Spatial pattern and scale of leaf N and P resorption efficiency and proficiency in a Quercus robur population

Abstract

Leaf nutrient resorption allows plants to lower their dependence on current soil nutrients, thereby influencing ecosystem-level processes such as litter decomposition and soil nutrient availability. Among different factors controlling nutrient resorption are the availability of plant resources such as nutrients, water or light. The heterogeneous spatial distribution of these resources in natural environments may influence the spatial pattern of nutrient resorption. The spatial variability of leaf N and P resorption efficiency and proficiency and their relationship to the spatial pattern of soil and light resources were evaluated by using descriptive statistics and geostatistics in a Quercus robur L. (pedunculate oak) population. Resorption efficiency and resorption proficiency were significantly higher for P than for N. Levels of N resorption proficiency indicated incomplete resorption in all individuals. However, 80% of individuals exhibited intermediate or complete levels of P resorption. Resorption efficiency and proficiency of leaf N and P showed spatial dependence at the studied scale, with the spatial distribution of P showing higher range (autocorrelation distance) than that of N. The spatial pattern and scale of nutrients in senesced leaves differed from that in green leaves, with senesced leaves having a higher spatial range but lower percentage of variance explained by distance. All soil variables and the light availability index showed spatial dependence at the examined spatial scale. However, only soil water content and extractable-P were significantly correlated with resorption proficiency and efficiency, although these relationships explained a low percentage (<5%) of total variance. The spatial dependence for resorption variables has important implications for sampling design because nearby individuals cannot be considered independent samples. It may also have implications for ecosystem-level processes related to litter quality, which might also exhibit spatial dependence.

The role of chemometrics in single and sequential extraction assays: A Review. Part II. Cluster analysis, multiple linear regression, mixture resolution, experimental design and other techniques

“…Several topics were addressed: i) the sources of the elements [41] and the properties influencing their concentrations [56][57][58][59][60][61]; ii) the seasonal variations [62]; iii) the identification of areas with different contamination levels and consequently different risks for plant growth or human health [47,57,[63][64][65]; iv) the effect of different data processing procedures [66,67]; v) the relationship between Cd content in durum wheat grain and in soil [68]; vi) the effect of gypsum addition and water management (flooded vs. non-flooded) on the yield of marsh vegetation [69]; vii) the identification of deficiency of micronutrients [56].…”

Spatial Variability of Some Chemical and Physical Soil Properties in Nesho Mountainous Rangelands

“…Cheng et al (2007) reported that spatial variability of aboveground biomass in shrub lands is greater than grasslands. There is clear special spatial relationship between plant and soil (Etemaand Wardle, 2002; Covelo et al, 2008;Zhao et al, 2007). Determining soil variability is important for ecological modeling, environmental predictions, precise agriculture and management of natural resources (Hangsheng et al, 2005;Wang, 2009).…”

Isolation and characterization of the Chrysanthemum nitrate transporter CmNRT1

“…Nitrogen, an essential macronutrient for plants (Covelo et al, 2008;Zhang et al, 2008), is a constituent of numerous important compounds including amino acids, proteins (enzymes), nucleic acids, chlorophyll, and several plant hormones (Britto and Kronzucker, 2004;Guo et al, 2008). Because of the important influence of nitrogen on plant growth, a number of studies have examined the basis of nitrogen use efficiency in cotton (Li et al, 2007), rice (Koutroubas and Ntanos, 2003) and maize (Presterl et al, 2002).…”