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SFS Annual Meeting

Monday, June 3, 2024
13:30 - 15:00

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S13 Insights of Patterns and Drivers of Freshwater Systems Gained from Regional and National Monitoring Datasets

13:30 - 13:45 | Philadelphia Ballroom | PROGRESS TOWARD DEVELOPING DNA-BASED DIATOM INDICATORS FOR STREAM MONITORING IN THE UNITED STATES: WATERSHED TO NATIONAL SCALE EFFORTS

6/03/2024  |   13:30 - 13:45   |  Philadelphia Ballroom

PROGRESS TOWARD DEVELOPING DNA-BASED DIATOM INDICATORS FOR STREAM MONITORING IN THE UNITED STATES: WATERSHED TO NATIONAL SCALE EFFORTS Benthic diatoms are highly responsive to changes in environmental conditions associated with human activities. Their changes in assemblage structure can be used to develop stressor-response relationships, metrics, and indices that can help identify effects of pollution and inform management targets in streams. Recent advancements in DNA techniques, metabarcoding, and bioinformatics could help expand the use of diatoms in monitoring and assessment programs by providing relatively quick, consistent, and increasingly cost-effective ways to quantify diatom diversity in environmental samples. However, these DNA-based approaches are relatively new and only a few studies have been conducted in the United States. Here, we provide an overview of multiple projects exploring how diatom DNA metabarcoding can be used for stream monitoring and assessment. Results from the first nationwide study to use diatom DNA metabarcoding (EPA’s National Rivers and Streams Assessment) showed that it is a robust approach that could be useful to monitoring and assessment programs throughout the conterminous United States, despite the heterogeneous conditions among ecoregions. Analyses also are contributing to understanding national patterns and drivers of diatom diversity. To help remove barriers and increase the usability of these approaches, we also are developing an R workflow and database to improve reproducibility and to simplify the integration and analysis of new DNA metabarcoding data. We will discuss possible applications of these approaches and how these developments have led to new collaborations and state partnerships with a variety of goals.

Nathan Smucker (Primary Presenter/Author), U.S. Environmental Protection Agency, smucker.nathan@epa.gov;

Erik Pilgrim (Co-Presenter/Co-Author), U.S. Environmental Protection Agency, pilgrim.erik@epa.gov;

Christopher Nietch (Co-Presenter/Co-Author), U.S. Environmental Protection Agency, nietch.christopher@epa.gov;

Lester Yuan (Co-Presenter/Co-Author), U.S. Environmental Protection Agency, yuan.lester@epa.gov;

Richard Mitchell (Co-Presenter/Co-Author), U.S. Environmental Protection Agency, mitchell.richard@epa.gov;

Charlie Carpenter (Co-Presenter/Co-Author), Neptune and Company, Inc., ccarpenter@neptuneinc.org;

Leslie Gains-Germain (Co-Presenter/Co-Author), Neptune and Company, Inc., lgermain@neptuneinc.org;

John Darling (Co-Presenter/Co-Author), U.S Environmental Protection Agency, darling.john@epa.gov;

Amina Pollard (Co-Presenter/Co-Author), U.S. Environmental Protection Agency, pollard.amina@epa.gov;

13:45 - 14:00 | Philadelphia Ballroom | NATIONAL, LONG-TERM CHLOROPHYLL RECORDS: CASE STUDIES IN LARGE RIVERS, OLIGOTROPHIC LAKES, AND EUTROPHIC LAKES

6/03/2024  |   13:45 - 14:00   |  Philadelphia Ballroom

NATIONAL, LONG-TERM CHLOROPHYLL RECORDS: CASE STUDIES IN LARGE RIVERS, OLIGOTROPHIC LAKES, AND EUTROPHIC LAKES The concentration of chlorophyll a in plankton and periphyton represent algal biomass in lakes, streams, and estuaries. To support efforts to develop process-based, machine learning, and remote sensing models for prediction of Harmful Algal Blooms (HABs), we compiled an 18-year record (2005-2022) of pigment data from water bodies across the United States (US) and its territories. These data are samples of planktonic or benthic algae, filtered in the field, extracted in the laboratory, and measured against chlorophyll a standards. Data were compiled from the Water Quality Portal (WQP) and US Geological Survey National Water Quality Lab (NWQL). Data were harmonized for reporting units, pigment type, duplicate values, collection depth, site name, negative values, and some extreme values. To our knowledge, this dataset is the largest compilation of harmonized discrete, lab-extracted chlorophyll data for the US. Uses for such data include the calibration of models, calibration of field sensors, examination of relationship to drivers, evaluation of temporal trends, and other local to national scale concerns. In this presentation, we examine temporal case studies from select large rivers, oligotrophic lakes, and eutrophic lakes.

Sarah Spaulding (Primary Presenter/Author), U.S. Geological Survey, Institute of Arctic and Alpine Research, University of Colorado Boulder, sarah.spaulding@colorado.edu;

14:00 - 14:15 | Philadelphia Ballroom | CHARACTERIZING LAKE CONDUCTIVITY IN THE CONTIGUOUS UNITED STATES USING SPATIALLY EXPLICIT MODELS FOR BIG SPATIAL DATA AND THE SPMODEL R PACKAGE

6/03/2024  |   14:00 - 14:15   |  Philadelphia Ballroom

Characterizing lake conductivity in the contiguous United States using spatially explicit models for big spatial data and the spmodel R package Conductivity is an important indicator of the health of aquatic ecosystems and is linked to anthropogenic activity. Understanding patterns and drivers of conductivity is important for effective management. We modeled lake conductivity data (n = 3,311) collected as part of the U.S. Environmental Protection Agency’s National Lakes Assessment (NLA) using spatial indexing, a flexible and efficient approach to fitting spatially explicit statistical models to big data sets. These spatial statistical models build spatial dependence directly into modeling and offer vast improvements over models that assume the data observations are independent (e.g., 45% reduction in mean-squared prediction error). We find lake conductivity is strongly related to calcium oxide rock content, crop production, human development, precipitation, and temperature. We used a final spatial model to predict lake conductivity at hundreds of thousands of lakes distributed throughout the contiguous United States. These maps revealed higher lake conductivities in the arid Southwest and several Midwestern states, such as the Dakotas. The combination of federal monitoring data with spatial modeling can offer important insights into the patterns and drivers of water quality nationally. Lake conductivity models fit using spatial indexing are nearly identical to lake conductivity models fit using traditional spatial analysis methods but are nearly 50 times faster. Spatial indexing and other tools for spatial statistical modeling and prediction are readily available in the spmodel R package. The views expressed in this article are those of the author(s) and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency.

Michael Dumelle (Primary Presenter/Author), U.S. Environmental Protection Agency, dumelle.michael@epa.gov;

Jay M Ver Hoef (Co-Presenter/Co-Author), NOAA, jay.verhoef@noaa.gov;

Amalia Handler (Co-Presenter/Co-Author), EPA, Handler.Amalia@epa.gov;

Ryan Hill (Co-Presenter/Co-Author), US Environmental Protection Agency, hill.ryan@epa.gov;

Matt Higham (Co-Presenter/Co-Author), St. Lawrence University, mhigham@stlaw.edu;

Anthony Olsen (Co-Presenter/Co-Author), EPA, olsen.tony@epa.gov;

14:15 - 14:30 | Philadelphia Ballroom | A STOICHIOMETRIC TRAIT DATABASE FOR NORTH AMERICAN BENTHIC INVERTEBRATES

6/03/2024  |   14:15 - 14:30   |  Philadelphia Ballroom

A Stoichiometric Trait Database for North American Benthic Invertebrates Trait-based ecology has expanded our understanding of how organisms both respond to and affect changes to the ecosystems they inhabit. Stoichiometric traits represent a promising link between these response and effect traits. Individual stoichiometry is affected by nutrient supply and selective pressures while also leading to direct effects on nutrient cycling and storage at the ecosystem scale. As scientists measure the stoichiometric traits of a growing number of organisms, a database of these stoichiometric trait data could expand the applicability of trait-based approaches to the field of ecological stoichiometry. We compiled the Stoichiometric Traits of Organisms In their Chemical Habitats (STOICH) database to fill this need. Here we present an overview of this continental-scale stoichiometric trait data of benthic invertebrates. We collected body stoichiometry and stable isotope data from common benthic invertebrate taxa from aquatic sites of the National Ecological Observatory Network (NEON) and supplemented NEON data with additional data from the published literature and targeted collections to fill geographic and taxonomic gaps. The database in its current form includes over 1,000 body stoichiometry measurements (i.e., measurement of at least two elements such as %C, %N, and %P) of invertebrates ranging from northern Alaska to eastern Panamá and represents many biomes and land use types in between. Our dataset contains data on over 200 genera of benthic invertebrates representing over 100 families in all major aquatic orders. Phylogenetic and geographical patterns in the data represent promising directions for future research in macroecological and trait-based applications of stoichiometric theory.

Eric Moody (Primary Presenter/Author), Middlebury College, ekmoody@middlebury.edu;

Baker Angstman (Co-Presenter/Co-Author), Middlebury College, bangstman@middlebury.edu;

Casey Brucker (Co-Presenter/Co-Author), University of Wyoming, cbrucker@uwyo.edu;

Qiting Cai (Co-Presenter/Co-Author), University of California Santa Cruz, qcai17@ucsc.edu;

Sarah Collins (Co-Presenter/Co-Author), University of Wyoming, sarah.collins@uwyo.edu;

Jessica Corman (Co-Presenter/Co-Author), University of Nebraska-Lincoln, jcorman3@unl.edu;

Molly Costanza-Robinson (Co-Presenter/Co-Author), Middlebury College, mcostanz@middlebury.edu;

Halvor Halvorson (Co-Presenter/Co-Author), University of Central Arkansas, hhalvorson@uca.edu;

Julia Keon (Co-Presenter/Co-Author), Middlebury College, jskeon@middlebury.edu;

Amy Krist (Co-Presenter/Co-Author), University of Wyoming, krist@uwyo.edu;

Erin Larson (Co-Presenter/Co-Author), University of Alaska-Anchorage, ern.larson@gmail.com;

Natalie Montano (Co-Presenter/Co-Author), Middlebury College, nmmontano@outlook.com;

Emma Neill (Co-Presenter/Co-Author), Middlebury College, neill.emmad@gmail.com;

Elizabeth Peebles (Co-Presenter/Co-Author), Harvard T.H. Chan School of Public Health, epeebles@hsph.harvard.edu;

Chad Petersen (Co-Presenter/Co-Author), University of Nebraska, cpetersen@unl.edu;

Kayley Porter (Co-Presenter/Co-Author), Middlebury College, kporter@middlebury.edu;

Ella Roelofs (Co-Presenter/Co-Author), Middlebury College, groelofs@middlebury.edu;

A.J. Rossbach (Co-Presenter/Co-Author), Middlebury College, arossbach@middlebury.edu;

Sophie Schuele (Co-Presenter/Co-Author), Middlebury College, sschuele@middlebury.edu;

Elle Thompson (Co-Presenter/Co-Author), Middlebury College, ellat@middlebury.edu;

Liza Toll (Co-Presenter/Co-Author), Middlebury College, etoll@middlebury.edu;

Katie Wagner (Co-Presenter/Co-Author), University of Wyoming, Catherine.Wagner@uwyo.edu;

14:30 - 14:45 | Philadelphia Ballroom | DOES COMPILING BIOLOGICAL DATA ACROSS MULTIPLE PROGRAMS YIELD A SUFFICIENT DATASET FOR REGIONAL ASSESSMENT OF TRENDS IN STREAM CONDITION?

6/03/2024  |   14:30 - 14:45   |  Philadelphia Ballroom

Does compiling biological data across multiple programs yield a sufficient dataset for regional assessment of trends in stream condition? Trend analysis is essential for tracking and understanding changes in stream condition, but biological stream datasets are rarely spatially and temporally robust enough for trend analysis over large regions. Extensive amounts of high-quality monitoring data have been collected in the Chesapeake Bay watershed, as it is experiencing rapid population growth and has strong management objectives to improve stream water quality and biological condition. However, long-term monitoring of biological data is limited, and the objectives and sampling structure of long-term monitoring programs differ among agencies within the watershed. Here we seek to determine if large scale compilations of fish and macroinvertebrate data from many sources can be used to determine watershed wide patterns in biological assemblages. We identified 118 fish sites and 221 macroinvertebrate sites with sufficient data (at least 7 years) for a preliminary trend analysis. Spatial bias was prevalent in the analysis since ninety nine percent of sites were in the southern half of the watershed. Preliminary results show no significant trends in either assemblage at the majority of sites, possibly due to the large number of high-quality reference sites with low amounts of anthropogenic disturbance in the dataset. Results indicate that though large-scale data compilations may provide enough data for analysis of trends, the spatial biases associated with such datasets may prevent adequate assessment of watershed-wide trends. As monitoring efforts continue, future data pulls may greatly increase available trend sites and expand spatial representation of trends.

Lindsey Boyle (Primary Presenter/Author), U.S. Geological Survey, lboyle@usgs.gov;

Matthew Cashman (Co-Presenter/Co-Author), U.S. Geological Survey, mcashman@usgs.gov;

Kelly Maloney (Co-Presenter/Co-Author), U.S. Geological Survey, kmaloney@usgs.gov;

14:45 - 15:00 | Philadelphia Ballroom | ECOLOGICAL FLOW VULNERABILITY ASSESSMENTS ACROSS LARGE LANDSCAPES

6/03/2024  |   14:45 - 15:00   |  Philadelphia Ballroom

ECOLOGICAL FLOW VULNERABILITY ASSESSMENTS ACROSS LARGE LANDSCAPES Fish responses to flow regimes can be used to assess potential vulnerability of stream ecosystems to climate change. These ecological flow relationships are hypothesized to vary regionally and our understanding the benefits from landscape-scale research that addresses complex variation in climatological, physical, and biological components. The U.S. Geological Survey (USGS) has identified priority ecosystems as regions that can be used for landscape-scale climate change vulnerability assessments. Ongoing work demonstrates how these USGS large landscapes can be used to facilitate partner-informed ecological flow vulnerability assessments and serve as potential pilot areas for larger scale ecological flow modeling and monitoring efforts. We highlight a collaborative research project bringing together ecologists, geospatial scientists, hydrologists, and partners working across scales in five USGS large landscapes: the Columbia, Upper Colorado River, and Mobile Basins, and the Great Lakes and Chesapeake Bay watersheds. Outreach and engagement efforts with local to national partners (government, non-governmental, and tribal) are being led by ecologists with experience in people-nature interactions to identify science needs. Geospatial scientists are summarizing landscape, climate, and land-use/land-cover (LULC) datasets for hydrology-informed machine-learning models that will predict time-varying streamflow characteristics (the magnitude, frequency, duration, and timing of droughts and floods) at ungaged locations and project them to future conditions. Ecologists will use estimated streamflow characteristics to perform vulnerability assessments of lotic fish to climate and LULC-induced changes in streamflow, as informed by partner needs and feedback. We’ll discuss opportunities and challenges in designing landscape-scale vulnerability assessments to meet science needs from local to national scales.

Taylor Woods (Primary Presenter/Author), US Geological Survey, tewoods@usgs.gov;

Tim Counihan (Co-Presenter/Co-Author), USGS, Western Fisheries Research Center, Columbia River Research Laboratory; 5501 Cook-Underwood Road, Cook, WA 98605, tcounihan@usgs.gov;

Sean Emmons (Co-Presenter/Co-Author), US Geological Survey, semmons@usgs.gov;

Ken Eng (Co-Presenter/Co-Author), U.S. Geological Survey, keng@usgs.gov;

Mary Freeman (Co-Presenter/Co-Author), US Geological Survey, mcfreeman@usgs.gov;

Benjamin Gressler (Co-Presenter/Co-Author), U.S. Geological Survey, bgressler@usgs.gov;

Joshua Hubbell (Co-Presenter/Co-Author), U.S. Geological Survey, jhubbell@usgs.gov;

James McKenna (Co-Presenter/Co-Author), US Geological Survey, Great Lakes Science Center, Tunison Laboratory of Aquatic Science, jemckenna@usgs.gov;

Kirk Rodgers (Co-Presenter/Co-Author), U.S. Geological Survey, krodgers@usgs.gov;

Jared Smith (Co-Presenter/Co-Author), U.S. Geological Survey, jsmith@usgs.gov;

Daniel Wieferich (Co-Presenter/Co-Author), United States Geological Survey, dwieferich@usgs.gov;

Tanja Williamson (Co-Presenter/Co-Author), U.S. Geological Survey, tnwillia@usgs.gov;

Robert Zuellig (Co-Presenter/Co-Author), U.S. Geological Survey, rzuellig@usgs.gov ;

Kelly Maloney (Co-Presenter/Co-Author), U.S. Geological Survey, kmaloney@usgs.gov;