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Testing assumptions of cost surface analysis—a tool for invasive species management

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Abstract

Applied ecology could benefit from new tools that identify potential movement pathways of invasive species, particularly where data are sparse. Cost surface analysis (CSA) estimates the permeability (friction) across a landscape and can be applied to dispersal modelling. Increasingly used in a diversity of applications, several fundamental assumptions that might influence the outputs of CSA (cost surfaces and least-cost pathways) have yet to be systematically examined. Thus, we explore two issues: the presumed relationship between habitat preferences and dispersal behaviour as well as the degree of landscape fragmentation through which an organism moves by modelling a total of 18 sensitivity and dispersal scenarios. We explored the effect of fragmentation by altering the friction values (generally assigned using expert opinion) associated with patch and linear features. We compared these sensitivity scenarios in two sites that differed in fragmentation. We also used eastern grey squirrels (Sciurus carolinensis) as an example invading species and compared diffusion models and two contrasting cost surface dispersal scenarios. The diffusion model underestimated spread because squirrels did not move randomly through the landscape. Despite contrasting assumptions regarding dispersal behaviour, the two cost surfaces were strikingly similar while the least-cost paths differed. Furthermore, while the cost surfaces were insensitive to changes in friction values for linear features, they were sensitive to assumptions made for patch features. Our results suggest that movement in fragmented landscapes may be more sensitive to assumptions regarding friction values than contiguous landscapes. Thus, the reliability of CSA may depend not only on the range of friction values used for patches but also the degree of contiguity in the landscape.

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Acknowledgements

Financial support for EKG was provided by a National Science and Engineering Research Council (NSERC) Industrial Postgraduate Scholarship, Pacific Region Environmental Systems Research Institute of Canada, Arthur Richmond Memorial Scholarship Graduate Scholarship at the University of Guelph, and the Mountain Equipment Co-Op Environment Fund. Digital data was generously provided by Geographic Data British Columbia, British Columbia Ministry of Environment, Land and Parks, Greater Vancouver Regional District, and Crown Lands Registry Services. EKG greatly appreciates the intellectual support from Dr. Tom Nudds and Dr. Peter Arcese. Thank you to the survey respondents, many of whom submitted interesting stories. We are grateful to Jessie Hui-Chung Wu, Janelle Curtis, Thorsten Wiegand, Patrick Lilley, and the anonymous reviewers who made improvements to earlier versions of the manuscript.

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Authors and Affiliations

  1. Integrative Biology, University of Guelph, Guelph, Ontario, Canada, N1G 2W1

    Emily K. Gonzales

  2. Forest Sciences, Centre for Applied Conservation Research, University of British Columbia, 3041-2424 Main Mall, Vancouver, British Columbia, Canada, V6T 1Z4

    Emily K. Gonzales & Sarah E. Gergel

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  1. Emily K. Gonzales
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  2. Sarah E. Gergel
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Correspondence to Emily K. Gonzales.

Appendix

Appendix

Digital data sources, scale, feature type and year last modified

Map

Source

Site

Scale

Feature type

Year

Greater Vancouver Generalized Land Use Map

The Policy and Planning Department, Regional Development Division of the Greater Vancouver Regional District

Fragmented

1:210000

Polygon

2001

Basemap Thematic Mapping Landuse

Geographic Data British Columbia and the BC Ministry of Environment, Land and Parks

Contiguous

1:250000

Polygon

1999

Municipal Cadastral Data

Crown Lands Registry Services

Contiguous

1:10000

Polygon

1999

Terrain Resource Information Management (TRIM) I

Geographic Data British Columbia and the BC Ministry of Environment, Land and Parks

Contiguous & Fragmented

1:10000

Line

2001

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Gonzales, E.K., Gergel, S.E. Testing assumptions of cost surface analysis—a tool for invasive species management. Landscape Ecol 22, 1155–1168 (2007). https://doi.org/10.1007/s10980-007-9106-6

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