Here, we present results from the most comprehensive compilation of Holocene peat soil properties with associated carbon and nitrogen accumulation rates for northern peatlands. Our database consists of 268 peat cores from 215 sites located north of 45°N. It encompasses regions within which peat carbon data have only recently become available, such as the West Siberia Lowlands, the Hudson Bay Lowlands, Kamchatka in Far East Russia, and the Tibetan Plateau. For all northern peatlands, carbon content in organic matter was estimated at 42 ± 3% (standard deviation) for Sphagnum peat, 51 ± 2% for non- Sphagnum peat, and at 49 ± 2% overall. Dry bulk density averaged 0.12 ± 0.07 g/cm3, organic matter bulk density averaged 0.11 ± 0.05 g/cm3, and total carbon content in peat averaged 47 ± 6%. In general, large differences were found between Sphagnum and non- Sphagnum peat types in terms of peat properties. Time-weighted peat carbon accumulation rates averaged 23 ± 2 (standard error of mean) g C/m2/yr during the Holocene on the basis of 151 peat cores from 127 sites, with the highest rates of carbon accumulation (25–28 g C/m2/yr) recorded during the early Holocene when the climate was warmer than the present. Furthermore, we estimate the northern peatland carbon and nitrogen pools at 436 and 10 gigatons, respectively. The database is publicly available at https://peatlands.lehigh.edu .

Tropical peatlands play an important role in the global carbon cycling but little is known about factors regulating carbon dioxide (CO 2 ) and methane (CH 4 ) fluxes from these ecosystems. Here, we test the hypotheses that (i) CO 2 and CH 4 are produced mainly from surface peat and (ii) that the contribution of subsurface peat to net C emissions is governed by substrate availability. To achieve this, in situ and ex situ CO 2 and CH 4 fluxes were determined throughout the peat profiles under three vegetation types along a nutrient gradient in a tropical ombrotrophic peatland in Panama. The peat was also characterized with respect to its organic composition using 13 C solid state crosspolarization magic-angle spinning nuclear magnetic resonance spectroscopy. Deep peat contributed substantially to CO 2 effluxes both with respect to actual in situ and potential ex situ fluxes. CH 4 was produced throughout the peat profile with distinct subsurface peaks, but net emission was limited by oxidation in the surface layers. CO 2 and CH 4 production were strongly substrate-limited and a large proportion of the variance in their production (30% and 63%, respectively) was related to the quantity of carbohydrates in the peat. Furthermore, CO 2 and CH 4 production differed between vegetation types, suggesting that the quality of plant-derived carbon inputs is an important driver of trace gas production throughout the peat profile. We conclude that the production of both CO 2 and CH 4 from subsurface peat is a substantial component of the net efflux of these gases, but that gas production through the peat profile is regulated in part by the degree of decomposition of the peat. , 2010). In areas of the tropics which are expected to experience reduced rainfall and more prolonged drought (Meehl et al., 2007), peatlands may become less important sources of atmospheric methane (CH 4 ). However, this would be offset by greatly increased rates of aerobic decomposition and carbon dioxide (CO 2 ) release, with the result that their combined global warming potential would increase (Hirano et al., 2009;Couwenberg et al., 2010;Sjö gersten et al., 2010). It is also plausible that old C stored deep in the peat profile would be metabolized if climatic conditions become more favourable for decomposition, as has been reported for northern peatlands (e.g. Dorrepaal et al., 2009). In tropical environments, a substantial draw down of the water table would be required to impact deep peat layers. Considerable variation in C fluxes occurs between vegetation types in tropical wetland systems (Melling et al., 2005a, b;Sjö gersten et al., 2010), suggesting that C inputs from the vegetation (Sebacher et al., 1985;Joabsson & Christensen, 2001;Konnerup et al., 2010) are strong drivers of C fluxes in these systems. However, it is not known if these differences in C fluxes result mainly from surface processes or are maintained throughout the peat profile. Tropical peatlands may reach depths of up to 15 m (Phillips et al., 1997;Page et al., 1999Page...

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International audienceWe present a method to quantify abrupt changes (or changepoints) in data series, represented as a function of depth or time. These changes are often the result of climatic or environmental variations and can be manifested inmultiple datasets as different responses, but all datasets can have the same changepoint locations/timings. The method we present uses transdimensional Markov chain Monte Carlo to infer probability distributions on the number and locations (in depth or time) of changepoints, the mean values between changepoints and, if required, the noise variance associated with each dataset being considered. This latter point is important as we generally will have limited information on the noise, such as estimates only of measurement uncertainty, and in most cases it is not practical to make repeat sampling/measurement to assess other contributions to the variation in the data.Wedescribe themain features of the approach (and describe themathematical formulation in supplementary material), and demonstrate its validity using synthetic datasets, with known changepoint structure (number and locations of changepoints) and distribution of noise variance for each dataset.We show that when using multiple data, we expect to achieve better resolution of the changepoint structure than when we use each dataset individually. This is conditional on the validity of the assumption of common changepoints between different datasets.We then apply themethod to two sets of real geochemical data, both from peat cores, taken from NE Australia and eastern Tibet. Under the assumption that changes occur at the same time for all datasets, we recover solutions consistent with those previously inferred qualitatively from independent data and interpretations. However, our approach provides a quantitative estimate of the relative probability of the inferred changepoints, allowing an objective assessment of the significance of each change