@article{DormannElithBacheretal.2013,
  author    = {Dormann, Carsten F. and Elith, Jane and Bacher, Sven and Buchmann, Carsten M. and Carl, Gudrun and Carre, Gabriel and Garcia Marquez, Jaime R. and Gruber, Bernd and Lafourcade, Bruno and Leitao, Pedro J. and M{\"u}nkem{\"u}ller, Tamara and McClean, Colin and Osborne, Patrick E. and Reineking, Bjoern and Schr{\"o}der-Esselbach, Boris and Skidmore, Andrew K. and Zurell, Damaris and Lautenbach, Sven},
  title     = {Collinearity a review of methods to deal with it and a simulation study evaluating their performance},
  series = {Ecography : pattern and diversity in ecology ; research papers forum},
  volume    = {36},
  journal   = {Ecography : pattern and diversity in ecology ; research papers forum},
  number    = {1},
  publisher = {Wiley-Blackwell},
  address   = {Hoboken},
  issn      = {0906-7590},
  doi       = {10.1111/j.1600-0587.2012.07348.x},
  pages     = {27 -- 46},
  year      = {2013},
  abstract  = {Collinearity refers to the non independence of predictor variables, usually in a regression-type analysis. It is a common feature of any descriptive ecological data set and can be a problem for parameter estimation because it inflates the variance of regression parameters and hence potentially leads to the wrong identification of relevant predictors in a statistical model. Collinearity is a severe problem when a model is trained on data from one region or time, and predicted to another with a different or unknown structure of collinearity. To demonstrate the reach of the problem of collinearity in ecology, we show how relationships among predictors differ between biomes, change over spatial scales and through time. Across disciplines, different approaches to addressing collinearity problems have been developed, ranging from clustering of predictors, threshold-based pre-selection, through latent variable methods, to shrinkage and regularisation. Using simulated data with five predictor-response relationships of increasing complexity and eight levels of collinearity we compared ways to address collinearity with standard multiple regression and machine-learning approaches. We assessed the performance of each approach by testing its impact on prediction to new data. In the extreme, we tested whether the methods were able to identify the true underlying relationship in a training dataset with strong collinearity by evaluating its performance on a test dataset without any collinearity. We found that methods specifically designed for collinearity, such as latent variable methods and tree based models, did not outperform the traditional GLM and threshold-based pre-selection. Our results highlight the value of GLM in combination with penalised methods (particularly ridge) and threshold-based pre-selection when omitted variables are considered in the final interpretation. However, all approaches tested yielded degraded predictions under change in collinearity structure and the folk lore'-thresholds of correlation coefficients between predictor variables of |r| >0.7 was an appropriate indicator for when collinearity begins to severely distort model estimation and subsequent prediction. The use of ecological understanding of the system in pre-analysis variable selection and the choice of the least sensitive statistical approaches reduce the problems of collinearity, but cannot ultimately solve them.},
  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}
}