Michael Greenfield | Search USGS

USGS2021

Evaluating common raven take for greater sage-grouse in Oregon’s Baker County Priority Conservation Area and Great Basin Region

The common raven ( Corvus corax ; raven) is a nest predator of species of conservation concern, such as the greater sage-grouse ( Centrocercus urophasianus ). Reducing raven abundance by take requires authorization under the Migratory Bird Treaty Act. To support U.S. Fish and Wildlife Service’s take decisions (e.g., those that authorize killing a specified proportion or number of individuals annually in a defined area), including the most recent one for Oregon’s Baker County Priority Area for Conservation (PAC), we modeled raven population dynamics under hypothetical scenarios with take rates ranging from below to above the maximum sustained yield (MSY; i.e., tr msy = 0.01-0.60). We fit a Bayesian state-space logistic model to estimate abundance based on the Breeding Bird Survey route-level count data for the PAC during 1997-2019 and Great Basin Region (GBR) during 1968-2019. We predicted abundance for 2019-2030 and evaluated potential take levels (PTL) for the PAC and GBR. Abundance averaged 682 (SE = 93) for the PAC during 1997-2019 and 333,027 (SE = 20,504) for the GBR during 1968-2019. With take rates between 0.41 and 0.60, predicted abundance averaged 308 (SD = 405) for the PAC and 142,258 (SD = 53,474) for the GBR during 2019-2030. With management factor F = 0.75-2 for takes ranging from below to above the MSY, the PTL 50 th percentiles were 150-401 yr -1 for the PAC and 60,457-161,219 yr -1 for the GBR. Our modeling framework is flexible and can be part of a comprehensive management strategy for ravens in the western United States.

California, Idaho, Nevada, Oregon, Utah↗

USGS2020

Probabilistic regional-scale liquefaction triggering modeling using 3D Gaussian processes

Liquefaction is a major cause of coseismic damages, occurring irregularly over hundreds or thousands of square kilometers in large earthquakes. Large variations in the extent and location of liquefaction have been observed in recent earthquakes, motivating the need for prediction methods that consider the spatial heterogeneity of geologic deposits at a regional scale. Contemporary regional-scale liquefaction hazard analyses are typically performed using only surficial data, which does not address the complicated subsurface mechanics and spatial variability associated with artificial fill and natural soil deposits. In this study, we develop a probabilistic, regional-scale, subsurface model using data from hundreds of borings to better understand subsurface conditions that could influence liquefaction. We then use this subsurface sample database to train Gaussian process models, yielding 3D independent random fields of groundwater depth, soil plasticity, and penetration resistance for each geologic unit. We incorporate the Gaussian process models into probabilistic liquefaction triggering procedures, producing 3D estimates of the probability of liquefaction for an example study area in Portland, Oregon. Near sampling locations, the variance of the Gaussian process models approaches the variance of site-specific liquefaction triggering procedures. Conversely, when no sample data are nearby to condition a Gaussian process, the variance approaches the marginal variance of the entire recorded dataset. Thus, the procedure described in this study unifies probabilistic site-specific and regional-scale liquefaction triggering procedures and provides an important step towards quantitative liquefaction hazard assessments for regionally distributed infrastructures, such as levees, pipelines, roadways, and electrical transmission facilities.

Oregon↗