As described in my previous blog post, my original intent was to investigate successional patterns of urban re-colonization by diurnal raptors using eBird data. I soon realized that Exercises 2 and 3 would not be possible with point data that I have because the data alone do not have any quantitative attributes that vary in any meaningful way in relation to my research question. After discussing other potential research questions with Julia, we decided that analyzing differing patterns of year-round residency across an urban/rural gradient would be a reasonable alternative that still addresses some of the same overarching themes.

My general approach to conducting this analysis was to calculate the ratio of the number of days a particular species was observed within a given year to the number of days observations of any species were made. This ratio is calculated per cell in a raster covering the extent of the study area. If the ratio is closer to 1 in certain places, then that species of bird is likely staying in that area for more of the year. A value of 0 would indicate that at least one other species was observed, but the species of interest was not observed at all at that location.

I initially thought I would get more meaningful results with a larger sample size so for this initial analysis, I chose to analyze the residency of red-tailed hawks (n = 6,607), one of the most commonly reported species of raptor. The year-round range of red-tailed hawks, however, overlaps with my study site in NW Oregon which might have influenced my results. I therefore repeated the analysis with observations of merlins (n = 589), a species without a year-round range overlapping the study site, to see if patterns in the data were different. Of course, merlins and red-tailed hawks may respond to urban and rural environmental characteristics differently. For both species, I used observations from 2014 only.

An overview of the workflow for this analysis is shown below , but I will also describe each step in detail.

Step 1 Dissolve all observations dataset and species observation dataset by date and observer ID fields.

Step 2 Point Density on both sets of dissolved observations with a cell size of 200 m and a neighborhood of 1 cell

Step 3 Raster Caculator on both points density rasters to multiply each by 200. This produces a raster where each cell is a count of the number of points within that cell. (Not shown in workflow schematic due to space limitations.)

Step 4 Raster Calculator to compute raster where each cell is a ratio of species observation count to total observation count

Step 5 Extract Values to Points with all observation dataset as input points and ratio raster as values to sample.

Step 6 Getis-Ord Gi* Hotspot analysis on sampled points with Fixed Distance Band as the Conceptualization of Spatial Relationships parameter.

While the results seem to reflect a pattern I would expect, I’m not sure that I trust them entirely. This is for several reasons:

• The merlin hotspot map is very similar despite the fact that the observations were much more sparse than red-tailed hawk observations. The merlin map also shows hotspots in locations where there aren’t any merlin observations too, and the sampled value of many of the points is 0 (meaning other birds were observed but no merlins were).

• I conducted the same sampling procedures using Extract Values to Points but with a grid of points even spaced 1km apart. The hotspot map is very different and seems to be mostly noise. There is only faint evidence of a discernible pattern.

• There are some data quality issues that I did not address here. For instance, some observers may have only been looking for certain species and might not have reported the species of interest even if it were present. The converse could be true as well. This is usually reported with those kinds of observations but I didn’t filter these out.
• Also, the data are very clustered to begin with and I may not have selected the right Conceptualization of Spatial Relationships for the data.