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Regional and Continental-Scale Perspectives on Landscape Pattern

  • Jeffrey A. CardilleEmail author
  • Monica G. Turner
Chapter

Abstract

Landscape patterns vary widely across Earth’s surface as a result of both anthropogenic and natural causes. This variation among landscapes can be quantified by using a large number of metrics developed to capture distinctive qualities of spatial pattern. An informed understanding of pattern–process relationships involves landscape comparisons among and within regions. Despite many advances in landscape pattern analysis, informed selection of landscapes for studying pattern–process relationships in real-world situations remains challenging. This lab explores these challenges with objectives designed to enable students to.

Keywords

Lyme Disease Landscape Pattern Landscape Metrics Landscape Composition Study Landscape 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References and Recommended Readings1

  1. Cardille JA, White JC, Wulder MA et al (2012) Representative landscapes in the forested area of Canada. Environ Manag 49(1):163–173CrossRefGoogle Scholar
  2. *Cardille JA, Lambois M (2010) From the redwood forest to the Gulf Stream waters: human signature nearly ubiquitous in representative US landscapes. Front Ecol Environ 8:130–134. Mines the Metaland database for 1992 using the “affinity propagation” algorithm, which clusters landscapes while selecting a representative of each group of landscapes. Supplementary material shows other landscapes that were evaluated as similar, using landscape metrics, to each of the chosen representatives. Google Scholar
  3. *Cardille JA, Turner MG, Clayton M et al (2005) METALAND: characterizing spatial patterns and statistical context of landscape metrics. BioScience 55:983–988. Presents the goals of the Metaland database, an overview of the Metaland interface, and maps and histograms of metrics across the state of Wisconsin, USA. Google Scholar
  4. Cushman SA, McGarigal K, Neel MC (2008) Parsimony in landscape metrics: strength, universality, and consistency. Ecol Indic 8(5):691–703CrossRefGoogle Scholar
  5. *Eigenbrod F, Hecnar SJ, Fahrig L (2011) Sub-optimal study design has major impacts on landscape-scale inference. Biol Conserv 144:298–305. Excellent demonstration of how poor sampling design can compromise the inference from a study that includes landscape pattern as a predictor—even causing the direction of relationships to switch. Tools like Metaland can be used to improve study design by quantifying the ranges and correlation structure of metrics used in an analysis and assuring that sample landscapes do not overlap in space. Google Scholar
  6. Frey BJ, Dueck D (2007) Clustering by passing messages between data points. Science 315(5814):972–976CrossRefPubMedGoogle Scholar
  7. *Fry JA, Coan MJ, Homer CJ et al (2009) Completion of the National Land Cover Database (NLCD) 1992–2001 land cover change retrofit product. US Geological Survey Open-File Report 2008-1379, p 18. This document provides details on differences between the 1992 and 2001 NLCD data, and is a necessary reading for anyone comparing the two time periods. Differences in the data can interfere with the ability to detect change. Google Scholar
  8. Fry JA, Xian G, Jin S et al (2011) Completion of the 2006 national land cover database for the conterminous United States. Photogramm Eng Remote Sens 77(9):858–864Google Scholar
  9. Gustafson EJ (1998) Quantifying landscape spatial pattern: what is the state of the art? Ecosystems 1(2):143–156CrossRefGoogle Scholar
  10. Homer C, Dewitz J, Fry J et al (2007) Completion of the 2001 National Land Cover Database for the Coterminous United States. Photogramm Eng Remote Sens 73(4):337Google Scholar
  11. Jin S, Yang L, Danielson P et al (2013) A comprehensive change detection method for updating the national land cover database to circa 2011. Remote Sens Environ 132:159–175CrossRefGoogle Scholar
  12. *Kupfer JA (2012) Landscape ecology and biogeography: rethinking landscape metrics in a post-FRAGSTATS landscape. Progr Phys Geogr 36:400–420. This paper summarizes the current pitfalls and limitations of landscape pattern analysis, arguing that the research community has focused on landscape structure at the expense of landscape function. It summarizes the state of affairs of landscape structure measures from several different perspectives, and makes clear recommendations for future research. Google Scholar
  13. Langford WT, Gergel SE, Dietterich TG et al (2006) Map misclassification can cause large errors in landscape pattern indices: examples from habitat fragmentation. Ecosystems 9(3):474–488CrossRefGoogle Scholar
  14. Li H, Wu J (2004) Use and misuse of landscape indices. Landsc Ecol 19(4):389–399CrossRefGoogle Scholar
  15. LoGiudice K, Ostfeld RS, Schmidt KA et al (2003) The ecology of infectious disease: effects of host diversity and community composition on Lyme disease risk. Proc Natl Acad Sci 100(2):567–571CrossRefPubMedPubMedCentralGoogle Scholar
  16. McGarigal K, Cushman SA, Ene E (2012) FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. Computer software program produced by the authors at the University of Massachusetts, Amhers. Available at the following web site: http://www.umass.edu/landeco/research/fragstats/fragstats.htmlGoogle Scholar
  17. *Proulx R, Fahrig L (2010) Detecting human-driven deviations from trajectories in landscape composition and configuration. Landsc Ecol 25:1479–1487. This paper offers a regional analysis of the relationship between two landscape metrics based on 16 Canadian landscapes that vary in land use and human disturbance, demonstrating the kind of analysis that can be facilitated by approaches similar to Metaland. Google Scholar
  18. Turner MG, Gardner RH (2015) Landscape ecology in theory and practice, 2nd edn. Springer, New YorkGoogle Scholar
  19. Vogelmann JE, Howard SM, Yang L et al (2001) Completion of the 1990s National Land Cover Data Set for the conterminous United States from Landsat Thematic Mapper data and ancillary data sources. Photogramm Eng Remote Sens 67(6):650–662Google Scholar
  20. Xian G, Homer C, Demitz J et al (2011) Change of impervious surface area between 2001 and 2006 in the conterminous United States. Photogramm Eng Remote Sens 77(8):758–762Google Scholar

Copyright information

© Springer-Verlag New York 2017

Authors and Affiliations

  1. 1.McGill UniversitySainte Anne de Bellevue, MontréalCanada
  2. 2.University of Wisconsin-MadisonMadisonUSA

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