东北红豆杉植物地理学研究

总结了东北红豆杉的地理分布概况和分布区特点，绘制了分布图，以在我国境内分布区域的主导气象因子构成指示指标组建立了东北红豆杉自然地理分布模型，并通过实例证明了该模型的有效性。     

南亚热带森林3层次物种多度之广义泊松分布模型

生物群落中物种多度分布(species abundance distribution)呈典型的倒J形,即其中存在许多稀有种、少量常见种.物种多度分布模型研究有助于解决森林生态恢复中的物种配置等实际问题.本研究考察了一种过分散(over-dispersion,或称超分布,即方差大于均值)的离散型分布,即具有λ和α两个参数...     

珍贵树种土沉香生长环境的土壤特性与营养特性分析

对我国野生土沉香4个主要分布区的土壤特性及自身营养特性进行了分析研究。结果表明,不同分布区的同一土层的土壤物理性质有明显的差异（P〈0.05）。海南临高分布区各层土壤容重最小,总空隙度、自然含水量毛管持水量比较高;海南屯昌分布区各层的土壤容重最大,总空隙度较低;广东东莞分布区与广东陆河分布区各层土壤松紧度比较适宜,透水性,通气性,持水能力比较协调。各分布区土壤均呈酸性,其有机质含量和营养元素含量偏低。土壤有效Mn、有效B均处于较低水平,有效Fe的含量均高于临界值;海南屯昌分布区土壤中有效Cu的含量极低,其余3分布区则均高于临界值;除海南临高分布区有效Zn的含量高于临界值,其他3个分布区均低于临界值;海南屯昌分布区土壤有效S含量稍低于临界值,为中等水平,广东陆河分布区高于临界值处于较高水平,广东东莞分布区为临界值的2.76倍,而海南临高分布区则高达临界值的7.31倍,均处于极高水平。野生土沉香叶片各营养元素之间的相关性总体不明显。     

能源植物黄连木在我国的地理分布规律

为了更好地开发利用生物柴油能源植物黄连木（Pistacia chinensis Bunge）,对其在我国的地理分布规律进行了系统调查研究,结果表明：黄连木的地理分布范围为北纬18°09′～40°09′、东经96°52′～123°14′,资源遍布我国华北、华南、西南、华中、华东与西北地区的25个省、自治区、直辖市;分布区地形以高原、山地为主,土壤母岩以石灰岩为主,土壤类型以褐土为主,跨越我国温带、亚热带、热带地区;黄连木的水平分布区主要位于云南潞西—西藏察隅—四川甘孜—青海循化—甘肃天水—陕西富县—山西阳城—河北顺平—北京西山一线以东、以南,整体上呈现连续分布的特征,局部地区有一定的间断分布;从我国西部到东部,其垂直分布的上限与下限均呈现逐渐降低的趋势,从南方到北方,这种降低趋势不太明显;黄连木在我国的资源分布区可以划分为集中分布区、次集中分布区、零星分布区和沿海地带零星分布区四种类型。     

Species abundance distributions result from body size-energetics relationships

Abundance distributions are a central characteristic of ecosystems. Certain distributions have been derived from theoretical models of community organization, and therefore the fit of data to these distributions has been proposed as a test of these theories. However, it is shown here that the geometric sequence distribution can be derived directly from the empirical relationship between population density and body size, with the assumption of random or uniform body size distributions on a log scale (as holds at local scales). The geometric sequence model provides a good to excellent fit to empirical data. The presence of noise in the relationship between population density and body size creates a curve that begins to approximate a lognormal species abundance distribution as the noise term increases. For continental-scale data in which the body size distribution is not flat, the result of sampling tends again toward the lognormal. Repeat sampling over time smooths out species population fluctuations and damps out the noise, giving a more precise geometric sequence abundance distribution. It is argued that the direct derivation of this distribution from empirical relationships gives it priority over distributions derived from complex theoretical community models.     

土壤、土地利用多样性及其与相关景观指数的关联分析

土壤作为人类赖以生存的物质基础,与土地利用之间存在密切联系,土壤类型及属性的差异很大程度上决定了土地利用的方式,同时土地利用方式的不同也间接地改变着土壤类型、性质及功能多样性的格局。在人地矛盾、人与自然矛盾不断加剧的今天,研究土地和土壤的空间分布格局变化及两者之间的交互关系具有重要意义。利用土壤多样性理论和景观生态学原理综合分析土壤、土地利用类型的空间分布格局,并借助空间网格的概念量化分析土壤和土地利用类型的空间分布情况。以河南省中南部样区为研究对象,利用仙农熵变形公式计算了1 km×1 km、3 km×3 km、5 km×5 km 3种网格尺度下的土壤和土地利用的空间分布多样性和各县市的土壤和土地利用构成组分多样性,运用ArcGIS探讨了多样性指数与景观指数之间的关联性,综合评价研究区的土壤类型空间分布与土地利用空间分布格局的相互性。研究表明：同一种土壤和土地利用类型的空间分布多样性指数在异网格尺度下具有相似的分异规律;土地利用与土壤类型的构成组分多样性之间不具有明显的相关性,土壤类型均匀分布的地区,土地利用类型不一定会均匀分布,而两者的平均斑块面积之间具有明显的相关性,土壤斑块的大小对土地利用斑块的大小影响较大,土壤类型的空间分布特征在一定程度上,将影响土地利用的空间分布格局和变化过程;土壤类型的面积指数、平均斑块面积指数分别与土壤空间分布多样性之间具有较好的相关性,相关系数分别为r2=0.990、r2=0.599。而土地利用的面积指数、平均斑块面积指数与土地利用空间分布多样性指数之间的相关性较差,相关系数分别为r2=0.437、r2=0.034。土壤的平均斑块形状指数与多样性指数之间存在一定正相关关系,相关系数为r2=0.612;但土地利用的形状指数与多样性之     

Fine-scale habitat modeling of a top marine predator: do prey data improve predictive capacity?

Predators and prey assort themselves relative to each other, the availability of resources and refuges, and the temporal and spatial scale of their interaction. Predictive models of predator distributions often rely on these relationships by incorporating data on environmental variability and prey availability to determine predator habitat selection patterns. This approach to predictive modeling holds true in marine systems where observations of predators are logistically difficult, emphasizing the need for accurate models. In this paper, we ask whether including prey distribution data in fine-scale predictive models of bottlenose dolphin (Tursiops truncatus) habitat selection in Florida Bay, Florida, U.S.A., improves predictive capacity. Environmental characteristics are often used as predictor variables in habitat models of top marine predators with the assumption that they act as proxies of prey distribution. We examine the validity of this assumption by comparing the response of dolphin distribution and fish catch rates to the same environmental variables. Next, the predictive capacities of four models, with and without prey distribution data, are tested to determine whether dolphin habitat selection can be predicted without recourse to describing the distribution of their prey. The final analysis determines the accuracy of predictive maps of dolphin distribution produced by modeling areas of high fish catch based on significant environmental characteristics. We use spatial analysis and independent data sets to train and test the models. Our results indicate that, due to high habitat heterogeneity and the spatial variability of prey patches, fine-scale models of dolphin habitat selection in coastal habitats will be more successful if environmental variables are used as predictor variables of predator distributions rather than relying on prey data as explanatory variables. However, predictive modeling of prey distribution as the response variable based on environmental variability did produce high predictive performance of dolphin habitat selection, particularly foraging habitat.     

Simulation model of universal law of school size distribution applied to southern bluefin tuna (Thunnus maccoyii) in the Great Australian Bight

A universal law of animal group size distribution correlates well to observed fish school size distribution from fisheries catch data. I applied the law to fisheries independent aerial survey data of southern bluefin tuna (Thunnus maccoyii) collected over a 10-year period in the Great Australian Bight. The law does not correlate to the observed school size distribution. A computer model originally demonstrated the formation of the universal law from simple rules. I redesigned this model as an individual-based simulation model calibrated from acoustic tag observations and state a mathematical formula for a resultant new family of transient group size distributions. The new formula correlates accurately to the simulation and to the aerial survey data. I use the mathematical model to estimate area of aggregation and total abundance. This approach is new as it does not seek stationary states of group size distribution and because it demonstrates a quantitative relationship between individual behaviour and group size distribution. This work elevates the pattern of group size distribution from a curiosity to a useful tool, and introduces a new family of transient distributions that may have a general application to other grouping phenomena.     

Identifying Conservation Areas on the Basis of Alternative Distribution Data Sets

Abstract:  Distribution data on biodiversity features is a major component of conservation planning that are often inaccurate; thus, the true distribution of each feature is commonly over- or underrepresented. The selection of distribution data sets may therefore lead to variability in the spatial configuration and size of proposed reserve networks and uncertainty regarding the extent to which these networks actually contain the biodiversity features they were identified to protect. Our goals were to investigate the impact on reserve selection of choosing different distribution data sets and to propose novel methods to minimize uncertainty about target attainment within reserves. To do so, we used common prioritization methods (richness mapping, systematic reserve design, and a novel approach that integrates multiple types of distribution data) and three types of data on the distribution of mammals (predicted distribution models, occurrence records, and a novel combination of the two) to simulate the establishment of regional biodiversity reserves for the state of Arizona (U.S.A.). Using the results of these simulations, we explored variability in reserve placement and size as a function of the distribution data set. Spatial overlap of reserve networks identified with only predicted distribution data or only occurrence distribution data never exceeded 16%. In pairwise comparisons between reserves created with all three types of distribution data, overlap never achieved 50%. The reserve size required to meet conservation targets also varied with the type of distribution data used and the conservation goal; the largest reserve system was 10 times the smallest. Our results highlight the impact of employing different types of distribution data and identify novel tools for application to existing distribution data sets that can minimize uncertainty about target attainment.     

Rarity in Neotropical Forest Mammals Revisited

The identification of rare species is an important goal in conservation biology. Recent attempts to classify rare species have emphasized dichotomies in such characteristics as local population density, area of distribution, and degree of ecological specialization. In particular, Arita et al. (1990) dichotomized 100 Neotropical forest mammals according to local population density and area of distribution. Among these species of mammals, mean body mass was significantly associated with local population density and area of distribution. We argue that the effects of body mass should be removed before species are classified with respect to rarity. We re-evaluated the data on Neotropical mammal species, using regression analyses to remove the effects of body mass on population density and area of distribution, followed by analysis of residuals. This new method resulted in substantial changes in the dichotomous classification of rare species. We combined the analysis of regression residuals with a ranking procedure that assumed that local population density and area of distribution were equally important in their effects on rarity. The new ranking technique produced another different classification of the rarity of the Neotropical forest mammal species. A graphical analysis showed that ranked species differed substantially in their degree of rarity, and in the importance of local population density, area of distribution, or both, to their degree of rarity. The ranking method allows the species of greatest concern to be singled out, it can be modified to include additional variables such as niche breadth, and it should be helpful for making conservation decisions.     

A simple random path method for the analysis of flow networks

The path of a particle through an ecosystem is modelled as a Markov chain. For a given flow network, powers of the transition matrix are used to calculate the distribution of the particles over the network after each transition. The method may be applied for the definition and calculation of trophic levels in food webs. The algorithm yields the trophic level distribution of species, the species composition of trophic levels, and the path length distribution of output flows. In addition, the network can be described as a linear chain, with the throughflows at each step identified. Data from several ecosystems are analyzed by the method, showing that surprising insights may result.     

Some effects of langmuir circulation on the quality of water resource systems

The quantitative extent to which the large-scale organized water motion in the surface waters of lakes and reservoirs, known as Langmuir circulation, affects the distribution and settling of algae and other suspended particles is not known and has thus been ignored in conventionally used water-quality models. Since the distribution and settling of these particles is important in determining water quality, this study set out to investigate these effects. Current literature which discusses this problem is reviewed and a mathematical model is developed based on the two-dimensional advection-diffusion mass transport describing the temporal and spatial distribution of suspended particles in a typical Langmuir cell; the Langmuir circulation flow field and turbulent diffusion coefficients are empirically modelled by relating them to environmental parameters.The results show that Langmuir circulation does affect particle distribution and settling. For particles with small sinking speeds, such as the lighter algae, the circulation causes intense mixing, resulting in essentially uniform distribution of particles over the cell (as assumed in the ‘well-mixed compartment model’). For particles with high sinking velocities, however, aggregation can occur, giving rise to significant reduction in sinking loss when compared with that predicted by conventional models. For diatoms, reductions of 6% and higher can occur depending on which conventionally used model is being considered, while for silt and sand particles in a cell of large width-to-depth ratio a reduction of more than 60% is possible.     

Kriging插值法在植物物种地理分布空间格局研究中的应用

Kriging插值法是一种定量化描述地理空间分布格局的方法,主要应用于空间采样以及相关的一些空间格局分析。而物种的地理空间分布是物种在自然及人类活动共同作用下的结果。物种分布的研究目前主要从气候的角度来探讨其与气候之间的关系,并取得了很好的结果,但是仅从气候考虑又有局限性,而且很难真实地反映出物种地理空间的分布格局。因此,将Kriging插值法引进到物种分布的研究中,并以我国广泛分布的物种——栓皮栎为例,探讨其可行性。结果表明：Kriging插值法能很好地拟合物种地理空间分布的实际情况,其误差程度较小。该研究的结果可为物种资源的开发利用和物种的引种栽培提供理论依据。     

村落生态系统分布特征和模式的探讨

村落生态系统的分布是地表突出和普遍的现象。本文概括总结了区域性村落生态系统分布的一般性原则，其分布特征表现为景观性、分散集聚性、动态迁移性、梯度性、趋适性、界限性以及人文性等各个方面；对诸如平原、山区、湖区、盆地、丘陵台地、岛屿以及过渡区等各种地貌类型区提出相应的理论分布模式。     

濒危植物脱皮榆种群结构与分布格局研究

对山西南部中条山和霍山脱皮榆的种群结构与分布格局进行了研究,其中根据大小结构图和存活曲线分析脱皮榆种群动态,应用扩散系数、聚集指数、平均拥挤度、聚块性指数、聚集强度、Cassie指数(1/K)、Poisson分布和负二项分布的χ2拟合检验等方法研究分布格局.结果表明,脱皮榆种群幼苗个体比例较大,总体上近于增长型种群.脱皮榆分布格局呈聚集型,这主要与物种本身的生物学和生态学特性密切相关,同时受群落内生境异质性的影响.图2表2参14     

水体沉积物中酸挥发性硫化物垂直分布模型的建立及应用

应用对流扩散理论，将沉积物固相、液相作为一个整体，建立并求解了沉积物中酸挥发性硫化物（AVS）的垂直分布模型，并应用于武汉东湖三个污染程度不同站点的AVS垂直分布研究。结果表明，所建立的模型能较好地描述沉积物中AVS的垂直分布，这种分布是非线性的，且时空分布不均匀。     

An Inverse Gaussian distribution of tree frequencies based on tree size

Forest stand management often depends on data from a single fixed area inventory plot located at random in a forest stand. The plot provides detailed information about tree size distribution but not about per unit area tree frequency distribution unless one assumes a Poisson (POI) distribution. The POI assumption ignores any relationship between a tree's size and its demand for growing space. This study argues for the Inverse Gaussian (IG) distribution as a more realistic model. Maximum likelihood estimates of the IG parameters are obtained from a transformation of tree size data (diameter) to proxies of tree counts. Data from two stands indicated that an IG model was better at predicting the tree frequency distribution than a POI model.     

基于MaxEnt的加拿大一枝黄花在中国的潜在分布区预测

基于采样后的加拿大一枝黄花(Solidago canadensis)在北半球的分布数据,以温度和降水作为预测的环境因子,以MaxEnt模型作为物种适生性预测模型,预测加拿大一枝黄花在我国的潜在分布。结果表明:加拿大一枝黄花在我国有广泛的潜在分布区,目前的实际分布远未达其最大潜在分布范围,仍有可能继续扩散。加拿大一枝黄花主要适生区包括上海、江苏、浙江、安徽、山东、河南、湖北、湖南、江西、福建、重庆、贵州、四川省东部、广东和广西2省北部、云南省东北部、山西和陕西2省南部地区。必须采取有效措施阻止加拿大一枝黄花扩散蔓延。     

Metal distribution in sediments from the Adriatic Sea

Total metal concentrations (Cu, Ni, Pb, Zn, Fe, and Mn) and their distribution in different chemical forms (speciation) were determined in sediments from the Adriatic Sea in order to evaluate contamination and mobility of metals. The study was carried out on marine sediments collected in two areas of the Adriatic Sea differently influenced by Po river waters. Metal distribution in the study area depends prevalently on water circulation and on organic matter distribution. Among metal levels, significant correlations between copper and zinc and between lead and nickel were found, suggesting common and specific sources. As regards metal speciation, the distribution of different chemical forms changes with the metal. The prevailing zinc phases, for example mobile forms, and sulphide together are greater than 50% of total metal. Nickel is mainly present in sediments as sulphide, and the preferential association of copper with organic matter is evident. Lead and manganese are preferentially found as mobile forms (more of 30% for lead, 40% or more for manganese).     

Uncertainty propagation in vegetation distribution models based on ensemble classifiers

Ensemble learning techniques are increasingly applied for species and vegetation distribution modelling, often resulting in more accurate predictions. At the same time, uncertainty assessment of distribution models is gaining attention. In this study, Random Forests, an ensemble learning technique, is selected for vegetation distribution modelling based on environmental variables. The impact of two important sources of uncertainty, that is the uncertainty on spatial interpolation of environmental variables and the uncertainty on species clustering into vegetation types, is quantified based on sequential Gaussian simulation and pseudo-randomization tests, respectively. An empirical assessment of the uncertainty propagation to the distribution modelling results indicated a gradual decrease in performance with increasing input uncertainty. The test set error ranged from 30.83% to 52.63% and from 30.83% to 83.62%, when the uncertainty ranges on spatial interpolation and on vegetation clustering, respectively, were fully covered. Shannon’s entropy, which is proposed as a measure for uncertainty of ensemble predictions, revealed a similar increasing trend in prediction uncertainty. The implications of these results in an empirical distribution modelling framework are further discussed with respect to monitoring setup, spatial interpolation and species clustering.