Enhanced understanding of physicochemical constraints on Corbicula japonica habitat in Lake Shinji assisted by machine learning

The catch of Corbicula japonica is one of the top three in Japan's inland water fisheries. Most of the Japanese fishing stock of this bivalve comes from Lake Shinji. Since the 1980's the catch of this species declined drastically with strong inter-annual variation. New recruitment and poor growth of the clams were thought to be the main reasons for this decline. This bivalve has a poor transportation ability after the settlement. Therefore, it is also important to know their suitable habit that is affected by multi interacting parameters of water qualities and sediments. In order to get a better understanding of their habitat, we used machine learning models to test abiotic limiting factors. The database that we used in our study included 337 sampling stations with 7 physicochemical variables and the Corbicula japonica counting which was divided into two size categories: small (shell length < 4 mm) and large (shell length ≥ 4 mm). Due to their low self-transportation ability, their survival is primarily influenced by the site environment, such as water quality and sediment conditions. To extract physicochemical thresholds that directly impact these clam populations, we applied a coupled methodology based on a random forest model and partial dependence plots.