Hello, Geo 584 readers!
In my previous blog post, I mentioned my main goal was to use statistical analysis including environmental variable mapping and spatial interpolation methods to quantify correlations between harbor porpoise movement patterns and distributions with biophysical variables.
However, to do all of this, I had to see exactly what kind of data I was working with. Over the previous two years I have collected spread sheet after spread sheet of survey effort, ship transect lines, marine mammal sighting data, oceanography data, acoustic data, and stranding data. This was my first chance to dive in and look at what all I was working with.
Step 1: Sort out all marine mammal sightings – this proved to be much more tedious than I thought J
Step 2: Plot all longitude and latitude gps points of sightings on a base map
Note: This is where the ship was when the marine mammal was spotted, not necessarily the exact location of the animal.
Step 3: Map a typical day of transect surveys from each of the three survey sites
Step 4: Add course bathymetry layer to the map – and add a 200m depth contour line
Note: Much as the name implies, Harbor Porpoise tend to be a near-shore species not typically inhabiting waters deeper than 200m.
Step 5: Distribution analysis using Arcmap tools – goal was to see if harbor porpoise had an overall different distribution vs all other marine mammals seen. My hypothesis was that harbor porpoise would have a more inshore distribution.
Results:
This was a map of my sightings based on the boat gps, again, this is not exactly where the animal was. This requires more triangulations and calculations which I will be taking a course on in August! So I wanted to save that analysis for then!
This second image is a map of a typical day of transect surveys in each of the three sites. I decided to do this because if you look at the sightings along you tend to see a funky pattern, but this is mainly due to the layout of the transects.
This third image has an added 200m depth contour line, again, this is because harbor porpoise ecology states that they tend to be a near shore species. The two ovals represented in this figure are the distributions of harbor porpoise in yellow vs all other species in green. The odd shape is due to the NH line of the surveys going about 15 more miles offshore than the other two. But it is easy to differentiate that harbor porpoise generally have a more inshore distribution as I predicted.
Walking through these steps was exciting for me this was my first chance to see visual representations of my data as well as learning GIS with using correct projections and distribution calculators.
What’s next – Plans for the next few weeks!
- Begin to focus only on harbor porpoises. I chose harbor porpoise for my indicator species for my thesis because they are abundant, sound sensitive, and most likely to overlap with marine renewable energy.
- Find fine scale layers: bathymetry, bottom type, etc. Using a basemap is great to look at the data visually, but it is hard to make any interpolations or statistical analysis without environmental covariates.
- Coordinating sightings vs effort: take into account unequal transects, length of transect line vs. odds of seeing porpoise
- Organize in-situ flow through oceanographic data collected concurrently with transect lines and then use spatial interpolation to create a fluid shape file of sea surface temperature, salinity, and chlorophyll a.
- Are environmental covariates determining distributions? SST, Distance to Shore, Depth, Season? What are driving these porpoise occurrences?
As you can see, I have plenty to work on! Thanks for reading!
