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USGS2021

Modeling at-sea density of marine birds to support renewable energy planning on the Pacific outer continental shelf of the contiguous United States

This report describes the at-sea spatial distributions of marine birds in Pacific OCS waters off the contiguous U.S. (Figure 1.1) to inform marine spatial planning in the region. The goal was to estimate long-term average spatial distributions for marine bird species using all available science-quality transect survey data and numerous bathymetric, oceanographic, and atmospheric predictor variables. We developed seasonal habitat-based spatial models of the at-sea distribution for 33 individual species and 13 taxonomic groups of marine birds throughout the study region. A statistical modeling framework was used to estimate numerical relationships between bird sighting data (i.e., standardized counts) and a range of temporal (e.g., Pacific Decadal Oscillation [PDO] index), spatially static (e.g., depth), and spatially dynamic (e.g., sea surface chlorophyll-a concentration) environmental variables. The estimated relationships were then used to predict spatially explicit long-term average density (individuals per km 2 ) throughout the study area for each species/group in each of four seasons. Bird sighting data came from multiple scientific survey programs and consisted of at-sea counts of birds collected between 1980 and 2017 using boat-based and fixed-wing aerial transect survey methods. Spatial environmental variables were derived from remote sensing satellite data and an ocean dynamics model.

California, Oregon, Washington↗

USGS2022

NABat ML: Utilizing deep learning to enable crowdsourced development of automated, scalable solutions for documenting North American bat populations

Bats play crucial ecological roles and provide valuable ecosystem services, yet many populations face serious threats from various ecological disturbances. The North American Bat Monitoring Program (NABat) aims to use its technology infrastructure to assess status and trends of bat populations, while developing innovative and community-driven conservation solutions. Here, we present NABat ML , an automated machine-learning algorithm that improves the scalability and scientific transparency of NABat acoustic monitoring. This model combines signal processing techniques and convolutional neural networks (CNNs) to detect and classify recorded bat echolocation calls. We developed our CNN model with internet-based computing resources (‘cloud environment’), and trained it on >600,000 spectrogram images. We also incorporated species range maps to improve the robustness and accuracy of the model for future ‘unseen’ data. We evaluated model performance using a comprehensive, independent, holdout dataset. NABat ML successfully distinguished 31 classes (30 species and a noise class) with overall weighted-average accuracy and precision rates of 92%, and ≥90% classification accuracy for 19 of the bat species. Using a single cloud-environment computing instance, the entire model training process took <16 h. Synthesis and applications . Our convolutional neural network (CNN)-based model, NABat ML , classifies 30 North American bat species using their recorded echolocation calls with an overall accuracy of 92%. In addition to providing highly accurate species-level classification, NABat ML and its outputs are compatible with Bayesian and other statistical techniques for measuring uncertainty in classification. Our model is open-source and reproducible, enabling future implementations as software on end-user devices and cloud-based web applications. These qualities make NABat ML highly suitable for applications ranging from grassroots community science initiatives to big-data methods developed and implemented by researchers and professional practitioners. We believe the transparency and accessibility of NABat ML will encourage broad-scale participation in bat monitoring, and enable development of innovative solutions needed to conserve North American bat species.

Journal of Applied Ecology↗

USGS2013

Correlating multispectral imaging and compositional data from the Mars Exploration Rovers and implications for Mars Science Laboratory

In an effort to infer compositional information about distant targets based on multispectral imaging data, we investigated methods of relating Mars Exploration Rover (MER) Pancam multispectral remote sensing observations to in situ alpha particle X-ray spectrometer (APXS)-derived elemental abundances and Mössbauer (MB)-derived abundances of Fe-bearing phases at the MER field sites in Gusev crater and Meridiani Planum. The majority of the partial correlation coefficients between these data sets were not statistically significant. Restricting the targets to those that were abraded by the rock abrasion tool (RAT) led to improved Pearson’s correlations, most notably between the red–blue ratio (673 nm/434 nm) and Fe 3+ -bearing phases, but partial correlations were not statistically significant. Partial Least Squares (PLS) calculations relating Pancam 11-color visible to near-IR (VNIR; ∼400–1000 nm) “spectra” to APXS and Mössbauer element or mineral abundances showed generally poor performance, although the presence of compositional outliers led to improved PLS results for data from Meridiani. When the Meridiani PLS model for pyroxene was tested by predicting the pyroxene content of Gusev targets, the results were poor, indicating that the PLS models for Meridiani are not applicable to data from other sites. Soft Independent Modeling of Class Analogy (SIMCA) classification of Gusev crater data showed mixed results. Of the 24 Gusev test regions of interest (ROIs) with known classes, 11 had >30% of the pixels in the ROI classified correctly, while others were mis-classified or unclassified. k-Means clustering of APXS and Mössbauer data was used to assign Meridiani targets to compositional classes. The clustering-derived classes corresponded to meaningful geologic and/or color unit differences, and SIMCA classification using these classes was somewhat successful, with >30% of pixels correctly classified in 9 of the 11 ROIs with known classes. This work shows that the relationship between SWIR multispectral imaging data and APXS- and Mössbauer-derived composition/mineralogy is often weak, a perhaps not entirely unexpected result given the different surface sampling depths of SWIR imaging (uppermost few microns) vs. APXS (tens of μm) and MB measurements (hundreds of μm). Results from the upcoming Mars Science Laboratory (MSL) rover’s ChemCam Laser Induced Breakdown Spectroscopy (LIBS) instrument may show a closer relationship to Mastcam SWIR multispectral observations, however, because the initial laser shots onto a target will analyze only the upper few micrometers of the surface. The clustering and classification methods used in this study can be applied to any data set to formalize the definition of classes and identify targets that do not fit in previously defined classes.

Icarus↗

USGS2018

Prevalence of seismic rate anomalies preceding volcanic eruptions in Alaska

Seismic rate increases often precede eruptions at volcanoes worldwide. However, many eruptions occur without such precursors. Additionally, identifying seismic rate increases near volcanoes with high levels of background seismicity is non-trivial and many periods of elevated seismicity occur without ensuing eruptions, limiting their usefulness for forecasting in some cases. Although these issues are commonly known, efforts to quantify them are limited. In this study, we consistently apply a common statistical tool, the β-statistic, to seismically monitored eruptions in Alaska of various styles to determine the overall prevalence of seismic rate anomalies immediately preceding eruptions. We find that 6 out of 20 (30%) eruptions have statistically significant precursory seismic rate increases. Of these 6 eruptions, 3 of them occur at volcanoes with relatively felsic compositions, repose periods >15 years, and VEI ≥ 3. Overall, our results confirm that seismic rate increases are common prior to larger eruptions at long dormant, “closed-system” volcanoes, but uncommon preceding smaller eruptions at more frequently active, “open-system” volcanoes with more mafic magmas. We also explore the rate of other anomalies not precursory to eruptions and investigate their origins. Some of these non-eruptive anomalies can be explained by aftershocks of regional seismic events, magmatic activity that did not lead to eruption, or unrest at other nearby volcanoes. Some open-system volcanoes have high non-eruptive anomaly rates and low pre-eruptive anomaly rates and are thus not amenable to forecasting based on earthquake catalogs. In this study, we find that 31% of anomalies lead to eruption. With continued calibration at more volcanoes, the β-statistic that we apply may be used more broadly to analyze future periods of seismic unrest at other volcanoes, properly placing such episodes into the context of the long-term background rate. These results may be useful for informing future eruption forecasts around the world, and the statistical tool may aid volcano observatories in identifying future seismic rate anomalies under changing network conditions.

Alaska↗

USGS2008

Advances and limitations of individual-based models to analyze and predict dynamics of mangrove forests: A review

Mangrove ecosystems are considered vulnerable to climate change as coastal development limits the ecosystem services and adaptations important to their survival. Although they appear rather simple in terms of species diversity, their ecology is complex due to interacting geophysical forces of tides, surface runoff, river and groundwater discharge, waves, and constituents of sediment, nutrients and saltwater. These interactions limit developing a comprehensive framework for science-based sustainable management practices. A suite of models have been developed independently by various academic and government institutions worldwide to understand the dynamics of mangrove ecosystems and to provide ecological forecasting capabilities under different management scenarios and natural disturbance regimes. The models have progressed from statistical tables representing growth and yield to more sophisticated models describing various system components and processes. Among these models are three individual-based models (IBMs) (FORMAN, KIWI, and MANGRO). A comparison of models’ designs reveal differences in the details of process description, particularly, regarding neighbor competition among trees. Each model has thus its specific range of applications. Whereas FORMAN and KIWI are most suitable to address mangrove forest dynamics of stands, MANGRO focuses on landscape dynamics on larger spatial scale. A comparison of the models and a comparison of the models with empirical knowledge further reveal the general needs for further field and validation studies to advance our ecological understanding and management of mangrove wetlands.

Aquatic Botany↗

USGS2016

Report from the workshop on climate downscaling and its application in high Hawaiian Islands, September 16–17, 2015

In the subtropical and tropical Pacific islands, changing climate is predicted to influence precipitation and freshwater availability, and thus is predicted to impact ecosystems goods and services available to ecosystems and human communities. The small size of high Hawaiian Islands, plus their complex microlandscapes, require downscaling of global climate models to provide future projections of greater skill and spatial resolution. Two different climate modeling approaches (physics-based dynamical downscaling and statistics-based downscaling) have produced dissimilar projections. Because of these disparities, natural resource managers and decision makers have low confidence in using the modeling results and are therefore are unwilling to include climate-related projections in their decisions. In September 2015, the Pacific Islands Climate Science Center (PICSC), the Pacific Islands Climate Change Cooperative (PICCC), and the Pacific Regional Integrated Sciences and Assessments (Pacific RISA) program convened a 2-day facilitated workshop in which the two modeling teams, plus key model users and resource managers, were brought together for a comparison of the two approaches, culminating with a discussion of how to provide predictions that are useable by resource managers. The proceedings, discussions, and outcomes of this Workshop are summarized in this Open-File Report.

Hawaii↗

USGS2010

Density matters: Review of approaches to setting organism-based ballast water discharge standards

As part of their effort to develop national ballast water discharge standards under NPDES permitting, the Office of Water requested that WED scientists identify and review existing approaches to generating organism-based discharge standards for ballast water. Six potential approaches were identified and the utility and uncertainties of each approach was evaluated. During the process of reviewing the existing approaches, the WED scientists, in conjunction with scientists at the USGS and Smithsonian Institution, developed a new approach (per capita invasion probability or "PCIP") that addresses many of the limitations of the previous methodologies. THE PCIP approach allows risk managers to generate quantitative discharge standards using historical invasion rates, ballast water discharge volumes, and ballast water organism concentrations. The statistical power of sampling ballast water for both the validation of ballast water treatment systems and ship-board compliance monitoring with the existing methods, though it should be possible to obtain sufficient samples during treatment validation. The report will go to a National Academy of Sciences expert panel that will use it in their evaluation of approaches to developing ballast water discharge standards for the Office of Water.

Report↗

USGS2021

Determination of vadose zone and saturated zone nitrate lag times using long-term groundwater monitoring data and statistical machine learning

In this study, we explored the use of statistical machine learning and long-term groundwater nitrate monitoring data to estimate vadose zone and saturated zone lag times in an irrigated alluvial agricultural setting. Unlike most previous statistical machine learning studies that sought to predict groundwater nitrate concentrations within aquifers, the focus of this study was to leverage available groundwater nitrate concentrations and other environmental variables to determine mean regional vertical velocities (transport rates) of water and solutes in the vadose zone and saturated zone (3.50 and 3.75 m yr −1 , respectively). The statistical machine learning results are consistent with two primary recharge processes in this western Nebraska aquifer, namely ( 1 ) diffuse recharge from irrigation and precipitation across the landscape and ( 2 ) focused recharge from leaking irrigation conveyance canals. The vadose zone mean velocity yielded a mean recharge rate (0.46 m yr −1 ) consistent with previous estimates from groundwater age dating in shallow wells (0.38 m yr −1 ). The saturated zone mean velocity yielded a recharge rate (1.31 m yr −1 ) that was more consistent with focused recharge from leaky irrigation canals, as indicated by previous results of groundwater age dating in intermediate-depth wells (1.22 m yr −1 ). Collectively, the statistical machine learning model results are consistent with previous observations of relatively high water fluxes and short transit times for water and nitrate in the primarily oxic aquifer. Partial dependence plots from the model indicate a sharp threshold in which high groundwater nitrate concentrations are mostly associated with total travel times of 7 years or less, possibly reflecting some combination of recent management practices and a tendency for nitrate concentrations to be higher in diffuse infiltration recharge than in canal leakage water. Limitations to the machine learning approach include the non-uniqueness of different transport rate combinations when comparing model performance and highlight the need to corroborate statistical model results with a robust conceptual model and complementary information such as groundwater age.

Nebraska↗

USGS2024

The impact of future changes in climate on breeding waterfowl pairs in the US Prairie Pothole Region

Millions of small (< 10 ha) waterbodies embedded in grassland and agroecosystems in midcontinental North America provide breeding habitat to an estimated 50–80% of North America’s migratory ducks. Tens of millions of dollars are invested annually to conserve and enhance upland and wetland habitats for breeding ducks by prioritizing locations predicted to have high densities of breeding pairs under average precipitation conditions. An implicit assumption of this approach is that the distribution of breeding habitat remains relatively static. Climate change is an identified risk to this strategy. To assess this assumption and plan for potential forthcoming conditions, we estimated changes in potential breeding duck pairs under different climate scenarios by combining results of 1) a mechanistic hydrology model that simulates ecosystem processes for a subset of wetlands distributed across the U.S. Prairie Pothole Region (USPPR); 2) four downscaled climate model projections at mid- and late-century time horizons; and 3) U.S. Fish and Wildlife Service multi-decadal datasets and predictive breeding waterfowl pair statistical models. We conducted virtual and in-person informational sessions with partners to inform them on the best practices of using downscaled global circulation models and approaches for climate scenario planning. This close coordination led to a joint presentation at a monthly North Central Climate Adaptation Science Center seminar. We are also co-developing simulated wetland- waterfowl responses under different climate futures for wetlands. Information from these robust predictions of waterfowl habitat and settling patterns in this region provides land-management agencies insights in prioritizing current conservation actions given uncertainty. In addition, understanding how many breeding pairs the USPPR might support in coming decades will likely influence overall breeding population sizes and sustainable harvest objectives across North America.

Iowa, Minnesota, Nebraska, North Dakota, South Dak↗

USGS2008

Evaluation of statistical treatments of left-censored environmental data using coincident uncensored data sets: I. Summary statistics

The main classes of statistical treatment of below-detection limit (left-censored) environmental data for the determination of basic statistics that have been used in the literature are substitution methods, maximum likelihood, regression on order statistics (ROS), and nonparametric techniques. These treatments, along with using all instrument-generated data (even those below detection), were evaluated by examining data sets in which the true values of the censored data were known. It was found that for data sets with less than 70% censored data, the best technique overall for determination of summary statistics was the nonparametric Kaplan-Meier technique. ROS and the two substitution methods of assigning one-half the detection limit value to censored data or assigning a random number between zero and the detection limit to censored data were adequate alternatives. The use of these two substitution methods, however, requires a thorough understanding of how the laboratory censored the data. The technique of employing all instrument-generated data - including numbers below the detection limit - was found to be less adequate than the above techniques. At high degrees of censoring (greater than 70% censored data), no technique provided good estimates of summary statistics. Maximum likelihood techniques were found to be far inferior to all other treatments except substituting zero or the detection limit value to censored data.

Environmental Science & Technology↗

USGS2013

A model of strength

In her AAAS News & Notes piece "Can the Southwest manage its thirst?" (26 July, p. 362), K. Wren quotes Ajay Kalra, who advocates a particular method for predicting Colorado River streamflow "because it eschews complex physical climate models for a statistical data-driven modeling approach." A preference for data-driven models may be appropriate in this individual situation, but it is not so generally, Data-driven models often come with a warning against extrapolating beyond the range of the data used to develop the models. When the future is like the past, data-driven models can work well for prediction, but it is easy to over-model local or transient phenomena, often leading to predictive inaccuracy (1). Mechanistic models are built on established knowledge of the process that connects the response variables with the predictors, using information obtained outside of an extant data set. One may shy away from a mechanistic approach when the underlying process is judged to be too complicated, but good predictive models can be constructed with statistical components that account for ingredients missing in the mechanistic analysis. Models with sound mechanistic components are more generally applicable and robust than data-driven models.

Science↗

USGS1987

Selected papers in the hydrologic sciences, 1986

Water-quality data from long-term (24 years), fixed- station monitoring at the Cape Fear River at Lock 1 near Kelly, N.C., and various measures of basin development are correlated. Subbasin population, number of acres of cropland in the subbasin, number of people employed in manufacturing, and tons of fertilizer applied in the basin are considered as measures of basinwide development activity. Linear correlations show statistically significant posi- tive relations between both population and manufacturing activity and most of the dissolved constituents considered. Negative correlations were found between the acres of harvested cropland and most of the water-quality measures. The amount of fertilizer sold in the subbasin was not statistically related to the water-quality measures considered in this report. The statistical analysis was limited to several commonly used measures of water quality including specific conductance, pH, dissolved solids, several major dissolved ions, and a few nutrients. The major dissolved ions included in the analysis were calcium, sodium, potassium, magnesium, chloride, sulfate, silica, bicarbonate, and fluoride. The nutrients included were dissolved nitrite plus nitrate nitrogen, dissolved ammonia nitrogen, total nitrogen, dissolved phosphates, and total phosphorus. For the chemicals evaluated, manufacturing and population sources are more closely associated with water quality in the Cape Fear River at Lock 1 than are agricultural variables.

Water Supply Paper↗

USGS2024

A benchmark dataset and workflow for landslide susceptibility zonation

Landslide susceptibility shows the spatial likelihood of landslide occurrence in a specific geographical area and is a relevant tool for mitigating the impact of landslides worldwide. As such, it is the subject of countless scientific studies. Many methods exist for generating a susceptibility map, mostly falling under the definition of statistical or machine learning. These models try to solve a classification problem: given a collection of spatial variables, and their combination associated with landslide presence or absence, a model should be trained, tested to reproduce the target outcome, and eventually applied to unseen data. Contrary to many fields of science that use machine learning for specific tasks, no reference data exist to assess the performance of a given method for landslide susceptibility. Here, we propose a benchmark dataset consisting of 7360 slope units encompassing an area of about 4,100 km 2 "> 4,100 km 2 in Central Italy. Using the dataset, we tried to answer two open questions in landslide research: (1) what effect does the human variability have in creating susceptibility models; (2) how can we develop a reproducible workflow for allowing meaningful model comparisons within the landslide susceptibility research community. With these questions in mind, we released a preliminary version of the dataset, along with a “call for collaboration,” aimed at collecting different calculations using the proposed data, and leaving the freedom of implementation to the respondents. Contributions were different in many respects, including classification methods, use of predictors, implementation of training/validation, and performance assessment. That feedback suggested refining the initial dataset, and constraining the implementation workflow. This resulted in a final benchmark dataset and landslide susceptibility maps obtained with many classification methods. Values of area under the receiver operating characteristic curve obtained with the final benchmark dataset were rather similar, as an effect of constraints on training, cross–validation, and use of data. Brier score results show larger variability, instead, ascribed to different model predictive abilities. Correlation plots show similarities between results of different methods applied by the same group, ascribed to a residual implementation dependence. We stress that the experiment did not intend to select the “best” method but only to establish a first benchmark dataset and workflow, that may be useful as a standard reference for calculations by other scholars. The experiment, to our knowledge, is the first of its kind for landslide susceptibility modeling. The data and workflow presented here comparatively assess the performance of independent methods for landslide susceptibility and we suggest the benchmark approach as a best practice for quantitative research in geosciences.

Earth-Science Reviews↗

USGS2011

Has the magnitude of floods across the USA changed with global CO2 levels?

Statistical relationships between annual floods at 200 long-term (85–127 years of record) streamgauges in the coterminous United States and the global mean carbon dioxide concentration (GMCO2) record are explored. The streamgauge locations are limited to those with little or no regulation or urban development. The coterminous US is divided into four large regions and stationary bootstrapping is used to evaluate if the patterns of these statistical associations are significantly different from what would be expected under the null hypothesis that flood magnitudes are independent of GMCO2. In none of the four regions defined in this study is there strong statistical evidence for flood magnitudes increasing with increasing GMCO2. One region, the southwest, showed a statistically significant negative relationship between GMCO2 and flood magnitudes. The statistical methods applied compensate both for the inter-site correlation of flood magnitudes and the shorter-term (up to a few decades) serial correlation of floods.

Hydrological Sciences Journal↗

USGS2011

U.S. Geological Survey Science for the Wyoming Landscape Conservation Initiative-2010 Annual Report

This is the third report produced by the U.S. Geological Survey (USGS) for the Wyoming Landscape Conservation Initiative (WLCI) to detail annual work activities. The first report described activities for 2007 and 2008, and the second report covered work activities for FY09. This third report covers work activities conducted in FY2010, and it continues the 2009 approach of reporting on all the individual activities to help give WLCI partners and other readers the full scope of what has been accomplished. New in this year's report is an additional section for each work activity that outlines the work planned for the following fiscal year. In FY2010, there were 35 ongoing/expanded, completed, or new projects conducted under the five major multi-disciplinary science and technical-assistance activities: (1) Baseline Synthesis; (2) Targeted Monitoring and Research; (3) Data and Information Management; (4) Integration and Coordination; and (5) Decisionmaking and Evaluation. The three new work activities were to (1) compile existing water data for the entire WLCI region and (2) develop regional curves (statistical models) for relating bankfull-channel geometry and discharge to drainages in the WLCI region, both of which will help guide long-term monitoring of water resources; and (3) initiate a groundwater-monitoring network to evaluate potential effects of energy-development activities on groundwater quality where groundwater is an important source of public/private water supplies. Results of the FY2009 work to develop methods for assessing soil organic matter and mercury indicated that selenium and arsenic levels may be elevated in the Muddy Creek Basin; thus, the focus of that activity was shifted in FY2010 to evaluate biogeochemical cycling of elements in the basin. In FY2010, two ongoing activities were expanded with the addition of more sampling plots: (a) the study of how greater sage-grouse (Centrocercus urophasianus) use vegetation-treatment areas (sites added to the Moxa Arch Natural Gas Development area) and (2) the study of cheatgrass (Bromus tectorum) occurrence in burn treatments of the Little Mountain Ecosystem. The activity that entails evaluating relationships between ungulate herbivory and fire on aspen (Populus tremuloides) recruitment also was expanded to include relationships between stand characteristics of and herbivory on aspen in various ecohydrological settings. The USGS continued compiling data and developing geospatial products from all of its WLCI activities to support (1) ranking and prioritizing of proposed conservation projects, (2) developing the WLCI Integrated Assessment, and (3) developing the WLCI 5-year Conservation Action Plan. Two activities were completed in FY2010: (1) the conceptual modeling and indicator selection for monitoring resource conditions across the WLCI region, and (2) the literature review on effects of oil and gas development in western regions of the United States, both of which are in the last stages of publication.

Open-File Report↗

USGS1995

Journal news

Statistical power (and conversely, Type II error) is often ignored by biologists. Power is important to consider in the design of studies, to ensure that sufficient resources are allocated to address a hypothesis under examination. Deter- mining appropriate sample size when designing experiments or calculating power for a statistical test requires an investigator to consider the importance of making incorrect conclusions about the experimental hypothesis and the biological importance of the alternative hypothesis (or the biological effect size researchers are attempting to measure). Poorly designed studies frequently provide results that are at best equivocal, and do little to advance science or assist in decision making. Completed studies that fail to reject Ho should consider power and the related probability of a Type II error in the interpretation of results, particularly when implicit or explicit acceptance of Ho is used to support a biological hypothesis or management decision. Investigators must consider the biological question they wish to answer (Tacha et al. 1982) and assess power on the basis of biologically significant differences (Taylor and Gerrodette 1993). Power calculations are somewhat subjective, because the author must specify either f or the minimum difference that is biologically important. Biologists may have different ideas about what values are appropriate. While determining biological significance is of central importance in power analysis, it is also an issue of importance in wildlife science. Procedures, references, and computer software to compute power are accessible; therefore, authors should consider power. We welcome comments or suggestions on this subject.

Journal of Wildlife Management↗

USGS2001

Geologic studies in Alaska by the U.S. Geological Survey, 1999

The collection of nine papers that follow continue the series of U.S. Geological Survey (USGS) investigative reports in Alaska under the broad umbrella of the geologic sciences. The series presents new and sometimes preliminary findings that are of interest to earth scientists in academia, government, and industry; to land and resource managers; and to the general public. Reports presented in Geologic Studies in Alaska cover a broad spectrum of topics from various parts of the State (fig. 1), serving to emphasize the diversity of USGS efforts to meet the Nation's needs for earth-science information in Alaska. The papers in this volume are organized under the topics: Hazards, Geologic Framework, Environment and Climate, and Resources. This organization is intended to reflect the scope and objectives of USGS geologic programs currently active in Alaska. The two Hazards studies discuss volcano-related topics in the seismically active southcentral Alaska region. The first paper revisits the eruptive events of Redoubt Volcano that occurred more than a decade ago and the subsequent development of the Alaska Volcano Observatory (AVO). This treatise documents the historic impact of this eruption and briefly summarizes the state of our knowledge of the other Cook Inlet, Alaska Peninsula, and Aleutian Island volcanoes. Finally, it discusses the recent role that AVO has had in seismic station installation and hazard assessment at volcanically active sites throughout the world. The second paper discusses the eruptive history of Snowy Mountain in the upper Alaska Peninsula. Because subsets of its 25-30 lava flows erupted as packages in short episodes, calculation of the volcano's lifetime average volumetric eruption rate is problematic. A portion of the cone was hydrothermally weakened and collapsed in the late Holocene producing a 22-km2 debris avalanche. Geologic Framework studies provide background information that is the scientific basis for present and future earth science investigations. The first paper compares and contrasts the Insular-Intermontane suture zone (IISZ) of southeast Alaska with the Adria-Europe suture zone (AESZ) of Switzerland and Hungary. The study develops the hypothesis that the zones have distinct differences as well as similarities and neither is a simple lithotectonic terrane boundary. The second paper discusses the relation among volcanic, glacial, and tectonic activity in the Cold Bay and False Pass 1 :250,000-scale quadrangles on the Alaska Peninsula. During Pleistocene time, continental-shelf glaciations and two massive volcanic centers were the dominant controls over landscape development. The third paper gives detailed geologic information for Paleozoic rocks within the Taylor Mountains D-1 quadrangle portion of the Holitna Lowland of southwestern Alaska. Because of the excellent preservation of megafossils, these Silurian and Ordovician strata lend themselves to detailed statigraphic investigations. Further, low thermal alteration indices of this area have made them a potential target of petroleum exploration. The final report in this section discusses the development of a new spectral enhancement approach for interpreting Multispectral Scanner (MSS) and Thematic Mapper (TM) satellite images. This technique enhances the use of remote sensing data in identifying geologic units in areas that have been poorly investigated. This study used this technique to better define the distribution of a JMtu (mafic, ultramafic, and sedimentary) unit and a PzZrqs (pelitic and quartzitic schist) unit. Environment and climate studies are the emphasis of two papers. One presents the first radiocarbon-dated postglacial vegetation history of the Kenai Mountains of southcentral Alaska. This reconstruction is the result of the analysis of pollen assemblages and peat from sediments collected in Tern Lake and presents a minimum age for deglaciation of these interior valleys at 9,31 0±200 yr B .P. Current vegetation, however, developed within the past ca. 2,500 years. A second study discusses the cycling of arsenic and cadmium in sub-arctic boreal forest ecosystems typical of interior Alaska and defines the importance of various natural (geogenic) sources. The transport and uptake into vegetation of these elements from soils developed from loess as well as soils developed from the major rock units is presented. The bioaccumulation of cadmium in willow (Salix sp.) and its potential consequence to the health of browsing animals is discussed. Papers related to resource issues comprise the topic of the final report. This paper presents a brief statistical summary of the geochemistry of rock samples collected in the east-central portion of the Eagle 1 :250,000-scale quadrangle. This study helps define the rock unit source of both resource- and environmental-based chemical elements of interest in the Fortymile mining district. Two bibliographies at the end of the volume list reports covering Alaska earth science topics in USGS publications during 1999 and reports about Alaska by USGS authors in non-USGS publications during the same period.

Alaska↗

USGS2019

Patterning emergent marsh vegetation assemblages in coastal Louisiana, USA, with unsupervised artificial neural networks

Question Are self‐organizing maps (SOMs) useful for patterning coastal wetland vegetation communities? Do SOMs provide robust alternatives to traditional classification methods, particularly when underlying species response functions are unknown or difficult to approximate, or when a need exists to continuously classify new samples obtained under ongoing long‐term ecosystem monitoring programs as they become available?. Location Coastal Louisiana, USA. Methods A SOM was trained from in situ observations of 559 vegetation species relative cover data from 2526 samples collected over eight years at 343 locations across coastal Louisiana. Hierarchical cluster analysis was applied to the SOM output to delineate vegetation community types, and indicator species analysis was conducted. Salinity and flood duration were compared across the delineated community types. Results The SOM patterned the 2526 training samples into 260 output neurons, which were further clustered into eleven community types. Clear gradients in salinity and flood duration existed among the community types, and geographic zonation of the communities was evident across the landscape. At some locations assemblages were temporally stable; at other locations they varied considerably. Samples not used in training the network were effectively projected onto the SOM and assigned to one of the delineated community types. Conclusions The SOM was effective in delineating plant communities in the region that were qualitatively similar to those obtained in previous investigations. Being robust to skewed distributions and the presence of outliers, SOMs provide an alternative to traditional distribution‐based statistical approaches. Their ability to efficiently classify new data into existing community types makes their use an ideal approach to classifying samples obtained from ongoing, long‐term ecological monitoring programs.

Louisiana↗