THE USE OF REMOTE SENSING, REGRESSION QUANTILES, AND GIS APPROACHES FOR MODELING OF SCALLOP LARVAE: A Case Study in Funka Bay, Hokkaido, Japan

Abstract

In the development of scallop cultivation in Japan, larvae collection and propagation become an important factor. Although the monitoring program has been conducted, modeling of species distribution is becoming an important tool for understanding the effects of environmental changes and resources management. This study was conducted to construct a model for providing estimation of the scallop larvae distribution in Funka Bay, Hokkaido, Japan using the integration of remote sensing, Regression Quantile (RQ) and Geographic Information System (GIS)-based model. Data on scallop larvae were collected during one year spawning season from April to July 2003. Environmental parameters were extracted from multi sensor remotely sensed data (chlorophyll-a and sea surface temperature) and a hydrographic chart (water depth). These parameters together with larvae data were then analyzed using RQ. Finally, spatial models were constructed within a GIS by combining the RQ models with digital map of environmental parameters. The results show that the model was best explained by using only sea surface temperature. The highest larvae densities were predicted in a relatively broad distribution along with the shallow water regions (Toyoura and Sawara to Yakumo) and the deeper water areas (center of the bay). The spatial model built from the RQ provided robust estimation of the scallop larvae distributions in the study area, as confirmed by model validation using independent data. These findings could contribute on the monitoring program in this region in order to distinguish the potential areas for an effective spat collection.