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USGS2024

Perspectives on equitable co-production workshop report

The co-production of knowledge is increasingly recognized as an approach to conducting research intended to achieve a societal impact. In this study, we used a broad definition of co-production, defining it as “a process that brings together diverse groups to iteratively create new knowledge and practices (1).” However, co-production has been defined and conceptualized in a variety of ways (2,3), across multiple domains, including public administration, conservation, health, education, and climate change. Theoretical definitions have been introduced by scholars like Jasanoff (4) and Ostrom (5), but definitions can also be grounded in practice (6). For example, unique definitions of co-production have been advanced for work with Arctic Indigenous Peoples (7), in the context of resource management (8), and for a specific program (9). Other similar processes of engagement, such as community-based participatory research (10), action research (11), civic science (12), community science (13), and post-normal science (14) may have overlap with the concept of co-production and have been used to describe similar processes of collaboration. These distinctions and varying definitions have been discussed extensively elsewhere (see Mach et al. 2020, Wyborn et al. 2019). In the context of co-production, power plays a crucial role in shaping interactions and outcomes. Some scholars and practitioners explicitly consider power dynamics as a central element in their definitions of co-production, recognizing how power imbalances can affect participation, decision-making, and the distribution of benefits. Others, however, might not emphasize power as prominently, focusing more on the collaborative aspects without explicitly addressing the underlying power structures. This leads to divergent objectives and priorities among projects claiming to be co-produced (2,3, 6). Chambers and colleagues (2) discussed how co-production projects in the context of sustainability usually emphasize one or more of six different goals, including: researching solutions, empowering voices, brokering power, reframing power, navigating differences, and reframing agency. Because power dynamics are inherent in co-production (15), equity dimensions should be considered in these definitions and conceptualizations. Yet, in the context of government or academically led climate change research and programs, equity is a relatively new focus, even among programs that have been engaging a co-production approach for decades (9). Alternatively, in some recent work the concept of equity in co-production is explicit, but it has only been considered in a limited context (7). Here, we present a discussion about co-production that is informed by research, practice, and community perspectives across partnerships from a range of regions and topics. We are specifically interested in how different actors in these projects think about equity and work towards more equitable approaches in the context of their co-production work. This understanding is needed, as the federal government has increasingly focused on co-production approaches in the design of their programs and funding calls, and most recently the Biden administration has called on federal agencies to more intentionally center equity for underserved groups of people in their work (16). Furthermore, with the Biden administration’s focus on environmental and climate justice, the opportunity for researchers and their societal partners to engage in co-production is expanding. Numerous programs within federal agencies have embraced a co-production approach, such as the National Oceanic and Atmospheric Administration (NOAA) Climate Adaptation Partnerships (CAP; formerly called the Regional Integrated Sciences and Assessments or RISA program) (9,17), Department of the Interior (DOI) Climate Adaptation Science Centers (CASCs)(18), and the US Department of Agriculture (USDA) Climate Hubs. However, the actual implementation of co-production processes varies significantly (1,19), with multiple implications for the design of equitable partnerships. Researchers, their partners, and funders have frequently cited many tensions and challenges in the successful implementation of co-production, including higher resource demands and few systemic structures for support (20). Practically implementing co-production, especially with people who have been underrepresented in or historically excluded from research activities, must consider fairness and the accessibility of co-production processes. While co-production is often cited as important for environmental governance, issues like power and equity are infrequently addressed (15). To explore this topic, we identified and studied three projects that centered on equity in co-production from three federal climate programs (CASC, CAP, USDA Climate Hubs) in three different regions of the U.S. (Alaska, Northeast, Southeast). We aimed to identify consensus or divergence in perspectives related to equitable co-production processes to elevate effective practices and link co-production research and practice. Findings from interviews and a survey (explained further in Akerlof et al., 2023) informed a twoday hybrid workshop involving participants from the three case studies, as well as individuals representing research, governmental, non-governmental, and community organizations across the United States. Participants also included scholars of co-production, program coordinators, and people who participated in co-production projects on behalf of their communities. Several boundary spanners, those practitioners who work at the intersection of the production and use of science (21,22), also attended the workshop. The goals of the workshop were to discuss and build on what was learned from the three case studies, discuss the three distinct perspectives on equitable co-production that emerged from the pre-workshop research, and draft a framework for equitable co-production processes. During the workshop, participants considered the three perspectives on equitable co-production, defining equitable co-production for each and discussing the practical implications of each, including barriers and priorities for overcoming them. We aimed to address the question: How can federal climate programs support equitable co-production processes?

Report↗

USGS2016

Estimating species – area relationships by modeling abundance and frequency subject to incomplete sampling

Models and data used to describe species–area relationships confound sampling with ecological process as they fail to acknowledge that estimates of species richness arise due to sampling. This compromises our ability to make ecological inferences from and about species–area relationships. We develop and illustrate hierarchical community models of abundance and frequency to estimate species richness. The models we propose separate sampling from ecological processes by explicitly accounting for the fact that sampled patches are seldom completely covered by sampling plots and that individuals present in the sampling plots are imperfectly detected. We propose a multispecies abundance model in which community assembly is treated as the summation of an ensemble of species-level Poisson processes and estimate patch-level species richness as a derived parameter. We use sampling process models appropriate for specific survey methods. We propose a multispecies frequency model that treats the number of plots in which a species occurs as a binomial process. We illustrate these models using data collected in surveys of early-successional bird species and plants in young forest plantation patches. Results indicate that only mature forest plant species deviated from the constant density hypothesis, but the null model suggested that the deviations were too small to alter the form of species–area relationships. Nevertheless, results from simulations clearly show that the aggregate pattern of individual species density–area relationships and occurrence probability–area relationships can alter the form of species–area relationships. The plant community model estimated that only half of the species present in the regional species pool were encountered during the survey. The modeling framework we propose explicitly accounts for sampling processes so that ecological processes can be examined free of sampling artefacts. Our modeling approach is extensible and could be applied to a variety of study designs and allows the inclusion of additional environmental covariates.

Ecology and Evolution↗

USGS2002

Establishing aquatic restoration priorities using a watershed approach

Since the passage of the Clean Water Act in 1972, the United States has made great strides to reduce the threats to its rivers, lakes, and wetlands from pollution. However, despite our obvious successes, nearly half of the nation’s surface water resources remain incapable of supporting basic aquatic values or maintaining water quality adequate for recreational swimming. The Clean Water Act established a significant federal presence in water quality regulation by controlling point and non-point sources of pollution. Point-sources of pollution were the major emphasis of the Act, but Section 208 specifically addressed non-point sources of pollution and designated silviculture and livestock grazing as sources of non-point pollution. Non-point source pollutants include runoff from agriculture, municipalities, timber harvesting, mining, and livestock grazing. Non-point source pollution now accounts for more than half of the United States water quality impairments. To successfully improve water quality, restoration practitioners must start with an understanding of what ecosystem processes are operating in the watershed and how they have been affected by outside variables. A watershed-based analysis template developed in the Pacific Northwest can be a valuable aid in developing that level of understanding. The watershed analysis technique identifies four ecosystem scales useful to identify stream restoration priorities: region, basin, watershed, and site. The watershed analysis technique is based on a set of technically rigorous and defensible procedures designed to provide information on what processes are active at the watershed scale, how those processes are distributed in time and space. They help describe what the current upland and riparian conditions of the watershed are and how these conditions in turn influence aquatic habitat and other beneficial uses. The analysis is organized as a set of six steps that direct an interdisciplinary team of specialists to examine the biotic and abiotic processes influencing aquatic habitat and species abundance. This process helps develop an understanding of the watershed within the context of the larger ecosystem. The understanding gained can then be used to identify and prioritize aquatic restoration activities at the appropriate temporal and spatial scale. The watershed approach prevents relying solely on site-level information, a common problem with historic restoration efforts. When the watershed analysis process was used in the Whitefish Mountains of northwest Montana, natural resource professionals were able to determine the dominant habitat forming processes important for native fishes and use that information to prioritize, plan, and implement the appropriate restoration activities at the watershed scale. Despite considerable investments of time and resources needed to complete an analysis at the watershed scale, the results can prevent the misdiagnosis of aquatic problems and help ensure that the objectives of aquatic restoration will be met.

Journal of Environmental Management↗

USGS2007

Interactions across spatial scales among forest dieback, fire, and erosion in northern New Mexico landscapes

Ecosystem patterns and disturbance processes at one spatial scale often interact with processes at another scale, and the result of such cross-scale interactions can be nonlinear dynamics with thresholds. Examples of cross-scale pattern-process relationships and interactions among forest dieback, fire, and erosion are illustrated from northern New Mexico (USA) landscapes, where long-term studies have recently documented all of these disturbance processes. For example, environmental stress, operating on individual trees, can cause tree death that is amplified by insect mortality agents to propagate to patch and then landscape or even regional-scale forest dieback. Severe drought and unusual warmth in the southwestern USA since the late 1990s apparently exceeded species-specific physiological thresholds for multiple tree species, resulting in substantial vegetation mortality across millions of hectares of woodlands and forests in recent years. Predictions of forest dieback across spatial scales are constrained by uncertainties associated with: limited knowledge of species-specific physiological thresholds; individual and site-specific variation in these mortality thresholds; and positive feedback loops between rapidly-responding insect herbivore populations and their stressed plant hosts, sometimes resulting in nonlinear “pest” outbreak dynamics. Fire behavior also exhibits nonlinearities across spatial scales, illustrated by changes in historic fire regimes where patch-scale grazing disturbance led to regional-scale collapse of surface fire activity and subsequent recent increases in the scale of extreme fire events in New Mexico. Vegetation dieback interacts with fire activity by modifying fuel amounts and configurations at multiple spatial scales. Runoff and erosion processes are also subject to scale-dependent threshold behaviors, exemplified by ecohydrological work in semiarid New Mexico watersheds showing how declines in ground surface cover lead to non-linear increases in bare patch connectivity and thereby accelerated runoff and erosion at hillslope and watershed scales. Vegetation dieback, grazing, and fire can change land surface properties and cross-scale hydrologic connectivities, directly altering ecohydrological patterns of runoff and erosion. The interactions among disturbance processes across spatial scales can be key drivers in ecosystem dynamics, as illustrated by these studies of recent landscape changes in northern New Mexico. To better anticipate and mitigate accelerating human impacts to the planetary ecosystem at all spatial scales, improvements are needed in our conceptual and quantitative understanding of cross-scale interactions among disturbance processes.

New Mexico↗

USGS1958

The solusphere - its inferences and study

Water is a fundamental geologic agent active in rock decomposition, erosion, and synthesis. Solutes in water are of particular interest to geochemists as sources of raw material for synthesis or as products of decomposition. When geochemical studies move from the laboratory into natural environment many variables relating to solute hydrology must be considered. As a focal point there has been designed a graphical representation of solute hydrology, the solusphere, which embodies the concepts of land-water occurrence and movement on which are superimposed geologic, biologic, physical, chemical, and cultural processes affecting solutes. The solusphere is demonstrated by passing an imaginary plane through the centre of the earth. This plane intercepts concentric zones designated as rock flowage, saturation, aeration, surface activity, and atmosphere. Transport processes carry solutes within and between zones without alteration or conversion. However, whether stationary or in motion, the water's solute character is constantly subject to (1) alteration processes that change concentration by addition or subtraction of solutes or solvent without loss of solute identities, and (2) conversion processes that change the chemical state and form of solutes. The geochemist is concerned with specific conversion processes, but he also must consider transport, alteration, and other conversion processes that are continually modifying the materials with which he is dealing in nature. The solusphere is an attempt to organize processes affecting the chemical quality of land waters into a unified field of science much like the field of marine chemistry.

Geochimica et Cosmochimica Acta↗

USGS2019

Geomorphic controls on hyporheic exchange across scales - Watersheds to particles

We examined the relationship between fluvial geomorphology and hyporheic exchange flows. We use geomorphology as a framework to understand hyporheic processes and how these processes change with location within a stream network, and over time in response to changes in stream discharge and catchment wetness. We focus primarily on hydrostatic and hydrodynamic processes—the processes where linkages to fluvial geomorphology are most direct. Hydrostatic processes result from morphologic features that create elevational head gradients whereas hydrodynamic processes result from the interaction between stream flow and channel morphologic features. We provide examples of the specific morphologic features that drive or enable hyporheic exchange and we examine how these processes interact in real stream networks to create complex subsurface flow nets through the hyporheic zone.

Book chapter↗

USGS2017

Observations and 3D hydrodynamics-based modeling of decadal-scale shoreline change along the Outer Banks, North Carolina

Long-term decadal-scale shoreline change is an important parameter for quantifying the stability of coastal systems. The decadal-scale coastal change is controlled by processes that occur on short time scales (such as storms) and long-term processes (such as prevailing waves). The ability to predict decadal-scale shoreline change is not well established and the fundamental physical processes controlling this change are not well understood. Here we investigate the processes that create large-scale long-term shoreline change along the Outer Banks of North Carolina, an uninterrupted 60 km stretch of coastline, using both observations and a numerical modeling approach. Shoreline positions for a 24-yr period were derived from aerial photographs of the Outer Banks. Analysis of the shoreline position data showed that, although variable, the shoreline eroded an average of 1.5 m/yr throughout this period. The modeling approach uses a three-dimensional hydrodynamics-based numerical model coupled to a spectral wave model and simulates the full 24-yr time period on a spatial grid running on a short (second scale) time-step to compute the sediment transport patterns. The observations and the model results show similar magnitudes (O(10 5 m 3 /yr)) and patterns of alongshore sediment fluxes. Both the observed and the modeled alongshore sediment transport rates have more rapid changes at the north of our section due to continuously curving coastline, and possible effects of alongshore variations in shelf bathymetry. The southern section with a relatively uniform orientation, on the other hand, has less rapid transport rate changes. Alongshore gradients of the modeled sediment fluxes are translated into shoreline change rates that have agreement in some locations but vary in others. Differences between observations and model results are potentially influenced by geologic framework processes not included in the model. Both the observations and the model results show higher rates of erosion (∼−1 m/yr) averaged over the northern half of the section as compared to the southern half where the observed and modeled averaged net shoreline changes are smaller (<0.1 m/yr). The model indicates accretion in some shallow embayments, whereas observations indicate erosion in these locations. Further analysis identifies that the magnitude of net alongshore sediment transport is strongly dominated by events associated with high wave energy. However, both big- and small- wave events cause shoreline change of the same order of magnitude because it is the gradients in transport, not the magnitude, that are controlling shoreline change. Results also indicate that alongshore momentum is not a simple balance between wave breaking and bottom stress, but also includes processes of horizontal vortex force, horizontal advection and pressure gradient that contribute to long-term alongshore sediment transport. As a comparison to a more simple approach, an empirical formulation for alongshore sediment transport is used. The empirical estimates capture the effect of the breaking term in the hydrodynamics-based model, however, other processes that are accounted for in the hydrodynamics-based model improve the agreement with the observed alongshore sediment transport.

North Carolina↗

USGS2004

Recycling of construction debris as aggregate in the Mid-Atlantic Region, USA

Reclaimed asphalt pavement (RAP) and portland cement concrete (RPCC) are abundant and available substitutes for natural aggregate in many areas. This paper presents an overview of factors that affect recycled aggregate cost, availability, and engineering performance, and the results of a survey of business practices in the Mid-Atlantic region. For RAP, processing costs are less than those for virgin natural aggregate. Use of efficient asphalt pavement stripping technology, on-site reclamation, and linked two-way transport of asphalt debris and processed asphalt paving mix between asphalt mix plants and paving sites has led to extensive recycling of asphalt pavement in the Mid-Atlantic region of the US. Most of the sites that recycle asphalt pavement (RAP) are located in or near urban areas close to important transportation corridors. RPCC is a viable aggregate source in urban settings where unit costs for processed aggregate from RPCC and natural aggregate are comparable. Disposal fees charged at RPCC recycling sites help defray processing costs and the significantly lower tipping fees at recycling sites versus landfill disposal sites encourage recycling of construction debris as aggregate. Construction contractors and construction debris recycling centers, many of which have the ability to crush and process concrete debris at the job site, produce most RPCC. Production of RPCC aggregate from construction debris that is processed on site using portable equipment moved to the construction site eliminates transportation costs for aggregate and provides an economic incentive for RPCC use. Processing costs, quality and performance issues, and lack of large quantities where needed limit RPCC use. Most RPCC suppliers in the Mid-Atlantic area are located in counties with population densities greater than 400 people/km2 (1036 people/mile2) and that have high unit-value costs and limited local availability of natural aggregate. ?? 2004 Published by Elsevier B.V.

Conference Paper↗

USGS2020

Animal movement models with mechanistic selection functions

A suite of statistical methods are used to study animal movement. Most of these methods treat animal trajectory data in one of three ways: as discrete pro- cesses, as continuous processes, or as point processes. We brie y review each of these approaches and then focus in on the latter. In the context of point processes, so-called resource selection analyses are among the most common way to statis- tically treat animal trajectory data. However, most resource selection analyses provide inference based on approximations of point process models. The forms of these models have been limited to a few types of specications that provide infer- ence about relative resource use and, less commonly, probability of use. For more general spatio-temporal point process models, the most common type of analysis often proceeds with a data augmentation approach that is used to create a binary data set that can be analyzed with conditional logistic regression. We show that the conditional logistic regression likelihood can be generalized to accommodate a variety of alternative specications related to resource selection. We then provide an example of a case where a spatio-temporal point process model coincides with that implied by a mechanistic model for movement expressed as a partial dier- ential equation derived from rst principles of movement. We demonstrate that inference from this form of point process model is intuitive (and could be useful for management and conservation) by analyzing a set of telemetry data from a mountain lion in Colorado, USA, to understand the eects of spatially explicit environmental conditions on movement behavior of this species.

Spatial Statistics↗

USGS2003

Analysis of a municipal wastewater treatment plant using a neural network-based pattern analysis

This paper addresses the problem of how to capture the complex relationships that exist between process variables and to diagnose the dynamic behaviour of a municipal wastewater treatment plant (WTP). Due to the complex biological reaction mechanisms, the highly time-varying, and multivariable aspects of the real WTP, the diagnosis of the WTP are still difficult in practice. The application of intelligent techniques, which can analyse the multi-dimensional process data using a sophisticated visualisation technique, can be useful for analysing and diagnosing the activated-sludge WTP. In this paper, the Kohonen Self-Organising Feature Maps (KSOFM) neural network is applied to analyse the multi-dimensional process data, and to diagnose the inter-relationship of the process variables in a real activated-sludge WTP. By using component planes, some detailed local relationships between the process variables, e.g., responses of the process variables under different operating conditions, as well as the global information is discovered. The operating condition and the inter-relationship among the process variables in the WTP have been diagnosed and extracted by the information obtained from the clustering analysis of the maps. It is concluded that the KSOFM technique provides an effective analysing and diagnosing tool to understand the system behaviour and to extract knowledge contained in multi-dimensional data of a large-scale WTP. ?? 2003 Elsevier Science Ltd. All rights reserved.

Water Research↗

USGS2018

Understanding how microbiomes influence the systems they inhabit

Translating the ever-increasing wealth of information on microbiomes (environment, host, or built environment) to advance the understanding of system-level processes is proving to be an exceptional research challenge. One reason for this challenge is that relationships between characteristics of microbiomes and the system-level processes they influence are often evaluated in the absence of a robust conceptual framework and reported without elucidating the underlying causal mechanisms. The reliance on correlative approaches limits the potential to expand the inference of a single relationship to additional systems and advance the field. We propose that research focused on how microbiomes influence the systems they inhabit should work within a common framework and target known microbial processes that contribute to the system-level processes of interest. Here we identify three distinct categories of microbiome characteristics (microbial processes, microbial community properties, and microbial membership) and propose a framework to empirically link each of these categories to each other and the broader system level processes they affect. We posit that it is particularly important to distinguish microbial community properties that can be predicted from constituent taxa (community aggregated traits) from and those properties that are currently unable to be predicted from constituent taxa (emergent properties). Existing methods in microbial ecology can be applied to more explicitly elucidate properties within each of these categories and connect these three categories of microbial characteristics with each other. We view this proposed framework, gleaned from a breadth of research on environmental microbiomes and ecosystem processes, as a promising pathway with the potential to advance discovery and understanding across a broad range of microbiome science.

Nature Microbiology↗

USGS2025

Wave driven cross shore and alongshore transport reveal more extreme projections of shoreline change in island environments

Coastal erosion, intensified by sea level rise, poses significant threats to coastal communities in Hawaiʻi and similar island communities. This study projects long-term shoreline change on the Hawaiian Island of O‘ahu using the data-assimilated CoSMoS-COAST shoreline change model. CoSMoS-COAST models four key shoreline processes: (1) Alongshore transport, (2) Recession due to sea level rise, (3) Cross-shore transport due to waves, and (4) Residual processes represented by a linear trend term. This study marks the first application of CoSMoS-COAST for an oceanic equatorial island with narrow beaches and a dynamic wave climate. The model is informed with a novel combination of shoreline data derived from high-resolution imagery from Planet, Sentinel-2, and Landsat satellites, wave-climate hindcasts specific to Hawai‘i, and regional beach-slope surveys. On a dynamic northern Oʻahu beach, the model achieved a root mean square error of 9.4 m between observations and model output. CoSMoS-COAST predicts that 81% of O‘ahu’s sandy beach coastline could experience beach loss by 2100; with 39.8% of this loss happening by 2030. This represents an increase, 43.3%, in net landward shoreline change compared to previous erosion forecasts, for 0.3 m of sea level rise (2050). Additionally, dynamic processes such as cross-shore equilibrium processes and alongshore sediment transport, play a large contribution to gross shoreline change within the next decade, particularly on O ‘ahu’s north and west shores. In the long term, we find that recession due to sea level rise and residual processes dominate, but dynamic, wave-driven processes (longshore and cross-shore transport) still account for 34% of shoreline change between present and 2100. We assert dynamic, wave-driven processes are a crucial addition for accurate modeling of island sandy beach environments. These findings have implications for O‘ahu’s coastal planning and development, suggesting updates to shoreline policies that rely upon erosion forecasting, and highlights the importance of incorporating wave and alongshore transport in erosion models for other Pacific islands.

Hawaii↗

USGS2010

Coupled arsenotrophy in a hot spring photosynthetic biofilm at Mono Lake, California

Red-pigmented biofilms grow on rock and cobble surfaces present in anoxic hot springs located on Paoha Island in Mono Lake. The bacterial community was dominated (∼ 85% of 16S rRNA gene clones) by sequences from the photosynthetic Ectothiorhodospira genus. Scraped biofilm materials incubated under anoxic conditions rapidly oxidized As(III) to As(V) in the light via anoxygenic photosynthesis but could also readily reduce As(V) to As(III) in the dark at comparable rates. Back-labeling experiments with 73 As(V) demonstrated that reduction to 73 As(III) also occurred in the light, thereby illustrating the cooccurrence of these two anaerobic processes as an example of closely coupled arsenotrophy. Oxic biofilms also oxidized As(III) to As(V). Biofilms incubated with [ 14 C]acetate oxidized the radiolabel to 14 CO 2 in the light but not the dark, indicating a capacity for photoheterotrophy but not chemoheterotrophy. Anoxic, dark-incubated samples demonstrated As(V) reduction linked to additions of hydrogen or sulfide but not acetate. Chemoautotrophy linked to As(V) as measured by dark fixation of [ 14 C]bicarbonate into cell material was stimulated by either H 2 or HS − . Functional genes for the arsenate respiratory reductase ( arrA ) and arsenic resistance ( arsB ) were detected in sequenced amplicons of extracted DNA, with about half of the arrA sequences closely related (∼98% translated amino acid identity) to those from the family Ectothiorhodospiraceae . Surprisingly, no authentic PCR products for arsenite oxidase ( aoxB ) were obtained, despite observing aerobic arsenite oxidation activity. Collectively, these results demonstrate close linkages of these arsenic redox processes occurring within these biofilms. Oxyanions of the group 15 element arsenic, arsenate [As(V)] and arsenite [As(III)], have been known for millennia to be potent poisons. Despite its well-established toxicity to life, the phenomenon of arsenic resistance was discovered whereby some microorganisms maintain an otherwise “normal” existence in the presence of high concentrations of As(V) or As(III) (17, 29, 31). More recently it has become recognized that certain representatives from the bacterial and archaeal domains can actually exploit the electrochemical potential of the As(V)/As(III) redox couple (+130 mV) to gain energy for growth. This can be achieved either by employing As(III) as an autotrophic electron donor or by using As(V) as a respiratory electron acceptor (18, 21, 34). The latter phenomenon, although most commonly associated with chemoheterotrophy, can also employ inorganic substances like sulfide or H 2 . Indeed, As(V)-respiring anaerobes displaying a capacity for chemoautotrophy with these electron donors have been isolated and described (5, 7, 16). We recently reported that photoautotrophy is supported by As(III) in anoxic biofilms located in hot springs on Paoha Island in Mono Lake, CA (15). This process represented a novel means of As(III) oxidation achieved via anoxygenic photosynthesis occurring in certain photosynthetic bacteria (i.e., Ectothiorhodospira ) and possibly within some cyanobacteria as well (e.g., “ Oscillatoria ”). Whether or not a microbial habitat is overtly oxic or anoxic, or temporally shifts between these two states over a diel cycle, critical energy linkages between aerobes and anaerobes have long been known for the biogeochemical cycles of key elements, such as sulfur, iron, and nitrogen. Most prominently studied is the case of nitrogen, whereby an ecological coupling exists between the processes of nitrification and denitrification (9, 10, 28). The former process provides energy to aerobic nitrifiers, while the latter process consumes the nitrate produced by this reaction, thereby meeting the energy needs of the denitrifiers. For arsenic, the detection of both As(III) oxidation and As(V) reduction in oxic and anoxic incubations of freshly collected periphyton suggested that an analogous coupled process may also occur for this element (12). Similarly, several uncontaminated soils in Japan displayed a capacity for either As(V) reduction or As(III) oxidation upon arsenic oxyanion amendment and whether they were incubated under oxic or anoxic conditions (39). A defined coculture consisting of an aerobic As(III) oxidizer (strain OL1) and an anaerobic As(V) respirer (strain Y5) was shown to function in this fashion under manipulated laboratory conditions of oxygen tension (26). We pursued the phenomenon of coupled arsenic metabolism further by using materials collected from the hot spring biofilms in Mono Lake, but we focused on examination of the cycling of arsenic under anoxic conditions. In this paper we report results obtained by manipulated incubations of red-pigmented biofilms found in the hot springs of Paoha Island. Preliminary community characterizations of these biofilms show that they are dominated by B acteria from the genus Ectothiorhodospira but also harbor an assemblage of Archaea related to the Halobacteriacaea . Incubation results have demonstrated the presence of the following arsenic metabolic activities: respiratory As(V) reduction, photosynthetic anaerobic As(III) oxidation, and aerobic As(III) oxidation, along with the ecophysiological conditions under which they occur. Surprisingly, we were unable to obtain authentic PCR products for arsenite oxidase genes ( aoxB ), despite observing aerobic As(III) oxidation activity. These biofilms serve as a model system for how anaerobic cycling of arsenic can be sustained with oxidation of As(III) by anoxygenic photosynthesis coupled to regeneration of this electron donor via dissimilatory As(V) reduction. The significance that such a light-driven anaerobic ecosystem may have played in the Archean Earth is discussed.

California↗

USGS2023

Introduction to the digitization of seismic data: A user’s guide

Modern seismic data are collected, distributed, and analyzed using digital formats, and this has become a standard for the field. Although most modern seismometers still make use of analog electronic circuits, their data are converted from an analog voltage output to time‐tagged counts by way of digitization. Although much of the digitization process is not complicated to conceptualize, there is a fair bit of jargon in digitizer specifications, and a few pitfalls that can arise in the processes of recording and analyzing ground‐motion data. In this article, we review some of the fundamental physical properties of data acquisition systems and the basic steps in digitizing data from an analog instrument (specifically a seismometer). We then briefly discuss the digitization process and some of the key properties needed to make these data useful for seismological applications. Finally, we discuss some of the filtering processes that naturally arise from digitization and how it can affect the processing workflow. The end goal is to provide a user guide that will enable seismologists to have a working knowledge of the digitization process. We focus on aspects central to seismological applications and have tried to avoid getting bogged down in signal processing formalism.

Seismological Research Letters↗

USGS2004

Archive of Digital Boomer Seismic Reflection Data Collected During USGS Field Activities 93LCA01 and 94LCA01 in Kingsley, Orange, and Lowry Lakes, Northeast Florida, 1993 and 1994

In August and September of 1993 and January of 1994, the U.S. Geological Survey, under a cooperative agreement with the St. Johns River Water Management District (SJRWMD), conducted geophysical surveys of Kingsley Lake, Orange Lake, and Lowry Lake in northeast Florida. This report serves as an archive of unprocessed digital boomer seismic reflection data, trackline maps, navigation files, GIS information, observer's logbook, Field Activity Collection System (FACS) logs, and formal FGDC metadata. A filtered and gained GIF image of each seismic profile is also provided. Refer to the Acronyms page for expansion of acronyms and abbreviations used in this report. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Examples of SU processing scripts and in-house (USGS) software for viewing SEG-Y files (Zihlman, 1992) are also provided. The data archived here were collected under a cooperative agreement with the St. Johns River Water Management District as part of the USGS Lakes and Coastal Aquifers (LCA) Project. For further information about this study, refer to http://coastal.er.usgs.gov/stjohns, Kindinger and others (1994), and Kindinger and others (2000). The USGS Florida Integrated Science Center (FISC) - Coastal and Watershed Studies in St. Petersburg, Florida, assigns a unique identifier to each cruise or field activity. For example, 93LCA01 tells us the data were collected in 1993 for the Lakes and Coastal Aquifers (LCA) Project and the data were collected during the first field activity for that project in that calendar year. For a detailed description of the method used to assign the field activity ID, see http://walrus.wr.usgs.gov/infobank/programs/html/definition/activity.html. The boomer is an acoustic energy source that consists of capacitors charged to a high voltage and discharged through a transducer in the water. The transducer is towed on a sled at the sea surface and when discharged emits a short acoustic pulse, or shot, that propagates through the water and sediment column. The acoustic energy is reflected at density boundaries (such as the seafloor or sediment layers beneath the seafloor), detected by the receiver, and recorded by a PC-based seismic acquisition system. This process is repeated at timed intervals (e.g., 0.5 s) and recorded for specific intervals of time (e.g., 100 ms). In this way, a two-dimensional vertical image of the shallow geologic structure beneath the ship track is produced. Acquisition geometery for 94LCA01 is recorded in the operations logbook. No logbook exists for 93LCA01. Table 1 displays acquisition parameters for both field activities. For more information about the acquisition equipment used, refer to the FACS equipment logs. The unprocessed seismic data are stored in SEG-Y format (Barry and others, 1975). For a detailed description of the data format, refer to the SEG-Y Format page. See the How To Download SEG-Y Data page for more information about these files. Processed profiles can be viewed as GIF images from the Profiles page. Refer to the Software page for details about the processing and examples of the processing scripts. Detailed information about the navigation systems used for each field activity can be found in Table 1 and the FACS equipment logs. To view the trackline maps and navigation files, and for more information about these items, see the Navigation page. The original trace files were recorded in nonstandard ELICS format and later converted to standard SEG-Y format. The original trace files for 94LCA01 lines ORJ127_1, ORJ127_3, and ORJ131_1 were divided into two or more trace files (e.g., ORJ127_1 became ORJ127_1a and ORJ127_1b) because the original total number of traces exceeded the maximum allowed by the processing system. Digital data were not recoverable for 93LCA

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USGS2001

The STRATAFORM Project: U.S. Geological Survey geotechnical studies

This report presents physical property logs of core samples from an offshore area near Eureka, CA. The cores were obtained as part of the STRATAFORM Program (Nittrouer and Kravitz, 1995, 1996), a study investigating how present sedimentation and sediment transport processes influence long-term stratigraphic sequences preserved in the geologic record. The core samples were collected during four separate research cruises to the northern California study area, and data shown in the logs of the cores were collected using a multi-sensor whole core logger. The physical properties collected are useful in identifying stratigraphic units, ground-truthing acoustic imagery and sub-bottom profiles, and in understanding mass movement processes. STRATA FOR mation on M argins was initiated in 1994 by the Office of Naval Research, Marine Geology and Geophysics Department as a coordinated multi-investigator study of continental-margin sediment transport processes and stratigraphy (Nittrouer and Kravitz, 1996). The program is investigating the stratigraphic signature of the shelf and slope parts of the continental margins, and is designed to provide a better understanding of the sedimentary record and a better prediction of strata. Specifically, the goals of the STRATAFORM Program are to (Nittrouer and Kravitz, 1995): - determine the geological relevance of short-term physical processes that erode, transport, and deposit particles and those processes that subsequently rework the seabed over time scales <10 2 years. - improve capabilities for identifying the processes that form the strata observed within the upper ~100 m of the seabed commonly representing 104-106 years of sedimentation. - synthesize this knowledge and bridge the gap between time scales of sedimentary processes and those of sequence stratigraphy. The STRATAFORM Program is divided into studies of the continental shelf and the continental slope; the geotechnical group within the U.S. Geological Survey provides support to both parts of the project.

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USGS2004

Incorporating uncertainty into mercury-offset decisions with a probabilistic network for National Pollutant Discharge Elimination System permit holders: An interim report

This interim report describes an alternative approach for evaluating the efficacy of using mercury (Hg) offsets to improve water quality. Hg-offset programs may allow dischargers facing higher-pollution control costs to meet their regulatory obligations by making more cost effective pollutant-reduction decisions. Efficient Hg management requires methods to translate that science and economics into a regulatory decision framework. This report documents the work in progress by the U.S. Geological Survey’s Western Geographic Science Center in collaboration with Stanford University toward developing this decision framework to help managers, regulators, and other stakeholders decide whether offsets can cost effectively meet the Hg total maximum daily load (TMDL) requirements in the Sacramento River watershed. Two key approaches being considered are: (1) a probabilistic approach that explicitly incorporates scientific uncertainty, cost information, and value judgments; and (2) a quantitative approach that captures uncertainty in testing the feasibility of Hg offsets. Current fate and transport-process models commonly attempt to predict chemical transformations and transport pathways deterministically. However, the physical, chemical, and biologic processes controlling the fate and transport of Hg in aquatic environments are complex and poorly understood. Deterministic models of Hg environmental behavior contain large uncertainties, reflecting this lack of understanding. The uncertainty in these underlying physical processes may produce similarly large uncertainties in the decisionmaking process. However, decisions about control strategies are still being made despite the large uncertainties in current Hg loadings, the relations between total Hg (HgT) loading and methylmercury (MeHg) formation, and the relations between control efforts and Hg content in fish. The research presented here focuses on an alternative analytical approach to the current use of safety factors and deterministic methods for Hg TMDL decision support, one that is fully compatible with an adaptive management approach. This alternative approach uses empirical data and informed judgment to provide a scientific and technical basis for helping National Pollutant Discharge Elimination System (NPDES) permit holders make management decisions. An Hg-offset system would be an option if a wastewater-treatment plant could not achieve NPDES permit requirements for HgT reduction. We develop a probabilistic decision-analytical model consisting of three submodels for HgT loading, MeHg, and cost mitigation within a Bayesian network that integrates information of varying rigor and detail into a simple model of a complex system. Hg processes are identified and quantified by using a combination of historical data, statistical models, and expert judgment. Such an integrated approach to uncertainty analysis allows easy updating of prediction and inference when observations of model variables are made. We demonstrate our approach with data from the Cache Creek watershed (a subbasin of the Sacramento River watershed). The empirical models used to generate the needed probability distributions are based on the same empirical models currently being used by the Central Valley Regional Water Quality Control Cache Creek Hg TMDL working group. The significant difference is that input uncertainty and error are explicitly included in the model and propagated throughout its algorithms. This work demonstrates how to integrate uncertainty into the complex and highly uncertain Hg TMDL decisionmaking process. The various sources of uncertainty are propagated as decision risk that allows decisionmakers to simultaneously consider uncertainties in remediation/implementation costs while attempting to meet environmental/ecologic targets. We must note that this research is on going. As more data are collected, the HgT and cost-mitigation submodels are updated and the uncertainties may be reduced. Subsequently, the value of using a probabilistic framework for estimating and explicitly stating these uncertainties within a decisionmaking process can be estimated when new data are collected. Future work includes the design and implementation of a Bayesian network decision support system (BN-DSS) to produce mitigation scenarios for offset-project evaluation in the Cache Creek watershed. The decisionmaker, a wastewater-treatment plant, is expected to evaluate potential Hg-offset programs in terms of changes in HgT load changes, MeHg-production potential, project cost, and other suitability criteria. Subsequently, scenarios can be analyzed by performing sensitivity analyses and ranking environmental and economic uncertainties in terms of the decisionmaker’s preferences and risk choices. Such an analysis allows decisionmakers and stakeholders to explore various scenarios and predict the consequences of different stated preferences over outcomes.

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USGS2006

Evaluation of some software measuring displacements using GPS in real-time

For the past decade, the USGS has been monitoring deformation at various locations in the western United States using continuous GPS. The main focus of these measurements are estimates of displacement averaged over one day. Essentially, these consist of recording at 30 seconds intervals the carrier-frequency phase-data (equivalent to travel-time) between a GPS receiver and the GPS satellite network. In turn, these observations, which are converted to pseudo—ranges, are processed using one of the “research grade” programs (GIPSY, Zumberge et al., or GAMIT, wwwgpsg.mit.edu/~simon/gtgk) to estimate the position of the GPS receiver averaged over 24 hours. However, it is possible and desirable to estimate the position of the receiver (actually the antenna) more frequently and to do this within a few seconds of the time actual measurement (known as real-time). A recent example, the 2004 Magnitude 6, Parkfield, California earthquake, demonstrated that having GPS estimates of position more frequently than simply a daily average is required if one requires discrimination between co-seismic and post-seismic deformation (Langbein et al., 2006). The high-rate estimates of position obtained at Parkfield show that post-seismic deformation started less than one-hour after the mainshock and that this deformation was roughly the same magnitude as the co-seismic deformation. The high-rate solutions for Parkfield were done by others including Yehuda Bock at UCSD and Kristine Larson at U. of Colorado, but not the USGS. The Parkfield experience points out the need for an in-house capability by the USGS to be able to accurately measure co-seismic displacements and other rapid, deformation signals using GPS. This applies to both the Earthquake and Volcano Hazard programs. Although at many locations where we monitor deformation, we have strainmeters and tiltmeters in addition to GPS which, in principle, are far more sensitive to rapid deformation over periods of less than a day (Langbein and Bock, 2004). But, not all locales include strain and tiltmeters. Thus, having the capability to extract signals with periods of less than a day is desirable since the distribution of GPS is more extensive than strain and tilt. At both Parkfield and Long Valley, the USGS has been using other software packages to process the GPS data at sub-daily intervals and in real-time. The underlying goal of these types of measurements is to detect any deformation event as it evolves; the 24 hour processing might not provide timely results if such a deformation event is precursory to a geologic hazard (an earthquake for Parkfield and either a volcanic event or an earthquake for Long Valley). In Long Valley, We use the software package called 3DTracker (http://www.3dtracker.com, http://www.condorearth.com) to estimate the changes of in position of a remote site relative to a “fixed” site. The 3DTracker software uses double difference GPS code measurements and receiversatellite-time triple differences from one epoch to the next of the GPS phase data (a proxy for travel-time measurements) and employs a Kalman filter to obtain stability in the estimate of position. That is, the estimate of the current position depends upon the estimate of the prior position. Hence, a time series of position looks fairly smooth depending upon the coefficient selected for the Kalman filter. With triple differences, the sometimes troublesome initial integer cycle ambiguity terms cancel (number of wavelengths between the receiver and each satellite), but only the incremental change in position is calculated. This triple difference Kalman filter solution is slow to converge and less accurate than a double difference (e.g., RTD, Track) solution, but it is robust and computationally efficient (Remondi and Brown, 2000). 3D-Tracker allows use of various single-frequency and dual-frequency GPS phase and code observables including the ionospheric-free combinations (known as LC or L3 and P(L3)) formed from an linear combination of the L1 and L2 carrier phase and code data. The lowest noise observable is the L1 carrier, but it is biased by ionospheric refraction that has amplitudes of about 1 to 10 ppm. This results in a systematic scale error in the relative positions. The L3 phase noise is about 3 times greater than the L1 phase noise, but it is generally used to solve for all but the shortest baselines (< 5 km). In addition, the software does output the position changes is a standard format that can be used for other analysis. At Parkfield, we use the software package called RTD (http://www.geodetics.com). The RTD software has been described in the literature (Bock et al., 2000) but basically, it estimates the position without the constraint of a Kalman filter. It uses double differences (in our studies the LC or ionospheric free observable is used) and the integer ambiguities are resolved independently for each 1-second measurement; Most GPS software that use double-differences require several epochs of measurements to resolve the integer ambiguities. The data files use a proprietary format and can not be read by me or others; rather, Yehuda Bock at UCSD (and author of RTD) translates these files into a standard format that can be read by me. Recently, Tom Herring of MIT has modified the GAMIT software to process kinematically GPS data (www-gpsg.mit.edu/~simon/gtgk/tutorial/Lecture_13.pdf). At this time, the software, known as TRACK, does not process the observations in real-time. Consequently, the latency between the time of the observation and the time when a position estimate is available depends upon the frequency that the data are downloaded and the speed of actually processing the observations; there could be a delay of an hour or two before the a position estimates are available. Unlike RTD and 3DTracker, TRACK comes with GAMIT (which is distributed freely) and is currently operating in a test mode at the USGS office in Pasadena. The LC or ionosphere free observable is used in our TRACK solutions. JPL has a version of their GIPSY software called “Real-time GIPSY (RTG)” (gipsy.jpl.nasa.gov/orms/rtg), which, like TRACK, can process the pseudo-range data “off—line”. However, this software is not freely distributed. Instead, at least one company, NAVCOM, has teamed with JPL to integrate RTG with GPS receivers and telemetry that yields positions in realtime. Kristine Larson of University of Colorado has modified the original GIPSY to estimate positions kinematically. Again, like TRACK, the positions are estimated off—line. Much of her research is described in Larson et al. (2003), and Choi et al. (2004). For Long Valley, out of the 17 GPS sites, we monitor 5 baselines within the caldera at 5 second intervals relative to the Bald Mountain site at the edge of the caldera using 3DTracker. The baseline measurement using 3DTracker consists of determination of the 3 dimensional positions of the 5 remote points (GPS receivers) relative to a GPS site at Bald. A second, independent system collects and downloads once a day the 30-second data used for the 24-hour solutions for the 12 sites not monitored with 3DTracker. For the sites monitored with 3DTracker, the pseudo—range data are decimated to 30 seconds and converted to a form used for the 24-hour solutions. Both sets of telemetry employ 900 MHz spread spectrum radios which require line of site between all of the links. The telemetry for the 3DTracker sites require a dedicated radios at each end and intermediate repeaters as needed, while the telemetry required for the other sites use a single master radio, repeaters as needed, and a radio at each remote site. (The 5 sites being monitored with 3DTracker require 13 radios.) At Parkfield, RTD is used to measure the position changes all 12 baselines at 1 second intervals relative to a site, Pomm, adjacent to the San Andreas Fault. The complete RTD package (hardware and software) collects all of the data and determines the position of each site relative to Pomm. In addition, the system stores both the 1-second and 30-second pseudo-range data for later downloading which are ultimately used in the 24-hour solutions. To do this, each site has a 2.4 GHz radio and a telemetry buffer. The telemetry buffer holds 24-hours of data (in the event that the telemetry link is broken) and converts the RS232 data stream from the GPS receiver into a form compatible with an IP (Internet protocol) network connection. In contrast with the Long Valley system, the telemetry link for GPS at Parkfield consists of a single radio at each remote sites and a single radio at the central site. Although position estimates are produced within 1-second of the observations, these results are not immediately available because there is no high speed Internet connection to Parkfield. Instead, the data are stored on a removable disk and sent to UCSD once per month. Below, I describe the results of a simple experiment to examine the response of some of these systems to simulated deformation that could be an analogue of a tectonic or volcanic event. In many engineering applications, the system response is tested by inputting a step to the system and measuring the output of the system. Essentially, this is what I've done. The experiment described below moves the GPS antenna from its original position to a new position within 1 second; the software tracks the translation. These measurements were conducted in August 2004 with the RTD software at Parkfield, and twice in Long Valley. The first Long Valley test was conducted in September 2004 using 3DTracker on a single baseline. The test was repeated in September 2005 using 3DTracker on two baselines and, importantly, saving the RINEX files of the data so that the data could be replayed through 3DTracker using other options in the program and, using other software packages including TRACK. In addition, we observed a short-term event at the Three Sisters volcano in Oregon. This event was snow melt at a remote GPS site which gave an apparent 15 cm displacement in vertical in less than one-day. 3DTracker is used to monitor this site, and the event was captured with this software. In addition, with the assistance of others, I got additional estimates of position using other software packages; those results are presented. Finally, the precision of both 3DTracker and RTD are compared using a power spectrum. Those results would suggest that 3DTracker using appropriate Kalman filter coefficients would have better precision than RTD; instead, the lower noise level from 3DTracker is a result of smoothing from the Kalman filter. Given the results described in this report, high-rate GPS is certainly capable of accurately measuring displacements of 1 centimeter with a high degree of statistical confidence. Plotting these results show that the time of the displacement can be visually determined to that of the sampling interval of the data. However, especially with small amplitude signals, any of the software packages can yield erroneous deformation “signals” that are either due excess travel-time of the GPS carrier frequency from multipath or a limitation in the software. Thus, the time series of displacements must be viewed with caution and knowledge of external circumstances that might cause a change in position. The casual reader should continue with the next section describing the methods then jump to the last two sections for the discussion and conclusions. I have made some recommendations there.

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