Distribution of tetraether lipids in agricultural soils – 1 differentiation between paddy and upland management 2

16 Rice paddies constitute almost a fifth of global cropland and provide more than half of the 17 world´s population with staple food. At the same time, they are a major source of methane 18 and therewith significantly contribute to the current warming of Earth’s atmosphere. Despite 19 their apparent importance in the cycling of carbon and other elements, however, the 20 microorganisms thriving in rice paddies are insufficiently characterized with respect to their 21 biomolecules. Hardly any information exists on human-induced alteration of biomolecules 22 from natural microbial communities in paddy soils through varying management types 23 (affecting e.g. soil/water and redox conditions, cultivated plants). Here, we determined the 24 influence of different land use types on the distribution of glycerol dialkyl glycerol tetraethers 25 (GDGTs), which serve as molecular indicators for microbial community structures, in rice 26 paddy (periodically flooded) and adjacent upland (non-flooded) soils, and for further 27 comparison forest, bushland and marsh soils.

0. The GDGT-0 / crenarchaeol ratio, indicating the enhanced presence of methanogenic archaea, was 3-27 times higher in paddy soils compared to other soils and increased with the number of rice cultivation cycles per year.The index of tetraethers consisting of 86 carbons (TEX 86 ) values were 1.3 times higher in upland, bushland and forest soils than in paddy soils, potentially due to differences in soil temperature.In all soils brGDGT predominated over iGDGTs with the relative abundance of brGDGTs increasing from subtropical to tropical soils.Higher branched vs. isoprenoid tetraether (BIT) values in paddy soils compared to upland soils together with higher BIT values in soils from subtropical climates indicated effects on the amounts of brGDGT induced by differences in management as well as climate.In acidic soils cyclization ratio of branched tetraethers (CBT) values correlated well with soil pH.In neutral to alkaline soils, however, no correlation but an offset in CBT between paddy and upland managed soils was detected.This is interpreted as indicating soil moisture exerting an additional control on the CBT in these soils.Lower modified methylation index of branched tetraether (MBT ) values and temperatures calculated from this (T MC ) in paddy soils compared to upland soils are attributed to a management-induced (e.g.enhanced soil moisture via flooding) effect on mean annual soil temperature (MST).
It is well known that archaea are involved in biogeochemically important processes, including methanogenesis, anaerobic methane oxidation (AMO) and aerobic ammonia oxidation (Boetius et al., 2000;Leininger et al., 2006;Thauer et al., 2008;Stahl and de la Torre, 2012;Offre et al., 2013).Distributions of isoprenoid GDGTs (iGDGTs) were initially used to characterize archaeal communities in marine environments with two major groups of archaea being distinguished: Thaumarchaeota (formerly recognized as mesophilic Crenarchaeota) and Euryarchaeota (see Pearson and Ingalls, 2013, and reference therein).Ammonia-oxidizing members of the Thaumarchaeota are currently the only known biological sources of crenarchaeol, a GDGT structure that contains four cyclopentane ring systems and an additional cyclohexane ring moiety (Sinninghe Damsté et al., 2002).In addition, Thaumarchaeota contain varying amounts of GDGTs with 0 to 4 cyclopentane rings (Sinninghe Damsté et al., 2012;Schouten et al., 2013;Pearson and Ingalls, 2013).
GDGT-0 is another common tetraether lipid that is present in a majority of archaea (Pearson and Ingalls, 2013;Schouten et al., 2013, and references therein;Villanueva et al., 2014), including for example mesophilic methanogens (Koga et al., 1998;Koga and Morii, 2005;Villanueva et al., 2014;Bauersachs et al., 2015).In addition, the presence of high abundances of GDGT-0 at sites with active AMO suggests a close relationship between microbial consortia involved in the production and consumption of methane (Pancost et al., 2001;Blumenberg et al., 2004;Schouten et al., 2013).In periodically flooded soils (paddy soils), methanogenic lineages, such as Methanosarcinales, Methanocellales, Methanobacteriales and Methanomicrobiales, were found (Liesack et al., 2000;Watanabe et al., 2006Watanabe et al., , 2013) ) at varying abundances in continuously flooded as well as in alternatingly flooded and dried paddy fields (Watanabe et al., 2013).The distribution of methanogens in soils has not yet been extensively studied by using the GDGT-0 vs. crenarchaeol ratio.However, this ratio in conjunction with stable isotope analysis has been applied successfully in soils, sediments and the water column of Lake Rotsee (Naeher et al., 2014) to identify methanogenic conditions.Likewise, Ayari et al. (2013) have shown that in a rice field, where samples were collected before and after flooding, the ratio of GDGT-0 / crenarchaeol increased upon flooding, when methanogenic conditions had been established.
iGDGTs with multiple cyclopentane rings have been reported from anaerobic methanotrophic archaea (ANME) of the ANME-1 cluster as well as Thaumarchaeota and extremophilic Euryarchaeota and Crenarchaeota (Blumenberg et al., 2004;Pearson and Ingalls, 2013;Schouten et al., 2013, and references therein).The presence of iGDGTs has been predominantly investigated in marine, limnic and other aquatic habitats, but they have also been reported from soils.Here, the specific environmental conditions controlling their distribution are less well studied (Weijers et al., 2006b;Leininger et al., 2006;Sinninghe Damsté et al., 2012;Ayari et al., 2013).An improved knowledge of environmental factors influencing iGDGT compositions has been gained from cultivation experiments, which demonstrated that growth temperature, pH and oxygen content affect GDGT synthesis (Wuchter et al., 2004;Elling et al., 2015;Qin et al., 2015).Probably the most commonly used archaeal-based proxy in marine systems is the TEX 86 (tetraether index of Thaumarchaeota-derived tetraethers consisting of 86 carbons), which correlates well with surface water temperatures (Schouten et al., 2002).Culture experiments revealed the effect of increasing temperature to raise the number of cyclopentane rings (Schouten et al., 2013, and references therein).Regional studies on altitudinal mountain transects confirmed a dependency of the iGDGT cyclization on temperatures in soil systems (Liu et al., 2013;Coffinet et al., 2014;Yang et al., 2016), but additional factors, e.g., pH or soil moisture, may influence the archaeal community and therefore the lipid composition found in soils as well (Wang et al., 2013;Xie et al., 2015).
However, factors other than temperature and pH also seem to affect the distribution of brGDGTs in natural ecosystems.For example, the relatively broad scatter of calculated MAT in arid soils (Peterse et al., 2012) as well as values deviating from the trend in the highest elevations of a transect sampled on Mt.Kilimanjaro (Sinninghe Damsté et al., 2008) have been interpreted as indicating an influence of water content and vegetation type on the brGDGT pool.In addition, several authors noted that changes in the distribution of brGDGT are strongly correlated with MAT on local scales as, for example, in altitudinal transects of Mt.Rungwe and Mt.Gongga (Peterse et al., 2009c;Coffinet et al., 2014).In agricultural soils from the same area, the type of soil management and the vegetation cover can differ, leading to variable soil water contents and soil temperatures (Liu et al., 2014;Awe et al., 2015), which affect the local microbial community.Soil microbes respond to environmental stress induced by, e.g., starvation, oxygen limitation or acidification (Frostegård et al., 1993;Aanderud et al., 2015).The last of these results in the predominance of brGDGTs without cyclopentyl moieties in soils and explains the dependency of soil pH and CBT (Weijers et al., 2007).
Besides pH, the redox potential (Eh) is an important factor that affects the diversity and abundance of soil microorganisms.The Eh expresses the activity of electrons which influence microbial metabolic reactions in soils.As individual microorganisms are adapted to specific Eh conditions, an increase in, e.g., soil moisture is accompanied by a decrease in Eh because of the consumption of oxygen by microbes (Husson, 2013).Further parameters, which regulate the Eh are temperature, organic matter content, or soil tillage, the last of these modifying the soil structure and soil aeration (Husson, 2013, and references therein).Agricultural management therefore may contribute to controlling redoximorphic conditions.In contrast to upland soil, i.e. without water flooding and associated crop plants, including corn or maize, wheat, barley, rape, cassava, sugar cane, cotton, banana and various vegetables, rice paddy soil management with repeated puddling of the surface soil as well as frequent flooding and alternating draining practices leads to a reduced Eh in the surface layer (Kögel-Knabner et al., 2010;Kölbl et al., 2014).Prevailing anoxic conditions are assumed to restrict the decomposition rate of organic matter (Lal, 2002;Sahrawat, 2005), leading to high activities of methanogenic archaea (Liesack et al., 2000) and, in combination with the application of mineral fertilizer, to high denitrification rates producing nitrous oxide (Xiong et al., 2007).In contrast, oxic conditions are associated with high Eh, as in upland soil and in paddy soil after draining where ammonia oxidation can occur.The last of these is either performed by ammonia-oxidizing archaea (AOA) or bacteria (AOB) (Leininger et al., 2006) depending on the soil pH, with AOA being more active in acidic soils and AOB in alkaline soils (Jiang et al., 2015).
Rice serves as major staple food for 50 % of the world's population and paddy rice cropland occupies an area of 157 million ha.This is equivalent to 18 % of the agricultural land use area of the 10 major crops worldwide and illustrates the importance of paddy agroecosystem utilization (FAO, 2003).This profound anthropogenic influence on aquatic agroecosystems will dictate their biogeochemical and geomicrobiological properties and processes, which were determined from GDGT distribution and warrant further investigation.Only limited information on microbial assemblages and their activity in paddy soils is currently available (Bannert et al., 2011;Ayari et al., 2013).The study of such agroecosystems is of particular interest for both soil scientists and geochemists for similar reasons, as man-made environmental constraints can be compared to natural ones.To identify the anthropogenically induced ecosystem properties, reflected in microbial community structures, we studied the tetraether lipid composition in soils of different agricultural management systems, which developed in subtropical (Italy, SW China) as well as in tropical (Indonesia, Philippines, Vietnam) climates.In addition to the management type, including differences in cropping style (upland crop plants vs. wetland rice), the intensity of the management and the duration of utilization were distinctive criteria in the investi-gation of effects on the microbial lipids in rice paddy soil (periodically flooded), upland (non-flooded) and forest soils.This study compares non-flooded and flooded agroecosystems of different agricultural uses with respect to their GDGT composition (including GDGT palaeoproxies) to widen our knowledge on the sources and properties of GDGTs in terrestrial agroecosystems on local, regional and global scales.

Sampling
From 2008 to 2014, a total of 170 Indonesian, Vietnamese, Philippine, Chinese and Italian soils with different land use systems were collected, including 119 paddy, 37 upland, 9 forest, 2 bushland and 3 marsh samples from the topsoil horizon (0-30 cm depth).The study sites are located in tropical as well as in subtropical climate zones (Fig. 1, Table 1) and agricultural soils were subject to different management techniques.Detailed soil characteristics and geographical positions of the sampling sites are given in Table S1 (Supplement).Topsoils were sampled with a soil auger as described by Klotzbücher et al. (2014).
In addition, successive land reclamation in the Chinese location Cixi via dyke construction on marine tidal flats over the last > 1000 years (Feng and Bao, 2005) led to differently aged soils, which allows us to study a 2000-year chronosequence.Based on the time of dyke construction and information from the Edit Committee of Chorography of Cixi County (1992), differently aged marsh soils (10-35 years) and agricultural soils under continuous non-irrigated upland use (50-700 years) as well as wetland rice cultivation (50-2000 years) were selected and sampled.The local cropping system on paddy fields is paddy-upland rotation, with one wetland rice season and one intercrop (vegetables, wheat or cereals) season per year (Cheng et al., 2009).Paddy and upland topsoils were sampled with a soil auger.Three composite samples, composed of seven subsamples each (taken in an area of 1 m 2 ) and being representative of the entire field (area of 120 m 2 ), were investigated at each location (for more details, see Mueller-Niggemann et al., 2012).

Bulk geochemistry
All soils were lyophilized, sieved to a size < 2 mm and ground to a fine powder using agate pestle and mortar prior to analysis.Soil pH was measured in a suspension of the soil in 0.01 M CaCl 2 , using a 1 : 2.5 (w/v) soil / liquid ratio.The pH was determined with a pH meter Model FG2-438 (Mettler-Toledo AG, Switzerland) at ambient temperature and atmospheric pressure.The total carbon (TC) and total nitrogen (TN) contents were measured on a CNS elemental analyser Vario EL III (Elementar Analysensysteme GmbH, Germany).The total inorganic carbon (TIC) content was determined using the Vario EL III elemental anal-yser coupled to the SoliTIC module.The soil organic carbon (SOC) was calculated as the difference between TC and TIC.

GDGT preparation and HPLC-MS analysis
Core lipids of iGDGTs and brGDGTs were obtained by automated solvent extraction using an ASE 200 (Dionex, USA) at a temperature of 75 • C and a pressure 5.0 × 10 6 Pa.Each sample was extracted for 20 min using a solvent mixture of dichloromethane (DCM) / MeOH (93 : 7, v/v).The total lipid extracts were separated over an aluminium oxide column into apolar and polar fractions using n-hexane / DCM (9 : 1, v/v) and DCM / MeOH (1 : 1, v/v) as respective eluents.The polar fractions were dried under a gentle stream of N 2 , redissolved in n-hexane / 2-propanol (99 : 1, v/v) and filtered through a 0.45 µm polytetrafluoroethylene (PTFE) filter prior to analysis.
All samples were analysed by atmospheric pressure chemical ionization-mass spectrometry (HPLC-APCI-MS) using an Alliance 2690 HPLC (Waters, UK) and a Quattro LC triple quadrupole mass spectrometer (Micromass, UK) and following the analytical protocol described by Hopmans et al. (2000) and Schouten et al. (2007).Briefly, 10 µL of the filtered polar fractions were injected into an analytical Prevail Cyano column (2.1 × 150 mm, 3 µm particle size; Grace, USA), maintained at a temperature of 30 • C with a constant flow rate of 0.2 mL min −1 .Tetraether lipids were eluted isocratically with 99 % n-hexane and 1 % 2-propanol for 5 min, followed by a linear gradient to 1.8 % 2-propanol in 36 min and subsequently to 10 % 2-propanol in 5 min, after which the system was held isocratically for 5 min.The column was re-equilibrated with 99 % n-hexane and 1 % 2-propanol for 12 min before the next injection.The MS was operated as outlined in Heyng et al. (2015), with isoprenoid and branched GDGTs being detected in the selected ion recording (SIR) mode of their protonated molecules [M+H] + .

Calculation of GDGT indices
Abbreviations in the equations below refer to GDGTs displayed in Appendix Fig. A1.The relationship between the cyclopentane-ring-containing iGDGTs (GDGT-1 to GDGT-3 vs. the crenarchaeol regioisomer) was used to calculate the TEX 86 as described by Schouten et al. (2002): The CBT was calculated using the relative abundance of tetra-and penta-methylated brGDGT according to Weijers et al. (2007): (2) The MBT index was calculated as given in Peterse et al. (2012): Table 1.List of sampling areas, environmental characteristics (mean annual air temperature (MAT), mean annual precipitation (MAP), soil organic carbon (SOC)) and minimum as well as maximum of GDGT proportions (expressed as a percentage of total GDGTs or as indices).(4) The branched and isoprenoid tetraether (BIT) index was determined as given in Hopmans et al. ( 2004): BIT = (Ia + IIa + IIIa)/(Ia + IIa + IIIa + Cren). (5)

Statistical analysis
Statistical analysis was conducted using the PASW Statistics 18 software.Principal component analysis (PCA) was performed on relative abundances of iGDGTs, brGDGTs and the different GDGT-based indices to explore and characterize the variability within the GDGT distribution in differently managed soils.To identify relationships between variables, a correlation analysis was performed.Results were given as r for Pearson's correlation regression coefficient together with the p value (two-tailed test), which is considered to be significant if p is < 0.001.The nonparametric Mann-Whitney U test was used to investigate the significance of differences in soil properties depending on management or geographical locations.Differences are significant if p is < 0.05. 1) varied from 0.4 to 5.0 % with highest contents present in paddy soils from the Philippine Ifugao (5.0 %) and Laguna (4.0 %), the Indonesian Sukabumi (4.4 %) and the Vietnamese Tien Giang (4.4 %) sites.The forest and bushland soils had a mean SOC of 2.7 ± 0.9 % (n = 11), which was higher than in most upland soils (1.6 ± 0.9 %, n = 37).

SOC (Table
The pH ranged from 3.7 in Tien Giang (Vietnam) to 8.2 in Cixi (China; Table 1).In general, no statistically significant differences in pH values were noticed for soils with paddy (5.3 ± 1.0, n = 119) or upland (5.3 ± 1.1, n = 37) management.Forest and bushland soils had the lowest mean pH of 4.5 ± 0.5 (n = 11).Both iGDGT and brGDGT were detected in variable abundances in all soils.The brGDGT / iGDGT ratio was > 80 in Indonesian paddy soils (Jasinga), varied between 20 and 55 in forest and bushland soils, and was between 20 and 1.9 in the remaining soils (Supplement, Fig. S1).The lowest proportion of brGDGT was noted in Italian upland soils and in very young Chinese marsh soils (< 30 years) and upland soils.A specific feature of soils from the Cixi area is their development on tidal wetland sediment.The GDGT signature of these soils was distinct from the one of other soils investigated in this study and represents a mixed signature of the parent substrate (tidal wetland sediments) and the recent soil organic matter (SOM).

Distribution of isoprenoid GDGTs in soils
iGDGTs constitute between 0.9 and 25.7 % (35 % in Cixi soils) of all GDGTs (Table 1), indicating substantial contributions of archaeal lipids to most of the investigated soils.Forest and bushland soils had lowest relative mean abundances of iGDGTs (5.8 ± 2.6 %), followed by tropical paddy (9.3 ± 4.0 %) and upland soils (9.8 ± 6.0 %).The proportion of iGDGTs was highest in Chinese and Italian upland soils (21.1 ± 8.0 %) compared to their adjacent paddy soils and all other remaining soils (13.3 ± 5.0 %).The fact that the iGDGT content was significantly (p < 0.01; Mann-Whitney U test) lower in tropical soils (including from the Philippines, Vietnam, Indonesia; n = 116) compared to subtropical soils (including from China and Italy; n = 51) suggests that the composition of the microbial consortia varies on regional to global scales.In addition, the differentiation between upland and paddy soils with higher amounts of iGDGTs in the former may indicate management-induced (regulating the water regime, nutrient availability, oxygen availability and/or redox conditions) variations of GDGT-containing microorganism.In general, at locations with the same climate and substrate, different management types best explain significantly different GDGT distributions (p < 0.05; Mann-Whitney U test).Regardless of whether paddy, upland or forest management, all soil types differ in their microbial lipid pattern that may be influenced by differing inputs of plant organic matter, differing fertilization practices and redox conditions.The last of these is controlled by flooding and draining practices on paddy soils, which seem to favour growth and input of brGDGT-containing bacteria and/or the improved preservation of fossil brGDGTs compared to the adjacently located aerated upland soils.
iGDGT distribution patterns described from cultured archaea (Koga et al., 1998;Pancost et al., 2001;Blumenberg et al., 2004;Koga and Morii, 2005) and their comparison with soils may provide insights into the archaeal community structure and the biological processes that they mediate (Ayari et al., 2013;Yang et al., 2016).The most abundant iGDGTs in our sample set are GDGT-0 and crenarchaeol.The latter is considered a highly specific biological marker for ammoniaoxidizing Thaumarchaeota (Leininger et al., 2006;Pitcher et al., 2010;Sinninghe Damsté et al., 2012;Pearson and Ingalls, 2013), which, in the form of groups 1.1a, 1.1b, 1.1c and 1.3, have been reported to be present in soils worldwide (Pester et al., 2012;Oton et al., 2016).Differences in the ammoniaoxidizing archaea community composition of group 1.1b Thaumarchaeota in soils may be influenced by climatic conditions, as demonstrated in soils of various geographical origins (Pester et al., 2012).This dependency was not found for the relative abundance of crenarchaeol in soils investigated here using the Mann-Whitney U test.To date, molecular investigations on cultivated Thaumarchaeota report GDGTs only for groups 1.1a and 1.1b (Pitcher et al., 2010(Pitcher et al., , 2011;;Sinninghe Damsté et al., 2012).Sinninghe Damsté et al. (2012) showed that group 1.1a Thaumarchaeota (marine and other environments) and group 1.1b Thaumarchaeota (soils and other environments) can be separated from each other based on the relative abundance of the crenarchaeol regioisomer, with a proportion of the crenarchaeol regioisomer < 5 % being indicative of group 1.1a and > 10-20 % of group 1.1b Thaumarchaeota (Sinninghe Damsté et al., 2012).The same authors observed higher abundances of the crenarchaeol regioisomer in soils rather than in marine or lacustrine environments (Sinninghe Damsté et al., 2012).Crenarchaeol and its regioisomer are present in all analysed soil samples, which is in agreement with a previous study (Weijers et al., 2006b).The amount of crenarchaeol is generally higher in upland soils (46.4 ± 12.9 %, n = 37) compared to adjacent paddy soils (22.5 ± 14.5 %, n = 119; Fig. 2a), possibly suggesting management-induced differences in the archaeal community structure.The abundance of the crenarchaeol regioisomer varies from 3 to 21 % compared to that of crenarchaeol (mean value of 9 ± 4 %, n = 170) and shows no differences between soils and/or management types (Fig. S2).Angel et al. (2012) observed that methanogenic archaea are ubiquitous in soils and are active only in anoxic, highly reducing environments, e.g.under flooded conditions.One distinct feature of paddy soil management vs. management of all other soils is the periodic flooding and draining of soils, which leads to highly variable redox conditions throughout the course of a year (Kögel-Knabner et al., 2010;Kölbl et al., 2014).Paddy soils are known for high methanogenic activity and as significant sources of atmospheric CH 4 (Conrad, 2007;Thauer et al., 2008;Serrano-Silva et al., 2014) with little changes in the methanogenic community structure between flooding events (Krüger et al., 2005;Watanabe et al., 2006Watanabe et al., , 2009)).In turn, this suggests that the overall lipid pool in paddies does not change significantly after draining the fields for harvesting.
Despite GDGT-0 being a common component in many archaea, an elevated ratio of GDGT-0 / crenarchaeol with a threshold > 2 has been used previously to indicate a dominance of methanogenic archaea in a given sedimentary environment.This notion primarily applied to for lake sediments, where the threshold in GDGT-0 / crenarchaeol > 2 has been attributed to methanogenesis occurring under anoxic and organic-matter-rich conditions (Blaga et al., 2009;Naeher et al., 2014).Paddy soils are known to release high amounts of methane during flooding periods (Thauer et al., 2008).Therefore, Ayari et al. (2013) suggested that the 3-to 6-fold increase in the GDGT-0 / crenarchaeol ratio, determined on the intact polar lipid fraction, in paddy soils after flooding is associated with GDGT-0 synthesis by methanogenic Euryarchaeota.We adopted this assumption and compared different kinds of soil management with respect to their iGDGT composition.In the investigated soils, the GDGT-0 / crenarchaeol ratio ranged from 0.1 to 121.6, with highest ratios observed in Philippine and Vietnamese paddy soils (Fig. 2c, Table 1).In oxic upland and forest soils the mean GDGT-0 / crenarchaeol ratio was ≤ 1, which indicates that methanogenic archaea are only a minor component of the microbial community at these sites.In addition, a few paddy soils (e.g.sites in Cixi and in Italy) had GDGT-0 / crenarchaeol ratios comparable to those observed in upland soils, which can be explained by the management form including higher intensities of crop rotation with upland crops under non-flooded conditions on these fields.However, if soils from the same region are compared, the ratio was generally 3-27 times higher in soils which are under paddy    S1.The dashed line in (c) marks the GDGT-0 / crenarchaeol value of 2 that is the boundary to higher proportions of methanogens, which reveal values > 2. Note the logarithmic scale for GDGT-0 / crenarchaeol ratios.Note different symbols (circle or asterisk) for outliers that are more than 1.5 (or 3) box lengths from one hinge of the box.management compared to adjacent upland soils, indicating increased abundances and activity of methanogens in flooded soils.
TEX 86 values from all sites ranged from 0.3 to 0.9 (Fig. 2d, Table 1) without an apparent geographical trend.However, TEX 86 values were on average 1.3 times higher in upland, bushland and forest soils compared to the adjacent paddy soils within the same region.For example, the ratios of upland and paddy soil TEX 86 values were highest in the subtropical locations of Cixi and Italy (∼ 1.5; Table 1).None or only minor differences in TEX 86 values were noted in the Jasinga and Ngawi upland and paddy soils of Indonesia.Because of the relation between the TEX 86 and temperature, one explanation for this difference could be that the periodic water layer on paddy soils may protect the soil surface from excessive heating and therefore results in lower mean annual soil temperatures (MSTs) in both soil types.Previous studies of altitudinal mountain transects support this suggestion, as the soil TEX 86 was negatively correlated with elevation and therefore with decreasing temperatures, e.g. in the Qinghai-Tibetan Plateau (r = −0.81,r 2 = 0.65, p < 0.01; Liu et al., 2013) and Tanzania (r = −0.71,r 2 = 0.50, p < 0.0001; Coffinet et al., 2014).In the soils investigated here, the relative proportion of GDGT-3 and the crenarchaeol regioisomer together with GDGT-1 mainly affected the TEX 86 .Low TEX 86 values, as observed in paddy soils, are the result of high relative abundances of GDGT-1 and low proportions of GDGT-3.This suggests that paddy soil characteristics such as alternating redox conditions and higher water content control the presence of GDGT-1.High contents of cyclopentyl moieties in archaeal membrane lipids are known to be associated with ANME archaea, which synthesize significant quantities of GDGT-1, GDGT-2 and GDGT-3 (Pancost et al., 2001;Blumenberg et al., 2004).Interestingly, two divergent trends in the direction of increased TEX 86 values were observed for GDGT-2 (Fig. 3a), with an increase in the GDGT-2 content to a TEX 86 value of 0.70 and a subsequent decrease if values exceed this threshold (Fig. 3a).This change may again indicate that the archaeal community differs in dry upland or forest soils and flooded soils.TEX 86 and (b) the relationship between the most abundant iGDGTs (GDGT-0 and crenarchaeol) and lower concentrated iGDGTs (GDGT-1, GDGT-2, GDGT-3, and crenarchaeol regioisomer) as TEX 86 .GDGT-0 / crenarchaeol > 2 and TEX 86 < 0.6 are diagnostic for methanogens.Two outliers from the Ifugao site (Philippines) with a GDGT-0 / crenarchaeol ratio > 69 were excluded from the figure.Note the logarithmic scale for GDGT-0 / crenarchaeol ratios.The filled circles in (a) denote paddy soils and the non-filled circles denote upland, marsh, forest, bamboo and bushland soils.
(logarithmic r = −0.67,r 2 = 0.45, p < 0.0001).However, both the TEX 86 and the GDGT-0 / crenarchaeol ratio show clear differences in soils under paddy (grey background in Fig. 3b) and upland management for adjacent sites, suggesting that a comparison of both parameters may allow distinguishing anoxic or oxic conditions in soils.In general, paddy soils plotted within a field characterized by GDGT-0 / crenarchaeol ratios > 2 and TEX 86 values < 0.6 (Fig. 3b), possibly denoting a diagnostic area for the abundance of methanogenic archaea.The GDGT-0 / crenarchaeol ratio also differs between the various paddy soils, with exceptionally high ratios in the Philippine Ifugao and Vietnamese Lao Cai soil (Table S1).At these sites, longer flooding periods (> 5 month per year) compared to Chinese and Indonesian soils are the likely explanation for the high ratios.

Distribution of branched GDGTs in soils
In the soils investigated here, the relative proportion of brGDGTs to the total GDGT pool was high and varied from 65.0 to 99.1 % (Table 1).Forest soils generally contained the highest abundances of brGDGTs (> 92 %), while they were significantly lower in upland and paddy soils (Fig. 4a).Pearson's correlation analysis indicated that the SOC content was not related to the relative abundance of brGDGT (r = 0.22, r 2 = 0.05, p < 0.01).
In general, the tetra-methylated GDGT-Ia was the most abundant brGDGT in acidic soil and was the only brGDGT to increase in relative abundance with decreasing pH (r = −0.75,r 2 = 0.56, p < 0.001; Fig. 5).All other brGDGTs increased in relative abundance with pH (p < 0.001; Table S2), with the highest correlations observed for GDGT Ib (r = 0.83, r 2 = 0.69), GDGT IIb (r = 0.79, r 2 = 0.62) and GDGT IIIb (r = 0.71, r 2 = 0.50).Our results thus suggest that the monocyclization, in particular, of brGDGT is strongly controlled by pH (r = 0.86, r 2 = 0.74,p < 0.001) with alkaline conditions favouring the synthesis of brGDGT with one cyclopentane moiety (Fig. 5).Similar observations have previously been made in a set of globally distributed upland soils (Weijers et al., 2007;Peterse et al., 2012).Weijers et al. (2007) proposed the lower number of cyclopentyl moieties in brGDGT as a protection mechanism of bacterial cell membranes within acidic soils.The decrease in the amount of cyclopentyl moieties in brGDGT is thought to be associated with a decrease in membrane permeability that regulates the internal pH of bacteria under acidic conditions (Weijers et al., 2007).In soils investigated here, the CBT ratio varied between −0.04 to 2.13 (Table 1) and showed a negative correlation with increasing soil pH (r = −0.81,r 2 = 0.65, p < 0.001; Fig. 6a).In neutral to alkaline soils (with pH values > 6.5) CBT values stayed rather constant with an offset observed between paddy soils (mean 0.34) and upland soils (mean −0.01; Fig. 6a).Wang et al. (2014) also found no apparent correlation between pH and CBT in alkaline soils in a study of arid and subhumid Chinese soils.However, a predominant dependency of CBT on soil water content and the mean annual precipitation (MAP) was observed (Wang et al., 2014).In our study, varying degrees of soil moisture may be one possible explanation for the varying CBT values in paddy and upland soil, especially under alkaline conditions (Fig. 6a).The degree of methylation of brGDGTs (MBT ) has previously been shown to correlate with MAT and pH (Weijers et al., 2007;Peterse et al., 2012).Our results demonstrate that the MBT generally shows low values in paddy soils compared to the adjacently located upland soils, except for the Chinese soils of Cixi (Table 1).The difference in MBT between soils from the same sampling area denotes a lower influence of MAT on the MBT than on the pH, which was weakly related to the MBT (r = −0.55,r 2 = 0.31, p < 0.001; Fig. 6b).The MBT was mainly controlled by the relative abundance of GDGT-Ia and GDGT-IIa, both of which were strongly related to MAP (Peterse et al., 2012).As the latter is largely similar at adjacent sites, we consider the paddy-soil-specific management techniques, including periodically flooding the soils, as responsible for the low GDGT-Ia and high GDGT-IIa content in paddy soils compared to upland soils (Table S1).The temperatures inferred from brGDGT patterns, i.e.T MC values, were generally lower in paddy soils compared to the adjacent upland soils (Table 1), suggesting that T MC reflects mean annual soil temperature rather than air temperature.Vegetation cover and soil moisture affect soil temperature, in particular in surface soils (Seneviratne et al., 2010;Liu et al., 2014;Awe et al., 2015).This led us to hypothesize that soil moisture and/or soil temperature regulates composition of brGDGTs in adjacent subaquatic and upland soils of identical air temperature as recognized by their respective T MC .
A recently developed method separates the structural isomers of brGDGTs from their methyl groups located at positions 5 and 6 (De Jonge et al., 2013). De Jonge et al. (2014) showed that the new CBT 5ME , calculated without 6-methyl brGDGTs, correlated more strongly with soil pH than the regular CBT, which includes both isomers, the 5-and 6methyl brGDGTs.In addition, these authors found no correlation between pH and the newly developed MBT 5ME , which is calculated without the 6-methyl isomer, but a stronger correlation of this index with MAT.De Jonge et al. ( 2014) thus demonstrated that co-elution of GDGTs can affect estimation of pH values.Conventional methods, such as the one employed in this study, are not suited to fully separate the different structural isomers of brGDGTs, and hence it is possible that some scatter observed between our CBTreconstructed and measured pH may result from the analytical set-up (Fig. 6a).However, the overall good covariation of CBT and pH for our sites suggests that the partial co-elution of brGDGT only had a minor effect on the calculation of the lipid-based proxies used in this study.Regressions line of all soils is black, the line of upland, marsh, forest, bamboo and bushland soils is brown and the line for paddy soils is blue.Abbreviations as in Fig. 2. Red lines in (a) show the offset between paddy and upland soil, which have > 6.2 pH values.

Influence of management systems on GDGT distributions
The BIT index quantifies the relationship between acyclic brGDGTs and crenarchaeol and has been used previously to determine the input of terrestrially derived organic matter to marine and lake environments (Hopmans et al., 2004;Weijers et al., 2007).The interpretation of BIT values in soil is not that straight forward as all GDGTs are terrestrially derived.Thus, variations in BIT values must be governed by a microbial input whose GDGT distribution is currently only incompletely known.Wang et al. (2013) observed a positive correlation between increasing soil water content and BIT values in Chinese marsh soils.In our sample set, the BIT index was slightly higher in paddy soils than in the adjacent upland soils (Fig. 4b).Furthermore, higher values were observed generally in paddy soils from tropic (1.02-1.04-fold)compared to subtropic (1.07-1.11-fold)locations.In contrast to the general trend, we found highest BIT values (1.27fold) in the subtropical paddy soils of the Cixi location.In this area, the BIT values in marsh and upland soils (0.61-0.89) were comparatively low, indicating that the latter have a mixed lipid composition, with crenarchaeol originating predominantly from the residual parent substrate (tidal wetland sediment) and in smaller quantities also from the current microbial soil community.Similar results were found in a study of plant wax lipids, which confirm the mixed organic matter composition in these soils (Mueller-Niggemann and Schwark, 2015).Except for the higher contribution of crenarchaeol to the marsh soils, our results show that brGDGTs clearly dominate over iGDGTs originating from Thaumarchaeota in all of the investigated soil types.Interestingly, based on relations of brGDGTs to crenarchaeol, Thaumar-chaeota seem to be more abundant in upland soils compared to forest and periodically flooded paddy soils (Fig. 4b).Low redox conditions as assumed for paddy soils may thus lead to an enrichment of brGDGTs either by higher production or increased preservation of brGDGTs compared to crenarin wetland soils.Our results thus contradict those of Peterse et al. (2015), who performed a 152-day experimental study, where soils were incubated under water to simulate the development of an aquatic environment under aerobic conditions.Contrastingly to our observations, lower BIT values were measured in flooded soils, potentially due to a higher contribution of crenarchaeol while brGDGTs remained unchanged until the end of the experiment.
PCA was performed to obtain information on the major factors that control the variability of the distribution of iGDGTs and brGDGTs.Results of this analysis indicate that crenarchaeol exerts a major control on the iGDGT composition in upland soils (Fig. 7a).The component loading score of GDGT-0 is opposite to crenarchaeol and has the highest negative score in PC1.In general, soils can be sorted into two groups on the basis of their scores on the first component.Paddy soils load negatively and all other soils load positively on PC1.Paddy soils that plot in the quadrant of upland soils are characterized by a higher intensity of crop rotation with upland crops on the fields.The iGDGT composition of periodically flooded paddy soils is mainly controlled by GDGT-0 and that of non-paddy upland soils by crenarchaeol derived from Thaumarchaeota.In flooded rice paddy soils, oxygen availability determines the development of microbial consortia adapted to more anoxic conditions such as GDGT-0synthesizing methanogenic archaea (Koga et al., 1998;Koga and Morii, 2005).The variance in PC2 is mainly associated with the relative abundance of GDGT-2 and separating forest and bushland soils from all other soils.The larger scatter of paddy soils on PC2 is explained by the number of rice cultivation cycles per year, which apparently influence the GDGT-2 content significantly (Fig. 7b).Methanogenic archaea were found to be phylogenetically related to ANME archaea (Krüger et al., 2003;Shima et al., 2012).ANME archaea are a well-known source of iGDGTs (including GDGT-2) in natural environments (Pancost et al., 2001;Blumenberg et al., 2004).Both the interaction of methanogenic and methanotrophic archaea as well as the fact that ANME are an abundant source of GDGT-2 could explain the relationship between higher numbers of rice cultivation cycles, which induce increased methanogenesis through abundant redox cycling, and the presence of GDGT-2.MAT and MAP had no obvious influence on discriminating between different types of agricultural soil via iGDGT distribution (Fig. S3).PCA analysis of the relative abundances of brGDGT shows an opposite relation of GDGT-Ia to all other brGDGTs, with the highest component loading score on PC1 for GDGT-Ia (Fig. 8).The cyclopentane-ring-containing GDGT-IIb and GDGT-IIIb plot negatively on PC1.Higher contents of GDGT-Ia in upland soils compared to adjacent paddy soils (Table S1) confirm that tetra-methylated brGDGTs may be useful in separating different agricultural soils.GDGT-IIa has the lowest loading score on PC1 but the highest on PC2.Upland soils load separately from paddy soils along the PC2, with a variation of relative abundance of the cyclic GDGT-Ib and GDGT-Ic playing the most important role.In contrast, paddy soils are mainly influenced by the abundance of GDGT-IIa and GDGT-IIIa, which both show only a low correlation with pH (Table S2).We rather assume that they are dependent on soil moisture, due to the lack of correlation between the GDGT distribution and soil properties (e.g.pH) as well as climate factors (e.g.precipitation, air temperature) in adjacently located paddy and upland soils.The main ecological difference between paddy and upland soil is the water budget, and thus we interpret this environmental variable as causing the offset in GDGTs.The first PC, explaining 69.11 % of the variance, indicates a separation between locations, with a strong negative score in subtropical Italian and Chinese soils and more positive scores in soils originating from the tropics (Fig. 8a).The MAP (Fig. 8b) and MAT (Fig. S4) gradients of sampling locations on PC1 confirm a relation of climatic parameters to the variation of acyclic brGDGTs.
PCA analysis of environmental parameters as well as of indices of bacterial and archaeal GDGTs indicated that separation of paddy and upland soil is mainly controlled by the intensity of methanogenesis (Fig. 9a).The GDGT-0 / crenarchaeol ratio and the BIT index had the highest positive loading score on PC2.The SOC and TN loaded in the same quadrant as the BIT index, suggesting that a positive correlation between the amount of organic matter and acyclic brGDGT, especially in paddy soils, prevailed.Alternating anoxic conditions in paddy soils are known to favour the preservation and therefore the accumulation of organic matter (Lal et al., 2002), which could lead to an increase in heterotrophic and brGDGT-producing bacteria.In general, the CBT loading was the opposite of that of the soil pH on PC1, indicating their negative relation to each other.The internal separation of paddy soils via the number of rice cultivation cycles is evident by high loading scores of the CBT and MBT (Fig. 9b).Apparently, the increase in the MBT is linked to the number of rice cycles and therefore to the lowering of penta-and hexa-methylated brGDGT during in- creasing redox cycles.Similar loading scores as well as similar directions of climatic parameters, such as MAP and MAT, and of CBT and MBT also indicated that these factors were linked to each other.In addition to methanogenesis, differences in MAT and soil water content seemed to be secondary factors controlling the distribution of brGDGT in soils, which also allowed a separation between upland and paddy management.It should be considered though that MAT is not identical to MST as the latter was also affected by, e.g., the albedo and soil management, which can be different in the adjacent soils (Liu et al., 2014;Awe et al., 2015, and references therein).The reflection coefficient of the surface differs in agricultural soils as a consequence of management practices, which influence the soil bulk density (via tillage), the plant cover (function of the crop leaf area index) and the soil water content.For example, Awe et al. (2015) found differences in soil temperature as a consequence of management practices, with lower temperatures in soils under chiselling and conventional tillage compared to no tillage.

Effects of long-term management on GDGT distributions
Changes in GDGT distribution within two Cixi chronosequences with different cropping systems, one under continuous non-flooded upland and the other under paddy management, indicated specific adaption processes during the long-term usage at each site.Marsh soils were the first soils to develop after the construction of dykes on tidal wetland sediments and therefore represent the starting point of the subsequent soil development.We observed high BIT values (∼ 0.77) already in the surface horizon of the marsh soils, indicating the rapid adaption of the microbial community to more terrestrial conditions.A plot of the brGDGT / iGDGT ratio over time provides evidence for a dominance of brGDGT over iGDGT in all soils, with values of this ratio varying between 2 and 6 in upland soils (Fig. 10a).In contrast to paddy soils, which had a 4-fold increase in the ratio after 2000 years of rice cultivation, such an increase suggests an influence of long-term processes on the proportion of archaeal and bacterial soil microorganisms.These processes may include desalinization, decalcification through leaching as shown in changes in soil pH values (Fig. S5a), fertilization activities, and organic matter input and accumulation (Fig. S5b).Paddy soil manage- ment is known to strongly affect the accumulation of organic matter (Wu, 2011;Mueller-Niggemann et al., 2012;Kölbl et al., 2014) as the periodically anaerobic conditions result in a slower degradation of organic matter (Lal et al., 2002).Kölbl et al. (2014) investigated the response of redox dynamics to changing water conditions over a 1-year time period in 100-, 700-and 2000-year-old paddy soils.They noted a change in the redox potential towards anoxic conditions, already after 5 days of flooding.After stabilization, the redox potential was in the same range in all soils (−170 to −200 mV), independent of the duration of paddy management.In upland soils, permanent oxic conditions were persistent throughout the time period investigated.The results of Kölbl et al. (2014) demonstrate that the rapid establishment of anoxic conditions and the long-term usage of paddy soils may lead to an increase in organic carbon concentrations over time.
Within the upland soil chronosequence, the TEX 86 does not change significantly over the 700-year cultivation time and averages 0.7 (Fig. 10b).In paddy soils, by contrast, the TEX 86 decreased from the initial marsh soil value of 0.7 to values of 0.3 within only 50 years of paddy management.Rotation between paddy-and upland-type cultivation resulted in a comparatively high TEX 86 value of 0.5 in the 2000year-old paddy soils (Fig. 10b).Our results thus suggest that management systems significantly affect the microbial soil community.Long-time paddy management also led to the successive increase in ammonia-oxidizing Thaumarchaeota based on high relative abundances of crenarchaeol, indicating either a recovery process of water-stressed soil Thaumarchaeota or the enrichment of fossil crenarchaeol.The latter is potentially explained by the management type used in the Cixi area, with one wetland rice season and one dry intercrop season per year that influence the presence of aerobic and anaerobic microbes in these paddy soils.In particular, the periodically anaerobic conditions may result in a slower degradation of organic matter (Lal et al., 2002).GDGTs may originate from a mixed source of microbial membrane lipids that were recently deposited (during the oxic as well as in the anoxic period) additionally to the previously preserved ones.
Thus, higher proportions of crenarchaeol, e.g., as marker for terrestrial ammonia oxidizers, being active during the oxic intercrop period, were detected but in lower amounts than commonly observed in upland soils (Table S1).At the same time, the proportion of methanogenic archaea, which was estimated by using the GDGT-0 / crenarchaeol ratio, decreased during the long-term paddy management from 5.0 in the 50year-old to 2.8 in the 2000-year-old paddy soil.
The pH values ranged between 8.0 in marsh soil and 5.5 in the 2000-year paddy soil.The paddy management (including flooding practices) thus leads to enhanced decalcification of soils compared to the non-flooded upland management.However, most soils have an alkaline or neutral pH with exception of the 700-year upland soil and the 2000-year paddy soils, which all had pH values < 6.5 (Fig. S5a).It has previously been demonstrated that the CBT is negatively correlated with increasing pH values (Weijers et al., 2007;Peterse et al., 2012).In the soils of the Cixi chronosequences a negative correlation was also observed, which was higher for paddy soils (r = −0.94,r 2 = 0.88, n = 4, p < 0.001) than for upland soils (r = −0.69,r 2 = 0.47, n = 5, p < 0.001).Interestingly, an offset of CBT values between paddy and upland soils with no apparent changes during cultivation time was noted (Fig. 10c).In addition, the CBT was higher in the younger of both marsh soils, probably because of the greater soil water content in the ∼ 10-year-old compared to the ∼ 35-year-old marsh soil as a result of the progressive dewatering during marsh soil pedogenesis.The observation regarding the CBT values supports the idea that soil moisture, in addition to pH, controls the degree of cyclization of brGDGTs under alkaline conditions, possibly as a reaction to water stress or oxygen deprivation in microorganisms.The increase in CBT values in acidic soils (Fig. 10c) also suggests that low soil pH results in the increased synthesis of brGDGTs with no cyclopentyl moieties.
Except for the youngest paddy soils (50 years), the MBT was slightly lower in Cixi upland soils compared to their corresponding paddy soils with identical cultivation time (Fig. 10d).This is in contrast to the observations that paddy soils in general showed a lower MBT compared to the adjacent upland soils (Fig. 6b).This may indicate that soil bacteria living under contrasting pH regimes adapt the composition of their membrane lipids in a different fashion, even if the agricultural management is comparable.
The CBT and MBT are both considered to be strongly related to MAT (Weijers et al., 2007;Peterse et al., 2012), which is largely similar for paddy and upland soils same sampling region.However, the calculated T MC was different in adjacent paddy and upland soils (Table S1) and gradually increased during long-term management in both chronosequences (Fig. 11) from 14.4 to 17.8 • C in paddy soils and from 17.1 to 19.3 • C in upland soils.In general, temperatures were approximately 1.4 • C higher in upland soils compared to soils under paddy management with the same cultivation time.This implies that the management type affects the MST, which in turn controls the membrane lipid composition of brGDGT-producing bacteria.

Conclusions
Our results show that archaeal and bacterial GDGTs were ubiquitously distributed in paddy, upland, forest, bushland and marsh soils of tropical and subtropical climate regimes.Independent of soil usage, brGDGTs predominated over iGDGTs in all soils but had lower relative proportions in soils located in the subtropics compared to soils at tropical latitudes.This implies that warm and humid environments favour an increased occurrence of brGDGT in the GDGT pool.The distribution patterns of iGDGTs indicate no differences in archaeal/thaumarchaeal composition dependent on climatic exposition.
Agricultural management was a major factor that controlled the distribution of the archaeal community in soils.In subaquatic paddy soils, the lower proportion of crenarchaeol compared to other iGDGTs indicates an enhanced presence of methanogenic archaea compared to ammonia-oxidizing Thaumarchaeota, which were more abundant in dry upland soils.In addition, the intensity and duration of rice cultivation significantly affected the composition of iGDGT with an increase in the GDGT-0 / crenarchaeol ratio in soils with a higher number of rice cultivation cycles per year.
CBT values were correlated with soil pH and were controlled by a predominance of acyclic brGDGT in acidic soils.In alkaline soils, CBT values were rather invariant but the offset between soils under periodical flooding (paddy soils) and soils under non-flooded upland management suggests that parameters other than pH affected the distribution of brGDGTs as well (e.g.soil moisture, which in addition to soil pH and MAT exerts a control on the degree of cyclization of brGDGTs).MBT values differed in adjacent paddy and upland soils, confirming that factors other than MAT and MAP affect the degree of methylation of brGDGT on a regional scale.brGDGT-based temperatures (T MC ) were higher in soils under upland management than under paddy management, and these differences in T MC suggest that the specific management influenced the soil moisture, which in turn affects MST.The results of the Cixi chronosequence covering 2000 years of soil development confirm that the SOC, the pH value and the soil moisture controlled the distribution of brGDGT during long-term paddy soil usage.

Figure 1 .
Figure 1.Map of sampling locations.Blue coloured area denotes subtropical sampling locations and green denotes tropical sampling locations.

Figure 4 .Figure 5 .
Figure 4. Box-plot diagrams of (a) the relative proportion of brGDGT in the total GDGT pool and (b) the BIT index in soil.Note different symbols (circle or asterisk) for outliers that are more than 1.5 (or 3) box lengths from one hinge of the box.Abbreviations and subdivisions as in Fig. 2.

Figure 6 .
Figure 6.Plot of (a) the cyclization ratio of branched tetraethers (CBT) vs. soil pH and of (b) the modified methylation index of branched tetraethers (MBT ) vs. soil pH.Dashed lines indicate neutral soil conditions, which delimitate the interval between 6.6 and 7.3 pH units.Regressions line of all soils is black, the line of upland, marsh, forest, bamboo and bushland soils is brown and the line for paddy soils is blue.Abbreviations as in Fig.2.Red lines in (a) show the offset between paddy and upland soil, which have > 6.2 pH values.

Figure 7 .
Figure 7. Principal component analysis (PCA) based on standardized relative abundances of six iGDGTs in 170 investigated soils.The first principal component (PC1) accounted for 53.9 % of the total variance and the second (PC2) for 29.9 %.Panel (a): symbols and colours in upper legend denote different management forms.Abbreviations as in Fig. 2. Panel (b): colours of the sample site symbols in lower legend are indicative of the number of rice cultivation cycles per year ("No. of rice yr −1 ").

Figure 8 .Figure 9 .
Figure 8. Principal component analysis (PCA) based on standardized relative abundances of nine brGDGTs in 170 investigated soils.The first principal component (PC1) accounts for 69.1 % of the variance and the second (PC2) for 14.3 %.Panel (a): symbols and colours in upper legend denote different management forms.Abbreviations as in Fig. 2. Panel (b): colours of the sample site symbols in lower legend are indicative of the mean annual precipitation.

Figure 10 .
Figure 10.Time plots of various GDGT ratios and indices in soils of the Chinese Cixi region: (a) ratio of branched vs. isoprenoid GDGTs, (b) the TEX 86 , (c) the CBT and (d) MBT .Note logarithmic scale for the cultivation time.Numbers in plot (c) reflect soil pH values.

Figure 11 .
Figure 11.Time plot of MBT -CBT-derived temperatures (T MC ) in soils of Cixi.Note logarithmic scale for cultivation time.