The virtual ecologist approach: simulating data and observers

By Damaris Zurell, Uta Berger, J. Cabral, Florian Jeltsch, C. Meynard, T. Münkemüller, Nana Nehrbass, Jörn Pagel, Björn Reineking, B. Schröder and 1 more. 2010

344 citations recorded by the source corpus.

Subfields: Ecological Modeling

View Paper: https://www.semanticscholar.org/paper/9d946b3e705a3897d41f44c9e6353c2532768098

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In Ecological Modeling

Citations in the corpus

Replication levels, false presences and the estimation of the presence/absence from eDNA metabarcoding data (2015, background): “In recent years, researchers are increasingly combining analyses of simulated and empirical data to assess the effect of incomplete sampling and for the evaluation of the performance of analytical methods (Guillera-Arroita et al. 2010; Zurell et al. 2010; Ficetola et al. 2014).”
Measuring the relative effect of factors affecting species distribution model predictions (2014, methodology): “Most such studies have used real data, but a few have used simulated, or so-called virtual, data (Zurell et al. 2010; Miller 2014).”
Tools for integrating range change, extinction risk and climate change information into conservation management (2013, background): “Depending on the case study, we estimated the following indices: 1) the smallest population size that is expected to between trends in range and abundance, potentially using simulated data and observer models to mimic real species (Zurell et al. 2010).”
How to understand species’ niches and range dynamics: a demographic research agenda for biogeography (2012, background): “But how good are these DRM estimates and forecasts? To evaluate this, Pagel & Schurr (2012) simulated the range dynamics of a virtual species and let a ‘virtual ecologist’ (Zurell et al., 2010) record: (1) abundance time series of 30 populations over 10 years, and (2) two presence/absence maps at th”
Assessing the suitability of diversity metrics to detect biodiversity change (2017, background): “Virtual datasets allow full control of both the community composition and the mechanism of decline, and thus allow the comparison of the relative responses of the diversity metrics (Zurell et al., 2010) by simulating ecological processes under alternative scenarios (Dornelas, 2010; Lamb et al.”
Current measures for distance decay in similarity of species composition are influenced by study extent and grain size (2012, background): “Simulated data enable work on a well-defined spatial setting of ecological objects (e.g. the distribution of species) and allow for the systematic variation of sampling design (Zurell et al., 2010).”
Selecting pseudo‐absences for species distribution models: how, where and how many? (2012, result): “Generalisation and application of the conclusions of these empirical studies are therefore of limited interest in general compared with conclusions from virtual experiments where results or patterns can be compared with the known truth (Zurell et al. 2010).”
Statistical inference for stochastic simulation models--theory and application. (2011, methodology): “In general, however, we will have to test (usually with artificially created data, see, e.g. Jabot & Chave 2009; Zurell et al. 2009) whether a statistic is sufficient with respect to a particular inferential task, and whether it can be further simplified.”