Aphid population response to agricultural landscape change: A spatially explicit,individual-based model

A spatially explicit individual-based simulation model has been developed to represent aphid population dynamics in agricultural landscapes. The application of the model to Rhopalosiphum padi (L.) population dynamics is detailed, including an outline of the construction of the model, its parameterisation and validation. Over time, the aphids interact with the landscape and with one another. The landscape is modified by varying a simple pesticide regime, and the multi-scale spatial and temporal implications for a population of aphids is analysed. The results show that a spatial modelling approach that considers the effects on the individual of landscape properties and factors such as wind speed and wind direction provides novel insight into aphid population dynamics both spatially and temporally. This forms the basis for the development of further simulation models that can be used to analyse how changes in landscape structure impact upon important species distributions and population dynamics.     

Using the ECLPSS software environment to build a spatially explicit component-based model of ozone effects on forest ecosystems

We have developed a modeling framework to support grid-based simulation of ecosystems at multiple spatial scales, the Ecological Component Library for Parallel Spatial Simulation (ECLPSS). ECLPSS helps ecologists to build robust spatially explicit simulations of ecological processes by providing a growing library of reusable interchangeable components and automating many modeling tasks. To build a model, a user selects components from the library, and then writes new components as needed. Some of these components represent specific ecological processes, such as how environmental factors influence the growth of individual trees. Other components provide simulation support such as reading and writing files in various formats to allow inter-operability with other software. The framework manages components and variables, the order of operations, and spatial interactions. The framework provides only simulation support; it does not include ecological functions or assumptions. This separation allows biologists to build models without becoming computer scientists, while computer scientists can improve the framework without becoming ecologists. The framework is designed to operate on multiple platforms and be used across networks via a World Wide Web-based user interface. ECLPSS is designed for use with both single processor computers for small models, and multiple processors in order to simulate large regions with complex interactions among many individuals or ecological compartments. To test Version 1.0 of ECLPSS, we created a model to evaluate the effect of tropospheric ozone on forest ecosystem dynamics. This model is a reduced-form version of two existing models: , which represents an individual tree, and , which represents forest stand growth and succession. This model demonstrates key features of ECLPSS, such as the ability to examine the effects of cell size and model structure on model predictions.     

大跨结构多维多点输入抗震研究进展

对于大跨度结构来说,应该考虑地震动的多维多点效应。从国内外发展现状及取得的主要研究进展和近期的发展动向等角度,对大跨度结构多维多点输入抗震的研究现状进行了系统的综述和总结。首先论述了多维地震动及其相关性,从地震动的各个分量之间的强度比、频率、持时及相关性等方面说明地震动分量的特性;然后论述地震动时空模型,侧重于反映地震动空间变化的相关函数模型的研究现状;最后比较了大跨度结构考虑多维多点输入的抗震计算方法,包括反应谱法、时程分析法以及随机振动分析法。对今后关于这一问题所应开展的研究提出了建议。     

Including Spatial Heterogeneity and Animal Dispersal When Evaluating Hunting: a Model Analysis and an Empirical Assessment in an Amazonian Community

Abstract:  Hunting in tropical forests is typically most intense near human settlements, and this creates gradients of decreasing animal densities toward those settlements. Within the context of this spatial pattern, we evaluated the status of game in the hunting grounds of an indigenous community in eastern Ecuador. We constructed a spatially explicit model of hunter-prey interactions that mimicked the hunting in the village and included realistic animal-dispersal rules. We compared predictions from the model with distributions of animal harvest rates and catch per unit effort of 12 game species. Six species were overharvested in part or all of the area, and two other species were probably being overharvested, although high dispersal rates complicated the interpretation. We then compared our method with methods that have been used previously. We argue that because our method provides information about the spatial extent of overharvesting, it could be particularly useful in informing decision makers about where to establish no-take areas and could therefore aid in improving the sustainability of hunting in tropical forests.     

Metapopulation Extinction Risk under Spatially Autocorrelated Disturbance

Abstract:  Recent extinction models generally show that spatial aggregation of habitat reduces overall extinction risk because sites emptied by local extinction are more rapidly recolonized. We extended such an investigation to include spatial structure in the disturbance regime. A spatially explicit metapopulation model was developed with a wide range of dispersal distances. The degree of aggregation of both habitat and disturbance pattern could be varied from a random distribution, through the intermediate case of a fractal distribution, all the way to complete aggregation (single block). Increasing spatial aggregation of disturbance generally increased extinction risk. The relative risk faced by populations in different landscapes varied greatly, depending on the disturbance regime. With random disturbance, the spatial aggregation of habitat reduced extinction risk, as in earlier studies. Where disturbance was spatially autocorrelated, however, this advantage was eliminated or reversed because populations in aggregated habitats are at risk of mass extinction from coarse-scale disturbance events. The effects of spatial patterns on extinction risk tended to be reduced by long-distance dispersal. Given the high levels of spatial correlation in natural and anthropogenic disturbance processes, population vulnerability may be greatly underestimated both by classical (nonspatial) models and by those that consider spatial structure in habitat alone.     

Incorporating the perceptual range of animals into connectivity models

The perceptual range of an animal towards different landscape elements affects its movements through heterogeneous landscapes. However, empirical knowledge and modeling tools are lacking to assess the consequences of variation in the perceptual range for movement patterns and connectivity. In this study we tested how changes in the assumed perception of different landscape elements affect the outcomes of a connectivity model. We used an existing individual-based, spatially explicit model for the dispersal of Eurasian lynx (Lynx lynx). We systematically altered the perceptual range in which animals recognize forest fragments, water bodies or cities, as well as the probability that they respond to these landscape elements. Overall, increasing the perceptual range of the animals enhanced connectivity substantially, both qualitatively and quantitatively. An enhanced range of attraction to forests had the strongest impact, doubling immigration success; an enhanced range of attraction to rivers had a slightly lower impact; and an enhanced range of avoidance of cities had the lowest impact. Correcting the enhancement in connectivity by the abundance of each of the landscape elements in question reversed the results, indicating the potential sensitivity of connectivity models to rare landscape elements (in our case barriers such as cities). Qualitatively, the enhanced perception resulted in strong changes in movement patterns and connectivity. Furthermore, model results were highly parameter-specific and patch-specific. These results emphasize the need for further empirical research on the perceptual capabilities of different animals in different landscapes and conditions. They further indicate the usefulness of spatially explicit individual-based simulation models for recognizing consistent patterns that emerge, despite uncertainty regarding animals’ movement behavior. Altogether, this study demonstrates the need to extend the concept of ‘perceptual ranges’ beyond patch detection processes, to encompass the wide range of elements that can direct animal movements during dispersal through heterogeneous landscapes.     

Optimization of tourism impacts within protected areas by means of genetic algorithms

The search for a balance between nature conservation and tourism development within protected areas is becoming an increasingly multifaceted problem worldwide, as outlined by an increasing number of authors and highlighted at several international events. Since it is unlikely that all management objectives will reach their optimum values simultaneously, an optimization approach is required to meet multiple, conflicting goals and to obtain an overall trade-off in terms of the conceived objectives.     

WATERSHEDSS: A DECISION SUPPORT SYSTEM FOR WATERSHED-SCALE NONPOINT SOURCE WATER QUALITY PROBLEMS1

ABSTRACT: A significant portion of all pollutants entering surface waters (streams, lakes, estuaries, and wetlands) derives from non-point source (NPS) pollution and, in particular, agricultural activities. The first step in restoring a water resource is to focus on the primary water quality problem in the watershed. The most appropriate NPS control measures, which include best management practices (BMPs) and landscape features, such as wetlands and riparian areas, can then be selected and positioned to minimize or mitigate the identified pollutant(s). A computer-based decision sup. port and educational software system, WATERSHEDSS (WATER, Soil, and Hydro-Environmental Decision Support System), has been developed to aid managers in defining their water quality problems and selecting appropriate NPS control measures. The three primary objectives of WATERSHEDSS are (1) to transfer water quality and land treatment information to watershed managers in order to assist them with appropriate land management/land treatment decisions; (2) to assess NPS pollution in a watershed based on user-supplied information and decisions; and (3) to evaluate, through geographical information systems-assisted modeling, the water quality effects of alternative land treatment scenarios. WATERSHEDSS is available on the World Wide Web (Web) at http://h2osparc.wq.ncsu.edu .     

STORM RUNOFF PREDICTION BASED ON A SPATIALLY DISTRIBUTED TRAVEL TIME METHOD UTILIZING REMOTE SENSING AND GIS1

ABSTRACT: In this study, remotely sensed data and geographic information system (GIS) tools were used to estimate storm runoff response for Simms Creek watershed in the Etonia basin in northeast Florida. Land cover information from digital orthophoto quarter quadrangles (DOQQ), and enhanced thematic mapper plus (ETM+) were analyzed for the years 1990, 1995, and 2000. The corresponding infiltration excess runoff response of the study area was estimated using the U.S. Department of Agriculture (USDA), Natural Resources Conservation Service Curve Number (NRCS‐CN) method. A digital elevation model (DEM)/GIS technique was developed to predict stream response to runoff events based on the travel time from each grid cell to the watershed outlet. A comparison of predicted to observed stream response shows that the model predicts the total runoff volume with an efficiency of 0.98, the peak flow rate at an efficiency of 0.85, and the full direct runoff hydrograph with an average efficiency of 0.65. The DEM/GIS travel time model can be used to predict the runoff response of ungaged watersheds and is useful for predicting runoff hydrographs resulting from proposed large scale changes in the land use.