Overview:

Established Range vs. eBird Range Comparison:

1. Compile spatial data –
   • Provincial Boundary
   • City locations
   • Highways and Freeways
   • Established Range
   • eBird Sightings
2. Buffer around sightings
   • 50 km border
3. Percent Cover Analysis

Analysis of eBird Data:

1. Analysis of Key Factors:
   • Transportation
   • Population
   • Land Use (Parks, Public Land, Nature Reserves, etc.)
   • Identified Range
2. MCE Identification of Ideal Bird Watching Locations
   1. Apply weight to factors
   2. Weighted sum for location identification
   3. Compare to identified eBird ranges
       

       

      Creation of Range Maps

      The official bird species ranges were clipped using a British Columbia boundary file in order to identify provincial ranges of birds. Individual sightings of birds reported through eBird were input as XY data points, identifying the specific latitude and longitude of each bird seen. A 50-kilometer buffer was applied to each bird sighting in order to represent the potential habitat usage of the bird within the time period it was seen. This 50-kilometer range represents the area a single bird could be using for resources, breeding, migration, or other uses at the time it was seen and reported. Layers identifying highways and freeways, provincial parks, and key population areas were added to provide further context. A comparison of eBird ranges and established ranges was quantified by calculating the percent coverage of established ranges by the projected eBird ranges.

      MCE for Identification of Hot Spots

      Land use, population, and transportation were identified as key factors in the reporting of bird sightings. A raster identifying the presence or absence of provincial parks was created by joining the provincial park polygons with an outline of the province, then converting the layer into a raster. Population density of census boundaries was calculated using Stats Canada census data from 2011. Population counts were divided by the calculated shape area, and converted into a raster. Distance to highways and freeways was calculated using the Euclidean Distance tool, creating a raster showing the distance of a point from transportation lines.

      The standardization of these factors was based on the premise that high population and proximity to transportation and public lands are favoured in this context. Euclidean Distance from roads was inverted (Field Calculator: Proximity=1/Distance) in order to have high values near roads. The resulting layer is referred to as Proximity to Transportation. Boolean presence/absence parameters were applied to the provincial park layer (Presence = 1, Absence = 0). The resulting layer is referred to as Land Use. Population density was represented as a proportion of the highest population density (Field Calculator: Pop_Ranking=Pop_Density/Max_Pop_Density). The resulting layer is referred to as Population Ranking. These new values were then used to create a Weighted Overlay identifying key areas of eBird data.

      Population ranking was identified as the most important factor for eBird suitability, followed closely by Proximity to Transportation. Land use had minimal impact on eBird data, so it was ranked low. Based on this ranking, a Weighted Overlay was employed with a weight of 0.71 for Population Ranking, 0.41 for Proximity to Transportation, and 0.04 for Land Use. The resulting raster was used to identify ideal locations for the use of eBird Data in British Columbia.