This week I began exploring the tools and techniques I proposed using in “My Spatial Problem” blog. The goal of my project is to investigate the foraging ranges of Adelie and Gentoo penguins over the course of a breeding season on the Western Antarctic Peninsula. Specifically, I’d like to calculate the total area each species utilizes for foraging, identify core foraging areas, and calculate the percent overlap between the ranges of these two species.

I began this process by importing XY Data (latitudinal and longitudinal coordinates in decimal degrees) into ArcMap. To start I’ve randomly chosen data from three Adelie and three Gentoo penguins, representing at-sea areas where PTT tags were able to successfully transmit location data to a satellite.

I utilized an online database called the Antarctic Digital Database (©1993-2015 Scientific Committee on Antarctic Research) to obtain basemaps for general orientation and reference. They aren’t perfect, but useful enough to show the general location of each species’ breeding colony, and the location of a nearby marine canyon that may be of ecological importance. I found these basemaps to be important right away. They aren’t directly necessary for spatial analysis, but they are critical in terms of initially assessing the space these species are utilizing and determining whether things make sense! I discovered two important things.

• In my first blog post, I briefly glossed over the importance of filtering these datapoints to eliminate poor quality data. Each datapoint (downloaded from ARGOS) comes with an associated estimation of accuracy. I decided to initially skip this step while I use this data to practice spatial analysis in Arc. This explains why some datapoints are on land, and it might explain outliers, like those seen in the Neumayer Channel (unlabeled, but at right in Figure 1 and 2 below).
• Upon visually inspecting these datapoints I realized there must be something wrong, because my initial map of Gentoo foraging locations showed a lot of clustering around Torgersen Island (Figure 1). Torgersen is the location of the Adelie colony, and Biscoe is the location of the Gentoo colony. It was unusual that the points of two individuals did not originate from Biscoe. In fact, the original data were from Adelie’s and were mixed up in the original datasource. This is corrected in Figure 2.

Next, I began to research kernel density estimation techniques. Much of the literature I’ve read where similar techniques have been used has alluded to kernel density estimation techniques, percent volume contours, and, a spatial analyst extension called Animal Movement. I was dismayed to find out that the Animal Movement Extension is no longer in commission and not available for Arc 9 and 10. The next extension/software I researched that provided these tools was called Hawths tools, and is also discontinued. Its replacement is called Geospatial Modeling Environment (http://www.spatialecology.com/gme/). I am still considering using this software, however it would require learning/using an entirely different program.

While considering these things, I attempted to search for tools in the ArcToolbox that might be useful. I used the Kernel Density tool to create kernel density layers for each species. I combined the individual datapoints from each Adelie into one layer (Fig 3), and the three Gentoo individuals into another (Fig 4), and then calculated separate kernel density layers for each. It was encouraging to find that the output of this was a pretty good visual representation of “hotspots”, however, I’ve since been stuck attempting to understand exactly what Arc did here. Specifically, I don’t understand the values that are associated with each contour.

 If I can figure this out I will be able to determine whether this tool will work for the purpose of my project. I’d like to determine the area within 50% and 95% contour lines. To do this I need to accurately create these contours, and this will require more knowledge about how the kernel density tool works. So far I’ve experimented changing different things associated with “Classification” in the Symbology tab of Layer Properties. Break Values seem to determine each contour, and there is an option to change these values. There is an option to specify %, but the units/area calculated by the % values do not seem right (Figure 5). The legend contains the values associated with 25, 50 and 95% breakpoints (0-25, 25-50, 50-95). I will continue to explore this function, as well as the Geospatial Modeling Environment program described above.

Looking closely at the Adelie datapoints (Fig 3) it appears that they are way too grid-like. It turns out that the original XY data (decimal degrees) is only to four decimal places. Eventually I will need to return to the original datasource for more fine-scale points (hopefully they exist).

My next steps include deciphering the kernel density output, and learning how changing factors such as grid cell size and search radius affect kernel density calculations. After that I will need to determine which tool/calculation will allow me to compute % overlap between the two species ranges.