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Abstract：
Acid phosphatase produced by plants and microbes plays a fundamental role in the recycling of soil phosphorus (P). A quantification of the spatial variation in potential acid phosphatase activity (AP) on large spatial scales and its drivers can help to reduce the uncertainty in our understanding of bio-availability of soil P. We applied two machine-learning methods (Random forests and back-propagation artificial networks) to simulate the spatial patterns of AP across Europe by scaling up 126 site observations of potential AP activity from field samples measured in the laboratory, using 12 environmental drivers as predictors. The back-propagation artificial network (BPN) method explained 58% of AP variability, more than the regression tree model (49%). In addition, BPN was able to identify the gradients in AP along three transects in Europe. Partial correlation analysis revealed that soil nutrients (total nitrogen, total P, and labile organic P) and climatic controls (annual precipitation, mean annual temperature, and temperature amplitude) were the dominant factors influencing AP variations in space. Higher AP occurred in regions with higher mean annual temperature, precipitation and higher soil total nitrogen. Soil TP and Po were non-monotonically correlated with modeled AP for Europe, indicating diffident strategies of P utilization by biomes in arid and humid area. This study helps to separate the influences of each factor on AP production and to reduce the uncertainty in estimating soil P availability. The BPN model trained with European data, however, could not produce a robust global map of AP due to the lack of representative measurements of AP for tropical regions. Filling this data gap will help us to understand the physiological basis of P-use strategies in natural soils.
摘要：
植物和微生物产生的酸性磷酸酶在土壤磷（P）的回收中起着基本作用。在大的空间尺度上量化潜在的酸性磷酸酶活性（AP）的空间变化及其驱动因素可以帮助减少我们对土壤P的生物有效性的理解的不确定性。我们应用了两种机器学习方法（随机森林和反向传播人工网络）通过扩大实验室测量的田间样品对潜在AP活性的126个现场观测来模拟整个欧洲AP的空间格局，使用12个环境驱动因素作为预测因素。反向传播人工网络(BPN)方法解释了58%的AP变异性，超过回归树模型(49%)。此外,BPN能够沿着欧洲的三个样点识别AP中的梯度。偏相关分析表明，土壤养分(全氮，总P,和不稳定的有机磷)和气候控制(年降水量，年平均气温,和温度振幅）是影响AP空间变化的主要因素。较高的AP发生在年平均温度较高的地区，降水和较高的土壤全氮。土壤TP和Po与欧洲的模拟AP非单调相关，表明干旱和潮湿地区生物群落利用磷的不同策略。这项研究有助于分离每个因素对AP产量的影响，并减少估算土壤P有效性的不确定性。用欧洲数据训练的BPN模型，然而,由于缺乏热带地区的代表性AP测量，因此无法生成强大的AP全球地图。填补这一数据空白将有助于我们了解自然土壤中磷利用策略的生理基础。
