An Overview of Wildlife Habitat Connectivity Modeling

Wildlife habitat connectivity describes the degree to which wildlife species can move across a landscape to access the habitat and resources they need to survive. In a fragmented landscape, suitable habitat is often isolated in patches surrounded by unsuitable habitat, like islands in an ocean; larger patches are more valuable because they have more interior area that can be used as habitat (Figure 3A). For example, in a developing area like western Whatcom County, forest-dwelling species may be limited to patches of forest surrounded by a matrix of urban, agricultural, and less suitable natural lands (Figure 3B). Connectivity between these patches in the form of permeable landscape, stepping stone habitat patches, or connected habitat corridors allows those species to move between habitat and supports the survival of the species overall (Figure 3C). These important habitat connections should allow multiple movement routes through a relatively permeable landscape (Figure 3D). And ideally, a connection between core habitat patches should be wide enough and suitable enough to act as intermediate species habitat in its own right (Figure 3E). Using multiple strategies to increase landscape permeability and movement pathways between core habitat patches is essential for maintaining wildlife habitat connectivity on the landscape level.

There are many ways to model wildlife habitat connectivity, from single-species models based on animal movement data to general landscape models for large groups of species. Since our mission was to provide a broad overview of connectivity in Whatcom County, we chose a structural landscape approach. Structural connectivity models, unlike species-specific models, use information about the landscape itself –including proxy variables related to naturalness, human landscape modification, or mortality risk – to create a generalized, species-agnostic model of wildlife habitat connectivity. Previous research, including some in Washington and the Columbia Plateau, has shown structural connectivity modeling to be a robust alternative to species-specific models that offers comparable insight into wildlife habitat connectivity with fewer data and analytical requirements (Marrec 2020, Krosby 2015).

The biggest limitation to this broad approach is that it cannot and does not accurately capture habitat connectivity for all wildlife due to the various needs of different species. Most importantly, our connectivity models are limited to terrestrial wildlife and do not reflect the network of habitats used by most aquatic species. But even among terrestrial wildlife, certain landscape features may facilitate or inhibit the movement of some species and not others. For example, a major highway poses a deadly barrier to animals that move on foot, but might have very little impact on the movement of many bird species. When creating a connectivity model, we must decide what to include as barriers to movement, and that brings with it certain assumptions about the species to which that model best applies. The connectivity models described in this report best reflect the needs of terrestrial species that: 1) avoid human development when possible, 2) avoid roads and/or risk mortality by crossing them, 3) avoid traversing steep slopes when possible, 4) cannot easily cross large bodies of water, and 5) prefer habitat consisting of unfragmented natural landcover in patches of 100 acres or larger.

We used two connectivity analysis methods to provide a more complete picture of connectivity in Whatcom County.

The first, Linkage Mapper, is a widely-used suite of connectivity tools that can be used to map wildlife linkages and corridors between core areas of habitat (McRae and Kavanagh 2011). The second, Omniscape, uses circuit theory to simulate wildlife movement across the entire landscape without defined habitat cores (Landau 2021).

Finally, we created a Connectivity Value index to summarize how much each part of the landscape contributes to wildlife habitat connectivity. This index combines the results of the analyses in this report with other connectivity data including Wildlands Network’s Pacific Climate and Non-Climate Connectivity Maps, created in partnership with the University of Washington (Nuñez et al. 2022).

Creating the landscape resistance layer Methods

Before conducting connectivity analyses using Linkage Mapper and Omniscape, we needed to create a landscape resistance data layer. Landscape resistance is a metric that represents the relative difficulty and/or risk to wildlife moving across a landscape due to characteristics of that landscape. This metric is often expressed as “cost distance.” Every piece of land that an animal must cross has some cost to the animal. This can mean the literal calorie cost of traversing that land or the relative risk of death from predation, hunting, vehicle collisions, and other natural or human causes. For example, it is generally much safer and easier for an animal to move through its preferred habitat in a remote area than to traverse the same distance across a human-populated landscape with dangerous roads. Creating a landscape resistance layer is an attempt to calculate the relative cost to wildlife moving through a landscape, here represented as 30-by-30-meter cells in a raster grid.

There are many ways to calculate landscape resistance ranging from structural resistance based on landcover type to localized, species-specific resistance generated from actual animal movement data. Since the purpose of this project was to give a broad overview of structural connectivity for all terrestrial wildlife, we used a general approach based on detailed landcover and dispersal barriers like roads. This approach was partially modeled after a recent connectivity modeling project in southwestern Washington completed for the Washington Wildlife Habitat Connectivity Working Group (Gallo et al. 2019).

For landcover information, we used the Ecological Systems of Washington in the U.S. (WDNR 2019) and 2015 Landcover of Canada in British Columbia (Natural Resources Canada 2015). The Ecological Systems of Washington is a 30-by-30-meter cell raster layer based on the U.S. National Land Cover Database (NLCD), but it includes many more land cover categories for specific ecosystem types (like “Northern Rocky Mountain Western Larch Savannah”) and human disturbance (like “Quarry, Mines, and Gravel Pits”). The 2015 Landcover of Canada is the same resolution (30-by-30-meters), but the categories are significantly less detailed, containing only general types of natural community and only one class for “urban and built-up” areas. Each landcover class was assigned a score from 1 to 1000, where 1 represents the least resistance to wildlife movement. In general, we borrowed these scores from Gallo et al. (2019), but because we used different landcover datasets, some scores had to be added or adjusted. A full list of the landcover types and the scores assigned to them can be found in

Fragmenting features such as roads impose significant additional costs to landscape connectivity, both due to physical risk to wildlife and avoidance of these areas by wildlife. We used data from the National Transportation Dataset (in the U.S.), Statistics Canada, and CanVec (in Canada) to show roads, railroads, and trails in our study area (USGS 2021, Statistics Canada 2020, CanVec 2019). The National Transportation Dataset and Statistics Canada both showed roads classified based on type (from highways to minor roads), but each used a different classification system. We assigned landscape resistance values to each analogous class from each dataset to match each other, and the road classes used by Gallo et al., as closely as possible (Appendix A). All railways were given a single resistance value of 500, and all trails were scored as 100.

The last resistance feature we calculated was slope, derived from the National Elevation Dataset (USGS 2021). Slope reflects the impact of steep areas on animal movement through avoidance or increased caloric cost of traversal. Although slope may not impact the movement of all species (and in fact some species may preferentially use steep slopes), in a general structural connectivity model, steep slopes especially can be an important factor limiting species movement. We followed the lead of Gallo et al. in assigning increasing resistance scores for low (<20 degrees = 1), medium, (20-40 degrees = 50) and steep slopes (>40 degrees = 500).

Finally, all the resistance factors were added together to create a single landscape resistance score with a resolution of 30-by-30-meter cells. The score ranges from 1 (a natural area with no human impacts and low slope) to 2,502 (a high-density urban cell containing a highway and a railroad).