗雪莲; 储文革; 高娇娇; 徐紫菡; 张言武; 程强; 白丽君; 魏倩楠
    • 摘要: 目的 探讨大气PM2.5和温度交互作用对精神分裂症患者入院的影响.方法 收集2014年1月1日至2017年12月31日安徽省铜陵市第三人民医院和市立医院精神分裂症患者的入院资料、铜陵市环境保护局和气象局的环境污染物资料和气象资料.采用分布滞后非线性模型联合广义相加模型,探讨PM2.5、多种污染物以及PM2.5和温度的交互作用对精神分裂症患者入院的影响,温度的分层按照3种标准进行划分,分别为<P 5、<P 10、<P 20分别为低温,P 5~P 95、P 10~P 90、P 20~P 80分别为中温,>P 95、>P 90、>P80分别为高温.结果 2014-2017年,铜陵市精神分裂症患者入院共计6 642人次,PM2.5和温度的中位数分别为47.0 μg/m3和17.5°C.以PM2.5暴露浓度P50为参照,当PM2.5暴露浓度为P90时,在滞后第1天精神分裂症患者入院RR值为1.03(95%CI:1.00~1.07),第5天达到最大,RR值为1.16(95%CI:1.13~1.19);多污染物模型中,同时纳入PM2.5和NO2时,PM2.5致精神分裂症患者入院风险较大,RR值为1.18 (95%CI:1.15~1.22)(P<0.001);3种温度分层标准下均为高温时PM2.5暴露致精神分裂症患者入院风险最大,超额风险度分别为12.1%(8.5%~15.7%)、9.7%(6.9%~12.6%)和17.1%(11.6%~22.8%),P值均<0.001.结论 随着PM2.5浓度的升高,精神分裂症的入院风险增加;并且在高温时PM2.5的危险效应更大.%Objective To explore the effect of ambient fine particulate matters (PM2.5) and temperature interaction on schizophrenia admission. Methods All admission data were retrieved from the Psychiatric Hospital and Municipal Hospital of TongLing from January 1, 2014 to December 31, 2017. Daily air pollution and meteorological data were collected from the Tongling Environmental Protection Agency and Meteorological Bureau, respectively. A distributed lag non-linear model combined with the generalized additive model were applied to explore the effects of PM2.5, multi-pollutants, and the interaction between temperature and PM2.5on schizophrenia admission. The stratification of temperature was divided by three criteria. The low temperature layer was defined as <P5 or <P10 or <P20; P5-P95 or P10-P90 or P20-P80 was defined as the middle temperature layer; >P95 or >P90 or >P80 was defined as the high temperature layer. Results From 2014 to 2017, 6 642 patients were admitted for schizophrenia in Tongling, and the median of PM2.5and temperature were 47.0 μg/m3and 17.5°C, respectively. The median concentration of PM2.5(P50) was taken as a reference. When the exposure concentration of PM2.5was P90, the lagged effect appeared in the first day with RR=1.03 (95%CI : 1.00-1.07) and reached the maximum in the fifth day with RR=1.16 (95%CI: 1.13-1.19). In the multi-pollutant models, it was found that the simultaneous inclusion of PM2.5and NO2had higher risk of schizophrenia admission, with the RR=1.18 (95%CI: 1.15-1.22), P<0.001. The risk of schizophrenia admission caused by PM2.5exposure at high temperature was greatest under the three temperature stratification standards, which were 12.1% (8.5%-15.7%), 9.7% (6.9%-12.6%) and 17.1% (11.6%-22.8%), all P values <0.001. Conclusion With the increase of PM2.5 concentration, the risk of schizophrenia admission is increased, and the risk effect of PM2.5 is stronger at high temperature.
    • 陈晨; 孙志颖; 孙庆华; 班婕; 李湉湉
    • 摘要: Objective To investigate the impact of persistent high ambient fine particulate matters (PM2.5) exposures on mortality in the polluted areas of 40 districts/counties in China. Methods Using a convenient sampling method, we selected 40 districts / counties as research sites from the Beijing-Tianjin-Hebei Metropolitan Region, the Yangtze River Delta, the Pearl River Delta, and Heilongjiang, Shanxi, and Sichuan province. The daily concentrations of PM2.5, meteorological data and population death data from January 1, 2013 to December 31, 2015 were collected. The six persistent PM2.5 pollution episode scenarios were defined by the average daily concentration of PM2.5(75 μg/m3, P75and P90of the average daily concentration of each district/county respectively) and the duration (≥2 days or 3 days). Generalized linear models and meta analyses were used to explore the impact of PM2.5pollution episodes on mortality in 40 districts/counties. Results The mean ± SD and P50(P25, P75) of average daily temperature, relative humidity and PM2.5were (15.26 ± 10.48) °C, 17.20 (7.50, 23.70) °C, (67.31 ± 19.26)% , 72.00% (57.00%, 81.00%), (72.81±60.93) μg/m3and 55.38 (33.77, 91.45) μg/m3, respectively in 40 districts/counties during 2013-2015. The average number of non-accidental, cardiovascular and cerebrovascular diseases deaths per day were (12 ± 7), (5 ± 4) and (2 ± 2) in each district/county, respectively. When the daily concentrations of PM2.5were≥75 μg/m3(≥2 days),≥P75(≥2 days),≥P90(≥2 days),≥75 μg/m3(≥3 days), and≥P75(≥3 days), the excess risk (95%CI ) of the total non-accidental deaths and cardiovascular diseases deaths were 1.77% (0.89%,2.66%), 2.69% (1.06%,4.35%), 1.67% (0.59%,2.76%), 2.31% (0.67%, 3.97%), 0.71% (-0.75%, 2.20%), 1.95% (0.08%, 3.86%), 1.15% (0.12%, 2.18%), 1.85% (0.25%, 3.47%), 1.39% (0.15%, 2.64%), 2.29% (0.39%, 4.23%), respectively. Conclusion Persistently high PM2.5exposures were associated with total non-accidental deaths and cardiovascular disease deaths.%目的 探讨大气PM2.5持续高暴露对中国40个区/县人群死亡的影响.方法 采用方便抽样的方法,在我国京津冀、长江三角洲、珠江三角洲地区及黑龙江、山西、太原、四川选取40个区/县作为研究现场,收集2013年1月1日至2015年12月31日的逐日PM2.5浓度、气象资料及人群死亡数据.以PM2.5日均浓度值(分别为75 μg/m3、各区/县日均浓度值的P75和P90)和持续时间(≥2 d和≥3 d)定义6种持续高PM2.5暴露情景,采用广义线性模型和Meta分析研究持续高PM2.5暴露对40区/县人群死亡的影响.结果 2013-2015年,40个区/县日均温度为(15.26±10.48)°C,P50P25,P75)为17.20(7.50, 23.70)°C;日均相对湿度为(67.31±19.26)%,P50P25,P75)为72.00%(57.00%,81.00%);PM2.5日均浓度为(72.81±60.93)μg/m3,P50P25,P75)为55.38(33.77,91.45)μg/m3;每个区/县每日非意外总死亡为(12± 7)例,心脑血管疾病死亡为(5±4)例,呼吸系统疾病死亡为(2±2)例.PM2.5日均浓度值≥75 μg/m3且持续≥2 d的暴露情景引起的人群非意外总死亡和心血管疾病死亡的ER(95%CI)值分别为1.77% (0.89%,2.66%)和2.69%(1.06%,4.35%);PM2.5日均浓度值≥P75且持续≥2 d的暴露情景引起的人群非意外总死亡和心血管疾病死亡的ER(95%CI)值分别为1.67%(0.59%,2.76%)和2.31%(0.67%, 3.97%);PM2.5日均浓度值≥P90且持续≥2 d的暴露情景引起的人群非意外总死亡和心血管疾病死亡的ER(95%CI)值分别为0.71%(-0.75%,2.20%)和1.95%(0.08%,3.86%);PM2.5日均浓度值≥75 μg/m3且持续≥3 d的暴露情景引起的人群非意外总死亡和心血管疾病死亡的ER(95%CI)值分别为1.15% (0.12%,2.18%)和1.85%(0.25%,3.47%);PM2.5日均浓度值≥P75且持续≥3 d的暴露情景引起的人群非意外总死亡和心血管疾病死亡的ER(95%CI)值分别为1.39%(0.15%,2.64%)和2.29%(0.39%, 4.23%).结论 持续高PM2.5暴露与人群非意外总死亡和心血管疾病死亡存在关联.
    • 林巧绚; 林梢; 周脉耕; 马文军; 王黎君; 林自强; 殷鹏; 黄正京; 刘涛; 肖建鹏; 李杏; 曾韦霖
    • 摘要: Objective To identify the definition of heat wave based on mortality risk assessment in different regions of China. Methods Daily mortality (from China Information System for Disease Control and Prevention) and meteorological data (from National Meteorological Information Center in China) from 66 counties with a population of over 200 000 were collected from 2006-2011. With the consideration of climate type and administrative division, China was classified as seven regions. Firstly, distributed lag non-linear model (DLNM) was used to estimate community-specific effects of temperature on non-accidental mortality. Secondly, a multivariate meta-analysis was applied to pool the estimates of community-specific effects to explore the region-specific temperature threshold and the duration for definition of heat wave. Results We defined regional heat wave of Northeast, North, Northwest, East, Central and Southwest China as being two or more consecutive days with daily mean temperature higher than or equal to the P64, P71, P85, P67, P75and P77of warm season (May to October) temperature, respectively, while the thresholds of temperature were 21.6, 23.7, 24.3, 25.7, 28.0 and 25.3°C. The heat wave in South China was defined as five or more consecutive days with daily mean temperature higher than or equal to the P93(30.4 °C) of warm season (May to October) temperature. Conclusion The region-specific definition of heat wave developed in our study may provide local government with the guidance of establishment and implementation of early heat-health response systems to address the negative health outcomes due to heat wave.%目的 通过分析中国不同地区极端高温与死亡的暴露-反应关系,构建不同区域的热浪定义.方法 收集2006-2011年中国人口超20万的66个疾病监测点气象(来源于中国气象数据网)和居民死亡数据(来源于中国疾病预防控制中心的中国疾病预防控制信息系统),结合气候类型、行政区域把中国分为7个区域.采用两阶段方法分析,第1阶段使用分布滞后非线性模型拟合每个监测点的温度-死亡关系,第2阶段采用多元Meta分析合并每个区域所有监测点的数据,获得区域性温度-死亡的关系,探索热浪的温度阈值和持续时间,建立每个区域基于死亡风险的热浪定义.结果 当日平均温度分别≥5-10月东北、华北、西北、华东、华中和西南日平均温度的P64、P71、P85、P67、P75、P77时,且该温度连续2 d及以上定义为热浪,对应的温度绝对阈值分别为21.6、23.7、24.3、25.7、28.0和25.3°C;华南地区热浪定义为日平均温度≥5-10月日平均温度的P9330.4°C),持续5 d及以上的炎热天气.结论 根据死亡风险评估建立起具有区域性特征的热浪定义,不同区域的热浪预警温度不同.
    • 汪庆庆; 叶云杰; 张嘉尧; 孙宏; 周连; 丁震; 徐燕
    • 摘要: Objective To explore the acute effect of fine particulate matters (PM2.5), O3, NO2on daily non-accidental mortality, cardiovascular disease mortality and respiratory mortality data in thirteen cities of Jiangsu province. Methods Daily average concentrations of non-accidental mortality, cardiovascular disease mortality, respiratory mortality data and environmental data were collected from January 1, 2015 to December 31, 2017 in thirteen cities of Jiangsu Province. Daily air quality, mortality and meteorology data were collected from the Information System of Air Pollution and Health Impact Monitoring of Chinese Center for Disease Control and Prevention. We used generalized additive model to evaluate the association between daily concentrations of air pollutants and mortality at single-city level and multi-city level, after adjusting the long-term and seasonal trend, as well as meteorological factors and the effect of"days and weeks". A multivariate Meta-analysis with random effects was applied to estimate dose-response relationship between air pollutants and mortality. Results At multi-city level, per interquartile range increase of PM2.5, O3, NO2was associated with an increase of 1.10% (95%CI: 0.66%, 1.54%), 0.59% (95%CI:0.18%, 1.00%), 2.00% (95%CI: 1.29%, 2.72%) of daily non-accidental mortality respectively; 1.01% (95%CI : 0.63%, 1.38%), 0.66% (95%CI : 0.02%, 1.30%), 1.62% (95%CI : 1.00%, 2.23%) of daily cardiovascular mortality respectively; 1.09% (95%CI: 0.35%, 1.82%), 0.44% (95%CI: -0.29%, 1.16%), 2.75% (95%CI: 1.42%, 4.08%) of daily respiratory mortality respectively. The air pollutants effect varied across different cities. The strongest effect of PM2.5was current day (excess risk (ER)=1.10%, 95%CI: 0.66%, 1.54%)), the strongest effect of O3was 2-day lag (ER=1.82%, 95%CI: 0.69%, 2.97%) and the strongest effect of NO2was 1-day lag (ER=2.09%, 95%CI: 1.34%, 2.83%) of daily non-accidental mortality respectively. Conclusion The increases of PM2.5and NO2concentration could result in the increases of daily non-accidental mortality, cardiovascular disease mortality and respiratory mortality. O3could result in the increases of daily non-accidental mortality and cardiovascular disease mortality. The acute effects for non-accidental mortality from high to low were NO2, PM2.5and O3,and the strongest effect of PM2.5was current day. O3and NO2had lagged effects.%目的 探讨江苏省大气PM2.5、O3、NO2污染对居民每日非意外、心血管疾病、呼吸系统疾病死亡的急性效应.方法 收集江苏省13个设区市2015年1月1日至2017年12月31日的PM2.5、O3、NO2日平均浓度,同期每日非意外、心血管疾病、呼吸系统死亡例数,以及气象资料,以上资料均来源于中国疾病预防控制中心空气污染人群健康影响监测信息系统.采用基于自然样条平滑函数的广义相加模型,分析不同城市大气污染物与每日死亡的相关性.采用多元Meta分析随机效应模型合并多个城市污染物与死亡风险的暴露-反应关系.结果 在13个设区市平均水平,PM2.5、O3、NO2浓度每升高1个四分位距,引起的居民非意外死亡率超额危险度(ER)分别为1.10%(95%CI :0.66%,1.54%)、0.59%(95%CI :0.18%,1.00%)、2.00%(95%CI :1.29%,2.72%);心血管疾病死亡率ER值分别为1.01% (95%CI :0.63%,1.38%)、0.66%(95%CI :0.02%,1.30%)、1.62%(95%CI :1.00%,2.23%);呼吸系统疾病死亡率ER值分别为1.09%(95%CI :0.35%,1.82%)、0.44%(95%CI :-0.29%,1.16%)、2.75%(95%CI :1.42% ,4.08%);对于非意外死亡,PM2.5在暴露当天效应最强(ER=1.10%,95%CI :0.66%,1.54%),O3在滞后第2天最强(ER=1.82%,95%CI :0.69%,2.97%),NO2在滞后第1天效应最强(ER=2.09%,95%CI :1.34%,2.83%).结论 PM2.5和NO2增加了非意外、呼吸系统疾病、心血管疾病死亡风险,O3污染增加了心血管疾病和非意外死亡风险;对非意外死亡的急性效应由高到低依次为NO2、PM2.5、O3,PM2.5在污染当天效应最强,NO2和O3有滞后效应.
    • 马克·波尔金; 钟义信
    • 摘要: 某个事物的信息,就是该事物所呈现的运动状态及其变化方式.因此,一切科学研究的活动,实质上是一种"获得信息、加工信息、提炼知识"的活动,也就是一种信息活动,服从信息科学的规律.而且,现代的科学研究通常是交叉科学的研究,需要按照生态学原理和谐地发挥学科群内所有学科的积极作用.因此,"信息生态方法学"将提供有效的方法,使人们能够更好地理解自然科学、社会科学、人文科学以及技术科学领域的信息生态过程,进而更好地利用科学研究中的信息生态过程,把科学研究提升到一个全新的高度.
    • 文胜1
    • 摘要: 在舞蹈课堂教学过程中,教师往往会忽视文化概念,一味地训练学生的身体技术技能,致使学生的大脑意识始终是空洞的。因此,在开设民族民间舞蹈课程时,教师还应要求学生学习相应领域的理论知识,以便学生更准确地理解舞蹈的含义。一、翼城浑身板在生态学研究下的表演形式翼城浑身板旧时是在打谷场上来进行表演的。谷场的四角分别站立着四个浑身板男演员,他们每个人各举一把大凉伞。当锣鼓敲起来后,男子手持浑身板,以洒脱的动作从谷场四边出场,走到场子的中间进行表演,像农民在田间播种劳作一般。四人以单打、对打、混合打的形式来表演,在舞动的过程中,将板子柄把上的丝穗甩起来。其画面优美,气势如虹。
  • 查看更多

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号