TY - JOUR A1 - Zurell, Damaris A1 - Berger, Uta A1 - Cabral, Juliano Sarmento A1 - Jeltsch, Florian A1 - Meynard, Christine N. A1 - Muenkemueller, Tamara A1 - Nehrbass, Nana A1 - Pagel, Jörn A1 - Reineking, Bjoern A1 - Schroeder, Boris A1 - Grimm, Volker T1 - The virtual ecologist approach : simulating data and observers N2 - 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. Y1 - 2010 UR - http://www3.interscience.wiley.com/cgi-bin/issn?DESCRIPTOR=PRINTISSN&VALUE=0030-1299 U6 - https://doi.org/10.1111/j.1600-0706.2009.18284.x SN - 0030-1299 ER - TY - INPR A1 - Wellstein, Camilla A1 - Schröder-Esselbach, Boris A1 - Reineking, Bjoern A1 - Zimmermann, Niklaus E. T1 - Understanding species and community response to environmental change - A functional trait perspective T2 - Agriculture, ecosystems & environment : an international journal for scientific research on the relationship of agriculture and food production to the biosphere KW - Functional traits KW - Functional diversity KW - Database KW - Land use KW - Management KW - Climate change KW - Landscape KW - Ecosystem function KW - Clonal plants KW - Dispersal KW - Plant growth KW - Orthoptera Y1 - 2011 U6 - https://doi.org/10.1016/j.agee.2011.06.024 SN - 0167-8809 VL - 145 IS - 1 SP - 1 EP - 4 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Dormann, Carsten F. A1 - Elith, Jane A1 - Bacher, Sven A1 - Buchmann, Carsten M. A1 - Carl, Gudrun A1 - Carre, Gabriel A1 - Garcia Marquez, Jaime R. A1 - Gruber, Bernd A1 - Lafourcade, Bruno A1 - Leitao, Pedro J. A1 - Münkemüller, Tamara A1 - McClean, Colin A1 - Osborne, Patrick E. A1 - Reineking, Bjoern A1 - Schröder-Esselbach, Boris A1 - Skidmore, Andrew K. A1 - Zurell, Damaris A1 - Lautenbach, Sven T1 - Collinearity a review of methods to deal with it and a simulation study evaluating their performance JF - Ecography : pattern and diversity in ecology ; research papers forum N2 - 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. Y1 - 2013 U6 - https://doi.org/10.1111/j.1600-0587.2012.07348.x SN - 0906-7590 SN - 1600-0587 VL - 36 IS - 1 SP - 27 EP - 46 PB - Wiley-Blackwell CY - Hoboken ER - TY - JOUR A1 - Bocedi, Greta A1 - Zurell, Damaris A1 - Reineking, Bjoern A1 - Travis, Justin M. J. T1 - Mechanistic modelling of animal dispersal offers new insights into range expansion dynamics across fragmented landscapes JF - Ecography : pattern and diversity in ecology ; research papers forum Y1 - 2014 U6 - https://doi.org/10.1111/ecog.01041 SN - 0906-7590 SN - 1600-0587 VL - 37 IS - 12 SP - 1240 EP - 1253 PB - Wiley-Blackwell CY - Hoboken ER -