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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Rangeland Resources & Systems Research » Research » Research Project #437928

Research Project: RoL: Using Reaction Norms To Link Genomic And Phenotypic Variation With Regional-Scale Population Responses To Environmental Change

Location: Rangeland Resources & Systems Research

Project Number: 3012-21610-003-29-R
Project Type: Reimbursable Cooperative Agreement

Start Date: Jan 1, 2020
End Date: Dec 31, 2020

The proposed research combines field experiments with DNA sequencing and statistical and mathematical modeling to predict future changes in the density of the invasive annual, Bromus tectorum, across western North America, quantify the accuracy and uncertainty of those predictions, and project resulting impacts on fire regimes and wildlife habitat. Specific objectives center on answering the following research questions: Q1. How do spatial and environmental gradients structure the genetic diversity of B. tectorum? Q2. How do demographic rates of B. tectorum genotypes vary in response to environmental gradients (GxE)? Q3. What are the most important sources of uncertainty in predicting B. tectorum population growth over the short-term: response to weather, competitors, or GxE? Q4. What are the most important sources of uncertainty in predicting B. tectorum population growth over the long-term: climate change, vegetation change, or genotypic change? Q5. How will long-term changes in B. tectorum abundance impact fire regimes and ecosystem structure across the western US?

Achieving our objectives will require an ambitious combination of seed collection and field experiments at dozens of sites across the interior West, DNA sequencing, and statistical and mathematical models. Our approach includes the following components: Step 1: Seed collection. BromeCast network participants will collect seeds from cheatgrass populations across this geographic area. For each seed collection site, we will also acquire data on a) plant species composition b) landscape context c) disturbance history, d) soils, and e) climate. Step 2: DNA sequencing. Co-PI Lasky will lead sequencing of the DNA of 300 genotypes from 100 collection locations. Step 3. Model the spatial distribution of genotypes. We will estimate genotype on the basis of associations between genomic data (Step 2) and spatial and environmental covariates for each seed collection location. Step 4: Common garden experiments. We will select a subset of 100 genotypes from the sequenced seed collections to grow in common gardens. The selected genotypes will maximize representation of both geographic and abiotic variation. Emergence will be censused after the first soaking rain, at which point we will mark plants and conduct repeat censuses bimonthly during the spring growing season. Step 5. Describe reaction norms and GxE. We will combine data from the DNA sequencing and common garden experiments to model the observed demographic rates as a function of abiotic drivers (soil moisture, air temperature, soil nutrient status), cheatgrass density, cheatgrass genotype, and perennial vegetation. Step 6. Build a population model. The statistical models of Steps 3 and 5 form the basis of a population model that predicts cheatgrass density at a particular location in year t + 1 given input data on cheatgrass density in year t, precipitation, temperature, soils, plant community composition, and genotype. Step 7. Validate short-term predictions of the population model. At dozens of satellite sites across the region, we will plant local B. tectorum ecotypes. In contrast to the core common gardens, seeds at these satellite sites will be planted into both intact vegetation (the control treatment) and into plots where existing vegetation has been removed (the removal treatment). We will monitor individual germination, survival, and seed production using the same methods as in the common garden sites. Data from satellite sites will be used for model calibration and validation. Step 8. Model fire regime. To project long-term impacts of the B. tectorum invasion on fire regimes, we will develop a model that will translate projected changes in climate and B. tectorum abundance at landscape scales into changes in fire regime attributes including size, frequency, and seasonality of fire. Step 9. Project future changes in cheatgrass abundance and fire regimes across the region and bound uncertainties. Our satellite sites will allow us to test short-term model predictions and quantify parameter uncertainty (Step 7). Projecting the response of B. tectorum populations to climate change over decadal scales (Question 4) requires consideration of additional uncertainties, which we will address here.