Cold and humid climatic conditions over the last millennium decreased the carbon accumulation in peatlands of the subtropical monsoon region

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In the subtropical monsoon region of China, the climate differs from that of most northern peatlands, where a significant number of peatlands have developed in mountainous areas. However, it remains unclear how the carbon dynamics of these subtropical peatlands respond to climate change. Here, we reconstructed the net carbon fluxes of a typical mountainous peatland in Tianmu Mountain, eastern China, over the past millennium. Climate records in the subtropical monsoon zone indicate fluctuating and declining temperatures alongside increasing humidity over the past thousand years. Drought and higher winter temperatures have facilitated the terrestrialization of waterlogged depressions and triggered the peatland formation in this region. The net carbon accumulation in the peatland has generally shown a downward trend due to the progressively decreasing winter temperature and increasing humidity. When winter temperatures decrease, the growing season for vegetation is shortened, resulting in less litter production and reduced carbon accumulation. Increased humidity leads to greater surface waterlogging and prolonged flooding of surface vegetation, which hampers vegetation growth, reduces litter production, and consequently lowers carbon accumulation. Despite the decline in carbon accumulation over the last millennium, the peatland’s net carbon balance remains in a 'carbon sink' state. This suggests that the risk of carbon release from the peatland carbon pool under natural climate change conditions is not substantial in the subtropical monsoon area. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Climate sciences subtropical monsoon peatland carbon accumulation winter temperature humidity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Peatlands account for only 3% to 4% of the global land area, but their carbon pool accounts for one-third of the global soil carbon pool, storing about 400-600 Gt carbon 1-4 . The formation of carbon pools in peatlands is intricately governed by the interplay of vegetation production and decomposition processes. Peat accumulation occurs when the rate of production exceeds the rate of decomposition 5 . Climate profoundly influences the thermal and humidity conditions experienced by peatlands, consequently shaping the dynamics of plant organisms and microbial communities within these ecosystems, which have an impact on the carbon accumulation in peatlands. The response of peatland’s carbon accumulation to temperature and precipitation patterns is linked to the future trajectory of carbon pools within these environments. On a millennial scale, the most significant carbon accumulation in northern peatlands occurred during the early Holocene, a period characterized by gradually rising temperatures 6 , 7 . On a centennial scale, Charman et al. 8 similarly suggest that temperature has had a stronger promoting effect on primary productivity compared to peat decomposition over the past millennium. This is evidenced by higher carbon accumulation rates during the Medieval Warm Period compared to the Little Ice Age 9 . These findings indicate that historical warm periods generally enhanced the rate of carbon accumulation in northern peatlands. In contrast, the climatic conditions for peatland formation and development in the low-medium elevation mountains of the subtropical monsoon region of China differ significantly from those in northern peatlands. This region experiences high temperatures and abundant precipitation 10 . The increase in carbon accumulation rates observed in the Dahu, Lantianyan, and Zhaogongting peatlands in eastern China's subtropical region correlates positively with gradually arid climatic conditions 10-13 . During the mid-Holocene, carbon accumulation rates peaked in Dahu peatland due to arid climatic conditions, which inhibited peatland development. Terrestrial and nearshore aquatic vegetation encroached upon central areas, resulting in an increase in organic matter content 14 . Similarly, the Zhaogongting peatland experienced notable increases in carbon accumulation during the mid-Holocene due to declining water levels under dry conditions. Huang et al. 13 argued that the arid environment enhanced organic matter accumulation. Research in Dajiuhu suggested that short-term rapid hydrological fluctuations promote carbon accumulation, whereas long-term drought leads to intensified peat decomposition and decreased carbon accumulation 15 . Zhao et al. 16 synthesized data from peatland resource surveys conducted in the 1980s and found that peaks of carbon accumulation in subtropical peatlands were predominantly observed during the Marine Isotope Stage 3 (MIS 3) and the Bølling-Allerød warm period, characterized by high solar radiation and intensified summer monsoons. Thus, it is evident that there are varying perspectives among different studies regarding carbon accumulation variations and their mechanisms in subtropical peatlands. The influence of climatic factors, such as temperature and precipitation, on peat carbon accumulation in subtropical regions remains incompletely understood. Over the past 1,000 years, the fundamental boundary conditions of Earth's climate, including factors such as land-sea distribution, topography, and orbital parameters, have remained relatively stable, providing a suitable backdrop for elucidating contemporary climate change trends. A large number of paleoclimate reconstruction and climate modeling studies have been carried out in the subtropical monsoon region of China. Historical and instrumental reconstructions of the climate of the Jianghuai region of China over the past millennium suggest an increasingly humid climate 17 . Additionally, Jiang et al. 18 observed a significant increase in precipitation in central-eastern China, a trend supported by a*/L* based on sediments from Nanyi Lake 19 . This trend has also been confirmed by diatom-based analyses 20 , grain size analyses 21 and magnetic analyses 22 . Regarding the temperature variations, Wang et al. 23 discovered through an analysis of the ECHO-G model that the temperature in the East Asian monsoon region of China exhibited fluctuations of “warmth-coldness-warmth” over the last millennium, which is consistent with the reconstruction results of Ge et al. 24 . There is a basic consensus pattern of gradually humid climate in the subtropical monsoon region over the last millennium. Therefore, understanding the historical relationships between carbon accumulation and climate factors over the past millennium is feasible, and will be instrumental in predicting the dynamics of carbon pools in peatlands under various climate change scenarios in the future. Here, we utilized two meticulously dated and high-resolution net carbon accumulation records obtained from a mountain peatland to investigate the response of carbon accumulation to climatic fluctuations over the past millennium within the subtropical monsoon region. The study aims to achieve two primary objectives: (1) reconstructing the peatland’s evolutionary history, and establishing correlations between observed shifts in carbon dynamics and variations in temperature and precipitation patterns over the last millennium in the subtropical monsoon region. Through the examination of the nexus between carbon accumulation and climatic factors, we hope our study can contribute to the assessment of the scale and direction of future global carbon cycle feedback. Results Lithostratigraphy and Chronology. The surface layer of core QMT22-1 from Qianmutian peatland (QMT), spanning 1 ~ 25 cm, constitutes the acrotelm and supports living fresh moss. From 25 cm to 117 cm, the core transitions into the catotelm, characterized by distinct layers: 25 ~ 35 cm consists of plant roots and humus, exhibiting weak decay; 35 ~ 55 cm comprises high humic sediments; and 55 ~ 117 cm is filled with yellow-brown mud, culminating in rock at the bottom. Similarly, in core QMT22-2, the acrotelm extends from the surface to a depth of 35 cm, cohabited by fresh moss. The subsequent 35 ~ 93 cm constitutes the catotelm, featuring distinctive layers: 35 ~ 50 cm comprises high humic sediments, while 51 ~ 93 cm is filled with yellow-brown mud exhibiting lower decomposition, ultimately concluding with rock at the base (refer to Fig. 1 ). Based on the observed variations in DBD and LOI observed in the sediment cores QMT22-1 and QMT22-2, four distinct sedimentary sections can be identified. The first segment, spanning depths of 13 ~ 39 cm in QMT22-1 and corresponding to 13 ~ 40 cm in QMT22-2, displays the highest DBD and LOI values within the entire catotelm layer, with a rapid decrease observed around 39 cm and 40 cm, respectively. The second segment covers depths of 39 ~ 73 cm in QMT22-1 and 40 ~ 56 cm in QMT22-2. In this segment, DBD variations remain relatively stable, while LOI values consistently increase with depth, abruptly decreasing around 73 cm and 56 cm. The third segment encompasses depths of 73 ~ 93 cm in QMT22-1 and 57 ~ 71 cm in QMT22-2, showing a declining trend in both DBD and LOI with increasing depth. The fourth segment, spanning depths of 93 ~ 117 cm in QMT22-1 and 71 ~ 93 cm in QMT22-2, exhibits significant fluctuations in DBD without a clear increasing or decreasing trend, while LOI remains stable within this sedimentary segment (Fig. 1 ). Consequently, we can establish the age depths of core QMT22-1 through core QMT22-2. Figure 2 illustrates that for core QMT22-2, samples from depths of 24 cm, 45 cm, 67 cm, and 93 cm were selected to determine ages. For QMT22-1, we determined the relative positions of age depths based on lithological characteristics. The first age depth is estimated at 23 cm, the second at 49 cm, the third at 86 cm, and the basal age depth at 117 cm (refer to Fig. 1 ). The basal ages of cores QMT22-1 and QMT22-2 are both estimated at 1160 ± 20 year 14 C BP (Table 1 ). Subsequently, using the R language, we established the chronology. Table 1 Results of AMS 14 C dating analysis of the QMT22-2 Laboratory number Sample ID Depth (cm) Analyzed material Conventional radiocarbon age (yr 14 C BP) Calibrated age (cal yr BP, mean, 2σ range) LZU23110 QMT22-2-24 23 ~ 24 Stem、leaf -140 ± 20 -41 (-53~-31) LZU23235 QMT22-2-45 44 ~ 45 Stem 140 ± 20 204 (82 ~ 276) LZU23111 QMT22-2-67 66 ~ 67 Stem 550 ± 30 569 (505 ~ 639) LZU23236 QMT22-2-93 92 ~ 93 Stem 1160 ± 20 1063 (972 ~ 1171) Carbon Accumulation Dynamics. As the acrotelm is inhabited by fresh moss, this study solely focuses on the catotelm. In the catotelm of core QMT22-1, the peat-addition rate is determined to be 123.5 g OM m − 2 yr − 1 , with a peat-decomposition rate of 0.0008 yr − 1 . Meanwhile, in core QMT22-2, the peat-addition rate is 67.8 g OM m − 2 yr − 1 , with a peat-decomposition rate of 0.0005 yr − 1 (refer to Fig. 2 ). Upon determining the peat decomposition rate (α), further calculations of the net carbon uptake (NCU), net carbon release (NCR), and net carbon balance (NCB) were conducted using the "super-peatland" approach. For core QMT22-1, the calculated NCU ranges from 40.79 to 82.82 g C m − 2 yr − 1 , with an average of 62.19 g C m − 2 yr − 1 . NCP ranges from 25.51 to 59.45 g C m − 2 yr − 1 , with an average of 41.57 g C m − 2 yr − 1 . Additionally, NCR ranges from 1.38 to 35.11 g C m − 2 yr − 1 , with an average of 20.63 g C m − 2 yr − 1 , resulting in NCB ranging from 5.68 to 81.44 g C m − 2 yr − 1 . Notably, these values are substantially higher compared to those of core QMT22-2. Specifically, the NCU of core QMT22-2 ranges from 26.20 to 52.51 g C m − 2 yr − 1 , with an average of 34.55 g C m − 2 yr − 1 . The NCP ranges from 18.40 to 35.17 g C m − 2 yr − 1 , with an average of 27.35 g C m − 2 yr − 1 . Moreover, NCR varies from 0.51 to 12.80 g C m − 2 yr − 1 , with an average of 7.21 g C m − 2 yr − 1 , leading to NCB ranging from 13.41 to 51.99 g C m − 2 yr − 1 , with an average of 27.35 g C m − 2 yr − 1 (see Fig. 3 ). Characteristics of solar radiation, temperature, and humidity. Solar radiation at 30°N has exhibited a fluctuating decreasing trend since the last millennium until approximately 600 years before the present (BP), followed by a fluctuating increasing trend since 600 years BP. However, the overall variation remains insignificant. Annual mean temperature anomalies in the Northern Hemisphere have undergone a notable change, displaying a decreasing trend since the last millennium until around 400 years BP, followed by an increasing trend from 400 years BP to the present. Similarly, annual mean temperature anomalies, as well as summer and winter annual temperature anomalies, in the subtropical monsoon region of China, followed a similar trend until approximately 450 years BP, indicating a fluctuating downward trend. However, a gradual warming trend has been observed after 450 years BP. Regarding changes in humidity indexes between 1200 and 800 years BP, variations are somewhat divergent, with some indicating a gradual decrease in humidity during this period, while others suggest an increase. Nevertheless, from 800 years BP to the present, all indicators point towards a gradual increase in humidity. By applying Z-scores to different humidity indicators, it becomes evident that humidity in the subtropical monsoon region of China has progressively increased over the past millennia (Fig. 4 ). Models of net carbon accumulation rate and climate factors. The AICs of GAMs: NCB = s ( SO ) (supplementary Fig. 1a), NCB = s ( NT ) (supplementary Fig. 1b), NCB = s ( ET ) (supplementary Fig. 1c), NCB = s ( WT ) (supplementary Fig. 1d), NCB = s ( ST ) (supplementary Fig. 1e), NCB = s ( MO ) (supplementary Fig. 1f), NCB = s ( SO ) + s ( MO ) (supplementary Fig. 2), NCB = s ( ET ) + s ( MO ) (supplementary Fig. 3), NCB = s ( WT ) + s ( MO ) (Fig. 5 ) and NCB = s ( ST ) + s ( MO ) (supplementary Fig. 4)was 321, 304, 314, 314, 306, 282, 277, 279, 273 and 282. Thus, the final GAM is NCB = s ( WT ) + s ( MO ), and this model explained 77.2% of the variance of the NCB. The winter temperature and humidity were significant meteorological parameters for NCB. Discussions Peatland development in the subtropical monsoon region over the past millennium. The QMT peatland was first formed at ~ 1063 year BP, Given that the QMT peatland in Tianmu Mountain is located in a low-lying area, it acts as a catchment area. QMT has runoff input, their water source is ultimately precipitation or the small watersheds to the peatland, the water level in peatland likely reflects regional humidity balance and regional climate because of a very small watershed. Less early precipitation resulted in less standing water in the QMT depression. At the same time, higher winter temperatures extended the growing season for vegetation and produced more litter, causing the terristrialization of the water area and the initiation of peatland. Therefore, it is mainly the lower level of humidity and higher winter temperature that triggered the initiation of the QMT peatland in the subtropical monsoon region. The QMT22-1 core is 24 cm deeper than the QMT22-2 core, indicating potential variations associated with the microtopography of peatland development. Peatland development typically progresses through three distinct stages 25 : the initial phase characterized by various water sources and rapid peat accumulation is termed ‘lowmoor’, the final stage where the peat layer thickens and peat accumulation decreases is known as ‘highmoor’, with rainfall being a significant recharge source. The transitional stage between lowmoor and highmoor is termed ‘mesomoor’. Although both QMT22-1 and QMT22-2 cores were collected from the same peatland, due to the higher landform, the QMT22-2 core can transition to the highmoor stage earlier than QMT22-1 in the stages of peatland development. When QMT22-2 reaches the highmoor stage, surface vegetation primarily relies on rainfall for replenishment, inhibiting peat accumulation. Meanwhile, the QMT22-1 core was still in the mesomoor stage, receiving recharge not only from rainfall but also influenced by peatland water levels, maintaining a high rate of peat accumulation (Fig. 6 ). The different sediment thicknesses caused by the different micro-geomorphic heights in the same peatland prove the role of water in the development of peatland over the last millennium. Impacts of climate factors on QMT peatland carbon fluxes over the past millennium. The GAMs model shows that Winter temperature and humidity significantly impact the NCB of the QMT peatland (Fig. 5 ). If the winter temperature anomaly is greater than − 0.2, winter temperature is negatively correlated with NCB. If the winter temperature index is less than − 0.2, winter temperature is positively correlated with NCB. Humidity indexes and NCB are negatively correlated when the humidity indicator is greater than − 0.5, and positively correlated when the humidity indicator is less than − 0.5. As each plant has its own suitable ecological niche 26 , vegetation growth in peatlands above this ecological niche is inhibited. Over the last thousand years, NCB has been positively correlated with the winter temperature anomaly ( -0.5) for most of the period (700 BP~) in QMT peatlands. When the winter temperature decreases, the length of the growing season for vegetation is shortened, resulting in reduced litter production and lower carbon accumulation. As humidity continues to increase, it leads to higher water levels in peatlands, which tends to accumulate more water on the surface, and peatland vegetation is flooded for a long period of time, inhibiting the production of vegetation, which produces less litter and leads to less carbon accumulation. Since the annual average temperature, summer temperature and winter temperature are basically similar in their changing trends, these parameters also have a strong correlation with NCB. However, the structure of the GAMs proves that the combination of winter temperature anomaly and humidity index has the strongest impact on NCB. Other climate parameters, such as solar radiation, have no correlation with NCB. It can be seen from this that winter temperature and humidity were the main climatic factors controlling the carbon accumulation in peatlands during the last thousand years. Over the last millennium, the humidity index in the eastern subtropical monsoon zone of China has generally increased, while the winter temperature anomaly has tended to decrease, resulting in a general decline in the NCB of the QMT peatlands. Although the responses of NCB to different ranges of winter temperature and humidity indices were not consistent, NCB was positively correlated with the winter temperature anomaly and negatively correlated with the humidity index for most of the period since 1000 BP. As winter temperatures decreased, the growing season for peatland vegetation shortened, suppressing litter production and leading to a lower carbon accumulation rate. Increased humidity raised water levels in the peatlands, causing more surface water accumulation and prolonged flooding of peatland vegetation, which hindered vegetation growth and reduced litter production, further decreasing the carbon accumulation rate. Thus, a cold and humid climate has been the primary factor influencing the decrease in carbon accumulation in the QMT peatland (Fig. 7 and Fig. 8 ). After the formation of the QMT peatland, it has consistently presented as a carbon sink. However, the net carbon balance of the peatland shows a downward trend since 1063 cal yr BP, yet still in the state of a carbon sink. Therefore, the carbon release risk of the mountainous peatland carbon pool in the subtropical monsoon area is not large under the change of the natural environment. Different climate-carbon accumulation patterns in subtropical monsoon regions compared to other peatlands globally. From a global perspective, carbon accumulation in northern peatlands is primarily driven by temperature, with higher temperatures promoting carbon accumulation 7, 8 . In contrast to northern peatlands, tropical peatlands are affected by summer monsoon intensity, sea-level change, and El Niño intensity 3 . Meanwhile, carbon accumulation in southern peatlands, primarily located in Patagonia, South America, is predominantly regulated by water balance 27 . Clearly, the factors governing carbon accumulation in peatlands vary significantly across different regions. Peatlands in the subtropical monsoon region belong to a subset of northern peatlands in terms of classification. However, the climatic mechanisms driving variations in carbon accumulation in subtropical peatlands differ from those in northern peatlands. In the early stage, less precipitation in the subtropical monsoon region led to a reduction in water levels in the depressions of low and medium mountains. At the same time, the higher winter temperature during that period prolonged the growing season of vegetation and increased the amount of litter. This was conducive to the terrestrialization of the water area and thereby promoted the development of peatland in this area. Analyses of carbon fluxes in the peatland showed that changes in winter temperature anomaly and humidity index have primarily controlled carbon accumulation in the peatlands over the past millennium. The gradual cooling and increase in humid over the past thousand years have reduced the rate of carbon accumulation. Lower winter temperatures shorten the length of the growing season of peatland vegetation, leading to a decrease in litter and a lower rate of carbon accumulation. A gradual increase in humidity led to the accumulation of more water in the surface layer of peatlands, which is detrimental to the growth of surface vegetation. This reduces net primary productivity, decreases litter, and consequently reduces net carbon accumulation. Conclusions In the subtropical monsoon region, drought and higher winter temperatures are conducive to the development and carbon accumulation of peatlands. Reduced precipitation lowers water levels in depressions of the mountains. Meanwhile, higher winter temperatures extend the vegetation growing season, increasing litter production and facilitating the terrestrialization of water areas, thereby promoting peatland development and carbon accumulation in this region. Over the past thousand years, carbon accumulation in the Qianmutian peatland has declined due to gradual decreases in winter temperatures and increases in humidity. Reduced winter temperatures have shortened the vegetation growing season, leading to decreased litter production and carbon accumulation. Increased humidity has raised peatland water levels, reducing vegetation productivity and causing a decline in carbon accumulation. Despite the decline in carbon accumulation over the last millennium, the peatland’s net carbon balance remains in a ‘carbon sink’ state. This suggests that the risk of carbon release from the peatland carbon pool under natural climate change conditions is not substantial in the subtropical monsoon area. Material and methods Study site and fieldwork. The Qianmutian (QMT) peatland is situated in the eastern region of China (30.4994°N, 119.4408°E), at an altitude of approximately 1300 meters above sea level (m a.s.l.) (Fig. 9 a). It located within a typical warm and humid monsoon climate zone characterized by abundant insolation relative to higher latitudes, elevated mean annual temperatures, and notable summertime warmth. Winter temperatures typically remain above 0°C, with monsoon precipitation concentrated primarily in the summer season, often accompanied by frequent thunderstorms and other extreme meteorological phenomena. Specifically, the mean annual temperature in this region averages around 9.5°C, annual precipitation is 1500mm. The QMT peatland, covering approximately 0.7 km 2 , is situated in a depression atop Tianmu Mountain (Fig. 9 b). The surface vegetation is dominated by Sphagnum junghuhnianum and Sphagnum magellanicum (Fig. 9 c). Accompanying with other plant species, such as Idesia polycarpa , Ligularia fischeri , Rubus trianthus , Rubus peltatus , Calamagrostis epigejos , Scirpus lushanensis , and Miscanthus sacchariflorus . In the surrounding upland vegetation, Cornus officinalis , Idesia polycarpa , and Viburnum opulus subsp . Calvescens are the dominant tree and shrub species. In November 2022, we retrieved two peat cores, QMT22-1 (117 cm) and QMT22-2 (93 cm), using both box corer and Russian peat corer methods. For QMT22-1, we initially hand-excavated the surface layer from 0 ~ 47cm before employing a box corer to extract a peat core spanning 0 ~ 97 cm. Subsequently, we attempted a second drilling session using the Russian peat core method to target depths of 64 ~ 117 cm; however, drilling was halted upon encountering gravel at the bottom. Similarly, for QMT22-2, we hand-excavated the surface layer from 0 ~ 38 cm and then employed a Russian peat core to extract the first core from 20 ~ 70 cm in depth. A subsequent drilling session aiming at depths of 43 ~ 93 cm was attempted, but drilling was again halted due to encountering gravel at the bottom. Following collection, the peat cores were placed into PVC pipes and transported back to the laboratory, where they were stored at 4°C for further analysis. 14 C dating. We collected samples from the QMT peatland using a Box peat corer and a Russian peat corer, resulting in QMT22-1 (117 cm) and QMT22-2 (93 cm). Samples from QMT22-2 at depths of 24 cm (stem and leaf), 45 cm (stem), 67 cm (stem), and 93 cm (stem) underwent Accelerator Mass Spectrometry (AMS) radiocarbon ( 14 C) dating at the 14 C Chronology Laboratory of Lanzhou University. Considering the close proximity and similar climatic context of the QMT22-1 and QMT22-2 cores, their stratigraphic positioning can be readily discerned based on their Dry Bulk Densities (DBD) and Loss on Ignition (LOI) values. Based on the depth of the dating points in the QMT22-2 core, the corresponding locations of the dating points in the QMT22-1 core can be inferred accordingly. Then we constructed an age-depth model using the Bayesian age-depth modeling package BACON 28 for R. The results were given in years before the present (BP), where “present” is defined as the year 1950 Common Era (CE). Acrotelm/catotelm boundary detection. The peatland sediment layers comprise the acrotelm and catotelm. The acrotelm is rich in fresh litter and has a high organic matter content. However, due to the rapid decomposition of organic matter in this layer, the surface carbon accumulation rate, based on LOI, is higher than the earlier carbon accumulation rate. Therefore, this study will focus exclusively on the catotelm sedimentary layer. The catotelm and acrotelm represent two distinct layers distinguished by their physical properties and functions 29 . In QMT22-1 and QMT22-2, the differentiation between the acrotelm and catotelm is evident from lithological variations observed in the cores (Fig. 10 ). In QMT22-1, the upper layer spanning 1 ~ 25 cm exhibits a brown-yellow sediment with low decomposition levels, abundant plant roots, and a relatively loose structure. Water content and LOI remain stable within this layer. Beyond 25 cm, the color gradually darkens to dark brown, accompanied by increased decomposition levels, rapid declines in water content and LOI, and the presence of underlying rocks. Consequently, the upper layer (1 ~ 25 cm) is identified as the acrotelm, comprising living fresh moss, while the subsequent layer (25 ~ 117 cm) is recognized as the catotelm. Similar changes are observed in the QMT22-2 core at a depth of 35cm, where the upper layer (1 ~ 35 cm) exhibits brown-yellow sediment, dense plant root systems, and a loose structure, with stable water content and LOI levels. Below 35 cm, the color transitions from brown-yellow to black-brown, accompanied by rapid decreases in water content and LOI, increased decomposition levels, and the presence of underlying rocks. Hence, the upper layer (1 ~ 35 cm) in QMT22-2 is identified as the acrotelm, comprising living fresh moss, while the subsequent layer (35 ~ 93 cm) is classified as the catotelm. Climate data integration. In investigating past climate changes over the last millennium in the subtropical monsoon region, researchers have leveraged various archives and proxies. These include solar radiation 30 , temperature 24, 31–33 , To indicate humidity changes, we collected several indicators, including a*/L* 18 , Rb/Sr 19 , mean size 21 , TOC 34 , and DPC-1 20 from lake sediments; SIRM 22 and precipitation 35 from peatland sediments; Total organic carbon (TOC) 36 and freshwater species 37 from offshore sediments; and a humidity index 17 based on instrumental stations and historical records in southern China (Table 2 ). The climate data used in this paper are published and peer-reviewed. When analyzing the correlation between humidity data and the NCB, we integrated the z-score values of these humidity records (Supplementary Fig. 5). Table 2 Site information on the sediment records used in climate data synthesis in this study Location Latitude Longitude Elevation/m Sediment type Indicator Reference Nvshan 32.996° 118.124° 12 Lake a*/L* Wang et al. 45 Nanyi 31.079° 118.944° 2.2 Lake Rb/Sr Liu et al 19 KET 24.095° 122.378° -2893 Sea TOC Wang et al 36 Wangdongyang 27.682° 119.637° 1300 Peatland SIRM Zhou et al 22 Gaoyou 32.84° 119.375° 4 Lake Mean size Li et al 21 Cuifeng 24.5° 121.6° 1850 Lake TOC Selvaraj et al 34 Cuifeng 24.5° 121.6° 1850 Lake DPC-1 Wang et al 20 Southern Okinawa 24.80067° 122.4892° -1275 Sea Freshwater species Li et al 37 Southern China - - - Historical and station records Dry-humidity index Zheng et al 17 Dajiu 31.47° 109.98° 1730 pealand Precipitation He et al 35 Carbon measurements and modeling. The cores QMT22-1 and QMT22-2 were sliced into contiguous sections each 1cm thick and subsequently measured. These samples underwent drying at 105°C for 12 hours to ascertain their DBD, defined as the ratio of dry weight to volume. A subset of the dried subsamples underwent combustion at 550°C in a muffle furnace for 4 hours to determine the LOI, serving as an indicator of Organic Matter content (OM) 38 . To calculate the Organic Carbon content 39 , the OM was multiplied by 50% 40 . Based on the chronologies and the measured DBD (g cm 3 ) and OC 41 , the peat aCAR was calculated as: \(aCAR=\frac{h}{t} \times DBD \times OC \times 100\) where t (yr) is the time experienced by peat thickness h (cm). Upon obtaining the LOI data from the QMT peatland, we apply the decomposition model proposed by Clymo 42 to derive the peat-addition rate (ρ) and peat decomposition rate (α), where M represents the organic matter and t signifies time. \(M=\frac{\rho }{\alpha } \times (1 - {e^{ - \alpha t}})\) Subsequently, employing the "super-peatland" methodology, we determine the net carbon fluxes of the QMT22-1 and QMT22-2 cores, encompassing net carbon uptake (NCU), net carbon release (NCR), and net carbon balance (NCB), with α derived from the modeled decomposition 43 . The observed carbon pool (NCP) enables the calculation of NCU for each 20-year interval, treating NCU as the initial mass and NCP as the remaining mass after time t. This equation is formulated as follows: \(NC{U_t}=\frac{{NC{P_t}}}{{{e^{ - \alpha t}}}}\) At a specific 20-year interval (k), the NCR is calculated. Therefore, the NCR at time t comprises the cumulative carbon release during that period from all peat cohorts older than time t. The carbon release during the 20-year period at time t can be determined by subtracting the potential NCU at time t from that at time t − 1. The equation can be expressed as follows: \(NC{R_t}=\sum\nolimits_{{k=t}}^{{initiation age}} {(\frac{{NC{P_k}}}{{{e^{ - \alpha t}}}} - \frac{{NC{P_k}}}{{{e^{ - \alpha \times (t - 1)}}}})}\) NCB was calculated as the difference between NCU and NCR as derived above \(NCB=NCU - NCR\) Regression models . Generalized Additive Models (GAMs) is a type of regression model that utilizes smoothing splines instead of linear coefficients for covariates 44 . The aim of using GAMs is to establish the connection between NCB and climate factors in the QMT peatland. The ultimate model was selected based on the lowest Akaike information criterion (AIC). Declarations Competing interests The authors declare no competing interests. Funding was provided by the National Natural Science Foundation of China (No. 42001081) and the Open Fund Project of Key Laboratory of Watershed Surface Processes and Ecological Security of Jinhua City (KF-2022-13) . Author contributions H.L. conceived the ideas; B.L., H.L., and Z.X. collected field samples; B.L. did the LOI measurement, and reconstructed the carbon flux history; B.L. and H.L. wrote the manuscript; Z.Y., Y.F., Z.X., and J.J. discussed results and commented on the manuscript. Data availability All carbon flux and 14 C data from QMT peatland are available in the online database and on request. References Davidson, E. A., Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173 (2006). Page, S. E., Rieley, J. O., Banks, C. J. Global and regional importance of the tropical peatland carbon pool. Glob. Chang. Bio. 17, 798–818 (2011). Yu, Z., Loisel, J., Brosseau, D. P., Beilman, D. W., Hunt, S. J. Global peatland dynamics since the Last Glacial Maximum. Geophys. Res. Lett. 37, 1–5 (2010). Programme, U. N. E. Global Peatlands Assessment- The State of the World's Peatlands: Evidence for action toward the conservation, restoration, and sustainable management of peatlands.). United Nations Environment Programme (2022). Gorham, E. The Development of Peat Lands. The Quarterly Review of Biology 32, 145–166 (1957). Chaudhary, N. et al. Modelling past and future peatland carbon dynamics across the pan-Arctic. Glob. Chang. Bio. 26, 4119–4133 (2020). Loisel, J. et al. A database and synthesis of northern peatland soil properties and Holocene carbon and nitrogen accumulation. Holocene 24, 1028–1042 (2014). Charman, D. J. et al. Climate-related changes in peatland carbon accumulation during the last millennium. Biogeosciences 10, 929–944 (2013). Liu, J. et al. Anthropogenic warming reduces the carbon accumulation of Tibetan Plateau peatlands. Quat. Sci. Rev. 281, 107449 (2022). Zhou, W. et al. High-resolution evidence from southern China of an early Holocene optimum and a mid-Holocene dry event during the past 18,000 years. Quat. Res. 62, 39–48 (2004). Yu, X. et al. Anti-phase Variation of Hydrology and In-Phase Carbon Accumulations in Two Wetlands in Southern and Northern China Since the Last Deglaciation. Front. Earth Sci. 8, (2020). Ma, T., Tarasov, P. E., Zheng, Z., Han, A., Huang, K. Pollen- and charcoal-based evidence for climatic and human impact on vegetation in the northern edge of Wuyi Mountains, China, during the last 8200 years. The Holocene 26, 1616–1626 (2016). Huang, X. et al. Holocene forcing of East Asian hydroclimate recorded in a subtropical peatland from southeastern China. Clim. Dynam. 60, 981–993 (2023). Wei, Z. et al. Carbon accumulation in Dahu Swamp in the eastern Nanling Mountains (south China) and its implications for hydroclimatic variability over the past 47,000 years. Boreas 47, 469–480 (2017). Liu, H. et al. The response of the Dajiuhu Peatland ecosystem to hydrological variations: Implications for carbon sequestration and peatlands conservation. J. Hydrol. 612, 128307 (2022). Zhao, Y. et al. Peatland initiation and carbon accumulation in China over the last 50,000 years. Earth Sci. Rev. 128, 139–146 (2014). Zheng, J., Wang, W. C., Ge, Q.-s., Man, Z., Zhang, P. Precipitation variability and extreme events in eastern China during the past 1500 years. Terr. Atmos. Ocean. Sci. 17, 579–592 (2006). Jiang, S. et al. Central eastern China hydrological changes and ENSO-like variability over the past 1800 year. Geology 49, 1386–1390 (2021). Liu, J. et al. Dipolar mode of precipitation changes between north China and the Yangtze River Valley existed over the entire Holocene: evidence from the sediment record of Nanyi Lake. Int. J. Climatol. 41, 1667–1681 (2020). Wang, L.-C. et al. Increased precipitation during the Little Ice Age in northern Taiwan inferred from diatoms and geochemistry in a sediment core from a subalpine lake. J. Paleolimnol. 49, 619–631 (2013). Li, S., Guo, W., Yin, Y., Jin, X., Tang, W. Environmental changes inferred from lacustrine sediments and historical literature: A record from Gaoyou Lake, eastern China. Quat. Int. 380–381, 350–357 (2015). Zhou, Y. et al. Magnetic properties of the Wangdongyang subalpine peatland in Zhejiang province, Eastern China and ITS paleoenvironmental implications. Quaternary Sciences 37, 1348–1356 (2017) (In Chinese). Wang, H., Liu, J., Wang, Z., Wang, S., Kuang, X. Simulated analysis of summer climate on centennial time scale in eastern China during the last millennium. Chin. Sci. Bull. 56, 1562–1567 (2011) (In Chinese). Ge, Q.-S. et al. Temperature variation through 2000 years in China: An uncertainty analysis of reconstruction and regional difference. Geophys. Res. Lett. 37, (2010). Weber, C. A. Über die Vegetation und Entstehung des Hochmoors von Augstumal im Memeldelta mit vergleichenden Ausblicken auf andere Hochmoore der Erde (1902). Sullivan, P. F., Arens, S. J. T., Chimner, R. A., Welker, J. M. Temperature and microtopography interact to control carbon cycling in a high arctic fen. Ecosystems 11, 61–76 (2008). Loisel, J., Yu, Z. Holocene peatland carbon dynamics in Patagonia. Quat. Sci. Rev. 69, 125–141 (2013). Blaauw, M., Christen, J. A. Flexible paleoclimate age-depth models using an autoregressive gamma process. Bayesian analysis 6, 457–474 (2011). Ingram, H. A. P. Size and shape in raised mire ecosystems: a geophysical model. Nature 297, 300–303 (1982). Steinhilber, F. et al. 9,400 years of cosmic radiation and solar activity from ice cores and tree rings. P. N. A. S. 109, 5967–5971 (2012). Ge, Q. et al. Winter half-year temperature reconstruction for the middle and lower reaches of the Yellow River and Yangtze River, China, during the past 2000 years. The Holocene 13, 933–940 (2003). Moberg, A. et al. Highly variable Northern Hemisphere temperatures reconstructed from low- and high-resolution proxy data. Nature 433, 613–617 (2005). Wang, J. et al. Causes of East Asian Temperature Multidecadal Variability Since 850 CE. Geophys. Res. Lett. 45, (2018). Selvaraj, K., Wei, K.-Y., Liu, K.-K., Kao, S.-J. Late Holocene monsoon climate of northeastern Taiwan inferred from elemental (C, N) and isotopic (δ13C, δ15N) data in lake sediments. Quat. Sci. Rev. 37, 48–60 (2012). He, B., Zhang, H., Cai, S. Climatic changes recorded in peat from the Dajiu lake basin in Shennongjia since the last 2600 years. Marine Geology & Quaternary Geology 23, 109–115 (2003) (In Chinese). Wang, Y., Song, J., Li, X., Wang, Q. Sedimentary records of sea-derived carbon and records of paleoproductivity and climate change in the Kuroshio mainstream over the past millennium. Haiyang Xuebao 40, 131–142 (2018)(In Chinese). Li, D., Jiang, H., Li, T., Zhao, M. Late Holocene paleoenvironmental changes in the southern Okinawa Trough inferred from a diatom record. Chin. Sci. Bull. 56, 1131–1138 (2011). Craft, C. B., Seneca, E. D., Broome, S. W. Loss on ignition and kjeldahl digestion for estimating organic carbon and total nitrogen in estuarine marsh soils: Calibration with dry combustion. Estuar. Coast. 14, 175–179 (1991). Gallego-Sala, A. V. et al. Latitudinal limits to the predicted increase of the peatland carbon sink with warming. Nat. Clim. Change 8, 907–913 (2018). Chambers, F. M., Beilman, D., Yu, Z. Methods for determining peat humification and for quantifying peat bulk density, organic matter and carbon content for palaeostudies of climate and peatland carbon dynamics. Mires Peat 7, 1–10 (2011). Yang, X., Wang, S., Tong, G. Character of a nology and changes of monsoon climate over the last 10000 years in gucheng lake, Jiangsu province. Acta Botanica Sinica 38, (1996)(In Chinese). Clymo, R. The limits to peat bog growth. Philosophical Transactions of the Royal Society of London Series B, Biological Sciences 303, 605–654 (1984). Yu, Z. Holocene carbon flux histories of the world's peatlands: Global carbon-cycle implications. Holocene 21, 761–774 (2011). Hastie, T., Tibshirani, R. Generalized additive models. Stat. Sci. 1, 297–318 (1986). Wang, J., Yang, B., Osborn, T. J., Ljungqvist, F. C., Luterbacher, J. Causes of East Asian temperature multidecadal variability since 850 CE. Geophys. Res. Lett. 45, 13485–13494 (2018). Chen, F. et al. Moisture changes over the last millennium in arid central Asia: a review, synthesis and comparison with monsoon region. Quat. Sci. Rev. 29, 1055–1068 (2010). Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryFig.docx Cite Share Download PDF Status: Published Journal Publication published 08 Jan, 2025 Read the published version in Communications Earth & Environment → Version 1 posted You are reading this latest preprint version 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-4875191","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":344720601,"identity":"eee10fdf-7652-471c-9216-3503de96d24d","order_by":0,"name":"Hanxiang Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBACPmYGNhDNA8SMD8AUA4MBXi1sSFqYDYjTwgDRAmZLQBkEtLDzmD34ueOwDP/s9muVP2TuJDawN2+TYKi5g8dhPOaGvWfSeCTunCm7IcHzLLGB51iZBMOxZ/i0mEnwttnwMNzISbthwHM4sUEix0yCseEwXi2Sf9skeOSBWgoSQFrk3xDWIg2yxeBG+jGGA2BbeAhpYSuTlm1L4zG8kcMs2cBz2LiNJ63YIuEYbi38/Ie3Sb5tO2wvdyP94cefPYdl+9kPb7zxoQa3FiTAY8DA2AONpgRiNDAwsD9gYPhBnNJRMApGwSgYWQAAyw5KoXABqL8AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-5603-6430","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Hanxiang","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-08-07 13:40:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4875191/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4875191/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s43247-025-01993-z","type":"published","date":"2025-01-08T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":65205567,"identity":"dd65d187-fb61-4afc-9e2d-aae1c174a9f4","added_by":"auto","created_at":"2024-09-24 18:10:25","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":235471,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe related position age-depth of the QMT22-1 core corrected from the QMT22-2 core. \u003c/strong\u003eA and B from the core QMT22-1, where A is dry bulk density, B is organic content; C and D from core QMT22-2, where C is dry bulk density, D is organic content; black dots are depth of age control.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4875191/v1/d1fcbd266cffcd700a89668b.jpeg"},{"id":65205024,"identity":"0106c2b5-a03c-4223-a5b5-4dcf4da429bc","added_by":"auto","created_at":"2024-09-24 18:02:25","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":100663,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCatotelm peat-addition and peat-decomposition model of QMT22-1 core and QMT22-2 core. \u003c/strong\u003eThe orange dashed line is the observed result and the green straight line is the result of the fit of the Clymo model.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4875191/v1/de549931a5aee5f85632ce09.jpeg"},{"id":65205566,"identity":"7c7c2da5-cacd-4373-8fe5-e230207a3e6d","added_by":"auto","created_at":"2024-09-24 18:10:25","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":117437,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCatotelm net carbon fluxes of QMT22-1 core and QMT 22-2 core by “super peatland”. \u003c/strong\u003eThe net carbon uptake (NCU) represents the average annual peat carbon flux over the past 1000 years, the net carbon pool (NCP) represents apparent peat stock as observed from peatland core, the net carbon release (NCR) represents the summed carbon release of all peat cohorts over time. The net carbon balance (NCB, black dots) was calculated as the difference between NCU and NCR. The smoothed lines are five-point moving averages (green line).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4875191/v1/7b28d97116c0aaf9a0a34d8a.jpeg"},{"id":65205029,"identity":"7a3e26b6-0b08-4139-bfb4-0ff63f7356d3","added_by":"auto","created_at":"2024-09-24 18:02:25","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":281160,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of NCB in the QMT peatlands of the subtropic monsoon and paleoclimate indicator records over the past 1000 years. \u003c/strong\u003e(a) 30°N solar irradiance. (b) north hemisphere annual temperature anomaly. (c) Central East China annual temperature anomaly. (d) Winter annual temperature anomaly of east China. (e) Summer annual temperature anomaly of east China. (f) The dark blue line is the composite z-score of the data for different humidity indicators in eastern China, and the sky-blue shading is the margin of error. (g) this study, the NCB is average for the core QMT22-1 and QMT22-2, short-term error bars.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4875191/v1/a08bf105d5fd4c894f14b76c.jpeg"},{"id":65205030,"identity":"19cc0833-a70a-4bd2-b8aa-8a295679a2f6","added_by":"auto","created_at":"2024-09-24 18:02:25","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":94111,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis employing GAMs between NCB and WT, MO.\u003c/strong\u003e (a) WT is winter time of east China annual temperature anomaly, (b) MO is the composite Z-score index of humidity.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4875191/v1/2610a8e8a0cbaa2d48033302.jpeg"},{"id":65205568,"identity":"fdb81745-e486-4d82-affe-9fc789e0d2d7","added_by":"auto","created_at":"2024-09-24 18:10:25","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":94581,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQMT Peatland Development Phase.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4875191/v1/d0c93a9d65bd4701ce6be512.jpeg"},{"id":65205571,"identity":"f76f936f-07a7-47b4-baee-d0c7e8e82fe5","added_by":"auto","created_at":"2024-09-24 18:10:25","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":49807,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe conceptual model of the climate-environmental mechanisms of peatland carbon accumulation in the subtropical monsoon zone. \u003c/strong\u003eNPP is the Net primary production, NCB is the net carbon accumulation rate balance.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4875191/v1/d65f89dab090c47f7766be24.jpeg"},{"id":65205031,"identity":"9a73f9d8-43dd-4c3e-8a99-a1a3757461dc","added_by":"auto","created_at":"2024-09-24 18:02:25","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":133335,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe conceptual model of the QMT peatland development and carbon accumulation influenced by winter temperature and precipitation over the 1000BP. \u003c/strong\u003eThe picture on the left shows the initial developmental stage, and the picture on the right shows the carbon accumulation stage,\u003cstrong\u003e \u003c/strong\u003e(+) indicates a positive effect impact, (-) indicates a negative effect impact. TOC is the total organic carbon. CAR is the carbon accumulation rate.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4875191/v1/2f7fc772557c83f0cfc0f85b.jpeg"},{"id":65205795,"identity":"6c832939-beef-4428-8f22-69b73addbb34","added_by":"auto","created_at":"2024-09-24 18:18:25","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":609199,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePeat records from the Qianmutian peatland on the Tianmu mountain. \u003c/strong\u003e(a) Elevation map of the subtropical monsoon zone of China showing the location of the study sites, black differently shaped points are the locations of the study sites for the collected climate background data, and this study is marked with a red star.\u0026nbsp; The colors represent elevations (m a.s.l.) as shown in the color bars right. The black arrow in the upper right corner indicates the direction of the East Asian monsoon, while the red dashed line represents the northern boundary of the East Asian monsoon. The boundary line of the East Asian monsoon is derived from \u003ca href=\"#_ENREF_46\" title=\"Chen, 2010 #179\"\u003eChen et al.\u003csup\u003e46\u003c/sup\u003e\u003c/a\u003e. (b) satellite image of the QMT peatland (source from Tianditu), the white line is counter. (c) close-up view of the QMT peatland.\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4875191/v1/b7a2c3b3b17c213a6fd6380b.jpeg"},{"id":65205569,"identity":"2d62a23c-9291-4093-9f06-cde01b6bda69","added_by":"auto","created_at":"2024-09-24 18:10:25","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":235287,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistinguish the acrotelm and catotelm of QMT22-1 core and QMT22-2 core.\u003c/strong\u003e The light blue wavy line represents the boundary between the acrotelm and catotelm layers. The dark blue represents the water content and the grey represents the organic matter content. (a) from the core QMT22-1; (b) from the core QMT22-2.\u003c/p\u003e","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4875191/v1/57f8e70bf6f9711314f2845c.jpeg"},{"id":73252503,"identity":"224723b5-b603-41be-be86-5d239429c32e","added_by":"auto","created_at":"2025-01-08 08:10:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2842223,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4875191/v1/4aebc564-9766-4ef8-8774-bc63150567fb.pdf"},{"id":65205026,"identity":"12192ed1-5219-485d-908b-f47a71d94456","added_by":"auto","created_at":"2024-09-24 18:02:25","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":665608,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFig.docx","url":"https://assets-eu.researchsquare.com/files/rs-4875191/v1/559378c761bd3f0019b9cec8.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Cold and humid climatic conditions over the last millennium decreased the carbon accumulation in peatlands of the subtropical monsoon region","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePeatlands account for only 3% to 4% of the global land area, but their carbon pool accounts for one-third of the global soil carbon pool, storing about 400-600 Gt carbon\u003csup\u003e1-4\u003c/sup\u003e. The formation of carbon pools in peatlands is intricately governed by the interplay of vegetation production and decomposition processes. Peat accumulation occurs when the rate of production exceeds the rate of decomposition\u003csup\u003e5\u003c/sup\u003e. Climate profoundly influences the thermal and humidity conditions experienced by peatlands, consequently shaping the dynamics of plant organisms and microbial communities within these ecosystems, which have an impact on the carbon accumulation in peatlands. The response of peatland\u0026rsquo;s carbon accumulation to temperature and precipitation patterns is linked to the future trajectory of carbon pools within these environments.\u003c/p\u003e\n\u003cp\u003eOn a millennial scale, the most significant carbon accumulation in northern peatlands occurred during the early Holocene, a period characterized by gradually rising temperatures\u003csup\u003e6\u003c/sup\u003e\u003csup\u003e, \u003c/sup\u003e\u003csup\u003e7\u003c/sup\u003e. On a centennial scale, Charman et al.\u003csup\u003e8\u003c/sup\u003e similarly suggest that temperature has had a stronger promoting effect on primary productivity compared to peat decomposition over the past millennium. This is evidenced by higher carbon accumulation rates during the Medieval Warm Period compared to the Little Ice Age\u003csup\u003e9\u003c/sup\u003e. These findings indicate that historical warm periods generally enhanced the rate of carbon accumulation in northern peatlands. In contrast, the climatic conditions for peatland formation and development in the low-medium elevation mountains of the subtropical monsoon region of China differ significantly from those in northern peatlands. This region experiences high temperatures and abundant precipitation\u003csup\u003e10\u003c/sup\u003e. The increase in carbon accumulation rates observed in the Dahu, Lantianyan, and Zhaogongting peatlands in eastern China\u0026apos;s subtropical region correlates positively with gradually arid climatic conditions\u003csup\u003e10-13\u003c/sup\u003e. During the mid-Holocene, carbon accumulation rates peaked in Dahu peatland due to arid climatic conditions, which inhibited peatland development. Terrestrial and nearshore aquatic vegetation encroached upon central areas, resulting in an increase in organic matter content\u003csup\u003e14\u003c/sup\u003e. Similarly, the Zhaogongting peatland experienced notable increases in carbon accumulation during the mid-Holocene due to declining water levels under dry conditions. Huang et al.\u003csup\u003e13\u003c/sup\u003e argued that the arid environment enhanced organic matter accumulation. Research in Dajiuhu suggested that short-term rapid hydrological fluctuations promote carbon accumulation, whereas long-term drought leads to intensified peat decomposition and decreased carbon accumulation\u003csup\u003e15\u003c/sup\u003e. Zhao et al.\u003csup\u003e16\u003c/sup\u003e synthesized data from peatland resource surveys conducted in the 1980s and found that peaks of carbon accumulation in subtropical peatlands were predominantly observed during the Marine Isotope Stage 3 (MIS 3) and the B\u0026oslash;lling-Aller\u0026oslash;d warm period, characterized by high solar radiation and intensified summer monsoons. Thus, it is evident that there are varying perspectives among different studies regarding carbon accumulation variations and their mechanisms in subtropical peatlands. The influence of climatic factors, such as temperature and precipitation, on peat carbon accumulation in subtropical regions remains incompletely understood. \u003c/p\u003e\n\u003cp\u003eOver the past 1,000 years, the fundamental boundary conditions of Earth\u0026apos;s climate, including factors such as land-sea distribution, topography, and orbital parameters, have remained relatively stable, providing a suitable backdrop for elucidating contemporary climate change trends. A large number of paleoclimate reconstruction and climate modeling studies have been carried out in the subtropical monsoon region of China. Historical and instrumental reconstructions of the climate of the Jianghuai region of China over the past millennium suggest an increasingly humid climate\u003csup\u003e17\u003c/sup\u003e. Additionally, Jiang et al.\u003csup\u003e18\u003c/sup\u003e observed a significant increase in precipitation in central-eastern China, a trend supported by a*/L* based on sediments from Nanyi Lake\u003csup\u003e19\u003c/sup\u003e. This trend has also been confirmed by diatom-based analyses\u003csup\u003e20\u003c/sup\u003e, grain size analyses\u003csup\u003e21\u003c/sup\u003eand magnetic analyses\u003csup\u003e22\u003c/sup\u003e. Regarding the temperature variations, Wang et al.\u003csup\u003e23\u003c/sup\u003e discovered through an analysis of the ECHO-G model that the temperature in the East Asian monsoon region of China exhibited fluctuations of \u0026ldquo;warmth-coldness-warmth\u0026rdquo; over the last millennium, which is consistent with the reconstruction results of Ge et al.\u003csup\u003e24\u003c/sup\u003e. There is a basic consensus pattern of gradually humid climate in the subtropical monsoon region over the last millennium. Therefore, understanding the historical relationships between carbon accumulation and climate factors over the past millennium is feasible, and will be instrumental in predicting the dynamics of carbon pools in peatlands under various climate change scenarios in the future.\u003c/p\u003e\n\u003cp\u003eHere, we utilized two meticulously dated and high-resolution net carbon accumulation records obtained from a mountain peatland to investigate the response of carbon accumulation to climatic fluctuations over the past millennium within the subtropical monsoon region. The study aims to achieve two primary objectives: (1) reconstructing the peatland\u0026rsquo;s evolutionary history, and establishing correlations between observed shifts in carbon dynamics and variations in temperature and precipitation patterns over the last millennium in the subtropical monsoon region. Through the examination of the nexus between carbon accumulation and climatic factors, we hope our study can contribute to the assessment of the scale and direction of future global carbon cycle feedback.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eLithostratigraphy and Chronology.\u003c/b\u003e The surface layer of core QMT22-1 from Qianmutian peatland (QMT), spanning 1\u0026thinsp;~\u0026thinsp;25 cm, constitutes the acrotelm and supports living fresh moss. From 25 cm to 117 cm, the core transitions into the catotelm, characterized by distinct layers: 25\u0026thinsp;~\u0026thinsp;35 cm consists of plant roots and humus, exhibiting weak decay; 35\u0026thinsp;~\u0026thinsp;55 cm comprises high humic sediments; and 55\u0026thinsp;~\u0026thinsp;117 cm is filled with yellow-brown mud, culminating in rock at the bottom. Similarly, in core QMT22-2, the acrotelm extends from the surface to a depth of 35 cm, cohabited by fresh moss. The subsequent 35\u0026thinsp;~\u0026thinsp;93 cm constitutes the catotelm, featuring distinctive layers: 35\u0026thinsp;~\u0026thinsp;50 cm comprises high humic sediments, while 51\u0026thinsp;~\u0026thinsp;93 cm is filled with yellow-brown mud exhibiting lower decomposition, ultimately concluding with rock at the base (refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the observed variations in DBD and LOI observed in the sediment cores QMT22-1 and QMT22-2, four distinct sedimentary sections can be identified. The first segment, spanning depths of 13\u0026thinsp;~\u0026thinsp;39 cm in QMT22-1 and corresponding to 13\u0026thinsp;~\u0026thinsp;40 cm in QMT22-2, displays the highest DBD and LOI values within the entire catotelm layer, with a rapid decrease observed around 39 cm and 40 cm, respectively. The second segment covers depths of 39\u0026thinsp;~\u0026thinsp;73 cm in QMT22-1 and 40\u0026thinsp;~\u0026thinsp;56 cm in QMT22-2. In this segment, DBD variations remain relatively stable, while LOI values consistently increase with depth, abruptly decreasing around 73 cm and 56 cm. The third segment encompasses depths of 73\u0026thinsp;~\u0026thinsp;93 cm in QMT22-1 and 57\u0026thinsp;~\u0026thinsp;71 cm in QMT22-2, showing a declining trend in both DBD and LOI with increasing depth. The fourth segment, spanning depths of 93\u0026thinsp;~\u0026thinsp;117 cm in QMT22-1 and 71\u0026thinsp;~\u0026thinsp;93 cm in QMT22-2, exhibits significant fluctuations in DBD without a clear increasing or decreasing trend, while LOI remains stable within this sedimentary segment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Consequently, we can establish the age depths of core QMT22-1 through core QMT22-2. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates that for core QMT22-2, samples from depths of 24 cm, 45 cm, 67 cm, and 93 cm were selected to determine ages. For QMT22-1, we determined the relative positions of age depths based on lithological characteristics. The first age depth is estimated at 23 cm, the second at 49 cm, the third at 86 cm, and the basal age depth at 117 cm (refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The basal ages of cores QMT22-1 and QMT22-2 are both estimated at 1160\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u0026nbsp;year \u003csup\u003e14\u003c/sup\u003eC BP (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Subsequently, using the R language, we established the chronology.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of AMS\u003csup\u003e14\u003c/sup\u003eC dating analysis of the QMT22-2\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDepth (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnalyzed material\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eConventional radiocarbon age (yr \u003csup\u003e14\u003c/sup\u003eC BP)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCalibrated age (cal yr BP, mean, 2σ range)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLZU23110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQMT22-2-24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u0026thinsp;~\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStem、leaf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e-140\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-41 (-53~-31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLZU23235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQMT22-2-45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u0026thinsp;~\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e140\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e204 (82\u0026thinsp;~\u0026thinsp;276)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLZU23111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQMT22-2-67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66\u0026thinsp;~\u0026thinsp;67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e550\u0026thinsp;\u0026plusmn;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e569 (505\u0026thinsp;~\u0026thinsp;639)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLZU23236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQMT22-2-93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92\u0026thinsp;~\u0026thinsp;93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1160\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1063 (972\u0026thinsp;~\u0026thinsp;1171)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCarbon Accumulation Dynamics.\u003c/b\u003e As the acrotelm is inhabited by fresh moss, this study solely focuses on the catotelm. In the catotelm of core QMT22-1, the peat-addition rate is determined to be 123.5 g OM m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with a peat-decomposition rate of 0.0008 yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Meanwhile, in core QMT22-2, the peat-addition rate is 67.8 g OM m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with a peat-decomposition rate of 0.0005 yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Upon determining the peat decomposition rate (α), further calculations of the net carbon uptake (NCU), net carbon release (NCR), and net carbon balance (NCB) were conducted using the \"super-peatland\" approach. For core QMT22-1, the calculated NCU ranges from 40.79 to 82.82 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with an average of 62.19 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. NCP ranges from 25.51 to 59.45 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with an average of 41.57 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Additionally, NCR ranges from 1.38 to 35.11 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with an average of 20.63 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, resulting in NCB ranging from 5.68 to 81.44 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Notably, these values are substantially higher compared to those of core QMT22-2. Specifically, the NCU of core QMT22-2 ranges from 26.20 to 52.51 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with an average of 34.55 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The NCP ranges from 18.40 to 35.17 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with an average of 27.35 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Moreover, NCR varies from 0.51 to 12.80 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with an average of 7.21 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, leading to NCB ranging from 13.41 to 51.99 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with an average of 27.35 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCharacteristics of solar radiation, temperature, and humidity.\u003c/b\u003e Solar radiation at 30\u0026deg;N has exhibited a fluctuating decreasing trend since the last millennium until approximately 600 years before the present (BP), followed by a fluctuating increasing trend since 600 years BP. However, the overall variation remains insignificant. Annual mean temperature anomalies in the Northern Hemisphere have undergone a notable change, displaying a decreasing trend since the last millennium until around 400 years BP, followed by an increasing trend from 400 years BP to the present. Similarly, annual mean temperature anomalies, as well as summer and winter annual temperature anomalies, in the subtropical monsoon region of China, followed a similar trend until approximately 450 years BP, indicating a fluctuating downward trend. However, a gradual warming trend has been observed after 450 years BP. Regarding changes in humidity indexes between 1200 and 800 years BP, variations are somewhat divergent, with some indicating a gradual decrease in humidity during this period, while others suggest an increase. Nevertheless, from 800 years BP to the present, all indicators point towards a gradual increase in humidity. By applying Z-scores to different humidity indicators, it becomes evident that humidity in the subtropical monsoon region of China has progressively increased over the past millennia (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eModels of net carbon accumulation rate and climate factors.\u003c/b\u003e The AICs of GAMs: \u003cem\u003eNCB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003es\u003c/em\u003e(\u003cem\u003eSO\u003c/em\u003e) (supplementary Fig.\u0026nbsp;1a), \u003cem\u003eNCB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003es\u003c/em\u003e(\u003cem\u003eNT\u003c/em\u003e) (supplementary Fig.\u0026nbsp;1b), \u003cem\u003eNCB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003es\u003c/em\u003e(\u003cem\u003eET\u003c/em\u003e) (supplementary Fig.\u0026nbsp;1c), \u003cem\u003eNCB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003es\u003c/em\u003e(\u003cem\u003eWT\u003c/em\u003e) (supplementary Fig.\u0026nbsp;1d), \u003cem\u003eNCB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003es\u003c/em\u003e(\u003cem\u003eST\u003c/em\u003e) (supplementary Fig.\u0026nbsp;1e), \u003cem\u003eNCB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003es\u003c/em\u003e(\u003cem\u003eMO\u003c/em\u003e) (supplementary Fig.\u0026nbsp;1f), \u003cem\u003eNCB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003es\u003c/em\u003e(\u003cem\u003eSO\u003c/em\u003e)\u0026thinsp;+\u0026thinsp;\u003cem\u003es\u003c/em\u003e(\u003cem\u003eMO\u003c/em\u003e) (supplementary Fig.\u0026nbsp;2), \u003cem\u003eNCB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003es\u003c/em\u003e(\u003cem\u003eET\u003c/em\u003e)\u0026thinsp;+\u0026thinsp;\u003cem\u003es\u003c/em\u003e(\u003cem\u003eMO\u003c/em\u003e) (supplementary Fig.\u0026nbsp;3), \u003cem\u003eNCB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003es\u003c/em\u003e(\u003cem\u003eWT\u003c/em\u003e)\u0026thinsp;+\u0026thinsp;\u003cem\u003es\u003c/em\u003e(\u003cem\u003eMO\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) and \u003cem\u003eNCB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003es\u003c/em\u003e(\u003cem\u003eST\u003c/em\u003e)\u0026thinsp;+\u0026thinsp;\u003cem\u003es\u003c/em\u003e(\u003cem\u003eMO\u003c/em\u003e) (supplementary Fig.\u0026nbsp;4)was 321, 304, 314, 314, 306, 282, 277, 279, 273 and 282. Thus, the final GAM is \u003cem\u003eNCB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003es\u003c/em\u003e(\u003cem\u003eWT\u003c/em\u003e)\u0026thinsp;+\u0026thinsp;\u003cem\u003es\u003c/em\u003e(\u003cem\u003eMO\u003c/em\u003e), and this model explained 77.2% of the variance of the NCB. The winter temperature and humidity were significant meteorological parameters for NCB.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussions","content":"\u003cp\u003e \u003cb\u003ePeatland development in the subtropical monsoon region over the past millennium.\u003c/b\u003e The QMT peatland was first formed at ~\u0026thinsp;1063\u0026nbsp;year BP, Given that the QMT peatland in Tianmu Mountain is located in a low-lying area, it acts as a catchment area. QMT has runoff input, their water source is ultimately precipitation or the small watersheds to the peatland, the water level in peatland likely reflects regional humidity balance and regional climate because of a very small watershed. Less early precipitation resulted in less standing water in the QMT depression. At the same time, higher winter temperatures extended the growing season for vegetation and produced more litter, causing the terristrialization of the water area and the initiation of peatland. Therefore, it is mainly the lower level of humidity and higher winter temperature that triggered the initiation of the QMT peatland in the subtropical monsoon region.\u003c/p\u003e \u003cp\u003eThe QMT22-1 core is 24 cm deeper than the QMT22-2 core, indicating potential variations associated with the microtopography of peatland development. Peatland development typically progresses through three distinct stages\u003csup\u003e25\u003c/sup\u003e: the initial phase characterized by various water sources and rapid peat accumulation is termed \u0026lsquo;lowmoor\u0026rsquo;, the final stage where the peat layer thickens and peat accumulation decreases is known as \u0026lsquo;highmoor\u0026rsquo;, with rainfall being a significant recharge source. The transitional stage between lowmoor and highmoor is termed \u0026lsquo;mesomoor\u0026rsquo;. Although both QMT22-1 and QMT22-2 cores were collected from the same peatland, due to the higher landform, the QMT22-2 core can transition to the highmoor stage earlier than QMT22-1 in the stages of peatland development. When QMT22-2 reaches the highmoor stage, surface vegetation primarily relies on rainfall for replenishment, inhibiting peat accumulation. Meanwhile, the QMT22-1 core was still in the mesomoor stage, receiving recharge not only from rainfall but also influenced by peatland water levels, maintaining a high rate of peat accumulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The different sediment thicknesses caused by the different micro-geomorphic heights in the same peatland prove the role of water in the development of peatland over the last millennium.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eImpacts of climate factors on QMT peatland carbon fluxes over the past millennium.\u003c/b\u003e The GAMs model shows that Winter temperature and humidity significantly impact the NCB of the QMT peatland (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). If the winter temperature anomaly is greater than \u0026minus;\u0026thinsp;0.2, winter temperature is negatively correlated with NCB. If the winter temperature index is less than \u0026minus;\u0026thinsp;0.2, winter temperature is positively correlated with NCB. Humidity indexes and NCB are negatively correlated when the humidity indicator is greater than \u0026minus;\u0026thinsp;0.5, and positively correlated when the humidity indicator is less than \u0026minus;\u0026thinsp;0.5. As each plant has its own suitable ecological niche\u003csup\u003e26\u003c/sup\u003e, vegetation growth in peatlands above this ecological niche is inhibited. Over the last thousand years, NCB has been positively correlated with the winter temperature anomaly (\u0026lt; -0.2) and negatively correlated with the humidity index (\u0026gt; -0.5) for most of the period (700 BP~) in QMT peatlands. When the winter temperature decreases, the length of the growing season for vegetation is shortened, resulting in reduced litter production and lower carbon accumulation. As humidity continues to increase, it leads to higher water levels in peatlands, which tends to accumulate more water on the surface, and peatland vegetation is flooded for a long period of time, inhibiting the production of vegetation, which produces less litter and leads to less carbon accumulation.\u003c/p\u003e \u003cp\u003eSince the annual average temperature, summer temperature and winter temperature are basically similar in their changing trends, these parameters also have a strong correlation with NCB. However, the structure of the GAMs proves that the combination of winter temperature anomaly and humidity index has the strongest impact on NCB. Other climate parameters, such as solar radiation, have no correlation with NCB. It can be seen from this that winter temperature and humidity were the main climatic factors controlling the carbon accumulation in peatlands during the last thousand years.\u003c/p\u003e \u003cp\u003eOver the last millennium, the humidity index in the eastern subtropical monsoon zone of China has generally increased, while the winter temperature anomaly has tended to decrease, resulting in a general decline in the NCB of the QMT peatlands. Although the responses of NCB to different ranges of winter temperature and humidity indices were not consistent, NCB was positively correlated with the winter temperature anomaly and negatively correlated with the humidity index for most of the period since 1000 BP. As winter temperatures decreased, the growing season for peatland vegetation shortened, suppressing litter production and leading to a lower carbon accumulation rate. Increased humidity raised water levels in the peatlands, causing more surface water accumulation and prolonged flooding of peatland vegetation, which hindered vegetation growth and reduced litter production, further decreasing the carbon accumulation rate. Thus, a cold and humid climate has been the primary factor influencing the decrease in carbon accumulation in the QMT peatland (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter the formation of the QMT peatland, it has consistently presented as a carbon sink. However, the net carbon balance of the peatland shows a downward trend since 1063 cal yr BP, yet still in the state of a carbon sink. Therefore, the carbon release risk of the mountainous peatland carbon pool in the subtropical monsoon area is not large under the change of the natural environment.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDifferent climate-carbon accumulation patterns in subtropical monsoon regions compared to other peatlands globally.\u003c/b\u003e From a global perspective, carbon accumulation in northern peatlands is primarily driven by temperature, with higher temperatures promoting carbon accumulation\u003csup\u003e7, 8\u003c/sup\u003e. In contrast to northern peatlands, tropical peatlands are affected by summer monsoon intensity, sea-level change, and El Ni\u0026ntilde;o intensity\u003csup\u003e3\u003c/sup\u003e. Meanwhile, carbon accumulation in southern peatlands, primarily located in Patagonia, South America, is predominantly regulated by water balance\u003csup\u003e27\u003c/sup\u003e. Clearly, the factors governing carbon accumulation in peatlands vary significantly across different regions.\u003c/p\u003e \u003cp\u003ePeatlands in the subtropical monsoon region belong to a subset of northern peatlands in terms of classification. However, the climatic mechanisms driving variations in carbon accumulation in subtropical peatlands differ from those in northern peatlands. In the early stage, less precipitation in the subtropical monsoon region led to a reduction in water levels in the depressions of low and medium mountains. At the same time, the higher winter temperature during that period prolonged the growing season of vegetation and increased the amount of litter. This was conducive to the terrestrialization of the water area and thereby promoted the development of peatland in this area.\u003c/p\u003e \u003cp\u003eAnalyses of carbon fluxes in the peatland showed that changes in winter temperature anomaly and humidity index have primarily controlled carbon accumulation in the peatlands over the past millennium. The gradual cooling and increase in humid over the past thousand years have reduced the rate of carbon accumulation. Lower winter temperatures shorten the length of the growing season of peatland vegetation, leading to a decrease in litter and a lower rate of carbon accumulation. A gradual increase in humidity led to the accumulation of more water in the surface layer of peatlands, which is detrimental to the growth of surface vegetation. This reduces net primary productivity, decreases litter, and consequently reduces net carbon accumulation.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn the subtropical monsoon region, drought and higher winter temperatures are conducive to the development and carbon accumulation of peatlands. Reduced precipitation lowers water levels in depressions of the mountains. Meanwhile, higher winter temperatures extend the vegetation growing season, increasing litter production and facilitating the terrestrialization of water areas, thereby promoting peatland development and carbon accumulation in this region. Over the past thousand years, carbon accumulation in the Qianmutian peatland has declined due to gradual decreases in winter temperatures and increases in humidity. Reduced winter temperatures have shortened the vegetation growing season, leading to decreased litter production and carbon accumulation. Increased humidity has raised peatland water levels, reducing vegetation productivity and causing a decline in carbon accumulation. Despite the decline in carbon accumulation over the last millennium, the peatland\u0026rsquo;s net carbon balance remains in a \u0026lsquo;carbon sink\u0026rsquo; state. This suggests that the risk of carbon release from the peatland carbon pool under natural climate change conditions is not substantial in the subtropical monsoon area.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003e \u003cb\u003eStudy site and fieldwork.\u003c/b\u003e The Qianmutian (QMT) peatland is situated in the eastern region of China (30.4994\u0026deg;N, 119.4408\u0026deg;E), at an altitude of approximately 1300 meters above sea level (m a.s.l.) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea). It located within a typical warm and humid monsoon climate zone characterized by abundant insolation relative to higher latitudes, elevated mean annual temperatures, and notable summertime warmth. Winter temperatures typically remain above 0\u0026deg;C, with monsoon precipitation concentrated primarily in the summer season, often accompanied by frequent thunderstorms and other extreme meteorological phenomena. Specifically, the mean annual temperature in this region averages around 9.5\u0026deg;C, annual precipitation is 1500mm.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe QMT peatland, covering approximately 0.7 km\u003csup\u003e2\u003c/sup\u003e, is situated in a depression atop Tianmu Mountain (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eb). The surface vegetation is dominated by \u003cem\u003eSphagnum junghuhnianum\u003c/em\u003e and \u003cem\u003eSphagnum magellanicum\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ec). Accompanying with other plant species, such as \u003cem\u003eIdesia polycarpa\u003c/em\u003e, \u003cem\u003eLigularia fischeri\u003c/em\u003e, \u003cem\u003eRubus trianthus\u003c/em\u003e, \u003cem\u003eRubus peltatus\u003c/em\u003e, \u003cem\u003eCalamagrostis epigejos\u003c/em\u003e, \u003cem\u003eScirpus lushanensis\u003c/em\u003e, and \u003cem\u003eMiscanthus sacchariflorus\u003c/em\u003e. In the surrounding upland vegetation, \u003cem\u003eCornus officinalis\u003c/em\u003e, \u003cem\u003eIdesia polycarpa\u003c/em\u003e, and \u003cem\u003eViburnum opulus subsp\u003c/em\u003e. \u003cem\u003eCalvescens\u003c/em\u003e are the dominant tree and shrub species.\u003c/p\u003e \u003cp\u003eIn November 2022, we retrieved two peat cores, QMT22-1 (117 cm) and QMT22-2 (93 cm), using both box corer and Russian peat corer methods. For QMT22-1, we initially hand-excavated the surface layer from 0\u0026thinsp;~\u0026thinsp;47cm before employing a box corer to extract a peat core spanning 0\u0026thinsp;~\u0026thinsp;97 cm. Subsequently, we attempted a second drilling session using the Russian peat core method to target depths of 64\u0026thinsp;~\u0026thinsp;117 cm; however, drilling was halted upon encountering gravel at the bottom. Similarly, for QMT22-2, we hand-excavated the surface layer from 0\u0026thinsp;~\u0026thinsp;38 cm and then employed a Russian peat core to extract the first core from 20\u0026thinsp;~\u0026thinsp;70 cm in depth. A subsequent drilling session aiming at depths of 43\u0026thinsp;~\u0026thinsp;93 cm was attempted, but drilling was again halted due to encountering gravel at the bottom. Following collection, the peat cores were placed into PVC pipes and transported back to the laboratory, where they were stored at 4\u0026deg;C for further analysis.\u003c/p\u003e \u003cp\u003e \u003csup\u003e \u003cb\u003e14\u003c/b\u003e \u003c/sup\u003e \u003cb\u003eC dating.\u003c/b\u003e We collected samples from the QMT peatland using a Box peat corer and a Russian peat corer, resulting in QMT22-1 (117 cm) and QMT22-2 (93 cm). Samples from QMT22-2 at depths of 24 cm (stem and leaf), 45 cm (stem), 67 cm (stem), and 93 cm (stem) underwent Accelerator Mass Spectrometry (AMS) radiocarbon (\u003csup\u003e14\u003c/sup\u003eC) dating at the \u003csup\u003e14\u003c/sup\u003eC Chronology Laboratory of Lanzhou University. Considering the close proximity and similar climatic context of the QMT22-1 and QMT22-2 cores, their stratigraphic positioning can be readily discerned based on their Dry Bulk Densities (DBD) and Loss on Ignition (LOI) values. Based on the depth of the dating points in the QMT22-2 core, the corresponding locations of the dating points in the QMT22-1 core can be inferred accordingly. Then we constructed an age-depth model using the Bayesian age-depth modeling package BACON\u003csup\u003e28\u003c/sup\u003e for R. The results were given in years before the present (BP), where \u0026ldquo;present\u0026rdquo; is defined as the year 1950 Common Era (CE).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAcrotelm/catotelm boundary detection.\u003c/b\u003e The peatland sediment layers comprise the acrotelm and catotelm. The acrotelm is rich in fresh litter and has a high organic matter content. However, due to the rapid decomposition of organic matter in this layer, the surface carbon accumulation rate, based on LOI, is higher than the earlier carbon accumulation rate. Therefore, this study will focus exclusively on the catotelm sedimentary layer. The catotelm and acrotelm represent two distinct layers distinguished by their physical properties and functions\u003csup\u003e29\u003c/sup\u003e. In QMT22-1 and QMT22-2, the differentiation between the acrotelm and catotelm is evident from lithological variations observed in the cores (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). In QMT22-1, the upper layer spanning 1\u0026thinsp;~\u0026thinsp;25 cm exhibits a brown-yellow sediment with low decomposition levels, abundant plant roots, and a relatively loose structure. Water content and LOI remain stable within this layer. Beyond 25 cm, the color gradually darkens to dark brown, accompanied by increased decomposition levels, rapid declines in water content and LOI, and the presence of underlying rocks. Consequently, the upper layer (1\u0026thinsp;~\u0026thinsp;25 cm) is identified as the acrotelm, comprising living fresh moss, while the subsequent layer (25\u0026thinsp;~\u0026thinsp;117 cm) is recognized as the catotelm. Similar changes are observed in the QMT22-2 core at a depth of 35cm, where the upper layer (1\u0026thinsp;~\u0026thinsp;35 cm) exhibits brown-yellow sediment, dense plant root systems, and a loose structure, with stable water content and LOI levels. Below 35 cm, the color transitions from brown-yellow to black-brown, accompanied by rapid decreases in water content and LOI, increased decomposition levels, and the presence of underlying rocks. Hence, the upper layer (1\u0026thinsp;~\u0026thinsp;35 cm) in QMT22-2 is identified as the acrotelm, comprising living fresh moss, while the subsequent layer (35\u0026thinsp;~\u0026thinsp;93 cm) is classified as the catotelm.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eClimate data integration.\u003c/b\u003e In investigating past climate changes over the last millennium in the subtropical monsoon region, researchers have leveraged various archives and proxies. These include solar radiation\u003csup\u003e30\u003c/sup\u003e, temperature\u003csup\u003e24, 31\u0026ndash;33\u003c/sup\u003e, To indicate humidity changes, we collected several indicators, including a*/L*\u003csup\u003e18\u003c/sup\u003e, Rb/Sr\u003csup\u003e19\u003c/sup\u003e, mean size\u003csup\u003e21\u003c/sup\u003e, TOC\u003csup\u003e34\u003c/sup\u003e, and DPC-1\u003csup\u003e20\u003c/sup\u003e from lake sediments; SIRM\u003csup\u003e22\u003c/sup\u003e and precipitation\u003csup\u003e35\u003c/sup\u003e from peatland sediments; Total organic carbon (TOC)\u003csup\u003e36\u003c/sup\u003e and freshwater species\u003csup\u003e37\u003c/sup\u003e from offshore sediments; and a humidity index\u003csup\u003e17\u003c/sup\u003e based on instrumental stations and historical records in southern China (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The climate data used in this paper are published and peer-reviewed. When analyzing the correlation between humidity data and the NCB, we integrated the z-score values of these humidity records (Supplementary Fig.\u0026nbsp;5).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSite information on the sediment records used in climate data synthesis in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eElevation/m\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSediment type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNvshan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.996\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118.124\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ea*/L*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWang et al.\u003csup\u003e45\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNanyi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.079\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118.944\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRb/Sr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLiu et al\u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.095\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122.378\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWang et al\u003csup\u003e36\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWangdongyang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.682\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119.637\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePeatland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSIRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZhou et al\u003csup\u003e22\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGaoyou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.84\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119.375\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLi et al\u003csup\u003e21\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCuifeng\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.5\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121.6\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelvaraj et al\u003csup\u003e34\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCuifeng\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.5\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121.6\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDPC-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWang et al\u003csup\u003e20\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern Okinawa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.80067\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122.4892\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFreshwater species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLi et al\u003csup\u003e37\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHistorical and station records\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDry-humidity index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZheng et al\u003csup\u003e17\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDajiu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.47\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109.98\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003epealand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePrecipitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHe et al\u003csup\u003e35\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCarbon measurements and modeling.\u003c/b\u003e The cores QMT22-1 and QMT22-2 were sliced into contiguous sections each 1cm thick and subsequently measured. These samples underwent drying at 105\u0026deg;C for 12 hours to ascertain their DBD, defined as the ratio of dry weight to volume. A subset of the dried subsamples underwent combustion at 550\u0026deg;C in a muffle furnace for 4 hours to determine the LOI, serving as an indicator of Organic Matter content (OM)\u003csup\u003e38\u003c/sup\u003e. To calculate the Organic Carbon content\u003csup\u003e39\u003c/sup\u003e, the OM was multiplied by 50%\u003csup\u003e40\u003c/sup\u003e. Based on the chronologies and the measured DBD (g cm\u003csup\u003e3\u003c/sup\u003e) and OC \u003csup\u003e41\u003c/sup\u003e, the peat aCAR was calculated as:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(aCAR=\\frac{h}{t} \\times DBD \\times OC \\times 100\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003ewhere t (yr) is the time experienced by peat thickness h (cm).\u003c/p\u003e \u003cp\u003eUpon obtaining the LOI data from the QMT peatland, we apply the decomposition model proposed by Clymo\u003csup\u003e42\u003c/sup\u003e to derive the peat-addition rate (ρ) and peat decomposition rate (α), where M represents the organic matter and t signifies time.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(M=\\frac{\\rho }{\\alpha } \\times (1 - {e^{ - \\alpha t}})\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003eSubsequently, employing the \"super-peatland\" methodology, we determine the net carbon fluxes of the QMT22-1 and QMT22-2 cores, encompassing net carbon uptake (NCU), net carbon release (NCR), and net carbon balance (NCB), with α derived from the modeled decomposition\u003csup\u003e43\u003c/sup\u003e. The observed carbon pool (NCP) enables the calculation of NCU for each 20-year interval, treating NCU as the initial mass and NCP as the remaining mass after time t. This equation is formulated as follows:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(NC{U_t}=\\frac{{NC{P_t}}}{{{e^{ - \\alpha t}}}}\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003eAt a specific 20-year interval (k), the NCR is calculated. Therefore, the NCR at time t comprises the cumulative carbon release during that period from all peat cohorts older than time t. The carbon release during the 20-year period at time t can be determined by subtracting the potential NCU at time t from that at time t\u0026thinsp;\u0026minus;\u0026thinsp;1. The equation can be expressed as follows:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(NC{R_t}=\\sum\\nolimits_{{k=t}}^{{initiation age}} {(\\frac{{NC{P_k}}}{{{e^{ - \\alpha t}}}} - \\frac{{NC{P_k}}}{{{e^{ - \\alpha \\times (t - 1)}}}})}\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003eNCB was calculated as the difference between NCU and NCR as derived above\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(NCB=NCU - NCR\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eRegression models\u003c/b\u003e. Generalized Additive Models (GAMs) is a type of regression model that utilizes smoothing splines instead of linear coefficients for covariates\u003csup\u003e44\u003c/sup\u003e. The aim of using GAMs is to establish the connection between NCB and climate factors in the QMT peatland. The ultimate model was selected based on the lowest Akaike information criterion (AIC).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003ewas provided by the National Natural Science Foundation of China (No. 42001081) and the Open Fund Project of Key Laboratory of Watershed Surface Processes and Ecological Security of Jinhua City (KF-2022-13) .\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eH.L. conceived the ideas; B.L., H.L., and Z.X. collected field samples; B.L. did the LOI measurement, and reconstructed the carbon flux history; B.L. and H.L. wrote the manuscript; Z.Y., Y.F., Z.X., and J.J. discussed results and commented on the manuscript.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eAll carbon flux and \u003csup\u003e14\u003c/sup\u003eC data from QMT peatland are available in the online database and on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDavidson, E. 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Rev. 29, 1055\u0026ndash;1068 (2010).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"subtropical monsoon, peatland, carbon accumulation, winter temperature, humidity","lastPublishedDoi":"10.21203/rs.3.rs-4875191/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4875191/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCarbon accumulation in most northern peatlands is generally positively correlated with temperature under natural climate change. In the subtropical monsoon region of China, the climate differs from that of most northern peatlands, where a significant number of peatlands have developed in mountainous areas. However, it remains unclear how the carbon dynamics of these subtropical peatlands respond to climate change. Here, we reconstructed the net carbon fluxes of a typical mountainous peatland in Tianmu Mountain, eastern China, over the past millennium. Climate records in the subtropical monsoon zone indicate fluctuating and declining temperatures alongside increasing humidity over the past thousand years. Drought and higher winter temperatures have facilitated the terrestrialization of waterlogged depressions and triggered the peatland formation in this region. The net carbon accumulation in the peatland has generally shown a downward trend due to the progressively decreasing winter temperature and increasing humidity. When winter temperatures decrease, the growing season for vegetation is shortened, resulting in less litter production and reduced carbon accumulation. Increased humidity leads to greater surface waterlogging and prolonged flooding of surface vegetation, which hampers vegetation growth, reduces litter production, and consequently lowers carbon accumulation. Despite the decline in carbon accumulation over the last millennium, the peatland\u0026rsquo;s net carbon balance remains in a 'carbon sink' state. This suggests that the risk of carbon release from the peatland carbon pool under natural climate change conditions is not substantial in the subtropical monsoon area.\u003c/p\u003e","manuscriptTitle":"Cold and humid climatic conditions over the last millennium decreased the carbon accumulation in peatlands of the subtropical monsoon region","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-24 18:02:20","doi":"10.21203/rs.3.rs-4875191/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-earth-and-environment","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsenv","sideBox":"Learn more about [Communications Earth and Environment](https://www.nature.com/commsenv/)","snPcode":"","submissionUrl":"","title":"Communications Earth \u0026 Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8a203461-2689-496f-b720-5bb91bade205","owner":[],"postedDate":"September 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":36534712,"name":"Earth and environmental sciences/Environmental sciences"},{"id":36534713,"name":"Earth and environmental sciences/Climate sciences"}],"tags":[],"updatedAt":"2025-01-08T08:10:07+00:00","versionOfRecord":{"articleIdentity":"rs-4875191","link":"https://doi.org/10.1038/s43247-025-01993-z","journal":{"identity":"communications-earth-and-environment","isVorOnly":false,"title":"Communications Earth \u0026 Environment"},"publishedOn":"2025-01-08 05:00:00","publishedOnDateReadable":"January 8th, 2025"},"versionCreatedAt":"2024-09-24 18:02:20","video":"","vorDoi":"10.1038/s43247-025-01993-z","vorDoiUrl":"https://doi.org/10.1038/s43247-025-01993-z","workflowStages":[]},"version":"v1","identity":"rs-4875191","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4875191","identity":"rs-4875191","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}