@article{ReinekingSchroederEsselbach2003,
  author    = {Reineking, Bj{\"o}rn and Schr{\"o}der-Esselbach, Boris},
  title     = {Computer-intensive methods in the analysis of species-habitat relationships},
  year      = {2003},
  language  = {en}
}
@article{SchroederEsselbachReineking2004,
  author    = {Schr{\"o}der-Esselbach, Boris and Reineking, Bj{\"o}rn},
  title     = {Validierung von Habitatmodellen},
  year      = {2004},
  language  = {de}
}
@article{ReinekingSchroederEsselbach2004,
  author    = {Reineking, Bj{\"o}rn and Schr{\"o}der-Esselbach, Boris},
  title     = {Variablenselektion : Strategien der Modellbildung in der Habitatmodellierung},
  year      = {2004},
  language  = {de}
}
@article{ReinekingSchroederEsselbach2004,
  author    = {Reineking, Bj{\"o}rn and Schr{\"o}der-Esselbach, Boris},
  title     = {G{\"u}temaße f{\"u}r Habitatmodelle},
  year      = {2004},
  language  = {de}
}
@article{ReinekingSchroederEsselbach2006,
  author    = {Reineking, Bj{\"o}rn and Schr{\"o}der-Esselbach, Boris},
  title     = {Constrain to perform : regularization of habitat models},
  issn      = {0304-3800},
  doi       = {10.1016/j.ecolmodel.2005.10.003},
  year      = {2006},
  abstract  = {Predictive habitat models are an important tool for ecological research and conservation. A major cause of unreliable models is excessive model complexity, and regularization methods aim to improve the predictive performance by adequately constraining model complexity. We compare three regularization methods for logistic regression: variable selection, lasso, and ridge. They differ in the way model complexity is measured: variable selection uses the number of estimated parameters, the lasso uses the sum of the absolute values of the parameter estimates, and the ridge uses the sum of the squared values of the parameter estimates. We performed a simulation study with environmental data of a real landscape and artificial species occupancy data. We investigated the effect of three factors on relative model performance: (1) the number of parameters (16, 10, 6, 2) in the 'true' model that determined the distribution of the artificial species, (2) the prevalence, i.e. the proportion of sites occupied by the species, and (3) the sample size (measured in events per variable, EPV). Regularization improved model discrimination and calibration. However, no regularization method performed best under all circumstances: the ridge generally performed best in the 16-parameter scenario. The lasso generally performed best in the 10-parameter scenario. Variable selection with AIC was best at large sample sizes (EPV >= 10) when less than half of the variables influenced the species distribution. However, at low sample sizes (EPV < 10), ridge and lasso always performed best, regardless of the parameter scenario or prevalence. Overall, calibration was best in ridge models. Other methods showed overconfidence, particularly at low sample sizes. The percentage of correctly identified models was low for both lasso and variable selection. Variable selection should be used with caution. Although it can produce the best performing models under certain conditions, these situations are difficult to infer from the data. Ridge and lasso are risk-averse model strategies that can be expected to perform well under a wide range of underlying species-habitat relationships, particularly at small sample sizes.},
  language  = {en}
}
@article{JeltschBontePeeretal.2013,
  author    = {Jeltsch, Florian and Bonte, Dries and Peer, Guy and Reineking, Bj{\"o}rn and Leimgruber, Peter and Balkenhol, Niko and Schr{\"o}der-Esselbach, Boris and Buchmann, Carsten M. and M{\"u}ller, Thomas and Blaum, Niels and Zurell, Damaris and B{\"o}hning-Gaese, Katrin and Wiegand, Thorsten and Eccard, Jana and Hofer, Heribert and Reeg, Jette and Eggers, Ute and Bauer, Silke},
  title     = {Integrating movement ecology with biodiversity research - exploring new avenues to address spatiotemporal biodiversity dynamics},
  doi       = {10.1186/2051-3933-1-6},
  year      = {2013},
  language  = {en}
}
@misc{JeltschBontePe'eretal.2013,
  author    = {Jeltsch, Florian and Bonte, Dries and Pe'er, Guy and Reineking, Bj{\"o}rn and Leimgruber, Peter and Balkenhol, Niko and Schr{\"o}der-Esselbach, Boris and Buchmann, Carsten M. and M{\"u}ller, Thomas and Blaum, Niels and Zurell, Damaris and B{\"o}hning-Gaese, Katrin and Wiegand, Thorsten and Eccard, Jana and Hofer, Heribert and Reeg, Jette and Eggers, Ute and Bauer, Silke},
  title     = {Integrating movement ecology with biodiversity research},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-401177},
  pages     = {13},
  year      = {2013},
  abstract  = {Movement of organisms is one of the key mechanisms shaping biodiversity, e.g. the distribution of genes, individuals and species in space and time. Recent technological and conceptual advances have improved our ability to assess the causes and consequences of individual movement, and led to the emergence of the new field of 'movement ecology'. Here, we outline how movement ecology can contribute to the broad field of biodiversity research, i.e. the study of processes and patterns of life among and across different scales, from genes to ecosystems, and we propose a conceptual framework linking these hitherto largely separated fields of research. Our framework builds on the concept of movement ecology for individuals, and demonstrates its importance for linking individual organismal movement with biodiversity. First, organismal movements can provide 'mobile links' between habitats or ecosystems, thereby connecting resources, genes, and processes among otherwise separate locations. Understanding these mobile links and their impact on biodiversity will be facilitated by movement ecology, because mobile links can be created by different modes of movement (i.e., foraging, dispersal, migration) that relate to different spatiotemporal scales and have differential effects on biodiversity. Second, organismal movements can also mediate coexistence in communities, through 'equalizing' and 'stabilizing' mechanisms. This novel integrated framework provides a conceptual starting point for a better understanding of biodiversity dynamics in light of individual movement and space-use behavior across spatiotemporal scales. By illustrating this framework with examples, we argue that the integration of movement ecology and biodiversity research will also enhance our ability to conserve diversity at the genetic, species, and ecosystem levels.},
  language  = {en}
}