Categorical, class-focused map patterns: characterization and comparison

Abstract

We present a rigorous and simple approach for the comparison of binary landscapes by class-focused metric values that complements the ease of computing these metrics for landscape ecology research. First, we assess whether a class-focused pattern metric value could have emerged due to random chance. Second, we compare two landscapes and assess whether class-focused pattern metrics computed for each landscape are significantly different or not. Our frameworks are based on conditional autoregressive simulations to derive empirical distributions for each metric where composition and configuration parameters are controlled. Our method permits the computation of probabilities that an observed metric value is either greater than or less than a given level of expectation. We also provide means for situating any landscape on a selected pattern metric-surface defined by parameters of composition and configuration. These surfaces illustrate which parameter would be most easily adjusted to effect a desired change in a selected class-focused pattern metric’s value. Implementation is fully within the R statistical computing environment.

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Notes

  1. 1.

    The code is available from the first author and will be converted into an R library for distribution on the Comprehensive R Archive Network (CRAN).

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Acknowledgments

This work was funded by NSERC Discovery Grant to T. Remmel and M.-J. Fortin. We thank Dr. Sándor Kabos and Dr. Ferko Csillag for programming and theoretical guidance. We thank Dr. Stephanie Melles for the use of her forest data. Finally, we thank the anonymous reviewers and editor for their valuable insights and comments that helped us to improve the impact of this article.

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Correspondence to Tarmo K. Remmel.

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Remmel, T.K., Fortin, M. Categorical, class-focused map patterns: characterization and comparison. Landscape Ecol 28, 1587–1599 (2013). https://doi.org/10.1007/s10980-013-9905-x

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Keywords

  • Expectation
  • Comparison
  • Random chance
  • Fragmentation
  • Spatial autocorrelation
  • Binary maps