Follow-up from Exercise 2
In Exercise 2, I compared Olympia oyster location data to the model of predicted suitable habitat that I developed in Exercise 1. Results from that analysis showed that 13 of the 18 observations within or near areas of high-quality habitat (type 4) indicated the presence of Olympia oysters (72%) versus 5 locations where oysters were not found (28%). No field survey locations fell within areas of majority lowest quality habitat (type 1). Seven observations were found within the second lowest quality habitat type (2), with 2 of those observations indicating presence (29%) and 5 indicating absence (71%).

*3 data points removed from analysis due to inconclusive search results.

To expand on this analysis, I used a confusion matrix to further examine the ‘errors’ in the data, or the observations that did not correlate with my model of predicted suitable habitat. For ease of interpretation, I removed habitat suitability type 1 since there were not observations in this category, and type 3 since it fell in between high and low-quality habitat.

Decimals reported indicate the proportion of total observations (n = 35) that fell within this category. The habitat suitability model predicted that oysters would be present within the highest quality habitat type and absent in low-quality habitat. The confusion matrix shows that the model was successful in predicting that 37% of the total observations where oysters were present were found within habitat type 4 (high), and 14% of the observations where oysters were absent were found in habitat type 2 (low).

In the type 4 habitat, 14% of the total observations found that oysters were absent, which goes against the model prediction. I suspect this is partly due to the patchy nature of substrate availability in Yaquina Bay and the low-resolution quality of the substrate raster layer used for analysis. For the 6% of observations that show oyster presence within habitat type 2, it’s possible that these points were juvenile oysters that were able to settle in year-1, but are less likely to survive into adulthood. Both of these errors could also indicate issues with the weights assigned in the model back in Exercise 1.

Question asked
For exercise 3, I wanted to expand on the habitat suitability analysis to see if I could more accurately predict oyster locations and account for the errors found in exercise 2. Here I asked:

Can the spatial pattern of Olympia oyster location data be more accurately described by manipulating the spatial pattern of one of the parameters of suitable habitat (salinity)?

I decided to modify the rank values of one of the model parameters: salinity. Based on my experience collecting oyster location data in the field, it seemed that salinity was the biggest influence in where oysters would be found. It was also the easiest parameter to change since it had the fewest rank categories. The excerpt below comes from the ranking value table I established for the habitat parameters in Exercise 1. Changes to rank value for salinity are indicated in the right-most column.

Name of tool or approach
I combined my approach from exercise 1 and exercise 2 to create a different model output based on the new rank values applied to the salinity parameter. The analysis was completed in ArcGIS Pro and the table of values generated was reviewed in Excel.

Brief description of steps to complete the analysis

1. After assigning new rank values to the salinity parameter, I applied a new ‘weighted overlay’ to the salinity raster layer in ArcGIS. As I did in exercise 1, I used the ‘weighted overlay’ tool again to combine the weighted substrate and bathymetry layers with the updated salinity layer. A new map of suitable habitat was created based on these new ranking values.
2. Then, I added the field observation data of oyster presence/absence to the map and created a new map of all the data points overlaid on habitat suitability.
   
3. I then created buffers around each of the points using the ‘buffer’ tool. In the last analysis, I used the ‘multiple ring buffer’, but was only able to analyze the largest buffer (300m). This time, I created only the one buffer around each point.
4. Using the ‘Zonal Statistics’ tool, I overlaid the newly created buffers on the updated raster of habitat suitability and viewed the results. I again chose ‘majority’ as my visual represented statistic, which categories the buffer based on the habitat suitability type occupying the largest area.
   
5. I also created a results table using the ‘Zonal Statistics as Table’ tool, then copied it over to Excel for additional analysis.

Results
An updated table based on manipulated salinity rank values was generated to compare to the table created from exercise 2 and displayed at the top of this blog post. Results from this analysis showed that only 2 of the 35 total observations fell within or near areas of high-quality habitat (type 4), one indicated presence and the other absence. The adjustments to the salinity rank value allowed the habitat type 3 to dominate the map, with 31 of the total 35 observations falling in this category. Of the 31 points, 18 showed presence data (58%) and 13 were absence data (42%). Again, no field survey locations fell within areas of majority lowest quality habitat (type 1). Two observations were found within the second lowest quality habitat type (2), both indicating absence (100%).

Again, I used a confusion matrix to further examine the ‘errors’ in the data, or the observations that did not correlate with my model of predicted suitable habitat. I removed habitat suitability type 1 since there were not observations in this category.

Decimals reported indicate the proportion of total observations (n = 35) that fell within this category. The confusion matrix shows that the model fit nearly all observations (31) into the type 3 habitat category, with a near even split between presence (18) and absence (13). In reference to the confusion matrix from exercise 2 at the top of this blog, it is difficult to make a direct comparison of the errors since most of the observations fell into type 3.

Critique of the method
I was surprised to see how drastically the map of suitable habitat changed by manipulating only one of the habitat parameters. The adjustment of the rank values for salinity resulted in a vast reduction in area attributed to the highest quality habitat (type 4). The results indicate that choosing the salinity parameter to manipulate did not result in a better fit model and that changes to salinity rank values were too drastic. Since the salinity parameter contains only 4 subcategories, or 4 different weighted salinity values, the impacts to the habitat suitability map were greater than if the parameter had had more nuance. For example, the bathymetry parameter has 10 subcategories and a reworking of the ranking values within could have made more subtle changes to the habitat suitability map.

The next steps would be to examine another parameter, either substrate or bathymetry, to see if adjustments to ranking values result in a more appropriate illustration of suitable habitat. Additionally, the collection of more oyster location data points will help in creating a better fit model and understanding the nuances of suitable habitat in Yaquina Bay.