Soil conditions are a key part of functioning ecosystem and affect the distribution and abundance of plants, forage production, and habitat patterns. The distribution of soil conditions, as well as other environmental factors such as precipitation, temperature, geology, topography, and vegetation determine the patterns and dynamics of wildlife habitats and biodiversity across the landscape. We used long-term averages of precipitation and temperature (1981–2010) to define environment conditions that affect the soil. Using climate averages supports understanding of persistent patterns such as distribution of forests, woodlands, shrublands and grasslands. Variability due to inter-annual differences is removed to focus on dominant vegetation patterns, habitat potential, fire frequency, and ecosystem processes. These efforts are intended to help researchers, wildlife and habitat managers, and policymakers, better understand climate patterns ecological implications, including restoration planning, exotic plant invasion risk to invasion, fuels and fire risk, and drought monitoring.
Background
Soil temperature and moisture (soil-climate) affect plant growth and microbial activity and provide important links between patterns in climate, soils, and flora. However, spatial data of soil-climate that represent variation in space and time have been lacking. By improving our ability to estimate and map soil-climate across large landscapes, we can better investigate important ecological relationships including vegetation abundance, diversity, habitat structure, and productivity. Understanding these relationships can improve restoration efforts, habitat management, mitigating risk of invasion by exotic plants and managing fuels.
Methods
We developed a framework that defines monthly estimates of soil-climate properties (moisture and temperature) using a modified version of the Newhall model that relies on spatial climate data and soil properties defined across the western United States. The Newhall model, originally developed by U.S. Department of Agriculture Natural Resources Conservation Service for non-spatial applications, is a daily simulation of vertical movement of water in a soil matrix.
We modified the Newhall model to include spatial data and other data related to climate data, snowmelt, soil properties affecting water holding capacity, multiple methods of estimating evapotranspiration (the dynamics of transferring water from land to the atmosphere by evaporation and transpiration, which results in the loss of water from plants), inclusion of organic material, air-soil temperature offsets, snow insulation, and improvements to the soil moisture and temperature classification system. We calculated an adjusted precipitation to account for snowmelt using daily snow data, raw precipitation data based on climate stations, and a custom equation. This approach allowed us to establish the appropriate timing of water that can infiltrate soils during snowmelt (Figure 1). For this study, we used climate averages (1981–2010) to estimate 1) soil-climate classes, 2) monthly, seasonal, and annual soil moisture estimates, 3) seasonality of soil moisture, and 4) trends in soil moisture. We also used statistical models to establish the connection between soil-climate conditions and the distribution of vegetation, habitat, and ecosystem processes. For more details see our paper on spatial estimates of soil moisture.
Results
Modeling soil-climate in the western United States using climate averages allowed us to produce high resolution (30 meters) spatial data products: monthly, seasonal, and annual soil moisture estimates; seasonality of soil moisture; and seasonal trends in soil moisture. When we examined soil-climate and vegetation patterns, we found that soil moisture was a good predictor of sagebrush cover, annual herbaceous plant cover, bare ground, and fire occurrence.
Research Implications
High spatial resolution (30 meters) data of soil-climate properties is beneficial for understanding ecological site potential, distribution patterns of species and habitats, the resilience of the system after disturbances, the resistance of invasive species and fires, and the response to restoration and other land treatments. These products can help to better understand the relationship between soil-climate and other landscape conditions. For example, typical (average) patterns in moisture availability from spring into summer are important for plant growth and reproduction in many areas and affect the distribution of habitat and effects of disturbance and treatments. Further, soil-climate seasonality estimates can help understand the variability of soil moisture occurring between seasons (Figure 2), which can have important implications for vegetation patterns, wildlife associations, and risk to invasion of exotic plants and fire. Soil moisture estimates can also be used to investigate their relationships with fire occurrences (Figure 3). Here our objective was to assess whether broader temporal and spatial patterns of soil moisture drive frequency and occurrences in fires.
Future research
The study area is being expanded beyond the sagebrush biome to include all the western United States. We are also developing an alternate evapotranspiration model and additional metrics that combine temperature and moisture (for example, growing period, soil degree days, soil moisture of wettest month). We are evaluating relationships of soil-climate and distributions of conifer pinyon-juniper. Finally, we are continuing to work with field and remote sensing data to investigate relations between soil-climate, vegetation and other land cover and habitat patterns.
Funders
U.S. Geological Survey (Ecosystem Mission Area and Wyoming Landscape Conservation Initiative) and North Central Climate Adaptation Science Center
Soil-climate for Managing Sagebrush Ecosystems
Future Scenarios of Soil-climate for Sagebrush Ecosystems
- Overview
Soil conditions are a key part of functioning ecosystem and affect the distribution and abundance of plants, forage production, and habitat patterns. The distribution of soil conditions, as well as other environmental factors such as precipitation, temperature, geology, topography, and vegetation determine the patterns and dynamics of wildlife habitats and biodiversity across the landscape. We used long-term averages of precipitation and temperature (1981–2010) to define environment conditions that affect the soil. Using climate averages supports understanding of persistent patterns such as distribution of forests, woodlands, shrublands and grasslands. Variability due to inter-annual differences is removed to focus on dominant vegetation patterns, habitat potential, fire frequency, and ecosystem processes. These efforts are intended to help researchers, wildlife and habitat managers, and policymakers, better understand climate patterns ecological implications, including restoration planning, exotic plant invasion risk to invasion, fuels and fire risk, and drought monitoring.
Background
Soil temperature and moisture (soil-climate) affect plant growth and microbial activity and provide important links between patterns in climate, soils, and flora. However, spatial data of soil-climate that represent variation in space and time have been lacking. By improving our ability to estimate and map soil-climate across large landscapes, we can better investigate important ecological relationships including vegetation abundance, diversity, habitat structure, and productivity. Understanding these relationships can improve restoration efforts, habitat management, mitigating risk of invasion by exotic plants and managing fuels.
Sources/Usage: Public Domain.Figure 1 Effects of snow accumulation and melt captured in precipitation data (1981 to 2010 average). Adjusted precipitation shows precipitation that can infiltrate soil by accounting for snowmelt. The unadjusted precipitation represents raw data and captures rain and snow (converted to snow water equivalency). Panel (a) illustrates precipitation data (raw data capturing rain and snow) minus adjusted (precipitation that can infiltrate soil by accounting for snowmelt) precipitation (March). Panel (b) illustrates how snow water equivalency is released during spring melt. The three histograms (panels [c–e]), correspond to the map and show mean differences of unadjusted and adjusted precipitation within example watersheds, which demonstrates the importance of considering snowmelt. Source: O'Donnell and Manier, Land: https://doi.org/10.3390/land11101856.
Methods
We developed a framework that defines monthly estimates of soil-climate properties (moisture and temperature) using a modified version of the Newhall model that relies on spatial climate data and soil properties defined across the western United States. The Newhall model, originally developed by U.S. Department of Agriculture Natural Resources Conservation Service for non-spatial applications, is a daily simulation of vertical movement of water in a soil matrix.
We modified the Newhall model to include spatial data and other data related to climate data, snowmelt, soil properties affecting water holding capacity, multiple methods of estimating evapotranspiration (the dynamics of transferring water from land to the atmosphere by evaporation and transpiration, which results in the loss of water from plants), inclusion of organic material, air-soil temperature offsets, snow insulation, and improvements to the soil moisture and temperature classification system. We calculated an adjusted precipitation to account for snowmelt using daily snow data, raw precipitation data based on climate stations, and a custom equation. This approach allowed us to establish the appropriate timing of water that can infiltrate soils during snowmelt (Figure 1). For this study, we used climate averages (1981–2010) to estimate 1) soil-climate classes, 2) monthly, seasonal, and annual soil moisture estimates, 3) seasonality of soil moisture, and 4) trends in soil moisture. We also used statistical models to establish the connection between soil-climate conditions and the distribution of vegetation, habitat, and ecosystem processes. For more details see our paper on spatial estimates of soil moisture.
Results
Modeling soil-climate in the western United States using climate averages allowed us to produce high resolution (30 meters) spatial data products: monthly, seasonal, and annual soil moisture estimates; seasonality of soil moisture; and seasonal trends in soil moisture. When we examined soil-climate and vegetation patterns, we found that soil moisture was a good predictor of sagebrush cover, annual herbaceous plant cover, bare ground, and fire occurrence.
Sources/Usage: Public Domain.Figure 2 Seasonality (variability among seasonal soil moisture [mm]) within the western United States under average climate conditions (1981–2010). Panel (a) shows the standard deviation in soil moisture among the four seasons (spring, summer, fall and winter). Panel (b) shows the standard deviation in the spring growing season (March, April, May and June). Source: O'Donnell and Manier, Land: https://doi.org/10.3390/land11101856.
Sources/Usage: Public Domain.Figure 3 Comparison of fire occurrence (1984–2016; Landsat burned area essential climate variable) and soil-climate trend during the growing season (March through September). A strong negative trend in soil moisture (x-axis) indicates high moisture early in the season and low moisture late in the season, and these conditions are associated with the highest average fire frequency (top left). On the other end of the axis, aridic soil-climate types (bottom-right) had a very low fire frequency. Source: O'Donnell and Manier, Land: https://doi.org/10.3390/land11101856. Research Implications
High spatial resolution (30 meters) data of soil-climate properties is beneficial for understanding ecological site potential, distribution patterns of species and habitats, the resilience of the system after disturbances, the resistance of invasive species and fires, and the response to restoration and other land treatments. These products can help to better understand the relationship between soil-climate and other landscape conditions. For example, typical (average) patterns in moisture availability from spring into summer are important for plant growth and reproduction in many areas and affect the distribution of habitat and effects of disturbance and treatments. Further, soil-climate seasonality estimates can help understand the variability of soil moisture occurring between seasons (Figure 2), which can have important implications for vegetation patterns, wildlife associations, and risk to invasion of exotic plants and fire. Soil moisture estimates can also be used to investigate their relationships with fire occurrences (Figure 3). Here our objective was to assess whether broader temporal and spatial patterns of soil moisture drive frequency and occurrences in fires.
Future research
The study area is being expanded beyond the sagebrush biome to include all the western United States. We are also developing an alternate evapotranspiration model and additional metrics that combine temperature and moisture (for example, growing period, soil degree days, soil moisture of wettest month). We are evaluating relationships of soil-climate and distributions of conifer pinyon-juniper. Finally, we are continuing to work with field and remote sensing data to investigate relations between soil-climate, vegetation and other land cover and habitat patterns.
Funders
U.S. Geological Survey (Ecosystem Mission Area and Wyoming Landscape Conservation Initiative) and North Central Climate Adaptation Science Center
- Science
Soil-climate for Managing Sagebrush Ecosystems
Soil-climate describes the temperature and moisture conditions important for plant growth and function. Soil condition patterns determine which vegetation is most abundant, thus controlling which habitats, invasive species, fuels, and economic activities are present in a region. Here, we use a model to simulate the vertical movement of water in a soil profile to provide insights into landscape...Future Scenarios of Soil-climate for Sagebrush Ecosystems
Climate forecasts provide a unique tool to researchers and wildlife managers, allowing for a look into potential future climate conditions. Climate models provide multiple scenarios that assume different mitigation polices implemented by governments. By using these data in a statistical model to estimate soil-climate conditions, we can investigate the connection between future climate and... - Data
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