Revista Mexicana de Ciencias Forestales Vol. 16 (90)

Julio - Agosto (2025)

Reception date/Fecha de recepción: 23 de enero de 2025.

Acceptance date/Fecha de aceptación: 6 de mayo de 2025.

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¹Instituto de Ciencias Agropecuarias y Rurales, Universidad Autónoma del Estado de México. México.

²Colegio de Postgraduados, Campus Montecillo. México.

³Institut National de Recherche Agronomique

⁴Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP), Centro Nacional de Investigación Disciplinaria en Conservación y Mejoramiento de Ecosistemas Forestales (CENID-COMEF). México.

*Corresponding author; e-mail: marpersua@gmail.com

*Autor para correspondencia; correo-e: marpersua@gmail.com

Abstract

High-elevation mountain forests (>2 500 m) play a crucial role in long-term carbon storage. This research aimed to determine how edaphic, climatic, and vegetal variables influence soil organic carbon (SOC) stocks along an elevation gradient in the Pinus hartwegii forest at Nevado de Toluca, Mexico. A total of 140 topsoil samples (0-15 cm depth) were collected at 100 m intervals between 3 400 and 4 000 masl. Soil samples were analyzed to SOC (oxide-reduction method), bulk density (BD; cylinder method), pH, and texture (Bouyoucos method). In addition, climate data, including mean annual temperature and precipitation, were obtained from the ClimateNA model v5.10. Relationships among soil properties, vegetation structure, and climatic variables were analyzed, comparing logged (3 400-3 800 m) and unlogged (3 900-4 000 m) plots. SOC stocks increased linearly with elevation (r²=0.70; p=0.02), peaking at 4 000 m (173.1±5.2 Mg C ha^-1) and reaching a minimum at 3 700 m (146.8±5.72 Mg C ha^-1). Higher SOC at 4 000 m was associated with lower temperatures and larger P. hartwegii trees. Findings highlight that SOC stocks vary along the elevation gradient, with reduced decomposition rates at higher elevations promoting accumulation. At lower elevations, logging reduced SOC due to vegetation alterations, disrupting organic matter inputs and microsite conditions. These results suggest that P. hartwegii logging may weaken the role of mountain forest soils in mitigating climate change by accelerating soil organic matter decomposition.

Key words: Climate change, elevation gradient, herbaceous vegetation, logging, organic matter, trees.

Resumen

Los bosques de alta montaña (>2 500 m) desempeñan un papel crucial en el almacenamiento de carbono a largo plazo. El objetivo de esta investigación fue determinar cómo las variables edáficas, climáticas y vegetales influyen en las existencias de carbono orgánico del suelo (COS) a lo largo de un gradiente altitudinal en el bosque de Pinus hartwegii en el Nevado de Toluca, México. Un total de 140 muestras de suelo superficial (0-15 cm de profundidad) fueron recolectadas a intervalos de 100 m entre los 3 400 y 4 000 m. El COS fue determinado por el método de óxido-reducción, y la densidad aparente (DA) por el método del cilindro. Se analizaron el pH (1:2 KCl) y la textura (método Bouyoucos) del suelo. Los datos climáticos, incluidas la temperatura media anual y las precipitaciones, se obtuvieron a partir del modelo ClimateNA v5.10. Se analizaron las relaciones entre las propiedades del suelo, la estructura de la vegetación y las variables climáticas, comparando parcelas taladas (3 400-3 800 m) y no taladas (3 900-4 000 m). Las existencias de COS aumentaron linealmente con la elevación (r²=0.70; p=0.02), y alcanzaron el máximo valor a los a 4 000 m (173.1±5.2 Mg C ha^-1) y el mínimo a 3 700 m (146.8±5.72 Mg C ha^-1). Un mayor COS a 4 000 m se asoció con temperaturas más bajas y árboles más grandes. La tala en cotas bajas redujo el COS debido a las alteraciones de la vegetación. Los hallazgos ponen de manifiesto que las reservas de COS varían a lo largo del gradiente de elevación, con menores tasas de descomposición a mayor altitud que favorecen la acumulación. La tala agrava la pérdida de COS al alterar los aportes de materia orgánica y las condiciones de los micrositios. Estos resultados sugieren que la tala puede debilitar el papel de los suelos forestales de montaña en la mitigación del cambio climático al acelerar la descomposición de la materia orgánica del suelo.

Palabras clave: Cambio climático, gradiente de elevación, vegetación herbácea, tala, materia orgánica, árboles.

Introduction

Mountain forests at elevations of 2 500 m or more account for 23 % of the Earth's forested area, covering about 9 000 000 km² (Price et al., 2011), which is crucial in regulating climate and water cycles. However, their capacity to store carbon (C) under cooler temperatures remains less understood. Globally, forests store 1.146 petagrams (Pg) of C, with two-thirds of this amount contained within the soil (Dixon et al., 1994). Mountain soils hold greater carbon reserves than forests at lower elevations (below 1 000 m) due to lower temperatures that reduce decomposition rates (Swetnam et al., 2017; Wiesmeier et al., 2019).

As sensitive terrestrial ecosystems, mountain forests face significant risks from climate change (Pepin et al., 2015). Increasing temperatures could impact soil carbon stocks, especially in tropical regions with more intense warming at higher elevations (Field et al., 2014). Temperature increases in mountain ecosystems may simultaneously enhance soil organic carbon (SOC) inputs and outputs (Davidson & Janssens, 2006; Wiesmeier et al., 2019).

Two hypotheses explain these effects. The first proposes that higher temperatures enhance the decomposition of soil organic matter (SOM), leading to increased CO₂ emissions from the soil, thereby creating a positive feedback loop (Kirschbaum, 2000). The second posits that faster SOM decomposition boosts soil nitrogen (N) availability, enhancing plant productivity, litter production, and SOC input (negative feedback) (Berg & Meentemeyer, 2002). The net warming effect remains uncertain, this is influenced by the spatial heterogeneity of mountain forests, which is driven by both biotic factors (such as plant species) and abiotic factors (including soil type, depth, slope, and elevation), along with their interactions (Kumar et al., 2012; Salomé et al., 2010).

SOC stability depends on physical protection (e. g., aggregates), chemical reactions, and soil biota activity (e. g., fungi, bacteria, roots) (Wiesmeier et al., 2019). Elevation gradients, used as proxies for temperature, provide realistic conditions to assess warming impacts on SOC (Körner, 2007; Tito et al., 2020). Research has revealed varying SOC-elevation relationships: negative (Kumar et al., 2012; Sheikh et al., 2009), positive (Du et al., 2014; Tashi et al., 2016), or none (Tewksbury & Van Miegroet, 2007; Zhu et al., 2010). High-elevation SOC accumulation is often linked to lower temperatures (Tashi et al., 2016), while factors like harvesting and logging strongly affect lowland forests (Jafari et al., 2013). Logging reduces SOC by 8-11.2 % globally, with effects lasting up to 45 years (Chiti et al., 2016; James & Harrison, 2016).

More than 50 % of Mexico's land area is situated at elevations exceeding 1 000 m (Challenger, 1998). Pinus hartwegii Lindl., a dominant mountain pine found at 3 500-4 000 masl, offers a unique opportunity to study ecological processes across elevation gradients. Climate change is driving an upward redistribution of this species, reducing its environmental suitability and potentially affecting ecosystem services like climate regulation and SOC storage (Cruz-Cárdenas et al., 2016; Gómez-Mendoza & Arriaga, 2007). These forests mainly thrive on Andosols, volcanic soils known for their high content of SOM, and their considerable potential for SOC sequestration (Neall, 2006). Including SOC stored in these soils in national carbon inventories is essential, as past efforts have focused mainly on above-ground carbon (Santini et al., 2019). This research aimed to determine how edaphic, climatic and vegetation variables influence the distribution of soil organic carbon (SOC) stocks along an elevation gradient in the P. hartwegii forest at Nevado de Toluca, Mexico, using elevation as a temperature proxy to understand warming's impact on mountain forest SOC stocks.

Materials and Methods

Study sites

The study took place in Nevado de Toluca Flora and Fauna Protected Area (FFPA) (19°06′06″ N, 99°46′03″ W), about 23 km Southwest of Toluca City, State of Mexico. Andosols cover 90 % of the area (Körner & Paulsen, 2004). The isothermal climate has a mean annual temperature (MAT) of -2 to 5 °C (García, 1990). Based on historical precipitation records from 1961 to 2016, rainfall in Toluca occurs mainly from May to October. July was the month with the most abundant rainfall, exceeding 200 mm (Figure 1).

San Francisco Oxtotilpan = San Francisco Oxtotilpan town; San Juan de las Huertas = San Juan de las Huertas town; San Miguel Balderas = San Miguel Balderas town. The region is dominated by Andosol soil (Organización de las Naciones Unidas para la Alimentación y la Agricultura [ONUAA], 2008). The climate diagram shows data for the 1961-2016 period from a meteorological station at 4 139 m (19°07'33" N, 99°46'15" W).

Figure 1. The geographical location of Mexico and the Nevado de Toluca Flora and Fauna Protected Area in the State of Mexico, where sampling was conducted.

Vegetation varies with altitude. P. hartwegii forests dominate the range between 3 500 and 4 000 m and typically reach heights of 25-35 m, but their size becomes smaller at higher elevations(Alfaro-Ramírez et al., 2017). Other tree species, such as Pinus L., Quercus L., Arbutus L. and Juniperus L. are occasionally interspersed. Understory includes grasses such as Penstemon gentianoides (Kunth) Poir., Eupatorium glabratum Kunth, Baccharis conferta Kunth, Muhlenbergia quadridentata (Kunth) Trin., M. macroura (Kunth) Hitchc., Festuca tolucensis Kunth, F. hephaestophila (Nees) Nees and patches of Lupinus sp. (Rzedowski, 1991).

Fieldwork sampling

Stratified random sampling was performed along an elevation gradient from 3 400 to 4 000 m on the Northeastern slope of Nevado de Toluca (Figure 1; Table 1). Seven elevation plots were established at 100 m intervals, ensuring homogeneity in slope and forest conditions. Soil sampling and vegetation measurements were done by plot after choosing ten healthy P. hartwegii trees with normal diameters (ND) between 10 and 30 cm.

Table 1. General characteristics of sampling sites in Pinus hartwegii Lindl. forest along an elevation gradient from 3 400 to 4 000 m at Nevado de Toluca, State of Mexico, Mexico.

Two soil samples, each 15 cm deep, were taken from the base of each tree and 50 cm from the trunk base (upslope and downslope), using a 10 cm diameter PVC pipe (20 samples per site; a total of 140 soil samples were obtained). PVC tubes were used to collect undisturbed soil samples, which were taken to the laboratory of the Institute of Agricultural and Rural Sciences of the Autonomous University of the State of Mexico, where they were processed.

In regard to vegetation sampling, it followed the National Forest and Soil Inventory (Infys) (Comisión Nacional Forestal [Conafor], 2025) method, organized in clusters following an inverted "Y" pattern. Each cluster covered one hectare (56.42 m radius) and included four permanent 400 m² plots (11.28 m radius). Within 400 m² plots, all woody plants with ND≥5 cm were measured, including stem ND (model 283D/5m Forestry Suppliers Inc^® diameter tape), total height (model FP550 Nikon Forestry Pro II^® laser hypsometer), crown diameter (model 283D/5m Forestry Suppliers Inc^® diameter tape) and condition (alive, dead, or damaged). In 80 m² plots (5.04 m radius), shrubs and trees with ND between 2.5 and 5 cm were measured (model 283D/5m Forestry Suppliers Inc^® diameter tape) to characterize the shrub layer. Herbaceous strata were evaluated in 9 m² plots (0.3 m radius).

Soil analysis

Each of the 140 soil samples was weighed (model AX423E Ohaus Adventurer® precision balance) for total weight. The cylinder method determined the bulk density (BD) of undisturbed soil samples (Elliott et al., 1999). The soil samples were carefully extracted from the PVC pipes. Rocks and visible organic material were removed by hand, and each component was weighed. The soil samples were then passed through a 2 mm sieve (Grainger^® stainless steel sieve). SOM content was determined following the oxide-reduction method (Walkley & Black, 1934), and subsequently, the SOC content (%) was estimated. SOC stocks were obtained using the Equation 1, considering a 15 cm depth sample:

Where:

SOC = Soil organic carbon

SOC (%) = Soil organic carbon percentage

BD = Bulk density (g cm^-3)

Results are reported in Mg C ha^-1. Soil pH was measured with 1:2 solutions of 1^-N KCl using a model pH/CON 500 OAKTON^® potentiometer (Robertson et al., 1999). Soil texture was determined following the Bouyoucos method (Bouyoucos, 1962) and characterized according to the United States Department of Agriculture’s textural triangle (United States Department of Agriculture [USDA], 2014). Climate data, including mean annual temperature (MAT) and precipitation (MAP), were derived from the ClimateNA model v5.10 (Wang et al., 2016).

Data analysis

To evaluate differences among elevation plots, one-way analysis of variance (ANOVA) was conducted for soil variables (SOC, BD, pH, sand, clay and silt), validating the normal distribution of residuals for all. Linear regressions analyzed elevation's effects regarding SOC stocks and related variables (Kutner et al., 2005). Soil samples from uphill and downhill positions near each tree were averaged, providing 70 data points per variable.

Pearson’s correlations examined relationships between soil variables, tree structure variables, grass cover, herb and shrub density, MAT and MAP. Principal Component Analysis (PCA) was conducted using 70 data points to analyze variation among trees within the plots, excluding one variable from each pair with a high correlation (r>0.90) to avoid multicollinearity (Chen, 2012). The logging impact was assessed using one-way ANOVA with a dichotomous variable ("logged" vs. "unlogged"). Logged plots were between 3 400 and 3 800 m, whereas unlogged plots were from 3 900 to 4 000 m. Analyses were conducted in R (v4.4.3) (R Core Team, 2017) and JMP (v9.1) (Statistical Analysis System [SAS], 2004), with a 95 % confidence level (α≤0.05), except for Pearson correlations, which were tested at a 99 % confidence level.

Results and Discussion

SOC stocks and soil characteristics along the elevation gradient

Soil characteristics varied significantly with elevation. SOC stocks were highest at 4 000 m, with a value of 173.0±5.2 Mg C ha-1, and lowest at 3 700 m, where SOC stocks were 146.8±5.72 Mg C ha-1, representing 16 % decrease (Figure 2). This aligns with the role of SOM derived from aboveground litter as the primary organic carbon source in soils (de la Cruz-Amo et al., 2020; Six et al., 2004).

The data are presented as means±standard error (SE), with the dotted lines indicating the 95 % confidence interval.

Figure 2. SOC stocks in the topsoil from 0 to 15 cm beneath the Pinus hartwegii Lindl. forest along a 600 m elevation gradient (3 400 to 4 000 masl).

The decline in MAT along the elevation gradient (0.5 °C per 100 m, ClimateNA), coupled with increased SOM and SOC stocks, specifies that lower temperatures at higher elevations reduce litter decomposition rates, slow turnover, and prolong SOC residence times (Becker et al., 2019; Tashi et al., 2016). Salinas et al. (2010) found that temperature accounts for up to 95 % of the variability in organic matter (OM) decomposition in forests at high elevations. In a similar way, Garten and Hanson (2006) reported SOC stocks increase with elevation in the Appalachian Mountains, with residence times rising from 11 to 31 years due to slower SOM decomposition. Tian et al. (2016) highlighted that the residence time of SOC also depends on labile SOC fractions, suggesting both factors likely affect SOC dynamics in P. hartwegii forests, though specific studies are lacking. At higher elevations (3 900-4 000 m), soil pH was lowest, reaching 4.92±0.11 at 3 900 m. Sand content increased with elevation, peaking at 63.30±1.40 % at 4 000 m; clay content was lowest at 3 900 m, measuring 13.7±0.97 %, while silt at 3 800 m (23.12±1.15 %). SOC stocks, SOM, and sand content positively correlated with elevation (r²=0.70, p=0.02; r²=0.55, p=0.01; r²=0.72, p=0.01, respectively). Other variables, including BD, pH, clay, and silt, showed no significant elevation-related trends. These results suggest that SOC stocks at higher elevations are more influenced by accumulation than stabilization processes (Figure 3).

A = BD; B = pH; C = Sand content; D = Clay content; E = Silt content. The data are presented as means±standard error (SE), with the dotted lines indicating the 95 % confidence interval. 

Figure 3. Variables of topsoil under Pinus hartwegii Lindl. forest along a 600 m elevation gradient (3 400 to 4 000 m). 

Soil texture influences both SOC and BD: soils with a high sand content generally exhibit higher BD and lower SOC, whereas soils with lower BD tend to have higher organic carbon content (Lukac & Godbold, 2011). In mountain volcanic soils, sand content tends to increase with elevation due to the presence of volcanic ash and diminished rock weathering, resulting in shallower and coarser soils (Simon et al., 2018). Andosols, characterized by low BD (<0.9 g cm^-3) due to the weathering of volcanic glass in the sand and silt fractions (Delmelle et al., 2015), display BD variations driven by the arrangement of minerals and organic matter (Neall et al., 2006). These soils store 42-207 Mg OC ha^-1, stabilized by aluminum-organic matter complexes in upper horizons (Covaleda et al., 2011; Msanya et al., 2007). The role of sand content in the stabilization of SOC in young soils derived from volcanic ash remains underexplored. PCA revealed variability across elevation plots: PC1 (38.56 % of variability) was driven by tree height, sand content, and SOC stocks, while PC2 (17.06 %) was influenced by clay, silt, and pH. High-elevation trees (3 800-4 000 m) showed greater dispersion along PC1, mid-elevation trees (3 700 m) clustered tightly, and lower-elevation trees (3 400-3 600 m) grouped along PC2 (Figure 4).

A = Orientation of the variable groups toward PC1 or PC2; B = Spatial variation of the sampling points about the first two PCs illustrates the structure and pattern of variation of the original variables assessed at the sampling sites.

Figure 4. The relationship between the primary original variables and the first two principal components (PCs) is shown.

Larger trees at higher elevations likely contributed to higher SOM levels due to increased litterfall, which could explain the higher SOC stocks observed in these plots. However, environmental constraints such as low CO₂ partial pressure, short growing seasons, soil nutrient limitations, and low temperatures typically restrict tree growth at higher elevations (Alfaro-Ramírez et al., 2017; Correa-Díaz et al., 2019).

Relationship between elevation, soil characteristics, climate and vegetation structure

Elevation exhibited a positive correlation with SOC stocks (r=0.84, p=0.04), sand content (r=0.85, p=0.03), and tree density of the sampled trees (r=0.83, p=0.04), but a negative correlation with MAT (r=-0.99, p=0.00). SOC stocks showed a positive correlation with sand content (r=0.80, p=0.04) and tree density of the sampled trees (r=0.95, p=0.00), and an inverse correlation with MAT (r=-0.84, p=0.04). Sand content was negatively correlated with clay content (r=-0.71, p=0.05) and MAT (r=-0.85, p=0.01). MAT and MAP displayed a strong negative relationship (r=-0.99, p=0.03). Grass cover increased as tree density (r=-0.82, p=0.02) and herb and shrub cover decreased (r=-0.75, p=0.04). Herb and shrub cover were reduced with the increase in tree height (r=-0.75, p= 0.04) (Table 2).

Logging negatively impacted soil (SOC stocks) and vegetation structure (ND of sampled trees) (Figure 5).

A = Effect of logging on SOC mean values; B = ND of sampled trees.

Figure 5. Effect of logging on SOC mean values and ND of sampled trees along the elevation gradient in the Pinus hartwegii Lindl. forest.

Smaller SOC stocks at lower elevations are due to the widespread harvesting of P. hartwegii, often illegal, for use in furniture and construction (Franco et al., 2006). Logging reduces OM input, alters decomposition rates, and changes litter quality and microsite conditions (Nave et al., 2010; Pérez-Suárez et al., 2012). Canopy gaps promote secondary succession, increasing degradable herbs and shrubs and reducing SOM pools (Bomfim et al., 2020; Cepáková & Frouz, 2015). Increased light and temperature exposure accelerates decomposition and CO₂ emissions for as much as 45 years (Chiti et al., 2016; Coletta et al., 2017). At lower elevations (≤3 700 m), smaller trees, noticeable degradation, and weak law enforcement exacerbate illegal logging, particularly near the Nevado de Toluca FFPA buffer zone. This reduces the forest's capacity to store organic carbon (OC). SOC stocks vary with elevation, influenced by MAT and soil texture. Higher SOC at high elevations reflects cooler temperatures and conservation practices, while lower SOC at low elevations is linked to human-driven forest structure changes. Mountain forest soils should be included in carbon inventories and reforestation strategies addressing elevation gradients.

Conclusions

SOC stocks along altitude are influenced by temperature (MAT) and BD. As elevation increases, MAT decreases, while SOM pools, sand content, and tree diameter rise. At lower elevations (around 3 700 m), selective logging can modify microsite conditions, potentially leading to increased SOC outputs. Conversely, at higher elevations from 3 900 to 4 000 m, the reduced MAT slows decomposition, resulting in higher SOC stocks. Understanding the dynamics of SOM and SOC along elevational gradients, as well as how temperature and factors like tree removal impact SOC regulation and stabilization in mountain forests, is crucial in the context of global warming projections.

Acknowledgments

The authors would like to thank Secihti for the scholarship granted to Lizbeth Carrillo Arizmendi to pursue her master's degree, as well as the Comecyt Researchers Program for allowing her to continue with the product of this article. The authors also thank the Comisión Estatal de Parques Naturales y de la Fauna (Cepanaf) and the Comisión Nacional de Áreas Naturales Protegidas (Conanp) for allowing them access to the Nevado de Toluca FFPA.

Conflict of interest

The authors declare that they have no conflict of interest.

Contribution by author

Lizbeth Carrillo Arizmendi: formal analysis, methodology, data curation, and writing of the original draft; Marlín Pérez Suárez: research initiation and conceptualization, supervision of project administration and funding acquisition, and review of statistical analyses; J. Jesús Vargas Hernández, Philippe Rozenberg and Arian Correa Díaz: supervision of statistical analyses, review and editing of the manuscript.

References

Alfaro-Ramírez, F. U., Arredondo-Moreno, J. T., Pérez-Suárez, M., & Endara-Agramont, Á. R. (2017). Pinus hartwegii Lindl. Treeline ecotone: Structure and altitudinal limits at Nevado de Toluca, Mexico. Revista Chapingo Serie Ciencias Forestales y del Ambiente, 23(2), 261-273. https://doi.org/10.5154/r.rchscfa.2016.10.055

Becker, J. N., Dippold, M. A., Hemp, A., & Kuzyakov, Y. (2019). Ashes to ashes: Characterization of organic matter in Andosols along a 3400 m elevation transect at Mount Kilimanjaro using analytical pyrolysis. Catena, 180, 271-281. https://doi.org/10.1016/j.catena.2019.04.033

Berg, B., & Meentemeyer, V. (2002). Litter quality in a north European transect versus carbon storage potential. Plant and Soil, 242, 83-92. https://doi.org/10.1023/A:1019637807021

Bomfim, B., Silva, L. C. R., Pereira, R. S., Gatto, A., Emmert, F., & Higuchi, N. (2020). Litter and soil biogeochemical parameters as indicators of sustainable logging in Central Amazonia. Science of the Total Environment, 714, Article 136780. https://doi.org/10.1016/j.scitotenv.2020.136780

Bouyoucos, G. J. (1962). Hydrometer method improved for making particle size analyses of soils. Agronomy Journal, 54(5), 464-465. https://doi.org/10.2134/agronj1962.00021962005400050028x

Cepáková, S., & Frouz, J. (2015). Changes in chemical composition of litter during decomposition: a review of published ¹³C NMR spectra. Journal of Soil Science and Plant Nutrition, 15(3), 805-815. http://dx.doi.org/10.4067/S0718-95162015005000055

Challenger, A. (1998). Utilización y conservación de los ecosistemas terrestres de México: pasado, presente y futuro. Comisión Nacional para el Conocimiento y Uso de la Biodiversidad. https://books.google.com.mx/books/about/Utilizaci%C3%B3n_y_conservaci%C3%B3n_de_los_ecos.html?id=M1RQAAAAYAAJ&redir_esc=y

Chen, G. J. (2012). A simple way to deal with multicollinearity. Journal of Applied Statistics, 39(9), 1893-1909. https://doi.org/10.1080/02664763.2012.690857

Chiti, T., Perugini, L., Vespertino, D., & Valentini, R. (2016). Effect of selective logging on soil organic carbon dynamics in tropical forests in central and western Africa. Plant and Soil, 399, 283-294. https://doi.org/10.1007/s11104-015-2697-9

Coletta, V., Pellicone, G., Bernardini, V., De Cinti, B., Froio, R., Marziliano, P. A., Matteucci, G., Ricca, N., Turco, R., & Veltri, A. (2017). Short-time effect of harvesting methods on soil respiration dynamics in a beech forest in southern Mediterranean Italy. iForest-Biogeosciences and Forestry, 10(3), 645-651. https://doi.org/10.3832/ifor2032-010

Comisión Nacional Forestal. (2025). INFYS Inventario Nacional Forestal y de Suelos (INFyS). Gobierno de Máxico. https://snmf.cnf.gob.mx/infys/

Correa-Díaz, A., Silva, L. C. R., Horwath, W. R., Gómez-Guerrero, A., Vargas-Hernández, J., Villanueva-Díaz, J., Velázquez-Martínez, A., & Suárez-Espinoza, J. (2019). Linking Remote Sensing and Dendrochronology to quantify climate-induced shifts in high-elevation forests over space and time. Journal of Geophysical Research: Biogeosciences, 124(1), 166-183. https://doi.org/10.1029/2018JG004687

Covaleda, S., Gallardo, J. F., Garcia-Oliva, F., Kirchmann, H., Prat, C., Bravo, M., & Etchevers, J. D. (2011). Land-use effects on the distribution of soil organic carbon within particle-size fractions of volcanic soils in the Transmexican Volcanic Belt (Mexico). Soil Use and Management, 27(2), 186-194. https://doi.org/10.1111/j.1475-2743.2011.00341.x

Cruz-Cardenas, G., López-Mata, L., Silva, J. T., Bernal-Santana, N., Estrada-Godoy, F., & López-Sandoval, J. A. (2016). Potential distribution model of Pinaceae species under climate change scenarios in Michoacán. Revista Chapingo Serie Ciencias Forestales y del Ambiente, 22(2), 135-148. https://doi.org/10.5154/r.rchscfa.2015.06.027

Davidson, E. A., & Janssens, I. A. (2006). Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature, 440, 165-173. https://doi.org/10.1038/nature04514

de la Cruz-Amo, L., Bañares-de-Dios, G., Cala, V., Granzow-de la Cerda, I., Espinosa, C. I., Ledo, A., Salinas, N., Macía, M. J., & Cayuela, L. (2020). Trade-offs among aboveground, belowground, and soil organic carbon stocks along altitudinal gradients in Andean tropical montane forests. Frontiers in Plant Sciences, 11, 106. https://doi.org/10.3389/fpls.2020.00106

Delmelle, P., Opfergelt, S., Cornelis, J.-T., & Ping, C.-L. (2015). Chapter 72-Volcanic Soils. In H. Sigurdsson (Ed.), The Encyclopedia of Volcanoes (2nd ed., pp. 1253-1264). Elsevier Inc. https://doi.org/10.1016/B978-0-12-385938-9.00072-9

Dixon, R. K., Solomon, A. M., Brown, S., Houghton, R. A., Trexier, M. C., & Wisniewski, J. (1994). Carbon pools and flux of global forest ecosystems. Science, 263(5144), 185-190. https://doi.org/10.1126/science.263.5144.185

Du, B., Kang, H., Pumpanen, J., Zhu, P., Yin, S., Zou, Q., Wang, Z., Kong, F., & Liu, C. (2014). Soil organic carbon stock and chemical composition along an altitude gradient in the Lushan Mountain, subtropical China. Ecological Research, 29(3), 433-439. https://doi.org/10.1007/s11284-014-1135-4

Elliot, E. T., Heil, J. W., Kelly, E. F., & Monger, H. C. (1999). 4 Soil structural and other physical properties. In G. P. Robertson, D. C. Coleman, C. S. Bledsoe & P. Sollins (Eds.), Standard soil methods for long-term ecological research (pp. 74-86). Oxford University Press. https://doi.org/10.1093/oso/9780195120837.003.0004

Field, C. B., Barros, V. R., Dokken, D. J., Mach, K. J., Mastrandrea, M. D., Bilir, T. E., Chatterjee, M., Ebi, K. L., Estrada, Y. O., Genova, R. C., Girma, B., Kissel, E. S., Levy, A. N., Maccracken, S., Mastrandrea, P. R., & White, L. L. (Eds.). (2014). Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Working group II contribution to the fifth assessment report of intergovernmental panel on climate change. Intergovernmental Panel on Climate Change. https://www.ipcc.ch/site/assets/uploads/2018/02/WGIIAR5-FrontMatterA_FINAL.pdf

Franco M., S., Regil G., H. H., y Ordóñez D., J. A. B. (2006). Dinámica de perturbación-recuperación de las zonas forestales en el Parque Nacional Nevado de Toluca. Madera y Bosques, 12(1), 17-28. https://doi.org/10.21829/myb.2006.1211247

García, E. (1990). Carta de climas. Atlas Nacional de México. Universidad Nacional Autónoma de México.

Garten Jr., C. T., & Hanson, P. J. (2006). Measured forest soil C stocks and estimated turnover times along an elevation gradient. Geoderma, 136(1-2), 342-352. https://doi.org/10.1016/j.geoderma.2006.03.049

Gómez-Mendoza, L., & Arriaga, L. (2007). Modeling the effect of climate change on the distribution of oak and pine species of Mexico. Conservation Biology, 21(6), 1545-1555. https://doi.org/10.1111/j.1523-1739.2007.00814.x

Jafari, S. M., Zarre, S., & Alavipanah, S. K. (2013). Woody species diversity and forest structure from lowland to Montane Forest in Hyrcanian Forest Ecorregion. Journal of Mountain Science, 10(4), 609-620. https://doi.org/10.1007/s11629-013-2652-2

James, J., & Harrison, R. (2016). The effect of harvest on forest soil carbon: A meta-analysis. Forests, 7, 308. https://doi.org/10.3390/f7120308

Kirschbaum, M. U. F. (2000). Will changes in soil organic carbon act as a positive or negative feedback on global warming? Biogeochemistry, 48, 21-51. https://doi.org/10.1023/A:1006238902976

Körner, C. (2007). The use of ‘altitude’ in ecological research. Trends in Ecology & Evolution, 22(11), 569-574. https://doi.org/10.1016/j.tree.2007.09.006

Körner, C., & Paulsen, J. (2004). A world-wide study of high altitude treeline temperatures. Journal of Biogeography, 31(5), 713-732. http://dx.doi.org/10.1111/j.1365-2699.2003.01043.x

Kumar, S., Lal, R., & Lloyd, C. D. (2012). Assessing spatial variability in soil characteristics with geographically weighted principal components analysis. Computational Geosciences, 16, 827-835. https://doi.org/10.1007/s10596-012-9290-6

Kutner, M. H., Nachtsheim, C. J., Neter, J., & Li, W. (2005). Applied Linear Statistical Models (5th ed.). McGraw-Hill Irwin. https://www.researchgate.net/publication/344587293_Applied_Linear_Statistical_Models

Lukac, M., & Godbold, D. L. (2011). Soil ecology in Northern forests. A belowground view of a changing world. Cambridge University Press. https://doi.org/10.1017/CBO9780511976100

Msanya, B. M., Otsuka, H., Araki, S., & Fujitake, N. (2007). Characterization of volcanic ash soils in southwestern Tanzania: Morphology, physicochemical properties, and classification. African Study Monographs, Supplementary Issue 34, 39-55. http://hdl.handle.net/2433/68484

Nave, L. E., Vance, E. D., Swanston, C. W., & Curtis, P. S. (2010). Harvest impacts on soil carbon storage in temperate forests. Forest Ecology and Management, 259(5), 857-866. https://doi.org/10.1016/j.foreco.2009.12.009

Neall, V. E. (2006). Volcanic Soils. In W. H. Verheye (Ed.), Encyclopedia of land use, land cover and soil sciences. Soils and soil Sciences. Vol. VII Encyclopedia of Life Support Systems (pp. 23-48). United Nations Educational, Scientific and Cultural Organization. https://edepot.wur.nl/484591

Organización de las Naciones Unidas para la Alimentación y la Agricultura. (2008). Base referencial mundial del recurso suelo. Un marco conceptual para clasificación, correlación y comunicación internacional (Informe sobre recursos mundiales de suelos 103). Organización de las Naciones Unidas para la Alimentación y la Agricultura. https://openknowledge.fao.org/server/api/core/bitstreams/afecdf0d-08a9-4754-8ebd-a8070aa71aea/content

Pepin, N., Bradley, R. S., Diaz, H. F., Baraer, M., Caceres, E. B., Forsythe, N., Fowler, H., Greenwood, G., Hashmi, M. Z., Liu, X. D., Miller, J. R., Ning, L., Ohmura, A., Palazzi, E., Rangwala, I., Schöner, W., Severskiy, I., Shahgedanova, M., Wang, M. B., … Yang, D. Q. (2015). Elevation-dependent warming in mountain regions of the world. Nature Climate Change, 5, 424-430. https://doi.org/10.1038/nclimate2563

Pérez-Suárez, M., Arredondo-Moreno, J. T., & Huber-Sannwald, E. (2012). Early stage of single and mixed leaf-litter decomposition in semiarid forest pine-oak: the role of rainfall and microsite. Biogeochemistry, 108, 245-258. https://doi.org/10.1007/s10533-011-9594-y

Price, M. F., Gratzer, G., Duguma, L. A., Kohler, T., Maselli, D., Romeo, R. (Edits.).(2011). Mountain forests in a changing world. Realizing values, addressing challenges. Food and Agriculture Organization of the United Nations. https://www.fao.org/4/i2481e/i2481e.pdf

R Core Team. (2017). R: A language and environment for statistical computing (v4.4.3) [Software]. R Foundation for Statistical Computing.https://www.R-project.org/

Robertson, G. P., Sollins, P., Ellis, B. G., & Lajtha, K. (1999). 6 Exchangeable ions, pH, and cation exchange capacity. In G. P. Robertson, D. C. Coleman, C. S. Bledsoe & P. Sollins (Eds.), Standard soil methods for long-term ecological research (pp. 106-114). Oxford University Press. https://doi.org/10.1093/oso/9780195120837.003.0006

Rzedowski, J. (1991). Diversidad y orígenes de la flora fanerogámica de México. Acta Botánica Mexicana, (14), 3-21. https://doi.org/10.21829/abm14.1991.611

Salinas, N., Malhi, Y., Meir, P., Silman, M., Cuesta, R. R., Huaman, J., Salinas, D., Huaman, V., Gibaja, A., Mamani, M., & Farfan, F. (2011). The sensitivity of tropical leaf litter decomposition to temperature: results from a large-scale leaf translocation experiment along an elevation gradient in Peruvian forest. New Phytologist, 189(4), 967-977. https://doi.org/10.1111/j.1469-8137.2010.03521.x

Salomé, C., Nunan, N., Pouteau, V., Lerch, T. Z., & Chenu, C. (2010). Carbon dynamics in topsoil and in subsoil may be controlled by different regulatory mechanisms. Global Change Biology, 16(1), 416-426. https://doi.org/10.1111/j.1365-2486.2009.01884.x

Santini, N. S., Adame, M. F., Nolan, R. H., Miquelajauregui, Y., Piñero, D., Mastretta-Yanes, A., Cuervo-Robayo, A. P., & Eamus, D. (2019). Storage of organic carbon in the soils of Mexican temperate forests. Forest Ecology and Management, 446, 115-125. https://doi.org/10.1016/j.foreco.2019.05.029

Sheikh, M. A., Kumar, M., & Bussmann, R. W. (2009). Altitudinal variation in soil organic carbon stock in coniferous subtropical and broadleaf temperate forests in Garhwal Himalaya. Carbon Balance and Management, 4, Article 6. https://doi.org/10.1186/1750-0680-4-6

Simon, A., Dhendup, K., Rai, P. B., & Gratzer, G. (2018). Soil carbon stocks along elevational gradients in Eastern Himalayan mountain forests. Geoderma Regional, 12, 28-38. https://doi.org10.1016/j.geodrs.2017.11.004

Six, J., Bossuyt, H., Degryze, S., & Denef, K. (2004). A history of research on the link between (micro)aggregates, soil biota, and soil organic matter dynamics. Soil and Tillage Research, 79(1), 7-31. https://doi.org/10.1016/j.still.2004.03.008

Statistical Analysis System. (2004). SAS/STAT^® 9.1 User’s Guide. SAS Institute Inc. https://support.sas.com/documentation/onlinedoc/91pdf/sasdoc_91/stat_ug_7313.pdf

Swetnam, T. L., Brooks, P. D., Barnard, H. R., Harpold, A. A., & Gallo, E. L. (2017). Topographically driven differences in energy and water constrain climatic control on forest carbon sequestration. Ecosphere, 8(4), Article e01797. https://doi.org/10.1002/ecs2.1797

Tashi, S., Singh, B., Keitel, C., & Adams, M. (2016). Soil carbon and nitrogen stocks in forest along an altitudinal gradient in the Eastern Himalayas and a meta-analysis of global data. Global Change Biology, 22(6), 2255-2268. https://doi.org/10.1111/gcb.13234

Tewksbury, C. E., & Van Miegroet, H. (2007). Soil organic carbon dynamics along a climatic gradient in a southern Appalachian spruce fir forest. Canadian Journal of Forest Research, 37(7), 1161-1172. https://doi.org/10.1139/X06-317

Tian, Q., He, H., Cheng, W., Bai, Z., Wang, Y., & Zhang, X. (2016). Factors controlling soil organic carbon stability along a temperate forest altitudinal gradient. Scientific Reports, 6, Article 18783. https://doi.org/10.1038/srep18783

Tito, R., Vasconcelos, H. L., & Feeley, K. J. (2020). Mountain ecosystems as natural laboratories for climate change experiments. Frontiers in Forests and Global Change, 3, Article 38. https://doi.org/10.3389/ffgc.2020.00038

United States Department of Agriculture. (2014). Keys to Soil Taxonomy. Natural Resources Conservation Service (12th ed.). United States Department of Agriculture and Natural Resources Conservation Service. https://ethz.ch/content/dam/ethz/special-interest/usys/ias/grassland-sciences-dam/documents/Education/Graslandsysteme/2014_USDA_Keys_to_Soil_Taxonomy.pdf

Walkley, A., & Black, I. A. (1934). An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Science, 37(1), 29-38. http://dx.doi.org/10.1097/00010694-193401000-00003

Wang, T., Hamann, A., Spittlehouse, D., & Carroll, C. (2016). Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS ONE, 11(6), Article e0156720. https://doi.org/10.1371/journal.pone.0156720

Wiesmeier, M., Urbanski, L., Hobley, E., Lang, B., von Lützow, M., Marin-Spiotta, E., van Wesemael, B., Rabot, E., Ließ, M., Garcia-Franco, N., Wollschläger, U., Vogel, H.-J., & Kögel-Knabner, I. (2019). Soil organic carbon storage as a key function of soils - A review of drivers and indicators at various scales. Geoderma, 333, 149-162. https://doi.org/10.1016/j.geoderma.2018.07.026

Zhu, B., Wang, X., Fang, J., Piao, S., Shen, H., Zhao, S., & Peng, C. (2010). Altitudinal changes in carbon storage of temperate forests on Mt Changbai, Northeast China. Journal of Plant Research, 123, 439-452. https://doi.org/10.1007/s10265-009-0301-1

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