Landscape Ecology

, Volume 31, Issue 1, pp 7–18 | Cite as

Cross-scale contradictions in ecological relationships

  • Kelly-Ann Dixon Hamil
  • Basil V. Iannone III
  • Whitney K. Huang
  • Songlin Fei
  • Hao Zhang
Research Article

Abstract

Context

Not accounting for spatial heterogeneity in ecological analyses can cause modeled relationships to vary across spatial scales, specifically different levels of spatial resolution. These varying results hinder both the utility of data collected at one spatial scale for analyses at others and the determination of underlying processes.

Objectives

To briefly review existing methods for analyzing data collected at multiple scales, highlight the effects of spatial heterogeneity on the utility of these methods, and to illustrate a practical statistical method to account for the sources of spatial heterogeneity when they are unknown.

Methods

Using simulated examples, we show how not accounting for the drivers of spatial heterogeneity in statistical models can cause contradictory findings regarding relationship direction across spatial scales. We then show how mixed effects models can remedy this multiscaling issue.

Results

Ignoring sources of spatial heterogeneity in statistical models with coarse spatial scales produced contradictory results to the true underlying relationship. Treating drivers of spatial heterogeneity as random effects in a mixed effects model, however, allowed us to uncover this true relationship.

Conclusions

Mixed effects models is advantageous as it is not always necessary to know the influential explanatory variables that cause spatial heterogeneity and no additional data are required. Furthermore, this approach is well documented, can be applied to data having various distribution types, and is easily executable using multiple statistical packages.

Keywords

Ecological fallacy MAUP Missing variables Multiscale Mixed effects models Spatial transmutation 

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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Kelly-Ann Dixon Hamil
    • 1
  • Basil V. Iannone III
    • 2
  • Whitney K. Huang
    • 1
  • Songlin Fei
    • 2
  • Hao Zhang
    • 1
  1. 1.Department of StatisticsPurdue UniversityWest LafayetteUSA
  2. 2.Department of Forestry and Natural ResourcesPurdue UniversityWest LafayetteUSA

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