@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} } @article{RadchukDeLaenderCabraletal.2019, author = {Radchuk, Viktoriia and De Laender, Frederik and Cabral, Juliano Sarmento and Boulangeat, Isabelle and Crawford, Michael Scott and Bohn, Friedrich and De Raedt, Jonathan and Scherer, Cedric and Svenning, Jens-Christian and Thonicke, Kirsten and Schurr, Frank M. and Grimm, Volker and Kramer-Schadt, Stephanie}, title = {The dimensionality of stability depends on disturbance type}, series = {Ecology letters}, volume = {22}, journal = {Ecology letters}, number = {4}, publisher = {Wiley}, address = {Hoboken}, issn = {1461-023X}, doi = {10.1111/ele.13226}, pages = {674 -- 684}, year = {2019}, abstract = {Ecosystems respond in various ways to disturbances. Quantifying ecological stability therefore requires inspecting multiple stability properties, such as resistance, recovery, persistence and invariability. Correlations among these properties can reduce the dimensionality of stability, simplifying the study of environmental effects on ecosystems. A key question is how the kind of disturbance affects these correlations. We here investigated the effect of three disturbance types (random, species-specific, local) applied at four intensity levels, on the dimensionality of stability at the population and community level. We used previously parameterized models that represent five natural communities, varying in species richness and the number of trophic levels. We found that disturbance type but not intensity affected the dimensionality of stability and only at the population level. The dimensionality of stability also varied greatly among species and communities. Therefore, studying stability cannot be simplified to using a single metric and multi-dimensional assessments are still to be recommended.}, language = {en} }