Habitat assessment considering habitat quality and quantity is a key approach in conservation and restoration works for biodiversity and ecosystems. In this regard, application of hydrodynamic model for modeling instream habitat conditions and machine learning (ML) methods for modeling habitat suitability of a target species can contribute to better modeling practices in ecohydraulics. Despite the importance of small streams for aquatic ecosystems, previous studies in ecohydraulics have been conducted mainly in medium to large rivers, often disregarding small-scale streams such as agricultural canals. The aim of this study is to demonstrate the applicability of a coupled use of ML and a two-dimensional (2D) hydrodynamic model for assessing spatial habitat heterogeneity in small-scale agricultural canals in Japan. Using abundance data of Japanese medaka (Oryzias latipes), four ML methods, namely artificial neural networks (ANNs), classification and regression trees (CARTs), random forests (RF) and support vector machines (SVMs), were applied to develop habitat suitability models considering water depth and flow velocity. A 2D hydrodynamic model was developed based on field surveys in two types of agricultural canals, namely earthen and concrete-lined canals. Information entropy was used for assessing the spatial heterogeneity of instream habitat conditions. As a result, the hydrodynamic models could model instream habitat conditions in a reasonable accuracy. Despite the differences in accuracies in habitat modeling, the four ML methods illustrated similar habitat suitability information for Japanese medaka. The coupled ecohydraulics modeling approach could quantify habitat quality and its spatial heterogeneity, based on which the differences between the earthen and concrete-lined canals were quantitatively assessed. This study demonstrated the applicability of ML-based habitat suitability evaluation and a 2D hydrodynamic model for modeling the spatial distribution of habitat suitability and assessing its spatial heterogeneity. Further study, assessing the spatial heterogeneity in various types of flows including natural/artificial and small/large streams, can contribute to establish quantitative criteria for an ecologically sound habitat and improved ecofriendly construction works in small-scale rivers and streams.
All Science Journal Classification (ASJC) codes
- Ecology, Evolution, Behavior and Systematics
- Modelling and Simulation
- Ecological Modelling
- Computer Science Applications
- Computational Theory and Mathematics
- Applied Mathematics