Habitat Classification Predictions on an Undeveloped Barrier Island Using a GIS-Based Landscape Modeling Approach

Abstract

Landscape models are essential tools that link landscape patterns to ecological processes. Barrier island vegetation communities are strongly correlated with geomorphology, which makes elevation-based metrics suitable for developing a predictive habitat classification model in these systems. In this study, multinomial logistic regression is used to predict herbaceous, sparse, and woody habitat distributions on the North End of Assateague Island from slope, distance to shore, and elevation change, all of which are derived from digital elevation models (DEMs). Sparse habitats, which were generally found closest to shore in areas that are exposed to harsh conditions, had the highest predictive accuracy. Herbaceous and woody habitats occupied more protected inland settings and had lower predictive accuracies. A majority of woody cells were misclassified as herbaceous likely because of the similarity in the predictive parameter distributions. This relatively simple model is transparent and does not rely on subjective interpretations. This makes it an effective tool that can directly aid practitioners making coastal management decisions surrounding storm response planning and conservation management. The model results were used in a nutrient sequestration application to quantify carbon and nitrogen stored in barrier island vegetation. This represents an example of how the model results can be used to assign economic value of ecosystem services in a coastal system to justify different management and conservation initiatives.

Keywords

• ecological model
• digital elevation model
• habitat classification
• multinomial logistic regression
• nutrient sequestration
• coastal management