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1. Stochastic nutrient-plankton models

   https://doi.org/10.1016/j.jde.2023.08.017  

   Hening, Alexandru; Hieu, Nguyen Trong; Nguyen, Dang Hai; Nguyen, Nhu Ngoc (December 2023, Journal of Differential Equations)
2. Elucidating organic and nutrient transport mechanisms in polyelectrolyte modified membranes for selective nutrient recovery

   https://doi.org/10.1016/j.memsci.2024.123438  

   Piash, Km_Prottoy Shariar; Sprouse, Noah; Hodges, Cole; Lin, Lian-Shin; Sanyal, Oishi (February 2025, Journal of Membrane Science)
3. Bryospheres in oligotrophic headwater streams provide nutrient-dense habitats and dominate stream nutrient cycling

   https://doi.org/10.1086/733067  

   Steele, Jessee_J B; Thellman, Audrey N; Vought, Olivia K; Rosi, Emma J; Wooster, Tammy; Solomon, Christopher T; Bernhardt, Emily S (December 2024, Freshwater Science)

   Stream bryophytes (mosses and liverworts) are widely recognized as important macroinvertebrate habitats, but their overall role in the stream ecosystem, particularly in nutrient cycling, remains understudied. Hubbard Brook Experimental Forest in New Hampshire, USA, contains some of the most extensively researched streams in the world, yet few studies mention their bryophytes. Perhaps this is because early estimates place bryophyte coverage in these streams at an insignificant 2%. However, data from 2019 show that contemporary coverage ranges from 4 to 40% among streams. To investigate how stream bryophyte cover may be changing over time and influencing stream nutrient stocks, we conducted field surveys, measured the mass of organic and inorganic bryophyte contents, and quantified nutrient uptake with bottle incubations of bryophyte mats. This study marks a novel attempt to map stream bryophyte coverage with estimates of C, P, and N stocks and fluxes. From our 2022 field surveys, we found that median bryophyte coverage varied across streams in the same catchment (0–41.4%) and shifted from just 3 y prior. We estimate that these bryophyte mats stored between 14 and 414 g of organic matter per m2 of stream in the form of live biomass and captured particulates. Within 12 h of light incubation, 35 out of 36 bryophyte clump samples sorbed peak historical water-column concentrations of PO43– as measured in the Hubbard Brook stream chemistry record. In Bear Brook, our scaled estimate of bryophyte mat NO3– uptake (2.3 g N/y) constitutes a substantial portion of previously estimated whole-stream NO3– uptake (12 g N/y). Cumulatively, our data demonstrate that bryophytes and their associated mineral substrates and biota—known as the bryosphere—are crucial in facilitating headwater stream nutrient cycling. These bryospheres may contribute significantly to interannual variability in stream nutrient concentrations within nutrient-poor streams, especially in climate-sensitive regions. 

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4. Limited progress in nutrient pollution in the U.S. caused by spatially persistent nutrient sources

   https://doi.org/10.1371/journal.pone.0258952  

   Frei, Rebecca J.; Lawson, Gabriella M.; Norris, Adam J.; Cano, Gabriel; Vargas, Maria Camila; Kujanpää, Elizabeth; Hopkins, Austin; Brown, Brian; Sabo, Robert; Brahney, Janice; et al (November 2021, PLOS ONE)

   Toor, Gurpal S. (Ed.)

   Human agriculture, wastewater, and use of fossil fuels have saturated ecosystems with nitrogen and phosphorus, threatening biodiversity and human water security at a global scale. Despite efforts to reduce nutrient pollution, carbon and nutrient concentrations have increased or remained high in many regions. Here, we applied a new ecohydrological framework to ~12,000 water samples collected by the U.S. Environmental Protection Agency from streams and lakes across the contiguous U.S. to identify spatial and temporal patterns in nutrient concentrations and leverage (an indicator of flux). For the contiguous U.S. and within ecoregions, we quantified trends for sites sampled repeatedly from 2000 to 2019, the persistence of spatial patterns over that period, and the patch size of nutrient sources and sinks. While we observed various temporal trends across ecoregions, the spatial patterns of nutrient and carbon concentrations in streams were persistent across and within ecoregions, potentially because of historical nutrient legacies, consistent nutrient sources, and inherent differences in nutrient removal capacity for various ecosystems. Watersheds showed strong critical source area dynamics in that 2–8% of the land area accounted for 75% of the estimated flux. Variability in nutrient contribution was greatest in catchments smaller than 250 km 2 for most parameters. An ensemble of four machine learning models confirmed previously observed relationships between nutrient concentrations and a combination of land use and land cover, demonstrating how human activity and inherent nutrient removal capacity interactively determine nutrient balance. These findings suggest that targeted nutrient interventions in a small portion of the landscape could substantially improve water quality at continental scales. We recommend a dual approach of first prioritizing the reduction of nutrient inputs in catchments that exert disproportionate influence on downstream water chemistry, and second, enhancing nutrient removal capacity by restoring hydrological connectivity both laterally and vertically in stream networks. 

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5. Combining nutrient, productivity, and landscape-based regressions improves predictions of lake nutrients and provides insight into nutrient coupling at macroscales: Joint nutrient-productivity model

   https://doi.org/10.1002/lno.10944  

   Wagner, Tyler; Schliep, Erin M. (January 2018, Limnology and Oceanography)

   Empirical nutrient models that describe lake nutrient, productivity, and water clarity relationships among lakes play a prominent role in limnology. Landscape-based regressions are also used to understand macroscale variability of lake nutrients, clarity, and productivity (hereafter referred to as nutrient-productivity). Predictions from both models are used to inform eutrophication management globally. To date, these two classes of models are generally conducted separately, which ignores the known dependencies among nutrient-productivity variables. We present a statistical model that integrates nutrient-productivity and landscape-based regressions—where lake nutrients, productivity, and clarity variables are modeled jointly. We fitted a joint nutrient-productivity model to over 7000 lakes with three nutrients (total phosphorus, total nitrogen, nitrate concentrations), chlorophyll a concentrations, and Secchi disk depth as response variables and landscape features as predictor variables. Because lakes in different regions respond to landscape features differently, we focused our analysis on two subregions with different dominant land uses, the agricultural Midwest and the forested Northeast U.S. Predictive performance was enhanced by modeling nutrientproductivity variables jointly. We also found strong evidence that nutrient-productivity variables were coupled, and that only nitrate may be decoupled from other nutrient-productivity variables in the forested region. We speculate that these regional differences may be related to differences in the strength of biogeochemical cycles and stoichiometric controls between these regions. Jointly modeling nutrient-productivity variables in lakes effectively integrates the two dominant approaches for studying lakes nutrient-productivity relationships and provides novel insight into macroscale patterns of the coupling of nutrients, chlorophyll, and water clarity in lakes. 

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