Using multi-objective classification to model communities of soil microarthropods

• Published in: Ecological modelling 2006-01, Vol.191 (1), p.131-143
• Main Authors: Demšar, Damjan, Džeroski, Sašo, Larsen, Thomas, Struyf, Jan, Axelsen, Jørgen, Pedersen, Marianne Bruus, Krogh, Paul Henning
• Format: Article
• Language: English
• Subjects: Animal, plant and microbial ecology; Biological and medical sciences; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Methods and techniques (sampling, tagging, trapping, modelling...); Modelling; Multi-objective classification; Soil microarthropods
• ISSN: 0304-3800; 1872-7026
• DOI: 10.1016/j.ecolmodel.2005.08.017

In agricultural soil, a suite of anthropogenic events shape the ecosystem processes and populations. However, the impact from anthropogenic sources on the soil environment is almost exclusively assessed for chemicals, although other factors like crop and tillage practices have an important impact as well. Thus, the farming system as a whole should be evaluated and ranked according to its environmental benefits and impacts. Our starting point is a data set describing agricultural events and soil biological parameters. Using machine learning methods for inducing regression and model trees, we produce empirical models able to predict the soil quality from agricultural measures in terms of quantities describing the soil microarthropod community. We are also interested in discovering additional higher level knowledge. In particular, we have identified the most important factors influencing the population densities of springtails and mites and their biodiversity. We also identify to which agricultural actions different microarthropods react distinctly. To obtain this higher level knowledge, we employ multi-objective regression trees.