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A Classification And Regression Tree Model Of Controls On Dissolved Inorganic Nitrogen Leaching From European Forests

机译:欧洲森林中溶解性无机氮淋溶控制的分类回归树模型

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Often, there is a non-linear relationship between atmospheric dissolved inorganic nitrogen (DIN) input and DIN leaching that is poorly captured by existing models. We present the first application of the non-parametric classification and regression tree approach to evaluate the key environmental drivers controlling DIN leaching from European forests. DIN leaching was classified as low (<3), medium (3-15) or high (>15 kg N ha~(-1) year~(-1)) at 215 sites across Europe. The analysis identified throughfall NO.V deposition, acid deposition, hydrology, soil type, the carbon content of the soil, and the legacy of historic N deposition as the dominant drivers of DIN leaching for these forests. Ninety four percent of sites were successfully classified into the appropriate leaching category. This approach shows promise for understanding complex ecosystem responses to a wide range of anthropogenic stressors as well as an improved method for identifying risk and targeting pollution mitigation strategies in forest ecosystems.
机译:通常,大气溶解的无机氮(DIN)输入和DIN浸出之间存在非线性关系,而现有模型很难捕捉到这一关系。我们介绍了非参数分类和回归树方法的首次应用,以评估控制欧洲森林中DIN浸出的关键环境驱动因素。在欧洲的215个站点中,DIN浸出被分类为低(<3),中(3-15)或高(> 15 kg N ha〜(-1)年〜(-1))。分析确定了全季NO的沉降,酸沉降,水文学,土壤类型,土壤的碳含量以及历史氮的沉积是这些森林进行DIN淋洗的主要驱动力。 94%的站点已成功分类为适当的浸出类别。这种方法显示出有望了解复杂的生态系统对各种人为压力源的反应,以及在森林生态系统中识别风险和确定污染缓解策略的改进方法。

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