Spatial Uncertainty Analysis in Ecological Biology

Spatial Uncertainty Analysis in Ecological Biology

Stelios Zimeras (Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean, Karlovassi, Samos, Greece) and Yiannis Matsinos (Biodiversity Conservation Laboratory, Department of Environmental Studies, University of the Aegean, Mytilene, Greece)
DOI: 10.4018/ijsbbt.2013010102
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Abstract

Uncertainty analysis is the part of risk analysis that focuses on the uncertainties in the data characteristics. Important components of uncertainty analysis include qualitative analysis that identifies the uncertainties, quantitative analysis of the effects of the uncertainties on the decision process, and communication of the uncertainty. (Funtowwicz & Ravetz 1990; Petersen, 2000; Regan et a1., 2002; Katz 2002). The analyses include simple descriptive procedures till quantitative estimation of uncertainty, and decision-based procedures. The analysis may be qualitative or quantitative, depending on the stage of analysis required and the amount of information available. When a neighbourhood structure lattice system is applied, a spatial connectivity between regions is defined where investigation of that structure includes modelling of the spatial homogeneity is introduced. Spatial investigation involves stochastic modelling especially in cases where the incomplete data involves hide information’s. In this work a spatial analysis methodology was introduced and procedures to solve the problem with spatial variability are described.
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Spatial Uncertainty Analysis

Interactions between biological cells at different scales are characterized by their local dynamics and the emergent spatial patterns are the outcome of different processes. The development of specific new, applied statistical techniques can be explained by the emerging field of specific regions of human body, which focuses on spatial processes operating over various spatial extents biologists are trying to collect quantified information about spatial pattern in order to answer questions regarding the underlying processes (e.g. competition) (Turner, 1989; Wiens, 1989). Although different processes could be responsible in generating the same spatial pattern, its quantification may help to identify these processes. Complementary to answering causal questions about biological processes, quantification of spatial pattern can be used to analyze spatial dependences. However, the sometimes hidden spatial dependence in data can lead to violations of the assumption (Legendre, 1993). It must be noted that there is considerable variety of statistical methods that have been applied in the analysis of spatial variation in biological data. These include dispersal analysis, spectral analysis, wavelet analysis, kriging, spatial Monte Carlo simulations and many geostatistics methods (Zimeras & Matsinos, 2011).

Uncertainty in models can be divided in a similar way by statistical and systematic uncertainty. The statistical uncertainties arise from the variability of input variables and parameters where the variability is known. This variability can be described by probability density functions (PDFs) describing the variability of the input variables and the parameters (Zimeras & Matsinos, 2011). Systematic uncertainties arise from variability in input variables and parameters when variability is unknown. Also unknown processes in the model e.g. incorrect model structure contribute to the systematic uncertainties.

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