A multi-scale feature selection approach for predicting benthic assemblages. (31st October 2022)

• Record Type: Journal Article
• Authors: Nemani, Shreya; Cote, David; Misiuk, Benjamin; Edinger, Evan; Mackin-McLaughlin, Julia; Templeton, Adam; Shaw, John; Robert, Katleen
• Is Part Of: Estuarine, coastal and shelf science. Volume 277(2022)
• Publication Date: 2022-10-31
• Subjects: Benthic ecology -- Habitat mapping -- Machine learning -- Multi-scale -- Predictive modelling -- Spatial scale -- Coastal management; Estuarine oceanography -- Periodicals; Coasts -- Periodicals; Estuarine biology -- Periodicals; Seashore biology -- Periodicals; Coasts; Estuarine biology; Estuarine oceanography; Seashore biology; Periodicals; 551.461805
• DOI: 10.1016/j.ecss.2022.108053
• Languages: English
• ISSNs: 0272-7714

Abstract

Abstract: Seafloor habitat maps are an important management tool used to delineate distinct regions of the seabed based on their biophysical properties. Spatially continuous bathymetry and backscatter-derived terrain features are commonly used as proxies for environmental conditions and processes that affect species distributions. Multi-scale approaches are increasingly applied to assess the relevant scales at which species co-occur. As the optimal scale(s) may be unknown, features can be calculated at multiple successive scales, yet this results in numerous highly correlated features that may negatively impact model interpretability. To address this increased dimensionality, feature selection approaches can be used to identify the most relevant features. Here, filter and wrapper approaches are assessed to select features from a highly dimensional multi-scale dataset. Terrain features describing the seabed were calculated across ten scales at two coastal sites in Placentia Bay, Newfoundland, Canada. Five species assemblages were identified using ground-truth underwater video sampling. Features predicting the presence of assemblages were assessed using the two selection methods, and the set of chosen features was modelled using three machine learning algorithms: extreme gradient boosting (XGB), random forest (RF), and support vector machines (SVM). The XGB model with features selected by scale-factor from the Boruta wrapper algorithm had the highest accuracy according to cross-validation- (61.67%, kappa 0.49). Bathymetry and terrain attributes were the most important predictors of assemblage occurrence across various analysis scales encompassing both broader and fine-scale variability of the seabed. The proposed feature reduction and selection approach improved the overall accuracy of predictions, and the resulting biological complexity captured in our habitat maps established baseline data for an ecologically significant coastal region. Graphical abstract: Image 1 Highlights: Classified five benthic assemblages among two coastal sites in an ecologically and biologically significant area (EBSA). Provided a feature selection framework across a high-dimensional multi-scale geomorphometric dataset. Compared three machine learning models to develop baseline habitat maps to support evidence-based decision-making.