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At least 1,117 records · Page 62Linked to original sources
USGS2024

Leveraging angler effort to inform fisheries management: Using harvest and harvest rate to estimate abundance of White Sturgeon

Traditional methods for estimating abundance of fish populations are not feasible in some systems due to complex population structure and constraints on sampling effort. Lincoln’s estimator provides a technique that uses harvest and harvest rate to estimate abundance. Using angler catch data allows assumptions of the estimator to be addressed without relying on methods that could be prohibitively field-intensive or costly. Historic estimates of White Sturgeon Acipenser transmontanus abundance in the Sacramento–San Joaquin River basin have been obtained using mark–recapture methods; however, White Sturgeon population characteristics often cause violations of model assumptions, such as population closure and independent capture probabilities. We developed a version of Lincoln’s estimator using a joint likelihood, estimated abundance of White Sturgeon in the Sacramento–San Joaquin River basin in 2015 using this method and empirical data and assessed accuracy and precision of estimates in a simulation study. Estimating abundance using harvest and harvest rate, as represented by our model framework, has the potential to be precise and accurate. The joint likelihood–based approach fitted using Bayesian methods is advantageous because it includes all sources of variation in a single model. Precision of abundance estimates was low with application of the model to White Sturgeon in the Sacramento–San Joaquin River basin and to similar conditions in a simulated dataset. Using simulation, precision and accuracy increased with increases in the number of high-reward and standard tags released, tag reporting rate, tag retention rate, and harvest rate. Results demonstrate potential sources of error when using this approach and suggest that increasing the number of tagged fish and tag reporting rate are potential actions to improve precision and accuracy of abundance estimates of the model.

California↗
USGS2009

Deep Resistivity Structure of Mid Valley, Nevada Test Site, Nevada

The U.S. Department of Energy (DOE) and the National Nuclear Security Administration (NNSA) at their Nevada Site Office (NSO) are addressing ground-water contamination resulting from historical underground nuclear testing through the Environmental Management (EM) program and, in particular, the Underground Test Area (UGTA) project. From 1951 to 1992, 828 underground nuclear tests were conducted at the Nevada Test Site northwest of Las Vegas (DOE UGTA, 2003). Most of these tests were conducted hundreds of feet above the ground-water table; however, more than 200 of the tests were near, or within, the water table. This underground testing was limited to specific areas of the Nevada Test Site including Pahute Mesa, Rainier Mesa/Shoshone Mountain (RM-SM), Frenchman Flat, and Yucca Flat. One issue of concern is the nature of the somewhat poorly constrained pre-Tertiary geology and its effects on ground-water flow in the area subsequent to a nuclear test. Ground-water modelers would like to know more about the hydrostratigraphy and geologic structure to support a hydrostratigraphic framework model that is under development for the Rainier Mesa/Shoshone Mountain (RM-SM) Corrective Action Unit (CAU) (National Security Technologies, 2007). During 2003, the U.S. Geological Survey (USGS), in cooperation with the DOE and NNSA-NSO collected and processed data at the Nevada Test Site in and near Yucca Flat (YF) to help define the character, thickness, and lateral extent of the pre-Tertiary confining units. We collected 51 magnetotelluric (MT) and audio-magnetotelluric (AMT) stations for that research (Williams and others, 2005a, 2005b, 2005c, 2005d, 2005e, and 2005f). In early 2005 we extended that research with 26 additional MT data stations (Williams and others, 2006) located on and near Rainier Mesa and Shoshone Mountain (RM-SM). The new stations extended the area of the hydrogeologic study previously conducted in Yucca Flat, further refining what is known about the pre-Tertiary confining units. In particular, a major goal was to define the extent of the upper clastic confining unit (UCCU). The UCCU is composed of late Devonian to Mississippian siliciclastic rocks assigned to the Eleana Formation and Chainman Shale (National Security Technologies, 2007). The UCCU underlies the Yucca Flat area and extends southwestward toward Shoshone Mountain, westward toward Buckboard Mesa, and northwestward toward Rainier Mesa. Late in 2005 we collected data at an additional 14 MT stations in Mid Valley, CP Hills, and northern Yucca Flat. That work was done to better determine the extent and thickness of the UCCU near the boundary between the southeastern RM-SM CAU and the southwestern YF CAU, and also in the northern YF CAU. The MT data have been released in a separate U.S. Geological Survey report (Williams and others, 2007). The Nevada Test Site magnetotelluric data interpretation presented in this report includes the results of detailed two-dimensional (2-D) resistivity modeling for each profile and inferences on the three-dimensional (3-D) character of the geology within the region.

Nevada↗
USGS2025

Current distribution of the nine-banded armadillo (Dasypus novemcinctus) in the United States

The nine-banded armadillo ( Dasypus novemcinctus : hereafter armadillo) was first recorded in the United States (U.S.) in the state of Texas in 1849 and has been expanding its range northward and eastward since then. With the widespread adoption of participatory science as well as the proliferation of nationwide wildlife game camera studies, occurrence data of armadillos can be compiled more rapidly and thoroughly than at any time in the past. Here, we use disparate data sources to update the current geographic distribution of the armadillo in the United States and use occurrence data from the leading edge of its range expansion to create a species distribution model to understand their relationship with landscape and bioclimatic factors. Since the last report on the geographic distribution of the armadillo in 2014, we show that armadillos have expanded to cover the entirety of Missouri and established in southern Iowa, expanded modestly within Kansas and Illinois, expanded northward and eastward in Indiana, expanded eastward in both Kentucky and Tennessee, established throughout the entirety of South Carolina and Georgia and established in the western third of North Carolina. Our species distribution model indicates that there is substantial opportunity for the species to continue to expand its geographic range, particularly in the Eastern United States. These results provide information to managers who are now or might soon be co-existing with the armadillo to proactively manage the species or inform the public regarding potential conflicts.

Diversity↗
USGS2013

Review of a model to assess stranding of juvenile salmon by ship wakes along the Lower Columbia River, Oregon and Washington

Long period wake waves from deep draft vessels have been shown to strand small fish, particularly juvenile Chinook salmon Oncorhynchus tschawytcha, in the lower Columbia River (LCR). The U.S. Army Corps of Engineers is responsible for maintaining the shipping channel in the LCR and recently conducted dredging operations to deepen the shipping channel from an authorized depth of 40 feet(ft) to an authorized depth of 43 ft (in areas where rapid shoaling was expected, dredging operations were used to increase the channel depth to 48 ft). A model was developed to estimate stranding probabilities for juvenile salmon under the 40- and 43-ft channel scenarios, to determine if channel deepening was going to affect wake stranding (Assessment of potential stranding of juvenile salmon by ship wakes along the Lower Columbia River under scenarios of ship traffic and channel depth: Report prepared for the Portland District U.S. Army Corps of Engineers, Portland, Oregon). The U.S. Army Corps of Engineers funded the U.S. Geological Survey to review this model. A total of 30 review questions were provided to guide the review process, and these questions are addressed in this report. In general, we determined that the analyses by Pearson (2011) were appropriate given the data available. We did identify two areas where additional information could have been provided: (1) a more thorough description of model diagnostics and model selection would have been useful for the reader to better understand the model framework; and (2) model uncertainty should have been explicitly described and reported in the document. Stranding probability estimates between the 40- and 43-ft channel depths were minimally different under most of the scenarios that were examined by Pearson (2011), and a discussion of the effects of uncertainty given these minimal differences would have been useful. Ultimately, however, a stochastic (or simulation) model would provide the best opportunity to illustrate uncertainty within a given set of model predictions, but such an approach would require a substantial amount of additional data collection. Several review questions focused on the accuracy and precision of the model estimates, but we were unable to address these questions because of the limited data that currently exists regarding wake stranding in the LCR. Additional field studies will be required to validate findings from Pearson (2011), if concerns regarding accuracy and precision remain a priority. Although the Pearson (2011) model provided a useful examination of stranding under pre-construction and post-construction conditions, future research will be required to better understand the effects of wake stranding on juvenile salmonids throughout the entire LCR. If additional information on wake stranding is desired in the future, the following topics may be of interest: (1) spatial examination of wake stranding throughout the entire LCR; (2) additional evaluation of juvenile salmonid behavior and population dynamics; (3) assessing and integrating predicted changes in ship development; and (4) assessing and integrating predicted changes in climate on environmental factors known to cause stranding.

Oregon;Washington↗