@article{BocediZurellReinekingetal.2014,
  author    = {Bocedi, Greta and Zurell, Damaris and Reineking, Bjoern and Travis, Justin M. J.},
  title     = {Mechanistic modelling of animal dispersal offers new insights into range expansion dynamics across fragmented landscapes},
  series = {Ecography : pattern and diversity in ecology ; research papers forum},
  volume    = {37},
  journal   = {Ecography : pattern and diversity in ecology ; research papers forum},
  number    = {12},
  publisher = {Wiley-Blackwell},
  address   = {Hoboken},
  issn      = {0906-7590},
  doi       = {10.1111/ecog.01041},
  pages     = {1240 -- 1253},
  year      = {2014},
  language  = {en}
}
@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}
}
@unpublished{WellsteinSchroederEsselbachReinekingetal.2011,
  author    = {Wellstein, Camilla and Schr{\"o}der-Esselbach, Boris and Reineking, Bjoern and Zimmermann, Niklaus E.},
  title     = {Understanding species and community response to environmental change - A functional trait perspective},
  series = {Agriculture, ecosystems \& environment : an international journal for scientific research on the relationship of agriculture and food production to the biosphere},
  volume    = {145},
  journal   = {Agriculture, ecosystems \& environment : an international journal for scientific research on the relationship of agriculture and food production to the biosphere},
  number    = {1},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0167-8809},
  doi       = {10.1016/j.agee.2011.06.024},
  pages     = {1 -- 4},
  year      = {2011},
  language  = {en}
}
@article{ZurellBergerCabraletal.2010,
  author    = {Zurell, Damaris and Berger, Uta and Cabral, Juliano Sarmento and Jeltsch, Florian and Meynard, Christine N. and Muenkemueller, Tamara and Nehrbass, Nana and Pagel, J{\"o}rn and Reineking, Bjoern and Schroeder, Boris and Grimm, Volker},
  title     = {The virtual ecologist approach : simulating data and observers},
  issn      = {0030-1299},
  doi       = {10.1111/j.1600-0706.2009.18284.x},
  year      = {2010},
  abstract  = {Ecologists carry a well-stocked toolbox with a great variety of sampling methods, statistical analyses and modelling tools, and new methods are constantly appearing. Evaluation and optimisation of these methods is crucial to guide methodological choices. Simulating error-free data or taking high-quality data to qualify methods is common practice. Here, we emphasise the methodology of the 'virtual ecologist' (VE) approach where simulated data and observer models are used to mimic real species and how they are 'virtually' observed. This virtual data is then subjected to statistical analyses and modelling, and the results are evaluated against the 'true' simulated data. The VE approach is an intuitive and powerful evaluation framework that allows a quality assessment of sampling protocols, analyses and modelling tools. It works under controlled conditions as well as under consideration of confounding factors such as animal movement and biased observer behaviour. In this review, we promote the approach as a rigorous research tool, and demonstrate its capabilities and practical relevance. We explore past uses of VE in different ecological research fields, where it mainly has been used to test and improve sampling regimes as well as for testing and comparing models, for example species distribution models. We discuss its benefits as well as potential limitations, and provide some practical considerations for designing VE studies. Finally, research fields are identified for which the approach could be useful in the future. We conclude that VE could foster the integration of theoretical and empirical work and stimulate work that goes far beyond sampling methods, leading to new questions, theories, and better mechanistic understanding of ecological systems.},
  language  = {en}
}