The main problem that I face with my humpback whale sighting data is that field efforts were not random and sightings reflect locations of predictable habitat use rather than sightings along survey transects. When asked to run a nearest neighbor analysis, Julia and I thought it might be neat to run the identical analysis at three different spatial scales in order to see how the results differ. I made three independent shapefiles for each spatial scale and ran the analysis for 1) all of southeastern Alaska, 2) just Glacier Bay and Icy Strait and 3) Point Adolphus.

These were the results for the nearest neighbor (NN) analyses:

SEAK (largest extent)

• Expected NN: 3795.7 m
• Observed NN: 485.9 m

Glacier Bay/Icy Strait (medium extent)

• Expected NN: 1174.3 m
• Observed NN: 353.6 m

Point Adolphus (smallest extent)

• Expected NN: 366.2 m
• Observed NN: 137.4 m

We can see that as we get down to a smaller spatial scale, the expected value becomes more similar to the observed. This is expected since the geography of the entire SEAK extent is no longer getting between groups of whales. Also, the distribution of whales is becoming be more evenly distributed.

Next, I ran a hot spot analysis of humpback whale sightings in Glacier Bay/Icy Strait. A layer of bathymetry was downloaded from the GEBCO “British Oceanographic Data Center” and I extracted raster values to each humpback whale sighting (first figure, below – green dots are deeper, red are more shallow). In the second figure below, red dots indicate significant clusters of high values (depths) and blue indicate significant clusters of whales at shallower depths.

Notes from today’s class discussion: How do we actually calculate the expected nearest neighbor value? This analysis is scale dependent. We must consider the spatial extent that goes into the calculation. Kate discovered that there is an option for defining the area of this analysis when you run the tool. The default is to use the extent of your feature class. Since I created a new feature class for each of the three analyses above, those different spatial scales are included in the output value for expected nearest neighbor distance. We must be sure to keep this methodology in mind when doing these calculations.

A potential next step for me is to run my hot spot analysis on more localized scales. The default spatial extent for the analysis I ran appears to be too expansive to really show fine-scale patterns. I also want to start looking at hot spot analyses based on mitochondrial DNA haplotypes.