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At least 1,117 records · Page 62Linked to original sources
USGS2023

Model-based assessment and mapping of total phosphorus enrichment in rivers with sparse reference data

Water nutrient management efforts are frequently coordinated across thousands of water bodies, leading to a need for spatially extensive information to facilitate decision making. Here we explore potential applications of a machine learning model of river low-flow total phosphorus (TP) concentrations to support landscape nutrient management. The model was trained, validated, and then applied for all rivers of Michigan, USA to identify potential drivers of nutrient variation, predict alteration in nutrient concentrations from minimally disturbed conditions, and explore reach specific sensitivity to riparian agricultural change. A boosted regression tree model of low-flow TP concentrations trained on natural and anthropogenic landscape predictors accounted for 53 % of variation in cross-validation data, had good accuracy, little bias, and plausible relationships between predictors and response. Percent riparian agricultural cover accounted for the greatest root mean square error reduction in the modeled response (33.2 %), followed by riparian soil permeability (12.9 %), watershed slope (9.6 %), and percent urban cover (9.6 %). An apparent non-linear relationship between TP concentrations and percent riparian agricultural cover suggested steep positive increases in stream TP concentrations between 10 and 30 % upstream riparian agricultural cover. Predicted minimally disturbed TP concentrations were spatially variable and ranged from 7.0 to 48.5 μg l −1 , with the highest concentrations in watersheds draining low-permeability lake plain soils. Comparison of minimally disturbed predictions to those from the early 2000s suggested that much of northern Michigan existed close to the reference condition, while lower Michigan streams were often substantially enriched. Our predicted values of minimally disturbed condition generally agree with previous studies but offer greater geographic specificity. Expanded application of machine learning modeling with landscape predictor data have great potential to inform large scale strategy development in landscapes with sparse reference data.

Michigan↗
USGS2024

Evaluation of debris-flow building damage forecasts

Reliable forecasts of building damage due to debris flows may provide situational awareness and guide land and emergency management decisions. Application of debris-flow runout models to generate such forecasts requires combining hazard intensity predictions with fragility functions that link hazard intensity with building damage. In this study, we evaluated the performance of building damage forecasts for the 9 January 2018 Montecito postfire debris-flow runout event, in which over 500 buildings were damaged. We constructed forecasts using either peak debris-flow depth or momentum flux as the hazard intensity measure and applied each approach using three debris-flow runout models (RAMMS, FLO-2D, and D-Claw). Generated forecasts were based on averaging multiple simulations that sampled a range of debris-flow volume and mobility, reflecting typical sources and magnitude of pre-event uncertainty. We found that only forecasts made with momentum flux and the D-Claw model could correctly predict the observed number of damaged buildings and the spatial patterns of building damage. However, the best forecast only predicted 50 % of the observed damaged buildings correctly and had coherent spatial patterns of incorrectly predicted building damage (i.e., false positives and false negatives). These results indicate that forecasts made at the building level reliably reflect the spatial pattern of damage but do not support interpretation at the individual building level. We found the event size strongly influences the number of damaged buildings and the spatial pattern of debris-flow depth and velocity. Consequently, future research on the link between precipitation and the volume of sediment mobilized may have the greatest effect on reducing uncertainty in building damage forecasts. Finally, because we found that both depth and velocity are needed to predict building damage, comparing debris-flow models against spatially distributed observations of building damage is a more stringent test for model fidelity than comparison against the extent of debris-flow runout.

Natural Hazards and Earth System Sciences↗
USGS2007

Role of sediment resuspension in the remobilization of particulate-phase metals from coastal sediments

The release of particulate-phase trace metals due to sediment resuspension has been investigated by combining erosion chamber experiments that apply a range of shear stresses typically encountered in coastal environments with a shear stress record simulated by a hydrodynamic model. Two sites with contrasting sediment chemistry were investigated. Sediment particles enriched in silver, copper, and lead, 4−50 times greater than the bulk surface-sediment content, were the first particles to be eroded. As the shear-stress level was increased in the chamber, the total mass eroded increased, but the enrichment of these trace metals fell, approaching the bulk-sediment content. From the temporal distribution of shear stress generated by the hydrodynamic model for a site in Boston Harbor, resuspension fluxes were estimated. The erosion threshold of this site is exceeded during spring tides, releasing the particles enriched in trace metals into the water column. Due to the higher trace metal content and the regularity of resuspension, low-energy resuspension events (up to a shear stress of 0.2 N/m 2 ) contribute up to 60% of the resuspension metal flux in an average year. The estimated annual quantity of copper and lead resuspended into the water column is higher than estimates of the total riverine flux for these metals. These results indicate that sediment resuspension is a very important mechanism for releasing metals into the water column and provide new insight into the chemical and physical processes controlling the long-term fate of trace metals in contaminated sediments.

Massachusetts↗
USGS2023

Joint spatiotemporal models to predict seabird densities at sea

Introduction: Seabirds are abundant, conspicuous members of marine ecosystems worldwide. Synthesis of distribution data compiled over time is required to address regional management issues and understand ecosystem change. Major challenges when estimating seabird densities at sea arise from variability in dispersion of the birds, sampling effort over time and space, and differences in bird detection rates associated with survey vessel type. Methods: Using a novel approach for modeling seabirds at sea, we applied joint dynamic species distribution models (JDSDM) with a vector-autoregressive spatiotemporal framework to survey data collected over nearly five decades and archived in the North Pacific Pelagic Seabird Database. We produced monthly gridded density predictions and abundance estimates for 8 species groups (77% of all birds observed) within Cook Inlet, Alaska. JDSDMs included habitat covariates to inform density predictions in unsampled areas and accounted for changes in observed densities due to differing survey methods and decadal-scale variation in ocean conditions. Results: The best fit model provided a high level of explanatory power (86% of deviance explained). Abundance estimates were reasonably precise, and consistent with limited historical studies. Modeled densities identified seasonal variability in abundance with peak numbers of all species groups in July or August. Seabirds were largely absent from the study region in either fall (e.g., murrelets) or spring (e.g., puffins) months, or both periods (shearwaters). Discussion: Our results indicated that pelagic shearwaters ( Ardenna spp.) and tufted puffin ( Fratercula cirrhata ) have declined over the past four decades and these taxa warrant further investigation into underlying mechanisms explaining these trends. JDSDMs provide a useful tool to estimate seabird distribution and seasonal trends that will facilitate risk assessments and planning in areas affected by human activities such as oil and gas development, shipping, and offshore wind and renewable energy.

Alaska↗
USGS2021

The response of streams in the Adirondack region of New York to projected changes in sulfur and nitrogen deposition under changing climate

Modeling studies project that in the future surface waters in the northeast US will continue to recover from acidification over decades following reductions in atmospheric sulfur dioxide and nitrogen oxides emissions. However, these studies generally assume stationary climatic conditions over the simulation period and ignore the linkages between soil and surface water recovery from acid deposition and changing climate, despite fundamental impacts to watershed processes and comparable time scales for both phenomena. In this study, the integrated biogeochemical model PnET-BGC was applied to two montane forest watersheds in the Adirondack region of New York, USA to evaluate the recovery of surface waters from historical acidification in response to possible future changes in climate and atmospheric sulfur and nitrogen deposition. Statistically downscaled climate scenarios on average project warmer temperatures and greater precipitation for the Adirondack by the end of the century. Model simulations suggest under constant climate, acid-sensitive Buck Creek would gain 12.8 μeq L −1 of acid neutralizing capacity (ANC) by 2100 from large reductions in deposition, whereas acid insensitive Archer Creek is projected to gain 7.9 μeq L −1 of ANC. However, climate change could limit those improvements in acid-base status. Under climate change, a negative offset relative to the ANC increases with no climate change are projected for both streams by 2100. In acid-insensitive Archer Creek the negative offset (−8.5 μeq L −1 ) was large enough that ANC is projected to decrease by −0.6 μeq L −1 , whereas in acid-sensitive Buck Creek, the negative offset (−0.4 μeq L −1 ) resulted in a slight decline of the projected future ANC increase to 12.4 μeq L −1 . Calculated target loads for 2150 for both sites decreased when future climate change was considered in model simulations, which suggests further reductions in acid deposition may be necessary to restore ecosystem structure and function under a changing climate.

New York↗
USGS2008

Interactive visualization to advance earthquake simulation

The geological sciences are challenged to manage and interpret increasing volumes of data as observations and simulations increase in size and complexity. For example, simulations of earthquake-related processes typically generate complex, time-varying data sets in two or more dimensions. To facilitate interpretation and analysis of these data sets, evaluate the underlying models, and to drive future calculations, we have developed methods of interactive visualization with a special focus on using immersive virtual reality (VR) environments to interact with models of Earth's surface and interior. Virtual mapping tools allow virtual "field studies" in inaccessible regions. Interactive tools allow us to manipulate shapes in order to construct models of geological features for geodynamic models, while feature extraction tools support quantitative measurement of structures that emerge from numerical simulation or field observations, thereby enabling us to improve our interpretation of the dynamical processes that drive earthquakes. VR has traditionally been used primarily as a presentation tool, albeit with active navigation through data. Reaping the full intellectual benefits of immersive VR as a tool for scientific analysis requires building on the method's strengths, that is, using both 3D perception and interaction with observed or simulated data. This approach also takes advantage of the specialized skills of geological scientists who are trained to interpret, the often limited, geological and geophysical data available from field observations. ?? Birkhaueser 2008.

Pure and Applied Geophysics↗
USGS2000

Occurrence of sulfonylurea, sulfonamide, imidazolinone, and other herbicides in rivers, reservoirs and ground water in the Midwestern United States, 1998

Sulfonylurea (SU), sulfonamide (SA), and imidazolinone (IMI) herbicides are relatively new classes of chemical compounds that function by inhibiting the action of a plant enzyme, stopping plant growth, and eventually killing the plant. These compounds generally have low mammalian toxicity, but plants demonstrate a wide range in sensitivity to SUs, SAs, and IMIs with over a 10 000-fold difference in observed toxicity levels for some compounds. SUs, SAs, and IMIs are applied either pre- or post-emergence to crops commonly at 1/50th or less of the rate of other herbicides. Little is known about their occurrence, fate, or transport in surface water or ground water in the USA. To obtain information on the occurrence of SU, SA, and IMI herbicides in the Midwestern United States, 212 water samples were collected from 75 surface-water and 25 ground-water sites in 1998. These samples were analyzed for 16 SU, SA and IMI herbicides by USGS Methods Research and Development Program staff using high-performance liquid chromatography/mass spectrometry. Samples were also analyzed for 47 pesticides or pesticide degradation products. At least one of the 16 SUs, SAs or IMIs was detected above the method reporting limit (MRL) of 0.01 μg/l in 83% of 130 stream samples. Imazethapyr was detected most frequently (71% of samples) followed by flumetsulam (63% of samples) and nicosulfuron (52% of samples). The sum of SU, SA and IMI concentrations exceeded 0.5 μg/l in less than 10% of stream samples. Acetochlor, alachlor, atrazine, cyanazine and metolachlor were all detected in 90% or more of 129 stream samples. The sum of the concentration of these five herbicides exceeded 50 μg/l in approximately 10% of stream samples. At least one SU, SA, or IMI herbicide was detected above the MRL in 24% of 25 ground-water samples and 86% of seven reservoir samples.

Science of Total Environment↗
USGS2025

Aggregating three sources of long-term trends of swallows and martins to identify priority conservation areas in the Great Lakes region

1. Long-term monitoring of bird populations across scales is important in evaluating conservation targets and creating effective conservation strategies. For nearly six decades, the Breeding Bird Survey (BBS) has served as the primary broad-scaled source of relative abundance trends of swallows and martins in North America. Recently, however, it has become possible to obtain breeding population trends using semi-structured eBird community science data. Moreover, weather surveil-lance radar data of swallow and martin roosting populations yield a third complementary source of trend information. 2. Using results from these three approaches, we propose a novel method of spatially combining estimates of percent change per year into a probability of directional agreement and/or disagreement that describes (1) the direction of the trend within a given region, (2) the amount of evidence associated with the estimate and (3) how much uncertainty surrounds it. We focus our efforts on an area of high Hirundinidae concentration in the North American Great Lakes region and predict trends from 2012 to 2022. 3. We found a high probability of agreement between all three sources about ob-served declines in swallow and martin trends in the region surrounding Lake Ontario and to the west of Lake Michigan. Focusing future research on these regions could improve our understanding of these declines and help build more targeted conservation initiatives. 4. Synthesis and applications. Our data integration methodology allows managers to identify regions that accumulate evidence of concerning trends across multiple wildlife monitoring schemes. These regions can thus be prioritized in conservation and management efforts. This approach can be generalized to other sources of long-term monitoring data of different species, at different stages of their annual cycle, in any geographic location.

Great Lakes region↗
USGS2020

Steps to develop early warning systems and future scenarios of wave-driven flooding along coral reef-lined coasts

Tropical coral reef-lined coasts are exposed to storm wave-driven flooding. In the future, flood events during storms are expected to occur more frequently and to be more severe due to sea-level rise, changes in wind and weather patterns, and the deterioration of coral reefs. Hence, disaster managers and coastal planners are in urgent need of decision-support tools. In the short-term, these tools can be applied in Early Warning Systems (EWS) that can help to prepare for and respond to impending storm-driven flood events. In the long-term, future scenarios of flooding events enable coastal communities and managers to plan and implement adequate risk-reduction strategies. Modeling tools that are used in currently available coastal flood EWS and future scenarios have been developed for open-coast sandy shorelines, which have only limited applicability for coral reef-lined shorelines. The tools need to be able to predict local sea levels, offshore waves, as well as their nearshore transformation over the reefs, and translate this information to onshore flood levels. In addition, future scenarios require long-term projections of coral reef growth, reef composition, and shoreline change. To address these challenges, we have formed the UFORiC (Understanding Flooding of Reef-lined Coasts) working group that outlines its perspectives on data and model requirements to develop EWS for storms and scenarios specific to coral reef-lined coastlines. It reviews the state-of-the-art methods that can currently be incorporated in such systems and provides an outlook on future improvements as new data sources and enhanced methods become available.

Frontiers in Marine Science↗
USGS2019

Fate and transport of nitrapyrin in agroecosystems: Occurrence in agricultural soils, subsurface drains, and receiving streams in the Midwestern US

Nitrapyrin is a nitrification inhibitor that is co-applied with nitrogen fertilizer in agroecosystems. There is limited information on the fate of nitrapyrin after it is applied to agricultural soils. Over the course of one year (March 2016 to June 2017), 192 water samples from seven streams across Iowa and Illinois were analyzed for nitrapyrin, its metabolite 6‑chloropicolinic acid (6‑CPA), and three widely used herbicides acetochlor, atrazine , and metolachlor. Additional environmental samples were collected and analyzed in spring 2017: 63 water samples from eight subsurface drains in Illinois, and 33 soil samples from a field in Iowa that received direct application of nitrapyrin. Nitrapyrin was detected in all seven streams (56% detection) with concentrations ranging from less than LOD to 1200 ng/L; 6‑CPA was detected in six of the seven streams (13% detection) with concentrations ranging from less than LOD to 13 ng/L. Nitrapyrin was detected in 10% of the subsurface drain samples with concentrations ranging from less than LOD to 12 ng/L; 6‑CPA was detected in six of the eight subsurface drains and in 33% of drain samples with concentrations ranging from less than LOD to 6 ng/L. Nitrapyrin was detected in 67% of the soil samples collected, and concentrations ranged from less than LOD to 42 ng/g. Generally, all three herbicides were detected more frequently and at higher concentrations than nitrapyrin in the streams, subsurface drains, and soils. The environmental fate of nitrapyrin after application is dominated by sorption to soil and off-field transport via leaching and overland flow.

Illinois, Iowa↗
USGS2020

Methylmercury exposure in wildlife: A review of the ecological and physiological processes affecting contaminant concentrations and their interpretation

Exposure to methylmercury (MeHg) can result in detrimental health effects in wildlife. With advances in ecological indicators and analytical techniques for measurement of MeHg in a variety of tissues, numerous processes have been identified that can influence MeHg concentrations in wildlife. This review presents a synthesis of theoretical principals and applied information for measuring MeHg exposure and interpreting MeHg concentrations in wildlife. Mercury concentrations in wildlife are the net result of ecological processes influencing dietary exposure combined with physiological processes that regulate assimilation, transformation, and elimination. Therefore, consideration of both physiological and ecological processes should be integrated when formulating biomonitoring strategies. Ecological indicators, particularly stable isotopes of carbon, nitrogen, and sulfur, compound-specific stable isotopes, and fatty acids, can be effective tools to evaluate dietary MeHg exposure. Animal species differ in their physiological capacity for MeHg elimination, and animal tissues can be inert or physiologically active, act as sites of storage, transformation, or excretion of MeHg, and vary in the timing of MeHg exposure they represent. Biological influences such as age, sex, maternal transfer, and growth or fasting are also relevant for interpretation of tissue MeHg concentrations. Wildlife tissues that represent current or near-term bioaccumulation and in which MeHg is the predominant mercury species (such as blood and eggs) are most effective for biomonitoring ecosystems and understanding landscape drivers of MeHg exposure. Further research is suggested to critically evaluate the use of keratinized external tissues to measure MeHg bioaccumulation, particularly for less-well studied wildlife such as reptiles and terrestrial mammals. Suggested methods are provided to effectively use wildlife for quantifying patterns and drivers of MeHg bioaccumulation over time and space, as well as for assessing the potential risk and toxicological effects of MeHg on wildlife.

Science of the Total Environment↗
USGS2022

Towards real-time probabilistic ash deposition forecasting for New Zealand

Volcanic ashfall forecasts are highly dependent on eruption source parameters (ESPs) and synoptic weather conditions at the time and location of the eruption. In New Zealand, MetService and GNS Science have been jointly developing an ashfall forecast system that incorporates four-dimensional high-resolution numerical weather prediction (NWP) and ESPs into the HYSPLIT model, a state-of-the art hybrid Eulerian and Lagrangian dispersion model widely used for volcanic ash. However, these forecasts are based on discrete ESPs combined with a deterministic weather forecast and thus provide no information on output uncertainty. This shortcoming hinders stakeholder decision making, particularly near the geographical margin of forecasted ashfall and in areas with large gradients in forecasted ash deposition. Our study presents a new approach that incorporates uncertainty from both eruptive and meteorological inputs to deliver uncertainty in the model output. To this end, we developed probability density functions (PDFs) for three key ESPs (plume height, mass eruption rate, eruption duration) tailored to New Zealand’s volcanoes and combine them with NWP ensemble datasets to generate probabilistic ashfall forecasts using the HYSPLIT model. We show that the Latin Hypercube Sampling (LHS) technique can be used to representatively span this four-dimensional parameter space and allow us to add uncertainty quantification to rapid response forecast systems. For a case study of a hypothetical eruption at Tongariro, New Zealand we suggest that large parts of New Zealand’s North Island would not receive adequate warning for potential ashfall if uncertainties were not included in the forecasts. We also propose new probabilistic summary products to support public information and emergency responders decision making.

Journal of Applied Volcanology↗
USGS2011

Using multi-source satellite data for lake level modelling in ungauged basins: A case study for Lake Turkana, East Africa

Managing limited surface water resources is a great challenge in areas where ground-based data are either limited or unavailable. Direct or indirect measurements of surface water resources through remote sensing offer several advantages of monitoring in ungauged basins. A physical based hydrologic technique to monitor lake water levels in ungauged basins using multi-source satellite data such as satellite-based rainfall estimates, modelled runoff, evapotranspiration, a digital elevation model, and other data is presented. This approach is applied to model Lake Turkana water levels from 1998 to 2009. Modelling results showed that the model can reasonably capture all the patterns and seasonal variations of the lake water level fluctuations. A composite lake level product of TOPEX/Poseidon, Jason-1, and ENVISAT satellite altimetry data is used for model calibration (1998-2000) and model validation (2001-2009). Validation results showed that model-based lake levels are in good agreement with observed satellite altimetry data. Compared to satellite altimetry data, the Pearson's correlation coefficient was found to be 0.81 during the validation period. The model efficiency estimated using NSCE is found to be 0.93, 0.55 and 0.66 for calibration, validation and combined periods, respectively. Further, the model-based estimates showed a root mean square error of 0.62 m and mean absolute error of 0.46 m with a positive mean bias error of 0.36 m for the validation period (2001-2009). These error estimates were found to be less than 15 % of the natural variability of the lake, thus giving high confidence on the modelled lake level estimates. The approach presented in this paper can be used to (a) simulate patterns of lake water level variations in data scarce regions, (b) operationally monitor lake water levels in ungauged basins, (c) derive historical lake level information using satellite rainfall and evapotranspiration data, and (d) augment the information provided by the satellite altimetry systems on changes in lake water levels. ?? Author(s) 2011.

Hydrology and Earth System Sciences Discussions↗
USGS2018

Chesapeake Bay dissolved oxygen criterion attainment deficit: Three decades of temporal and spatial patterns

Low dissolved oxygen (DO) conditions are a recurring issue in waters of Chesapeake Bay, with detrimental effects on aquatic living resources. The Chesapeake Bay Program partnership has developed criteria guidance supporting the definition of state water quality standards and associated assessment procedures for DO and other parameters, which provides a binary classification of attainment or impairment. Evaluating time series of these two outcomes alone, however, provides limited information on water quality change over time or space. Here we introduce an extension of the existing Chesapeake Bay water quality criterion assessment framework to quantify the amount of impairment shown by space-time exceedance of DO criterion (“attainment deficit”) for a specific tidal management unit (i.e., segment). We demonstrate the usefulness of this extended framework by applying it to Bay segments for each 3-year assessment period between 1985 and 2016. In general, the attainment deficit for the most recent period assessed (i.e., 2014–2016) is considerably worse for deep channel (DC; n = 10) segments than open water (OW; n = 92) and deep water (DW; n = 18) segments. Most subgroups – classified by designated uses, salinity zones, or tidal systems – show better (or similar) attainment status in 2014–2016 than their initial status (1985–1987). Some significant temporal trends (p < 0.1) were detected, presenting evidence on the recovery for portions of Chesapeake Bay with respect to DO criterion attainment. Significant, improving trends were observed in seven OW segments, four DW segments, and one DC segment over the 30 3-year assessment periods (1985–2016). Likewise, significant, improving trends were observed in 15 OW, five DW, and four DC segments over the recent 15 assessment periods (2000–2016). Subgroups showed mixed trends, with the Patuxent, Nanticoke, and Choptank Rivers experiencing significant, improving short-term (2000–2016) trends while Elizabeth experiencing a significant, degrading short-term trend. The general lack of significantly improving trends across the Bay suggests that further actions will be necessary to achieve full attainment of DO criterion. Insights revealed in this work are critical for understanding the dynamics of the Bay ecosystem and for further assessing the effectiveness of management initiatives aimed toward Bay restoration.

Maryland, Virginia↗
USGS2025

Land application of biosolid, livestock, and drilling wastes to US farmland: A potential pathway for the redistribution of contaminants in the environment

In the United States (U.S.), waste byproducts generated from the treatment of municipal waste (biosolids), production of livestock (livestock waste), and drilling of oil and gas wells (drilling waste) are commonly applied to agricultural lands. Although this can be a cost-effective reuse/disposal practice, there is limited research on the potential for contaminant exposures and effects on ecosystems, wildlife, and human health from such land applications. In this study, we conducted extensive chemical, microbial, and toxicity analyses of biosolid, livestock, and drilling wastes just prior to land application on agricultural lands at 34 sites across the U.S. Twenty-two analytical methods were used to determine potential contaminant exposures profiles for 452 organic and 114 inorganic chemicals, nine microbial groups, estrogenicity, and cytotoxicity. Analytical results document unique and substantial chemical, microbial, and toxicity profiles for these land-applied wastes. Of the three waste byproducts, biosolids contained the greatest concentrations of household chemicals, pesticides, pharmaceuticals, per-/polyfluoroalkyl substances, calcium, and phosphorus. Livestock waste contained the greatest concentrations of total and leachable dissolved organic carbon, biogenic hormones, mycotoxins, plant estrogens, total inorganic nitrogen, and potassium. Drilling waste contained the greatest concentrations of BTEX compounds (benzene, toluene, ethylbenzene, and xylenes), polycyclic aromatic hydrocarbons, rare-earth elements, barium, strontium, and uranium–thorium series radioisotopes. Biosolid and livestock wastes had greater culturable heterotrophic bacteria, halophilic bacteria, Escherichia coli ( E. coli ), enterococci, and staphylococci concentrations, and greater microbial diversity than drilling waste. Bioassay analyses indicated that exposure to contaminants in livestock wastes and biosolids could result in estrogenic effects, whereas exposure to contaminants in drilling waste could result in cytotoxic effects. Our study documents that current reuse/disposal practices for biosolid, livestock, and drilling wastes on agricultural lands could provide a potential pathway for the redistribution of unique and complex contaminant mixtures into the environment that have bioactive, endocrine disrupting, and carcinogenic characteristics. Results of this study provide a snapshot of chemical compositions and concentrations that can be used to inform the development of best-management practices to help maximize beneficial reuse of these wastes and minimize risk to the environment and human health.

Environmental Science: Processes & Impacts↗
USGS2016

Application of a hybrid model to reduce bias and improve precision in population estimates for elk (Cervus elaphus) inhabiting a cold desert ecosystem

Accurately estimating the size of wildlife populations is critical to wildlife management and conservation of species. Raw counts or “minimum counts” are still used as a basis for wildlife management decisions. Uncorrected raw counts are not only negatively biased due to failure to account for undetected animals, but also provide no estimate of precision on which to judge the utility of counts. We applied a hybrid population estimation technique that combined sightability modeling, radio collar-based mark-resight, and simultaneous double count (double-observer) modeling to estimate the population size of elk in a high elevation desert ecosystem. Combining several models maximizes the strengths of each individual model while minimizing their singular weaknesses. We collected data with aerial helicopter surveys of the elk population in the San Luis Valley and adjacent mountains in Colorado State, USA in 2005 and 2007. We present estimates from 7 alternative analyses: 3 based on different methods for obtaining a raw count and 4 based on different statistical models to correct for sighting probability bias. The most reliable of these approaches is a hybrid double-observer sightability model (model M H ), which uses detection patterns of 2 independent observers in a helicopter plus telemetry-based detections of radio collared elk groups. Data were fit to customized mark-resight models with individual sighting covariates. Error estimates were obtained by a bootstrapping procedure. The hybrid method was an improvement over commonly used alternatives, with improved precision compared to sightability modeling and reduced bias compared to double-observer modeling. The resulting population estimate corrected for multiple sources of undercount bias that, if left uncorrected, would have underestimated the true population size by as much as 22.9%. Our comparison of these alternative methods demonstrates how various components of our method contribute to improving the final estimate and demonstrates why each is necessary.

Colorado↗
USGS2025

Upriver migrating sea lamprey exhibit similar responses to hydrodynamic features as other up and downriver-moving species

Identifying commonalities in how fish navigate rivers near infrastructure will enhance water operations and design by improving our ability to predict engineering outcomes (e.g., barrier construction/removal, fish passage installation) in novel settings before the cost of real-world implementation. Evidence from intermediate-scale computer models (time scales of minutes to days and spatial scales <2 km) suggests that fish movement behavior in rivers is frequently governed by responses to one or more of the following hydrodynamic features: (1) flow direction (i.e., rheotaxis), (2) flow velocity magnitude, (3) turbulence, and (4) depth, plus (5) the integration of information over recent time periods (i.e., memory/experience). However, the lack of consistent modeling approaches, infrequent assessment of each response in isolation and combination, and a focus on a limited number of species means the generality of these responses is uncertain. We use a computer model, specifically a pattern-oriented modeling approach incorporating individual based models (IBMs), to apply responses to the four hydrodynamic features plus memory/experience in different combinations to study their value for reproducing the movement of an infrequently modeled species and lifestage, upriver migrating adult sea lamprey, Petromyzon marinus . The study site was the region downstream of the Sault Ste. Marie lock and dam complex located between Canada and the U.S.A on the St. Marys River joining Lake Superior and Lake Huron. Our analysis indicates that rheotaxis and a response to velocity magnitude as well as recent past experience improve sea lamprey spatio-temporal movement prediction compared to other, simpler forms of modeled behavior. Sea lamprey movement is also biased toward lower levels of turbulence (e.g., turbulent kinetic energy) or its precursor (i.e., the spatial gradient in water speed). A response to water depth was not found to be important, but the modeled domain was two-dimensional which limited our assessment. As similar responses to hydrodynamic features are found in very different fish, commonalities appear to underlie river navigation across a range of species and life stages that share the goal-oriented behavior of upriver and downriver movement. The systematic approach of our analysis highlights the accuracy trade-offs of each response, individually and in combination, that often accompany alternative behavioral formulations in a computer model of fish movement. The model structure provides a framework to which future findings from the analyses of additional species in different contexts can be added.

Sault Ste. Marie lock and dam complex in the St. M↗
USGS2010

Composition, distribution, and potential toxicity of organochlorine mixtures in bed sediments of streams

Mixtures of organochlorine compounds have the potential for additive or interactive toxicity to organisms exposed in the stream. This study uses a variety of methods to identify mixtures and a modified concentration-addition approach to estimate their potential toxicity at 845 stream sites across the United States sampled between 1992 and 2001 for organochlorine pesticides and polychlorinated biphenyls (PCBs) in bed sediment. Principal-component (PC) analysis identified five PCs that account for 77% of the total variance in 14 organochlorine compounds in the original dataset. The five PCs represent: (1) chlordane-related compounds and dieldrin; (2) p,p′ -DDT and its degradates; (3) o,p′ -DDT and its degradates; (4) the pesticide degradates oxychlordane and heptachlor epoxide; and (5) PCBs. The PC analysis grouped compounds that have similar chemical structure (such as parent compound and degradate), common origin (in the same technical pesticide mixture), and(or) similar relation of concentrations to land use. For example, the highest concentrations of chlordane compounds and dieldrin occurred at urban sites, reflecting past use of parent pesticides for termite control. Two approaches to characterizing mixtures—PC-based mixtures and unique mixtures—were applied to all 299 samples with a detection of two or more organochlorine compounds. PC-based mixtures are defined by the presence (in the sample) of one or more compounds associated with that PC. Unique mixtures are defined as a specific combination of two or more compounds detected in a sample, regardless of how many other compounds were also detected in that sample. The simplest PC-based mixtures (containing compounds from 1 or 2 PCs) commonly occurred in a variety of land use settings. Complex mixtures (containing compounds from 3 or more PCs) were most common in samples from urban and mixed/urban sites, especially in the Northeast, reflecting high concentrations of multiple chlordane, dieldrin, DDT-related compounds, and(or) PCBs. The most commonly occurring unique mixture ( p,p′ -DDE, p,p′ -DDD) occurred in both simple and complex PC-based mixtures, and at both urban and agricultural sites. Mean Probable Effect Concentration Quotients (PEC-Q) values, which estimate the potential toxicity of organochlorine contaminant mixtures, were highest for complex mixtures. Mean PEC-Q values were highest for urban sites in the Northeast, followed by mixed/urban sites in the Northeast and agricultural sites in cotton growing areas. These results demonstrate that the PEC-Q approach can be used in combination with PC-based and unique mixture analyses to relate potential aquatic toxicity of contaminant mixtures to mixture complexity, land use, and other surrogates for contaminant sources.

Science of the Total Environment↗