Dataset Information

Is leaf dry matter content a better predictor of soil fertility than specific leaf area?

ABSTRACT:

Background and aims

Specific leaf area (SLA), a key element of the 'worldwide leaf economics spectrum', is the preferred 'soft' plant trait for assessing soil fertility. SLA is a function of leaf dry matter content (LDMC) and leaf thickness (LT). The first, LDMC, defines leaf construction costs and can be used instead of SLA. However, LT identifies shade at its lowest extreme and succulence at its highest, and is not related to soil fertility. Why then is SLA more frequently used as a predictor of soil fertility than LDMC?

Methods

SLA, LDMC and LT were measured and leaf density (LD) estimated for almost 2000 species, and the capacity of LD to predict LDMC was examined, as was the relative contribution of LDMC and LT to the expression of SLA. Subsequently, the relationships between SLA, LDMC and LT with respect to soil fertility and shade were described.

Key results

Although LD is strongly related to LDMC, and LDMC and LT each contribute equally to the expression of SLA, the exact relationships differ between ecological groupings. LDMC predicts leaf nitrogen content and soil fertility but, because LT primarily varies with light intensity, SLA increases in response to both increased shade and increased fertility.

Conclusions

Gradients of soil fertility are frequently also gradients of biomass accumulation with reduced irradiance lower in the canopy. Therefore, SLA, which includes both fertility and shade components, may often discriminate better between communities or treatments than LDMC. However, LDMC should always be the preferred trait for assessing gradients of soil fertility uncoupled from shade. Nevertheless, because leaves multitask, individual leaf traits do not necessarily exhibit exact functional equivalence between species. In consequence, rather than using a single stand-alone predictor, multivariate analyses using several leaf traits is recommended.

SUBMITTER: Hodgson JG

PROVIDER: S-EPMC3197453 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature

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Is leaf dry matter content a better predictor of soil fertility than specific leaf area?

Hodgson J G JG Montserrat-Martí G G Charles M M Jones G G Wilson P P Shipley B B Sharafi M M Cerabolini B E L BE Cornelissen J H C JH Band S R SR Bogard A A Castro-Díez P P Guerrero-Campo J J Palmer C C Pérez-Rontomé M C MC Carter G G Hynd A A Romo-Díez A A de Torres Espuny L L Royo Pla F F

Annals of botany 20110921 7

<h4>Background and aims</h4>Specific leaf area (SLA), a key element of the 'worldwide leaf economics spectrum', is the preferred 'soft' plant trait for assessing soil fertility. SLA is a function of leaf dry matter content (LDMC) and leaf thickness (LT). The first, LDMC, defines leaf construction costs and can be used instead of SLA. However, LT identifies shade at its lowest extreme and succulence at its highest, and is not related to soil fertility. Why then is SLA more frequently used as a pr ...[more]

PMID: 21948627

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Project description:Assessment and monitoring of soil organic matter (SOM) quality are important for understanding SOM dynamics and developing management practices that will enhance and maintain the productivity of agricultural soils. Visible and near-infrared (Vis-NIR) diffuse reflectance spectroscopy (350-2500 nm) has received increasing attention over the recent decades as a promising technique for SOM analysis. While heterogeneity of sample sets is one critical factor that complicates the prediction of soil properties from Vis-NIR spectra, a spectral library representing the local soil diversity needs to be constructed. The study area, covering a surface of 927 km2 and located in Yujiang County of Jiangsu Province, is characterized by a hilly area with different soil parent materials (e.g., red sandstone, shale, Quaternary red clay, and river alluvium). In total, 232 topsoil (0-20 cm) samples were collected for SOM analysis and scanned with a Vis-NIR spectrometer in the laboratory. Reflectance data were related to surface SOM content by means of a partial least square regression (PLSR) method and several data pre-processing techniques, such as first and second derivatives with a smoothing filter. The performance of the PLSR model was tested under different combinations of calibration/validation sets (global and local calibrations stratified according to parent materials). The results showed that the models based on the global calibrations can only make approximate predictions for SOM content (RMSE (root mean squared error) = 4.23-4.69 g kg-1; R2 (coefficient of determination) = 0.80-0.84; RPD (ratio of standard deviation to RMSE) = 2.19-2.44; RPIQ (ratio of performance to inter-quartile distance) = 2.88-3.08). Under the local calibrations, the individual PLSR models for each parent material improved SOM predictions (RMSE = 2.55-3.49 g kg-1; R2 = 0.87-0.93; RPD = 2.67-3.12; RPIQ = 3.15-4.02). Among the four different parent materials, the largest R2 and the smallest RMSE were observed for the shale soils, which had the lowest coefficient of variation (CV) values for clay (18.95%), free iron oxides (15.93%), and pH (1.04%). This demonstrates the importance of a practical subsetting strategy for the continued improvement of SOM prediction with Vis-NIR spectroscopy.

Project description:Background and Aims:How functional traits vary with environmental conditions is of fundamental importance in trait-based community ecology. However, how intraspecific variability in functional traits is connected to species distribution is not well understood. This study investigated inter- and intraspecific variation of a key functional trait, i.e. specific leaf area (leaf area per unit dry mass; SLA), in relation to soil factors and tested if trait variation is more closely associated with specific environmental regimes for low-variability species than for high-variability species. Methods:In a subtropical evergreen forest plot (50 ha, southern China), 106 700 leaves from 5335 individuals of 207 woody species were intensively collected, with 30 individuals sampled for most species to ensure a sufficient sample size representative of intraspecific variability. Soil conditions for each plant were estimated by kriging from more than 1700 observational soil locations across the plot. Intra- and interspecific variation in SLA were separately related to environmental factors. Based on the species-specific variation of SLA, species were categorized into three groups: low-, intermediate- and high-intraspecific variability. Intraspecific habitat ranges and the strength of SLA-habitat relationships were compared among these three groups. Key Results:Interspecific variation in SLA overrides the intraspecific variation (77 % vs. 8 %). Total soil nitrogen (TN, positively) and total organic carbon (TOC, negatively) are the most important explanatory factors for SLA variation at both intra- and interspecific levels. SLA, both within and between species, decreases with decreasing soil nitrogen availability. As predicted, species with low intraspecific variability in SLA have narrower habitat ranges with respect to soil TOC and TN and show a stronger SLA-habitat association than high-variability species. Conclusions:For woody plants low SLA is a phenotypic and probably adaptive response to nitrogen stress, which drives the predominance of species with ever-decreasing SLA towards less fertile habitats. Intraspecific variability in SLA is positively connected to species' niche breadth, suggesting that low-variability species may play a more deterministic role in structuring plant assemblages than high-variability species. This study highlights the importance of quantifying intraspecific trait variation to improve our understanding of species distributions across a vegetated landscape.

Dataset Information

Is leaf dry matter content a better predictor of soil fertility than specific leaf area?

Background and aims

Methods

Key results

Conclusions

Publications

Is leaf dry matter content a better predictor of soil fertility than specific leaf area?

Similar Datasets

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