SFS Annual Meeting

6/03/2024  |   10:30 - 10:45   |  Philadelphia Ballroom

National Aquatic Resource Surveys (NARS) Data for Addressing Research Questions at Broad Spatial and Temporal Scales The National Aquatic Resource Surveys (NARS) program, a collaboration among United States Environmental Protection Agency (US EPA) Office of Water, Office of Research and Development (ORD), and states and tribes, assesses progress towards the objective of the Clean Water Act through field-based assessments conducted annually on a five-year cycle in lakes, rivers, streams, coasts, and wetlands. NARS is the only national program that produces publicly available, long-term, probabilistic, field-based data for the nation’s aquatic resources. Since its inception in 2007, NARS has amassed physical, chemical, and biological data from over 16,000 sites. The probabilistic design enables the use of data collected in the field from individual sites to make estimates with confidence for regional and national populations of any given aquatic resource. However, in recent years, these data have been increasingly leveraged to address a variety of large-scale ecological questions that would not have been possible before. In this talk, we provide an overview of the NARS program and a short tutorial covering the survey design and how to leverage and utilize the publicly available data (https://www.epa.gov/national-aquatic-resource-surveys). We also highlight published examples from US EPA ORD that demonstrate the power of NARS data and analysis methods to answer research questions focused on patterns and drivers of freshwater biodiversity and physiochemical conditions across broad spatial extents and over long temporal scales. The views expressed in this abstract are those of the author(s) and do not necessarily represent the views or policies of the US EPA.

Amanda M. Nahlik (Primary Presenter/Author), U.S. Environmental Protection Agency, Nahlik.Amanda@epa.gov;

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

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

Presentation:
This presentation has not yet been uploaded.

6/03/2024  |   10:45 - 11:00   |  Philadelphia Ballroom

Leveraging regional and national datasets compiled from multiple sources to improve quantitative ecology: process, lessons, and research opportunities afforded Through combinations of data from multiple survey programs, universities, and government agencies, datasets of greater sample density, and spatial and temporal coverage are now more available to researchers than ever before. These datasets allow for a wider breadth of modeling techniques including machine learning and causal inference, but such combinations come with some issues. Here, we present on several current projects that have used or are using large sample size, multi-program datasets. First, we will discuss our ongoing regional dataset compilations that explore aquatic physical habitat, specific conductance, benthic macroinvertebrates, and fish within the Chesapeake Bay. We will discuss how these data are being used in watershed-wide assessments of biological and habitat condition, freshwater salinization, and identification of potential environmental stressors. Then, we will discuss our current nation-wide project combining benthic macroinvertebrates and fish data from the US Environmental Protection Agency-National Rivers and Streams Assessment and US Geological Survey-National Water-Quality Assessment and how we have adapted densely sampled regional studies to evaluate largescale alterations to instream stressors (e.g., flow, salinity, water quality) and their impact on biological communities. For each example, we will showcase analyses and research projects that would not be possible without the availability of widespread data sources. We will also highlight lessons learned and some key insights into caveats when combing data from different sources.

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

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

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

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

John Young (Co-Presenter/Co-Author), USGS, jyoung@usgs.gov ;

Alexander Kiser (Co-Presenter/Co-Author), US Geological Survey, akiser@usgs.gov;

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

Rosemary Fanelli (Co-Presenter/Co-Author), USGS South Atlantic Water Science Center, rfanelli@usgs.gov;

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

Daren Carlisle (Co-Presenter/Co-Author), U.S. Geological Survey, dcarlisle@usgs.gov;

Presentation:
This presentation has not yet been uploaded.

6/03/2024  |   11:00 - 11:15   |  Philadelphia Ballroom

finsyncR: an R package for synchronizing 27 years of fish and invertebrate biomonitoring data across the United States United States (US) federal biomonitoring programs collect data on freshwater assemblages across the contiguous US using standardized sampling protocols to produce data that is of great spatial extent, long temporal scope, and wide diversity of taxa surveyed. These data are used by the US government in assessment frameworks, but a wealth of additional knowledge can be gleaned from unique applications of these data. However, barriers exist for new users to apply these data without institutional guidance. `finsyncR` (fish and invertebrate synchronizer in R) is an R package that streamlines the process of acquiring, processing, and integrating fish and macroinvertebrate datasets collected in streams and rivers, increasing access to cleaned data and ensuring the straightforward application of these data. The data sources are the US Environmental Protection Agency’s National River and Streams Assessment and US Geological Survey’s BioData. Resulting datasets span 27 years (1993 to 2019) and include 8,115 sites, 15,169 sampling events, 963 macroinvertebrate genera, and 687 fish species. Common challenges to working with these data include understanding sampling designs, harmonizing taxonomy, calculating densities and standardized abundances, accounting for sampling effort differences, and considering improvements in taxonomic identifications. Here, we document these barriers and strategies to overcome them. We anticipate this package will spur research exploring changes in fish and macroinvertebrate communities across space and time and the causes and consequences of those changes. The views expressed in this presentation are those of the authors and do not necessarily represent the views or policies of the US Environmental Protection Agency.

Michael Mahon (Primary Presenter/Author), US Environmental Protection Agency, mahon.michael@epa.gov;

Devin Jones (Co-Presenter/Co-Author), US Environmental Protection Agency, jones.devin@epa.gov;

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

Terry Brown (Co-Presenter/Co-Author), US Environmental Protection Agency, brown.terry@epa.gov;

Ethan Brown (Co-Presenter/Co-Author), University of Notre Dame, ebrown23@nd.edu;

Stefan Kunz (Co-Presenter/Co-Author), Institute for Environmental Sciences, RPTU Kaiserslautern Landau, Stefan.Kunz@bgr.de;

Samantha Rumschlag (Co-Presenter/Co-Author), U.S. Environmental Protection Agency, rumschlag.samantha@epa.gov;

Presentation:
This presentation has not yet been uploaded.

6/03/2024  |   11:15 - 11:30   |  Philadelphia Ballroom

Density declines, richness increases, and composition shifts in stream macroinvertebrates Documenting trends of stream macroinvertebrate biodiversity is challenging because biomonitoring often has limited spatial, temporal, and taxonomic scopes. We analyzed biodiversity and composition of assemblages of >500 genera, spanning 27 years, and 6131 stream sites across forested, grassland, urban, and agricultural land uses throughout the United States. In this dataset, macroinvertebrate density declined by 11% and richness increased by 12.2%, and insect density and richness declined by 23.3 and 6.8%, respectively, over 27 years. In addition, differences in richness and composition between urban and agricultural versus forested and grassland streams have increased over time. Urban and agricultural streams lost the few disturbance-sensitive taxa they once had and gained disturbance-tolerant taxa. These results suggest that current efforts to protect and restore streams are not sufficient to mitigate anthropogenic effects.

Samantha Rumschlag (Primary Presenter/Author), U.S. Environmental Protection Agency, rumschlag.samantha@epa.gov;

Michael Mahon (Co-Presenter/Co-Author), US Environmental Protection Agency, mahon.michael@epa.gov;

Devin Jones (Co-Presenter/Co-Author), US Environmental Protection Agency, jones.devin@epa.gov;

Wiliam Battaglin (Co-Presenter/Co-Author), USGS, wbattagl@usgs.gov;

Johnny Behrens (Co-Presenter/Co-Author), Duke University, jonathan.behrens@duke.edu;

Emily Bernhardt (Co-Presenter/Co-Author), Duke University, emily.bernhardt@duke.edu;

Paul Bradley (Co-Presenter/Co-Author), United States Geological Survey, pbradley@usgs.gov;

Ethan Brown (Co-Presenter/Co-Author), University of Notre Dame, ebrown23@nd.edu;

Frederik De Laender (Co-Presenter/Co-Author), University of Namur, frederik.delaender@unamur.be;

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

Stefan Kunz (Co-Presenter/Co-Author), Institute for Environmental Sciences, RPTU Kaiserslautern Landau, Stefan.Kunz@bgr.de;

Sylvia Lee (Co-Presenter/Co-Author), U.S. Environmental Protection Agency, lee.sylvia@epa.gov;

Emma Rosi (Co-Presenter/Co-Author), Cary Institute of Ecosystem Studies, rosie@caryinstitute.org;

Ralf Schäfer (Co-Presenter/Co-Author), University of Koblenz-Landau, Institute for Environmental Sciences, schaefer-ralf@uni-landau.de;

Travis Schmidt (Co-Presenter/Co-Author), USGS WY-MT Water Science Center, tschmidt@usgs.gov;

Marie Simonin (Co-Presenter/Co-Author), Duke University, simonin.marie@gmail.com;

Kelly Smalling (Co-Presenter/Co-Author), USGS, ksmall@usgs.gov;

Kristofor Voss (Co-Presenter/Co-Author), Regis University, kvoss@regis.edu;

Jason Rohr (Co-Presenter/Co-Author), University of Notre Dame, jrohr2@nd.edu;

Presentation:
This presentation has not yet been uploaded.

6/03/2024  |   11:30 - 11:45   |  Philadelphia Ballroom

OVERLAPS & DEVIATIONS IN THE SPATIAL DRIVERS OF MACROINVERTEBRATE ASSSEMBLAGES IN LENTIC & LOTIC WATERS ACROSS THE CONTERMINOUS US The relative role of natural and anthropogenic drivers on freshwater biodiversity across different aquatic ecosystem types is not well understood. The U.S. Environmental Protection Agency’s National Aquatic Surveys (NARS) offers a unique opportunity to compare lentic and lotic ecosystems at a continental scale. We compared patterns and key drivers of macroinvertebrate diversity and composition in streams and lakes across the conterminous US, using machine learning methods. For both streams and lakes, multivariate random forest (MVRF) models performed better than stacked single random forest models. The MVRF models explained about 40% of the spatial variation in macroinvertebrate richness in both streams and lakes. Based on the MVRF models, many macroinvertebrate genera were strongly influenced by geography, temperature, and the amount of runoff, regardless of ecosystem type. Multiple genera were common in both streams and lakes. Two common genera, the mayfly Caenis and the damselfly Agria, were more likely to be found in streams and lakes in the Western US with warmer maximum temperatures (>20°C). However, the extent of forest cover and nitrogen deposition was more important for macroinvertebrates in lakes than in streams. Stream macroinvertebrates were more influenced by the extent of row crops and elevation. Varying key anthropogenic drivers of biodiversity between streams and lakes may suggest prioritizing different management strategies such as stream buffers in agricultural areas and air pollution mitigation for lakes. The views expressed in this abstract are those of the author(s) and do not necessarily represent the views or policies of the U.S. EPA.

Lara Jansen (Primary Presenter/Author), Oak Ridge Institute for Science and Education Fellow c/o U.S. Environmental Protection Agency, jansen.lara@epa.gov;

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

Darin Kopp (Co-Presenter/Co-Author), U.S. Environmental Protection Agency, Kopp.Darin@epa.gov;

Presentation:
This presentation has not yet been uploaded.

6/03/2024  |   11:45 - 12:00   |  Philadelphia Ballroom

DETERMINING BENCHMARKS FOR STREAM PHYSICAL HABITAT INDICATORS USING INTERAGENCY NETWORKS OF REFERENCE SITES The Clean Water Act addresses chemical, physical, and biological integrity of the Nation’s waters, but few states have numerical physical habitat criteria. Large-scale monitoring datasets produced from the EPA’s NRSA program, the BLM’s Assessment Inventory and Monitoring Program, the USFS Northwest Forest Plan’s Aquatic Riparian Effectiveness Monitoring Program, and the USFS PacFish InFish Biological Opinion Monitoring Program provide opportunities to understand how natural factors influence stream physical habitat and thus how to effectively assess how anthropogenic activities affect stream physical integrity. Our goal was to develop site-specific models to predict naturally occurring spatial variation across western USA streams in percent fine sediment (< 2 mm), geometric mean particle diameter, percent and frequency of pools, percent canopy cover (overhead and bank), large wood frequency and volume, bank angle, and percent undercut. We used these models to assess 1) how predictable physical habitat indicators are across natural gradients and 2) what physical habitat indicators are most responsive to anthropogenic disturbance. Random forest models explained 26-53% of the natural variation in metric values. Sediment and overhead canopy cover were most predictable. Channel slope was the top predictor of most physical habitat indicators. All habitat indicators were only weakly responsive to anthropogenic disturbance after accounting for natural gradients. This result may have been caused by unaccounted for natural environmental variation, confounding of natural and anthropogenic factors, and legacy and lag effects of past disturbances, which highlights the need for improved site-level disturbance datasets or a different approach that incorporates riverscape-scale assessments of geomorphic status.

Jennifer Courtwright (Primary Presenter/Author), Utah State University National Aquatic Monitoring Center, jennifer.courtwright@usu.edu;

Charles Hawkins (Co-Presenter/Co-Author), Utah State University, chuck.hawkins@usu.edu;

Joe Wheaton (Co-Presenter/Co-Author), Utah State University, joe.wheaton@usu.edu;

Presentation:
This presentation has not yet been uploaded.