《昆明-蒙特利尔全球生物多样性框架》下的生物多样性监测

生物多样性监测是执行和评估《昆明-蒙特利尔全球生物多样性保护框架》（简称《昆-蒙框架》）长期目标和行动指标所需要的关键数据的主要来源。简要介绍了《昆-蒙框架》下的全球生物多样性观测网络（The global biodiversity observation network,GEO BON）概况、中国已有的生物多样性监测/观测网络,构建全球生物多样性观测系统（A global biodiversity observation system,GBiOS）的目的和核心监测指标,分析了生物多样性监测网络建设的一般步骤和GEO BON建议的4种生物多样性监测新方法。提出,地理空间单元与生物类群的全覆盖监测是建设生物多样性监测网络的主要目标,传统监测方法和新监测技术的互补是当前生物多样性监测的重点工作内容。     

Trophic field overlap: A new approach to quantify keystone species

It is a current challenge to better understand the relative importance of species in ecosystems, and the network perspective is able to offer quantitative tools for this. It is plausible to assume, in general, that well-linked species, being key interactors, are also more important for the community. Recently a number of methods have been suggested for quantifying the network position of species in ecological networks (like the topological importance metric, TI). Most of them are based on node centrality indices and it may happen that the two most important species in a food web have very similar interaction structure and they can essentially replace each other if one becomes extinct. For conservation considerations it is a challenge to identify species that are richly connected and, at the same time, have a relatively unique and irreplaceable interaction pattern. We present a new method and illustrate our approach by using the Kuosheng Bay trophic network in Taiwan. The new method is based on the interaction matrix, where the strength of the interaction between nodes i and j depends only on topology. By defining a threshold separating weak and strong interactors, we define the effective range of interactions for each graph node. If the overlaps between pairs of these ranges are quantified, we gain a metric expressing how unique is the interaction pattern of a focal node (TO). The combination of centrality (TI) and uniqueness (TO) is called topological functionality (TF). We compare the nodal importance rank provided by this metric to others based on a variety of centrality measures. The main conclusion is that shrimps seem to have the most unique interaction pattern despite that their structural importance has been underestimated by all conventional centrality indices. Also, our network analysis suggests that fisheries disturb the ecosystem in a more critical network position than the impingement by the local power plant.     

A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia,China

Two models, artificial neural network (ANN) and multiple linear regression (MLR), were developed to estimate typical grassland aboveground dry biomass in Xilingol River Basin, Inner Mongolia, China. The normalized difference vegetation index (NDVI) and topographic variables (elevation, aspect, and slope) were combined with atmospherically corrected reflectance from the Landsat ETM+ reflective bands as the candidate input variables for building both models. Seven variables (NDVI, aspect, and bands 1, 3, 4, 5 and 7) were selected by the ANN model (implemented in Statistica 6.0 neural network module), while six (elevation, NDVI, and bands 1, 3, 5 and 7) were picked to fit the MLR function after a stepwise analysis was executed between the candidate input variables and the above ground dry biomass. Both models achieved reasonable results with RMSEs ranging from 39.88% to 50.08%. The ANN model provided a more accurate estimation (RMSE_r = 39.88% for the training set, and RMSE_r = 42.36% for the testing set) than MLR (RMSE_r = 49.51% for the training, and RMSE_r = 53.20% for the testing). The final above ground dry biomass maps of the research area were produced based on the ANN and MLR models, generating the estimated mean values of 121 and 147 g/m², respectively.     

Comparing network analysis methodologies for consumer–resource relations at species and ecosystems scales

This research compares two existing methodologies, mixed trophic impact analysis and utility analysis, which use network analysis to evaluate the direct, pair-wise, and indirect, holistic, ecological relations between ecosystem compartments. The two approaches have many similarities, but differ in some key assumptions which affect both the final results and interpretations. Here, we briefly introduce both methodologies through a series of two simple examples; a 3-compartment competition model and a 3-compartment food chain model, and then apply the methodologies to a 15-compartment ecosystem model of the Chesapeake Bay. This example demonstrates how implementing the various conceptual and methodological assumptions lead to differing results. Notably, the overall number of positive relations is greatly affected by the treatment of the self-interactions and the handling of detritus compartments lead to a distinction between ecological or trophic relations. We recommend slight changes to both methodologies, not necessarily in order to bring them completely together, but because each has some points which are stronger and better defensible.     

Comparative network analysis toward characterization of systemic organization for human–environmental sustainability

A preliminary study in comparative ecological network analysis was conducted to identify key assumptions and methodological challenges, test initial hypotheses and explore systemic and network structural characteristics for environmentally sustainable ecosystems. A nitrogen network for the U.S. beef supply chain – a small sub-network of the industrial food system analyzed as a pilot study – was constructed and compared to four non-human carbon and nitrogen trophic networks for the Chesapeake Bay and the Florida Everglades. These non-human food webs served as sustainable reference systems. Contrary to the main original hypothesis, the “window of vitality” and the number of network roles did not clearly differentiate between a human sub-network and the more complete non-human networks. The effective trophic level of humans (a partial estimate of trophic level based on the single food source of beef) was much higher (8.1) than any non-human species (maximum of 4.88). Network connectance, entropy, total dependency coefficients, trophic efficiencies and the ascendency to capacity ratio also indicated differences that serve as hypotheses for future tests on more comprehensive human food webs. The study elucidated important issues related to (1) the steady state assumption, which is more problematic for industrial human systems, (2) the absence or dearth of data on contributions of dead humans and human wastes to feed other species in an integrated food web, (3) the ambiguity of defining some industrial compartments as living versus non-living, and (4) challenges with constructing compartments and trophic transfers in industrial versus non-human food webs. The two main novel results are (1) the progress made toward adapting ecological network analysis (ENA) methodology for analysis of human food networks in industrial cultures and (2) characterizing the critical aspects of comparative ENA for understanding potential causes of the problems, and providing avenues for solutions, for environmental sustainability. Based on this work, construction and comparative network analysis of a more comprehensive industrial human food network seems warranted and likely to provide valuable insights for modifying structures of industrial food networks to be more like natural networks and more sustainable.     

Young-boy networks without kin clusters in a lek-mating manakin

I use 10 years of data from a long-term study of lek-mating long-tailed manakins to relate the social network among males to their spatial and genetic structure. Previously, I showed that the network connectivity of young males predicts their future success. Here, I ask whether kinship might shape the organization of this “young-boy network”. Not surprisingly, males that were more socially distant (linked by longer network paths) were affiliated with perch zones (lek arenas) that were further apart. Relatedness (r) among males within the network decreased as social distance increased, as might be expected under kin selection. Nevertheless, any role for indirect inclusive fitness benefits is refuted by the slightly negative mean relatedness among males at all social distances within the network (overall mean r = −0.06). That is, relatedness ranged from slightly negative (−0.04) to more negative (−0.2). In contrast, relatedness in dyads for which at least one of the males was outside the social network (involving at least one blood-sampled male not documented to have interacted with other banded males) was slightly above the random expectation (mean r = 0.05). The slight variations around r = 0 among males of different categories likely reflect dispersal dynamics, rather than any influence of kinship on social organization. Relatedness did not covary with the age difference between males. These results, together with previous results for lack of relatedness between alpha and beta male partners, refute any role for kin selection in the evolution of cooperative display in this lek-mating system. This contribution is part of the special issue “Social Networks: new perspectives” (Guest Editors: J. Krause, D. Lusseau, and R. James).     

Approximating SWAT Model Using Artificial Neural Network and Support Vector Machine1

Abstract: With the popularity of complex, physically based hydrologic models, the time consumed for running these models is increasing substantially. Using surrogate models to approximate the computationally intensive models is a promising method to save huge amounts of time for parameter estimation. In this study, two learning machines [Artificial Neural Network (ANN) and support vector machine (SVM)] were evaluated and compared for approximating the Soil and Water Assessment Tool (SWAT) model. These two learning machines were tested in two watersheds (Little River Experimental Watershed in Georgia and Mahatango Creek Experimental Watershed in Pennsylvania). The results show that SVM in general exhibited better generalization ability than ANN. In order to effectively and efficiently apply SVM to approximate SWAT, the effect of cross‐validation schemes, parameter dimensions, and training sample sizes on the performance of SVM was evaluated and discussed. It is suggested that 3‐fold cross‐validation is adequate for training the SVM model, and reducing the parameter dimension through determining the parameter values from field data and the sensitivity analysis is an effective means of improving the performance of SVM. As far as the training sample size, it is difficult to determine the appropriate number of samples for training SVM based on the test results obtained in this study. Simple examples were used to illustrate the potential applicability of combining the SVM model with uncertainty analysis algorithm to save efforts for parameter uncertainty of SWAT. In the future, evaluating the applicability of SVM for approximating SWAT in other watersheds and combining SVM with different parameter uncertainty analysis algorithms and evolutionary optimization algorithms deserve further research.     

西北地区能源-环境-经济可持续发展预警研究——以陕西省为例

能源、环境与经济是人类社会生存与发展的物质基础,能源经济发展在带来区域经济快速繁荣的同时,往往造成当地社会生活质量的下降和生态环境的破坏。陕西作为我国21世纪重要的能源接续地,在能源开发利用的同时也受到脆弱生态环境的约束,本文以此为例进行可持续发展预警实证研究具有一定的代表性和现实意义。通过设计能源-环境-经济可持续发展预警指标体系,构建出可持续发展预警模型,采用人工神经网络预警方法和层次分析法,编写出计算运行程序,运用Matlab7.1软件中人工神经网络工具箱,将有关数据信息和预警模型有机结合,探讨能源-环境-经济可持续发展理论构建与应用问题。研究结果表明:陕西能源-环境-经济的可持续发展趋势在2000-2014年间分别处于重警、中警、轻警和无警四种状态,基本符合陕西能源产业发展现状;所设计的预警指标能较好地反映出能源-环境-经济可持续发展的特征;基于BP人工神经网络的预警方法具有较强的仿真能力,能提高可持续发展预警的准确性,在能源产业可持续发展预警研究中具有一定的应用价值。     

太湖流域上游河流污染空间分布特征研究

为把握太湖流域上游西北部河网区域水体的N、P污染特征及其空间分布,2010年8月在该区域内的武进港 直湖港水系、洮滆水系、丹扁孟河（丹金溧漕运河、扁担河及孟津河）以及宜溧河水系的河网,监测了39个河流断面的水质。研究表明,4水系TN平均浓度分别为388、283、291及193 mg/L,DTN占TN比重分别为9180%、7843%、7001%及8444%,河网氮输出均以DTN为主。在DTN中NO-3 N浓度所占比重分别为6747%、5275%、7748%及4281%,NH+4 N浓度所占比重分别为1633%、3316%、1127%及4351%。TP平均浓度分别为059、030、026及015 mg/L,DTP所占比重分别为6213%、4019%、4673%及3197%,而PO3-4 P浓度占DTP比重分别为2073%、5461%、4327%及7772%。武进港 直湖港水系N平均浓度显著（〖WTBX〗p〖WTBZ〗<001）高于宜溧河水系,与其它两水系无显著差异,P平均浓度显著（〖WTBX〗p〖WTBZ〗<005）高于宜溧河水系和丹扁孟河,与洮滆水系无显著差异。影响太湖流域上游西北部区域河流水网水质的主导因素为氮污染,而其中以DTN污染最为严重。太湖西北部是蓝藻水华暴发的重灾区,上游区域DTN的大量输入,将成为引发太湖水体生态系统灾变的潜在风险     

区域高速公路网络构建对可达性空间格局的影响——以安徽沿江地区为实证 

以安徽沿江为实证，采用加权平均旅行时间指标，分析了高速公路网构建对节点区内联系及区外联系可达性格局的影响，并以此为基础，综合分析了节点总体可达性格局的变动；随后对总体可达性的变动幅度及其相对可达性变动格局进行了探讨。结果表明：安徽沿江高速公路网络的构建显著提高了区域整体可达性，且促进了可达性的均衡化；区内联系可达性、区外联系可达性及总体可达性三者格局特征不同，路网构建所形成的影响也各有差别；东部及北部地区可达性变动幅度小于西南部地区；根据可达性系数的变化幅度及方向，可将节点归为相对可达性下降、相对可达性稳定及相对可达性上升3类。