Sediment organic matter, grain size, and results of prediction models from northern North Atlantic and Arctic Seas@en

Sediment samples and hydrographic conditions were studied at 28 stations around Iceland. At these sites, Conductivity-Temperature-Depth (CTD) casts were coducted to collect hydrographic data and multicorer casts were conducted to collect data on sediment characteristics including grain size distribution, carbon and nitrogen concentration, and chloroplastic pigment concentration. A total of 14 environmental predictors were used to model sediment characteristics around Iceland on regional scale. Two approaches were used: Multivariate Adaptation Regression Splines (MARS) and randomForest regression models. RandomForest outperformed MARS in predicting grain size distribution. MARS models had a greater tendency to over-and underpredict sediment values in areas outside the environmental envelope defined by the training dataset. We provide first GIS layers on sediment characteristics around Iceland, that can be used as predictors in future models. Although models performed well, more samples, especially from the shelf areas, will be needed to improve the models in future.本研究针对冰岛周边28个站位的沉积物样品与水文状况开展了系统调研。在各站位中，通过施放导电率-温度-深度（Conductivity-Temperature-Depth, CTD）剖面仪采集水文数据，并通过多管沉积物采样器（multicorer）投放作业获取沉积物特征相关数据，涵盖粒度分布、碳氮浓度以及叶绿素类色素浓度等指标。本研究共选取14项环境预测因子，用于构建冰岛周边区域尺度的沉积物特征预测模型。建模过程采用两种方法：多元自适应回归样条（Multivariate Adaptation Regression Splines, MARS）与随机森林（randomForest）回归模型。在粒度分布预测任务中，随机森林模型的表现优于多元自适应回归样条。多元自适应回归样条模型更易在训练数据集所定义的环境包络域之外的区域，对沉积物数值出现过度预测或低估的偏差。本研究首次发布了冰岛周边沉积物特征的地理信息系统（Geographic Information System, GIS）图层，可作为后续模型构建的预测因子加以使用。尽管现有模型表现良好，但未来仍需采集更多样品（尤其是陆架区域的样品）以进一步优化模型。