Abstract
The paper shows the use of Kohonen's network for classification of basaltoides on the base of chemical properties of soils and <em>Polypodium vulgare</em> L. The study area was Lower Silesia (Poland). The archival data were: chemical composition of types of basaltoides from 89 sites (Al<sub>2</sub>O<sub>3</sub>, CaO, FeO, Fe<sub>2</sub>O<sub>3</sub>, K2O, MgO, MnO, Na<sub>2</sub>O, P<sub>2</sub>O<sub>5</sub>, SiO<sub>2</sub> and TiO<sub>2</sub>), elements contents in soils (Cd, Co, Cu, Fe, Mn, Mo, Ni, Pb, S, Ti and Zn) and leaves of <em>P. vulgare</em> (Ca, Cd, Co, Cu, Fe, K, Mg, Mn, Mo, N, Ni, P, Pb, S, Ti and Zn) from 20 sites. Descriptive statistical parameters of soils and leaves chemical properties have been shown, statistical analyses using ANOVA and relationships between chemical elements were carried out, and SOFM models have been constructed. The study revealed that the ordination of individuals and groups of neurons in topological maps of plant and soil chemical properties are similar. The constructed models are related with significantly different contents of elements in plants and soils. These models represent different chemical types of soils and are connected with ordination of types of basaltoides worked out by SOFM model of TAS division. The SOFM appeared to be a useful technique for ordination of ecological data and provides a novel framework for the discovery and forecasting of ecosystem properties.
Highlights
Nowadays, particular attention is being paid to search of methods and solutions enabling higher verification possibilities of study results
Researchers use a lot of numerical methods, mainly classical ones (Sokal and Rohlf 2003), and techniques based on artificial neural networks (ANNs) (Ray and Klindworth 2000; Soko3owski 2002; Tadeusiewicz 2006)
Tadeusiewicz (2000) draws attention to the application of neural networks in biotechnology and biomaterials, which offer an attractive solution to lots of problems in many critical applications, and Kosiba (2010; and other references cited by this author) to their superiority to classical and advanced statistical methods in case of ecological studies
Summary
Particular attention is being paid to search of methods and solutions enabling higher verification possibilities of study results. Moreno-Sanches (2004) points to the use of various image and numerical techniques, which are precise and practical in ecological studies and Soko3owski (2010) points to the abundance of basic and advanced statistical methods in research. Their use in order to recognize the regularities occurring in phenomenons and processes remaining under the influence of the main reasons (systematic component) and indirect ones (accidental component) (Soko3owski 2010). Tadeusiewicz (2000) draws attention to the application of neural networks in biotechnology and biomaterials, which offer an attractive solution to lots of problems in many critical applications, and Kosiba (2010; and other references cited by this author) to their superiority to classical and advanced statistical methods in case of ecological studies. One of the ANNs is Kohonens network (Kohonen 2001), which can be used for ordination and visualization of complex ecological data (Chon et al 1996; Recknagel 2001)
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