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Landslides are a hazard for humans and artificial structures. From an ecological point of view, they represent an important ecosystem disturbance, especially in tropical montane forests. Here, shallow translational landslides are a frequent natural phenomenon and one local determinant of high levels of biodiversity. In this paper, we apply weighted ensembles of advanced phenomenological models from statistics and machine learning to analyze the driving factors of natural landslides in a tropical montane forest in South Ecuador. We exclusively interpret terrain attributes, derived from a digital elevation model, as proxies to several driving factors of landslides and use them as predictors in our models which are trained on a set of five historical landslide inventories. We check the model generality by transferring them in time and use three common performance criteria (i.e. AUC, explained deviance and slope of model calibration curve) to, on the one hand, compare several state-of-the-art model approaches and on the other hand, to create weighted model ensembles. Our results suggest that it is important to consider more than one single performance criterion. Approaching our main question, we compare responses of weighted model ensembles that were trained on distinct functional units of landslides (i.e. initiation, transport and deposition zones). This way, we are able to show that it is quite possible to deduce driving factors of landslides, if the consistency between the training data and the processes is maintained. Opening the ‘black box’ of statistical models by interpreting univariate model response curves and relative importance of single predictors regarding their plausibility, we provide a means to verify this consistency. With the exception of classification tree analysis, all techniques performed comparably well in our case study while being outperformed by weighted model ensembles. Univariate response curves of models trained on distinct functional units of landslides exposed different shapes following our expectations. Our results indicate the occurrence of landslides to be mainly controlled by factors related to the general position along a slope (i.e. ridge, open slope or valley) while landslide initiation seems to be favored by small scale convexities on otherwise plain open slopes. [Copyright &y& Elsevier]

Both the effects of earthworms on soils and the effects of soil conditions on earthworms have been studied with the help of experiments and modelling. This paper provides a model architecture allowing coupling both effects to a dynamic interaction in changing environmental conditions. We chose for a spatio-temporally explicit model and focussed on wetland conditions. Soil temperature and humidity have been modelled by means of finite volumes and were used to determine the spatial habitat suitability. The life cycles of earthworms have been modelled by Leslie matrices where soil humidity, soil temperature and population densities have been used to parametrize survival and transition probabilities. Earthworm dispersion has been described by a cellular automaton of the domain providing spatial population densities for both the life cycle submodel and the soil conditions submodel. The resulting integrated model allowed simulating the spatio-temporal population dynamics of three selected earthworm species at once in a two dimensional topological context. [Copyright &y& Elsevier]