Understanding carbonate factories through palaeoecological and sedimentological signals – Tribute to Luis Pomar

The carbonate factories, their controlling factors and their palaeoecological and sedimentological signals recorded in sedimentary successions are key elements for understanding the evolution of carbonate platform systems. Luis Pomar has dedicated most of his academic life to the study of carbonate rocks and carbonate factories. The idea for this special issue to celebrate Pomar’s career arose during a session at the 34^th International Association of Sedimentologists meeting held in Rome, entitled ‘Understanding carbonate factories through palaeoecological and geochemical signals’. The proposal encountered great response among participants, and additional contributions followed an email invitation to other specialists. This issue contains a variety of papers on carbonate sedimentology and carbonate factories. Here, an introduction that contextualizes the papers and key concepts discussed in this thematic issue is provided. It reviews Luis Pomar’s major achievements in carbonate sedimentology and discusses the evolution of the concept of the carbonate factory and the series of palaeoecological and sedimentological signals used to characterize the wide spectrum of carbonate depositional systems found in the natural world.     

Quantifying the variability and allocation patterns of aboveground carbon stocks across plantation forest types,structural attributes and age in sub-tropical coastal region of KwaZulu Natal,South Africa using remote sensing

Quantifying the variability and allocation patterns of aboveground carbon stocks across plantation forests is central in deriving accurate and reliable knowledge and understanding of the extent to which these species contribute to the global carbon cycle and towards minimizing climate change effects. The principal objective of this study was to quantify the variability and allocation patterns of aboveground carbon stocks across Pinus and Eucalyptus plantation forests, tree-structural attributes (i.e. stems, barks, branches and leaves) and age groups, using models developed based on remotely sensed data. The results of this study demonstrate that aboveground carbon stocks significantly (α = 0.05) vary across different plantation forest species types, structural attributes and age. Pinus taeda and Eucalyptus grandis species contained aboveground carbon stocks above 110 t C ha⁻¹, and Eucalyptus dunii had 20 t C ha⁻¹. Across plantation forest tree structural attributes, stems contained the highest aboveground carbon stocks, when compared to barks, branches and leaves. Aboveground carbon stock estimates also varied significantly (α = 0.05) with stand age. Mature plantation forest species (i.e. between 7 and 20 years) contained the highest aboveground carbon stock estimates of approximately 120 t C ha⁻¹, when compared to younger species (i.e. between 3 and 6 years), which had approximately 20 t C ha⁻¹. The map of aboveground carbon stocks showed distinct spatial patterns across the entire study area. The findings of this study are important for understanding the contribution of different plantation forest species, structural attributes and age in the global carbon cycle and possible climate change moderation measures. Also, this study demonstrates that data on vital tree structural attributes, previously difficult to obtain, can now be easily derived from cheap and readily-available satellite data for inventorying carbon stocks variability.     

Geochemical constraints on the contribution of Louisville seamount materials to magmagenesis in the Lau back-arc basin,SW Pacific

Seamounts are an integral part of element recycling in global subduction zones. The published trace element and Pb-Sr-Nd isotope data for basaltic lavas from three key segments (Central Lau Spreading Ridge (CLSR), Eastern Lau Spreading Ridge (ELSR), and Valu Fa Ridge (VFR)) of the Lau back-arc basin were compiled to evaluate the contribution of Louisville seamount materials to their magma genesis. Two geochemical transitions, separating three provinces with distinct geochemical characteristics independent of ridge segmentation, were identified based on abrupt geochemical shifts. The origin of the geochemical transitions was determined to be the result of drastic compositional changes of subduction components added into the mantle source, rather than the transition from Indian to Pacific mid-ocean ridge basalt (MORB) mantle, or due to variable mantle fertilities. The most likely explanation for the drastic shifts in subduction input is the superimposition of Louisville materials on ‘normal’ subduction components consisting predominantly of aqueous fluids liberated from the down-going altered oceanic crust and minor pelagic sediment melts. Quantitative estimation reveals that Louisville materials contributed 0–74% and 21–83% of the Th budget, respectively, to CLSR and VFR lavas, but had no definite contribution to the lavas from the ELSR, which lies farthest away from the subducted Louisville seamount chain (LSC). The spatial association of the subducted LSC with the Louisville-affected segments suggests that the Louisville signature is regionally but not locally available in the Tonga subduction zone. Besides, the preferential melting of subducted old Cretaceous LSC crust instead of the old normal Pacific oceanic crust at similar depths implies that elevated temperature across the subduction interface or seamount erosion and rupture were required to trigger melting. A wider implication of this study, thus, is that seamount subduction may promote efficiency of element recycling in subduction zones.     

Ensemble data assimilation in a simple coupled climate model: The role of ocean-atmosphere interaction

A conceptual coupled ocean-atmosphere model was used to study coupled ensemble data assimilation schemes with a focus on the role of ocean-atmosphere interaction in the assimilation. The optimal scheme was the fully coupled data assimilation scheme that employs the coupled covariance matrix and assimilates observations in both the atmosphere and ocean. The assimilation of synoptic atmospheric variability that captures the temporal fluctuation of the weather noise was found to be critical for the estimation of not only the atmospheric, but also oceanic states. The synoptic atmosphere observation was especially important in the mid-latitude system, where oceanic variability is driven by weather noise. The assimilation of synoptic atmospheric variability in the coupled model improved the atmospheric variability in the analysis and the subsequent forecasts, reducing error in the surface forcing and, in turn, in the ocean state. Atmospheric observation was able to further improve the oceanic state estimation directly through the coupled covariance between the atmosphere and ocean states. Relative to the mid-latitude system, the tropical system was influenced more by ocean-atmosphere interaction and, thus, the assimilation of oceanic observation becomes more important for the estimation of the ocean and atmosphere.     

Polynomial expansions in likelihoods for spatial data: A case study

This paper illustrates the computational benefits of polynomial representations for quantities in the likelihood function for the spatial linear model based on the power covariance scheme. These benefits include a comprehensive study of likelihoods and maximum likelihood estimators for data. For simplicity, we focus on a relatively simple covariance scheme and data observed at equal intervals along a transect; we briefly indicate how generalizations to more complicated covariance functions and higher dimensions will operate.     

THE FREE-WILSON PARADIGM REDUX:SIGNIFICANCE OF THE FREE-WILSON COEFFICIENTS,INSIGNIFICANCE OF COEFFICIENT ‘UNCERTAINTIES’ AND STATISTICAL SINS

The Free-Wilson paradigm is an established and powerful tool for quantitatively relating activity withchemical structure.Current implementations of the paradigm,however,are flawed both conceptually andin execution.As part of an attempt to more fully realize the promise of the paradigm,it was necessaryto examine these limitations in detail.This report introduces a robust,theory-founded Free-Wilson implementation:stepwise principalcomponents regression analysis(SPCRA).SPCRA is computationally superior to previousimplementations but does not in itself correct their conceptual flaws.The development of SPCRA did,however,facilitate derivation of a simple and chemically significantinterpretation of the Free-Wilson structure-activity model.A number of statistical aspects of this modelcommonly misused in previous applications are discussed at length.These discussions provide criticalbackground for the development of an alternative implementation of the Free-Wilson paradigm.     

Detecting subtle hydrochemical anomalies with multivariate statistics: an example from ‘homogeneous’ groundwaters in the Great Artesian Basin,Australia

The major ion composition of Great Artesian Basin groundwater in the lower Namoi River valley is relatively homogeneous in chemical composition. Traditional graphical techniques have been combined with multivariate statistical methods to determine whether subtle differences in the chemical composition of these waters can be delineated. Hierarchical cluster analysis and principal components analysis were successful in delineating minor variations within the groundwaters of the study area that were not visually identified in the graphical techniques applied. Hydrochemical interpretation allowed geochemical processes to be identified in each statistically defined water type and illustrated how these groundwaters differ from one another. Three main geochemical processes were identified in the groundwaters: ion exchange, precipitation, and mixing between waters from different sources. Both statistical methods delineated an anomalous sample suspected of being influenced by magmatic CO₂ input. The use of statistical methods to complement traditional graphical techniques for waters appearing homogeneous is emphasized for all investigations of this type. Copyright © 2006 John Wiley & Sons, Ltd.     

PRINCIPAL COMPONENTS REGRESSION FOR ROUTINE MULTICOMPONENT UV DETERMINATIONS: A VALIDATION PROTOCOL

A validation protocol for multicomponent spectroscopic assays based on principal componentsregression is described. Factorial design and hypothesis tests are used to establish the linearity andabsence of interaction between components in the regression model. Testing considers multiple responsevariables simultaneously so that correlation between residuals is properly treated. Assay reproducibilityand sensitivity to related substances are evaluated.     

MULTIVARIATE RESPONSE SURFACE MODELLING BY PRINCIPAL COMPONENTS ANALYSIS

Analysis of multivariate response data by modelling the principal components of the response has beenapplied to two sets of data. In both cases principal components analysis revealed the relationships amongthe response variables and exploited them to simplify the problem of modelling and optimizing themultivariate response. The models and optima obtained from the principal components comparedfavourably with the individual models and simultaneous optima.