Stream hydrology and temperature are among the most influential regulators of life-history traits and community structure of aquatic organisms. Hydrologic and thermal gradients strongly affect individual fitness and ultimately species success by imposing fundamental constraints on behavior, metabolic rates, reproduction, growth, and ecological interactions. Stream hydrology and water temperature are also among the most frequently altered components of lotic systems due to human activities and other environmental disturbance. Despite their critical role in sustaining native aquatic biodiversity, few studies have examined the cross-scale influence of hydrology and water temperature on freshwater biota using a multi-species and flow regime analytical framework.

The Ozark and Ouachita Interior Highlands and Gulf Coastal Plains regions are characterized by high biological diversity and species endemism, in addition to a rapidly growing human population dependent on freshwater resources. Human activities leading to hydrologic and temperature alteration are common in this region. Additionally, climate change scenarios predict more frequent temperature and precipitation extremes leading to increased warming, increased flooding in winter and spring, and increased drought during summer and fall in streams in the Interior Highlands and Gulf Coastal Plain. Due to these changes, a growing percentage of streams are predicted to experience flow and temperature regime shifts over the next decades.

The research team is linking large species taxonomic and functional trait databases with hydrologic metrics derived from the USGS national streamgaging network, and satellite remote-sensing data. Data include daily National Aeronautics and Space Administration’s Integrated Multi-satellite Retrievals for Global Precipitation Measurements (IMERG-GPM) and daily land surface temperature and emissivity from the Moderate Resolution Imaging Spectroradiometer (MODIS).

The research team is using a new machine learning approach, Gradient Forest modeling, that is based on Random Forest models to examine non-linear environmental thresholds. Gradient Forests split values of a predictor variable and evaluate where species composition or traits change along an environmental gradient leading to the identification of environmental thresholds, or not, dependent on the underlying data.

Results are being used to indicate significant hydrologic, temperature, and land use thresholds for individual fish species and functional traits. This approach can focus on entire assemblages, species of greatest conservation need, or those of management concern. The research team presented this approach to examining hydrologic thresholds of fish assemblages and species of greatest conservation need at multiple venues to great interest by researchers, managers, and decision makers.

The ONB features articles from Cooperative Fish and Wildlife Research Units across the country. Working with key cooperators, including WMI, Units are leading exciting, new fish and wildlife research projects that we believe our readers will appreciate reading about. This article was written by Daniel Magoulick, Assistant Unit Leader, danmag@usgs.gov or danmag@uark.edu, USGS Arkansas Cooperative Fish and Wildlife Research Unit on the University of Arkansas campus.