Geometric versus topological clustering: An insight into conformation mapping

Clustering molecular conformations into “families” is a common procedure in conformational analysis of molecular systems. An implicit assumption which often underlies this clustering approach is that the resulting geometric families reflect the energetic structure of the system's potential energy surface. In a broader context we address the question whether structural similarity is correlated with energy basins, i.e., whether conformations that belong to the same energy basin are also geometrically similar. “Topological mapping” and principal coordinate projections are used here to address this question and to assess the quality of the “family clustering” procedure. Applying the analysis to a small tetrapeptide it was found that the general correlation that exists between energy basins and structural similarity is not absolute. Clusters generated by the geometric “family clustering” procedure do not always reflect the underlying energy basins. In particular it was found that the “family tree” that is generated by the “family clustering” procedure is completely inconsistent with its real topological counterpart, the “disconnectivity” graph of this system. It is also demonstrated that principal coordinate analysis is a powerful visualization technique which, at least for this system, works better when distances are measured in dihedral angle space rather than in cartesian space. © 1997 Wiley-Liss, Inc.     

Spatial Epidemiology and GIS in Marine Mammal Conservation Medicine and Disease Research

The use of spatial epidemiology and geographical information systems (GIS) facilitates the incorporation of spatial relationships into epidemiological investigations of marine mammal diseases and conservation medicine. Spatial epidemiology is the study of the spatial variation in disease risk or incidence and explicitly addresses spatial structures and functions that factor into disease. The GIS consists of input, management, analysis, and presentation of spatial disease data and can act as an integrative tool so that a range of varied data sources can be combined to describe different environmental aspects of wild animals and their diseases. The use of modern spatial analyses and GIS is becoming well developed in the field of marine mammal ecology and biology, but has just recently started to gain more use in disease research. The use of GIS methodology and spatial analysis in nondisease marine mammal studies is briefly discussed, while examples of the specific uses of these tools in mapping, surveillance and monitoring, disease cluster detection, identification of environmental predictors of disease in wildlife populations, risk assessment, and modeling of diseases, is presented. Marine mammal disease investigations present challenges, such as less consistent access to animals for sampling, fewer baseline data on diseases in wild populations, and less robust epidemiologic study designs, but several recommendations for future research are suggested. Since location is an integral part of investigating disease, spatial epidemiology and GIS should be incorporated as a data management and analysis tool in the study of marine mammal diseases and conservation medicine.     

Indicators of hemeroby for the monitoring of landscapes in Germany

The article discusses the concepts of “closeness to nature” and “hemeroby”, and outlines a method to establish two indicators of hemeroby. Until now Germany's national land use monitoring systems have lacked an indicator to capture the naturalness respectively hemeroby of the landscape. Based on digital spatial data on land use (DLM-DE) and the mapping of potential natural vegetation, these indicators have now been estimated for the whole of Germany and illustrated cartographically. The indicators have been integrated into a land use monitoring system (IOER-Monitor). A hemeroby index that considers all hemeroby classes of a reference area (e.g. administrative unit and regular grid cell) is presented as well as an indicator named “Proportion of certain natural areas”. The results on hemeroby of several time-cuts can be used to estimate the cumulative impact of land use changes on the environmental status.     

Reconciling neuronal representations of schema,abstract task structure,and categorization under cognitive maps in the entorhinal-hippocampal-frontal circuits

Learning leads to a neuronal representation of acquired knowledge. This idea of knowledge representation was traditionally developed as a “cognitive map” of spatial memory represented in the hippocampus. The framework of cognitive mapping has been extended in the past decade to include not only spatial memory, but also non-spatial factual and temporal memory. Following this conceptual advancement, a line of recent neurophysiological research discovered such knowledge representations not only in the hippocampus, but also in the entorhinal cortex and frontal cortex. Although the distinct terms “cognitive map,” “schema,” “abstract task structure” or “categorization” were used in these studies, it is likely that these terms can be reconciled as a common mechanism of learned knowledge representations. Future experimental work will be required to differentiate the parametric nature of knowledge representations across brain areas.     

基于GIS的产业生态学研究述评

产业生态学由于缺少关于空间分析的工具,使得研究结果因缺乏空间维度信息而影响对管理效率和精准度的支持。基于GIS的产业生态学相关研究已成为产业生态学研究的一个新的方向。为总结已有的研究成果并展望未来的研究方向,运用文献计量及对比分析的手段,系统分析了国内外基于GIS的产业生态学的相关研究进展,得出以下结论:当前基于GIS的产业生态研究主要集中在物质代谢、产业共生和生命周期评价3个方面,将GIS技术引入到物质代谢研究中,可以更好的展示物质代谢的时空分布格局,为物质代谢研究提供了一种新的方法;基于GIS技术,不仅可以更加高效地挖掘潜在的产业共生机会,还可应用于生态产业园的规划管理如企业的选址、空间布局等以及废弃物的回收再利用方面;将GIS与LCA耦合在一起,可以很好地补充、完善和管理传统数据,有助于探索产品、活动或工艺的环境影响的空间特性以及进行土地利用相关的环境影响评价。另外,国内外研究的侧重点也不尽相同。在物质代谢研究中,国内研究较少,仅在城市尺度上进行了基础设施的物质代谢及其存量分析,国外在国家、城市尺度上研究了铜、锌等金属的物质代谢情况;在产业共生研究中,国内侧重于生态产业园的研究,而国外侧重于城市尺度的产业共生机会识别的研究;在LCA的研究中,国内开展了基于GIS的生命周期评价数据库和产品材料信息管理系统的研究,而国外侧重于进行区域化的生命周期评价、进行土地利用影响类型的相关评价以及污染物的追踪,国内在该方面尚处于起步阶段。国内外在研究方法上存在共性,都是基于GIS的空间分析方法、缓冲区分析方法以及数据库技术等。未来将GIS作为一个平台,面向产业转型展开产业生态学综合理论方法的研究,可以为产业的可持续性管理提供有效支持。     

A global organism detection and monitoring system for non-native species

Harmful invasive non-native species are a significant threat to native species and ecosystems, and the costs associated with non-native species in the United States is estimated at over $120 Billion/year. While some local or regional databases exist for some taxonomic groups, there are no effective geographic databases designed to detect and monitor all species of non-native plants, animals, and pathogens. We developed a web-based solution called the Global Organism Detection and Monitoring (GODM) system to provide real-time data from a broad spectrum of users on the distribution and abundance of non-native species, including attributes of their habitats for predictive spatial modeling of current and potential distributions. The four major subsystems of GODM provide dynamic links between the organism data, web pages, spatial data, and modeling capabilities. The core survey database tables for recording invasive species survey data are organized into three categories: “Where, Who & When, and What.” Organisms are identified with Taxonomic Serial Numbers from the Integrated Taxonomic Information System. To allow users to immediately see a map of their data combined with other user's data, a custom geographic information system (GIS) Internet solution was required. The GIS solution provides an unprecedented level of flexibility in database access, allowing users to display maps of invasive species distributions or abundances based on various criteria including taxonomic classification (i.e., phylum or division, order, class, family, genus, species, subspecies, and variety), a specific project, a range of dates, and a range of attributes (percent cover, age, height, sex, weight). This is a significant paradigm shift from “map servers” to true Internet-based GIS solutions. The remainder of the system was created with a mix of commercial products, open source software, and custom software. Custom GIS libraries were created where required for processing large datasets, accessing the operating system, and to use existing libraries in C++, R, and other languages to develop the tools to track harmful species in space and time. The GODM database and system are crucial for early detection and rapid containment of invasive species.     

A structural equation modeling approach for formalizing and evaluating ecological integrity in terrestrial ecosystems

Ecological integrity is a functional property that integrates habitat functions and species information for maintaining key ecological interactions in predator-prey systems. As a functional property, ecological integrity can be modeled as a latent concept from observable spatial attributes that measure the ecosystem's capacity to provide suitable habitat conditions for apex predators. Ecological integrity is a tri-dimensional concept that stems from “stable”, “concurrent” and “intact” conditions. A theoretical framework and a methodology is presented here for modeling ecological integrity from observable attributes (as GIS layers) to obtain a spatial representation of the integrity condition. From a theoretical framework, the ecological integrity concept is obtained with a structural equation modeling approach, where several other latent variables are obtained for characterizing a hierarchical network of spatial information. Later on, these observable attributes, and several latent modeled variables are translated into sources of geographic information that can be used to monitor changes in the natural remnant areas due to human impacts. When examining the direct, indirect and total effects of habitat loss and fragmentation on ecological integrity, spatial intactness (e.g., the amount of remnant habitat and connectivity) and stability (resistance in the interaction network and mobile links) are the attributes more affected by the pathway effects. The balance of the formative parameters obtained for the model supports the idea that ecosystems that have a high degree of integrity should maintain a high level of stability, self-organization and naturalness. These attributes are achieved when spatial habitat intactness and species interactions are maintained.     

A multifunctional peroxidase-based reaction for imaging,sensing and networking of spatial biology

Peroxidase is a heme-containing enzyme that reduces hydrogen peroxide to water by extracting electron(s) from aromatic compounds via a sequential turnover reaction. This reaction can generate various aromatic radicals in the form of short-lived “spray” molecules. These can be either covalently attached to proximal proteins or polymerized via radical–radical coupling. Recent studies have shown that these peroxidase-generated radicals can be utilized as effective tools for spatial research in biological systems, including imaging studies aimed at the spatial localization of proteins using electron microscopy, spatial proteome mapping, and spatial sensing of metabolites (e.g., heme and hydrogen peroxide). This review may facilitate the wider utilization of these peroxidase-based methods for spatial discovery in cellular biology.     

The rule of normal neighbors: A hypothesis for morphogenetic pattern regulation

The rule of normal neighbors, a generalization of the polar coordinate model of V. French, P. J. Bryant, and S. V. Bryant (1976, Science193, 969–981), can predict topological features of epimorphic regeneration without ascribing a particular coordinate system of positional values to cells. The rule correctly predicts the types and symmetries of structures which regenerate after grafting operations on the distal segments of crayfish legs. The rule of normal neighbors should be valid for any regenerating system in which short-range interactions between cells near the site of wound healing suffice to restore normal structures. Two criteria for defining “minimal regeneration” are proposed: (1) a “shortest-path rule” which generalizes the corresponding rule in the polar coordinate model; and (2) a “minimal free energy” hypothesis.     

土地利用变化影响下洞庭湖地区生态系统服务价值的时空演变

对洞庭湖地区多年遥感影像进行解译,结合其经济发展统计数据,在地理信息系统技术支持下,估算各土地利用类型的生态价值系数,并运用空间自相关、高地聚类及热点分析等空间地统计分析方法,探究洞庭湖地区生态系统服务价值(ESV)的时空变化特征.结果表明: 2000—2013年间,洞庭湖地区的ESV小幅上升,由3868.39亿元上升至3886.62亿元,湿地面积的增加是ESV上升的主要原因;洞庭湖地区的ESV高值区与低值区相互溶解渗透,次高值区对高值及低值区呈包围态势;洞庭湖地区的ESV及其动态演化表现出明显的空间自相关与高低值聚集现象,但其自相关与聚集程度趋于减弱;研究期间洞庭湖地区的ESV热点区重心主要向西稍偏北方向移动,其轨迹大体上呈 “S”型态势,迁移速度缓慢但具有明显的阶段性,经历了 “剧烈—缓慢—剧烈”的变化阶段.     

Spatial Access Priority Mapping (SAPM) with Fishers: A Quantitative GIS Method for Participatory Planning

Spatial management tools, such as marine spatial planning and marine protected areas, are playing an increasingly important role in attempts to improve marine management and accommodate conflicting needs. Robust data are needed to inform decisions among different planning options, and early inclusion of stakeholder involvement is widely regarded as vital for success. One of the biggest stakeholder groups, and the most likely to be adversely impacted by spatial restrictions, is the fishing community. In order to take their priorities into account, planners need to understand spatial variation in their perceived value of the sea. Here a readily accessible, novel method for quantitatively mapping fishers’ spatial access priorities is presented. Spatial access priority mapping, or SAPM, uses only basic functions of standard spreadsheet and GIS software. Unlike the use of remote-sensing data, SAPM actively engages fishers in participatory mapping, documenting rather than inferring their priorities. By so doing, SAPM also facilitates the gathering of other useful data, such as local ecological knowledge. The method was tested and validated in Northern Ireland, where over 100 fishers participated in a semi-structured questionnaire and mapping exercise. The response rate was excellent, 97%, demonstrating fishers’ willingness to be involved. The resultant maps are easily accessible and instantly informative, providing a very clear visual indication of which areas are most important for the fishers. The maps also provide quantitative data, which can be used to analyse the relative impact of different management options on the fishing industry and can be incorporated into planning software, such as MARXAN, to ensure that conservation goals can be met at minimum negative impact to the industry. This research shows how spatial access priority mapping can facilitate the early engagement of fishers and the ready incorporation of their priorities into the decision-making process in a transparent, quantitative way.     

Consumer preference for edible insect‐containing cookies determined by conjoint analysis: An exploratory study of Korean consumers

Consumers are the principal agents of consumption when edible insect cookies are launched into the market; therefore, there is a need for research into consumer perception of this product and the factors that could affect their choice. Therefore, through this study, we aimed to provide such information for the development and launch of edible insect cookies. We investigated the importance of product features of edible insect cookies and the part‐worth of the feature levels while predicting the market share of edible insect cookies that are currently in development. We constructed 16 main and four hold‐out profiles from six product features and 16 levels of features. Following analysis of data collected from 203 of 250 respondents, product features were found to be important in the descending order of “type”, “price”, “size”, “availability of nutrition facts”, “thickness” and “use of edible insects”. When the market share of an edible insect cookie with the optimal combination of product features was analyzed through choice simulation, it had the second‐highest share in the cookie market, thus suggesting a direction for the development of edible insect cookies.     

A review of techniques for spatial modeling in geographical, conservation and landscape genetics

Most evolutionary processes occur in a spatial context and several spatial analysis techniques have been employed in an exploratory context. However, the existence of autocorrelation can also perturb significance tests when data is analyzed using standard correlation and regression techniques on modeling genetic data as a function of explanatory variables. In this case, more complex models incorporating the effects of autocorrelation must be used. Here we review those models and compared their relative performances in a simple simulation, in which spatial patterns in allele frequencies were generated by a balance between random variation within populations and spatially-structured gene flow. Notwithstanding the somewhat idiosyncratic behavior of the techniques evaluated, it is clear that spatial autocorrelation affects Type I errors and that standard linear regression does not provide minimum variance estimators. Due to its flexibility, we stress that principal coordinate of neighbor matrices (PCNM) and related eigenvector mapping techniques seem to be the best approaches to spatial regression. In general, we hope that our review of commonly used spatial regression techniques in biology and ecology may aid population geneticists towards providing better explanations for population structures dealing with more complex regression problems throughout geographic space.     

The past and future influence of geographic information systems on hybrid zone, phylogeographic and speciation research

Over the past two decades geographers have developed an increasingly sophisticated technology termed a geographic information system (GIS). A GIS has the ability to store, map and analyse spatial data. The powerful analytical capabilities of a GIS could serve to enhance our understanding of the spatial component of the evolutionary process. In particular, phylogeographers, hybrid zone and speciation researchers could benefit enormously from incorporating this sophisticated technology from the discipline of geography, as they have done so readily from other disciplines (e.g. genetics). Indeed, an increasing number of researchers in these fields are beginning to include GIS analyses into their research programmes. Some of this integration has taken the form of analysing the spatial relationship between populations and hybrid zones. Several other researchers have also begun to incorporate GIS into their work through the use of GIS-based niche models. These models estimate a multidimensional niche for a species using known geo-referenced populations and digital climate maps. Here, I review the recent integration of GIS and GIS-based predictive niche models into the above evolutionary sub-disciplines. I also describe evolutionary analyses that could be further enhanced through the implementation of GIS.     

Hybrid modeling in bioprocess dynamics: Structural variabilities,implementation strategies,and practical challenges

Hybrid modeling, with an appropriate blend of the mechanistic and data-driven framework, is increasingly being adopted in bioprocess modeling, model-based experimental design (digital-twin), identification of critical process parameters, and optimization. However, the development of a hybrid model from experimental data is an inherently complex workflow, involving designed experiments, selection of the data-driven process, identification of model parameters, assessment fitness, and generalization capability. Depending on the complexity of the process system and purpose, each piece of these modules can flexibly be incorporated into the puzzle. However, this extra flexibility can be a cause of concern to trace an “optimal” model structure. In this paper, the development of hybrid models in a common bioprocess system, selection of data-driven components and their mapping to states, choice of parameter identification techniques, and model quality assurance are revisited. The challenges associated with hybrid-model development, and corrective actions have also been reviewed. The review also suggests the lack of data, and code sharing in communal repositories can be a hurdle in the exploration, and expansion of those tools in a bioprocess system.     

The TRM Model of Potential Natural Vegetation in Mountain Forests

Due to advances in spatial modeling and improved availability of digital geodata, traditional mapping of potential natural vegetation (PNV) can be replaced by ecological modeling approaches. We developed a new model to map forest types representing the potential natural forest vegetation in the Bavarian Alps. The TRM model is founded on a three-dimensional system of the ecological gradients temperature (T), soil reaction (R), and soil moisture (M). Within such a “site cube” forest types are defined as homogenous site units that give rise to forest communities with comparable species composition, structure, production and protective functions. The three gradients were modeled using regression algorithms with area-wide, high resolution geodata on climate, relief and soil as predictors and average Ellenberg indicator values for temperature, acidity and moisture of vegetation plots as dependent variables summarizing plant responses to ecological gradients. The resulting predictor-response relationships allowed us to predict gradient positions of each raster cell in the region from geodata layers. The three-dimensional system of gradients was partitioned into 26 forest types, which can be mapped for the whole region. TRM-based units are supplemented by 22 forest types of special sites defined by other ecological factors such as geomorphology, for which individual GIS rules were developed. The application of our model results in an intermediate-scale map of potential natural forest vegetation, which is based on an explicit function of temperature, reaction and moisture and is therefore consistent and repeatable in contrast to traditional PNV maps.     

鄂尔多斯沙地草地实验站生态制图系统的建立及应用

生态制图是借助于图形对植被及生态因子的格局及它们之间的相互关系进行分析。应用地理信息系统技术进行生态制图使得植被生态分析不仅由定性走向定量,而且由定量向图形和图象化发展。本项研究利用EPPL7地理信息系统软件建立了鄂尔多斯沙化草地实验站生态制图系统并用该系统编制了潜在沙化强度分级图、植被生态规划图、综合生态图及沙化监测图。     

集成的专家系统和神经网络应用于大熊猫生境评价

充分了解大熊猫生境的时空格局及其变化,对有效保护大熊猫非常重要.绘制生境图既是野生动物生境评价和监测的一个有效方式,也是一个必要的步骤.新发展起来的人工智能方法(包括专家系统和神经网络方法),在模拟复杂系统过程中能够同时综合定性和定量信息,并可集成于GIS中,有助于大熊猫复杂生境的制图及评价.为了对大熊猫生境进行评价,本文建立了一个较全面的综合制图方法,将专家系统、神经网络和多类型数据全部集成在GIS环境下.结果表明,采用专家系统和神经网络集成方法绘制的大熊猫生境图的精度达到80％以上,高于单一的专家系统方法、神经网络方法和传统的最大似然法制图的精度.Z统计方法也证实了新建立的专家系统和神经网络集成方法要显著好于3种单一方法.     

Linking a spatially-explicit model of acacias to GIS and remotely-sensed data

Spatially-explicit and landscape-related simulation models are increasingly used in ecology, but are often criticized because their parameterization has high data requirements. A frequently suggested approach to overcome this difficulty is the linkage of spatially-explicit or landscape-related models with GIS (geographic information system) and remote-sensing technology. GIS can provide data on relevant landscape features, such as topography, and satellite images can be used to identify spatial vegetation distribution. In this paper, we use these techniques for simple, cost-inexpensive (in both time and money) parameterization based on readily-available GIS and remotely-sensed data. We use a previously developed, spatially-explicit model of the population dynamics of anAcacia species in the Negev desert of Israel (SAM, spatialAcacia model) to investigate if model initialization (measurement of current tree distribution) can be obtained from readily-available satellite images using a radiometric vegetation index (NDVI, normalized difference vegetation index). Furthermore, we investigate the applicability and the advantages of using an explicit consideration of landscape features in the model based on topographic data from a GIS. Using a DEM (digital elevation model), we compare the wadi topography to the current tree distribution observed in the field.