The goal of this exercise was to investigate spatial autocorrelation in the movement parameters of a foraging humpback whale. I used location points of sightings of the whale at the water surface between consecutive dives to infer the path of the whale (Friedlaender et al., 2009). To facilitate the analysis, I assumed linear travel of the whale below the surface between consecutive surfacings. I projected the location points and used the adehabitatLT package in R CRAN (Calenge, 2011) to plot the location points of the whale as well as the linear travel segments between these points (see graph below).
The blue triangle indicates the first, the red square the last observation of the whale at the surface.
Using projected data, the adehabitatLT package calculates the distance traveled by the whale between consecutive observations, the turning angle between consecutive linear segments of the whale’s path as well as the duration between the observations. Using the distance and duration data I calculated the swimming speed of the whale.
Then I calculated the spatial autocorrelation in swimming speed and turning angles using Moran’s I for the entire path, and also for a small section of the path which I assumed to be a foraging area of the whale (cluster of points southeast of the blue triangle). In this small area, the whale spent a comparatively large amount of its time and swam shorter distances between consecutive surfacings, possibly indicating foraging activity.
A small p-value in one of the parameters would provide convincing evidence for the hypothesis that the movement of the whale is autocorrelated in the respective parameter, i.e. that neighboring locations have more similar values than locations that are further apart.
The results from the current analysis (see table below) provide moderate evidence for spatial autocorrelation in swimming speed for the analysis of the entire path, indicating that the whale swam slower in certain parts of its path and faster in other parts (Calenge 2011). However there was no evidence to suggest that travel speed in the small foraging area was autocorrelated. This could be explained by the fact that in the foraging area, the whale swam at a constant, slow speed to the probability of prey detection or encounter (Benhamou, 1992). When leaving this foraging area, the whale is likely to increase its speed, resulting in separate areas of the whale’s path with lower and higher swimming speeds, which would explain the autocorrelation in swimming speed observed for the entire path.
| Entire path | Foraging area | |||
| Speed | Angle | Speed | Angle | |
| p-value | 0.017 | 0.841 | 0.702 | 0.651 |
Benhamou, S. (1992). Efficiency of area-concentrated searching behaviour in a continuous patchy environment. Journal of Theoretical Biology – J THEOR BIOL, 159(1), 67–81. http://doi.org/10.1016/S0022-5193(05)80768-4
Calenge, C. (2011). Analysis of Animal Movements in R: the adehabitatLT Package. Saint Benoist, Auffargis, France: Office Nationale de La Chasse. Retrieved from http://cran.gis-lab.info/web/packages/adehabitatLT/vignettes/adehabitatLT.pdf
Friedlaender, A. S., Hazen, E. L., Nowacek, D. P., Halpin, P. N., Ware, C., Weinrich, M. T., Hurst, T., Wiley, D. (2009). Diel changes in humpback whale Megaptera novaeangliae feeding behavior in response to sand lance Ammodytes spp. behavior and distribution. Marine Ecology Progress Series, 395, 91–100. http://doi.org/10.3354/meps08003
Hi Theresa,
Nice work on the latest blog post/class assignment. I hadn’t realized you were using adehabitat, I’ll have to ask you about that later. Since our next assignment is to bring independent/explanatory variables into our analyses I thought I’d share this paper with you. It used a wide range of metrics (e.g. first passage time and the straightness index) to assess foraging behavior and it provided further evidence that seabirds use environmental cues to locate profitable foraging areas. It could be a useful resource for developing methods to investigate how foraging behavior is influenced by different environmental variables.
A brief summary and link below in case anyone else in class is interested!
Trathan, P. N., Bishop, C., Maclean, G., Brown, P., Fleming, A., & Collins, M. A. (2008). Linear tracks and restricted temperature ranges characterise penguin foraging pathways. Marine Ecology Progress Series, 370, 285-294. http://nora.nerc.ac.uk/11641/1/m370p285.pdf.
This study utilized a combination of ARGOS PTTs, GPS loggers and TDR tags to investigate the movement patterns of female King penguins (Aptenodytes patagonicus) during post-laying foraging trips off of South Georgia Island. The study was formed around the idea that optimal foragers (like seabirds) do not search randomly for prey; rather they seek predictable prey patches in order to maximize energy gain. In the open ocean where locating these areas may be more difficult, foragers will rely on environmental cues to direct them toward profitable prey patches. This behavior is highly scale dependent and difficult to study without the use of satellite telemetry. This study had two objectives (1) To compare movement data derived from GPS and ARGOS satellite tags and (2) To determine whether the foraging behavior of king penguins varies in relation to sea surface temperature (SST).
Area restricted search patterns were identified using a measurement called the straightness index. First passage time analysis revealed the scale of these search patterns, and additional foraging metrics (travel speed, dive depth, duration and frequency) revealed foraging behavior at varying spatial and temporal scales. Analysis showed that birds commuted north to the Antarctic Polar Frontal Zone where 45% of birds spent their time in water temperatures between 5-5.5C. In these areas, dive rate and traveling speed decreased while the degree of meandering, dive depth and dive duration increased. While ARGOS transmitters and GPS loggers were both useful in determining that the large-scale foraging behavior of king penguins is correlated with SST, ARGOS tags were more limited at finer spatial and temporal scales.
Also, I did a quick Google Scholar search of “Augur shell paths” and did not find anything so I propose we explore the concept search behavior further in the warm, clear waters of the tropical Pacific….
Erin