Evaluating carbon stocks in soils of fragmented Brazilian Atlantic Forests (BAF): How soil features and different methodologies influence its assessment

doi:10.21203/rs.3.rs-3688344/v1

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Evaluating carbon stocks in soils of fragmented Brazilian Atlantic Forests (BAF): How soil features and different methodologies influence its assessment

https://doi.org/10.21203/rs.3.rs-3688344/v1

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published 01 May, 2024

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Brazil’s Atlantic Forest (BAF) is a highly fragmented, strategic environmental and socio-economic region that represents the fourth biodiversity hotspot while also producing many commodities that are exported globally. Human disturbance plays a pivotal role as a driver of BAF’s soil dynamics and behaviors. The soils below Riparian Forests (RFs) and some Semideciduous Primary Forests (SFs) were characterized by high to moderate resilience, with improved chemical properties as human disturbance decreased. The Transitional Forest to Cerrado (TFC) had the worst soil conditions. Accordingly, SOCs stocks and soil carbon credits (SCC) increased from TFC << SFs < RFs. In BAF soils, measurements of SOC quantity and behavior should be conducted to at least 1 m soil depth to avoid unreliable data. Furthermore, quantifying SOC with a CHN analyzer was more reliable than other methods. A positive feedback was observed between soil and BAF vegetation, with human disturbance strongly affecting it and related SOCs dynamics. Soil development decreased as human disturbance increased, thus negatively affecting SOC. Soils in the BAF require a long time to recover after the end of human disturbance, thus suggesting that preservation strategies should be prioritized in remnant BAF fragments.

Biological sciences/Ecology/Forest ecology

Earth and environmental sciences/Environmental sciences/Environmental impact

After the oceans, the soil is the main carbon sink on our planet, storing about 2500 Pg C in the first 100 cm depth¹, a value corresponding to three times the carbon stock in the atmosphere and about four and a half times the stock in the biosphere².

As first reviewed by Harrison et al.³, most studies assess soil organic carbon (SOC) levels in the first 0–20 cm. The Intergovernmental Panel on Climate Change^4–6 IPCC 2019 recommends sampling the first 0–30 cm to assess the carbon stock to generate carbon credits. In both cases, the sampled layers represent only part of the overall carbon stock present in the soil⁷. Soils can be very deep under tropical conditions due to a combination of age and high precipitation, which suggests that soil sampling for carbon quantification cannot be limited to the first 30 cm⁸ and that a significant accumulation of SOC can occur in deeper soil horizons^9,10.

Canary et al.¹¹ and Liebig et al.¹² compared the C stocks in the first 20/30 cm of mineral soil surface horizons with their maximum depth and observed that the 0–20/30 cm soil contained an average of 44% of SOC vs 56% in deeper horizons (20/30–100 cm). The results show the inadequacy of sampling the first 20/30 cm soil surface if we want to assess the total SOC pool¹³. In an analysis of 2,700 soil profiles in three global databases covering shrubland, grassland, and forest ecosystems, the 0–20/30 cm vs 20/30–100 soil depth contained 42% vs 58%, 33% vs 77%, and 50% vs 50% of the total SOC, respectively^14,15.

The most commonly used technique for laboratory SOC measurement^16–18 is the wet oxidation method by Walkley-Black¹⁹ (WBm, hereafter). This method determines SOC content by oxidizing SOC with a potassium dichromate (K₂Cr₂O₇)-sulfuric acid (H₂SO₄) mixture followed by back titration of excess dichromate. However, there are several issues with the WBm method. First, dichromate is a carcinogenic compound and thus laboratory use generates toxic wastes that are hazardous to human health and the environment and requires complex and expensive management and disposal¹⁸. Second, the method does not result in complete SOM oxidation, causing underestimation of total SOC content²⁰. To avoid such a problem, a “correction factor,” which ranges greatly according to soil features, is applied according to investigated pedo-environmental conditions¹⁸.

More accurate measurements can be done using an elemental analyzer, such as a CHN analyzer (CHNa, hereafter), which is able to detect all SOC forms in the analyzed soil samples. Additionally, the method is safe, repeatable, and can be automated while avoiding hazardous reagents. However, widespread use of CHNa in soil laboratories worldwide is limited by the high up-front costs of the machine as well as maintenance expenses²¹. This is a dramatic situation since reliable soil organic carbon measurement is pivotal in studies of greenhouse gas (GHG) emissions caused by human activities and responsible for global warming and climate change processes²². Thus, solutions to encourage and promote its worldwide adoption should be proposed.

Most SOC measurements in the literature come from a few countries, which over-represents to regions in our scientific understanding of human disturbance effects on soils and ecosystems²³. At the continental scale, the imbalance in study density is stark^24,25: i) studies conducted in Asia, Europe, and North America represent 90% of the global measurements; ii) in Africa and South America, reliable data dramatically lacks. Nonetheless, soils and ecosystems in Africa and South America have great significance to worldwide GHG budgets¹⁸.

Another pivotal question is related to the fact that soil physical and chemical properties (such as depth, pH, bulk density, macro- and micronutrient content and behavior, etc.) strongly influence SOC²⁶. Thus, an in-depth soil analysis can avoid bias in SOC assessment²⁰.

Brazil is one of the largest emerging economies. It is the tenth-largest world economy and the biggest within Latin American countries, reaching a gross domestic product (GDP) of nearly 8% immediately before the pandemic period (+ 1.9% in 2023²⁷). The agribusiness and forestry sectors played a pivotal role in the historical expansion of the Brazilian economy, and output from these sectors continue to grow at substantial rates²⁷. However, the same sector is responsible for important and large-scale land-use change, which affects soil C stocks and, consequently, GHG emissions²⁶.

This strong relationship between the Brazilian economy and the agroforestry sector brought an extraordinary increase in agreements and collaborations among private companies and research centers (Universities, Governmental Bodies, etc.), which promoted development in soil laboratory technologies. Subsequently, tools such as elemental analyzers (e.g., CHNa) are now commonly used and widespread in the country. This represents an important opportunity to fill the knowledge gap still affecting the literature on C soil content in Brazilian soils, a unique pedoecosystem worldwide. This can also allow comparisons between different SOC stock assessment methods.

Brazil’s Atlantic Forest (BAF, hereafter) is a strategic biodiversity hotspot²⁶ and is one of the most species-rich ecosystems on Earth²⁸. Despite being considered under threat due to human-related degradation, it still represents the fourth most biodiverse hotspot in the world and is Brazil's second-most diverse ecosystem (after the Amazon Forest)²⁹. After European colonization (1500 AD), the original 130 million hectares of BAF³⁰ were drastically destroyed and reduced, now hosting around 70% of Brazil’s population²⁷. Only 26–28% of BAF continuous cover remains, most of which is in different stages of regeneration²⁶.

The BAF region is also pivotal in the Brazilian and worldwide agroeconomy³¹. The agribusiness sector based in the BAF region is the global leader for the production of many commodities (such as coffee, corn, ethanol, meat, soybean, sugarcane, etc.), which are exported around the world and which make Brazil's agroindustry the “breadbasket of the world”³². About 80% of the Brazilian Gross Domestic Product (GDP) is based on BAF region activities²⁷. Yet, studies on the remaining native BAF can bring new knowledge and opportunities to reconcile conservation and ecosystem services purposes vs socio-economic activities. This represents an enormous challenge that science must face to safeguard this important, still understudied ecosystem²⁶, trying, when possible, to allow the economic development of an area that produces essential goods. Among these challenges, estimating C stocks stored in these unique and fragile pedosystems represents a starting point of knowledge on which to make future choices of how and in which way to reconcile ecosystem protection and socio-economic development. Indeed, the soil degradation historically affecting BAF causes drastic changes in their bio-physical-chemical properties, negatively affecting fertility, nutrient/water storage availability, and globally, being responsible for GHG emissions³³. On a global scale, deforestation is estimated to contribute 8–15% of annual GHG emissions³⁴. In Brazil, which is the world’s fifth-largest GHG emitter, the leading causes of CO₂ emissions are changes in land use, representing 75% of the total emissions vs the 20–25% coming from burning fossil fuels³⁵. Therefore, there is a need to better understand, by using and comparing advanced vs commonly used tools of measurements, the SOC sequestration in tropical pedosystems, aiming to outline conservation and management policies for reducing CO₂ emissions in a critical ecosystem.

Overall, taking into account all the previous considerations, this research aimed to: i) quantify C stocks in BAF soils by ii) comparing WBm vs CHNa methods, along iii) different soil pedosystems (investigated to 1 m soil depth) featuring iv) five different vegetation physiognomies belonging to one of the most important, human-affected, and fragile ecosystem worldwide. All the previously reported points will also be investigated to understand (vi) how soil features and applied different methodologies influence SOCs assessment.

Soil physical-chemical features

Table 1 shows selected soil physical-chemical features of all five investigated BAF fragments along the five different depths (0-20 to 80-100 cm). The mean pH-CaCl₂ value was 4.9±0.1, ranging from 3.4 (very acidic) to 6.9 (neutral). Inside each investigated area, pH usually significantly decreased (p < 0.05) with depth, except for TFC, where it maintained similar low (acidic) values. Comparing different fragments, pH decreased from RFs > SFs > TFC. The BD ranged from very low (0.6) up to extremely high (1.9 g cm^-3) with a mean of 1.3±0.1 g cm^-3. As expected, BD increased with soil depth in all investigated BAF fragments. In this case, a decreasing trend from TFC > SFs > RFs was observed among environments. Soil organic matter (SOM) and macronutrients N and P concentrations had variable contents too, with usually low mean values of 19.0±0.9 g kg^-1 (min: 0.1; max: 86.3 g kg^-1), 0.3±0.1 % (0.1-3.2 %), and 30.1±2.2 mg kg^-1 (min: 1.6; max: 546.2 mg kg^-1), respectively. Their values generally decreased along the soil depth, showing the same pattern when compared to BAF fragments (RFs > SFs > TFC). Cation-exchange capacity (CEC: mean 109.1±3.4 mmol_c dm^-3, range 24.0-381.6 mmol_c dm^-3), exchangeable basic cations (K⁺: 1.8±0.2 mmol_c dm^-3, 0.2-7.8 mmol_c dm^-3; Ca²⁺: 57.2±2.8 mmol_c dm^-3, 2.4-297.0 mmol_c dm^-3;Mg²⁺: 15.4±0.6 mmol_c dm^-3, 0.6.0-43.2 mmol_c dm^-3),and, consequently, base saturation (BS: 74±3%, 3-343%), showed a strict positive relationship with SOM (vide infra) and all exchangeable basic cations, while acidic cations (Al³⁺: 3.4±0.3 mmol_c dm^-3, 0.0-57.0 mmol_c dm^-3) and total acidity (H+Al: 34±1.3 mmol_c dm^-3, 5.9-296.7 mmol_c dm^-3) had a negative relationship with SOM. Thus, CEC and all basic cations usually decreased along soil depth, while the opposite was usually observed for Al³⁺ and H+Al. Comparing the five BAF fragments, the trend SFs > RFs >> TFC was observed for CEC and all basic cations, while TFC > SFs ≃ RFs was observed for Al³⁺ and H+Al. Soil micronutrients broadly varied (S: 9.79±0.29 mg kg^-1, 1.57-51.68 mg kg^-1; B: 0.32±0.00 mg kg^-1, 0.05-1.39 mg kg^-1; Cu: 3.11±0.15 mg kg^-1, 0.11-13.68 mg kg^-1; Fe: 56.26±1.94 mg kg^-1, 1.78-170.69 mg kg^-1; Mn: 28.22±1.30 mg kg^-1, 0.64-104.0 mg kg^-1; Zn: 8.76±0.48 mg kg^-1, 0.16-57.72 mg kg^-1) usually showing a decreasing trend with soil depth (except Cu and Zn) and decreased across environments from RFs > SFs > TFC.

Soil organic carbon stocks (SOCs)

Table 2 reports the SOCs values, for each of the five investigated vegetation physiognomies, also comparing the used methodologies for SOC quantification.

The largest difference in SOCs amounts was observed for SF1 (Primary Semideciduous Seasonal Forest) with values of SOC extracted by CHNa 99% higher than SOC values obtained with the WBm. The smallest differences were observed for both investigated Primary Riparian Forests (RF1: 14%, RF2: 19%). Soil organic carbon stocks and CO₂e, when quantified through the CHNa, were 40% higher on average, considering all investigated physiognomies to 1 m soil depth, when compared with the WBm.

Regarding soil carbon credits (SCC, Table 3), we found a statistically significant difference between the number of SCC calculated using CHNa (102.3 t ha^-1) and using WBm (92.3 t ha^-1), which corresponded to a 10% greater estimate of SCC. Regardless of the method used to measure SOC, the quantity of soil carbon credits increased with decreasing intensity and recency of human disturbance (e.g., RFs >> SFs). However, in SFs fragments, a net difference of 130 (SF1) and 86% (SF2) was observed between WBm vs CHNa.

As previously observed (Table 2), the WBm showed a mean value of 128.6±14.3 t ha^-1 of SOCs stocked up to 1 m soil depth. Of this, 62% (79.7 t ha^-1) was concentrated in the first 0-40 cm, while the remaining 38% (48.9 t ha^-1) was in the 40-100 cm soil depth. By using CHNa extraction, SOCs mean values up to 1 m depth were 180.9±18.6 t ha^-1 (Table 2). In this case, 58% (104.9 t ha^-1) was in the first 0-40 cm soil depth, while the remaining 76.0 t ha^-1 (42%) was found in the last 40-100 cm soil depth. Table 4 shows SOC values according to the method of SOC measurement, vegetation physiognomy, and investigated soil depth. Looking at the SOC content vs soil depth, considering all investigated vegetation’s physiognomies, an inverse correlation was observed, with very similar behaviors for both applied SOC extraction methodologies, even if the WBm resulted in a lower estimate of SOCs compared to CHNa. Also in this case, the RFs fragments had the highest (p < 0.05) SOC contents compared to all other investigated physiognomies, regardless of the investigated depth. The SFs and TFC do not show statistically significant differences except when SOC was analyzed through CHNa. Indeed, in this case, at least for the soil depth to 60 cm, a clear statistical difference was observed, with the following trend in SOC contents: RF1 ≥ RF2 >> SF1 ≥ SF2 >> TFC. Soil organic carbon stocks at the 80-100 cm depth were three (CHNa) to two (WBm) times lower compared to the surface A horizons (0-20 cm).

Factor analyses

The factor analyses (FA) extracted a three-component model of robust statistical performance (all eigenvalues are over 1). The three extracted Factors explain 80% of the overall variance, meaning high statistical reliability. The first factor (F1) explained 56% of all observed variance in the data. F1 was positively concordant with pH, SOM, C-CHN, N, P, all basic exchangeable cations (K⁺_,Ca²⁺,Mg²⁺), CEC, BS, Cu, Mn, and both SOCs (regardless of the used method), while Al³⁺ was negatively correlated. F2 (14%) was positively correlated with SOM, C-CHN, Fe, and both SOCs. Finally, F3 (10%) showed BD as inversely correlated to C-CHN, N, K+, S, B, and both SOCs.

Principal component analyses

Figure 1 shows the biplot from PCA. Overall, the two main components explain alone the 69% of all observed variability. All investigated soil parameters (arrows) are over 1, thus showing a high statistical significance inside the model. The five mains investigated BAF’s physiognomies (points rounded by areas underlined by different colors) are grouped in a clear way, showing a clear difference among each other.

Soil physical-chemical features

Features of investigated soils among the five different BAF fragments show interesting similarities and differences. In most of the analyzed soil physical-chemical properties, we observed that RFs ≠ SFs ≠ TFC. Regarding human effects, past disturbances played a pivotal role in creating such clear differences. As argued by Roder et al.²⁶, in more natural conditions like the RFs in this study, soils develop under mature forest vegetation, with higher spatial canopy cover and a well-developed organic surface horizon (O). Thus, O horizons can release basic cations in greater amounts, increasing soil pH³⁶, while the forest canopy intercepts precipitation and limits leaching processes, thus limiting the loss of more soluble nutrients (basic cations)³⁷. Conversely, environments like the TFC, representing the most disturbance, have less developed vegetation cover with a following enhancement of leaching processes and, consequently, decrease in soil pH. Pearson correlation coefficients (Supplementary Material 1) clearly confirmed such a behavior, showing a strong positive correlation between pH vs SOM (r = 0.62**), CEC (r = 0.53**) and, consequently, all basic cations (K⁺, r = 0.61**; Ca²⁺, r = 0.70***; Mg²⁺, r = 0.72***); an inverse correlation was observed for SOM vs Al³⁺ (r = - 0.35**).

Soil macro (N and P) and micronutrients (S, B, Cu, Fe, Mn, and Zn) showed a clear decreasing trend from RFs to TFC, i.e., from more preserved natural environments to less preserved ones. They all correlate to SOM (N: r = 0.50**, P: r = 0.43**, S: r = 0.37**, B: r = 0.65***, Cu: r = 0.44**, Fe: r = 0.27**, Mn: r = 0.70***, Zn: r = 0.32**), confirming previous studies showing SOM as the main source of such elements in tropical acidic and dystrophic pedosystems^{9,10,26,38–40}.

Results on investigated soil physical-chemical parameters show the pivotal role of human disturbance as a driving-factor in affecting pedoenvironment dynamics and behaviors. Even if pedoenvironments like those featuring RFs and somewhat even SFs are characterized by high to moderate resilience, with most of the investigated parameters improving as human disturbance decreases, recovery seems to be highly time-dependent. Indeed, the best soil conditions were found in those BAF fragments with no history of deforestation, exploitation for livestock, or agriculture, at least during the last century (RF1), while the worst in the TFC, where humans acted as driving force until recently (30-40 years ago). Such observations suggest an efficient but highly time-dependent, slow recovery of BAF ecosystems from human disturbance, underlying the importance of their conservation and recovery.

Soil organic carbon stocks (SOCs)

The carbon concentrations measured using CHNa were significantly higher than those measured using WBm, with mean values of 180.9±18.6 t ha^-1 and 128.6±14.3 t ha^-1 respectively. On average, CHNa measurements of SOC were 40% higher than WBm, with the greatest difference in the SF1 plots, where SOC was 99% higher with CHNa. Such results demonstrate that CHNa can play a pivotal role in detecting significant differences in SOCs values among different environments. Walkley-Black method is less able to quantify SOC, and thus may under- or over-estimate SOC change when comparing between disturbed environments or between different management strategies. This outcome is in line with studies conducted worldwide that reported an underestimation in SOC values by using WBm compared to CHNa^21,41–44. This is an intrinsic problem of the WBm, since it exhibits great variability in the dichromate oxidation degree and efficiency depending on factors such as SOC composition, soil bio-chemical-physical features, land cover and use, investigated soil horizon, etc.^42,45. The CHNa detected higher SOC concentrations than the WBm, especially in soil horizons of BAF fragments with higher C content (Tables 1 and 2). Similar results between the two methods were observed in lower C content conditions, as for the TFC or for deeper soil horizons. The decreased efficiency of the WBm compared to CHNa can be explained by the well-known affinity between iron-enriched tropical soils for OC⁴⁶; thus, at increasing SOC amount, WBm underestimated its content, for previously explained reasons (vide supra), while CHNa is more reliable. Another factor could be associated with past land uses. Indeed, Davis et al.⁴⁷ reviewed the SOC methods for tropical soils of Brazil, showing that in more anthropized or less natural areas (like those featured by relatively recent fires) WBm resulted in smaller and more variable results, especially for soil surface mineral A and Ap horizons. Thus, the authors concluded that underestimation of SOC content in such soils should be expected with WBm measurements. Segnini et al.⁴⁸ and Tivet et al.⁴⁹ confirmed that in the tropical soil of Brazil, WBm strongly underestimated SOC contents; Tivet et al.⁴⁹ also identified historical land use as a factor affecting the efficiency of WBm.

Due to the concomitant combination of several issues (vide supra), conversion factors are extremely site-specific⁴⁹, thus demonstrating the need to develop models calibrated for each investigated pedosystem to achieve a reliable comparison among SOC stocks. Despite SOC underestimation, WBm was the only method available to researchers in many cases; consequently, developing site-specific correction factors represents a strategy to improve data quality. Researchers worldwide proposed the application of a large range in correction factors (from 0.09 to 2.21) to increase WBm reliability⁴⁴. A contradiction arises from the literature that authors often report the need to use CHNa to calibrate WBm measurements; this was done by sending samples outside investigated countries with related costs²¹. So, even if it is still widely used as a relatively low-cost method, it frequently requires a review/calibration process due to the large variability in the conversion factor. For instance, Gessesse and Khamzina²¹ revealed that in soil samples collected in several locations in Ethiopia, the most common correction factors did not improve the reliability of WB vs CHNa derived results. Thus, they proposed using the Bland and Altman method⁵⁰, commonly applied in clinical research, for assessing the conversion factor, concluding that a correction factor of 1.32 for non-calcareous, carbon-poor Ethiopian soils can be considered reliable. This research, as with many others^{41,42,44,45,51–59}, confirmed that correction factors are extremely site-specific and, thus not easily applicable without a double-check control with more expensive and sophisticated laboratory tools. As concluded by the review of Pribyl⁶⁰, any factor used for OC conversion in SOM cannot be assumed as a universal constant; indeed, such numbers may be influenced by a combination of many factors (vide supra) that have the potential for serious error. Consequently, even if in the absence of other alternatives WBm often remains the only possibility to assess SOC, it is urgent to find new solutions that increase the reliability of data in unfavored socio-economic conditions. From this point of view, participatory and networking-based research promoted by Universities, Research Centers, Governments, and other institutional bodies, non-governmental organizations (NGO), represent opportunities to remedy this gap in data reliability. Implementing such kinds of projects is not only of scientific importance but also a moral question¹⁸. Indeed, GHG emissions, prevalently produced by developed countries, are responsible for climate change, mainly affecting developing ones⁶¹.

The use of CHNa is also recommended in soil carbon credit (SCC) evaluation⁶². Our results confirm such a recommendation, with SCC being 10% greater when assessed through CHNa vs WBm (Table 3). We also observed that in most well-preserved soil conditions (as in RFs fragments), the quantified amount of SCC is up to two times higher compared to less preserved ones (like TFC) (Table 2). Funding provided by selling SCC could play an important role in promoting BAF recovery activities. Indeed, since they are already well established in the agricultural and forestry sectors⁶³, it could be proposed to increase their use in the voluntary offset market, where they could be sold to contribute to governance environmental policies, such as the Kyoto Protocol's Clean Development Mechanism (CDM), or the Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD+) program^4,64. Principles developed to ensure the quality of agricultural and forestry-based carbon credits could be also useful for evaluating and promoting the environmental and socio-economic value of BAF conservation and restoration programs. Accurate and reliable SOC measurement is a prerequisite for developing a BAF carbon crediting program, as well as for monitoring SOC changes following project implementation. Assessing the correct amounts of SOC represents a challenge to reverse the loss of forests and carbon stocks in threatened natural ecosystems⁶⁴.

Looking at the SOCs vs soil depth, the RFs forest’s physiognomies showed the highest values in all investigated depths compared to the other BAF fragments. In particular, if compared to the TFC (the most degraded fragment or the least natural one), RFs stocked around 4.5-5 times more SOC across each depth for both CHNa and WBm methods. The SFs seem to represent an intermediate stage between the aforementioned physiognomies. Depending on the measurement method (CHNa or WBm), SFs contained 2.5-3.5 times less SOC than RFs, but contained 1-1.5 times more than TFC.

Our results also show the deficiency in the IPCC recommended depth (0-30 cm) for soil sampling to quantify SOC⁴. Another important fact relates to the IPCC recommendation⁴ for 0-30 soil sampling in SOCs quantification purposes. As argued by Jandl et al.⁸, such pre-established depth guidelines can dramatically underestimate overall SOC quantification, and sampling activities to at least 1 m soil depth can reveal additional important information. The present research confirmed that: i) the SOCs between 40-100 cm were only slightly lower (≤10%) than stocks found in the first 0-40 cm; ii) by extrapolating SOCs data from 30-100 vs 0-30 cm soil depth, the amount is significantly comparable, confirming Muños-Rojas et al.⁶⁵ suggestion of soil sampling up to 1 m depth for SOCs quantification. Indeed, if SOC stock into the 30-100 cm soil depth was not measured, up to 50% of overall SOC (along the soil profile) would not be quantified.

We observed a clear increasing trend with decreased human disturbance level in both SOC stocks and CO₂e amounts. As argued by Chenu et al.⁶⁶, under most preserved environmental conditions, SOC stock can approach a long-term equilibrium, with SOM inputs coming from vegetation and animal residues, roots and their exudates, etc., balanced by its degradation from mineralization processes. This equilibrium that can be shifted, as observed in the investigated areas, by changes in land management. Our results agree with Roder et al.²⁶, which demonstrated a decrease in SOM as human disturbance increases in fragmented BAF. Souza et al.⁴⁰ demonstrated that BAF recovery results from a large increase in SOCs after stopping human disturbance. In the present study, we demonstrated that such an increase in SOM was particularly due to Riparian Forests (RFs), which contained a significantly higher amount of SOCs compared to Semideciduous Seasonal Forest (SFs) and the Transitional Forest to Cerrado (TFC). Previous research by Britez⁶⁷ confirmed that among BAF physiognomies, the Semideciduous Seasonal Forest can be characterized by low SOCs stock (between 31.7 and 37.5 t ha^-1 up to 100 cm soil depth), especially in the case of relatively recent human disturbance. The results from our present research validate such a hypothesis, clearly showing a decreasing SOCs trend as SF1 → SF2, i.e., at increasing human disturbance.

Multivariate statistics

The FA and PCA combine soil physical-chemical features and SOCs, providing a more complete and complex perspective by quantifying relationship with factors other than human disturbance and depth. Furthermore, these models allow hypotheses about mechanisms involved in SOCs behavior compared to pedoenvironmental features to be explored.

Factor analyses (FA)

Factor 1 (F1) shows that more natural, well-preserved fragments, i.e., the Primary Riparian Forests (RFs), which feature a higher amount of SOM, thanks to a more developed forest cover and species complexity, are also characterized by (i) higher macronutrient contents (N and P), and (ii) higher CEC, exchangeable cation, and % base saturation, thus reflecting overall higher soil fertility. Under these conditions of high naturality, SOCs amounts increased, with the CHNa method showing a higher statistical correlation than the WBm, confirming its higher analytical reliability. F1 also showed that increasing SOM led to a significant increase in Cu and Mn soil availability, which is consistent with SOM being one of the most influential factors modulating macro- and micro-nutrient availability in tropical soils². Factor F1 can be explained as the influence of BAF naturality in stocking higher amounts of SOC thanks to favorable pedoenvironmental conditions. Factor F2, confirmed the strong relationship between SOM and SOCs, showing that at increasing BAF fragments naturality, we observed an increase in SOM and consequently stocked SOC. This factor adds to the pivotal role played by iron, showing that it increased at increasing SOM amounts. This was due to the well-known processes of SOM accumulation enhanced by organo-mineral interactions in tropical soils, with iron oxides playing an important role in stabilization⁶⁸. Thus, F2 can be explained as the Fe vs SOM interaction. Factor F3 showed that at increasing bulk density (BD), i.e., soil depth (vide supra), a decrease in SOC and consequently soil macro (N), micronutrients (S, B), and exchangeable cations (K+), was observed. Since we know that such an increase in BD along soil depth follows the TFC > SFs > RFs trend, i.e., is inversely correlated to the BAF fragment conservation state, this factor can be explained as the key role of BD in indicating BAF conservation state. This interpretation is consistent with previous studies^69,70, which showed that mean bulk density increased with soil depth, with this process being particularly enhanced by passing from well-preserved to degraded areas. Pontes et al.⁷¹ argued that human activities increased soil BD while decreasing porosity and creating a hostile edaphic environment for plant roots. Consequently, after human disturbance ends, the regeneration processes will be affected by BD initial conditions and the time required for establishing good edaphic conditions for plant roots. As the first pioneer species started colonization, porosity improved, creating more favorable conditions for hosting species from later successional stages. During this process, which requires a long time depending on starting conditions, BD will decrease, thus indicating improved conditions in the time-dependent steps toward BAF recovery.

Principal component analyses (PCA)

The PCA further visually helps us in confirming the FA outcomes. The five investigated BAF fragments are distinctively grouped with the greatest difference between group centroids along the first principal component (Fig. 2). Soil samples collected from the most developed, natural BAF physiognomies, i.e., RF1 and 2, form a distinct group along the first principal component. Arrows (indicating soil physical-chemical parameters) showed that these environments correlate with increased SOM and, consequently, SOC stocks and related extraction methods, together with N and B that are typically macro- and microelements in well-structured Atlantic Forest pedoenvironments²⁶. The worst preserved BAF fragment (TFC) is located on the opposite position of RFs, visually confirming that soil samples collected in this environment have lower levels of all measured parameters besides bulk density. This environment is mainly influenced by Al³⁺ and BD, i.e., by acidic and more compacted soils. Again, PCA also confirmed that SFs physiognomies represent a transitional stage between highly-preserved (RFs) and most degraded (TFC) BAF fragments. Samples collected in SFs environments form two distinct groups located along the passage from TFC to RFs. The most important parameters in these environments are mainly soil micronutrients, which are pivotal in enhancing forest development towards more stable conditions²⁶.

Overall, soil development and conservation features are strongly influenced by vegetation/forest conditions and vice versa, thus showing a positive feedback relationship (Fig. 2). As human activities (Fig. 2, number “1”) with related disturbance end (Fig. 2, letter “a”), after a variable period of time (depending on human disturbance intensity and years passed after last activities), vegetation start a relatively slow process of recovery (Fig. 2, letter “b”). Such a process strongly improves (Fig. 2, number “2”) soil's overall physical-chemical properties (Fig. 2, letter “c”), including fertility and the amounts of SOC stocked in the entire profile. Indeed, as vegetation reaches higher maturity, canopy cover increases, which results in (a) increased organic material inputs and (b), the formation of a thick soil organic (O) horizon; factors (a) and (b) preserve SOCs from mineralization and leaching processes thus substantially increasing its reserves along the entire soil profile¹⁰. Indeed, shrub and tree species create more organic complexes and deeper root networks, up to 18/20 meters^9,10. Consequently (Fig. 2, number “3”), the overall soil-plant system improves over time with respect to nutrient exchange, overall fertility, quantity of SOCs, complexity, biodiversity, etc., until reaching a dynamic equilibrium representing the forest/soil climax. The duration to reach this climax is strongly dependent on human disturbance, including the number of years since anthropogenic activity ended and whether additional effects such as climate change affect forest growth and development patterns.

Assessing soil organic carbon stocks (SOCs) is a pivotal step in the challenging research field of monitoring and evaluating GHG emissions responsible for climate change processes as well as carbon credits associated with forestry offset projects. These activities are based on field data collection and laboratory analysis, requiring reliable instruments. A great gap can exist in underdeveloped and developing countries due to resource constraints, and these same countries will experience some of the most severe impacts from climate change, including damages in both environmental and socio-economic terms. Additionally, all of these impacts are enhanced in tropical systems, which play a pivotal role in the global carbon cycle and, consequently, in climate behavior. Brazil represents a paradigmatic example of a previously undeveloped country that, in a relatively short time, completely changed its economy, including dramatic land-use changes, that had both positive and negative, direct and indirect consequences on SOC stocks and behavior. The Brazilian Atlantic Forest (BAF) has been one of the most damaged ecosystems, together with the Amazon, making research on it strategically important worldwide. Soil organic carbon stocks quantification results showed that the use of CHN analyzer (CHNa) was highly reliable compared to the Walkley-Black method (WBm). We suggest promoting collaborations between under-equipped laboratories and research groups with high experience with CHNa use to increase network-based research programs, thus providing more reliable data. Additionally, SOCs should be evaluated to at least 1 m depth in tropical soils. A large amount of SOCs is not properly considered following the IPCC protocol of 30 cm sampling depth, which can make further uses of these data (for instance, for GHG emission models) unreliable. Considering investigated BAF physiognomies, representing fragments with different levels and recency in human disturbance, the Riparian Primary Forests (RFs) are the most developed and well-preserved, with soils showing the highest SOC stocks. The Transitional Semideciduous Seasonal Forest to Cerrado (TFC) has the least developed forest canopy and worst preserved soil conditions, thus showing the lowest SOC stocks. The Primary Semideciduous Seasonal Forests (SFs) represent a transitional stage among the RFs and TFC, with SOCs values slightly lower than in RFs while higher than in TFC physiognomies. Multivariate statistics showed that the investigated environments are featured by complex dynamic feedback where the influence of multiple parameters can play a pivotal role in determining differences and commonalities among recovery forests. Regarding management practices, we demonstrated that BAF requires a long time to fully recover its initial conditions after the end of human disturbance; consequently, preservation and conservation strategies should be privileged in remnant fragments.

Study area

The research was conducted inside the “Edgardia and Lageado” (EL) experimental farms belonging to the Capivara River basin, Tietê Valley region, Botucatu Municipality in the south-east of Brazil (22° 47′ 30” – 22° 50′ S and 48° 26′ 15′′ to 48° 22′ 30′′ W; Fig. 3). The climate is classified as Cfa (Köeppen criteria⁷²), i.e., subtropical, which features hot temperatures (mean annual T: 20.6 ºC) and high air humidity (mean annual P: 1,707 mm).

The sites are located in the geological dominion of the western São Paulo Plateau, which is composed of igneous rocks. The research area is specifically a basaltic cuesta (from Spanish “slope”), which is a geological structure (technically an “homoclinal ridge”) with cliff/escarpment on one side and a dip/backslope on the other⁷³.

Soils vary from low to medium-high fertility, depending on site overall features²⁶. However, most are considered extremely fragile and under threat due to erosion processes enhanced by human activities and morphological conditions (high slopes). From a classification viewpoint⁷⁴, we investigated “young” Entisols, passing to more developed Inceptisols (with a deep diagnostic Cambic Bw horizon), and finally to older Alfisols, Ultisols (both featured by an Argillic Bt horizon), and Oxisols (deep Oxic Bo horizon). This great pedovariability was mainly due to changes in morphology, land use, and cover²⁶.

Experimental design

The vegetation cover greatly varied along the entire “Edgardia and Lageado” experimental farms. Indeed, as with the entire BAF over the last five-hundred years, the vegetation has been dramatically affected by anthropogenic disturbance and harvest, which is now found in fragmented patches with their own specific physiology and flora associations. For the purpose of classifying vegetation physiognomies into a gradient of human disturbance and recovery, we performed an ad-hoc investigation of: i) historical land-use changes compared using aerial and satellite images; ii) physiognomic features; iii) species composition; iv) phytosociological associations; and; v) ecological indicators, in addition to review of previous studies^26,75–77. Based upon this investigation, we grouped sites into five main vegetation physiognomies, ordered from least to most human disturbance:

Primary Riparian Forest (RF1): are those BAF fragments located along the river. The term primary indicates that the BAF experienced a low level of human disturbance, with no history of deforestation, selective logging, livestock or agriculture, at least during the last century (Fig. 3a);
Secondary Riparian Forest (RF2): represent a case of regenerative RF1 phase, after an intensive human disturbance (such as logging of forest species for economic purposes), usually occurring more than 30-40 years ago;
Primary Semideciduous Seasonal Forest (SF1): are characterized by the occurrence of a seasonal climate (drought-cold winters-rainy summers) responsible for the foliage’s semi-deciduousness, i.e., partial leaf fall. The term primary was used for the same reasons explained for RF1 (vide supra);
Secondary Semideciduous Seasonal Forest Primary (SF2): are the BAF fragments of SF classified as secondary for the same reasons explained for RF2 (vide supra);
Transitional Semideciduous Seasonal Forest to Cerrado (TFC): are those BAF fragments affected by a clear decrease in forest canopy cover due to selective logging, grazing, agricultural activities, etc., usually occurring more than 30-40 years ago. Even if the term Cerrado, indicates a specific Tropical savanna ecoregion, in the investigated area, it represents an ecotonal transition between BAF and itself³⁸.

Eighteen 20 × 100 m (2000 m²) permanent plots (Fig. 3b) were randomly distributed in SF1-2, RF-1-2, and TFC. The plot size and distribution were defined according to the methodology proposed by Roder et al²⁶. Specifically, the sampling unit and their distribution along the five investigated vegetation physiognomies were selected after careful in-field study and GIS investigation carried out before the beginning of the research by considering the difference between: i) the vegetation from several perspectives (vide supra); ii) morphological and slope aspects; iii) soil features; iv) history in human disturbance; v) area extension of each investigated kind of vegetation. Consequently, the eighteen plots were distributed in numbers of 2, 1, 10, 3, and 2 in RF1, RF2, SF1, SF2, and TFC, respectively (Fig. 3b). Due to the previously reported factor, the uneven distribution was necessary to capture the whole variance among investigated environments. Milliren et al⁷⁸ assessed whether uneven plot distribution can bias random effect estimation and, consequently, observed variance and obtained outcomes, and concluded that applied plot distribution captured a truly random effect without affecting the statistical significance.

The edge effect was avoided by distancing 500 and 200 m from each plot’s and forest’s border, respectively. Replications consisted of subdividing the eighteen permanent plots (vide supra) into twenty subplots of 10 × 10 m (100 m²) (Fig. 3b). To completely randomize soil collection within the sampling unit (plots of 20 × 10 m), five subplots (10 × 10 m) were randomly selected to take a composite sample from each, using the method defined by Husch⁷⁹.

Soil sampling and analyses

Within each of the five randomly selected subplots (vide supra), soil samples were collected from 5 soil depths, i.e., 0-20 cm, 20-40 cm, 40-60 cm, 60-80 cm, and 80-100 cm. In RF plots, soil sampling stopped at 80 cm because these soils were less deep since they were located on steep slopes. For SFs and TFC plots, 25 composite samples were gathered per plot (5 subplots/replication × 5 soil depths); for RFs, 20 composite samples were gathered per plot (5 subplots × 4 soil depths) (SFs and TFC); with 15 non-RF and 3 RF plots, a total of 435 soil samples were collected.

From each horizon, undisturbed soil mineral samples were collected with metal cylinders for bulk density determination⁸⁰. Soil analyses were then performed in the laboratory on air-dried ⌀ < 2 mm sieved soil, as recommended by Brazilian official procedures⁸¹. Soil pH-CaCl₂ and H+Al (potential acidity) were assessed potentiometrically with a glass electrode in a 1:2.5 1 N CaCl₂soil/solution mixture. Total N was analyzed using a CHN analyzer (dry combustion). Total P was analyzed through a NH₄Cl/HCl acid digestion. Cation-exchange capacity (CEC) was assessed via saturation with BaCl₂ (pH 8.2). Calcium and Mg were determined by extraction in 1M KCl. Concentrations of soil micronutrients (Al, B, Cu, Fe, K, Mn, and Zn) were measured by the Olsen extraction method (pH 8.5).

Soil organic carbon was determined using two methodologies: i) the Walkley–Black method; ii) an elemental (CHN) analyzer (2400 Series II System, Perkin Elmer, US). In the (i) case, soil organic matter was obtained by multiplying C results for a conversion factor of 1.724 (assuming that SOM contains 58% of OC), commonly used and recommended by IPCC⁵ and, as reviewed by Kim et al.¹⁸, several authors regardless of the pedoenvironmental conditions.

Soil organic carbon stocks (SOCs, hereafter) were calculated as follows:

SOCs (t ha^-1) = HD × BD × SOC × 10 (1)

where, HD was the soil horizon depth (cm), BD was the bulk density (g cm⁻³), and SOC was the soil organic carbon content of the investigated horizon (%).

Total SOCs were converted into CO₂-equivalent(CO₂-e, hereafter) using the conversion factor 3.67, i.e., by the ratio between carbon dioxide's molecular mass and carbon's atomic mass⁸². Finally, the CO₂-e quantification was used for conversion in soil carbon credits (SCC), where one soil carbon credit represents 1 metric ton of CO₂-e that has been reduced, avoided, or removed from the atmosphere. To quantify SCC, we used a modified version of the equation proposed by Zhou et al¹³:

SCC (t ha^-1) = SOC_sps – SOC_sws (2)

where SOC_sps is the SOC stock in preserved scenarios (ps; Riparian (RF) and Seasonal Forests (SF) in our case), while SOC_sws is the SOC stock in the worst scenario (ps; the TFC in our case).

Soil carbon credits have been mainly thought of and used for farming activities, in order to promote best practices (such as reduced or no tillage practices, adding organic cover crops, rotational grazing and crops, reducing use in fertilizers, etc.), which maintain higher amounts of soil carbon⁶². We hypothesized that comparison among the five investigated fragments could represent a situation where intact BAF is preserved, starting from the most degraded phase (the TFC), followed by different natural restoration stages (according to their increasing naturality the temporal order is: SF2 → SF1 → RF2 → RF1). In short, we calculated soil carbon credit as the change in soil carbon stock under better-preserved soil conditions (RF and SF fragments) compared with the worst situation (the TFC), representing the t₀, in the restoration stage steps.

Statistical analyses

Statistics were done by using the software program R⁸³. The analysis of variance (ANOVA) was conducted to assess statistical differences (p < 0.05) in soil C stocks in the following cases: i) by using different methodologies (WBm vs elemental analyzer); ii) when comparing the five different fragments; and, iii) when comparing the five different investigated soil depths. A posteriori comparison was also done by using the Tukey-HSD (honestly significant difference) test at p < 0.05. In case of parametric requirements were not satisfied, the Kruskal-Wallis test was used at p < 0.05. Several multivariate statistical techniques were also used: i) a factor analysis (FA) used for data interpretation and hypothesis testing; ii) a principal component analysis (PCA) for data compression and visualization, without making any assumptions about the causal relationships among investigated variables. The procedure proposed by Reimann et al.⁸⁴ and modified by Capra et al.⁸⁵ for soil physical-chemical datasets, was applied for factor analysis (FA). In particular: i) the entire dataset was tested for normal distribution; ii) the raw datasets were Box–Cox transformed, thus approaching normality; iii) a Pearson correlation matrix (CM) was constructed with the Box–Cox transformed data; and, iv) FA was obtained based on the CM as elaborated in point (iii); v) varimax rotation was applied for a more robust statistical approach and to facilitate the results’ interpretation. Principal component analysis (PCA) was implemented by using the robust procedure proposed by Filzmoser et al.⁸⁶: i) dataset was isometric log-ratio transformed (ilr); ii) since the transformation results in uninterpretable variables in terms of their original names, data was; iii) back-transformed to the centered log-ratio (clr) transformation to allow for an interpretation in terms of the original variable names.

Acknowledgements

Coordenaçao de Aperfeiçoamento de Pessoal de Nivel Superior (CAPES–PrInt Program), Brasil [grant number: 88887.194785/2018–00, 2018]; CNPq-National Council for Scientific and Technological Development [grant numbers: 308916/2017–4, 2017; 311623/2020–4, 2020]; the Federal Institute of Education, Science, and Technology of Rondônia (IFRO).

Contributions

I.A.G. acquired the funds, conceptualized the idea and the methodology; J.P.S., D.C.L.S., F.G.M., and C.A.Y.P. conducted filed and laboratory analyses; C.M.S.N. supervised field and laboratory analyses; R.B.S., S.T.P.J., and L.R.R. elaborated the data, realized the graphs, performed the statistical analyses; J.N.J. write and edited the final draft of the paper, G.F.C. and A.G. validate the data, wrote the first draft of the paper, supervised the statistical analyses.

Ethics declarations

Competing interests: the authors declare no competing interests.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Tables 1 to 5 are available in the Supplementary Files section

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Evaluating carbon stocks in soils of fragmented Brazilian Atlantic Forests (BAF): How soil features and different methodologies influence its assessment

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