Spatially Explicit Individual-Based Ecological Modeling with Mobile Fuzzy Agents

Robinson, Vincent Β.; Graniero, Phil A.

doi:10.1007/3-540-26886-3_14

Cited by 8 publications

(10 citation statements)

References 39 publications

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“…ECO-COSM was used to explore the use of fuzzy logic in an individual-based model of natal dispersal behaviour of eastern grey squirrels (Sciurus carolinensis) in an area near the Land Between the Lakes National Recreation Area (Robinson and Graniero 2005a). The motivation for this exploration derives from the problems of uncertainty in both data and model parameters when using GIS-based spatially explicit population models.…”

Section: Agent Model Examplementioning

confidence: 99%

A probe mechanism to couple spatially explicit agents and landscape models in an integrated modelling framework

Graniero

Robinson

2006

International Journal of Geographical Information Science

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Many environmental, ecological, and social problems require investigation using a mixture of landscape models, individual-based models, and some level of interaction between them. Few simulation-modelling frameworks are structured to handle both styles of model in an integrated fashion. ECO-COSM is a framework that is capable of handling complex models with both landscape and agent components. Its Probe-based architecture allows model components to have controlled access to the state of other components. The ProbeWrapper is a modification of this common design approach which allows alterations to the state retrieved from the model and is a critical component of ECO-COSM's broad modelling capability. It allows agents to apply perceptual filters or measurement errors to their observations of the landscape, or apply decisionmaking strategies in the face of incomplete or uncertain observations. ECO-COSM is demonstrated with a landscape model of metapopulation dynamics, an agent model of squirrel dispersal, and a coupled landscape-agent model to evaluate field-data-acquisition strategies for identifying nutrient or contaminant hotspots.

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Section: Agent Model Examplementioning

confidence: 99%

A probe mechanism to couple spatially explicit agents and landscape models in an integrated modelling framework

Graniero

Robinson

2006

International Journal of Geographical Information Science

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“…The representation of environmental phenomena as fuzzy sets (Fisher, 2000a, b;Petry et al, 2005;Robinson, 2003) has included soils (Burrough, 1989;Lagacherie et al, 1997) vegetation (Moraczewski, 1993a, b), geology (Brown et al, 2003;Luo and Dimitrakopoulos, 2003), land cover classification from remotely sensed data (Foody, 1992(Foody, , 1996, landform classes (Cheng and Molenaar, 1999;MacMillan et al, 2000), and been extended to the agent-based modelling of the ecological movement of animals (Robinson and Graniero, 2005;Elith et al, 2002). Most research has been grounded in the argument that many class descriptions for natural resources are inherently vague.…”

Section: Introductionmentioning

confidence: 99%

Higher Order Vagueness in a Dynamic Landscape: Multi-Resolution Morphometric Analysis of a Coastal Dunefield

Fisher

Wood²,

Cheng³

2007

J ENV INFORM

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ABSTRACT. Many environmental phenomena can be conceptualised as vague and so may be suitable for storage as fuzzy sets and analysis by fuzzy logic. Fuzzy sets directly address the vagueness in the information, but many consider that any statement about a vague phenomenon must itself be vague. This is known as higher order vagueness, and is handled in fuzzy set theory by type-2 and, by extension, type-n fuzzy sets. In this paper we use the recognition of, and change in, a system of coastal sand dunes as an environmental example in which to explore the use of type-2 fuzzy sets. The crests and troughs of the dunes are identified as fuzzy sets from geomorphometric analysis of high resolution digital elevation models from two years (1998 and 2000). By varying the parameters of the morphometric extraction, multiple instances of type-1 fuzzy sets can be defined, and these can be summarised to yield type-2 fuzzy sets. The logic of change analysis is presented, and two alternative approaches to change analysis of type-2 fuzzy sets implemented. In one approach changes in the multiple instances of type-1 fuzzy sets are analysed and summarised as type-2 sets. The second approach directly examines change in the parameters of type-2 fuzzy sets, viewing the results for the different parameters as separate instances of change. All analyses produce satisfactory results which, although they are hard to verify, make sense, yielding a range of possible but small degrees of fuzzy change. In a rather sedentary dune system (as is usual in a coastal location in mid-latitudes) this is to be expected. Change analysis in most applications of type-2 fuzzy sets, which might be based on expert advice for defining memberships, would have to rely on this second approach, and it is therefore interesting to note that this yields the largest range of possible change results.

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“…It has been shown that such context dependent perceptual ranges can have an effect on the movement behavior of individuals (Olden et al, 2004). Robinson and Graniero (2005) describe how an object-oriented probe mechanism can be used to incorporate fuzziness in an effective manner at the level of model parameters such as the perceptual range. Essentially the individual animals are simulated as a population of objects where each class has a different decision-making model of whether to move, where to move, and when to stop moving.…”

Section: Fuzziness and Model Parametersmentioning

confidence: 99%

“…However, each object does not directly draw its information from the GIS-based database, but rather through a probe mechanism (Graniero and Robinson, in press). Robinson and Graniero (2005) model squirrel dispersal using a fuzzy set representation of perceptual range. It is modeled as a function of distance on an isotropic surface.…”

Section: Fuzziness and Model Parametersmentioning

confidence: 99%

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Issues and Challenges of Incorporating Fuzzy Sets in Ecological Modeling

Robinson¹

Geographic Uncertainty in Environmental Security

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Abstract. An information-based framework is presented for spatially explicit GIS-based ecological modeling. Within this framework some of the important issues and challenges of incorporating fuzzy sets in spatially explicit population models (SEPM) are discussed. Examples of current work are used to illustrate the main issues and challenges facing the incorporation of fuzzy sets in ecological modeling. Among the challenges to be discussed are fuzzy-based techniques for data acquisition, model control/ evaluation, heterogeneous representations of spatial data, parameterization of models, and hypothesis testing. There is special attention given to the issue of habitat modeling and presence/absence problem. Many scientific issues facing the incorporation of ecological models will be raised such as hypothesis testing, and relationship between statistical analysis and fuzzy

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Spatially Explicit Individual-Based Ecological Modeling with Mobile Fuzzy Agents

Cited by 8 publications

References 39 publications

A probe mechanism to couple spatially explicit agents and landscape models in an integrated modelling framework

A probe mechanism to couple spatially explicit agents and landscape models in an integrated modelling framework

Higher Order Vagueness in a Dynamic Landscape: Multi-Resolution Morphometric Analysis of a Coastal Dunefield

Issues and Challenges of Incorporating Fuzzy Sets in Ecological Modeling

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