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!
