The spatial problem I explored this quarter was about quantifying the extent of the foraging ranges of Adelie and Gentoo penguins breeding at Palmer Station, Antarctica. My original research question was whether interspecific competition could be a possible mechanism driving penguin population trends at Palmer Station. In retrospect, this question was a bit beyond the scope of the spatial analysis I proposed to conduct. However, the approach I used to test my hypothesis (that the foraging ranges of these two species would overlap) is an important first step in starting to answer this question.

The dataset I used to conduct this analysis consisted of location data (Lat/Long coordinates) obtained from platform terminal transmitters (PTTs). Over the course of the 2015 breeding season (5 January-2 February), 20 penguins (n=5 Adelie, n=15 Gentoo) were outfitted with PTT tags for roughly 3 days each. Over these three days, tags transmitted location data to ARGOS satellite system. With the specific purpose of learning spatial analysis techniques in mind, all datapoints were treated as foraging locations. Further analysis of PTT data combined with TDR (time depth recorder) data would need to be conducted in order to separate foraging locations from travelling locations. Location data from individual birds were grouped together by species (n=522 Adelie, n=147 Gentoo). The purpose of this was to analyze each species foraging distribution as a whole rather than look at individual tracks.

I used a kernel density (KD) approach to answer my question of interest. I chose this approach because it is one of the most widely used techniques to apply to tracking data for hot spot analysis, and because it appeared to be relatively easy and quick to learn. My goal was to create isopleths of utilization in order to identify areas used for foraging (95% KDE) and core use areas (50% KDE). The general idea being that the area contained within the 50% contour line would be the smallest area encompassing 50% of the datapoints used to create the entire KDE. I also sought to determine the area (km²) within of each of these contour lines and calculate the proportion of overlap between the two species ranges.

My results are summarized in table 1 (below). Gentoo penguins have a larger foraging range (core use and overall) concentrated around the colony where they were tagged, as well as near the head of Palmer deep canyon (figure 1). Adelie penguins have a more densely concentrated (near shore) range centered around the colonies where they were tagged. These results provide evidence to support my hypothesis that the ranges of the two species overlap. A greater percentage of Adelie foraging area overlaps with Gentoo area, due to the fact that their range is smaller.

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. Map depicting kernel density contours for 95% and 50% KDEs. Dark blue and red symbolize overall and core use areas of Adelies and lighter blue and red represent Gentoo ranges.

The significance of these results is questionable due to issues I was unable to address by the end of the quarter. Kernel density estimates are influenced significantly by the smoothing factor (search radius) used, which in turn is influenced by the density of datapoints considered. Therefore, sampling size, or the number of datapoints used in each kernel density estimate, has a big effect on the final KDEs. In this analysis, I used a much larger sample of Adelie locations then I did Gentoo locations. I have begun testing the effect of sample size on these KDEs, but have yet to come to any conclusions about the appropriate number of datapoints to use in order to gain an accurate estimation of foraging range.

Once I’ve addressed this issue of unbalanced sampling, I will be more confident in drawing conclusions about the foraging ranges of these two species. In the future I intend to use this information to make comparisons of these ranges between species and across years of variable prey. Ultimately, this knowledge will inform larger questions of the Palmer Long Term Ecological Research (LTER) project (e.g. how do changes in the marine environment affect the behavior and distribution of penguins? Are penguins competing with each other and/or other krill predators (e.g. whales) in the Palmer area? How does prey variability affect these relationships?).

Over the quarter I’ve gained more knowledge of ArcMap, specifically the spatial analyst toolbox and the kernel density tool. I’ve also begun to learn these same techniques in R and I hope to continue to expand on that in the future.  Thanks Julia and Mark!

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.

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.