Combining Climate Model Predictions, Hydrological Modeling, and Ecological Niche Modeling Algorithms to Predict the Impacts of Climate Change on Aquatic BiodiversityEPA Grant Number: R834195
Title: Combining Climate Model Predictions, Hydrological Modeling, and Ecological Niche Modeling Algorithms to Predict the Impacts of Climate Change on Aquatic Biodiversity
Investigators: Knouft, Jason
Institution: Saint Louis University - Main Campus
EPA Project Officer: Hiscock, Michael
Project Period: August 1, 2009 through July 31, 2011 (Extended to January 31, 2014)
Project Amount: $246,147
RFA: Consequences of Global Change for Water Quality (2008) RFA Text | Recipients Lists
Research Category: Ecological Indicators/Assessment/Restoration , Global Climate Change , Water and Watersheds , Ecosystems , Climate Change , Water
The primary objective of this research is to predict the impacts of climate change on aquatic biodiversity in United States river drainages. Global climate models will be integrated with a landscape hydrologic model and an ecological niche modeling algorithm to test three general hypotheses: 1) Climate data, when integrated with landscape hydrologic models, can accurately predict variation in current and future flow regimes in United States river drainages; 2) Ecological niche modeling algorithms, when used in conjunction with hydrologic model outputs and species distribution data, can accurately predict current and future distributions of aquatic taxa; and 3) Predicted changes in climate will differentially impact aquatic taxa, with some species experiencing decreases in future habitat availability while other species experience increases in the amount of available habitat.
The objectives of this research will be achieved by integrating data derived from three regionally downscaled global climate models with the landscape-based Soil and Water Assessment Tool (SWAT) hydrologic model to predict changes in flow characteristics and water temperatures in United States river drainages based on climate change scenarios. These hydrologic data will then be used to predict the potential impacts of climate change on distributions of a variety of taxonomic groups, including fishes, crayfishes, and mollusks, in Illinois and Alabama using a maximum entropy ecological niche modeling algorithm (Maxent). Illinois and Alabama support relatively high levels of biodiversity and threatened species and there is also an unusually robust amount of species locality data available for these states. In this study, these areas will serve as models to understand the potential impacts of climate change on aquatic communities in the coming decades. The impact of climate change on fishes will be similarly assessed in five major river drainages in different regions of the United States. The predicted changes in hydrologic characteristics in each region will be integrated with freshwater fish species distribution data to predict the response of these species to changes in climate and assess potential regional differences in biodiversity impacts based on climate change scenarios.
The results of this research will provide a broad taxonomic and regional assessment of the impacts of climate change on aquatic species in the United States by producing predictions of current and future habitat quality for aquatic taxa based on multiple climate change scenarios. The results will address key questions detailed in the program announcement including: 1) What are the potential impacts of climate change on streamflow regimes in different regions of the United States, and how will these changes affect aquatic ecosystems; and 2) How will climate change influence the availability of suitable aquatic habitat for vulnerable species?