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USGS2016

Daniel Goodman’s empirical approach to Bayesian statistics

Bayesian statistics, in contrast to classical statistics, uses probability to represent uncertainty about the state of knowledge. Bayesian statistics has often been associated with the idea that knowledge is subjective and that a probability distribution represents a personal degree of belief. Dr. Daniel Goodman considered this viewpoint problematic for issues of public policy. He sought to ground his Bayesian approach in data, and advocated the construction of a prior as an empirical histogram of “similar” cases. In this way, the posterior distribution that results from a Bayesian analysis combined comparable previous data with case-specific current data, using Bayes’ formula. Goodman championed such a data-based approach, but he acknowledged that it was difficult in practice. If based on a true representation of our knowledge and uncertainty, Goodman argued that risk assessment and decision-making could be an exact science, despite the uncertainties. In his view, Bayesian statistics is a critical component of this science because a Bayesian analysis produces the probabilities of future outcomes. Indeed, Goodman maintained that the Bayesian machinery, following the rules of conditional probability, offered the best legitimate inference from available data. We give an example of an informative prior in a recent study of Steller sea lion spatial use patterns in Alaska.

PeerJ↗

USGS2023

Mapping landslide susceptibility over large regions with limited data

Landslide susceptibility maps indicate the spatial distribution of landslide likelihood. Modeling susceptibility over large or diverse terrains remains a challenge due to the sparsity of landslide data (mapped extent of known landslides) and the variability in triggering conditions. Several different data sampling strategies of landslide locations used to train a susceptibility model are used to mitigate this challenge. However, to our knowledge, no study has systematically evaluated how different sampling strategies alter a model's predictor effects (i.e., how a predictor value influences the susceptibility output) critical to explaining differences in model outputs. Here, we introduce a statistical framework that examines the variation in predictor effects and the model accuracy (measured using receiver operator characteristics) to highlight why certain sampling strategies are more effective than others. Specifically, we apply our framework to an array of logistic regression models trained on landslide inventories collected at sub-regional scales over four terrains across the United States. Results show significant variations in predictor effects depending on the inventory used to train the models. The inconsistent predictor effects cause low accuracies when testing models on inventories outside the domain of the training data. Grouping test and training sets according to physiographic and ecological characteristics, which are thought to share similar triggering mechanisms, does not improve model accuracy. We also show that using limited landslide data distributed uniformly over the entire modeling domain is better than using dense but spatially isolated data to train a model for applications over large regions.

California, Kentucky, North Carolina, West Virgini↗

USGS2017

Using variance structure to quantify responses to perturbation in fish catches

We present a case study evaluation of gill-net catches of Walleye Sander vitreus to assess potential effects of large-scale changes in Oneida Lake, New York, including the disruption of trophic interactions by double-crested cormorants Phalacrocorax auritus and invasive dreissenid mussels. We used the empirical long-term gill-net time series and a negative binomial linear mixed model to partition the variability in catches into spatial and coherent temporal variance components, hypothesizing that variance partitioning can help quantify spatiotemporal variability and determine whether variance structure differs before and after large-scale perturbations. We found that the mean catch and the total variability of catches decreased following perturbation but that not all sampling locations responded in a consistent manner. There was also evidence of some spatial homogenization concurrent with a restructuring of the relative productivity of individual sites. Specifically, offshore sites generally became more productive following the estimated break point in the gill-net time series. These results provide support for the idea that variance structure is responsive to large-scale perturbations; therefore, variance components have potential utility as statistical indicators of response to a changing environment more broadly. The modeling approach described herein is flexible and would be transferable to other systems and metrics. For example, variance partitioning could be used to examine responses to alternative management regimes, to compare variability across physiographic regions, and to describe differences among climate zones. Understanding how individual variance components respond to perturbation may yield finer-scale insights into ecological shifts than focusing on patterns in the mean responses or total variability alone.

Transactions of the American Fisheries Society↗

USGS2018

Analysis of vegetation recovery surrounding a restored wetland using the normalized difference infrared index (NDII) and normalized difference vegetation index (NDVI)

Watershed restoration efforts seek to rejuvenate vegetation, biological diversity, and land productivity at Cienega San Bernardino, an important wetland in southeastern Arizona and northern Sonora, Mexico. Rock detention and earthen berm structures were built on the Cienega San Bernardino over the course of four decades, beginning in 1984 and continuing to the present. Previous research findings show that restoration supports and even increases vegetation health despite ongoing drought conditions in this arid watershed. However, the extent of restoration impacts is still unknown despite qualitative observations of improvement in surrounding vegetation amount and vigor. We analyzed spatial and temporal trends in vegetation greenness and soil moisture by applying the normalized difference vegetation index (NDVI) and normalized difference infrared index (NDII) to one dry summer season Landsat path/row from 1984 to 2016. The study area was divided into zones and spectral data for each zone was analyzed and compared with precipitation record using statistical measures including linear regression, Mann– Kendall test, and linear correlation. NDVI and NDII performed differently due to the presence of continued grazing and the effects of grazing on canopy cover; NDVI was better able to track changes in vegetation in areas without grazing while NDII was better at tracking changes in areas with continued grazing. Restoration impacts display higher greenness and vegetation water content levels, greater increases in greenness and water content through time, and a decoupling of vegetation greenness and water content from spring precipitation when compared to control sites in nearby tributary and upland areas. Our results confirm the potential of erosion control structures to affect areas up to 5 km downstream of restoration sites over time and to affect 1 km upstream of the sites.

Cuenca Los Ojos, San Bernardino National Wildlife ↗

USGS1998

Geospatial Technology Applications and Infrastructure in the Biological Resources Division

Executive Summary -- Automated spatial processing technology such as geographic information systems (GIS), telemetry, and satellite-based remote sensing are some of the more recent developments in the long history of geographic inquiry. For millennia, humankind has endeavored to map the Earth's surface and identify spatial relationships. But the precision with which we can locate geographic features has increased exponentially with satellite positioning systems. Remote sensing, GIS, thematic mapping, telemetry, and satellite positioning systems such as the Global Positioning System (GPS) are tools that greatly enhance the quality and rapidity of analysis of biological resources. These technologies allow researchers, planners, and managers to more quickly and accurately determine appropriate strategies and actions. Researchers and managers can view information from new and varying perspectives using GIS and remote sensing, and GPS receivers allow the researcher or manager to identify the exact location of interest. These geospatial technologies support the mission of the U.S. Geological Survey (USGS) Biological Resources Division (BRD) and the Strategic Science Plan (BRD 1996) by providing a cost-effective and efficient method for collection, analysis, and display of information. The BRD mission is 'to work with others to provide the scientific understanding and technologies needed to support the sound management and conservation of our Nation's biological resources.' A major responsibility of the BRD is to develop and employ advanced technologies needed to synthesize, analyze, and disseminate biological and ecological information. As the Strategic Science Plan (BRD 1996) states, 'fulfilling this mission depends on effectively balancing the immediate need for information to guide management of biological resources with the need for technical assistance and long-range, strategic information to understand and predict emerging patterns and trends in ecological systems.' Information sharing plays a key role in nearly everything BRD does. The Strategic Science Plan discusses the need to (1) develop tools and standards for information transfer, (2) disseminate information, and (3) facilitate effective use of information. This effort centers around the National Biological Information Infrastructure (NBII) and the National Spatial Data Infrastructure (NSDI), components of the National Information Infrastructure. The NBII and NSDI are distributed electronic networks of biological and geographical data and information, as well as tools to help users around the world easily find and retrieve the biological and geographical data and information they need. The BRD is responsible for developing scientifically and statistically reliable methods and protocols to assess the status and trends of the Nation's biological resources. Scientists also conduct important inventory and monitoring studies to maintain baseline information on these same resources. Research on those species for which the Department of the Interior (DOI) has trust responsibilities (including endangered species and migratory species) involves laboratory and field studies of individual animals and the environments in which they live. Researchboth tactical and strategicis conducted at the BRD's 17 science centers and 81 field stations, 54 Cooperative Fish and Wildlife Research Units in 40 states, and at 11 former Cooperative Park Study Units. Studies encompass fish, birds, mammals, and plants, as well as their ecosystems and the surrounding landscape. Biological Resources Division researchers use a variety of scientific tools in their endeavors to understand the causes of biological and ecological trends. Research results are used by managers to predict environmental changes and to help them take appropriate measures to manage resources effectively. The BRD Geospatial Technology Program facilitates the collection, analysis, and dissemination of data and informat

Information and Technology Report↗

USGS2011

Derived crop management data for the LandCarbon Project

The LandCarbon project is assessing potential carbon pools and greenhouse gas fluxes under various scenarios and land management regimes to provide information to support the formulation of policies governing climate change mitigation, adaptation and land management strategies. The project is unique in that spatially explicit maps of annual land cover and land-use change are created at the 250-meter pixel resolution. The project uses vast amounts of data as input to the models, including satellite, climate, land cover, soil, and land management data. Management data have been obtained from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) and USDA Economic Research Service (ERS) that provides information regarding crop type, crop harvesting, manure, fertilizer, tillage, and cover crop (U.S. Department of Agriculture, 2011a, b, c). The LandCarbon team queried the USDA databases to pull historic crop-related management data relative to the needs of the project. The data obtained was in table form with the County or State Federal Information Processing Standard (FIPS) and the year as the primary and secondary keys. Future projections were generated for the A1B, A2, B1, and B2 Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) scenarios using the historic data values along with coefficients generated by the project. The PBL Netherlands Environmental Assessment Agency (PBL) Integrated Model to Assess the Global Environment (IMAGE) modeling framework (Integrated Model to Assess the Global Environment, 2006) was used to develop coefficients for each IPCC SRES scenario, which were applied to the historic management data to produce future land management practice projections. The LandCarbon project developed algorithms for deriving gridded data, using these tabular management data products as input. The derived gridded crop type, crop harvesting, manure, fertilizer, tillage, and cover crop products are used as input to the LandCarbon models to represent the historic and the future scenario management data. The overall algorithm to generate each of the gridded management products is based on the land cover and the derived crop type. For each year in the land cover dataset, the algorithm loops through each 250-meter pixel in the ecoregion. If the current pixel in the land cover dataset is an agriculture pixel, then the crop type is determined. Once the crop type is derived, then the crop harvest, manure, fertilizer, tillage, and cover crop values are derived independently for that crop type. The following is the overall algorithm used for the set of derived grids. The specific algorithm to generate each management dataset is discussed in the respective section for that dataset, along with special data handling and a description of the output product.

Open-File Report↗

USGS2014

Elk monitoring in Lewis and Clark National Historical Park: 2008-2012 synthesis report

Maintaining elk (Cervus elaphus roosevelti) herds that frequent Lewis and Clark National Historical Park (NHP) is central to the park’s purpose of preserving the historic, cultural, scenic, and natural resources associated with the winter encampment of the Lewis and Clark expedition. Elk were critically important to the Lewis and Clark expedition in providing food and hides that sustained the expedition during the winter of 1805-06 and supplied them for their return east during 1806. Today, elk remain a key component of interpreting the Lewis and Clark story to over 200,000 park visitors each year at the Fort Clatsop visitor center. In 2008, the US Geological Survey (USGS) began collaborating with Lewis and Clark NHP and the NPS North Coast and Cascades Network to develop a protocol for monitoring long-term changes in the magnitude and spatial patterns of elk use within and adjacent to Lewis and Clark NHP (Griffin et al. 2011). Specific objectives of the monitoring program were to measure trends in (1) relative use of the Fort Clatsop unit by elk during winter; (2) the proportion of areas where elk sign is present in the Fort Clatsop unit in winter; and (3) the frequency of elk sightings from roads in and around the Fort Clatsop unit. This report synthesizes the results of the first four years of monitoring elk distribution and use in Lewis and Clark NHP from 2008-2012. We also present data from FY2012 (Appendix 1), in lieu of an annual report for that year. We used fecal pellet group surveys as the cornerstone for monitoring trends in both relative use of the Fort Clatsop Unit by elk and the proportion of areas where elk sign was present at the end of winter. We estimated pellet group density based on data collected from a network of fecal pellet plots distributed systematically throughout the unit. We developed a double observer sampling scheme that enabled us to estimate detection biases and improve the accuracy of pellet group density estimates. We computed the estimated detection probability for any pellet group observed; this probability was a function of the pellet group size and stage of decay, as well as lighting and vegetation conditions, and the number of observers (one or two) searching for pellets in that subplot. We then used these estimated detection probabilities to adjust the raw counts of the detected pellet groups to account for groups that likely went undetected under similar pellet and environmental conditions (each observed pellet group was weighted by the inverse of its estimated detection probability). We also used results from the late winter fecal pellet surveys to quantify the proportion of areas where elk pellets occurred (PAO), which was based on the presence of fecal pellet groups and estimation of detection biases (i.e., accounting for pellet groups that likely went undetected by both observers). In this synthesis, we report temporal trends in both pellet group density and PAO from 2008-2012, based on weighted linear regression analyses as well as spatial variation of pellet group densities over time. We completed late winter fecal pellet surveys at 61-66 plots annually, depending on yearly variation in access. We cleared fecal pellets at survey points in late October / early November each year and returned in late February / early March to count pellet groups left by elk over the winter. The estimated probability that a pellet group was detected by any one observer during late winter was affected most by the pellet group size and was less affected by decay class and lighting conditions. Per-observer detection probabilities ranged from as low as ~10-15% for single pellets to ~85-90% for pellet groups with 50 pellets. Average pellet group density in the Fort Clatsop unit ranged annually from 0.58 (+/- 1.43 standard error [SE]) to 0.93 (+/- 2.25 SE) pellet groups per 3-m radius subplot. Pellet group density declined over time, at approximately 8.8% per year (+/- 2.5% SE), but that slope was not statistically distinguishable from zero (2- tailed P=0.16). Following correction for detection biases, the proportion of surveyed points used by elk (i.e., PAO) ranged from 0.44 (+/- 0.07 SE) to 0.53 (+/- 0.07 SE) during the 4 winters. The estimated proportion of areas where elk pellets occurred (PAO) declined at a rate of 2.6% per year (+/- 1.2% per year SE), but that trend also was not statistically distinguishable from zero (2- tailed P=0.17). Statistical significance of a measure’s trend depends on both the magnitude (i.e., slope) of the observed trend and the number of years the trend continues in the same increasing or decreasing direction. Through simulation modeling we determined how many additional years of surveys would be required to reveal a statistically significant trend, based on the same trends in pellet group density and PAO, and associated variation, observed from 2009-2012. Assuming the same trends persist in the future, simulations indicated that there is a 70% probability that a statistically significant trend would be detected after two more years of conducting pellet group surveys. Relative use by elk during winter, as indexed by elk pellet group density, was generally greatest in the southeast region of the Fort Clatsop unit in or near the large freshwater marsh at the mouth of Colewort Creek and adjacent upland areas. Pellet group density was also higher than average in the north-central forested area, not far from a privately-owned pasture north of the park boundary. This spatial pattern in pellet group densities across the Fort Clatsop unit was consistent across all four years, although specific pellet group densities varied from year to year. Pellet group density declined significantly over time at two points in the southeast of the Fort Clatsop unit, even though pellet group density at those points remained higher than the unit average. Pellet group density increased significantly over time at one point in the north-central region, and at one point in the south-central region of the unit, indicating a slight shift in the distribution of elk use within the Fort Clatsop Unit over the four years. As an index of visitors’ opportunities to see elk in and around the Fort Clatsop Unit, we conducted replicated roadside elk surveys 3-5 times monthly during February, April, June, August, October and December 2008-2012. During each morning of survey, we searched for elk along four routes that totaled 32 km. We examined bimonthly trends in the numbers of elk groups seen, the total number of elk seen, and the observed composition ratios for those six months of the year. The average number of elk groups seen per survey ranged from 0.75 (+/- 0.32 SE) during February to a peak of 1.95 (+/- 0.36 SE) during June. Despite this seasonal variation in numbers of elk groups seen, the average total number of elk seen per morning was less variable. The average ratios of antlered elk to antlerless adult elk (i.e., bulls:cows) and calves to antlerless adult elk (i.e. calves:cows) varied seasonally, with the highest of both average ratios observed in August. We detected no significant trends in the average number of elk groups and total numbers of elk seen per survey from 2008-2012. Similarly, ratios of calves and antlered elk per antlerless elk did not differ over time. Elk groups were frequently seen from January to August in the southeast region of the Fort Clatsop unit, in the vicinity of Colewort Creek. Outside of NPS lands, we observed elk most frequently in open areas near the Astoria regional airport, in the pastures and forests immediately north of the Fort Clatsop unit and, prior to the construction of a residential development, in a pasture northwest of the Fort Clatsop unit. Elk monitoring at Lewis and Clark NHP is still in its initial years and additional monitoring will be required to verify trends that appear to be emerging. For example, the initial monitoring suggested incipient declining trends in both pellet group density and proportion of plots with pellets present, as well as, potentially, a small shift in elk distribution away from a new trail that was recently constructed in the southeast portion of the Fort Clatsop unit. Continued monitoring will aid in determining whether this local change in distribution persists (or, alternatively, resulted from short-term random variation), and whether there will be any positive or negative effect in the northern portion of the unit where a new trail has been constructed. High variability in road counts prevented our ability to find any clear trend in numbers or composition of elk observed in and near Fort Clatsop, but changes in the patterns of observations of elk from roadways suggest that residential development outside the park has reduced the available habitat for elk in some of the areas surveyed, and may have affected spatial use patterns of elk adjacent to some areas of the park. In addition to monitoring future effects of land use changes outside the park, continued monitoring may also prove useful for assessing elk responses to natural succession in forests disturbed by windthrow in December 2007 and to NPS vegetation management activities such as variable density thinning in the forest, trail development, and restoration at Otter Point tidal area and Colewort Creek Slough.

Oregon;Washington↗

USGS2011

Understanding interaction effects of climate change and fire management on bird distributions through combined process and habitat models

Avian conservation efforts must account for changes in vegetation composition and structure associated with climate change. We modeled vegetation change and the probability of occurrence of birds to project changes in winter bird distributions associated with climate change and fire management in the northern Chihuahuan Desert (southwestern U.S.A.). We simulated vegetation change in a process-based model (Landscape and Fire Simulator) in which anticipated climate change was associated with doubling of current atmospheric carbon dioxide over the next 50 years. We estimated the relative probability of bird occurrence on the basis of statistical models derived from field observations of birds and data on vegetation type, topography, and roads. We selected 3 focal species, Scaled Quail ( Callipepla squamata ), Loggerhead Shrike ( Lanius ludovicianus ), and Rock Wren ( Salpinctes obsoletus ), that had a range of probabilities of occurrence for our study area. Our simulations projected increases in relative probability of bird occurrence in shrubland and decreases in grassland and Yucca spp. and ocotillo ( Fouquieria splendens ) vegetation. Generally, the relative probability of occurrence of all 3 species was highest in shrubland because leaf-area index values were lower in shrubland. This high probability of occurrence likely is related to the species' use of open vegetation for foraging. Fire suppression had little effect on projected vegetation composition because as climate changed there was less fuel and burned area. Our results show that if future water limits on plant type are considered, models that incorporate spatial data may suggest how and where different species of birds may respond to vegetation changes.

Chihuahuan Desert↗

USGS2011

Modelling the distribution of chickens, ducks, and geese in China

Global concerns over the emergence of zoonotic pandemics emphasize the need for high-resolution population distribution mapping and spatial modelling. Ongoing efforts to model disease risk in China have been hindered by a lack of available species level distribution maps for poultry. The goal of this study was to develop 1 km resolution population density models for China's chickens, ducks, and geese. We used an information theoretic approach to predict poultry densities based on statistical relationships between poultry census data and high-resolution agro-ecological predictor variables. Model predictions were validated by comparing goodness of fit measures (root mean square error and correlation coefficient) for observed and predicted values for 1/4 of the sample data which were not used for model training. Final output included mean and coefficient of variation maps for each species. We tested the quality of models produced using three predictor datasets and 4 regional stratification methods. For predictor variables, a combination of traditional predictors for livestock mapping and land use predictors produced the best goodness of fit scores. Comparison of regional stratifications indicated that for chickens and ducks, a stratification based on livestock production systems produced the best results; for geese, an agro-ecological stratification produced best results. However, for all species, each method of regional stratification produced significantly better goodness of fit scores than the global model. Here we provide descriptive methods, analytical comparisons, and model output for China's first high resolution, species level poultry distribution maps. Output will be made available to the scientific and public community for use in a wide range of applications from epidemiological studies to livestock policy and management initiatives.

Agriculture, Ecosystems and Environment↗

USGS2014

A sub-national scale geospatial analysis of diamond deposit lootability: the case of the Central African Republic

The Central African Republic (CAR), a country with rich diamond deposits and a tumultuous political history, experienced a government takeover by the Seleka rebel coalition in 2013. It is within this context that we developed and implemented a geospatial approach for assessing the lootability of high value-to-weight resource deposits, using the case of diamonds in CAR as an example. According to current definitions of lootability, or the vulnerability of deposits to exploitation, CAR's two major diamond deposits are similarly lootable. However, using this geospatial approach, we demonstrate that the deposits experience differing political geographic, spatial location, and cultural geographic contexts, rendering the eastern deposits more lootable than the western deposits. The patterns identified through this detailed analysis highlight the geographic complexities surrounding the issue of conflict resources and lootability, and speak to the importance of examining these topics at the sub-national scale, rather than relying on national-scale statistics.

The Extractive Industries and Society↗

USGS2012

Advances in the simulation and automated measurement of well-sorted granular material: 1. Simulation

1. In this, the first of a pair of papers which address the simulation and automated measurement of well-sorted natural granular material, a method is presented for simulation of two-phase (solid, void) assemblages of discrete non-cohesive particles. The purpose is to have a flexible, yet computationally and theoretically simple, suite of tools with well constrained and well known statistical properties, in order to simulate realistic granular material as a discrete element model with realistic size and shape distributions, for a variety of purposes. The stochastic modeling framework is based on three-dimensional tessellations with variable degrees of order in particle-packing arrangement. Examples of sediments with a variety of particle size distributions and spatial variability in grain size are presented. The relationship between particle shape and porosity conforms to published data. The immediate application is testing new algorithms for automated measurements of particle properties (mean and standard deviation of particle sizes, and apparent porosity) from images of natural sediment, as detailed in the second of this pair of papers. The model could also prove useful for simulating specific depositional structures found in natural sediments, the result of physical alterations to packing and grain fabric, using discrete particle flow models. While the principal focus here is on naturally occurring sediment and sedimentary rock, the methods presented might also be useful for simulations of similar granular or cellular material encountered in engineering, industrial and life sciences.

Journal of Geophysical Research F: Earth Surface↗

USGS2022

The 2020 Westmorland, California earthquake swarm as aftershocks of a slow slip event sustained by fluid flow

Swarms are bursts of earthquakes without an obvious mainshock. Some have been observed to be associated with transient aseismic fault slip, while others are thought to be related to fluids. However, the association is rarely quantitative due to insufficient data quality. We use high-quality GPS/GNSS, InSAR, and relocated seismicity to study a swarm of >2,000 earthquakes which occurred between 30 September and 6 October 2020, near Westmorland, California. Using 5 min sampled Global Positioning System (GPS) supplemented with InSAR, we document a spontaneous shallow M w 5.2 slow slip event that preceded the swarm by 2–15 hr. The earthquakes in the early phase were predominantly non-interacting and driven primarily by the slow slip event resulting in a nonlinear expansion. A stress-driven model based on the rate-and-state friction successfully explains the overall spatial and temporal evolution of earthquakes, including the time lag between the onset of the slow slip event and the swarm. Later, a distinct back front and a square root of time expansion of clustered seismicity on en-echelon fault structures suggest that fluids helped sustain the swarm. Static stress triggering analysis using Coulomb stress and statistics of interevent times suggest that 45%–65% of seismicity was driven by the slow slip event, 10%–35% by inter-earthquake interactions, and 10%–30% by fluids. Our model also provides constraints on the friction parameter and the pore pressure and suggests that this swarm behaved like an aftershock sequence but with the mainshock replaced by the slow slip event.

California↗

USGS2024

Detecting avalanche path ground cover and vegetation change across multiple scales through time using remote sensing tools

Large-magnitude avalanches often alter vegetation composition, avalanche path dimensions, and subsequent avalanche return periods. Understanding temporal changes in individual avalanche path trimlines, runout zones, and geomorphic characteristics helps forecasters, planners, and engineers estimate potential avalanche destructive size and impact on infrastructure or settlements in the runout zone. Understanding these changes on a large scale also provides information on post-cycle avalanche distribution. Here, we use remote sensing platforms and change detection techniques to examine vegetation change in avalanche paths in Montana and Colorado. In northwest Montana, we implemented a novel approach using lidar, aerial imagery, and a random forest model to classify imagery-observed vegetation within avalanche paths. We calculated spatially explicit avalanche return periods using a physically based spatial interpolation method and characterized the vegetation within those return period zones. In Colorado, we investigated changes in avalanche path vegetation characteristics prior to and after a widespread large-magnitude avalanche cycle. The highest frequency of avalanche return periods was broadly characterized by grassland and shrubland, but topography greatly influences vegetation classes and return periods. Furthermore, statistically significant differences in lidar-derived vegetation canopy height exist between categorical return periods. We used optical sensors from satellite imagery to analyze changes in Normalized Difference Vegetation Index (NDVI) to calculate ground cover change over time. NDVI, a measure of near-infrared and red bands within the imagery, allowed us to distinguish between green vegetation (e.g., trees and shrubs) and non-vegetated ground cover (e.g., dead and downed trees, rocks, and dirt) within avalanche paths. For this study, we calculated changes in NDVI values by comparing imagery from 2018 to imagery from 2019 after a widespread large magnitude avalanche cycle occurred in March 2019 in Colorado, United States. We applied a filtering process to reduce error, classified NDVI change based on the value distribution, and then calculated area change of all areas within each avalanche path. We completed this process for 1633 avalanche paths throughout Colorado. We found that using NDVI difference values pre- and post-avalanche cycle allowed us to identify ground cover change in avalanche paths throughout Colorado. These changes span from a slight expansion of existing avalanche paths to substantial landscape disturbance. For example, a size D5 avalanche caused severe ground cover change in 18% of one single path near Aspen, Colorado. This suggests that large magnitude avalanches can redefine avalanche path dimensions and could impact subsequent avalanche size and frequency. Using NDVI from satellite imagery is a simple way to detect ground cover changes in avalanche paths on a large scale or in remote areas. In general, remote sensing products to detect and examine vegetation and ground cover change in avalanche paths can help inform avalanche distribution and benefit planning efforts.

Montana↗

USGS2018

A new modeling approach to prioritize riparian restoration to reduce sediment loading in two Virginia river basins

Human impact, particularly land cover changes (e.g., agriculture, construction) increase erosion and sediment loading into streams. Benthic species are negatively affected by silt deposition that coats and embeds stream substrate. Given that riparian buffers are effective sediment filters, riparian restoration is increasingly implemented by conservation groups to protect stream habitats. Limited funding and a multitude of impaired streams warrant the need for cost-effective prioritization of potential restoration actions. We created a decision-support framework for conservation agencies and aquatic resource managers to prioritize riparian restoration efforts. Our framework integrates GIS data and field surveys into a statistical model to predict instream silt from estimates of upland soil loss and riparian filtration capacity. We focus specifically on prioritizing sites in upper sections of the Roanoke and Nottoway river basins (Virginia, US) based on observed records of Roanoke logperch ( Percina rex ), an imperiled sediment-sensitive species. Our statistical approach examines soil characteristics, land cover, precipitation, topography, and annual soil loss estimates from the empirically derived Revised Universal Soil Loss Equation, combined with land cover-based riparian filtration capacity as potential stream habitat predictors. We found riparian filtration capacity to be a significant predictor of silt cover, while precipitation was a significant predictor of embeddedness. Spatial scale was also a factor, in that spatial variance in silt cover and embeddedness was more accurately predicted at smaller spatial extents. Ultimately, our model can be used as a prioritization tool for mitigating high siltation areas, or for protecting low soil erosion areas.

North Carolina, Virginia↗

USGS2025

Evidence for nonlocal sediment transport on hillslopes from fault scarp morphology

Hillslope sediment transport processes such as bioturbation, rainsplash, and granular mechanics occur across the entire planet. Yet, it remains uncertain how these small-scale processes act together to shape landscapes. Longstanding hillslope diffusion theory posits that hillslope processes are spatially limited, whereas new concepts of nonlocal sediment transport argue otherwise. However, each theory produces subtly different, but distinct, predictions for the evolution of fault scarps. We use the topographic change of fault scarps to demonstrate that hillslope processes produce nonlocal sediment transport. Analysis of a global compilation of 340 dated single-earthquake scarp profiles reveals a statistically significant ( p < 0.05 ) relationship between scarp age and scarp asymmetry, here defined as the ratio of imaginary to real components of the Fourier transform of absolute slope. Numerical simulations show that nonlocal models predict this relationship, whereas hillslope diffusion models do not. To further investigate this result, we examined the depositional geometry of a well-exposed colluvial wedge along the Wasatch fault in central Utah, United States. Our quantitative comparison between the exposure and numerical simulations reveals better agreement with the nonlocal model. Nonlocal sediment transport theory appears to best capture the physics of how hillslope processes shape fault scarps, yet hillslope diffusion provides a useful approximation in many cases. As the processes that act on fault scarps are nearly identical to those acting on hillslopes, our results provide evidence supporting nonlocality as a generalized model of hillslope sediment transport.

Utah↗

USGS2021

Citizen science data collection for integrated wildlife population analyses

Citizen science, or community science, has emerged as a cost-efficient method to collect data for wildlife monitoring. To inform research and conservation, citizen science sampling designs should collect data that match the robust statistical analyses needed to quantify species and population patterns. Further increasing the contributions of citizen science, integrating citizen science data with other datasets and datatypes can improve population estimates and expand the spatiotemporal extent of inference. We demonstrate these points with a citizen science program called iSeeMammals developed in New York state in 2017 to supplement costly systematic spatial capture-recapture sampling by collecting opportunistic data from one-off observations, hikes, and camera traps. iSeeMammals has initially focused on the growing population of American black bear ( Ursus americanus ), with integrated analysis of iSeeMammals camera trap data with systematic data for a region with a growing bear population. The triumvirate of increased spatial and temporal coverage by at least twofold compared to systematic sampling, an 83% reduction in annual sampling costs, and improved density estimates when integrated with systematic data highlight the benefits of collecting presence-absence data in citizen science programs for estimating population patterns. Additional opportunities will come from applying presence-only data, which are oftentimes more prevalent than presence-absence data, to integrated models. Patterns in data submission and filtering also emphasize the importance of iteratively evaluating patterns in engagement, usability, and accessibility, especially focusing on younger adult and teenage demographics, to improve data quality and quantity. We explore how the development and use of integrated models may be paired with citizen science project design in order to facilitate repeated use of datasets in standalone and integrated analyses for supporting wildlife monitoring and informing conservation.

Frontiers in Ecology and Evolution↗

USGS2024

Pragmatically mapping Phragmites with unoccupied aerial systems: A comparison of invasive species land cover classification using RGB and multispectral imagery

Unoccupied aerial systems (UASs) are increasingly being deployed in coastal environments to rapidly map and monitor changes to geomorphology, vegetation, and infrastructure, particularly in difficult to access areas. UAS data, relative to airplane or satellite data, typically have higher spatial resolution, sensor customization, and increased flexibility in temporal resolution, which benefits monitoring applications. UAS data have been used to map and monitor invasive species occurrence and expansion, such as Phragmites australis , a reed species in wetlands throughout the eastern United States. To date, the work on this species has been largely opportunistic or ad hoc. Here, we statistically and qualitatively compare results from several sensors and classification workflows to develop baseline understanding of the accuracy of different approaches used to map Phragmites . Two types of UAS imagery were collected in a Phragmites -invaded salt marsh setting—natural color red-green-blue (RGB) imagery and multispectral imagery spanning visible and near infrared wavelengths. We evaluated whether one imagery type provided significantly better classification results for mapping land cover than the other, also considering trade-offs like overall accuracy, financial costs, and effort. We tested the transferability of classification workflows that provided the highest thematic accuracy to another barrier island environment with known Phragmites stands. We showed that both UAS sensor types were effective in classifying Phragmites cover, with neither resulting in significantly better classification results than the other for Phragmites detection (overall accuracy up to 0.95, Phragmites recall up to 0.86 at the pilot study site). We also found the highest accuracy workflows were transferrable to sites in a barrier island setting, although the quality of results varied across these sites (overall accuracy up to 0.97, Phragmites recall up to 0.90 at the additional study sites).

Massachusetts↗

USGS2024

Groundwater hydrology, groundwater and surface-water interactions, aquifer testing, and groundwater-flow simulations for the Fountain Creek alluvial aquifer, near Colorado Springs, Colorado, 2018–20

From 2018 through 2020, the U.S. Geological Survey, in cooperation with the Air Force Civil Engineering Center, conducted an integrated study of the Fountain Creek alluvial aquifer located near Colorado Springs, Colorado. The objective of the study was to characterize hydrologic conditions for the alluvial aquifer pertinent to the potential for transport of solutes. Specific goals of this report were to characterize the groundwater hydrology of the area, to quantify groundwater and surface-water interactions, to estimate hydraulic properties of the aquifer using aquifer testing, and to complete numerical simulations of groundwater flow. Synoptic groundwater-level elevation measurements completed throughout this study, and as part of other U.S. Geological Survey programs between 1994 and 2020, indicate groundwater-level elevations fluctuate on annual and interannual timeframes. Groundwater-level fluctuations likely were caused by temporally variable groundwater recharge and discharge components in the area, with many wells showing maximum groundwater-level elevations during the winter months (November through March). From an interannual perspective, groundwater-level fluctuations appear to have reached maximum values during 2000 to 2003, decreased during 2003 to 2006, and remained relatively constant since that time, with the exception of several wells which have displayed rising groundwater-level elevations since 2018. Spatial evaluation of groundwater-level elevations indicates groundwater flow is generally from northeast to southwest within the vicinity of several alluvial paleochannels occurring along the northeastern margin of the aquifer. Within the center of the aquifer along Fountain Creek, groundwater flow is generally from north to south, approximately paralleling surface-water flow. To quantitatively understand the potential effect of groundwater recharge and groundwater pumping on fluctuations in groundwater-level elevation, a statistical transfer-function-noise model was applied. Results of the statistical model indicate throughout most of the aquifer, fluctuations were primarily the result of recharge seasonality. In the main stem of the aquifer where groundwater pumping wells were more concentrated, however, groundwater-level elevation fluctuations were also attributable to groundwater pumping through time. Three-dimensional evaluation of the aquifer geometry near Fountain Creek was combined with synoptic streamflow measurement and accounting of stream gains and losses to evaluate groundwater and surface-water interactions in the study area. Streamflow gain or loss calculations indicate Fountain Creek both gains from and loses flow to the alluvial aquifer, and gaining or losing reaches of the stream may be partially controlled by the depth to bedrock near the stream. Reaches with streamflow gains tend to coincide with areas where the estimated depth to bedrock is decreasing, meaning the alluvial aquifer is likely thinning in these areas and groundwater-flow paths may be converging and discharging groundwater to the stream. Losing reaches tended to coincide with locally greater depth to bedrock where the alluvial aquifer is likely thicker and has greater storage potential for surface water lost from Fountain Creek. Results of aquifer testing indicate hydraulic conductivity, estimated from slug tests and single-well pumping tests, ranged from 0.32 to 1,410 feet per day (ft/d) and 4.13 to 664 ft/d, respectively. These results are similar to the range of values from previous aquifer tests in the study area. Hydraulic conductivities from aquifer testing for this study were generally greater than the estimates of previous slug tests and had a mean value less than the estimates from previous pumping tests. Spatial evaluation of aquifer testing results indicates hydraulic conductivity tends to be greater in the main stem of the alluvial aquifer and lower in paleochannels upgradient from the main stem of the aquifer. The spatial variation in hydraulic conductivity may be attributed to the geomorphologic processes that formed the alluvial aquifer. Compacted sediment in the paleochannels has not been potentially transported sufficient distance to cause grain-size sorting, resulting in a poorly sorted deposit and lower hydraulic conductivities. In the central portion of the alluvial aquifer, near Fountain Creek, the sediments have been transported farther from their source areas and are likely better sorted, removing finer grained sediments that would cause lower hydraulic conductivity. A numerical groundwater-flow model was calibrated for the Fountain Creek alluvial aquifer for 2000–19 to simulate water-budget components, groundwater-flow directions, and groundwater-flow paths. The model simulated precipitation recharge, groundwater and surface-water interactions, evapotranspiration, high-volume groundwater pumping by pumping wells, and external inflows and outflows occurring along the boundaries of the alluvial aquifer. Model calibration was completed using manual and automated approaches, the latter of which assisted in quantifying model results sensitivity to input parameters. The calibrated model corresponds well with groundwater-level elevation observations, with a mean residual (observed minus simulated groundwater-level elevation) equal to −0.60 feet. Simulated groundwater base flow to streams was typically within 10 percent of base flow estimated by independent methods. Groundwater and surface-water interactions represented the largest water-budget components of the aquifer, with the second largest groundwater discharge component coming from pumping wells. Groundwater and surface-water interactions represent both the largest gain and loss terms in the water budget, because these interactions differ spatially, meaning in some areas of the model domain groundwater is being recharged by streams, whereas in other areas, groundwater is discharged to streams. Estimates of advective groundwater-flow paths indicate pumping wells may capture groundwater recharged from losing streams and groundwater that flows into the main stem of the alluvial aquifer from paleochannels.

Colorado↗