Predictive Modeling of Soil and Plant Distributions

Ali Keshavarzi, Fereydoon Sarmadian, Antonia Odagiu


Predictive modeling of plant species’ distributions based on their relationship with environmental variables is important for a range of management activities. Examples include management of threatened species and communities, risk assessment of non-native species in new environments and the estimation of the magnitude of biological responses to environmental changes. Variability is one of the intrinsic characteristics of the soil properties. Within an ecosystem, soil properties have vast spatial variations which mainly arise from factors and processes of pedogenesis and land use. Spatial variability in the soil is natural, but understanding these changes, particularly in agricultural lands for planning and management is inevitable. Soil properties change with time and space of the small scales to large scales, which are influenced by intrinsic properties (such as soil parent materials) and non-inherent characteristics (such as management, fertilizer and crop rotation). To plants predictive mapping, it is necessary to prepare the maps of all affective factors of models. Geostatistics is a useful tool for analyzing the structure of spatial variability, interpolating between point observations and creating the map of interpolated values with an associated error map. In this paper, we are focusing on the spatial variation of plant diversity with respect to soil and environmental impacts


geostatistics, soil properties, plant diversity, regression models

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