Biology subjects

Mueller, K. R.

Publications and source records attributed to Mueller, K. R..

3 recordsLinked to original sources

bioRxiv2026

Shifting Resilience: Trends and Predictors of Mesic Resource Productivity in Western U.S. Rangelands

Mesic resources, the late-season herbaceous vegetation found in riparian areas and wet meadows, provide disproportionately important forage and habitat across western U.S. rangelands, yet their response to climatic variability and anthropogenic influences remains poorly understood. Using a 40-year Landsat time series (1984-2024), we quantified trends in late-season productivity (NDVI) across 4.5 million hectares of the sagebrush biome and applied random forest models to distinguish between temporal and spatial predictors of mesic resource productivity. We identified a fundamental shift in how mesic resources respond to drought: from 1984 to 2004, mesic productivity was strongly correlated with drought severity (Palmer Drought Severity Index, R{superscript 2} = 0.92), but this relationship weakened substantially in the next two decades (2005-2024; R{superscript 2} = 0.28), during which time productivity increased despite persistent aridity. Temporal modeling identified rising atmospheric CO2 concentrations as the strongest predictor of this shift, consistent with enhanced plant water-use efficiency under CO2 fertilization. Spatially, large agricultural valley floodplains act as anthropogenic refugia, sustaining productive mesic resources through flood irrigation and subsequent groundwater recharge into late summer. These findings suggest that human water management and physiological shifts in vegetation are currently buffering mesic systems against meteorological drought throughout U.S. rangelands. However, this apparent buffering is spatially heterogeneous and may mask vulnerability to groundwater depletion, shifts in precipitation regimes, and woody encroachment. Sustaining these vital ecosystems will require conservation approaches that go beyond climate monitoring to include balanced management considering both agricultural and ecological water needs and constraints.

ecology↗

bioRxiv2025

Beyond Uplands: Integrating Riparian Areas into Sagebrush Conservation Design for a More Resilient West

Riparian and mesic habitats are essential biological hotspots in the American West, supporting 80% of wildlife, yet they are often assessed in isolation from surrounding uplands. To address this, we developed a framework that complements the Sagebrush Conservation Design to incorporate these habitats into broader landscape conservation planning. By analyzing 40 years of riparian vegetation data alongside sagebrush ecosystems, we classified watersheds into three actionable management tiers based on water availability and vegetation health. This prioritization is operationalized through the Mesic Analysis Platform, an interactive tool that identifies stream reaches with floodplain disconnection as key targets for restoration. Notably, the greatest restoration potential exists on private lands, where 63-65% of identified mesic habitats are found. This framework provides a practical strategy to integrate social feasibility with ecological priority, fostering the multi-jurisdictional partnerships vital to connecting water resources with the broader sagebrush ecosystem.

ecology↗

bioRxiv2022

Mapping Tree Cover Expansion in Montana, U.S.A. Rangelands using High-Resolution Historical Aerial Imagery

Worldwide, trees are colonizing rangelands with high conservation value. The introduction of trees into grasslands and shrublands causes large-scale changes in ecosystem structure and function, which have cascading impacts to ecosystem services, biodiversity, and agricultural economies. Satellites are increasingly being used to track tree cover at continental to global scales, but these methods can only provide reliable estimates of change over recent decades. Given the slow pace of tree cover expansion, remote sensing techniques that can extend this historical record provide critical insights into the magnitude of environmental change. Here, we estimate conifer expansion in rangelands of the northern Great Plains, United States, North America, using historical aerial imagery from the mid-20th century and modern aerial imagery. We analyzed 19.3 million hectares of rangelands in Montana, USA, using a convolutional neural network (U-Net architecture) and cloud computing to detect tree features and tree cover change. Our bias-corrected results estimate 3.0 {+/-} 0.2 million hectares of conifer tree cover expansion in Montana rangelands, which accounts for 15.4% of the total study area. Overall accuracy was greater than 91%, but producer accuracy was lower than user accuracy (0.60 vs. 0.88). Nonetheless, the omission errors were not spatially correlated, indicating that the method is reliable for estimating the spatial extent of tree cover expansion. Using the model results in conjunction with historical and modern imagery allows for effective communication of the scale of tree expansion while overcoming the recency effect caused by shifting environmental baselines.

ecology↗