The resistance surface that formed the basis of our coastal marten connectivity model is comprised of several data layers that represent forested and non-forested land cover, waterbodies, rivers, roads, and serpentine soils.

This dataset contains the roads data used in the resistance surface. To see actual resistance values assigned to the road classes in this raster when the resistance surface is compiled, see the associated spreadsheet of resistance surface data sources and resistance values.

To build the roads resistance layer, we used the OpenStreetMap dataset (OpenStreetMap contributors 2018), which is a global, collaborative project to create a free, editable map of the world’s roads. OpenStreetMap contains 33 different types of roadway in the modeled landscape that are classified based on type, size, usage, etc. (https://wiki.openstreetmap.org/wiki/Map_Features #Highway). We selected 14 of these that seemed likely to have the potential to impact coastal marten habitat and movements, modeling them as having higher resistance with increasing width and the amount and speed of vehicle traffic, as implied in the classification descriptions.

We examined descriptions of the 33 roadway classifications in OpenStreetMap that occurred in our modeled landscape. Because of the origins of this global dataset, most of these road types have been given names more typically used in Great Britain than in the USA (e.g. “Motorway” instead of “Freeway”). However, good descriptions of all classifications were available in supporting documentation online (https://wiki.openstreetmap.org/wiki/Map_Features#Highway), allowing us to assess which categories were likely to impact coastal marten habitat and movements based on road width and the amount and speed of vehicle traffic implied in the classifications (we lacked data on actual traffic levels or speed limits). We assumed that freeways and major divided highways would act as strong “psychological” deterrents to martens crossing them because of the noise and disturbance from traffic and the likelihood of having to cross a wide area with little or no cover (Forman and Alexander 1998, Alexander and Waters 2000). These roads also probably pose the greatest risk of mortality from being struck by a vehicle; we assigned them a resistance value of 150. We then assigned decreasing resistance values through smaller highways and roads (“Trunk”, “Primary”, “Secondary”, and “Tertiary” roads and their associated “link roads”, which are generally very short offshoots connecting to other roads) (Fig. 4D, Table 2 of report). Many of the smallest roads (such as residential roads and logging roads) overlapped with data represented in the OGSI or ESLF layers, which could have led to “double counting” the resistance of these features and modeling them as more significant barriers than we intended them to be. Therefore, we ended up not assigning resistance values to anything smaller than “Unclassified” roads, which are usually two-lane roads connecting to small rural communities. Ultimately, 11 road classifications were assigned resistance values >0 (see Appendix 1 for full list).

Rivers and roads were represented on the resistance surface as linear features a single pixel 30m wide. Because many of these features were modeled as representing significant barriers to movement by martens, we took care to minimize the occurrence of any breaks in these linear features that would encourage LCPs to pass through them. In many instances, roads and rivers are in fact wider than 30m, and in these cases the pixels were classified by the relevant surrounding land cover type (OGSI or ESLF).

This is an abbreviated and incomplete description of the dataset. Please refer to the spatial metadata for a more thorough description of the methods used to produce this dataset, and a discussion of any assumptions or caveats that should be taken into consideration.