@article{MeyerRaudnitschkaSteinhauseretal.2008,
  author    = {Meyer, Jork and Raudnitschka, Dorit and Steinhauser, J. and Jeltsch, Florian and Brandl, Roland},
  title     = {Biology and ecology of "Thallomys nigricauda" (Rodentia, Muridae) in the Thornveld savannah of South Africa},
  issn      = {1616-5047},
  doi       = {10.1016/j.mambio.2006.11.002},
  year      = {2008},
  language  = {en}
}
@article{HothornMuellerSchroederetal.2011,
  author    = {Hothorn, Torsten and M{\"u}ller, J{\"o}rg and Schroeder, Boris and Kneib, Thomas and Brandl, Roland},
  title     = {Decomposing environmental, spatial, and spatiotemporal components of species distributions},
  series = {Ecological monographs : a publication of the Ecological Society of America.},
  volume    = {81},
  journal   = {Ecological monographs : a publication of the Ecological Society of America.},
  number    = {2},
  publisher = {Wiley},
  address   = {Washington},
  issn      = {0012-9615},
  doi       = {10.1890/10-0602.1},
  pages     = {329 -- 347},
  year      = {2011},
  abstract  = {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.},
  language  = {en}
}
@article{MeyerKohnenDurkaetal.2009,
  author    = {Meyer, Jork and Kohnen, Annette and Durka, Walter and W{\"o}stemeyer, Johannes and Blaum, Niels and Rossmanith, Eva and Brandl, Roland},
  title     = {Genetic structure and dispersal in a small South African Rodent : is dispersal female-biased?},
  issn      = {1616-5047},
  doi       = {10.1016/j.mambio.2008.11.004},
  year      = {2009},
  abstract  = {Dispersal greatly determines genetic structure of populations, although it is influenced by landscape heterogeneity, quality of the matrix, resource distribution and local population densities and dynamics. To get insights into some of those processes we analysed the genetic structure of the hairy-footed gerbil Gerbillurus paeba (Rodentia, Murinae, Gerbillinae) in the southern Kalahari (South Africa). Samples were taken from 20 populations covering an area of about 2200 km2. Genetic data were related to landscape characters and population dynamics. We used newly developed microsatellites and found at all loci some indication for the presence of null alleles. However, null alleles seem to have little influence on the general results of our analyses. Altogether we found even nearby populations of G. paeba to be significantly differentiated, although assignment tests revealed 24\% of individuals as immigrants. Genetic structure was independent of landscape heterogeneities at all spatial scales. Autocorrelation analyses (range 50-90 km) revealed significant genetic structure within populations on distances <3 km. We found some indication for female-biased dispersal. Our study suggests that dispersing individuals have little influence on the long-term genetic structure and that drift is the major cause of genetic diversity. The observed genetic pattern likely derives from strong population fluctuations of G. paeba. The landscape structure has little influence on the genetic differentiation between populations.},
  language  = {en}
}