Monte Carlo-Based Analysis of the Effect of Positional and Thematic Uncertainties on Biodiversity Models

  • Patrick J. KirbyEmail author
  • Scott W. Mitchell
Part of the Advances in Geographic Information Science book series (AGIS)


Monte Carlo methods are a common approach to quantifying uncertainty propagation. We used Monte Carlo simulation to quantify the effects of positional and thematic uncertainties in a set of landscape maps on model averaged regression coefficients that were based on metrics derived from these maps. Results indicate that the uncertainty estimates from model averaging outweigh the effects of positional and thematic uncertainties in the landscape maps. Shifts between reference and simulated coefficients indicate a need for further research into simulation approaches that account for spatial autocorrelation.


Monte Carlo Positional uncertainty Thematic uncertainty Biodiversity 



Assistance and guidance from Andrew Davidson, Dennis Duro, Lenore Fahrig, Jude Girard, Steve Javorek, Doug King, Kathryn Lindsay, Jon Pasher, Murray Richardson, Adam Smith, Lutz Tischendorf, Jessica van den Berg as well as others involved in the Farmland Biodiversity Project. Ground reference data from Agriculture Canada was used to supplement training samples. Funding was provided through Environment Canada. Additional thanks to Dan Patterson.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Geomatics and Landscape Ecology Laboratory, Department of Geography and Environmental StudiesCarleton UniversityOttawaCanada

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