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USGS2018

Geomorphometry in landscape ecology: Issues of scale, physiography, and application

Topographic measures are frequently used in a variety of landscape ecology applications, in their simplest form as elevation, slope, and aspect, but increasingly more complex measures are being employed. We explore terrain metric similarity with changes in scale, both grain and extent, and examine how selecting the best measures is sensitive to changes in application. There are three types of topographic measures: 1) those that relate to orientation for approximating solar input, 2) those that capture variability in terrain configuration, and 3) those that provide metrics about landform features. Many biodiversity hotspots and predators have been found to coincide with areas of complexity, yet most complexity measures cannot differentiate between terrain steepness and uneven and broken terrain. Currently characterizing terrain in landscape-level analyses can be challenging, especially at coarser spatial resolutions but developing methods that improve landscape-level assessments include multivariate approaches and the use of neighborhood statistics. Some measures are sensitive to the spatial grain of calculation, the physiography of the landscape, and the scale of application. We show which measures have the potential to be multi-collinear, and illustrate with a case study how the selection of the best measures can change depending on the question at hand using mountain lion (Puma concolor) occurrence data. The case study showed a combination of infrequently employed metrics, such as view-shed analysis and focal statistics, outperform more commonly employed singular metrics. The use of focal statistics as a measure of topographic complexity shows promise for improving how mountain lions use terrain features.

Environment and Ecology Research↗
USGS2018

A causal partition of trait correlations: using graphical models to derive statistical models from theoretical language

Recent studies hypothesize various causes of species‐level trait covariation, namely size (e.g., metabolic theory of ecology and leaf economics spectrum), pace‐of‐life (e.g., slow‐to‐fast continuum; lifestyle continuum), evolutionary history (e.g., phylogenetic conservatism), and ecological conditions (e.g., stabilizing selection). Various methods have been used in attempts to partition trait correlation among these influences (e.g., univariate analysis, principal components analysis, and factor analysis). However, it is not clear that the implied causal structure assumed by these methods matches the hypothesized causal structure driving trait correlations, a situation that can potentially lead to biased estimates and incorrect partitioning among mechanisms. Here, we propose the application of graphical causal models (GCM) for across‐kingdom synthesis and to aid researchers in their selection of correct analytical strategies. Graphical causal models use causal diagrams (i.e., box‐and‐arrow graphs) to represent expert knowledge of the data‐generating processes to analytically investigate the possibility of identifying hypothesized causal associations. We developed a causal diagram that synthesizes prominent hypotheses of trait covariation. Using the causal diagram, we (1) derived a quantitative expression to partition trait covariance among its hypothesized causal elements (i.e., size, pace‐of‐life, evolutionary history, and ecological conditions) and (2) developed analytic strategies to attribute trait covariance among the hypothesized causal elements under real‐world data availability, namely unobserved variables (i.e., pace‐of‐life) and confounding variables (i.e., evolutionary history and ecological conditions). Finally, we tested each analytic strategy by simulating trait datasets and, after incorporating the data limitations, tested their ability to correctly partition trait covariance. The analytical strategies were able to correctly partition trait covariance into the hypothesized causal elements of size, pace‐of‐life, and the historical effects of evolutionary history and ecological conditions. We demonstrate the efficacy of these strategies by applying them to a widely used trait dataset. Overall, the application of GCM revealed that researchers have used inappropriate measures to represent their theoretical constructs and have relied on analytical strategies that violated their causal assumptions, likely resulting in biased estimates. We discuss how this mismatch between theoretical language and statistical methods is prevalent in species‐level, trait‐based research and call for future studies to address these limitations.

Ecosphere↗
USGS2017

Methods for estimating selected low-flow frequency statistics and harmonic mean flows for streams in Iowa

A statewide study was conducted to develop regression equations for estimating six selected low-flow frequency statistics and harmonic mean flows for ungaged stream sites in Iowa. The estimation equations developed for the six low-flow frequency statistics include: the annual 1-, 7-, and 30-day mean low flows for a recurrence interval of 10 years, the annual 30-day mean low flow for a recurrence interval of 5 years, and the seasonal (October 1 through December 31) 1- and 7-day mean low flows for a recurrence interval of 10 years. Estimation equations also were developed for the harmonic-mean-flow statistic. Estimates of these seven selected statistics are provided for 208 U.S. Geological Survey continuous-record streamgages using data through September 30, 2006. The study area comprises streamgages located within Iowa and 50 miles beyond the State's borders. Because trend analyses indicated statistically significant positive trends when considering the entire period of record for the majority of the streamgages, the longest, most recent period of record without a significant trend was determined for each streamgage for use in the study. The median number of years of record used to compute each of these seven selected statistics was 35. Geographic information system software was used to measure 54 selected basin characteristics for each streamgage. Following the removal of two streamgages from the initial data set, data collected for 206 streamgages were compiled to investigate three approaches for regionalization of the seven selected statistics. Regionalization, a process using statistical regression analysis, provides a relation for efficiently transferring information from a group of streamgages in a region to ungaged sites in the region. The three regionalization approaches tested included statewide, regional, and region-of-influence regressions. For the regional regression, the study area was divided into three low-flow regions on the basis of hydrologic characteristics, landform regions, and soil regions. A comparison of root mean square errors and average standard errors of prediction for the statewide, regional, and region-of-influence regressions determined that the regional regression provided the best estimates of the seven selected statistics at ungaged sites in Iowa. Because a significant number of streams in Iowa reach zero flow as their minimum flow during low-flow years, four different types of regression analyses were used: left-censored, logistic, generalized-least-squares, and weighted-least-squares regression. A total of 192 streamgages were included in the development of 27 regression equations for the three low-flow regions. For the northeast and northwest regions, a censoring threshold was used to develop 12 left-censored regression equations to estimate the 6 low-flow frequency statistics for each region. For the southern region a total of 12 regression equations were developed; 6 logistic regression equations were developed to estimate the probability of zero flow for the 6 low-flow frequency statistics and 6 generalized least-squares regression equations were developed to estimate the 6 low-flow frequency statistics, if nonzero flow is estimated first by use of the logistic equations. A weighted-least-squares regression equation was developed for each region to estimate the harmonic-mean-flow statistic. Average standard errors of estimate for the left-censored equations for the northeast region range from 64.7 to 88.1 percent and for the northwest region range from 85.8 to 111.8 percent. Misclassification percentages for the logistic equations for the southern region range from 5.6 to 14.0 percent. Average standard errors of prediction for generalized least-squares equations for the southern region range from 71.7 to 98.9 percent and pseudo coefficients of determination for the generalized-least-squares equations range from 87.7 to 91.8 percent. Average standard errors of prediction for weighted-least-squares equations developed for estimating the harmonic-mean-flow statistic for each of the three regions range from 66.4 to 80.4 percent. The regression equations are applicable only to stream sites in Iowa with low flows not significantly affected by regulation, diversion, or urbanization and with basin characteristics within the range of those used to develop the equations. If the equations are used at ungaged sites on regulated streams, or on streams affected by water-supply and agricultural withdrawals, then the estimates will need to be adjusted by the amount of regulation or withdrawal to estimate the actual flow conditions if that is of interest. Caution is advised when applying the equations for basins with characteristics near the applicable limits of the equations and for basins located in karst topography. A test of two drainage-area ratio methods using 31 pairs of streamgages, for the annual 7-day mean low-flow statistic for a recurrence interval of 10 years, indicates a weighted drainage-area ratio method provides better estimates than regional regression equations for an ungaged site on a gaged stream in Iowa when the drainage-area ratio is between 0.5 and 1.4. These regression equations will be implemented within the U.S. Geological Survey StreamStats web-based geographic-information-system tool. StreamStats allows users to click on any ungaged site on a river and compute estimates of the seven selected statistics; in addition, 90-percent prediction intervals and the measured basin characteristics for the ungaged sites also are provided. StreamStats also allows users to click on any streamgage in Iowa and estimates computed for these seven selected statistics are provided for the streamgage.

Iowa↗
USGS2017

Computing spatial correlation of ground motion intensities for ShakeMap

Modeling the spatial correlation of ground motion residuals, caused by coherent contributions from source, path, and site, can provide valuable loss and hazard information, as well as a more realistic depiction of ground motion intensities. The U.S. Geological Survey (USGS) software package, ShakeMap, utilizes a deterministic empirical approach to estimate median ground shaking in conjunction with observed seismic data. ShakeMap-based shaking estimates are used in concert with loss estimation algorithms to estimate fatalities and economic losses after significant seismic events around the globe. Incorporating the spatial correlation of ground motion residuals has been shown to improve seismic loss estimates. In particular, Park, Bazzuro, and Baker (Applications of Statistics and Probability in Civil Engineering, 2007) investigated computing spatially correlated random fields of residuals. However, for large scale ShakeMap grids, computational requirements of the method are prohibitive. In this work, a memory efficient algorithm is developed to compute the random fields and implemented using the ShakeMap framework. This new, iterative parallel algorithm is based on decay properties of an associated ground motion correlation function and is shown to significantly reduce computational requirements associated with adding spatial variability to the ShakeMap g round motion estimates. Further, we demonstrate and quantify the impact of adding peak ground motion spatial variability on resulting earthquake loss estimates.

Computers & Geosciences↗
USGS2004

Comparison of fluorescence microscopy and solid-phase cytometry methods for counting bacteria in water

Total direct counts of bacterial abundance are central in assessing the biomass and bacteriological quality of water in ecological and industrial applications. Several factors have been identified that contribute to the variability in bacterial abundance counts when using fluorescent microscopy, the most significant of which is retaining an adequate number of cells per filter to ensure an acceptable level of statistical confidence in the resulting data. Previous studies that have assessed the components of total-direct-count methods that contribute to this variance have attempted to maintain a bacterial cell abundance value per filter of approximately 10 6 cells filter -1 . In this study we have established the lower limit for the number of bacterial cells per filter at which the statistical reliability of the abundance estimate is no longer acceptable. Our results indicate that when the numbers of bacterial cells per filter were progressively reduced below 10 5 , the microscopic methods increasingly overestimated the true bacterial abundance (range, 15.0 to 99.3%). The solid-phase cytometer only slightly overestimated the true bacterial abundances and was more consistent over the same range of bacterial abundances per filter (range, 8.9 to 12.5%). The solid-phase cytometer method for conducting total direct counts of bacteria was less biased and performed significantly better than any of the microscope methods. It was also found that microscopic count data from counting 5 fields on three separate filters were statistically equivalent to data from counting 20 fields on a single filter.

Applied and Environmental Microbiology↗
USGS2013

Crater-based dating of geological units on Mars: methods and application for the new global geological map

The new, post-Viking generation of Mars orbital imaging and topographical data provide significant higher-resolution details of surface morphologies, which induced a new effort to photo-geologically map the surface of Mars at 1:20,000,000 scale. Although from unit superposition relations a relative stratigraphical framework can be compiled, it was the ambition of this mapping project to provide absolute unit age constraints through crater statistics. In this study, the crater counting method is described in detail, starting with the selection of image data, type locations (both from the mapper’s and crater counter’s perspectives) and the identification of impact craters. We describe the criteria used to validate and analyse measured crater populations, and to derive and interpret crater model ages. We provide examples of how geological information about the unit’s resurfacing history can be retrieved from crater size–frequency distributions. Three cases illustrate short-, intermediate, and long-term resurfacing histories. In addition, we introduce an interpretation-independent visualisation of the crater resurfacing history that uses the reduction of the crater population in a given size range relative to the expected population given the observed crater density at larger sizes. From a set of potential type locations, 48 areas from 22 globally mapped units were deemed suitable for crater counting. Because resurfacing ages were derived from crater statistics, these secondary ages were used to define the unit age rather than the base age. Using the methods described herein, we modelled ages that are consistent with the interpreted stratigraphy. Our derived model ages allow age assignments to be included in unit names. We discuss the limitations of using the crater dating technique for global-scale geological mapping. Finally, we present recommendations for the documentation and presentation of crater statistics in publications.

Icarus↗
USGS1980

Evaluating methods for determining water use in the High Plains in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming, 1979

The volume and areal distribution of ground-water withdrawals (pumpage) for irrigation during 1980 are required for the High Plains Regional Aquifer-System Analysis. In 1979, approaches and instrumentation that might be suitable for application to 1980 water-use determinations were tested. Pumpage was sampled by monitoring time of operation and discharge of irrigation wells during the growing season. The total volume pumped during the irrigation season was compared to the crop type and acreage irrigated. This comparison provided a means of extending sampled pumpage information to unmonitored areas using irrigated cropland maps. A transient-time flowmeter and a vibration-sensitive timing device proved to be reliable in providing discharge and time of operation information, respectively. Statistical analysis of comparisons between pumpage and irrigated cropland indicated that significant differences existed in the amounts of water applied between flood and sprinkler irrigation systems. However, statistical analyses of differences in amounts of water applied for different crop types and for selected climatic factors were inconclusive. A variety of approaches were tested to develop the irrigated cropland maps needed to extend sampled pumpage data. Of the methods tested, only Landsat data proved to be effective for application to an area as large as the High Plains. The results obtained in the 1979 evaluation of instrumentation and pumpage sampling approaches have been used to formulate a strategy for monitoring irrigation water use in the High Plains in 1980. (USGS)

Water-Resources Investigations Report↗
USGS2026

The use of fluorite geochemistry and machine learning to identify critical mineral systems

Fluorite (CaF 2 ) is a potential pathfinder to critical mineral and rare earth element (REE) deposits but its application has been limited to a narrow range of mineralization types. I show that fluorite is a robust recorder of mineralization fertility by applying statistical and machine-learning methods to a new global fluorite geochemical database. Distinct median rare earth and trace element patterns are observed among deposit types and genetic environments. Fluorite associated with carbonatites and REE deposits are relatively enriched in Sr and have minimal Eu anomalies. These characteristics define new bivariate discrimination diagrams that correctly identify 78% of carbonatite-related fluorite and 88% of fluorite from REE deposits. Random forest classifiers were developed for a wide range of mineralization types and genetic settings. Trained solely on rare earth element patterns, these models achieve accuracies of 77–79%. Higher classification accuracies (up to 88–96%) are obtained when including elements such as Sr, highlighting the significance of trace elements for optimal fluorite classification. The recognition of diagnostic fluorite compositional fingerprints, particularly in REE-fertile systems, underscores its potential as a pathfinder and indicator for critical mineral exploration in F-bearing environments.

Mineralium Deposita↗
USGS2022

Velocity modeling of supercritical pore fluids through porous media under reservoir conditions with applications for petroleum secondary migration and carbon sequestration plumes

Computational methods to characterize secondary migration in porous media traditionally rely on fluid transport equations with assumptions of time invariance, such as flowpath modeling of buoyancy vectors, statistical percolation algorithms, capillary pressure curves, or a form of Darcy’s Law which presumes instantaneous fluid transport. However, in petroleum systems modeling, the timeframe of secondary migration from source to reservoir is important to quantify in relation to other geologic factors such as timing of petroleum generation, fault movement, and seal formation. Additionally, quantifying migration velocities enables an estimation of the distance a plume of geologically sequestered carbon dioxide travels over time, as well as the identification of low-permeability strata appropriate for long-term containment. This study introduces a method to quantify transport velocities of supercritical fluids in low-permeability lithologies for a broad range of rock and fluid properties likely encountered in the sedimentary sequence. A time-dependent form of Darcy’s Law for pressure-driven viscous flow through homogeneous isotropic porous media was used to model flow velocities within a carrier bed. Thermodynamic equations of state were used to determine thermophysical properties of supercritical pore fluids under reservoir pressures ranging from 0–200 MPa (0–29,000 psi) to constrain the momentum equations. Three case studies were examined that (1) estimated fluid flow velocities of methane within the low-permeability Upper Jurassic Haynesville Formation, (2) defined permeability-based flow units to evaluate saline formations for long-term geologic carbon sequestration, and (3) calculated the migration distance of carbon dioxide plumes at the Decatur, Illinois injection and sequestration project.

SEG-AAPG Interpretation↗
USGS2023

Advancing best practices for the analysis of the vulnerability of military installations in the Pacific Basin to coastal flooding under a changing climate – RC-2644

Coastal flooding takes many forms, ranging from major flooding associated with storms to minor flooding associated with exceptionally high tides and other oceanic and atmospheric phenomena on storm-free days. A major societal challenge is to understand and predict how flood magnitude and frequency will manifest at particular places and times, now and in the future. Of particular interest here is how coastal flooding will impact Department of Defense (DoD) installations. In response to this need, this work aims to advance the practical application of statistical and other analytical techniques that can be used to assess the exposure, and ultimately the vulnerability, of built and natural environments to the impacts of coastal flooding. A variety of methods are described and applied to assess exposure. This includes tide gauge station-based diagnosis and prognosis of patterns and trends of Still Water Level, techniques to characterize the expression of ‘lesser extremes’ (e.g., sub-annual to subdecadal event probabilities), and region-wide analysis that improves upon results obtained from conventional single-tide gauge analyses. A novel hybrid statistical and dynamical modeling approach is applied to the analysis of Total Water Levels, necessary for exposure assessment along shorelines exposed to wave action. The hybrid exposure assessment modeling approach is incorporated into a broader mission-based protocol for the assessment of resilience to coastal flooding at the installation level. Demonstrated via an exemplar assessment, which takes into account functional (lost day) as well as financial impacts (lost dollars), the protocol meets the demand for an actionable characterization of how DoD installations will be affected by coastal flooding and improves DoD’s ability to make informed decisions about how to adapt to its effects. The methods described, evaluated, and applied here, including innovative approaches and proof-of-concept products developed through this work, are incorporated into and considered within an analytical framework that serves as guidance as to their relative merits with respect to coastal flood exposure assessment in various circumstances and settings, and illustrates best practices. This will provide engineers, scientists and other practitioners with an enhanced capability to generate information that can be used to support area-wide assessment related to climate adaptation planning and disaster risk reduction as well as site-specific analysis related to design and maintenance of facilities and infrastructure. While the focus is on a select set of DoD sites in the Pacific Basin, the results have broad applicability nationally as well as globally.

Final Report↗
USGS2019

A Generalized Additive Model approach to evaluating water quality: Chesapeake Bay Case Study

Nutrient-reduction efforts have been undertaken in recent decades to mitigate the impacts of eutrophication in coastal and estuarine systems worldwide. To track progress in response to one of these efforts we use Generalized Additive Models (GAMs) to evaluate a diverse suite of water quality constituents over a 32-year period in the Chesapeake Bay, an estuary on the east coast of the United States. Model development included selecting a GAM structure to describe nonlinear seasonally-varying changes over time, incorporating hydrologic variability via either river flow or salinity, and using interventions to model method or laboratory changes suspected to impact data. This approach, transferable to other systems, allows for evaluation of water quality data in a statistically rigorous way, while being suitable for application to many sites and variables. This enables consistent generation of annual updates, while providing a tool for developing insights to a range of management- and research-focused questions.

Chesapeake Bay↗
USGS2014

Using mark-recapture distance sampling methods on line transect surveys

Mark–recapture distance sampling (MRDS) methods are widely used for density and abundance estimation when the conventional DS assumption of certain detection at distance zero fails, as they allow detection at distance zero to be estimated and incorporated into the overall probability of detection to better estimate density and abundance. However, incorporating MR data in DS models raises survey and analysis issues not present in conventional DS. Conversely, incorporating DS assumptions in MR models raises issues not present in conventional MR. As a result, being familiar with either conventional DS methods or conventional MR methods does not on its own put practitioners in good a position to apply MRDS methods appropriately. This study explains the sometimes subtly different varieties of MRDS survey methods and the associated concepts underlying MRDS models. This is done as far as possible without giving mathematical details – in the hope that this will make the key concepts underlying the methods accessible to a wider audience than if we were to present the concepts via equations. We illustrate use of the two main types of MRDS model by using data collected on two different types of survey: a survey of ungulate faecal pellets where two observers searched independently of each other; and a cetacean survey that used a search protocol that could accommodate responsive movement, with only one observer searching independently and the other being aware of all detections. Synthesis and applications . Mark–recapture DS is a widely used method for estimating animal density and abundance when detection of animals at distance zero is not certain. Two observer configurations and three statistical models are described, and it is important to choose the most appropriate model for the observer configuration and target species in question. By way of making the methods more accessible to practicing ecologists, we describe the key ideas underlying MRDS methods, the sometimes subtle differences between them, and we illustrate these by applying different kinds of MRDS method to surveys of two different target species using different survey configurations.

Methods in Ecology and Evolution↗
USGS2020

Estimating flood magnitude and frequency on streams and rivers in Connecticut, based on data through water year 2015

The U.S. Geological Survey, in cooperation with the Connecticut Department of Transportation, updated flood-frequency estimates with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities (2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals, respectively) for 141 streamgages in Connecticut and 11 streamgages in adjacent States using annual peak-flow data through water year 2015. Peak-flow regression equations were derived for estimating flows at ungaged stream sites with annual exceedance probabilities from 50 to 0.2 percent. Methods for estimating prediction intervals for the peak-flow regression equations are presented. The regression equations are applicable for basins in Connecticut with drainage areas ranging from 0.69 to 325 square miles that are not affected by flood-control regulation or flow diversions. The flood discharges for select annual exceedance probabilities were estimated following new (2018) national guidelines for flood-frequency analyses. New guidelines have improved statistical methods for flood-frequency analysis including (1) the expected moments algorithm to help describe uncertainty in annual peak flows and to better represent missing and historical record and (2) the generalized multiple Grubbs-Beck test to screen out potentially influential low outliers and to better fit the upper end of the peak-flow distribution. Additionally, a new regional skew (0.37) derived for New England was used in the flood-frequency analysis for the streamgages. Annual peak flows were analyzed for trends for four time periods (30, 50, 70, and 90 years) through 2015. Trend results show some statistical evidence of increasing peak flows in each of the time periods analyzed; however, multidecadal climate cycles may be influencing the number and magnitude of the trends. Historical peak-flow trends in and near Connecticut do not offer clear and convincing evidence for incorporating trends into flood-frequency analyses. For this study, the traditional assumption of stationarity is used with no adjustment for trends. Generalized least squares regression techniques were used to develop the final set of multivariable regression equations for estimating flood discharges with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities. The standard error of prediction for the regional regression equations ranged from 26.3 to 45.0 percent. The standard error of prediction was slightly smaller in the current study compared to the 2004 study, indicating an improvement in the predictive ability of the equations (6 percent smaller at the 50-percent annual exceedance probability to about 1 percent smaller at the 1-percent annual exceedance probability). Generalized least squares regression techniques also were used to develop a one-variable (drainage-area-only) equation. Drainage-area-only equations can be used as an alternative to the multiexplanatory variable statewide regression equations if decreased accuracy is acceptable. The revised statistical procedures and additional streamgage data applied in the current study result in a more accurate representation of peak-flow conditions in Connecticut than was previously available. The regional regression equations will be integrated in the U.S. Geological Survey StreamStats program, which estimates basin and climatic characteristics and streamflow statistics at user-selected ungaged stream sites.

Connecticut↗
USGS2022

Understanding the Avian-Impact Offset Method—A tutorial

Biodiversity offsetting, or compensatory mitigation, is increasingly being used in temperate grassland and wetland ecosystems to compensate for unavoidable environmental damage from anthropogenic disturbances such as energy development and road construction. Energy-extraction and -generation facilities continue to proliferate across the natural landscapes of the United States, yet mitigation tools to ameliorate the negative behavioral effects on wildlife from these types of facilities are rarely implemented. Scientists from the U.S. Geological Survey conducted a 10-year before-after-control-impact (commonly referred to as BACI) study that evaluated the displacement effects of wind facilities on breeding grassland birds. The study determined behavioral avoidance for 7 of 9 species. This research is notable because of its design, geographical scope, and duration, which allowed for the determination of immediate, short-term effects; delayed or sustained effects; and discrete distances at which effects occurred. In addition, the U.S. Fish and Wildlife Service and Ducks Unlimited conducted a 3-year concurrent-year paired-reference study to determine behavioral avoidance for five species of dabbling ducks. By quantifying displacement rate from these two studies, U.S. Geological Survey and U.S. Fish and Wildlife Service scientists developed the Avian-Impact Offset Method (AIOM) to quantify and compensate for loss in value of breeding habitat. The AIOM converts the biological value (that is, number of bird pairs) lost by way of avoidance and estimates the site-specific number of hectares of grasslands and number of wetlands needed to compensate for displaced pairs of grassland birds and waterfowl. By converting biological value to traditional units of measure in which land is described and purchased or sold, the AIOM lends itself readily to the delivery of offsetting measures such as easement protections and restoration projects. The AIOM tool is applicable to wind, solar, oil, gas, and transportation infrastructure. This tutorial was designed to increase awareness of the AIOM and to promote its proper application. The tutorial is divided into four sections, each of which explains a discrete topic concerning aspects of behavioral displacement. The first section provides geographical and biological context, and the second section describes the field and statistical methods and results. The third section provides step-by-step instructions for applying the AIOM to several scenarios involving grassland birds or waterfowl at wind or oil facilities. The fourth section describes decision-support tools created to implement the AIOM. The appendices provide the actual field protocols constituting the methods for the research, provide detailed results by species and wind facility for that research, and provide detailed instructions for downloading and applying the decision-support tools.

Open-File Report↗
USGS2018

A practical primer on geostatistics

Introduction The Challenge —Most geological phenomena are extraordinarily complex in their interrelationships and vast in their geographical extension. Ordinarily, engineers and geoscientists are faced with corporate or scientific requirements to properly prepare geological models with measurements involving a small fraction of the entire area or volume of interest. Exact description of a system such as an oil reservoir is neither feasible nor economically possible. The results are necessarily uncertain. Note that the uncertainty is not an intrinsic property of the systems; it is the result of incomplete knowledge by the observer. The Aim of Geostatistics —The main objective of geostatistics is the characterization of spatial systems that are incompletely known, systems that are common in geology. A key difference from classical statistics is that geostatistics uses the sampling location of every measurement. Unless the measurements show spatial correlation, the application of geostatistics is pointless. Ordinarily the need for additional knowledge goes beyond a few points, which explains the display of results graphically as fishnet plots, block diagrams, and maps. Geostatistical Methods —Geostatistics is a collection of numerical techniques for the characterization of spatial attributes using primarily two tools: probabilistic models, which are used for spatial data in a manner similar to the way in which time-series analysis characterizes temporal data, or pattern recognition techniques. The probabilistic models are used as a way to handle uncertainty in results away from sampling locations, making a radical departure from alternative approaches like inverse distance estimation methods. Differences with Time Series —On dealing with time-series analysis, users frequently concentrate their attention on extrapolations for making forecasts. Although users of geostatistics may be interested in extrapolation, the methods work at their best interpolating. This simple difference has significant methodological implications. Historical Remarks —As a discipline, geostatistics was firmly established in the 1960s by the French engineer Georges Matheron, who was interested in the appraisal of ore reserves in mining. Geostatistics did not develop overnight. Like other disciplines, it has built on previous results, many of which were formulated with different objectives in various fields. Pioneers —Seminal ideas conceptually related to what today we call geostatistics or spatial statistics are found in the work of several pioneers, including: 1940s: A.N. Kolmogorov in turbulent flow and N. Wiener in stochastic processing; 1950s: D. Krige in mining; 1960s: B. Mathern in forestry and L.S. Gandin in meteorology Calculations —Serious applications of geostatistics require the use of digital computers. Although for most geostatistical techniques rudimentary implementation from scratch is fairly straightforward, coding programs from scratch is recommended only as part of a practice that may help users to gain a better grasp of the formulations. Software —For professional work, the reader should employ software packages that have been thoroughly tested to handle any sampling scheme, that run as efficiently as possible, and that offer graphic capabilities for the analysis and display of results. This primer employs primarily the package Stanford Geomodeling Software (SGeMS) - recently developed at the Energy Resources Engineering Department at Stanford University - as a way to show how to obtain results practically. This applied side of the primer should not be interpreted as the notes being a manual for the use of SGeMS. The main objective of the primer is to help the reader gain an understanding of the fundamental concepts and tools in geostatistics. Organization of the Primer —The chapters of greatest importance are those covering kriging and simulation. All other materials are peripheral and are included for better comprehension of these main geostatistical modeling tools. The choice of kriging versus simulation is often a big puzzle to the uninitiated, let alone the different variants of both of them. Chapters 14, 18, and 19 are intended to shed light on those subjects. The critical aspect of assessing and modeling spatial correlation is covered in chapter 7. Chapters 2 and 3 review relevant concepts in classical statistics. Course Objectives —This course offers stochastic solutions to common problems in the characterization of complex geological systems. At the end of the course, participants should have: an understanding of the theoretical foundations of geostatistics; a good grasp of its possibilities and limitations; and reasonable familiarity with the SGeMS software, thus opening the possibility of practically applying geostatistics.

Open-File Report↗
USGS2024

Novel approach for ranking DEMs: Copernicus DEM improves one arc second open global topography

We present a practical approach to intercompare a range of candidate digital elevation models (DEMs) based on predefined criteria and a statistically sound ranking approach. The presented approach integrates the randomized complete block design (RCBD) into a novel framework for DEM comparison. The method presented provides a flexible, statistically sound, and customizable tool for evaluating the quality of any raster—in this case, a DEM—by means of a ranking approach, which takes into account a confidence level and can use both quantitative and qualitative criteria. The users can design their own criteria for the quality evaluation in relation to their specific needs. The application of the RCBD method to rank six 1′′ global DEMs, considering a wide set of study sites, covering different morphological and land cover settings, highlights the potentialities of the approach. We used a suite of criteria relating to the differences in the elevation, slope, and roughness distributions compared to reference DEMs aggregated from 1- to 5-m light detection and ranging (LiDAR)-derived DEMs. Results confirmed the significant superiority of Copernicus DEM (CopDEM) 1′′ and its derivative forests and buildings removed DEM (FABDEM) as the overall best 1′′ global DEMs. They are slightly better than Advanced Land Observing Satellite (ALOS) and clearly outperform NASADEM and SRTM, which are, in turn, much better than advanced spaceborne thermal emission and reflection radiometer (ASTER).

IEEE Transactions on Geoscience and Remote Sensing↗
USGS2009

A cautionary note on substituting spatial subunits for repeated temporal sampling in studies of site occupancy

1. Assessing the probability that a given site is occupied by a species of interest is important to resource managers, as well as metapopulation or landscape ecologists. Managers require accurate estimates of the state of the system, in order to make informed decisions. Models that yield estimates of occupancy, while accounting for imperfect detection, have proven useful by removing a potentially important source of bias. To account for detection probability, multiple independent searches per site for the species are required, under the assumption that the species is available for detection during each search of an occupied site. 2. We demonstrate that when multiple samples per site are defined by searching different locations within a site, absence of the species from a subset of these spatial subunits induces estimation bias when locations are exhaustively assessed or sampled without replacement. 3. We further demonstrate that this bias can be removed by choosing sampling locations with replacement, or if the species is highly mobile over a short period of time. 4. Resampling an existing data set does not mitigate bias due to exhaustive assessment of locations or sampling without replacement. 5. Synthesis and applications. Selecting sampling locations for presence/absence surveys with replacement is practical in most cases. Such an adjustment to field methods will prevent one source of bias, and therefore produce more robust statistical inferences about species occupancy. This will in turn permit managers to make resource decisions based on better knowledge of the state of the system.

Journal of Applied Ecology↗
USGS2025

Statistical approaches for modeling correlated grade and tonnage distributions and applications for mineral resource assessments

Correlations between grade and tonnage exist in mineral resource data compiled from published reports, but they are not always addressed during quantitative assessment of undiscovered mineral resources. Failure to account for correlated grade and tonnage distributions can result in geologically unrealistic assessment results. Current software tools simulate univariate ore tonnage and multivariate resource grades of undiscovered deposits independently. As a result, analysts are forced to rely on ad-hoc solutions to minimize the correlation issues by: 1) creating subsets of data with restricted criteria; 2) truncating grade and tonnage distributions; and 3) testing model robustness using exploratory data analysis. While these methods represent pragmatic solutions, the statistical solutions presented here provide additional options to address real correlations in grade and tonnage data used for mineral resource assessments. We present a modified version of the MapMark4 package in R that introduces two alternatives for modeling grade and tonnage distributions, consisting of a multivariate solution that accounts for correlations between ore tonnage and metal grades and an empirical solution that utilizes simple random sampling with replacement to reproduce coupled grades and tonnages from the input data. We present simulations for contained ore and metal for three case studies representing tungsten skarn, komatiite-hosted nickel, and sediment-hosted carbonate amagmatic zinc-lead (Mississippi Valley-type) deposits. Employing the methods presented here yields quantitative mineral resource assessment results that more closely reflect the empirical distributions of grades and tonnages observed in nature and expands the applicability of these tools for ongoing critical mineral resource assessments.

Applied Computing and Geosciences↗