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Landscape Ecology

, Volume 28, Issue 7, pp 1269–1281 | Cite as

Finding the functional grain: comparing methods for scaling resistance surfaces

  • Paul GalpernEmail author
  • Micheline Manseau
Research article

Abstract

The influence of landscape features on the movement of an organism between two point locations is often measured as an effective distance. Typically, raster models of landscape resistance are used to calculate effective distance. Because organisms may experience landscape heterogeneity at different scales (i.e. functional grains), using a raster with too fine or too coarse a spatial grain (i.e. analysis grain) may lead to inaccurate estimates of effective distance. We adopted a simulation approach where the true functional grain and effective distance for a theoretical organism were defined and the analysis grains of landscape connectivity models were systematically changed. We used moving windows and grains of connectivity, a recently introduced landscape graph method that uses an irregular tessellation of the resistance surface to coarsen the landscape data. We then used least-cost path metrics to measure effective distance and found that matching the functional and analysis grain sizes was most accurate at recovering the expected effective distance, affirming the importance of multi-scale analysis. Moving window scaling with a maximum function (win.max) performed well when the majority of landscape structure influencing connectivity consisted of high resistance features. Moving window scaling with a minimum function (win.min) performed well when the relevant landscape structure consisted of low resistance regions. The grains of connectivity method performed well under all scenarios, avoiding an a priori choice of window function, which may be challenging in complex landscapes. Appendices are provided that demonstrate the use of grains of connectivity models.

Keywords

Grains of connectivity Functional grain Landscape graphs Minimum planar graph Voronoi tessellation Effective distance Least-cost paths Landscape pattern Simulation 

Notes

Acknowledgments

We thank A. Fall, D. Fortin, T. Keitt and two anonymous reviewers for comments that improved the manuscript. Funding for this work was provided by an NSERC Collaborative Research and Development Grant in collaboration with Manitoba Hydro (MM) and an NSERC Post-Graduate Scholarship (PG).

Supplementary material

10980_2013_9873_MOESM1_ESM.pdf (2.4 mb)
Supplementary material 1 (PDF 2433 kb)

References

  1. Adriaensen F, Chardon JP, De Blust SG, Swinnen E, Villalba S, Gulinck H, Matthysen E (2003) The application of ‘least-cost’ modelling as a functional landscape model. Landsc Urban Plan 64:233–247CrossRefGoogle Scholar
  2. Anderson C, Epperson BK, Fortin M-J, Holderegger R, James PM, Rosenberg MS, Scribner KT, Spear S (2010) Considering spatial and temporal scale in landscape-genetic studies of gene flow. Mol Ecol 19:3565–3575PubMedCrossRefGoogle Scholar
  3. Baguette M, Van Dyck H (2007) Landscape connectivity and animal behavior: functional grain as a key determinant for dispersal. Landscape Ecol 22:1117–1129CrossRefGoogle Scholar
  4. Beier P, Majka DR, Newell SL (2009) Uncertainty analysis of least-cost modeling for designing wildlife linkages. Ecol Appl 19:2067–2077PubMedCrossRefGoogle Scholar
  5. Brooks CP (2003) A scalar analysis of landscape connectivity. Oikos 102:433–439CrossRefGoogle Scholar
  6. Bunn AG, Urban DL, Keitt TH (2000) Landscape connectivity: A conservation application of graph theory. J Environ Manag 59:265–278CrossRefGoogle Scholar
  7. Calabrese JM, Fagan WF (2004) A comparison-shopper’s guide to connectivity metrics. Front Ecol Environ 2:529–536CrossRefGoogle Scholar
  8. Chetkiewicz CLB, Clair CCS, Boyce MS (2006) Corridors for conservation: Integrating pattern and process. Annu Rev Ecol Evol Syst 37:317–342CrossRefGoogle Scholar
  9. Coulon A, Morellet N, Goulard M, Cargnelutti B, Angibault JM, Hewison AJM (2008) Inferring the effects of landscape structure on roe deer (Capreolus capreolus) movements using a step selection function. Landscape Ecol 23:603–614CrossRefGoogle Scholar
  10. Cushman SA, Landguth EL (2010) Scale dependent inference in landscape genetics. Landscape Ecol 25:967–979CrossRefGoogle Scholar
  11. Cushman SA, Lewis JS (2010) Movement behavior explains genetic differentiation in American black bears. Landscape Ecol 25:1613–1625CrossRefGoogle Scholar
  12. Cushman SA, McKelvey KS, Hayden J, Schwartz MK (2006) Gene flow in complex landscapes: testing multiple hypotheses with causal modeling. Am Nat 168:486–499PubMedCrossRefGoogle Scholar
  13. Dijkstra EW (1959) A note on two problems in connexion with graphs. Numerische Mathematik 1959:269–271CrossRefGoogle Scholar
  14. Fall A, Fortin M-J, Manseau M, O’Brien D (2007) Spatial graphs: principles and applications for habitat connectivity. Ecosystems 10:448–461CrossRefGoogle Scholar
  15. Galpern P, Manseau M, Fall A (2011) Patch-based graphs of landscape connectivity: a guide to construction, analysis and application for conservation. Biol Conserv 144:44–55CrossRefGoogle Scholar
  16. Galpern P, Fall A, Manseau M (2012a) Grainscape package for R: grains of connectivity and minimum planar graph modelling of landscape connectivity using resistance surfaces. Package version 0.1. http://grainscape.r-forge.r-project.org. Accessed 1 March 2013
  17. Galpern P, Manseau M, Wilson P (2012b) Grains of connectivity: analysis at multiple spatial scales in landscape genetics. Mol Ecol 21:3996–4009PubMedCrossRefGoogle Scholar
  18. Gustafson EJ (1998) Quantifying landscape spatial pattern: what is the state of the art? Ecosystems 1:143–156CrossRefGoogle Scholar
  19. Hijmans RJ, van Etten J (2011) Raster: geographic analysis and modeling with raster data. R package version 1.9–58Google Scholar
  20. Jaquiéry J, Broquet T, Hirzel A, Yearsley J, Perrin N (2011) Inferring landscape effects on dispersal from genetic distances: how far can we go? Mol Ecol 20:692–705PubMedCrossRefGoogle Scholar
  21. Legendre P, Fortin M-J (2010) Comparison of the Mantel test and alternative approaches for detecting complex multivariate relationships in the spatial analysis of genetic data. Mol Ecol Res 10:831–844CrossRefGoogle Scholar
  22. Levin SA (1992) The problem of pattern and scale in ecology: the Robert H. MacArthur award lecture. Ecology 73:1943–1967CrossRefGoogle Scholar
  23. Luque S, Saura S, Fortin M-J (2012) Landscape connectivity analysis for conservation: insights from combining new methods with ecological and genetic data. Landscape Ecol 27:153–157CrossRefGoogle Scholar
  24. Margosian ML, Garrett KA, Hutchinson JMS, With KA (2009) Connectivity of the American agricultural landscape: assessing the National Risk of Crop Pest and Disease Spread. Bioscience 59:141–151CrossRefGoogle Scholar
  25. McComb WC, McGrath MT, Spies TA, Vesely D (2002) Models for mapping potential habitat at landscape scales: an example using northern spotted owls. For Sci 48:203–216Google Scholar
  26. McRae BH, Dickson BG, Keitt TH, Shah VB (2008) Using circuit theory to model connectivity in ecology, evolution, and conservation. Ecology 89:2712–2724PubMedCrossRefGoogle Scholar
  27. O’Brien D, Manseau M, Fall A, Fortin MJ (2006) Testing the importance of spatial configuration of winter habitat for woodland caribou: an application of graph theory. Biol Conserv 130:70–83CrossRefGoogle Scholar
  28. Okabe A, Boots B, Sugihara K, Chiu SN (2000) Spatial tessellations: concepts and applications of Voronoi diagrams, 2nd edn. Wiley, ChichesterCrossRefGoogle Scholar
  29. Pinto N, Keitt TH (2009) Beyond the least-cost path: evaluating corridor redundancy using a graph-theoretic approach. Landscape Ecol 24:253–266CrossRefGoogle Scholar
  30. Polfus JL, Hebblewhite M, Heinemeyer K (2011) Identifying indirect habitat loss and avoidance of human infrastructure by northern mountain Woodland Caribou. Biol Conserv 144:2637–2646CrossRefGoogle Scholar
  31. R Development Core Team (2012) R: a language and environment for statistical computing. Austria, ViennaGoogle Scholar
  32. Rayfield B, Fortin MJ, Fall A (2010) The sensitivity of least-cost habitat graphs to relative cost surface values. Landscape Ecol 25:519–532CrossRefGoogle Scholar
  33. Richard Y, Armstrong DP (2010) Cost distance modelling of landscape connectivity and gap-crossing ability using radio-tracking data. J Appl Ecol 47:603–610CrossRefGoogle Scholar
  34. Sawyer SC, Epps CW, Brashares JS (2011) Placing linkages among fragmented habitats: do least-cost models reflect how animals use landscapes? J Appl Ecol 48:668–678CrossRefGoogle Scholar
  35. Schwartz MK, Copeland JP, Anderson NJ, Squires JR, Inman RM, McKelvey KS, Pilgrim KL, Waits LP, Cushman SA (2009) Wolverine gene flow across a narrow climatic niche. Ecology 90:3222–3232PubMedCrossRefGoogle Scholar
  36. Shirk A, Wallin D, Cushman S, Rice C, Warheit K (2010) Inferring landscape effects on gene flow: a new model selection framework. Mol Ecol 19:3603–3619PubMedCrossRefGoogle Scholar
  37. Spear SF, Balkenhol N, Fortin MJ, McRae BH, Scribner K (2010) Use of resistance surfaces for landscape genetic studies: considerations for parameterization and analysis. Mol Ecol 19:3576–3591PubMedCrossRefGoogle Scholar
  38. Storfer A, Murphy MA, Spear SF, Holderegger R, Waits LP (2010) Landscape genetics: where are we now? Mol Ecol 19:3496–3514PubMedCrossRefGoogle Scholar
  39. Thompson CM, McGarigal K (2002) The influence of research scale on bald eagle habitat selection along the lower Hudson River, New York (USA). Landscape Ecol 17:569–586CrossRefGoogle Scholar
  40. Turner MG, O’Neill RV, Gardner RH, Milne BT (1989) Effects of changing spatial scale on the analysis of landscape pattern. Landscape Ecol 3:153–162CrossRefGoogle Scholar
  41. Urban D, Keitt T (2001) Landscape connectivity: a graph-theoretic perspective. Ecology 82:1205–1218CrossRefGoogle Scholar
  42. Urban DL, Minor ES, Treml EA, Schick RS (2009) Graph models of habitat mosaics. Ecol Lett 12:260–273PubMedCrossRefGoogle Scholar
  43. van Etten J (2010) Package gdistance: distances and movements on geographical grids. R package version 1.0Google Scholar
  44. Wasserman TN, Cushman SA, Schwartz MK, Wallin DO (2010) Spatial scaling and multi-model inference in landscape genetics: Martes americana in northern Idaho. Landscape Ecol 25:1601–1612CrossRefGoogle Scholar
  45. Wiens JA (1989) Spatial scaling in ecology. Funct Ecol 3:385–397CrossRefGoogle Scholar
  46. Wu JG (2004) Effects of changing scale on landscape pattern analysis: scaling relations. Landscape Ecol 19:125–138CrossRefGoogle Scholar
  47. Zeller KA, McGarigal K, Whiteley AR (2012) Estimating landscape resistance to movement: a review. Landscape Ecol 27:777–797CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Natural Resources Institute, University of ManitobaManitobaCanada
  2. 2.Parks CanadaManitobaCanada

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