Structure-mediated carbon–biodiversity relationships across agroforestry typologies in tropical Thailand

Structure-mediated carbon–biodiversity relationships across agroforestry typologies in tropical Thailand | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Structure-mediated carbon–biodiversity relationships across agroforestry typologies in tropical Thailand Chattanong Podong, Krissana Khamfong, Supawadee Noinamsai, Sukanya Mhon-ing This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9392281/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Agroforestry is increasingly framed as a nature-based climate change mitigation strategy, but the relationship between biodiversity and carbon storage across structurally and management-divergent systems remains unclear. This study compares carbon stocks and biodiversity of six agroforestry systems representing four typologies in Thailand, with an emphasis on the degree to which stand structure plays a role. Carbon stocks were predicted from above-ground biomass, below-ground biomass, and soil organic carbon—while biodiversity was described by species richness, Shannon diversity, and evenness. Structurally defined properties were used to determine how spatial patterns of biomass affect biodiversity–carbon relationships. Carbon stocks varied significantly across systems. Rubber monoculture systems, known for strong dominance of one plant, contained the most biomass carbon; however, more diverse systems stored less overall. When all the systems were examined in an integrated way, greater diversity meant lower carbon. However, that relationship weakened when dominance-driven systems were removed and evenness had a positive association with carbon storage. These findings suggest that the detected biodiversity–carbon relationship is highly dependent on stand structure. Instead of being a universal trade-off, the relationship depends on how biomass is partitioned among species. This illustrates the influence of structural attributes in ecosystem functioning and indicates how agroforestry systems may be configured to improve carbon storage with regard to biodiversity goals. Agroforestry systems Carbon stock Biodiversity–ecosystem functioning Stand structure Tropical ecosystems Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Agroforestry systems are becoming increasingly identified as a contributing strategy for climate change adaptation and mitigation in tropical systems experiencing high land-use pressure and where carbon sequestration potential is significant. These systems, based on tree–crop or tree–livestock combinations, can sequester biomass carbon and provide essential ecosystem services (e.g., soil regulation, biodiversity support) (Nair 1993 ; Nair et al. 2010 ; Jose 2009 ). In terrestrial ecosystems, especially forest systems, tree-based systems are the most significant carbon pools which makes them vital in climate regulation at a global scale (Pan et al. 2011 ). Agroforestry under this context is commonly lauded as a nature-based solution with potential to contribute both to carbon sequestration and ecosystem multifunctionality. Despite this interesting interplay, the link between biodiversity and carbon stocks in agroforestry systems remains elusive. In the biodiversity–ecosystem functioning (BEF) paradigm, increased biodiversity tends to lead to stronger ecosystem processes via mechanisms like niche complementarity and facilitation (Tilman et al., 1997; Hooper et al., 2005 ; Cardinale et al., 2012 ). In contrast, evidence from managed systems is more mixed. In agroforestry systems, biodiversity does not always translate into higher biomass or carbon storage as species composition and structure are closely related to human management. This indicates that biodiversity–carbon relationships are context dependent: they can differ according to system structure. A major limitation of many studies is the assumption that biodiversity and carbon are necessarily correlated without taking the role of stand structure into consideration. Management determining structural factors such as species dominance, canopy layering and biomass allocation drives ecosystem outcomes in agroforestry systems. Highly dominated systems may have high biomass accumulation but low diversity and more evenly distributed biomass often support higher diversity though lower carbon. This raises a question on whether the observed trade-offs between biodiversity and carbon is due to fundamental ecological constraints, or rather structural differences in organization. Recent syntheses highlight the need for agroforestry typologies to be clearly defined, and carbon pools integrated when evaluating carbon stocks (Cardinael et al., 2025 ). Moreover, meta-analyses have demonstrated that agroforestry systems can increase both biodiversity and ecosystem services but that the results of their application are very heterogeneous depending on management practices and the system design (Torralba et al., 2016). And yet there remain few comparative efforts, particularly for tropical areas, to identify how structural variation drives biodiversity–carbon relationships. Agroforestry systems in Thailand range from rubber-based systems to a number of configurations including multistrata and agri-silvicultural [16]. However, these systems are seldom considered within the framework of a unified theory allowing for direct comparison of structural properties. That inhibits our understanding of how differences in system design affect both carbon storage and biodiversity outcomes. Filling these gaps, we investigate carbon storage and biodiversity in contrasting agroforestry typologies throughout tropical Thailand. Specifically, it aims to (1) quantify carbon stocks in ecosystems; (2) characterize biodiversity patterns using multiple indices; and (3) assess the influence of stand structure on mediating the relationship between biodiversity and carbon. By focussing on (the structural variation an ecosystem harbours rather than) diversity in itself, this study gives readers a mechanistic understanding of biodiversity–carbon relationships and facilitates designing agroforestry systems that target towards carbon storage along with diversity conservation. 2. Materials and Methods 2.1 Study area and site selection The study was carried out at six agroforestry systems located across Thailand along a north–south latitudinal gradient of 7°N to 17°N and aiming to cover different climate environments with humid tropical conditions in the southern part compared to more seasonal climate at the northern sites. Sites were chosen as representative of a range of differences in agroforestry structure, species composition and management practices. All systems had been in place for a minimum of 10 y, providing adequate time for vegetation infrastructure and building out the biomass. Geographic coordinates of the sites were recorded by a hand-held GPS unit, and the elevation ranged from 32 to 150 m above sea level (Fig. 1 ). 2.2 Agroforestry typology classification Field observations of species composition, canopy structure and management practices were used to classify each site into an agroforestry typology. This classification was based on standard agroforestry systems (Nair, 1993 ); it was then adapted to align with recent recommendations for the carbon assessment of agroforestry systems (Cardinael et al., 2025 ). Four typologies were identified which are; agri-silvicultural systems, multistrata systems, farm forestry systems and rubber-based agroforestry. We verified that each site corresponded to its assigned category based on field-measured structural indicators (i.e., basal area, canopy layering and species dominance). 2.3 Plot establishment and vegetation measurements At each site, we collected data from ten square plots (20 × 20 m). Plot locations were chosen to capture the dominant vegetation features of each system while avoiding obvious disturbances such as road or recent clearings. All trees with DBH ≥ 10 cm were identified and measured within each plot. Diameter at 1.3 m above ground was recorded with a diameter tape. Tree height was measured with a hypsometer when possible. In the field were identified species and subsequently verified according to local floristic references. Be contiguously plot paired into little join plots to catch regeneration. Saplings (≥ 1.5 m tall and < 10 cm DBH) were recorded in 5 × 5 m subplots; seedlings (< 1.5 m tall) were surveyed in subplots of dimensions 2 × 2 m. These measurements were made to characterize stand structure and species composition within size classes. 2.4 Biomass and carbon estimation Where species-specific allometric equations did exist, above-ground biomass was estimated from them. In the absence of such equations, a widely used pantropical model (Chave et al., 2014 ) was utilized with wood density, diameter and tree height as input variables. Wood density values were taken from published databases and those reported at the species or genus level. These measurements did not directly measure below-ground biomass in the field, but estimated it from above-ground using a root-to-shoot ratio of 0.24 (IPCC, 2006 ; refining the method in 2019) that is considered appropriate for tropical systems. Soil samples were collected from each plot at 0–10, 10–20 and 20–30 cm soil depth. Soil organic carbon (SOC), measured as percent carbon, was analyzed in a laboratory. SOC stocks were estimated based on a representative value of bulk density for tropical mineral soils at depth, which is within commonly observed ranges found in the literature (Post and Kwon, 2000 ; Don et al., 2011 ). In regions of the world with no site measurements of bulk density, SOC estimates are viewed as approximate values serving to enable comparison between sites rather than absolute quantification. 2.5 Biodiversity assessment Species richness, Shannon diversity index, and Pielou’s evenness were used to describe species diversity. Also, biomass-weighted proportions were used to handle the contribution of any species on total stand biomass. This type of diversity can account for both species composition in the ecosystem, and their functional significance. 2.6 Soil analysis The soil samples were analyzed for basic physicochemical properties: pH, electrical conductivity, organic matter, total nitrogen, available phosphorus and exchangeable cations (K, Ca, Mg) and total deficiencies. Routine laboratory techniques were used for subsequent analyses.It measures soil over many depth intervals, enabling exploration of vertical patterns in soil properties. The identified agroforestry variables were subsequently applied in multivariate analysis and used to compare patterns across the different categories of agroforestry systems. 2.7 Data analysis Differences between agroforestry typologies were tested using standard statistical tests, including analysis of variance when assumptions were satisfied. Non-parametric alternatives were used where appropriate). Correlation and regression analysis were performed to explore relationships between biodiversity and carbon. We constructed additional models to assess the role of stand structure, incorporating structural variables: basal area and species dominance. PCA (principal component analysis) was used to summarize the variation in soil properties and establish gradients among sites. All analyses were conducted in R statistical software. 3. Results 3.1 Agroforestry Typologies and Structural Characteristics Significant differences in vegetation structure were found among the six agroforestry systems, characterized by variation in species composition, dominance patterns and biomass distribution (Table 1 ; Fig. 2 ). We recorded 175 tree species in total across all sites but their distribution was very uneven across the systems. In rubber-based systems, however, biomass was dominated by one or few dominant species. In Songkhla Hevea brasiliensis contributed 77.8% of total stand biomass and in Phatthalung Hopea odorata contributed 53.4% (Table 2 ). These figures are indicative of a clear dominance-driven structure where the biomass of stands is determined mainly by one species. In contrast, biomass allocation was more evenly distributed in the non-rubber systems. It can obviously be observed, in the agri-silvicultural system (Uttaradit), no species contributed > 20.2% of total biomass and the most even distribution was shown (J′ = 0.864). In a similar mixed system, Chanthaburi's multistrata system was the most species rich (S = 54), but distribution across species also differed strongly with Dipterocarpus alatus dominating and being responsible for 70.3% of stand biomass.The dominant species contribution to biomass proportion is especially different among the sites as illustrated in Fig. 2 . Systems based on rubber are highly clustered at the top end of dominance, while low to mid-paid systems show a more uniform distribution across dominance. Multistrata systems are a grey area; high species richness with the extent of dominance varying. Collectively, these results suggest that the agroforestry systems in this study form a structural gradient instead of discrete categories. Dominance-driven systems with high biomass concentration on one species appear at the extreme end of gradient, while structurally heterogeneous systems featuring more evenly distributed biomass lie at the other end. This gradient serves as a valuable benchmark for interpreting differential carbon storage and biodiversity in subsequent comparisons. Table 1 Structural and biodiversity characteristics of agroforestry systems across study sites. Site S H′ J′ AGB (Mg ha⁻¹) C stock (Mg C ha⁻¹) CO₂eq (Mg CO₂ ha⁻¹) Dominant species (% biomass) Typology Phatthalung 16 0.899 0.324 28.43 13.36 48.99 H. odorata (53.4%) Rubber AF (Mature) Songkhla 34 0.797 0.495 25.70 12.08 44.29 H. brasiliensis (77.8%) Rubber AF (Young) Uttaradit 16 2.397 0.864 8.92 4.19 15.37 L. domesticum (20.2%) Agri-silvicultural Chanthaburi 54 1.190 0.298 3.73 1.75 6.42 D. alatus (70.3%) Multistrata Chachoengsao 21 2.013 0.661 1.91 0.90 3.29 D. alatus (37.9%) Farm forest Prachinburi 33 2.451 0.701 1.66 0.78 2.87 A. indica (28.3%) Bush fallow 3.2 Biomass carbon distribution across agroforestry systems Above-ground carbon stocks varied significantly between the six agroforestry systems, supporting the structural differences outlined in the previous section (Table 2 ; Fig. 3 ). For above-ground carbon, values across all sites ranged from 0.78 to 13.36 Mg C ha⁻¹, more than an order-of-magnitude difference. The resulting highest values were observed within the rubber-based systems with site Phatthalung (13.36 Mg C ha⁻¹) and Songkhla (12.08 Mg C ha⁻¹) having the highest average carbon stock overall. Very low carbon stocks (< 1 Mg C ha⁻¹; for example, Prachinburi and Chachoengsao) were recorded in non-rubber vegetation (Table 2 ). When carbon is considered relative to biomass distribution, this contrast becomes apparent. An over-dominated high carbon stock system is when there are only one or a few species that account for the vast majority of biomass (Fig. 3 a). For example, Hevea brasiliensis in Songkhla contributed around 9.40 Mg C ha⁻¹ of carbon, which was responsible for most of the total stand carbon. This pattern also holds for Phatthalung, with a similarly large biomass share contributed by Hopea odorata .More evenly distributed biomass yields lower carbon values but greater diversity, however. This pattern is illustrated by the agri-silvicultural system in Uttaradit: despite the biomass gained from various species, no single dominant species exists, and total above-ground carbon remains relatively low (4.19 Mg C ha⁻¹). This indicates that carbon accumulation is more dependent on biomass density than species richness. We further address this relationship in Fig. 3 b, which shows the fractional carbon contributions of dominant taxa among systems. Higher carbon stocks are consistently found in more dominant systems and lower values in more evenly represented contribution of species. The presence of this pattern is consistent across sites which are geographically well separated (not driven by local condition alone) and reflects broader structural characteristics of the agroforestry typologies. Overall, our results indicate that variation in above-ground carbon across different agroforestry systems is not random but follows a clear structural trend. Systems that accumulate a larger fraction of biomass in few species establish higher amount of carbon, while systems that maintain even distribution of biomass across many more species stabilize lower climate-carbon stocks at high diversity. This pattern serves as a starting point for interpreting the opposing associations between diversity and carbon seen in later analyses. 3.3 Biodiversity patterns across agroforestry systems Research Highlights: Biodiversity was very different in the 6 agroforestry systems studied but this could not be explained by species richness alone. Instead, differences in biomass distribution and dominance were pivotal in determining diversity indices (Table 2 ; Fig. 4 ). Species richness was between 16 and 54 species per site, with a maximum of the multistrata system in Chanthaburi (S = 54). Nonetheless, high richness often did not translate to high diversity in functional terms. The evenness was low (J′ = 0.298) in Chanthaburi because a large proportion of total biomass existed in Dipterocarpus alatus even though many species were found (Table 2 ). In contrast, Uttaradit exhibited another trend associated with its agri-silvicultural system. Although spacing of species was minimal (S = 16), biomass distribution among species was more uniform leading to maximal evenness (J′ = 0.864) and comparatively high Shannon diversity values (H′ = 2.397). This suggests that diversity high rarefies when a single taxon is not made up of biomass.Rubber-based systems showed consistently low biodiversity values. In both Songkhla and Phatthalung, diversity indices were low (H′ < 1.0), reflecting strong dominance by one species and limited contribution from other taxa. These systems represent the extreme end of the structural gradient, where biodiversity is constrained by management practices and species selection. The contrasting patterns among sites are illustrated in Fig. 4 , where richness, Shannon diversity, and evenness show different trends across agroforestry typologies. While richness varies widely, evenness appears to be more closely aligned with biomass distribution. Systems with high dominance exhibit low evenness regardless of species count, whereas systems with balanced biomass contributions maintain higher evenness even with fewer species. 3.4 Biodiversity–carbon relationships How biodiversity was represented and which systems were included in the analysis affected how this relationship looked (Fig. 5 ). For all agroforestry systems, Shannon diversity was negatively associated with above-ground carbon. The higher carbon stock sites, especially rubber-derived systems were always found close to the lower end of diversity (Fig. 5 a). This pattern could be seen by a trade-off between biodiversity and carbon storage. Remove systems driven by dominance, and a different picture emerges. Removing the rubber-based systems (Fig. 5 b) weakens this negative relationship, and evenness (J′) becomes positively linked to carbon. Within this subset, structures that show a more equal distribution of biomass across species generally yield higher carbon values than those with less evenness. An example of this contrast can be seen in the agri-silvicultural system found in Uttaradit. Species richness in these systems is low relative to their diversity, but a combination of high evenness with moderate carbon distinguishes it from dominance-driven systems and low-carbon systems. Conversely, sites like Prachinburi and Chachoengsao with low stocks of carbon store less-balanced biomass. When considered together, these results indicate that the biodiversity–carbon relationship is not consistent in agroforestry systems but varies as a result of structural context. When systems with very different structures are analysed together (which we will discuss in the next section), a negative relationship emerges. When controlling for structurally similar networks, the effect weakens and may even reverse. 3.5 Structural mediation of carbon storage The trends observed in Fig. 5 suggest a steady effect of stand architecture on carbon outcomes across agroforestry systems. Instead of being more directly associated with species richness or diversity alone, carbon storage seems to track how biomass is distributed among the different species. Carbon also helps paint a clearer picture when in conjunction with dominance patterns. High carbon stock systems are defined as those with high covariation of biomass in one or a few species. This is most noticeable in the rubber-based systems, where one species provides the lion's share of stand biomass and a considerable amount of above-ground carbon. Here, structural dominance offers a direct route to increased carbon accumulation. On the contrary, systems characterized by low stocks of carbon generally register higher dispersion for biomass. Biomass is shared between multiple species in the provinces of Prachinburi and Chachoengsao, for example, but no one major contributor. We identify systems characterized by relatively low carbon stocks and moderate- or low- structural concentration, indicating that weak dominance prevents the accumulation of biomass at the stand scale. In Uttaradit, the agri-silvicultural system is intermediate. While relative biomass dominance is not high, this group retains moderate to high evenness and a moderate carbon fate in storage. This means that carbon accumulation is not purely a result of being dominant, but rather in some structural conditions can be maintained through even distribution of biomass. The differences contribute in understanding the shift seen in Fig. 5 . When systems with strong dominance are considered, carbon is largely driven by a few species which leads to a negative relationship between carbon and biodiversity indices. Then the contribution of biomass distribution becomes clearer and evenness a more important metric of carbon variation. Overall, these results indicate that the relationship between biodiversity and carbon storage is mediated by stand structure. Species dominance and biomass allocation varied between systems, affecting both variables in parallel and causing differences across agroforestry systems. This means that biodiversity–carbon relationships should not be read separately from structural context. 3.6 Soil properties and carbon relationships The characteristics of the soil varied substantially between the six existing agroforestry systems, both within sites and along a vertical profile of the soil (Fig. 6 ; Table 2 ). The observed differences in pH, organic matter, available nutrients and exchangeable cations were related to contrasting environmental conditions of the study areas and their management history. Soil properties, however, were not consistently related to above-ground carbon across the systems. Higher carbon stocks were not necessarily found at the sites with relatively more fertile soil. Prachinburi, for example, which presented comparatively higher organic matter and nutrient conditions had among the lowest above-ground carbon values. Meanwhile, Phatthalung with more acidic soils and low nutrients had the biggest carbon stock. The difference highlights that soil fertility does not directly control above-ground carbon on its own. Instead, it seems that soil conditions influence carbon storage indirectly, through their effects on vegetation structure and composition. Systems with high carbon stocks are mainly those systems in which biomass is aggregated into few dominants, irrespective of the degree of soil nutrient limitation. Soil properties are still relevant for understanding ecosystem functioning. Differences in organic matter and nutrient availability likely impact on productivity, regeneration and long-term stability of agroforestry systems even if these crop yield effects are not necessarily manifested in the measured above-ground carbon at the time it is performed. Overall, the findings suggest that soil and vegetation are only weakly coupled at these agroforestry systems. Although soil properties vary widely between sites, their relationship with above-ground carbon is not a simple one and needs to be viewed in the context of stand structure and system typology. Table 2 Soil physicochemical properties across agroforestry systems Site pH OM (%) Total N (%) Avail. P (mg kg⁻¹) Exch. K (cmolc kg⁻¹) Exch. Ca (cmolc kg⁻¹) Exch. Mg (cmolc kg⁻¹) Uttaradit (Agri-silvicultural) 5.8 2.10 0.18 8.5 0.21 3.20 1.10 Chanthaburi (Multistrata) 5.2 1.75 0.15 6.8 0.18 2.60 0.95 Chachoengsao (Farm forestry) 6.3 1.90 0.16 7.2 0.25 3.80 1.35 Prachinburi (Bush fallow) 6.6 2.45 0.21 9.8 0.32 4.50 1.60 Songkhla (Rubber AF Young) 4.9 1.60 0.14 5.5 0.17 2.10 0.80 Phatthalung (Rubber AF Mature) 4.7 1.85 0.17 6.2 0.19 2.30 0.90 4. Discussion 4.1 Apparent trade-offs as structural artefacts rather than ecological constraints The apparent negative relationship between Shannon diversity and above-ground carbon when all agroforestry systems are analysed collectively should be interpreted cautiously. This pattern is thus not necessarily indicative of an underlying trade-off between biodiversity and carbon storage, but may instead arise from structural heterogeneity among systems considered within a single analysis framework. Such a distinction is widely accepted in biodiversity–ecosystem functioning (BEF) research, with the direction and strength of biodiversity–function relationships being strongly dependent upon community composition and environmental context (Cardinale et al., 2012 ). In contrast, in agroforestry systems structural variability is not incidental but significantly influenced by management (species selection, planting density and canopy configuration). Thus fundamentally different architectural systems are likely to reflect alternative structural regimes rather than similar ecological units, such as rubber-dominated stands and multistrata systems.Statistical relationships may therefore reflect differences in biomass organization rather than underlying ecological processes when such systems are analysed jointly. This interpretation is corroborated by the shift seen after discarding systems driven by dominance. Once systems with comparable structural properties are contrasted, the inverse relationship is diminished (Fig. 1 ), suggesting that this apparent trade-off results from mixing structurally dissimilar systems instead of a universal ecological constraint. This result aligns with recent research emphasizing the need of system definition and classification used in agroforestry studies. Notably, the absence of universally accepted typologies and nonuniform system boundaries have been pointed out as a major factor contributing to discrepancies in reported carbon pools and ecosystem relationships (Cardinael et al., 2025 ). The current results build on this argument by demonstrating how structural heterogeneity can also confound biodiversity–carbon relationships and interpretations over extrapolations that are likely invalid within structurally comparable systems. Gradually these results show that the relationship between biodiversity and carbon in agroforestry systems cannot be understood without simultaneously considering stand structure. Trade-offs visible at the level of pooled datasets may be apparent, but not necessarily represent concrete ecological constraints. Instead, they mirror how different structural forms get amalgamated in analytic schemas. 4.2 Dominance-driven carbon accumulation and the limits of the mass ratio paradigm The tight relationship between carbon storage and species mean dominance found here also supports the mass ratio hypothesis, which states that ecosystem function is determined mainly by the traits, abundance or biomass of dominant species (Grime, 1998 ). In the rubber-based systems explored here, carbon accumulation is strongly regulated by one functional type, leading to high biomass with low diversity. This pattern represents a structural pathway in which the storage of carbon is maximized due to the concentration of biomass rather than due to species complementarity. Nonetheless, mass ratio hypothesis in agroforestry systems cannot be generalized without reservation. In natural ecosystems, dominance often arises as a result of ecological processes like intra-specific competition and environmental eigenfactors. In contrast, in agroforestry dominance is often exerted via management decisions made by the farmer, e.g., species choice (more dominant vs. less dominant), planting density and canopy design. Thus, observed mass ratio effects may result from engineered structural configurations and not emergent ecological dynamics. This is consistent with evidence from experimental and synthesis studies. Usually, the mass ratio framework causes biomass production to depend effectively on dominant species, but community composition could temper this (Grime, 1998 ; Sonkoly et al., 2019). Carbon storage varies widely in agroforestry systems depending on system structure, species composition, and management intensity rather than just biodiversity alone (Cardinael et al., 2025 ). These results point to structural organization, rather than species richness per se, as a central determinant of carbon outcomes. This distinction has important implications. Now, while dominance-driven systems can have high levels of above-ground carbon, they achieve that at the expense of functional redundancy and structural complexity. It is already well established that such systems could optimize short-term carbon gains − but be more sensitive to environmental stress and disturbance (Nair et al., 2010 ; Jose, 2009 ). The amount of biomass in just one species can reduce the buffering power of the system and reduce stability over a geologic timeframe. The current findings support this view. High-carbon systems are not merely low-diversity systems, but structurally simplified systems where the biomass is disproportionately packed into a dominating single species. This classification reframes carbon buildup as a structural product, rather than a biodiversity deficit. In this sense, the mass ratio pathway is an effective but potentially unstable flux of carbon storage conditioned less by inherent ecosystem processes and more by management-induced dominance. 4.3 Evenness, complementarity, and context-dependent biodiversity effects The positive relationship between biodiversity and carbon storage seen here is not universal to all agroforestry systems, but dependent on the structural context. When all systems are assessed jointly, diversity metrics including the Shannon index exhibit a negative relationship with above-ground carbon. However, this pattern breaks down for dominance-driven systems suggesting that the apparent trade-off is not universal but rather dependent on weight distributions in the system. There is a more regular nomenclature when one uses evenness as an indicator. Systems where biomass is more evenly distributed among species, without a single dominant species, tend to have relatively high carbon levels. This indicates that carbon storage of these ecosystems is not only driven by the functional properties of dominant species, but also by how biomass is distributed among the community. In this respect evenness reflects a different structural pathway where carbon accumulation can occur alongside more diverse assemblages. These results conform to the biodiversity–ecosystem functioning paradigm, whereby ecosystem functioning can be underpinned by dominance, that is, mass ratio effects or through interactions between species (complementarity effects) (Hooper et al., 2005 ; Cardinale et al., 2012 ; Tilman et al., 2006 ). However, the current results reveal that the relative contribution of these mechanisms is structure dependent. In agroforestry systems, where species composition and spatial arrangement are purposefully managed, the balance of dominance and complementarity is constructed through design rather than solely by ecological processes. 4.4 Soil–vegetation decoupling and the hierarchical control of carbon stocks The weak relationship of this study between soil properties and above-ground carbon (AGC) indicates that belowground conditions in the spatial scale considered are decoupled from biomass accumulation. While soil fertility did vary among sites and accounted for a measure of system differentiation from the principal component analysis, it was not consistently reflective of AGC gradients (as expected if carbon stocks were directly controlled by short-term variation in soil nutrients) which indicates this is not the case. For example, in tropical systems above-ground biomass is typically more closely linked to stand structure, disturbance history and species composition than solely to soil fertility (Quesada et al., 2012 ; Paustian et al., 2016 ). As with the current findings, this trend was maintained across systems that appeared structurally different but shared similar soil conditions and climate (i.e landscape type). In systems subject to management (e.g. agroforestry), such relationships are additionally formatted by managerial choices (e.g. species) such as selection, density and stand configuration (Nair et al., 2010 ; Somarriba et al., 2013 ). Our findings provide support for a hierarchical framing of carbon control, with stand structure as the overarching driver of biomass allocation and soil properties mediating long-term productivity through indirect channels. Under this framework, the influence of soil conditions on carbon stocks is indirect, operating through its effect on vegetation structure instead. This notion explains why high-carbon systems often develop on relatively nutrient-poor soils — especially where structural development is well-advanced, while more fertile systems show lower biomass when limited structural complexity features. Such patterns highlight the fact that high levels of biomass accumulation are constrained more by structure than by soil fertility alone. Importantly, this decoupling does not mean soil properties are unimportant. Instead, their influence is indirect and is increasingly salient across longer temporal scales. The dynamics of soil organic carbon stabilization and turnover processes are heavily driven by edaphic factors (e.g. mineral composition, microbial activity; Doetterl et al., 2015 ), which may diverge from trajectories of above-ground biomass. 4.5 Implications for carbon accounting frameworks and system desig n Consequences for carbon accounting frameworks and agroforestry system design Relatedly, one of the major findings is that agroforestry systems are not functional uniformities. Structural variation—especially in species dominance and biomass distribution—results in considerable differences in carbon storage under similar environmental conditions. This challenges common accounting conventions that treat agroforestry systems as homogeneous aggregates (Wigley et al., 2012), which neglects to incorporate internal heterogeneity, a limitation that is increasingly being acknowledged within global assessments of tree-based systems on agricultural land (Zomer et al., 2016 ). A second implication relates to system boundaries and carbon pools. The lack of consideration of above-ground biomass, below-ground biomass and soil organic carbon in the context of agroforestry typologies consistency may result in inaccuracy during the estimation of carbon stocks. Owing to recent developments in carbon accounting, robust measures should be put in place for the purposes of measurement, reporting and verification (MRV) to reduce uncertainty and enhance the comparability of systems Rosenstock et al., 2019; Smith et al., 2020. The current results highlight the necessity of including structural characteristics rather than relying solely on generalized land-use types in these frameworks. A third implication is about system design. The observed differences in carbon are informed by the dominance-driven and more even structure of systems, suggesting that the organizational configuration of biomass is critical to carbon outcomes. Specialization towards carbon sequestration is dominated by structuration and, conversely, building systems for a broader ecosystem service requires more equal distribution of species composition and biomass. This correlates with recent views of agroforestry and other multifunctional land-use strategies in which trade-offs and synergies between carbon, biodiversity and productivity need to be managed explicitly (Mbow et al., 2014 ). In practical terms, this evidence favours a transition to structure-based design practices in agroforestry. Instead of applying general models, system configuration should be determined by particular goals and local factors such as climate, soil and management context. Reed et al. (2017) emphasize integrated landscape approaches require alignment of farm-level design at larger environmental and socio-economic levels. Structural features should be integrated in system-design and carbon-accounting frameworks as it can help to bring more accurate estimates of carbon and its contribution to climate mitigation potential, as well as sustainable land management capacity of agroforestry systems. 4.6 Limitations and directions for future research Several important limitations must be considered in the interpretation of these results. The number of sites per typology was small and the study represented a snapshot rather than temporal analysis. Replication and longitudinal data are both needed to further bolster the findings. Furthermore, soil organic carbon estimates are based on assumed bulk density values adding uncertainty in absolute carbon estimates. Although relative comparisons are useful, direct measurements would strengthen future analyses. The functional traits approaches, disturbance regimes and socio-economic drivers also need to be more widely integrated. Agroforestry systems are a product of ecological processes and management decisions, and understanding their interdependent relationship is essential to developing effective climate mitigation strategies. 5. Conclusion This study demonstrates that structural configuration determines carbon storage of tropical agroforestry systems, and not just biodiversity alone. If you have a different type of agroforestry typology in another country like Thailand, it has been shown to be quite clear that variation in carbon stocks followed the following structural gradient: dominant-driven systems with higher concentration of biomass such as those found in plantations differ from more heterogeneous systems where biomass is much less diverse. The findings suggest that the widely reported trade-off between biodiversity and carbon is not a universal phenomenon. That negative relationship arises primarily from studying structurally dissimilar systems together, and mainly reflects accounts of cases where biomass is dominated by a single species. But when these systems are treated independently, the pattern dissipates and evenness becomes more related to carbon variation. This shows that biodiversity–carbon relationships are dependent on how biomass is structured in the system, not just by species richness. The association of carbon storage with dominance and biomass distribution underscores stand structure as an important mediating mechanism. This view helps to explain the otherwise conflicting findings in the literature and suggests that mass ratio and complementarity effects are structural continuum not competing explanation. Practically, the results highlight the need to integrate structural characteristics in agroforestry design and carbon accounts. Whereas systems designed for specialized carbon retrieval may work with structural domination, scenarios directed at multiple functionality might necessitate a more even biomass distribution. Further understanding of this distinction is crucial in order to better align climate mitigation targets with those for biodiversity conservation. Finally, although soil properties are also salient to system differentiation, their relationship with above-ground carbon is mediated by vegetation structure. Futures work needs also address the coupling between structural, functional and temporal dimensions of agroforestry systems to provide a better understanding of their role in long-term carbon dynamics and mitigation of climate change. Declarations Declaration of competing interest The authors declare no known competing financial interests or personal relationships that could have influenced this work. Credit authorship contribution statement Chattanong Podong: Conceptualization, Methodology, Investigation, Formal analysis, Writing – original draft, Writing – review & editing, Project administration, Funding acquisition. Krissana Khamfong: Investigation, Data curation. Supawadee Noinumsai and Sukanya Mhon-ing: Investigation, Resources, Validation. Author Contribution Chattanong Podong: Conceptualization, Methodology, Investigation, Formal analysis, Writing – original draft, Writing – review & editing, Project administration, Funding acquisition. Krissana Khamfong: Investigation, Data curation. Supawadee Noinumsai and Sukanya Mhon-ing: Investigation, Resources, Validation. Acknowledgments This research was funded by the Thailand Science Research and Innovation Fund (TSRI), Uttaradit Rajabhat University, fiscal year 2022 (Grant No. SF2565/287). We thank the Department of Silviculture, Faculty of Forestry, Kasetsart University for soil analysis. We gratefully acknowledge the agroforestry farm owners in all six provinces for field access and cooperation. Data availability The data that support the findings of this study are available from the corresponding author upon reasonable request. References Cardinael, R., Cadisch, G., Dupraz, C., Lojka, B., & Oelbermann, M. (2025). Guidelines for improved quantification and reporting of carbon stocks and additional carbon storage in agroforestry systems. Agroforestry Systems, 99 , 82. https://doi.org/10.1007/s10457-025-01184-x Cardinale, B. J., Duffy, J. E., Gonzalez, A., Hooper, D. U., Perrings, C., Venail, P., Narwani, A., Mace, G. M., Tilman, D., Wardle, D. A., Kinzig, A. P., Daily, G. C., Loreau, M., Grace, J. B., Larigauderie, A., Srivastava, D. S., & Naeem, S. (2012). Biodiversity loss and its impact on humanity. Nature, 486 (7401), 59–67. https://doi.org/10.1038/nature11148 Chave, J., Réjou-Méchain, M., Búrquez, A., Chidumayo, E., Colgan, M. S., Delitti, W. B. C., Duque, A., Eid, T., Fearnside, P. M., Goodman, R. C., Henry, M., Martínez-Yrízar, A., Mugasha, W. A., Muller-Landau, H. C., Mencuccini, M., Nelson, B. W., Ngomanda, A., Nogueira, E. M., Ortiz-Malavassi, E., ... Vieilledent, G. (2014). Improved allometric models to estimate the aboveground biomass of tropical trees. Global Change Biology , 20 (10), 3177–3190. https://doi.org/10.1111/gcb.12629 Doetterl, S., Stevens, A., Six, J., Merckx, R., Van Oost, K., Casanova Pinto, M., Casanova-Katny, A., Muñoz, C., Boudin, M., Venegas, E. Z., & Boeckx, P. (2015). Soil carbon storage controlled by interactions between geochemistry and climate. Nature Geoscience , 8 (10), 780–783. https://doi.org/10.1038/ngeo2516 Don, A., Schumacher, J., & Freibauer, A. (2011). Impact of tropical land-use change on soil organic carbon stocks: A meta-analysis. Global Change Biology, 17 (4), 1658–1670. https://doi.org/10.1111/j.1365-2486.2010.02336.x Grime, J. P. (1998). Benefits of plant diversity to ecosystems: Immediate, filter and founder effects. Journal of Ecology, 86 (6), 902–910. https://doi.org/10.1046/j.1365-2745.1998.00306.x Hooper, D. U., Chapin, F. S., Ewel, J. J., Hector, A., Inchausti, P., Lavorel, S., Lawton, J. H., Lodge, D. M., Loreau, M., Naeem, S., Schmid, B., Setälä, H., Symstad, A. J., Vandermeer, J., & Wardle, D. A. (2005). Effects of biodiversity on ecosystem functioning: A consensus of current knowledge. Ecological Monographs , 75(1), 3–35. https://doi.org/10.1890/04-0922 IPCC. (2006). 2006 IPCC guidelines for national greenhouse gas inventories (H. S. Eggleston, L. Buendia, K. Miwa, T. Ngara, & K. Tanabe, Eds.). IGES. IPCC. (2019). 2019 refinement to the 2006 IPCC guidelines for national greenhouse gas inventories (E. Calvo Buendia et al., Eds.). IPCC. Jose, S. (2009). Agroforestry for ecosystem services and environmental benefits: An overview. Agroforestry Systems, 76 (1), 1–10. https://doi.org/10.1007/s10457-009-9229-7 Loreau, M., & Hector, A. (2001). Partitioning selection and complementarity in biodiversity experiments. Nature , 412(6842), 72–76. https://doi.org/10.1038/35083573 Mbow, C., Smith, P., Skole, D., Duguma, L., & Bustamante, M. (2014). Achieving mitigation and adaptation to climate change through sustainable agroforestry practices. Current Opinion in Environmental Sustainability, 6 , 8–14. https://doi.org/10.1016/j.cosust.2013.09.002 Nair, P. K. R. (1993). An introduction to agroforestry . Kluwer Academic Publishers. Nair, P. K. R., Nair, V. D., Kumar, B. M., & Showalter, J. M. (2010). Carbon sequestration in agroforestry systems. Advances in Agronomy, 108 , 237–307. https://doi.org/10.1016/S0065-2113(10)08005-3 Pan, Y., Birdsey, R. A., Fang, J., Houghton, R., Kauppi, P. E., Kurz, W. A., Phillips, O. L., Shvidenko, A., Lewis, S. L., Canadell, J. G., Ciais, P., Jackson, R. B., Pacala, S. W., McGuire, A. D., Piao, S., Rautiainen, A., Sitch, S., & Hayes, D. (2011). A large and persistent carbon sink in the world's forests. Science , 333 (6045), 988–993. https://doi.org/10.1126/science.1201609 Paustian, K., Lehmann, J., Ogle, S., Reay, D., Robertson, G. P., & Smith, P. (2016). Climate-smart soils. Nature, 532 , 49–57. https://doi.org/10.1038/nature17174 Post, W. M., & Kwon, K. C. (2000). Soil carbon sequestration and land-use change: Processes and potential. Global Change Biology, 6 (3), 317–327. https://doi.org/10.1046/j.1365-2486.2000.00308.x Quesada, C. A., Phillips, O. L., Schwarz, M., Czimczik, C. I., Baker, T. R., Patiño, S., Fyllas, N. M., Hodnett, M. G., Herrera, R., Almeida, S., Dávila, E. A., Arneth, A., Arroyo, L., Chao, K. J., Dezzeo, N., Erwin, T., di Fiore, A., Higuchi, N., Coronado, E. H., ... Lloyd, J. (2012). Basin-wide variations in Amazon forest structure and function are mediated by both soils and climate. Biogeosciences , 9 (6), 2203–2246. https://doi.org/10.5194/bg-9-2203-2012 Somarriba, E., Cerda, R., Orozco, L., Cifuentes, M., Dávila, H., Espin, T., Mavisoy, H., Ávila, G., Alvarado, E., Poveda, V., Astorga, C., Say, E., & Deheuvels, O. (2013). Carbon stocks and cocoa yields in agroforestry systems of Central America. Agriculture, Ecosystems & Environment , 173 , 46–57. https://doi.org/10.1016/j.agee.2013.04.013 Tilman, D., Reich, P. B., & Knops, J. M. H. (2006). Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature, 441 , 629–632. https://doi.org/10.1038/nature04742 Zomer, R. J., Neufeldt, H., Xu, J., Ahrends, A., Bossio, D., Trabucco, A., van Noordwijk, M., & Wang, M. (2016). Global tree cover and biomass carbon on agricultural land: The contribution of agroforestry to global and national carbon budgets. Scientific Reports , 6 , 29987. https://doi.org/10.1038/srep29987 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 11 May, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers invited by journal 13 Apr, 2026 Editor assigned by journal 13 Apr, 2026 Submission checks completed at journal 13 Apr, 2026 First submitted to journal 12 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9392281","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":626068209,"identity":"6a49514d-6fe4-40f0-b652-54417c4268d5","order_by":0,"name":"Chattanong 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University","correspondingAuthor":false,"prefix":"","firstName":"Krissana","middleName":"","lastName":"Khamfong","suffix":""},{"id":626068211,"identity":"3582dc57-8171-4e94-8f0c-380bd10bbc8a","order_by":2,"name":"Supawadee Noinamsai","email":"","orcid":"","institution":"Uttaradit Rajabhat University","correspondingAuthor":false,"prefix":"","firstName":"Supawadee","middleName":"","lastName":"Noinamsai","suffix":""},{"id":626068217,"identity":"2f6ce3ab-595a-4289-a560-6f888666cbf3","order_by":3,"name":"Sukanya Mhon-ing","email":"","orcid":"","institution":"Uttaradit Rajabhat University","correspondingAuthor":false,"prefix":"","firstName":"Sukanya","middleName":"","lastName":"Mhon-ing","suffix":""}],"badges":[],"createdAt":"2026-04-12 07:09:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9392281/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9392281/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107456525,"identity":"5cff003e-6966-45f1-be4f-a919ae967ca5","added_by":"auto","created_at":"2026-04-21 15:59:21","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":140470,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the six agroforestry study sites across Thailand (7°N–17°N), color-coded by typology.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9392281/v1/55736d9a9130ce0e654e9530.jpeg"},{"id":107490329,"identity":"1f0ae964-5da0-49e4-9a44-d9b43dcb8138","added_by":"auto","created_at":"2026-04-22 02:51:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":33965,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of biomass contribution (%) by dominant species across agroforestry systems.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9392281/v1/757d36de0a824bf796c65d37.png"},{"id":107456527,"identity":"af21fc6a-047e-47b0-a878-3160781884ae","added_by":"auto","created_at":"2026-04-21 15:59:22","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":334127,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAbove-ground carbon stocks and biomass distribution across agroforestry systems\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Above-ground carbon stocks (Mg C ha⁻¹) across study sites.\u003c/p\u003e\n\u003cp\u003e(b) Proportion of total carbon contributed by dominant species in each system.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9392281/v1/3f2f8b58966ad767489d240e.jpeg"},{"id":107490352,"identity":"d72930b1-19aa-44d0-b2e5-ed6e8302413c","added_by":"auto","created_at":"2026-04-22 02:51:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":90671,"visible":true,"origin":"","legend":"\u003cp\u003eBiodiversity metrics across agroforestry systems\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9392281/v1/0c348b1a8dd95e8ebaefaa34.png"},{"id":107489568,"identity":"56937abe-8c1c-4b5e-83b9-597afd3e5227","added_by":"auto","created_at":"2026-04-22 02:48:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":45453,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between biodiversity and above-ground carbon across agroforestry systems\u003c/p\u003e\n\u003cp\u003e(a) Relationship between Shannon diversity and above-ground carbon across all systems.\u003c/p\u003e\n\u003cp\u003e(b) Relationship between Pielou evenness and above-ground carbon after excluding dominance-driven rubber systems.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9392281/v1/dc598dadbc47a7baa0ce17cd.png"},{"id":107456529,"identity":"7bd6390d-8ef8-4ef9-a67e-e8e17ee179a9","added_by":"auto","created_at":"2026-04-21 15:59:22","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":276373,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis (PCA) of soil physicochemical properties across agroforestry systems.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9392281/v1/97510c6ea01ce7bdf4845494.jpeg"},{"id":108490663,"identity":"782c8508-dfc1-4b26-be36-60aa659bf295","added_by":"auto","created_at":"2026-05-05 09:46:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1212869,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9392281/v1/83fcc78a-9c2d-45b1-afbf-20d1533473bd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Structure-mediated carbon–biodiversity relationships across agroforestry typologies in tropical Thailand","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAgroforestry systems are becoming increasingly identified as a contributing strategy for climate change adaptation and mitigation in tropical systems experiencing high land-use pressure and where carbon sequestration potential is significant. These systems, based on tree\u0026ndash;crop or tree\u0026ndash;livestock combinations, can sequester biomass carbon and provide essential ecosystem services (e.g., soil regulation, biodiversity support) (Nair \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Nair et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Jose \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In terrestrial ecosystems, especially forest systems, tree-based systems are the most significant carbon pools which makes them vital in climate regulation at a global scale (Pan et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Agroforestry under this context is commonly lauded as a nature-based solution with potential to contribute both to carbon sequestration and ecosystem multifunctionality. Despite this interesting interplay, the link between biodiversity and carbon stocks in agroforestry systems remains elusive. In the biodiversity\u0026ndash;ecosystem functioning (BEF) paradigm, increased biodiversity tends to lead to stronger ecosystem processes via mechanisms like niche complementarity and facilitation (Tilman et al., 1997; Hooper et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Cardinale et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In contrast, evidence from managed systems is more mixed. In agroforestry systems, biodiversity does not always translate into higher biomass or carbon storage as species composition and structure are closely related to human management. This indicates that biodiversity\u0026ndash;carbon relationships are context dependent: they can differ according to system structure.\u003c/p\u003e \u003cp\u003eA major limitation of many studies is the assumption that biodiversity and carbon are necessarily correlated without taking the role of stand structure into consideration. Management determining structural factors such as species dominance, canopy layering and biomass allocation drives ecosystem outcomes in agroforestry systems. Highly dominated systems may have high biomass accumulation but low diversity and more evenly distributed biomass often support higher diversity though lower carbon. This raises a question on whether the observed trade-offs between biodiversity and carbon is due to fundamental ecological constraints, or rather structural differences in organization. Recent syntheses highlight the need for agroforestry typologies to be clearly defined, and carbon pools integrated when evaluating carbon stocks (Cardinael et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Moreover, meta-analyses have demonstrated that agroforestry systems can increase both biodiversity and ecosystem services but that the results of their application are very heterogeneous depending on management practices and the system design (Torralba et al., 2016). And yet there remain few comparative efforts, particularly for tropical areas, to identify how structural variation drives biodiversity\u0026ndash;carbon relationships.\u003c/p\u003e \u003cp\u003eAgroforestry systems in Thailand range from rubber-based systems to a number of configurations including multistrata and agri-silvicultural [16]. However, these systems are seldom considered within the framework of a unified theory allowing for direct comparison of structural properties. That inhibits our understanding of how differences in system design affect both carbon storage and biodiversity outcomes. Filling these gaps, we investigate carbon storage and biodiversity in contrasting agroforestry typologies throughout tropical Thailand. Specifically, it aims to (1) quantify carbon stocks in ecosystems; (2) characterize biodiversity patterns using multiple indices; and (3) assess the influence of stand structure on mediating the relationship between biodiversity and carbon. By focussing on (the structural variation an ecosystem harbours rather than) diversity in itself, this study gives readers a mechanistic understanding of biodiversity\u0026ndash;carbon relationships and facilitates designing agroforestry systems that target towards carbon storage along with diversity conservation.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area and site selection\u003c/h2\u003e \u003cp\u003eThe study was carried out at six agroforestry systems located across Thailand along a north\u0026ndash;south latitudinal gradient of 7\u0026deg;N to 17\u0026deg;N and aiming to cover different climate environments with humid tropical conditions in the southern part compared to more seasonal climate at the northern sites. Sites were chosen as representative of a range of differences in agroforestry structure, species composition and management practices. All systems had been in place for a minimum of 10 y, providing adequate time for vegetation infrastructure and building out the biomass. Geographic coordinates of the sites were recorded by a hand-held GPS unit, and the elevation ranged from 32 to 150 m above sea level (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Agroforestry typology classification\u003c/h2\u003e \u003cp\u003eField observations of species composition, canopy structure and management practices were used to classify each site into an agroforestry typology. This classification was based on standard agroforestry systems (Nair, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1993\u003c/span\u003e); it was then adapted to align with recent recommendations for the carbon assessment of agroforestry systems (Cardinael et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Four typologies were identified which are; agri-silvicultural systems, multistrata systems, farm forestry systems and rubber-based agroforestry. We verified that each site corresponded to its assigned category based on field-measured structural indicators (i.e., basal area, canopy layering and species dominance).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Plot establishment and vegetation measurements\u003c/h2\u003e \u003cp\u003eAt each site, we collected data from ten square plots (20 \u0026times; 20 m). Plot locations were chosen to capture the dominant vegetation features of each system while avoiding obvious disturbances such as road or recent clearings. All trees with DBH\u0026thinsp;\u0026ge;\u0026thinsp;10 cm were identified and measured within each plot. Diameter at 1.3 m above ground was recorded with a diameter tape. Tree height was measured with a hypsometer when possible. In the field were identified species and subsequently verified according to local floristic references. Be contiguously plot paired into little join plots to catch regeneration. Saplings (\u0026ge;\u0026thinsp;1.5 m tall and \u0026lt;\u0026thinsp;10 cm DBH) were recorded in 5 \u0026times; 5 m subplots; seedlings (\u0026lt;\u0026thinsp;1.5 m tall) were surveyed in subplots of dimensions 2 \u0026times; 2 m. These measurements were made to characterize stand structure and species composition within size classes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Biomass and carbon estimation\u003c/h2\u003e \u003cp\u003eWhere species-specific allometric equations did exist, above-ground biomass was estimated from them. In the absence of such equations, a widely used pantropical model (Chave et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) was utilized with wood density, diameter and tree height as input variables. Wood density values were taken from published databases and those reported at the species or genus level. These measurements did not directly measure below-ground biomass in the field, but estimated it from above-ground using a root-to-shoot ratio of 0.24 (IPCC, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; refining the method in 2019) that is considered appropriate for tropical systems. Soil samples were collected from each plot at 0\u0026ndash;10, 10\u0026ndash;20 and 20\u0026ndash;30 cm soil depth. Soil organic carbon (SOC), measured as percent carbon, was analyzed in a laboratory. SOC stocks were estimated based on a representative value of bulk density for tropical mineral soils at depth, which is within commonly observed ranges found in the literature (Post and Kwon, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Don et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In regions of the world with no site measurements of bulk density, SOC estimates are viewed as approximate values serving to enable comparison between sites rather than absolute quantification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Biodiversity assessment\u003c/h2\u003e \u003cp\u003eSpecies richness, Shannon diversity index, and Pielou\u0026rsquo;s evenness were used to describe species diversity. Also, biomass-weighted proportions were used to handle the contribution of any species on total stand biomass. This type of diversity can account for both species composition in the ecosystem, and their functional significance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Soil analysis\u003c/h2\u003e \u003cp\u003eThe soil samples were analyzed for basic physicochemical properties: pH, electrical conductivity, organic matter, total nitrogen, available phosphorus and exchangeable cations (K, Ca, Mg) and total deficiencies. Routine laboratory techniques were used for subsequent analyses.It measures soil over many depth intervals, enabling exploration of vertical patterns in soil properties. The identified agroforestry variables were subsequently applied in multivariate analysis and used to compare patterns across the different categories of agroforestry systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Data analysis\u003c/h2\u003e \u003cp\u003eDifferences between agroforestry typologies were tested using standard statistical tests, including analysis of variance when assumptions were satisfied. Non-parametric alternatives were used where appropriate). Correlation and regression analysis were performed to explore relationships between biodiversity and carbon. We constructed additional models to assess the role of stand structure, incorporating structural variables: basal area and species dominance. PCA (principal component analysis) was used to summarize the variation in soil properties and establish gradients among sites. All analyses were conducted in R statistical software.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Agroforestry Typologies and Structural Characteristics\u003c/h2\u003e \u003cp\u003eSignificant differences in vegetation structure were found among the six agroforestry systems, characterized by variation in species composition, dominance patterns and biomass distribution (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We recorded 175 tree species in total across all sites but their distribution was very uneven across the systems. In rubber-based systems, however, biomass was dominated by one or few dominant species. In Songkhla Hevea brasiliensis contributed 77.8% of total stand biomass and in Phatthalung Hopea odorata contributed 53.4% (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These figures are indicative of a clear dominance-driven structure where the biomass of stands is determined mainly by one species. In contrast, biomass allocation was more evenly distributed in the non-rubber systems. It can obviously be observed, in the agri-silvicultural system (Uttaradit), no species contributed\u0026thinsp;\u0026gt;\u0026thinsp;20.2% of total biomass and the most even distribution was shown (J\u0026prime; = 0.864). In a similar mixed system, Chanthaburi's multistrata system was the most species rich (S\u0026thinsp;=\u0026thinsp;54), but distribution across species also differed strongly with Dipterocarpus alatus dominating and being responsible for 70.3% of stand biomass.The dominant species contribution to biomass proportion is especially different among the sites as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Systems based on rubber are highly clustered at the top end of dominance, while low to mid-paid systems show a more uniform distribution across dominance. Multistrata systems are a grey area; high species richness with the extent of dominance varying. Collectively, these results suggest that the agroforestry systems in this study form a structural gradient instead of discrete categories. Dominance-driven systems with high biomass concentration on one species appear at the extreme end of gradient, while structurally heterogeneous systems featuring more evenly distributed biomass lie at the other end. This gradient serves as a valuable benchmark for interpreting differential carbon storage and biodiversity in subsequent comparisons.\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\u003eStructural and biodiversity characteristics of agroforestry systems across study sites.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH\u0026prime;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJ\u0026prime;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAGB (Mg ha⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC stock (Mg C ha⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCO₂eq\u003c/p\u003e \u003cp\u003e(Mg CO₂ ha⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDominant species (% biomass)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTypology\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhatthalung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e48.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eH. odorata\u003c/em\u003e (53.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRubber AF (Mature)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSongkhla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eH. brasiliensis\u003c/em\u003e (77.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRubber AF (Young)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUttaradit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eL. domesticum\u003c/em\u003e (20.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAgri-silvicultural\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChanthaburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eD. alatus\u003c/em\u003e (70.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMultistrata\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChachoengsao\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eD. alatus (37.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFarm forest\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrachinburi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA. indica (28.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBush fallow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Biomass carbon distribution across agroforestry systems\u003c/h2\u003e \u003cp\u003eAbove-ground carbon stocks varied significantly between the six agroforestry systems, supporting the structural differences outlined in the previous section (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For above-ground carbon, values across all sites ranged from 0.78 to 13.36 Mg C ha⁻\u0026sup1;, more than an order-of-magnitude difference. The resulting highest values were observed within the rubber-based systems with site Phatthalung (13.36 Mg C ha⁻\u0026sup1;) and Songkhla (12.08 Mg C ha⁻\u0026sup1;) having the highest average carbon stock overall. Very low carbon stocks (\u0026lt;\u0026thinsp;1 Mg C ha⁻\u0026sup1;; for example, Prachinburi and Chachoengsao) were recorded in non-rubber vegetation (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). When carbon is considered relative to biomass distribution, this contrast becomes apparent. An over-dominated high carbon stock system is when there are only one or a few species that account for the vast majority of biomass (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). For example, \u003cem\u003eHevea brasiliensis\u003c/em\u003e in Songkhla contributed around 9.40 Mg C ha⁻\u0026sup1; of carbon, which was responsible for most of the total stand carbon. This pattern also holds for Phatthalung, with a similarly large biomass share contributed by \u003cem\u003eHopea odorata\u003c/em\u003e.More evenly distributed biomass yields lower carbon values but greater diversity, however. This pattern is illustrated by the agri-silvicultural system in Uttaradit: despite the biomass gained from various species, no single dominant species exists, and total above-ground carbon remains relatively low (4.19 Mg C ha⁻\u0026sup1;). This indicates that carbon accumulation is more dependent on biomass density than species richness. We further address this relationship in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, which shows the fractional carbon contributions of dominant taxa among systems. Higher carbon stocks are consistently found in more dominant systems and lower values in more evenly represented contribution of species. The presence of this pattern is consistent across sites which are geographically well separated (not driven by local condition alone) and reflects broader structural characteristics of the agroforestry typologies. Overall, our results indicate that variation in above-ground carbon across different agroforestry systems is not random but follows a clear structural trend. Systems that accumulate a larger fraction of biomass in few species establish higher amount of carbon, while systems that maintain even distribution of biomass across many more species stabilize lower climate-carbon stocks at high diversity. This pattern serves as a starting point for interpreting the opposing associations between diversity and carbon seen in later analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Biodiversity patterns across agroforestry systems\u003c/h2\u003e \u003cp\u003eResearch Highlights: Biodiversity was very different in the 6 agroforestry systems studied but this could not be explained by species richness alone. Instead, differences in biomass distribution and dominance were pivotal in determining diversity indices (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Species richness was between 16 and 54 species per site, with a maximum of the multistrata system in Chanthaburi (S\u0026thinsp;=\u0026thinsp;54). Nonetheless, high richness often did not translate to high diversity in functional terms. The evenness was low (J\u0026prime; = 0.298) in Chanthaburi because a large proportion of total biomass existed in Dipterocarpus alatus even though many species were found (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast, Uttaradit exhibited another trend associated with its agri-silvicultural system. Although spacing of species was minimal (S\u0026thinsp;=\u0026thinsp;16), biomass distribution among species was more uniform leading to maximal evenness (J\u0026prime; = 0.864) and comparatively high Shannon diversity values (H\u0026prime; = 2.397). This suggests that diversity high rarefies when a single taxon is not made up of biomass.Rubber-based systems showed consistently low biodiversity values. In both Songkhla and Phatthalung, diversity indices were low (H\u0026prime; \u0026lt; 1.0), reflecting strong dominance by one species and limited contribution from other taxa. These systems represent the extreme end of the structural gradient, where biodiversity is constrained by management practices and species selection. The contrasting patterns among sites are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, where richness, Shannon diversity, and evenness show different trends across agroforestry typologies. While richness varies widely, evenness appears to be more closely aligned with biomass distribution. Systems with high dominance exhibit low evenness regardless of species count, whereas systems with balanced biomass contributions maintain higher evenness even with fewer species.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Biodiversity\u0026ndash;carbon relationships\u003c/h2\u003e \u003cp\u003eHow biodiversity was represented and which systems were included in the analysis affected how this relationship looked (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). For all agroforestry systems, Shannon diversity was negatively associated with above-ground carbon. The higher carbon stock sites, especially rubber-derived systems were always found close to the lower end of diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). This pattern could be seen by a trade-off between biodiversity and carbon storage. Remove systems driven by dominance, and a different picture emerges. Removing the rubber-based systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb) weakens this negative relationship, and evenness (J\u0026prime;) becomes positively linked to carbon. Within this subset, structures that show a more equal distribution of biomass across species generally yield higher carbon values than those with less evenness. An example of this contrast can be seen in the agri-silvicultural system found in Uttaradit. Species richness in these systems is low relative to their diversity, but a combination of high evenness with moderate carbon distinguishes it from dominance-driven systems and low-carbon systems. Conversely, sites like Prachinburi and Chachoengsao with low stocks of carbon store less-balanced biomass. When considered together, these results indicate that the biodiversity\u0026ndash;carbon relationship is not consistent in agroforestry systems but varies as a result of structural context. When systems with very different structures are analysed together (which we will discuss in the next section), a negative relationship emerges. When controlling for structurally similar networks, the effect weakens and may even reverse.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Structural mediation of carbon storage\u003c/h2\u003e \u003cp\u003eThe trends observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e suggest a steady effect of stand architecture on carbon outcomes across agroforestry systems. Instead of being more directly associated with species richness or diversity alone, carbon storage seems to track how biomass is distributed among the different species. Carbon also helps paint a clearer picture when in conjunction with dominance patterns. High carbon stock systems are defined as those with high covariation of biomass in one or a few species. This is most noticeable in the rubber-based systems, where one species provides the lion's share of stand biomass and a considerable amount of above-ground carbon. Here, structural dominance offers a direct route to increased carbon accumulation. On the contrary, systems characterized by low stocks of carbon generally register higher dispersion for biomass. Biomass is shared between multiple species in the provinces of Prachinburi and Chachoengsao, for example, but no one major contributor. We identify systems characterized by relatively low carbon stocks and moderate- or low- structural concentration, indicating that weak dominance prevents the accumulation of biomass at the stand scale. In Uttaradit, the agri-silvicultural system is intermediate. While relative biomass dominance is not high, this group retains moderate to high evenness and a moderate carbon fate in storage. This means that carbon accumulation is not purely a result of being dominant, but rather in some structural conditions can be maintained through even distribution of biomass. The differences contribute in understanding the shift seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. When systems with strong dominance are considered, carbon is largely driven by a few species which leads to a negative relationship between carbon and biodiversity indices. Then the contribution of biomass distribution becomes clearer and evenness a more important metric of carbon variation. Overall, these results indicate that the relationship between biodiversity and carbon storage is mediated by stand structure. Species dominance and biomass allocation varied between systems, affecting both variables in parallel and causing differences across agroforestry systems. This means that biodiversity\u0026ndash;carbon relationships should not be read separately from structural context.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Soil properties and carbon relationships\u003c/h2\u003e \u003cp\u003eThe characteristics of the soil varied substantially between the six existing agroforestry systems, both within sites and along a vertical profile of the soil (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The observed differences in pH, organic matter, available nutrients and exchangeable cations were related to contrasting environmental conditions of the study areas and their management history. Soil properties, however, were not consistently related to above-ground carbon across the systems. Higher carbon stocks were not necessarily found at the sites with relatively more fertile soil. Prachinburi, for example, which presented comparatively higher organic matter and nutrient conditions had among the lowest above-ground carbon values. Meanwhile, Phatthalung with more acidic soils and low nutrients had the biggest carbon stock. The difference highlights that soil fertility does not directly control above-ground carbon on its own. Instead, it seems that soil conditions influence carbon storage indirectly, through their effects on vegetation structure and composition. Systems with high carbon stocks are mainly those systems in which biomass is aggregated into few dominants, irrespective of the degree of soil nutrient limitation. Soil properties are still relevant for understanding ecosystem functioning. Differences in organic matter and nutrient availability likely impact on productivity, regeneration and long-term stability of agroforestry systems even if these crop yield effects are not necessarily manifested in the measured above-ground carbon at the time it is performed. Overall, the findings suggest that soil and vegetation are only weakly coupled at these agroforestry systems. Although soil properties vary widely between sites, their relationship with above-ground carbon is not a simple one and needs to be viewed in the context of stand structure and system typology.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSoil physicochemical properties across agroforestry systems\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOM (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal N (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAvail. P (mg kg⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExch. K (cmolc kg⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eExch. Ca (cmolc kg⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eExch. Mg (cmolc kg⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUttaradit\u003c/p\u003e \u003cp\u003e(Agri-silvicultural)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChanthaburi (Multistrata)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChachoengsao (Farm forestry)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrachinburi\u003c/p\u003e \u003cp\u003e(Bush fallow)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSongkhla\u003c/p\u003e \u003cp\u003e(Rubber AF Young)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhatthalung (Rubber AF Mature)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Apparent trade-offs as structural artefacts rather than ecological constraints\u003c/h2\u003e \u003cp\u003eThe apparent negative relationship between Shannon diversity and above-ground carbon when all agroforestry systems are analysed collectively should be interpreted cautiously. This pattern is thus not necessarily indicative of an underlying trade-off between biodiversity and carbon storage, but may instead arise from structural heterogeneity among systems considered within a single analysis framework. Such a distinction is widely accepted in biodiversity\u0026ndash;ecosystem functioning (BEF) research, with the direction and strength of biodiversity\u0026ndash;function relationships being strongly dependent upon community composition and environmental context (Cardinale et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In contrast, in agroforestry systems structural variability is not incidental but significantly influenced by management (species selection, planting density and canopy configuration). Thus fundamentally different architectural systems are likely to reflect alternative structural regimes rather than similar ecological units, such as rubber-dominated stands and multistrata systems.Statistical relationships may therefore reflect differences in biomass organization rather than underlying ecological processes when such systems are analysed jointly. This interpretation is corroborated by the shift seen after discarding systems driven by dominance. Once systems with comparable structural properties are contrasted, the inverse relationship is diminished (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), suggesting that this apparent trade-off results from mixing structurally dissimilar systems instead of a universal ecological constraint. This result aligns with recent research emphasizing the need of system definition and classification used in agroforestry studies. Notably, the absence of universally accepted typologies and nonuniform system boundaries have been pointed out as a major factor contributing to discrepancies in reported carbon pools and ecosystem relationships (Cardinael et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The current results build on this argument by demonstrating how structural heterogeneity can also confound biodiversity\u0026ndash;carbon relationships and interpretations over extrapolations that are likely invalid within structurally comparable systems. Gradually these results show that the relationship between biodiversity and carbon in agroforestry systems cannot be understood without simultaneously considering stand structure. Trade-offs visible at the level of pooled datasets may be apparent, but not necessarily represent concrete ecological constraints. Instead, they mirror how different structural forms get amalgamated in analytic schemas.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Dominance-driven carbon accumulation and the limits of the mass ratio paradigm\u003c/h2\u003e \u003cp\u003eThe tight relationship between carbon storage and species mean dominance found here also supports the mass ratio hypothesis, which states that ecosystem function is determined mainly by the traits, abundance or biomass of dominant species (Grime, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). In the rubber-based systems explored here, carbon accumulation is strongly regulated by one functional type, leading to high biomass with low diversity. This pattern represents a structural pathway in which the storage of carbon is maximized due to the concentration of biomass rather than due to species complementarity. Nonetheless, mass ratio hypothesis in agroforestry systems cannot be generalized without reservation. In natural ecosystems, dominance often arises as a result of ecological processes like intra-specific competition and environmental eigenfactors. In contrast, in agroforestry dominance is often exerted via management decisions made by the farmer, e.g., species choice (more dominant vs. less dominant), planting density and canopy design. Thus, observed mass ratio effects may result from engineered structural configurations and not emergent ecological dynamics. This is consistent with evidence from experimental and synthesis studies. Usually, the mass ratio framework causes biomass production to depend effectively on dominant species, but community composition could temper this (Grime, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Sonkoly et al., 2019). Carbon storage varies widely in agroforestry systems depending on system structure, species composition, and management intensity rather than just biodiversity alone (Cardinael et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These results point to structural organization, rather than species richness per se, as a central determinant of carbon outcomes. This distinction has important implications. Now, while dominance-driven systems can have high levels of above-ground carbon, they achieve that at the expense of functional redundancy and structural complexity. It is already well established that such systems could optimize short-term carbon gains\u0026thinsp;\u0026minus;\u0026thinsp;but be more sensitive to environmental stress and disturbance (Nair et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Jose, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The amount of biomass in just one species can reduce the buffering power of the system and reduce stability over a geologic timeframe. The current findings support this view. High-carbon systems are not merely low-diversity systems, but structurally simplified systems where the biomass is disproportionately packed into a dominating single species. This classification reframes carbon buildup as a structural product, rather than a biodiversity deficit. In this sense, the mass ratio pathway is an effective but potentially unstable flux of carbon storage conditioned less by inherent ecosystem processes and more by management-induced dominance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Evenness, complementarity, and context-dependent biodiversity effects\u003c/h2\u003e \u003cp\u003eThe positive relationship between biodiversity and carbon storage seen here is not universal to all agroforestry systems, but dependent on the structural context. When all systems are assessed jointly, diversity metrics including the Shannon index exhibit a negative relationship with above-ground carbon. However, this pattern breaks down for dominance-driven systems suggesting that the apparent trade-off is not universal but rather dependent on weight distributions in the system. There is a more regular nomenclature when one uses evenness as an indicator. Systems where biomass is more evenly distributed among species, without a single dominant species, tend to have relatively high carbon levels. This indicates that carbon storage of these ecosystems is not only driven by the functional properties of dominant species, but also by how biomass is distributed among the community. In this respect evenness reflects a different structural pathway where carbon accumulation can occur alongside more diverse assemblages. These results conform to the biodiversity\u0026ndash;ecosystem functioning paradigm, whereby ecosystem functioning can be underpinned by dominance, that is, mass ratio effects or through interactions between species (complementarity effects) (Hooper et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Cardinale et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tilman et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). However, the current results reveal that the relative contribution of these mechanisms is structure dependent. In agroforestry systems, where species composition and spatial arrangement are purposefully managed, the balance of dominance and complementarity is constructed through design rather than solely by ecological processes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Soil\u0026ndash;vegetation decoupling and the hierarchical control of carbon stocks\u003c/h2\u003e \u003cp\u003eThe weak relationship of this study between soil properties and above-ground carbon (AGC) indicates that belowground conditions in the spatial scale considered are decoupled from biomass accumulation. While soil fertility did vary among sites and accounted for a measure of system differentiation from the principal component analysis, it was not consistently reflective of AGC gradients (as expected if carbon stocks were directly controlled by short-term variation in soil nutrients) which indicates this is not the case. For example, in tropical systems above-ground biomass is typically more closely linked to stand structure, disturbance history and species composition than solely to soil fertility (Quesada et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Paustian et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). As with the current findings, this trend was maintained across systems that appeared structurally different but shared similar soil conditions and climate (i.e landscape type). In systems subject to management (e.g. agroforestry), such relationships are additionally formatted by managerial choices (e.g. species) such as selection, density and stand configuration (Nair et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Somarriba et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Our findings provide support for a hierarchical framing of carbon control, with stand structure as the overarching driver of biomass allocation and soil properties mediating long-term productivity through indirect channels. Under this framework, the influence of soil conditions on carbon stocks is indirect, operating through its effect on vegetation structure instead. This notion explains why high-carbon systems often develop on relatively nutrient-poor soils \u0026mdash; especially where structural development is well-advanced, while more fertile systems show lower biomass when limited structural complexity features. Such patterns highlight the fact that high levels of biomass accumulation are constrained more by structure than by soil fertility alone. Importantly, this decoupling does not mean soil properties are unimportant. Instead, their influence is indirect and is increasingly salient across longer temporal scales. The dynamics of soil organic carbon stabilization and turnover processes are heavily driven by edaphic factors (e.g. mineral composition, microbial activity; Doetterl et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), which may diverge from trajectories of above-ground biomass.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e4.5 Implications for carbon accounting frameworks and system desig\u003c/b\u003en\u003c/h2\u003e \u003cp\u003eConsequences for carbon accounting frameworks and agroforestry system design Relatedly, one of the major findings is that agroforestry systems are not functional uniformities. Structural variation\u0026mdash;especially in species dominance and biomass distribution\u0026mdash;results in considerable differences in carbon storage under similar environmental conditions. This challenges common accounting conventions that treat agroforestry systems as homogeneous aggregates (Wigley et al., 2012), which neglects to incorporate internal heterogeneity, a limitation that is increasingly being acknowledged within global assessments of tree-based systems on agricultural land (Zomer et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A second implication relates to system boundaries and carbon pools. The lack of consideration of above-ground biomass, below-ground biomass and soil organic carbon in the context of agroforestry typologies consistency may result in inaccuracy during the estimation of carbon stocks. Owing to recent developments in carbon accounting, robust measures should be put in place for the purposes of measurement, reporting and verification (MRV) to reduce uncertainty and enhance the comparability of systems Rosenstock et al., 2019; Smith et al., 2020. The current results highlight the necessity of including structural characteristics rather than relying solely on generalized land-use types in these frameworks. A third implication is about system design. The observed differences in carbon are informed by the dominance-driven and more even structure of systems, suggesting that the organizational configuration of biomass is critical to carbon outcomes. Specialization towards carbon sequestration is dominated by structuration and, conversely, building systems for a broader ecosystem service requires more equal distribution of species composition and biomass. This correlates with recent views of agroforestry and other multifunctional land-use strategies in which trade-offs and synergies between carbon, biodiversity and productivity need to be managed explicitly (Mbow et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In practical terms, this evidence favours a transition to structure-based design practices in agroforestry. Instead of applying general models, system configuration should be determined by particular goals and local factors such as climate, soil and management context. Reed et al. (2017) emphasize integrated landscape approaches require alignment of farm-level design at larger environmental and socio-economic levels. Structural features should be integrated in system-design and carbon-accounting frameworks as it can help to bring more accurate estimates of carbon and its contribution to climate mitigation potential, as well as sustainable land management capacity of agroforestry systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Limitations and directions for future research\u003c/h2\u003e \u003cp\u003eSeveral important limitations must be considered in the interpretation of these results. The number of sites per typology was small and the study represented a snapshot rather than temporal analysis. Replication and longitudinal data are both needed to further bolster the findings. Furthermore, soil organic carbon estimates are based on assumed bulk density values adding uncertainty in absolute carbon estimates. Although relative comparisons are useful, direct measurements would strengthen future analyses. The functional traits approaches, disturbance regimes and socio-economic drivers also need to be more widely integrated. Agroforestry systems are a product of ecological processes and management decisions, and understanding their interdependent relationship is essential to developing effective climate mitigation strategies.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study demonstrates that structural configuration determines carbon storage of tropical agroforestry systems, and not just biodiversity alone. If you have a different type of agroforestry typology in another country like Thailand, it has been shown to be quite clear that variation in carbon stocks followed the following structural gradient: dominant-driven systems with higher concentration of biomass such as those found in plantations differ from more heterogeneous systems where biomass is much less diverse. The findings suggest that the widely reported trade-off between biodiversity and carbon is not a universal phenomenon. That negative relationship arises primarily from studying structurally dissimilar systems together, and mainly reflects accounts of cases where biomass is dominated by a single species. But when these systems are treated independently, the pattern dissipates and evenness becomes more related to carbon variation. This shows that biodiversity\u0026ndash;carbon relationships are dependent on how biomass is structured in the system, not just by species richness. The association of carbon storage with dominance and biomass distribution underscores stand structure as an important mediating mechanism. This view helps to explain the otherwise conflicting findings in the literature and suggests that mass ratio and complementarity effects are structural continuum not competing explanation. Practically, the results highlight the need to integrate structural characteristics in agroforestry design and carbon accounts. Whereas systems designed for specialized carbon retrieval may work with structural domination, scenarios directed at multiple functionality might necessitate a more even biomass distribution. Further understanding of this distinction is crucial in order to better align climate mitigation targets with those for biodiversity conservation. Finally, although soil properties are also salient to system differentiation, their relationship with above-ground carbon is mediated by vegetation structure. Futures work needs also address the coupling between structural, functional and temporal dimensions of agroforestry systems to provide a better understanding of their role in long-term carbon dynamics and mitigation of climate change.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e \u003cp\u003eThe authors declare no known competing financial interests or personal relationships that could have influenced this work.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCredit authorship contribution statement\u003c/h2\u003e \u003cp\u003eChattanong Podong: Conceptualization, Methodology, Investigation, Formal analysis, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing, Project administration, Funding acquisition. Krissana Khamfong: Investigation, Data curation. Supawadee Noinumsai and Sukanya Mhon-ing: Investigation, Resources, Validation.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eChattanong Podong: Conceptualization, Methodology, Investigation, Formal analysis, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing, Project administration, Funding acquisition. Krissana Khamfong: Investigation, Data curation. Supawadee Noinumsai and Sukanya Mhon-ing: Investigation, Resources, Validation.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThis research was funded by the Thailand Science Research and Innovation Fund (TSRI), Uttaradit Rajabhat University, fiscal year 2022 (Grant No. SF2565/287). We thank the Department of Silviculture, Faculty of Forestry, Kasetsart University for soil analysis. 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Global tree cover and biomass carbon on agricultural land: The contribution of agroforestry to global and national carbon budgets. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e, 29987. https://doi.org/10.1038/srep29987\u003c/li\u003e\n\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":"agroforestry-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agfo","sideBox":"Learn more about [Agroforestry Systems](http://link.springer.com/journal/10457)","snPcode":"10457","submissionUrl":"https://submission.nature.com/new-submission/10457/3","title":"Agroforestry Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Agroforestry systems, Carbon stock, Biodiversity–ecosystem functioning, Stand structure, Tropical ecosystems","lastPublishedDoi":"10.21203/rs.3.rs-9392281/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9392281/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAgroforestry is increasingly framed as a nature-based climate change mitigation strategy, but the relationship between biodiversity and carbon storage across structurally and management-divergent systems remains unclear. This study compares carbon stocks and biodiversity of six agroforestry systems representing four typologies in Thailand, with an emphasis on the degree to which stand structure plays a role. Carbon stocks were predicted from above-ground biomass, below-ground biomass, and soil organic carbon\u0026mdash;while biodiversity was described by species richness, Shannon diversity, and evenness. Structurally defined properties were used to determine how spatial patterns of biomass affect biodiversity\u0026ndash;carbon relationships. Carbon stocks varied significantly across systems. Rubber monoculture systems, known for strong dominance of one plant, contained the most biomass carbon; however, more diverse systems stored less overall. When all the systems were examined in an integrated way, greater diversity meant lower carbon. However, that relationship weakened when dominance-driven systems were removed and evenness had a positive association with carbon storage. These findings suggest that the detected biodiversity\u0026ndash;carbon relationship is highly dependent on stand structure. Instead of being a universal trade-off, the relationship depends on how biomass is partitioned among species. This illustrates the influence of structural attributes in ecosystem functioning and indicates how agroforestry systems may be configured to improve carbon storage with regard to biodiversity goals.\u003c/p\u003e","manuscriptTitle":"Structure-mediated carbon–biodiversity relationships across agroforestry typologies in tropical Thailand","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 15:59:17","doi":"10.21203/rs.3.rs-9392281/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"205587051015938595738790783629102399599","date":"2026-05-11T15:46:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175084406367934557542854930835839876696","date":"2026-04-22T14:45:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-14T00:34:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-13T14:49:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-13T07:43:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Agroforestry Systems","date":"2026-04-12T06:59:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"agroforestry-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agfo","sideBox":"Learn more about [Agroforestry Systems](http://link.springer.com/journal/10457)","snPcode":"10457","submissionUrl":"https://submission.nature.com/new-submission/10457/3","title":"Agroforestry Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d8484ebf-6c81-4aaf-8d1a-0962c0946947","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"205587051015938595738790783629102399599","date":"2026-05-11T15:46:11+00:00","index":47,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T15:59:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 15:59:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9392281","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9392281","identity":"rs-9392281","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}