Climate-Driven Changes to Forested Wetland Inundation Dynamics: Implications for Frogs and Toads
Active
By Climate Adaptation Science Centers
December 31, 2023
Project Overview
Wetlands in forested areas of the Upper Midwest provide vital habitat for amphibians, but changing patterns of drying under climate change can disrupt species reproduction and growth. Researchers supported by this Midwest CASC project will use water depth sensor data, machine learning models, and long-term amphibian surveys to predict wet and dry periods for wetlands under future climate scenarios. Results will offer actionable information for State, Federal, and Tribal partners to prioritize wetland and species conservation efforts.
Public Summary
Across the Upper Midwest, many wetlands in forested areas are ephemeral, meaning they dry up periodically. These drying periods benefit many amphibian species by preventing predatory fish from living there. However, if drying periods are too long, or occur too early in the spring, amphibians may not have enough time to develop from eggs to tadpoles to adults in the water. Because of this, managers seek to understand and predict when and where ephemeral wetlands will dry up, both now and in the future under climate change. Using modern approaches, like satellite imagery, are complicated by dense tree cover, which obstructs the ability to determine whether small, forested wetlands are wet or dry from images, so other approaches are needed to make these predictions.
Researchers supported by this project will use data from water depth sensors placed in ephemeral forest wetlands in the Midwest for over a decade. Machine learning models will use these water level data, climate information, and detailed land surface maps to predict when forested wetlands are wet and dry. The team will then examine the effects of past wet and dry periods on frog and toad species using a public 20-year dataset of species surveys. Finally, all this information will be combined to make projections of wetland wet and dry periods under various future climate change scenarios.
This research will be informed by close collaboration with State, Federal, and Tribal partners who will use the project outputs to better understand current and future threats to aquatic species and prioritize wetlands for management and restoration. The results will improve species-based climate resilience, identify potential refugia habitats in the region, and help protect the biota and environments that contribute to healthy ecosystems and, in turn, human communities.
Wetlands in forested areas of the Upper Midwest provide vital habitat for amphibians, but changing patterns of drying under climate change can disrupt species reproduction and growth. Researchers supported by this Midwest CASC project will use water depth sensor data, machine learning models, and long-term amphibian surveys to predict wet and dry periods for wetlands under future climate scenarios. Results will offer actionable information for State, Federal, and Tribal partners to prioritize wetland and species conservation efforts.
Public Summary
Across the Upper Midwest, many wetlands in forested areas are ephemeral, meaning they dry up periodically. These drying periods benefit many amphibian species by preventing predatory fish from living there. However, if drying periods are too long, or occur too early in the spring, amphibians may not have enough time to develop from eggs to tadpoles to adults in the water. Because of this, managers seek to understand and predict when and where ephemeral wetlands will dry up, both now and in the future under climate change. Using modern approaches, like satellite imagery, are complicated by dense tree cover, which obstructs the ability to determine whether small, forested wetlands are wet or dry from images, so other approaches are needed to make these predictions.
Researchers supported by this project will use data from water depth sensors placed in ephemeral forest wetlands in the Midwest for over a decade. Machine learning models will use these water level data, climate information, and detailed land surface maps to predict when forested wetlands are wet and dry. The team will then examine the effects of past wet and dry periods on frog and toad species using a public 20-year dataset of species surveys. Finally, all this information will be combined to make projections of wetland wet and dry periods under various future climate change scenarios.
This research will be informed by close collaboration with State, Federal, and Tribal partners who will use the project outputs to better understand current and future threats to aquatic species and prioritize wetlands for management and restoration. The results will improve species-based climate resilience, identify potential refugia habitats in the region, and help protect the biota and environments that contribute to healthy ecosystems and, in turn, human communities.
- Source: USGS Sciencebase (id: 66746589d34e68d163086ba5)
Jacob LaFontaine
Chief, Modeling Support and Coordination Branch
Chief, Modeling Support and Coordination Branch
Email
Phone
Ryan C Burner, PhD
Research Wildlife Biologist
Research Wildlife Biologist
Email
Phone
Project Overview
Wetlands in forested areas of the Upper Midwest provide vital habitat for amphibians, but changing patterns of drying under climate change can disrupt species reproduction and growth. Researchers supported by this Midwest CASC project will use water depth sensor data, machine learning models, and long-term amphibian surveys to predict wet and dry periods for wetlands under future climate scenarios. Results will offer actionable information for State, Federal, and Tribal partners to prioritize wetland and species conservation efforts.
Public Summary
Across the Upper Midwest, many wetlands in forested areas are ephemeral, meaning they dry up periodically. These drying periods benefit many amphibian species by preventing predatory fish from living there. However, if drying periods are too long, or occur too early in the spring, amphibians may not have enough time to develop from eggs to tadpoles to adults in the water. Because of this, managers seek to understand and predict when and where ephemeral wetlands will dry up, both now and in the future under climate change. Using modern approaches, like satellite imagery, are complicated by dense tree cover, which obstructs the ability to determine whether small, forested wetlands are wet or dry from images, so other approaches are needed to make these predictions.
Researchers supported by this project will use data from water depth sensors placed in ephemeral forest wetlands in the Midwest for over a decade. Machine learning models will use these water level data, climate information, and detailed land surface maps to predict when forested wetlands are wet and dry. The team will then examine the effects of past wet and dry periods on frog and toad species using a public 20-year dataset of species surveys. Finally, all this information will be combined to make projections of wetland wet and dry periods under various future climate change scenarios.
This research will be informed by close collaboration with State, Federal, and Tribal partners who will use the project outputs to better understand current and future threats to aquatic species and prioritize wetlands for management and restoration. The results will improve species-based climate resilience, identify potential refugia habitats in the region, and help protect the biota and environments that contribute to healthy ecosystems and, in turn, human communities.
Wetlands in forested areas of the Upper Midwest provide vital habitat for amphibians, but changing patterns of drying under climate change can disrupt species reproduction and growth. Researchers supported by this Midwest CASC project will use water depth sensor data, machine learning models, and long-term amphibian surveys to predict wet and dry periods for wetlands under future climate scenarios. Results will offer actionable information for State, Federal, and Tribal partners to prioritize wetland and species conservation efforts.
Public Summary
Across the Upper Midwest, many wetlands in forested areas are ephemeral, meaning they dry up periodically. These drying periods benefit many amphibian species by preventing predatory fish from living there. However, if drying periods are too long, or occur too early in the spring, amphibians may not have enough time to develop from eggs to tadpoles to adults in the water. Because of this, managers seek to understand and predict when and where ephemeral wetlands will dry up, both now and in the future under climate change. Using modern approaches, like satellite imagery, are complicated by dense tree cover, which obstructs the ability to determine whether small, forested wetlands are wet or dry from images, so other approaches are needed to make these predictions.
Researchers supported by this project will use data from water depth sensors placed in ephemeral forest wetlands in the Midwest for over a decade. Machine learning models will use these water level data, climate information, and detailed land surface maps to predict when forested wetlands are wet and dry. The team will then examine the effects of past wet and dry periods on frog and toad species using a public 20-year dataset of species surveys. Finally, all this information will be combined to make projections of wetland wet and dry periods under various future climate change scenarios.
This research will be informed by close collaboration with State, Federal, and Tribal partners who will use the project outputs to better understand current and future threats to aquatic species and prioritize wetlands for management and restoration. The results will improve species-based climate resilience, identify potential refugia habitats in the region, and help protect the biota and environments that contribute to healthy ecosystems and, in turn, human communities.
- Source: USGS Sciencebase (id: 66746589d34e68d163086ba5)
Jacob LaFontaine
Chief, Modeling Support and Coordination Branch
Chief, Modeling Support and Coordination Branch
Email
Phone
Ryan C Burner, PhD
Research Wildlife Biologist
Research Wildlife Biologist
Email
Phone