@article{fdi:010077381,
  title = {{T}esting methods in species distribution modelling using virtual species : what have we learnt and what are we missing ?},
  author = {{M}eynard, {C}. {N}. and {L}eroy, {B}. and {K}aplan, {D}avid},
  editor = {},
  language = {{ENG}},
  abstract = {{S}pecies distribution models ({SDM}s) have become one of the major predictive tools in ecology. {H}owever, multiple methodological choices are required during the modelling process, some of which may have a large impact on forecasting results. {I}n this context, virtual species, i.e. the use of simulations involving a fictitious species for which we have perfect knowledge of its occurrence-environment relationships and other relevant characteristics, have become increasingly popular to test {SDM}s. {T}his approach provides for a simple virtual ecologist framework under which to test model properties, as well as the effects of the different methodological choices, and allows teasing out the effects of targeted factors with great certainty. {T}his simplification is therefore very useful in setting up modelling standards and best practice principles. {A}s a result, numerous virtual species studies have been published over the last decade. {T}he topics covered include differences in performance between statistical models, effects of sample size, choice of threshold values, methods to generate pseudo-absences for presence-only data, among many others. {T}hese simulations have therefore already made a great contribution to setting best modelling practices in {SDM}s. {R}ecent software developments have greatly facilitated the simulation of virtual species, with at least three different packages published to that effect. {H}owever, the simulation procedure has not been homogeneous, which introduces some subtleties in the interpretation of results, as well as differences across simulation packages. {H}ere we 1) review the main contributions of the virtual species approach in the {SDM} literature; 2) compare the major virtual species simulation approaches and software packages; and 3) propose a set of recommendations for best simulation practices in future virtual species studies in the context of {SDM}s.},
  keywords = {artificial species ; environmental niche models ; niche ; simulations ; species distribution modelling ; virtual ecologist},
  booktitle = {},
  journal = {{E}cography},
  volume = {42},
  numero = {12},
  pages = {2021--2036},
  ISSN = {0906-7590},
  year = {2019},
  DOI = {10.1111/ecog.04385},
  URL = {https://www.documentation.ird.fr/hor/fdi:010077381},
}