Living with exotic annual grasses in the sagebrush ecosystem

Exotic annual grasses dominate millions of hectares and increase fire frequency in the sagebrush ecosystem of North America. This devastating invasion is so costly and challenging to revegetate with perennial vegetation that restoration efforts need to be prioritized and strategically implemented. Management needs to break the annual grass-fire cycle and prevent invasion of new areas, while research is needed to improve restoration success. Under current land management and climate regimes, extensive areas will remain annual grasslands, because of their expansiveness and the low probability of transition to perennial dominance. We propose referring to these communities as Intermountain West Annual Grasslands, recognizing that they are a stable state and require different management goals and objectives than perennial-dominated systems. We need to learn to live with annual grasslands, reducing their costs and increasing benefits derived from them, at the same time maintaining landscape-level plant diversity that could allow transition to perennial dominance under future scenarios. To accomplish this task, we propose a framework and research to improve our ability to live with exotic annual grasses in the sagebrush biome.

Harvester ant seed removal in an invaded sagebrush ecosystem: Implications for restoration

A better understanding of seed movement in plant community dynamics is needed, especially in light of disturbance-driven changes and investments into restoring degraded plant communities. A primary agent of change within the sagebrush-steppe is wildfire and invasion by non-native forbs and grasses, primarily cheatgrass (Bromus tectorum). Our objectives were to quantify seed removal and evaluate ecological factors influencing seed removal within degraded sagebrush-steppe by granivorous Owyhee harvester ants (Pogonomyrmex salinus Olsen). In 2014, we sampled 76 harvester ant nests across 11 plots spanning a gradient of cheatgrass invasion (40%-91% cover) in southwestern Idaho, United States. We presented seeds from four plant species commonly used in postfire restoration at 1.5 and 3.0 m from each nest to quantify seed removal. We evaluated seed selection for presented species, monthly removal, and whether biotic and abiotic factors (e.g., distance to nearest nest, temperature) influenced seed removal. Our top model indicated seed removal was positively correlated with nest height, an indicator of colony size. Distance to seeds and cheatgrass canopy cover reduced seed removal, likely due to increased search and handling time. Harvester ants were selective, removing Indian ricegrass (Achnatherum hymenoides) more than any other species presented. We suspect this was due to ease of seed handling and low weight variability. Nest density influenced monthly seed removal, as we estimated monthly removal of 1,890 seeds for 0.25 ha plots with 1 nest and 29,850 seeds for plots with 15 nests. Applying monthly seed removal to historical restoration treatments across the western United States showed harvester ants can greatly reduce seed availability at degraded sagebrush sites; for instance, fourwing saltbush (Atriplex canescens) seeds could be removed in <2 months. Collectively, these results shed light on seed removal by harvester ants and emphasize their potential influence on postfire restoration within invaded sagebrush communities.

Validating a Landsat Time-Series of Fractional Component Cover Across Western U.S. Rangelands

Western U.S. rangelands have been quantified as six fractional cover (0%–100%) components over the Landsat archive (1985–2018) at a 30 m resolution, termed the “Back-in-Time” (BIT) dataset. Robust validation through space and time is needed to quantify product accuracy. Here, we used field data collected concurrently with high-resolution satellite (HRS) images over multiple locations (n = 42) and years. Field observations were used to train regression tree models, predicting the component cover across each HRS image. Our objectives were to evaluate the spatial and temporal relationships between HRS and BIT component cover and compare spatio-temporal climate responses. First, for each HRS site-year (n = 77) we averaged both the HRS and BIT predictions within each site separately and regressed the averages to quantify the temporal accuracy. Next, we regressed individual pixel values of corresponding HRS and BIT predictions to quantify the spatio-temporal accuracy. Results showed strong temporal correlations with an average R2 of 0.63 and Root Mean Square Error (RMSE) of 5.47% as well as strong spatio-temporal correlations with an average R2 of 0.52 and RMSE of 7.89% across components. Our approach increased the validation sample size relative to direct comparison of field observations. Validation results showed robust spatio-temporal relationships between HRS and BIT data, providing increased user confidence in the data.