We have developed habitat suitability indiex (HSI) to quantify physical habitat requirements of freshwater fishes and guide stream ecosystem restoration. Using field surveys and statistical models, we are examining relationships between fish occurrence and physical habitat conditions, including water depth, velocity, and substrate. Our indices represent both general habitat preferences and variation in habitat selection among stream types. We are applying these indices in hydrualic modeling (e.g., River2D) to estimate suitable habitats and environmental flows. Our research assesses how modifications to physical stream environments can improve fish habitats and uses this information to guide restoration planning.

One of our studies is to examine how climate change may alter freshwater fish distributions and biodiversity patterns across stream networks. We use machine learning algorithms and ensemble modeling to develop species distribution models that relate fish occurrence to physical and climatic conditions at the stream-segment level. These models estimate current suitable habitats and predicted changes in their distributions under future climate scenarios. We compare climate responses among species, including coldwater and headwater fishes, and examine the potential consequences of these changes for species richness and community composition. Our research aims to identify species and regions that are particularly vulnerable to climate change and guide freshwater biodiversity conservation.

We examine how biotic interactions, physical habitat, and climate contribute to fish biodiversity patterns from local to metacommunities. We use joint species distribution model (JSDM) to evaluate species–environment relationships and patterns of species co-occurrence across stream networks. We also investigate how invasive fishes, including largemouth bass, are associated with taxonomic and functional diversity in reservoirs. Our research considers diversity within local communities (alpha diversity), differences among communities (beta diversity), and total diversity across a metacommunity (gamma diversity). By comparing these patterns, we aim to understand how the relative importance of biotic and abiotic factors varies across spatial scales.

We use camera recording systems installed in fishways to collect videos of fish passage. We are developing methods that combine computer vision and deep learning algorithms (i.e., YOLO) to detect fish, identify species, and count individuals. These methods are designed to quantify the number of fish passing through fishways and examine how passage varies among species and over time. Our goal is to improve the efficiency of video analysis and provide data on species composition and passage patterns for evaluating fishway use and guiding fishway management.

Fish Ecology

Analysis of Ecological Community

Disturbance and Restoration Ecology

Climate Change Ecology