Question that you asked:
My goal for my thesis work is to evaluate the distribution of native Olympia oysters in Yaquina Bay, Oregon by assessing habitat suitability through spatial analysis of three habitat parameters: salinity, substrate availability, and elevation. A map of predicted suitable habitat as a result of the spatial analysis will be compared with field observations of oyster locations within Yaquina Bay. The main research question I am examining in this project is:
How is the spatial pattern of three habitat parameters (salinity, substrate, elevation) [A] related to the spatial pattern of Olympia oysters in the Yaquina estuary [B] over time [C]?
For this blog post, I will be evaluating the [A] portion of this question and the three habitat parameters simultaneously to identify where habitat is least suitable to most suitable. To better understand the spatial pattern of the habitat parameters, I am evaluating a raster layer for each parameter, then combining them to determine where overlap between the layers shows the best environmental conditions for oysters to survive.
Name of the tool or approach that you used:
For this portion of my research analysis, I wanted to be able to make an educated guess about where the best and worst habitat for Olympia oysters would be located within Yaquina Bay by ranking different subcategories within each of the salinity, substrate, and elevation datasets.
To do this, I started by looking through the available literature on the subject and consulting with shellfish biologists to get an idea of what conditions oysters prefer in order to apply a ranking value. The following table is a compilation of that information:
|Habitat parameter||Subcategories||Subcategory variable range||Olympia oyster tolerance||Rank value applied|
|Mean wet-season salinity (psu)||Upper estuary||< 16 psu||somewhat, but not long-term||1|
|Upper mid estuary||16.1 – 23 psu||X||4|
|Lower mid estuary||23.1 – 27 psu||X||3|
|Lower estuary||> 27 psu||somewhat||2|
|Substrate availability||1.2||Unconsolidated mineral substrate||possible||3|
|3.1.2||Anthropogenic rock rubble||unlikely||2|
|3.1.3||Anthropogenic rock hash||no||1|
|Bathymetric depth (compared to MLLW)||1.5 – 2.5m||supratidal||no||1|
|1 – 1.5m||supratidal||no||1|
|0.5 – 1m||intertidal||maybe||2|
|0 – 0.5m||intertidal||yes||3|
|-2 – 0m||intertidal||yes||4|
|-3 – -2m||subtidal||yes||4|
|-4 – -3m||subtidal||yes||4|
|-6 – -4m||subtidal||yes||4|
|-8 – -6m||subtidal||yes||3|
|-12.5 – -8m||subtidal||yes||3|
Once I established my own ranking values, I decided to use the ‘weighted overlay’ function, found within the Spatial Analyst toolbox in ArcGIS Pro. Weighted overlay applies a numeric rank to values within the raster inputs on a scale that the ArcGIS user is able to set. For example, on a scale from 1-9 ranking 1 as areas of least fit and 9 as areas of best fit. This can be used to determine the most appropriate site or location for a desired product or phenomenon. I used the ranking value scale 1-4 where 1 indicates the lowest suitability of subcategories for that parameter and 4 indicates the highest suitability.
Brief description of steps you followed to complete the analysis:
To apply the weighted overlay function:
- Open the appropriate raster layers for analysis in ArcGIS Pro. Weighted overlay will only work with a raster input, specifically integer raster data. Here, I pulled all three of my habitat parameter raster layers from my geodatabase into the Contents pane and made each one visible in turn as I applied the weighted overlay function.
- In the Geoprocessing pane, type ‘weighted overlay’ into the search box. Weighted overlay can also be found in the Spatial Analyst toolbox.
- Once in the weighted overlay window within the Geoprocessing pane, determine the appropriate scale or ranking values for the analysis. I used a scale from 1-4, where 1 was low suitability and 4 was high suitability.
- Add raster layers for analysis by selecting them from your geodatabase and adding them into the window at the top left. To add more than one raster, click ‘Add raster’ at the bottom of the window.
- Select one of the raster inputs and see the subcategories for that raster appear on the upper right. Here, ranking values within the predetermined can be individually applied to the subcategories by clicking from a drop-down list. Do this for each subcategory within each raster input. I ranked each subcategory within each of my habitat rasters according to the ranks listed on the table above.
- Determine the weights of each raster input. The weights must add up to 100, but can be manipulated according to the needs of the analysis. A raster input can be given greater or lesser influence if that information is known. For my analysis, I made all three of my habitat raster inputs nearly equal weight (two inputs were assigned a weight of 33, one was weighted 34 to equal 100 total).
- Finally, run the tool and assuming no errors, an output raster will appear in the Contents pane and in the map window.
Brief description of results you obtained:
The first three images show each habitat parameter weighted by suitability, with green indicating most suitable and red indicating least suitable.
The results of the final weighted overlay show that the oysters are most likely to be in the mid estuary where salinity, bathymetry, and substrate are appropriate.
Critique of the method – what was useful, what was not?:
The weighted overlay was a simple approach to combining all of the raster layers for habitat and creating something spatially meaningful for my research analysis. The areas indicated in green in the resulting map generally reinforce what was found in the literature and predicted by local shellfish biologists. While the weighted overlay tool did generate a useful visual, it is highly dependent on the quality of the raster inputs. In my analysis, the detailed resolution of the bathymetry layer was very helpful, but the substrate layer is a more generalized assessment of sediment types within Yaquina Bay. It doesn’t show the nuances of substrate availability that might be important for finding exactly where an opportunistic species like Olympia oysters might actually have settled. For example, in Coos Bay Olympia oysters have been found attached to shopping carts that have been dumped. The substrate raster is a generalized layer that uses standardized subcategories and does not pinpoint such small features.
Additionally, the salinity layer is an average of wet-season salinity, but it can change dramatically throughout the year. Some in situ measurements from Yaquina Bay this April showed that the surface salinity with the subcategory range of 16-23 psu were actually <10 psu. While it is more reasonable to generalize salinity for the purposes of this analysis, it is important to note that the oysters are exposed to a greater range over time.
This spatial information serves as a prediction of suitable oyster habitat. The next step is to compare this predicted suitability to actual field observations. I’ve recently completed my first round of field surveys and will be analyzing how closely the observations align with the prediction in Exercise #2.