Geology topics

Christopher T. Filstrup

Publications and source records attributed to Christopher T. Filstrup.

12 recordsLinked to original sources

USGS2025

Connecting tributary mercury loads to nearshore and offshore sediments in Lake Superior

Lake Superior has a vast and largely undeveloped watershed in comparison to the other Great Lakes, which makes it challenging to study mercury (Hg) sources and cycling. To examine Hg inputs to Lake Superior, we conducted an expansive binational assessment in 40 watersheds from a diverse range of landcover types. We further paired tributary Hg data to sediment source portfolios in the nearshore and offshore zones of Lake Superior through partnership with the Great Lakes Sediment Surveillance Program. We observed that total Hg loads were highest in the spring driven by the combination of elevated Hg concentrations and increased water discharge from snowmelt. In addition, total Hg concentrations in tributaries from remote, heavily forested regions, such as Pukaskwa National Park and the Minnesota Northshore, were higher than the Southshore and Thunder Bay regions. Methylmercury concentrations and loads were more spatially dependent, often corresponding to regions with more wetlands (e.g., Michigan Upper Peninsula). We estimated that the total Hg tributary load to Lake Superior in 2021 was 126 kg per year. To further examine the fate of watershed Hg sources, we examined sediments from 28 sites in Lake Superior using Hg stable isotopes. At open water sites, precipitation was the primary Hg source to sediments, but within nearshore sites Hg originated predominantly from watershed runoff. This work further defines the sources and fate of Hg within Lake Superior and highlights how Hg delivery is intrinsically tied to varying hydrologic regimes.

Michigan, Minnesota, Ontario, Wisconsin↗

USGS2023

Fire characteristics and hydrologic connectivity influence short-term responses of north temperate lakes to wildfire

Despite increasing wildfires, few studies have investigated seasonal water quality responses to wildfire characteristics (e.g., burn severity) across a large number of lakes. We monitored 30 total lakes (15 burned, 15 control) monthly following the Greenwood Fire in Minnesota, USA, a lake-rich region with historically prevalent wildfire. We found increases in median concentrations of total nitrogen (68%), total phosphorus (70%), dissolved organic carbon (127%), total suspended solids (71%), and reduced water clarity (48%) and pH (0.45) in burned lakes. Post-wildfire responses in drainage lakes were often persistent or cumulative throughout the open-water season, compared to isolated lakes. Total phosphorus (TP) increased linearly with watershed high-severity burns, and shoreline high-severity burns explained more variation in TP than lake morphometry and watershed variables. Post-wildfire chlorophyll-a responses were nonsignificant and inconsistent, possibly due to light limitation. Our results suggest that increasing wildfires have significant potential to affect water quality of inland lakes.

Geophysical Research Letters↗

USGS2020

Ecological prediction at macroscales using big data: Does sampling design matter?

Although ecosystems respond to global change at regional to continental scales (i.e., macroscales), model predictions of ecosystem responses often rely on data from targeted monitoring of a small proportion of sampled ecosystems within a particular geographic area. In this study, we examined how the sampling strategy used to collect data for such models influences predictive performance. We subsampled a large and spatially-extensive dataset to investigate how macroscale sampling strategy affects prediction of ecosystem characteristics in 6,784 lakes across a 1.8 million km2 area. We estimated model predictive performance for different subsets of the dataset to mimic three common sampling strategies for collecting observations of ecosystem characteristics: random sampling design, stratified random sampling design, and targeted sampling. We found that sampling strategy influenced model predictive performance such that (1) stratified random sampling designs did not improve predictive performance compared to simple random sampling designs and (2) although one of the scenarios that mimicked targeted (non-random) sampling had the poorest performing predictive models, the other targeted sampling scenarios resulted in models with similar predictive performance to that of the random sampling scenarios. Our results suggest that although potential biases in datasets from some forms of targeted sampling may limit predictive performance, compiling existing spatially-extensive datasets can result in models with good predictive performance that may inform a wide range of science questions and policy goals related to global change.

Ecological Applications↗

USGS2019

Increasing accuracy of lake nutrient predictions in thousands of lakes by leveraging water clarity data

Aquatic scientists require robust, accurate information about nutrient concentrations and indicators of algal biomass in unsampled lakes in order to understand and predict the effects of global climate and land-use change. Historically, lake and landscape characteristics have been used as predictor variables in regression models to generate nutrient predictions, but often with significant uncertainty. An alternative approach to improve predictions is to leverage the observed relationship between water clarity and nutrients, which is possible because water clarity is more commonly measured than lake nutrients. We used a joint-nutrient model that conditioned predictions of total phosphorus, nitrogen, and chlorophyll a on observed water clarity. Our results demonstrated substantial reductions (8–27%; median = 23%) in prediction error when conditioning on water clarity. These models will provide new opportunities for predicting nutrient concentrations of unsampled lakes across broad spatial scales with reduced uncertainty.

Limnology and Oceanography Letters↗

USGS2018

Combining sediment fingerprinting with age-dating sediment using fallout radionuclides for an agricultural stream, Walnut Creek, Iowa, USA

Purpose The main purpose of this study was to demonstrate the utility of the sediment fingerprinting approach to apportion surface-derived sediment, and then age date that portion using short-lived fallout radionuclides. In systems where a large mass of mobile sediment is in channel storage, age dating provides an understanding of the transfer of sediment through the watershed and the time scales over which management actions to reduce sediment loadings may be effective. Materials and methods In the agricultural Walnut Creek watershed, Iowa, the sediment-fingerprinting approach with elemental analysis was used to apportion the sources of fine-grained sediment (croplands, prairie, unpaved roads, and channel banks). Fallout radionuclides ( 7 Be, 210 Pb ex ) were used to age the portion of suspended sediment that was derived from agricultural topsoil. Age dating was performed at two different scales: 210 Pb ex which can date sediment to ~ 85 years and 7 Be to ~ 1 year. Results and discussion Sediment fingerprinting results indicated that the majority of suspended sediment is derived from cropland (62%) with streambanks contributing 36%, and prairie, pasture, and unpaved roads each contributing ≤ 1%. The topsoil–derived portion of sediment (primarily agriculture) dated using 210 Pb ex has ages ranging from 1 to 58 years, and using 7 Be, a component of much younger sediment that yields ages ranging from 44 to 205 days. The occurrence of 7 Be indicates that some portion of the sediment is young, on the order of months, whereas the dating based on 210 Pb ex indicates that some of the surface-derived sediment has been in channel storage for decades. Published studies in Walnut Creek indicate that a large component of sediment is stored in the channel bed. Conclusions We conclude that the 210 Pb ex -based ages are a reasonable estimate for the mean age of the surface-derived fraction and that 7 Be activities are evidence that there is a smaller fraction of very young sediment in the stream. We propose a geomorphic model where agricultural soil is delivered to the channel and conveyed to the watershed outlet at three time scales: a geologic-millennial time scale, decades, and a young time scale (< 1 year).

Iowa↗

USGS2017

Evidence for regional nitrogen stress on chlorophyll a in lakes across large landscape and climate gradients

Nitrogen (N) and phosphorus (P) commonly stimulate phytoplankton production in lakes, but recent observations from lakes from an agricultural region suggest that nitrate may have a subsidy‐stress effect on chlorophyll a (Chl a ). It is unclear, however, how generalizable this effect might be. Here, we analyzed a large water quality dataset of 2385 lakes spanning 60 regions across 17 states in the Northeastern and Midwestern U.S. to determine if N subsidy‐stress effects on phytoplankton are common and to identify regional landscape characteristics promoting N stress effects in lakes. We used a Bayesian hierarchical modeling framework to test our hypothesis that Chl a –total N (TN) threshold relationships would be common across the central agricultural region of the U.S. (“the Corn Belt”), where lake N and P concentrations are high. Data aggregated across all regions indicated that high TN concentrations had a negative effect on Chl a in lakes with concurrent high total P. This large‐scale pattern was driven by relationships within only a subset of regions, however. Eight regions were identified as having Chl a –TN threshold relationships, but only two of these regions located within the Corn Belt clearly demonstrated this subsidy‐stress relationship. N stress effects were not consistent across other intense agricultural regions, as we hypothesized. These findings suggest that interactions among regional land use and land cover, climate, and hydrogeology may be important in determining the synergistic conditions leading to N subsidy‐stress effects on lake phytoplankton.

Limnology and Oceanography↗

USGS2017

Creating multithemed ecological regions for macroscale ecology: Testing a flexible, repeatable, and accessible clustering method

Understanding broad-scale ecological patterns and processes often involves accounting for regional-scale heterogeneity. A common way to do so is to include ecological regions in sampling schemes and empirical models. However, most existing ecological regions were developed for specific purposes, using a limited set of geospatial features and irreproducible methods. Our study purpose was to: (1) describe a method that takes advantage of recent computational advances and increased availability of regional and global data sets to create customizable and reproducible ecological regions, (2) make this algorithm available for use and modification by others studying different ecosystems, variables of interest, study extents, and macroscale ecology research questions, and (3) demonstrate the power of this approach for the research question—How well do these regions capture regional-scale variation in lake water quality? To achieve our purpose we: (1) used a spatially constrained spectral clustering algorithm that balances geospatial homogeneity and region contiguity to create ecological regions using multiple terrestrial, climatic, and freshwater geospatial data for 17 northeastern U.S. states (~1,800,000 km 2 ); (2) identified which of the 52 geospatial features were most influential in creating the resulting 100 regions; and (3) tested the ability of these ecological regions to capture regional variation in water nutrients and clarity for ~6,000 lakes. We found that: (1) a combination of terrestrial, climatic, and freshwater geospatial features influenced region creation, suggesting that the oft-ignored freshwater landscape provides novel information on landscape variability not captured by traditionally used climate and terrestrial metrics; and (2) the delineated regions captured macroscale heterogeneity in ecosystem properties not included in region delineation—approximately 40% of the variation in total phosphorus and water clarity among lakes was at the regional scale. Our results demonstrate the usefulness of this method for creating customizable and reproducible regions for research and management applications.

Ecology and Evolution↗

USGS2016

Seasonal patterns in carbon dioxide in 15 mid-continent (USA) reservoirs

Evidence suggests that lakes are important sites for atmospheric CO 2 exchange and so play a substantial role in the global carbon budget. Previous research has 2 weaknesses: (1) most data have been collected only during the open-water or summer seasons, and (2) data are concentrated principally on natural lakes in northern latitudes. Here, we report on the full annual cycle of atmospheric CO 2 exchanges of 15 oligotrophic to eutrophic reservoirs in the Glacial Till Plains of the United States. With one exception, these reservoirs showed an overall loss of CO 2 during the year, with most values within the lower range reported for temperate lakes. There was a strong cross-system seasonal pattern: an average of 70% of total annual CO 2 efflux occurred by the end of spring mixis; some 20% of annual flux was reabsorbed during summer stratification; and the remaining 50% of efflux was lost during autumnal mixing. Net annual flux was negatively correlated with depth and positively correlated with both water residence time and DOC, with the smallest annual CO 2 efflux measured in shallow fertile impoundments. Strong correlations yield relationships allowing regional up-scaling of CO 2 evasion. Understanding lacustrine CO 2 uptake and evasion requires seasonal analyses across the full range of lake trophic states and morphometric attributes.

Missouri↗

USGS2015

Building a multi-scaled geospatial temporal ecology database from disparate data sources: Fostering open science through data reuse

Although there are considerable site-based data for individual or groups of ecosystems, these datasets are widely scattered, have different data formats and conventions, and often have limited accessibility. At the broader scale, national datasets exist for a large number of geospatial features of land, water, and air that are needed to fully understand variation among these ecosystems. However, such datasets originate from different sources and have different spatial and temporal resolutions. By taking an open-science perspective and by combining site-based ecosystem datasets and national geospatial datasets, science gains the ability to ask important research questions related to grand environmental challenges that operate at broad scales. Documentation of such complicated database integration efforts, through peer-reviewed papers, is recommended to foster reproducibility and future use of the integrated database. Here, we describe the major steps, challenges, and considerations in building an integrated database of lake ecosystems, called LAGOS (LAke multi-scaled GeOSpatial and temporal database), that was developed at the sub-continental study extent of 17 US states (1,800,000 km 2 ). LAGOS includes two modules: LAGOS GEO , with geospatial data on every lake with surface area larger than 4 ha in the study extent (~50,000 lakes), including climate, atmospheric deposition, land use/cover, hydrology, geology, and topography measured across a range of spatial and temporal extents; and LAGOS LIMNO , with lake water quality data compiled from ~100 individual datasets for a subset of lakes in the study extent (~10,000 lakes). Procedures for the integration of datasets included: creating a flexible database design; authoring and integrating metadata; documenting data provenance; quantifying spatial measures of geographic data; quality-controlling integrated and derived data; and extensively documenting the database. Our procedures make a large, complex, and integrated database reproducible and extensible, allowing users to ask new research questions with the existing database or through the addition of new data. The largest challenge of this task was the heterogeneity of the data, formats, and metadata. Many steps of data integration need manual input from experts in diverse fields, requiring close collaboration.

Connecticut, Delaware, Illinois, Indiana, Iowa, Ma↗

USGS2014

Regional variability among nonlinear chlorophyll-phosphorus relationships in lakes

The relationship between chlorophyll a (Chl a ) and total phosphorus (TP) is a fundamental relationship in lakes that reflects multiple aspects of ecosystem function and is also used in the regulation and management of inland waters. The exact form of this relationship has substantial implications on its meaning and its use. We assembled a spatially extensive data set to examine whether nonlinear models are a better fit for Chl a —TP relationships than traditional log-linear models, whether there were regional differences in the form of the relationships, and, if so, which regional factors were related to these differences. We analyzed a data set from 2105 temperate lakes across 35 ecoregions by fitting and comparing two different nonlinear models and one log-linear model. The two nonlinear models fit the data better than the log-linear model. In addition, the parameters for the best-fitting model varied among regions: the maximum and lower Chl a asymptotes were positively and negatively related to percent regional pasture land use, respectively, and the rate at which chlorophyll increased with TP was negatively related to percent regional wetland cover. Lakes in regions with more pasture fields had higher maximum chlorophyll concentrations at high TP concentrations but lower minimum chlorophyll concentrations at low TP concentrations. Lakes in regions with less wetland cover showed a steeper Chl a —TP relationship than wetland-rich regions. Interpretation of Chl a —TP relationships depends on regional differences, and theory and management based on a monolithic relationship may be inaccurate.

Iowa, Maine, Michigan, New Hampshire, Ohio, Wiscon↗

USGS2014

Cross-scale interactions: Quantifying multi-scaled cause–effect relationships in macrosystems

Ecologists are increasingly discovering that ecological processes are made up of components that are multi-scaled in space and time. Some of the most complex of these processes are cross-scale interactions (CSIs), which occur when components interact across scales. When undetected, such interactions may cause errors in extrapolation from one region to another. CSIs, particularly those that include a regional scaled component, have not been systematically investigated or even reported because of the challenges of acquiring data at sufficiently broad spatial extents. We present an approach for quantifying CSIs and apply it to a case study investigating one such interaction, between local and regional scaled land-use drivers of lake phosphorus. Ultimately, our approach for investigating CSIs can serve as a basis for efforts to understand a wide variety of multi-scaled problems such as climate change, land-use/land-cover change, and invasive species.

Frontiers in Ecology and the Environment↗

USGS2014

Approaches for advancing scientific understanding of macrosystems

The emergence of macrosystems ecology (MSE), which focuses on regional- to continental-scale ecological patterns and processes, builds upon a history of long-term and broad-scale studies in ecology. Scientists face the difficulty of integrating the many elements that make up macrosystems, which consist of hierarchical processes at interacting spatial and temporal scales. Researchers must also identify the most relevant scales and variables to be considered, the required data resources, and the appropriate study design to provide the proper inferences. The large volumes of multi-thematic data often associated with macrosystem studies typically require validation, standardization, and assimilation. Finally, analytical approaches need to describe how cross-scale and hierarchical dynamics and interactions relate to macroscale phenomena. Here, we elaborate on some key methodological challenges of MSE research and discuss existing and novel approaches to meet them.

Frontiers in Ecology and the Environment↗