Smith uses a predictive spatial habitat model using aerial surveys of humpback whales to identify and describe the wintering areas for humpback whales in the Great Barrier Reef Marine Park (GBRMP). Sighting data in addition to environmental variables derived from GIS (sea surface temperature, distance from coast, seafloor slope) was incorporated into this predictive model. Predictive modeling of humpback distribution was performed using Maxent. The output values, a suitability value, was incorporated into GIS as a 4.8×4.8km cell within a GBRMP base map. A “frequency distribution of environmental suitability values” was constructed and the area of these intervals was calculated using the Spatial Analyst Tool Zonal statistics within ArcGIS 9.3. Habitat model validation was performed by overlaying satellite tagged whale tracks and underlying habitat suitability values were assessed.
This study exemplified the ability of GIS to help to predict and identify potential habitat of a whale species. This type of modeling can be performed on numerous other whale species and subspecies to help inform management decisions. For my own purposes, factoring in data such as oil spills into habitat suitability could help to explain changes in species distribution over time.
Smith J, Grantham H, Gales N, et al (2012) Identification of humpback whale breeding and calving habitat in the Great Barrier Reef. Mar Ecol Prog Ser 447:259–272. https://doi.org/10.3354/meps09462
