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Lakes and reservoirs provide multiple essential ecosystem services to humans, and several of these require the presence of a cool, oxygen‐rich hypolimnion. These ecosystem services include supporting habitat for recreational fish species and the maintenance of a non‐eutrophic state by limiting internal nutrient loading. However, changes in land use and global warming are modifying the thermal structure of lakes worldwide, creating episodic or prolonged periods of hypoxia, changing a lakes ability to deliver certain ecosystem services. Here, we used National Lake Assessment data and machine learning approaches to identify which lake or watershed features determined the presence of a hypolimnion and which contributed to the development of hypoxia. A random forest of 1000 trees predicted the presence of a hypolimnion with 85% accuracy using commonly measured morphometric and physicochemical variables as well as land use. Mean and maximum depths had a disproportionate influence, entirely obscuring the influence of variables commonly associated with lake mixing, such as light penetration and fetch. In contrast, depth had a more restrained role to predict the presence of hypoxia; the latter was predicted with an overall accuracy of 85%, from epilimnetic nitrogen and carbon concentrations. Although the nutrient‐color groups had a minor influence in the random forests, there was a clear trend of increased hypoxia with higher turbidity. Our approach allows for the broad‐scale assessment of lakes with hypolimnia and suggests that increasing eutrophication and browning will promote profundal hypoxia, altering the delivery of certain ecosystems services. [ABSTRACT FROM AUTHOR]

Terrestrial and aquatic ecosystems are intimately linked by the export of elements from watersheds. Although export is influenced by land cover within watersheds, few models evaluate how the spatial configuration of land cover influences loading. In this study we examined spatial variation of land cover at a 10 X 10 m resolution by developing a mass balance, maximum likelihood model of lake iron (Fe) concentrations in 93 watersheds. The model estimated lake iron concentrations based on loading, within-lake processes and losses. Two models were developed. One considered loading from eight land cover types, whereas the second model included the distance of each grid cell to account for Fe losses along flow paths to the lake. In-lake production and losses were accounted for as a function of lake area, water color, and discharge. If we treated watersheds as homogeneous source areas, export was estimated as 450 mg Fe [m.sup.-2] [yr.sup.-1] ; however, in spatial models export varied from negligible to 5,400 mg Fe [m.sup.-2] [yr.sup.-1] based on differential loadings from eight cover types. Accounting for losses of Fe based on distance from the lake did not improve the model. Although areal export of Fe was greater from wetlands, upland forests dominate the landscape and thus accounted for on average 75% of the total Fe load. Fe losses from lakes were primarily regulated by discharge; however, water color and lake depth were also important. Overall, the analysis revealed that lake Fe concentrations are related to land cover based on strong differential Fe loadings.

Diverse prokaryotic communities consume and transform a broad suite of molecules in the dissolved organic matter (DOM) pool, which controls major biogeochemical cycles. Despite methodological advancements that provide increasingly more detailed information on the diversity of both prokaryotic communities and DOM components, understanding how these two component parts are structured to influence ecosystem functioning remains a major challenge in microbial ecology. Using empirical data collected along a gradient of productivity in the Labrador Sea, we characterized relationships among DOM compounds, metabolic processing, and prokaryotic diversity by structuring prokaryotic communities using spatial abundance distribution (SpAD) modeling. We identified strong associations of different SpAD taxonomic groups with specific organic substrates as well as with metabolic rates. Amplicon sequence variants (ASVs) with more cosmopolitan distributions (i.e. normal- like) such as Bacteroidia were related to fresher DOM substrates such as free and combined amino acids whereas rare ASVs (i.e. logistic) like δ-proteobacteria were associated with complex forms of organic matter. In terms of ecosystem function, rates of respiration and production were most strongly predicted by the abundance of certain SpAD taxonomic groups. Given the importance and complexity of linking environmental conditions, prokaryotic community structure, and ecosystem function, we propose a framework to bridge the gap between prokaryotic diversity, microbial ecology, and biogeochemistry among methods and across scales. Our work suggests that SpAD modeling can be used as an intermediate step to link prokaryotic community structure to both finer DOM details and larger ecosystem scale processes. [ABSTRACT FROM AUTHOR]