Identification of Distinct Functional Microstructural Domains

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Identification of distinct functional microstructural domains controlling C storage in soil Markus Steffens, Derek M Rogge, Carsten W. Mueller, Carmen Höschen, Johann Lugmeier, Angelika Kölbl, and Ingrid Kogel-Knabner Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b03715 • Publication Date (Web): 28 Sep 2017 Downloaded from http://pubs.acs.org on September 28, 2017

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Environmental Science & Technology

TOC/graphical abstract

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Identification of distinct functional microstructural domains controlling C storage in soil

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Markus Steffens1,2*, Derek Rogge3, Carsten W. Mueller1, Carmen Höschen1, Johann Lugmeier1, Angelika Kölbl1, Ingrid Kögel-Knabner1,4

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Lehrstuhl für Bodenkunde, Wissenschaftszentrum Weihenstephan, Technische Universität München, Emil-Ramann-Str. 2, D-85354 Freising, Germany

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*[email protected]

Department of Soil Sciences, Research Institute of Organic Agriculture (FiBL), Ackerstrasse 113, CH-5070 Frick, Switzerland Applied spectroscopy group, Deutsche Forschungsanstalt für Luft- und Raumfahrt Oberpfaffenhofen, D-82234 Wessling, Germany Institute of Advanced Study, Technische Universität München, Lichtenbergstraße 2a, D85748 Garching, Germany

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The physical, chemical, and biological processes forming the backbone of important soil

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functions (e.g. carbon sequestration, nutrient and contaminant storage, and water transport)

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take place at reactive interfaces of soil particles and pores. The accessibility of these

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interfaces is determined by the spatial arrangement of the solid mineral and organic soil

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components, and the resulting pore system. Despite the development and application of novel

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imaging techniques operating at the micro- and even nanometer scale, the microstructure of

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soils is still considered as a random arrangement of mineral and organic components. Using

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nanoscale secondary ion mass spectroscopy (NanoSIMS) and a novel digital image processing

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routine adapted from remote sensing (consisting of image preprocessing, endmember

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extraction and a supervised classification), we extensively analyzed the spatial distribution of

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secondary ions characteristic for mineral and organic soil components on the sub-micrometer-

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scale in an intact soil aggregate (40 measurements, each covering 30×30 µm2 with a lateral

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resolution of 100×100 nm). We were surprised that the 40 spatially independent

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measurements clustered in just two, complimentary types of micrometer-sized domains. Each

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domain is characterized by a micro-architecture built of a definite mineral assemblage with


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various organic matter forms and a specific pore system, each fulfilling different functions in

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soil. Our results demonstrate that these micro-architectures form due to self-organization of

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the manifold mineral and organic soil components to distinct mineral assemblages, which are

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in turn stabilized by biophysical feedback mechanisms acting through pore characteristics and

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microbial accessibility. These microdomains are the smallest units in soil that fulfill specific

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functionalities.



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Introduction

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Soil functions are an essential component of ecosystem services, and cover complex processes

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as provision of water and nutrients for plant production, purification of water and air, and

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regulation of the global climate via carbon sequestration.1, 2 These functions emerge from the

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soil architecture - the three-dimensional spatial arrangement of a soil’s manifold mineral and

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organic components, and pores. This architecture determines the reaction space for all basic

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physical, chemical, and biological processes3 that are the backbone of soil functions. Up to

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now soil functions are deduced from studies investigating either properties of isolated mineral

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and organic soil components4, or the pore system with aggregates as a key unit of soil

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structure5. However, to mechanistically understand how soil functions emerge from these

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components and their spatial arrangement, it is necessary to study the interplay between solid

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components and the pore system within intact soil samples5-10. Prevailing concepts postulate

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that different soil minerals like phyllosilicates and oxy-hydroxides form definite spatial

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arrangements with distinct organic components on the micrometer scale11-13. Until today,

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direct evidence for these concepts is missing. Contrastingly, current spectromicroscopic

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investigations describe soil microstructures as heterogeneous mixtures of mineral and organic

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components in random arrangement14-16. In order to elucidate the spatial heterogeneity of

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microstructures in an intact soil macroaggregate, we extensively investigated the spatial

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arrangement of mineral and organic components as well as pores within a millimeter-sized

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intact soil aggregate using nanoscale secondary ion mass spectroscopy (NanoSIMS; Figure 1).

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To account for the spatial heterogeneity of the soil aggregate, we recorded 40 images (each

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30 µm × 30 µm) in a regular grid on an intact soil aggregate. We combined digital image

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processing, supervised classification and cluster analyses in a new image data analyses routine

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in order to synoptically analyze the multivariate information in this large, spatially resolved

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data set. Using this routine, we were able to group the various spatial arrangements of the 40

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images in three distinct, recurring types of micro-architectures. These micro-architectures ACS Paragon Plus Environment

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showed clearly different soil properties like local pore size distribution, microbial

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accessibility, and sorption potential and could be identified as either storage or transport

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domain. Here we show that the mineral composition of the mineral grains in combination with

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the organic matter are the nucleus for the formation of distinct micro-architectures that allow

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specific functionalities in soils - functional microdomains.

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Materials and Methods

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The sampling site is located at the Research Station Scheyern of the Technische Universität

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München, 40 km north of München in southern Germany (445-498 m above sea level), with a

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mean annual temperature of 7.4 °C and a mean annual precipitation of 803 mm. The intact

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soil macroaggregate was taken from the Ap-horizon (0-20 cm) of a conventionally managed,

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loamy Cambisol (27% sand, 52% silt, 21% clay), derived from coarse and fine-grained

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tertiary sediments covered by Quaternary Loess17 - a soil material typical and representative

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for many agriculturally managed areas in Europe. Mineralogy of the bulk material and the

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clay-sized fraction was assessed by X-ray diffraction (XRD). Prior to XRD analyses, the

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samples were treated with hydrogen peroxide to remove organic matter (H2O2). XRD was

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performed on random powder samples and on oriented samples (Co-Kα; Philips

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diffractometer PW 1830) after saturation with Ca2+ and glycerol (room temperature) and K+

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(room temperature and stepwise heated to 560°). The bulk sample consists of 50% quartz,

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30% micas, and 20% feldspars, while the clay-sized fraction contained 70% illite, 20%

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smectite, and 10% kaolinite.

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The millimeter-sized, air-dried intact soil aggregate was embedded using epoxy resin

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(Araldite kit 502, electron microscope sciences, Hatfield, USA). For larger specimen as the

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analyzed macroaggregate (>10 mm in diameter) consisting of a prominent amount of quartz

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grains, the chemical embedding and sectioning approach is a secure approach to obtain intact

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microscale structures with no distortion due to drying18. We used an epoxy resin that ACS Paragon Plus Environment


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demonstrates a unique matrix effect with clearly distinguishable differences between resin and

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organic matter derived signals in the NanoSIMS19. The embedded sample was cured for 48

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hours at 60°C, cut, and subsequently polished in order to obtain a sample surface with low

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topography20. Prior to NanoSIMS analysis, the sample was investigated using a reflectance

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light microscope (Axio Imager Z2m, Zeiss, Germany). We designed a regular, staggered

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sampling grid with 400 µm sampling distance yielding 40 locations for NanoSIMS imaging

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(Figure 1). In total 1.2% of the polished sample surface were analyzed with NanoSIMS.

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NanoSIMS measurements were carried out with a NanoSIMS 50L instrument (Cameca,

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Gennevilliers, France) at the Lehrstuhl für Bodenkunde, Technische Universität München,

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Freising, Germany18. To compensate for charging on the non-conductive mineral soil

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particles, we sputter-coated (SCD 005 sputter coater, Bal-Tec GmbH, Germany) our sample

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with a conductive layer of Au/Pd (∼30 nm) prior to analysis and used the electron ﬂood gun

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during the NanoSIMS measurements. The Cs+ primary beam was used at an approximate

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lateral resolution of 100 nm. The primary beam current was ∼1-2 pA, with 2 ms pix-1 dwell

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time, and 5 planes were recorded. The size of each image was 30 µm × 30 µm at 512 × 512

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pixels. In preparation, an area larger than the spot to be measured was pre-sputtered with a

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high primary beam current (∼500 pA). This eliminated the Au-coating layer, cleaned the

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sample of air adsorbates, and promoted ionization of the elements by Cs implantation. When

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signal equilibrium was reached, the NanoSIMS measurement was initiated. Electron

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multiplier secondary ion collectors were used for

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organic components. Nitrogen does not ionize, and was detected as

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secondary ions

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components. The secondary ions are retrieved from the sample surface with a depth resolution

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of approximately 10 nm. We calibrated the machine using a Si grid sample and regularly

Fe16O-. The

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C- and

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Si-,

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C-,

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O-,

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C14N-,

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Si-,

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Al16O- and

12 14

C N- secondary ions were recorded to monitor the distribution of

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Al16O-, and

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12 14

C N- cluster ion. The

Fe16O- were recorded as representative of the mineral


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checked for optimal beam centering using the Cameca integrated tuning software. We

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suppose that ratios between secondary ions remain constant for one matrix if measured at

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different locations or dates, even if the absolute counts can vary.

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To allow a direct comparison of a large number of NanoSIMS images measured at several

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days and different sample locations, we developed a new routine in IDL/ENVI 5.2 (Exelis

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Visual Information Solutions, Boulder, Colorado, USA) for NanoSIMS image analyses. The

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key advantage of this routine for NanoSIMS image analysis is the ability to evaluate the

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whole data set simultaneously across all measured secondary ions at all measured locations.

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This routine makes it possible to sort every pixel into a class with a similar or comparable

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“mass signature” and map these classes across all images. It is an important prerequisite for

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the synoptical analyses of spectromicroscopic data in geoscience. In order to analyse the

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whole data set simultaneously we first need to concatenate the 40 images, each containing the

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images of six secondary ions, to produce an L×S×E image cube (L = number of lines; S =

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number of samples; E = number of ion species) where each pixel of the contiguous image

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represents a vector, or mass signature, comprising all measured secondary ions. This image

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cube being analogous to a reflectance image in imaging spectroscopy comprising spectral

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measurements at specific wavelengths, either contiguous or not, resulting in each pixel having

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a reflectance profile. With such an image cube array it is now possible to analyse all pixels as

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vectors comprising all measured secondary ions.

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The developed routine consists of three main steps: Step 1 is the data preparation including

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filtering to reduce random noise and intra-class variability, and normalization to allow for

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transferability across different measurement dates or locations. Data preparation is followed

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by an image-based endmember extraction (step 2) and a supervised classification of the whole

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image using a spectral angle approach on the extracted endmembers (step 3).



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The high sensitivity (ppm to ppb range) of the NanoSIMS can lead to an imaging “noise” in

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the recorded measurements that can hamper image segmentation. Reduction of this noise can

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be applied per ion-species image using simple approaches such as median or mean filtering.

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In this study we make use of the iterative adaptive spatial-spectral filter (IAS filter) that is

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designed to reduce random noise and intra-class variability, while at the same time being

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effective at retaining edges and boundaries across materials (step 1). The advantage of this

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particular filter is that it smooths the image data by considering the local spatial variance of

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each pixel compared to its surroundings using the vector based standard spectral similarity

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measures spectral angle21 and root-mean-squared error. The IAS filter is a simplified variant

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of a vector based anisotropic diffusion approach22 and is characterized by minimal

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algorithmic complexity and computational load.

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Once filtering is complete the next data preparation step is to apply a per-pixel spectral mean

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normalization23 to normalize varying count rates between the six secondary ion

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measurements. The result of this normalization is to retain the ratios between secondary ions,

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while removing overall amplitude. The approach allows for transferability across the different

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measurement locations and is vital for the synoptical analysis of the six secondary ion images.

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A mean normalized secondary ion-profile is calculated per-pixel with the following equation:

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, =

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where c is the ion species identifier, x,y is the pixel vector location in the image, Nx,y(c) is the

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normalized secondary ion profile for a specific pixel, Rx,y(c) is the original secondary ion

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profile, and Mx,y is the mean value across all secondary ions for the pixel. By applying this

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normalization, the ratio of all secondary ions to each other is kept constant for one pixel but

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the absolute count numbers are normalized and made comparable between different pixels

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and therefore between matrices and measurement locations. In this step we suppose that the

, ,

(1)


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absolute counts vary between sampling locations, matrices and sampling dates but the ratios

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between the measured secondary ions remain constant (Figure S5, S6, and S7).

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Endmember extraction (step 2) is an approach commonly used in remote sensing to extract

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pure spectra of unique materials24. We use the approach to identify pixels, which are

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characterised by a unique mass signature, so called endmembers from the full L×S×E image

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cube and use their signature for further steps. These endmembers define the extreme points

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(vertices) of the six-dimensional data space (simplex) spanned by the six analysed secondary

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ions, and all other pixels are considered mixtures of these few identified endmembers. In this

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study we use the Spatial Spectral Endmember Extraction (SSEE) method25 which is

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commonly used and established in the analysis of hyperspectral remote sensing imagery. The

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advantage of this specific approach over many other endmember extraction methods is its

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ability to use both spectral and spatial information to define unique signatures, resulting in a

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larger set of potential endmembers that may include spectrally similar, but spatially

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independent materials. This approach allows for a good representation of the intra-class

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variability within a given class type and allows for expert knowledge to be integrated in the

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selection process to select a final endmember set appropriate for a specific application. We

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identified 30 endmembers with unique mass signatures in the 40 NanoSIMS images (Figure

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S7). We analysed the likeliness of the mass signatures of the 30 endmembers using a

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hierarchical cluster analysis based on the squared Euclidean distance (SPSS 23; IBM SPSS

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Statistics for Windows, Armonk, NY). This analysis in combination with the reference spectra

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from test data sets 1 and 2 showed that the 30 endmembers could be clustered into 10

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compound groups with comparable mass signatures (Figure S9, table S3).

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For the classification of the 11,010,048 pixels in the NanoSIMS images into these 30 mass

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signature classes we used the spectral angle mapper (SAM) algorithm26 to assign each pixel to

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one of the unique endmember classes (step 3; Figure S8). SAM is a nonparametric classifier



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that determines the signature similarity between each pixel’s signature with the set of

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endmembers. Each pixel is assigned to the endmember class with the highest similarity based

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on the smallest angle to the reference signature. In this study, we use the ion profile

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endmembers derived from SSEE as our reference to classify the image cube. The angle (ө)

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between two ion-signatures si = si1, …, siC and sj = sj1, …, sjC, where c is the number of bands

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(or ion-species), is calculated as:

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, = ө , =

∑!" # "⁄% "⁄% % %) $∑!" & (∑!" #

* (2)

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Based on the contribution of these 30 mass signature classes to each image, we clustered the

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40 images in three types of micro-architectures using a second cluster analyses based on their

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squared Euclidean distance (Figure S10, table S4).

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We used two test data sets with increasing spatial complexity to evaluate the power of our

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NanoSIMS image analyses routine. Test data set 1: We applied the technique on seven pure

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standard materials representing pedogenic aluminium and iron oxides, and phyllosilicates in

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order to evaluate the power of our routine to identify homogeneous and pure minerals. These

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materials were measured one by one so that no mixing of materials could hamper their

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identification. The iron oxide goethite was synthesized following the procedure of

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Schwertmann & Cornell27 in three varieties with increasing Al substitution (Figure S1 a-c).

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Additionally we purchased four commercial minerals – Fe oxide Goethite (‘Bayferrox 920’;

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Lanxess Deutschland GmbH, Leverkusen, Germany), Al oxide Boehmite (‘APYRAL AOH

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20’; Nabaltec AG, Schwandorf, Germany), and the phyllosilicates Illite (Inter-ILI Engineering

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Co. Ltd., Kosd, Hungary) and Montmorillonite (‘Ceratosil® WG’, Süd-Chemie AG,

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Moosburg, Germany). All standard materials were attached to adhesive carbon foil

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(polycarbonate graphite mixture; PLANO Leit-Tabs; Plano, Marburg, Germany) and ACS Paragon Plus Environment

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measured individually on the NanoSIMS 50L at the Lehrstuhl für Bodenkunde at the TU

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München. All seven measurements were conducted under the same settings as the main

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experiment and the following six masses were recorded:

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adhesive carbon film, and 16O, 28Si, 27Al16O, 56Fe16O, and 56Fe16O2 for the identification of the

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different mineral species (Figures S1). In test data set 1, the routine identified the seven pure

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mineral grains on the adhesive carbon film and clearly discriminated the different mineral

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species (Figures S1 and S2, table S1). Even the increasing Al substitution in the synthesized

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goethite minerals could be visualized with this digital image processing routine.

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Test data set 2: We prepared an artificial soil to evaluate the power of our routine to identify

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pure materials in more complex soil-like mixtures, and especially to test the discrimination

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between organic matter and resin. We built the artificial soil by mixing the two pure minerals

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Quartz (Carl Roth, Karlsruhe, Germany) and Illite (Inter-ILI Engineering Co. Ltd., Kosd,

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Hungary) with organic matter (needle from Norway Spruce (Picea abies L. Karst)), and

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embedded this mixture in resin (Araldite kit 502, electron microscope sciences, Hatfield,

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USA). The embedded sample (10 mm in diameter) was cured for 48 hours at 60°C, cut, and

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subsequently polished in order to obtain a sample surface with low topography20. Prior to

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NanoSIMS analysis, the sample was investigated using a reflectance light microscope (Axio

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Imager Z2m, Zeiss, Germany) in order to identify 18 locations for the more detailed

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NanoSIMS measurements. We used the same settings as the main experiment and the

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following six masses were recorded: 12C and 12C14N for the identification of resin and organic

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matter, and

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species (Figures S3). In test data set 2, we could show that the discrimination of different

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mineral species is possible in artificial and resin-embedded soil mixtures, too (Figures S3 and

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S4, table S2). Furthermore, this test data set showed that resin and organic matter can be

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discriminated based on their unique mass signatures, even in very complex cases in which

16

O,

28

Si,

27

Al16O, and

56

12

C for the identification of the

Fe16O for the identification of the different mineral



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resin infiltrated the internal structure of the spruce needle and lead to pixels with a mixed

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mass signature showing characteristics of both organic matter and resin.

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Results and discussion

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By using digital image processing consisting of data preparation, endmember extraction, and

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supervised classification, we were able to analyze all 40 images in one approach. We were

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surprised that these 40 images did not show various spatial arrangements14, 15, but clustered in

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just three distinct, recurring types of micro-architectures (Figure 2, table 1, figure S8). These

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three types of micro-architectures can be distinguished by their specific mineral composition

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(Figure S11), quantity and quality of organic matter, and their pore system. Our findings show

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for the first time that mineral grains differing in size and mineral composition are not

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randomly distributed in the soil matrix14-16 but are arranged in well-defined mineral

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assemblages. Therefore, we define the combination of specific mineral assemblages with

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different quantities and qualities of organic matter as arranged in distinct micro-architectures

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as microdomains.

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Microdomain A is characterized by weathering-resistant, coarse, Si-rich mineral grains (mass

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signature classes 23-27; figure S7), glued together by thin layers of phyllosilicates (mass

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signatures classes 15-22; for the separation of different types of phyllosilicates see figures S1

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and S2 for NanoSIMS, and S11 for SEM-EDX experiments). This microdomain is low in

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organic matter (mass signature classes 5 and 6), but high in porosity (mass signature classes

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1-4; Figure 3, table 1). Microdomain B contains coarse, Si-rich mineral grains in a dense

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matrix of phyllosilicates, and shows low porosity and intermediate amounts of organic matter.

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In contrast to Microdomain A, here the coarse grains are connected to the dense phyllosilicate

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matrix via thin Fe oxide coatings (mass signature classes 28-30). Microdomain C has low

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porosity as it is built up by densely arranged Al-rich phyllosilicates (mass signature classes 7-

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10) in association with high proportions of organic matter. Single, coarse, Al-rich mineral ACS Paragon Plus Environment

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grains (mass signature classes 11-14) remain as parent minerals embedded in the

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phyllosilicate matrix. Overall, we classified 19 out of 40 images as Microdomain A and 13 as

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Microdomain C. Four images showed the characteristics of Microdomain B and four images

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were taken on large pure mineral grains without any micro-architecture (Table S4). It is

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important to note that the hierarchical cluster analyses showed a clear discrimination between

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the three microdomains and no smooth transition from one to the other (Figure S10).

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We assume these microdomains with their unique micro-architectures to be the result of their

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specific mineral assemblages that in turn induced different feedback mechanisms leading to

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the formation and stabilization of the respective microdomain28. Large, Si-rich mineral grains,

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most likely quartz crystals, are the main mineral components of microdomain A. These

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smooth grains are weathering slowly and produce little, Si-rich phyllosilicates that bind the

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coarse mineral grains into the soil structure via mineral-mineral interactions. Here, attractive

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forces of physical origin (water menisci, swelling pressure) pull the particles together29 and

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stabilize this porous micro-architecture. Binding via organic matter plays a minor role due to

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low amounts and the low sorptive capacities of the mineral grains13. In Microdomain C, we

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assume coarse, Al-rich mineral grains to have higher weathering rates producing plenty of Al-

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rich phyllosilicates (Table 1 and figure 2) with high isomorphic substitution, surface charge,

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and sorption capacity. This results in a microstructure dominated by a phyllosilicate matrix

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that promotes accumulation of organic matter28, which in turn increases the association to

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other reactive mineral surfaces and results in a dense packing with low porosity29, 30. This low

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accessibility hampers the decomposition of the organic matter, keeping the structure intact,

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and the organic matter protected. In Microdomain B similar mechanisms are active as in

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Microdomain C with the exception that coarse, Si-rich mineral grains are embedded in the

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dense phyllosilicate matrix. This is possible because thin Fe oxide coatings add sorption

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potential to the otherwise low sorptive capacity of the Si-rich mineral grains and enable the

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binding of these minerals in the phyllosilicate matrix31. The different feedback mechanisms ACS Paragon Plus Environment


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described above keep the three microdomains stable and support the concept of soil self-

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organization forwarded by Young and Crawford32, and Smucker et al.33. Going beyond this

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assessment of organic and mineral soil components and their interactions, it is now possible to

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link their spatial arrangement with distinct pore characteristics.

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The three microdomains are characterized by different pore systems, and in consequence

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contribute differently to important soil functions, i.e. carbon sequestration, microbial habitat,

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nutrient and contaminant exchange and storage, and water transport (Table 1). The pore

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system of Microdomain A is characterized by large pore sizes between 0.2 and 14.7 µm

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(mean = 7 µm), and high pore volumes (Figure 3). It allows the fast and advective transport

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and exchange of water, nutrients, contaminants, and dissolved organic matter. We assume that

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this pore system provides the living space for soil microorganisms7, 16, and its soil solution to

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be available to plants. We did not see particulate organic matter, indicative for fresh or partly

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decomposed litter material (Table 1, figure 2, and figure S8). We assume the particulate

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organic matter in Microdomain A to be easily accessible and thus prone to microbial

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degradation8, 34. Organic matter is found only in thin layers intimately associated with the

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surfaces of phyllosilicates and may in this configuration be protected from degradation35-37.

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Overall, the carbon sequestration in this microdomain is comparably low (Figure 3 and

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table 1). We define microdomain A as the supply domain, holding low but readily available

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amounts of water, nutrients, contaminants, and living biomass. In contrast, the microdomains

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B and C are characterized by small pore sizes (0.2 to 7.2 µm, mean = 2 µm) and a generally

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low porosity (Figure 3). The accessibility of the phyllosilicate surfaces with their high

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sorption capacity is limited for nutrients, contaminants, and dissolved organic matter because

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of restriction to diffusive transport38. Therefore, their exchange and cycling can be considered

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as strongly retarded. The small pore sizes also hamper the access of microorganisms39, 40. This

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reduced microbial accessibility promotes the accumulation of both, particulate and mineral


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bound organic matter. We define the microdomains B and C as the storage domains with high

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but not readily available amounts of organic matter, nutrients and contaminants.

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We were surprised to find recurring spatial arrangements of mineral and organic components,

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and pores in 40 spatially independent NanoSIMS images on an intact soil aggregate.

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Moreover, these micro-architectures were always associated with the same specific mineral

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assemblages, characteristic amounts and forms of organic matter, and distinct pore systems.

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Based on the extensive measurements we were able to statistically cluster these manifold

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components in just three clearly different microdomains based on the contribution of the

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different mineral and organic components. We assume that self-organization and feedback

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mechanisms enable their development and keep these microdomains stable. It is striking that

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these microdomains are associated with clearly different functions (Table 1). We suppose

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these microdomains as the smallest spatial units of a soil that fulfil specific functions. Based

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on these findings we propose these functional microdomains to be an adequate level for

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generalization in soil modelling. Functional microdomains are the threshold where basic

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physical, chemical, and biological properties reach a maximum level of complexity. On the

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scale of functional microdomains, these basic properties can be standardized and used as

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characteristic for a given type of microdomain. We suggest functional microdomains to be the

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smallest building block of soil functionality and to define the lowest level of generalization as

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proposed by Vereecken et al.9. We assume that the contribution of the different types of

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functional microdomains is different in other soil materials and their variability represents the

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variety of soil functionality. However, the surprising results steming from the extensive

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measurement and the applied statistical methods used for their identification encourage us to

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propose these functional microdomains as the smallest functional unit of soils and as an

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important spatial scale in soil structure. We call for more studies on this scale that combine

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different disciplines and data analyses techniques in order to proof or challenge our findings

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of functional microdomains and their underlying micro-architectures. Our findings do not ACS Paragon Plus Environment


336

redundantise studies on the smaller, molecular or coarser, aggregate-sized scales. We want to

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set the threshold where generalization of soil materials can start. The characterization of

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physico-chemical processes on the one and the understanding of the temporal more dynamic

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aggregate-scale processes are vital for the understanding of soil systems. The functional

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microdomains define the threshold where basic physico-chemical processes become functions

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and where heterogeneity of soil functionality begins.


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Acknowledgements

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We are

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Forschungsgemeinschaft (KO 1035/38-1). We kindly acknowledge financial support by the

463

Deutsche Forschungsgemeinschaft within the framework of the research unit “MAD Soil -

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Microaggregates: Formation and turnover of the structural building blocks of soils” (DFG RU

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2179) through project KO 1035/48-1. We thank Lars R. Zimmermann for designing figure 1,

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Stefan Elgeti for SEM-EDX measurements, and Werner Häusler for XRD measurements.

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Correspondence and requests should be addressed to M.S. Supplementary Information

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accompanies this paper.

grateful for funding

of

the

NanoSIMS instrument by


the

Deutsche

Identification of Distinct Functional Microstructural Domains

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