Author Archives: John

Characterizing melon-headed whale resting/socializing habitat in the Marquesas Islands

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Where did the time go ?! Seems like we just began this term and it is already over. While I feel pretty unproductive right now, especially after seeing the outstanding work my fellow grad students have accomplished, I am extremely appreciative of the opportunity to explore the statistical tools available in ArcGIS and elsewhere that this course has provided. I found the freedom to explore our own data sets and the advice and encouragement provided by Julia and everyone else in the class incredibly rewarding. Thanks Julia for a great class!

I began the term with a pretty undefined thesis project (its still fairly fuzzy but at least I can start to make out a faint outline now). My data set is small and limited to a single survey season. As depicted in Figure 1, I have GPS track logs and the encounter locations  for 20 cetacean surveys conducted in the Marquesas Islands of French Polynesia.

Map of the study area in the Marquesas Islands of French Polynesia showing the extent of small boat surveys and cetacean encounter locations.
Figure 1. Map of the study area in the Marquesas Islands of French Polynesia showing the extent of surveys and the location of cetaceans encountered during small boat surveys in 2012.

My primary focus is on a poorly understood species,  melon-headed whales (Peponocephala electra).

Melon-headed whales courtesy of ARKive, http://www.arkive.org/melon-headed-whale/peponocephala-electra/image-G85523.html.
Melon-headed whales courtesy of ARKive, http://www.arkive.org/melon-headed-whale/peponocephala-electra/image-G85523.html.

These members of the dolphin family form very large “herds” (50  to as many as 1000 individuals) and have been observed congregating near the shore of Nuku Hiva in very specific locations on a regular, daily basis (Figure 2).

Encounter locations for melon-headed whales in the Marquesas Islands. Many of these locations are identical to those documented by Gannier in the mid-90s.
Figure 2. Encounter locations for melon-headed whales in the Marquesas Islands. Many of these locations are identical to those documented by Gannier in the mid-90s.

I spent most of this term finding, accessing, downloading, importing, reclassifying, converting, re-downloading, cussing at, etc., etc.,  environmental and bathymetric data. Using this data and other environmental data such as information on currents and sea surface height, I hope to investigate the differences and similarities between melon-headed whale encounter locations in order to 1) characterize these resting/socializing areas and 2) develop a model to predict possible resting/socializing locations in areas that have not been surveyed.

I was able to explore some of the tools in the Spatial Statistics Toolbox but for this data many of the tools are not applicable. For example, Ordinary Least Squares and Geographically Weighted Regression assume that there is linearity in the data. My data does not show linearity, even after transformation, and my response variable is not continuous. Running the Average Nearest Neighbor Tool produced the results that one would predict after looking at the maps provided above – the encounter locations are more clustered than predicted (Figure 3).

Figure . Results of a Nearest Neighbor analysis for melon-headed whales. As predicted, encounter locations appear to be more clustered than expected by chance.
Figure . Results of a Nearest Neighbor analysis for melon-headed whales. As predicted, encounter locations appear to be more clustered than expected by chance.

All of these results brought me to a point where I needed to take a step back and reexamine my data and my objectives. I felt like I was attempting to ask questions that just aren’t going to be answered by my data. My main question involves the characteristics of the encounter locations that define melon-headed whale resting locations. To get at this question, I plan on defining encounter locations spatially, i.e. delineate polygons of a certain size around encounter locations, and statistically examine the similarities and differences between the polygons using the environmental and oceanographic data mentioned above. I will continue to explore the tools available in ArcMap as well as the plethora of non-ArcGIS tools to answer this question.

Environmental/oceanographic characteristics, habitat utilization, and species distribution

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My data consists of points derived from a GPS track log, which contains spatial information for GPS points taken at 30-second intervals along with a time stamp for each point. I also have a spreadsheet of field data containing location information for the start and end of an encounter with a species of cetaceans, the time the encounter started and when it ended and other important information such as the species, the number of animals, etc. In order to pair species’ encounters with the GPS tracklog, I use the time information of the encounter and associate those points in the tracklog that correspond with the beginning and ending times.

 

I am interested in a couple of spatial aspects of this data that are pertinent to this class:

  1. Patterns in the environmental and oceanographic characteristics of the encounter locations that may explain melon-headed whale (Peponocephala electra) utilization of these locations.
  2. The spatial distribution of melon-headed whales and other small cetaceans and the patterns in the presence or absence of melon-headed whales and the presence or absence of other species.

 

These areas of interest bring up the following spatial statistics related questions:

  1. Do environmental and oceanographic characteristics differ significantly between locations?
  2. Which variables are significant predictors of melon-headed whale utilization of these areas?
  3. Do encounter locations differ significantly from locations where melon-headed whales were not seen?
  4. Is there a relationship between the presence (or absence) of melon-headed whales and the presence of other species of small cetaceans?

 

I am sure there will be more questions that present themselves once I begin delving into the data.

ModelBuilder for automating tasks and using ArcGIS with R

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Areas of interest

1. Using  ModelBuilder  to manage data downloaded from the Internet.

http://www.arcgis.com/home/item.html?id=7180ba6e9d8845128eaadf70a4b6bf7e

This tutorial piqued my interest because my data will come from a variety of sources. I will likely encounter a variety of formatting, labeling, and quality differences among datasets so standardizing the process would be beneficial. This tutorial illustrates some of the pertinent considerations, such as no spaces in field names, when importing data into ArcGIS as well as how to use ModelBuilder to plan and automate tasks.

2. Using R in ArcGIS 10.

http://www.arcgis.com/home/item.html?id=a5736544d97a4544aa47d06baf910f6d

Extending ArcGIS with R – presentation from the 2010 Users Conference

http://www.arcgis.com/home/item.html?id=547085ee428f4141b2cacb338f8f61a3

Since ArcGIS can experience limited functionality working with large datasets and spatial statistics needs can extend beyond its capabilities, being able to integrate with software that is more capable, such as R, could be very useful.

To Do:

  1. I am still early in my thesis development but one of the things that I would like to investigate is habitat use of melon-headed whales around French Polynesia and compare that to habitat use around other islands. I would like to continue to investigate the spatial statistics tools that are out there and see what the best approach will be for my project.
  2. I am also interested in looking at spatial distributions of small cetaceans in the Pacific and test for relationships between these distributions and the presence or absence of melon-headed whales. So again, investigation into the spatial statistics relevant to this type of study is on the to do list.