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Spatial modelling and landscape-level approaches for visualizing intra-specific variation

  1. HENRI A. THOMASSEN1,
  2. ZACHARY A. CHEVIRON1,2,
  3. ADAM H. FREEDMAN1,3,
  4. RYAN J. HARRIGAN1,
  5. ROBERT K. WAYNE1,3,
  6. THOMAS B. SMITH1,3

Article first published online: 13 AUG 2010

DOI: 10.1111/j.1365-294X.2010.04737.x

Issue

Molecular Ecology

Molecular Ecology

Special Issue: SPECIAL ISSUE ON LANDSCAPE GENETICS

Volume 19, Issue 17, pages 3532–3548, September 2010

Additional Information

How to Cite

THOMASSEN, H. A., CHEVIRON, Z. A., FREEDMAN, A. H., HARRIGAN, R. J., WAYNE, R. K. and SMITH, T. B. (2010), Spatial modelling and landscape-level approaches for visualizing intra-specific variation. Molecular Ecology, 19: 3532–3548. doi: 10.1111/j.1365-294X.2010.04737.x

Author Information

  1. 1

    Center for Tropical Research, Institute of the Environment, University of California, Los Angeles, La Kretz Hall, Suite 300, 619 Charles E. Young Dr. East, Los Angeles, CA 90095, USA

  2. 2

    School of Biological Sciences, University of Nebraska, Lincoln, NE 68588, USA

  3. 3

    Department of Ecology and Evolutionary Biology, University of California, Los Angeles, 621 Charles E. Young Dr. East, Los Angeles, CA 90095, USA

*Henri A. Thomassen, Fax: +1 310 825 5446; E-mail: hathomassen@ucla.edu

Publication History

  1. Issue published online: 25 AUG 2010
  2. Article first published online: 13 AUG 2010
  3. Received 2 February 2010; revision received 3 May 2010; accepted 9 May 2010

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Keywords:

  • biodiversity;
  • conservation biology;
  • ecological modelling;
  • landscape genetics;
  • population genetics;
  • spatial statistics

Abstract

Spatial analytical methods have been used by biologists for decades, but with new modelling approaches and data availability their application is accelerating. While early approaches were purely spatial in nature, it is now possible to explore the underlying causes of spatial heterogeneity of biological variation using a wealth of environmental data, especially from satellite remote sensing. Recent methods can not only make inferences regarding spatial relationships and the causes of spatial heterogeneity, but also create predictive maps of patterns of biological variation under changing environmental conditions. Here, we review the methods involved in making continuous spatial predictions from biological variation using spatial and environmental predictor variables, provide examples of their use and critically evaluate the advantages and limitations. In the final section, we discuss some of the key challenges and opportunities for future work.

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