Global patterns and drivers of dissolved organic matter across Earth systems: Insights from H/C and O/C ratios

Global patterns and drivers of dissolved organic matter across Earth systems: Insights from H/C and O/C ratios | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Global patterns and drivers of dissolved organic matter across Earth systems: Insights from H/C and O/C ratios Ang Hu, Lei Han, Xiancai Lu, Ganlin Zhang, Jianjun Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3324551/v2 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jan, 2024 Read the published version in Fundamental Research → Version 2 posted You are reading this latest preprint version Show more versions Abstract Dissolved organic matter (DOM) is ubiquitous and contains a complex pool of thousands of distinct molecules, and their chemical characteristics help us inform the fate of global carbon. Yet, a more holistic perspective of molecular characteristics of DOM and underlying mechanisms across Earth systems and climates remain understudied. Here, we present a comprehensive analysis of the molecular characteristics of DOM using two abundance-weighted average indices, i.e., H/C and O/C ratios by compiling 3,558 samples from 317 studies covering the waters, land, plant, petroleum, and atmosphere systems, and the climatic regions from tropics to tundra. H/C ratios are lower on average in waters (H/C = 1.15 ± 0.005) and land (H/C = 1.20 ± 0.010) than the other systems, while their O/C ratios rank between plant and atmosphere. In the waters and land systems, the H/C ratios of DOM vary from the highest to the lowest in the habitats of land-to-ocean continuum generally as snow > glacier > marine ≥ freshwater/soil > groundwater. The H/C ratios show predictably U-shaped patterns along latitudinal gradients indicating the lowest abundance of more hydrogen saturated molecules at around mid-latitudes of 40°-50° in river water, lake water, and forest soil. The two ratios are primarily controlled by the environmental factors such as pH, dissolved oxygen, and carbon and nitrogen contents. We further unveil additional and considerable links between the ratios and the extremes of climatic factors such as precipitation of warmest quarter and maximum temperature of warmest month. Our synthesis provides molecular-level perspectives to characterize the global distribution and underlying drivers of DOM, which is complementary for our understanding global carbon cycle’s processes under future global change. Biogeography Geochemistry Molecular characteristics dissolved organic matter Earth systems habitats latitudinal pattern environmental drivers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Dissolved organic matter (DOM) is an essential component of the Earth’s biogeochemical cycles in determining carbon source or sink and is ubiquitous within and across Earth systems [ 1 – 3 ]. Terrestrial soil, inland waters, and ocean are key carbon reservoirs controlling atmosphere greenhouse gases and Earth’s climate as they not only transport and process, but also bury large amounts of organic carbon [ 2 , 4 – 6 ]. For example, global carbon sequestration in soil, inland waters, and ocean are estimated at ~ 0.9, 0.15, and 0.2 Pg per year, respectively [ 7 , 8 ]. These systems are interconnected in the emergent view of global carbon cycle, which leads to their exchange of dissolved carbon and nutrients and, in turn, should impact the fate of DOM such as decomposition and production rates [ 5 , 9 ]. The global carbon cycle’s processes could be effectively informed from the molecular-level perspectives of DOM characteristics (i.e., molecular traits) [ 3 , 10 – 12 ]. A better understanding of the regulatory mechanisms of the global variation in DOM traits resulting from the spatial heterogeneity of climatic and environmental variables is important for estimating the responses of carbon cycle’s processes to environmental changes. Incorporation of the previously over-looked drivers into the predictive models (e.g., Earth system models) is needed to reduce the uncertainty of estimation [ 13 , 14 ]. Therefore, it is crucial to develop a more holistic perspective of the distribution and underlying drivers of DOM molecular traits across Earth systems and multigradient environments, which ultimately helps inform modelling for predicting future global carbon cycle’s processes. Organic matter chemistry is a complex pool of thousands of distinct molecules, with unique molecular traits such as the two primary dimensions of hydrogen to carbon ratio (H/C) and oxygen to carbon ratio (O/C) [ 15 , 16 ]. H/C ratio is relevant to biogeochemical reactions of hydrogenation or dehydrogenation, reflecting the degree of hydrogen saturation. Higher H/C ratio reflects higher degree of hydrogen saturation. H/C ratio can also be applied to indicate the capacity for a molecule to be degraded [ 17 , 18 ]. O/C ratio is relevant to chemical reactions of oxidation or reduction. Higher O/C ratio reflects higher degree of oxygenation and more oxygen-containing functional groups such as carboxyl or hydroxyl groups [ 19 ]. These two dimensions of traits could be constrained by microbes and environmental conditions like nutrients, temperature, and sunlight, and further inform the transformation of organic matter [ 20 ]. For example, the environmental condition such as low oxygen availability can enrich compounds with lower oxygenation and make organic matter degradation thermodynamically unfeasible [ 21 , 22 ]. The climatic factors such as mean annual temperature can also affect the DOM’s characteristics with more hydrogen saturated compounds enriched in the higher temperature conditions [ 19 ]. Despite the well-known importance of average state of the climatic variables, there are few investigations considering the influences of extremes or variability of climatic variables on DOM composition. Here, we compiled compositional-level H/C and O/C ratios of DOM of 3,558 samples derived from 317 studies spanning diverse systems and climates worldwide (Figs. 1 , S1, and Table S1). The datasets included waters, land, plant, petroleum, and atmosphere systems, covering the climatic regions from tropics to tundra (Figs. 1 , S1, and Table S1). There were 2,876 samples (80.8%) from waters and land, covering the habitats of land-to-ocean continuum, e.g., soil, peatland, glacier, pond, reservoir, lake, river, and ocean. The availability of such big datasets is benefited from the recent advance of ultrahigh-resolution Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) [ 15 , 16 ]. FT-ICR MS has been applied to numerous natural organic matter such as terrestrial, aquatic and marine DOM, microbial-derived DOM, and petroleum-derived materials, and to further determine chemical characteristics (i.e., molecular traits) as a function of (bio)geochemical or anthropogenic processes [ 12 , 23 , 24 ]. The compositional-level DOM traits reduce the complex mass spectrum data for the individual peaks to abundance-weighted average indices [ 15 ]. This is beneficial for an intersample comparison by incorporating climatic and environmental factors, and has already been well applied in DOM studies [ 1 , 10 , 19 ]. We aimed to provide a comprehensive survey on molecular-level perspectives of DOM characteristics at a global scale. Specifically, the synthesis explores the distribution patterns of H/C and O/C ratios of DOM across Earth systems and along latitudinal gradients, and elucidates the roles of climatic and environmental variables in driving these traits. Such global patterns and drivers for DOM via a meta-synthesis study could be more important when estimating the effects of global environmental change on carbon cycle’s processes given the limited scopes of individual studies. Materials and Methods Data collection We systematically searched all peer-reviewed publications that were published prior to June 2022, which investigated the molecular traits (i.e., H/C and O/C ratios) of DOM measured by FT-ICR MS using the Web of Science (Core Collection; http://www.webofknowledge.com ) and Google Scholar ( http://scholar.google.com ) via the search term: “organic matter AND FT-ICR MS AND van Krevelen”. The molecular traits of thousands of molecular formulae (hereafter refer to as “molecules”) for each sample’s FT-ICR MS spectrum were evaluated on van Krevelen diagrams on the basis of their molar H/C ratios (y axis) and molar O/C ratios (x axis) [ 15 ]. The van Krevelen diagrams enable the comparison of molecular properties of organic matter and the ability to assign molecules to major biochemical categories, which included amino sugar-, lipid-, protein-, lignin-, carbohydrate-, tannin-, and condensed aromatic-like compounds. However, it should be noted that we here used van Krevelen diagrams to visualize the H/C and O/C ratios at the compositional level, and the sample points in the diagrams do not intend to assign the samples to these biochemical categories. We employed the criteria to select the studies as follows. (1) They had raw mass spectrometry data, from which the compositional-level H/C and O/C ratios could be calculated. (2) They had compositional-level H/C and O/C ratios, that is weighted means of formula-based H/C and O/C ratios in a given sample, which are calculated as the sum of the H/C (or O/C) ratio for each molecule and its relative intensity divided by the sum of all intensities [ 1 , 10 ]. (3) They focused on the DOM extracted from natural and engineered environments, rather than manipulated experiments. In total, H/C and O/C ratios of 3,558 samples from 317 studies met these criteria (Table S1). To minimize the challenges in data comparison and interpretation across studies with different instrument type and settings [ 25 , 26 ], we employed the following criteria to further subset the data: (1) DOM trait datasets obtained by FT-ICR MS were retained, but not by other instrument types such as Orbitrap MS. (2) Negative ESI mode was retained for the following statistical analyses, as it is most frequently documented in literature by comprising 88.9% of the total datasets and is the most suitable ionization method for the analysis of natural DOM. (3) We focused on the compositional-level H/C and O/C ratios calculated based on all molecules in a given sample, rather than the samples with only subsets of molecules. In total, there were H/C and O/C ratios of 2,995 samples from 270 studies using (-)ESI-FT-ICR MS for the robust data comparison among various systems and habitats. The collected dataset included various Earth systems, such as waters, land, plant, petroleum, and atmosphere, covering climatic regions from tropics to tundra (Figs. 1 , S1, S2, and Table S2). We further binned the dataset of each system into fine habitats (Figs. S3, S4, Table S3). Specifically, the waters includes habitats of marine water, marine sediment, marine hydrothermal fluid, lake water, lake sediment, reservoir, pond, river water, river sediment, stream, drink water, groundwater, spring, glacier, snow, rainwater, and wastewater. The land includes habitats of peatland, permafrost, forest soil, grassland, cropland, paddy soil, riparian soil, and coastal soil. The plant includes habitats of phycophyta, herbage, arbor, and shrub. The atmosphere includes habitats of aerosol, particulate matter (PM) 2.5, and PM 10. The glacier is mainly derived from marine ice and lake ice. There were several habitats categorized as “Others”, including virus, melanin, murchison, mineral, coal, biochar, and manure. Waters and land systems were discussed in more detail than plant, petroleum, and atmosphere systems, as more sufficient data derived from these two systems and their finely-categorized habitats were available in the literature. It should be noted that we also included the rarely reported systems like plant, petroleum, and atmosphere systems, as this synthesis was aimed to provide an overview for comparing DOM traits derived from as many Earth systems as possible. Besides the molecular traits of H/C and O/C ratios, the datasets also included climatic and environmental variables for each sample when possible. A total of 15 environmental variables were collected, including salinity, temperature, pH, conductivity, and the concentrations of dissolved oxygen (DO), total organic carbon (TOC), total nitrogen (TN), total dissolved nitrogen (TDN), dissolved organic carbon (DOC), ammonium (NH 4 + ), nitrate (NO 3 − ), nitrite (NO 2 − ), phosphate (PO 4 3− ), iron (Fe), and manganese (Mn). In addition, climatic variables were derived using latitude, longitude and digital elevation data with a spatial resolution of 0.5°. The gridded data were obtained from the WorldClim dataset ( https://www.worldclim.org ) for the 19 bioclimatic variables [ 27 , 28 ], including annual mean temperature (BIO1), mean diurnal range (BIO2), isothermality (BIO3), temperature seasonality (BIO4), maximum temperature of warmest month (BIO5), minimum temperature of coldest month (BIO6), temperature annual range (BIO7), mean temperature of wettest quarter (BIO8), mean temperature of driest quarter (BIO9), mean temperature of warmest quarter (BIO10), mean temperature of coldest quarter (BIO11), annual precipitation (BIO12), precipitation of wettest month (BIO13), precipitation of driest month (BIO14), precipitation seasonality (BIO15), precipitation of wettest quarter (BIO16), precipitation of driest quarter (BIO17), precipitation of warmest quarter (BIO18), and precipitation of coldest quarter (BIO19). Statistical analysis The significance of differences in H/C or O/C ratios between Earth systems was performed using a Kruskal-Wallis test. Pairwise comparison was performed for the magnitude of variances of H/C or O/C ratios between habitats using Wilcoxon test. These analyses were performed using R package stats V4.1.3. We further explored the distribution patterns of compositional-level H/C or O/C ratios along latitudinal gradients, and the influences of explanatory variables on these two ratios. The explanatory variables included 19 bioclimatic and 15 collected environmental variables. It should be noted that although DOM molecular traits are also dependent on microbes and sunlight, we here focused on climatic vs. environmental constraints due to the following reasons: (1) There were important roles of climatic and environmental variables documented in previous literature such as Roth et al ., (2019) [ 1 ] and Hu et al ., (2022) [ 10 ]; (2) Microbial data are not always available along with DOM mass spectral data in the same literature, and thus the influences of microbes and sunlight on DOM could not be well quantified in our meta-analysis study. For better statistical power, we performed the analyses with the sample size over 30 for the waters or land, or each of their habitats. The latitudinal patterns of H/C and O/C ratios were fitted using generalized additive models [ 29 ]. The influences of climatic and environmental variables on H/C and O/C ratios were evaluated by linear mixed-effects models [ 30 ]. In each model, we modeled H/C or O/C ratios in every Earth system (that is, waters or land) as a function of a climatic or environmental variable, and used studies and habitats as random effects. The omnibus test was used to evaluate model significance, and the conditional explained heterogeneity represented the influence of each explanatory variable on the H/C or O/C ratios accounting for the random effects [ 31 ]. To minimize the potential biases of data discrepancies across various instruments or laboratories, we specified random effects in our model, which are able to factor out the idiosyncrasies of our samples and obtain a more general estimate of the fixed effects of interest [ 32 ]. We further examined the influences of each of these explanatory variables on the H/C or O/C ratios in each habitat of waters and land, in which we used the identity of data-source studies as random effects. The analyses of linear mixed-effects models were performed by using lmer function in the R package lme4 V1.1.28. This approach enabled us to obtain reliable results of the latitudinal patterns of H/C or O/C ratios and the influences of climatic and environmental variables on the ratios. Further partitioning analysis in linear mixed-effects models provided an estimate of the total contribution of a fixed effect of each climatic variable to the overall prediction of H/C or O/C ratio. We selected climatic variables for partitioning analyses by dereplicating strongly correlated variables by a threshold of Pearson correlation over 0.8. Partitioning analysis was performed with R package partR2 V0.9.1 [ 33 ]. Results and Discussion Variation of DOM traits across Earth systems The molecular traits of DOM, measured by H/C and O/C ratios at the compositional level (hereafter, H/C and O/C ratios), were highly divergent across Earth systems, such as waters, land, plant, petroleum, and atmosphere systems (Figs. 2 a, S2, Table S2). H/C and O/C ratios varied from 0.22 to 2.14, and 0.01 to 1.04, with mean values of 1.17 and 0.41, respectively, in all systems (Fig. 2 a, Table S2). H/C ratios were lower than 1.5 in 92.0% of samples, indicating that DOM generally contained a high abundance of recalcitrant (i.e., less hydrogen saturated) molecules in each system [ 18 ]. Among these systems, atmosphere samples showed the highest mean values for H/C (mean ± s.e = 1.47 ± 0.030) and O/C ratios (0.45 ± 0.017), indicating a higher abundance of more hydrogen saturated molecules and more abundant oxygen-containing functional groups, respectively (Fig. 2 b, Table S2). Atmosphere experiences rapid photochemical transformation and, therefore, indicates that DOM contains a higher abundance of more hydrogen saturated molecules than other systems and thus the highest H/C mean value. In comparison, petroleum samples had relatively intermediate H/C (mean = 1.37 ± 0.034) and the lowest O/C (mean = 0.29 ± 0.020) ratios, while plant had similar H/C and O/C ratios to those in waters and land (Fig. 2 , Table S2). There were 2,140 and 401 samples for waters and land, comprising 71.5% and 13.4% of the collected datasets, respectively (Fig. 1 , Table S2). The mean values of O/C ratios were significantly ( P ≤ 0.05) lower in waters (0.40 ± 0.002) than land (0.43 ± 0.005), and their H/C ratios showed the similar pattern with mean values of 1.15 ± 0.005 and 1.20 ± 0.011, respectively (Fig. 2 , Table S2). We recognized the overlapped nature between waters and land systems, where the mean values of H/C ratios were lower than the other systems and their O/C ratios ranked between plant and atmosphere (Figs. 2 , S2). This indicates that DOM contains a higher abundance of molecules with more recalcitrant and relatively intermediate oxygenation than the other systems. Variation of DOM traits across habitats of waters and land When considering individual habitats of waters or land, we also found some distinct variations in H/C or O/C ratios between habitats (Figs. 3 , S4, Table S3), and the significance tests are shown in Fig. S5. For the waters, H/C ratios were over 1.4 in 12.9% of samples and showed the significantly highest mean values in snow and rainwater ( P ≤ 0.05; Figs. 3 , S5). Relatively high values of these two natural aquatic habitats were similar to those of atmosphere system, which could be explained by their shared atmospheric source of DOM. Subsequently, the habitats like stream, pond and spring had the means of H/C ratios ranging from 1.29 to 1.31 (Fig. 3 c). Similar H/C ratios were also observed in glacier and ocean with mean values of 1.27 to 1.34 (Fig. 3 c). These habitats had relatively higher mean values of H/C ratios over 1.2, while their O/C ratios ranged between 0.4 and 0.5 except for those in snow (Fig. 3 c). Generally, O/C ratios in each habitat showed significantly higher variation than H/C ratios ( P ≤ 0.05; Fig. 4 ). In contrast, river and drinking water showed the lower means of H/C ratios ranging from 1.08 to 1.10, followed by groundwater with the lowest values of 0.98 (Fig. 3 c). We observed significantly lower values of these habitats than most of the other aquatic habitats ( P ≤ 0.05; Fig. S5). Like lake, pond and stream, DOM characteristics in river were also influenced by terrestrial inputs [ 34 ]; however, lower H/C mean values were associated with lower carbon productivity and turnover rates due to flowing waters with short residence time [ 35 ]. As an engineered aquatic habitat, DOM in drinking water is characterized as rapidly microbial processing of labile DOM [ 36 ], leaving recalcitrant molecules behind and thus low H/C mean values. The lowest H/C ratios in groundwater indicate higher aromaticity than usually experienced for those in aquatic and terrestrial surface environments, which is consistent with previous reports [ 37 , 38 ]. Notably, H/C ratios in lakes showed relatively large variation between sediment and water ( P ≤ 0.05), and were lower in the former with mean values of 1.01 and 1.25, respectively (Figs. 3 c, S5). The lower hydrogen saturation of DOM in lake sediment is associated with accumulated recalcitrant molecules, likely due to higher microbial diversity and carbon metabolism that in turn lead to faster microbial processing of labile organic matter [ 39 , 40 ]. For O/C ratios, the highest and lowest mean values of 0.51 and 0.31 were observed in drinking water and river sediment, respectively (Fig. 3 c). For the land, mean H/C ratios showed the highest and lowest values of 1.32 and 1.04–1.07 observed in coastal and cropland/riparian soils, respectively (Fig. 3 c). Interestingly, H/C ratios were not significantly different ( P > 0.05) between coastal soil and ocean, while those in cropland and riparian soil were similar to river and lake sediment (Fig. S5). This phenomenon agrees with the fact that there is a land-water continuum for the spatial dynamics of DOM to be transported from terrestrial soils to inland waters and from tidal wetlands to the ocean. In the context of future climate warming, the increased extreme rainfall intensity and frequency would enhance the carbon and nutrient transports from soil to inland waters and to ocean [ 41 , 42 ]. Consequently, this mixing of organic carbon sources of contrasting reactivity might result in the changes in organic matter degradation through priming processes [ 43 ]. Compared to H/C ratios, O/C ratios showed significantly higher variation across most habitats of land ( P ≤ 0.05; Fig. 4 ). This suggests that the variations of DOM characteristics especially O/C ratios in each habitat should be constrained such as by climate and environmental conditions resulting from their global spatial heterogeneity. Collectively, DOM traits particularly H/C ratios showed clear variations across Earth systems and habitats. Specifically, H/C ratios were on average lower in waters and land than other systems such as plant, petroleum and atmosphere. In these two systems, the H/C ratios of DOM varied from the highest to the lowest in the habitats of land-to-ocean continuum generally as snow, rainwater > glacier > coastal soil, ocean, stream, pond, permafrost > lake water, reservoir, peatland, paddy soil, forest soil, grassland > river > lake sediment, riparian soil, cropland > groundwater. Based on smaller number of observations, previous studies have also tried to compare the H/C ratios of DOM across a limited number of habitats. For example, H/C ratios were higher for glacier, followed by ocean and freshwater [ 18 , 44 ], higher in lake water relative to lake sediment [ 39 ], and higher in paddy soil than upland soil [ 45 ]. Different from previous studies, our synthesized global datasets, for the first time, extended such findings with unprecedently finely-categorized and comprehensive habitats, which provides an overview for comparing DOM traits along the aquatic-terrestrial continuum. Variation of DOM traits with latitudes H/C and O/C ratios also showed predictable patterns along latitudinal gradients for the systems of waters and land (Fig. 5 ). Specifically, H/C ratios generally showed a significant U-shaped pattern in both systems, with the lowest values occurring in latitudes of absolute 40° − 50° ( P ≤ 0.05, generalized additive models; Fig. 5 a, c). This U-shaped pattern was also observed in specific habitats such as river water, lake water, and forest soil (Fig. 5 b, d). In comparison, O/C ratios also showed a significant ( P ≤ 0.05) U-shaped pattern in the waters, but a nonsignificant ( P > 0.05) pattern in the land (Fig. 5 a, c). In contrast, previous studies show that DOM traits have monotonically decreasing or increasing patterns along latitudinal gradients [ 46 , 47 ]. For example, bacterial production of fluorescent DOM in alpine and polar lakes, indicated by optical properties like absorption at 250 nm and total fluorescence, shows a decreasing pattern within absolute latitudes of 30–75° but is lower at around mid-latitudes of ~ 50° [ 46 ]. Considering our compiled datasets with a larger spatial scale spanned from tropics to polar and covered broad gradients of ecosystem properties such as climatic and environmental factors, we thus show, for the first time, that H/C ratios had the predictably latitudinal pattern for both waters and land systems. Together, our results suggest that DOM is more hydrogen saturated towards the extremes at the polar regions and at the equator, while less hydrogen saturated in the mid-latitudes of 40°-50°. Drivers of DOM traits in waters and land The distribution patterns of H/C and O/C ratios of DOM were both significantly affected by climate and environmental variables across waters and land, indicated by linear mixed models (Figs. 6 , S6, S7, and Table S4). For the waters, environmental variables such as dissolved oxygen and ammonium had the strongest effects on H/C ( R 2 = 0.775, P ≤ 0.05) and O/C ( R 2 = 0.605, P ≤ 0.05) ratios, respectively (Fig. 6 ). Climatic variables, such as mean annual precipitation and mean temperature of warmest quarter, also showed strong effects on O/C ratios, with the explained variation of 0.564 and 0.527, respectively (Fig. 6 ). For the land, H/C and O/C ratios also showed significant variation explained by climatic variables, such as maximum temperature of warmest month ( R 2 = 0.332, P ≤ 0.05) and mean temperature of wettest quarter ( R 2 = 0.425, P ≤ 0.05), respectively (Fig. 6 ). Further partitioning analyses confirmed the stronger relative importance of extremes of climatic factors on H/C and O/C ratios than mean annual climates (Fig. S6). Specifically, the variances of H/C and O/C ratios were mostly explained by isothermality and precipitation of coldest quarter, respectively, in the waters, while by precipitation of coldest quarter and mean temperature of wettest quarter, respectively, in the land (Fig. S6). However, the dominant effects of climatic and environmental factors were not always consistent across the individual habitats (Figs. 6 , S7). For example, H/C and O/C ratios in marine water, reservoir water, river water, and peatland were most strongly affected by environmental variables, such as total dissolved nitrogen, dissolved organic carbon, nitrate, and pH, followed by extremes of climatic variables, such as minimum temperature of coldest month, maximum temperature of warmest month, and mean temperature of warmest quarter (Fig. 6 ). In contrast, H/C and O/C ratios in river sediment and soil were dominantly affected by mean annual temperature and extremes of climatic variables such as precipitation of warmest quarter, mean diurnal range, isothermality, and temperature seasonality (Fig. 6 ). These findings support previous reports showing the important roles of ecosystem properties in controlling DOM traits and decomposition rates, such as temperature [ 48 , 49 ], precipitation [ 50 ], carbon and nitrogen contents [ 51 , 52 ], and acidity [ 53 ]. Further, our findings reveal additional links of DOM traits to the extremes of climatic variables beyond those drivers known from previous studies. Specifically, H/C and O/C ratios were more closely related to extremes (e.g., monthly or quarterly maximum) of temperature or precipitation than to mean annual temperature or precipitation. Earth’s average temperature has been increased by 1.5℃ since pre-industrial baseline, and even relatively small incremental increases in global warming (+ 0.5℃) can cause statistically significant changes in extremes on the global scale and for large regions [ 54 ]. Extremes of climatic factors are key to understanding the effect of climate change on primary producers, such as plant species diversity and growth [ 55 , 56 ] and decomposers like microbes [ 57 ], which would affect organic carbon characteristics. Thus, our findings highlight the need to integrate extremes of climatic factors into climate change modelling when making current inferences and future predictions of organic carbon processes. The implications of this study First, our synthesized analysis provided a comprehensive survey of molecular-level perspectives of the global DOM characteristics across Earth systems and climates. The trait-based metrics like H/C and O/C ratios are relevant to the chemical reaction processes of molecules and thus effectively inform the fate of DOM such as decomposition processes. For example, the utility of H/C ratio as a surrogate for reactivity studies could reveal quantifiable and comparative labile nature of DOM [ 18 ]. Our utility of these two metrics provides an overview of the current state of knowledge on spatial distribution of DOM characteristics via a meta-synthesis approach by compiling data from the unprecedently finely-categorized and comprehensive habitats [ 18 , 58 ]. Second, rather than considering these systems independent of one another, a more holistic perspective of DOM characteristics is needed to be developed in a system-to-system continuum like that in Lake Nam Co [ 59 ]. Considering that the intensity and frequency of extreme rainfall would increase with climate warming, the carbon and nutrient transport from soil to inland waters and to ocean are anticipated to increase [ 41 , 42 ]. Consequently, the global patterns of DOM characteristics across Earth systems or habitats could help understand how the mixing of organic carbon sources of contrasting reactivity would influence the changes in carbon cycle’s processes. Third, a better understanding of molecular-level perspectives of the global DOM characteristics in response to environmental constraints is of great interest to a wide readership, as this ultimately helps understand and predict future global carbon cycle’s processes. It is critical to understand the global variations of DOM characteristics resulting from the spatial heterogeneity of climatic and environmental variables, which is important for estimating the potential constraints of DOM characteristics. Our synthesized analysis highlights the potential influence of climatic constraints especially extremes of climatic factors (e.g., monthly or quarterly maximum of temperature or precipitation) on the DOM traits. The inclusion of these novel drivers of climate extremes could help predict DOM characteristics and further carbon cycle’s processes under the future climate change scenarios. Future perspectives Although our mass spectrum datasets were selected considering FT-ICR MS instrument and ESI negative ionization method, there may still be uncertainties in data comparison and interpretation across studies. For example, the differences in analytical equipment between laboratories and studies, data acquisition and processing, or sample preparation techniques could be important considerations in achieving reproducible results [ 26 ]. Our findings should be, if possible, validated using the consistent measurement methods across systems and habitats. We, however, could utilize proper statistical models to account for idiosyncrasies of the data [ 32 ] and minimize the potential biases of data discrepancies across various instruments or laboratories. Further, our datasets were based on the compositional-level H/C and O/C ratios of DOM. We did not consider other information in this study such as the full dataset with individual molecules, or other trait metrics like the number of N, P, and S, aromaticity, and nominal oxidation state of carbon. The additional trait information especially with consistent measurement methods would be helpful to fully understand the global carbon cycle’s processesing mechanisms and DOM transformations. In addition, much larger FT-ICR MS datasets in literature are derived from waters compared to other systems and mainly from the habitats in subtropical and temperate climates. Further studies are encouraged to extend the coverage of habitats such as groundwater, spring, soils and coastal areas to a larger spatial scale spanned from tropics to polar. Although our main aim is to explore the important roles of climatic and environmental factors in controlling DOM characteristics across Earth systems, other potential drivers such as microbial communities [ 1 , 10 ], minerals [ 60 ], and water retention time [ 61 ] have been reported to influence DOM characteristics. Future studies are encouraged to focus more on the causal relationships among these potential drivers, climate, and DOM characteristics, which will improve our understanding of underlying mechanisms of global carbon cycle’s processes. Declarations Conflict of interests The authors declare no conflict of interests. Author contributions JW conceived the review. LH synthesized and analyzed the data with the contributions of AH and JW. AH and JW finished the first draft. JW and AH finalized the manuscript with the contributions of all authors. Acknowledgements This study was supported by National Natural Science Foundation of China (42225708, 92251304, 42377122, 42077052), the Second Tibetan Plateau Scientific Expedition and Research (STEP) Program (2019QZKK0503), Research Program of Sino-Africa Joint Research Center, Chinese Academy of Sciences (151542KYSB20210007), Science and Technology Planning Project of NIGLAS (NIGLAS2022GS09), and CAS Key Research Program of Frontier Sciences (QYZDB-SSW-DQC043). References V.-N. Roth et al. , 2019. Persistence of dissolved organic matter explained by molecular changes during its passage through soil. Nat. Geosci. 12, 755–761. T. Dittmar, A. Stubbins, 2014. 12.6—Dissolved organic matter in aquatic systems. Treatise on Geochemistry, 2nd edn. Elsevier: Oxford , 125–156. A. M. Kellerman, D. N. Kothawala, T. Dittmar, L. J. Tranvik, 2015. Persistence of dissolved organic matter in lakes related to its molecular characteristics. Nat. Geosci. 8, 454–457. J. Lehmann, M. Kleber, 2015. The contentious nature of soil organic matter. Nature 528, 60–68. T. J. Battin et al. , 2009. The boundless carbon cycle. Nat. Geosci. 2, 598–600. L. J. Tranvik et al. , 2009. Lakes and reservoirs as regulators of carbon cycling and climate. Limnol. Oceanogr. 54, 2298–2314. R. Mendonca et al. , 2017. Organic carbon burial in global lakes and reservoirs. Nat. Commun. 8, 1694. R. Lal, 2004. Soil carbon sequestration to mitigate climate change. Geoderma 123, 1–22. P. Regnier, L. Resplandy, R. G. Najjar, P. Ciais, 2022. The land-to-ocean loops of the global carbon cycle. Nature 603, 401–410. A. Hu et al. , 2022. Ecological networks of dissolved organic matter and microorganisms under global change. Nat. Commun. 13, 3600. J. C. Stegen et al. , 2018. Influences of organic carbon speciation on hyporheic corridor biogeochemistry and microbial ecology. Nat. Commun. 9, 585. R. L. Sleighter, P. G. Hatcher, 2007. The application of electrospray ionization coupled to ultrahigh resolution mass spectrometry for the molecular characterization of natural organic matter. J. Mass Spectrom. 42, 559–574. H. Zhang et al. , 2020. Microbial dynamics and soil physicochemical properties explain large-scale variations in soil organic carbon. Global Change Biol. 26, 2668–2685. J. Lee et al. , 2023. Soil organic carbon is a key determinant of CH(4) sink in global forest soils. Nat. Commun. 14, 3110. S. Kim, R. W. Kramer, P. G. Hatcher, 2003. Graphical Method for Analysis of Ultrahigh-Resolution Broadband Mass Spectra of Natural Organic Matter, the Van Krevelen Diagram. Anal. Chem. 75, 5336–5344. M. Ruan et al. , 2023. Molecular-level exploration of properties of dissolved organic matter in natural and engineered water systems: A critical review of FTICR-MS application. Crit. Rev. Environ. Sci. Technol. 10.1080/10643389.2022.2157167 . A. Hu et al. , 2022. Microbial and Environmental Processes Shape the Link between Organic Matter Functional Traits and Composition. Environ. Sci. Technol. 56, 10504–10516. J. D'Andrilli, W. T. Cooper, C. M. Foreman, A. G. Marshall, 2015. An ultrahigh-resolution mass spectrometry index to estimate natural organic matter lability. Rapid Commun. Mass Spectrom. 29, 2385–2401. V.-N. Roth, T. Dittmar, R. Gaupp, G. Gleixner, 2013. Latitude and pH driven trends in the molecular composition of DOM across a north south transect along the Yenisei River. Geochim. Cosmochim. Acta 123, 93–105. D. N. Kothawala, A. M. Kellerman, N. Catalan, L. J. Tranvik, 2021. Organic Matter Degradation across Ecosystem Boundaries: The Need for a Unified Conceptualization. Trends Ecol. Evol. 36, 113–122. D. E. LaRowe et al. , 2020. The fate of organic carbon in marine sediments - New insights from recent data and analysis. Earth-Science Reviews 204, 103146. X. Chen et al. , 2022. Oxygen availability driven trends in DOM molecular composition and reactivity in a seasonally stratified fjord. Water Res. 220, 118690. J. J. Melendez-Perez et al. , 2016. Characterization and comparison of riverine, lacustrine, marine and estuarine dissolved organic matter by ultra-high resolution and accuracy Fourier transform mass spectrometry. Org. Geochem. 101, 99–107. S. Kim, D. Kim, M. J. Jung, S. Kim, 2022. Analysis of environmental organic matters by Ultrahigh-Resolution mass spectrometry-A review on the development of analytical methods. Mass Spectrom. Rev. 41, 352–369. J. D'Andrilli et al. , 2010. Comprehensive characterization of marine dissolved organic matter by Fourier transform ion cyclotron resonance mass spectrometry with electrospray and atmospheric pressure photoionization. Rapid Commun. Mass Spectrom. 24, 643–650. J. A. Hawkes et al. , 2020. An international laboratory comparison of dissolved organic matter composition by high resolution mass spectrometry: Are we getting the same answer? Limnol. Oceanogr. Meth. 18, 235–258. R. J. Hijmans, S. E. Cameron, J. L. Parra, P. G. Jones, A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25, 1965–1978. M. Delgado-Baquerizo et al. , 2020. The proportion of soil-borne pathogens increases with warming at the global scale. Nature Climate Change 10, 550–554. P. Royston, G. Ambler, 1998. Generalized additive models. Stata Technical Bulletin 7. A. Galecki, T. Burzykowski, Linear Mixed Effects Models Using R.: A Step-by-Step Approach (2013), 10.1007/978-1-4614-3900-4 . S. Nakagawa, H. Schielzeth, R. B. O'Hara, 2013. A general and simple method for obtainingR2from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142. H. Singmann, D. Kellen, "An introduction to mixed models for experimental psychology" in New methods in cognitive psychology. (Routledge, 2019), pp. 4–31. M. A. Stoffel, S. Nakagawa, H. Schielzeth, 2021. partR2: partitioning R(2) in generalized linear mixed models. PeerJ 9, e11414. S. Wagner et al. , 2015. Linking the Molecular Signature of Heteroatomic Dissolved Organic Matter to Watershed Characteristics in World Rivers. Environ Sci Technol 49, 13798–13806. I. F. Creed et al. , 2015. The river as a chemostat: fresh perspectives on dissolved organic matter flowing down the river continuum. Can. J. Fish. Aquat. Sci. 72, 1272–1285. M. Heibati et al. , 2017. Assessment of drinking water quality at the tap using fluorescence spectroscopy. Water Res. 125, 1–10. W. Qiao, H. Guo, C. He, Q. Shi, B. Zhao, 2021. Unraveling roles of dissolved organic matter in high arsenic groundwater based on molecular and optical signatures. J. Hazard. Mater. 406, 124702. K. Kovács, A. Gaspar, C. Sajgó, P. Schmitt-Kopplin, E. Tombácz, 2012. Comparative study on humic substances isolated in thermal groundwaters from deep aquifers below 700 m. Geochem. J. 46, 211–224. J. Valle et al. , 2020. Molecular differences between water column and sediment pore water SPE-DOM in ten Swedish boreal lakes. Water Res. 170, 115320. Y. Dai et al. , 2016. Spatiotemporal variation of planktonic and sediment bacterial assemblages in two plateau freshwater lakes at different trophic status. Appl. Microbiol. Biotechnol. 100, 4161–4175. M. V. Rantala, L. Nevalainen, M. Rautio, A. Galkin, T. P. Luoto, 2016. Sources and controls of organic carbon in lakes across the subarctic treeline. Biogeochemistry 129, 235–253. C. T. Solomon et al. , 2015. Ecosystem Consequences of Changing Inputs of Terrestrial Dissolved Organic Matter to Lakes: Current Knowledge and Future Challenges. Ecosystems 18, 376–389. M. M. Bengtsson, K. Attermeyer, N. Catalán, 2018. Interactive effects on organic matter processing from soils to the ocean: are priming effects relevant in aquatic ecosystems? Hydrobiologia 822, 1–17. L. Zhai, Y. An, L. Feng, X. Qin, J. Xu, 2022. Contrasting the physical and chemical characteristics of dissolved organic matter between glacier and glacial runoff from a mountain glacier on the Tibetan Plateau. Sci. Total Environ. 848, 157784. X. Chen et al. , 2014. Fate of 14C-labeled dissolved organic matter in paddy and upland soils in responding to moisture. Sci. Total Environ. 488–489, 268–274. N. Mladenov et al. , 2011. Dust inputs and bacteria influence dissolved organic matter in clear alpine lakes. Nat. Commun. 2, 405. R. Li, B. Xi, W. Tan, Y. Yuan, 2022. Spatiotemporal heterogeneous effects of microplastics input on soil dissolved organic matter (DOM) under field conditions. Sci. Total Environ. 847, 157605. E. A. Davidson, I. A. Janssens, 2006. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173. T. W. Crowther et al. , 2016. Quantifying global soil carbon losses in response to warming. Nature 540, 104–108. A. M. Kellerman, T. Dittmar, D. N. Kothawala, L. J. Tranvik, 2014. Chemodiversity of dissolved organic matter in lakes driven by climate and hydrology. Nat. Commun. 5, 3804. C. Orland, K. M. Yakimovich, N. C. S. Mykytczuk, N. Basiliko, A. J. Tanentzap, 2020. Think global, act local: The small-scale environment mainly influences microbial community development and function in lake sediment. Limnol. Oceanogr. 65, S88-S100. A. N. Bulseco et al. , 2019. Nitrate addition stimulates microbial decomposition of organic matter in salt marsh sediments. Glob Chang Biol 25, 3224–3241. C. D. Evans et al. , 2012. Acidity controls on dissolved organic carbon mobility in organic soils. Global Change Biol. 18, 3317–3331. V. r. Masson-Delmotte, Global warming of 1.5°C: an IPCC Special Report on impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty (Cambridge University Press, Cambridge, 2022), pp. 1 online resource (xiii, 616 pages): digital, PDF file(s). D. M. J. S. Bowman, G. J. Williamson, R. J. Keenan, L. D. Prior, 2014. A warmer world will reduce tree growth in evergreen broadleaf forests: evidence from Australian temperate and subtropical eucalypt forests. Glob. Ecol. Biogeogr. 23, 925–934. I. Gwitira, A. Murwira, M. D. Shekede, M. Masocha, C. Chapano, 2014. Precipitation of the warmest quarter and temperature of the warmest month are key to understanding the effect of climate change on plant species diversity in Southern African savannah. Afr. J. Ecol. 52, 209–216. D. Costa, R. M. Tavares, P. Baptista, T. Lino-Neto, 2022. The influence of bioclimate on soil microbial communities of cork oak. BMC Microbiol. 22, 163. V. A. Garayburu-Caruso et al. , 2020. Using Community Science to Reveal the Global Chemogeography of River Metabolomes. Metabolites 10, 518. P. Maurischat, M. Seidel, T. Dittmar, G. Guggenberger, 2022. A DOM continuum from the roof of the world – Tibetan molecular dissolved organic matter characteristics track sources, land use effects, and processing along the fluvial-limnic pathway. EGUsphere 2022, 1–31. S. Qin et al. , 2019. Temperature sensitivity of SOM decomposition governed by aggregate protection and microbial communities. Science Advances 5, eaau1218. N. Catalán, R. Marcé, D. N. Kothawala, L. J. Tranvik, 2016. Organic carbon decomposition rates controlled by water retention time across inland waters. Nat. Geosci. 9, 501–504. R. J. Whittaker, D. J. Futuyma, 1976. Communities and Ecosystems. The Quarterly Review of Biology 51, 159–160. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Published Journal Publication published 01 Jan, 2024 Read the published version in Fundamental Research → Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3324551","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":231342348,"identity":"dcdd2204-89d9-422f-8803-b9423b4e47b1","order_by":0,"name":"Ang Hu","email":"","orcid":"","institution":"Nanjing Institute of Geography and Limnology, Chinese Academic of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ang","middleName":"","lastName":"Hu","suffix":""},{"id":231342349,"identity":"ae4ea9af-3cb8-4754-a96a-f6d7449024b5","order_by":1,"name":"Lei Han","email":"","orcid":"","institution":"Nanjing Institute of Geography and Limnology, Chinese Academic of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Han","suffix":""},{"id":231342350,"identity":"b1153902-1104-4112-a875-de8e25fddbcf","order_by":2,"name":"Xiancai Lu","email":"","orcid":"","institution":"Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Xiancai","middleName":"","lastName":"Lu","suffix":""},{"id":231342351,"identity":"ff298647-fabf-43c4-896d-d1e2abc971c7","order_by":3,"name":"Ganlin Zhang","email":"","orcid":"","institution":"Institute of Soil Science, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ganlin","middleName":"","lastName":"Zhang","suffix":""},{"id":231342352,"identity":"4ae19afc-4695-40fd-885a-1a3da096e57d","order_by":4,"name":"Jianjun Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYBACxoYDIMqGgY2BuYEkLWlALYxEaoGCw2DdxKllbjxj+Lng1/k8Pv6FjR9/MNjJM7CfPUDAYWeMpWf23S5mk3jYLM3DkGzYwJOXQEDL2Q3SvD23E9skDjZIA21NYJDgMSCkZfNv3p5zIC3NP38w1BOlZZs0z48DiW38jW0SPAyHidFy/ps1b0My0BbGNmseg+OGbTw5+LUYzjiWfJvnj13i/P7Dh2/+qKiW52c/Q0jLAaBVbUCWRAKQACpmw6seCOT5G4DkHyDmP0BI7SgYBaNgFIxUAACm9EcgSwKP4wAAAABJRU5ErkJggg==","orcid":"","institution":"Nanjing Institute of Geography and Limnology, Chinese Academic of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Jianjun","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2023-09-04 13:00:57","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-3324551/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-3324551/v2","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1016/j.fmre.2023.11.018","type":"published","date":"2024-01-01T09:09:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":50875352,"identity":"8107549e-d88f-4183-85dd-cde308b63ffd","added_by":"auto","created_at":"2024-02-08 18:58:22","extension":"tif","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1208467,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMap of samples included in the compiled dataset of H/C and O/C ratios of DOM.\u003c/strong\u003e We obtained a total of 3,558 samples from 317 studies that measure H/C and O/C ratios across Earth systems including waters, land, plant, petroleum, and atmosphere prior to June 2022. The geographic locations of samples across different habitats are shown with colored dots. Numbers of studies (N) and samples (n) per system are given in the parentheses. Inset figure shows the distribution of samples across the gradients of temperature and precipitation. Polygons depict Whittaker’s biomes [62] according to mean annual temperature (°C) and mean annual precipitation (mm yr\u003csup\u003e-1\u003c/sup\u003e), following: (1) tropical rainforest; (2) tropical seasonal rainforest/savanna; (3) subtropical desert; (4) temperate rainforest; (5) temperate seasonal forest; (6) woodland/shrubland; (7) temperate grassland/desert; (8) boreal forest; and (9) tundra. The colored dots indicate the samples across different habitats. The full publication list for the global synthesis is shown in Supporting Information Table S1.\u003c/p\u003e","description":"","filename":"Fig.1.tif","url":"https://assets-eu.researchsquare.com/files/rs-3324551/v2/477bf406ed41581a3b5c8000.tif"},{"id":50875350,"identity":"5af7aa31-6762-45a0-b12e-43449e031fe6","added_by":"auto","created_at":"2024-02-08 18:58:22","extension":"tif","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2068151,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVariation of H/C and O/C ratios of DOM across Earth systems.\u003c/strong\u003e(a) Van Krevelen diagram shows the means of compositional-level H/C and O/C ratios measured by (-)ESI-FT-ICR MS in Earth systems including waters, land, plant, petroleum, and atmosphere. The means ± s.e of H/C and O/C ratios are shown with colored dots, and the number of samples in each system is indicated by the dot size and in the parentheses. Black dashed lines represent the direction of change in H/C and O/C ratios for chemical reactions including hydrogenation/dehydrogenation, and oxidation/reduction [12]. Marginal density plot shows the distribution of compositional-level H/C and O/C ratios in each system, and the small colored dots are their means. (b) Boxplots of compositional-level H/C (left panel) and O/C (right panel) ratios in each Earth system. Colored dots in the boxplots are the H/C or O/C values for individual samples, and black dots indicate their mean values. Different letters (a-c) indicate a significant difference (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) by a Kruskal-Wallis test. Regarding the organization of figure panels, we could first look at the overall view of means ± s.e of H/C and O/C ratios among all systems in Panel (a), and then zoom in to the samples within each system in Panel (b).\u003c/p\u003e","description":"","filename":"Fig.2.tif","url":"https://assets-eu.researchsquare.com/files/rs-3324551/v2/9674d232e458dfde62430296.tif"},{"id":50875349,"identity":"46df8736-8f4d-4241-9706-5c0249d30c11","added_by":"auto","created_at":"2024-02-08 18:58:22","extension":"tif","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1857824,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVariation of H/C and O/C ratios of DOM across habitats of waters and land. \u003c/strong\u003eVan Krevelen diagram shows the means of compositional-level H/C and O/C ratios measured by (-)ESI-FT-ICR MS in the habitats of waters (blue, a) and land (orange, b). Black dashed lines represent the direction of change in H/C and O/C ratios for chemical reactions including hydrogenation/dehydrogenation, and oxidation/reduction [12]. The number of samples in each habitat is indicated by the dot size. (c) Boxplots of compositional-level H/C (top panel) and O/C (bottom panel) ratios for better comparisons among the habitats. Significances for pairwise comparisons between habitats by a Wilcoxon test are provided in Fig. S5. Colored dots in the boxplots are the H/C or O/C values for individual samples, and black dots indicate their mean values. The dots with color gradients of blue or orange from light to dark represent H/C ratios varying from low to high, respectively. The labels with numbers 1 to 25 indicate all habitats. Marine HF: marine hydrothermal fluid. Regarding the organization of figure panels, we could first look at the overall view of means ± s.e of H/C and O/C ratios among all habitats in Panel (a) and (b), and then zoom in to the samples within each habitat in Panel (c).\u003c/p\u003e","description":"","filename":"Fig.3.tif","url":"https://assets-eu.researchsquare.com/files/rs-3324551/v2/a82d446598874f04276e2cb6.tif"},{"id":50875771,"identity":"e693031c-af0a-4069-b201-c115b0c58a94","added_by":"auto","created_at":"2024-02-08 19:06:22","extension":"tif","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":523477,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVariability of H/C and O/C ratios of DOM within each habitat of waters and land. \u003c/strong\u003eViolin plots of variability of the H/C (a) and O/C (b) ratios of DOM measured by (-)ESI-FT-ICR MS across the samples in the waters or land and their corresponding habitats. The variability was calculated as the ratio of standardized deviation and mean of the ratios by randomly selecting 50% samples (100 bootstraps) for each system or habitat. Asterisks indicate the significant (\u003csup\u003e***\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e ≤ 0.001) differences between two ratios by t-test analysis. ns: non-significant.\u003c/p\u003e","description":"","filename":"Fig.4.tif","url":"https://assets-eu.researchsquare.com/files/rs-3324551/v2/59558a05966cf883ebde9f46.tif"},{"id":50875353,"identity":"66fe6c8c-31f0-4127-aa7c-ca610fd18157","added_by":"auto","created_at":"2024-02-08 18:58:22","extension":"tif","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1990166,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe distribution patterns of H/C and O/C ratios of DOM along latitudinal gradients. \u003c/strong\u003eWe plotted the compositional-level H/C and O/C ratios measured by (-)ESI-FT-ICR MS against latitudes for the waters (blue, a) and land (orange, c) and the corresponding habitats (b, d). Latitudinal patterns are visualized with generalized additive models with 2 knots, and the significant patterns are indicated by asterisks (\u003csup\u003e***\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e ≤ 0.001; \u003csup\u003e**\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e ≤ 0.01; \u003csup\u003e*\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e ≤ 0.05). North and South latitudes were assigned as absolute latitudes. It should be noted that it would be challenging to show the equator and polar regions in the figures, as we did not obtain samples located in the latitudes of \u0026gt; 80°.\u003c/p\u003e","description":"","filename":"Fig.5.tif","url":"https://assets-eu.researchsquare.com/files/rs-3324551/v2/50ee0194fd7f2b4269c13550.tif"},{"id":50875354,"identity":"ba1505cf-9322-4c85-98cc-460c9e57f863","added_by":"auto","created_at":"2024-02-08 18:58:22","extension":"tif","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":297074,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe influences of climatic and environmental variables on H/C and O/C ratios of DOM.\u003c/strong\u003e The influences of each explanatory variable on the compositional-level H/C (a) and O/C (b) ratios measured by (-)ESI-FT-ICR MS were examined with linear mixed-effects models for the waters and land and their finely-categorized habitats. The significant (\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05) conditional explained heterogeneity (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e) are shown for these variables according to their driver categories of temperature, precipitation and environments. Smaller solid dots are the average \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e for each driver category, and open circles are the maximum \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e among the variables in each category. We included the habitats with the samples size over 30. The abbreviations of the explanatory variables are shown in the Material and Methods.\u003c/p\u003e","description":"","filename":"Fig.6.tif","url":"https://assets-eu.researchsquare.com/files/rs-3324551/v2/62cc99dbb705ac42ba138b8a.tif"},{"id":53328746,"identity":"5b3168c6-2099-4cd4-81d5-fd9562e03d56","added_by":"auto","created_at":"2024-03-24 09:10:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2970992,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3324551/v2/72a6d5e0-0409-43e9-91ac-2cefdb04a058.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eGlobal patterns and drivers of dissolved organic matter across Earth systems: Insights from H/C and O/C ratios\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDissolved organic matter (DOM) is an essential component of the Earth\u0026rsquo;s biogeochemical cycles in determining carbon source or sink and is ubiquitous within and across Earth systems [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Terrestrial soil, inland waters, and ocean are key carbon reservoirs controlling atmosphere greenhouse gases and Earth\u0026rsquo;s climate as they not only transport and process, but also bury large amounts of organic carbon [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. For example, global carbon sequestration in soil, inland waters, and ocean are estimated at ~\u0026thinsp;0.9, 0.15, and 0.2 Pg per year, respectively [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These systems are interconnected in the emergent view of global carbon cycle, which leads to their exchange of dissolved carbon and nutrients and, in turn, should impact the fate of DOM such as decomposition and production rates [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The global carbon cycle\u0026rsquo;s processes could be effectively informed from the molecular-level perspectives of DOM characteristics (i.e., molecular traits) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. A better understanding of the regulatory mechanisms of the global variation in DOM traits resulting from the spatial heterogeneity of climatic and environmental variables is important for estimating the responses of carbon cycle\u0026rsquo;s processes to environmental changes. Incorporation of the previously over-looked drivers into the predictive models (e.g., Earth system models) is needed to reduce the uncertainty of estimation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Therefore, it is crucial to develop a more holistic perspective of the distribution and underlying drivers of DOM molecular traits across Earth systems and multigradient environments, which ultimately helps inform modelling for predicting future global carbon cycle\u0026rsquo;s processes.\u003c/p\u003e \u003cp\u003eOrganic matter chemistry is a complex pool of thousands of distinct molecules, with unique molecular traits such as the two primary dimensions of hydrogen to carbon ratio (H/C) and oxygen to carbon ratio (O/C) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. H/C ratio is relevant to biogeochemical reactions of hydrogenation or dehydrogenation, reflecting the degree of hydrogen saturation. Higher H/C ratio reflects higher degree of hydrogen saturation. H/C ratio can also be applied to indicate the capacity for a molecule to be degraded [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. O/C ratio is relevant to chemical reactions of oxidation or reduction. Higher O/C ratio reflects higher degree of oxygenation and more oxygen-containing functional groups such as carboxyl or hydroxyl groups [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These two dimensions of traits could be constrained by microbes and environmental conditions like nutrients, temperature, and sunlight, and further inform the transformation of organic matter [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. For example, the environmental condition such as low oxygen availability can enrich compounds with lower oxygenation and make organic matter degradation thermodynamically unfeasible [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The climatic factors such as mean annual temperature can also affect the DOM\u0026rsquo;s characteristics with more hydrogen saturated compounds enriched in the higher temperature conditions [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Despite the well-known importance of average state of the climatic variables, there are few investigations considering the influences of extremes or variability of climatic variables on DOM composition.\u003c/p\u003e \u003cp\u003eHere, we compiled compositional-level H/C and O/C ratios of DOM of 3,558 samples derived from 317 studies spanning diverse systems and climates worldwide (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, S1, and Table S1). The datasets included waters, land, plant, petroleum, and atmosphere systems, covering the climatic regions from tropics to tundra (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, S1, and Table S1). There were 2,876 samples (80.8%) from waters and land, covering the habitats of land-to-ocean continuum, e.g., soil, peatland, glacier, pond, reservoir, lake, river, and ocean. The availability of such big datasets is benefited from the recent advance of ultrahigh-resolution Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. FT-ICR MS has been applied to numerous natural organic matter such as terrestrial, aquatic and marine DOM, microbial-derived DOM, and petroleum-derived materials, and to further determine chemical characteristics (i.e., molecular traits) as a function of (bio)geochemical or anthropogenic processes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The compositional-level DOM traits reduce the complex mass spectrum data for the individual peaks to abundance-weighted average indices [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This is beneficial for an intersample comparison by incorporating climatic and environmental factors, and has already been well applied in DOM studies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. We aimed to provide a comprehensive survey on molecular-level perspectives of DOM characteristics at a global scale. Specifically, the synthesis explores the distribution patterns of H/C and O/C ratios of DOM across Earth systems and along latitudinal gradients, and elucidates the roles of climatic and environmental variables in driving these traits. Such global patterns and drivers for DOM via a meta-synthesis study could be more important when estimating the effects of global environmental change on carbon cycle\u0026rsquo;s processes given the limited scopes of individual studies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eWe systematically searched all peer-reviewed publications that were published prior to June 2022, which investigated the molecular traits (i.e., H/C and O/C ratios) of DOM measured by FT-ICR MS using the Web of Science (Core Collection; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.webofknowledge.com\u003c/span\u003e\u003cspan address=\"http://www.webofknowledge.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Google Scholar (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://scholar.google.com\u003c/span\u003e\u003cspan address=\"http://scholar.google.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) via the search term: \u0026ldquo;organic matter AND FT-ICR MS AND van Krevelen\u0026rdquo;. The molecular traits of thousands of molecular formulae (hereafter refer to as \u0026ldquo;molecules\u0026rdquo;) for each sample\u0026rsquo;s FT-ICR MS spectrum were evaluated on van Krevelen diagrams on the basis of their molar H/C ratios (y axis) and molar O/C ratios (x axis) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The van Krevelen diagrams enable the comparison of molecular properties of organic matter and the ability to assign molecules to major biochemical categories, which included amino sugar-, lipid-, protein-, lignin-, carbohydrate-, tannin-, and condensed aromatic-like compounds. However, it should be noted that we here used van Krevelen diagrams to visualize the H/C and O/C ratios at the compositional level, and the sample points in the diagrams do not intend to assign the samples to these biochemical categories.\u003c/p\u003e \u003cp\u003eWe employed the criteria to select the studies as follows. (1) They had raw mass spectrometry data, from which the compositional-level H/C and O/C ratios could be calculated. (2) They had compositional-level H/C and O/C ratios, that is weighted means of formula-based H/C and O/C ratios in a given sample, which are calculated as the sum of the H/C (or O/C) ratio for each molecule and its relative intensity divided by the sum of all intensities [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. (3) They focused on the DOM extracted from natural and engineered environments, rather than manipulated experiments. In total, H/C and O/C ratios of 3,558 samples from 317 studies met these criteria (Table S1).\u003c/p\u003e \u003cp\u003eTo minimize the challenges in data comparison and interpretation across studies with different instrument type and settings [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], we employed the following criteria to further subset the data: (1) DOM trait datasets obtained by FT-ICR MS were retained, but not by other instrument types such as Orbitrap MS. (2) Negative ESI mode was retained for the following statistical analyses, as it is most frequently documented in literature by comprising 88.9% of the total datasets and is the most suitable ionization method for the analysis of natural DOM. (3) We focused on the compositional-level H/C and O/C ratios calculated based on all molecules in a given sample, rather than the samples with only subsets of molecules. In total, there were H/C and O/C ratios of 2,995 samples from 270 studies using (-)ESI-FT-ICR MS for the robust data comparison among various systems and habitats.\u003c/p\u003e \u003cp\u003eThe collected dataset included various Earth systems, such as waters, land, plant, petroleum, and atmosphere, covering climatic regions from tropics to tundra (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, S1, S2, and Table S2). We further binned the dataset of each system into fine habitats (Figs. S3, S4, Table S3). Specifically, the waters includes habitats of marine water, marine sediment, marine hydrothermal fluid, lake water, lake sediment, reservoir, pond, river water, river sediment, stream, drink water, groundwater, spring, glacier, snow, rainwater, and wastewater. The land includes habitats of peatland, permafrost, forest soil, grassland, cropland, paddy soil, riparian soil, and coastal soil. The plant includes habitats of phycophyta, herbage, arbor, and shrub. The atmosphere includes habitats of aerosol, particulate matter (PM) 2.5, and PM 10. The glacier is mainly derived from marine ice and lake ice. There were several habitats categorized as \u0026ldquo;Others\u0026rdquo;, including virus, melanin, murchison, mineral, coal, biochar, and manure. Waters and land systems were discussed in more detail than plant, petroleum, and atmosphere systems, as more sufficient data derived from these two systems and their finely-categorized habitats were available in the literature. It should be noted that we also included the rarely reported systems like plant, petroleum, and atmosphere systems, as this synthesis was aimed to provide an overview for comparing DOM traits derived from as many Earth systems as possible.\u003c/p\u003e \u003cp\u003eBesides the molecular traits of H/C and O/C ratios, the datasets also included climatic and environmental variables for each sample when possible. A total of 15 environmental variables were collected, including salinity, temperature, pH, conductivity, and the concentrations of dissolved oxygen (DO), total organic carbon (TOC), total nitrogen (TN), total dissolved nitrogen (TDN), dissolved organic carbon (DOC), ammonium (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e), nitrate (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e), nitrite (NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e), phosphate (PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e), iron (Fe), and manganese (Mn). In addition, climatic variables were derived using latitude, longitude and digital elevation data with a spatial resolution of 0.5\u0026deg;. The gridded data were obtained from the WorldClim dataset (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.worldclim.org\u003c/span\u003e\u003cspan address=\"https://www.worldclim.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for the 19 bioclimatic variables [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], including annual mean temperature (BIO1), mean diurnal range (BIO2), isothermality (BIO3), temperature seasonality (BIO4), maximum temperature of warmest month (BIO5), minimum temperature of coldest month (BIO6), temperature annual range (BIO7), mean temperature of wettest quarter (BIO8), mean temperature of driest quarter (BIO9), mean temperature of warmest quarter (BIO10), mean temperature of coldest quarter (BIO11), annual precipitation (BIO12), precipitation of wettest month (BIO13), precipitation of driest month (BIO14), precipitation seasonality (BIO15), precipitation of wettest quarter (BIO16), precipitation of driest quarter (BIO17), precipitation of warmest quarter (BIO18), and precipitation of coldest quarter (BIO19).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe significance of differences in H/C or O/C ratios between Earth systems was performed using a Kruskal-Wallis test. Pairwise comparison was performed for the magnitude of variances of H/C or O/C ratios between habitats using Wilcoxon test. These analyses were performed using R package stats V4.1.3.\u003c/p\u003e \u003cp\u003eWe further explored the distribution patterns of compositional-level H/C or O/C ratios along latitudinal gradients, and the influences of explanatory variables on these two ratios. The explanatory variables included 19 bioclimatic and 15 collected environmental variables. It should be noted that although DOM molecular traits are also dependent on microbes and sunlight, we here focused on climatic vs. environmental constraints due to the following reasons: (1) There were important roles of climatic and environmental variables documented in previous literature such as Roth \u003cem\u003eet al\u003c/em\u003e., (2019) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and Hu \u003cem\u003eet al\u003c/em\u003e., (2022) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]; (2) Microbial data are not always available along with DOM mass spectral data in the same literature, and thus the influences of microbes and sunlight on DOM could not be well quantified in our meta-analysis study. For better statistical power, we performed the analyses with the sample size over 30 for the waters or land, or each of their habitats.\u003c/p\u003e \u003cp\u003eThe latitudinal patterns of H/C and O/C ratios were fitted using generalized additive models [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The influences of climatic and environmental variables on H/C and O/C ratios were evaluated by linear mixed-effects models [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In each model, we modeled H/C or O/C ratios in every Earth system (that is, waters or land) as a function of a climatic or environmental variable, and used studies and habitats as random effects. The omnibus test was used to evaluate model significance, and the conditional explained heterogeneity represented the influence of each explanatory variable on the H/C or O/C ratios accounting for the random effects [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. To minimize the potential biases of data discrepancies across various instruments or laboratories, we specified random effects in our model, which are able to factor out the idiosyncrasies of our samples and obtain a more general estimate of the fixed effects of interest [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. We further examined the influences of each of these explanatory variables on the H/C or O/C ratios in each habitat of waters and land, in which we used the identity of data-source studies as random effects. The analyses of linear mixed-effects models were performed by using lmer function in the R package lme4 V1.1.28. This approach enabled us to obtain reliable results of the latitudinal patterns of H/C or O/C ratios and the influences of climatic and environmental variables on the ratios. Further partitioning analysis in linear mixed-effects models provided an estimate of the total contribution of a fixed effect of each climatic variable to the overall prediction of H/C or O/C ratio. We selected climatic variables for partitioning analyses by dereplicating strongly correlated variables by a threshold of Pearson correlation over 0.8. Partitioning analysis was performed with R package partR2 V0.9.1 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eVariation of DOM traits across Earth systems\u003c/h2\u003e \u003cp\u003eThe molecular traits of DOM, measured by H/C and O/C ratios at the compositional level (hereafter, H/C and O/C ratios), were highly divergent across Earth systems, such as waters, land, plant, petroleum, and atmosphere systems (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, S2, Table S2). H/C and O/C ratios varied from 0.22 to 2.14, and 0.01 to 1.04, with mean values of 1.17 and 0.41, respectively, in all systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Table S2). H/C ratios were lower than 1.5 in 92.0% of samples, indicating that DOM generally contained a high abundance of recalcitrant (i.e., less hydrogen saturated) molecules in each system [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong these systems, atmosphere samples showed the highest mean values for H/C (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;s.e\u0026thinsp;=\u0026thinsp;1.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.030) and O/C ratios (0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.017), indicating a higher abundance of more hydrogen saturated molecules and more abundant oxygen-containing functional groups, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Table S2). Atmosphere experiences rapid photochemical transformation and, therefore, indicates that DOM contains a higher abundance of more hydrogen saturated molecules than other systems and thus the highest H/C mean value. In comparison, petroleum samples had relatively intermediate H/C (mean\u0026thinsp;=\u0026thinsp;1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.034) and the lowest O/C (mean\u0026thinsp;=\u0026thinsp;0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.020) ratios, while plant had similar H/C and O/C ratios to those in waters and land (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table S2).\u003c/p\u003e \u003cp\u003eThere were 2,140 and 401 samples for waters and land, comprising 71.5% and 13.4% of the collected datasets, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table S2). The mean values of O/C ratios were significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) lower in waters (0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002) than land (0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005), and their H/C ratios showed the similar pattern with mean values of 1.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005 and 1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.011, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table S2). We recognized the overlapped nature between waters and land systems, where the mean values of H/C ratios were lower than the other systems and their O/C ratios ranked between plant and atmosphere (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, S2). This indicates that DOM contains a higher abundance of molecules with more recalcitrant and relatively intermediate oxygenation than the other systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eVariation of DOM traits across habitats of waters and land\u003c/h2\u003e \u003cp\u003eWhen considering individual habitats of waters or land, we also found some distinct variations in H/C or O/C ratios between habitats (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, S4, Table S3), and the significance tests are shown in Fig. S5. For the waters, H/C ratios were over 1.4 in 12.9% of samples and showed the significantly highest mean values in snow and rainwater (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05; Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, S5). Relatively high values of these two natural aquatic habitats were similar to those of atmosphere system, which could be explained by their shared atmospheric source of DOM. Subsequently, the habitats like stream, pond and spring had the means of H/C ratios ranging from 1.29 to 1.31 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Similar H/C ratios were also observed in glacier and ocean with mean values of 1.27 to 1.34 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). These habitats had relatively higher mean values of H/C ratios over 1.2, while their O/C ratios ranged between 0.4 and 0.5 except for those in snow (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Generally, O/C ratios in each habitat showed significantly higher variation than H/C ratios (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn contrast, river and drinking water showed the lower means of H/C ratios ranging from 1.08 to 1.10, followed by groundwater with the lowest values of 0.98 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). We observed significantly lower values of these habitats than most of the other aquatic habitats (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05; Fig. S5). Like lake, pond and stream, DOM characteristics in river were also influenced by terrestrial inputs [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]; however, lower H/C mean values were associated with lower carbon productivity and turnover rates due to flowing waters with short residence time [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. As an engineered aquatic habitat, DOM in drinking water is characterized as rapidly microbial processing of labile DOM [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], leaving recalcitrant molecules behind and thus low H/C mean values. The lowest H/C ratios in groundwater indicate higher aromaticity than usually experienced for those in aquatic and terrestrial surface environments, which is consistent with previous reports [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Notably, H/C ratios in lakes showed relatively large variation between sediment and water (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05), and were lower in the former with mean values of 1.01 and 1.25, respectively (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, S5). The lower hydrogen saturation of DOM in lake sediment is associated with accumulated recalcitrant molecules, likely due to higher microbial diversity and carbon metabolism that in turn lead to faster microbial processing of labile organic matter [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. For O/C ratios, the highest and lowest mean values of 0.51 and 0.31 were observed in drinking water and river sediment, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eFor the land, mean H/C ratios showed the highest and lowest values of 1.32 and 1.04\u0026ndash;1.07 observed in coastal and cropland/riparian soils, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Interestingly, H/C ratios were not significantly different (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) between coastal soil and ocean, while those in cropland and riparian soil were similar to river and lake sediment (Fig. S5). This phenomenon agrees with the fact that there is a land-water continuum for the spatial dynamics of DOM to be transported from terrestrial soils to inland waters and from tidal wetlands to the ocean. In the context of future climate warming, the increased extreme rainfall intensity and frequency would enhance the carbon and nutrient transports from soil to inland waters and to ocean [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Consequently, this mixing of organic carbon sources of contrasting reactivity might result in the changes in organic matter degradation through priming processes [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Compared to H/C ratios, O/C ratios showed significantly higher variation across most habitats of land (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This suggests that the variations of DOM characteristics especially O/C ratios in each habitat should be constrained such as by climate and environmental conditions resulting from their global spatial heterogeneity.\u003c/p\u003e \u003cp\u003eCollectively, DOM traits particularly H/C ratios showed clear variations across Earth systems and habitats. Specifically, H/C ratios were on average lower in waters and land than other systems such as plant, petroleum and atmosphere. In these two systems, the H/C ratios of DOM varied from the highest to the lowest in the habitats of land-to-ocean continuum generally as snow, rainwater\u0026thinsp;\u0026gt;\u0026thinsp;glacier\u0026thinsp;\u0026gt;\u0026thinsp;coastal soil, ocean, stream, pond, permafrost\u0026thinsp;\u0026gt;\u0026thinsp;lake water, reservoir, peatland, paddy soil, forest soil, grassland\u0026thinsp;\u0026gt;\u0026thinsp;river\u0026thinsp;\u0026gt;\u0026thinsp;lake sediment, riparian soil, cropland\u0026thinsp;\u0026gt;\u0026thinsp;groundwater. Based on smaller number of observations, previous studies have also tried to compare the H/C ratios of DOM across a limited number of habitats. For example, H/C ratios were higher for glacier, followed by ocean and freshwater [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], higher in lake water relative to lake sediment [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], and higher in paddy soil than upland soil [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Different from previous studies, our synthesized global datasets, for the first time, extended such findings with unprecedently finely-categorized and comprehensive habitats, which provides an overview for comparing DOM traits along the aquatic-terrestrial continuum.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eVariation of DOM traits with latitudes\u003c/h2\u003e \u003cp\u003eH/C and O/C ratios also showed predictable patterns along latitudinal gradients for the systems of waters and land (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Specifically, H/C ratios generally showed a significant U-shaped pattern in both systems, with the lowest values occurring in latitudes of absolute 40\u0026deg; \u0026minus;\u0026thinsp;50\u0026deg; (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05, generalized additive models; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, c). This U-shaped pattern was also observed in specific habitats such as river water, lake water, and forest soil (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, d). In comparison, O/C ratios also showed a significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) U-shaped pattern in the waters, but a nonsignificant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) pattern in the land (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, c). In contrast, previous studies show that DOM traits have monotonically decreasing or increasing patterns along latitudinal gradients [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. For example, bacterial production of fluorescent DOM in alpine and polar lakes, indicated by optical properties like absorption at 250 nm and total fluorescence, shows a decreasing pattern within absolute latitudes of 30\u0026ndash;75\u0026deg; but is lower at around mid-latitudes of ~\u0026thinsp;50\u0026deg; [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Considering our compiled datasets with a larger spatial scale spanned from tropics to polar and covered broad gradients of ecosystem properties such as climatic and environmental factors, we thus show, for the first time, that H/C ratios had the predictably latitudinal pattern for both waters and land systems. Together, our results suggest that DOM is more hydrogen saturated towards the extremes at the polar regions and at the equator, while less hydrogen saturated in the mid-latitudes of 40\u0026deg;-50\u0026deg;.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDrivers of DOM traits in waters and land\u003c/h2\u003e \u003cp\u003eThe distribution patterns of H/C and O/C ratios of DOM were both significantly affected by climate and environmental variables across waters and land, indicated by linear mixed models (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, S6, S7, and Table S4). For the waters, environmental variables such as dissolved oxygen and ammonium had the strongest effects on H/C (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.775, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) and O/C (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.605, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) ratios, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Climatic variables, such as mean annual precipitation and mean temperature of warmest quarter, also showed strong effects on O/C ratios, with the explained variation of 0.564 and 0.527, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). For the land, H/C and O/C ratios also showed significant variation explained by climatic variables, such as maximum temperature of warmest month (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.332, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) and mean temperature of wettest quarter (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.425, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Further partitioning analyses confirmed the stronger relative importance of extremes of climatic factors on H/C and O/C ratios than mean annual climates (Fig. S6). Specifically, the variances of H/C and O/C ratios were mostly explained by isothermality and precipitation of coldest quarter, respectively, in the waters, while by precipitation of coldest quarter and mean temperature of wettest quarter, respectively, in the land (Fig. S6).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHowever, the dominant effects of climatic and environmental factors were not always consistent across the individual habitats (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, S7). For example, H/C and O/C ratios in marine water, reservoir water, river water, and peatland were most strongly affected by environmental variables, such as total dissolved nitrogen, dissolved organic carbon, nitrate, and pH, followed by extremes of climatic variables, such as minimum temperature of coldest month, maximum temperature of warmest month, and mean temperature of warmest quarter (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In contrast, H/C and O/C ratios in river sediment and soil were dominantly affected by mean annual temperature and extremes of climatic variables such as precipitation of warmest quarter, mean diurnal range, isothermality, and temperature seasonality (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese findings support previous reports showing the important roles of ecosystem properties in controlling DOM traits and decomposition rates, such as temperature [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], precipitation [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], carbon and nitrogen contents [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], and acidity [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Further, our findings reveal additional links of DOM traits to the extremes of climatic variables beyond those drivers known from previous studies. Specifically, H/C and O/C ratios were more closely related to extremes (e.g., monthly or quarterly maximum) of temperature or precipitation than to mean annual temperature or precipitation. Earth\u0026rsquo;s average temperature has been increased by 1.5℃ since pre-industrial baseline, and even relatively small incremental increases in global warming (+\u0026thinsp;0.5℃) can cause statistically significant changes in extremes on the global scale and for large regions [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Extremes of climatic factors are key to understanding the effect of climate change on primary producers, such as plant species diversity and growth [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] and decomposers like microbes [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], which would affect organic carbon characteristics. Thus, our findings highlight the need to integrate extremes of climatic factors into climate change modelling when making current inferences and future predictions of organic carbon processes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eThe implications of this study\u003c/h2\u003e \u003cp\u003eFirst, our synthesized analysis provided a comprehensive survey of molecular-level perspectives of the global DOM characteristics across Earth systems and climates. The trait-based metrics like H/C and O/C ratios are relevant to the chemical reaction processes of molecules and thus effectively inform the fate of DOM such as decomposition processes. For example, the utility of H/C ratio as a surrogate for reactivity studies could reveal quantifiable and comparative labile nature of DOM [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Our utility of these two metrics provides an overview of the current state of knowledge on spatial distribution of DOM characteristics via a meta-synthesis approach by compiling data from the unprecedently finely-categorized and comprehensive habitats [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSecond, rather than considering these systems independent of one another, a more holistic perspective of DOM characteristics is needed to be developed in a system-to-system continuum like that in Lake Nam Co [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Considering that the intensity and frequency of extreme rainfall would increase with climate warming, the carbon and nutrient transport from soil to inland waters and to ocean are anticipated to increase [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Consequently, the global patterns of DOM characteristics across Earth systems or habitats could help understand how the mixing of organic carbon sources of contrasting reactivity would influence the changes in carbon cycle\u0026rsquo;s processes.\u003c/p\u003e \u003cp\u003eThird, a better understanding of molecular-level perspectives of the global DOM characteristics in response to environmental constraints is of great interest to a wide readership, as this ultimately helps understand and predict future global carbon cycle\u0026rsquo;s processes. It is critical to understand the global variations of DOM characteristics resulting from the spatial heterogeneity of climatic and environmental variables, which is important for estimating the potential constraints of DOM characteristics. Our synthesized analysis highlights the potential influence of climatic constraints especially extremes of climatic factors (e.g., monthly or quarterly maximum of temperature or precipitation) on the DOM traits. The inclusion of these novel drivers of climate extremes could help predict DOM characteristics and further carbon cycle\u0026rsquo;s processes under the future climate change scenarios.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFuture perspectives\u003c/h2\u003e \u003cp\u003eAlthough our mass spectrum datasets were selected considering FT-ICR MS instrument and ESI negative ionization method, there may still be uncertainties in data comparison and interpretation across studies. For example, the differences in analytical equipment between laboratories and studies, data acquisition and processing, or sample preparation techniques could be important considerations in achieving reproducible results [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Our findings should be, if possible, validated using the consistent measurement methods across systems and habitats. We, however, could utilize proper statistical models to account for idiosyncrasies of the data [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and minimize the potential biases of data discrepancies across various instruments or laboratories. Further, our datasets were based on the compositional-level H/C and O/C ratios of DOM. We did not consider other information in this study such as the full dataset with individual molecules, or other trait metrics like the number of N, P, and S, aromaticity, and nominal oxidation state of carbon. The additional trait information especially with consistent measurement methods would be helpful to fully understand the global carbon cycle\u0026rsquo;s processesing mechanisms and DOM transformations.\u003c/p\u003e \u003cp\u003eIn addition, much larger FT-ICR MS datasets in literature are derived from waters compared to other systems and mainly from the habitats in subtropical and temperate climates. Further studies are encouraged to extend the coverage of habitats such as groundwater, spring, soils and coastal areas to a larger spatial scale spanned from tropics to polar. Although our main aim is to explore the important roles of climatic and environmental factors in controlling DOM characteristics across Earth systems, other potential drivers such as microbial communities [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], minerals [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], and water retention time [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] have been reported to influence DOM characteristics. Future studies are encouraged to focus more on the causal relationships among these potential drivers, climate, and DOM characteristics, which will improve our understanding of underlying mechanisms of global carbon cycle\u0026rsquo;s processes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interests\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eJW conceived the review. LH synthesized and analyzed the data with the contributions of AH and JW. AH and JW finished the first draft. JW and AH finalized the manuscript with the contributions of all authors.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis study was supported by National Natural Science Foundation of China (42225708, 92251304, 42377122, 42077052), the Second Tibetan Plateau Scientific Expedition and Research (STEP) Program (2019QZKK0503), Research Program of Sino-Africa Joint Research Center, Chinese Academy of Sciences (151542KYSB20210007), Science and Technology Planning Project of NIGLAS (NIGLAS2022GS09), and CAS Key Research Program of Frontier Sciences (QYZDB-SSW-DQC043).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eV.-N. Roth \u003cem\u003eet al.\u003c/em\u003e, 2019. Persistence of dissolved organic matter explained by molecular changes during its passage through soil. Nat. Geosci. 12, 755\u0026ndash;761.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eT. Dittmar, A. Stubbins, 2014. 12.6\u0026mdash;Dissolved organic matter in aquatic systems. \u003cem\u003eTreatise on Geochemistry, 2nd edn. Elsevier: Oxford\u003c/em\u003e, 125\u0026ndash;156.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. M. Kellerman, D. N. Kothawala, T. Dittmar, L. J. Tranvik, 2015. Persistence of dissolved organic matter in lakes related to its molecular characteristics. Nat. Geosci. 8, 454\u0026ndash;457.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. Lehmann, M. Kleber, 2015. The contentious nature of soil organic matter. Nature 528, 60\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eT. J. Battin \u003cem\u003eet al.\u003c/em\u003e, 2009. The boundless carbon cycle. Nat. Geosci. 2, 598\u0026ndash;600.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL. J. Tranvik \u003cem\u003eet al.\u003c/em\u003e, 2009. Lakes and reservoirs as regulators of carbon cycling and climate. Limnol. Oceanogr. 54, 2298\u0026ndash;2314.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR. Mendonca \u003cem\u003eet al.\u003c/em\u003e, 2017. Organic carbon burial in global lakes and reservoirs. Nat. Commun. 8, 1694.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR. Lal, 2004. Soil carbon sequestration to mitigate climate change. Geoderma 123, 1\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP. Regnier, L. Resplandy, R. G. Najjar, P. Ciais, 2022. The land-to-ocean loops of the global carbon cycle. Nature 603, 401\u0026ndash;410.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. Hu \u003cem\u003eet al.\u003c/em\u003e, 2022. Ecological networks of dissolved organic matter and microorganisms under global change. Nat. Commun. 13, 3600.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. C. Stegen \u003cem\u003eet al.\u003c/em\u003e, 2018. Influences of organic carbon speciation on hyporheic corridor biogeochemistry and microbial ecology. Nat. Commun. 9, 585.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR. L. Sleighter, P. G. Hatcher, 2007. The application of electrospray ionization coupled to ultrahigh resolution mass spectrometry for the molecular characterization of natural organic matter. J. Mass Spectrom. 42, 559\u0026ndash;574.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH. Zhang \u003cem\u003eet al.\u003c/em\u003e, 2020. Microbial dynamics and soil physicochemical properties explain large-scale variations in soil organic carbon. Global Change Biol. 26, 2668\u0026ndash;2685.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. Lee \u003cem\u003eet al.\u003c/em\u003e, 2023. Soil organic carbon is a key determinant of CH(4) sink in global forest soils. Nat. Commun. 14, 3110.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Kim, R. W. Kramer, P. G. Hatcher, 2003. Graphical Method for Analysis of Ultrahigh-Resolution Broadband Mass Spectra of Natural Organic Matter, the Van Krevelen Diagram. Anal. Chem. 75, 5336\u0026ndash;5344.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. Ruan \u003cem\u003eet al.\u003c/em\u003e, 2023. Molecular-level exploration of properties of dissolved organic matter in natural and engineered water systems: A critical review of FTICR-MS application. Crit. Rev. Environ. Sci. Technol. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/10643389.2022.2157167\u003c/span\u003e\u003cspan address=\"10.1080/10643389.2022.2157167\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. Hu \u003cem\u003eet al.\u003c/em\u003e, 2022. Microbial and Environmental Processes Shape the Link between Organic Matter Functional Traits and Composition. Environ. Sci. Technol. 56, 10504\u0026ndash;10516.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. D'Andrilli, W. T. Cooper, C. M. Foreman, A. G. Marshall, 2015. An ultrahigh-resolution mass spectrometry index to estimate natural organic matter lability. Rapid Commun. Mass Spectrom. 29, 2385\u0026ndash;2401.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eV.-N. Roth, T. Dittmar, R. Gaupp, G. Gleixner, 2013. Latitude and pH driven trends in the molecular composition of DOM across a north south transect along the Yenisei River. Geochim. Cosmochim. Acta 123, 93\u0026ndash;105.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD. N. Kothawala, A. M. Kellerman, N. Catalan, L. J. Tranvik, 2021. Organic Matter Degradation across Ecosystem Boundaries: The Need for a Unified Conceptualization. Trends Ecol. Evol. 36, 113\u0026ndash;122.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD. E. LaRowe \u003cem\u003eet al.\u003c/em\u003e, 2020. The fate of organic carbon in marine sediments - New insights from recent data and analysis. Earth-Science Reviews 204, 103146.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eX. Chen \u003cem\u003eet al.\u003c/em\u003e, 2022. Oxygen availability driven trends in DOM molecular composition and reactivity in a seasonally stratified fjord. Water Res. 220, 118690.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. J. Melendez-Perez \u003cem\u003eet al.\u003c/em\u003e, 2016. Characterization and comparison of riverine, lacustrine, marine and estuarine dissolved organic matter by ultra-high resolution and accuracy Fourier transform mass spectrometry. Org. Geochem. 101, 99\u0026ndash;107.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Kim, D. Kim, M. J. Jung, S. Kim, 2022. Analysis of environmental organic matters by Ultrahigh-Resolution mass spectrometry-A review on the development of analytical methods. Mass Spectrom. Rev. 41, 352\u0026ndash;369.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. D'Andrilli \u003cem\u003eet al.\u003c/em\u003e, 2010. Comprehensive characterization of marine dissolved organic matter by Fourier transform ion cyclotron resonance mass spectrometry with electrospray and atmospheric pressure photoionization. Rapid Commun. Mass Spectrom. 24, 643\u0026ndash;650.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. A. Hawkes \u003cem\u003eet al.\u003c/em\u003e, 2020. An international laboratory comparison of dissolved organic matter composition by high resolution mass spectrometry: Are we getting the same answer? Limnol. Oceanogr. Meth. 18, 235\u0026ndash;258.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR. J. Hijmans, S. E. Cameron, J. L. Parra, P. G. Jones, A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25, 1965\u0026ndash;1978.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. Delgado-Baquerizo \u003cem\u003eet al.\u003c/em\u003e, 2020. The proportion of soil-borne pathogens increases with warming at the global scale. Nature Climate Change 10, 550\u0026ndash;554.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP. Royston, G. Ambler, 1998. Generalized additive models. Stata Technical Bulletin 7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. Galecki, T. Burzykowski, \u003cem\u003eLinear Mixed Effects Models Using R.: A Step-by-Step Approach\u003c/em\u003e (2013), \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-1-4614-3900-4\u003c/span\u003e\u003cspan address=\"10.1007/978-1-4614-3900-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Nakagawa, H. Schielzeth, R. B. O'Hara, 2013. A general and simple method for obtainingR2from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133\u0026ndash;142.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH. Singmann, D. Kellen, \"An introduction to mixed models for experimental psychology\" in New methods in cognitive psychology. (Routledge, 2019), pp. 4\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. A. Stoffel, S. Nakagawa, H. Schielzeth, 2021. partR2: partitioning R(2) in generalized linear mixed models. PeerJ 9, e11414.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Wagner \u003cem\u003eet al.\u003c/em\u003e, 2015. Linking the Molecular Signature of Heteroatomic Dissolved Organic Matter to Watershed Characteristics in World Rivers. Environ Sci Technol 49, 13798\u0026ndash;13806.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eI. F. Creed \u003cem\u003eet al.\u003c/em\u003e, 2015. The river as a chemostat: fresh perspectives on dissolved organic matter flowing down the river continuum. Can. J. Fish. Aquat. Sci. 72, 1272\u0026ndash;1285.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. Heibati \u003cem\u003eet al.\u003c/em\u003e, 2017. Assessment of drinking water quality at the tap using fluorescence spectroscopy. Water Res. 125, 1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eW. Qiao, H. Guo, C. He, Q. Shi, B. Zhao, 2021. Unraveling roles of dissolved organic matter in high arsenic groundwater based on molecular and optical signatures. J. Hazard. Mater. 406, 124702.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK. Kov\u0026aacute;cs, A. Gaspar, C. Sajg\u0026oacute;, P. Schmitt-Kopplin, E. Tomb\u0026aacute;cz, 2012. Comparative study on humic substances isolated in thermal groundwaters from deep aquifers below 700 m. Geochem. J. 46, 211\u0026ndash;224.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. Valle \u003cem\u003eet al.\u003c/em\u003e, 2020. Molecular differences between water column and sediment pore water SPE-DOM in ten Swedish boreal lakes. Water Res. 170, 115320.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eY. Dai \u003cem\u003eet al.\u003c/em\u003e, 2016. Spatiotemporal variation of planktonic and sediment bacterial assemblages in two plateau freshwater lakes at different trophic status. Appl. Microbiol. Biotechnol. 100, 4161\u0026ndash;4175.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. V. Rantala, L. Nevalainen, M. Rautio, A. Galkin, T. P. Luoto, 2016. Sources and controls of organic carbon in lakes across the subarctic treeline. Biogeochemistry 129, 235\u0026ndash;253.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eC. T. Solomon \u003cem\u003eet al.\u003c/em\u003e, 2015. Ecosystem Consequences of Changing Inputs of Terrestrial Dissolved Organic Matter to Lakes: Current Knowledge and Future Challenges. Ecosystems 18, 376\u0026ndash;389.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. M. Bengtsson, K. Attermeyer, N. Catal\u0026aacute;n, 2018. Interactive effects on organic matter processing from soils to the ocean: are priming effects relevant in aquatic ecosystems? \u003cem\u003eHydrobiologia\u003c/em\u003e 822, 1\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL. Zhai, Y. An, L. Feng, X. Qin, J. Xu, 2022. Contrasting the physical and chemical characteristics of dissolved organic matter between glacier and glacial runoff from a mountain glacier on the Tibetan Plateau. Sci. Total Environ. 848, 157784.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eX. Chen \u003cem\u003eet al.\u003c/em\u003e, 2014. Fate of 14C-labeled dissolved organic matter in paddy and upland soils in responding to moisture. Sci. Total Environ. 488\u0026ndash;489, 268\u0026ndash;274.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eN. Mladenov \u003cem\u003eet al.\u003c/em\u003e, 2011. Dust inputs and bacteria influence dissolved organic matter in clear alpine lakes. Nat. Commun. 2, 405.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR. Li, B. Xi, W. Tan, Y. Yuan, 2022. Spatiotemporal heterogeneous effects of microplastics input on soil dissolved organic matter (DOM) under field conditions. Sci. Total Environ. 847, 157605.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eE. A. Davidson, I. A. Janssens, 2006. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165\u0026ndash;173.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eT. W. Crowther \u003cem\u003eet al.\u003c/em\u003e, 2016. Quantifying global soil carbon losses in response to warming. Nature 540, 104\u0026ndash;108.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. M. Kellerman, T. Dittmar, D. N. Kothawala, L. J. Tranvik, 2014. Chemodiversity of dissolved organic matter in lakes driven by climate and hydrology. Nat. Commun. 5, 3804.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eC. Orland, K. M. Yakimovich, N. C. S. Mykytczuk, N. Basiliko, A. J. Tanentzap, 2020. Think global, act local: The small-scale environment mainly influences microbial community development and function in lake sediment. Limnol. Oceanogr. 65, S88-S100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. N. Bulseco \u003cem\u003eet al.\u003c/em\u003e, 2019. Nitrate addition stimulates microbial decomposition of organic matter in salt marsh sediments. Glob Chang Biol 25, 3224\u0026ndash;3241.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eC. D. Evans \u003cem\u003eet al.\u003c/em\u003e, 2012. Acidity controls on dissolved organic carbon mobility in organic soils. Global Change Biol. 18, 3317\u0026ndash;3331.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eV. r. Masson-Delmotte, \u003cem\u003eGlobal warming of 1.5\u0026deg;C: an IPCC Special Report on impacts of global warming of 1.5\u0026deg;C above pre-industrial levels and related global greenhouse gas emission pathways, in context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty\u003c/em\u003e (Cambridge University Press, Cambridge, 2022), pp. 1 online resource (xiii, 616 pages): digital, PDF file(s).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD. M. J. S. Bowman, G. J. Williamson, R. J. Keenan, L. D. Prior, 2014. A warmer world will reduce tree growth in evergreen broadleaf forests: evidence from Australian temperate and subtropical eucalypt forests. Glob. Ecol. Biogeogr. 23, 925\u0026ndash;934.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eI. Gwitira, A. Murwira, M. D. Shekede, M. Masocha, C. Chapano, 2014. Precipitation of the warmest quarter and temperature of the warmest month are key to understanding the effect of climate change on plant species diversity in Southern African savannah. Afr. J. Ecol. 52, 209\u0026ndash;216.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD. Costa, R. M. Tavares, P. Baptista, T. Lino-Neto, 2022. The influence of bioclimate on soil microbial communities of cork oak. BMC Microbiol. 22, 163.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eV. A. Garayburu-Caruso \u003cem\u003eet al.\u003c/em\u003e, 2020. Using Community Science to Reveal the Global Chemogeography of River Metabolomes. Metabolites 10, 518.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP. Maurischat, M. Seidel, T. Dittmar, G. Guggenberger, 2022. A DOM continuum from the roof of the world \u0026ndash; Tibetan molecular dissolved organic matter characteristics track sources, land use effects, and processing along the fluvial-limnic pathway. \u003cem\u003eEGUsphere\u003c/em\u003e 2022, 1\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Qin \u003cem\u003eet al.\u003c/em\u003e, 2019. Temperature sensitivity of SOM decomposition governed by aggregate protection and microbial communities. Science Advances 5, eaau1218.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eN. Catal\u0026aacute;n, R. Marc\u0026eacute;, D. N. Kothawala, L. J. Tranvik, 2016. Organic carbon decomposition rates controlled by water retention time across inland waters. Nat. Geosci. 9, 501\u0026ndash;504.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR. J. Whittaker, D. J. Futuyma, 1976. Communities and Ecosystems. The Quarterly Review of Biology 51, 159\u0026ndash;160.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"Nanjing Institute of Geography and Limnology, Chinese Academic of Sciences","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Molecular characteristics, dissolved organic matter, Earth systems, habitats, latitudinal pattern, environmental drivers","lastPublishedDoi":"10.21203/rs.3.rs-3324551/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3324551/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDissolved organic matter (DOM) is ubiquitous and contains a complex pool of thousands of distinct molecules, and their chemical characteristics help us inform the fate of global carbon. Yet, a more holistic perspective of molecular characteristics of DOM and underlying mechanisms across Earth systems and climates remain understudied. Here, we present a comprehensive analysis of the molecular characteristics of DOM using two abundance-weighted average indices, i.e., H/C and O/C ratios by compiling 3,558 samples from 317 studies covering the waters, land, plant, petroleum, and atmosphere systems, and the climatic regions from tropics to tundra. H/C ratios are lower on average in waters (H/C = 1.15 ± 0.005) and land (H/C = 1.20 ± 0.010) than the other systems, while their O/C ratios rank between plant and atmosphere. In the waters and land systems, the H/C ratios of DOM vary from the highest to the lowest in the habitats of land-to-ocean continuum generally as snow \u0026gt; glacier \u0026gt; marine ≥ freshwater/soil \u0026gt; groundwater. The H/C ratios show predictably U-shaped patterns along latitudinal gradients indicating the lowest abundance of more hydrogen saturated molecules at around mid-latitudes of 40°-50° in river water, lake water, and forest soil. The two ratios are primarily controlled by the environmental factors such as pH, dissolved oxygen, and carbon and nitrogen contents. We further unveil additional and considerable links between the ratios and the extremes of climatic factors such as precipitation of warmest quarter and maximum temperature of warmest month. Our synthesis provides molecular-level perspectives to characterize the global distribution and underlying drivers of DOM, which is complementary for our understanding global carbon cycle’s processes under future global change.\u003c/p\u003e","manuscriptTitle":"Global patterns and drivers of dissolved organic matter across Earth systems: Insights from H/C and O/C ratios","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2024-02-08 18:58:17","doi":"10.21203/rs.3.rs-3324551/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2023-09-06 14:39:15","doi":"10.21203/rs.3.rs-3324551/v1","editorialEvents":[{"type":"communityComments","content":1}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1a096416-2066-470a-bea4-afa132434046","owner":[],"postedDate":"February 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":28656638,"name":"Biogeography"},{"id":28656639,"name":"Geochemistry"}],"tags":[],"updatedAt":"2024-03-24T09:09:54+00:00","versionOfRecord":{"articleIdentity":"rs-3324551","link":"https://doi.org/10.1016/j.fmre.2023.11.018","journal":{"identity":"fundamental-research","isVorOnly":true,"title":"Fundamental Research"},"publishedOn":"2024-01-01 09:09:54","publishedOnDateReadable":"January 1st, 2024"},"versionCreatedAt":"2024-02-08 18:58:17","video":"","vorDoi":"10.1016/j.fmre.2023.11.018","vorDoiUrl":"https://doi.org/10.1016/j.fmre.2023.11.018","workflowStages":[]},"version":"v2","identity":"rs-3324551","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3324551","identity":"rs-3324551","version":["v2"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}