Landscape Ecology

, Volume 28, Issue 2, pp 247–257 | Cite as

Underestimating the effects of spatial heterogeneity due to individual movement and spatial scale: infectious disease as an example

  • Paul C. Cross
  • Damien Caillaud
  • Dennis M. Heisey
Research Article


Many ecological and epidemiological studies occur in systems with mobile individuals and heterogeneous landscapes. Using a simulation model, we show that the accuracy of inferring an underlying biological process from observational data depends on movement and spatial scale of the analysis. As an example, we focused on estimating the relationship between host density and pathogen transmission. Observational data can result in highly biased inference about the underlying process when individuals move among sampling areas. Even without sampling error, the effect of host density on disease transmission is underestimated by approximately 50 % when one in ten hosts move among sampling areas per lifetime. Aggregating data across larger regions causes minimal bias when host movement is low, and results in less biased inference when movement rates are high. However, increasing data aggregation reduces the observed spatial variation, which would lead to the misperception that a spatially targeted control effort may not be very effective. In addition, averaging over the local heterogeneity will result in underestimating the importance of spatial covariates. Minimizing the bias due to movement is not just about choosing the best spatial scale for analysis, but also about reducing the error associated with using the sampling location as a proxy for an individual’s spatial history. This error associated with the exposure covariate can be reduced by choosing sampling regions with less movement, including longitudinal information of individuals’ movements, or reducing the window of exposure by using repeated sampling or younger individuals.


Source-sink metapopulation Epidemiological model Observational bias Disease transmission Host density Modifiable areal unit problem 



We thank M. Ebinger for help with the figures. PCC’s work was supported by U.S. Geological Survey, the NSF/NIH Ecology of Infectious Disease program DEB-1067129 and some ideas stem from working groups sponsored by the NIH/DHS funded RAPIDD program. DC’s work was supported by NSF Grant DEB-0749097 to L.A. Meyers. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Supplementary material

10980_2012_9830_MOESM1_ESM.docx (1.4 mb)
Supplementary material 1 (DOCX 1456 kb)


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Copyright information

© Springer Science+Business Media Dordrecht (outside the USA) 2012

Authors and Affiliations

  • Paul C. Cross
    • 1
  • Damien Caillaud
    • 2
  • Dennis M. Heisey
    • 3
  1. 1.U.S. Geological Survey, Northern Rocky Mountain Science CenterBozemanUSA
  2. 2.Section of Integrative BiologyUniversity of Texas at AustinAustinUSA
  3. 3.U. S. Geological Survey, National Wildlife Health CenterMadisonUSA

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