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Geology topics

Hilary A. Dugan

Publications and source records attributed to Hilary A. Dugan.

17 recordsLinked to original sources
USGS2026

Nutritional content of phytoplankton communities under ice

The effects of winter environmental conditions on lake ecology can be highly variable. Ice and overlying snow isolate the water below, impeding exchange of materials among water, atmosphere, and the watershed. Even with these limitations, biological communities under the ice can persist, where cold tolerant taxa can be especially abundant. Because many under-ice taxa tend to have a suite of unsaturated lipids that enable them to maintain metabolic function during cold conditions, under-ice biological communities may contain highly nutritious resources for higher level consumers relative to warmer, open-water conditions. To examine the effects of ice presence on under-ice biological productivity and nutritional quality, we aggregated data on under-ice physical, chemical, and biological variables from several existing databases. These data are among the most comprehensive, publicly available environmental and phytoplankton data that can be used to compare under-ice and open-water conditions at the community level. To understand how phytoplankton compositional shifts under ice can precipitate shifts in total algal nutritional content available to consumers relative to open water conditions, we merged characteristic phytoplankton fatty acid profiles with phytoplankton community composition data. Our results suggest that even though photosynthetically active radiation under ice is dramatically reduced by snow cover and opaque ice conditions, algal communities can be highly productive. Furthermore, these under-ice communities tend to be dominated by coldwater taxa, which are associated with elevated essential polyunsaturated fatty acids and reduced saturated fatty acids. Saturated fatty acids tend to be most associated with phytoplankton communities in the summer when cyanobacteria are prevalent, while winter communities tend to be most associated with high polyunsaturated fatty acids. As ice duration and quality continue to shift with increasing temperatures worldwide, phytoplankton assemblages are likely to exhibit altered nutritional profiles that have direct and indirect effects on food webs.

Ecopshere↗
USGS2026

The Zooplankton International Geospatial (ZIG) dataset: A global repository of spatiotemporal freshwater zooplankton community composition data from lakes and reservoirs to support ecological research

Zooplankton transfer substantial energy in aquatic food webs and are used as indicators of environmental change. Syntheses of zooplankton community dynamics globally require datasets that span a wide range of environmental gradients; however, these datasets are limited due to methodological differences across programs, taxonomic inconsistencies, and a lack of standardized metadata. To reconcile these challenges, we created the Zooplankton International Geospatial (ZIG) dataset, which includes original zooplankton, water physical and chemical variables, and lake morphometric data from 311 inland lakes and reservoirs. ZIG includes waterbodies ranging in size from 0.005 to 82,100 km 2 and spanning broad latitudinal (−47.26 to 64.90) and longitudinal ranges (−165.04 to 176.53). Temporal coverage for individual waterbodies ranges between 1 and 60 yr with sampling frequency ranging from annually to weekly. With its extensive coverage and content, we consider ZIG to be a cornerstone for future investigations of global scale lake biodiversity change.

Limnology & Oceanography - Letters↗
USGS2025

Ice thickness regulates heat flux in permanently ice-covered lakes

The permanently ice-covered lakes of Taylor Valley, Antarctica, are rare ecosystems where permanent ice cover and year-round vertically stable water columns provide critical redox zones for cold-adapted microorganisms. Using 30 yr of limnological data from the McMurdo Dry Valleys Long-Term Ecological Research program, we assessed the water column heat flux of four permanently ice-covered lakes in the context of global lake ice decline and lake warming. Our study reveals that heat flux in Taylor Valley lakes is driven by ice cover dynamics, both annual changes in ice thickness as well as overall ice thickness. During periods of ice thinning, like those observed from 2020 to 2023, the lakes accumulate heat. Lake Fryxell, Lake Hoare, and West Lake Bonney have repeatedly cooled and warmed over our record, with only East Lake Bonney cooling due to lake level rise. Ice thickness is largely synchronous among the four lakes, with periods of asynchronicity likely caused by lake-specific changes in surface albedo driven by changes in optical properties of the ice covers and in-ice sediment dynamics.

Limnology and Oceanography↗
USGS2025

One-hundred fundamental, open questions to integrate methodological approaches in lake ice research

The rate of technological innovation within aquatic sciences outpaces the collective ability of individual scientists within the field to make appropriate use of those technologies. The process of in situ lake sampling remains the primary choice to comprehensively understand an aquatic ecosystem at local scales; however, the impact of climate change on lakes necessitates the rapid advancement of understanding and the incorporation of lakes on both landscape and global scales. Three fields driving innovation within winter limnology that we address here are autonomous real-time in situ monitoring, remote sensing, and modeling. The recent progress in low-power in situ sensing and data telemetry allows continuous tracing of under-ice processes in selected lakes as well as the development of global lake observational networks. Remote sensing offers consistent monitoring of numerous systems, allowing limnologists to ask certain questions across large scales. Models are advancing and historically come in different types (process-based or statistical data-driven), with the recent technological advancements and integration of machine learning and hybrid process-based/statistical models. Lake ice modeling enhances our understanding of lake dynamics and allows for projections under future climate warming scenarios. To encourage the merging of technological innovation within limnological research of the less-studied winter period, we have accumulated both essential details on the history and uses of contemporary sampling, remote sensing, and modeling techniques. We crafted 100 questions in the field of winter limnology that aim to facilitate the cross-pollination of intensive and extensive modes of study to broaden knowledge of the winter period.

Water Resources Research↗
USGS2025

Climate and connectivity mediate overwintering habitat suitability for centrarchids in a large floodplain river network

Availability of suitable overwintering habitat is crucial for the survival of centrarchids in large floodplain rivers. However, there remains uncertainty in the spatiotemporal drivers of suitable conditions. We paired hydrogeomorphic characteristics with environmental data from 1994 to 2018 in individual lentic areas to (1) assess overwintering habitat availability throughout the Upper Mississippi River System using existing habitat suitability indices (HSIs) and (2) explore potential drivers of overall habitat suitability (HSI O ) and its components (dissolved oxygen, temperature, and flow). We found that flow velocities that exceeded suitable thresholds were independently responsible for 53% of nonsuitable habitats, and connectivity with lotic channels and river discharge increased velocity within lentic habitats. Additionally, colder winter conditions reduced water temperature, reducing availability of highly suitable habitat. Our results indicate that although warmer winters could increase the availability of highly suitable habitat for centrarchids, changes in flow regimes could lead to more connected areas becoming unsuitable. Our results provide critical information on factors that can be prioritized to manage centrarchid habitat, which is especially important in the context of uncertain future climate.

upper Mississippi River↗
USGS2024

National-scale remotely sensed lake trophic state from 1984 through 2020

Lake trophic state is a key ecosystem property that integrates a lake’s physical, chemical, and biological processes. Despite the importance of trophic state as a gauge of lake water quality, standardized and machine-readable observations are uncommon. Remote sensing presents an opportunity to detect and analyze lake trophic state with reproducible, robust methods across time and space. We used Landsat surface reflectance data to create the first compendium of annual lake trophic state for 55,662 lakes of at least 10 ha in area throughout the contiguous United States from 1984 through 2020. The dataset was constructed with FAIR data principles (Findable, Accessible, Interoperable, and Reproducible) in mind, where data are publicly available, relational keys from parent datasets are retained, and all data wrangling and modeling routines are scripted for future reuse. Together, this resource offers critical data to address basic and applied research questions about lake water quality at a suite of spatial and temporal scales.

Scientific Data↗
USGS2024

Environmental and societal consequences of winter ice loss from lakes

More than half a billion people live near lakes that freeze over in the winter. However, lakes are rapidly losing winter ice cover in response to warming, and the rate of loss has accelerated over the past 25 years. Hampton et al . reviewed the state of seasonal ice cover on lakes and discuss some of the consequences of its disappearance. Ice loss will affect culture, economy, water quality, fisheries, and biodiversity, as well as weather and climate. —Jesse Smith

Science↗
USGS2023

Modular compositional learning improves 1D hydrodynamic lake model performance by merging process-based modeling with deep learning

Hybrid Knowledge-Guided Machine Learning (KGML) models, which are deep learning models that utilize scientific theory and process-based model simulations, have shown improved performance over their process-based counterparts for the simulation of water temperature and hydrodynamics. We highlight the modular compositional learning (MCL) methodology as a novel design choice for the development of hybrid KGML models in which the model is decomposed into modular sub-components that can be process-based models and/or deep learning models. We develop a hybrid MCL model that integrates a deep learning model into a modularized, process-based model. To achieve this, we first train individual deep learning models with the output of the process-based models. In a second step, we fine-tune one deep learning model with observed field data. In this study, we replaced process-based calculations of vertical diffusive transport with deep learning. Finally, this fine-tuned deep learning model is integrated into the process-based model, creating the hybrid MCL model with improved overall projections for water temperature dynamics compared to the original process-based model. We further compare the performance of the hybrid MCL model with the process-based model and two alternative deep learning models and highlight how the hybrid MCL model has the best performance for projecting water temperature, Schmidt stability, buoyancy frequency, and depths of different isotherms. Modular compositional learning can be applied to existing modularized, process-based model structures to make the projections more robust and improve model performance by letting deep learning estimate uncertain process calculations.

Journal of Advances in Modeling Earth Systems↗
USGS2022

Towards critical white ice conditions in lakes under global warming

The quality of lake ice is of uppermost importance for ice safety and under-ice ecology, but its temporal and spatial variability is largely unknown. Here we conducted a coordinated lake ice quality sampling campaign across the Northern Hemisphere during one of the warmest winters since 1880 and show that lake ice during 2020/2021 commonly consisted of unstable white ice, at times contributing up to 100% to the total ice thickness. We observed that white ice increased over the winter season, becoming thickest and constituting the largest proportion of the ice layer towards the end of the ice cover season when fatal winter drownings occur most often and light limits the growth and reproduction of primary producers. We attribute the dominance of white ice before ice-off to air temperatures varying around the freezing point, a condition which occurs more frequently during warmer winters. Thus, under continued global warming, the prevalence of white ice is likely to substantially increase during the critical period before ice-off, for which we adjusted commonly used equations for human ice safety and light transmittance through ice.

Nature Communications↗
USGS2022

Long-term change in metabolism phenology in north temperate lakes

The phenology of dissolved oxygen (DO) dynamics and metabolism in north temperate lakes offers a basis for comparing metabolic cycles over multi-year time scales. Although proximal control over lake DO can be attributed to metabolism and physical processes, how those processes evolve over decades largely remains unexplored. Metabolism phenology may reveal the importance of coherence among lakes and facilitate general conclusions about the controls on lake metabolism at regional scales. We developed a Bayesian modeling framework to estimate DO concentrations and metabolism in eight lakes in contrasting landscapes in Wisconsin, USA. We identify the DO and metabolism phenologies for each lake, and use those to compare how decadal patterns relate to trophic state and landscape setting. We show that lakes can be categorized by their hypolimnetic oxygen consumption dynamics, with oligotrophic lakes having a diverse set of patterns and eutrophic lakes having uniform trends of increased oxygen consumption over the last decade. Metabolism phenology is likewise diverse for oligotrophic lakes, whereas eutrophic lakes in southern Wisconsin share consistent long-term patterns of metabolic trends and seasonal DO consumption highlighting the importance of trophic state driving metabolism. Eutrophic lakes had higher magnitudes and more seasonal variation in net ecosystem production in contrast to oligotrophic lakes. Generally, long-term metabolic trends of north temperate lakes suggest a limited influence of climate on lake metabolism and that temporal coherence of long-term metabolism change is driven primarily by the landscape setting.

Wisconsin↗
USGS2021

LakeEnsemblR: An R package that facilitates ensemble modelling of lakes

Model ensembles have several benefits compared to single-model applications but are not frequently used within the lake modelling community. Setting up and running multiple lake models can be challenging and time consuming, despite the many similarities between the existing models (forcing data, hypsograph, etc.). Here we present an R package, LakeEnsemblR, that facilitates running ensembles of five different vertical one-dimensional hydrodynamic lake models (FLake, GLM, GOTM, Simstrat, MyLake). The package requires input in a standardised format and a single configuration file. LakeEnsemblR formats these files to the input required by each model, and provides functions to run and calibrate the models. The outputs of the different models are compiled into a single file, and several post-processing operations are supported. LakeEnsemblR's workflow standardisation can simplify model benchmarking and uncertainty quantification, and improve collaborations between scientists. We showcase the successful application of LakeEnsemblR for two different lakes.

Environmental Modelling & Software↗
USGS2018

Consequences of gas flux model choice on the interpretation of metabolic balance across 15 lakes

Ecosystem metabolism and the contribution of carbon dioxide from lakes to the atmosphere can be estimated from free-water gas measurements through the use of mass balance models, which rely on a gas transfer coefficient ( k ) to model gas exchange with the atmosphere. Theoretical and empirically based models of k range in complexity from wind-driven power functions to complex surface renewal models; however, model choice is rarely considered in most studies of lake metabolism. This study used high-frequency data from 15 lakes provided by the Global Lake Ecological Observatory Network (GLEON) to study how model choice of k influenced estimates of lake metabolism and gas exchange with the atmosphere. We tested 6 models of k on lakes chosen to span broad gradients in surface area and trophic states; a metabolism model was then fit to all 6 outputs of k data. We found that hourly values for k were substantially different between models and, at an annual scale, resulted in significantly different estimates of lake metabolism and gas exchange with the atmosphere.

Inland Waters↗
USGS2018

LakeMetabolizer: An R package for estimating lake metabolism from free-water oxygen using diverse statistical models

Metabolism is a fundamental process in ecosystems that crosses multiple scales of organization from individual organisms to whole ecosystems. To improve sharing and reuse of published metabolism models, we developed LakeMetabolizer, an R package for estimating lake metabolism from in situ time series of dissolved oxygen, water temperature, and, optionally, additional environmental variables. LakeMetabolizer implements 5 different metabolism models with diverse statistical underpinnings: bookkeeping, ordinary least squares, maximum likelihood, Kalman filter, and Bayesian. Each of these 5 metabolism models can be combined with 1 of 7 models for computing the coefficient of gas exchange across the air–water interface ( k ). LakeMetabolizer also features a variety of supporting functions that compute conversions and implement calculations commonly applied to raw data prior to estimating metabolism (e.g., oxygen saturation and optical conversion models). These tools have been organized into an R package that contains example data, example use-cases, and function documentation. The release package version is available on the Comprehensive R Archive Network (CRAN), and the full open-source GPL-licensed code is freely available for examination and extension online. With this unified, open-source, and freely available package, we hope to improve access and facilitate the application of metabolism in studies and management of lentic ecosystems.

Inland Waters↗
USGS2017

Water quality data for national-scale aquatic research: The Water Quality Portal

Aquatic systems are critical to food, security, and society. But, water data are collected by hundreds of research groups and organizations, many of which use nonstandard or inconsistent data descriptions and dissemination, and disparities across different types of water observation systems represent a major challenge for freshwater research. To address this issue, the Water Quality Portal (WQP) was developed by the U.S. Environmental Protection Agency, the U.S. Geological Survey, and the National Water Quality Monitoring Council to be a single point of access for water quality data dating back more than a century. The WQP is the largest standardized water quality data set available at the time of this writing, with more than 290 million records from more than 2.7 million sites in groundwater, inland, and coastal waters. The number of data contributors, data consumers, and third-party application developers making use of the WQP is growing rapidly. Here we introduce the WQP, including an overview of data, the standardized data model, and data access and services; and we describe challenges and opportunities associated with using WQP data. We also demonstrate through an example the value of the WQP data by characterizing seasonal variation in lake water clarity for regions of the continental U.S. The code used to access, download, analyze, and display these WQP data as shown in the figures is included as supporting information.

Water Resources Research↗
USGS2017

Meteorological drivers of hypolimnetic anoxia in a eutrophic, north temperate lake

Oxygen concentration is both an indicator and driver of water quality in lakes. Decreases in oxygen concentration leads to altered ecosystem function as well as harmful consequences for aquatic biota, such as fishes. The responses of oxygen dynamics in lakes to climate-related drivers, such as temperature and wind speed, are well documented for lake surface waters. However, much less is known about how the oxic environment of bottom waters, especially the timing and magnitude of anoxia in eutrophic lakes, responds to changes in climate drivers. Understanding how important ecosystem states, such as hypolimnetic anoxia, may respond to differing climate scenarios requires a model that couples physical-biological conditions and sufficiently captures the density stratification that leads to strong oxygen gradients. Here, we analyzed the effects of changes in three important meteorological drivers (air temperature, wind speed, and relative humidity) on hypolimnetic anoxia in a eutrophic, north temperate lake using the anoxic factor as an index that captures both the temporal and spatial extent of anoxia. Air temperature and relative humidity were found to have a positive correlation with anoxic factor, while wind speed had a negative correlation. Air temperature was found to have the greatest potential impact of the three drivers on the anoxic factor, followed by wind speed and then relative humidity. Across the scenarios of climate variability, variation in the simulated anoxic factor was primarily due to changes in the timing of onset and decay of stratification. Given the potential for future changes in climate, especially increases in air temperature, this study provides important insight into how these changes will alter lake water quality.

Ecological Modelling↗
USGS2015

Supporting open collaboration in science through explicit and linked semantic description of processes

The Web was originally developed to support collaboration in science. Although scientists benefit from many forms of collaboration on the Web (e.g., blogs, wikis, forums, code sharing, etc.), most collaborative projects are coordinated over email, phone calls, and in-person meetings. Our goal is to develop a collaborative infrastructure for scientists to work on complex science questions that require multi-disciplinary contributions to gather and analyze data, that cannot occur without significant coordination to synthesize findings, and that grow organically to accommodate new contributors as needed as the work evolves over time. Our approach is to develop an organic data science framework based on a task-centered organization of the collaboration, includes principles from social sciences for successful on-line communities, and exposes an open science process. Our approach is implemented as an extension of a semantic wiki platform, and captures formal representations of task decomposition structures, relations between tasks and users, and other properties of tasks, data, and other relevant science objects. All these entities are captured through the semantic wiki user interface, represented as semantic web objects, and exported as linked data.

Conference Paper↗