Bayesian Inference of Benthic Infauna Habitat Suitability along the U.S. West Coast Chris Goldfinger 1, Sarah Henkel 2, Bruce Marcot 3, Chris Romsos 1, Lisa Gilbane 4 1Active Tectonic and Seafloor Mapping Lab, College of Earth, Ocean, and Atmospheric Sciences, Oregon State University 2Benthic Ecology Lab, Hatfield Marine Science Center, Depart. of Zoology, Oregon State University 3U.S. Forest Service, USDA 4Bureau of Ocean Energy Management Andrea Havron M.S. Candidate, Marine Resource Management, Oregon State University

Development of offshore renewable energy Limited knowledge of benthic infauna Need to understand suitable habitat Goals –Habitat Suitability Maps –Uncertainty Maps –Communicate use and limitation maps Oregon State University Conceptual Wave Park Wave Energy Anchor on Seafloor

BOEM development of renewable energy Limited knowledge of benthic infauna Need to understand suitable habitat Goals –Habitat Suitability Maps –Uncertainty Maps –Communicate use and limitation maps

Benefits of Bayesian Approach –Can handle missing data –Remains robust with small datasets –Easily incorporates multi-collinearity –Easily tracks uncertainty Model Usability –Limitations of Benthic Infauna Models Probability of suitable habitat Static - based on data from summer Focus on Physical Parameters –Reusability Updateable with new information Structure can be reapplied to new infauna species P(A|B) = P(B|A)P(A) P(B)

Species Data – 218 benthic grab samples Sternaspis fossor Axinopsida serricata Aystris gausapata Habitat Data –Percent Silt/Sand –Total Nitrogen (TN) –Total Organic Carbon (TOC) –Mean Grain Size –Latitude –Depth –Distance to Shore Local In Situ Data

Species Data – 218 benthic grab samples Sternaspis fossor Axinopsida serricata Aystris gausapata Habitat Data –Percent Silt/Sand –Total Nitrogen (TN) –Total Organic Carbon (TOC) –Mean Grain Size –Latitude –Depth –Distance to Shore Local In Situ Data Regional Raster Data

Absent Present Absent Present Mean Grain Size density Mean Discretize continuous variables Breakpoints decided from field data

Basic Net –Arrows indicate correlation/causation –No multi-collinearity considered

Supervised Structure –Correlations from Field Data –Scientific Review

Intermediate nodes re-discretize regional raster variables to best predict local in situ variables

Training –Inserted priors Trained model with regional Grain Size database –Expectation Maximization Learning Algorithm Calculates conditional probabilities Can incorporate values with missing data Testing –Performed 4-fold cross validation –Evaluated performance metrics Error Rates (0-100%) Spherical Payoff (0-1) True Skill Statistic (-1, 1)

Model Selection Prediction of habitat suitability based on four regional raster layers: –Depth –Mean Grain Size –Distance to Shore –Latitude Uncertainty Maps –Measure of confidence in probabilities –Percent field data used to inform probabilities

Train Test Error Rate SPTSS All Data8 % fold cv10 % Probability of Habitat Suitability ExperienceUncertainty Very Unlikely Somewhat Unlikely Completely Unknown Somewhat Likely Very Likely Low: 0 High: 0.5 High: 1 Low: 0

Train Test Error Rate SPTSS All Data11 % fold cv15 % Probability of Habitat Suitability ExperienceUncertainty Very Unlikely Somewhat Unlikely Completely Unknown Somewhat Likely Very Likely High: 1 Low: 0 High: 0.5

Train Test Error Rate SPTSS All Data21 % fold cv42 % Probability of Habitat Suitability ExperienceUncertainty Very Unlikely Somewhat Unlikely Completely Unknown Somewhat Likely Very Likely High: 1 Low: 0 High: 0.5

Communicating Uncertainty –Renewable energy development –Limited knowledge of benthic infauna –Extrapolating sample data to regional maps Bayesian Network Models –Habitat Suitability –Uncertainty –Experience Aystris gausapataAystris gausapata Ennucula tenuisEnnucula tenuis Callinax pycnaCallinax pycna Axinopsida serricataAxinopsida serricata Sternaspis fossorSternaspis fossor Onuphis iridescensOnuphis iridescens Magelona berkeleyiMagelona berkeleyi