Humans rely on the natural environment and benefit from the goods and services provided by natural ecosystems. Quantification and mapping of ecosystem services (ES) is required to better protect valued ES benefits under pressure from anthropogenic activities. The removal of excess nitrogen, a recognized catchment‐derived pollutant, by biota in estuarine soft sediments is an important ES that potentially ameliorates the development of eutrophication symptoms. Here, we quantified estuarine benthic sediment characteristics and denitrification enzyme activity (DEA), a proxy of inorganic N removal, at 109 sites in four estuaries to develop a general (“global”) model for predicting DEA. Our initial global model for linking DEA and environmental characteristics had good explanatory power, with sediment mud content having the strongest influence on DEA (60%), followed by sediment organic matter content (≈35%) and sediment chlorophyll a content (≈5%). Predicted and empirically evaluated DEA values in a fifth estuary (Whitford, n  = 90 validation sites) were positively correlated (r  = 0.77), and the fit and certainty of the model (based on two types of uncertainty measures) increased further after the validation sites were incorporated into it. The model tended to underpredict DEA at the upper end of its range (at the muddier, more organically enriched sites), and the relative roles of the three environmental predictors differed in Whitford relative to the four previously sampled estuaries (reducing the explained deviance relative to the initial global model). Our detailed quantification of DEA and methodological description for producing empirically validated maps, complete with uncertainty information, represents an important first step in the construction of nutrient pollution removal ES maps for use in coastal marine spatial management. This technique can likely be adapted to map other ecosystem functions and ES proxies worldwide.

中文翻译：

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使用经验验证的增强回归树模型绘制河口生态系统服务污染物的去除图。

人类依赖自然环境，并从自然生态系统提供的商品和服务中受益。需要对生态系统服务（ES）进行量化和制图，以便在人为活动的压力下更好地保护有价值的ES效益。河口软沉积物中的生物区系去除过量的氮（一种公认的集水源性污染物）是一项重要的ES，可能会改善富营养化症状的发展。在这里，我们量化了四个河口109个站点的河口底栖沉积物特征和反硝化酶活性（DEA），这是无机氮去除的代表，从而开发了预测DEA的通用（“全局”）模型。我们最初的将DEA与环境特征联系起来的全局模型具有很好的解释力，一个内容（≈5％）。第五个河口（惠特福德，n  = 90个验证点）的经预测和经验评估的DEA值呈正相关（r = 0.77），并且在将验证位点并入模型后，模型的拟合度和确定性（基于两种类型的不确定性度量）进一步提高。该模型倾向于在其范围的上限（泥泞，有机富集的地区）低估DEA，并且在惠特福德，相对于先前取样的四个河口，这三种环境预测因子的相对作用有所不同（相对于先前的四种，减少了解释的偏差）。初始全局模型）。我们对DEA的详细量化和方法描述，以产生经过经验验证的地图，以及不确定性信息，代表了建设用于沿海海洋空间管理的营养物去除ES地图的重要第一步。