Leaf stoichiometry of Leontopodium lentopodioides at high altitudes on the northeastern Qinghai-Tibetan Plateau,China

Altitude affects leaf stoichiometry by regulating temperature and precipitation,and influencing soil properties in mountain ecosystems.Leaf carbon concentration(C),leaf nitrogen concentration(N),leaf phosphorous concentration(P),and their stoichiometric ratios of Leontopodium lentopodioides(Willd.)Beauv.,a widespread species in degraded grasslands,were investigated to explore its response and adaptation strategy to environmental changes along four altitude gradients(2500,3000,3500,and 3800 m a.s.l.)on the northeastern Qinghai-Tibetan Plateau(QTP),China.The leaf C significantly varied but without any clear trend with increasing altitude.Leaf N showed an increasing trend,and leaf P showed a little change with increasing altitude,with a lower value of leaf P at 3500 m than those at other altitudes.Similarity,leaf C:P and N:P exhibited a little change with increasing altitude,which both had greater values at 3500 m than those at other altitudes.However,leaf C:N exhibited a decreasing trend with increasing altitude.Soil NH^(+)_(4)-N,soil pH,soil total phosphorus(STP),mean annual temperature(MAT),and mean annual precipitation(MAP)were identified as the main factors driving the variations in leaf stoichiometry of L.lentopodioides across all altitudes,with NH^(+)_(4)-N alone accounting for 50.8%of its total variation.Specifically,leaf C and N were mainly controlled by MAT,soil pH,and NH^(+)_(4)-N,while leaf P by MAP and STP.In the study area,it seems that the growth of L.lentopodioides may be mainly limited by STP.The results could help to strengthen our understanding of the plasticity of plant growth to environmental changes and provide new information on global grassland management and restoration.

祁连山植物共有种沿海拔梯度的叶片化学计量特征

研究不同海拔地区植物共有种叶片化学计量的变化及其影响因素和适应策略,对于理解全球环境变化中的生物地球化学循环具有重要意义。在本研究中,我们测量了中国西北部祁连山5个海拔高度(海拔2400-3200 m,间隔200 m)的土壤有机碳和养分浓度,以及植物共有种的叶片化学计量,以期能更好地了解山区植物共有种如何对海拔变化表现出适应性反应及未来潜在的环境变化如何影响其叶片功能。研究结果表明,随海拔高度的增加,植物共有种叶片C:N:P化学计量的变化不同。在不同的海拔梯度上,年平均气温(MAT)、土壤全磷(STP)、年平均降水量(MAP)、土壤含水量(SWC)和土壤硝态氮(NO3-N)是影响植物共有种叶片元素浓度的主要因素。而叶片化学计量比主要由MAT、MAP和土壤全氮(STN)决定。MAT和MAP对植物共有种叶片元素浓度和叶化学计量比均有显著影响。研究区植物生长主要受到磷的限制。研究结果不仅凸显了植物的可塑性生存策略,还有助于加深我们对植物叶片化学计量学的理解,建立单个植物物种和植物群落与共有种之间的联系。

Sampling Designs for Validating Digital Soil Maps: A Review

Sampling design(SD) plays a crucial role in providing reliable input for digital soil mapping(DSM) and increasing its efficiency.Sampling design, with a predetermined sample size and consideration of budget and spatial variability, is a selection procedure for identifying a set of sample locations spread over a geographical space or with a good feature space coverage. A good feature space coverage ensures accurate estimation of regression parameters, while spatial coverage contributes to effective spatial interpolation.First, we review several statistical and geometric SDs that mainly optimize the sampling pattern in a geographical space and illustrate the strengths and weaknesses of these SDs by considering spatial coverage, simplicity, accuracy, and efficiency. Furthermore, Latin hypercube sampling, which obtains a full representation of multivariate distribution in geographical space, is described in detail for its development, improvement, and application. In addition, we discuss the fuzzy k-means sampling, response surface sampling, and Kennard-Stone sampling, which optimize sampling patterns in a feature space. We then discuss some practical applications that are mainly addressed by the conditioned Latin hypercube sampling with the flexibility and feasibility of adding multiple optimization criteria. We also discuss different methods of validation, an important stage of DSM, and conclude that an independent dataset selected from the probability sampling is superior for its free model assumptions. For future work, we recommend: 1) exploring SDs with both good spatial coverage and feature space coverage; 2) uncovering the real impacts of an SD on the integral DSM procedure;and 3) testing the feasibility and contribution of SDs in three-dimensional(3 D) DSM with variability for multiple layers.

Comparison of sampling designs for calibrating digital soil maps at multiple depths

Digital soil mapping (DSM) aims to produce detailed maps of soil properties or soil classes to improve agricultural management and soil quality assessment. Optimized sampling design can reduce the substantial costs and efforts associated with sampling, profile description, and laboratory analysis. The purpose of this study was to compare common sampling designs for DSM, including grid sampling (GS), grid random sampling (GRS), stratified random sampling (StRS), and conditioned Latin hypercube sampling (cLHS). In an agricultural field (11 ha) in Quebec, Canada, a total of unique 118 locations were selected using each of the four sampling designs (45 locations each), and additional 30 sample locations were selected as an independent testing dataset (evaluation dataset). Soil visible near-infrared (Vis-NIR) spectra were collected in situ at the 148 locations (1 m depth), and soil cores were collected from a subset of 32 locations and subdivided at 10-cm depth intervals, totaling 251 samples. The Cubist model was used to elucidate the relationship between Vis-NIR spectra and soil properties (soil organic matter (SOM) and clay), which was then used to predict the soil properties at all 148 sample locations. Digital maps of soil properties at multiple depths for the entire field (148 sample locations) were prepared using a quantile random forest model to obtain complete model maps (CM-maps). Soil properties were also mapped using the samples from each of the 45 locations for each sampling design to obtain sampling design maps (SD-maps). The SD-maps were evaluated using the independent testing dataset (30 sample locations), and the spatial distribution and model uncertainty of each SD-map were compared with those of the corresponding CM-map. The spatial and feature space coverage were compared across the four sampling designs. The results showed that GS resulted in the most even spatial coverage, cLHS resulted in the best coverage of the feature space, and GS and cLHS resulted in similar prediction accuracies and spatial distributions of soil properties. The SOM content was underestimated using GRS, with large errors at 0–50 cm depth, due to some values not being captured by this sampling design, whereas larger errors for the deeper soil layers were produced using StRS. Predictions of SOM and clay contents had higher accuracy for topsoil (0–30 cm) than for deep subsoil (60–100 cm). It was concluded that the soil sampling designs with either good spatial coverage or feature space coverage can provide good accuracy in 3D DSM, but their performances may be different for different soil properties.

入侵植物与重金属胁迫的相互作用研究进展

全球变化改变了植物群落的分布格局,包括入侵植物,而人为污染可能降低本地植物对入侵植物的抗性。因此,本文总结了近几十年本地植物、入侵植物和植物-土壤生态系统中重金属生物地球化学行为的研究,以加深我们对入侵植物与环境胁迫因子相互作用的认识。我们的研究结合已有文献报道表明:(i)入侵物种对环境胁迫的影响具有异质性,(ii)影响的大小是多变的,(iii)即使在同一影响类型内,影响类型也具有多向性。然而,入侵植物暴露在重金属环境中表现出更强的自我保护机制,对重金属的生物可利用性和毒性有正向或负向的影响。另一方面,由于入侵植物普遍具有较高的耐受性,加之本地植物暴露于有毒重金属污染时具有“逃逸行为”,重金属胁迫环境更有利于植物的成功入侵。但是,对于入侵植物的重金属等元素组成是否与污染地区的本地植物不同,目前尚无共识。因此,在全球范围内对外来入侵植物与本土植物的植物体内、凋落物和土壤污染物含量进行定量比较是今后研究的一个重要方向。