Data from: Climatic water availability mainly drives context-dependency of tree functional diversity effects on soil organic carbon storage in European forests

AbstractThe interplay of forest stand and environmental factors shape soil organic C (SOC) storage in forest ecosystems but little is known about their relative impacts in different soil layers. Moreover, how environmental factors modulate the impact of stand factors, particularly species mixing, on SOC storage, is largely unexplored. In this study conducted in 21 forest triplets (two-species mixed stand and respective monocultures nearby) distributed in Europe, we tested the hypothesis that stand factors (functional identity and diversity) have stronger effects on topsoil (FF+0-10 cm) C storage than environmental factors (climatic water availability, clay+silt content, oxalate-extractable Al - Alox) but that the opposite occurs in the subsoil (10-40 cm). We also tested the hypothesis that functional diversity improves SOC storage under high climatic water availability, clay+silt contents, Alox. We characterized functional identity as the proportion of broadleaved species (beech and/or oak), and functional diversity as the product of broadleaved and conifer (pine) proportions. The results show that functional identity was the main driver of topsoil C storage while climatic water availability had the largest control on subsoil C storage. Contrary to expectations, functional diversity decreased topsoil C storage under increasing climatic water availability but the opposite was observed in the subsoil. Functional diversity effects on topsoil C increased with increasing clay+silt content, while its effects on subsoil C was negative at increasing Alox content. This suggests that functional diversity effect on SOC storage along environmental gradients depends on the specific environmental factor and the soil depth under consideration., MethodsThis study was conducted in 21 forest triplets across Europe. A triplet consisted of two-species mixed stand and their corresponding monocultures at the same site. The three forest stands in each triplet were of similar ages (based on tree cores and forest archives) and had homogenous soil conditions based on texture analyses on soil samples in the 10-20 cm depth. The triplets were of three types: five beech-oak (Fagus sylvatica L. - Quercus petraea (Matt.) Liebl.), eight pine-beech (Pinus sylvestris L. - Fagus sylvatica L.), and eight pine-oak (Pinus sylvestris L. - Quercus robur L. / Quercus petraea (Matt.) Liebl.). These tree species are widely distributed in Europe and are very important for forestry. We placed ten (10) sampling points in each mixed stand and five (5) points each in the corresponding monocultures. At each sampling point, we sampled the forest floor (organic layer above the mineral soil) with 30 cm x 30 cm metal frame. Subsequently, we dug sampling pits in 10 cm interval until 40 cm depth. We estimated total volume (soil + voids + stones) of soil samples in each 10 cm pit by the volume replacement method (Al-Shammary and others 2018) with glass beads. Samples were air-dried, crushed, then passed through 2 mm sieve to separate fine soil (<2 mm), coarse roots (>2 mm), and stones. We picked visible roots in fine soil to reduce their influence on C contents. We separately weighed all the fine soil and the stone fractions. We determined stone volume by water displacement method. Sub-samples of fine soils were ground into powder with Vibratory Disc Mill (Retsch RS 200, Germany) for C and N analyses on all samples (totaling 2080) using CN Analyzer (FlashEA® 1112, USA). Computation of SOC stocks have been described in Osei and others (2021). Soil pH, particle size distribution, and oxalate-extractable metals (Alox, Feox) were determined on samples from the 10-20 cm depth. We determined soil pH in deionized water at a ratio of 1:10 using inoLab pH Level 1 (WTW GmbH, Germany). Particle size distribution was determined by sedimentation method following protocol NF X31-107. The oxalate-extractable metals (Alox, Feox) were extracted by 0.2M ammonium-oxalate at pH 3 according to Blackmore and others (1981), and the concentrations of Al and Fe were determined by ICP. We characterized the combined effect of precipitation (P, mm) and temperature (T, °C) by the de Martonne aridity index (DMI; P/T+10), Usage notesIt is saved in txt, which is accessible by almost all softwares.摘要 林分（forest stand）与环境因子（environmental factors）的交互作用塑造了森林生态系统的土壤有机碳（soil organic C, SOC）储量，但目前对不同土层中二者相对影响的认知仍较为匮乏。此外，环境因子如何调控林分因子（尤其是物种混交）对SOC储量的影响，在很大程度上仍未被探索。本研究在欧洲分布的21组林分三联体（即2个物种组成的混交林（two-species mixed stand）及其邻近的对应纯林（monocultures））中开展实验，验证两个假说：其一，林分因子（功能属性（functional identity）与功能多样性（functional diversity））对表层土（含枯落物层，0~10 cm）碳储量的影响强于环境因子（气候水分可获得性（climatic water availability）、黏粒+粉粒含量（clay+silt content）、草酸盐浸提态铝（oxalate-extractable Al, Alox）），而在下土层（subsoil, 10~40 cm）中则反之；其二，在高气候水分可获得性、高黏粒+粉粒含量及高Alox的条件下，功能多样性可提升SOC储量。本研究将功能属性表征为阔叶树种（broadleaved species，山毛榉和/或栎树）的占比，将功能多样性定义为阔叶树与针叶树（conifer，松树）占比的乘积。研究结果显示：功能属性是表层土碳储量的主要驱动因子，而气候水分可获得性则对下层土碳储量的调控作用最强。与预期相反，随着气候水分可获得性提升，功能多样性会降低表层土碳储量，但在下土层中观察到了相反的结果。功能多样性对表层土碳储量的影响随黏粒+粉粒含量升高而增强，而其对下层土碳储量的影响则随Alox含量升高呈负向变化。上述结果表明，沿环境梯度的功能多样性对SOC储量的影响，取决于具体的环境因子与所研究的土壤深度。 方法 本研究在欧洲范围内的21组林分三联体中开展。每组三联体包含同一立地的2物种混交林及其对应的纯林。每组三联体中的3个林分年龄相近（基于树木年轮数据与森林档案确定），且根据10~20 cm土层的土壤样品质地分析结果，其土壤条件均一。三联体共分为3类：5组山毛榉-栎树（Fagus sylvatica L. - Quercus petraea (Matt.) Liebl.）林、8组松树-山毛榉（Pinus sylvestris L. - Fagus sylvatica L.）林，以及8组松树-栎树（Pinus sylvestris L. - Quercus robur L. / Quercus petraea (Matt.) Liebl.）林。这些树种在欧洲分布广泛，对林业生产具有重要价值。我们在每个混交林内设置10个采样点，在对应的纯林内各设置5个采样点。在每个采样点，先用30 cm×30 cm的金属框采集枯落物层（forest floor，矿质土壤上方的有机层）样品；随后开挖采样坑，按10 cm间隔分层至40 cm深度。采用玻璃珠置换法（Al-Shammary等，2018）测定每个10 cm土层的土壤总体积（包括土壤、孔隙与石块）。将样品风干、粉碎后过2 mm筛，分离出细土（<2 mm）、粗根（>2 mm）与石块；挑出细土中可见的根系，以降低其对碳含量测定的干扰。分别称量细土与石块组分的重量，通过排水法测定石块体积。取细土子样品经振动圆盘磨（Vibratory Disc Mill, Retsch RS 200, 德国）研磨成粉末，使用碳氮分析仪（CN Analyzer, FlashEA® 1112, 美国）测定全部2080个样品的碳、氮含量。SOC储量的计算方法参见Osei等（2021）的研究。对10~20 cm土层的样品测定了土壤pH值、粒径分布以及草酸盐浸提态金属（oxalate-extractable metals, Alox、Feox）含量：采用inoLab pH Level 1（WTW GmbH, 德国）以1:10的土水比在去离子水中测定土壤pH值；参照NF X31-107标准采用沉降法测定粒径分布；参照Blackmore等（1981）的方法，使用0.2M草酸铵（pH 3）浸提草酸盐态金属，并用电感耦合等离子体法（inductively coupled plasma, ICP）测定铝与铁的浓度。本研究采用德马顿干旱指数（de Martonne aridity index, DMI; P/(T+10)，其中P为降水量（mm）、T为气温（℃））表征降水与温度的综合效应。 使用说明 本数据集以txt格式存储，几乎可被所有软件读取。