New indicators of ecological resilience and invasion resistance to support prioritization and management in the sagebrush biome, United States
Creators: name: Chambers, Jeanne
name: Brown, Jessi
name: Bradford, John
name: Board, David
name: Campbell, Steven
name: Clause, Karen
name: Hanberry, Brice
name: Schlaepfer, Daniel
name: Urza, Alexandra
Publisher: name: Zenodo
Year: 2023
Subjects: sagebrush biome
ecological resilience
resistance to invasion
Bromus tectorum
ecohydrological simulation
Climate
Soil water availability
random forest
prioritization

description: Ecosystem transformations to altered or novel ecological states are accelerating across the globe. Indicators of ecological resilience to disturbance and resistance to invasion can aid in assessing risks and prioritizing areas for conservation and restoration. The sagebrush biome encompasses parts of 11 western states and is experiencing rapid transformations due to human population growth, invasive species, altered disturbance regimes, and climate change. We built on prior use of static soil moisture and temperature regimes to develop new, ecologically relevant and climate-responsive indicators of both resilience and resistance. Our new indicators were based on climate and soil water availability variables derived from process-based ecohydrological models that allow predictions of future conditions. We asked: (1) Which variables best indicate resilience and resistance? (2) What are the relationships among the indicator variables and resilience and resistance categories? (3) How do patterns of resilience and resistance vary across the area? We assembled a large database (n = 24,045) of vegetation sample plots from regional monitoring programs and derived multiple climate and soil water availability variables for each plot from ecohydrological simulations. We used USDA Natural Resources Conservation Service National Soils Survey Information, Ecological Site Descriptions, and expert knowledge to develop and assign ecological types and resilience and resistance categories to each plot. We used random forest models to derive a set of 19 climate and water availability variables that best predicted resilience and resistance categories. Our models had relatively high multiclass accuracy (80% for resilience; 75% for resistance). Top indicator variables for both resilience and resistance included mean temperature, coldest month temperature, climatic water deficit, and summer and driest month precipitation. Variable relationships and patterns differed among ecoregions but reflected environmental gradients; low resilience and resistance were indicated by warm and dry conditions with high climatic water deficits, and moderately high to high resilience and resistance were characterized by cooler and moister conditions with low climatic water deficits. The new, ecologically-relevant indicators provide information on the vulnerability of resources and likely success of management actions and can be used to develop new approaches and tools for prioritizing areas for conservation and restoration actions.
description: All code scripts are RStudio notebooks, which are RMarkDown files additionally formatted to render to HTML. Input files can be .csv files (plain text, common-separated files) or RDS files (R data objects).Funding provided by: Joint Fire Sciences Program*<br>Crossref Funder Registry ID: <br>Award Number: Project 19-2-02-11Funding provided by: Rocky Mountain Research Station<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/100014586<br>Award Number:
description: We assembled a large database (n = 24,045) of vegetation sample plots from regional monitoring programs and derived multiple climate and soil water availability variables for each plot from ecohydrological simulations. We used USDA Natural Resources Conservation Service National Soils Survey Information, Ecological Site Descriptions, and expert knowledge to develop and assign ecological types and resilience and resistance categories to each plot. We used random forest models to derive a set of 19 climate and water availability variables that best predicted resilience and resistance categories.