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Species distribution models are an important tool to predict the impact of global change on species distributional ranges and community assemblages. Although considerable progress has been made in the statistical modeling during the last decade, many approaches still ignore important features of species distributions, such as nonlinearity and interactions between predictors, spatial autocorrelation, and nonstationarity, or at most incorporate only some of these features. Ecologists, however, require a modeling framework that simultaneously addresses all these features flexibly and consistently. Here we describe such an approach that allows the estimation of the global effects of environmental variables in addition to local components dealing with spatiotemporal autocorrelation as well as nonstationary effects. The local components can be used to infer unknown spatiotemporal processes; the global component describes how the species is influenced by the environment and can be used for predictions, allowing the fitting of many well-known regression relationships, ranging from simple linear models to complex decision trees or from additive models to models inspired by machine learning procedures. The reliability of spatiotemporal predictions can be qualitatively predicted by separately evaluating the importance of local and global effects. We demonstrate the potential of the new approach by modeling the breeding distribution of the Red Kite (Milvus milvus), a bird of prey occurring predominantly in Western Europe, based on presence/absence data from two mapping campaigns using grids of 40 km 2 in Bavaria. The global component of the model selected seven environmental variables extracted from the CORINE and WorldClim databases to predict Red Kite breeding. The effect of altitude was found to be nonstationary in space, and in addition, the data were spatially autocorrelated, which suggests that a species distribution model that does not allow for spatially varying effects and spatial autocorrelation would have ignored important processes determining the distribution of Red Kite breeding across Bavaria. Thus, predictions from standard species distribution models that do not allow for real-world complexities may be considerably erroneous. Our analysis of Red Kite breeding exemplifies the potential of the innovative approach for species distribution models. The method is also applicable to modeling count data.
The public promotion of renewable energies is expected to increase the number of biogas plants and stimulate energy crops cultivation (e. g. maize) in Germany. In order to assess the indirect effects of the resulting land-use changes on biodiversity, we developed six land-use scenarios and simulated the responses of six farmland wildlife species with the spatially explicit agent-based model system ALMaSS. The scenarios differed in composition and spatial configuration of arable crops. We implemented scenarios where maize for energy production replaced 15% and 30% of the area covered by other cash crops. Biogas maize farms were either randomly distributed or located within small or large aggregation clusters. The animal species investigated were skylark (Alauda arvensis), grey partridge (Perdix perdix), European brown hare (Lepus europaeus), field vole (Microtus agrestis), a linyphiid spider (Erigone atra) and a carabid beetle (Bembidion lampros). The changes in crop composition had a negative effect on the population sizes of skylark, partridge and hare and a positive effect on the population sizes of spider and beetle and no effect on the population size of vole. An aggregated cultivation of maize amplified these effects for skylark. Species responses to changes in the crop composition were consistent across three differently structured landscapes. Our work suggests that with the compliance to some recommendations, negative effects of biogas-related land-use change on the populations of the six representative farmland species can largely be avoided.
Mountain ecosystems are commonly regarded as being highly sensitive to global change. Due to the system complexity and multifaceted interacting drivers, however, understanding current responses and predicting future changes in these ecosystems is extremely difficult. We aim to discuss potential effects of global change on mountain ecosystems and give examples of the underlying response mechanisms as they are understood at present. Based on the development of scientific global change research in mountains and its recent structures, we identify future research needs, highlighting the major lack and the importance of integrated studies that implement multi-factor, multi-method, multi-scale, and interdisciplinary research.
In this paper we evaluate different methods to predict soil erosion processes. We derived different layers of predictor variables for the study area in the Northern Chianti, Italy, describing the soil-lithologic complex, land use, and topographic characteristics. For a subcatchment of the Orme River, we mapped erosion processes by interpreting aerial photographs and field observations. These were classified as erosional response units (ERU), i.e. spatial areas of homogeneous erosion processes. The ERU were used as the response variable in the soil erosion modelling process. We applied two models i) bootstrap aggregation (Random Forest: RF), and ii) stochastic gradient boosting (TreeNet: TN) to predict the potential spatial distribution of erosion processes for the entire Orme River catchment. The models are statistically evaluated using training data and a set of performance parameters such as the area under the receiver operating characteristic curve (AUC), Cohen's Kappa, and pseudo R2. Variable importance and response curves provide further insight into controlling factors of erosion. Both models provided good performance in terms of classification and calibration; however, TN outperformed RF. Similar classes such as active and inactive landslides can be discriminated and well interpreted by considering response curves and relative variable importance. The spatial distribution of the predicted erosion susceptibilities generally follows topographic constraints and is similar for both models. Hence, the model-based delineation of ERU on the basis of soil and terrain information is a valuable tool in geomorphology; it provides insights into factors controlling erosion processes and may allow the extrapolation and prediction of erosion processes in unsurveyed areas.
Northwest Europe's largest heather-dominated sandy habitats are located in the nature reserve Luneburger Heide, Germany. Yet, even these appear to be losing their ability to support some of their stenotopic species such as the ladybird spider, Eresus kollari Rossi 1846, and are thus becoming increasingly important for the preservation of these species. The habitat requirements of this endangered spider species were investigated in order to obtain data that will help stabilize the last remnants of the species' population in northwest Germany. Several heathland habitats were surveyed by pitfall trapping during the mate-search period of the males. Two statistical methods were applied: logistic regression and boosted regression trees (BRT). Both methods showed that three habitat variables are of prime relevance in predicting the occurrence of E. kollari: a) thickness of the organic layer (a negative effect), b) soil temperature at a depth of 10 cm, and c) Calluna cover in the herb layer (both have positive effect). Our results show that choppering (removing above-ground biomass and most of O-layer) and burning are likely appropriate heathland management measures for the conservation of E. kollari. Such measures improve the species' habitat quality by creating a heterogenic (small-scaled) heathland structure with suitable microhabitats. As Calluna heathlands show a clear senescence of the dominant heather, it is essential that those habitat patches be conserved. Further measures, such as transfer experiments, are recommended.