Spatio-temporal variation of vegetation in an arid and vulnerable coal mining region
武汉东湖沉积物好氧氨氧化微生物时空分布
中国环境科学 2021,41(4)：1917~1924 China Environmental Science 武汉东湖沉积物好氧氨氧化微生物时空分布张志忠1,2,程德玺1,廖明军1,2,汪淑廉1,李祝1,2*(1.湖北工业大学土木建筑与环境学院,湖北武汉 430068；2.河湖生态修复及藻类利用湖北省重点实验室,湖北武汉 430068)摘要：采用实时荧光定量PCR(qPCR)技术,测定了武汉东湖沉积物中氨氧化古菌(AOA)和氨氧化细菌(AOB)氨单加氧酶基因(amoA)的丰度,并结合沉积物水体环境中各形态氮素的含量,分析氮素含量对AOA和AOB的时空分布的影响.结果显示, AOA amoA基因丰度大于AOB amoA基因丰度,表明AOA 对氨氧化过程的贡献较大.同时,AOA和AOB amoA基因丰度都随深度增加而降低.此外,间隙水的总氮、氨氮、硝酸盐氮以及亚硝酸盐氮浓度分别为6.28~33.56、2.71~22.7、0.12~0.98、0.01~0.13mg/L;上覆水的总氮、氨氮、硝酸盐氮以及亚硝酸盐氮平均浓度分别为1.68,0.79,0.16,0.04mg/L;表层水的总氮、氨氮、硝酸盐氮以及亚硝酸盐氮平均浓度分别为1.34,0.62,0.11,0.03mg/L,表明东湖东湖沉积物相对于水体呈营养盐可释放状态.相关性分析表明:AOA amoA基因丰度与间隙水氨氮和亚硝酸盐氮浓度呈显著正相关(P<0.05),AOB amoA基因丰度与间隙水亚硝酸盐氮(NO2--N)浓度呈显著正相关(P<0.05).关键词：实时荧光定量PCR；沉积物；氨氧化古菌；氨氧化细菌；丰度中图分类号：X524 文献标识码：A 文章编号：1000-6923(2021)04-1917-08Spatiotemporal distribution of aerobic ammonia-oxidizing microorganisms in sediments of Lake Donghu, Wuhan.ZHANG Zhi-zhong1,2, CHENG De-xi1, LIAO Ming-jun1,2, WANG Shu-lian1, LI Zhu1,2* (1.School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, China；2.Hubei Key Laboratory of Ecological Remediation for Rivers-Lakes and Algal Utilization, Wuhan 430068, China). China Environmental Science, 2021,41(4)：1917~1924Abstract：The amoA gene abundances of ammonia-oxidizing archaea (AOA-amoA) and bacteria (AOB-amoA) in the sediments of Lake Donghu, Wuhan, was determined by the real-time quantitative polymerase chain reaction (qPCR). Moreover, the influence of nitrogen content on the temporal and spatial distribution of AOA and AOB was analyzed. The results showed that the abundance of AOA-amoA was higher than that of AOB-amoA, indicating greater contribution of AOA-amoA to the ammonia oxidation process. Meanwhile, the abundances of AOA-amoA and AOB-amoA decreased with elevated sediment depth. In addition, the concentrations of total nitrogen (TN), ammonia nitrogen (NH4+-N), nitrate nitrogen (NO3--N) and nitrite nitrogen (NO2--N) in interstitial water were 6.28~33.56, 2.71~22.7, 0.12~0.98 and 0.01~0.13mg/L, respectively. The average concentrations of TN, NH4+-N, NO3--N and NO2--N in overlying water were 1.68, 0.79, 0.16 and 0.04mg/L, respectively, while in surface water were 1.34, 0.62, 0.11 and 0.03mg/L, respectively. These results suggested that the sediments of Lake Donghu were important sources of nutrient. In addition, the abundance of AOA-amoA was positively correlated with the concentrations of NH4+-N and NO2--N in interstitial water (P<0.05), while the abundance of AOB-amoA was positively correlated with the concentration of NO2--N in interstitial water (P<0.05).Key words：qPCR；sediment；ammoxidation archaea；ammonia oxidizing bacteria；abundance沉积物作为湖泊水体中氮的重要“源”和“汇”,是水体中氮的主要贡献者[1-2].栖息于沉积物中的部分微生物通过参与氮循环过程,包括氮的固定、硝化作用、反硝化作用和氨氧化作用[3],促进氮的转化.其中氨氧化作用作为硝化作用的首要环节,是硝化过程的关键限速反应[4].氨氧化作用的速率取决于氨氧化古菌(AOA)和氨氧化细菌(AOB)的丰度[5].而AOA与AOB在不同的生境中,受温度、溶解氧、有机质、pH值、氮素浓度和盐度等环境因子的影响,两者的丰度、群落结构、多样性以及氨氧化效率等不尽相同[6-8],氨氧化微生物的分布与氮循环氨氧化过程之间的联系复杂.由于生理生化机制的差异,AOA与AOB具有不同的环境偏好[9-10].在大多数湖泊环境中,AOA的丰度均高于AOB[11-12],但也存在AOB的丰度高于AOA的情况[13-15].总体来说,AOA往往在低pH值、低氧和寡营养的条件下比AOB更占优势.在巢湖以及太湖的沉积物中,AOA的丰度与硝酸盐氮浓度呈收稿日期：2020-08-24基金项目：国家自然科学基金资助项目(51579092,51879099);国家重点基础研究项目(2016YFC0401702);广东省水利科技创新项目(2017—14)* 责任作者, 副教授,****************1918 中国环境科学 41卷显著负相关,AOB的丰度与氨氮和亚硝酸盐氮浓度呈显著正相关[14].而在若尔盖花湖的沉积物中,总氮、氨氮、硝酸盐氮以及亚硝酸盐氮与AOA的丰度均呈显著正相关[16].造成不同湖泊中氮素含量与好氧氨氧化微生物分布之间联系差异的原因尚不明确,需要进一步研究.根据近几年对武汉东湖各子湖水质的监测结果发现,牛巢湖水质为Ⅳ类,官桥湖水质为劣Ⅴ类,郭郑湖水质状况次于牛巢湖.其次,牛巢湖靠近风景区,周边污染源较少,官桥湖和郭郑湖靠近学校生活区,有利于对比研究.因此,本研究选取具有代表性的郭郑湖、官桥湖、牛巢湖作为研究对象,监测表层水、上覆水及沉积物间隙水中的氮素浓度.同时,采集沉积物泥样提取泥样中微生物的DNA,采用实时荧光定量PCR 技术,以功能基因amoA作为分子标记,定量分析沉积物中AOA和AOB amoA基因丰度.研究AOA和AOB 在沉积物中不同时空以及深度下的分布特征,分析其与表层水、上覆水和沉积物间隙水中氮素浓度之间的相关性.旨在揭示武汉东湖不同营养水体中氨氧化微生物的动态变化,为进一步研究湖泊沉积物氮循环机理和开展湖泊生态修复提供理论参考.1材料与方法1.1样品采集本研究以东湖为研究对象,在东湖子湖郭郑湖(采样点A)、官桥湖(采样点B)和牛巢湖(采样点C)各设置一个采样点(图1).采样坐标为A:(30°33'19''N, 114°23’26''E);B:(30°31'51''N,114°23'31''E); C:(30°33'39''N, 114°25'43''E).采样时间为2016年7月30日、2016年11月28日、2017年4月4日及2017年9月10日,分别代表夏(SU)、冬(WI)、春(SP)、秋(AU)4个季节.沉积物采样的同时采集表层水、上覆水和间隙水.A(30°33′19″N,114°23′26″E)(30°31′51″N,114°23′31″E)BC(30°33′39″N,114°25′43″E)图1 东湖采样点Fig.1 Sampling sites in Lake Donghu1.2理化性质测定以《水和废水监测分析方法》第四版[17]方法测定硝酸盐氮(NO3--N)、亚硝酸盐氮(NO2--N)、氨氮(NH4+-N)以及总氮(TN).参考国标《CJ/T221-2005》[18]方法测定沉积物含水率.1.3 AOA和AOB丰度测定1.3.1 沉积物DNA的提取参考陆诗敏等[19]报道的方法,提取沉积物中总基因组.以λ-Hind DNAⅢMaker为标准,在1%的琼脂糖凝胶电泳上进行检测,观察目的基因是否有条带,用Nanodrop ND-1000测定样品的浓度及纯度,-20℃保存,以待后续分析.表1氨氧化微生物amoA基因PCR扩增引物及条件Table 1 Primers and procedures for PCR amplification of amoA genes目的基因引物引物序列(5’-3’) 反应条件AOA amoA CrenamoA23f CrenamoA616rATGGTCTGGCTWAGACGGCCATCCABCKRTANGTCCA[20]95,30s;(95,5s,53,1min,72,1min)×35℃℃℃℃AOB amoA amoA-1F amoA-2RGGGGTTTCTACTGGTGGTCCCCTCKGSAAAGCCTTCTTC[21]95,30s,(95,5s,54,35s,72,1min)×35℃℃℃℃1.3.2 标准质粒的构建 AOA和AOB所用的PCR 引物、扩增的目的片段及扩增条件见表1,扩增体系为:2μL模板,上下游引物各0.25μL (10μmol/L), 2× Taq Master Mix 10μL加上无菌双蒸水补足至20μL. PCR产物经切胶纯化后,用DNA连接试剂盒将纯化后的PCR产物连接到pMD18-T载体上,产物再经过转化、筛选后测序.AOA和AOB amoA序列的阳性质粒克隆菌株用LB液体培养基扩大培养,用TIAN prep Mini Plasmid Kit质粒小提试剂盒提取质粒备用.1.3.3AOA和AOB amoA基因丰度测定采用SYBR Green法进行qPCR,反应体系使用SYBR Premix Ex TaqTMⅡ替代普通PCR反应体系中的4期 张志忠等：武汉东湖沉积物好氧氨氧化微生物时空分布 19192×Taq Master Mix,其余条件与普通PCR 相同,得到标准曲线.每次扩增都设置阴性对照组,分别对4个季节中3个子湖的不同分层沉积物样品中的AOA 和AOB amoA 基因进行扩增,得到每个样品提取的DNA 中amoA 基因丰度. 1.4 数据处理与分析利用Originpro 2019b 软件对东湖表层水、上覆水以及间隙水理化指标数据进行处理,利用SPSS 22.0统计分析软件对丰度和环境因子数据进行方差分析和相关性分析. 2 结果与分析 2.1 表层水、上覆水和间隙水不同季节氮含量的变化如图2(a)所示,表层水的总氮、氨氮、硝酸盐氮以及亚硝酸盐氮平均浓度分别为1.34,0.62,0.11, 0.03mg/L.在4个季节中,官桥湖的表层水中硝酸盐氮、亚硝酸盐氮、氨氮和总氮的含量均显著高于郭郑湖和牛巢湖(P <0.05).对于不同的季节而言,3个子湖表层水中春季总氮含量最高,而秋季总氮含量最低.对于同一子湖的表层水,3种无机氮中氨氮含量明显高于硝酸盐氮和亚硝酸盐氮的含量,且氨氮的含量与总氮含量变化一致,而硝酸盐氮和亚硝酸盐氮的含量较低且无明显变化趋势.如图2(b),上覆水的总氮、氨氮、硝酸盐氮以及亚硝酸盐氮平均浓度分别为1.68,0.79,0.16,0.04mg/ L.与表层水相似,在4个季节中官桥湖的上覆水中硝酸盐氮、亚硝酸盐氮、氨氮和总氮的含量均显著高于牛巢湖和郭郑湖(P <0.05).同时,官桥湖春季上覆水中总氮含量远高于其它季节,而郭郑湖和牛巢湖的上覆水在不同季节中的氮含量无明显差异(P > 0.05).同样地,上覆水中3种无机氮中氨氮含量最高,且氨氮含量的变化趋势与总氮一致,而硝酸盐氮和亚硝酸盐氮的含量较低且无明显变化趋势.如图2(c),间隙水的总氮、氨氮、硝酸盐氮以及亚硝氮浓度分别为6.28~33.56、2.71~22.7、0.12~ 0.98、0.01~0.13mg/L.分析3个子湖表层沉积物(0~ 5cm)间隙水在不同季节氮含量的变化,发现氮的各种存在形态在间隙水中的含量比在表层水和上覆水中的含量高.官桥湖的表层沉积物间隙水中氨氮和总氮的含量显著高于牛巢湖和郭郑湖(P <0.05),而硝酸盐氮和亚硝酸盐氮的含量在3个子湖中则无明显差异(P >0.05).对于同一子湖中无机氮的含量,氨氮含量最高,其次是硝酸盐氮,亚硝酸盐氮含量最低.综上分析,硝酸盐氮和亚硝酸盐氮的含量在表层水、上覆水和间隙水中无明显差异;而对于总氮和氨氮的含量依次是间隙水>上覆水>表层水.表明3个子湖的氨氮在4个季节中不断由沉积物向湖水中释放,导致湖水中氨氮和总氮浓度不断上升,这可能是东湖一直处于重度污染的内在原因.图2 不同季节研究水体中4种氮素含量变化情况Fig.2 Changes of four nitrogen contents in water in different seasons2.2 AOA 与AOB amoA 基因丰度时空分布特征东湖3个子湖中AOA 与AOB 丰度有明显的季节差异性;同时,在沉积物的分层采样结果中发现,丰度随空间变化较大,结果见图3.1920 中 国 环 境 科 学 41卷不同季节中AOA amoA 基因丰度不同,春季的丰度为4.50×105~3.68×108copies/g,夏季的丰度为8.07×105~1.58×108copies/g,秋季的丰度为6.21×105~ 4.99×108copies/g,冬季的丰度为8.27×105~4.13× 108copies/g.同样的,AOB amoA 基因丰度在不同的季节也明显不同,春夏秋冬4个季节中AOB amoA 基因丰度分别为1.14×105~5.32×106,1.66×104~1.14× 106,9.57×104~4.21×106和5.18×104~8.89×105copies/ g.总体来看,AOA amoA 基因丰度在夏季最高,而在其它3个季节无明显差异(P >0.05);AOB amoA 基因丰度在春季最高,冬季最低,而在其它2个季节无明显差异(P >0.05).不同深度沉积物的AOA amoA 基因丰度见图3(a)~(d).表层沉积物丰度为 2.38×106~4.99× 108copies/g,中层沉积物AOA amoA 基因丰度为6.21×105~1.25×108copies/g,底层沉积物丰度为4.50× 105~5.06×107copies/g.在东湖的3个子湖中,夏季AOA amoA 基因丰度随着采样深度的增加而降低,官桥湖的AOA amoA 基因丰度比郭郑湖和牛巢湖高(图3a).而在春、秋和冬季,AOA 主要存在于表层沉积物中(0~5cm),而在5cm 以下的深度,基本无AOA 的存在,且都在0~1cm 深度时AOA 丰度最大;在这3个季节中,在0~1cm 深度均是官桥湖中AOA amoA 基因丰度最高,郭郑湖次之,牛巢湖最低(图3b -d).图3 不同季节研究区域沉积物中AOA 和AOB amoA 基因丰度垂直分布Fig.3 The vertical distribution of amoA gene abundance of AOA and AOB in sediments of different seasons不同深度沉积物的AOB amoA 基因丰度见图3(e)~(h).表层沉积物AOB amoA 基因丰度为1.25×105~5.32×106copies/g,中层和底层沉积物AOB amoA 基因丰度分别为7.31×104~7.68×105copies/g 和1.66×4期张志忠等：武汉东湖沉积物好氧氨氧化微生物时空分布 1921104~5.15×105copies/g.在夏季和冬季,3个子湖不同深度(0~20cm)沉积物中均有AOB的分布,表层(0~5cm)AOB amoA基因丰度最高;在夏季最高的是郭郑湖,而冬季最高的是官桥湖.在春季和秋季,AOB 主要分布在0~4cm深度,同样是在表层(0~1cm)最高;在春季最高的是官桥湖郭郑湖,而冬季最高的是郭郑湖.2.3 AOA/AOB(amoA基因丰度比)在所有采样点中,AOA amoA基因丰度均高于AOB,AOA/AOB为2~546.相关性分析表明,AOA amoA基因丰度与AOB amoA基因丰度呈极显著正相关(P<0.01,相关系数为0.572).如图4所示,郭郑湖和牛巢湖的AOA/AOB在夏季均呈现先增加后降低的变化趋势,郭郑湖AOA/AOB在15~20cm达到峰值(图4a),而牛巢湖AOA/AOB在10~15cm达到峰值(图4c).在其他季节,AOA/AOB在不同深度变化不大.图4 不同子湖AOA/AOB(amoA基因丰度比)垂直变化Fig.4 Vertical variation of AOA/AOB(ratio of amoA gene abundance) in different sub-lakes2.4 氨氧化微生物amoA基因丰度与间隙水理化性质相关性如表2所示,夏季AOA amoA基因丰度与间隙水氨氮和亚硝酸盐氮浓度呈极显著正相关(P<0.01), AOB amoA基因丰度与间隙水氨氮和亚硝酸盐氮浓度呈极显著正相关(P<0.01);冬季AOA amoA基因丰度与间隙水氨氮浓度呈显著正相关(P<0.05),AOB amoA基因丰度与间隙水亚硝酸盐氮浓度呈显著正相关(P<0.05);AOA/AOB与间隙水硝酸盐氮呈显著正相关(P<0.05);秋季AOA/AOB与氨氮呈显著负相关(P<0.05).按照不同沉积物层数来分类,中层沉积物AOA amoA基因丰度与间隙水氨氮浓度呈显著正相关(P<0.05).按照不同子湖来分类,官桥湖AOA和AOB amoA基因丰度均与间隙水氨氮和亚硝酸盐氮浓度呈极显著正相关(P<0.01),AOB amoA基因丰度与间隙水总氮浓度呈显著负相关(P<0.05),AOA/AOB与氨氮呈显著正相关(P<0.05).表2 AO A和AOB amoA基因丰度与间隙水氮浓度皮尔逊相关系数Table 2 Pearson correlation coefficients between AOA andAOB amoA gene abundance and nitrogen content ininterstitial water项目 AOAamoA基因丰度AOB amoA基因丰度AOA/AOB硝酸盐氮0.097 0.136-0.242*亚硝酸盐氮0.294** 0.215*0.100氨氮 0.215* 0.103 0.127总氮-0.101 -0.177 0.100注:* P<0.05 ; **P<0.01.1922 中国环境科学 41卷3 讨论AOA和AOB广泛生存于水生环境中, 氨氮作为氨氧化微生物的基质,与氨氧化微生物丰度关系密切[22].一般而言,在低氨的贫营养水体中,AOA占绝对优势[23],而AOB生长受到限制.适当提高氨氮浓度会提高AOA的生长速率[24].3个子湖间隙水中,官桥湖的氨氮要显著高于郭郑湖和牛巢湖,这很可能是官桥湖沉积物中AOA远高于其他2个子湖的原因.同时,3个子湖不同季节不同深度的沉积物中, AOA amoA基因丰度高于AOB,可能是由于3个子湖间隙水的氨氮浓度为2.71~22.7mg/L,有利于AOA 生存而限制AOB生长[25].因此推测东湖属于一个低氨氮环境,导致AOB生长受氨氮影响被抑制.AOA 处于优势地位,表明AOA可能在东湖沉积物氨氧化过程中发挥着主要作用.湖泊沉积物生境较为复杂,也存在其他环境因子对氨氧化微生物分布有不同程度影响的可能性,在目前的研究中已发现NO3--N、NH4+-N、TOC 、TP、TN和pH值等因素对AOA amoA基因丰度的影响较多,AOB amoA 基因丰度受NO3--N、NH4+-N和pH值的影响更多[22].夏季AOA amoA基因丰度与间隙水氨氮和亚硝酸盐氮浓度呈极显著正相关,冬季AOA amoA基因丰度只与间隙水氨氮浓度呈显著正相关,原因可能是冬季温度低,AOA的代谢活性降低,较少的氨氮转化为亚硝酸盐氮,导致亚硝酸盐氮的浓度降低.夏冬季节AOB amoA基因丰度与间隙水氨氮和亚硝酸盐氮浓度分别呈极显著正相关和显著正相关,表明温度变化对AOB的代谢活性影响不大.而在沉积物不同分层水平上,仅中层沉积物AOA amoA基因丰度与间隙水氨氮浓度呈显著正相关,这可能与沉积物中溶解氧和有机质有关.对于不同子湖,官桥湖AOA和AOB amoA基因丰度均与间隙水氨氮和亚硝酸盐氮浓度呈极显著正相关.3个子湖中官桥湖水质最差、富营养化最严重,可能是由于官桥湖间隙水的氨氮浓度适宜氨氧化微生物生长,而亚硝酸盐氮浓度对好氧氨氧化微生物无明显的抑制作用.不同季节带来的温度差异会影响AOA和AOB amoA基因丰度.在3个子湖表层和中层沉积物中,AOA amoA基因丰度在夏季明显高于其他3个季节.这与Auguet等[26]人在研究高山湖泊中泉古菌分布规律时发现的AOA amoA基因丰度在夏季最高,在春季和冬季均有下降趋势相一致. 在东湖表层沉积物中,AOB amoA基因丰度在冬季显著低于其他3个季节,在春季显著高于其他3个季节.不同季节对于AOB amoA基因丰度的影响因素可能在于温度的差异.温度是影响AOB分布的关键性因素,同时随着季节的变化,氨浓度和pH值也会改变,从而影响AOB的群落结构[27].Avrahamk等[28]人发现,在土壤中的湿度、氨氮和pH值处于稳定状态时,AOB amoA 基因丰度以及其硝化活性在15~25℃时最高.这与Godde等[29]研究的N2O释放率中温时低而低温时高的结论恰好相反,其原因可能在于两者所研究对象的营养水平不同.因此,在研究氨氧化微生物不同季节分布特征时,各种因素对其的影响情况不能一概而论,应结合实地水文、水质等情况深入研究.普遍发现在空间垂直方向上,随着沉积物深度的增加,AOA与AOB amoA基因丰度均有下降的趋势[16,30-32],本研究中AOA与AOB amoA基因丰度在垂直方向上的变化趋势亦是如此,这可能与溶解氧浓度的变化有关.AOA和AOB均属于好氧型,随深度的增加,溶解氧浓度逐渐降低,amoA基因丰度逐渐减少.在东湖沉积物中,AOA和AOB amoA基因丰度都在0~1cm最高,在前3cm迅速下降,然后趋于平缓.这表明表层沉积物与上覆水氧气交换频繁,溶解氧浓度相对较高,该趋势与其他关于氨氧化微生物垂直分布的研究结论一致[33-34].对于同一季节,秋季的官桥湖和郭郑湖以及冬季的官桥湖沉积物中随着深度增加,AOA/AOB呈上升的趋势.这与AOA比AOB更适于厌氧浓度条件有关[35].亚硝酸盐氮作为氨氧化反应的产物,其浓度也会影响AOA和AOB amoA基因丰度[36].本研究中发现AOA/AOB与硝酸盐氮呈显著负相关. AOA 广泛地分布于海洋、土壤、沉积物这样的中好氧到缺氧的环境中[37],相比于AOB, AOA更适应缺氧的环境,硝酸盐氮作为硝化反应的最终产物,其浓度一定程度上反映了氧气浓度的多少,硝化速率越低的区域其溶解氧一般越低,而AOA比AOB更耐低氧环境,所以硝酸盐氮越低的区域,其AOA/AOB反而较高.湖泊沉积物中氮循环相关的微生物受多种环境因子的影响,比如溶解氧以及有机质含量.溶解氧4期张志忠等：武汉东湖沉积物好氧氨氧化微生物时空分布 1923会影响氨氧化微生物的丰度,进而影响间隙水氨氮的浓度;高的有机质含量会使异养细菌(如硝化细菌)的丰度增加,从而降低亚硝酸盐氮的浓度,减低亚硝酸盐氮对好氧氨氧化微生物的产物抑制作用.从而达到增加氨氧化微生物的氨氧化活性,降低氨氮浓度的目的.因此可以通过生物法如种植水生植物使溶解氧含量升高,以及向湖泊沉积物投加外源碳增加有机质含量等措施来实现定向促进氨氮向亚硝酸盐氮的转化.4 结论4.1 武汉东湖不同水层各营养盐(氮)浓度表现为间隙水>上覆水>表层水, 上覆水的总氮和氨氮浓度显著高于表层水,显示东湖沉积物相对于水体呈营养盐可释放状态.4.2 武汉东湖沉积物中AOA amoA基因丰度为4.50×105~4.99×108copies/g,AOB amoA基因丰度为1.66×104~5.32×106copies/g,AOA/AOB amoA基因丰度比为2~546,AOA可能在东湖沉积物氨氧化过程中发挥着主要作用.4.3 AOA和AOB amoA基因丰度随沉积物深度增加而降低.在夏季,AOA amoA基因丰度在15cm以上的沉积物中下降速率更快,AOB amoA基因丰度在15cm以上的沉积物中下降速率更快,其他3个季节普遍在前3cm快速下降,之后趋于平缓.4.4 AOA amoA基因丰度与间隙水氨氮和亚硝酸盐氮浓度呈显著正相关(P<0.05);AOB amoA基因丰度与间隙水亚硝酸盐氮浓度呈显著正相关(P<0.05); AOA/AOB amoA基因比值与间隙水硝酸盐氮呈显著负相关(P<0.05).参考文献：[1] 朱元荣,张润宇,吴丰昌.滇池沉积物中氮的地球化学特征及其对水环境的影响 [J]. 中国环境科学, 2011,31(6):978-983.Zhu Y R, Zhang R Y, Wu F C. 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Differences between ammonia- oxidizing microorganisms in phylogeny and physiological ecology [J].Microbiology China, 2015,42(12):2457-2465.[35] Abell G C, Banks J, Ross D J, et al. Effects of estuarine sedimenthypoxia on nitrogen fluxes and ammonia oxidizer gene transcription [J]. Fems Microbiology Ecology, 2011,75(1):111-122.[36] 鲍林林,陈永娟,王晓燕.北运河沉积物中氨氧化微生物的群落特征[J]. 中国环境科学, 2015,35(1):179-189.Bao L L, Chen Y , Wang X Y. Diversity and abundance of ammonia-oxidizing prokaryotes in surface sediments in Beiyun River [J]. china environmental science, 2015,35(1):179-189.[37] Wang X L, Han C, Zhang J B, et al. Longterm fertilization effects onactive ammonia oxidizers in an acid upland soil in China [J] Soil Biology and Biochemistry, 2015,84:28-37.作者简介：张志忠(1996-),男,湖北仙桃人,湖北工业大学硕士研究生,主要研究方向环境微生物生态.发表论文1篇.。
spatio-temporall...
Spatio-Temporal LSTM with Trust Gates for3D Human Action Recognition817 respectively,and utilized a SVM classifier to classify the actions.A skeleton-based dictionary learning utilizing group sparsity and geometry constraint was also proposed by[8].An angular skeletal representation over the tree-structured set of joints was introduced in[9],which calculated the similarity of these fea-tures over temporal dimension to build the global representation of the action samples and fed them to SVM forfinal classification.Recurrent neural networks(RNNs)which are a variant of neural nets for handling sequential data with variable length,have been successfully applied to language modeling[10–12],image captioning[13,14],video analysis[15–24], human re-identification[25,26],and RGB-based action recognition[27–29].They also have achieved promising performance in3D action recognition[30–32].Existing RNN-based3D action recognition methods mainly model the long-term contextual information in the temporal domain to represent motion-based dynamics.However,there is also strong dependency between joints in the spatial domain.And the spatial configuration of joints in video frames can be highly discriminative for3D action recognition task.In this paper,we propose a spatio-temporal long short-term memory(ST-LSTM)network which extends the traditional LSTM-based learning to two con-current domains(temporal and spatial domains).Each joint receives contextual information from neighboring joints and also from previous frames to encode the spatio-temporal context.Human body joints are not naturally arranged in a chain,therefore feeding a simple chain of joints to a sequence learner can-not perform well.Instead,a tree-like graph can better represent the adjacency properties between the joints in the skeletal data.Hence,we also propose a tree structure based skeleton traversal method to explore the kinematic relationship between the joints for better spatial dependency modeling.In addition,since the acquisition of depth sensors is not always accurate,we further improve the design of the ST-LSTM by adding a new gating function, so called“trust gate”,to analyze the reliability of the input data at each spatio-temporal step and give better insight to the network about when to update, forget,or remember the contents of the internal memory cell as the representa-tion of long-term context information.The contributions of this paper are:(1)spatio-temporal design of LSTM networks for3D action recognition,(2)a skeleton-based tree traversal technique to feed the structure of the skeleton data into a sequential LSTM,(3)improving the design of the ST-LSTM by adding the trust gate,and(4)achieving state-of-the-art performance on all the evaluated datasets.2Related WorkHuman action recognition using3D skeleton information is explored in different aspects during recent years[33–50].In this section,we limit our review to more recent RNN-based and LSTM-based approaches.HBRNN[30]applied bidirectional RNNs in a novel hierarchical fashion.They divided the entire skeleton tofive major groups of joints and each group was fedSpatio-Temporal LSTM with Trust Gates for3D Human Action RecognitionJun Liu1,Amir Shahroudy1,Dong Xu2,and Gang Wang1(B)1School of Electrical and Electronic Engineering,Nanyang Technological University,Singapore,Singapore{jliu029,amir3,wanggang}@.sg2School of Electrical and Information Engineering,University of Sydney,Sydney,Australia******************.auAbstract.3D action recognition–analysis of human actions based on3D skeleton data–becomes popular recently due to its succinctness,robustness,and view-invariant representation.Recent attempts on thisproblem suggested to develop RNN-based learning methods to model thecontextual dependency in the temporal domain.In this paper,we extendthis idea to spatio-temporal domains to analyze the hidden sources ofaction-related information within the input data over both domains con-currently.Inspired by the graphical structure of the human skeleton,wefurther propose a more powerful tree-structure based traversal method.To handle the noise and occlusion in3D skeleton data,we introduce newgating mechanism within LSTM to learn the reliability of the sequentialinput data and accordingly adjust its effect on updating the long-termcontext information stored in the memory cell.Our method achievesstate-of-the-art performance on4challenging benchmark datasets for3D human action analysis.Keywords:3D action recognition·Recurrent neural networks·Longshort-term memory·Trust gate·Spatio-temporal analysis1IntroductionIn recent years,action recognition based on the locations of major joints of the body in3D space has attracted a lot of attention.Different feature extraction and classifier learning approaches are studied for3D action recognition[1–3].For example,Yang and Tian[4]represented the static postures and the dynamics of the motion patterns via eigenjoints and utilized a Na¨ıve-Bayes-Nearest-Neighbor classifier learning.A HMM was applied by[5]for modeling the temporal dynam-ics of the actions over a histogram-based representation of3D joint locations. Evangelidis et al.[6]learned a GMM over the Fisher kernel representation of a succinct skeletal feature,called skeletal quads.Vemulapalli et al.[7]represented the skeleton configurations and actions as points and curves in a Lie group c Springer International Publishing AG2016B.Leibe et al.(Eds.):ECCV2016,Part III,LNCS9907,pp.816–833,2016.DOI:10.1007/978-3-319-46487-950。
Title Toward Spatio-temporal Models of Biogeophysical Fields for Ecological Forecasting
Title: Toward Spatio-temporal Models of Biogeophysical Fieldsfor Ecological ForecastingAuthors: Geoffrey M. Henebry1*, Tony Fountain2, Jan Chomicki3, and K. Jon Ranson4 Address: 1Center for Advanced Land Management Information Technologies (CALMIT) University of Nebraska-Lincoln113 Nebraska HallL incoln, NE 68588-0517P hone: 402-472-6158F ax: 402-472-4608E mail: ghenebry@2San Diego Supercomputer CenterU niversity of California-San Diego9500 Gilman DriveL a Jolla, CA 92093-0505P hone: 858-534-8374F ax: 858-534-5152E mail: fountain@3Department of Computer Science and EngineeringUniversity at Buffalo, The State University of New York201 Bell HallBuffalo, NY 14260-2000Phone: 716-645-3180 x 103Fax: 716-645-3464Email: chomicki@4Biospheric Sciences BranchCode 923NASA GSFCGreenbelt, MD 20771Phone: 301-286-4041Fax: 301-286-1757Email: jon.ranson@TYPE: SHORT PAPERDEMO?: NO* Corresponding authorC ONTEXT We are now in an era of intensive earth observation.Orbital platforms generate myriad remote sensing datastreams across a range of spatial, temporal, spectral, and radiometric resolutions. The number and variety of “eyes in the skies” are scheduled to increase significantly over the next few years. This veritable data deluge necessitates new ways of thinking about transforming remote sensing data into information about ecological patterns and processes. These datastreams hold the promise for environmental decision support. Effective use of remote sensing datastreams to characterize and monitor landscape dynamics requires analysis of the temporal variations in spatial patterns. We can distinguish four main phases in the analysis of image time series (Henebry and Goodin 2002): (1) change detection—perceiving the differences; (2) change quantification—measuring the magnitudes of the differences; (3) change assessment—determining whether the differences are significant; and (4) change attribution—identifying or inferring the proximate cause of the change. There is a critical need for theories and tools that will enable efficient and reliable characterization of spatio-temporal patterns contained in image time series. Such tools ought to be based on ecological expectations of land surface dynamics, analogous to climatological expectations. Ecological expectations would summarize across specific regions the typical temporal development of spatial pattern in biogeophysical fields. To make ecological forecasting an operational possibility (Clark et al. 2001; Maier et al. 2001), there is need for computational strategies to establish and to update complex spatio-temporal baselines that will enable prediction of the usual and the identification, quantification, and assessment of the unusual. Beyond computational considerations, there is an urgent need for environmental scientists to dialogue with computer scientists to develop effective and robust spatio-temporal models of biogeophysical fields for database and datamining applications aimed at the investigation of these baselines and their associated anomalies in datastreams. One objective in our Biodiversity and Ecosystem Informatics (BDEI) project is to sponsor an international cross-disciplinary workshop to examine the challenges facing the development and implementation of the next generation of spatio-temporal data models. This workshop is to held 8-10 April 2002 at the San Diego Supercomputer Center. We shall report the results at the dg.o conference. Here we offer some background on principal issues and a list of questions to be examined at the workshop.T HE C HALLENGES OF G EOSPATIAL D ATA IN T IME Burrough and Frank (1995), inquiring about the generality of GIS implementations, observed an unresolved and possibly irresolvable tension between the universal data models that computer scientists seek and the ad hoc data models that GIS practitioners use to address specific problems. They further identified three major groups of GIS users: managers of defined objects (e.g., cadastres, utilities, facilities management); planners and resource managers (e.g., multi-attribute evaluation & decision-making); and space-time modelers (e.g., environmental scientists broadly construed). What Burrough and Frank (1995) discovered was a profound conceptual disconnect in the GIS community between the units of analysis and the baseline models employed by different disciplinary subgroups. Current GIS implementations are not generic and they do not adequately support space-time modeling (Burrough and Frank 1995; Couclelis 1999; Peuquet 2001).Inclusion of time in GIS is not as straightforward an exercise as might be expected (Ott and Swiaczny 2000). A major source of difficulty stems from how the increased dimensionality of the data affects what can be assumed about the data. Consider an unordered list, the simplest database structure. It is a collection of zero-dimensional data, database records that lack spatial or temporal relationships with other records. While this structure is easy to implement and enables efficient querying about the relationships between records, it can permit inferences about relationships that are nonsensical when viewed within the broader context of the data. Temporal databases introduce an explicit, unidirectional, one-dimensional structure to the data. The “arrow of time” makes temporally oriented queries and logical inferences possible (Snodgrass 1992; Chomicki and Toman 1998). Spatial databases represent spatial relationships as locations (raster/fields) and/or as entities (vector/objects). Although coordinate systems supply topology, there is no a priori ordering of the directionality of causation in space. This has the important consequence of requiring the user to inform the database about the flows of influence among spatially ordered data. The user must specify a model of spatial relationships in order to make meaningful queries. For example, many GIS implemenations have a module that introduces the influence of gravity into the database topology in order to analyze drainage patterns. While topological relationships indicate who is the neighbor of whom, additional information is required to know who are the effective neighbors. Different processes can have different effective neighborhoods or corridors at different scales. The addition of time into a spatial model further complicates the issue of influence and places more responsibility on the user to identify relevant neighborhoods and to supply meaningful orderings (Henebry 1995; Peuquet 2001; Henebry and Merchant 2002).Databases that contain data with both spatial and temporal dimensions need to support sophisticated spatio-temporal queries. For example, let’s consider some spatio-temporal queries that could be asked of a time series of vegetation index composites (e.g., AVHRR or MODIS NDVI) concerning the “green wave” that accompanies the onset of spring in the northern temperate and boreal zones (Myneni et al. 1997; Schwartz and Reed 1999; Cayan et al. 2001; Shabanov et al. 2002):[Q1] Where did spring arrive earlier this year than last? Assume SP is a predicate with the following meaning: SP(Y, P, x,y) is true if the database contains information that indicates Spring is present in the year Y, instant P (measured in days), and location x,y. Thus, SP assumes that data have been processed to bring a series of remotely-sensed images into a series of images of a biogeophysical variable (here, fractional vegetation cover). The query [Q1] can then be expressed by the logical formula:∃ Pa.∃ Pb. ¬SP(2000, Pa, x,y)∧ SP(2000, Pa+1, x,y)∧ ¬SP(2001, Pb, x,y)∧ SP(2001, Pb+1, x,y)∧Pb<Pa.[Q2] Was the area in which spring arrived earlier than the previous year greater than the area in which spring arrived later than the previous year?[Q3] Where is spring likely to arrive earlier next year than this year?The first query [Q1] requires a point-wise comparison of relationships between different time instants. The second query [Q2] additionally requires the ability to do spatial aggregation and its translation to a logical formula would require non-standard constructs. The third query [Q3]moves beyond querying about what has been observed to ask for a forecast, a prediction about the future based on prior and current knowledge. This is the kind of predictive query could be addressed using machine learning/data mining techniques informed by domain expertise and applied to the image time series and its derived products. These sorts of quantitative results are what biogeophysical scientists want to elicit from time series of images and/or GIS data layers.A N U NORDERED LIST OF Q UESTIONS TO BE E XAMINED AT THE W ORKSHOP• Which kinds of spatio-temporal questions are of interest? What do these data look like?What do investigators want to get out of these data?• Which types of data mining techniques are useful for analyzing biogeophysical data for ecological forecasting?• Which types of data mining algorithms are appropriate for biogeophysical data?• Which kind of spatio-temporal data model is appropriate to enable/facilitate the queries of interest? How generic is this model?• How should querying to spatio-temporal vs. spatio-spectral vs. spectral-temporal slices from image time series be handled?• How should domain expertise be integrated into advanced query processing and mining? • Which interface tools are needed to enable the scientists to accomplish their goals with the database and mining tools.• Which types of computational infrastructure would be useful for efficient querying/mining/analysis? How should tasks be mapped onto hardware and software resources; in particular, what are the key connections between the database and the other analysis/mining routines.• Which operators of the relational algebra should be available to formulate spatio-temporal queries? Should operator nesting be restricted? Should new operators be introduced? What about relational calculus and SQL?• Which query evaluation and optimization techniques may be suitable in this context?• What is impact of missing and/or uncertain data on efficiency and accuracy of pattern characterization/query processing?• How achieve spatio-temporal queries that imply change detection and/or change quantification?• How to handle missing or poor quality data in spatio-temporal queries?• How to quantify/evaluate the success of a complex query?• How to model propagation of error or uncertainty in complex queries and/or mined relationships?• How much statistical analysis can be productively moved into a RDBMS?A CKNOWLEDGEMENTS We are grateful for support from BDEI NSF #0131937.R EFERENCESBehnke, J., E. Dobinson, S. Graves, T. Hinke, D. Nichols, P. Stolorz, and P. Newsome. 2000. Final Report: NASA Workshop on Issues in the Application of Data Mining to Scientific Data.Available online at /NASA_Mining.Burrough, P.A., and Frank, A.U. 1995. Concepts and paradigms in spatial information: are current geographical information systems truly generic? International Journal ofGeographical Information Systems 9:101-116.Cayan, D.R., Kammerdiener, S.A., Dettinger, M.D., Caprio, J.M., and Peterson, D.H., 2001, Changes in the onset of spring in the western United States. Bulletin of the AmericanMeteorological Society 82:399-415.Chomicki, J. and D. Toman. 1998. “Temporal logic in information systems.” In Logics for Databases and Information Systems, edited by J. Chomicki and G. Saake, 31-70. Boston: Kluwer.Couclelis, H. 1999. Space, time, geography. In Geographical Information Systems. Volume 1 Principles and Technical Issues 2/e, edited by P.A. Longley, M.F. Goodchild, D.J. Maguire, and D.W. Rhind, 29-38. New York: Wiley.Clark, J.S., and 16 others. 2001. Ecological forecasts: An emerging imperative. Science 293:657-660.Henebry, G.M. 1995. Spatial model error analysis using autocorrelation indices. Ecological Modelling 82:75-91.Henebry, G.M., and D.G. Goodin. 2002. Spatio-temporal analysis of landscape dynamics from image time series. Photogrammetric Engineering and Remote Sensing. In review. Henebry, G.M., and J.W. Merchant. 2002. Geospatial data in time: limits and prospects for predicting species occurrences. In Predicting Species Occurrences: Issues of Scale and Accuracy, (J.M. Scott, P.J. Heglund, M. Morrison, editors). Island Press, Covello, CA.Chapter 23. pp 291-309.Maier, D., E. Landis, J. Cushing, A. Frondorf, A. Silberschatz, M. Frame, and J.L. Schnase. 2001.Research Directions in Biodiversity and Ecosystem Informatics. Report of an NSF, USGS, NASA Workshop on Biodiversity and Ecosystem Informatics (NASA Goddard Space Flight Center, June 22-23, 2000, Greenbelt, Maryland). 30 pp.Myneni, R. B., C. D. Keeling, C. J. Tucker, G. Asrar, and R. R. Nemani. 1997. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386:698-702.Ott, T., and F. Swiaczny. 2000. Time-Integrative Geographic Information Systems: Management and Analysis of Spatio-Temporal Data, Springer-Verlag, New York. 234 pp.Peuquet, D.J. 2001. Making space for time: Issues in space-time data representation.Geoinformatica 5(1):1-32.Schwartz, M.D., and B.C. Reed. 1999. Surface phenology and satellite sensor-derived onset of greenness: an initial comparison. International Journal of Remote Sensing 20(17):3451-3457. Shabanov, N.V., L. Zhou, Y. Knyazikhin, , R.B. Myneni, and C.J. Tucker. 2002. Analysis of interannual changes in northern vegetation activity observed in AVHRR data from 1981-1994. IEEE Transactions on Geoscience and Remote Sensing 40:115-130.Snodgrass, T.R. 1992. Temporal databases. In Theories and Methods of Spatio-Temporal Reasoning in Geographic Space, edited by A.U. Frank, I. Campari, and U. Formentini, 22-64. New York: Springer-Verlag.。
MULTIPLE INVARIANCE SPATIO-TEMPORAL SPECTRAL ESTIMATION
In recent years, a number of high resolution eigenstructure based methods have been developed for the 2D narrow-band source location problem, many of which are based on variants of the ESPRIT algorithm. The ESPRIT family of methods have some well-known desirable features that motivates their popularity; they do not require array calibration, they are search free and are fairly robust against array imperfections. ESPRIT can be applied in frequency-DOA estimation either as a 1D method in a separated approach [1]–[4], to yield separate estimates of the frequencies and DOAs, or as a 2D method on the full problem [5],[6]. In the former case one needs a pairing procedure to find the correct frequencyDOA pairs, and one must also by some means ascertain that the pairing problem has a unique solution. The latter can be achieved e.g. with the marked subspace device [3],[4]. Methods for the full 2D problem, such as the recently developed 2D unitary ESPRIT [6], avoid this obstacle but the price paid is a higher numerical cost. Typically, 2D methods require eigendecompositions on systems of at least the size
中国玉米秸秆草谷比及其资源时空分布特征
第36卷第21期农业工程学报V ol.36 No.212020年11月Transactions of the Chinese Society of Agricultural Engineering Nov. 2020 227 中国玉米秸秆草谷比及其资源时空分布特征霍丽丽1，赵立欣1，姚宗路1※，贾吉秀1，赵亚男1，傅国浩1，丛宏斌2（1.中国农业科学院农业环境与可持续发展研究所，北京100081；2. 农业农村部规划设计研究院农村能源与环保研究所，北京100125）摘要：针对玉米秸秆资源量及时空区域分布不清等问题，该研究分析9个典型省的玉米秸秆草谷比差异性，并基于草谷比实测值，评价近10a中国玉米秸秆资源量的时空变化情况，预测玉米秸秆的资源潜力。
研究结果表明，玉米秸秆草谷比实测值为（0.84±0.23），不同地区、不同品种草谷比差异显著，随着年份变化，玉米品种和种植方式在不断变化，草谷比逐年变小，从2009年1.2减小到2018年的0.84，估算2018年全国玉米秸秆理论资源量为2.16×108t，比2009年仅增加3.9%。
玉米秸秆东北和华北地区资源量最高，占50%以上，与2009年相比，东北、华北、西北地区资源量有所增加，华东、华中、西南、华南略有下降；单位面积玉米秸秆可收集资源量4.51 t/hm2，比2009年增加23%，东北地区最高，其次华北、华东和西北地区，然后是华中和西南地区，华南地区最低。
预测2025年玉米秸秆的理论资源量为(2.53±0.58)×108t，可收集资源量为(1.86±0.51)×108t。
研究为全国各个地区的秸秆合理规划利用提供基本参考数据。
关键词：秸秆；资源评价；谷物；草谷比；理论资源量；可收集资源量；时空变化doi：10.11975/j.issn.1002-6819.2020.21.027中图分类号：TK6 文献标志码：A 文章编号：1002-6819(2020)-21-0227-08霍丽丽，赵立欣，姚宗路，等. 中国玉米秸秆草谷比及其资源时空分布特征[J]. 农业工程学报，2020，36(21)：227-234. doi：10.11975/j.issn.1002-6819.2020.21.027 Huo Lili, Zhao Lixin, Yao Zonglu, et al. Difference of the ratio of maize stovers to grain and spatiotemporal variation characteristics of maize stovers in China[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2020, 36(21): 227-234. (in Chinese with English abstract) doi：10.11975/j.issn.1002-6819.2020.21.027 0 引 言中国秸秆资源丰富，尤其是玉米秸秆分布范围广，资源量约占总秸秆资源量的1/3[1]，2018年，全国玉米种植面积为42.1×106hm2，是2006年的1.5倍，而玉米产量为2.57×108 t，是2006年的1.7倍[2]，玉米秸秆资源量随着种植面积和玉米产量增加而逐年增加，但秸秆资源量的统计值差异较大[3]，难以为秸秆综合利用的区划提供相对准确的数据支撑[4-5]。
河南省耕地“非农化”时空演变特征分析
第 41 卷 ，第 2 期 2024 年4 月15 日国土资源科技管理Vol. 41，No.2Apr. 15，2024 Scientific and Technological Management of Land and Resourcesdoi：10.3969/j.issn.1009-4210.2024.02.005河南省耕地“非农化”时空演变特征分析余庆年，王虎威（河海大学 公共管理学院，江苏 南京 211100）摘 要：深入理解和掌握耕地“非农化”的时空演变特征及其原因对保护耕地并确保粮食安全具有重要意义。
本文针对中国产粮第二大省河南，以县域为单位，基于河南省1980—2020年土地利用长时序空间数据，采用重心转移模型和空间自相关分析等方法，定量揭示全省158个县级评价单元1980—2020年来耕地“非农化”的空间分布特征、时空迁移路径和集聚特征，以期为河南省耕地资源的保护与可持续利用提供依据。
结果表明：（1）河南省耕地资源东西分布差异大，集中连片耕地主要集中在东部，耕地总面积随时间推移呈波动减少。
（2）耕地“非农化”等级时空差异较大，豫西地区耕地“非农化”较为缓和，中部和东部地区呈先快速上升后缓慢下降的态势。
（3）河南省耕地“非农化”空间不均衡性增强，空间格局小幅波动，耕地“非农化”重心以先向东南和西南后向东北的路径迁移。
（4）河南省耕地“非农化”空间分布格局在4个时期均呈现出集聚特性，空间集聚程度先增大后减小，高—高和低—低聚类主导格局变化。
本研究揭示了1980—2020年河南省耕地“非农化”的时空演变格局，为政府管控耕地“非农化”现象、实现耕地资源的可持续发展提供参考和借鉴。
关键词：耕地“非农化”；时空演变特征；重心模型；空间自相关中图分类号：F323.21 文献标志码：Ａ 文章编号：1009-4210-（2024）02-50-12Analysis on Temporal and Spatial Evolution Characteristics of FarmlandConversion in Henan ProvinceYU Qingnian，WANG Huwei（School of Public Administration，Hohai university，Nanjing 211100，Jiangsu，China）Abstract: Understanding and mastering the characteristics and underlying reasons for the spatial-temporal evolution of cultivated land “non-agriculturalization” is of paramount importance to ensure food security. Based on the long-term spatial data of land use in Henan from 1980 to 2020，the second largest grain-producing province in China，this paper adopts the methods of gravity shift model and spatial autocorrelation analysis. To provide a solid foundation for the protection and sustainable use of cultivated land resources in Henan Province，this study quantitatively examines the spatial distribution patterns，temporal-spatial migration routes，and agglomeration characteristics of cultivated land “non-agriculturalization” across 158 county-level units from 1980 through to 2020. The results show that：（1）the distribution of cultivated land resources in Henan Province varies greatly from east to west. The收稿日期：2023-10-31作者简介：余庆年（1977—），女，博士，副教授，硕士生导师，从事土地政策与制度研究。
近10年中国耕地变化的区域特征及演变态势
第37卷第1期农业工程学报V ol.37 No.12021年1月Transactions of the Chinese Society of Agricultural Engineering Jan. 2021 267 近10年中国耕地变化的区域特征及演变态势袁承程1，张定祥2，刘黎明1※，叶津炜1（1. 中国农业大学土地科学与技术学院，北京100193；2. 中国国土勘测规划院，北京100035）摘要：随着工业化、城市化进程推进,中国耕地在数量和质量方面均发生了显著变化。
通过分析2009－2018年中国耕地的时空变化，掌握中国耕地变化的区域特征与变化态势，有助于制定差别化的区域耕地保护政策与管理策略，为保障粮食安全提供科学依据。
该研究基于2009－2018年土地调查格网数据，利用GIS空间分析、数学指数模型等方法，从耕地数量、空间以及立地条件等方面研究近10年来中国的耕地时空变化特征。
研究表明：1）2009－2018年间中国耕地数量总体稳定，但是耕地数量变化的区域差异较大。
全国耕地共减少39.37万hm2，减少幅度为0.29%。
2）从市域尺度分析，呈现以“哈尔滨-郑州-昆明”带为中心的东-中-西分异特征，该中心带内耕地净减少面积与全国耕地净减少总量基本持平，而该中心带以东地区的耕地净减少量与中心带以西地区的耕地净增加量相近。
3）耕地空间变化率在长江以北的长江中下游平原区、黄淮海平原区以及四川盆地及其周边地区相对较高，表明这些区域人为调整耕地空间布局的强度较大，但其市域内净增加耕地面积总量却不大。
4）耕地减少主要分布在距离主要城市中心30 km以内的区域，而耕地增加主要发生在离城市中心40 km以外区域，这进一步说明城市化发展仍然是当前耕地减少的主导因子。
此外，石嘴山、延安、雅安、榆林、张家口、丽水和泉州等地的耕地平均海拔增加较大，说明这些地区耕地“上山”现象较为严重。
因此，今后应根据耕地变化“热点地区”的动态识别，提升自然资源管理和督察的精准定位和因地施策的能力。
群落的结构
型、肉质多浆、丛生型、根茎型
一年生植物(Th) ：如沙漠短命植物、北方早春植物
物种组成 Species composition
优势种（dominant species）和从属种 （subordinate）： 关键种（keystone species）： 冗余种（redundancy species）：
相对频数％ Relative frequency
70 60 50 40 30 20 10 0 1 2 3 4 5 6 7 8 9 冠 III 全 冠 I II
叶层 Leaf layers
样冠深度 Sample crown depth (m)
各叶层的样冠深度的相对频数 The frequencies of sample canopy in different leaf layers
3.3 群落三维结构 (3-dimensional structure)
3.4 空间异质性(Spatial heterogeneity) 4. 时间结构 (Temporal pattern )
4.1 昼夜
4.2 季节 4.3 演替
5. 营养结构
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1.1 生活型结构 (2) 常规生活型 乔木: 常绿、落叶、针叶、阔叶、藤本
0.14 植 冠 空 间 占 有 比 0.12 0.10 0.08 0.06 0.04 0.02 0.00
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宿州市耕地土壤养分时空变化特征分析
Temporal and spatial characteristics of soil nutrients in cultivated land in Suzhou CityDING Qixun 1,ZHAN Xuejie 1,ZHANG Tian′en 1,XU Nuo 2,MA Xiuting 3,ZHANG Changkun 3,MA Youhua 1*（1.Key Laboratory of Farmland Ecological Conservation and Pollution Control of Anhui Province,College of Resources and Environment,Anhui Agricultural University,Hefei 230036,China;2.Suzhou Agriculture and Rural Affairs Bureau,Suzhou 234000,China;3.Anhui Huacheng Seed Co.,Ltd.,Suzhou 234000,China ）Abstract ：Analyzing the temporal and spatial evolution of soil nutrients is a prerequisite for implementing precision agriculture and sustainable soil management.The spatial and temporal variation characteristics of soil organic matter,total N,available P,and available K in arable soil in Suzhou City in 2010and 2019were analyzed by inverse distance weighted spatial interpolation analysis method.The results showed that the soil nutrients of arable soil in Suzhou increased slightly in 2019compared with 2010.The soil organic matter of arable soil was relatively scarce in Dangshan County,Xiaoxian County,and Sixian County,and abundant in the middle towns of YongqiaoDistrict,with an average value of 17.95g·kg -1,an increase of 6.15%.The area with intermediate soil organic matter content accounted for76.00%of the total cultivated land area;the soil total N content was the same,with an average value of 1.06g·kg -1.The area with medium宿州市耕地土壤养分时空变化特征分析丁琪洵1，詹雪洁1，张天恩1，许诺2，马秀婷3，张长坤3，马友华1*（1.农田生态保育与污染防控安徽省重点实验室，安徽农业大学资源与环境学院，合肥230036；2.宿州市农业农村局，安徽宿州234000；3.安徽华成种业股份有限公司，安徽宿州234000）收稿日期：2021-11-26录用日期：2022-03-02作者简介：丁琪洵（1997—），女，江苏泰州人，硕士研究生，主要从事耕地质量评价与提升研究。
2000-2020_年内蒙古草地植被覆盖度时空变化及趋势预测
第 32 卷第 8 期Vol.32，No.81-132023 年 8 月草业学报ACTA PRATACULTURAE SINICA张慧龙，杨秀春，杨东，等. 2000-2020年内蒙古草地植被覆盖度时空变化及趋势预测. 草业学报， 2023， 32（8）： 1−13.ZHANG Hui-long， YANG Xiu-chun， YANG Dong，et al. Spatio-temporal changes in grassland fractional vegetation cover in Inner Mongolia from 2000 to 2020 and a future forecast. Acta Prataculturae Sinica， 2023， 32（8）： 1−13.2000-2020年内蒙古草地植被覆盖度时空变化及趋势预测张慧龙，杨秀春*，杨东，陈昂，张敏（北京林业大学草业与草原学院，北京 100083）摘要：草地植被覆盖度（FVC）是反映草地生态状况最直接的指标之一。
目前在大区域内构建准确的FVC估算模型，进行长时间序列的动态分析，仍是一个挑战。
基于大量地面调查样本，使用2000-2020年MODIS遥感数据、气象数据，通过随机森林模型进行FVC分区建模与结果估算。
利用Sen+Mann-Kendall趋势分析法、Hurst指数法等，分析FVC时空变化特征和未来变化情况。
研究表明：1）内蒙古草地FVC随机森林模型精度表现为分区优于全区，有效地降低了空间异质性的影响。
2）内蒙古草地FVC总体上呈东高西低的分布格局，空间差异明显。
3）近21年，内蒙古草地FVC总体呈波动上升趋势，年增长率为0.2%·a-1；增长区域面积占比（79.5%）大于减少区域面积占比（20.5%），并且极显著增长和显著增长占比（28.3%）远大于极显著减少和显著减少（1.6%）。
4）未来内蒙古草地FVC总体为正持续性发展。
植物叶形态的多样性及其进化适应意义
植物叶形态的多样性及其进化适应意义张玉芬罗文韬(北京师范大学生命科学学院，生物多样性与生态工程教育部重点实验室北京100875)摘要植物的叶形态是环境因子与遗传因子共同作用的结果，为了适应复杂多变的环境，植物在叶形上演化出了丰富的多样性。
植物叶形态与其诸多功能密切相关。
综述了植物叶形多 样性及其进化的适应意义，特别是关于异形叶性和发育调控叶异形性，厘清了二者之间的概念差异。
这二者虽然都表现为叶形态的变化，但是具有不同的机制。
未来研究应建立综合遗传多样性、发育背景和环境可塑性的定量模型，以揭示叶形多样性演化发育过程及其对环境压力的响应机制，为可持续地保护生物多样性，并且为有方向、有目标地利用植物的适应性特征提供科学依据。
关键词叶形多样性异形叶性发育调控的叶异形性进化适应意义中国图书分类号：Q94 文献标识码：A叶是植物进行光合作用和蒸腾作用的主要场所，为了最有效地行使其功能，植物能精准地控制叶片的初始发育和形态变化:1]。
被子植物是陆生植物的主要类群，为了进一步适应复杂多变的陆生环境，该类群在叶形上演化出了巨大的多样性。
不仅大的分支类群间、物种间具有变化多端的叶形，物种之内也往往存在各种因素作用下的变异，其中包括单叶形态的多样性，例如，全缘、锯齿 状、不同程度（深浅）和不同式样（羽状或掌状、回数等）的裂刻等，也有单、复叶的差异，例如，广泛 分布于热带、亚热带地区的蝶形花科猪屎豆属(Crota〗aria)是被子植物最大的属之一，在全球约有700余种，其中71%的种为三出复叶或羽状复叶，仅29%的种为单叶，研究显示，其复叶类型更适应于干燥的气候，而单叶类型在潮湿气候中占据主导地位。
这可能是由于复叶与同等大小的单叶相比，空气界面层阻力更小，气体交换量更大，散热效果更好，同时，复叶通过调节单个小叶的倾斜角度，能最大限度地捕获光能[2]。
本文综述了植物叶形多样性及其进化适应意义，尤其是异形叶性（heterophylly)和发育调控叶异形性（heteroblasty),二者虽都表现为在同一植株上会生长不同形态的叶，但具有不同的机制[3]。
Exploring the Spatio-temporal Variation of Seagrass
Exploring the Spatio-temporal Variation of Seagrass Ecosystems in Southern Tampa BayRon Li and Xutong NiuDepartment of CEEGS, The Ohio State University470 Hitchcock Hall, 2070 Neil AvenueColumbus, OH 43210614-292-4303li.282@ABSTRACTThis paper presents the status and outcomes of our efforts in using developed coastal geospatial information technology for studying on seagrass degradation in Tampa Bay, FL, in the last period of our NSF-funded Digital Government project. The research results of the project in the first three years are summarized. In the fourth year, the project is focused on seagrass degradation monitoring and restoration in Tampa Bay, FL. In this paper, the spatio-temporal variation of seagrass coverage in southern Tampa Bay was investigated. The effects of several physical factors on seagrass distribution were also examined. It was found that seagrass distribution is not only influenced by biochemical factors such as nutrient loading, water quality, and light, but also by the underwater landscape and other physical factors.Categories and Subject Descriptors:I.6.7 [Simulation Support Systems]: EnvironmentsGeneral Terms:Management, ExperimentationKey Words:Seagrass degradation and restoration, GIS, Spatio-temporal change analysis, Ecosystem1. INTRODUCTIONThe goal of this project is to investigate and develop technologies to enhance the operational capabilities of federal, state, and local agencies, which are responsible for coastal management and decision-making. We have completed coastal spatio-temporal data collection, set up a spatial data inventory system, and conducted multi-source spatial data integration [5]. In order to facilitate the administrative processes of state and local government agencies, we have developed a web-based system for coastal management and decision making [7]. A shoreline prediction model has also been designed [10]. Based on historic shorelines, future shorelines are predicted and published in a shoreline erosion awareness subsystem [7]. In multi-source spatial data integration, three-year (1999-2001) satellite altimetric data, water-gauge data, and hydrodynamic model hindcast results are compared with the goal of improving operational lake and coastal circulation nowcast and forecasting capabilities[6]. Sub-meter high-resolution QuickBird satellite stereo imagery was also acquired for the southern Tampa Bay area. Applying four different adjustment methods, geo-positioning accuracy of the QuickBird was achieved at a level of 71 cm in the horizontal and 65 cm in the vertical directions. A 3-D shoreline and a high-resolution coastal DEM were also derived [8]. In the fourth year of the project, the technologies are evaluated for monitoring and modeling seagrass degradation and restoration in Tampa Bay, Florida. During the late 1800s before human influence, approximately 31,000 ha of seagrass were distributed in Tampa Bay. In 1982, seagrass coverage was reduced to about 8,800 ha. The loss of as much as 70% of the seagrass is due to the impact of human activities on the bay, such as the excessive nutrient loading, increased commercial activities, and dredging operations during the 1950s, ’60s and ’70s [3]. After that, substantial seagrass expansion has occurred throughout the bay because of improved water quality. However, the recovery rate and expansion of seagrasses in several areas have been much slower. In general, the factors controlling seagrass distribution include light, water depth, tide and water movement, salinity, temperature, climate change, and anthropogenic impacts [9]. The natural habitat of seagrass ecosystem also provides a growth bed for seagrass evolution [2]. Through the investigation and study of the spatio-temporal variations of the seagrass in Tampa Bay, the relationships between underwater landscape and the physical factors and seagrass changes are observed.2. SEAGRASS CHANGE ANALYSISThe study site, Cockroach Bay, is located in the southern coast of Tampa Bay. A segment of natural shoreline extends 11 km in the northeast–southwest direction. Without experiencing shoreline development, the impact of human activities in this area is relatively less than that in other coastal areas in Tampa Bay. Baywide seagrass coverage maps in 1988, 1990, 1992, 1994, 1996, 1999, and 2001 were generated by the Surface Water Improvement and Management Section of the Southwest Florida Water Management District from 1:24,000 aerial photographs. Two types of seagrass polygons were classified: patchy (>25% of polygon unvegetated) or continuous (<25% of polygon unvegetated) [4]. Other types of non-seagrass polygons, such as tidal flats and water bodies, were also included in the maps. From these maps, four seagrass variation patterns can be observed:Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and thatcopies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.The 2005 Annual National Conference on Digital Government Research, May 15–18, 2005, Atlanta, GA, USA.•Degradation zones, where either the density of seagrass polygons has decreased or the seagrasses have completely disappeared.•Restoration zones, where either the density of seagrass polygons has increased or the seagrasses have emergedfrom non-seagrass polygons.•Dynamic zones, where both degradation and restoration of the seagrass happened at least once during the timeperiod from 1988 to 2001.•Stable zones, where the types of the seagrass polygons have not changed.In this research, a transition matrix was used to quantify the seagrass variations. By comparing the elements in the matrices, a seagrass variation map was generated. It is found that seagrass variations are dependent on locations of the seagrass habitat: •Most of the degradation zones were located farther away from the shoreline and exposed to the open ocean water. The farther away the zones are from the shoreline, the deeper the seagrass is under the water.•The restoration zones appeared at the isolated area north of the bay entrance. They also appeared at the entrance of Cockroach Bay.•Dynamic zones were exposed to open ocean water, but closer to the land than that of the degradation zones.•The stable zones are located adjacent to the island boundaries or the shoreline.The findings indicated that there is a relationship between seagrass habitat and seagrass geospatial variations.3. PHYSICAL FACTORSTo evaluate the effects of seagrass geospatial variations, three physical factors were examined, including bathymetry, slope, and annual water level changes.A 30-m resolution topo-bathy dataset generated by USGS/NOAA was used as the bathymetry map of seagrass habitat. Based on the bathymetry, a slope map was calculated using ESRI ArcGIS Desktop 9.0. Due to water level changes, bathymetry data should not be invariant. Annual water level changes from 1988 to 2001 were calculated from observations obtained from the water gauge station (station No. 8726520) in St. Petersburg, Florida. Each annual water level was deducted as a constant from every point in the bathymetry data to generate a sequence of simulated dynamic bathymetry data. The water depths and slopes were then extracted for each seagrass variation zone and it is observed that:•The mean depth of the degradation zones is the greatest, which is well matched with its horizontal locations, which are farther away from the shoreline. The mean depth of the restoration zones is the smallest. The mean depths of both stable and dynamic zones are the same. However dynamic zones have a larger standard deviation for depth, which is corresponding to dynamic seagrass changes.•The degradation zone has the biggest mean slope and the biggest standard deviation as well. The other three zones have the similar mean values.4. FUTURE RESEARCHIn this paper, spatio-temporal seagrass variation in southern Tampa Bay was investigated. Four kinds of seagrass variation zones were defined according to the seagrass distribution changes: stable, degradation, restoration, and dynamic zones. From the seagrass variation map, it was found that the underwater landscape and location of the seagrass habitat play an important role in seagrass evolution.For future research, the micro impacts of shoreline changes, sea level changes, bathymetric changes, sandbar movement, waves and currents, and biochemical influences on coastal ecosystem will be investigated in a small coastal region with the support of high-resolution geospatial data. A self-adaptive modeling system will be developed to investigate coastal environmental changes, predict ecological variation, and provide critical information for coastal ecosystem monitoring, management, and decision making. ACKNOWLEDGMENTSThis project is supported by the National Science Foundation—Digital Government Program.REFERENCES[1]COUCLELIS, H., 1985. Cellular worlds: a framework for modellingmicro-macro dynamics. Environment and Planning A. 17: 585-96. [2]GREENING, H.S., 2004. Factors Influencing seagrass recovery inFeather Sound, Tampa Bay, Florida. Report submitted to Pinellas County Environmental Foundation by the Feather Sound Seagrass Recovery Workgroup.[3]JOHANSSAN, J.O.R. 2000. Historical overview of Tampa Baywater quality and seagrass: issues and trends. In Seagrass Management: It’s Not Just Nutrients!, St. Petersburg, Florida, Aug 22–24, 2000, H.S. GREENING, Eds. Tampa Bay Estuary Program, 1-10.[4]KURZ, R.C., 2000. Seagrass mapping: accuracy issues. In SeagrassManagement: It’s Not Just Nutrients!, St. Petersburg, Florida, Aug 22–24, 2000, H.S. GREENING, Eds. Tampa Bay, Florida, 109 - 213[5]LI, R., BEDFORD, K.W., SHUM, C.K., RAMIREZ, J.R., ZHANG,A., AND DI, K. 2002. Digitalization of Coastal Management andDecision Making Supported by Multi-dimensional Geospatial Information and Analysis. In: Proceedings of the NSF National Conference for Digital Government Research "dg.o 2002", Los Angeles, CA, May 20-22, 2002,.53-59.[6]NIU, X., KUO, C.-Y., VELISSARIOU, V., LI, R., BEDFORD, K.W.,AND SHUM, C. K. 2003. Multi-source Coastal Data Analysis. In: Proceedings of the NSF National Conference on Digital Government Research, Boston , MA., May 18-21, 2003, 227-230.[7]NIU, X. MA, R. ALI, T., AND LI, R. 2005. Mobile GIS and CoastalManagement. Photogrammetric Engineering and Remote Sensing, 71(4). (In Press)[8]NIU, X., WANG, J., DI, K., AND LI, R. 2004. Geometric Modelingand Processing of QuickBird Stereo Imagery. Proceedings of ASPRS Annual Conference, Denver, Colorado , May 23-28, 2004 . (CDROM) [9]SHORT, F.T., COLES, R.G., AND PERGENT-MARTINI, C. 2001.Global seagrass distribution. In: Global Seagrass Research Methods,F.T. SHORT AND R.G. COLES, Eds. Elsevier Science B.V.,Amsterdam, The Netherlands, 5-30.[10]SRIVASTAVA, A., NIU, X., DI, K., AND LI, R. 2005. ShorelineModeling and Erosion Prediction. In: Proceedings of the ASPRS Annual Conference, Baltimore, MD, March 7-11, 2005.。
南京都市圈耕地利用效率的时空格局及动态演进
摘要：基于2000~2020年南京都市圈的耕地利用投入产出面板数据，采用超效率EBM 、标准差椭圆、核密度估计函数和马尔科夫链等方法，对耕地利用效率进行了系统研究。
结果表明：（1）2000~2020年南京都市圈的耕地利用效率总体偏高，但距离有效前沿面仍存在一定的提升空间。
其中，南京的耕地利用综合效率稳步提升且数值最高，淮安和扬州的耕地利用综合效率>1，芜湖、马鞍山和宣城的耕地利用综合效率均值未达到有效前沿面。
（2）南京都市圈的耕地利用纯技术效率处于较高水平，规模效率处于较低水平，从未达到有效前沿面。
（3）南京都市圈耕地利用效率分布呈现偏“东北—西南”格局，且具有向东北方向偏移趋势。
耕地利用效率重心集中于南京市，并呈现“西南—东北—西南”方向的阶段性转移。
（4）南京都市圈的耕地利用效率一直存在明显的两极分化，不同地级市之间耕地利用效率绝对差值随着时间的推移扩大，应重点提高低值区域耕地利用效率。
（5）从长期看，南京都市圈内耕地利用效率呈现出向下减少趋势，且各状态间的内部流动性比较小，保持原有状态的可能性较大。
提出因地制宜，因城施策；加强地市间相互联系，促进资源优化配置；控制碳排放，促进耕地的低碳利用；加强农业科技创新力度的对策建议。
关键词：南京都市圈；超效率EBM ；时空格局中图分类号：F301.2文献标识码：A 文章编号：1008-1631（2023）05-0088-07收稿日期：2023-02-14基金项目：国家自然科学基金面上项目“都市圈乡村土地利用转型的格局、机理与效应———以南京都市圈为例”（42371269）；安徽省自然科学基金面上项目“都市圈跨界区域土地利用转型的格局、机理与效应———以南京与合肥都市圈为例”（2208085MD86）；2021年国家级大学生创新训练项目（202110370228）作者简介：吴九兴（1980-），男，江西宜春人，副教授，博士，主要从事城乡发展与土地利用研究。
野慈姑和矮慈姑种间花粉传递与生殖
基金项目: 国家自然科学基金(３１９７０２５０) ꎮ
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植物科学学报 ２０２２ꎬ ４０(６) : ７６２ ~７７０
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唐莎莎ꎬ 费采虹ꎬ 杨聪ꎬ 尚书禾ꎬ 熊浩镧ꎬ 王欣怡ꎬ 汪小凡. 野慈姑和矮慈姑种间花粉传递与生殖干扰不对称性[ Ｊ] . 植物科学学报ꎬ ２０２２ꎬ ４０
缘本地物种对可能为同域分布ꎬ 并占据相同的栖息
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同域分布的近缘物种之间生殖干扰的式样和机制有
待深入研究ꎬ 以拓展对于物种间相互作用与共存机
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面向疾病的空间聚集性与影响因素分析方法
2097-3012(2024)01-0065-09 Journal of Spatio-temporal Information 时空信息学报收稿日期: 2022-06-30；修订日期: 2023-12-15 基金项目: 国家自然科学基金（42201490）作者简介: 胡涛，研究方向为时空大数据分析与可视化。
E-mail:*****************通信作者: 王丽娜，研究方向为地理信息可视化、疾病制图。
E-mail:***************面向疾病的空间聚集性与影响因素分析方法胡涛1，王丽娜2，李响1，张正斌3，俞鑫楷11. 信息工程大学 地理空间信息学院，郑州450052；2. 郑州轻工业大学 计算机科学与技术学院，郑州 450001；3. 武汉市结核病防治所结核病控制办公室，武汉430030摘 要：疾病的发生与自然环境、社会环境和人群特点密切相关，其发生与流行通常具有一定的空间分布特征。
目前在疾病空间聚集特征与影响因素的已有研究中缺少两者关联关系的探讨，以及空间尺度多集中于省、市和县域，因此，本研究提出一种面向疾病空间聚集性与影响因素分析的方法。
以武汉市的历史肺结核数据为例，进行基于乡镇尺度的肺结核发病率数据及影响因素数据的处理与整合，基于空间自相关方法分析2011年、2013年和2015年肺结核空间聚集情况；并运用地理探测器探测肺结核发病率空间分布的影响因素及交互作用，探究肺结核空间聚集的成因。
结果表明：肺结核热点聚集乡镇主要分布在新洲区、江夏区和蔡甸区，冷点聚集乡镇主要分布在洪山区；植被指数、人口密度、人均GDP 及五类兴趣点密度（医疗保健类、生活服务类、餐饮类、住宅类和农林牧渔类）为肺结核发病率空间分布的主要影响因素，其交互作用对肺结核发病率影响显著增强。
研究成果可为武汉市肺结核防治提供科学参考。
关键词：肺结核；空间聚集性；空间自相关；地理探测器；兴趣点引用格式：胡涛, 王丽娜, 李响, 张正斌, 俞鑫楷. 2024. 面向疾病的空间聚集性与影响因素分析方法. 时空信息学报, 31(1): 65-73Hu T, Wang L N, Li X, Zhang Z B, Yu X K. 2024. Analysis method for disease-oriented spatial clustering and influencing factors. Journal of Spatio-temporal Information, 31(1): 65-73, doi: 10.20117/j.jsti.2024010091 引 言计算机科学、地理信息系统和空间分析技术快速发展，为挖掘多维、海量的疾病数据提供了坚实的技术基础，并广泛应用于流行病的预警、聚类分析、疾病制图等方面（施迅和王法辉，2016；李杰等，2020；陈曦和闫广华，2021）。
大九湖泥炭藓沼泽植被指数时空变化研究
泥炭藓沼泽（sphagnum swamp ）经过长时间的生长积累，可以将大气碳以平均每年每平方米12~23g 的速度固定[1]。
尽管泥炭藓沼泽仅仅拥有相当于地球陆地表面的3%的覆盖面积[2]，但其储存了地球上约1/3的碳源[3]。
而泥炭藓(Sphagnum)作为泥炭藓沼泽的优势物种，每年固定的碳[4]要远多于其他世界陆生植物积累的碳[5]。
赵素婷[6]等于2011年中旬对大鄂西亚高山区泥炭藓沼泽开展了深入研究，并考察了该地区的高级植被群类型多样性，对大九湖泥碳藓沼泽在生态系统稳定领域的重要作用，以及主要植被群丛种类等做出了判断和研究。
因此对泥炭藓沼泽植被生长趋势以及长时间序列变化监测是了解气候变化对泥炭藓沼泽影响、预估未来泥炭藓沼泽如何变化、检测生态系统是否稳定的有效工具［7］。
归一化植被指数（NDVI ）作为应用最广泛的指数之一，能对较大空间范围内的植被生长状况进行监测。
Boelman [8]等以由泥炭藓组成的苔原植物种群为主要研究目标，研究成果指出，NDVI 在控制南北极苔原植物引文格式：张唯,张振泽,刘福江,等.大九湖泥炭藓沼泽植被指数时空变化研究[J].地理空间信息,2024,22(4):106-110.doi:10.3969/j.issn.1672-4623.2024.04.025Apr.,2024Vol.22,No.4地理空间信息GEOSPATIAL INFORMATION2024年4月第22卷第4期大九湖泥炭藓沼泽植被指数时空变化研究张唯1，张振泽1，刘福江1*，梁天欣1（1.中国地质大学（武汉）地理与信息工程学院，湖北武汉430000）摘要：基于Landsat 系列卫星数据，计算大九湖泥炭藓沼泽地区1986-2021年的归一化植被指数（NDVI ），利用Theil-Senmedian 趋势分析和Mann-Kendall 显著性检验方法，结合研究区气象资料，分析近36a 来植被指数的时空演变规律。
基于RS与GIS的伏牛山区植被NDVI时空变化分析
总735期第一期2021年1月河南科技Henan Science and Technology基于RS与GIS的伏牛山区植被NDVI时空变化分析禹倩业张静静高莎（郑州师范学院地理与旅游学院，河南郑州450044）摘要：基于2000—2017年MODIS-NDVI数据集，在MRT、ArcGIS、SPSS等软件的辅助下，采用最大值合成法、均值法、一元线性回归法来分析近18年来伏牛山区植被NDVI时空变化特征。
结果表明：2000—2017年伏牛山区植被NDVI呈增长趋势，增长率为0.024/10a（P＜0.05），其中秋季植被NDVI的增加速率（0.042/10a）最为显著，明显高于春季（0.018/10a，P＜0.05），表明在全球变暖背景下，伏牛山区植被变化情况表现为生长季的延长趋势强于生长季的提前趋势。
从空间上来看，伏牛山植被覆盖状况较好，大部分地区植被NDVI都在0.4～0.8，所占比例达到97.03%，高值区主要分布在中西部海拔较高的山区，低值区零星地分布在研究区南部、东部和北部的城镇中心。
关键词：植被；MODIS-NDVI；时空变化；伏牛山区中图分类号：Q948；TP79文献标识码：A文章编号：1003-5168（2021）01-0144-03 Analysis of Spatio-temporal Changes of Vegetation NDVIin Funiu Mountain Area Based on RS and GISYU Qianye ZHANG Jingjing GAO Sha（College of Geography and Tourism,Zhengzhou Normal University，Zhengzhou Henan450044）Abstract:Based on the MODIS-NDVI data set of vegetation in Funiu Mountain Area from2000to2017,with the help of MRT,ArcGIS,SPSS and other software,the spatial and temporal variation characteristics of vegetation NDVI in Funiu Mountain Area in recent18years were analyzed by using the maximum value synthesis method,mean value method and univariate linear regression method.The results showed that:from2000to2017,the vegetation NDVI in Funiu Mountain area showed an increasing trend,with the growth rate of0.024/10a(P＜0.05).The growth rate of vegetation NDVI in autumn(0.042/10a)was the most significant,which was significantly higher than that in spring (0.018/10a,P＜0.05),indicating that under the background of global warming,the vegetation change in Funiu Mountain area showed that the extension trend of growing season was stronger than the advance trend of growing sea⁃son.Spatially,the vegetation coverage of Funiu Mountain is good.The NDVI of most areas is between0.4and0.8,ac⁃counting for97.03%.The high-value areas are mainly distributed in the high-altitude mountainous areas in the cen⁃tral and western regions,and the low-value areas are scattered in the urban centers in the south,east and north of the study area.Keywords:vegetation；MODIS-NDVI；temporal and spatial variation；Funiu Mountain植被覆盖度可作为生态建设和生态系统评价的综合性量化指标和重要参数，某一地区植被覆盖占该地区比重多少是衡量该地区环境变化的重要依据［1-4］。
在链霉菌中高效敲除中的两个pHZ1358衍生型载体
J. Microbiol. Biotechnol. (2010),20(4), 678–682doi: 10.4014/jmb.0910.10031First published online 29 January 2010Two pHZ1358 Derivative V ectors for Efficient Gene Knockout in Streptomyces He, Yunlong, Zhijun Wang, Linquan Bai, Jingdan Liang, Xiufen Zhou, and Zixin Deng*Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiaotong University, Shanghai 200030, ChinaReceived:October 22, 2009 / Revised:November 26, 2009 / Accepted:November 28, 2009The deletion of sti from the Streptomyces plasmid pIJ101 made its derivative pHZ1358 an efficient vector for gene disruption and replacement. Here, pHZ1358 was further optimized by the construction of a derivative plasmid pJTU1278, in which a cassette carrying multiple cloning sites and a lacZ selection marker were introduced for convenient plasmid construction in E. coli. In addition, the ori T region of pJTU1278 was also deleted, generating a vector (pJTU1289) that can be used specifically for PCR-targ eting. The efficient usag e of these vectors was demonstrated by the deletion of the g ene involved in avermectin biosynthesis in S.avermitilis.Keywords: Shuttle vector, pHZ1358, pJTU1278, pJTU1289, conjugation, PCR-targetingStreptomyces, soil-inhabiting Gram-positive bacteria, occupya significant niche in biotechnology research because oftheir ability to produce a wide variety of bioactive compounds. Streptomyces are also unusual among prokaryotes as theyundergo a complex cycle of morphological differentiation,forming spores, vegetative substrate hyphae, and aerial hyphaeduring different stages of their life cycle [4]. Although newtechniques are continually being developed for differentresearch purposes in this area, gene deletion/disruptionremains one of the most efficient and indispensable means.For gene disruption and replacement in Streptomycesspecies, pHZ1358 [7] has been developed as an efficientpIJ101 [3] derivative vector by the removal of a 574-bpDNA region containing an sti (strong incompatibility locus)that causes the accumulation of single-strand plasmids inthe host [1,7]. Further deletion of 36-bp direct repeatsmakes the plasmid stably replicate as a shuttle vector in E. coli.Here, we report on the construction of two pHZ1358derivatives, pJTU1278 and pJTU1289, where the former contains the additional virtues of multiple cloning sites anda LacZ selection marker (for convenient construction in E. coli), and the latter has a deleted ori T, allowing it to beused specifically for PCR-targeting. The usage of pJTU1289was also demonstrated by the deletion of an avermectinbiosynthetic gene.M ATERIALS AND M ETHODSBacterial Strains and Plasmids Used in This StudyDH10B (GIBCO-BRL) and ET12567 (pUZ8002) [5] were used as the E. coli host for cloning and conjugation, respectively. S. avermitilis NRRL 8165 was used for the gene deletion.The fosmid vector pCC2FOS TM (EPICENTRE Biotechnologies) was used to construct the genomic library of S. avermitilis NRRL 8165 [2]. Plasmid pJTU412 was a gift from Sun et al. [7].The DNA isolation and plasmid manipulation from E. coli were performed according to Sambrook and Russell[6]. Construction of pJTU1278 and pJTU1289For the construction of pJTU1278, pJTU412 was first digested with Kpn I, blunted with the Klenow fragment of E. coli DNA polymerase I, self-ligated to generate pJTU1275, and further digested with Bgl II and Sph I. The resulting 8.7-kb Bgl II-Sph I fragment from pJTU1275 was then treated with the Klenow fragment and self-ligated to generate pJTU1276, which was digested with Xba I, treated with the Klenow fragment, and self-ligated to generate pJTU1277. A 585-bp PCR fragment from pBluescript II SK(+) using primers L acZ-P1 (5'-AACAA TTGCCA TTCGCCA TTCAGG-3') and LacZ-P2 (5'-AA-CAA TTGCCCAA TACGCAAACC-3') was digested with Mun I and inserted into the Eco RI site of pJTU1277 to generate pJTU1278. The LacZ was in the same direction as the resistant gene bla in pJTU1278.For the construction of pJTU1289, pJTU1278 was digested with Sse8387I, and the 8.4-kb Sse8387I fragment from pJTU1278 was then self-ligated to generate pJTU1289.Gene Deletion of aveA4 (S. avermetilis HYL16)The 4,671-bp Bam HI fragment containing the portion of aveA4 from cosmid 14A7 of the S. avermitilis NRRL 8165 genome library was cloned into pJTU1289 to generate pJTU1314. The aac(3)IV and*Corresponding authorPhone: +86-21-62933404; Fax: +86-21-62932418;E-mail: zxdeng@679He et al.Fig. 1. Construction of pJTU1278 and pJTU1289.pJTU1278 and pJTU1289 were optimized from pJTU412. After two rounds of digestion, blunting with polymerase, and re-ligation using Kpn I and Bgl II-Sph I, respectively, Kpn I and Hin dIII were eliminated from the vector. A multiple cloning site (Sac I, Xba I, Spe I, Bam HI, Eco RI, Hin dIII, Kpn I) and lacZ gene (for clone selection in E. coli) were then inserted. Eco RV and Xho I can also be used for cloning. ori pIJ101, ori ColE1, and ori T were kept for the shuttle between Streptomyces and E. coli. The vector can also be used for cosmid library construction.P JTU1278 AND P JTU1289 FOR G ENE K NOCKOUT IN S TREPTOMYCES680ori T cassettes were amplified from the pIJ733 disruption cassette using the primers aveA4-T1 (5'-ACTCCCGCCTGCACGACGCC-ACTCCCCCAGCCCACAAGGA TTCCGGGGA TCCGTCGACC-3') and aveA4-T2 (5'-GCGGAGCCA TGGTGGCCA TCGAGGCGTCC-GAGGACGAGA TGTAGGCTGGAGCTGCTTC-3'). The resulting PCR product was then used to replace the 1,150-bp DNA region of aveA4 in pJTU1314 to generate pJTU1317, and then in strain NRRL 8165 to generate S. avermetilis HYL16. A comparison of the wild-type and mutant S. avermetilis HYL16 was conducted through fermentation, extraction, and an LC-MS analysis.Cultivation of S. avermitilisThe Streptomyces culture conditions were as described by Kieser et al.[4]: An SFM solid medium was used for the S. avermitilis sporulation, fermentation, and conjugation between E. coli and Streptomyces. A TSB culture supplemented with 10.3% sucrose and 1% yeast extract was used for the growth of mycelia for the isolation of total DNA. Thiostrepton and apramycin were used at a concentration of 12.5µg/ml and 15µg/ml in the solid and liquid medium, respectively, when necessary for the screening of conjugants.Analysis of AvermectinAfter 7 days of growth on an SFM agar plate, the mycelia of S. avermitilis and the S. avermetilis HYL16 mutants were collected and extracted with ethanol. The extract was concentrated under reduced pressure to yield an oily substance, which was further extracted with 1ml of methanol. The methanol extract was analyzed directly using an HPLC-MS Agilent 1100 equipped with an Agilent ZORBAX SB-C18 (2.1×150mm) column and using a linear gradient program of acetonitrile/H2O: 60% acetonitrile over 3min, 60%-90% over 5min, 90% over 2min, 90%-100% over 2min, and constant 100% acetonitrile over 3min at a flow rate of 0.5ml/min. An iontrap mass spectrometer was operated with an electrospray ionization source in the positive-ion mode, where the drying gas flow was 8l/min, the nebulizer pressure was 30psi, the drying gas temperature was 325o C, and the fragmentation amplitude was varied between 1.0 and 1.8V.R ESULTS AND D ISCUSSIONConstruction of pJTU1278 and pJTU1289Derived from pIJ101, pHZ1358 is an efficient Escherichia coli/Streptomyce s shuttle vector for targeted gene deletion and disruption. However, a further improvement of pHZ1358 would be removing the 36-bp direct repeats, which often cause a problem of plasmid recombination in E. coli [7]. For the convenience of gene cloning, a multiple cloning site is needed. Therefore, the vector optimization was started from the pHZ1358 derivative pJTU412. By digesting with Kpn I and then blunt-ending by filling-in to eliminate the Kpn I site in pJTU412, followed by further digestion with Bgl II-Sph I, blunt-ending by filling-in, and re-ligation to eliminate the Hin dIII site, a cassette carrying multiple cloning sites was introduced, along with the selection marker lac Z gene with a PCR fragment from pBluescript II SK(+) (Fig.1). Successful cloning was confirmed by restriction digestion and sequencing (Fig.2).Fig. 2. Confirmation of successful cloning of pJTU1278 and pJTU1289.The successful construction of pJTU1275 was indicated by the loss of the Kpn I restriction site at 2,999-bp (lane 2), whereas pJTU412 was linearized by Kpn I (lane 1). The loss of the Bgl II and Hin dIII sites confirmed the successful cloning of pJTU1276 (lanes 4, 6), whereas pJTU1275 was linearized by Bgl II and Hin dIII (lanes 3, 5). The cloning of pJTU1276, pJTU1277, pJTU1278, and pJTU1289 was confirmed with Xba I (B). The loss of the Xba I site confirmed the successful cloning of pJTU1277 (lane 8), whereas pJTU1267, pJTU1278, and pJTU1289 were linearized by Xba I (lanes 7, 9, 10). All the clones were confirmed with Pvu II (C). La nes 11-16 show the digestion of pJTU412, pJTU1275, pJTU1267, pJTU1277, pJTU1278, a nd pJTU1289 with Pvu II, separately.681He et al.Based on the above manipulations, the pHZ1358 derivative pJTU1278 contained the following characteristics: ori , rep from pIJ101, and resistant gene tsr for operation in Streptomyces ;ori (ColE1), lac Z, and resistance gene bla for manipulation in E. coli ; and ori T for conjugation from E. coli to Streptomyce s. In addition, owing to its segregational instability from its progenitor pHZ1358, it can be used for efficient gene deletion/disruption in Streptomyces . Since a single cos was included, it can also be used for cosmid library construction.For the specific purpose of PCR-targeting, an additional optimization of pJTU1278 was carried out to generate pJTU1289, where the 0.8-kb Sse83871 fragment containing ori T was deleted.Deletion of aveA4The usage of pJTU1278 and pJTU1289 as gene disruption vectors was demonstrated by the deletion of a gene essential for avermectin biosynthesis in S. avermitilis MA-4680. As shown in Fig.3, the 1,150-bp DNA region of aveA4 was replaced by the ori T -aac(3)IV cassette to generate S.avermetilis HYL 16. The successful construction of S.avermetilis HYL16 was confirmed using a PCR (Fig. 3B),and the successful deletion of aveA4 was confirmed by a comparison of the production of metabolites between the wild-type and the mutant S. avermetilis HYL16 using an HPLC analysis. Clearly, the production of avermectin “a”was completely abolished in S. avermetilis HYL16, in sharp contrast to the wild-type strain, whereas the oligomycin “o” was still produced in S. avermet ilis HYL 16 at the same level as that in the wild-type strain NRRL 8165(Fig. 3C).In addition to the S. avermitilis tested in this study, the two plasmids pJTU1278 and pJTU1289 were also successfully used for gene deletion in several other Streptomyces strains,including S. nanchangensis , S. hygroscopicus , Streptomyces sp. FR-008, and Actinosynnema pretiosum , confirming their efficiency as gene deletion/disruption vectors.AcknowledgmentsThe authors are very grateful to Dr. Tobias Kieser for hiscritical and patient editing of the manuscript. The authorsFig. 3. Deletion of the aveA4 gene.The 1.2-kb region of aveA4 was replaced with the ori T-aac(3)IV cassette using a double crossover (A ). The successful construction of the S. avermetilis HYL16 was confirmed by PCR (B ) with a 1.5-kb PCR product as expected, in contrast to the 1.2-kb DNA fragment from the wild type strain. Comparison of metabolites between the wild-type strain and the mutant S. avermetilis HYL16 showed that the avermectin corresponding to “a” peaks of the wild-type strain NRRL 8165 disappeared in S. avermetilis HYL16. Oligomycin corresponding to “o” peaks still produced in HYL16 (C ).P JTU1278 AND P JTU1289 FOR G ENE K NOCKOUT IN S TREPTOMYCES682also wish to thank the National Science Foundation of China, the Ministry of Science and Technology (973 and 863 programs), the Ministry of Education of China, the Shanghai Municipal Council of Science and Technology and Shanghai L eading Academic Discipline Project, and the State Key Laboratory of Bio-organic and Natural Products Chemistry (CAS) for their research support.R EFERENCES1.Deng, Z. X., T. Kieser, and D. A. Hopwood. 1988. “Strongincompatibility” between derivatives of the Streptomyces multi-copy plasmid pIJ101. Mol. Gen. Genet. 214: 286-294.2.He, Y., D. Zhu, L. Bai, and Z. Deng. 2009. Cloning ofantibiotic biosynthetic gene clusters from genomic library by narrow-down polymerase chain reaction. J. Shanghai Jiaotong Univ.43: 5-8.3.Kendall, K. J. and S. N. Cohen. 1988. Complete nucleotidesequence of the Streptomy ces lividans plasmid pIJ101 and correlation of the sequence with genetic properties. J. Bacteriol. 170: 4634-4651.4.Kieser, T., M. J. Bibb, M. J. Buttner, K. F. Chater, and D. A.Hopwood. 2000. Practical Streptomy ces Genetics. John Innes Centre.5.Paget, M. S., L. Chamberlin, A. Atrih, S. J. Foster, and M. J.Buttner. 1999. Evidence that the extracytoplasmic function sigma factor sigmaE is required for normal cell wall structure in Streptomyces coelicolor A3(2). J. Bacteriol.181: 204-211.6.Sambrook, J. and D. Russell. 2000. Molecular Cloning: ALaboratory Manual, 3rd Ed. Cold Spring Harbor L aboratory Press, NY, U.S.A.7.Sun, Y., X. He, J. Liang, X. Zhou, and Z. Deng. 2009. Analysisof functions in plasmid pHZ1358 influencing its genetic and structural stability in S treptomyces lividans 1326. Appl. Microbiol.Biotechnol.82: 303-310.。
【高中生物】一项4亿美元的测试
【高中生物】一项4亿美元的测试当小货车沿着美国弗吉尼亚州西部一片森林中的土路颠簸前行时，茂密的灌木丛击打着车身两侧。
车上，生态学家TyLindberg大声叫着挤在路两边的入侵物种的名字。
这是绰号“一分钟一英里”、以惊人速度扩散的薇甘菊。
一种被称为亚洲高跷草的一年生开花植物则铺满了地面，并且扼杀了当地植物。
同时，一种异常多刺的玫瑰会将靠近它的任何人的衣服刮破。
再往前走，Lindberg将车停在矮树丛中的空地上，然后自己穿过灌木，朝伸出地面的塑料和铝桩走去。
它们划出一个40米×40米的地块，而这是散布在弗兰特罗亚尔镇附近1300公顷森林和草地上的几十个地块中的一个。
从4月到10月，野外技术人员每天花时间记录地块中几乎每棵树的位置、直径和高度，收集“陷阱”中的落叶，并且将压制的入侵植物存档。
他们的主要目标是测定生态系统的新陈代谢，尤其是其每年能产生多少生物质。
在其他地块，技术人员会诱捕啮齿类动物，并且提取血液样本以检测疾病，包括那些可能传染给人类的疾病。
工作人员收集并储存蜱虫和甲虫，同时采集土壤样本，用于研究地下的细菌。
在更高的山上，一座50米高的金属塔架从树林中伸出，上面布满了持有传感器的长吊杆。
这些传感器能监控不同海拔的气温、风速和太阳辐射。
当最终的仪器“套装”在被全部安装上时，这座塔架将通过监控二氧化碳和水蒸气浓度如何升降，观测底下的这片森林。
这是规划的美国国家生态观测站网络（NEON）80多个站点中的一个。
NEON是一个耗资4.34亿美元的项目，旨在建造跨越全美的生态观测站。
它的目标很宏大。
如果一切进展顺利，NEON将记录下气候变化和土地使用对生态系统造成的影响，并且为科学家提供针对该国生态系统关键特征的近乎实时的测量结果。
很多站点将运行30年，其他的则会被定期关停并重新选址，以应对环境变化。
收集的数据将通过一个门户网站对所有人免费开放。
NEON的由来回到2000年，新墨西哥大学生态学家ScottCollins是美国国家科学基金会（NSF）所属NEON的首个项目负责人。