Test of validity of a dynamic soil carbon model using data from leaf litter decomposition in a West African tropical forest

GMDD 6, 3003–3032, 2013 Soil carbon modeling in tropical forests G. H. S. Guendehou et al. Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Geosci. Model Dev. Discuss., 6, 3003–3032, 2013 www.geosci-model-dev-discuss.net/6/3003/2013/ doi:10.5194/gmdd-6-3003-2013 © Author(s) 2013. CC Attribution 3.0 License. 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Please refer to the corresponding final paper in GMD if available. Test of validity of a dynamic soil carbon model using data from leaf litter decomposition in a West African tropical forest G. H. S. Guendehou1,2, J. Liski3, M. Tuomi3, M. Moudachirou4, B. Sinsin5, and R. Mäkipää2 1Centre Béninois de la Recherche Scientifique et Technique, 03 BP 1665 Cotonou, Bénin 2Finnish Forest Research Institute, P.O. Box 18, 01301 Vantaa, Finland 3Finnish Environment Institute, P.O. Box 140, 00251 Helsinki, Finland 4Laboratoire de Pharmacognosie, Faculté des Sciences et Techniques, Université d’Abomey-Calavi, 01 BP 918 Cotonou, Bénin 5Laboratoire d’Ecologie Appliquée, Faculté des Sciences Agronomiques, Université d’Abomey-Calavi, 01 BP 526 Cotonou, Bénin Received: 8 April 2013 – Accepted: 6 May 2013 – Published: 28 May 2013 Correspondence to: G. H. S. Guendehou ([email protected], [email protected]) Published by Copernicus Publications on behalf of the European Geosciences Union. 3003 GMDD 6, 3003–3032, 2013 Soil carbon modeling in tropical forests G. H. S. Guendehou et al. Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Abstract We evaluated the applicability of the dynamic soil carbon model Yasso07 in tropical conditions in West Africa by simulating the litter decomposition process using as required input into the model litter mass, litter quality, temperature and precipitation collected during a litterbag experiment. The experiment was conducted over a six-month5 period on leaf litter of five dominant tree species, namely Afzelia africana, Anogeissus leiocarpa, Ceiba pentandra, Dialium guineense and Diospyros mespiliformis in a semideciduous vertisol forest in Southern Benin. Since the predictions of Yasso07 were not consistent with the observations on mass loss and chemical composition of litter, Yasso07 was fitted to the dataset composed of global data and the new experimen-10 tal data from Benin. The re-parameterized versions of Yasso07 had a good predictive ability and refined the applicability of the model in Benin to estimate soil carbon stocks, its changes and CO2emissions from heterotrophic respiration as main outputs of the model. The findings of this research support the hypothesis that the high variation of litter quality observed in the tropics is a major driver of the decomposition and needs15 to be accounted in the model parameterization. 1 Introduction In tropical conditions in Africa, little attention has been paid to litter decomposition and quantification of changes in the soil organic carbon (SOC), though forest soil in this region accounts for 11 % of the world’s soil carbon pool (FAO, 2010). Quantification of20 SOC dynamics in tropical Africa is required to improve the estimation of the global carbon balance. The SOC changes are reported under the Climate Change Convention as a part of the national greenhouse gas inventories of the forestry sector (UNFCCC, 2008, 2010), but majority of the countries in tropical Africa either report no changes in SOC stocks or apply default stock change factors of the Intergovernmental Panel25 on Climate Change methodologies (par ex. IPCC, 2003, 2006) together with rough 3004 GMDD 6, 3003–3032, 2013 Soil carbon modeling in tropical forests G. H. S. Guendehou et al. Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | estimation of the land use and land use change. Under the United Nations Framework Convention on Climate Change (UNFCCC) mechanism of the Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD), the countries have economic incentives for the conservation, the sustainable management and the enhancement of their forest carbon stocks and robust methods leading to confident5 and verified carbon stock estimates are increasingly required. A number of studies have reported SOC stocks estimated from only sporadic soil sampling and digital maps (for example Manu et al., 1991; Prudencio, 1993; Volkoffet al., 1999; Henry et al., 2009) and the reported values in the existing databases in Africa (Batjes, 1996, 2002, 2005, 2006; FAO, 2008) consist of global estimates with limited indications on changes and10 distribution according to ecosystems. The dynamics of SOC is influenced by litter quantity and quality, climate and metabolism of decomposing organisms and governed by other physical, chemical and biological factors, such as soil properties, which all may be difficult to quantify (Swift and Anderson, 1989; Aerts, 1997; Coûteaux et al., 1998; Lavelle et al., 1993). Overall15 changes in the SOC stock may be quantified with measurements, but repeated measurements of soil carbon stocks are laborious, time and resources consuming efforts with the added drawback of the difficulty of predicting future levels. Also, extrapolating only a few SOC measurements to a large scale may lead to high uncertainty due to the spatial variation of SOC. Thus, processes taking place in soil and SOC stock20 changes are studied mostly through the use of decomposition models (Coleman and Jenkinson, 1996; Currie and Aber, 1997; Kurz and Apps, 1999; Chertov et al., 2001; Liski et al., 2005; Sierra et al., 2012). The application of the model-based approach could also help countries to meet SOC reporting requirements in tropical Africa, where resources are limited. However, model results and their applicability depend on model25 structure and parameters, as well as on available input information and assumptions used (Peltoniemi et al., 2007; Palosuo et al., 2012). To our knowledge, no SOC model calibrated using litter decomposition data from forests in Africa is available. The litter quality is known to be the most important 3005 GMDD 6, 3003–3032, 2013 Soil carbon modeling in tropical forests G. H. S. Guendehou et al. Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | determinant of the decomposition rate at regional scale (e.g. Tian et al., 1992; Berg et al., 1993; Lavelle et al., 1993; Aerts, 1997; Loranger et al., 2002; Guendehou et al., 2014) and the plant species richness of tropical forests yields high variation in the litter quality (Goma-Tchimbakala and Bernhard-Reversat, 2006; Barbhuiya et al., 2008; Cusack et al., 2009). However, only a few litter types from tropical tree species were5 included in the dataset that was used for the parameterization of the widely applied soil carbon model (Coleman and Jenkinson, 1996; Chertov et al. 2001; Tuomi et al., 2009) and many soil models are parameterized only for temperate and boreal conditions. In this paper, we assessed the applicability of the dynamic soil carbon model Yasso07 in tropical conditions in West Africa, by simulating the litter decomposition10 process using data on litter mass, litter quality, temperature and precipitation. The data was collected from a litterbag experiment conducted on leaf litter from five dominant tree species, namely Afzelia africana,Anogeissus leiocarpa,Ceiba pentandra,Dialium guineense, and Diospyros mespiliformis, in the natural semi-deciduous forest Lama in Southern Benin. We tested the hypotheses that the decomposition process in tropics15 is affected by the high variation of litter quality and re-parameterization of the Yasso07 model with the diverse litter data from tropics improves the precision of the mass loss predictions. 2 Material and methods 2.1 Experimental site20 The experimental site is the Lama forest, a natural semi-deciduous forest located in a humid tropical climate in Southern Benin (Nagel et al., 2004) at 6◦550–7◦000N, 2◦040– 2◦120E (Fig. 1). The site falls within the tropical moist zone according to the classification scheme for climate regions of the IPCC (IPCC, 2006). The highest (38 ◦C) and the lowest (15 ◦C) temperatures were usually recorded in February–March and in De-25 cember, respectively. The mean annual temperature is 27 ◦C. The precipitation shows 3006 GMDD 6, 3003–3032, 2013 Soil carbon modeling in tropical forests G. H. S. Guendehou et al. Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | a bimodal distribution pattern. The mean annual precipitation in the experimental site is 1100 mm; rainfall is, in general, more than 100 mmmonth−1throughout the year, except in January, February and December. Years are divided into four seasons: two rainy and two dry seasons. The principal rainy season occurs between mid-March and mid-July and the shorter rainy season between mid-September and mid-November.5 The monthly average of relative humidity is always more than 51 %. Table 1 shows the monthly climatic data recorded by the national meteorological service at the time of the decomposition experiment. The soil of the Lama forest is reported as a rare hydromorphic clayey vertisol (40 to 60 % of clay) in West Africa, with a poor drainage and a pH range of 5–5.5 in the 0–10 30 cm horizon (Küppers et al., 1998). The pH increases up to 6.5–7 in deeper horizons due to the appearance of limestone at a depth of 150 cm. This soil has been described as rich in calcium (Ca) and magnesium (Mg) due to a “granito-gneissic” parent material from the secondary and tertiary ages. The mean altitude in the forest is 60 m (von Bothmer et al., 1986).15 The tree species richness of the Lama forest was described by Akoègninou (1984), Mondjannagni (1969), Paradis and Houngnon (1977). Küppers et al. (1998) reported 67 families based on an inventory carried out in 1998. The average density in the natural dense part of the forest where the experiment took place is 12 species/400 m2, and the relative abundance of dominant tree species is about 40 trees/400 m2(Küppers20 et al., 1998). The current research focused on five dominant tree species including A. africana,A. leiocarpa,C. pentandra,D. guineense and D. mespiliformis (von Bothmer et al., 1986; Küppers et al., 1998; Nagel et al., 2004). No human activities such as harvesting or fertilization are implemented in the experimental site. In Lama forest, the amount of leaf litter fall ranges from 26 to 42 t dry matter yr−1; the litter fall follows25 a unimodal distribution pattern, with the maximum litter production observed during the dry season, often in January (Djego, 2006). 3007 GMDD 6, 3003–3032, 2013 Soil carbon modeling in tropical forests G. H. S. Guendehou et al. Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 2.2 Litterbag experiment, mass loss measurement, and chemical analyses The material used consisted of leaf litter of A. africana, A. leiocarpa, C. pentandra, D. guineense, and D. mespiliformis. Only senescent leaves ready to fall from the trees were collected. Leaves were dried in open-air and then oven-dried at 75 ◦C to constant weight. In order to use all the amount of litter collected, an initial mass of dried leaves5 of A. africana (20 g), A. leiocarpa (30 g), C. pentandra (20 g), D. guineense (30 g), and D. mespiliformis (30 g) was placed in the litterbags in polyester (20 cm ×20 cm, mesh size 0.33 mm) on the forest floor. Litterbags were divided between four plots established in a nearly rectangular configuration; the distance between the plots (between 25 and 30 m) was assumed large enough to minimize the spatial autocorrelation be-10 tween plots. In each plot, litterbags were placed in rows and columns on the forest floor: there were five columns each containing the five litter species and six rows each containing the six collections. In total, 30 litterbags were placed in a plot. In each row, the five bags were placed. Bags were not moved until collection date and no disturbances occurred during the experiment. Bags were collected every four weeks from the four15 plots (between February and July 2010), the remaining litter was dried in open air and in the oven at 75 ◦C to constant weight. The remaining mass was measured and the mass loss estimated. The remaining dried litter was kept in a freezer in a plastic bag before the chemical analyses. The chemical analyses on litter prior to decomposition and on decomposed litter20 were carried out in the laboratory of the Finnish Forest Research Institute. Based on the solubility difference of the essential constituents of leaf litter in different solvents, the analyses enabled to determine the concentration of compounds soluble in ethanol, compounds soluble in water, compounds hydrolysable in acid, and compounds neither soluble nor hydrolysable (hereinafter referred to as Klason lignin). Extraction was25 conducted in a sonicating water bath, first for 90 min with ethanol, then for 90 min with water (Karhu et al., 2010). The ethanoland water extracted residue was divided into acid-hydrolysable (72 % H2SO4) and non-hydrolysable fractions using the Klason lignin 3008 GMDD 6, 3003–3032, 2013 Soil carbon modeling in tropical forests G. H. S. Guendehou et al. Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | method (Effland, 1977). Samples were filtered, oven-dried at 75 ◦C to constant weight and weighed between the extractions, and the amounts of different fractions were determined as proportions of the mass of organic matter. The ash content of the samples was determined after keeping the dried samples in a muffle furnace at 550 ◦C overnight. Due to the high number of chemical parameters to determine on each collected sam-5 ples (160 parameters), samples of the same litter species and same collection were pooled for chemical analyses in order to reduce the amount of work, and assuming this yields the average chemical composition of the four plots. 2.3 Model description, simulations of litter decomposition process and data analysis10 In the model Yasso07, fresh organic matter in leaf, fine root, and woody litter is divided into four chemically distinguishable compound groups: acid-hydrolysable compounds (A), water-soluble compounds (W), ethanol-soluble compounds (E), neither soluble, nor hydrolysable compounds (N) that decompose at their unique rates (Tuomi et al., 2009). In addition, there is a humus (H) fraction, assumed to consist of more recalcitrant15 compounds, that receives a part of products resulting from the decomposition of A, W, E, N (Fig. 2). The decay rates (as measure of microbial activity) of the compound groups depend on the climatic conditions described by temperature and precipitation (Meentemeyer, 1978; Berg et al., 1993; Aerts, 1997; Liski et al., 2003; Parton et al., 2007). The decomposition of compound groups results in mass loss from the system20 and in mass flows between the compound groups. The mass loss consists of removal from the soil as heterotrophic respiration (CO2) and leaching while the remaining mass forms more recalcitrant compounds, for example humus. Mathematically, Yasso07 is a linear compartmental system, a set of first order differential equations (Tuomi et al., 2011b):25 ˙ x(t)=A(C)x(t)+b(t)−ωiIPa,x(0) =x0(1) 3009 GMDD 6, 3003–3032, 2013 Soil carbon modeling in tropical forests G. H. S. Guendehou et al. Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | where x=(xA,xW,xE,xN,xH)Tis a vector describing the masses of the five compartments as a function of time (t); A(C) is a matrix describing the decomposition rates and the mass flows between the compartments as a function of climatic conditions (C); vector b(t) is the litter input to the soil; ωiare free parameters describing the precipitation induced leaching rates; I=(1,1,1,1,1)Tis a constant column vector; Pais the annual5 precipitation. Vector x0=(xA,0,xW,0,xE,0,xN,0,xH,0) describes the initial chemical composition state of the system. Matrix Ais defined as a product of the mass flow matrix Apand the diagonal decomposition coefficient matrix k(C)=diag(kA,kW,kE,kN,kH)(C), where kiare the decomposition rate coefficients of the compartments (Tuomi et al., 2009).10 Ap=       −1p1p2p30 p4−1p5p60 p7p8−1p90 p10 p11 p12 −1 0 pHpHpHpH−1       where pi∈[0,1] are relative mass flow parameters between the compartments. p1:. relative mass flow magnitude, W →A; p2: relative mass flow magnitude, E →A; p3:. relative mass flow magnitude, N →A; p4: relative mass flow magnitude, A →W; p5:. relative mass flow magnitude, E →W; p6: relative mass flow magnitude, N →W;15 p7:. relative mass flow magnitude, A →E; p8: relative mass flow magnitude, W →E; p9:. relative mass flow magnitude, N →E; p10: relative mass flow magnitude, A →N; p11:. relative mass flow magnitude, W →N; p12: relative mass flow magnitude, E →N; pH:. mass flow to humus. 3010 GMDD 6, 3003–3032, 2013 Soil carbon modeling in tropical forests G. H. S. Guendehou et al. Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | The climate dependence of the decomposition rate factors kiformulated in Eq. (2) was justified earlier by Tuomi et al. (2009): ki(C)=αiexpβ1T+β2T2(1 −expγPa) (2) where Tis temperature (Celsius scale), Pa: annual precipitation, αi: decomposition rate parameter, β1and β2: temperature dependence parameters, γ: precipitation de-5 pendence parameter. αi,β1,β2, and γare free parameters. The structure and mathematical formulas of Yasso07 are described in more detail in previous publications (Tuomi et al., 2009, 2011a). The data needed to run the model Yasso07 include: litter amount, litter quality (distribution of litter between A, W, E, N) together with uncertainty data (expressed as10 standard deviations) and climatic data (temperature and precipitation). The simulation was conducted on each individual studied litter species. An initial mass of litter with its chemical composition (Figs. 3–7) and climatic data (Table 1) were used as inputs into the dynamic soil carbon model Yasso07. The predictions of Yasso07 were compared with the observations on change in mass, and change in chemical15 composition. Then, Yasso07 was fitted to a dataset where new data from Benin were merged with a global data (from Europe; Berg et al., 1991a,b, 1993, Northern and Central America, Gholz et al., 2000; Trofymow et al., 1998). Also, Yasso07 was fitted only to the new experimental data from Benin. This resulted in two new versions of the Yasso07: Y07A to refer to Yasso07 fitted to the dataset including global and Benin20 data, and Y07B to refer to Yasso07 fitted only to new data from Benin. The predictions of these two versions were compared with the observations using mean residuals and standard deviations thereof. When analysing the data, the Bayesian inference of information from the measurements to the parameter values (see for example, Ellison, 2004) was used. Following the25 method of Tuomi et al. (2009, 2011a), the Markov chain Monte Carlo (MCMC) posterior sampling technique was used in data analyses. The reason for selecting this method was its ability to produce a sample from the posterior probability density of the model 3011 GMDD 6, 3003–3032, 2013 Soil carbon modeling in tropical forests G. H. S. Guendehou et al. 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M., and Patinvoh, P.: Plan Directeur de la Forêt Classée de la Lama, Projet de développement de l’économie foresti‘ere et production de bois, Projet GTZ No. 79.2038.2.01-200, 1986. 3023 GMDD 6, 3003–3032, 2013 Soil carbon modeling in tropical forests G. H. S. Guendehou et al. Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Table 1. Monthly climatic data recorded during the litterbag experiment in 2010 by the meteorological stations Bohicon (for temperature) and Toffo (for precipitation) closest to the Lama forest. Month Temperature Precipitation (◦C) (mm) Feb 31.4 80.1 Mar 30.6 192.4 Apr 30.2 100.3 May 29.0 157.1 Jun 28.1 90.1 Jul 26.6 149.0 3024 GMDD 6, 3003–3032, 2013 Soil carbon modeling in tropical forests G. H. S. Guendehou et al. Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Table 2. Parameter values of the original calibration of Yasso07 and of the parameterization of Yasso07 using only data from Benin and thereafter all data (global data and data from Benin together). Parameter Unit Benin data All data Original calibration∗ Interpretation MAP 95 % CI MAP 95 % CI MAP 95 % CI αAyr−13.97 ±0.74 0.39 ±0.03 0.72 ±0.09 drate of A αWyr−120.55 ±3.10 4.54 ±0.30 5.9 ±0.8 drate of W αEyr−117.00 ±3.50 0.34 ±0.04 0.28 +0.07, −0.04 drate of E αNyr−13.57 ±0.50 0.13 ±0.02 0.031 +0.011, −0.004 drate of N p1– 0.02 ±0.02 0.00 +0.01, −0.00 0.48 ±0.06 mflow, W →A p2– 0.01 +0.04, −0.01 0.01 ±0.01 0.01 +0.15, −0.01 mflow, E →A p3– 0.79 ±0.33 0.87 ±0.03 0.83 +0.16, −0.23 mflow, N →A p4– 0.71 ±0.15 0.99 ±0.01 0.99 +0.01, −0.05 mflow, A →W p5– 0.05 ±0.05 0.05 +0.01, −0.00 0.00 +0.08, −0.00 mflow, E →W p6– 0.04 +0.16, −0.04 0.00 +0.01, −0.00 0.01 +0.20, −0.01 mflow, N →W p7– 0.05 ±0.05 0.00 +0.01, −0.00 0.00 +0.01, −0.00 mflow, A →E p8– 0.06 +0.07, −0.06 0.00 +0.01, −0.00 0.00 +0.01, −0.00 mflow, W →E p9– 0.13 ±0.08 0.133 ±0.03 0.02 +0.23, −0.02 mflow, N →E p10 – 0.01 +0.04, −0.01 0.01 ±0.01 0.00 +0.01, −0.00 mflow, A →N p11 – 0.02 +0.05, −0.02 0.19 ±0.01 0.02 ±0.02 mflow, W →N p12 – 0.88 ±0.11 0.44 ±0.01 0.95 +0.05, −0.16 mflow, E →N β110−2◦C−10.087 +0.01, −0.00 0.069 ±0.01 0.095 ±0.02 Tdependence β210−3◦C−2−0.0029 +0.01, −0.00 −0.00050 0.001 −0.0014 +0.0006, −0.0009 Tdependence γm−1−2.500 ±0.05 −0.939 ±0.09 −1.21 ±0.14 Pdependence ωEyr−1m−1– 0.005 −0.2008 ±0.01 −0.151 ±0.008 Pleaching Europe ωAyr−1m−1–+0.01, −0.00 −0.0006 ±0.01 0.000 +0.0, −0.002 Pleaching America ωByr−1m−1−0.061 ±0.19 −0.943 ±0.04 – – Pleaching Benin 3025 GMDD 6, 3003–3032, 2013 Soil carbon modeling in tropical forests G. H. S. Guendehou et al. Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 21 583 584 Figure 1: Location of the study area in Benin 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 Fig. 1. Location of the study area in Benin. 3026 GMDD 6, 3003–3032, 2013 Soil carbon modeling in tropical forests G. H. S. Guendehou et al. Title Page Abstract Introduction Conclusions References Tables Figures J I J I Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 22 614 Figure 2: Flow diagram of the model Yasso07 and the relative magnitudes of each mass flow between 615 labile compound groups of organic carbon and more recalcitrant humus; acid-hydrolysable (A), water-616 soluble (W), ethanol-soluble (E), and compounds neither soluble nor hydrolysable (N). The carbon flows 617 whose magnitudes differ statistically (95% confidence) from zero (solid arrows) between and out of the 618 A, W, E, and N fractions (square boxes); the small flows (dotted arrows) into humus (bottom box), each 619 approximately 0.5%; and the mass flows (dashed arrows) whose maximum a posteriori (MAP) estimates 620 were indistinguishable from zero but whose 95% Bayesian confidence interval was broader than 0.05 621 (Tuomi et al. 2011b). 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 Fig. 2. Flow diagram of the model Yasso07 and the relative magnitudes of each mass flow between labile compound groups of organic carbon and more recalcitrant humus; acidhydrolysable (A), water-soluble (W), ethanol-soluble (E), and compounds neither soluble nor hydrolysable (N). The carbon flows whose magnitudes differ statistically (95 % confidence) from zero (solid arrows) between and out of the A, W, E, and N fractions (square boxes); the small flows (dotted arrows) into humus (bottom box), each approximately 0.5 %; and the mass flows (dashed arrows) whose maximum a posteriori (MAP) estimates were indistinguishable from zero but whose 95 % Bayesian confidence interval was broader than 0.05 (Tuomi et al., 2011b). 3027