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At least 613 records · Page 34Linked to original sources
USGS1999

A comparison of the weights-of-evidence method and probabilistic neural networks

The need to integrate large quantities of digital geoscience information to classify locations as mineral deposits or nondeposits has been met by the weights-of-evidence method in many situations. Widespread selection of this method may be more the result of its ease of use and interpretation rather than comparisons with alternative methods. A comparison of the weights-of-evidence method to probabilistic neural networks is performed here with data from Chisel Lake-Andeson Lake, Manitoba, Canada. Each method is designed to estimate the probability of belonging to learned classes where the estimated probabilities are used to classify the unknowns. Using these data, significantly lower classification error rates were observed for the neural network, not only when test and training data were the same (0.02 versus 23%), but also when validation data, not used in any training, were used to test the efficiency of classification (0.7 versus 17%). Despite these data containing too few deposits, these tests of this set of data demonstrate the neural network's ability at making unbiased probability estimates and lower error rates when measured by number of polygons or by the area of land misclassified. For both methods, independent validation tests are required to ensure that estimates are representative of real-world results. Results from the weights-of-evidence method demonstrate a strong bias where most errors are barren areas misclassified as deposits. The weights-of-evidence method is based on Bayes rule, which requires independent variables in order to make unbiased estimates. The chi-square test for independence indicates no significant correlations among the variables in the Chisel Lake–Andeson Lake data. However, the expected number of deposits test clearly demonstrates that these data violate the independence assumption. Other, independent simulations with three variables show that using variables with correlations of 1.0 can double the expected number of deposits as can correlations of −1.0. Studies done in the 1970s on methods that use Bayes rule show that moderate correlations among attributes seriously affect estimates and even small correlations lead to increases in misclassifications. Adverse effects have been observed with small to moderate correlations when only six to eight variables were used. Consistent evidence of upward biased probability estimates from multivariate methods founded on Bayes rule must be of considerable concern to institutions and governmental agencies where unbiased estimates are required. In addition to increasing the misclassification rate, biased probability estimates make classification into deposit and nondeposit classes an arbitrary subjective decision. The probabilistic neural network has no problem dealing with correlated variables—its performance depends strongly on having a thoroughly representative training set. Probabilistic neural networks or logistic regression should receive serious consideration where unbiased estimates are required. The weights-of-evidence method would serve to estimate thresholds between anomalies and background and for exploratory data analysis.

Natural Resources Research↗
USGS2004

The effects of macroinvertebrate taxonomic resolution in large landscape bioassessments: An example from the Mid-Atlantic Highlands, U.S.A.

1. During late spring 1993-1995, the U.S. Environmental Protection Agency's Environmental Monitoring and Assessment Program (EMAP) sampled 490 wadeable streams in the mid-Atlantic Highlands (MAH) of the U.S. for a variety of physical, chemical and biological indicators of environmental condition. We used the resulting data set to evaluate the importance of differing levels of macroinvertebrate taxonomic resolution in bioassessments by comparing the ability of family versus genus to detect differences among sites classified by type and magnitude of human impact and by stream size. We divided the MAH into two physiographic regions: the Appalachian Plateau where mine drainage (MD) and acidic deposition are major stressors, and the Ridge and Valley where nutrient enrichment is a major stressor. Stream sites were classified into three or four impact classes based on water chemistry and habitat. We used stream order (first to third Strahler order) in each region as a measure of stream size. Ordination, 2 x 2 chi-square and biotic metrics were used to compare the ability of family and genus to detect differences among both stressor and size classes. 2. With one notable exception, there were only a small number of different genera per family (interquartile range = 1-4). Family Chironomidae, however, contained 123 different genera. As a result, significant information loss occurred when this group was only classified to family. The family Chironomidae did not discriminate among the predefined classes but many chironomid genera did: by chi-square analysis, 10 and 28 chironomid genera were significant in discriminating MD and nutrient impacts, respectively. 3. Family and genus data were similar in their ability to distinguish among the coarse impacts (e.g. most severe versus least severe impact classes) for all cases. Though genus data in many cases distinguished the subtler differences (e.g. mixed/moderate impacts versus high or low impacts) better than family, differences in significance levels between family and genus analyses were relatively minor. However, genus data detected differences among stream orders in ordination analyses that were not revealed at the family level. In the ordinations, both family and genus levels of analysis responded to similar suites of environmental variables. 4. Our results suggest that identification to the family level is sufficient for many bioassessment purposes. However, identifications to genus do provide more information in genera-rich families like Chironomidae. Genus or finer levels of identification are important for investigating natural history, stream ecology, biodiversity and indicator species. Decisions about the taxonomic level of identification need to be study specific and depend on available resources (cost) and study objectives.

Freshwater Biology↗
USGS2026

Requirements, capabilities, and analysis for Earth observations—From user needs to Earth observation solutions

Introduction At the core of the Nation’s land imaging enterprise, the Requirements, Capabilities, and Analysis for Earth Observations (RCA-EO) project serves as the backbone for understanding user needs and shaping the future of Earth observation (EO) systems, products, and technology development. Understanding user needs and continuously tracking observing system capabilities is essential for EO because it ensures that the U.S. Government investments remain aligned with the real-world decisions, applications, and societal benefits they are intended to support. The RCA-EO project collects data users’ land imaging needs, focusing on what needs to be measured rather than specific technology. Collecting this data has resulted in a comprehensive understanding of civil land imaging user needs across scientific and operational applications. Over the past 10 years, the RCA-EO project has interviewed hundreds of subject matter experts, yielding more than a thousand user needs. The RCA-EO project’s analytical tools, models, and methods help inform the development of land imaging systems and products, determine appropriate data purchases, and prioritize partnerships for access to data from missions outside of the USGS. Our tools and databases support decisions with credible, needs-based information.

Fact Sheet↗
USGS2013

Satellite images of the September 2013 flood event in Lyons, Colorado

The U.S. Geological Survey (USGS) Special Applications Science Center (SASC) produced an image base map showing high-resolution remotely sensed data over Lyons, Colorado—a city that was severely affected by the flood event that occurred throughout much of the Colorado Front Range in September of 2013. The 0.5-meter WorldView-2 data products were created from imagery collected by DigitalGlobe on September 13 and September 24, 2013, during and following the flood event. The images shown on this map were created to support flood response efforts, specifically for use in determining damage assessment and mitigation decisions. The raw, unprocessed imagery were orthorectified and pan-sharpened to enhance mapping accuracy and spatial resolution, and reproduced onto a cartographic base map. These maps are intended to provide a snapshot representation of post-flood ground conditions, which may be useful to decisionmakers and the general public. The SASC also provided data processing and analysis support for other Colorado flood-affected areas by creating cartographic products, geo-corrected electro-optical and radar image mosaics, and GIS water cover files for use by the Colorado National Guard, the National Park Service, the U.S. Forest Service, and the flood response community. All products for this International Charter event were uploaded to the USGS Hazards Data Distribution System (HDDS) website (http://hdds.usgs.gov/hdds2/) for distribution.

Colorado↗
USGS2025

Tracking status and trends in seven key indicators of river and stream condition in the Chesapeake Bay watershed

Freshwater streams and rivers are recognized as vital habitats within the Chesapeake Bay watershed, which has been undergoing extensive restoration efforts for more than 30 years. Resource managers need to understand stream and river condition and how these conditions are changing over time to determine whether regional long-term restoration and conservation goals are being met. The objective of this report was to document the spatial and temporal variability of conditions for seven indicators of river and stream health across the nontidal Chesapeake Bay watershed. The framework for the U.S. Geological Survey’s Nontidal Network (NTN), a network of more than 100 nutrient and suspended sediment monitoring locations, was extended to assess conditions for six additional indicators of stream health: temperature, salinity, toxic contaminants, streamflow, hydromorphology, and biological aquatic communities. For each indicator, the latest available data from multiple sources were compiled and harmonized, and key metrics were identified to describe indicator conditions across space and time. A status condition was defined for each indicator to describe overall spatial variability in recent condition, and trend analyses were used to describe changes in each indicator metric over time. The analysis revealed clear differences in spatial and temporal data coverage across the seven indicators, so individual indicator trend analyses were not constrained to a common time interval. However, a status snapshot was conducted across all indicators for the 2015–17 period to simultaneously explore spatial variability across all indicators. The status snapshot highlighted general degraded conditions across multiple indicators in large metropolitan regions, such as the Baltimore–Washington, D.C., metropolitan area. Regression analysis between indicator status metrics and major land cover for the sites suggest urbanization as a potential driver of degraded conditions for many of the indicator metrics, including total phosphorus, salinity, temperature, high-flow frequency, and metrics of habitat and biological assemblage quality. A final analysis exploring the spatial representation of each indicator network showed that some indicator monitoring networks did not cover certain settings, such as small watersheds. These results provided an initial assessment of stream health status and trends and will continue to be leveraged to describe conditions across the Chesapeake Bay watershed to help inform local and regional management decisions. These results also highlighted the need for improved coordination among monitoring organizations to support long-term multi-indicator monitoring and assessment.

Chesapeake Bay watershed↗
USGS2022

Balancing future renewable energy infrastructure siting and associated habitat loss for migrating whooping cranes

The expansion of human infrastructure has contributed to novel risks and disturbance regimes in most ecosystems, leading to considerable uncertainty about how species will respond to altered landscapes. A recent assessment revealed that whooping cranes ( Grus americana ), an endangered migratory waterbird species, avoid wind-energy infrastructure during migration. However, uncertainties regarding collective impacts of other types of human infrastructure, such as power lines on migration, variable drought conditions, and continued construction of wind energy infrastructure may compromise ongoing recovery efforts for whooping cranes. Droughts are increasing in frequency and severity throughout the whooping crane migration corridor, and the impacts of drought on stopover habitat use are largely unknown. Moreover, decision-based analyses are increasingly advocated to guide recovery planning for endangered species, yet applications remain rare. Using GPS locations from 57 whooping cranes from 2010 through 2016 in the United States Great Plains, we assessed habitat selection and avoidance of potential disturbances during migration relative to drought conditions, and we used these results in an optimization analysis to select potential sites for new wind energy developments that minimize relative habitat loss for whooping cranes and maximize wind energy potential. Drought occurrence and severity varied spatially and temporally across the migration corridor during our study period. Whooping cranes rarely used areas <5 km from human settlements and wind energy infrastructure under both drought and non-drought conditions, and <2 km from power lines during non-drought conditions, with the lowest likelihood of use near wind energy infrastructure. Whooping cranes differed in their selection of wetland and cropland land cover types depending on drought or non-drought conditions. We identified scenarios for wind energy expansion across the migration corridor and in select states, which are robust to uncertain drought conditions, where future loss of highly selected stopover habitats could be minimized under a common strategy. Our approach was to estimate functional habitat loss while integrating current disturbances, potential future disturbances, and uncertainty in drought conditions. Therefore, dynamic models describing potential costs associated with risk-averse behaviors resulting from future developments can inform proactive conservation before population impacts occur.

Kansas, Montana, Nebraska, North Dakota, South Dak↗
USGS2014

Virtual Beach 3: user's guide

Virtual Beach version 3 (VB 3 ) is a decision support tool that constructs site-specific statistical models to predict fecal indicator bacteria (FIB) concentrations at recreational beaches. VB 3 is primarily designed for beach managers responsible for making decisions regarding beach closures or the issuance of swimming advisories due to pathogen contamination. However, researchers, scientists, engineers, and students interested in studying relationships between water quality indicators and ambient environmental conditions will find VB 3 useful. VB 3 reads input data from a text file or Excel document, assists the user in preparing the data for analysis, enables automated model selection using a wide array of possible model evaluation criteria, and provides predictions using a chosen model parameterized with new data. With an integrated mapping component to determine the geographic orientation of the beach, the software can automatically decompose wind/current/wave speed and magnitude information into along-shore and onshore/offshore components for use in subsequent analyses. Data can be examined using simple scatter plots to evaluate relationships between the response and independent variables (IVs). VB 3 can produce interaction terms between the primary IVs, and it can also test an array of transformations to maximize the linearity of the relationship The software includes search routines for finding the "best" models from an array of possible choices. Automated censoring of statistical models with highly correlated IVs occurs during the selection process. Models can be constructed either using previously collected data or forecasted environmental information. VB 3 has residual diagnostics for regression models, including automated outlier identification and removal using DFFITs or Cook's Distances.

Report↗
USGS2023

Using public litigation records to identify priority science needs for managing public lands

Relevant science is essential for effective natural resource decision making, including on public lands managed by the United States Department of the Interior (DOI) Bureau of Land Management (BLM), that cover 1/10th of the United States. Most of the BLM’s management decisions require analyses under the National Environmental Policy Act, and the use of science in these decisions is often challenged. Using coproduction, we assembled an interagency team of scientists and resource managers to develop a method for using public litigation to identify priority science needs for the BLM. We searched publicly available case documents finalized from 2015–2019 in Wyoming, Colorado, Utah, and New Mexico within federal courts and the DOI Office of Hearings and Appeals, and identified 108 case documents that involved challenges to the BLM’s use of science. We retained 48 case documents that contained at least one challenge about the BLM’s use of science for a specific resource. We categorized all challenges in each case document according to the proposed action, affected resource, type of science challenged (data about resources, science relevant to potential impacts, methods for analyzing potential impacts, and mitigation actions), and specific nature of the challenge (e.g., challenging direct effects analysis). We identified priority science needs based on the frequency of challenges, the number of states where similar challenges occurred, whether the BLM lost the challenge, and whether the case was remanded. Top needs related to oil and gas development actions and included science about effects on air quality and climate, water, and socioeconomics; data for air quality and climate; and methods for analyzing potential impacts to cultural resources and air quality and climate. The BLM can use this information to prioritize actions (e.g., funding new research or science syntheses) to strengthen its science foundation for decision-making.

Colorado, New Mexico, Utah, Wyoming↗
USGS2017

Motivations for enrollment into the Conservation Reserve Enhancement Program in the James River Basin of South Dakota

The Conservation Reserve Enhancement Program (CREP) targets high-priority conservation needs (e.g., water quality, wildlife habitat) by paying landowners an annual rental rate to remove environmentally sensitive or agriculturally unproductive lands from rowcrop production, and then implement conservation practices on these lands. This study examined motivations of South Dakota landowners for enrolling in the James River Basin CREP. All 517 newly enrolled landowners were mailed a questionnaire in 2014 measuring demographics, behaviors, opinions, and motivations (60% response rate). Cluster analysis of 10 motivations for enrolling identified three motivation groups (wildlife = 40%, financial = 35%, environmental = 25%). The financial group had the youngest mean age (62 years), followed by the wildlife (65) and environmental groups (68). Among respondents, 43% favored the public access requirement of this CREP with the environmental group most in favor. Understanding landowner enrollment motivations and decision criteria will assist in strategies (e.g., financial incentives, increasing yield via habitat restoration) for increasing future participation.

South Dakota↗
USGS2018

Refining aging criteria for northern sea otters in Washington State

Measurement of skull ossification patterns is a standard method for aging various mammalian species and has been used to age sea otters Enhydra lutris from Russia, California, and Alaska. Cementum annuli counts have also been verified as an accurate aging method for sea otters in Alaska. In this study, we compared cementum annuli count results and skull ossification patterns as methods for aging the northern sea otter, E. l. kenyoni, in Washington State. We found significant agreement between the two methods, suggesting that either method could be used to age sea otters in Washington. We found that ossification of the squamosal–jugal suture at the ventral glenoid fossa can be used to differentiate male subadults from adults. To assist field biologists or others without access to cementum annuli or skull ossification analysis techniques, we analyzed a suite of morphologic, physiologic, and developmental characteristics to assess whether a set of these more easily accessible parameters could also predict age class. We identified tooth condition score, evidence of reproductive activity in females, and tooth eruption pattern as the most useful criteria for classifying sea otters in Washington. We created a simple decision tree based on characteristics accessible in the field or at necropsy, which can be used to reliably predict age class of Washington sea otters as determined by cementum annuli. These techniques offer field biologists and marine mammal stranding networks a replicable, cost-conscious methodology to gather useful biological information from sea otters.

Washington↗
USGS1983

Evaluating environmental and economic consequences of alternative pest management strategies: results of modeling workshops

The U.S. Environmental Protection Agency (EPA) needs a comprehensive method to evaluate the human health and environmental effects of alternative agricultural pest management strategies. This project explored the utility of Adaptive Environmental Assessment (AEA) techniques for meeting this need. The project objectives were to produce models for environmental impact analysis, improve communications, identify research needs and data requirements, and demonstrate a process for resolving conflicts. The project was structured around the construction (in an initial 2 1/2-day workshop) and examination (in a second 2 1/2-day workshop) of a simulation model of a corn agroecosystem. The model conceptualized at the first workshop simulates the effect of corn agrecosystem decisions on crop production, economic returns, and environmental indicators. The model is composed of five interacting submodels: 1) a Production Strategies submodel which makes decisions concerning tillage, planting, fertilizer and pesticide applications, and harvest; 2) a Hydrology/Chemical Transport submodel which represents soil hydrology, erosion, and concentrations of fertilizers and pesticides in the soil, runoff, surface waters, and percolation; 3) a Vegetation submodel which simulates growth of agricultural crops (corns and soybeans) and weeds; 4) a Pests submodel which calculates pest population levels and resulting crop damage; and 5) an Environmental Effects submodel which calculates indicators of potential fish kills, human health effects, and wildlife habitat. The most persistent data gaps encountered in quantifying the model were coefficients to relate environmental consequences to alternative pest management strategies. While the model developed in the project is not yet accurate enough to be used for real-world decisions about the use of pesticides on corn, it does contain the basic structure upon which such a model could be built. More importantly at this stage of development, the project has shown that very complex systems can be modeled in short periods of time and that the process of building such models increases understanding among disciplinary specialists and between diverse institutional interests. This process can be useful to EPA as the agency cooperates with other institutions to meet its responsibilities in less costly ways. Activities at the second 2 1/2-day workshop included a review of the model, incorporation of necessary corrections, simulation of policy scenarios, and examination of techniques to address remaining institutional conflicts. Participants were divided into three groups representing environmental, production or industry, and regulatory interests. Each group developed scenarios that would be most appealing to their particular interest and the scenarios were simulated by the agroecosystem computer model. Negotiators from each of the interest groups decided whether a hypothetical herbicide should be relabeled and if certain restrictions should be imposed on its use. Other participants functioned as experts and consultants on caucus teams. A solution to the hypothetical problem was successfully negotiated. Workshop participants and project staff agreed that the model and processes developed during the project should be used in training students, extension specialists, farmers, researchers, and chemical producers in collaborative problem solving methods. More productive research can be planned, and more realistic models of complex systems can be built in this way. More importantly, greater trust of decisionmakers in computer models, better understanding by technical experts about disciplines other than their own, and improved cooperation between institutional interests can be achieved. This trust, understanding, and cooperation are critical ingredients in solving problems that are too complex to be resolved by independent disciplinary activity and unilateral decision authority.

Report↗
USGS2022

Conflict of energies: Spatially modeling mule deer caloric expenditure in response to oil and gas development

Context Wildlife avoid human disturbances, including roads and development. Avoidance and displacement of wildlife into less suitable habitat due to human development can affect their energy expenditures and fitness. The heart rate and oxygen uptake of large mammals varies with both natural aspects of their habitat (terrain, climate, predators, etc.) and anthropogenic influence (noise, light, fragmentation, etc.). Although incorporating physiological analyses of energetics can inform the impacts of both development and conservation, management decisions rarely incorporate individuals’ energetic requirements when deciding on locations for potential development. Objectives We aimed to estimate the change in expected energy expenditure, numerically and spatially, for mule deer to traverse a landscape with varying levels of oil and gas development through time. Methods Using calculations of energy expenditure of mule deer ( Odocoileus hemionus ) by weight, in relation to physical terrain components, plus avoidance factors for anthropogenic disturbance, we developed a spatiotemporal model of the minimum energy required for mule deer to traverse a landscape. We compared expected energy expenditure across 12 study sites with increasing levels of oil and gas development and over time in our study area, on the northern Colorado Plateau of Utah. Results We found that energy expenditure can be increased by development, regardless of terrain, through increased travel distance associated with avoidance behavior. Maximum median energy expenditure to traverse a 1400 ha sample area rose from 1135 to 1935 kilocalories, a 70% increase in energy required of a mule deer. There was a significant relationship between energy expenditure and the size of oil and gas development (p < 0.001), its compactness (p < 0.05), and its ‘thinness’ (p < 0.001), but not terrain ruggedness (p = 0.25). Conclusion As the energy costs of movement correlate across multiple species of large mammals, our analysis of the energetic cost, for mule deer, associated with development can serve as a quantitative representative of the impacts of oil and gas development for multiple mammals—including threatened or endangered species. Our bioenergetic cost-distance model provides a means of delineating impediments to efficient movement and can be used to quantify the expected energetic costs of proposed future developments. As wildlife are exposed to increasing anthropogenic stressors which reduce fitness, it is important to make strategic siting decisions to reduce energetic costs imposed by human activities.

Utah↗
USGS2022

The Hawai'i groundwater recharge tool

This article discusses the design and implementation of the Hawai’i Groundwater Recharge Tool, an application for providing data and analyses of the impacts of land-cover modifications and changes in precipitation on groundwater-recharge rates for the island of O’ahu. This application uses simulation data based on a set of 29 land-cover types and 2 precipitation conditions to provide users with real-time recharge calculations for interactively defined land-cover modifications. The tool provides two visualizations, representing the land cover for the island and the resultant groundwater-recharge rates, and a set of metrics indicating the changes to groundwater recharge for relevant areas to present a set of easily interpretable outcomes based on user-defined scenarios. Users have varying degrees of control over the granularity of data input and output, allowing for the quick production of a roughly defined scenario, or more precise land-cover definitions. These modifications can be exported for further analysis. Heuristics are used to provide a responsive user interface and performant integration with the database containing the full set of simulation data. This tool is designed to provide user-friendly access to the information on the impacts of land-cover and precipitation changes on groundwater-recharge rates needed to assist in making data-driven decisions.

Hawai'i↗
USGS2015

Terrain parameters of glide snow avalanches and a simple spatial glide snow avalanche model

Glide snow avalanches are dangerous and difficult to predict. Despite substantial recent research there is still inadequate understanding regarding the controls of glide snow avalanche release. Glide snow avalanches often occur in similar terrain or the same locations annually, and repeat observations and prior work suggest that specific topography may be critical. Thus, to gain a better understanding of the terrain component of these types of avalanches we examined terrain parameters associated with the specific area of glide snow avalanche release in comparison to avalanche starting zones where no glide snow avalanches were observed (i.e. non-glide snow avalanche terrain). Glide snow avalanche occurrences visible from the Going-to-the-Sun Road corridor in Glacier National Park, Montana from 2003 to 2013 are investigated using a database of all avalanche occurrences derived of daily observations each year from 1 April to 1 June. This yielded 192 glide snow avalanches in 53 distinct avalanche paths. Each avalanche was digitized in a GIS using satellite, oblique, and aerial imagery as reference. A set of 117 non-glide snow avalanche starting zones were also selected in this manner. These were start zones with avalanche activity potential, but without glide avalanches observed. Topographical parameters such as area, slope, aspect, curvature, potential incoming solar radiation, distance from ridge, and elevation were then derived for the entire dataset utilizing tools with a GIS and a 10 m DEM. Ground class and a glide factor were calculated using a four level classification index with in-situ observations and a land surface type layer in a GIS. A total of 21 terrain variables were examined using a univariate analysis between areas where glide snow avalanches occurred and areas where glide snow avalanches were never observed, despite crack formation. Only two variables were not significantly different. The significantly different variables were then used to train a classification tree to distinguish between glide and non-glide snow avalanche terrain. A 10-fold cross validated tree resulted in four decision nodes to classify the data. The nodes split on glide factor, maximum slope angle, seasonal sum of incoming solar radiation, and maximum curvature to distinguish between glide snow avalanche and non-glide snow avalanche terrain with an unweighted average accuracy (RPC) of 0.95 and probability of detection of events (POD) of 0.99. Finally, the results of the cross-validated tree were used in a GIS to examine other areas, not used in the training dataset of the classification tree, of potential glide snow avalanche release within Glacier National Park. Using this understanding of the role of topographic parameters on glide snow avalanche activity, a spatial terrain based model was developed to identify other areas with high glide snow avalanche potential outside of the immediate observation area. This simple spatial model correctly classified 78 percent of actual glide snow avalanche terrain (pixel count) of a small test area of four independent observed glide snow avalanches.

Montana↗
USGS2017

Coupling ecological and social network models to assess “transmission” and “contagion” of an aquatic invasive species

Network analysis is used to address diverse ecological, social, economic, and epidemiological questions, but few efforts have been made to combine these field-specific analyses into interdisciplinary approaches that effectively address how complex systems are interdependent and connected to one another. Identifying and understanding these cross-boundary connections improves natural resource management and promotes proactive, rather than reactive, decisions. This research had two main objectives; first, adapt the framework and approach of infectious disease network modeling so that it may be applied to the socio-ecological problem of spreading aquatic invasive species, and second, use this new coupled model to simulate the spread of the invasive Chinese mystery snail ( Bellamya chinensis ) in a reservoir network in Southeastern Nebraska, USA. The coupled model integrates an existing social network model of how anglers move on the landscape with new reservoir-specific ecological network models. This approach allowed us to identify 1) how angler movement among reservoirs aids in the spread of B . chinensis , 2) how B . chinensis alters energy flows within individual-reservoir food webs, and 3) a new method for assessing the spread of any number of non-native or invasive species within complex, social-ecological systems.

Journal of Environmental Management↗
USGS2024

Analysis and review of fishery-dependent data for Hawaiian nearshore noncommercial fisheries

Noncommercial, shore-based fisheries provide economic, social, and cultural services to communities throughout the Hawaiian Islands. The State of Hawai‘i Department of Land and Natural Resources (DLNR), Division of Aquatic Resources (DAR) routinely conducts surveys to monitor noncommercial fisheries such that estimates of fishing effort and catch by gear type can be generated and used to implement more sustainable management practices. DAR executes both the Hawai‘i Marine Recreational Fishery Survey (HMRFS), a nationally standardized survey that focuses on intercepting fishers at access points (i.e., boat ramps) across the main Hawaiian Islands, and a set of roving creel surveys on O‘ahu, Maui Nui, and Kaua‘i that observe and intercept fishers at locations along the shoreline outside of those targeted by HMRFS. The latter set of creel surveys were designed to complement HMRFS by expanding its geographic coverage and thus providing a more representative picture of noncommercial fishing in Hawai‘i. Sustainable management priorities set by DAR rely on the availability of statewide, fishery- dependent data. Thus, we collate information from island-based roving creel surveys into a cohesive Statewide Creel Survey Database. Further, we provide preliminary analyses and describe ways that surveys could be streamlined to improve future data collection, analysis, and utility. In so doing, we synthesize the most detailed information to-date about noncommercial shore-based fisheries of Hawai‘i. The unprecedented spatial and temporal coverage of DAR’s dataset reveals the value of their survey efforts over the last decade to address fishery management needs. Our primary objectives, results, and conclusions are summarized below: 1) Integrate DAR roving creel survey data from different islands into a single Statewide Creel Survey Dataset (Chapter II). We describe the collation of creel survey data from O‘ahu, Maui Nui, and Kaua‘i into a statewide dataset. We also offer ways in which these surveys could be streamlined to meet the needs of managers and decision makers. Briefly, these are to create a statewide strategic plan, standardize the execution of standard operating procedures, centralize the creel survey database and associated metadata, and consider using technology that improves the data pipeline, including transitioning from paper-based to electronic systems for data entry and processing. 2) Assess whether the new Statewide Creel Survey Dataset can provide inputs for length-based stock assessments (Chapter III). Only on Maui were interviews conducted with associated catch data. There was reasonably high taxonomic coverage (42 species from 186 interviews with 310 fishers), but low sample sizes for nearly all species precluded the development of length-based stock assessments. We provide summary statistics from the existing data and briefly discuss how technologies could be used to automate analysis of images of noncommercial catch. 3) Analyze the Statewide Creel Survey Dataset for spatial and temporal patterns in fishing effort (Chapter IV): a. Visualizing noncommercial fishing pressure . We found that fishing effort (mean number of fishers observed per survey event at a site) on O'ahu was over three times greater than that recorded during similar surveys conducted on Maui or Kaua'i. We create maps that display the distribution of angling and spearfishing effort around each of the three islands. b. Factors that predict fishing “hotspots” around Maui . Fishing effort on Maui was associated with areas with more wave power and less parking availability. There were half as many fishers in areas with parking lots than in areas with parking on the road shoulder only. c. Changes in fishing effort during the COVID-19 pandemic . There was no change in fishing effort on O‘ahu during the first year of the pandemic, but there was a 20% decline in year 2 and a 33% decline in year 3, both in comparison to pre-pandemic levels. Pre-pandemic creel survey data were unavailable for Maui and Kaua‘i, but fishing effort on these islands also declined as the pandemic progressed at similar or greater rates than those observed on O‘ahu. 4) Quantify potential bias in survey methods by experimentally deriving fisher detection probabilities of shore-based and drone-based surveys (Chapter V): a. Shore-based surveys . We conducted roving creel surveys for four months at three locations around Hilo Bay, designed to emulate and estimate the efficacy of DAR standard operating procedures. There was high agreement between paired observers in counting fishers, leading to near-perfect detection probabilities of both anglers (94%) and spearfishers (97%), but relatively low agreement and detection probabilities of other fishers (throw net, ‘opihi picking, etc.) (52%). b. Drone-based surveys . We used an unmanned aerial vehicle (UAV; operated by DAR staff) to collect imagery of fishers along the Hilo Bay shoreline. We used still images and video clips (with known fishing activity) to build an online survey that was distributed to DAR and HCFRU personnel, asking them to count and categorize resource users as a snorkeler, spearfisher, angler, or other fisher. Only 40.0% of the responses correctly counted and categorized resource users in the image. Anglers were correctly identified and enumerated in 90.0% of the responses, but the correct response rates of the other three user categories ranged from 67.8% – 79.4%. Snorkelers and anglers tended to be undercounted while spearfishers and other fishers were overcounted. 5) Review the potential for incorporating emerging technologies that will improve, augment, and evolve creel survey data collection, especially for spearfishing (Chapter VI). Within the context of monitoring shore-based noncommercial fishing, we review the use of electronic data entry/processing systems with geospatial and image capabilities, field cameras, drones, smart buoys, citizen science apps, data mining social media, artificial intelligence and machine learning. We highlight several of the challenges and considerations when implementing these technologies into creel surveys and provide a synthesis of options that could be used to better estimate spearfishing. The general conclusion of this assessment is that the DAR roving creel survey program is collecting valuable data that supplement the existing HMRFS efforts. However, there are a number of areas that could be improved to make these efforts a more effective tool for decision-making processes in resource management and conservation: 1) Establishment of clear statewide and island objectives for the Statewide Creel Survey Dataset. Currently, data collection efforts are focused towards addressing a very broad purpose – supplementing the HMRFS data collection efforts. However, the results of the preliminary analyses conducted as part of this project suggest that the data could be used to address other areas of need if these objectives were clearly defined. Further, the design of the creel survey would benefit from greater standardization of survey protocols between islands and an effort to define a) the acceptable margins of error associated with the estimates generated by these data and b) the minimum level of change that the surveys would need to detect to be useful to managers. 2) Centralization of data entry, data quality assessment, and data accessibility. Currently, each DAR office manages data entry, checks the data for errors, and is responsible for managing and storing the data. Instituting a centralized data entry system, particularly an online database that can receive survey data from tablets or smartphones running a standardized data collection application would improve efficiency, reduce data entry errors, and accelerate the availability of data to managers. A substantial amount of time and effort from the project described in this report was devoted to checking the dataset for errors. The development and application of data quality assurance protocols would ensure that the data are reliable and available in a timely fashion to support management decisions. 3) Address lingering questions regarding the efficacy of current survey protocols to capture and characterize the spearfishing component of the noncommercial fishery. The results presented in the report suggest that the current creel survey protocols do a good job detecting spearfishers when present but are not capturing sufficient data about their catch or total effort. There are also questions remaining as to whether the survey times and sites are sufficiently capturing the behavior of spearfishers in Hawai‘i. A more thorough assessment – whether through additional research, alteration of survey design, or review of data by representatives of the spearfishing community – would provide insight on how to use the Statewide Creel Survey Database to inform management of spearfishing. 4) Investigate the integration of technological advancements into the creel survey methods. As priorities and needs are developed and formalized, it would be valuable to consider how various technological advancements might enhance and streamline data collection or open new avenues of inquiry.

Hawaii↗
USGS2023

Incorporating uncertainty in susceptibility criteria into probabilistic liquefaction hazard analysis

Most conventional approaches for assessing liquefaction triggering hazards generally rely on simplified procedures that involve identifying liquefaction susceptible layers and calculating a factor of safety against liquefaction (FSL) in each layer. Such procedures utilize deterministic semi-empirical models for standard penetration test (SPT), cone penetrometer test (CPT), or shear wave velocity (Vs)-based subsurface data. This general approach largely neglects considerable uncertainties in ground shaking, as well as aleatory variabilities and epistemic uncertainties inherent to liquefaction susceptibility and triggering prediction. A more robust methodology introduced by Kramer and Mayfield (2007) is known as probabilistic liquefaction hazard analysis (PLHA), which integrates the full ground motion hazard space with probabilistic forms of liquefaction triggering models (e.g., Boulanger and Idriss 2014), resulting in the computation of FSL profiles with consistent return periods. Multiple PLHA computational platforms have been developed over the years, with the computational framework from Makdisi (2021) serving as the basis for a new Liquefaction Hazard Tool under development at the U.S. Geologic Survey (USGS). Despite significant improvements in recent years to the availability of seismic hazard data and probabilistic triggering and effects models, the issue of incorporating uncertainty in characterizing liquefaction susceptibility remains a challenge. Most compositional susceptibility criteria (i.e., whether or not the soil exhibits sand-like behavior) currently in use are presented as deterministic bounds based on in-situ or laboratory test data; similarly, determination of soil saturation is often based on a single groundwater level from in-situ testing. As a result, the same types of binary decisions must be made in PLHA as in more conventional methods. With the expansion and availability of field and laboratory data pertaining to liquefaction through resources such as the Next Generation Liquefaction (NGL) project, there exists the potential for an improved set of susceptibility models for CPT, SPT, and Vs-based applications. Presented here is a brief discussion on how probabilistic susceptibility modeling can be accommodated in PLHA calculations, as well as how the use of multiple models can be leveraged within a logic tree to improve the representation of epistemic uncertainty in liquefaction hazard analysis.

Conference Paper↗
USGS2013

Detection of tamarisk defoliation by the northern tamarisk beetle based on multitemporal Landsat 5 thematic mapper imagery.

The spread of tamarisk ( Tamarix spp., also known as saltcedar) is a significant ecological disturbance in western North America and has long been targeted for control, leading to the importation of the northern tamarisk beetle ( Diorhabda carinulata ) as a biological control agent. Following its initial release along the Colorado River near Moab, Utah in 2004, the beetle has successfully established and defoliated tamarisk across much of the upper Colorado River Basin. However, the spatial distribution and seasonal timing of defoliation are complex and difficult to quantify over large areas. To address this challenge, we tested and compared two remote sensing approaches to mapping tamarisk defoliation: Disturbance Index (DI) and a decision tree method called Random Forest (RF). Based on multitemporal Landsat 5 TM imagery for 2006-2010, changes in DI and defoliation probability from RF were calculated to detect tamarisk defoliation along the banks of Green, Colorado, Dolores and San Juan rivers within the Colorado Plateau area. Defoliation mapping accuracy was assessed based on field surveys partitioned into 10 km sections of river and on regions of interest created for continuous riparian vegetation. The DI method detected 3711 ha of defoliated area in 2007, 7350 ha in 2008, 10,457 ha in 2009 and 5898 ha in 2010. The RF method detected much smaller areas of defoliation but proved to have higher accuracy, as demonstrated by accuracy assessment and sensitivity analysis, with 784 ha in 2007, 960 ha in 2008, 934 ha in 2009, and 1008 ha in 2010. Results indicate that remote sensing approaches are likely to be useful for studying spatiotemporal patterns of tamarisk defoliation as the tamarisk leaf beetle spreads throughout the western United States.

Arizona, Colorado, New Mexico, Utah, Wyoming↗