Oikos 00: 1–14, 2009 doi: 10.1111/j.1600-0706.2009.18284.x © 2009 he Authors. Journal compilation © 2009 Oikos Subject Editor: José Alexandre Felizola Diniz-Filho. Accepted 16 November 2009 1 The virtual ecologist approach: simulating data and observers Damaris Zurell, Uta Berger, Juliano S. Cabral, Florian Jeltsch, Christine N. Meynard, Tamara Münkemüller, Nana Nehrbass, Jörn Pagel, Björn Reineking, Boris Schröder and Volker Grimm D. Zurell (damaris.zurell@uni-potsdam.de) and B. Schröder, Inst. of Geoecology, Univ. of Potsdam, Karl-Liebknecht-Str. 24/25, DE–14476 Potsdam, Germany. BS also at: ZALF e.V., Leibniz-Centre for Agricultural Landscape Research, Soil Landscape Modelling, Eberswalder Straße 84, DE–15374 Müncheberg, Germany. – U. Berger, Inst. of Forest Growth and Computer Sciences, Dresden Univ. of Technology, Pienner Straße 8, DE–01737 harandt, Germany. – J. S. Cabral, F. Jeltsch and J. Pagel, Inst. for Biochemistry and Biology, Univ. of Potsdam, Maulbeerallee 2, DE–14469 Potsdam, Germany. – C. N. Meynard, Inst. des Sciences de l’Evolution, Univ. de Montpellier II, UMR CNRS 5554, Place Eugène Bataillon, CC 065, FR–34095 Montpellier Cedex 5, France. – T. Münkemüller, Laboratoire d’Ecologie Alpine, Univ. J. Fourier, UMR CNRS 5553, BP 53, FR–38041 Grenoble Cedex 9, France. – N. Nehrbass and V. Grimm, UFZ, Helmholtz Centre of Environmental Research – UFZ, Dept of Ecological Modelling, Permoserstr. 15, DE–04318 Leipzig, Germany. Present address for NN: Stünz-Mölkauer Weg 18, DE–04318 Leipzig, Germany. – B. Reineking, Biogeographical Modelling, BayCEER, Univ. of Bayreuth, Universitätsstraße 30, DE–95440 Bayreuth, Germany. 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 method- ological 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. his virtual data is then subjected to statistical analyses and modelling, and the results are evaluated against the ‘true’ simulated data. he 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 practi- cal relevance. We explore past uses of VE in diferent ecological research ields, 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 beneits as well as potential limitations, and provide some practical considerations for designing VE studies. Finally, research ields are identiied 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. Models permeate every ield in ecology. hey have become an indispensable tool for a wide range of tasks, including the understanding of mechanisms, capturing the processes behind the emergence of ecological phenomena, quantify- ing relationships between species presence or abundance and environmental conditions, and forecasting efects of changing environments on broad spatial and temporal scales (DeAngelis and Mooij 2005, Araújo and Rahbek 2006, huiller et al. 2008). here is, however, a further important ield of applica- tion of ecological models that so far has not been thoroughly acknowledged in ecological research: evaluating methods for data sampling, analysis and modelling methods by means of virtual data. Here, the idea is to generate virtual data by simulating not only ecological processes, but also the sam- pling processes that are used to collect these data in real- ity and the methodological tools used to analyse them. We propose to call this the ‘virtual ecologist’ (VE) approach (see Glossary). he virtue of this approach is its ability to rigor- he virtue of this approach is its ability to rigor- ously test method performance against a known truth. he VE approach is concerned with practical questions regard- ing ecological methods: Is a method able to identify patterns that we know exist (Grimm et al. 1999)? Can we infer the mechanisms underlying these patterns given a certain set of data (Tyre et al. 2001)? Can we correctly and reliably predict future events (Zurell et al. 2009)? To evaluate methods of data collection, statistical analy- sis, and modelling we would ideally compare their outcome to reality. his would allow us to assess whether existing patterns were detected correctly, whether correct estimates of process rates were obtained, or whether the distribu- tion of a species was predicted correctly. However, we have no privileged access to reality independent of and beyond ield observations and analytical methods. he ability of ield data to represent reality depends not only on the time interval and the spatial extent of observation but also on the disturbances