All Publications

Introduction: Terrestrial species in riverine ecosystems face unique constraints leading to diverging patterns of population structure, connectivity, and disease dynamics. Desert bighorn sheep (Ovis canadensis nelsoni) in Grand Canyon National Park, a large native population in the southwestern USA, offer a unique opportunity to evaluate population patterns and processes in a remote riverine syste

The U.S. Geological Survey (USGS) provided technical assistance to the National Park Service (NPS) as part of the USGS-NPS Water-Quality Partnership, by gathering references related to water-quality research conducted in the three units of Gateway National Recreation Area (GATE): Jamaica Bay and Staten Island in New York, and Sandy Hook in New Jersey. As part of this effort, a literature search wa

IntroductionThe Southeast Alaska (SE) stock of northern sea otters (Enhydra lutris kenyoni) ranges from Cape Yakataga on the north to the Dixon Entrance on the south. During the maritime fur trade, sea otters were commercially harvested to near extinction in SE for their pelts and were presumed unlikely to naturally repopulate the region.

We present a hydroclimate synthesis of the southern Great Basin over the last two glacial-interglacial cycles focused on paleolakes in Death Valley (core DV93-1), Searles Valley (core SLAPP-SRLS17), Owens Valley (core OL92), and the Devils Hole cave. There is close agreement between the occurrence of lakes in Death Valley and the height of the water table in the Devils Hole (50 km east of Death Va

Context.—Overexposure to respirable coal mine dust can cause severe lung disease including progressive massive fibrosis (PMF). Field emission scanning electron microscopy with energy dispersive x-ray spectroscopy (FESEM-EDS) has been used for in situ lung dust particle analysis for evaluation of disease etiology. Automating such work can reduce time, costs, and user bias.Objective.—To develop and

Rangeland ecosystems in the western United States are vulnerable to climate change, fire, and anthropogenic disturbances, yet classification of rangeland areas remains difficult due to frequently sparse vegetation canopies that increase the influence of soils and senesced vegetation, the overall abundance of senesced vegetation, heterogeneity of life forms, and limited ground-based data. The Range

BackgroundEnhancing detection of cryptic snakes is critical for the development of conservation and management strategies; yet, finding methods that provide adequate detection remains challenging. Issues with detecting snakes can be particularly problematic for some species, like the invasive Burmese python (Python bivittatus) in the Florida Everglades.MethodsUsing multiple survey methods, we pred

With growing global capability for satellite measurement of river discharge (flow) comes a need to understand and reduce error in satellite-based discharge measurements. Satellite-based discharge estimates are based on measurements of water surface width, elevation, slope, and potentially velocity. Site selection is important for reducing error and uncertainty in both conventional and satellite-ba

Scientists and resource managers increasingly use Markdown-based tools to create reproducible reports and manuscripts. These workflows allow people to use standardized methods that are more reproducible, efficient, and transparent than other standard office tools. We present a Quarto template and demonstrate how this template may be used for a journal, the Journal of Fish and Wildlife Management,

The vertical accuracy of elevation data in coastal environments is critical because small variations in elevation can affect an area’s exposure to waves, tides, and storm-related flooding. Elevation data contractors typically quantify the vertical accuracy of lidar-derived digital elevation models (DEMs) on a per-project basis to gauge whether the datasets meet quality and accuracy standards. Here

Spatial machine learning models can be developed from observations with substantial unexplainable variability, sometimes called ‘noise’. Traditional point-scale metrics (e.g., R2) alone can be misleading when evaluating these models. We present a multi-scale performance evaluation (MPE) using two additional scales (distributional and geostatistical). We apply the MPE framework to predictions of de