Evidence for the Stepwise Behavioral Response Model (SBRM): the effects of Carbamate Pesticides on medaka (Oryzias latipes) in an online monitoring system

The Stepwise Behavioral Response Model (SBRM), which is a conceptual model, postulated that an organism displays a time-dependent sequence of compensatory Stepwise Behavioral Response (SBR) during exposure to pollutants above their respective thresholds of resistance. In order to prove the model, in this study, the behavioral responses (BRs) of medaka (Oryzias latipes) in the exposure of Arprocarb (A), Carbofuran (C) and Methomyl (M) were analyzed in an online monitoring system (OMS). The Self-Organizing Map (SOM) was utilized for patterning the obtained behavioral data in 0.1 TU (Toxic Unit), 1 TU, 2 TU, 5 TU, 10 TU and 20 TU treatments with control. Some differences among different Carbamate Pesticides (CPs) were observed in different concentrations and the profiles of behavior strength (BS) on SOM were variable depending upon levels of concentration. The time of the first significant decrease of BS (SD-BS) was in inverse ratio to the CP concentrations. Movement behavior showed by medaka mainly included No effect, Stimulation, Acclimation, Adjustment (Readjustment) and Toxic effect, which proved SBRM as a time-dependence model based on the time series BS data. Meanwhile, it was found that SBRM showed evident stress-dependence. Therefore, it was concluded that medaka SBR was both stress-dependent and time-dependent, which supported and developed SBRM, and data mining by SOM could be efficiently used to illustrate the behavioral processes and to monitor toxic chemicals in the environment.     

The Time Invariance Principle,the absence of ecological chaos,and a fundamental pitfall of discrete modeling

This paper is to show that most discrete models used for population dynamics in ecology are inherently pathological that their predications cannot be independently verified by experiments because they violate a fundamental principle of physics. The result is used to tackle an on-going controversy regarding ecological chaos. Another implication of the result is that all dynamical systems must be modeled by differential equations. As a result it suggests that researches based on discrete modeling must be closely scrutinized and the teaching of calculus and differential equations must be emphasized for students of biology.     

Development of fire risk visualization tool based on heat map

At present, relatively few tools are able to visualize regional fire risks both dynamically and efficiently. As a general method of density clustering and visualization, heat maps can be used to analyze the spatial distribution characteristics of fire risks. In this study, based on combining a transformed sigmoid function with a heat map method, a transformed sigmoid risk model (TSRM) was proposed. A numerical mapping method between fire risk values and colors was established based on an RGB color model. By using HTML 5 and JavaScript, a fire risk visualization tool (FRVT) was developed based on the TSRM. Baidu Map was used as the geographic information system engine for the FRVT by calling its JavaScript application program interface. To validate the TSRM and FRVT with case studies, 16,448 historical fires from 2013 to 2017 in Changsha, China, were gathered and visualized. The visualization results indicated that the high-risk areas were mainly distributed in urban areas, and that the medium-risk and low-risk areas were primarily distributed in rural areas. The FRVT can also be applied to visualize and analyze process-related fire risks from a regional perspective. In further research, it will be important to update the FRVT, e.g., to visualize the fire risks posed by the transportation of hazardous materials.     

Increasing accuracy of dispersal kernels in grid-based population models

Dispersal kernels in grid-based population models specify the proportion, distance and direction of movements within the model landscape. Spatial errors in dispersal kernels can have large compounding effects on model accuracy. Circular Gaussian and Laplacian dispersal kernels at a range of spatial resolutions were investigated, and methods for minimizing errors caused by the discretizing process were explored. Kernels of progressively smaller sizes relative to the landscape grid size were calculated using cell-integration and cell-center methods. These kernels were convolved repeatedly, and the final distribution was compared with a reference analytical solution. For large Gaussian kernels (σ > 10 cells), the total kernel error was <10⁻¹¹ compared to analytical results. Using an invasion model that tracked the time a population took to reach a defined goal, the discrete model results were comparable to the analytical reference. With Gaussian kernels that had σ ≤ 0.12 using the cell integration method, or σ ≤ 0.22 using the cell center method, the kernel error was greater than 10%, which resulted in invasion times that were orders of magnitude different than theoretical results. A goal-seeking routine was developed to adjust the kernels to minimize overall error. With this, corrections for small kernels were found that decreased overall kernel error to <10⁻¹¹ and invasion time error to <5%.     

Bird distributions relative to remotely sensed habitats in Great Britain: towards a framework for national modelling

This paper develops a comprehensive and objective picture of bird distributions relative to habitats across Britain. Bird species presence/absence data from an extensive field survey and habitat data from the remotely sensed UK Land Cover Map 2000 were analysed in 36,920 tetrads (2 kmx2 km) across Britain (a 65% sample of Britain's c. 240,000 km2). Cluster analysis linked birds to generalised landscapes based on distinctive habitat assemblages. Maps of the clusters showed strong regional patterns associated with the habitat assemblages. Cluster centroid coordinates for each bird species and each habitat were combined across clusters to derive individualised bird-habitat preference indices and examine the importance of individual habitats for each bird species. Even rare species and scarce habitats showed successful linkages. Results were assessed against published accounts of bird-habitat relations. Objective corroboration strongly supported the associations. Relatively scarce coastal and wetland habitats proved particularly important for many birds. However, extensive arable farmland and woodland habitats were also favoured by many species, despite reported declines in bird numbers in these habitats. The fact that habitat-specialists do not or cannot move habitat is perhaps a reason for declining numbers where habitats have become unsuitable. This study showed that there are unifying principles determining bird-habitat relations which apply and can be quantified at the national scale, and which corroborate and complement the cumulative knowledge of many and varied surveys and ecological studies. This 'generality' suggests that we may be able, reliably and objectively, to integrate and scale up such disparate studies to the national scale, using this generalised framework. It also suggests the potential for a landscape ecology approach to bird-habitat analyses. Such developments will be important steps in building models to develop and test the sustainable management of landscapes for birds.     

SOFM神经网络在道路交通事故分类评价中的应用

随着我国道路交通需求的持续增长和交通建设的快速发展,交通环境和条件有了很大改善,但交通事故仍频频发生,且呈不断增多的趋势,安全已成为交通管理当中一个不容忽视的问题。为了减少交通事故发生次数,降低事故损失程度,需要对交通事故进行分类管理,以便针对不同种类和特征的交通事故采取专门的措施。笔者应用SOFM(自组织特征映射)神经网络对不同原因的道路交通事故进行了分类评价,并根据实际数据的计算和分析提出了相应的防护和控制措施。     

Comparing Landslide Maps: A Case Study in the Upper Tiber River Basin, Central Italy

/ The preparation of landslide maps is an important step in any landslide hazard assessment. Landslides maps are prepared around the world, but little effort is made to assess their reliability, outline their main characteristics, and pinpoint their limitations. In order to redress this imbalance, the results of a long-term research project in the Upper Tiber River basin in central Italy are used to compare reconnaissance and detailed landslide inventory maps, statistical and geomorphologically based density maps, and landslide hazard maps obtained by multivariate statistical modeling. An attempt is made to discuss advantages and limitations of the available maps, outlining possible applications for decision-makers, land developers, and environmental and civil defence agencies. The Tiber experiment has confirmed that landslides can be cost-effectively mapped by interpreting aerial photographs coupled with field surveys and that errors and uncertainties associated with the inventory can be quantified. The experiment has shown that GIS makes it easy to prepare landslide density maps and facilitates the production of statistically based landslide hazard models. The former supply an overview of the distribution of landslides that is easily comprehended but do not provide insight on the causes of instability. The latter, giving insight into the causes of instability, are diagnostically powerful, but are difficult to prepare and exploit.     

Forecasting Air Quality Parameters Using Hybrid Neural Network Modelling

Urban air pollution has emerged as an acute problem in recent years because of its detrimental effects on health and living conditions. The research presented here aims at attaining a better understanding of phenomena associated with atmospheric pollution, and in particular with aerosol particles. The specific goal was to develop a form of air quality modelling which can forecast urban air quality for the next day using airborne pollutant, meteorological and timing variables.Hourly airborne pollutant and meteorological averages collected during the years 1995–1997 were analysed in order to identify air quality episodes having typical and the most probable combinations of air pollutant and meteorological variables. This modelling was done using the Self-Organising Map (SOM) algorithm, Sammon's mapping and fuzzy distance metrics. The clusters of data that were found were characterised by statistics. Several overlapping Multi-Layer Perceptron (MLP) models were then applied to the clustered data, each of which represented one pollution episode. The actual levels for individual pollutants could then be calculated using a combination of the MLP models which were appropriate in that situation.The analysis phase of the modelling gave clear and intuitive results regarding air quality in the area where the data had been collected. The resulting forecast showed that the modelling of gaseous pollutants is more reliable than that of the particles.     

Utility assessment of a map-based online geo-collaboration tool

Spatial group decision-making processes often include both informal and analytical components. Discussions among stakeholders or planning experts are an example of an informal component. When participants discuss spatial planning projects they typically express concerns and comments by pointing to places on a map. The Argumentation Map model provides a conceptual basis for collaborative tools that enable explicit linkages of arguments to the places to which they refer. These tools allow for the input of explicitly geo-referenced arguments as well as the visual access to arguments through a map interface. In this paper, we will review previous utility studies in geo-collaboration and evaluate a case study of a Web-based Argumentation Map application. The case study was conducted in the summer of 2005 when student participants discussed planning issues on the University of Toronto St. George campus. During a one-week unmoderated discussion phase, 11 participants wrote 60 comments on issues such as safety, facilities, parking, and building aesthetics. By measuring the participants' use of geographic references, we draw conclusions on how well the software tool supported the potential of the underlying concept. This research aims to contribute to a scientific approach to geo-collaboration in which the engineering of novel spatial decision support methods is complemented by a critical assessment of their utility in controlled, realistic experiments.     

Revisiting Kappa to account for change in the accuracy assessment of land-use change models

Land-use change models are typically calibrated to reproduce known historic changes. Calibration results can then be assessed by comparing two datasets: the simulated land-use map and the actual land-use map at the same time. A common method for this is the Kappa statistic, which expresses the agreement between two categorical datasets corrected for the expected agreement. This expected agreement is based on a stochastic model of random allocation given the distribution of class sizes. However, when a model starts from an initial land-use map and makes changes to it, that stochastic model does not pose a meaningful reference level. This paper introduces K_Simulation, a statistic that is identical in form to the Kappa statistic but instead applies a more appropriate stochastic model of random allocation of class transitions relative to the initial map. The new method is illustrated on a simple example and then the results of the Kappa statistic and K_Simulation are compared using the results of a land-use model. It is found that only K_Simulation truly tests models in their capacity to explain land-use changes over time, and unlike Kappa it does not inflate results for simulations where little change takes place over time.