| Summary: | Dissolved oxygen is essential for all marine life, especially for benthic organisms that live on the seafloor and are unable to escape if oxygen concentrations fall below critical thresholds. Therefore, near-bottom oxygen concentrations are a key component of environmental assessments and are measured widely. To gain the full picture of hypoxic areas, spatial gaps between monitoring stations must be closed. Therefore, we applied two spatial interpolation methods, where estimated near-bottom oxygen concentrations were solely based on measurements. Furthermore, two variants of the machine learning algorithm Quantile Regression Forest were applied, and any uncertainties in the results were evaluated. All geostatistical methods were evaluated for one year and over a longer period, showing that Quantile Regression Forest methods achieved better results for both. Afterward, all geostatistical methods were applied to estimate the areas below different critical oxygen thresholds from 1950 to 2019 to compute oxygen-deficient areas and how they changed when faced with anthropogenic pressures, especially in terms of increased nutrient inputs.
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