Author Archives: havrona

Final Post: Putting Error on a Map

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Recap:

•Using Bayesian Nets to model species habitat suitability of benthic infauna
•Creating predictive maps of the likelihood of suitable habitat
•Explanatory variables: Depth, Grain Size, Organic Carbon, Nitrogen, Percent Silt/Sand, Latitude
Bayes Net Model
sfossor_netsfossor_s2n4legend

I was interested in two questions:
1. Is there spatial pattern to the observed error?
2. Does the scale of prediction increase or decrease error?

I started noticing some of my points with error contained samples of gravel. The focus of my study was on soft bottom sediment and the regional maps I am using for prediction have areas of rock and gravel masked out. There were only eight records in 218 samples that contained gravel. Finally, as gravel skews grain size measurements to the right, I decided these eight samples were outliers and removed them from the analysis.

The species I decided to focus on is a marine worm that had a strong response to higher values of grain size and was found at Depths greater than 55 meters. Due to its strong preference to grain size and depth, I started created plots to determine if error was occurring within a certain range of these two variables.

DepthvsGS_error

 

Most of the error was occurring at depth around a grain size of 4. Coloring the plots by sampling site provided some useful feedback.

DepthvsGS_site

 

This graph depicts records of species absence on the left and species presence on the right. While the species preferred deep and high grain size, areas within this range where the species was absent was predominantly from one site, NSAF. This site also happened to be the most southern site in the region. Also, the shallowest region where the species was found to be present was in the next site just north of NSAF, off the coast of Eureka, CA. So, I wanted to look at the data again, isolating these two most southern sites from the rest.

DepthvsGS_latitude

 

In this graph, the two most southern sites are on the bottom graph while the remaining sites are on the top graph. The species response to grain size appears shifted to higher values and there appears to be more records of absence at depth. I different response to physical features in the southern range could be due to a number of factors, such as a more narrow shelf (less opportunity for larval dispersal) and a different characteristic to the silt and sand particles as the rivers along the southern region drain the Southern Cascade Mountains as opposed to the Coast Range further north.

Pooling all this information together, I made several changes to the model. I simplified the model by removing the explanatory variables: Organic Carbon, Nitrogen, and Percent Silt/Sand. I added a latitude variable and categorized it into north and south. Due to the simplification of the model, I was able to add more categories to both the depth and grain size variable to capture more of the relationships between them and the species response.

The final model.

e10_model

 

Model performance improved.

Original Model: 11.0% error

s2n4_Norths2n4_Middlen2s2_South

Revised Model: 6.9% error

e10_northe10_Middlen2s2_South

 

Original Model on Left, Revised Model on Right:

sfossor_s2n4 e10

I also calculated a difference between the two maps. On the left is a map depicting the change in habitat suitability. On the right is a map of regions where there was a shift in prediction (i.e. absent to present).

sf_diff_resize  ChangePred_resize

Next step: Does the scale of prediction increase or decrease error?

•Current predictive maps limited by resolution of regional dataset (250 meter)
•Local sampling sites:
–Surveyed with high resolution multi-beam
–Higher density of Grain Size samples than region
•Goal:
–Create a high resolution, spatially interpolated Grain Size surface
–Combine with high resolution bathymetry
–Make prediction for this new high resolution surface
–Compare error rates with original and revised maps
mgs Depth

Putting error on a map

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I have 218 benthic sediment grabs from the continental shelf ranging from 20 to 130 meters deep. These samples were taken from eight sites spread from Northern California to Southern Washington. Within each site, samples were randomized along depth gradients.

Each sample consists of species counts plus depth, latitude, and sediment characteristics such as grain size (i.e., sand versus silt), organic carbon, and nitrogen concentrations. Using Bayesian Belief Networks, species– habitat associations were calculated and established relationships were used to make regional predictive maps. Final map products depict the spatial distribution of suitable habitat where a high probability indicates a high likelihood of finding a species given a location and its combination of environmental factors. While sampling points were not taken across a consistent gridded scale, suitability maps were scaled to 250 meter resolution.

As in any habitat modeling process, the “best” model was chosen by looking at model performance and the amount of error, or misclassification between what was observed and what was predicted. Error of commission occurred when probability scores were high for a location where the species was actually observed to be absent. Errors of omission occurred when probability scores were low for a location where the species was actually observed to be present.

I am interested in two questions. The first is whether there is a spatial pattern to the error observed and the second question is at what scale is this error significant? Error may be caused by variation in the environment that occurs at a finer scale than what my modeling structure captures.

To explore these two questions, I intend to conduct a spatial autocorrelation analysis on the error for each local site to determine if there is any potential spatial pattern, and if so, if there is an associated environmental pattern to the error (i.e., does most of the errors occur in shallower or deeper water?). I am also interested in creating high resolution local maps of sediment characteristics (grain size, organic carbon, and nitrogen) through spatial interpolation technique using sediment grab data. For these local sites, I will then recreate predictive maps and compare to the 250 meter predictive maps.

samples Etenuis et_errorNetNet_Key