Publications
This list of publications includes peer-review journal articles, official USGS publications series, reports and more authored by scientists in the Ecosystems Mission Area. A database of all USGS publications, with advanced search features, can be accessed at the USGS Publications Warehouse.
Changes in sand storage in the Colorado River in Grand Canyon National Park from July 2017 through June 2020
Decomposition rates appear stable despite elevated shrimp abundances following hurricanes in montane streams, Puerto Rico
Hurricanes: An unexpected mechanism linking disturbance and seed production in trees
Resource-driven pattern formation in consumer-resource systems with asymmetric dispersal on a plane
Validation of a molecular sex marker in three sturgeons from eastern North America
U.S. Geological Survey Grand Canyon Monitoring and Research Center: Proceedings of the fiscal year 2023 annual reporting meeting to the Glen Canyon Dam Adaptive Management Program
Demography with drones: Detecting growth and survival of shrubs with unoccupied aerial systems
Large-scale disturbances, such as megafires, motivate restoration at equally large extents. Measuring the survival and growth of individual plants plays a key role in current efforts to monitor restoration success. However, the scale of modern restoration (e.g., >10,000 ha) challenges measurements of demographic rates with field data. In this study, we demonstrate how unoccupied aerial system (UAS
Seasonal differences in larval sea lamprey (Petromyzon marinus) sensitivity to the pesticide TFM
The addition of 144Nd atomic mass to routine ICP-MS analysis as a Quick Screening Tool for Approximating Rare Earth Elements (Q-STAR) in natural waters
Rare earth elements (REEs) are a class of critical minerals, all of which can have supply chain vulnerability that impacts economic security. These elements are widely measured in environmental matrices via inductively coupled plasma mass spectrometry (ICP-MS); however, successful quantification can require time-consuming, sample-specific optimization. While a sample-by-sample approach is appropri