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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.
In order to investigate the potential role of arctic geese in the epidemiology, the spatial and temporal spread of selected avian diseases, in autumn 2002, a virological and serological survey designed as capture-mark-resighting study was conducted in one of the most important coastal resting sites for migratory waterfowl in Germany. Orophatyngeal, cloacal swabs and blood samples were collected from a total of 147 birds comprising of three different arctic geese species including White-fronted Goose (Anser albifrons), Tundra Bean Goose (Anser fabalis rossicus), Pink-footed Goose (Anser brachyrhynchus) as well as from 29 non-migratory Canada Geese (Branta canadensis). Altogether, six adeno-like viruses (ALV; 95% CI, 1.74-9.92%) and two avian paramyxoviruses (APMV-4; 95% Cl, 0.19-5.53%) were isolated mainly from juvenile White-fronted Geese. In addition, four Canada Geese were infected with lentogenic APMV-1 (95% CI, 3.89-31.66%) at the date of sampling. No avian influenza viruses, reo-like viruses could be isolated despite serological evidence. Likewise, no evidence of current or previous infection by West Nile virus was found. Of the 147 birds tagged in the following years, 137 birds were resighted between 2002 and 2008 accumulating to 1925 sightings. About 90% of all sightings were reported from the main wintering and resting sites in Germany and The Netherlands. Eight of the resighted geese were virus positive (ALV and APMV-4) at the time point of sampling in 2002.
We investigate concentrated solutions of poly(styrene-b-N-isopropyl acrylamide) (P(S-b-NIPAM)) diblock copolymers in deuterated water (D2O). Both structural changes and the changes of the segmental dynamics occurring upon heating through the lower critical solution temperature (LCST) of PNIPAM are studied using small-angle neutron scattering and neutron spin-echo spectroscopy. The collapse of the micellar shell and the cluster formation of collapsed micelles at the LCST as well as an increase of the segmental diffusion coefficient after crossing the LCST are detected. Comparing to our recent results on a triblock copolymer P(S-b-NIPAM-b-S) [25], we observe that the collapse transition of P(S-b-NIPAM) is more complex and that the PNIPAM segmental dynamics are faster than in P(S-b-NIPAM-b-S).