Evaluating the Suitability of Roadway Corridors for Use by Monarch Butterflies (2020)

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B-1 A P P E N D I X B User Guide for the Landscape Prioritization Model for Roadside Habitat for Monarchs Landscape Prioritization Model Roadside Habitat for Monarchs Model Overview The Landscape Prioritization Model of Roadside Habitat for Monarchs analyses and combines factors related to monarch habitat suitability along roadways. The model helps roadside rights-of-way managers assess how the roads in their state relate to landscape-scale factors affecting monarch habitat quality. The Landscape Prioritization Model may be used in several ways: To locate areas where roadside habitat may complement high functioning surrounding habitats. To identify places where roadside habitat could be developed as a habitat corridor in areas where the landscape is providing little monarch habitat. To compare roadsides within a given area using the roadside suitability index. To select sample locations for surveys of monarch habitat within roadside rights-of-way, across the spectrum of roadside suitability index values. If used in conjunction with manual surveys of rights-of-way with the Rapid Assessment of Roadside Habitat for Monarchs tool, the model can help target roads with high suitability index values but low actual habitat quality scores for enhancement through altered management. The model is downloaded as a .zip drive containing the model’s code and relevant source data. To open the model, the user must access the uncompressed folder in ArcGIS and open the ArcGIS Python Toolbox within. The user must provide the land-cover (USDA Cropland Data Layer) and road data layers (USGS National Roads Dataset), as well as a study area of interest, as specified in the Model User-Interface section below. The user can substitute state-specific road data for traffic volume, right-of-way (ROW) width, and speed limits to improve the model’s accuracy by replacing the generalized metrics derived from the USGS National Roads Dataset. ArcGIS Licensing The model requires an ArcGIS for Desktop Basic license with the Spatial Analyst extension.

B-2 Evaluating the Suitability of Roadway Corridors for Use by Monarch Butterflies Graphical Abstract Figure 1. Graphical abstract of the Landscape Prioritization Model Downloaded Files Source_Files: A folder containing the required source data (ArcGIS Layer files for cartographic consistency) and back-up python code for the model. Monarch Roadside Suitability.pyt: The ArcGIS Python Toolbox containing the Landscape Prioritization Model of Roadside Habitat for Monarchs interface and code. Open the folder in ArcGIS to access the tool.

User Guide for the Landscape Prioritization Model for Roadside Habitat for Monarchs B-3 Model User-Interface Inputs Cropland Data Layer: The raster dataset containing the USDA Cropland Data Layer, downloadable from the USDA National Agricultural Statistics Service website: https://www.nass.usda.gov/Research_and_Science/Cropland/Release/index.php USGS National Roads Dataset: A Shapefile or ArcGIS Feature Class containing the USGS National Transportation Map’s road layer, downloadable on a state-by-state basis from the USGS National Map website: https://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Tran/GDB/ National coverage is available via ftp here: ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Tran/Shape/ Study Area: A Shapefile or ArcGIS Feature Class denoting the full extent of the study area, typically a state or county. Results Folder: Folder location where the model will deposit results. Also acts as the working folder for any intermediate data created during the modeling process, all of which is subsequently deleted upon model completion. Key Monarch Habitat (optional): A Shapefile or ArcGIS Feature Class featuring areas of known high-quality monarch habitat (e.g. conservation areas, habitat reserves, etc.). Results Suffix (optional): A text suffix appended to all files generated by the model. Used to distinguish results from different runs of the model in the same folder. Traffic Volume (optional): Dataset (optional): A Shapefile or ArcGIS Feature Class of roads in the study area, containing data on Average Annual Daily Traffic volumes (number of cars per day). Field containing the data (required if using Traffic Volume data): The relevant data field in the Traffic Volume dataset. Speed Limit (optional): Dataset (optional): A Shapefile or ArcGIS Feature Class of roads in the study area, containing data on speed limits (miles-per-hour). Field containing the data (required if using Speed Limit data): The relevant data field in the Speed Limit dataset. Right-of-Way Width (optional): Dataset (optional): A Shapefile or ArcGIS Feature Class of roads in the study area, containing data on roadside right-of-way widths (feet). Field containing the data (required if using Right-of-Way Width data): The relevant data field in the Right-of-Way Width dataset. Outputs ArcMap Layer Files: Roadside Suitability.lyr: A map of roads in the study area and their predicted roadside monarch habitat suitability from 0 (low-quality) to 1 (high-quality) Patch Habitat.lyr: A map of predicted high quality monarch habitat ‘patches’ Habitat Quality.lyr: A map of landscape-scale predicted monarch habitat quality from 0 (low-quality) to 1 (high-quality) Nectar Availability.lyr: A map of predicted monarch-specific nectar availability from 0 (low availability) to 1 (high availability)

B-4 Evaluating the Suitability of Roadway Corridors for Use by Monarch Butterflies Milkweed Quality.lyr: A map of predicted monarch-specific milkweed quality from 0 (low-quality) to 1 (high- quality) Pesticide Exposure.lyr: A map of predicted pesticide exposure risk from 0 (low-risk) to 1 (high-risk) Landcover.lyr: A map of the underlying land use/land cover data for the study area. Other Files: Results.gdb: An ESRI File GeoDatabase containing the source data for the layer files (see above). Default.gdb: A vestige of the modeling process, an ESRI File GeoDatabase containing nothing. Methodology Overview Summary There are two distinct parts to this model, (1) predicting landscape-scale monarch habitat quality and (2) assessing roadsides for monarch habitat suitability based on a combination of road- and landscape- derived attributes. The habitat model is built from national land-cover data to highlight areas likely to provide high quality monarch habitat: where milkweed (host plants) and blooming plants (nectar sources) are likely to be abundant with a low likelihood of exposure to herbicides or insecticides. The habitat model then interacts with road data layers containing specific relevant attributes: potential roadside habitat area as determined by roadside right-of-way width, chemical exposure from traffic volume, and the threat of vehicular collision based on traffic volume and speed. Together these habitat- and road-based metrics combine into a single index of ‘roadside suitability’—the predicted habitat value of each road segment within the given landscape. The model can run at any spatial scale (e.g., state, county) within its specified ecological zone (e.g. North Core, South Core, or Western states, see Fig. 2). The model outputs a map of the ‘roadside suitability’ index as well as the habitat quality across the landscape (see Fig. 1). The model does not incorporate field data and may or may not predict well the actual field conditions within the right-of-way; to find out what habitat values exist on the ground in the rights-of-way, field data may be collected by the Rapid Assessment of Roadside Habitat for Monarchs tool. Figure 2. Monarch bioregions

User Guide for the Landscape Prioritization Model for Roadside Habitat for Monarchs B-5 Habitat Quality Maps of nectar availability, milkweed quality, and pesticide exposure are derived from the USDA Cropland Data Layer using data and equations from a variety of monarch ecological literature (see ‘Methodology’ file for more detailed information). The associated reclassification table is within the Source_Files folder. These three maps are combined to create an overall habitat quality map. If the user provides data on Key Monarch Habitat (see Model User-Interface above), the model increases the habitat quality in those areas. Habitat ‘patches’—areas of exceptional habitat quality—are selected from this map. Two of these habitat-based maps are used in predicting roadside suitability: (1) the distance of a given roadside to the nearest high quality habitat patch and (2) the ‘adjacent’ habitat quality, meaning the average habitat quality score (excluding pesticide exposure) within 120m of the roadside. Roadside Suitability Roads in the study area are derived from the USGS National Transportation Dataset. The ‘classification’ of these roads (e.g. highway, local road, etc.) translates to monarch-relevant metrics: roadside right-of-way width: predicts potential roadside habitat area traffic volume: predicts roadside chemical exposure risk (and see below) speed limit: combined with traffic volume, predicts monarch-vehicular collision risk These metrics act as baseline datasets. The user may provide more specific maps of right-of-way width, traffic volume, and speed limits, which will replace the baseline datasets wherever they overlap. For instance, users can provide an incomplete map of traffic volume and still retain the baseline data from the National Transportation Dataset for the incomplete areas. These roadside metrics each predict potential risks or benefits to monarch butterflies. Wide rights-of-way equate to higher potential roadside habitat area within the roadside buffer strips. Higher traffic volumes can lead to increased risk of roadside chemical exposure, and also higher vehicular collision risk in areas with speed limits between 15 and 55mph. For computational efficiency, the road data are converted into raster format before introducing the landscape-scale habitat metrics. Once converted, each section of road is analyzed for the two key metrics of the nearby habitat (distance to the nearest high quality habitat patch, adjacent habitat quality). Finally, for each section of road, the three road-derived metrics (potential roadside habitat area, roadside chemical exposure risk, monarch-vehicular collision risk) and two habitat-derived metrics are combined into the single roadside suitability index, using a weighted average approach that weights habitat benefits (potential roadside habitat area, distance to the nearest high quality habitat patch, adjacent habitat quality) higher than roadside risks (chemical exposure risk, vehicular collision risk).