Environmental Engineering Reference
In-Depth Information
(Fig.
14.1
) shows that this functional group of earthworms prefers less clay and
more silt soil with medium pH. It has been shown that the seasonal effect (autumn/
spring sampling) has stronger influence on anecic biomass compared to the inter-
annual effect (autumn 2002/autumn 2003). Indeed, it is very well known that in
temperate arable ecosystems, anecic earthworms reach their minimum in winter,
due to low temperature, and their maximum in autumn, after spring and summer
reproduction and development. Finally, agricultural practices, such as tillage or
maize variety have no effects on anecic earthworm biomass.
Soil dwelling populations in arable ecosystems are exposed to various anthropo-
genic pressures. To identify attributes influencing the abundance of soil mites and
springtails and the biodiversity of soil micro-arthropods, a multi objective regres-
sion tree has been induced from data collected under different crop management
practices (Demˇar et al. 2006). Figure
14.2
shows an example of such a decision
tree predicting the target attributes abundances of Acari (
r
2
¼
0.653) and Collem-
bola (
r
2
0.675) and the diversity of Collembola (
r
2
0.562). The model indi-
cates that the most important parameters are the soil type, the time (number of
months) since the establishment of the current situation, and the different forms of
tillage. Hence, the model can adequately reproduce the known empirical knowledge
on this phenomenon.
¼
¼
14.5 Habitat Modelling Using Decision Trees
Habitat modelling typically relates properties of the environment with the pres-
ence, abundance or diversity of organisms (for other detailed examples, see
Chap. 13 on spatial distribution models). For example, one might study the
influence of soil characteristics, such as soil temperature, water content, and
proportion of mineral soil on the abundance and species richness of Collembola
(springtails; the most abundant insects in soil (Kampichler et al. 2000)). Habitat
modelling can also be linked with spatial information derived from geographic
information systems (GIS) on the studied area (Debeljak et al. 2001; Jerina et al.
2003) (see also Chap. 22).
A number of habitat-suitability modelling applications of other machine learning
methods (e.g. neural networks, genetic algorithms) were surveyed by Fielding
(1999). Lek-Ang et al. (1999) used neural networks to build a number of predictive
models for Collembola diversity. Bell (1999) used decision trees to describe the
winter habitat of pronghorn antelope. Jeffers (1999) used a genetic algorithm to
discover rules that describe habitat preferences for aquatic species in British rivers.
Rule inductions were also used to relate the presence or absence of a number of
species in Slovenian rivers to physical and chemical properties of river water, such
as temperature, dissolved oxygen, pollutant concentrations, chemical oxygen
demand, etc. (Dˇeroski and Grbovi´ 1995).
Decision trees are applied widely in habitat modelling. Dˇeroski and Drumm
(2003) have used classification tree models to predict the suitability for the sea
