Quality and quantity of topsoil organic matter in monospecific and mixed pine forests: role of overstory and understory vegetation

doi:10.21203/rs.3.rs-3773229/v1

Download PDF

Research Article

Quality and quantity of topsoil organic matter in monospecific and mixed pine forests: role of overstory and understory vegetation

https://doi.org/10.21203/rs.3.rs-3773229/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Hypothesis and aims

The topsoil organic matter dynamics in mixed vs. monospecific pine stands responds to the vegetation composition. Thus, we tested the effect of the stand type and every under- and overstory species as well as the understory richness in forest floor and topsoil organic matter variability in a fine-scale approach.

Methods

The study of topsoil (5cm depth), forest floor (25 cm x 25 cm), understory (1 m x 1 m), and overstory (4 m radio) in 180 plots [6 triplets x 3 forest stands (PS: P. sylvestris monospecific stand; PP: P. pinaster monospecific stand; MM: mixed stand of both Pinus species) x 10 plots/stand] was implemented in a triplets’ essay in North-Central Spain.

Results

The topsoil organic matter quantity (~ TOC) described an expected trend given the overstory composition (PS > MM > PP) but quality (~ - C/N) showed an unexpected trend (PS ~ MM > PP) that we found related to the understory richness.

Conclusion

The quantity and quality of topsoil organic matter in mixed pine stands respond to the overstory, but also to the understory. Management strategies oriented to understory conservation to maintain dynamics of organic matter that promote carbon sequestration are recommended.

Monospecific vs mixed pine forest

Overstory composition

Pinus pinaster

Pinus sylvestris

Topsoil TOC and C/N

Understory alpha richness

It is widely known that vegetation is the main source of organic matter in soils, particularly in forest soils (Augusto et al. 2015) contributing through aboveground litterfall production and root exudates and roots decomposition below the ground (Rasse et al. 2005; Andivia et al. 2016; López-Marcos et al. 2018; Yeste et al. 2021; Laganière et al. 2022; Xu et al. 2022). Those inputs are decomposed and partially transformed into soil organic matter within the mineral matrix (Xu et al. 2022) in a process that is crucial for both, the sequestration of atmospheric carbon and the maintenance of soil fertility (Xu et al. 2022) since it’s able to drive the carbon cycle (Laganière et al. 2022).

Among the different layers, the forest overstory partly determines the biological, physical, and chemical functioning of the whole forest (Augusto et al. 2015). Many authors suggest that the forest soil properties depend on the tree species composition (Augusto et al. 2015; Andivia et al. 2016), and/or their species richness (Ruiz-Peinado et al. 2017) having a stronger influence on the topsoil (López-Marcos et al. 2018). In addition, the decomposition of that organic matter, being a biological process mainly correlated, inter alia, with the characteristics and composition of foliage, also determines the characteristics of the soil organic matter produced (Prescott and Vesterdal 2021). For instance, gymnosperms have more sclerophyllous leaves which generate litter richer in carbon and lignin and with higher carbon to nitrogen ratios than angiosperms (Augusto et al. 2015; Yeste et al. 2021; Scher et al. 2022) that some authors relate to a slower decomposition foliage process (Prescott and Vesterdal 2021; De Marco et al. 2022).

Therefore, there is a potential to modify soil organic matter dynamics, and consequently the ecosystem services supply, through vegetation changes induced by forest management (Laganière et al. 2022). Indeed, the forest management practices that contribute to the creation and sustainability of mixed-species forests have gained great relevance over the last decades (Gamfeldt et al. 2013; Bravo-Oviedo et al. 2014) being considered a strategy for enhancing carbon sequestration and promoting resilient soil functions (Laganière et al. 2022). Because there is the evidence that these mixed stands can supply multiple ecosystem services more efficiently than monospecific stands (Jactel et al. 2009; Gamfeldt et al. 2013; Del Río et al. 2015) including biodiversity conservation (Felton et al. 2010; López-Marcos et al. 2019, 2020) or carbon sequestration (European Commission 2010; López-Marcos et al. 2018). However, with only 19% of Spanish forests (MAGRAMA 2012) and 40% of European forests (MCPFE 2003) being mixed forests, and with the still urgent need for appropriate management recommendations, the objective of maintaining and improving the soil organic matter dynamics that allows better forest service’s provision is far from achieved.

Despite the need, a general understanding of the tree species’ effect on soil has not been yet reached (Jandl et al. 2007). Perhaps, the rest of the vegetation that makes up the forest ecosystem, such as the understory, also plays an important role and, therefore, deserves more detailed study. In this sense, Augusto et al. (2015) point out the critical role of the understory on soil properties; not only direct effects through nutrient recycling and litter inputs which depend on understory nutrient content but also a high number of indirect, cascade effects, such as damped temperature oscillations and a modified water regime. Therefore, notwithstanding the small contribution of the understory to the forest biomass (Pan et al. 2018), the understory is an important component of forest ecosystems that also drives ecosystem processes such as carbon cycling (Chen et al. 2017).

In this regard, the understory is the largest plant biodiversity reservoir of the forest ecosystems (Mestre et al. 2017). With a diversity of sizes (Puentes et al. 2016; Vega et al. 2022), compositions (Louault et al. 2005; Ansquer et al. 2009), and strategies (Hodgson et al. 1999; Louault et al. 2005; Ansquer et al. 2009), typically combining herb and woody species, it contributes significantly to litter diversity. These physicochemical differences in litter can have an important impact on the structure and operation of the litter microbial community when both tree types cohabit. Mixed litter has been observed to have higher fungal and bacterial abundances, as well as greater microbial community diversity than litter from a single source (Liu et al. 2022).

In the end, both the quantity and quality of topsoil organic matter can have an impact on carbon sequestration in monospecific and mixed stands through the different processes involved in its dynamics and hence they need to be explored. The contribution of the understory also needs to be clarified. Also, because it is strongly affected by forest management (Augusto et al. 2015) we think it deserves more attention.

Our study about the quantity and quality of topsoil organic matter in mixed vs. monospecific stands, and their relationship with the understory contributes to filling this knowledge gap. We specifically target the admixtures of Scots pine (Pinus sylvestris L.) and Maritime pine (Pinus pinaster Aiton) because despite being increasingly common in many environments in Spanish, similar research has not yet been conducted.

Both pine species hold great ecological and socio-economic value (Riofrío et al. 2016). Maritime pine is an important species in western Mediterranean forests, whereas Scots pine is also the most widely distributed pine species in the world (Bogino and Bravo 2014). Both are among the main forest species in Spain (Scots pine: 1.20 million ha; Maritime pine: 0.68 million ha) and can be found in monospecific and mixed stands, either naturally or because of species selection for afforestation (Serrada et al. 2008). Both species show similar crown architecture and slight differences in shade tolerance (Riofrío et al. 2017). Even though root distribution is not conclusive, since rooting depth may vary depending on the moisture conditions (Bakker et al. 2006) both Pinus species differ in drought tolerance (López-Marcos et al. 2019) and in leaf traits (e.g. more recalcitrant leaf litter for P. pinaster; Herrero et al. 2016; longer and coarser P. pinaster needles; Amaral Franco 1986 and higher carbon:nitrogen litter ratio of P. pinaster; López-Marcos et al. 2018). Mixed stands where these two species coexist are particularly interesting because of their location at the rear edges of P. sylvestris forests where ecological conditions (high temperatures, frequent droughts) approach the species tolerance limits, and at the geographical context where the most drastic effects of climate change are predicted (Matías and Jump 2012).

In previous studies in these areas, we found that the stand type influenced several soil organic matter properties, particularly those related to the forest floor, and the different trails on carbon distribution in the soil profile (López-Marcos et al. 2018). However, the pine overstory is not the only stratum important for the dynamics of the whole forest. The relevance of understory vegetation in explaining many aspects of forest ecosystem dynamics has also been highlighted (López-Marcos et al. 2019, 2020, 2021), for instance, the understory richness in those mixed forest stands was found to be related to Quercus pyrenaica regeneration (López-Marcos et al. 2020). However, further research is needed. Therefore, a fine-scale approach is welcome here to assess the relationship among understory, overstory, and topsoil as other authors considered (López-Marcos et al. 2021a; Osei et al. 2021; Getino-Álvarez et al. 2023). Indeed, mixed forests can be considered a network of local interactions of individuals of different species (Vandermeer 1989; Michalet et al. 2015), and consequently, the mixture response should be the result of aggregated fine-scale variations in neighbourhood interactions (López-Marcos et al. 2021).

Within this context, we hypothesize that (1) the stand type influences the fine-scale properties of the topsoil organic matter quantity and quality and the forest floor biomass; and (2) this fine-scale variability on topsoil properties is related to the overstory but also to the understory. Therefore the aims of this study were (1) to test the stand type effect at fine-scale on the topsoil organic matter and the forest floor biomass; (2) to know the variability at fine-scale of the properties related to the topsoil organic matter and forest floor biomass, and their relationship with vegetation to assess (3) the niche amplitude and the individual effect of each under- and overstory species but also of the understory richness on the topsoil properties that better explain the topsoil organic matter quantity and quality.

Study area

The study area was in the Iberian System mountains, in North-Central Spain (41º47'35''N and 41º53'41''N latitude, and 2º56'12''W and 3º20'46''W longitude; Fig. 1). The dominant climate is Temperate with dry or temperate summer (Cfb, Csb) according to the Köppen (1936) classification for the Iberian Peninsula. The mean annual temperature ranges from 8.7 to 9.8 ºC and the annual precipitation ranges from 684 to 833 mm (Nafría-García et al. 2013). The altitude varies from 1093 m to 1277 m.a.s.l. and the slope is from 0.9 to 20% (López-Marcos et al. 2018, 2019). The geological parent materials are sandstones and marl from the Mesozoic era (IGME 2015). The soils are Inceptisols with a xeric soil moisture regime and mesic soil temperature regime and they are classified as Typic Dystroxerept or Typic Humixerept (sensu Soil Survey Staff 2022). The sandy soil texture is dominant, and the pH varies from extremely acid to strongly acid (see López-Marcos et al. 2018, 2021b). Dominant forests in the study area are pine forests and the close vegetation, highly degraded by anthropogenic action, is characterized by Pyrenean oak (Quercus pyrenaica Willd.) forests or communities dominated by junipers.

Experimental set-up

The research was carried out in six triplets distributed over the study area. Each triplet consists of three circular plots of 15 m radius, two monospecific plots dominated either by P. sylvestris (PS) or P. pinaster (PP) and one mixed plot that contains both species (MM), located less than 1 km from each other to ensure homogenous environmental conditions within each triplet (Fig. 1). In this same sense, the stand characteristics as age, density, basal area, and management they were intended to be similar within the triplets even though could differ among distinct triplets (López-Marcos et al. 2018).

The percentage of the basal area of the dominant species in the monospecific plots was greater than 83% and 95% for P. sylvestris and P. pinaster respectively, whereas the basal area percentage of both species in the mixed plots ranged from 33 to 67%. The age of the selected plots ranged between 44 and 151 years, the density between 509 and 1429 trees ha^− 1, the basal area between 33.3 and 70.3 m² ha^− 1 and the dominant height between 15.60 and 25.04 m. Traditional Forest management consists of strip clear-cutting with plowing and planting or sowing when necessary, and moderate thinning from below (Riofrío et al. 2019), promoting P. sylvestris when convenient (López-Marcos et al. 2019). The stands have had no silvicultural intervention or damage in the last ten years. Triplets belong to the network of permanent plots of the Sustainable Forest Management Research Institute UVa (iuFOR) and they have been previously used in a series of recent studies (Riofrío et al. 2017a, b; 2019; López-Marcos et al. 2018, 2019; 2020, 2021a,b; Cattaneo et al. 2020).

Within each plot, we implemented a fine-scale approach, so 180 small-size sampling units randomly located used for understory, overstory, forest floor and topsoil sampling [6 triplets x 3 forest stands (2 monospecific + 1 mixed) x 10 subplots/plot], following previous works in mixed forest (López-Marcos et al. 2021a; Osei et al. 2021; Getino-Álvarez et al. 2023). Each small-size sampling unit included one circular 4 m radius overstory inventory, one 1 m x 1 m square understory inventory, one 25 cm x 25 cm square forest floor sample and one 4 cm radio x 5 cm depth topsoil core. Those four analyses shared the same point centre. (Fig. 1; Appendix A).

Overstory sampling

Following Rodríguez-Calcerrada et al. (2011), the overstory vegetation was measured. So, the number and diameter of all stems > 7.5 cm in diameter were computed for every Pinus species. Total basal area (GT), and the basal area of each Pinus species were calculated (GPS: Pinus sylvestris basal area; GPP: Pinus pinaster basal area). In addition, the percentage of P. pinaster basal area (%GPP) was calculated as the ratio between the basal area of P. pinaster and the total basal area, and the same for the percentage of P. sylvestris basal area (%GPS).

Understory sampling

The cover (%) of every understory vascular plant species, including tree regeneration was visually estimated by the same observer in June 2016 (López-Marcos et al. 2019) to encompass and better identify the maximum number of vascular plant species (Alday et al. 2010). Richness was calculated per quadrat (Colwell 2009), including understory vegetation and tree regeneration (see López-Marcos et al. 2019).

Forest floor and topsoil sampling and laboratory analyses

Before forest floor sampling, coarse woody materials, such as large branches, were carefully removed from the forest floor (Andivia et al. 2016) to collect all the leaf litter from within the sampling square for further analysis. Once the forest floor biomass was taken, we collected an undisturbed topsoil sample using steel cylinders (251.3 cm³) to preserve its original structure.

Then, at the laboratory, both, the undisturbed topsoil and the forest floor samples were dried. The leaf litter was dried at 60°C for 48 hours and then was weighed (± 0.01 g) to determine the biomass (FFB). The soil samples were dried at 105°C for 24 h and then weighed (± 0.001 g) to calculate the soil bulk density (bD). Subsequently, the soil was sieved (2 mm) to calculate the percentage by weight of coarse fraction (> 2 mm; stones) and earth fraction (< 2 mm; %EF).

Trying to keep it as simple as possible and to focus on the role of the quantity and quality of topsoil organic matter, the analyses included quantifying the total organic carbon (TOC) and total nitrogen (TN) both by dry combustion using a Leco CHN 2000 elemental analyzer. Then eventually, the carbon:nitrogen ratio (C/N = TOC / TN) and Cstock (Cstock = TOC x bD x·%EF x T, where T: thickness; thickness was in all cases 5 cm depth) were calculated for each topsoil sample. Here, was used only the upper 5 cm of soil to quantify the Cstock since it is most clearly affected by the vegetation (Velázquez et al. 2023).

Data analyses

First, we analyzed the differences among stand types in topsoil TOC, TN, Cstock, C/N, bD and FFB using linear mixed models (LMM; Pinheiro and Bates 2000) with the restricted maximum likelihood method (REML; Richards 2005), considering triplet and subplot as the random terms.

Second, the subplots were ordered according to the topsoil properties and FFB in a principal component analysis (PCA). Standard deviational ellipses of each stand type were then used to illustrate the position of each stand type on the biplot (Oksanen 2016). The PCA let us identify the linkage of the axes to some variables using Pearson’s coefficient (p < 0.05).

Then the response pattern of both percentages of basal area of Pinus sylvestris (%GPS) and Pinus pinaster (%GPP), understory richness (Sα) and the cover percentage of each understory species along the PC1 and PC2 soil gradients were modeled by Huisman–Olff–Fresco (HOF) models (Huisman et al. 1993). These are a hierarchical set of five response models, ranked by their increasing complexity (Model I, monotone trend, i.e., with constant abundance; Model II, increasing or decreasing trend where the maximum is equal to the upper bound; Model III, increasing or decreasing trend where the maximum is below the upper bound; Model IV, symmetrical response curve; Model V, skewed response curve. The Akaike Information Criterion (AIC; Akaike 1973) was used to select the most appropriate response model (Johnson and Omland 2004); smaller values of AIC indicate better models. Finally, the location of species optima (µ) and niche widths (2t) for those species with unimodal responses were derived from the HOF models (Lawesson and Oksanen 2002). The 2t values were found by solving for the gradient points of the fitted HOF model relative to a strict Gaussian model at 2t (Lawesson and Oksanen 2002). In the case of a symmetric unimodal response, the lower and upper t values are identical, while with a skewed model, the 2t intervals are not necessarily equal. In addition, a bidimensional niche model was built to better detect the vegetation trend to the topsoil variability.

All statistical analyses were implemented in the R software environment (version 3.3.3; R Development Core Team 2016) using the nlme package for Linear Mixed Models (LMM, version 3.1–137; Pinheiro et al. 2018), the vegan package for multivariate analyses (version 2.3-5; Oksanen 2016) and the eHOF package for HOF modeling (version 3.2.2; Jansen and Oksanen 2013). One monospecific plot of P. sylvestris was treated as an outlier and excluded from all analyses because it was the only one that presented aquic conditions (see López-Marcos et al. 2019). Soils that have an aquic moisture regime are saturated long enough to cause anaerobic conditions (Soil Survey Staff 2022).

Differences in topsoil properties and forest floor biomass among stand types

The topsoil (5 cm depth) TOC, TN, C/N and bD differed significantly among stand types (Table 1). TOC and TN were higher in PS, lower in PP and intermediate in MM. C/N was higher in PP than in the other stand types (PS ~MM), and bD was lower in PS than in the other stand types (MM ~PP). No significant differences in the topsoil Cstock and FFB were found among monospecific and mixed stand types, although Cstock, showed patterns like TOC.

Topsoil variability

The two first dimensions of the PCA applied to the topsoil properties dataset, PC1 (λ₁ = 1.68) and PC2 (λ₁ = 1.13), explained together 68.47% of the subplot's ordination variability according to the topsoil properties and FFB (Figure 2).TOC, TN and C_stock increased in the negative end of PC1, being TOC (r = - 0.97), TN (r = - 0.97) and C_stock (r = -0.948) negatively correlated with the subplot coordinates along PC1. Furthermore, bD and C/N, and to a lesser extent FFB, increased towards the negative end of PC2, being bD (r = - 0.75) and C/N (r = - 0.74) negatively correlated with the subplot coordinates along PC2. Therefore, the ordination of the subplots by the PCA follows two gradients associated with the heterogeneity of the topsoil organic matter-related properties: the first gradient is mostly associated with the change in TOC of the soil samples and the second one is associated with C/N.

There is no clear subplot segregation according to the stand type in the bi-dimensional space delimited by PC1 and PC2 (Figure 2), and the standard deviational ellipses of the three stand types overlap, and the spatial arrangement of the subplots shows a massive group close to the origin of coordinates with two branches stretched over the negative side of PC1, mostly with and, with certain deviation, over the negative side of PC2, mostly parallel to C/N. Within this arrangement, PS subplots showed the highest dispersion over PC1, whereas PP did over PC2.

HOF models

The different variables related to the understory and overstory were modelled against the soil gradients identified in PC1 and PC2.

The understory richness (Sα) showed an increasing trend (HOF model II) along the PC1 (Figure 3.a1) and PC2 (Figure 3.a2) gradients. The %GPS showed a decreasing trend (HOF model II) along PC1 (Figure 3.a1) and an increasing trend (HOF model II) along PC2 (Figure 3.a2), whereas the %GPP exhibited an increasing trend bounded below the maximum attainable along PC1 (Figure 3.a1) and a unimodal skewed trend along PC2 (Figure 3.a2). The responses of individual understory species along PC1 and PC2 gradients were categorized into eight groups. Group 1 comprised ten species with a monotone response along PC1 and PC2 gradients (HOF model I) and are not depicted in Figure 3; they mostly had cover ≤ 1%: Arenaria montana L. (0.09%), Asphodelus albus Mill. (0.24%), Galium saxatile L. (0.62%), Geranium robertianum L. (0.02%), Hypochaeris radicata L. (0.11%), Ilex aquifolium L. (0.06%), Juncus conglomeratus L. (0.04%), Lotus corniculatus L. (0.04%), Quercus faginea Lam. (0.16%) and Sanguisorba minor Scop. (0.04%). Group 2 included three species that exhibited a HOF model II along PC1 gradient: Agrostis castellana Boiss. & Reut and Juniperus oxycedrus L with an increasing trend (Figure 3.e1), and Pinus sylvestris (regeneration) with a decreasing trend (Figure 3.c1). Group 3 included four species that showed a HOF Model II with an increasing trend along PC2 gradient: Erica australis L. (Figure 3.b2), Agrostis castellana, Pteridium aquilinum (L.) Kuhn, and Quercus pyrenaica Willd. (Figure 3.d2). Group 4 consisted solely of Hypnum spp. that showed a HOF Model III to both gradients, but along PC1 described a decreasing trend below maximum attainable response (Figure 3.b1) and along PC2 an increasing trend below maximum attainable response (Figure 3.b2). Group 5 included nine species exhibiting a symmetrical unimodal response (HOF Model IV) along the PC1 gradient: Arctostaphylos uva-ursi (L.) Spreng, Erica australis L. (Figure 3.b1), Calluna vulgaris (L.) Hull (Figure 3.d1), Deschampsia flexuosa (L.) Trin., Erica arborea L. (Figure 3.c1), Aira caryophyllea L., Melampyrum pratense L., Simethis mattiazzii (Vand.) Sacc. and Viola montcaunica Pau (Figure 3.e1). Group 6 comprised six species displaying a symmetrical unimodal response (HOF Model IV) along the PC2 gradient: Viola montcaunica (Figure 3.c2), Erica arborea, Juniperus oxycedrus (Figure 3.d2), Aira caryophyllea, Melampyrum pratense and Simethis mattiazzii (Figure 3.e2). Group 7 contained five species exhibiting a skewed unimodal response along the PC1 gradient: Pteridium aquilinum (Figure 3.c1), Cistus laurifolius L., Pinus pinaster (regeneration), Potentilla montana Brot. and Quercus pyrenaica (Figure 3.d1). Finally, group 8 consisted of seven species displaying a skewed unimodal response along the PC2 gradient: Arctostaphylos uva-ursi, Calluna vulgaris (Figure 3.b2), Pinus sylvestris (regeneration), Deschampsia flexuosa (Figure 3.c2), Pinus pinaster (regeneration), Cistus laurifolius (Figure 3.d2) and Potentilla montana (Figure 3.e2).

Probability of occurrence, optimum location, and niche amplitude

The %GPS achieves a higher probability of occurrence (h) along the PC1 gradient compared to the PC2 gradient, whereas the %GPP exhibited the opposite trend. The optimum (μ) of the %GPS was located at the beginning of PC1 and the ends of PC2 gradients. On the contrary, the optimum of the %GPP was located at the beginning of PC2 and at the ends of PC1 gradients. The niche width (2t) along PC1 gradient was slightly larger for %GPS than for %GPP, whereas the opposite pattern was found along PC2. On the other hand, Sα showed a higher probability of occurrence along PC2 than PC1 but a similar niche width, and its optimum was located both at the ends of the PC1 and PC2 gradients (Table 1 in Appendix B; Figure 4.a1 and Figure 3.a2).

Attending to the understory species, Arctostaphylos uva-ursi, Erica australis, and Hypnum spp. showed the highest probabilities of occurrence (10 < h < 40), being higher along the PC2 than the PC1 gradient (Table 1. in Appendix B; Figure 3.b1 and 3.b2). The optima of Erica australis and Hypnum spp were located at the ends of PC1 and PC2 gradients. Contrary, the Arctostaphylos uva-ursi optimum was located at the ends of PC1 but at the beginning of the PC2 gradient. The Calluna vulgaris optimum occupied an intermediate position along the PC2 gradient also with a high probability of occurrence to that gradient (Figure 3. b2; h >10). Deschampsia flexuosa, Erica arborea, Pteridium aquilinum and the regeneration of Pinus sylvestris showed a medium probability of occurrence (5<h<10) along the PC1 gradient (Figure 4.c1). Deschampsia flexuosa and Erica arborea optima occupied the end of PC1 whereas the optima of Pteridium aquilinum and Pinus sylvestris regeneration occupied the opposite PC1 gradient end. Deschampsia flexuosa, Pinus sylvestris regeneration and Viola montcaunica also showed a medium probability of occurrence (5<h<10) along the PC2 gradient (Figure 3.c2). The optimum of the Pinus sylvestris regeneration appeared in the end of PC2 whereas the optima of Deschampsia flexuosa and Viola montcaunica occupied the opposite PC2 gradient end. Calluna vulgaris, Cistus laurifolius, Pinus pinaster regeneration, Potentilla montana and Quercus pyrenaica showed a low probability of occurrence (2<h<5) along PC1 gradient (Figure 3.d1) with their optima towards the end of PC1. Agrostis castellana, Cistus laurifolius, Erica arborea, Juniperus oxycedrus, Pinus pinaster regeneration, Potentilla montana, Pteridium aquilinum and Quercus pyrenaica showed a low probability of occurrence (2<h<5) along the PC2 gradient (Figure 3.d2) with the optima of Cistus laurifolius, Pinus pinaster regeneration and Quercus pyrenaica in the end of PC2, and the optima of Erica arborea, Juniperus oxycedrus, Potentilla montana and Pteridium aquilinum in the opposite PC2 gradient end. Agrostis castellana, Aira caryophyllea, Juniperus oxycedrus, Melampyrum pratense, Simethis mattiazzii and Viola montcaunica showed a very low probability of occurrence (h<2) along PC1 gradient (Figure 3.e1) with Aira caryophyllea optimum in the PC1 gradient end with a higher quantity of soil organic matter. Lastly, Aira caryophyllea, Melampyrum pratense, and Simethis mattiazzii showed a very low probability of occurrence (h<2) along PC2 gradient (Figure 3.e2) with the Melampyrum pratense optimum appearing in the PC2 end (Table 1 in Appendix B).

Bidimensional niche model of under- and overstory vegetation along the topsoil organic matter gradients

Analysing the position of under- and overstory species concerning the PC1 and PC2 gradients using a bidimensional niche model three groups were identified (Figure 4). The first group of species displayed their optimum at the beginning of PC1 and the end of PC2: Aira caryophyllea, Pteridium aquilinum and the regeneration of Pinus sylvestris (Figure 4.a). The %GPS exhibited its optimum in this same area (Figure 4.b) where the majority of the PS plots are located (Figure 4.c). The second group of species showed their optimum at the end of PC1 and the beginning of PC2: Arctostaphylos uva-ursi, Calluna vulgaris, Cistus laurifolius, Melampyrum pratense, and the regeneration of Pinus pinaster and Quercus pyrenaica (Figure 4.a). Also, %GPP aligns its optima in this region (Figure 4.b) and it corresponds to most of the PP plots' location (Figure 4.c). The final group of species exhibited their optimum at the end of both PC1 and PC2 gradients: Agrostis castellana, Deschampsia flexuosa, Erica arborea, Erica australis, Hypnum spp., Juniperus oxycedrus, Potentilla montana, Simethis mattiazzii and Viola montcaunica (Figure 4.a). In these locations, Sα reached its optimum (Figure 4.b) and it coincides with the location of most of the MM plots (Figure 4.c). Notably, there were no species observed in the area at the beginning of both PC1 and PC2 gradients.

Our results investigating fine scale in mixed vs. monospecific pine stands indicate that the variations in quantity and quality of topsoil organic matter are related to the overstory but also to the understory vegetation composition. Here, the PC1, highly correlated to TOC, serves as a predictor of organic matter quantity since soil organic matter is a complex mixture of plant, animal, and microbial residues in several decomposition stages (Brady and Weil 2017) and it is primarily composed of carbon, oxygen, hydrogen, and small amounts of sulphur, nitrogen, phosphorus, potassium, calcium, and magnesium (Bot and Benites 2005). In addition, we use the PC2, highly correlated to the C/N, as a predictor of organic matter quality since the decomposition of organic matter is a process where factors such as a high lignin content of their sclerophyllous foliage with high carbon:nitrogen, carbon:phosphorous, lignin:nitrogen or lignin:phosphorus ratios can slow down the decomposition process, and thus the incorporation to the soil organic matter (Prescott and Vesterdal 2021; De Marco et al. 2022).

Topsoil organic matter as a function of the stand type using a fine-scale approach

Our analysis of fine-scale variations of organic matter-related topsoil properties and forest floor biomass using LMM indicates that there are stand-type effects on TOC, TN, C/N, and bD similar to those described by López-Marcos et al. (2018) at the stand level in Cstock and FFB. Within-stand variance, instead, is too high to find differences among stand-types, although Cstock follows a trend like TOC.

On the one hand, the monospecific P. sylvestris (PS) stands is linked to higher TOC values, and PP to higher C/N values, and this is very likely a consequence of P. pinaster recalcitrant (Herrero et al. 2016), coarse (Amaral Franco 1986) and high C/N leaf litter (López-Marcos et al. 2018). Thus, PS stands have high quantity and quality topsoil organic matter in contrast to monospecific P. pinaster (PP) stands. Even though intermediate values of TOC and C/N are expected in mixed stands (MM) according to the overstory composition, we only observed an intermediate quantity (TOC: PS > MM > PP) but a higher topsoil organic matter quality than expected because the topsoil C/N in MM stands is lower-than-expected from the overstory composition (C/N expected: PP > MM > PS vs. C/N observed: PP > MM ~ PS). There might be some effect of the understory, as indicated by Augusto et al. (2015) or Chen et al. (2019).

Regarding the other topsoil variables, TN follows the same pattern as TOC and Cstock given the close linkage between carbon and nitrogen cycling (Elbasiouny and Elbehiry 2019). Soil bulk density, which is a measure of the weight of the soil per unit volume, is related to the relative proportion of solid organic and inorganic particles and the porosity of the soil and varies inversely with respect to soil organic matter content (Saini 1966). Accordingly, we find opposite trends between TOC and bD across the different stand compositions. The absence of differences in the forest floor biomass (FFB) among stand types found in our study can be related to the multiple processes that affect the forest floor accumulation (Andivia et al. 2016; Prescott and Vesterdal 2021).

Topsoil organic matter heterogeneity. Fine-scale vs large scale approach

The topsoil TOC and Cstock (first 5 cm depth) measured in the 4-m radius subplot responded to the stand type effect in the same way that it was already described by López-Marcos et al. (2018) for the (one pit) upper 10 cm soil of 15-m radius plot in the same study area. At both spatial scales, topsoil TOC and Cstock were higher in PS, lower in PP and intermediate in MM stands, showing a fine-scale trend related to the dominant tree species, as López-Marcos et al. (2018) previously reported at the stand level.

On the other hand, topsoil C/N, which is expected to decreae during organic matter decomposition (Getino-Álvarez et al. 2023), at a smaller spatial scale showed an unexpected trend according to the forest floor C/N ratio pattern previously described by López-Marcos et al. (2018) and at the stand level. The topsoil C/N here was higher in PP than in the other stand types, among which there were no differences. This led us to think that the topsoil organic matter variability at a smaller spatial scale in pine stands is related not only to the overstory but also to the understory composition, as discussed below. Therefore, composite samples as in Cools et al. (2014) may hide understory effects such as those demonstrated in Rodríguez et al. (2009)

The different topsoil organic matter described at the stand level (previously reported by López-Marcos et al., 2018) and at a smaller spatial scale (just described here) could be linked to various trends in tree density and basal area at both spatial scales (López-Marcos et al., 2021c). The impact of different mixing percentages of stands on soil organic matter also influences, as reported by Getino-Álvarez et al. (2023). This is because, as suggested by Yeste et al. (2021), not only litterfall production but also root systems and secretions may play a crucial role. Also, as indicated above, the topsoil properties are the result of aggregated small-scale variations in neighbourhood interactions (López-Marcos et al. 2021a), this neighbourhood including both overstory and understory species, and being more complex in mixed forests (Vandermeer 1989; Michalet et al. 2015).

Defining the quantity and quality of soil organic matter as latent variables that explain their variability

Cstock and FFB, but also the other topsoil variables, showed very high within-stand-type and within-stand heterogeneity in both monospecific and mixed stands so that the differentiation of stand types (monospecific vs mixed stands) was not prioritized in the first dimensions of the PCA. The fine-scale heterogeneity in forest soils is often high as demonstrated by others (Rodríguez et al. 2009; Yeste et al. 2021).

Although the PCA applied on the topsoil organic matter and forest floor variability didn’t show clear subplot segregation according to the stand type, latent variables associated with the principal components, such as the quantity (PC1) and quality (PC2) of topsoil organic matter, were identified, and they explain almost 70% of the total variability found.

TOC, a proxy of organic matter (Howard and Howard 1990; Augusto et al. 2015; Gasch and DeJong-Hughes 2019), increased towards the negative end of PC1, so did Cstock and TN. C/N being almost orthogonal to PC1, means that higher TN in PC1 mostly indicates high organic matter content instead of higher organic matter quality, and hence that PC1 is equivalent with a gradient of quantity of soil organic matter.

On the other side, the C/N ratio, a proxy of the lower quality of organic matter (Yeste et al. 2021; Getino-Álvarez et al. 2023) increased towards the negative end of PC2, meaning that the highest organic matter quality is found in the subplots towards the positive end of PC2. bD over the PC2 is likely to be an indirect effect of the change of the soil's physical properties associated with the formation of aggregates during organic matter decomposition (Prescott and Vesterdal 2021) and, therefore, provides here similar information as C/N. These results suggested that PC1 and PC2 were related, respectively, to the quantity and quality of the topsoil organic matter, which represents the main gradients generating soil variability; almost 70% of the total variation here. The only variable with a weak relationship with both gradients is FFB, which is independent of PC1, the soil organic matter quantity, and is only associated with the soil quality.

The first principal component (PC1) is likely prioritizing the organic matter quantity because of the spatial patterns of trees and other plants in the forest, as the main contributor to the soil organic matter (Augusto et al. 2015; Prescott and Vesterdal 2021), is far more important than the stand type and the mean characteristics of the triplets (i.e., age, density) in our dataset.

The quality of topsoil organic matter was associated with PC2 given its high correlation with C/N ratio, considered an indicator of the soil organic matter quality (Ostrowska and Porębska 2015); the lower the C/N ratio the higher the soil organic matter quality, due to its higher humification degree and more stable soil organic matter (Prescott and Vesterdal 2021; Getino-Álvarez et al. 2023). Compared to organic matter quantity, the differences in quality are relatively small in our experimental set-up because the litter produced by the two dominant trees is relatively similar.

Linking topsoil organic matter quantity and quality with under- and overstory vegetation

The carbon input to forest soil is primarily determined by the type of vegetation, which means, the amount and characteristics of litter inputs that will determine the rate of soil organic matter formation (Cools et al. 2014; Andivia et al. 2016). Therefore, differences between Pinus sylvestris and Pinus pinaster leaf litter found in the same experimental set-up were related to the carbon input (see López-Marcos et al 2018); Pinus pinaster stands presenting more sclerophyllous foliage than Pinus sylvestris stands (Kattge et al. 2020). This finding is supported by the positive relationship found in this study between the C/N ratio and the %GPP since species with more sclerophyllous foliage have higher lignin content and higher C/N ratio (Augusto et al. 2015).

Hence, mixed pine stands (MM) are expected to have intermediate values for both variables (TOC, C/N) according to the overstory composition. However, in mixed forests, we found a C/N underperformance, that means a lower C/N of topsoil organic matter than what would be expected from the overstory composition. These results suggest that the dominant tree species is probably not the only one responsible for the variability in topsoil organic matter. As previously mentioned, these findings open the door to interpretations related to the rest of the vegetation that makes up the forest ecosystem, i.e. the understory. Some authors have already revealed that understory plants are more important to mediate litter decomposition than overstory (Augusto et al. 2015; Chen et al. 2019), e.g., by affecting enzymatic activity and microbial C use efficiency (Deng et al. 2023). In this regard, understory plants have already been demonstrated to generate soil heterogeneity in pine forests (Rodríguez et al. 2009).

According to Raunkiær’s life-forms classification of the understory species found in the same experimental set-up (see López-Marcos et al. 2019), low C/N litter is produced from parts of therophytes, geophytes, and hemicryptophytes that disappear massively every year during the most unfavorable season(s), and high C/N litter, related to high lignin content, recalcitrant organic matter of woody parts (López-Marcos et al. 2019) is expected from chamaephytes and phanerophytes inputs. This is also consistent with the common use of leaf C/N ratio and N content as a predictor of plant strategies (Louault et al. 2005; Ansquer et al. 2009) and, in particular, with the low C/N ratio of the litterfall of many hemicryptophytes of temperate terrestrial ecosystems (Kattge et al. 2020). Therefore, associating the plant's strategies to pass the unfavourable season (see Raunkiær’s classification of life-forms) to the litter inputs nature, and knowing the foliage characteristics of the dominant tree species is necessary to understand the quantity and quality of topsoil organic matter variability.

When the under- and overstory species and understory richness were modeled along the quantity (PC1) and quality (PC2) topsoil organic matter gradients (bidimensional niche analysis) three tendencies were reported. First, in locations with low quantity and quality of the topsoil organic matter, most of the PP subplots are located as well as the optimum of %GPP and the cover percentage of Arctostaphylos uva-ursi, Calluna vulgaris, Cistus laurifolius, Quercus pyrenaica (regeneration), Melampyrum pratense, and Pinus pinaster regeneration. Given the high presence of chamaephytes and phanerophytes in the understory and of Pinus pinaster in the overstory, a woodier understory litter input, and a more sclerophyllous overstory litter input are expected.

Second, in locations with high quantity and quality of the topsoil organic matter, most of the PP subplots are located as well as the optimum of %GPS and the cover percentage of Aira caryophyllea, Pteridium aquilinum and Pinus sylvestris regeneration. Given the high presence of therophytes and geophytes in the understory and Pinus sylvestris in the overstory, a more herbaceous understory litter input, and a less sclerophyllous overstory litter input are expected.

Third, in locations with low quantity but high quality of the topsoil organic matter, most of the MM subplots are located as well as the optimum cover percentage of Agrostis castellana, Deschampsia flexuosa, Erica arborea, Erica australis, Hypnum spp., Juniperus oxycedrus, Potentilla montana, Simethis mattiazzii and Viola montcaunica as well as the optimum of the understory richness (Sα). The greatest diversity of litter inputs both from the understory and the overstory is expected there. There is a high presence of hemicryptophytes, phanerophytes, and bryophytes from the understory that showed the greatest species richness. Consequently, there is a more heterogeneous understory litter input with a mixture of herbaceous and woody litter. Also, there is a codominance of both Pinus species in the overstory that, in turn, provides a more heterogeneous overstory litter input with a mixture of more and less sclerophyllous foliage.

Trends first and second are consistent with the assumptions that woodier and more sclerophyllous litter inputs present higher lignin content associated with a higher C/N ratio. The presence of more recalcitrant chemical compounds such as lignin could explain the lower decomposition rate of litter (Wang et al. 2016) and, in turn, the lower carbon input into the soil (López-Marcos et al. 2018). However, the third trend deserves further discussion.

In mixed stands, we expected to find intermediate values of both quantity and quality of topsoil organic matter given the overstory composition. The higher-than-expected quality of topsoil organic matter in mixed stands could be related to the higher diversity of litter inputs derived from the greater diversity of the understory, not only to the overstory mixture. The litter physicochemical diversity can have an important impact on the structure and operation of the litter microbial community when trees with different traits cohabit, since mixed litter has higher fungal and bacterial abundances and microbial community diversity than litter from a single source (Liu et al. 2022), being able to find a positive priming effect (Getino-Álvarez et al. 2023). Fontaine et al. (2003) have already postulated that the higher the chemical diversity of litter inputs is the higher the diversity of the produced enzymes and the probability of occurrence of the priming effect will be. This positive priming effect is defined as changes in the carbon and nitrogen mineralization of soil organic matter (acceleration, or positive priming effect, vs reduction, or negative priming effect) as a result of an increase in general microbial activity due to the greater availability of energy and nutrients released by fresh organic matter (plant residues, dead microorganisms, organic or mineral fertilizers; Fontaine et al. 2003; Getino-Álvarez et al. 2023). Fanin et al. (2020) have already reported a greater impact of labile over recalcitrant substrates on the priming effect magnitude. Indeed, here Sα is related to a high-quality topsoil organic matter since showing its optimum at the end of PC2 gradient. In particular, some understory species, such as Viola montcaunica and Potentilla montana and Desclampsia flexuosa, which have been previously described as key species in the maintenance of high understory richness in the MM stands (López-Marcos et al. 2021a) and codified as hemicryptophytes (López-Marcos et al. 2019), display its optimum in the same areas as Sα. However, even though the priming effect has been already reported in mixtures combining species with contrasting traits (Getino-Álvarez et al. 2023; Tian et al. 2023) little is still known about the mixture effect in pine forests with species belonging to the same genus, as is the case of the tree-dominant species, and even less about its relationship with the understory.

The results here presented suggest that the topsoil organic matter quantity, despite all the fine-scale heterogeneity at the stand level, is related to the overstory litter input, and the topsoil organic matter quality is related not only to the overstory but also to the understory litter inputs. These findings stress the need to include this reviled/unwanted vegetation in the management strategies of pine forest stands in order to conserve a biodiverse understory that contributes to topsoil organic matter of high quality.

The topsoil properties related to organic matter quantity (TOC, Cstock, and TN) have a decreasing trend as the overstory is more sclerophyllous, whereas the topsoil properties related to organic matter quality (C/N) follow an unexpected trend according to composition, suggesting that understory vegetation is also contributing. Thus, these results highlight the importance of the relationship between under- and overstory vegetation and the quantity and quality of topsoil organic matter in mixed vs monospecific Pine stands.

In PP, where more sclerophyllous overstory species and woodier understory species dominate, the quantity and quality of topsoil organic matter is lower. In PS, where less sclerophyllous overstory species and more herbaceous understory species dominate, the quantity and quality of topsoil organic matter is higher. Nevertheless, in MM, where there is a more diverse overstory (cohabit diverse sclerophyll tree species) and understory (a mixture of woody and herbaceous species), the quantity of topsoil organic matter shows intermediate values whereas the organic matter quality shows higher values than expected given their overstory composition. We argue that the higher diversity of under- and overstory, and the presence of some hemicryptophytes such as Viola montcaunica, Potentilla montana, and Deschampsia flexuosa are responsible for a higher quality of topsoil organic matter and as a result, the mixed pine forests maintain the topsoil organic matter quality thanks to the understory richness.

These findings make us reflect on the need to include the understory vegetation in the management strategies of these forest stands to conserve a biodiverse understory that lets us maintain a topsoil organic matter of high quality.

Funding. This research was funded by a predoctoral grant to DLM (BES-2015-072852) and the Projects FORMIXING (AGL2014- 51964-C2-1-R), IMFLEX (PID2021-126275OB-C22 from the Spanish Ministry of Science and Innovation and of Economy and Competitiveness and also by the Project ‘Project ‘CLU-2019-01-iuFOR Institute Unit of Excellence’ and its International Reinforce (CL-EI-2021-05) of the University of Valladolid, funded by the Junta de Castilla and co-financed by the European Union (ERDF “Europe drives our growth”)

Conflicts of interest/Competing interests. The authors declare that they have no conflict of interest.

Availability of data and material. Available upon reasonable request.

Authors' contributions. DLM, MBT, FB and CMR conceptualized the sampling design. FB carried out the overstory vegetation fieldwork. DLM carried out the soil and understory vegetation field and laboratory work, ran the data analysis and discussed the results. DLM, JGD and CMR discussed data analysis and commented on the results and discussion. CMR and JGD supported DLM with the statistical analysis. MBT supported DLM with the laboratory analysis. DLM, CMR, JGD, MBT and FB edited the manuscript. FB coordinated the research project.

Acknowledgements. We would like to thank Luis Alfonso Ramos Calvo for his invaluable help with soil sampling. Carmen Blanco and Juan Carlos Arranz for their advice in the laboratory analyses. José Riofrío and Cristóbal Ordóñez for their assistance in the characterization and location of plots in the field, and the Regional Forest Service of Castilla and León for facilitating the triplet’s installation and monitoring.

Akaike H (1973) Information theory as an extension of the maximum likelihood principle. In: Brillinger D, Gani J, Hartigan J (eds) Second International Symposium on Information Theory. Akademiai Kiado, Budapest (Hungary), pp 267–281
Alday JG, Martínez-Ruiz C, Marrs RH, Bravo F (2010) Influence of harvesting intensity on the floristic composition of natural Mediterranean maritime pine forest. Acta Oecol 36:349–356. https://doi.org/10.1016/j.actao.2010.03.001
Amaral Franco J (1986) Género Pinus L. In: Castroviejo S, Aedo C, Laínz M et al (eds) Flora Iberica. Real Jardín Botánico. CSIC, Madrid, pp 168–174
Andivia E, Rolo V, Jonard M et al (2016) Tree species identity mediates mechanisms of topsoil carbon sequestration in a Norway spruce and European beech mixed forest. 437–447. https://doi.org/10.1007/s13595-015-0536-z
Ansquer P, Duru M, Theau JP, Cruz P (2009) Convergence in plant traits between species within grassland communities simplifies their monitoring. Ecol Indic 9:1020–1029. https://doi.org/10.1016/j.ecolind.2008.12.002
Augusto L, De Schrijver A, Vesterdal L et al (2015) Influences of evergreen gymnosperm and deciduous angiosperm tree species on the functioning of temperate and boreal forests. Biol Rev 90:444–466. https://doi.org/10.1111/brv.12119
Bakker M, Augusto L, Achat DL (2006) Fine root distribution of trees and understory in mature stands of maritime pine (Pinus pinaster) on dry and humid sites. Plant Soil 286:37–51. https://doi.org/10.1007/s11104-006-9024-4
Bogino SM, Bravo F (2014) Carbon stable isotope-climate association in tree rings of Pinus pinaster and Pinus sylvestris in Mediterranean environments. Bosque 35:175–184. https://doi.org/10.4067/S0717-92002014000200005
Bot A, Benites J (2005) The importance of soil organic matter Key to drought-resistant soil and sustained food production. FAO Soils Bulletin 80, FAO, Rome (Italy)
Brady N, Weil R (2017) The Nature and Properties of Soils. 15th Edition, Pearson, New York
Bravo-Oviedo A, Pretzsch H, Ammer C et al (2014) European Mixed Forests: definition and research perspectives. For Syst 23:518–533
Cattaneo N, Schneider R, Bravo F, Bravo-Oviedo A (2020) Inter-specific competition of tree congeners induces changes in crown architecture in Mediterranean pine mixtures. For Ecol Manage 476:118471. https://doi.org/10.1016/j.foreco.2020.118471
Chen Y, Cai X, Zhang Y et al (2017) Dynamics of understory shrub biomass in six young plantations of southern subtropical China. Forests 8:1–12. https://doi.org/10.3390/f8110419
Chen Y, Zhang Y, Cao J et al (2019) Stand age and species traits alter the effects of understory removal on litter decomposition and nutrient dynamics in subtropical Eucalyptus plantations. Glob Ecol Conserv 20:e00693. https://doi.org/10.1016/j.gecco.2019.e00693
Colwell RK (2009) Biodiversity: Concepts, Patterns and Measurement. In: Simon AL (ed) The Princeton Guide to Ecology. Princeton University Press, Princeton (USA), pp 257–263
Cools N, Vesterdal L, De Vos B et al (2014) Tree species is the major factor explaining C:N ratios in European forest soils. For Ecol Manage 311:3–16. https://doi.org/10.1016/j.foreco.2013.06.047
De Marco A, Panico SC, Memoli V et al (2022) Differences in soil carbon and nitrogen pools between afforested pine forests and natural shrublands in a Mediterranean area. Appl Soil Ecol 170:104262. https://doi.org/10.1016/j.apsoil.2021.104262
Del Río M, Pretzsch H, Alberdi I et al (2015) Characterization of the structure, dynamics, and productivity of mixed-species stands: review and perspectives. Eur J For Res 135:23–49. https://doi.org/10.1007/s10342-015-0927-6
Deng C, Lyu M, Xiong X et al (2023) Understory ferns removal downregulates microbial carbon use efficiency and carbon accrual in previously degraded lands. Agric For Meteorol 340:109631. https://doi.org/10.1016/j.agrformet.2023.109631
Elbasiouny H, Elbehiry F (2019) Soil carbon and nitrogen stocks and fractions for improving soil quality and mitigating climate change. Egypt J Soil Sci 59:131–144. https://doi.org/10.21608/ejss.2019.9984.1251
European Commission (2010) Green Paper on Forest protection and information in the EU: Preparing forests for climate change. Brussels - Belgium
Fanin N, Alavoine G, Bertrand I (2020) Temporal dynamics of litter quality, soil properties and microbial strategies as main drivers of the priming effect. Geoderma 377:114576. https://doi.org/10.1016/j.geoderma.2020.114576
Felton A, Lindbladh M, Brunet J, Fritz Ö (2010) Replacing coniferous monocultures with mixed-species production stands: An assessment of the potential benefits for forest biodiversity in northern Europe. For Ecol Manage 260:939–947. https://doi.org/10.1016/j.foreco.2010.06.011
Fontaine S, Mariotti A, Abbadie L (2003) The priming effect of organic matter: A question of microbial competition? Soil Biol Biochem 35:837–843. https://doi.org/10.1016/S0038-0717(03)00123-8
Gamfeldt L, Snäll T, Bagchi R et al (2013) Higher levels of multiple ecosystem services are found in forests with more tree species. Nat Commun 4:1340. https://doi.org/10.1038/ncomms2328
Gasch C, DeJong-Hughes J (2019) Soil organic matter. Does matter. North Dakota State University. Extension publication SF1942. Available at: https://www.ndsu.edu/agriculture/sites/default/files/2022-08/sf1942.pdf
Getino-Álvarez M, San-Martin R, Pretzsch H et al (2023) Assessing soil C stock and C to N ratio of soil organic matter under mixed pine-beech forests at different scales. Eur J For Res 142:1081–1098. https://doi.org/10.1007/s10342-023-01578-5
Herrero C, Turrión MB, Pando V, Bravo F (2016) Carbon content of forest floor and mineral soil in Mediterranean Pinus spp. and Oak stands in acid soils in Northern Spain. For Syst 25:e065. https://doi.org/10.5424/fs/2016252-09149
Hodgson AJG, Wilson PJ, Hunt R et al (1999) Allocating C-S-R Plant Functional Types: A Soft Approach to a Hard Problem Published by : Wiley on behalf of Nordic Society Oikos Stable URL : http://www.jstor.org/stable/3546494 REFERENCES Linked references are available on JSTOR fo. Oikos 85:282–294
Howard PJA, Howard DM (1990) Use of organic carbon and loss-on-ignition to estimate soil organic matter in different soil types and horizons. Biol Fertil Soils 9:306–310. https://doi.org/10.1007/BF00634106
Huisman J, Olff H, Fresco LFM (1993) A hierarchical set of models for species response analysis. J Veg Sci CN – 3619 4:37–46
IGME (2015) Mapa Geológico de la Península Ibérica, Baleares y Canarias a escala 1M
Jactel H, Nicoll BC, Branco M et al (2009) The influences of forest stand management on biotic and abiotic risks of damage. Ann For Sci 66:1–18. https://doi.org/10.1051/forest/2009054
Jandl R, Lindner M, Vesterdal L et al (2007) How strongly can forest management influence soil carbon sequestration? 137:253–268. https://doi.org/10.1016/j.geoderma.2006.09.003
Jansen F, Oksanen J (2013) How to model species responses along ecological gradients - Huisman-Olff-Fresco models revisited. J Veg Sci 24:1108–1117. https://doi.org/10.1111/jvs.12050
Johnson JB, Omland KS (2004) Model selection in ecology and evolution. Trends Ecol Evol 19:101–108. https://doi.org/10.1016/j.tree.2003.10.013
Kattge J, Bönisch G, Díaz S et al (2020) TRY plant trait database – enhanced coverage and open access. Glob Chang Biol 26:119–188. https://doi.org/10.1111/gcb.14904
Köppen W (1936) Das geographische System der Klimate. In: Handbuch der Klimatologie. pp 7–30
Laganière J, Augusto L, Hatten JA, Spielvogel S (2022) Vegetation Effects on Soil Organic Matter in Forested Ecosystems. Front Glob Chang 4:828701. https://doi.org/10.3389/ffgc.2021.828701
Lawesson JE, Oksanen J (2002) Niche characteristics of Danish woody species as derived from coenoclines. J Veg Sci 13:279–290
Liu J, Ding C, Zhang W et al (2022) Litter mixing promoted decomposition rate through increasing diversities of phyllosphere microbial communities. Front Microbiol 13:1–15. https://doi.org/10.3389/fmicb.2022.1009091
López-Marcos D, Martínez-Ruiz C, Turrión M-B et al (2018) Soil carbon stocks and exchangeable cations in monospecific and mixed pine forests. Eur J For Res 137:831–847. https://doi.org/10.1007/s10342-018-1143-y
López-Marcos D, Turrión MB, Bravo F, Martínez-Ruiz C (2019) Understory response to overstory and soil gradients in mixed vs. monospecific Mediterranean pine forests. Eur J For Res 138:939–955. https://doi.org/10.1007/s10342-019-01215-0
López-Marcos D, Turrión M, Bravo F, Martínez-Ruiz C (2020) Can mixed pine forests conserve understory richness by improving the establishment of understory species typical of native oak forests? Ann For Sci 77:15. https://doi.org/10.1007/s13595-020-0919-7
López-Marcos D, Turrión MB, Bravo F, Martínez-Ruiz C (2021a) The over-yielding at a small scale in mixed vs. monospecific pine forest is a result of the soil niche complementarity: understory richness implications. Eur J For Res 40:777–791. https://doi.org/10.1007/s10342-021-01365-0
Louault F, Pillar VD, Aufrère J et al (2005) Plant traits and functional types in response to reduced disturbance in a semi-natural grassland. J Veg Sci 16:151–160. https://doi.org/10.1111/j.1654-1103.2005.tb02350.x
MAGRAMA (2012) Infraestructura de datos espaciales. https://sig.mapama.gob.es/geoportal/
Matías L, Jump AS (2012) Interactions between growth, demography and biotic interactions in determining species range limits in a warming world: The case of Pinus sylvestris. For Ecol Manage 282:10–22. https://doi.org/10.1016/j.foreco.2012.06.053
MCPFE (2003) Improved pan-European indicators for sustainable forest management as adopted by the MCPFE Expert Level Meeting. In: Ministerial Conference on the Protection of Forests in Europe. Vienna (Austria)
Mestre L, Toro-Manríquez M, Soler R et al (2017) The influence of canopy-layer composition on understory plant diversity in southern temperate forests. For Ecosyst 4:6. https://doi.org/10.1186/s40663-017-0093-z
Michalet R, Chen SY, An LZ et al (2015) Communities: Are they groups of hidden interactions? J Veg Sci 26:207–218. https://doi.org/10.1111/jvs.12226
Nafría-García DA, Garrido-del-Pozo N, Álvarez-Arias MV et al (2013) Atlas agroclimático de Castilla y León, 1st edn. Instituto Tecnológico Agrario, Junta de Castilla y León- Agencia Estatal de Meteorología
Oksanen J (2016) Vegan : an introduction to ordination. 1–12
Osei R, Titeux H, Bielak K et al (2021) Tree species identity drives soil organic carbon storage more than species mixing in major two-species mixtures (pine, oak, beech) in Europe. For Ecol Manage 481:118752. https://doi.org/10.1016/j.foreco.2020.118752
Ostrowska A, Porębska G (2015) Assessment of the C/N ratio as an indicator of the decomposability of organic matter in forest soils. Ecol Indic 49:104–109. https://doi.org/10.1016/j.ecolind.2014.09.044
Pan P, Zhao F, Ning J et al (2018) Impact of understory vegetation on soil carbon and nitrogen dynamic in aerially seeded Pinus massoniana plantations. PLoS ONE 13:1–13. https://doi.org/10.1371/journal.pone.0191952
Pinheiro J, Bates D (2000) Mixed-effects models in S and S-Plus. Springer-Verlag, New York (USA)
Pinheiro J, Bates D, DebRoy S et al (2018) Package nlme
Prescott CE, Vesterdal L (2021) Decomposition and transformations along the continuum from litter to soil organic matter in forest soils. For Ecol Manage 498:119522. https://doi.org/10.1016/j.foreco.2021.119522
Puentes A, Pías B, Basanta M (2016) Vertical structure of Erica umbellata, a representative species of European Ibero-Atlantic dry heaths. Plant Biosyst - An Int J Deal with all Asp Plant Biol 152:110–119. https://doi.org/10.1080/11263504.2016.1255270
Rasse DP, Rumpel C, Dignac MF (2005) Is soil carbon mostly root carbon? Mechanisms for a specific stabilisation. Plant Soil 269:341–356. https://doi.org/10.1007/s11104-004-0907-y
Richards A (2005) Testing ecological theory using the information-theoretic approach: examples and cautionary results. Ecology 86:2805–2814. https://doi.org/10.1890/05-0074
Riofrío J, del Río M, Maguire D, Bravo F (2019) Species Mixing Effects on Height–Diameter and Basal Area Increment Models for Scots Pine and Maritime Pine. Forests 10:249. https://doi.org/10.3390/f10030249
Riofrío J, del Río M, Pretzsch H, Bravo F (2017) Changes in structural heterogeneity and stand productivity by mixing Scots pine and Maritime pine. For Ecol Manage 405:219–228. https://doi.org/10.1016/j.foreco.2017.09.036
Riofrío J, del Río M, Bravo F (2016) Mixing effects on growth efficiency in mixed pine forests. Forestry 90:381–392. https://doi.org/10.1093/forestry/cpw056
Rodríguez-Calcerrada J, Nanos N, del Rey MC et al (2011) Small-scale variation of vegetation in a mixed forest understorey is partly controlled by the effect of overstory composition on litter accumulation. J For Res 16:473–483. https://doi.org/10.1007/s10310-010-0237-2
Rodríguez A, Durán J, Fernández-Palacios JM, Gallardo A (2009) Spatial pattern and scale of soil N and P fractions under the influence of a leguminous shrub in a Pinus canariensis forest. Geoderma 151:303–310. https://doi.org/10.1016/j.geoderma.2009.04.019
Ruiz-Peinado R, Bravo-Oviedo A, López-Senespleda E et al (2017) Forest management and carbon sequestration in the Mediterranean region: A review. For Syst 26:1–25. https://doi.org/10.5424/fs/2017262-11205
Saini GR (1966) Organic Matter as a measure of bulk density of soil. Nature 210:1295–1296
Scher MA, Barclay RS, Baczynski AA et al (2022) The effect of CO₂ concentration on carbon isotope discrimination during photosynthesis in Ginkgo biloba: implications for reconstructing atmospheric CO₂ levels in the geologic past. Geochim Cosmochim Acta 337:82–94. https://doi.org/10.1016/j.gca.2022.09.033
Serrada R, Montero G, Reque JA (2008) Compendio de selvicultura aplicada en España. Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria-INIA., Mdrid. (Spain)
Soil Survey Staff (2022) Keys to soil taxonomy, 13th edn
Vandermeer J (1989) The ecology of intercropping. Cambridge University Press, UK
Vega JA, Arellano-Pérez S, Álvarez-González JG et al (2022) Modelling aboveground biomass and fuel load components at stand level in shrub communities in NW Spain. For Ecol Manage 505:119926. https://doi.org/10.1016/j.foreco.2021.119926
Velázquez E, Turrión MB, Bravo F (2023) Carbon accumulation over 70 years of old-field succession driven by live biomass in a Mediterranean continental environment. For Ecol Manage 548:121422. https://doi.org/10.1016/j.foreco.2023.121422
Wang J, You Y, Tang Z et al (2016) A comparison of decomposition dynamics among green tree leaves, partially decomposed tree leaf litter and their mixture in a warm temperate forest ecosystem. J For Res 27:1037–1045. https://doi.org/10.1007/s11676-016-0248-8
Xu Y, Liu K, Yao S et al (2022) Formation efficiency of soil organic matter from plant litter is governed by clay mineral type more than plant litter quality. Geoderma 412:115727. https://doi.org/10.1016/j.geoderma.2022.115727
Yeste A, Blanco JA, Imbert JB et al (2021) Pinus sylvestris L. and Fagus sylvatica L. effects on soil and root properties and their interactions in a mixed forest on the Southwestern Pyrenees. For Ecol Manage 481:118726. https://doi.org/10.1016/j.foreco.2020.118726

Table 1. Summary results of the linear mixed models testing the effect of stand type [PS: P. sylvestris monospecific stands (n=50), MM: mixed stands (n=60), and PP: P. pinaster monospecific stands (n=60)] on the organic matter topsoil (first 5 cm depth) variables and the forest floor biomass (mean ± standard error). Different letters indicate differences between pairs of stand types (p<0.05). Abbreviations: TOC: total organic carbon; TN: total nitrogen; C/N: TOC/TN ratio; Cstock: carbon stock; bD: bulk density; FFB: forest floor biomass.

		PS				MM				PP				F _{[2, 162]}	p
		Mean	±	SE		Mean	±	SE		Mean	±	SE		F _{[2, 162]}	p
TOC	g kg^-1	94.61	±	13.17	b	72.76	±	4.56	ab	66.34	±	4.20	a	3.62	0.03
TN	g kg^-1	2.43	±	0.31	c	1.87	±	0.12	b	1.48	±	0.10	a	6.78	0.00
C/N		40.87	±	1.71	a	42.02	±	1.67	a	50.34	±	3.05	b	5.22	0.01
Cstock	Mg ha^-1	29.21	±	5.22	a	25.97	±	2.28	a	19.55	±	1.62	a	2.27	0.11
bD	g cm^-3	0.61	±	0.03	a	0.71	±	0.02	b	0.67	±	0.03	b	6.04	0.00
FFB	Mg ha^-1	13.41	±	1.52	a	12.68	±	0.93	a	14.96	±	1.44	a	0.91	0.40

Download PDF

Version 1

posted

You are reading this latest preprint version

Quality and quantity of topsoil organic matter in monospecific and mixed pine forests: role of overstory and understory vegetation

Status:

Version 1

Abstract

Hypothesis and aims

Methods

Results

Conclusion

Figures

Introduction

Material and methods

Study area

Experimental set-up

Overstory sampling

Understory sampling

Forest floor and topsoil sampling and laboratory analyses

Data analyses

Results

Differences in topsoil properties and forest floor biomass among stand types

Topsoil variability

HOF models

Probability of occurrence, optimum location, and niche amplitude

Bidimensional niche model of under- and overstory vegetation along the topsoil organic matter gradients

Discussion

Topsoil organic matter as a function of the stand type using a fine-scale approach

Topsoil organic matter heterogeneity. Fine-scale vs large scale approach

Linking topsoil organic matter quantity and quality with under- and overstory vegetation

Conclusions

Declarations

References

Tables

Supplementary Files

Status:

Version 1