Understanding the future of big sagebrush ecological integrity

Project Description

Background: Sagebrush ecosystems are a major component of landscapes in the Western U.S.  Climate change has the potential to substantially alter big sagebrush plant communities as their structure and function are closely tied to soil moisture and temperature. Ecosystem services provided by the sagebrush biome are already threatened by multiple simultaneous processes, including invasive annual plants, altered fire regimes, conifer expansion, climate change, and human land use. Defending and growing core sagebrush habitats in the face of these threats requires information that enables spatially targeted management.


Objectives:  

Broadly, our goal is to understand the climatic, and edaphic controls over sagebrush ecosystems and forecasting how sagebrush ecosystems may change in the future. We strive to understand impacts of feedbacks between wildfire, grazing, and invasive species as well as a broad array of climate projections to evaluate uncertainty due to climate variability. 


Specifically, we are working on addressing the following questions: 


Approach:  We use the SOILWAT2 ecosystem water balance model to simulate sagebrush plant community responses to future climate, wildfire, grazing, and invasion by annual grasses. To improve our representation of wildfire we created a statistical model using historical wildfire occurrence, gridded climate data, and remotely sensed herbaceous biomass data which we then incorporated into SOILWAT2 (wildfire dynamics are relevant to all questions). To create projections of sagebrush core habitats under future conditions (questions 2-3), we combine simulated sagebrush community responses with remote sensing based estimates of sagebrush ecosystem integrity that have been developed as part of the ongoing Sagebrush Conservation Design effort (Doherty et al., 2022). To better understand climate-grazing interactions we simulate several levels of forage utilization into STEPWAT2 (questions 4-5). 


Outcomes

Question 1: How do climate and fine fuels drive long term wildfire probability in sagebrush ecosystems and how might fire regimes change in the future? We fit a climate-sensitive statistical model to observed fire-occurrence, climate (temperature, annual precipitation, and the proportion of precipitation received in June-August), and fine fuels (remote sensing based estimates of aboveground biomass of annual and perennial herbaceous plants). This relatively simple model (which is a closed form equation) predicts annual wildfire probability and has been incorporated into SOILWAT2. In ongoing work we are using SOILWAT2 to explore wildfire-vegetation-climate feedbacks.  

This figure shows relationship between wildfire probability, climate and fine fuels across the sagebrush region. Panels show mean observed (black circles) and predicted (blue triangles) annual wildfire probability for each percentile of a) mean temperature, b) annual precipitation, c) proportion summer precipitation (PSP), d) aboveground biomass of annual forbs and grasses, and e) aboveground biomass of perennial forbs and grasses (values of all predictor variables were calculated as 3-year running averages). Corresponding fire return intervals (FRI) are shown on the secondary (right) y-axis. Data were binned by percentile (i.e., 100 bins) of a given predictor variable, and the x-axis (panels a-e) shows the mean value of each percentile of that variable. Each point represents the mean of ~250,000 observations (i.e., 1% of the entire dataset used). In panel f) mean observed and predicted annual wildfire probability values shown in panels a-e are plotted against each other (1:1 line shown for reference). 

Question 2: How will biome-wide patterns of sagebrush core habitat areas be altered in coming decades by the combination of climate change and feedbacks between vegetation and wildfires? Here we show the potential changes core sagebrush areas (CSA), growth opportunity areas (GOA) and other rangeland areas (ORA). The map shows the median change in this sagebrush ecological integrity classification from current (2017-2021) to future (RCP 4.5, 2071-2100) climate conditions. Results are based on simulations that incorporated vegetation-wildfire feedbacks. Reds denote areas where we projected losses of CSA or GOA. 

Question 3: How robust are projected 21st century changes in sagebrush ecological integrity to uncertainty in future climate conditions represented by climate models and representative concentration pathways? Total area in the nine possible changes of  sagebrush ecological integrity (SEI) classification for two emissions scenarios (RCP4.5 and RCP8.5) and two time periods (2031-2060, 2071-2100). The bars with no hash marks (RCP4.5, 2071-2100) correspond to the areas shown in the map above. Bars show the area based on calculating the median future SEI across 13 GCMs at each grid-cell. Error bars show the range in area based on using the 2nd lowest and 2nd highest SEI values across GCMs at each grid-cell. Note that while 9 changes in sagebrush ecological integrity classification are possible, the ‘ORA becomes CSA’ (black) and ‘CSA becomes ORA’ (dark red) categories do not appear on the map (because they represent approximately zero area). 

a) Agreement among climate models for change in sagebrush ecological integrity classification (under RCP4.5 2071-2100) for areas that are currently Core Sagebrush Areas (CSA) or Growth Opportunity Areas (GOA).  b) Area of the categories shown in panel a. ‘Non-robust agreement’ indicates agreement among 7-11 models out of 13 (light colors, not a robust signal), and ‘robust agreement’ means agreement among 12-13 models (dark colors, a robust signal). Loss of CSA means future classification is GOA or ORA. Loss of GOA means future classification is ORA. 

Question 4: What plant functional types are driving projected shifts in sagebrush ecological integrity (SEI)? a) Relative contribution of changing abundance of sagebrush (red), perennial grasses (green), and annual grasses (blue) to changes in SEI, for RCP4.5 2071-2100.  b) Region-wide area most impacted by changes in each component for each SEI class change category. The primary driver of change was defined as the plant functional type that had the greatest impact on the change in SEI in a given grid-cell. Areas where no single plant functional type was the primary driver of change are shown in gray. Abbreviations: CSA, Core Sagebrush Area; GOA, Growth Opportunity Area; ORA, Other Rangeland Area. 

More results to come...

Other Outcomes

Publications:

 Data releases:

Presentations:

Collaborators

Chad Boyd - U.S. Department of Agriculture, Agricultural Research Service

Megan Creutzburg - Oregon State University, Institute for Natural Resources

Michele Crist - Bureau of Land Management, Fire Planning and Fuels Management Division

Kevin Doherty, John Tull - U.S. Fish and Wildlife Service, Science Applications Program 

William Lauenroth - Yale University, School of the Environment,

Kyle Palmquist - Marshall University, Department of Biological Sciences

Thomas Remington - Western Association of Fish and Wildlife Agencies 

Karin Riley,  Karen Short - U.S. Forest Service, Rocky Mountain Research Station

Lief Wiechman - U.S. Geological Survey, Ecosystems Mission Area 

Support 

U.S. Fish and Wildlife Service, Bureau of Land Management, USGS Ecosystems Mission Area, and the USGS Climate Adaptation Science Centers