环境DNA技术在地下生态学中的应用

地下生态过程是生态系统结构、功能和过程研究中最不确定的因素。由于技术和方法的限制,作为"黑箱"的地下生态系统已经成为限制生态学发展的瓶颈,也是未来生态学发展的主要方向。环境DNA技术,是指从土壤等环境样品中直接提取DNA片段,然后通过DNA测序技术来定性或定量化目标生物,以确定目标生物在生态系统中的分布及功能特征。环境DNA技术已成功用于地下生态过程的研究。目前,环境DNA技术在土壤微生物多样性及其功能方面的研究相对成熟,克服了土壤微生物研究中不能培养的问题,可以有效地分析土壤微生物的群落组成、多样性及空间分布,尤其是宏基因组学技术的发展,使得微生物生态功能方面的研究成为可能;而且,环境DNA技术已经在土壤动物生态学的研究中得到了初步应用,可快速分析土壤动物的多样性及其分布特征,更有效地鉴定出未知的或稀少的物种,鉴定土壤动物类群的幅度较宽;部分研究者通过提取分析土壤中DNA片段信息对生态系统植物多样性及植物分类进行了研究,其结果比传统的植物分类及物种多样性测定更精确,改变了以往对植物群落物种多样性模式的理解。同时,环境DNA技术克服传统根系研究方法中需要洗根、分根、只能测定单物种根系的局限,降低根系研究中细根区分的误差,并探索性地用于细根生物量的研究。主要综述了基于环境DNA技术的分子生物学方法在土壤微生物多样性及功能、土壤动物多样性、地下植物多样性及根系生态等地下生态过程研究中的应用进展。环境DNA技术对于以土壤微生物、土壤动物及地下植物根系为主体的地下生态学过程的研究具有革命性意义,并展现出良好的应用前景。可以预期,分子生物学技术与传统的生态学研究相结合将成为未来地下生态学研究的一个发展趋势。     

Manta Matcher: automated photographic identification of manta rays using keypoint features

For species which bear unique markings, such as natural spot patterning, field work has become increasingly more reliant on visual identification to recognize and catalog particular specimens or to monitor individuals within populations. While many species of interest exhibit characteristic markings that in principle allow individuals to be identified from photographs, scientists are often faced with the task of matching observations against databases of hundreds or thousands of images. We present a novel technique for automated identification of manta rays (Manta alfredi and Manta birostris) by means of a pattern‐matching algorithm applied to images of their ventral surface area. Automated visual identification has recently been developed for several species. However, such methods are typically limited to animals that can be photographed above water, or whose markings exhibit high contrast and appear in regular constellations. While manta rays bear natural patterning across their ventral surface, these patterns vary greatly in their size, shape, contrast, and spatial distribution. Our method is the first to have proven successful at achieving high matching accuracies on a large corpus of manta ray images taken under challenging underwater conditions. Our method is based on automated extraction and matching of keypoint features using the Scale‐Invariant Feature Transform (SIFT) algorithm. In order to cope with the considerable variation in quality of underwater photographs, we also incorporate preprocessing and image enhancement steps. Furthermore, we use a novel pattern‐matching approach that results in better accuracy than the standard SIFT approach and other alternative methods. We present quantitative evaluation results on a data set of 720 images of manta rays taken under widely different conditions. We describe a novel automated pattern representation and matching method that can be used to identify individual manta rays from photographs. The method has been incorporated into a website (mantamatcher.org) which will serve as a global resource for ecological and conservation research. It will allow researchers to manage and track sightings data to establish important life‐history parameters as well as determine other ecological data such as abundance, range, movement patterns, and structure of manta ray populations across the world.     

宜昌百里荒草山草坡群落物种分布的空间趋势分析

通过基于CCA的趋势面分析和空间插值方法,研究了宜昌百里荒山地草场的群落结构空间变化,以及群落结构空间趋势与主要环境因子的相关性。结果表明,该群落物种空间中的群落结构面和物理空间中的空间趋势面可以很好地吻合,说明该群落的结构由一种具有强烈空间结构化特征的机制控制。对群落结构和空间趋势影响最显著的环境因素是土壤有效磷。     

Identifying Transmission Cycles at the Human-Animal Interface: The Role of Animal Reservoirs in Maintaining Gambiense Human African Trypanosomiasis

Many infections can be transmitted between animals and humans. The epidemiological roles of different species can vary from important reservoirs to dead-end hosts. Here, we present a method to identify transmission cycles in different combinations of species from field data. We used this method to synthesise epidemiological and ecological data from Bipindi, Cameroon, a historical focus of gambiense Human African Trypanosomiasis (HAT, sleeping sickness), a disease that has often been considered to be maintained mainly by humans. We estimated the basic reproduction number of gambiense HAT in Bipindi and evaluated the potential for transmission in the absence of human cases. We found that under the assumption of random mixing between vectors and hosts, gambiense HAT could not be maintained in this focus without the contribution of animals. This result remains robust under extensive sensitivity analysis. When using the distributions of species among habitats to estimate the amount of mixing between those species, we found indications for an independent transmission cycle in wild animals. Stochastic simulation of the system confirmed that unless vectors moved between species very rarely, reintroduction would usually occur shortly after elimination of the infection from human populations. This suggests that elimination strategies may have to be reconsidered as targeting human cases alone would be insufficient for control, and reintroduction from animal reservoirs would remain a threat. Our approach is broadly applicable and could reveal animal reservoirs critical to the control of other infectious diseases.     

Fuzzy clustering of fuzzy ecological data

Besides the problem of searching for effective methods for data analysis there are some additional problems with handling data of high uncertainty. Uncertainty problems often arise in an analysis of ecological data, e.g. in the cluster analysis of ecological data. Conventional clustering methods based on Boolean logic ignore the continuous nature of ecological variables and the uncertainty of ecological data. That can result in misclassification or misinterpretation of the data structure. Clusters with fuzzy boundaries reflect better the continuous character of ecological features. But the problem is, that the common clustering methods (like the fuzzy c-means method) are only designed for treating crisp data, that means they provide a fuzzy partition only for crisp data (e.g. exact measurement data). This paper presents the extension and implementation of the method of fuzzy clustering of fuzzy data proposed by Yang and Liu [Yang, M.-S. and Liu, H-H, 1999. Fuzzy clustering procedures for conical fuzzy vector data. Fuzzy Sets and Systems, 106, 189-200.]. The imprecise data can be defined as multidimensional fuzzy sets with not sharply formed boundaries (in the form of the so-called conical fuzzy vectors). They can then be used for the fuzzy clustering together with crisp data. That can be particularly useful when information is not available about the variances which describe the accuracy of the data and probabilistic approaches are impossible. The method proposed by Yang has been extended and implemented for the Fuzzy Clustering System EcoFucs developed at the University of Kiel. As an example, the paper presents the fuzzy cluster analysis of chemicals according to their ecotoxicological properties. The uncertainty and imprecision of ecotoxicological data are very high because of the use of various data sources, various investigation tests and the difficulty of comparing these data. The implemented method can be very helpful in searching for an adequate partition of ecological data into clusters with similar properties.     

Optimum-transformation of plant species cover-abundance values

Most of the methods used in the multivariate analysis of data on vegetation and environment, or transformations implied in such methods, put disproportionate emphasis on species with a relatively wide ecological amplitude occurring with relatively high cover-abundance values, and/or rare species. This problem can be overcome to some extent by reducing the cover-abundance values to presence-absence data, but this means a severe loss of information. A standardization of values by species maxima as is done automatically in some programs, may lead to an undesirable emphasis on species represented with low values only.In this paper a method is presented, by which relatively low cover-abundance values of species are upweighted to an arbitrarily chosen higher value, if these low values are considered to indicate an optimum response of that particular species. The method has been tested on a selection of 40 phytosociological relevés from dune slacks in the Voorne dunes, as well as on the Dune Meadow data set used in the textbook of Jongman et al. (1987). The cluster structure obtained with the optimum-transformation appears to be clearer and the contribution of typical dune slack species to the cluster structure increased significantly. Canonical correspondence analysis of the transformed data gave slightly more important main axes.Abbreviations CA = Correspondence Analysis - CCA = Canonical Correspondence Analysis - DOL = Detection of Optimality Level - SWOM = Standardized Weighted Optimality Measure - WPGMA = Weighted Pair Group Method Average linking clustering     

Predicting the likely response of data‐poor ecosystems to climate change using space‐for‐time substitution across domains

Predicting ecological response to climate change is often limited by a lack of relevant local data from which directly applicable mechanistic models can be developed. This limits predictions to qualitative assessments or simplistic rules of thumb in data‐poor regions, making management of the relevant systems difficult. We demonstrate a method for developing quantitative predictions of ecological response in data‐poor ecosystems based on a space‐for‐time substitution, using distant, well‐studied systems across an inherent climatic gradient to predict ecological response. Changes in biophysical data across the spatial gradient are used to generate quantitative hypotheses of temporal ecological responses that are then tested in a target region. Transferability of predictions among distant locations, the novel outcome of this method, is demonstrated via simple quantitative relationships that identify direct and indirect impacts of climate change on physical, chemical and ecological variables using commonly available data sources. Based on a limited subset of data, these relationships were demonstrably plausible in similar yet distant (>2000 km) ecosystems. Quantitative forecasts of ecological change based on climate‐ecosystem relationships from distant regions provides a basis for research planning and informed management decisions, especially in the many ecosystems for which there are few data. This application of gradient studies across domains – to investigate ecological response to climate change – allows for the quantification of effects on potentially numerous, interacting and complex ecosystem components and how they may vary, especially over long time periods (e.g. decades). These quantitative and integrated long‐term predictions will be of significant value to natural resource practitioners attempting to manage data‐poor ecosystems to prevent or limit the loss of ecological value. The method is likely to be applicable to many ecosystem types, providing a robust scientific basis for estimating likely impacts of future climate change in ecosystems where no such method currently exists.     

Modelling the adaptive dynamics of traits involved in inter- and intraspecific interactions: An assessment of three methods

In recent years, three related methods have been used to model the phenotypic dynamics of traits under the influence of natural selection. The first is based on an approximation to quantitative genetic recursion equations for sexual populations. The second is based on evolution in asexual lineages with mutation-generated variation. The third method finds an evolutionarily stable set of phenotypes for species characterized by a given set of fitness functions, assuming that the mode of reproduction places no constraints on the number of distinct types that can be maintained in the population. The three methods share the property that the rate of change of a trait within a homogeneous population is approximately proportional to the individual fitness gradient. The methods differ in assumptions about the potential magnitude of phenotypic differences in mutant forms, and in their assumptions about the probability that invasion or speciation occurs when a species has a stable, yet invadable phenotype. Determining the range of applicability of the different methods is important for assessing the validity of optimization methods in predicting the evolutionary outcome of ecological interactions. Methods based on quantitative genetic models predict that fitness minimizing traits will often be evolutionarily stable over significant time periods, while other approaches suggest this is likely to be rare. A more detailed study of cases of disruptive selection might reveal whether fitness-minimizing traits occur frequently in natural communities.     

合并与不合并：两个相似性聚类分析方法比较

以山西省4638种昆虫在7个地理小区的分布、内蒙古7766种昆虫在14个地理小区的分布和中国16804属昆虫在67个生态区域的分布3组数据为样本,用传统的层层合并的相似性聚类分析法(SCA)和新的不需合并的多元相似性聚类分析法(MSCA)进行运算分析,对比结果表明,不合并法都能得到既符合统计学逻辑,又符合地理学、生物学逻辑的结果;合并法在参与小区较少时,还能够得到与不合并法类似的结果,随着参与小区的增多,聚类结构发生变化,以致聚类功能彻底丧失.无论两种聚类结果差异大小,其性质都迥然不同:不合并法的相似性系数是固有的、互相独立的、同时存在的,聚类结果是所有小区之间关系亲疏、距离远近的状态;合并法的每个相似性系数都是合并的依据或结果,前一个系数是后一个系数产生的条件,后一个系数是前一个系数消亡的结果,严格按照顺序,当最后一个系数产生时,前面所有系数和所有小区都已不复存在,聚类结果只是记录不断合并、不断消亡的过程.因此在肯定合并法历史价值的同时,认为申效诚等创建的多元相似性系数公式及多元相似性聚类分析法摈弃合并降阶这一产生偏差和错误的根源,能够得出相对客观的聚类结果,是生物地理学研究领域有效的聚类分析工具,必将推动生物地理学定量研究迈入一个新阶段.     

Combining capture–recapture data and known ages allows estimation of age‐dependent survival rates

In many animal populations, demographic parameters such as survival and recruitment vary markedly with age, as do parameters related to sampling, such as capture probability. Failing to account for such variation can result in biased estimates of population‐level rates. However, estimating age‐dependent survival rates can be challenging because ages of individuals are rarely known unless tagging is done at birth. For many species, it is possible to infer age based on size. In capture–recapture studies of such species, it is possible to use a growth model to infer the age at first capture of individuals. We show how to build estimates of age‐dependent survival into a capture–mark–recapture model based on data obtained in a capture–recapture study. We first show how estimates of age based on length increments closely match those based on definitive aging methods. In simulated analyses, we show that both individual ages and age‐dependent survival rates estimated from simulated data closely match true values. With our approach, we are able to estimate the age‐specific apparent survival rates of Murray and trout cod in the Murray River, Australia. Our model structure provides a flexible framework within which to investigate various aspects of how survival varies with age and will have extensions within a wide range of ecological studies of animals where age can be estimated based on size.     

Mapping nucleic acid structure by hydroxyl radical cleavage

Hydroxyl radical footprinting is a widely used method for following the folding of RNA molecules in solution. This method has the unique ability to provide experimental information on the solvent accessibility of each nucleotide in an RNA molecule, so that the folding of all domains of the RNA species can be followed simultaneously at single-nucleotide resolution. In recent work, hydroxyl radical footprinting has been used, often in combination with other global measures of structure, to work out detailed folding pathways and three-dimensional structures for increasingly large and complicated RNA molecules. These include synthetic ribozymes, and group I and group II ribozymes, from yeast, the Azoarcus cyanobacterium and Tetrahymena thermophila. Advances have been made in methods for analysis of hydroxyl radical data, so that the large datasets that result from kinetic folding experiments can be analyzed in a semi-automated and quantitative manner.     

激光雷达技术在动物生态学领域的研究进展

激光雷达(light detection and ranging, LiDAR)作为一门新兴的主动遥感技术, 近年来由于在提取和反演森林参数水平上不断提高, 被越来越多地应用于动物生态学研究中。本文通过整理和搜集国内外文献, 对激光雷达的技术特点及其在森林参数提取和动物生境上的研究进展进行综述, 指出当前基于LiDAR的森林参数反演算法主要服务于森林资源调查或林学研究, 缺少对动物生态或生理意义相关的参数量化信息。目前该技术在国内的动物生态学方面的应用较少, 尚未见文章发表。通过总结国外学者的研究, 分别从动物生境选择与三维森林结构的关系、栖息地立体生境制图、生物多样性评估和物种分布模型预测三个方面综述了LiDAR在动物生态学研究中的应用现状。相比传统方法, LiDAR技术提供的高精度三维结构信息, 能够显著提高动物生境质量的评估、生物多样性的监测水平和物种分布模型的评价精度, 有利于从机理上加深对物种生境选择和集群过程的理解。但目前LiDAR技术的应用主要集中在对已知的生态关系研究, 尤其是冠层结构与动物分布的关系, 缺少对林下层生活的动物生境质量和生物多样性的监测和评估, 同时很多有关动物生存和繁衍与立体生境的关系研究有待从LiDAR数据中进一步挖掘分析。未来应加强对森林林下层三维信息的提取, 提高林下层动物生境质量和生物多样性的监测水平, 同时建立适用于动物生态和生理意义相关的参数, 为动物生境质量和生物多样性的评估提供标准的量化指标。     

Thermodynamic extremization principles and their relevance to ecology

Theories based on simple principles have provided much insight into the common processes that underpin complex ecological systems. Although such theories (e.g. neutral theory, metabolic theories) often neglect specific ecological details, they compensate for this with their generality and broad applicability. We review several simple principles based on ‘thermodynamic extremization’ (the minimization or maximization of a thermodynamic quantity) and explore their application and relevance to ecology. Thermodynamic extremization principles predict that certain energetic quantities (e.g. entropy production) will tend towards maxima or minima within ecological systems, subject to local constraints (e.g. resource availability). These principles have a long history in ecology, but existing applications have had a theoretical focus and have made few quantitative predictions. We show that the majority of existing theories can be unified conceptually and mathematically, a result that should facilitate ecological applications of thermodynamic extremization principles. Recent developments in broader ecological research (e.g. metabolic theories) have allowed quantitative predictions of ecological patterns from thermodynamic extremization principles, and initial predictions have been supported by empirical data. We discuss how the application of extremization principles could be extended and demonstrate one possible extension, using an extremization principle to predict individual size distributions. A key focus in the application of thermodynamic extremization principles to mainstream ecological questions should be the generation of quantitative predictions and subsequent empirical validation.     

基于BP神经网络的流域生态恢复度计算——以福建长汀朱溪小流域为例

以福建省长汀县朱溪小流域为研究对象,通过野外调查、室内分析以及遥感影像提取相结合的方法获取数据。利用Matlab7.0软件建立BP神经网络生态恢复模型,定量评价退化生态系统的恢复程度。选择土壤理化性质(有机质、全N、全P、全K、容重和p H)、植被结构(植被盖度)、物种多样性指数(Shannon-Wiener指数)和热环境(地表温度)等4个方面的9个指标建立退化生态系统评价体系,并作为生态恢复模型的输入层数据,生态恢复度作为输出层数据。使用Matlab7.0进行数据预处理、样本训练、样本检验并建立生态恢复模型。利用建立的生态恢复模型对整个朱溪小流域生态恢复度进行定量评价。结果表明,生态恢复模型预测结果与流域生态恢复的实际情况基本吻合,利用BP神经网络模型定量评价退化生态系统的恢复程度具有可行性。朱溪小流域内生态恢复程度极低的区域面积仅占0.94%,95.48%区域为中等恢复程度,说明生态保护措施已初见成效;生态恢复程度高的区域面积仅占3.62%,意味着未来仍需加强治理和保护工作。     

MartiTracks: a geometrical approach for identifying geographical patterns of distribution

Panbiogeography represents an evolutionary approach to biogeography, using rational cost-efficient methods to reduce initial complexity to locality data, and depict general distribution patterns. However, few quantitative, and automated panbiogeographic methods exist. In this study, we propose a new algorithm, within a quantitative, geometrical framework, to perform panbiogeographical analyses as an alternative to more traditional methods. The algorithm first calculates a minimum spanning tree, an individual track for each species in a panbiogeographic context. Then the spatial congruence among segments of the minimum spanning trees is calculated using five congruence parameters, producing a general distribution pattern. In addition, the algorithm removes the ambiguity, and subjectivity often present in a manual panbiogeographic analysis. Results from two empirical examples using 61 species of the genus Bomarea (2340 records), and 1031 genera of both plants and animals (100118 records) distributed across the Northern Andes, demonstrated that a geometrical approach to panbiogeography is a feasible quantitative method to determine general distribution patterns for taxa, reducing complexity, and the time needed for managing large data sets.     

How to infer population trends in sparse data: examples with opportunistic sighting records for great white sharks

Aim Biodiversity is declining at accelerating rates. Understanding past and ongoing changes to biodiversity is paramount in prioritizing conservation action and restoring functional ecosystems. Yet long‐term, systematic data on the distribution and abundance of species are sparse. For many organisms, specimen collections and anecdotal accounts of chance sightings or captures constitute the only source of information. Such data have the potential to provide valuable insights on long‐term ecosystem changes, but are often neglected because they are difficult to analyse quantitatively. Here we review available methods and introduce a new approach. Location Historic data on sightings and captures of great white sharks in the eastern Adriatic and off eastern Canada serve to illustrate the utility of both the existing methods and the new approach. Method Unlike existing methods, the new approach focuses on estimating population trends rather than verifying extinction and explicitly addresses uncertainty over observation effort via two tiers of sensitivity analysis. It fits a series of generalized linear models that provide multiple estimates of declines under alternate scenarios regarding the appropriate reference period and observer trends. Programming code to implement the approach in freely available software is provided as supplementary material. Results Example analyses of great white shark sightings suggest that local populations of this species have suffered dramatic declines, both in the eastern Adriatic and along Canada’s eastern coast. Although not yet extinct, this top predator may therefore no longer be able to fulfil its former ecological role. Main conclusions Careful quantitative analyses of imperfect historical data can provide valuable insights into past ecological changes. Such insights are crucial to improved management and restoration of individual species and their ecosystems. We therefore hope that our review of available methods will facilitate quantitative evaluations of species for which analysis was previously impeded by a lack of systematically collected data.     

Inferring species interactions from ecological survey data: A mechanistic approach to predict quantitative food webs of seed feeding by carabid beetles

1. Ecological networks are valuable for ecosystem analysis but their use is often limited by a lack of data because many types of ecological interaction, for example, predation, are short‐lived and difficult to observe or detect. While there are different methods for inferring the presence of interactions, they have rarely been used to predict the interaction strengths that are required to construct weighted, or quantitative, ecological networks.
2. Here, we develop a trait‐based approach suitable for inferring weighted networks, that is, with varying interaction strengths. We developed the method for seed‐feeding carabid ground beetles (Coleoptera: Carabidae) although the principles can be applied to other species and types of interaction.
3. Using existing literature data from experimental seed‐feeding trials, we predicted a per‐individual interaction cost index based on carabid and seed size. This was scaled up to the population level to create inferred weighted networks using the abundance of carabids and seeds from empirical samples and energetic intake rates of carabids from the literature. From these weighted networks, we also derived a novel measure of expected predation pressure per seed type per network.
4. This method was applied to existing ecological survey data from 255 arable fields with carabid data from pitfall traps and plant seeds from seed rain traps. Analysis of these inferred networks led to testable hypotheses about how network structure and predation pressure varied among fields.
5. Inferred networks are valuable because (a) they provide null models for the structuring of food webs to test against empirical species interaction data, for example, DNA analysis of carabid gut regurgitates and (b) they allow weighted networks to be constructed whenever we can estimate interactions between species and have ecological census data available. This permits ecological network analysis even at times and in places when interactions were not directly assessed.

     

排序法在植物群落与环境关系研究中的应用述评

自然环境对植物的影响主要表现在气候、水文、土壤及地形方面。大尺度上,气候类型明显影响植物的带状分布与物种空间格局;中小尺度上,土壤、水文、地形以及三者的交互作用影响植物生长必需的环境与资源条件,并对植物群落物种多样性起决定性作用。多元数量分析是研究植物群落生态关系的重要方法,在揭示植物群落与环境关系方面起到关键作用。排序法作为数量分析的重要手段,经常在植物生态学研究中扮演重要角色,尤其是在植物群落分布以及群落结构方面的应用已形成一种趋势。主要从植物群落分布以及群落结构的角度综述了当今排序法的应用,分析了面临的主要问题,并提出了未来可能发展方向,以期为今后排序方法的选择应用提供参考。     

The origins and detection of plant community structure: Reproductive versus vegetative processes

The exchange of ideas and information between vegetation ecology and pollination ecology is relatively restricted, yet both fields have devised methods to detect the structure of species assemblages and communities. To promote the exchange of ideas between fields I compare approaches, concepts, and problems faced by researchers working in each area. Both vegetative and reproductive interactions may generate assemblage structure through ecological sorting or through character displacement. Vegetative interactions may lead to assemblage organization more often by ecological sorting and reproductive interactions more often by character displacement. Vegetative interactions generally operate over shorter temporal and smaller spatial scales than reproductive interactions and may be affected more strongly by temporal and spatial heterogeneity in abiotic and biotic environments. These differences affect how the concept of ecological niche should be applied to plants. The Hutchinsonian concept of niche needs to be significantly modified before it can be usefully applied to plants. Null models are a valuable tool for investigating both vegetative and reproductive structuring of plant assemblages; however, the procedures followed in the application of null models need further refinement. The appropriate formulation of the null model may require information that is unavailable, hence multiple models may have to be employed to “bracket” conclusions. The literature on pollination community ecology demonstrates that difficult decisions must be made about the likely processes that have generated the structure being tested, the relevant definition of sympatry, how guid membership should be defined and employed, and what constraints should be incorporated into the null model to impose realism. Differences in these decisions will affect the outcome of the analysis. While top-down studies of pattern have numerous advantages, they usually cannot identify the process(es) that have generated the patterns. Bottom-up, experimental studies can be useful for identifying the processes, but they can rarely be used to assess the structure of an entire natural assemblage. The optimal approach to studying assemblage structure is to detect patterns with top-down analysis and use experiments to identify the processes that generate and maintain the patterns.