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This is a summary of major ion concentrations for lake water at selected depths as well as for the inlets and outlets of Green Lakes 1, 4, and Lake Albion. On some occasions the same samples were also taken from other lakes in the Green Lakes Valley, such as Green Lakes 2, 3 and 5. 

This dataset contains water quality measurements made on Green Lakes 1, 2, 3, 4, 5, and Lake Albion. Green Lake 4 was initially sampled in 2000 and is ongoing. Ongoing sampling of Green Lakes 1 was started in 2014 and ongoing sampling of Lake Albion was started in 2016. Water samples were collected for analysis of chlorophyll a and nutrient analysis (which is available in glvwatsolu.dm.data) and field measurements for pH, temperature, specific conductivity, dissolved oxygen (DO), % saturation, secchi depth, PAR. Secchi depth is recorded at the 0m row however it is a measurement of depth and so the units are meters. Most samples were collected between 0800 and 1200 MST. The first sampling date each summer occurs shortly after the ice had melted. Data are collected from an inflatable raft at the point of deepest depth or from the lake inlet and outlet when surface flow is present. The majority of chlorophyll-a the measurements were taken at the surface (0m), the metalimnion (3m), and the hypolimnion nine (usually 8-11m). However, additional measurements were taken for side projects of the long-term dataset during several of the years and are included in this dataset. Water samples from the metalimnion or hypolimnion were collected using a Van Dorne sampler, and surface samples were collected as grab samples from the water column surface, the inlet and outlet. Field measurements were conducted using a YSI either DO or multiple probe meter (2014-2017, YSI MPS 556)(2018-ongoing, YSI ProPlus) and a Li-Cor meter with a quantum sensor. Chlorophyll-a was extracted from filtered samples and absorbance was measured before and after acidification to quantify chlorophyll a concentration. 

Flow pulses mobilize particulate organic matter (POM) in streams from the surrounding landscape and streambed. This POM serves as a source of energy and nutrients, as well as a means for organismal dispersal, to downstream communities. In the barren terrestrial landscape of the McMurdo Dry Valleys (MDV) of Antarctica, benthic microbial mats occupying different in-stream habitat types are the dominant POM source in the many glacier-fed streams. Many of these streams experience daily flow peaks that mobilize POM, and diatoms recovered from underlying stream sediments suggest that mat-derived diatoms in the POM are retained there through hyporheic exchange. Yet, ‘how much’ and ‘when’ different in-stream habitat types contribute to POM diatom assemblages is unknown. To quantify the contribution of different in-stream habitat types to POM diatom assemblages, we collected time-integrated POM samples over four diel experiments, which spanned a gradient of flow conditions over three summers. Diatoms from POM samples were identified, quantified, and compared with dominant habitat types (i.e., benthic ‘orange’ mats, marginal ‘black’ mats, and bare sediments). Like bulk POM, diatom cell concentrations followed a clockwise hysteresis pattern with stream discharge over the daily flow cycles, indicating supply limitation. Diatom community analyses showed that different habitat types harbor distinct diatom communities, and mixing models revealed that a substantial proportion of POM diatoms originated from bare sediments during baseflow conditions. Meanwhile, orange and black mats contribute diatoms to POM primarily during daily flow peaks when both cell concentrations and discharge are highest, making mats the most important contributors to POM diatom assemblages at high flows. These observations may help explain the presence of mat-derived diatoms in hyporheic sediments. Our results thus indicate a varying importance of different in-stream habitats to POM generation and export on daily to seasonal timescales, with implications for biogeochemical cycling and the local diatom metacommunity. 

LakeBeD-US: Ecology Edition is a harmonized lake water quality dataset containing time series and vertical profiles of 21 lakes in the United States monitored by long-term monitoring institutions. These institutions include the North Temperate Lakes Long-Term Ecological Research program (NTL-LTER), Niwot Ridge Long-Term Ecological Research program (NWT-LTER), National Ecological Observatory Network (NEON), and the Carey Lab at Virginia Tech as part of the Virginia Reservoirs Long-Term Research in Environmental Biology (LTREB) site in collaboration with the Western Virginia Water Authority. The data include depth-discrete observations of 17 water quality variables including temperature, dissolved oxygen, chemical properties, Secchi depth, and more. Observations are divided into data collected by automated sensors at a relatively high temporal frequency and manually sampled data at a relatively low temporal frequency. All data were collected in situ. The data are available as Apache Parquet files, and the included R scripts give guidance on how to utilize and query the dataset in R. LakeBeD-US: Ecology Edition is an ecological science-oriented companion to LakeBeD-US: Computer Science Edition. The Computer Science Edition is available on the Hugging Face Hub. 

Riverine silicon (Si) plays a vital role in governing primary production, water quality, and carbon sequestration. The Global Aggregation of Stream Silica (GlASS) database was constructed to assess changes in riverine Si concentrations and fluxes, their relationship to available nutrients, and to evaluate mechanisms driving these patterns. GlASS includes dissolved Si (DSi), dissolved inorganic nitrogen, and dissolved inorganic phosphorus concentrations at daily to quarterly time steps, daily discharge, and watershed characteristics for rivers with drainage areas ranging < 1 km2 to 3 million km2 and spanning eight climate zones, mainly in the northern hemisphere. Data range between years 1963 and 2023. GlASS uses publicly available datasets, ensuring transparency and reproducibility. Original data sources are cited, data quality assurance workflows are public, and input files to a common load estimator are provided.