洞庭湖滩地杨树抑螺防病林研究

在滩地杨树抑螺林12年轮伐期内,采取沿时间序列定位观测及以空间换时间的调查研究方法,对滩地杨树抑螺防病林的造林地选择、钉螺种群消长与水因子、林龄及整地措施的相关关系进行了研究,对滩地杨树林的防浪护堤、阻滞水流和泥沙、降低地下水位等生态效应进行了分析,对林下草本植物的物种多样性进行了动态监测和研究,为有螺滩地的综合治理和开发提供依据。     

长江滩地不同林龄杨树人工林植物群落物种多样性研究

本文以石首、公安长江外滩不同林龄杨树人工群落为研究对象,研究了林下植物群落物种多样性的变化规律,结果表明:滩地立地条件较好,适合多种植物生长;滩地造林对土壤养分和草本植物种群数量分布的影响不大;有林地的多样性指数比无林地略有上升,不同林龄草本植物群落的丰富度指数R和多样性指数H′大于1.0、多样性指数D和均匀度指数J小于1.0,且与林龄的变化基本没有相关性,确定了物种多样性指数模型。     

区域气候与中国柳属物种多样性格局的关系研究

利用地理信息系统技术与空间统计相结合的方法,研究了柳属物种多样性在中国区域尺度上的空间分布格局,并且采用逐步广义线性回归分析方法研究了柳属物种多样性与主要气候因子的关系。结果表明:1)柳属物种多样性在中国的分布格局表现为区域广泛,局部聚集:聚集区分布在新疆阿勒泰地区、东北地区和西南地区,其中西南分布区的青藏高原与横断山脉毗连处为中国柳属物种多样性分布中心。2)柳属物种多样性在经度、纬度、气温、降水、蒸散量等气候气候因子一维格局上的分布随着因子梯度先增加后减小,均存在一个最适区间。3)柳属物种多样性与水分因子呈正相关、与能量因子呈负相关关系。水分因子是影响柳属物种多样性的主导因子,能量与水分间的交互作用对物种多样性影响微弱。4)结论部分地支持了水—热动态假说和生态位保守假说,但柳属物种多样性格局成因并非由任何单一假说来完全地解释,这表明其他因子如历史因素、地形异质性、土壤因素或扰动等也可能是形成柳属物种多样性格局的重要因素。     

Diatom assemblages and water depth in Lake 239 (Experimental Lakes Area,Ontario): implications for paleoclimatic studies

Diatom assemblages in surface sediments were sampled along three transects in Lake 239, from the Experimental Lakes Area (NW Ontario), and analyzed in order to explore the relationship between modern species distributions and water depth. Approximately 170 diatom species were identified in surficial sediments at lake depths from 2 to 30 m. The species composition varied with sample depth but remained highly similar across all three transects. The main patterns of variation in the diatom assemblages across transects, derived from a detrended correspondence analysis (DCA), showed that assemblages were highly correlated (r = 0.97 to 0.98). At depths > 8 m the pattern of predominantly benthic composition changed to a planktonic assemblage dominated by Cyclotella stelligera. This depth currently corresponds to the depth of 1% light penetration as assessed from extinction coefficient measurements. Diatom species diversity increases with the switch to the near-shore benthic taxa in all three transects. Additionally, there is a large decrease in the ratio of chrysophyte scales to diatoms at depths < 8 m. Light transmission data from wet and dry periods over the last 35 years suggests that during dry periods the extent of the littoral zone should change by over 2 m. We suggest that cores along a transect from 8 to 14 m should provide a highly sensitive location for detailed paleoclimatic study.     

大亚湾潮间带底栖生物种类组成与分布

于１９８８年９月－１９８９年１月在大亚湾潮间带采集底栖动物标本，经分类研究发现，共有５４７种，其中藻类９１种，多毛类１２０种，软体动物２０９种，甲壳动物８３种，棘皮动物１９种，其他动物２５种。研究表明，其区系性质以热带亚热带暖水种占多数，有许多热带种发展成优势种。在水平分布上，岩石相种类（３１６）＞沙滩种数（２２２）＞红树林泥滩（５１）；在垂直分布上，中潮区种数（３９８）＞低潮区（２２１）＞红树林     

深圳湾浮游生物和底栖动物现状调查研究

对深圳湾浮游植物、浮游动物和底栖动物的种类、数量及分布现状进行了研究。结果表明 ,淡水种类占绝对优势 ,藻类5门32属45种 ,其中硅藻12属18种 ,绿藻1属15种 ,蓝藻7属10种 ,裸藻、金藻各1属1种 ;浮游动物共计37种 ,其中原生动物9种,轮虫14种 ,枝角类2种 ,桡足类8种 ,浮游幼虫及其它4种 ;底栖动物20种 ,分别为环节动物的多毛类7种 ,寡毛类2种 ,软体动物8种 ,甲壳动物2种 ,鱼类1种。浮游生物和底栖动物数量和分布的特征是 :藻类细胞密度为2.4×106～1.4×107个/L ,平均6.43×106 个/L。浮游动物为514.0～8049.2个/L,平均1690.8个/L。底栖动物个体数为180～8860个/m2,平均为2364个/m2。这三类生物的种类和数量分布与距深圳河河口的距离有密切关系。因此 ,三类生物多样性指数湾内段与湾口段差异明显,且平均值偏低 ,说明深圳湾水体有一定程度的污染     

金焰笛鲷与金带笛鲷的RAPD分析

应用RAPD技术，对笛鲷属的金焰笛鲷(Lutjanus fulviflamma)、金带笛鲷(Lutjanus vaigiensis)进行种群内及种群间遗传学分析。结果表明两种鱼的种内遗传多样性指数(H)分别为：金带笛鲷0．1107；金焰笛鲷0．1673。二者的多态位点比例(P)分别为47．8％和59．0％。2种笛鲷种间平均遗传相似系数为0．4640，遗传距离Dpd为0．5360。2种鱼共检出6个可作为种的特异性鉴定的条带。     

中国马尾藻属的研究 Ⅶ.宾德种群的三个新种

在研究中国真马尾藻亚属SubgenusSargassum刺托组SectionAcanthocarpicae团徽托序亚组SubsectionGlomerulatae宾德系SeriesBinderiana宾德种群SpeciesGroupBinderia过程中，发现该种群新种3个，它们是中间马尾藻SargassumIntermediumTsengetLusp．nov.，模式标本于1975年5月15日由李伟新采自广西涸洲岛，模式标本号AST75－0594；琼海马尾藻SargassumqionghaienseTsengetLusp．nov，模式标本于1983年7月12日由陆保仁采自海南省琼海县沙，模式标本号CA83－0016；裂开马尾藻SargasumerumensTsengetLusp．nov，模式标本于1982年4月23日由陆探仁采自海南省三亚市鹿回头，模式标本号AST82－0413。模式标本均存于中国科学院海洋研究所植物标本室。     

中国东方鲀属鱼类一新种——晕环东方鲀

在1979年7月—1981年11月间,在长江口采集到24尾体长为32—214mm的东方鲀属鱼类标本,经形态特征和解剖比较研究后,认为是一新种,定名为晕环东方纯Takifugu coronoidus Ni et Li nov. sp.。正副模式标本存放于中国水产科学研究院东海水产研究所和中科院海洋研究所。     

Incorporating movement in species distribution models: how do simulations of dispersal affect the accuracy and uncertainty of projections?

Species distribution models (SDMs) are one of the most important GIScience research areas in biogeography and are the primary means by which the potential effects of climate change on species’ distributions and ranges are investigated. Dispersal is an important ecological process for species responding to changing climates, however, SDMs and their subsequent spatial products rarely reflect accessibility to any future suitable environment. Dispersal-related movement can be confounded by factors that vary across landscapes and climates, as well as within and among species, and it has therefore remained difficult to parametrise in SDMs. Here we compared 20 models that have previously been used (or have the potential to be used) to represent dispersal processes in SDM to predict future range shifts in response to climate change. We assessed the different dispersal models in terms of their accuracy at predicting future distributions, as well as the uncertainty associated with their predictions. Atlas data for 50 bird species from 1988 to 1991 in Great Britain were treated as base distributions (t₁), with the species–environment relationships extrapolated (using three commonly used statistical methods) to 2008–2011 (t₂). Dispersal (in the form of the 20 different models) was simulated from the base distribution (t₁) to 2008–2011 (t₂). The results were then combined and used to identify locations that were both abiotically suitable (obtained from the statistical methods) and accessible (obtained from the dispersal models). The accuracy of these coupled projections was assessed with the 2008–2011 atlas data (the observed t₂ distribution). There was substantial variation in the accuracy of the different dispersal models, and in general, the more restrictive dispersal models (e.g. fixed rate dispersal) resulted in lower accuracy for the metrics which reward correct prediction of presences. Ensemble models of the dispersal methods (generated by combining multiple projection outcomes) were created for each species, and a new Ensemble Agreement Index (EAI), which ranges from 0 (no agreement among models) to 1 (full agreement among models) was developed to quantify uncertainty among the projections. EAI values ranged from 0.634 (some areas of disagreement and therefore medium uncertainty among dispersal models) to 0.999 (large areas of agreement and low uncertainty among dispersal models). The results of this research highlight the importance of incorporating dispersal and also illustrate that the method with which dispersal is simulated greatly impacts the projected future distribution. This has important implications for studies aimed at predicting the effects of changing environmental conditions on species’ distributions.