Chapter 13: Terrestrial Permeability of the Pacific Northwest

Dataset Overview

This dataset provides a map of landscape permeability, which represents how easy it is for a variety of organisms to move through or across the landscape (figure 13.1; see chapter glossary for definitions of terms). In addition, the concept of permeability can extend to ecological processes such that more permeable landscapes are those with fewer geographic barriers to those processes. The process to calculate permeability involved the creation of a resistance dataset based on land use, infrastructure features, transportation networks, and other landscape features that might serve as deterrents or barriers to the movement of organisms.

Data Access:

Figure 13.1


Conservation Applications

Potential conservation applications of this dataset could include the following:

  • This dataset could be used to evaluate the potential of a current network of protected areas to facilitate species’ movements. For example, the study that produced this dataset (Buttrick et al., 2015) examined how well an existing biodiversity-based portfolio of protected areas performed in representing sites with high permeability and high topoclimate diversity. The study found that, using a 30% conservation target, ecofacets with high topoclimate diversity and high permeability were well represented in the portfolio in 9 of the 11 ecoregions examined.
  • This dataset can also be used to identify the types of land facets and ecofacets that are underrepresented in the current protected area network. For example, Buttrick et al. (2015) discussed the possibility that protection and ecological restoration of some ecofacets with low permeability, e.g., those currently used for agriculture, could improve representation of those ecofacets in the overall protected-area network. Buttrick et al. (2015) also provided a conceptual example of a process for conservation planning that incorporates current protection status, a conservation risk assessment, current biodiversity, and the data layers produced in the study (topoclimate diversity and landscape permeability).

Applicable scales for detailed spatial assessments:

  • For conservation applications requiring detailed assessment of spatial variation of the dataset within a geographic boundary (such as a protected area), the following geographic scales may be most appropriate (see appendix 3 for more information).
  • At the scale of: a local watershed (12-digit hydrologic unit code [HUC-12]), a Bureau of Land Management (BLM) district, a river watershed (8-digit hydrologic unit code [HUC-8]), an individual county, a national forest, a level-3 ecoregion (e.g. the North Cascades), a single state (Washington, Oregon, or Idaho), a region (multiple states in the Pacific Northwest).

Applicable scales for assessing general patterns:

  • Due to spatial resolution, the dataset may not show detailed spatial variation at the following geographic scales, however, the dataset may be useful to assess general patterns or for comparison to other locations (see appendix 3 for more information).
  • At the scale of: a small (< 1 km2) nature preserve, a state park or state wildlife area.

Use of the dataset in conservation applications may be limited by the following considerations:

  • Because different species disperse across the landscape in different ways (e.g., with different modes of dispersal, at different rates, and with different sensitivities to different types of barriers), this dataset cannot necessarily be used to evaluate movement potential for an individual species. If conservation decision-making requires predictions of species-specific movement potential, this dataset could be used to supplement species-specific information on dispersal and habitat requirements.
  • When applied to long-range conservation planning in the face of climate change, it may be important to remember that this dataset was created using input data layers (i.e., land cover and locations of infrastructure and transportation networks) that were current at the time the study was conducted (these datasets were generally from 2010-2014). Because the creation of the permeability data layer did not incorporate any models of future land use, landscape permeability in some regions by mid-century (i.e., 2050s) or by the end of the 21st century could be substantially different than what is depicted in this dataset.

Past or current conservation applications:

Chapter 13 Case Study

Dataset citation:

Buttrick, S., K. Popper, M. Schindel, B. McRae, B. Unnasch, A. Jones, and J. Platt. 2015. Conserving nature’s stage: identifying resilient terrestrial landscapes in the Pacific Northwest. The Nature Conservancy, Portland, Oregon, USA.

Dataset documentation link: (open access)

Data access:

The dataset can be downloaded from

The zipped folder linked above contains scripts used in the study that produced the dataset and a geodatabase containing several geospatial files. Terrestrial permeability is represented by the data layer PERM_90_ALL_ECOREG. Please consult the dataset citation for explanations for each file.

The dataset available for interactive online map viewing at

Metadata access:

Formal metadata files are included with the datasets available from

Dataset corresponding authors:
Michael Schindel
The Nature Conservancy in Oregon
[email protected]

Ken Popper
self-employed; formerly with The Nature Conservancy in Oregon
[email protected]

Data type category (as defined in the Introduction to this guidebook): Landscape connectivity

Species or ecosystems represented: This dataset does not represent any individual species or ecosystems.

Units of mapped values: unitless

Range of mapped values: 0 to 1.0

Spatial data type: a raster dataset (grid)

Data file format(s): GeoTiff (.tif)

Spatial resolution: 90 m

Geographic coordinate system: North American Datum of 1983

Projected coordinate system: USA Contiguous Albers Equal Area Conic

Spatial extent: Regional (Pacific Northwest of the United States)

Dataset truncation: The dataset is truncated at non-ecological borders along the northern edge at the United States border with Canada.

Time period represented: Current or recent (2000 to 2018)

Methods overview:

Spatial information on land use, infrastructure features, and transportation networks were compiled and assigned resistance values. Higher resistance values were assigned to features or land-use types believed to impose greater barriers to movement of organisms and ecological processes. To this resistance data layer (originally at 30-m resolution, then resampled to 90-m resolution), kernel analysis was applied (Compton et al., 2007; Compton, 2012). This analysis produces, for each pixel, a measure of the extent to which movement outward from the pixel to neighboring pixels is impeded (low permeability) or enabled (high permeability). For more information, please consult the dataset citation listed in section 2 of this chapter.

This dataset relied on the following general types of models: Connectivity models

This dataset employed the following specific models: Conservation Assessment and Prioritization System (CAPS) traversability model (Compton et al., 2007; Compton, 2012)

Major input data sources for this dataset included: Indicators of human presence on the landscape; current land use

The mapped values of the dataset may be interpreted as follows:

Higher values indicate higher landscape permeability, i.e., greater connectedness of natural systems, which facilitates movement of many species and helps sustain ecological processes. Higher values are generally found in areas with more natural land cover, while lower values are generally found in areas of more intensive human land use (e.g., urban areas and agriculture) and in the vicinity of infrastructure and transportation networks.

Representations of key concepts in climate-change ecology:

This dataset primarily represents climate-change adaptive capacity, in that it seeks to quantify the ease with which species can move across the landscape in response to climate change and thus sustain ecological processes. The dataset does not represent climate-change exposure, in that it does not represent the direction or magnitude of projected climate change. The dataset may represent climate-change sensitivity to the extent that areas of more intensive human land use are more sensitive to climate change. Because sensitivity and adaptive capacity are components of climate-change vulnerability, this dataset can help contribute to an assessment of climate-change vulnerability (i.e., less permeable areas may be more vulnerable because they may have lower adaptive capacity and/or higher sensitivity).

The capacity for species to adapt to climate change, such as through migration to other regions with more suitable climate, can be constrained by human influences on the landscape (Beever et al., 2016). This dataset incorporates some of those constraints on adaptive capacity by showing reduced landscape permeability in areas of more intensive human land use. However, species may face other potential constraints on their adaptive capacity not represented in this dataset, such as changing pest and pathogen dynamics, invasive species, and changes to disturbance (e.g., fire) regimes.

This dataset involves the following assumptions, simplifications, and caveats:

The permeability values in this dataset were created using spatial data layers representing land cover, infrastructure features, and transportation networks that were current at the time the study was conducted (i.e., input data layers were generally from 2010-2014). Permeability values in this dataset thus do not reflect possible future land use, such as urban sprawl. In addition, the dataset does not consider barriers to movement created by other characteristics of the landscape, such as topographic features (e.g., cliffs) or areas of inhospitable climate (e.g., warm valleys separating cold microclimates on mountain summits). The dataset represents permeability only for terrestrial areas and should not be used to evaluate aquatic or marine ecosystems including streams, lakes, or estuaries.

Quantification of uncertainty:

This dataset does not include any quantification of uncertainty relating to the mapped values.

Field verification:

Creation of this dataset did not involve any field verification of the mapped values.

Prior to dataset publication, peer review was conducted by internal review (reviewers were from the same institution).

Beever, E., J. O’Leary, C. Mengelt, J. M. West, S. Julius, N. Green, D. Magness, L. Petes, B. Stein, A. B. Nicotra, J. J. Hellmann, A. L. Robertson, M. D. Staudinger, A. Rosenberg, E. Babij, J. Brennan, G. W. Schuurman, and G. E. Hofmann. 2016. Improving conservation outcomes with a new paradigm for understanding species’ fundamental and realized adaptive capacity. Conservation Letters 9: 131–137.

Buttrick, S., K. Popper, M. Schindel, B. McRae, B. Unnasch, A. Jones, and J. Platt. 2015. Conserving nature’s stage: identifying resilient terrestrial landscapes in the Pacific Northwest. The Nature Conservancy, Portland, Oregon, USA.

Compton, B. 2012. CAPS traversability metric in R. Landscape Ecology Program. University of Massachusetts, Amherst, MA.

Compton, B. W., K. McGarigal, S. A. Cushman, and L. R. Gamble. 2007. A resistant-kernel model of connectivity for amphibians that breed in vernal pools. Conservation Biology 21:788–799.