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  - JOUR
A1  - Dormann, Carsten F.
A1  - Schymanski, Stanislaus J.
A1  - Cabral, Juliano Sarmento
A1  - Chuine, Isabelle
A1  - Graham, Catherine
A1  - Hartig, Florian
A1  - Kearney, Michael
A1  - Morin, Xavier
A1  - Römermann, Christine
A1  - Schröder-Esselbach, Boris
A1  - Singer, Alexander
T1  - Correlation and process in species distribution models: bridging a dichotomy
JF  - Journal of biogeography
N2  - Within the field of species distribution modelling an apparent dichotomy exists between process-based and correlative approaches, where the processes are explicit in the former and implicit in the latter. However, these intuitive distinctions can become blurred when comparing species distribution modelling approaches in more detail. In this review article, we contrast the extremes of the correlativeprocess spectrum of species distribution models with respect to core assumptions, model building and selection strategies, validation, uncertainties, common errors and the questions they are most suited to answer. The extremes of such approaches differ clearly in many aspects, such as model building approaches, parameter estimation strategies and transferability. However, they also share strengths and weaknesses. We show that claims of one approach being intrinsically superior to the other are misguided and that they ignore the processcorrelation continuum as well as the domains of questions that each approach is addressing. Nonetheless, the application of process-based approaches to species distribution modelling lags far behind more correlative (process-implicit) methods and more research is required to explore their potential benefits. Critical issues for the employment of species distribution modelling approaches are given, together with a guideline for appropriate usage. We close with challenges for future development of process-explicit species distribution models and how they may complement current approaches to study species distributions.
KW  - Hypothesis generation
KW  - mechanistic model
KW  - parameterization
KW  - process-based model
KW  - species distribution model
KW  - SDM
KW  - uncertainty
KW  - validation
Y1  - 2012
U6  - https://doi.org/10.1111/j.1365-2699.2011.02659.x
SN  - 0305-0270
VL  - 39
IS  - 12
SP  - 2119
EP  - 2131
PB  - Wiley-Blackwell
CY  - Hoboken
ER  -