Detecting hot and cold spots in a seagrass landscape using local indicators of spatial association

Abstract

Eelgrass (Zostera marina) is an important feature of coastal ecosystems in Atlantic Canada, providing a suite of valuable ecosystem services. These services, and its sensitivity to stressors, have prompted efforts to characterize the spatial and temporal dynamics of eelgrass landscapes in order to facilitate management and monitoring of coastal ecosystem health. Current methods for broad-scale mapping of eelgrass rely on aerial remote sensing and may not be appropriate in certain types of landscapes, particularly in turbid waters and areas lacking distinct boundaries. This study takes a novel approach to the quantification and analysis of seagrass landscape structure at multiple spatial scales using acoustic data and local spatial statistics. Data from a single-beam acoustic survey in Richibucto, New Brunswick, Canada were analyzed with geostatistical techniques and the Getis-Ord Gi* local spatial statistic in order to detect statistically significant zones of high and low cover in an estuarine seagrass bed. Results showed distinct and significant patterns in seagrass cover at multiple spatial scales within a region of apparently continuous spatial cover. Boundaries between areas of high and low cover were also detected. This study demonstrates how acoustic data and local spatial statistics can be used to quantify landscape pattern and to further the application of landscape techniques in the marine environment.