Geology topics

Sabina R. Gifford

Publications and source records attributed to Sabina R. Gifford.

5 recordsLinked to original sources

USGS2026

Evaluating drivers of environmental change in a lake sediment core: Insights from spectroscopic metrics of water-extractable organic matter and stable carbon isotopes

Freshwater lakes play a critical role in the global carbon cycle by storing and transforming organic matter (OM) from both terrestrial and aquatic sources. Small lakes in northern temperate regions, despite their limited surface area, disproportionately influence regional carbon budgets. Buried sediments integrate OM inputs over time and archive ecosystem responses to natural and anthropogenic disturbances. However, the direction and magnitude of recent environmental changes on sediment carbon (C) dynamics remain poorly understood. A 23-cm core was collected from a small temperate lake in northeastern USA to evaluate sediment OM content and composition over timescales relevant to historical land-use change, damming, and recovery from acid deposition. Patterns in OM burial and source contributions were revealed via elemental and isotopic analyses of bulk OM and ultraviolet-visible spectrophotometry of water-extractable organic matter (WEOM). The optical metrics expanded observations of likely OM sources beyond the information gained from bulk carbon metrics (total carbon, δ 13 C). The aromaticity of WEOM increased downcore, which is consistent with a shift from increased terrestrial inputs during early logging and damming activity (pre ∼1920) to more microbial-derived OM in recent surficial sediments. Future applications of WEOM optical properties as complements to traditional geochemical metrics can enhance interpretations of lake ecosystem responses recorded in lake sediments to environmental perturbations in temperate lakes.

New York↗

USGS2025

From sample to sonde to Sentinel-2: Insights from a multi-scale chlorophyll-a monitoring effort in the Hudson River, New York

Monitoring cyanobacteria and other nuisance phytoplankton in the Hudson River is of great interest given its societal and ecological importance. Satellite remote sensing provides a cost-effective method to monitor chlorophyll- a (chl-a), a common proxy for algal biomass; however, the dynamic nature of rivers complicates approaches traditionally applied to lakes and oceans. During 2021–2023, we collected discrete samples for laboratory measurement of chl-a and measured in situ chl-a fluorescence during a series of longitudinal boat surveys along a 220-km reach of the lower Hudson River. Surveys were timed to coincide with Sentinel-2 satellite overpasses. We first investigated relations between laboratory-measured chl-a concentration and field-measured chl-a fluorescence, observing a weak correlation ( r 2 = 0.25) that improved substantially after splitting data by day (mean r 2 = 0.53). Separately, to estimate chl-a fluorescence using satellite data, we developed a series of random forest models leveraging the rich fluorescence dataset collected. We tested three model types: individual day models, leave-one-out models trained on all days except a holdout test day, and a single pooled model trained on all days. Generally, individual day models exhibited lowest error (mean of mean absolute error [MAE] = 0.16 relative fluorescence units [RFU]), followed by the single pooled model (MAE = 0.22 RFU). Daily holdout models showed highest error (mean MAE = 0.40 RFU); this approach was intended to represent model performance on a day unseen in the training set, providing a more conservative estimate of performance than the more traditional pooled approach. Findings from both analyses emphasize the importance of considering temporal variability when modeling riverine systems.

New Jersey, New York↗

USGS2025

Spatiotemporal variability of algal biomass and nitrate in Owasco and Seneca Lakes in the Finger Lakes Region, New York, in 2019

Cyanobacterial harmful algal blooms (CyanoHABs) have become increasingly common, threatening the security of water resources globally. The U.S. Geological Survey conducted high-resolution nearshore mapping surveys using boat-mounted multiparameter sondes and nitrate sensors during the summer and fall of 2019 on Owasco Lake and Seneca Lake, two lakes with documented CyanoHABs in the Finger Lakes region of New York State. Discrete sensor measurements and water-quality samples were collected at fixed points along survey routes and continuous data were generated at open-water monitoring platforms. This investigation examined whether water-quality information from nearshore surveys was representative of open-water conditions and if nearshore surveys could be used to identify areas with localized nearshore CyanoHABs and potential sources of nutrients not captured by tributary sampling. In addition to comparisons across methods, nearshore concentrations of nitrate and chlorophyll were evaluated relative to tributary outlets, cyanobacterial abundance and biovolume at discrete locations, and the locations of near-surface CyanoHABs that were designated as “confirmed with high toxins” by the New York State Department of Environmental Conservation. Nitrate and chlorophyll concentrations were comparable across methods for each lake, although concentration ranges were typically higher for nearshore mapping datasets than for nearshore discrete datasets. Nearshore surveys indicated areas of nitrate enrichment that varied temporally in both lakes. Orthophosphate was not routinely detected. Across methods, median chlorophyll concentrations were higher for the summer survey than for the fall survey in Owasco Lake. Nearshore chlorophyll concentrations varied more temporally in Owasco Lake than in Seneca Lake. Phytoplankton and cyanobacterial abundance and biovolume were about five times higher in Owasco Lake than in Seneca Lake. Cyanobacteria dominated the phytoplankton community in most samples, and Microcystis comprised the bulk of the cyanobacterial biovolume. The most abundant potential cyanotoxin-producing (specifically microcystins) genera were Microcystis , Synechococcus , Aphanocapsa , and Pseudanabaena . The cyanobacterial community composition was comparable between open-water monitoring platforms and nearshore samples. Microcystins were detected in seven survey samples from Owasco Lake, in one survey sample from Seneca Lake, and in one sample each from the open-water monitoring platforms on Owasco and Seneca Lakes that were collected about 7 days before the fall surveys. Microcystin detections were not consistently associated with high cyanobacterial cell counts or cyanotoxin-producing genera. Results from nearshore surveys were comparable to open-water monitoring platforms and discrete nearshore observations in the absence of nearshore or open-water CyanoHABs in these systems during the study. Patterns of nearshore concentrations of nitrate and chlorophyll from nearshore surveys may aid in the identification of areas with localized nitrate loading and shifts in phytoplankton abundance and community composition.

New York↗

USGS2024

Comparison of imaging flow cytometry and microscopy for freshwater algal bloom detection

Imaging flow cytometry (IFC) is an emerging tool that allows for rapid identification and enumeration of phytoplankton in freshwater systems. However, few studies have assessed the effects of preservation on IFC results or compared live IFC and microscopy results in freshwater systems. Understanding the effects of preservation and differences between IFC and microscopy will improve interpretation of these data and inform strategies to use IFC-based approaches in freshwater systems. Our study objectives were to compare IFC and phase contrast with epifluorescence microscopy as techniques for phytoplankton identification and enumeration, and the effects of sample preservation with an emphasis on taxa forming harmful cyanobacterial blooms (HCBs). During June through October 2020, samples were collected from 2 lakes in the Finger Lakes region of New York. Live and preserved samples were analyzed by laboratory-based IFC, and preserved samples were analyzed by microscopy. The IFC approach captured community dynamics while detecting potential cyanobacterial bloom-forming taxa earlier and at lower abundances than microscopy. Laboratory-based IFC allowed for an intermediate level of taxonomic information when compared to microscopy, gross techniques, such as extracted chlorophyll a or fluorescence sensors, and field-based operation of IFC approaches. The laboratory-based application of IFC in this study allowed receipt of results in 5 d or less, a substantial improvement over microscopy, which can be time-consuming to conduct. However, the laboratory-based IFC approach had some limitations. Imaging flow cytometry-estimated biovolume may be less accurate than microscopy for some taxa because of the algorithms used to calculate biovolume, particularly for chrysophytes and coccoid cyanobacteria. Colonial dissociation during preservation appeared to affect detection of Microcystis by IFC less than for other fragile bloom-forming taxa like chrysophytes. Our study results advance understanding of how IFC may translate to field-based approaches for early harmful algal bloom indicators in freshwater.

Lake and Reservoir Management↗

USGS2024

Evaluation of sensors for continuous monitoring of harmful algal blooms in the Finger Lakes region, New York, 2019 and 2020

In response to the increasing frequency of cyanobacterial harmful algal blooms (CyanoHABs) in the Finger Lakes region of New York State, a pilot study by the U.S. Geological Survey, in collaboration with the New York State Department of Environmental Conservation, was conducted to enhance CyanoHAB monitoring and understanding. High-frequency sensors were deployed on open water monitoring-station platforms at Seneca Lake in 2019–20, at Owasco Lake in 2019–20, and at Skaneateles Lake in 2019. One of the goals of this study was to evaluate the ability of in-place sensors to make representative measurements of dissolved organic matter, nutrients, and algal pigments (as indicators of phytoplankton biomass) while collecting routine field parameters (water temperature, specific conductance, pH, dissolved oxygen, turbidity, weather, and light) to provide additional information about environmental conditions. Despite challenges like power issues and sensor fouling, the sensors performed well overall. However, correlation analyses between sensor readings and laboratory measurements revealed variable performance. Results indicate the relation between the fluorescent dissolved organic matter sensor and laboratory-measured dissolved organic carbon was weak at all study lakes. The nitrate sensors can be sensitive to ambient temperature and have a substantial power requirement, and the relation between sensor- and laboratory-measured nitrate values differed among lakes. The orthophosphate sensors, which were complex and prone to data loss, yielded results that were difficult to interpret because orthophosphate detections are rare in the study lakes. The multichannel fluorometer was also complex to use and required several unique procedures for its operation. Chlorophyll measurements from the fluorometers correlated moderately well with laboratory-measured chlorophyll- a , although relations with total phytoplankton biovolume were weaker. Relations between phycocyanin concentration measurements from the dual-channel fluorometers and cyanobacterial biovolume were not significant; however, the cyanobacterial biovolume correlation was moderately strong with chlorophyll contribution from cyanobacteria measurements from the multichannel fluorometer. Of all collected parameters, water temperature was among the strongest correlated with chlorophyll- a , total phytoplankton biovolume, and cyanobacterial biovolume. Stepwise regression analysis was used to identify the best parameters for modeling variance in laboratory measures of phytoplankton biomass. This analysis included factors such as chlorophyll fluorescence, pH, water temperature, and others, which varied by lake. Overall, the models had limited explanatory power for chlorophyll- a and other biovolumes, possibly due to the absence of CyanoHABs at the open-water monitoring locations. Multivariate models did not outperform simple fluorescence-based models. Notably, turbidity was a more significant indicator of cyanobacterial biovolume variability than phycocyanin from dual-channel fluorometers. The study concludes that while single and multivariate models based on sensor data are useful, they did not explain any more variance than fluorescence-based models. Broader data collection, including more CyanoHAB events, is necessary to refine these models. Integrating machine learning could leverage large, complex datasets to improve CyanoHAB predictions, thereby enhancing the management and understanding of these blooms.

New York↗