Since my last update I’ve made significant progress in estimating the foraging ranges and overlap between Adelie and Gentoo penguins at Palmer Station over the 2014/15 breeding season.

With the help of a classmate (thanks Steven!) and a few online forums (GIS in Ecology & GIS 4 Geomorphology), I was able to figure out how to calculate kernel density estimates (KDE) without Arc’s outdated Animal Movement Extension or Hawth’s Analysis Tools.

Objective: Quantify the geographical extent of the distribution of Adelie and Gentoo penguins foraging around Palmer Station

• Create kernel density estimates to identify areas used for foraging (95% KDE) and core use areas (50% KDE)
• Calculate the area (km²) within 95% and 50% kernel density contours
• Calculate the % overlap between the ranges of Adelie and Gentoo penguins

Methods:

1. Filter data points whose estimated error is >1500m
2. Combine location data points for all Adelie (ADPE) individuals n=15 (522 data points) and all Gentoo individuals (GEPE) n=5 (147 data points)
3. Create kernel density estimates using the kernel density tool and default parameters
4. Extract values by points from the output obtained above, determine 50% and 95% of observations using values of extracted points from attribute tables
5. Reclassify kernel density raster so values >50^th percentile have a new value of 50 and all others have a new value of NoData, use the same steps to create additional rasters representing 95% of points
6. Convert rasters to polygons, calculate area of each polygon using calculate geometry tool
7. Use union function to determine area of overlap between polygons

Results:

Table 1. Estimates of core use (50% KDE) and total (95% KDE) foraging areas used by Adelie and Gentoo penguins with associated overlap between species.

Figure 1. Visual representation of Adelie core use (red) and total foraging area (pink) and Gentoo core use (dark blue) and total (light blue) foraging areas. Despite poor image quality it is obvious that these ranges are closely associated with the colonies that the respective species are from, and there appears to be some association with bathymetry as the range of Gentoo’s is dense at the head of Palmer deep canyon.

Figure 2. Close up visual representation of Adelie core use (red) and total foraging area (pink) and Gentoo core use (dark blue) and total (light blue) foraging areas. Despite poor image quality it is obvious that these ranges are closely associated with the colonies that the respective species are from, and there appears to be some association with bathymetry as the range of Gentoo’s is clustered at the head of Palmer deep canyon. Note overlap between species.

Discussion:

The results of this analysis indicate that Gentoo penguins occupy a larger foraging range (core use and overall) and because of this, the portion of their range that overlaps with that of the Adelie penguins is minimal to moderate. The opposite is seen in Adelie penguins, who appear to have a smaller foraging range and thus a higher proportion of it overlaps with Gentoo penguins. Also notable is the fact that Gentoo penguins appear to be foraging farther away from their colony than Adelie penguins, which is surprising as the opposite is usually true. The main caveat of these results is the difference in sample size between data points of Gentoo (n=147) and Adelie (n=522) penguins. This was not accounted for in this analysis and is likely skewing these results. The fact that Gentoo’s have a larger range could be because there were fewer data points used in the creation of the KDEs.

The next step in this process will be to research methods that take sample size into account. One possibility is taking a random sample of Adelie location points from the total sample so that Adelie’s are represented equally to Gentoo penguins.

I will also be experimenting with KDE in R. This will allow me to compare results between the two methods (and R should speed this process up down the road)!

I am also in the process of determining whether a bathymetric layer and/or accurate basemap exists for this region. So far I’ve had difficulty finding these things but they would be very useful to compare these results to co-variates such as bathymetry and distance to shore.

I conducted a hot spot analysis for the cackling goose use of the Willamette Valley. As previously mentioned, I was curious how the geese are using the area throughout the winter season (October – April). I conducted a hot spot analysis for for each month using location points from the entire time series, 1997-2011, to attempt to discern any changes in landscape level use of the valley throughout the winter. All the maps passed the common sense test (the clusters were right over the refuges, no floating hot spots, etc.) which was somewhat heartening.

My first step was separating the data into month files and creating maps of each month in Arc. I also had not seen the data before, so it was nice to pull the points into Arc finally.

Cackling goose flock locations throughout the Willamette Valley, Oregon from 1997-2011.

Secondly I ran hot spot analyses on each month and compared. The hot spots were centered on the four federal refuges in the valley, Finley, Ankney, Baskett, and Sauvie Island (from south to north). Most of the winter months looked more or less the same, except the beginning and end of the winter season.

October vs. November

March vs. April

The addition of two hot spots between October and November, and the loss of one (Sauvie Island) in April likely reflects what sort of agriculture timing the refuges are on, but I am working on exploring why these particular locations are/are not used in those months.