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At least 181 records · Page 10Linked to original sources
USGS2000

Using structural equation modeling to investigate relationships among ecological variables

Structural equation modeling is an advanced multivariate statistical process with which a researcher can construct theoretical concepts, test their measurement reliability, hypothesize and test a theory about their relationships, take into account measurement errors, and consider both direct and indirect effects of variables on one another. Latent variables are theoretical concepts that unite phenomena under a single term, e.g., ecosystem health, environmental condition, and pollution (Bollen, 1989). Latent variables are not measured directly but can be expressed in terms of one or more directly measurable variables called indicators. For some researchers, defining, constructing, and examining the validity of latent variables may be the end task of itself. For others, testing hypothesized relationships of latent variables may be of interest. We analyzed the correlation matrix of eleven environmental variables from the U.S. Environmental Protection Agency's (USEPA) Environmental Monitoring and Assessment Program for Estuaries (EMAP-E) using methods of structural equation modeling. We hypothesized and tested a conceptual model to characterize the interdependencies between four latent variables-sediment contamination, natural variability, biodiversity, and growth potential. In particular, we were interested in measuring the direct, indirect, and total effects of sediment contamination and natural variability on biodiversity and growth potential. The model fit the data well and accounted for 81% of the variability in biodiversity and 69% of the variability in growth potential. It revealed a positive total effect of natural variability on growth potential that otherwise would have been judged negative had we not considered indirect effects. That is, natural variability had a negative direct effect on growth potential of magnitude -0.3251 and a positive indirect effect mediated through biodiversity of magnitude 0.4509, yielding a net positive total effect of 0.1258. Natural variability had a positive direct effect on biodiversity of magnitude 0.5347 and a negative indirect effect mediated through growth potential of magnitude -0.1105 yielding a positive total effects of magnitude 0.4242. Sediment contamination had a negative direct effect on biodiversity of magnitude -0.1956 and a negative indirect effect on growth potential via biodiversity of magnitude -0.067. Biodiversity had a positive effect on growth potential of magnitude 0.8432, and growth potential had a positive effect on biodiversity of magnitude 0.3398. The correlation between biodiversity and growth potential was estimated at 0.7658 and that between sediment contamination and natural variability at -0.3769.

Environmental and Ecological Statistics↗
USGS2023

Stream temperature prediction in a shifting environment: The influence of deep learning architecture

Stream temperature is a fundamental control on ecosystem health. Recent efforts incorporating process guidance into deep learning models for predicting stream temperature have been shown to outperform existing statistical and physical models. This performance is in part because deep learning architectures can actively learn spatiotemporal relationships that govern how water and energy propagate through a river network. However, exploration of how spatiotemporal awareness and process guidance influence a model's generalizability under shifting environmental conditions such as climate change is limited. Here, we use Explainable Artificial Intelligence (XAI) to interrogate how differing deep learning architectures affect a model's learned spatial and temporal dependencies, and how those learned dependencies affect a model's ability to maintain high accuracy when applied to unseen environmental conditions. Using the Delaware River Basin in the northeastern United States as a test case, we compare two spatiotemporally aware process-guided deep learning models for predicting stream temperature (a recurrent graph convolution network—RGCN, and a temporal convolution graph model—Graph WaveNet). Both models achieve equally high predictive performance when testing data are well represented in the training data (test root mean squared errors of 1.64°C and 1.65°C); however, Graph WaveNet significantly outperforms RGCN in 4 out of 5 experiments where test partitions represent different types of unseen environmental conditions. XAI results show that the architecture of Graph WaveNet leads to learned spatial relationships with greater fidelity to physical processes, and that this fidelity improves the generalizability of the model when applied to shifting and/or unseen environmental conditions.

Delaware River Basin↗
USGS2024

Illuminating the “invisible”: Substantial deep respiration and lateral export of dissolved carbon from beneath soil

Dissolved organic and inorganic carbon (DOC and DIC) influence water quality, ecosystem health, and carbon cycling. Dissolved carbon species are produced by biogeochemical reactions and laterally exported to streams via distinct shallow and deep subsurface flow paths. These processes are arduous to measure and challenge the quantification of global carbon cycles. Here we ask: when, where, and how much is dissolved carbon produced in and laterally exported from the subsurface to streams? We used a catchment-scale reactive transport model, BioRT-HBV, with hydrometeorology and stream carbon data to illuminate the “invisible” subsurface processes at Sleepers River, a carbonate-based catchment in Vermont, United States. Results depict a conceptual model where DOC is produced mostly in shallow soils (3.7 ± 0.6 g/m 2 /yr) and in summer at peak root and microbial respiration. DOC is flushed from soils to the stream (1.0 ± 0.2 g/m 2 /yr) especially during snowmelt and storms. A large fraction of DOC (2.5 ± 0.2 g/m 2 /yr) percolates to the deeper subsurface, fueling deep respiration to generate DIC. DIC is exported predominantly from the deeper subsurface (7.1 ± 0.4 g/m 2 /yr, compared to 1.3 ± 0.3 g/m 2 /yr from shallow soils). Deep respiration reduces DOC and increases DIC concentrations at depth, leading to commonly observed DOC flushing (increasing concentrations with discharge) and DIC dilution patterns (decreasing concentrations with discharge). Surprisingly, respiration processes generate more DIC than weathering in this carbonate-based catchment. These findings underscore the importance of vertical connectivity between the shallow and deep subsurface, highlighting the overlooked role of deep carbon processing and export.

Vermont↗
USGS2024

Climate-driven increases in stream metal concentrations in mineralized watersheds throughout the Colorado Rocky Mountains, USA

Increasing stream metal concentrations apparently caused by climate warming have been reported for a small number of mountain watersheds containing hydrothermally altered bedrock with abundant sulfide minerals (mineralized watersheds). Such increases are concerning and could negatively impact downstream ecosystem health, water resources, and mine-site remediation efforts. However, the pervasiveness and typical magnitude of these trends remain uncertain. We aggregated available streamwater chemistry data collected from late summer and fall over the past 40 years for 22 mineralized watersheds throughout the Colorado Rocky Mountains. Temporal trend analysis performed using the Regional Kendall Test indicates significant regional upward trends of ∼2% of the site median per year for sulfate, zinc, and copper concentrations in the 17 streams affected by acid rock drainage (ARD; median pH ≤ 5.5), equivalent to concentrations roughly doubling over the past 30 years. An examination of potential load trends utilizing streamflow data from eight “index gages” located near the sample sites provides strong support for regionally increasing sulfate and metal loads in ARD-affected streams, particularly at higher elevations. Declining streamflows are likely contributing to regionally increasing concentrations, but increasing loads appear to be on average an equal or greater contributor. Comparison of selected site characteristics with site concentration trend magnitudes shows the highest correlation for mean annual air temperature and mean elevation (R 2 of 0.42 and 0.35, respectively, with all others being ≤0.14). Future research on climate-driven controlling mechanisms should therefore focus on processes such as melting of frozen ground directly linked to site mean temperature and elevation.

Colorado↗
USGS2025

The role of bedrock circulation depth and porosity in mountain streamflow response to prolonged drought

Quantitative understanding is lacking on how the depth of active groundwater circulation in bedrock affects mountain streamflow response to a multi‐year drought. We use an integrated hydrological model to explore the sensitivity of a variety of streamflow metrics to bedrock circulation depth and porosity under a plausible extreme drought scenario lasting up to 5 years. Endmember depth versus hydraulic conductivity relationships and porosity values for fractured crystalline rock are simulated. With drought, a deeper circulation system with higher drainable porosity more effectively buffers minimum flow and significantly limits perennial stream loss in comparison to a shallow circulation system. Streamflow buffering is accomplished through extensive groundwater storage loss. However, deeper circulation systems experience prolonged recovery from drought in comparison to storage‐limited shallow systems. Research highlights the importance of characterizing the deeper bedrock hydrogeology in mountainous watersheds to better understand and predict drought impacts on stream ecosystem health and water resource sustainability.

Colorado↗
USGS2024

Longitudinal and seasonal changes of organic matter sources through a semi-arid river-reservoir system

The quality and quantity of organic matter (OM) in a river system directly affects ecosystem health; thus, managers benefit from an in-depth understanding of the drivers and sources of OM. In the Snake River, a highly altered river-reservoir system in the semi-arid western United States, OM production and loading are key drivers of reservoir anoxia, which leads to several deleterious processes such as mercury methylation. However, sources and quantities of OM to the Snake River, and the effects of impoundment on OM moving through the river-reservoir system, are not well understood. Particulate organic carbon (POC), dissolved organic carbon (DOC), particulate nitrogen (PN), chlorophyll a (chl- a ), and δ 15 N–PN and δ 13 C–POC isotopic ratios were measured bi-weekly for over 2 years at four locations through the Snake River Hells Canyon Reservoir Complex to determine spatial and temporal patterns of OM quantities and sources. POC concentrations increased through the riverine zone upstream of the reservoirs, likely due to in situ primary production and/or inputs from tributaries and agricultural drains; then decreased through the most upstream reservoir likely due to particle settling. Isotopic ratios and other OM source indicators (δ 15 N–PN, δ 13 C–POC, POC:PN, chl- a :POC) show that the dominant source of particulate OM was phytoplankton with seasonal terrestrial/macrophytic inputs. Results highlight the effects of major tributary and agricultural drain inputs, primary production, and impoundment on OM composition and concentration through a large river-reservoir system and may inform water quality management efforts in this and similar systems.

Idaho, Oregon↗
USGS2026

Subsurface vertical connectivity shapes solute transport to montane streams: Insights from watershed-scale geophysics-informed modeling

Headwater catchments provide essential water and nutrients to downstream ecosystems. The timing and evolution of their transport is shaped in part by the “invisible” subsurface structure but have been largely unexplored at the watershed scale. This raises a fundamental question: how and to what extent does subsurface structure influence streamflow and solute transport in montane watersheds? Here, we test controls of three-dimensionally resolved subsurface structure on solute transport, which reflects source waters and water residence time. We integrate the geophysically mapped architecture of Coal Creek, a headwater catchment in the Colorado River Basin, into numerical flow-transport models. Our results reveal greater vertical connectivity elongates flow paths and enhances deep groundwater contributions to streams, therefore substantially influencing the timing and magnitude of solute transport. In contrast, watershed-integrated outlet discharge can be predicted without high-resolution subsurface information. These findings demonstrate incorporating subsurface architecture is critical for predicting ecosystem health and contaminant transport.

Colorado↗
USGS2016

Land–atmosphere feedbacks amplify aridity increase over land under global warming

The response of the terrestrial water cycle to global warming is central to issues including water resources, agriculture and ecosystem health. Recent studies indicate that aridity, defined in terms of atmospheric supply (precipitation, P) and demand (potential evapotranspiration, Ep) of water at the land surface, will increase globally in a warmer world. Recently proposed mechanisms for this response emphasize the driving role of oceanic warming and associated atmospheric processes. Here we show that the aridity response is substantially amplified by land–atmosphere feedbacks associated with the land surface’s response to climate and CO2 change. Using simulations from the Global Land Atmosphere Coupling Experiment (GLACE)-CMIP5 experiment, we show that global aridity is enhanced by the feedbacks of projected soil moisture decrease on land surface temperature, relative humidity and precipitation. The physiological impact of increasing atmospheric CO2 on vegetation exerts a qualitatively similar control on aridity. We reconcile these findings with previously proposed mechanisms by showing that the moist enthalpy change over land is unaffected by the land hydrological response. Thus, although oceanic warming constrains the combined moisture and temperature changes over land, land hydrology modulates the partitioning of this enthalpy increase towards increased aridity.

Nature Climate Change↗
USGS2018

Exploring the pathology of an epidermal disease affecting a circum-Antarctic sea star

Over the past decade, unusual mortality outbreaks have decimated echinoderm populations over broad geographic regions, raising awareness globally of the importance of investigating such events. Echinoderms are key components of marine benthos for top-down and bottom-up regulations of plants and animals; population declines of these individuals can have significant ecosystem-wide effects. Here we describe the first case study of an outbreak affecting Antarctic echinoderms and consisting of an ulcerative epidermal disease affecting ~10% of the population of the keystone asteroid predator Odontaster validus at Deception Island, Antarctica. This event was first detected in the Austral summer 2012–2013, coinciding with unprecedented high seawater temperatures and increased seismicity. Histological analyses revealed epidermal ulceration, inflammation, and necrosis in diseased animals. Bacterial and fungal alpha diversity was consistently lower and of different composition in lesioned versus unaffected tissues (32.87% and 16.94% shared bacterial and fungal operational taxonomic units OTUs respectively). The microbiome of healthy stars was more consistent across individuals than in diseased specimens suggesting microbial dysbiosis, especially in the lesion fronts. Because these microbes were not associated with tissue damage at the microscopic level, their contribution to the development of epidermal lesions remains unclear. Our study reveals that disease events are reaching echinoderms as far as the polar regions thereby highlighting the need to develop a greater understanding of the microbiology and physiology of marine diseases and ecosystems health, especially in the era of global warming.

Scientific Reports↗
USGS2019

Coral skeleton δ15N as a tracer of historic nutrient loading to a coral reef in Maui, Hawaii

Excess nutrient loading to nearshore environments has been linked to declining water quality and ecosystem health. Macro-algal blooms, eutrophication, and reduction in coral cover have been observed in West Maui, Hawaii, and linked to nutrient inputs from coastal submarine groundwater seeps. Here, we present a forty-year record of nitrogen isotopes (δ 15 N) of intra-crystalline coral skeletal organic matter in three coral cores collected at this site and evaluate the record in terms of changes in nitrogen sources. Our results show a dramatic increase in coral δ 15 N values after 1995, corresponding with the implementation of biological nutrient removal at the nearby Lahaina Wastewater Reclamation Facility (LWRF). High δ 15 N values are known to be strongly indicative of denitrification and sewage effluent, corroborating a previously suggested link between local wastewater injection and degradation of the reef environment. This record demonstrates the power of coral skeletal δ 15 N as a tool for evaluating nutrient dynamics within coral reef environments.

Hawaii↗
USGS2026

Permafrost thaw controls iron flux from wetlands and sulfide-bearing rocks to Arctic rivers and streams

Recent warming has caused widespread iron mobilization into Arctic waterbodies that degrades ecosystems and threatens natural resources. Yet, understanding where and when iron flux occurs remains limited. Here, we investigate iron loading across regional to local scales in Arctic Alaska using climate, water chemistry, and borehole data together with mapped geology and permafrost presence. We show that both anoxic microbial iron reduction and acid rock drainage from iron-sulfide oxidation mobilize iron. Iron influx is strongly associated with lowland wetlands, sulfide-rich upland bedrock, and near-surface permafrost. Acid rock drainage chemistry correlates very strongly with the depth of seasonal thaw above permafrost from the previous year, indicating a one-year lag. These findings clarify the spatial and temporal dynamics of Arctic river rusting, provide a mechanistic understanding of the phenomenon, and may allow anticipation of its occurrence and assessment of its implications for aquatic ecosystem health and subsistence resources under ongoing climate change.

Alaska↗
USGS2013

A review on cylindrospermopsin: the global occurrence, detection, toxicity and degradation of a potent cyanotoxin

Cylindrospermopsin is an important cyanobacterial toxin found in water bodies worldwide. The ever-increasing and global occurrence of massive and prolonged blooms of cylindrospermopsin-producing cyanobacteria poses a potential threat to both human and ecosystem health. Its toxicity is associated with metabolic activation and may involve mechanisms that adversely affect a wide variety of targets in an organism. Cylindrospermopsin has been shown to be cytotoxic, dermatotoxic, genotoxic, hepatotoxic in vivo, developmentally toxic, and may be carcinogenic. Human exposure may occur through drinking water, during recreational activities and by consuming foods in which the toxin may have bioaccumulated. Drinking water shortages of sufficient quality coupled with growing human pressures and climate variability and change necessitate an integrated and sustainable water management program. This review presents an overview of the importance of cylindrospermopsin, its detection, toxicity, worldwide distribution, and lastly, its chemical and biological degradation and removal by natural processes and drinking water treatment processes.

Environmental Science: Processes and Impacts↗
USGS2023

Two-dimensional inverse energy cascade in a laboratory surf zone for varying wave directional spread

Surfzone eddies enhance the dispersion and transport of contaminants, bacteria, and larvae across the nearshore, altering coastal water quality and ecosystem health. During directionally spread wave conditions, vertical vortices (horizontal eddies) are injected near the ends of breaking crests. Energy associated with these eddies may be transferred to larger-scale, low-frequency rotational motions through an inverse energy cascade, consistent with two-dimensional turbulence. However, our understanding of the relationships between the wave conditions and the dynamics and energetics of low-frequency surfzone eddies are largely based on numerical modeling. Here, we test these relationships with remotely sensed and in situ observations from large-scale directional wave basin experiments with varying wave conditions over alongshore-uniform barred bathymetry. Surface velocities derived with particle image velocimetry were employed to assess the spatial scales of low-frequency surfzone eddies and compute structure functions with alongshore velocities. Second-order structure functions for directionally spread waves ( ⁠

Physics of Fluids↗
USGS2025

Strategic planning of prevention and surveillance for emerging diseases and invasive species

Emerging infectious diseases and biological invasions pose increasing threats to public and ecosystems health. Proactive measures—such as prevention and surveillance taken before initial detection of the pathogen or species—are essential to ensure minimal spread prior to first detection. We developed an optimization model to determine where, when, and how much effort should be allocated to prevention versus surveillance. The model accounts for imperfect detection, system dynamics, spatial heterogeneity in risk and costs and is scalable to large landscapes. We found that the most cost-effective strategy is to maintain the prevention and surveillance efforts at stable equilibrium for the majority of the time, with deviations occurring only initially to steer the system toward the equilibrium. The equilibrium effort is jointly determined by the introduction risk, management costs, and total budget. Application of this model to chronic wasting disease in New York State suggests that the optimal strategy could reduce the cumulative disease cases before initial detection by an average of 22% compared to current practice. The optimal surveillance strategy could detect the disease on average over 8 mo earlier than the current strategy.

PNAS↗
USGS2025

Using a BACI design to assess stream ecology and chemistry response after wildfire

Wildfires can have complex effects on aquatic communities, which can be either beneficial or adverse depending on the setting. In California, USA, the size, frequency, and severity of wildfires has greatly increased over the last few decades. Understanding the effects of fire on aquatic ecosystem health and human land use has become increasingly important in managing the interface of the natural and urban landscape. In this study, we used a before–after control–impact (BACI) study design to test for differences in bed sediment chemistry, water chemistry, benthic algal biomass, and benthic macroinvertebrate communities between burned and unburned (control) watersheds, pre- and post-fire, using data collected before and after California’s 2017 Atlas, Nuns, and Tubbs wildfires. We found a treatment effect (burned vs control) on water chemistry, sediment trace-metal concentrations, algae, and community structure of benthic macroinvertebrates, but we found no evidence of an interaction between time period (pre- vs post-fire) and treatment. Both the difference in environmental factors between our control and burned sample sites and the proximity and degree of wildfire burn severity may have contributed to the results of this BACI study. Our results also highlight the importance of including control sites when assessing the effects of discrete disturbances, such as wildfire, to avoid spurious conclusions of disturbance effects that may be attributable to innate ecological variability.

Freshwater Science↗
USGS2020

Coordinated river infrastructure decisions improve net social-ecological benefits

We explore the social, ecological, economic, and technical dimensions of sustainable river infrastructure development and the potential benefits of coordinating decisions such as dam removal and stream crossing improvement. Dam removal is common practice for restoring river habitat connectivity and ecosystem health. However, stream crossings such as culverts are often 15 times more abundant than dams and may pose similar ecological impacts. Using multi-objective optimization for a model system of 6100 dams and culverts in Maine, USA, we demonstrate substantial benefit-cost improvements provided by coordinating habitat connectivity decisions. Benefit-cost efficiency improves by two orders of magnitude when coordinating more decisions across wider areas, but this approach may cause inequitable resource distribution. Culvert upgrades improve roadway safety and habitat connectivity, creating cost-effective opportunities for coordinating and cost-sharing projects between conservationists and safety managers. Benefit-cost trends indicate significant overlaps in habitat and safety goals, encouraging flexible stakeholder collaborations and cost-sharing strategies.

Maine↗
USGS2020

Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data

Stream water temperature ( T s ) is a variable of critical importance for aquatic ecosystem health. T s is strongly affected by groundwater-surface water interactions which can be learned from streamflow records, but previously such information was challenging to effectively absorb with process-based models due to parameter equifinality. Based on the long short-term memory (LSTM) deep learning architecture, we developed a basin-centric lumped daily mean T s model, which was trained over 118 data-rich basins with no major dams in the conterminous United States, and showed strong results. At a national scale, we obtained a median root-mean-square error of 0.69°C, Nash–Sutcliffe model efficiency coefficient of 0.985, and correlation of 0.994, which are marked improvements over previous values reported in literature. The addition of streamflow observations as a model input strongly elevated the performance of this model. In the absence of measured streamflow, we showed that a two-stage model could be used, where simulated streamflow from a pre-trained LSTM model ( Q sim ) still benefited the T s model even though no new information was brought directly into the inputs of the T s model. The model indirectly used information learned from streamflow observations provided during the training of Q sim , potentially to improve internal representation of physically meaningful variables. Our results indicate that strong relationships exist between basin-averaged forcing variables, catchment attributes, and T s that can be simulated by a single model trained by data on the continental scale.

Environmental Research Letters↗
USGS2017

The Nitrogen Footprint Tool network: A multi-institution program to reduce nitrogen pollution

Anthropogenic sources of reactive nitrogen have local and global impacts on air and water quality and detrimental effects on human and ecosystem health. This paper uses the nitrogen footprint tool (NFT) to determine the amount of nitrogen (N) released as a result of institutional consumption. The sectors accounted for include food (consumption and upstream production), energy, transportation, fertilizer, research animals, and agricultural research. The NFT is then used for scenario analysis to manage and track reductions, which are driven by the consumption behaviors of both the institution itself and its constituent individuals. In this paper, the first seven completed institution nitrogen footprint results are presented. The institution NFT network aims to develop footprints for many institutions to encourage widespread upper-level management strategies that will create significant reductions in reactive nitrogen released to the environment. Energy use and food purchases are the two largest sectors contributing to institution nitrogen footprints. Ongoing efforts by institutions to reduce greenhouse gas emissions also help to reduce the nitrogen footprint, but the impact of food production on nitrogen pollution has not been directly addressed by the higher-ed sustainability community. The NFT Network found that institutions could reduce their nitrogen footprints by optimizing food purchasing to reduce consumption of animal products and minimize food waste, as well as reducing dependence on fossil fuels for energy.

Sustainability↗