Author Archives: Sophie

Investigating spatial patterns in haplotype and sex of humpback whales in southeastern Alaska

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In the North Pacific, humpback whales feed in various locations along the Pacific Rim including in the US, Canada, Russia and eastern Asia during summer. In winter, they migrate south to mate and calve along Pacific coasts as well as the offshore islands of Mexico, Hawaii, and Japan (including Ogasawara and Ryukyu Islands). Fidelity to feeding areas is high, and is thought to be maternally directed; mothers take their calves to their specific feeding ground, and these offspring subsequently return to this region each year after independence.

This maternally directed fidelity is reflected in studies of maternally inherited mitochondrial DNA (mtDNA). In an ocean-wide survey of genetic diversity and subsequent analysis of population structure in North Pacific humpback whales (Structure of Populations, Levels of Abundance, and Status of Humpbacks; SPLASH), sequencing of the mtDNA control region resolved 28 unique mtDNA haplotypes showing marked frequency differences among breeding grounds (overall FST=0.106, p<0.001, n=825) and among feeding regions (overall FST=0.179, p<0.001, n=1031; Baker et al. 2008).

Despite genetic evidence of regional population structure in the North Pacific (i.e. separation of humpback whales into various stocks), there have been few studies to investigate the possibility of finer-scale structure within a single North Pacific feeding ground. For example, it is unclear whether maternally directed site fidelity at smaller scales within southeastern Alaska results in discernible differences in haplotype and sex frequencies.

For my final investigation in this course, I decided to look at fine-scale population structure of humpback whales in southeastern Alaska by exploring spatial patterns in haplotype and sex distribution. Specifically, I wanted to answer the following questions:

  • Are haplotypes (A+, A-, E2) differentially distributed by latitude?
  • Are males and females differentially distributed by latitude?
  • Are certain maternal lineages more spatially clustered than others?
  • Are males or females more spatially clustered?
Methods and Results
First, I isolated haplotype and sex layers by using the “split layer by attribute” tool in XToolsPro. I then went into Excel and produced latitude bins throughout southeastern Alaska (54.1-54.5, 54.6-55, 55.1-55.5, 55.6-56, 56.1-56.5, 56.6-57, 57.1-57.5, 57.6-58, 58.1-58.5, 58.6-59, 59.1-59.5). Next, I totaled the number of encounters of each class variable in each bin and calculated the percent of each class variable in each bin.
Haplotype Distribution:
Haplotypes_Split3Untitled
Sex Distribution:
Sex_SplitUntitledgraph2

It appears as though there is a peak in percent of sex and haplotypes observations between 56.6-58.5 degrees. After looking closer at this, I realized that this peak is a function of my bin selection. After visualizing my population distribution within each bin, it is clear that most of my encounters occurred between 56.6-58.5 degrees. However, there are some patterns in differential class variable percents. For example, more A+ haplotypes are found near 58 degrees than A- and E2 haplotypes. Also, the E2 haplotype seems to be more represented at lower latitudes than A+ and A- haplotypes. Males and females seem to be fairly similar in their latitudinal distribution.

Nearest Neighbor Analysis:

Screen Shot 2013-06-09 at 1.05.25 PM
All haplotype classes are significantly clustered. The E2 haplotype has the highest z-score and is therefore the least clustered. The A- haplotype appears to be most clustered with the lowest z-score. Based on the z-score, males appear to be more spatially clustered than females, although both are significantly clustered. A nearest neighbor ratio of 1 indicates that the observed mean distance is equal to the expected mean distance based on a random distribution. Smaller nearest neighbor ratios indicate a larger deviation from 1 and, therefore, a more clustered class variable. It should be noted that the study area varies for each class variable. In my analysis, I was not able to standardize the study area to make these comparisons more meaningful. I am curious to know how these values vary across a standardized study area and with equal sample sizes.

 

Three spatial extents.

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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.

Depths at humpback whale sightings.
Results from my hot spot analysis.

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.

 

Non-random field effort

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Today, I solved my first problem (thanks Jen!) and successfully projected my data so that I could begin running spatial statistic analyses. My data went from GCS_WGS_1984 (unprojected) to NAD_1983_StatePlane_Alaska_1_FIPS_5001 which will allow for improved accuracy in spatial calculations for whale sighting data in southeastern Alaska. I ran an Average Nearest Neighbor analysis on humpback whales sightings in southeastern Alaska and found that the observed nearest neighbor distance was significantly smaller than the expected value. This significant difference is most likely due to the complex geography of southeastern Alaska which creates a clustering of individuals. I also learned that results of my spatial statistics analyses will be presented in meters. I look forward to running additional analyses next week!

My spatial problem is that my data were not collected randomly and field efforts were influenced by predicted habitat use or confirmed sightings of whales. Thus, what appear to be hot spots or patterns of habitat use within southeastern Alaska, might actually be areas of increased field effort. This will undoubtedly complicate my analyses and I continue to turn to the Arc Blog (and Dori) for answers.

We have talked about creating a random sample of whales in southeastern Alaska and comparing their patterns of habitat “use” to what we actually have in our data. Stay tuned for more on that…

Hot Spot Analysis

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In my initial exploration of the ESRI spatial statistics website, I focused on tools that might be useful in my proposed research of population structure and behavioral ecology of humpback whales (Megaptera novaeangliae) in Glacier Bay/Icy Strait, Alaska. One objective of my master’s thesis is investigating the mechanisms of population increase within Glacier Bay/Icy Strait, Alaska since the early 1970s/1980s. I was initially struck by the hot spot analysis, thinking it might be informative to visualize habitat use of humpback whales within Glacier Bay/Icy Strait. This region has undergone massive geological change in the past decades and has become deglaciated relatively recently, i.e. over the past 200 years. Visualizing the habitat use (depth, slope, distance from shore, etc.) of the contemporary population of humpback whales in Glacier Bay/Icy Strait might help inform why there has been an increase in abundance in this region. This would be done by importing layers of oceanographic features under humpback whale encounters to detect patterns of habitat use.

Links:

How to do it:

http://resources.arcgis.com/gallery/file/geoprocessing/details?entryID=604B4BD9-1422-2418-A0F3-77076337D488

http://www.arcgis.com/home/item.html?id=dea008bcc77d4fd485abdf8121190b82

How it works:

http://help.arcgis.com/en/arcgisdesktop/10.0/help/#/How_Hot_Spot_Analysis_Getis_Ord_Gi_works/005p00000011000000/

TO DO: After visualizing my humpback whale encounters in ArcGIS, it occurred to me that what appear to be hot spots within Glacier Bay/Icy Strait, might actually be areas of increased field effort. My data was not collected using random transect lines and thus, this is going to complicate any potential hotspot analysis.