Lohrer Andrew M, Stephenson Fabrice, Douglas Emily J, Townsend Michael
National Institute of Water & Atmospheric Research, PO Box 11115, Hillcrest, Hamilton, 3214, New Zealand.
Waikato Regional Council, Private Bag 3038, Hamilton, 3240, New Zealand.
Ecol Appl. 2020 Jul;30(5):e02105. doi: 10.1002/eap.2105. Epub 2020 Mar 20.
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.
人类依赖自然环境，并从自然生态系统提供的产品和服务中受益。为了在人为活动的压力下更好地保护有价值的生态系统服务效益，需要对生态系统服务（ES）进行量化和制图。河口软沉积物中的生物群去除过量氮（一种公认的源自流域的污染物）是一项重要的生态系统服务，它有可能缓解富营养化症状的发展。在这里，我们对四个河口的109个站点的河口底栖沉积物特征和反硝化酶活性（DEA，无机氮去除的一个指标）进行了量化，以建立一个预测DEA的通用（“全球”）模型。我们最初将DEA与环境特征联系起来的全球模型具有良好的解释力，沉积物泥质含量对DEA的影响最大（60%），其次是沉积物有机质含量（约35%）和沉积物叶绿素a含量（约5%）。在第五个河口（惠特福德，n = 90个验证站点）预测的和经实证评估的DEA值呈正相关（r = 0.77），在将验证站点纳入模型后，模型的拟合度和确定性（基于两种不确定性度量）进一步提高。该模型在其范围的上限（在泥质更多、有机富集程度更高的站点）往往会低估DEA，并且相对于之前采样的四个河口，惠特福德的三个环境预测因子的相对作用有所不同（相对于初始全球模型，减少了解释偏差）。我们对DEA的详细量化以及生成经实证验证的地图的方法描述，包括不确定性信息，是构建用于沿海海洋空间管理的营养物污染去除生态系统服务地图的重要第一步。这种技术可能可以适用于绘制全球其他生态系统功能和生态系统服务指标的地图。
