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USGS2002

Yellowstone grizzly bear investigations: Annual report of the Interagency Grizzly Bear Study Team, 2001

The contents of this Annual Report summarize results of monitoring and research from the 2001 field season. The report also contains a summary of nuisance grizzly bear ( Ursus arctos horribilis ) management actions. In addition to our normal monitoring, we completed an array of studies addressing the potential impacts of winter recreation on denning grizzly bears. This research was in response to a lawsuit filed against the Gallatin National Forest and subsequent need to develop a biological assessment addressing effects of snowmobile use on grizzly bears (Chemy 2001). Research results were also used by the National Park Service for a biological assessment and winter use plan (U.S. Department of the Interior 2001). The Interagency Grizzly Bear Study Team (IGBST) was able to use existing data collected from collared bears to address several issues and data needs for both agencies. Denning chronology (Haroldson et al. 2002), denning areas (Podruzny et al. 2002), and grizzly distribution (Schwartz et al. 2002) were all addressed. Information from these studies was presented at the International Association for Bear Research and Management (IBA) in Jackson Hole, Wyoming in 2001, and all 3 manuscripts have been officially accepted for publication in the journal Ursus . Abstracts are attached to this report (Appendices A, B. and C). Additionally, members of the study team participated in a workshop held by the National Park Service to develop monitoring protocols addressing the impacts of snowmobiles on wildlife (Graves and Reams 2001). The study team has also been working on issues associated with counts of unduplicated females with cubs-of-the-year (COY). These counts are used to establisha minimum population size, which is then used to establish mortality thresholds for the Recovery Plan (U.S. Fish and Wildlife Service [USFWS) 1993). Efforts by the Study Team to calculate more statistically sound estimates of population size have been underway for sometime. Eberhardt and Knight (1996) applied a Peterson-type capturemark-recapture estimator to unduplicated counts, and Boyce et al. (1999) recommended a maximum likelihood method. These methods assumed equal sightability of families, which was unrealistic for the Yellowstone population. Consequently, Boyce et al. (2001) recommended using a negative binomial distribution but found that they obtained reasonable results only when the coefficient of variation among sightings was assumed to be constant overtime. This assumption is also difficult to justify. Recent work by the study team (Keating et al. 2002) evaluated the application of 7 nonparametric estimators to assess their performance in determining the number of females with COY in a given year. This work identified 2 estimators that performed well using Monte Carlo simulations over a range of sampling conditions deemed plausible for the Yellowstone population: Chao's estimator (Chao 1984) and the sample coverage estimator (Chao and Lee 1992, Lee and Chao 1994). This work was presented at the IBA meeting in Jackson and the manuscript has been accepted in the journal Ursus . An abstract of this work is attached to this annual report (Appendix D). We are currently refining the application of these techniques to expand the predicted number of females with COY into a total population estimate. We anticipate completion of that work in 2002 or 2003.

Idaho, Montana, Wyoming↗

USGS2025

Valuing recreational fishing using creel survey statistics

Recreation demand analysis has relied on mail and internet surveys to collect information on individual recreators. However, conducting these surveys is costly and time-consuming. Alternative sources that report aggregate visitation may go unused due to a lack of information about trip starting points. We set up and solve a system of equations that predict reservoir visits and the home locations of recreational anglers. Using mode-level effort statistics from Nebraska creel surveys, we separate the effects of travel cost and site attributes between bank and boat anglers, which allows us to measure heterogenous values for public reservoir access.

Nebraska↗

USGS2018

Analysis of population change and movement using robust design removal data

In capture-mark-reencounter studies, Pollock’s robust design combines methods for open populations with methods for closed populations. Open population features of the robust design allow for estimation of rates of death or permanent emigration, and closed population features enhance estimation of population sizes. We describe a similar design, but for use with removal data. Data collection occurs on secondary sampling occasions clustered within primary sampling periods. Primary sampling periods are intervals of brief enough duration that it can be safely assumed that the population is unchanged by births, deaths, immigration or emigration during them; all population change and movement occurs between primary sampling periods. Our model provides a basis for inference about population size, changes in population size, and movement rates among sample locations between primary sampling periods. Movement rates are modeled as functions of distance and time. Capture probabilities are modeled as a function of effort. We apply the model to data obtained in attempting to eradicate an introduced population of veiled chameleons ( Chamaeleo calyptratus ) on the island of Maui in Hawaii.

Journal of Agricultural, Biological, and Environme↗

USGS2024

Cross-fade sampling: Extremely efficient Bayesian inversion for a variety of geophysical problems

This paper introduces cross-fade sampling, a computationally efficient Markov Chain Monte Carlo simulation method that uses a semi-analytical approach to quickly solve Bayesian inverse problems that do not themselves have an analytical solution. Cross-fading is efficient in two ways. First, it requires fewer samples to obtain the same quality simulation of the target probability density function (PDF). Secondly, it is much faster to evaluate the posterior probability of each sample than conventional sampling methods for simulating Bayesian posterior PDFs. Conventional methods require evaluating the prior probability (which describes your a priori constraints) and data likelihood (which describes the fit between the observations and the predictions of the model) for each sample model. However, cross-fading does not require evaluating the data likelihood, meaning that ‘big data’ can be fit with zero additional computational cost. Further, the cross-fading approach can be used to calculate the marginal likelihood associated with a model design, facilitating model comparison and Bayesian model averaging. Topics covered in this paper include derivation of the cross-fade approach and how it can be used to simulate Bayesian posterior PDFs and compute the marginal likelihood, discussion of the class of problems to which cross-fading can be applied (with examples from earthquake statistics, earthquake ground motion modelling, volcanic eruption forecasting, and finite fault slip modelling), demonstration of efficiency relative to existing sampling methods and discussion of how cross-fading can be used to account for prediction errors (i.e. epistemic errors) as part of the geophysical inverse problem.

Geophysical Journal International↗

USGS2023

Bridging the gap between mathematical biology and undergraduate education using applicable natural resource modeling

Mathematical biology is a wide field of study with many venues that undergraduate students can access through research. However, the topics of study for these students can be overwhelming, and many topics of study yield either only trivial results or abstract outcomes that are nonintuitive and diffcult to understand. We have used natural resource modeling, and more specifically, a partnership between academic researchers and federal scientists, as a bridge between undergraduate research and mathematical biology. Our collaboration is an interdisciplinary team that combines biology, mathematics, and statistics professors with government research scientists. As a team, we have mentored students through opportunities such as a Research Experiences for Undergraduates and other projects. In this article, we provide an overview of how we develop questions for undergraduates and outline two case studies, both of which resulted in peer reviewed journal articles. Last, we describe how we also transfer the results from these undergraduate projects to resource managers so the results may be applied to real world problems.

Letters in Biomathematics↗

USGS2015

Spatial occupancy models for predicting metapopulation dynamics and viability following reintroduction

The reintroduction of a species into its historic range is a critical component of conservation programmes designed to restore extirpated metapopulations. However, many reintroduction efforts fail, and the lack of rigorous monitoring programmes and statistical models have prevented a general understanding of the factors affecting metapopulation viability following reintroduction. Spatially explicit metapopulation theory provides the basis for understanding the dynamics of fragmented populations linked by dispersal, but the theory has rarely been used to guide reintroduction programmes because most spatial metapopulation models require presence–absence data from every site in the network, and they do not allow for observation error such as imperfect detection. We develop a spatial occupancy model that relaxes these restrictive assumptions and allows for inference about metapopulation extinction risk and connectivity. We demonstrate the utility of the model using six years of data on the Chiricahua leopard frog Lithobates chiricahuensis , a threatened desert-breeding amphibian that was reintroduced to a network of sites in Arizona USA in 2003. Our results indicate that the model can generate precise predictions of extinction risk and produce connectivity maps that can guide conservation efforts following reintroduction. In the case of L. chiricahuensis , many sites were functionally isolated, and 82% of sites were characterized by intermittent water availability and high local extinction probabilities (0·84, 95% CI: 0·64–0·99). However, under the current hydrological conditions and spatial arrangement of sites, the risk of metapopulation extinction is estimated to be <3% over a 50-year time horizon. Low metapopulation extinction risk appears to result from the high dispersal capability of the species, the high density of sites in the region and the existence of predator-free permanent wetlands with low local extinction probabilities. Should management be required, extinction risk can be reduced by either increasing the hydroperiod of existing sites or by creating new sites to increase connectivity. Synthesis and applications . This work demonstrates how spatio-temporal statistical models based on ecological theory can be applied to forecast the outcomes of conservation actions such as reintroduction. Our spatial occupancy model should be particularly useful when management agencies lack the funds to collect intensive individual-level data.

Arizona, New Mexico↗

USGS2021

Assessing specific-capacity data and short-term aquifer testing to estimate hydraulic properties in alluvial aquifers of the Rocky Mountains, Colorado, USA

Study Region : Rocky Mountains, United States Study Focus : Groundwater-flow modeling requires estimates of hydraulic properties, namely hydraulic conductivity. Hydraulic conductivity values commonly vary over orders of magnitudes however and estimation may require extensive field campaigns applying slug or pumping tests. As an alternative, specific-capacity tests can be used to estimate hydraulic properties for large areas when benchmarked with slug or pumping tests. This study combined aquifer testing with specific capacity data to estimate hydraulic properties in a large alluvial aquifer. New hydrological insights for region : In the Wet Mountain Valley, Colorado, both slug tests and pumping tests were conducted, resulting in a likely range of hydraulic-conductivity values. Aquifer-testing results were related to specific-capacity data, a more spatially distributed dataset, to expand the area of aquifer characterization beyond the distribution of wells included in aquifer testing. Specific-capacity data were used in two ways: (1) a regression was built between specific-capacity values and transmissivity derived from aquifer testing; and (2) an iterative method was utilized to estimate transmissivity from specific capacity at all sites (including sites lacking aquifer tests). Study results indicate that there is a statistically significant difference between hydraulic-conductivity values estimated using the two approaches and that the regression method yields systematically greater values. These results indicate that careful consideration of methods that use specific capacity for extrapolating aquifer properties is warranted as bias could be introduced depending on the applied methodology.

Colorado↗

USGS1983

Statistical evaluation of oil and gas prospects in the outer continental shelf of the U.S. Gulf Coast

Areas of the U.S. Gulf Coast, both onshore and offshore, are among the petroliferous regions of the United States. In offshore Louisiana and Texas, most oil and gas is associated with structurally controlled traps on the crest or flanks of domes created by the diapiric movement of salt. These structures can be detected and mapped by seismic techniques that directly indicate favorable prospective areas. The characteristics of seismically defined structures provide information in advance of drilling about the probable size of reservoirs contained within these structures. Simple statistical relationships can be determined between the size, shape, and other attributes of structures detected by seismic mapping and the presence and size of petroleum and natural gas reservoirs. Gulf Coast OCS lease tracts presently are evaluated by the U.S. government using a Monte Carlo simulation procedure adapted from reservoir analysis. Specification of the parameters of the Monte Carlo model are obtained subjectively in advance of drilling and thus are highly uncertain. Comparisons of predrill predictions with postdiscovery reservoir evaluations show very low correlations. In contrast, predrill predictions based on regressions between seismic structural properties and reservoir volumes show highly significant correlations. In addition, statistical analysis of seismic structure is much simpler than the Monte Carlo procedure, and can be applied rapidly and inexpensively. A statistical analysis of the "Pleistocene Trend" of offshore Louisiana and Texas, an area of about 3 million acres, yielded results that are sufficiently encouraging to suggest that similar evaluations be made of the remaining Gulf Coast OCS and of OCS regions off of other coasts of the United States. ?? 1983 Plenum Publishing Corporation.

Journal of the International Association for Mathe↗

USGS1985

Streamflow variability in the United States: 1931-1978.

Systematic modes of spatial and temporal variation in a 48-year record of streamflow are defined using principal components. The components were calculated from a matrix of annual streamflow departures for 106 grid cells covering the United States in the years 1931-78. Five statistically significant components are found to account for more than 56% of the total variance. A varimax orthogonal rotation of the original components describes regional anomaly cores located in the middle Mississippi Valley, Pacific Northwest, Far West, Northeast, and northern Great Plains. -from Author

Journal of Climate and Applied Meteorology↗

USGS2012

Estimation of rod scale errors in geodetic leveling

Comparisons among repeated geodetic levelings have often been used for detecting and estimating residual rod scale errors in leveled heights. Individual rod-pair scale errors are estimated by a two-step procedure using a model based on either differences in heights, differences in section height differences, or differences in section tilts. It is shown that the estimated rod-pair scale errors derived from each model are identical only when the data are correctly weighted, and the mathematical correlations are accounted for in the model based on heights. Analyses based on simple regressions of changes in height versus height can easily lead to incorrect conclusions. We also show that the statistically estimated scale errors are not a simple function of height, height difference, or tilt. The models are valid only when terrain slope is constant over adjacent pairs of setups (i.e., smoothly varying terrain). In order to discriminate between rod scale errors and vertical displacements due to crustal motion, the individual rod-pairs should be used in more than one leveling, preferably in areas of contrasting tectonic activity. From an analysis of 37 separately calibrated rod-pairs used in 55 levelings in southern California, we found eight statistically significant coefficients that could be reasonably attributed to rod scale errors, only one of which was larger than the expected random error in the applied calibration-based scale correction. However, significant differences with other independent checks indicate that caution should be exercised before accepting these results as evidence of scale error. Further refinements of the technique are clearly needed if the results are to be routinely applied in practice.

Journal of Geophysical Research B: Solid Earth↗

USGS2012

Evaluation of NDVI to assess avian abundance and richness along the upper San Pedro River

Remote-sensing models have become increasingly popular for identifying, characterizing, monitoring, and predicting avian habitat but have largely focused on single bird species. The Normalized Difference Vegetation Index (NDVI) has been shown to positively correlate with avian abundance and richness and has been successfully applied to southwestern riparian systems which are uniquely composed of narrow bands of vegetation in an otherwise dry landscape. Desert riparian ecosystems are important breeding and stopover sites for many bird species but have been degraded due to altered hydrology and land management practices. Here we investigated the use of NDVI, coupled with vegetation, to model the avian community structure along the San Pedro River, Arizona. We also investigated how vegetation and physical features measured locally compared to those data that can be gathered through remote-sensing. We found that NDVI has statistically significant relationships with both avian abundance and species richness, although is better applied at the individual species level. However, the amount of variation explained by even our best models was quite low, suggesting that NDVI habitat models may not presently be an accurate tool for extensive modeling of avian communities. We suggest additional studies in other watersheds to increase our understanding of these bird/NDVI relationships.

Arizona↗

USGS2023

Flow development and entrainment in turbulent particle-laden jets

Explosive eruptions expel volcanic gases and particles at high pressures and velocities. Within this multiphase fluid, small ash particles affect the flow dynamics, impacting mixing, entrainment, turbulence, and aggregation. To examine the role of turbulent particle behavior, we conducted an analogue experiment using a particle-laden jet. We used compressed air as the carrier fluid, considering turbulent conditions at Reynolds numbers from approximately 5,000 to 20,000. Two different particles were examined: 14-μm diameter solid nickel spheres and 13-μm diameter hollow glass spheres. These resulted in Stokes numbers between 1 and 35 based on the convective scale. The particle mass percentage in the mixture is varied from 0.3% to more than 20%. Based on a 1-D volcanic plume model, these Stokes numbers and mass loadings corresponded to millimeter-scale particle diameters at heights of 4–8 km above the vent during large, sustained eruptions. Through particle image velocimetry, we measured the mean flow behavior and the turbulence statistics in the near-exit region, primarily focusing on the dispersed phase. We show that the flow behavior is dominated by the particle inertia, with high Stokes numbers reducing the entrainment by more than 40%. When applied to volcanic plumes, these results suggest that high-density particles can greatly increase the probability of column collapse.

Journal of Geophysical Research: Atmospheres↗

USGS2002

Estimation of shoreline position and change using airborne topographic lidar data

A method has been developed for estimating shoreline position from airborne scanning laser data. This technique allows rapid estimation of objective, GPS-based shoreline positions over hundreds of kilometers of coast, essential for the assessment of large-scale coastal behavior. Shoreline position, defined as the cross-shore position of a vertical shoreline datum, is found by fitting a function to cross-shore profiles of laser altimetry data located in a vertical range around the datum and then evaluating the function at the specified datum. Error bars on horizontal position are directly calculated as the 95% confidence interval on the mean value based on the Student's t distribution of the errors of the regression. The technique was tested using lidar data collected with NASA's Airborne Topographic Mapper (ATM) in September 1997 on the Outer Banks of North Carolina. Estimated lidar-based shoreline position was compared to shoreline position as measured by a ground-based GPS vehicle survey system. The two methods agreed closely with a root mean square difference of 2.9 m. The mean 95% confidence interval for shoreline position was ?? 1.4 m. The technique has been applied to a study of shoreline change on Assateague Island, Maryland/Virginia, where three ATM data sets were used to assess the statistics of large-scale shoreline change caused by a major 'northeaster' winter storm. The accuracy of both the lidar system and the technique described provides measures of shoreline position and change that are ideal for studying storm-scale variability over large spatial scales.

Maryland, North Carolina, Virginia↗

USGS2022

Subsampling large-scale digital elevation models to expedite geospatial analyses in coastal regions

Large-area, high-resolution digital elevation models (DEMs) created from light detection and ranging (LIDAR) and/or multibeam echosounder data sets are commonly used in many scientific disciplines. These DEMs can span thousands of square kilometers, typically with a spatial resolution of 1 m or finer, and can be difficult to process and analyze without specialized computers and software. Such DEMs often can be subsampled to expedite analysis with negligible impact on results for large-scale geospatial analyses. Subsampling can be achieved by creating a grid of points that specify the locations from which to extract elevation values from the DEM. This paper presents a method that can be used to accurately perform subsampling of large-scale, high-resolution DEMs using GIS software. This subsampling method was applied to two LIDAR-derived DEMs encompassing 242 km 2 of the northern Florida Reef Tract as an example application and to test subsampling accuracy. Results indicate that subsampling 1-m-resolution DEMs using a 2-m-spaced grid results in no significant difference in mean elevation or other basic statistics for analyses performed over multiple spatial scales ranging from 1 km 2 to 242 km 2 .

Journal of Coastal Research↗

USGS2001

Statistical properties of relative weight distributions of four salmonid species and their sampling implications

We assessed relative weight (Wr) distributions among 291 samples of stock-to-quality-length brook trout Salvelinus fontinalis, brown trout Salmo trutta, rainbow trout Oncorhynchus mykiss, and cutthroat trout O. clarki from lentic and lotic habitats. Statistics describing Wr sample distributions varied slightly among species and habitat types. The average sample was leptokurtotic and slightly skewed to the right with a standard deviation of about 10, but the shapes of Wr distributions varied widely among samples. Twenty-two percent of the samples had nonnormal distributions, suggesting the need to evaluate sample distributions before applying statistical tests to determine whether assumptions are met. In general, our findings indicate that samples of about 100 stock-to-quality-length fish are needed to obtain confidence interval widths of four Wr units around the mean. Power analysis revealed that samples of about 50 stock-to-quality-length fish are needed to detect a 2% change in mean Wr at a relatively high level of power (beta = 0.01, alpha = 0.05).

North American Journal of Fisheries Management↗

USGS2023

Dynamic population models with temporal preferential sampling to infer phenology

To study population dynamics, ecologists and wildlife biologists typically use relative abundance data, which may be subject to temporal preferential sampling. Temporal preferential sampling occurs when the times at which observations are made and the latent process of interest are conditionally dependent. To account for preferential sampling, we specify a Bayesian hierarchical abundance model that considers the dependence between observation times and the ecological process of interest. The proposed model improves relative abundance estimates during periods of infrequent observation and accounts for temporal preferential sampling in discrete time. Additionally, our model facilitates posterior inference for population growth rates and mechanistic phenometrics. We apply our model to analyze both simulated data and mosquito count data collected by the National Ecological Observatory Network. In the second case study, we characterize the population growth rate and relative abundance of several mosquito species in the Aedes genus. Supplementary materials accompanying this paper appear on-line.

Journal of Agricultural, Biological, and Environme↗

USGS2012

Estimating discharge measurement uncertainty using the interpolated variance estimator

Methods for quantifying the uncertainty in discharge measurements typically identify various sources of uncertainty and then estimate the uncertainty from each of these sources by applying the results of empirical or laboratory studies. If actual measurement conditions are not consistent with those encountered in the empirical or laboratory studies, these methods may give poor estimates of discharge uncertainty. This paper presents an alternative method for estimating discharge measurement uncertainty that uses statistical techniques and at-site observations. This Interpolated Variance Estimator (IVE) estimates uncertainty based on the data collected during the streamflow measurement and therefore reflects the conditions encountered at the site. The IVE has the additional advantage of capturing all sources of random uncertainty in the velocity and depth measurements. It can be applied to velocity-area discharge measurements that use a velocity meter to measure point velocities at multiple vertical sections in a channel cross section.

Journal of Hydraulic Engineering↗

USGS2011

A case study of green tree frog population size estimation by repeated capture-mark-recapture method with individual tagging: A parametric bootstrap method vs. Jolly-Seber method

This paper deals with estimation of a green tree frog population in an urban setting using repeated capture–mark–recapture (CMR) method over several weeks with an individual tagging system which gives rise to a complicated generalization of the hypergeometric distribution. Based on the maximum likelihood estimation, a parametric bootstrap approach is adopted to obtain interval estimates of the weekly population size which is the main objective of our work. The method is computation-based; and programming intensive to implement the algorithm for re-sampling. This method can be applied to estimate the population size of any species based on repeated CMR method at multiple time points. Further, it has been pointed out that the well-known Jolly–Seber method, which is based on some strong assumptions, produces either unrealistic estimates, or may have situations where its assumptions are not valid for our observed data set.

Journal of Statistical Computation and Simulation↗