Carbon stocks in natural forests of Colombia

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Peña, Claudia P. Olarte, Sebastián González-Caro, Sebastián Ramírez, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8621469/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 15 You are reading this latest preprint version Abstract Background Tropical forests play a key role in the development of climate policy frameworks. However, field-based assessments integrating multiple carbon pools in tropical regions remain scarce at national or regional scales. In Colombia, natural forests cover more than half of the continental territory, yet comprehensive estimates of carbon stocks across aboveground biomass, woody debris, and soils are lacking. This study provides the first national-scale, standardized, field-based quantification of these carbon pools and examines their environmental and structural drivers. Results Using data from 265 clusters of Colombia’s National Forest Inventory, we estimated a national mean total carbon stock of 184.8 ± 3.6 Mg C ha − 1 , which represents a total estimated amount of 10.96 ± 0.21 Pg C stored in natural forests. Aboveground carbon (AGC) represented 55.4% of total carbon stocks, soil organic carbon (SOC) stored 37.1%, and woody debris carbon (WDC) 7.6%. Significant regional variation was observed, with Amazonia showing the highest total carbon mean and Caribe the lowest. Structural equation models revealed that AGC was mainly driven by the abundance of large trees, followed by climatic and soil fertility gradients. WDC was influenced by both climate and forest structure, while SOC was primarily determined by climate and soil properties. Conclusions Colombian natural forests store substantial carbon stocks whose distribution and drivers vary markedly among biogeographic regions and carbon pools. The Amazonia region contains about 70% of the country’s total forest carbon, emphasizing its importance for national mitigation strategies. These findings provide critical empirical evidence to improve greenhouse gas inventories and support regionally tailored policies for forest conservation, REDD+, and carbon offset initiatives. Tropical forests aboveground biomass soil organic carbon woody debris National Forest Inventory carbon accounting Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Tropical forests play a central role in the global carbon cycle [ 1 ], making them critical for the development of international climate policy frameworks. However, field-based, integrated estimates of carbon stocks across multiple forest compartments remain limited in tropical regions, especially at national or regional scales [ 2 , 3 ]. Most of the regional and global forest carbon estimates that include compartments beyond aboveground biomass (AGB), such as soils and woody debris, typically rely on generalized expansion factors [ 4 , 5 ], default values from meta-analyses [ 1 , 6 , 7 ], or remote sensing products [ 5 , 8 , 9 ]. These approaches often do not distinguish between individual carbon pools or identify the main environmental drivers underlying their variability, resulting in significant uncertainties in carbon accounting. In tropical forest ecosystems, carbon stocks are primarily distributed among compartments such as the aboveground biomass, soils, and woody debris [ 10 ]. Field-based studies in regions such as the Amazon [ 11 , 12 ], Southeast Asia [ 13 ], and tropical Andes[ 14 – 17 ], have demonstrated that the relative contribution of these pools to the total carbon stock changes widely with local climate and soil fertility. For instance, aboveground carbon (AGC) typically dominates in tropical lowland forests, while soil organic carbon (SOC) is the main component in highland mountain forests [ 14 , 15 ]. In contrast, woody debris carbon (WDC) is highly variable across space and time [ 11 , 14 , 15 , 17 , 18 ]. Understanding these variations is essential for evaluating the resilience and vulnerability of forest carbon stocks as well as to inform effective land-based climate mitigation strategies [ 19 ]. Carbon stocks are not controlled by a single set of drivers; rather, the dominant controls shift across compartments because the mechanisms generating each pool differ. AGC primarily reflects the cumulative outcome of tree growth and biomass allocation and therefore scales strongly with forest structural attributes, especially stem density and the size distribution of trees [ 20 , 21 ], while being further constrained by temperature [ 22 ] and soil fertility [ 23 ] through their effects on productivity and biomass accumulation. WDC is a short-term, transient pool governed by the balance between episodic inputs from tree mortality (often disturbance-driven) and losses through decomposition. Consequently, it is closely tied to forest dynamics and disturbance regimes, with climate and stand structure jointly modulating both mortality rates and decay activity [ 15 , 24 ]. SOC, in contrast, integrates longer-term inputs and stabilization pathways, emerging from the interaction between litter supply, temperature and moisture constraints on microbial processing, and soil properties that regulate retention and turnover [ 23 , 25 , 26 ]. Taken together, these contrasts show that carbon storage is compartment-specific; thus, identifying both shared controls and pool-specific mechanisms is key to improving predictions of carbon accumulation at local and regional scales. In Colombia, a megadiverse country with a wide range of climates and soil types, natural forests cover approximately 59 million hectares, representing about 52% of its continental territory (Table S1 ). The Agriculture, Forestry, and Other Land Use (AFOLU) sector is the main source of the country's net greenhouse gas (GHG) emissions. Combined, the land use, land-use change, and forestry (LULUCF) and agriculture sectors account for about 55% of total national GHG emissions [ 27 ]. Accurate forest carbon estimates are therefore crucial for climate policy and for fulfilling international commitments under mechanisms such as Reducing Emissions from Deforestation and Forest Degradation (REDD+). To address this need, the National Forest Inventory (NFI), initiated in 2015, provides standardized, spatially explicit data on forest structure and carbon content. Here we present the first national-scale study to evaluate AGC, CWD, and SOC using harmonized field-based methods across Colombia. To address this critical knowledge gap, we analyzed data from 265 clusters collected as part of Colombia's NFI, which provide information on carbon stocks in aboveground biomass (AGC), woody debris (WDC), and soils (SOC). Specifically, we address the following research questions: (1) How much carbon is stored in the aboveground biomass, coarse wood debris, and soils of Colombian natural forests? (2) What is the relative contribution of each forest carbon pool (i.e., AGC, WDC, and SOC) to the total forest carbon stocks at both national and regional scales? (3) To what extent can forest carbon stocks be explained by climatic conditions, soil fertility, and/or forest structural features? Methods Study sites Colombia is located in the northwest extreme of South America, between latitudes 4° S and 12° N, and longitudes 67° W and 79° W. The country has a continental area of 1,140,509 km 2 , that has been divided into five natural regions due to differences in geology, climate, and ecosystems: Amazonia, Andes, Caribe, Orinoquia, and Pacific (Fig. 1 , Table S1 ). The Colombian landscape is dominated by hills (35%) and mountains (26%) [ 28 ], with an elevation range from 0 to 5775 m asl. Approximately 91% of the territory has a warm climate, with a mean annual temperature (MAT) > 24°C, 5% has a more temperate climate (18–24°C), and the remaining 2% includes cold climate zones with MAT < 18°C [ 29 ]. Precipitation patterns vary across regions: the Caribe and Andean regions have a bimodal rainfall regime throughout the year, averaging 500–2000 mm y − 1 and 2000–6000 mm y − 1 , respectively. In contrast, the Orinoquia and Amazonia regions exhibit a unimodal precipitation pattern, ranging from 2000 to 4500 mm y − 1 . The Pacific region, however, has a more uniform rainfall distribution, with annual averages ranging between 3000 and 12000 mm y − 1 [ 29 ]. Sample design The data analyzed were collected between 2015 and 2019 as part of the ongoing Colombia's National Forest Inventory (NFI), led by the Instituto de Hidrología, Meteorología y Estudios Ambientales (IDEAM), using a post-stratified systematic sampling design [ 30 ]. Sampling points were randomly selected within a 24 × 24 km grid cell to ensure comprehensive coverage of the country. Each sampling unit (cluster) consists of five circular plots arranged in a cross-shaped pattern (Figure S1 A). Each circular plot has an area of 0.07 ha (radius 15 m), resulting in a total sampled area of 0.35 ha per cluster. Trees were measured within concentric circular plots of different radii and recorded based on their diameter measured at 1.3 m height (DBH) as follows: i) saplings (2.5 cm ≤ DBH < 10 cm): measured within a radius of 3 m; ii) trees (10 cm ≤ DBH < 30 cm): measured within a radius of 7 m; and iii) large trees (DBH ≥ 30 cm): measured within a radius of 15 m (Figure S1 B). Here, we use data from 265 NFI clusters established between 2015 and 2019, representing approximately 18% of the total expected NFI sample size [ 30 ]. We included only those clusters located in natural forests, as defined by the 2016 forest cover map produced by IDEAM (Table S1 ). These clusters are distributed across Colombia’s five natural regions as follows: 52% in Amazonia (139 clusters), 20% in Andes (52 clusters), 12% in Caribe (33 clusters), 9% in Orinoquia (24 clusters), and 6% in the Pacific region (17 clusters) (Table 1 , Fig. 1 ). Table 1 Mean (± standard error) and total (± standard error) values of accumulated carbon in aboveground biomass (AGC), woody debris (WD), soils (SOC), and total accumulated carbon in forests by biogeographic region and the total for the country. n: number of clusters used for the analyses. Different letters indicate statistically significant differences between regions. Region Compartment Amazonia (139) Andes (52) Caribe (33) Orinoquia (24) Pacific (17) Country (265) AGC Mg ha − 1 124.72 a (2.54) 44.53 cb (4.49) 35.92 d (5.11) 63.10 bc (8.26) 86.71 b (12.56) 102.30 (2.04) Pg 4.95 (0.10) 0.47 (0.05) 0.06 (0.01) 0.14 (0.02) 0.45 (0.07) 6.07 (0.12) WDC Mg ha − 1 17.76 a (0.93) 5.17 bc (1.13) 3.52 c (1.10) 1.11 c (0.44) 11.46 ab (2.14) 13.95 (0.61) Pg 0.70 (0.04) 0.05 (0.01) 0.01 (0.002) 0.002 (0.001) 0.06 (0.01) 0.83 (0.04) SOC Mg ha − 1 57.42 c (2.05) 100.03 a (9.79) 72.83 abc (6.57) 55.00 bc (5.80) 93.51 ab (25.29) 68.54 (2.95) Pg 2.28 (0.08) 1.06 (0.10) 0.13 (0.01) 0.12 (0.01) 0.49 (0.13) 4.07 (0.18) Total Mg ha − 1 199.91 a (3.52) 149.73 bc (10.56) 112.27 d (9.45) 119.21 cd (10.02) 191.68 ab (24.99) 184.79 (3.59) Pg 7.93 (0.14) 1.58 (0.12) 0.19 (0.01) 0.26 (0.02) 1.00 (0.13) 10.96 (0.21) Carbon stocks in live aboveground biomass In each cluster, trees were measured, mapped, and collected for species identification following standardized methods [ 31 ]. The identification and homologation of the botanical vouchers were carried out by the members of the Colombian Herbaria Association (ACH). Trees with stem deformations, the presence of stem spurs, tabular or columnar roots were measured where the stem was regular, and the height of the measurement point recorded. Total height (H, in m) was measured to approximately 40% of the individuals within each cluster, using a clinometer (Suunto). Tree height of all stems that were not measured were estimated using local allometric models built for each different region (see Methods SI for details, Table S2). Individual tree aboveground biomass (AGB in kg) was estimated using the general allometric model developed for tropical forests [ 32 ]: $$\:AGB=0.0673{(WD\times\:{DBH}^{2}\times\:h)}^{0.976}$$ Where: AGB is the tree aboveground biomass (kg), WD is the wood density (gr cm − 3 ), DBH is the diameter at 1.3 m height, and H total tree height (m). WD data for each species were assigned using the BIOMASS library for R [ 33 ]. When species-level WD values were not available, we used genus, family or cluster-level averages. The carbon stored in AGB (AGC, in Mg C ha − 1 ) was obtained by multiplying AGB by a factor of 0.456 [ 34 ]. We did not use a specific equation for palms, given their low abundance (~ 3 % of th total individuals measured). The associated uncertainty of using a tree allometry instead of a specific palm allometry is expected to be very low and to do not have any significant effect on the overall AGB estimates, as assessed by other studies [ 14 , 18 ]. Woody debris carbon stocks The carbon stored in woody debris within each cluster was calculated by summing the dry mass of standing coarse woody debris (SCWD) and fallen trees and branches, categorized as fallen coarse woody debris (FCWD) and fallen fine woody debris (FFWD). Then, the total dry mass was multiplied by a factor of 0.456 [ 34 ]. SCWD was defined as all standing dead trees and stumps with a diameter (D) ≥ 10 cm, and these were censused within each cluster. Line-transect methods [ 14 , 35 ] were used to census both FCWD (pieces with D ≥ 20 cm) and FFCW (D < 20 cm). Fallen woody debris was sampled in the circular plots numbered as 2 and 4 of each cluster (Figure S1 A). In these plots, four (4) transects of 30 meters in length (equivalent to two 60-meter transects) were established and divided into 10-meter sections. FCWD were censused in all four (4) transects, while FFWD were censused in the first meter of each 10-m transect section (Figure S1 C). Additional technical details for calculating the amount of carbon stored in woody debris can be found in the supporting information. Soil carbon stocks In each circular plot, soil samples were taken at a point located at a distance of two meters from the center of the plot and at an azimuth of 45° (Figure S1 A). Two soil samples were collected by perpendicularly inserting stainless steel rings to a depth of 0–30 cm, which were employed to calculate bulk density (BD; g cm − 3 ) and soil organic carbon concentration (%). All samples were packaged and labeled for analysis at the Biogeochemistry Laboratory of Universidad Nacional de Colombia, Medellín. Bulk density (BD) of each sample was calculated using the stone-free dry weight (g) and the volume of steel ring (cm 3 ). Organic carbon was quantified using the loss-on-ignition (LOI) method [ 36 ], which estimates carbon content based on the mass of organic matter lost during controlled combustion. Additionally, we measured SOC directly with an Elemental Analyzer for a subset of 262 soil samples collected across 60 NFI clusters. Using these paired measurements generated in this study (LOI-derived organic matter and Elemental Analyzer SOC), we calibrated a regression model to convert LOI organic matter to SOC, enabling SOC estimation for sampled analyzed only by LOI (C = 0.38 × OM; R 2 = 0.94; Figure S2). Mean and total carbon stocks To estimate the carbon stocks in AGC, WDC, and SOC in natural forests of Colombia, we applied a post-stratified systematic sampling design, employing the biogeographic regions as strata. The mean carbon stocks ( \(\:\stackrel{-}{Y}\) , Eq. 1 ) and its associated standard error ( SE , Eq. 2 ) were calculated as follows: $$\:\stackrel{-}{Y}=\frac{\sum\:_{i=1}^{h}{N}_{i}{\stackrel{-}{Y}}_{i}}{N}$$ 1 $$\:SE=\:\sqrt{\frac{\left(1-f\right)}{n}\sum\:_{i}^{h}\frac{{N}_{i}}{N}{S}_{i}^{2}+\frac{1}{{n}^{2}}\sum\:\left(1-\frac{{N}_{i}}{N}\right){S}_{i}^{2}}$$ 2 Where \(\:{\stackrel{-}{Y}}_{i}\) is the arithmetic mean carbon stock of each stratum (i.e., biogeographic region); \(\:{N}_{i}\) and N are the total number of plots per stratum and country, respectively; f is the fraction of sample; n is the total number of sampled plots; \(\:{S}_{i}^{2}\) is the variance in the stratum i . Finally, the total carbon stock in AGC, WDC, and SOC for the national forests of Colombia was estimated by multiplying the mean carbon stock per hectare by the total forest cover. The area of natural forest by biogeographic region was obtained following the 2016 forest cover map produced by IDEAM (Table S1 ). Climate Climate variables were extracted from CHELSA database [ 37 , 38 ] employing the geographic coordinates of the center of each NFI cluster. We selected a set of variables that reflect the main climatic constraints on ecosystem carbon cycling in tropical forests: energy availability (mean annual temperature, MAT; potential evapotranspiration, PET), water supply and its intra-annual variability (mean annual precipitation, MAP; precipitation seasonality, PS), thermal variability (temperature seasonality, TS), and atmospheric controls on plant water stress and decomposition (vapor pressure deficit, VPD; cloud area fraction, CLT, as a proxy for persistent cloudiness and associated microclimatic conditions). We then applied a Principal Component Analysis (PCA) to these seven climatic variables to reduce collinearity and summarize major climatic gradients across clusters. The first two principal components were retained for subsequent analyses. Soil fertility In parallel with soil carbon sampling, we collected mineral soil from the A horizon (i.e., after removing the organic layer) in each circular plot (five per cluster) to characterize soil fertility and chemical constraints relevant to carbon storage and turnover. The five plot-level samples within each cluster were homogenized to obtain a 500 g composite sample, which was air-dried for laboratory analyses. We quantified total phosphorus (P; mg kg⁻¹) and key exchangeable cations (Ca, Mg, K; mg kg⁻¹), along with aluminum (Al), soil pH, and cation exchange capacity (CEC), as these properties jointly reflect nutrient availability, base status, acidity/toxicity constraints, and the soil’s capacity to retain nutrients. To reduce collinearity among soil properties and summarize major fertility gradients across clusters, we applied a Principal Component Analysis (PCA) to the soil variables and retained the first two principal components for subsequent analyses. Statistical analyses We performed a one-way analysis of variance (ANOVA) to assess whether carbon stocks of each compartment evaluated (AGC, WDC, SOC, and total) differed among Colombia’s biogeographic regions. When significant differences were found, post-hoc comparisons were carried out using Tukey’s Honestly Significant Difference test (Tukey HSD) to identify which regional means differed. We used structural equation modelling (SEM) [ 39 ] to evaluate the direct and indirect effects of climatic variability (PCA axis), soil fertility (PCA axis), and tree size (number of trees with DBH ≥ 40cm) on determining the spatial variation in carbon stocks. The response variables (AGC, WDC, and SOC) and tree size were log-transformed. All variables were standardized before their inclusion in the SEM. Our SEM structure assessed the premise that climatic variables affected all variables in the model. Soils fertility affected tree size and carbon stocks. Tree size was affected by all explanatory variables, and affect only the carbon stocks (Fig. 2 A). We used a Satorra-Bentler scaled Chi-square test statistic to determine whether the covariance matrix observed in our data significantly deviated from that predicted by the SEM [ 39 ]. Results Total carbon stocks at national and regional scales Total carbon stocks (AGC, WDC, and SOC) in the natural forests of Colombia had a national mean (± SE) estimate of 184.79 ± 3.59 Mg C ha − 1 (range: 37.01–528.92 Mg C ha − 1 ), which represents a total estimated amount of 10.96 ± 0.21 Pg C. Significant variability was observed across biogeographic regions, with mean estimates ranging from 112.27 ± 9.45 Mg C ha − 1 in the Caribe to 199.91 ± 3.52 Mg C ha − 1 in the Amazonia region (Table 1 ). AGC represented 55.36% of the total carbon stocks (range: 29.74–62.39%), SOC stored 37.09% (range: 28.72–66.81%), and WDC 7.55% (range: 0.93–8.89%) (Fig. 3 ). The national mean of AGC in natural forests was 102.30 ± 2.04 Mg C ha − 1 (range 3.44–231.66 Mg C ha − 1 ), corresponding to 6.07 ± 0.12 Pg C stored in the AGB of natural forests of Colombia. AGC stocks varied significantly among biogeographic regions, with the highest mean in the Amazonia and the lowest in the Caribe region (Table 1 ). When considering trees with DBH ≥ 10cm, the national mean of AGC was 93.08 ± 2.07 Mg C ha − 1 , representing a total estimate of 5.52 ± 0.12 Pg C. Carbon stocks in woody debris (WDC) had a national mean of 13.95 ± 0.61 Mg C ha − 1 (range: 0.00–48.03 Mg C ha − 1 ), which represents a total estimated of 0.83 ± 0.04 Pg C (Table 1 ). The average of carbon stocks in standing coarse woody debris (SCWD) was 4.01 ± 0.28 Mg C ha − 1 (28.76% of WDC), in fallen coarse woody debris (FCWD) was 6.53 ± 0.39 Mg C ha − 1 (46.84%), and in fallen fine woody debris (FFWD) was 3.40 ± 0.25 Mg C ha − 1 (24.39%). Carbon stocks in woody debris varied significantly among biogeographic regions, with the highest average in the Amazonia and the lowest in the Orinoquia region (Table 1 ). The mean SOC stock in the first 30 cm of soil was 68.54 ± 2.95 Mg C ha − 1 (range: 3.02–487.64 Mg C ha − 1 ), which represents a total of 4.07 ± 0.18 Pg C in the soils of natural forests. SOC varied significantly among regions, with the highest mean in the Andes and the lowest in the Orinoquia region. In three regions (Andes, Caribe, and Pacific), SOC accounted for the largest proportion of the estimated total carbon (Table 1 , Fig. 3 ). Environmental gradients PCA climate 1 explained 55% of the total variance of the climatic variables and had high PET, VPD, CLT, and TS loadings. This axis is related to seasonal and dry climates, and reflects a gradient from warm, seasonal, and drier climates to cooler, less seasonal, and more humid conditions. PCA climate 2 explained 22% of the total variance and is determined mainly by water availability (MAP and PS), where high values indicate warm, humid conditions, while low values reflect cooler, drier environments with more pronounced precipitation seasonality (Fig. 4 A, Table S3). The PCA soils 1 explained 51% of the total variance in soil variables and is associated with fertility, with higher values indicating more fertile, base-rich soils. PCA soils 2 explained 16% of the total variance and is related to a drainage and aluminum toxicity gradient, where lower values indicate poorly drained, acidic soils with high Al concentrations (Fig. 4 B, Table S3). Drivers of carbon stocks in natural forests of Colombia We found that 75% of the variation of aboveground carbon (AGC) stocks was explained by tree size (number of trees with DBH ≥ 40 cm), climatic gradients (PCA climate 2, associated mainly with water availability, Fig. 4 A), and soil fertility (PCA soils 1, Fig. 4 B). Tree size had the strongest influence, followed by the climate and soil gradients, which had similar effect sizes based on standardized coefficients. Both tree size and PCA climate 2 had positive effects, whereas soil fertility (PCA soils 1) had a negative effect on AGC (Fig. 2 B). In total, 56% of carbon in woody debris (WDC) was explained by the combined effect of climate (PCA climate 1 and PCA climate 2) and tree size. All of these variables exerted positive effects, with similar standardized coefficients. Soil gradients (PCA soils 1 and PCA soils 2) had no significant direct effect on WDC stocks (Fig. 2 C). SOC stocks were explained in 27% by climate and soil gradients, with no significant contribution from tree size. Specifically, PCA climate 1 and PCA soils 1 showed positive effects, while PCA climate 2 and PCA soils 2 had negative effects. Among these, PCA climate 1 (related to seasonal and drier conditions, Fig. 4 A) and PCA soils 2 (associated with soil drainage, Fig. 4 B) showed the strongest standardized effects, followed by PCA soils 1 and PCA climate 2 (Fig. 2 D). Additionally, the SEM models showed that tree size was positively influenced by climate gradients (PCA climate 1 and PCA climate 2), whereas soil fertility (PCA soils 1) had a negative effect. Finally, soil fertility (PCA soils 1) was influenced negatively by climatic conditions (PCA climate 1 and PCA climate 2), and PCA climate 1 negatively affected soil drainage (PCA soils 2). Discussion This study presents the first national-scale in situ assessment of carbon stocks across multiple forest compartments (aboveground biomass, woody debris, and soils) in natural forests of Colombia based on field-based standardized data. By integrating data from 265 National Forest Inventory (NFI) clusters spanning diverse biogeographic regions, we found that variability in carbon stocks at national and regional scales is driven by environmental and structural factors with contrasting effects on across compartments. Understanding the spatial variation of carbon stocks in different forest compartments can improve the accuracy of national inventories and inform mitigation strategies that consider the vulnerability of each carbon reservoir under the ongoing climate change. Total carbon stocks at national and regional scale Our results confirm the large carbon storage capacity of Colombia's forests, with a national carbon stock estimate of 10.96 ± 0.21 Pg C, considering carbon in the aboveground biomass (AGC), woody debris (WDC), and the top 30 cm of soil (SOC). A previous estimate for Colombia’s natural forest carbon stocks based on non-random plot networks that only included trees with DBH > = 10 cm [ 40 ], reported an AGC estimate of 6.44 ± 0.21 Pg C, which is 17% higher than our estimate for trees with DBH ≥ 10cm (5.52 ± 0.12 Pg C). This overestimation likely results from the use of existing plot networks that lack a probabilistic sampling design and mostly focus on old-growth, well-preserved forests, thereby underrepresenting the contribution of secondary and disturbed forests [ 40 ]. By integrating standardized measurements of AGC, WDC, and SOC, the NFI not only improves the accuracy of national carbon accounting but also strengthens the country’s capacity to meet international reporting requirements. The overall estimation of carbon stocks presented in this study, differ from other broad-scale estimates based on plot networks or remote sensing-based maps, which tend to overestimate national means. For instance, applying the regional mean total carbon for tropical South America [ 1 ] (Extended Data Table 2: 277.5 Mg C ha − 1 ), would overestimate Colombia’s carbon storage capacity by approximately 31% (16.46 Pg C). Similarly, the mean AGC reported for Colombia [ 5 ] of 148.5 Mg C ha − 1 (see Table S2 in [ 41 ]) is higher than our national AGC estimate (102.30 ± 2.04 Mg C ha − 1 ), resulting in a national AGC overestimation of about 45% (8.80 Pg C). Such discrepancies emphasize the importance of using field-based inventories to validate and calibrate estimations of forest carbon stocks at regional or national scales. Remote sensing products, while essential for wall-to-wall mapping, are typically calibrated with limited ground data and may fail to capture the structural and ecological heterogeneity of tropical forests [ 9 ]. The Colombian NFI provides a probabilistic, spatially representative sampling design that captures the diversity of forest types, disturbance histories, and environmental conditions across the country. However, the grain or plot size employed with a sampling area of 0.35 ha is smaller than the most common grain size of 50 m x 50 m employed by satellite missions, such as GEDI [ 42 ] and BIOMASS [ 43 ], which could hamper its use to validate this kind of remote sensing data [ 44 ]. To scale up the observed values of AGC assessed with the NFI in Colombian forests, we will need to use technologies such as LiDAR [ 9 , 45 , 46 ], which are definitively more expensive and logistically difficult to acquire than the freely available satellite data. The distribution of carbon among pools reveals crucial regional variations, supporting previous landscape-scale studies in Colombia that show a quite significant spatial heterogeneity in carbon stocks throughout the country [ 11 , 14 , 18 , 40 , 47 , 48 ]. At the national level, AGC is the largest contributor to total carbon stocks (55.4%), followed by SOC (37.1%) and WDC (7.6%). However, this balance shifts at the regional level: AGC is the main reservoir in Amazonia and Orinoquia, while SOC dominates in the Andes, Caribe, and Pacific (Fig. 3 ). Amazonian forests store the highest AGC stocks, principally due to the presence of large trees [ 11 , 20 ]. In contrast, lower AGC values in the Caribe and Andes are in line with earlier studies reporting reduced biomass and greater dominance of SOC in montane forests [ 14 – 16 , 40 ]. As claimed above, our findings show that AGC stocks decrease with elevation (r = -0.36, P < 0.001), whereas SOC increases (r = 0.40, P < 0.001), a pattern reported in other studies along elevational gradients in the tropics [ 14 , 15 , 48 ]. However, it is important to note that our SOC estimates are based on only the upper 30 cm of soil, providing a standardized comparison at the national level, but potentially underestimating total SOC contributions [ 11 , 14 , 16 ]. These findings highlight the regional variation in carbon stocks, which may be relevant for improving the accuracy of national carbon estimates and guiding equitable forest conservation and climate mitigation policies. Environmental and structural drivers of carbon stocks Our analyses reveal a functional decoupling of carbon pools, with each compartment responding to a distinct set of environmental and structural drivers. AGC stocks are primarily influenced by forest structure, particularly the abundance of large-diameter trees (DBH ≥ 40 cm), followed by soil fertility, and climatic variables related to water availability. This finding is consistent with global studies showing that large-diameter trees disproportionately contribute to forest biomass and carbon storage [ 20 , 21 , 49 , 50 ]. The presence of large trees is influenced not only by soil and climatic conditions, as revealed by our SEM analysis (Fig. 2 B), but also by disturbance, with their abundance increasing from secondary to primary forests. This suggest that conservation of primary forests may be important for maintaining carbon storage and contributing to climate change mitigation [ 1 ]. Interestingly, we found a negative association between soil fertility and AGC, a counterintuitive result that may be explained by historical anthropogenic pressure. Fertile lowland dry and montane regions of Colombia have been subject to intensive land use and selective logging [ 40 , 51 ], potentially reducing biomass despite favorable nutrient conditions. Because our NFI sampling is not restricted to primary forests, the inclusion of secondary or degraded forests is critical for identifying the often-overlooked impact of anthropogenic pressures on forest carbon distribution. Previous studies in tropical forests often report positive associations between water availability and biomass [ 22 , 47 ], however, the relationship observed in the present study may be strongly influenced by the disturbance process indicated above. Indeed, forests in dry (Caribe) or higher-elevation (Andes) regions, where land-use impacts are more pronounced, tend to exhibit lower AGC. In the tropical dry forest, tree species with higher wood density are present but are generally smaller in size, which may result from disturbance, underscoring the importance of long periods for dry forest regeneration. Likewise, the loss of large trees in high elevation but remote forest [ 52 , 53 ] suggests a similar pattern of anthropogenic pressure. Together, these findings indicate that both environmental conditions and land-use history may influence carbon stocks across diverse tropical landscapes [ 40 , 51 ]. Woody debris, although representing a smaller and more variable fraction of total carbon [ 11 , 14 , 15 ], constitute a vital component of forest carbon stocks, especially in the context of disturbance regimes [ 17 ]. WDC shows a combined response to both stand structure, as a source of fallen wood, and climate, which regulates decomposition rates. A strong influence of climate is expected, given that both productivity (which determines woody input) and decomposition rates (which determine residence time of debris) are sensitive to moisture and temperature conditions [ 22 ]. The tight coupling between mortality processes, decomposition, and climate indicates that this pool may be sensitive to changes in disturbance frequency or intensity, as has been observed in tropical forests [ 14 , 17 ]. Importantly, because WDC can act as either a short-term carbon source or a long-term reservoir depending on decomposition rates, its inclusion in carbon accounting frameworks is may improve the accuracy of national greenhouse gas inventories and inform REDD+ strategies. SOC was influenced by climate and soil conditions, with no significant contribution from stand structure. As expected, SOC stocks were highest in cooler, high-elevation sites, where lower temperatures reduce microbial decomposition rates and favor carbon accumulation [ 26 , 54 ]. This positive relationship between SOC and elevation has been reported previously for montane forests [ 15 , 55 ]. The positive relationship between SOC stock and soil fertility (PCA soil 1) may be partly influenced, again, by the elevation gradient. Soils derived from volcanic ashes (Andisols), mainly distributed above 2000 m asl, are characterized by strong acidity, high phosphorus retention (see Fig. 4 B), low cation exchange capacity, and conversely, high anion exchange capacity [ 56 ]. It is important to note that our study was limited to the upper 30 cm of the soil profile, which likely underestimates total SOC stocks. Deeper soil layers often reveal that soils can surpass biomass as the dominant carbon pool when sampled at greater depths [ 11 , 14 , 17 ]. Finally, the contrasting drivers of carbon stocks identified in this study underscore the importance of locally-tailored conservation strategies. For example, protecting large trees in lowland forests may help maintain aboveground carbon, whereas conserving forests in high-altitude, colder, and wetter regions may help safeguard substantial soil carbon stocks that are vulnerable to decomposition as temperatures rise. By providing robust, field-based data across multiple carbon pools and diverse regions, this study contributes information relevant to national climate policies and the understanding of forest carbon dynamics. Relevance for climate policy and carbon accounting Our findings have important contributions to improving the accuracy of Colombia's national greenhouse gas (GHG) inventory. While most previous studies have focused primarily on improving estimations of biomass, often overlooking other compartments such as soils and woody debris, our results show that these compartments can represent a considerable proportion of total carbon stocks [ 11 , 14 ]. The marked heterogeneity in carbon compartments across regions suggests that carbon accounting and climate mitigation strategies must be tailored to regional characteristics and carbon allocation patterns, rather than relying on generalized assumptions. This has direct implications for REDD + and other climate mitigation initiatives, as carbon offset and payment for ecosystem service schemes must reflect regional differences in carbon distribution to ensure accurate resource allocation. The Amazonia region has a disproportionate role as a national carbon reservoir, accounting for approximately 70% of Colombia’s total carbon stocks (Table 1 ). Given that 68% of deforestation reported for 2024 occurred in Amazonia [ 57 ], these findings highlight the vulnerability of national carbon stocks to forest loss in this region. The latest national GHG inventory corroborates this concern, identifying the Land Use, Land-Use Change, and Forestry (LULUCF) sector as the largest source of national emissions (~ 35%), with forest conversion to pastures and degradation (~ 60%) as the primary emission drivers within the sector [ 27 ]. Deforestation primarily affects AGC and WDC pools, while SOC, although more stable, can also be at risk under land-use conversion, especially when substantial soil disturbance occurs. Illegal mining, in particular, poses a major threat by directly disrupting soils and releasing large amounts of SOC that may have accumulated over centuries. Once disturbed, these soils lose not only their stored carbon but also their long-term capacity to sequester additional carbon, given the slower recovery rates of soil carbon compared to biomass [ 17 ]. Finally, our results have important implications for forest conservation and climate policy. First, conservation strategies may benefit from recognizing the role of soil carbon, particularly in highland ecosystems where it represents the dominant carbon pool. Protecting these areas may help maintain long-term carbon storage under changing climatic conditions. Second, carbon offset initiatives and payment for ecosystem services (PES) schemes may benefit from incorporating regional differences in carbon pool composition to improve valuation and resource allocation. Neglecting this variation could result in an underestimation of carbon stocks in certain ecosystems, especially those with high SOC. Third, the Colombian NFI provides a robust and underutilized platform for enhancing national Measurement, Reporting, and Verification (MRV) systems under REDD+, by offering spatially explicit, field-based data across a wide range of environmental conditions. Declarations Acknowledgments M.A. Peña was funded by Minciencias (Convocatoria 909 de 2021). The authors thank the Instituto de Hidrología, Meteorología y Estudios Ambientales (IDEAM), which led Colombia’s National Forest Inventory (NFI), and all those who participated in data collection. Funding M.A. Peña was funded by Minciencias (Convocatoria 909 de 2021). Author Contribution M.A.P. and A.D. conceived and designed the study and wrote the main manuscript. S.G. and J.L. contributed to writing, review, and editing. C.O., S.R., J.R., O.M., and R.J. data collection and curation. All authors reviewed and approved the final manuscript before submission. Data Availability All metadata used in the analyses will be deposited in a repository for free access. Consent to Publish declaration Not applicable. Ethics and Consent to Participate declarations Not applicable. Competing Interest The authors declare no competing interests. References Pan Y, Birdsey RA, Phillips OL, Houghton RA, Fang J, Kauppi PE, et al. The enduring world forest carbon sink. Nature. 2024;631:563–9. https://doi.org/10.1038/s41586-024-07602-x . Nesha MK, Herold M, De Sy V, Duchelle AE, Martius C, Branthomme A, et al. An assessment of data sources, data quality and changes in national forest monitoring capacities in the Global Forest Resources Assessment 2005–2020. Environ Res Lett. 2021. https://doi.org/10.1088/1748-9326/abd81b . Romijn E, Lantican CB, Herold M, Lindquist E, Ochieng R, Wijaya A, et al. Assessing change in national forest monitoring capacities of 99 tropical countries. Ecol Manage. 2015;352:109–23. https://doi.org/10.1016/j.foreco.2015.06.003 . 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Resumen de resultados de monitoreo. Bogotá D.C.: Colombia; 2025. Additional Declarations No competing interests reported. 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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-8621469","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":583765647,"identity":"d1b8c8a1-45d2-472f-bb92-f5aabfa1e07e","order_by":0,"name":"Miguel A. 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(A) The framework path model including all the hypothetical drivers analyzed in this study. SEM path result for the aboveground carbon (B) woody debris (C) and soil organic carbon (D). Numbers indicate path coefficients and arrow colors indicate the direction of the relationship, black arrows indicate positive relationships and red arrows negative relationships. Non significant paths were excluded on each SEM diagram.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8621469/v1/3e855e741e4d6c039970ee1e.png"},{"id":101656097,"identity":"bb9d8ee7-e887-4e7b-89e4-62da7b64f3dc","added_by":"auto","created_at":"2026-02-02 10:07:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33580,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of total estimated carbon stored in each of the compartments at the national scale and by biogeographic region. AGC: aboveground carbon. WDC: woody debris carbon. SOC: soil organic carbon.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8621469/v1/46612a24f41040550adfef30.png"},{"id":101656099,"identity":"a6f6f7ef-64a5-4d59-a739-501570a53047","added_by":"auto","created_at":"2026-02-02 10:07:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":59507,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal components analysis (PCA) of the climatic (\u003cstrong\u003eA\u003c/strong\u003e) and soils (\u003cstrong\u003eB\u003c/strong\u003e) variables employed to define the gradients of climatic and soils variation across Colombia. Variable loadings are in the supplementary Table S3. MAT: mean annual temperature. TS: temperature seasonality. MAP: mean annual precipitation. PS: precipitation seasonality. CLT: cloud area fraction. VPD: vapour pressure deficit. PET: potential evapotranspiration. P: total phosphorus. Al: aluminum. Ca: calcium. Mg: magnesium. K: potassium: CEC: cation exchange capacity.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8621469/v1/de3fab61d1351c9fc27ac251.png"},{"id":101754259,"identity":"ff657557-5436-43b2-8f4b-a7b172bca55f","added_by":"auto","created_at":"2026-02-03 10:42:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1335484,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8621469/v1/23453bbb-32b0-42a8-8441-1b9b2e85db6a.pdf"},{"id":101656100,"identity":"5ad917e0-3fbf-4c30-a744-aef64ceed9c8","added_by":"auto","created_at":"2026-02-02 10:07:11","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":255183,"visible":true,"origin":"","legend":"","description":"","filename":"SICtotal20260115.docx","url":"https://assets-eu.researchsquare.com/files/rs-8621469/v1/ddbc0bd36c47259007615ae7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Carbon stocks in natural forests of Colombia","fulltext":[{"header":"Background","content":"\u003cp\u003eTropical forests play a central role in the global carbon cycle [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], making them critical for the development of international climate policy frameworks. However, field-based, integrated estimates of carbon stocks across multiple forest compartments remain limited in tropical regions, especially at national or regional scales [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Most of the regional and global forest carbon estimates that include compartments beyond aboveground biomass (AGB), such as soils and woody debris, typically rely on generalized expansion factors [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], default values from meta-analyses [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], or remote sensing products [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These approaches often do not distinguish between individual carbon pools or identify the main environmental drivers underlying their variability, resulting in significant uncertainties in carbon accounting.\u003c/p\u003e \u003cp\u003eIn tropical forest ecosystems, carbon stocks are primarily distributed among compartments such as the aboveground biomass, soils, and woody debris [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Field-based studies in regions such as the Amazon [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], Southeast Asia [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and tropical Andes[\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], have demonstrated that the relative contribution of these pools to the total carbon stock changes widely with local climate and soil fertility. For instance, aboveground carbon (AGC) typically dominates in tropical lowland forests, while soil organic carbon (SOC) is the main component in highland mountain forests [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In contrast, woody debris carbon (WDC) is highly variable across space and time [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Understanding these variations is essential for evaluating the resilience and vulnerability of forest carbon stocks as well as to inform effective land-based climate mitigation strategies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCarbon stocks are not controlled by a single set of drivers; rather, the dominant controls shift across compartments because the mechanisms generating each pool differ. AGC primarily reflects the cumulative outcome of tree growth and biomass allocation and therefore scales strongly with forest structural attributes, especially stem density and the size distribution of trees [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], while being further constrained by temperature [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and soil fertility [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] through their effects on productivity and biomass accumulation. WDC is a short-term, transient pool governed by the balance between episodic inputs from tree mortality (often disturbance-driven) and losses through decomposition. Consequently, it is closely tied to forest dynamics and disturbance regimes, with climate and stand structure jointly modulating both mortality rates and decay activity [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. SOC, in contrast, integrates longer-term inputs and stabilization pathways, emerging from the interaction between litter supply, temperature and moisture constraints on microbial processing, and soil properties that regulate retention and turnover [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Taken together, these contrasts show that carbon storage is compartment-specific; thus, identifying both shared controls and pool-specific mechanisms is key to improving predictions of carbon accumulation at local and regional scales.\u003c/p\u003e \u003cp\u003eIn Colombia, a megadiverse country with a wide range of climates and soil types, natural forests cover approximately 59\u0026nbsp;million hectares, representing about 52% of its continental territory (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The Agriculture, Forestry, and Other Land Use (AFOLU) sector is the main source of the country's net greenhouse gas (GHG) emissions. Combined, the land use, land-use change, and forestry (LULUCF) and agriculture sectors account for about 55% of total national GHG emissions [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Accurate forest carbon estimates are therefore crucial for climate policy and for fulfilling international commitments under mechanisms such as Reducing Emissions from Deforestation and Forest Degradation (REDD+). To address this need, the National Forest Inventory (NFI), initiated in 2015, provides standardized, spatially explicit data on forest structure and carbon content. Here we present the first national-scale study to evaluate AGC, CWD, and SOC using harmonized field-based methods across Colombia.\u003c/p\u003e \u003cp\u003eTo address this critical knowledge gap, we analyzed data from 265 clusters collected as part of Colombia's NFI, which provide information on carbon stocks in aboveground biomass (AGC), woody debris (WDC), and soils (SOC). Specifically, we address the following research questions: (1) How much carbon is stored in the aboveground biomass, coarse wood debris, and soils of Colombian natural forests? (2) What is the relative contribution of each forest carbon pool (i.e., AGC, WDC, and SOC) to the total forest carbon stocks at both national and regional scales? (3) To what extent can forest carbon stocks be explained by climatic conditions, soil fertility, and/or forest structural features?\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy sites\u003c/h2\u003e \u003cp\u003eColombia is located in the northwest extreme of South America, between latitudes 4\u0026deg; S and 12\u0026deg; N, and longitudes 67\u0026deg; W and 79\u0026deg; W. The country has a continental area of 1,140,509 km\u003csup\u003e2\u003c/sup\u003e, that has been divided into five natural regions due to differences in geology, climate, and ecosystems: Amazonia, Andes, Caribe, Orinoquia, and Pacific (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The Colombian landscape is dominated by hills (35%) and mountains (26%) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], with an elevation range from 0 to 5775 m asl. Approximately 91% of the territory has a warm climate, with a mean annual temperature (MAT)\u0026thinsp;\u0026gt;\u0026thinsp;24\u0026deg;C, 5% has a more temperate climate (18\u0026ndash;24\u0026deg;C), and the remaining 2% includes cold climate zones with MAT\u0026thinsp;\u0026lt;\u0026thinsp;18\u0026deg;C [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Precipitation patterns vary across regions: the Caribe and Andean regions have a bimodal rainfall regime throughout the year, averaging 500\u0026ndash;2000 mm y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 2000\u0026ndash;6000 mm y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. In contrast, the Orinoquia and Amazonia regions exhibit a unimodal precipitation pattern, ranging from 2000 to 4500 mm y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The Pacific region, however, has a more uniform rainfall distribution, with annual averages ranging between 3000 and 12000 mm y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample design\u003c/h3\u003e\n\u003cp\u003eThe data analyzed were collected between 2015 and 2019 as part of the ongoing Colombia's National Forest Inventory (NFI), led by the Instituto de Hidrolog\u0026iacute;a, Meteorolog\u0026iacute;a y Estudios Ambientales (IDEAM), using a post-stratified systematic sampling design [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Sampling points were randomly selected within a 24 \u0026times; 24 km grid cell to ensure comprehensive coverage of the country. Each sampling unit (cluster) consists of five circular plots arranged in a cross-shaped pattern (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). Each circular plot has an area of 0.07 ha (radius 15 m), resulting in a total sampled area of 0.35 ha per cluster. Trees were measured within concentric circular plots of different radii and recorded based on their diameter measured at 1.3 m height (DBH) as follows: i) saplings (2.5 cm\u0026thinsp;\u0026le;\u0026thinsp;DBH\u0026thinsp;\u0026lt;\u0026thinsp;10 cm): measured within a radius of 3 m; ii) trees (10 cm\u0026thinsp;\u0026le;\u0026thinsp;DBH\u0026thinsp;\u0026lt;\u0026thinsp;30 cm): measured within a radius of 7 m; and iii) large trees (DBH\u0026thinsp;\u0026ge;\u0026thinsp;30 cm): measured within a radius of 15 m (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eHere, we use data from 265 NFI clusters established between 2015 and 2019, representing approximately 18% of the total expected NFI sample size [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. We included only those clusters located in natural forests, as defined by the 2016 forest cover map produced by IDEAM (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). These clusters are distributed across Colombia\u0026rsquo;s five natural regions as follows: 52% in Amazonia (139 clusters), 20% in Andes (52 clusters), 12% in Caribe (33 clusters), 9% in Orinoquia (24 clusters), and 6% in the Pacific region (17 clusters) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003eMean (\u0026plusmn;\u0026thinsp;standard error) and total (\u0026plusmn;\u0026thinsp;standard error) values of accumulated carbon in aboveground biomass (AGC), woody debris (WD), soils (SOC), and total accumulated carbon in forests by biogeographic region and the total for the country. n: number of clusters used for the analyses. Different letters indicate statistically significant differences between regions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003cp\u003eCompartment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmazonia\u003c/p\u003e \u003cp\u003e(139)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAndes\u003c/p\u003e \u003cp\u003e(52)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCaribe\u003c/p\u003e \u003cp\u003e(33)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOrinoquia\u003c/p\u003e \u003cp\u003e(24)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePacific\u003c/p\u003e \u003cp\u003e(17)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003cp\u003e(265)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124.72\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.53\u003csup\u003ecb\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(4.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.92\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(5.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.10\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(8.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e86.71\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e 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\u003cp\u003e6.07\u003c/p\u003e \u003cp\u003e(0.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWDC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.76\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.17\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.52\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.11\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.46\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(2.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.95\u003c/p\u003e \u003cp\u003e(0.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003cp\u003e(0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003cp\u003e(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003cp\u003e(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003cp\u003e(0.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.42\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.03\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(9.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72.83\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(6.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.00\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(5.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e93.51\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(25.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e68.54\u003c/p\u003e \u003cp\u003e(2.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003cp\u003e(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003cp\u003e(0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003cp\u003e(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003cp\u003e(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003cp\u003e(0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.07\u003c/p\u003e \u003cp\u003e(0.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e199.91\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(3.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149.73\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(10.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112.27\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(9.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e119.21\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(10.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e191.68\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(24.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e184.79\u003c/p\u003e \u003cp\u003e(3.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.93\u003c/p\u003e \u003cp\u003e(0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003cp\u003e(0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003cp\u003e(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003cp\u003e(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e(0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.96\u003c/p\u003e \u003cp\u003e(0.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eCarbon stocks in live aboveground biomass\u003c/h3\u003e\n\u003cp\u003eIn each cluster, trees were measured, mapped, and collected for species identification following standardized methods [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The identification and homologation of the botanical vouchers were carried out by the members of the Colombian Herbaria Association (ACH). Trees with stem deformations, the presence of stem spurs, tabular or columnar roots were measured where the stem was regular, and the height of the measurement point recorded. Total height (H, in m) was measured to approximately 40% of the individuals within each cluster, using a clinometer (Suunto). Tree height of all stems that were not measured were estimated using local allometric models built for each different region (see Methods SI for details, Table S2).\u003c/p\u003e \u003cp\u003eIndividual tree aboveground biomass (AGB in kg) was estimated using the general allometric model developed for tropical forests [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:AGB=0.0673{(WD\\times\\:{DBH}^{2}\\times\\:h)}^{0.976}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere: \u003cem\u003eAGB\u003c/em\u003e is the tree aboveground biomass (kg), \u003cem\u003eWD\u003c/em\u003e is the wood density (gr cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), DBH is the diameter at 1.3 m height, and H total tree height (m). WD data for each species were assigned using the BIOMASS library for R [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. When species-level WD values were not available, we used genus, family or cluster-level averages. The carbon stored in AGB (AGC, in Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) was obtained by multiplying AGB by a factor of 0.456 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. We did not use a specific equation for palms, given their low abundance (~\u0026thinsp;3 % of th total individuals measured). The associated uncertainty of using a tree allometry instead of a specific palm allometry is expected to be very low and to do not have any significant effect on the overall AGB estimates, as assessed by other studies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eWoody debris carbon stocks\u003c/h3\u003e\n\u003cp\u003eThe carbon stored in woody debris within each cluster was calculated by summing the dry mass of standing coarse woody debris (SCWD) and fallen trees and branches, categorized as fallen coarse woody debris (FCWD) and fallen fine woody debris (FFWD). Then, the total dry mass was multiplied by a factor of 0.456 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. SCWD was defined as all standing dead trees and stumps with a diameter (D)\u0026thinsp;\u0026ge;\u0026thinsp;10 cm, and these were censused within each cluster. Line-transect methods [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] were used to census both FCWD (pieces with D\u0026thinsp;\u0026ge;\u0026thinsp;20 cm) and FFCW (D\u0026thinsp;\u0026lt;\u0026thinsp;20 cm). Fallen woody debris was sampled in the circular plots numbered as 2 and 4 of each cluster (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). In these plots, four (4) transects of 30 meters in length (equivalent to two 60-meter transects) were established and divided into 10-meter sections. FCWD were censused in all four (4) transects, while FFWD were censused in the first meter of each 10-m transect section (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC). Additional technical details for calculating the amount of carbon stored in woody debris can be found in the supporting information.\u003c/p\u003e\n\u003ch3\u003eSoil carbon stocks\u003c/h3\u003e\n\u003cp\u003eIn each circular plot, soil samples were taken at a point located at a distance of two meters from the center of the plot and at an azimuth of 45\u0026deg; (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). Two soil samples were collected by perpendicularly inserting stainless steel rings to a depth of 0\u0026ndash;30 cm, which were employed to calculate bulk density (BD; g cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) and soil organic carbon concentration (%). All samples were packaged and labeled for analysis at the Biogeochemistry Laboratory of Universidad Nacional de Colombia, Medell\u0026iacute;n.\u003c/p\u003e \u003cp\u003eBulk density (BD) of each sample was calculated using the stone-free dry weight (g) and the volume of steel ring (cm\u003csup\u003e3\u003c/sup\u003e). Organic carbon was quantified using the loss-on-ignition (LOI) method [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], which estimates carbon content based on the mass of organic matter lost during controlled combustion. Additionally, we measured SOC directly with an Elemental Analyzer for a subset of 262 soil samples collected across 60 NFI clusters. Using these paired measurements generated in this study (LOI-derived organic matter and Elemental Analyzer SOC), we calibrated a regression model to convert LOI organic matter to SOC, enabling SOC estimation for sampled analyzed only by LOI (C\u0026thinsp;=\u0026thinsp;0.38 \u0026times; OM; R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.94; Figure S2).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMean and total carbon stocks\u003c/h2\u003e \u003cp\u003eTo estimate the carbon stocks in AGC, WDC, and SOC in natural forests of Colombia, we applied a post-stratified systematic sampling design, employing the biogeographic regions as strata. The mean carbon stocks (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{Y}\\)\u003c/span\u003e\u003c/span\u003e, Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and its associated standard error (\u003cem\u003eSE\u003c/em\u003e, Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were calculated as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\stackrel{-}{Y}=\\frac{\\sum\\:_{i=1}^{h}{N}_{i}{\\stackrel{-}{Y}}_{i}}{N}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:SE=\\:\\sqrt{\\frac{\\left(1-f\\right)}{n}\\sum\\:_{i}^{h}\\frac{{N}_{i}}{N}{S}_{i}^{2}+\\frac{1}{{n}^{2}}\\sum\\:\\left(1-\\frac{{N}_{i}}{N}\\right){S}_{i}^{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\stackrel{-}{Y}}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the arithmetic mean carbon stock of each stratum (i.e., biogeographic region); \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{N}_{i}\\)\u003c/span\u003e\u003c/span\u003e and \u003cem\u003eN\u003c/em\u003e are the total number of plots per stratum and country, respectively; \u003cem\u003ef\u003c/em\u003e is the fraction of sample; \u003cem\u003en\u003c/em\u003e is the total number of sampled plots; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{S}_{i}^{2}\\)\u003c/span\u003e\u003c/span\u003e is the variance in the stratum \u003cem\u003ei\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eFinally, the total carbon stock in AGC, WDC, and SOC for the national forests of Colombia was estimated by multiplying the mean carbon stock per hectare by the total forest cover. The area of natural forest by biogeographic region was obtained following the 2016 forest cover map produced by IDEAM (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClimate\u003c/h3\u003e\n\u003cp\u003eClimate variables were extracted from CHELSA database [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] employing the geographic coordinates of the center of each NFI cluster. We selected a set of variables that reflect the main climatic constraints on ecosystem carbon cycling in tropical forests: energy availability (mean annual temperature, MAT; potential evapotranspiration, PET), water supply and its intra-annual variability (mean annual precipitation, MAP; precipitation seasonality, PS), thermal variability (temperature seasonality, TS), and atmospheric controls on plant water stress and decomposition (vapor pressure deficit, VPD; cloud area fraction, CLT, as a proxy for persistent cloudiness and associated microclimatic conditions). We then applied a Principal Component Analysis (PCA) to these seven climatic variables to reduce collinearity and summarize major climatic gradients across clusters. The first two principal components were retained for subsequent analyses.\u003c/p\u003e\n\u003ch3\u003eSoil fertility\u003c/h3\u003e\n\u003cp\u003eIn parallel with soil carbon sampling, we collected mineral soil from the A horizon (i.e., after removing the organic layer) in each circular plot (five per cluster) to characterize soil fertility and chemical constraints relevant to carbon storage and turnover. The five plot-level samples within each cluster were homogenized to obtain a 500 g composite sample, which was air-dried for laboratory analyses. We quantified total phosphorus (P; mg kg⁻\u0026sup1;) and key exchangeable cations (Ca, Mg, K; mg kg⁻\u0026sup1;), along with aluminum (Al), soil pH, and cation exchange capacity (CEC), as these properties jointly reflect nutrient availability, base status, acidity/toxicity constraints, and the soil\u0026rsquo;s capacity to retain nutrients. To reduce collinearity among soil properties and summarize major fertility gradients across clusters, we applied a Principal Component Analysis (PCA) to the soil variables and retained the first two principal components for subsequent analyses.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eWe performed a one-way analysis of variance (ANOVA) to assess whether carbon stocks of each compartment evaluated (AGC, WDC, SOC, and total) differed among Colombia\u0026rsquo;s biogeographic regions. When significant differences were found, post-hoc comparisons were carried out using Tukey\u0026rsquo;s Honestly Significant Difference test (Tukey HSD) to identify which regional means differed.\u003c/p\u003e \u003cp\u003eWe used structural equation modelling (SEM) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] to evaluate the direct and indirect effects of climatic variability (PCA axis), soil fertility (PCA axis), and tree size (number of trees with DBH\u0026thinsp;\u0026ge;\u0026thinsp;40cm) on determining the spatial variation in carbon stocks. The response variables (AGC, WDC, and SOC) and tree size were log-transformed. All variables were standardized before their inclusion in the SEM. Our SEM structure assessed the premise that climatic variables affected all variables in the model. Soils fertility affected tree size and carbon stocks. Tree size was affected by all explanatory variables, and affect only the carbon stocks (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). We used a Satorra-Bentler scaled Chi-square test statistic to determine whether the covariance matrix observed in our data significantly deviated from that predicted by the SEM [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTotal carbon stocks at national and regional scales\u003c/h2\u003e \u003cp\u003eTotal carbon stocks (AGC, WDC, and SOC) in the natural forests of Colombia had a national mean (\u0026plusmn;\u0026thinsp;SE) estimate of 184.79\u0026thinsp;\u0026plusmn;\u0026thinsp;3.59 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (range: 37.01\u0026ndash;528.92 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), which represents a total estimated amount of 10.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21 Pg C. Significant variability was observed across biogeographic regions, with mean estimates ranging from 112.27\u0026thinsp;\u0026plusmn;\u0026thinsp;9.45 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in the Caribe to 199.91\u0026thinsp;\u0026plusmn;\u0026thinsp;3.52 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in the Amazonia region (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). AGC represented 55.36% of the total carbon stocks (range: 29.74\u0026ndash;62.39%), SOC stored 37.09% (range: 28.72\u0026ndash;66.81%), and WDC 7.55% (range: 0.93\u0026ndash;8.89%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe national mean of AGC in natural forests was 102.30\u0026thinsp;\u0026plusmn;\u0026thinsp;2.04 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (range 3.44\u0026ndash;231.66 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), corresponding to 6.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 Pg C stored in the AGB of natural forests of Colombia. AGC stocks varied significantly among biogeographic regions, with the highest mean in the Amazonia and the lowest in the Caribe region (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). When considering trees with DBH\u0026thinsp;\u0026ge;\u0026thinsp;10cm, the national mean of AGC was 93.08\u0026thinsp;\u0026plusmn;\u0026thinsp;2.07 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, representing a total estimate of 5.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 Pg C.\u003c/p\u003e \u003cp\u003eCarbon stocks in woody debris (WDC) had a national mean of 13.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (range: 0.00\u0026ndash;48.03 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), which represents a total estimated of 0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 Pg C (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The average of carbon stocks in standing coarse woody debris (SCWD) was 4.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (28.76% of WDC), in fallen coarse woody debris (FCWD) was 6.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (46.84%), and in fallen fine woody debris (FFWD) was 3.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (24.39%). Carbon stocks in woody debris varied significantly among biogeographic regions, with the highest average in the Amazonia and the lowest in the Orinoquia region (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe mean SOC stock in the first 30 cm of soil was 68.54\u0026thinsp;\u0026plusmn;\u0026thinsp;2.95 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (range: 3.02\u0026ndash;487.64 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), which represents a total of 4.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18 Pg C in the soils of natural forests. SOC varied significantly among regions, with the highest mean in the Andes and the lowest in the Orinoquia region. In three regions (Andes, Caribe, and Pacific), SOC accounted for the largest proportion of the estimated total carbon (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental gradients\u003c/h2\u003e \u003cp\u003ePCA\u003csub\u003eclimate\u003c/sub\u003e1 explained 55% of the total variance of the climatic variables and had high PET, VPD, CLT, and TS loadings. This axis is related to seasonal and dry climates, and reflects a gradient from warm, seasonal, and drier climates to cooler, less seasonal, and more humid conditions. PCA\u003csub\u003eclimate\u003c/sub\u003e2 explained 22% of the total variance and is determined mainly by water availability (MAP and PS), where high values indicate warm, humid conditions, while low values reflect cooler, drier environments with more pronounced precipitation seasonality (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, Table S3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe PCA\u003csub\u003esoils\u003c/sub\u003e1 explained 51% of the total variance in soil variables and is associated with fertility, with higher values indicating more fertile, base-rich soils. PCA\u003csub\u003esoils\u003c/sub\u003e2 explained 16% of the total variance and is related to a drainage and aluminum toxicity gradient, where lower values indicate poorly drained, acidic soils with high Al concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, Table S3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDrivers of carbon stocks in natural forests of Colombia\u003c/h2\u003e \u003cp\u003eWe found that 75% of the variation of aboveground carbon (AGC) stocks was explained by tree size (number of trees with DBH\u0026thinsp;\u0026ge;\u0026thinsp;40 cm), climatic gradients (PCA\u003csub\u003eclimate\u003c/sub\u003e2, associated mainly with water availability, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), and soil fertility (PCA\u003csub\u003esoils\u003c/sub\u003e1, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Tree size had the strongest influence, followed by the climate and soil gradients, which had similar effect sizes based on standardized coefficients. Both tree size and PCA\u003csub\u003eclimate\u003c/sub\u003e2 had positive effects, whereas soil fertility (PCA\u003csub\u003esoils\u003c/sub\u003e1) had a negative effect on AGC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eIn total, 56% of carbon in woody debris (WDC) was explained by the combined effect of climate (PCA\u003csub\u003eclimate\u003c/sub\u003e1 and PCA\u003csub\u003eclimate\u003c/sub\u003e2) and tree size. All of these variables exerted positive effects, with similar standardized coefficients. Soil gradients (PCA\u003csub\u003esoils\u003c/sub\u003e1 and PCA\u003csub\u003esoils\u003c/sub\u003e2) had no significant direct effect on WDC stocks (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eSOC stocks were explained in 27% by climate and soil gradients, with no significant contribution from tree size. Specifically, PCA\u003csub\u003eclimate\u003c/sub\u003e1 and PCA\u003csub\u003esoils\u003c/sub\u003e1 showed positive effects, while PCA\u003csub\u003eclimate\u003c/sub\u003e2 and PCA\u003csub\u003esoils\u003c/sub\u003e2 had negative effects. Among these, PCA\u003csub\u003eclimate\u003c/sub\u003e1 (related to seasonal and drier conditions, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) and PCA\u003csub\u003esoils\u003c/sub\u003e2 (associated with soil drainage, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) showed the strongest standardized effects, followed by PCA\u003csub\u003esoils\u003c/sub\u003e1 and PCA\u003csub\u003eclimate\u003c/sub\u003e2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eAdditionally, the SEM models showed that tree size was positively influenced by climate gradients (PCA\u003csub\u003eclimate\u003c/sub\u003e1 and PCA\u003csub\u003eclimate\u003c/sub\u003e2), whereas soil fertility (PCA\u003csub\u003esoils\u003c/sub\u003e1) had a negative effect. Finally, soil fertility (PCA\u003csub\u003esoils\u003c/sub\u003e1) was influenced negatively by climatic conditions (PCA\u003csub\u003eclimate\u003c/sub\u003e1 and PCA\u003csub\u003eclimate\u003c/sub\u003e2), and PCA\u003csub\u003eclimate\u003c/sub\u003e1 negatively affected soil drainage (PCA\u003csub\u003esoils\u003c/sub\u003e2).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study presents the first national-scale \u003cem\u003ein situ\u003c/em\u003e assessment of carbon stocks across multiple forest compartments (aboveground biomass, woody debris, and soils) in natural forests of Colombia based on field-based standardized data. By integrating data from 265 National Forest Inventory (NFI) clusters spanning diverse biogeographic regions, we found that variability in carbon stocks at national and regional scales is driven by environmental and structural factors with contrasting effects on across compartments. Understanding the spatial variation of carbon stocks in different forest compartments can improve the accuracy of national inventories and inform mitigation strategies that consider the vulnerability of each carbon reservoir under the ongoing climate change.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eTotal carbon stocks at national and regional scale\u003c/h2\u003e \u003cp\u003eOur results confirm the large carbon storage capacity of Colombia's forests, with a national carbon stock estimate of 10.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21 Pg C, considering carbon in the aboveground biomass (AGC), woody debris (WDC), and the top 30 cm of soil (SOC). A previous estimate for Colombia\u0026rsquo;s natural forest carbon stocks based on non-random plot networks that only included trees with DBH\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;10 cm [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], reported an AGC estimate of 6.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21 Pg C, which is 17% higher than our estimate for trees with DBH \u0026ge;\u0026thinsp;10cm (5.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 Pg C). This overestimation likely results from the use of existing plot networks that lack a probabilistic sampling design and mostly focus on old-growth, well-preserved forests, thereby underrepresenting the contribution of secondary and disturbed forests [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. By integrating standardized measurements of AGC, WDC, and SOC, the NFI not only improves the accuracy of national carbon accounting but also strengthens the country\u0026rsquo;s capacity to meet international reporting requirements.\u003c/p\u003e \u003cp\u003eThe overall estimation of carbon stocks presented in this study, differ from other broad-scale estimates based on plot networks or remote sensing-based maps, which tend to overestimate national means. For instance, applying the regional mean total carbon for tropical South America [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] (Extended Data Table\u0026nbsp;2: 277.5 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), would overestimate Colombia\u0026rsquo;s carbon storage capacity by approximately 31% (16.46 Pg C). Similarly, the mean AGC reported for Colombia [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] of 148.5 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (see Table S2 in [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]) is higher than our national AGC estimate (102.30\u0026thinsp;\u0026plusmn;\u0026thinsp;2.04 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), resulting in a national AGC overestimation of about 45% (8.80 Pg C).\u003c/p\u003e \u003cp\u003eSuch discrepancies emphasize the importance of using field-based inventories to validate and calibrate estimations of forest carbon stocks at regional or national scales. Remote sensing products, while essential for wall-to-wall mapping, are typically calibrated with limited ground data and may fail to capture the structural and ecological heterogeneity of tropical forests [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The Colombian NFI provides a probabilistic, spatially representative sampling design that captures the diversity of forest types, disturbance histories, and environmental conditions across the country. However, the grain or plot size employed with a sampling area of 0.35 ha is smaller than the most common grain size of 50 m x 50 m employed by satellite missions, such as GEDI [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and BIOMASS [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], which could hamper its use to validate this kind of remote sensing data [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. To scale up the observed values of AGC assessed with the NFI in Colombian forests, we will need to use technologies such as LiDAR [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], which are definitively more expensive and logistically difficult to acquire than the freely available satellite data.\u003c/p\u003e \u003cp\u003eThe distribution of carbon among pools reveals crucial regional variations, supporting previous landscape-scale studies in Colombia that show a quite significant spatial heterogeneity in carbon stocks throughout the country [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. At the national level, AGC is the largest contributor to total carbon stocks (55.4%), followed by SOC (37.1%) and WDC (7.6%). However, this balance shifts at the regional level: AGC is the main reservoir in Amazonia and Orinoquia, while SOC dominates in the Andes, Caribe, and Pacific (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Amazonian forests store the highest AGC stocks, principally due to the presence of large trees [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In contrast, lower AGC values in the Caribe and Andes are in line with earlier studies reporting reduced biomass and greater dominance of SOC in montane forests [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs claimed above, our findings show that AGC stocks decrease with elevation (r = -0.36, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas SOC increases (r\u0026thinsp;=\u0026thinsp;0.40, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), a pattern reported in other studies along elevational gradients in the tropics [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. However, it is important to note that our SOC estimates are based on only the upper 30 cm of soil, providing a standardized comparison at the national level, but potentially underestimating total SOC contributions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These findings highlight the regional variation in carbon stocks, which may be relevant for improving the accuracy of national carbon estimates and guiding equitable forest conservation and climate mitigation policies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental and structural drivers of carbon stocks\u003c/h2\u003e \u003cp\u003eOur analyses reveal a functional decoupling of carbon pools, with each compartment responding to a distinct set of environmental and structural drivers. AGC stocks are primarily influenced by forest structure, particularly the abundance of large-diameter trees (DBH\u0026thinsp;\u0026ge;\u0026thinsp;40 cm), followed by soil fertility, and climatic variables related to water availability. This finding is consistent with global studies showing that large-diameter trees disproportionately contribute to forest biomass and carbon storage [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The presence of large trees is influenced not only by soil and climatic conditions, as revealed by our SEM analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), but also by disturbance, with their abundance increasing from secondary to primary forests. This suggest that conservation of primary forests may be important for maintaining carbon storage and contributing to climate change mitigation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInterestingly, we found a negative association between soil fertility and AGC, a counterintuitive result that may be explained by historical anthropogenic pressure. Fertile lowland dry and montane regions of Colombia have been subject to intensive land use and selective logging [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], potentially reducing biomass despite favorable nutrient conditions. Because our NFI sampling is not restricted to primary forests, the inclusion of secondary or degraded forests is critical for identifying the often-overlooked impact of anthropogenic pressures on forest carbon distribution. Previous studies in tropical forests often report positive associations between water availability and biomass [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], however, the relationship observed in the present study may be strongly influenced by the disturbance process indicated above. Indeed, forests in dry (Caribe) or higher-elevation (Andes) regions, where land-use impacts are more pronounced, tend to exhibit lower AGC. In the tropical dry forest, tree species with higher wood density are present but are generally smaller in size, which may result from disturbance, underscoring the importance of long periods for dry forest regeneration. Likewise, the loss of large trees in high elevation but remote forest [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] suggests a similar pattern of anthropogenic pressure. Together, these findings indicate that both environmental conditions and land-use history may influence carbon stocks across diverse tropical landscapes [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWoody debris, although representing a smaller and more variable fraction of total carbon [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], constitute a vital component of forest carbon stocks, especially in the context of disturbance regimes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. WDC shows a combined response to both stand structure, as a source of fallen wood, and climate, which regulates decomposition rates. A strong influence of climate is expected, given that both productivity (which determines woody input) and decomposition rates (which determine residence time of debris) are sensitive to moisture and temperature conditions [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The tight coupling between mortality processes, decomposition, and climate indicates that this pool may be sensitive to changes in disturbance frequency or intensity, as has been observed in tropical forests [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Importantly, because WDC can act as either a short-term carbon source or a long-term reservoir depending on decomposition rates, its inclusion in carbon accounting frameworks is may improve the accuracy of national greenhouse gas inventories and inform REDD+ strategies.\u003c/p\u003e \u003cp\u003eSOC was influenced by climate and soil conditions, with no significant contribution from stand structure. As expected, SOC stocks were highest in cooler, high-elevation sites, where lower temperatures reduce microbial decomposition rates and favor carbon accumulation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. This positive relationship between SOC and elevation has been reported previously for montane forests [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. The positive relationship between SOC stock and soil fertility (PCA\u003csub\u003esoil\u003c/sub\u003e1) may be partly influenced, again, by the elevation gradient. Soils derived from volcanic ashes (Andisols), mainly distributed above 2000 m asl, are characterized by strong acidity, high phosphorus retention (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), low cation exchange capacity, and conversely, high anion exchange capacity [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. It is important to note that our study was limited to the upper 30 cm of the soil profile, which likely underestimates total SOC stocks. Deeper soil layers often reveal that soils can surpass biomass as the dominant carbon pool when sampled at greater depths [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, the contrasting drivers of carbon stocks identified in this study underscore the importance of locally-tailored conservation strategies. For example, protecting large trees in lowland forests may help maintain aboveground carbon, whereas conserving forests in high-altitude, colder, and wetter regions may help safeguard substantial soil carbon stocks that are vulnerable to decomposition as temperatures rise. By providing robust, field-based data across multiple carbon pools and diverse regions, this study contributes information relevant to national climate policies and the understanding of forest carbon dynamics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eRelevance for climate policy and carbon accounting\u003c/h2\u003e \u003cp\u003eOur findings have important contributions to improving the accuracy of Colombia's national greenhouse gas (GHG) inventory. While most previous studies have focused primarily on improving estimations of biomass, often overlooking other compartments such as soils and woody debris, our results show that these compartments can represent a considerable proportion of total carbon stocks [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The marked heterogeneity in carbon compartments across regions suggests that carbon accounting and climate mitigation strategies must be tailored to regional characteristics and carbon allocation patterns, rather than relying on generalized assumptions. This has direct implications for REDD\u0026thinsp;+\u0026thinsp;and other climate mitigation initiatives, as carbon offset and payment for ecosystem service schemes must reflect regional differences in carbon distribution to ensure accurate resource allocation.\u003c/p\u003e \u003cp\u003eThe Amazonia region has a disproportionate role as a national carbon reservoir, accounting for approximately 70% of Colombia\u0026rsquo;s total carbon stocks (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Given that 68% of deforestation reported for 2024 occurred in Amazonia [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], these findings highlight the vulnerability of national carbon stocks to forest loss in this region. The latest national GHG inventory corroborates this concern, identifying the Land Use, Land-Use Change, and Forestry (LULUCF) sector as the largest source of national emissions (~\u0026thinsp;35%), with forest conversion to pastures and degradation (~\u0026thinsp;60%) as the primary emission drivers within the sector [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Deforestation primarily affects AGC and WDC pools, while SOC, although more stable, can also be at risk under land-use conversion, especially when substantial soil disturbance occurs. Illegal mining, in particular, poses a major threat by directly disrupting soils and releasing large amounts of SOC that may have accumulated over centuries. Once disturbed, these soils lose not only their stored carbon but also their long-term capacity to sequester additional carbon, given the slower recovery rates of soil carbon compared to biomass [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, our results have important implications for forest conservation and climate policy. First, conservation strategies may benefit from recognizing the role of soil carbon, particularly in highland ecosystems where it represents the dominant carbon pool. Protecting these areas may help maintain long-term carbon storage under changing climatic conditions. Second, carbon offset initiatives and payment for ecosystem services (PES) schemes may benefit from incorporating regional differences in carbon pool composition to improve valuation and resource allocation. Neglecting this variation could result in an underestimation of carbon stocks in certain ecosystems, especially those with high SOC. Third, the Colombian NFI provides a robust and underutilized platform for enhancing national Measurement, Reporting, and Verification (MRV) systems under REDD+, by offering spatially explicit, field-based data across a wide range of environmental conditions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.A. Peña was funded by Minciencias (Convocatoria 909 de 2021). The authors thank the Instituto de Hidrología, Meteorología y Estudios Ambientales (IDEAM), which led Colombia’s National Forest Inventory (NFI), and all those who participated in data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.A. Peña was funded by Minciencias (Convocatoria 909 de 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.A.P. and A.D. conceived and designed the study and wrote the main manuscript. S.G. and J.L. contributed to writing, review, and editing. C.O., S.R., J.R., O.M., and R.J. data collection and curation. All authors reviewed and approved the final manuscript before submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll metadata used in the analyses will be deposited in a repository for free access.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics and Consent to Participate declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePan Y, Birdsey RA, Phillips OL, Houghton RA, Fang J, Kauppi PE, et al. The enduring world forest carbon sink. 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Bogot\u0026aacute; D.C.: Colombia; 2025.\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"carbon-balance-and-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cbam","sideBox":"Learn more about [Carbon Balance and Management](https://cbmjournal.biomedcentral.com/)","snPcode":"13021","submissionUrl":"https://submission.nature.com/new-submission/13021/3","title":"Carbon Balance and Management","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Tropical forests, aboveground biomass, soil organic carbon, woody debris, National Forest Inventory, carbon accounting","lastPublishedDoi":"10.21203/rs.3.rs-8621469/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8621469/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTropical forests play a key role in the development of climate policy frameworks. However, field-based assessments integrating multiple carbon pools in tropical regions remain scarce at national or regional scales. In Colombia, natural forests cover more than half of the continental territory, yet comprehensive estimates of carbon stocks across aboveground biomass, woody debris, and soils are lacking. This study provides the first national-scale, standardized, field-based quantification of these carbon pools and examines their environmental and structural drivers.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eUsing data from 265 clusters of Colombia\u0026rsquo;s National Forest Inventory, we estimated a national mean total carbon stock of 184.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, which represents a total estimated amount of 10.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21 Pg C stored in natural forests. Aboveground carbon (AGC) represented 55.4% of total carbon stocks, soil organic carbon (SOC) stored 37.1%, and woody debris carbon (WDC) 7.6%. Significant regional variation was observed, with Amazonia showing the highest total carbon mean and Caribe the lowest. Structural equation models revealed that AGC was mainly driven by the abundance of large trees, followed by climatic and soil fertility gradients. WDC was influenced by both climate and forest structure, while SOC was primarily determined by climate and soil properties.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eColombian natural forests store substantial carbon stocks whose distribution and drivers vary markedly among biogeographic regions and carbon pools. The Amazonia region contains about 70% of the country\u0026rsquo;s total forest carbon, emphasizing its importance for national mitigation strategies. These findings provide critical empirical evidence to improve greenhouse gas inventories and support regionally tailored policies for forest conservation, REDD+, and carbon offset initiatives.\u003c/p\u003e","manuscriptTitle":"Carbon stocks in natural forests of Colombia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-02 10:07:06","doi":"10.21203/rs.3.rs-8621469/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-27T13:22:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-23T14:05:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-22T20:04:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-17T17:04:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-15T03:37:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"294486490520097460066673548244252460121","date":"2026-02-03T16:28:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250696457753852303263725218085281977089","date":"2026-02-03T15:09:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-03T13:43:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"322294552130016296526260758617620637722","date":"2026-02-02T12:33:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"296182148695531953096575391590211152964","date":"2026-01-30T10:12:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"319824402908716225678389627499095639000","date":"2026-01-29T17:38:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-29T11:06:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-23T14:52:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-23T14:47:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Carbon Balance and Management","date":"2026-01-16T17:52:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"carbon-balance-and-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cbam","sideBox":"Learn more about [Carbon Balance and Management](https://cbmjournal.biomedcentral.com/)","snPcode":"13021","submissionUrl":"https://submission.nature.com/new-submission/13021/3","title":"Carbon Balance and Management","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1e07ba25-4fd9-4d8c-a8e5-55b5f7e68b24","owner":[],"postedDate":"February 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-02-27T13:38:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-02 10:07:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8621469","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8621469","identity":"rs-8621469","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}