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SFS Annual Meeting

2021 Detailed Schedule

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Michelle Catherine Kelly (Primary Presenter/Author)
Michigan Technological University,;

Erin Eberhard (Co-Presenter/Co-Author)
Michigan Technological University ,;

Kevin Nevorski (Co-Presenter/Co-Author)
Michigan Technological University,;

Amy Marcarelli (Co-Presenter/Co-Author)
Michigan Technological University,;

Abstract: While we focus on the aerobic processes of gross primary production (GPP) and ecosystem respiration (ER) to parameterize stream metabolism, we also know that anaerobic, energy-releasing processes are common in streams. Denitrification, which releases just 5% less free energy than aerobic respiration, is one such process. We ask: are anaerobic energy-generation pathways significant contributors to stream energy budgets? We measured denitrification (acetylene block) and aerobic metabolism in 25 study streams in the USA (NEON n=13, StreamPULSE n=12), modeling GPP and ER using a one or two-station inverse Bayesian approach. This dataset was supplemented with denitrification (15-N tracer) and metabolism from LINXII (n=28). Areal denitrification and ER rates were converted to rates of energy release using Gibbs free energy values. Total energy released was calculated as free energy from denitrification plus ER. Linear, nonlinear, and tree-based regression models, where the best fit model was determined by testing set performance, were used to determine which environmental variables were most important to predicting the proportion of the total energy budget released via denitrification. Preliminary results show denitrification contributing to as much as 9.6% of the total energy budget of a single stream.


Umarfarooq Abdulwahab (Primary Presenter/Author)
Utah State University,;

Edward Hammill (Co-Presenter/Co-Author)
Utah State University,;

Charles Hawkins (Co-Presenter/Co-Author)
Utah State University,;

Abstract: The proliferation of readily-accessible climate data is stimulating increasing use of species distribution models (SDMs) in conservation planning and management. However, it is uncertain how sensitive models are to the choice of climate data used. We compared the performance of maximum-entropy SDMs developed for six imperiled California species (4 amphibians, 2 reptiles) based on seven different datasets (WorldClim, Chelsa, TerraClimate, Climate Research Unit Time-Series [CRU-TS], PRISM, StreamCat, and EarthEnv). For each species, we used a standardized, objective procedure to select climate predictors from each dataset; assessed performance with standard metrics; and compared variable importance scores, partial dependence plots, and predicted distributions. SDM performance was sensitive to the climate dataset used. For some species, use of similar climate predictors from different datasets produced opposite responses. Models based on the spatially coarsest data (CRU-TS) performed less well than models based on the other data sources. For some species, freshwater-focused datasets produced better models than terrestrial-focused datasets. Some models overpredicted and others underpredicted distributions compared with historical range maps. Care should be taken when developing, applying, and interpreting climate-based SDMs, and ensembles of models may be needed to estimate uncertainties in predictions.


Michelle Catherine Kelly (Co-Presenter/Co-Author)
Michigan Technological University,;

Admin Husic (Co-Presenter/Co-Author)
University of Kansas,;

Amy Burgin (Co-Presenter/Co-Author)
University of Kansas,;

AMIRREZA ZARNAGHSH (Primary Presenter/Author)
University of Kansas,;

Abstract: Effective management of water quality requires a detailed understanding of how point-source nitrate pollution is dispersed and cycled in large rivers. While numerical models have been helpful, the reliability of model predictions is limited by the quality of calibration data, which is rarely recorded at the same temporal frequencies at which cycling and dispersion occur. In this study, we integrate 15-minute nitrate data from four sensors along a 33 km stretch of the Kansas River, the world’s largest prairie river. During a six-month period, nitrogen waste was strategically released by local authorities into the Kansas River, providing a unique contamination event. We utilized this event and the associated sensing data to simulate the dispersion and cycling of nitrogen in the river using an unsteady 2-D ecohydraulics model. Numerical model results validate sensing data and indicate an elevated degree of uptake and lateral turbulent mixing in response to the waste release. Further, calibrating model performance with high-frequency data helped simulate dispersion and cycling at novel timescales not possible with traditional grab-sampling. Our study demonstrates the utility of sensors and modeling to transform our understanding of nitrate dynamics in rivers.


Charles McIntire (Co-Presenter/Co-Author)
OptiO2, Inc.,;

Erin Cantrell (Co-Presenter/Co-Author)
US Forest Service,;

Evan Matusz (Co-Presenter/Co-Author)
OptiO2, Inc.,;

Phillip Vivek (Co-Presenter/Co-Author)
OptiO2, Inc.,;

Ruby Ghosh (Co-Presenter/Co-Author)
OptiO2, Inc.,;

Michael Gooseff (Primary Presenter/Author)
University of Colorado,;

Abstract: Dissolved oxygen (DO) is requisite for healthy aquatic ecosystems, however, few studies have focused on the long-term dynamics and controls of DO in hyporheic zones. Here we explore controls on hyporheic DO dynamics at 10, 20, and 35 cm depth beneath the East River, Colorado, from July-October, 2017 (normal water year) and April to October, 2018 (low flow water year). We expect hyporheic DO is supplied by the surface water and therefore negatively correlated with vertical flux (q, estimated with VFLUX2 model, where q <0 is downwelling). We also predict that hyporheic DO removal (?DO<0; i.e., decrease from river) is controlled by heterotrophic microbial communities, which are likely to be more active at warmer temperatures. Thus, ?DO should be negatively correlated with temperature. Ubiquitously, we estimate downwelling at all depths. DO becomes anoxic briefly at 20 and 35 cm depths in 2017, and at all 3 depths for extended periods in 2018. Correlations between ?DO and temperature are not strong suggesting a dynamic microbial community (perhaps because of changes to their needs such as DOC) may be the reason for the temporal dynamics in this hyporheic zone.


J. E. Barrett (Co-Presenter/Co-Author)
Virginia Tech,;

Tyler Kohler (Co-Presenter/Co-Author)
École Polytechnique Fédérale de Lausanne, Switzerland,;

Lee Stanish (Co-Presenter/Co-Author)
University of Colorado, Boulder,;

Diane McKnight (Co-Presenter/Co-Author)
University of Colorado,;

Mark Salvatore (Co-Presenter/Co-Author)
Northern Arizona University,;

Eric Sokol (Primary Presenter/Author)
Battelle, National Ecological Observatory Network (NEON),;

Abstract: Because of the importance of diatoms as bioindicators, it is important that we develop a process-based understanding of the factors that organize their biodiversity at multiple spatial scales in heterogeneous and dynamic ecosystems. In the McMurdo Dry Valleys of Antarctica, well-described diatom assemblages are found within two cyanobacterial mat types, which occupy different habitats and vary in coverage within and among streams. Here, we use MCSim, a spatially explicit metacommunity simulation package for R, to test alternative hypotheses about the roles of dispersal and species sorting in maintaining the biodiversity of diatom assemblages. The spatial distribution and patchiness of cyanobacterial mat habitats was characterized by remote imagery of the Lake Fryxell sub-catchment in Taylor Valley. The available species pool for diatom metacommunity simulation scenarios was informed by the Antarctic Freshwater Diatoms Database, maintained by the McMurdo Dry Valleys Long Term Ecological Research program. We used simulation outcomes to test the plausibility of alternative community assembly hypotheses to explain empirically observed patterns of freshwater diatom biodiversity in the long-term record. The results point to the importance of dispersal for understanding current and future biodiversity patterns for diatoms in this ecosystem.


Robert Bailey (Primary Presenter/Author)
Ontario Tech University,;

Abstract: Macrophyte over-abundance is a serious issue for seasonal, year-round, and Indigenous communities on the semi-regulated Trent-Severn Lakes of southern Ontario. I will illustrate development of a multi-level, causal model designed to predict the annual ups and downs of the "weed" population as quantified by multi-spectral Landsat satellite images. Candidate predictors in the model will include within-lake, time-invariant factors such as depth and fetch, annually varying, among-lake factors including nutrients and water level, and regionally invariant factors such as temperature, precipitation and wind in the previous year. Ground-truthing of the 30x30m grain satellite images will use cm-scale, multi-spectral drone images as well as in-water observations. Ultimately, I will determine the degree to which management actions can influence macrophyte dynamics in this complex, semi-regulated lake system.


Jaime Ricardo García Márquez (Co-Presenter/Co-Author)
Leibniz-Institute of Freshwater Ecology and Inland Fisheries,;

Sami Domisch (Co-Presenter/Co-Author)
Leibniz Institute of Freshwater Ecology and Inland Fisheries,;

Afroditi Grigoropoulou (Primary Presenter/Author,Co-Presenter/Co-Author)
IGB Leibniz-Institute of Freshwater Ecology and Inland Fisheries,;

Abstract: Along with the ongoing decline in freshwater biodiversity emerges the urgent need to protect what is being threatened. The accurate mapping of global freshwater habitats and biodiversity at a fine resolution is fundamental for assessing their vulnerability and directing effective conservation planning. Our work is one of the first coordinated efforts to map freshwater habitats and capture the actual spatial patterns of aquatic biodiversity on a fine resolution, at the global extent. We are using a newly developed seamless, standardized stream network of 90m spatial resolution to model the potential genus distributions of four insect orders that are used as proxies for the overall state of freshwater biodiversity (Ephemeroptera, Plecoptera, Trichoptera and Odonata; EPTO). In this talk, we will explore the environmental and topographical variables that mostly influence EPTO distributions based on an extensive dataset of insect occurrence records and the Random Forest algorithm. The most relevant predictors will be used for mapping genus distributions using spatially explicit Species Distribution Models (SDM), taking into account the connectivity between basins. The output of these analyses will aid in indicating irreplaceable habitats worldwide under a novel freshwater-specific spatial conservation planning framework.


Corey Krabbenhoft (Co-Presenter/Co-Author)
University of Minnesota,;

Ashley Burtner (Co-Presenter/Co-Author)
Cooperative Institute for Great Lakes Research,;

Brenna Friday (Co-Presenter/Co-Author)
Wayne State University,;

Donna Kashian (Co-Presenter/Co-Author)
Wayne State University,;

Jacob Wielgomas (Primary Presenter/Author)
Wayne State University,;

Abstract: Recognizing the consequences of anthropogenic actions on stream health is challenging on a landscape scale. Logging, mining, and road construction are such actions affecting watersheds of Marquette County, Michigan. Although there are well-established modeling methods in freshwater ecology, such as traditional linear models, non-traditional and newly emerging methods may be able to provide valuable insights into macroinvertebrate changes with potentially better accuracy. Our objective is to use the natural distribution of our response variable (EPT) to justify beta and gamma regression analysis and compare these to traditional linear modeling. Emerging modeling techniques new to the field of statistics – topological data analysis – may also have promising growth in displaying patterns of water quality within long-term datasets. Long-term data sets are a unique and invaluable resource in providing vital background information for biotic models that may help differentiate between impacts of natural variation and anthropogenic changes in stream ecosystems. Through careful and extensive traditional and nontraditional modeling techniques of ecological datasets, there is a high potential to have more comprehensive, evidence-based conclusions.

Minding the data gap: Comparing probabilistic approaches to estimate missing data in time-series [Oral Presentation]

Robert O. Hall (Co-Presenter/Co-Author)
Flathead Lake Biological Station, University of Montana,;

Laurel Genzoli (Co-Presenter/Co-Author)
University of Montana,;

Matt Trentman (Primary Presenter/Author)
Flathead Lake Biological Station, University of Montana,;

Abstract: Missing data are common in research, and are particularly problematic when observations are auto-correlated in time. In aquatic ecosystems, sensors collect continuous data, but these data are prone to missingness due to sensor malfunction. It is unclear how missing data gaps affect parameter estimation in time-series models. We tested the effectiveness of two probabilistic approaches, multiple imputations (MI) and Bayesian parameter estimation (Bayes), to estimate missing data and recover known parameters of a time-series model of simulated light-controlled gross primary production. The MI approach imputes missing data points five times, generating five complete datasets, with each dataset modeled individually and inference made on model-averaged parameters. The Bayes approach lists missing data as parameters to be estimated in a Bayesian time-series model. Both approaches effectively estimated a range of randomly removed data (1-40% of the dataset) in single-day and weeklong blocks. Furthermore, both approaches returned the known time-series model parameters, and more importantly, parameter estimation was not affected by varying the amount of missing data (up to 40%). Probabilistic approaches to estimating missing data may be useful to fill data gaps without sacrificing the accuracy of parameter estimation for simple models.


Elizabeth Mohr (Primary Presenter/Author)
Montana State University,;

Geoffrey Poole (Co-Presenter/Co-Author)
Montana State University, Montana Institute on Ecosystems, ;

Hayley Oakland (Co-Presenter/Co-Author)
Montana State University,;

Katie Fogg (Co-Presenter/Co-Author)
Montana State University,;

Ann Marie Reinhold (Co-Presenter/Co-Author)
Montana State University, Montana Institute on Ecosystems,;

Byron Amerson (Co-Presenter/Co-Author)
Montana State University,;

Sam Carlson (Co-Presenter/Co-Author)
Montana State University,;

Abstract: Biogeochemical cycling within hyporheic zones can substantially influence stream solute dynamics, yet quantifying this influence at the scale of stream reaches and networks is challenged by the need to represent both hyporheic hydrology and biogeochemistry. By conceptualizing an arbitrarily complex relationship between solute concentration and hyporheic water age while considering a power law hyporheic residence time distribution, we developed a mathematical model for simulating the influence of hyporheic flowpath biogeochemistry on entire stream reaches. To assess model predictions of both hyporheic hydrology and biogeochemistry, we conducted a co-release of conservative (salt) and reactive (nitrate) tracer in a recirculating flume with a sinusoidal dune comprised of stream-incubated gravel. We measured conductivity and nitrate concentrations several times following the release and fit our simulation model to both time series. Our approach provides a simple way to incorporate hyporheic heterogeneity into models of solute dynamics at stream reach and network scales.


Jonathan Tonkin (Primary Presenter/Author)
University of Canterbury,;

Abstract: Extreme events such as anomalous floods and droughts will continue to increase in frequency and severity with ongoing climate change. This presents a major challenge for protecting the biodiversity that inhabits freshwaters globally. Faced with insufficient information about how ecosystems might respond to such events over both short and long timescales, managers may be forced to make decisions that lack sufficient scientific credibility. Here, I present a review of extreme events in running waters and their potential to shift ecosystems into new states, generate ecological lock-ins, and alter freshwater biodiversity across the globe, with a particular focus on potential outcomes into an uncertain future. I outline potential modelling solutions to the challenges associated with increased extreme events, including the emerging field of near-term ecological forecasting and more mechanistic approaches particularly suited to far-term scenario-based projections such as community-wide matrix population models.

Open Science: Modeling NEON Open Data with USGS Open Source Software [Oral Presentation]

Nora Catolico (Primary Presenter/Author)
National Ecological Observatory Network,;

Abstract: In the age of open science, ever-growing datasets and tools are freely accessible to users. This unprecedented resource pool enables on-demand analysis without significant time and cost barriers such as fieldwork and proprietary software. Here we present an example of a groundwater/surface-water flow model created using solely open science sources. Model inputs are derived from National Ecological Observatory Network (NEON) continuous water table elevation data. NEON is a continental-scale observatory that provides long-term ecological open data. There are 34 NEON aquatic sites consisting of lakes, wadeable streams, and large rivers, where continuous high frequency sensor data is integrated with physical, organismal, and biogeochemical observational sampling. Of these sites, 30 are equipped with instrumented shallow groundwater wells. MODFLOW, an open-source hydrologic modeling software developed by the U.S. Geological Survey (USGS) was used to simulate the response of groundwater levels to variations in head-dependent flux boundaries using the Time-Variant Specified Head (CDH) and River (RIV) packages. Results are plotted with ModelMuse, a free USGS graphical user interface. This simple model illustrates how NEON surface and groundwater elevation data can be used to analyze flow paths through integrating open science data sources.


Michelle Tarian (Primary Presenter/Author)
Cal State University Monterey Bay,;

John Olson (Co-Presenter/Co-Author)
Dept of Applied Environmental Science, California State University Monterey Bay, CA, USA,;

Abstract: Estuaries are critical habitat for many fish species in California, including salmon, steelhead, and tide water Gobi. This habitat is often managed by breaching sandbars, but the timing of when this is done is guided only by conventional wisdom. To understand how the fish assemblage changes in response to chemical and physical changes in the estuary caused by opening of sandbars, we modeled fish responses in the Russian River Estuary in Central California using random forest. Seven fish species were modeled and included the presence of the other fish species as predictor variables to understand species interactions. Models performance was evaluated by calculating the area under the curve (AUCs), which ranged from 0.65 to 0.89. These models can then be applied to predict the entire fish assemblage , providing mangers with data driven guidance on management actions and can be used to develop the Aquatic Species Assessment Tool (ASAT). The ASAT aims to capture the complexity of species-habitat interactions in a simple end-user interface that can be used to predict impacts of estuary management actions on multiple aquatic species and will eventually be applied to estuaries throughout California.

Predicting lake temperature to understand ecological change using process-guided deep learning models [Oral Presentation]

Gretchen Hansen (Primary Presenter/Author)
University of Minnesota,;

Jordan Read (Co-Presenter/Co-Author)
US Geological Survey,;

Alison Appling (Co-Presenter/Co-Author)
US Geological Survey,;

Jonah Bacon (Co-Presenter/Co-Author)
University of Minnesota,;

Haley Corson-Dosch (Co-Presenter/Co-Author)
United States Geological Survey,;

Vipin Kumar (Co-Presenter/Co-Author)
University of Minnesota,;

Ashley LaRoque (Co-Presenter/Co-Author)
University of Minnesota,;

Samantha Oliver (Co-Presenter/Co-Author)
United States Geological Survey, ;

Lindsay Platt (Co-Presenter/Co-Author)
United States Geological Survey,;

Kelsey Vitense (Co-Presenter/Co-Author)
University of Minnesota,;

Jared Willard (Co-Presenter/Co-Author)
University of Minnesota,;

Abstract: Climate change impacts on lakes are complex and variable. Lake temperature data are lacking on the spatiotemporal scales and resolution required to understand how changing temperatures influence ecological processes. We integrated deep neural networks with knowledge of lake physics to develop process-guided deep learning models of lake temperature. We applied these models to 638 lakes in Minnesota, USA to predict continuous, daily water temperature profiles from 1980-2018. The median lake-specific root-mean square error was 1.75°C. On average, lakes got warmer, although rates of change varied. Surface and bottom temperatures increased most in fall months, and both surface and bottom temperatures of lakes cooled on average in April and May. The number of days in which lakes contained optimal temperatures for cold, cool, and warmwater fish increased on average, while the area of optimal thermal habitat generally decreased during the same period. Spawning dates occurred earlier for spring and summer spawners and later for fall spawners, although these trends varied among lakes. These models advance understanding of lake temperature response to climate change and demonstrate that the impacts of these changes on fish and other biota are not straightforward.


Admin Husic (Primary Presenter/Author)
University of Kansas,;

Alexander Michalek (Co-Presenter/Co-Author)
University of Kansas,;

Abstract: Hydrologic connectivity is defined as the degree to which a system facilitates the transfer of water from source zones to river networks. Quantifying spatial trends in connectivity across large areas, such as continents, is complicated by substantial data requirements and computational demands. In this study, we utilize high-performance computing to calculate the Index of Connectivity (IC) for all 332 HUC-6 basins in the continental United States (CONUS) at 10-m DEM resolution. Results show significant differences in connectivity across CONUS with the most well-connected areas in the Pacific Northwest, Colorado Rockies, and the Appalachian Mountains. Further, typically only 1% or less of a basin area is considered “highly-connected”, which has implications for how limited resources for watershed management should be distributed to maximize ecosystem benefits.

Refining global spatial conservation planning considering environmental uniqueness, connectivity, and biodiversity of freshwater habitats [Oral Presentation]

Maria Magdalena Üblacker (Primary Presenter/Author)
Leibniz Institute of Freshwater Ecology and Inland Fisheries,;

Jaime García-Márquez (Co-Presenter/Co-Author)
Leibniz Institute of Freshwater Ecology and Inland Fisheries ,;

Sami Domisch (Co-Presenter/Co-Author)
Leibniz Institute of Freshwater Ecology and Inland Fisheries,;

Abstract: Global freshwater biodiversity is in a constant decline and more efficient protection schemes are needed. To date, however, existing protected areas are rarely tailored towards freshwater biodiversity and habitats since they do not account for freshwater species or longitudinal connectivity along hydrographical networks. In addition, coarse spatial data resolution and related data uncertainties further limit systematic spatial conservation planning. This study addresses these limitations and aims to identify freshwater regions that are potentially deemed irreplaceable regarding their environmental uniqueness, connectivity, and their importance for freshwater biodiversity. We first delineate sub-catchments of individual stream reaches at a spatial resolution of ~90m globally. To identify environmentally unique freshwater habitats, we aggregate environmental catchment characteristics and human stressors, and employ a k-means cluster analysis. In addition, we determine the relative importance of stream reaches regarding the river network connectivity using betweenness centrality indices and patch-based graphs. The resulting clustered sub-catchments and connectivity indices are then combined with Species Distribution Models (SDMs) of aquatic insects and implemented in integer linear programming (ILP) solvers to identify potential areas for protection. The talk will outline the overall project and present first results of the cluster analysis.


Noel Juvigny-Khenafou (Co-Presenter/Co-Author)
University of Koblenz-Landau, Institute for Environmental Sciences,;

Ralf Schäfer (Co-Presenter/Co-Author)
University of Koblenz-Landau, Institute for Environmental Sciences,;

Lucas Streib (Primary Presenter/Author)
University of Koblenz-Landau, Institute for Environmental Sciences,;

Abstract: In most ecosystems, multiple stressors co-occur in space and time. Stressor interactions can be additive or non-additive (antagonistic or synergistic). To date, most multiple stressor studies focused on effects at the local scale (i.e., patch experiments). However, it remains largely unknown how spatio-temporally differing stressors interact in their effects. Indeed, at larger spatial scales, biotic dynamics within ecological networks complicate our understanding. We simulated a wide range of scenarios of two stressors using a spatially-explicit meta-population model for a generic, hemimetabolous freshwater insect (Streib et al. 2020 - Ecol. Model. 416). Stressors were land-use and climatic extreme events, implemented in spatio-temporally different profiles with repeated co-occurrence. Land-use permanently influenced meta-population network connectivity and patch qualities, whereas climatic extreme events resulted in periodic mortality. Overall, we ran 27.000 simulations. We found that the type of interaction between land-use and climatic extreme events depends on the stressor intensity, with higher levels of both stressors resulting in synergistic effects. Conversely, low land-use stress buffered long-term effects of climatic extreme events at all levels, by supporting recovery dynamics via the network. We discuss the relevance of the spatial dimension when assessing the effects of multiple stressors.

Synthesizing Long-term Data to Explain an Unexpected Streamwater Nitrate Pulse [Oral Presentation]

Mark Green (Primary Presenter/Author)
Case WEstern Reserve University,;

John Campbell (Co-Presenter/Co-Author)
US Forest Service, Northern Research Station,;

Linda Pardo (Co-Presenter/Co-Author)
US Forest Service,;

Emma Rosi (Co-Presenter/Co-Author)
Cary Institute of Ecosystem Studies,;

Pamela Templer (Co-Presenter/Co-Author)
Boston University,;

Timothy Fahey (Co-Presenter/Co-Author)
Cornell University,;

Charles Driscoll (Co-Presenter/Co-Author)
Syracuse University, ;

Nicholas LoRusso (Co-Presenter/Co-Author)
Syracuse University,;

Jackie Matthes (Co-Presenter/Co-Author)
Wellesley College,;

Abstract: The Hubbard Brook Ecosystem Study has involved long-term observation of catchment ecosystem behavior in response to atmospheric deposition and climate changes. A major pulse of streamwater nitrate from the reference catchment occurred in 2013-14, however there was no clear perturbation to the ecosystem that might have caused the pulse. We quantitatively synthesized the suite of ecosystem data collected at Hubbard Brook to objectively identify the most likely causes for this nitrate pulse. The analysis required handling time lags in the catchment since the diversity of potential drivers are operating at different time scales. The approach identified a novel combination of warm air temperature, heavy soil frost, low forest gross primary production, and a series of large storms that we hypothesize together produced excess nitrate and transported it to the catchment outlet. This data-driven synthesis required novel data analysis techniques to objectively arrive at new hypotheses about how this forested ecosystem is responding to climate change.


Brian Brown (Primary Presenter/Author)
Brigham Young University, Provo,;

Aimee Fullerton (Co-Presenter/Co-Author)
Northwest Fisheries Science Center, NOAA,;

Darin Kopp (Co-Presenter/Co-Author)
University of Oklahoma,;

Flavia Tromboni (Co-Presenter/Co-Author)
University of Nevada, Reno,;

Arial Shogren (Co-Presenter/Co-Author)
Department of Earth and Environmental Sciences, Michigan State University,;

Jeremy Jones (Co-Presenter/Co-Author)
University of Alaska Fairbanks,;

Lenka Kuglerova (Co-Presenter/Co-Author)
Swedish University of Agricultural Sciences,;

Christopher Sergeant (Co-Presenter/Co-Author)
University of Montana, Flathead Lake Biological Station,;

Angus Webb (Co-Presenter/Co-Author)
The University of Melbourne,;

Claire Ruffing (Co-Presenter/Co-Author)
University of British Columbia,;

Jay Zarnetske (Co-Presenter/Co-Author)
Department of Earth and Environmental Sciences, Michigan State University,;

Matthew Heaton (Co-Presenter/Co-Author)
Brigham Young University, Department of Statistics,;

Lillian McGill (Co-Presenter/Co-Author)
University of Washington ,;

Benjamin Abbott (Co-Presenter/Co-Author)
Brigham Young University, Department of Plant and Wildlife Sciences,;

Abstract: The amount of water flowing through streams changes through time. While seemingly banal, this phenomenon controls aquatic habitat, biogeochemical flux, and societal freshwater availability. Not only are the factors driving temporal changes in streamflow still uncertain, there is not even agreement about how to describe streamflow regime, with hundreds of metrics proposed and used by managers and researchers in hydrology and ecology. Here, we show that the mathematics of waves, including concepts of frequency, amplitude, phase, and waveform, are adept at describing most of the variance in streamflow regime in a comprehensive, concise, and organized way. We focus on the concept of frequency, and demonstrate that just as the complex sound of a symphony can be decomposed into a collection of individual frequencies played together, the complex dynamics of a hydrograph can be decomposed into discrete frequencies. We find that many of the flow metrics currently available in the scientific literature relate to these frequencies. Furthermore we show how various weather and geographical features influencing hydraulic connectivity control annual and sub-annual streamflow dynamics, and how climatic features influencing water balance control multi-year dynamics in streamflow regime on a global scale.


Daniel McGarvey (Primary Presenter/Author)
Center for Environmental Studies, Virginia Commonwealth University,;

Alexander Brown (Co-Presenter/Co-Author)
Virginia Commonwealth University, Department of Biology, BROWNA13@MYMAIL.VCU.EDU;

Elsa Chen (Co-Presenter/Co-Author)
Virginia Commonwealth University, Department of Biology, CHENE7@MYMAIL.VCU.EDU;

Catherine Viverette (Co-Presenter/Co-Author)
Virginia Commonwealth University, Center for Environmental Studies,;

Philip Tuley (Co-Presenter/Co-Author)
Virginia Commonwealth University, Department of Biology, TULEYPA@MYMAIL.VCU.EDU;

Olivia Latham (Co-Presenter/Co-Author)
Virginia Commonwealth University, Center for Environmental Studies,;

Phillip Gibbs (Co-Presenter/Co-Author)
Virginia Commonwealth University, Center for Environmental Studies,;

Allyson Richins (Co-Presenter/Co-Author)
Virginia Commonwealth University, Department of Biology, RICHINSAE@MYMAIL.VCU.EDU;

Michelle Deadwyler (Co-Presenter/Co-Author)
Virginia Commonwealth University, Center for Environmental Studies,;

Baron Lin (Co-Presenter/Co-Author)
Virginia Commonwealth University, Department of Biology,;

Erik Kaseloo (Co-Presenter/Co-Author)
Virginia Commonwealth University, Center for Environmental Studies,;

Abstract: National and state scenic rivers programs seek to showcase and protect lotic ecosystems, but few efforts have been made to assess their benefits for freshwater biota. Using the Virginia Scenic Rivers Program as a case study, we built species distribution models for a representative set of 31 Virginia freshwater fishes, then tested whether model-predicted habitat quality is consistently higher in designated scenic rivers than elsewhere in the state. Two criteria were used to select model species: (i) select species represented the full breadth of functional trait space comprised by Virginia freshwater fishes; and (ii) a subset of state-listed imperiled species was used to highlight species of special concern. Maximum entropy distribution models were built for each of the 31 fishes and used to predict habitat suitability throughout the state. Habitat suitability within designated scenic rivers was then summarized and compared with the complete, state-wide distribution of habitat suitability for each species. Results indicate that the Virginia Scenic Rivers Program may include some of the best habitat within the state for many native fishes. Furthermore, our flexible approach can be used to assess scenic river benefits in other regions.


Kelly Loria (Primary Presenter/Author)
University of Nevada Reno,;

Joanna Blaszczak (Co-Presenter/Co-Author)
Global Water Center and Department of Natural Resources and Environmental Science, University of Nevada, Reno,;

Abstract: Headwater streams draining mountain watersheds can retain or remove nitrogen (N) and reduce nutrient loading to downstream lake ecosystems sensitive to nutrient loading. Gross primary productivity and the rate of biomass accrual can drive in-stream N uptake, but the strength of this relationship and how it varies seasonally among streams with different flow regimes is unclear. We estimated gross primary production (GPP) and ecosystem respiration (ER) rates from September 18th to October 29th 2020, in three comparative streams draining catchments along the western shore of Lake Tahoe in the Sierra Nevada mountains. GPP and ER ranged from 0.172 to 5.482 and -17.043 to -2.152 g O2 m-2 d-1, respectively. This preliminary analysis is a first step towards linking upland processes (i.e. stream metabolism) with downstream processes (i.e. nearshore productivity) to assess the degree of control mountain stream biota exert on watershed nutrient export as well as the degree of synchrony between in-stream and nearshore lake productivity. Future work seeks to apply modeled GPP and ER and intra-seasonal rates of stream biota N uptake to identify when and where maximum nitrogen uptake may occur in streams draining to Lake Tahoe.

New Life to Old Data: Building a Global Database of Freshwater Predator-Prey Interactions [Poster Presentation]

Jeff Wesner (Co-Presenter/Co-Author)
University of South Dakota,;

Jacob Ridgway (Primary Presenter/Author)
University of South Dakota ,;

Abstract: Most of STEM is dominated by “long tail” science. Simply put, this is science conducted by independent groups over limited spatial and temporal scales under funding systems that undermine data organization and sharing. The scientific literature, because of this, lacks organization and consists of individual papers using variable formats. This is particularly limiting to the field of trophic ecology, which requires vast amounts of geographically and biologically diverse data to understand food webs within and between ecosystems that are highly complex, susceptible to change, and indicative of ecological health. Whereas most solutions to this data crisis involve standardizing and storing modern and future ecological data, our approach attempts to recapture the massive amount of past data. Our project is digitizing approximately 140 years of data from the fish predation literature to address this modern data crisis and to provide an open-source reference for pertinent ecological questions relating to trophic interactions within freshwater ecosystems.