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Article

Understanding Soil Respiration Dynamics in Temperate Forests in Northwestern Mexico

by
José Alexis Martínez-Rivas
1,
Benedicto Vargas-Larreta
1,*,
Jorge Omar López-Martínez
2,3,
Cristóbal Gerardo Aguirre-Calderón
1,
Francisco Javier Hernández
1 and
Gregorio Ángeles-Pérez
4
1
Tecnológico Nacional de México/Instituto Tecnológico de El Salto, Tecnológico 101, Colonia La Forestal, El Salto 34942, Mexico
2
Investigador por México (IxM), CONACYT, Avenida Insurgentes Sur 1582, Colonia Crédito Constructor, Benito Juárez, Mexico City 03940, Mexico
3
Departamento de Agricultura, Sociedad y Ambiente, El Colegio de la Frontera Sur Unidad Chetumal, Avenida Centenario km 5.5, Chetumal 77014, Mexico
4
Colegio de Postgraduados, Campus Montecillo, Texcoco 56264, Mexico
*
Author to whom correspondence should be addressed.
Forests 2023, 14(9), 1763; https://doi.org/10.3390/f14091763
Submission received: 17 July 2023 / Revised: 23 August 2023 / Accepted: 25 August 2023 / Published: 31 August 2023
(This article belongs to the Special Issue Carbon, Water and Energy Fluxes in Forest Ecosystems)
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Abstract

:
Temperate mixed forests in Mexico are considered highly important ecosystems because of their high levels of biodiversity and capacity to store carbon. The aim of this study was to evaluate temporal and between-forest soil respiration (CO2 efflux) variability, and to assess the effect of vegetation diversity metrics on soil CO2 fluxes in mixed-uneven-aged forests in Durango, Northwestern Mexico. Soil CO2 efflux, soil moisture, and soil temperature were measured in three temperate forest types. A generalized linear model (GLM) was fitted to analyze the relationship between soil CO2 fluxes and stand variables, diversity metrics, soil moisture, and soil temperature. Furthermore, a two-way analysis of variance was used to assess the effect of forest type, month of the year, and their interaction on soil respiration. Annual average, minimum, and maximum soil CO2 efflux rate values were 3.81 (±2.94), 2.28 (±1.47), and 7.97 (±2.94) µmol m−2 s−1, respectively. Soil respiration was positively related to species richness, aboveground biomass, and quadratic mean diameter; however, forest type did not contribute to understanding the dynamics of soil CO2 fluxes. The results highlight the importance of seasonality, species diversity and aboveground biomass stocks to preserve the ecosystem processes driving soil respiration in temperate forests.

1. Introduction

The soils of forest ecosystems are the main C pool and contain about 1500 Pg worldwide [1], representing about 80% of terrestrial carbon stocks [2]. In this scenario, soil respiration (also known as soil CO2 efflux) is the second most important carbon flux between forest ecosystems and the atmosphere [3]. It has been observed that variation in soil carbon stocks in forest systems are the result of multiple dynamic processes (e.g., vegetation growth and mortality, litter production, the decomposition of dead organic matter, as well as natural and anthropogenic disturbances), which interact from the scale of a single tree to an entire landscape. Consequently, changes in species diversity and composition, as well as abiotic factors such as temperature and humidity, can significantly affect the dynamics of soil CO2 fluxes, the atmospheric CO2 concentration, as well as the overall carbon balance of ecosystems [4,5].
At certain spatial scales, more diverse plant communities tend to show higher primary productivity [6,7]. However, the relationship between plant diversity and soil respiration has not proved to be strong [8], but it has been observed that plant diversity can favour soil respiration by incorporating more C into the system.
Soil CO2 efflux is conditioned by soil temperature and moisture content, as well as by plant photosynthetic activity [9]. Of these conditioning factors, soil moisture content modifies the soil aeration and the circulation of different gases [10], which leads to variations in the ability of soil microorganisms to carry out organic matter oxidation and litter decomposition [11]. Therefore, environmental conditions and vegetation types, as driving factors that control organic matter inputs and their adjacent processes into the soil, must be taken into account in soil respiration estimates [12]. On the other hand, seasonal fluctuations in soil respiration are commonly found in nearly all ecosystems and are frequently linked to shifts in temperature, moisture, photosynthetic activity, root growth, or a combination of these factors [13]. Conversely, the spatial variability in soil respiration arises from considerable changes in soil physical characteristics, fine-root biomass, fungal and bacterial activity, and nutrient availability [14].
In the future, additional increases in the atmospheric CO2 concentrations are expected [15]; these changes, together with the expected global temperature increase, may substantially affect the soil CO2 fluxes in forest soils worldwide. Moreover, some activities, such as land use change, deforestation, and silviculture, increase CO2 emissions to the atmosphere. For instance, forest management significantly alters soil properties and environmental conditions [16], affecting soil micro-organism activity and, consequently, soil CO2 dynamics.
The Sierra Madre Occidental in Mexico is considered a special ecological area because of the high levels of biodiversity, which are attributed to diverse physiographic and climatic conditions [17], making it the largest continuous forested area in Mexico. It is also of high importance to forest management for timber production. Despite its ecological, economical, and environmental importance, research on soil CO2 fluxes in these forests is scarce; consequently, this topic represents a knowledge gap in the carbon cycle in Mexican forests. Based on the above, we selected three forest types in Northwestern Mexico to (i) evaluate temporal and between-forest soil CO2 efflux variability; (ii) to identify the main environmental determinants of soil CO2 fluxes; and (iii) to assess the effect of stand variables and diversity metrics on soil respiration.

2. Materials and Methods

2.1. Site Description

The study was conducted in the El Salto forest region, situated in Southwest Durango State, Mexico (23°06′59″ and 24°11′38″ N and 105°55′56″ and 105°10′31″ W) (Figure 1). The elevation in this region varies from 1200 to 3200 m, with an average of 2264 m. The climate of the area is classified according to the Köppen classification modified by García [18] as temperate subhumid C(w2). The temperature ranges from 8.2 to 26.2 °C, with an annual average of 13.3 °C. The mean annual rainfall is 917 mm, varying between 443 and 1452 mm. The study area is characterized by two main soil groups: Luvisol and Umbrisol. These soil types are common in temperate and cold or warm–humid climates with a seasonal pattern of rain and drought, and they are frequently found in the temperate forests of the Sierra Madre Occidental [19]. Regarding vegetation, the area comprises several forest types, including pine forest, pine–oak forest, and associations of pine with other conifer and broadleaved species.

2.2. Inventory Data and Sampling Design

To examine the variation in soil respiration and the relationship between the CO2 efflux and the vegetation composition, three dominant forest types were selected (pure pine, mixed pine–oak, and mixed oak–pine) (Table 1). Nine permanent sampling plots (50 m × 50 m), three for each forest type, were established. In each plot, the following variables by tree species were recorded: diameter at breast height (DBH, measured at 1.3 m above ground level, cm), total tree height (m), height to the live crown (m), azimuth (°), and radius (m) from the centre of the plot to all trees equal or larger than 7.5 cm in DBH.
A total of 27 CO2 flux measurement points were located, 3 points within each permanent plot (3 forest types × 3 plots × 3 replicated measurement points); the distance between the measurement points within the plots was 4.5 m (Figure 1). The sample size was determined based on forest types and species composition within each ecosystem. The selection of nine measurement points, each with three replicates in each forest type, was deemed adequate to effectively represent the CO2 flux across the study area. Furthermore, spatial independence among these locations was ensured. At each measurement point, a polyvinyl-chloride (PVC) collar (20 cm in diameter × 12 cm in height) was installed in the soil at 5 cm depth. To reduce the influence of vegetation and soil disturbances on the CO2 fluxes, the PVC collars were placed two weeks prior to the first field data collection. At the 27 points, soil CO2 flux, soil temperature (Tsoil), and soil moisture (Msoil) were measured.

2.3. Soil CO2 Flux Measurements

Soil CO2 flux (µmol m−2 s−1) was measured in situ with a portable infrared gas analyzer model LICOR-8100A (LI-COR Inc., Lincoln, NE, USA), equipped with a closed dynamic chamber where soil CO2 emissions are calculated. Measurements were made twice monthly, from January to December 2021, except in September, when the records were lost. On each sampling date, the measurements were made twice daily: 07:00 to 10:00 and 15:00 to 18:00 h. Each measurement lasted 120 s, separated by 60 s between repeated measurements, during which the chamber opened and closed automatically to achieve ambient CO2 concentrations. At the same time, during the CO2 flux measurement, Tsoil (°C) and Msoil (%) at 10 cm below the soil surface were measured [20]. Tsoil was measured with a temperature probe model LICOR 8100-201 Omega (LI-COR Inc., Lincoln, NE, USA), and Msoil with a ThetraProbe ML2x (Delta-T Devices Ltd., Cambridge, UK) soil moisture probe, respectively, which were placed adjacent to the soil respiration chamber.

2.4. Diversity Metrics and Aboveground Biomass Estimations

To analyse the species diversity of woody plants, we used Hill numbers, which are a mathematically unified family of diversity indices that incorporate both relative abundance and species richness and overcome many of these shortcomings. In this sense, we calculated q0 (species richness [S], q1 (Shannon index (H′)), and q2 (Simpson’s index (λ)). The analyses were carried out using the iNEXT library in R 4.3.1 software [21]. In addition, the relative importance of the species composition, the index of importance value (IVI), was computed.
The aboveground biomass (AGB) of live trees in each plot was estimated using species-specific allometric equations based on DBH and total height developed for all major coniferous and broadleaf tree species of Durango forests [22].

2.5. Statistical Analyses

Pearson’s correlation coefficient (r) was performed to identify relationships between CO2 flux and Tsoil, Msoil, and AGB, as well as Hill’s number indexes. After that, a generalized linear model (GLM) was fitted to estimate CO2 efflux based on the measured variables, assuming a gamma distribution of the data. To evaluate the goodness of fit of the GLM model, a cross-validation was performed, fitting the model with 10% of the data to compare it with the null model. Prior to the analysis, the multicollinearity of variables was established to avoid redundancy. Furthermore, a two-way analysis of variance (ANOVA) was used to assess the effects of forest types, months, and the interaction between these both on soil CO2 fluxes; then, the means of soil CO2 fluxes by forest type and month were compared with Tukey’s multiple means comparison test. Statistical analyses were performed with the R software [21].

3. Results

3.1. Diversity Metrics and Aboveground Biomass

A total of 1499 trees (DBH ≥ 7.5 cm) belonging to twenty species, seven genera, and five families were recorded. The abundance among forest types was distributed as follows: 553 trees in oak–pine forest, 539 in pine–oak forest, and 407 in pine forest. The richness estimated with a similar number of individuals (150) was 13.27 (CI = 12.20–14.33; cover estimator C.hat = 0.98) for oak–pine forest, 11.55 (CI = 10.73–12.37; cover estimator C.hat = 0.983) for pine–oak forest, and 6.86 (CI = 6.52–7.20; cover estimator C.hat = 0.78) for pine forest. The Shannon index (H′) showed the highest values in the oak–pine forest (2.14, CI = 2.06–2.22), followed by the pine–oak forest (1.95, CI = 1.88–2.03) and the pine forest (1.14, CI = 1.03–1.24). According to the H′ values, the oak–pine and pine–oak forests were in the medium-diversity range, while the pine forest had low diversity. Conversely, Simpson’s diversity index (λ) showed that the pine forests had a higher dominance of one species assemblage (0.47, CI = 0.42–0.52) than the oak–pine (0.17, CI = 0.15–0.2) and pine–oak (0.19, CI = 0.17–0.21) forests.
The most important species in the oak–pine and pine–oak forests were Q. sideroxila (26.9% and 23.3%), P. durangensis (13.7% and 19.6%), and P. teocote (10.2% and 12.4%). These three species together accounted for 50.8% and 55.2% of the IVI in these forests. In pine forests, the most important species was P. cooperi (53.9%) (Figure 2). The maximum AGB values by forest type were recorded in the oak–pine forest (211.83 Mg ha−1), followed by pine forest (208.33 Mg ha−1) and pine–oak forest (177.36 Mg ha−1).

3.2. Monthly CO2 Efflux

The mean soil respiration in the study area was 4.97 (±3.07) µmol m−2 s−1. Between January and December, CO2 fluxes spanned from 0.04 (May) to 15.5 µmol m−2 s−1 (June). Across the measurement period, Tsoil ranged from around 5.1–20.6 °C, with an average of 11.9 °C (±2.96), whereas Msoil also varied temporally from around 8.8 to 95% (Figure 3). Mean CO2 efflux reached its highest value in July (8.79 ± 1.8 µmol m−2 s−1), while the lowest occurred in April (2.28 ± 1.47 µmol m−2 s−1) (Table 2). From January to April, the average CO2 efflux values showed no significant variation, with a value around 2.2–2.8 µmol m−2 s−1. From June, the mean CO2 efflux increased considerably, reaching about four times higher (July) than in the previous months (Table 2).
There were statistically significant differences in CO2 efflux among months (F-value = 71.113, p < 0.0000, α = 0.05), as well as in the interaction forest type: month (F-value = 2.547, p < 0.0003, α = 0.05); forest type had no significant effect on CO2 efflux (F-value = 0.847, p = 0.429, α = 0.05). Through Tukey’s means comparison test, a significant difference in CO2 fluxes between most months (Table 2) was observed. Similarly, the CO2 efflux in July and August was significantly higher than in the other months. The ANOVA for Tsoil and Msoil also indicated significant differences between months (F-value = 80.464, p < 0.0000, α = 0.05; F-value = 65.645, p < 0.0000, α = 0.05, respectively). It is worth noting that significant differences in Tsoil among months mainly occurred between seasons; for example, soil temperature in spring (March, April, and May) was not significantly different between those months, but it was different from the winter months (January and February) and the beginning of summer (June). The maximum monthly Tsoil occurred in June (20.6 °C) and the minimum occurred in February (5.1 °C). Soil moisture was significantly different between months, except January and June. The dynamic of Msoil showed a maximum in July (68.4%) and a minimum in April (9.05%) (Figure 3).

3.3. Soil Respiration by Forest Type

Mean CO2 efflux by forest type was 5.08 (±3.40), 5.03 (±2.96), and 4.81 (±2.84) µmol m−2 s−1, in oak–pine, pine, and pine–oak forest, respectively (Table 3). The three forest types showed a similar monthly CO2 efflux trend (Figure 3).
The maximum value of CO2 efflux was found in the oak–pine forest (15.5 µmol m−2 s−1), while the lowest was in the pine forest (0.04 µmol m−2 s−1). The mean soil CO2 flux reached peaks of 7.87 µmol m−2 s−1 (pine), 6.72 µmol m−2 s−1 (pine–oak), and 9.32 µmol m−2 s−1 (oak–pine). The maximum values of CO2 flux recorded in June corresponded to the highest average Tsoil (14.58, 15.37, and 15.46 °C) in pine, pine–oak, and oak–pine forest, respectively. As for temperature, the highest average CO2 flux values coincided with the monthly highest Msoil records (June) in the three forest types: 88.61 (pine), 94.85 (pine–oak), and 75.94% (oak–pine) (Figure 3).

3.4. Relationships of Soil CO2 Fluxes with Forest Diversity Metrics, Soil Temperature, Soil Moisture, and Forest Stand Variables

Forest type had no statistically significant effects on soil CO2 (F-value = 1.586, p = 0.2068, α = 0.05), Tsoil (F-value = 1.776, p = 0.175, α = 0.05), or Msoil (F-value = 0.423, p = 0.656, α = 0.05). However, the analysis of variance indicated that the interaction between forest type and months had a significant effect on soil CO2 flux (F-value = 13.28, p = 0.0001, α = 0.05) and on Msoil (F-value = 4.21, p = 0.0002, α = 0.05). Tsoil and Msoil did not differ significantly among forest types.
Soil respiration was positively correlated with soil temperature (r = 0.44) and soil moisture (r = 0.45) at 0.1 m depth. Likewise, CO2 flux showed significant positive correlations with stand variables, such as basal area (r = 0.48), tree density (r = 0.48), and aboveground biomass (r = 0.36) (Figure 4). Soil respiration was similarly positively correlated with diversity metrics (species richness, r = 0.25; Shannon, r = 0.25; and Simpson, r = 0.13). In contrast, Tsoil was negatively correlated with basal area (r = −0.51, p = 0.003), biomass (r = −0.45, p < 0.000), number of species (r = −0.35, p = 0.033), and Shannon (r = −0.18, p = 0.037) and Simpson indices (r = −0.23, p = 0.016). Regarding Msoil, a positive correlation was observed with aboveground biomass (r = 0.48, p = 0.021), basal area (r = 0.42, p = 0.029), Simpson’s index (r = 0.35, p = 0.016), and species richness (r = 0.31, p = 0.021).

3.5. Model for Estimating Soil Respiration

Soil CO2 efflux was estimated with the following generalized linear model:
CO2 efflux = b1 (Tsoil) + b2 (Msoil) + b3 (S) +b4 (Simpson) + b5 (Dq) + b6 (AGB)
The model explained 48.3% of the variance in soil CO2 flux in the three forest types. All parameters were highly significant at a 99% confidence level. The sign of the parameters was, in all cases, biologically logical, so the model is considered adequate (Table 4). The residuals of the model did not show any trends indicating no problems of heteroscedasticity.

4. Discussion

Despite the importance of soil organic carbon in terrestrial carbon cycle dynamics, few studies have analysed the influence of forest type, soil temperature, and soil moisture on CO2 fluxes in managed forests in Mexico. This study provides key information for understanding the dynamics of soil respiration in three forest types.
The average CO2 effluxes observed for the three forest types fell within the range of soil respiration values reported in similar research [4,23,24,25,26,27]. Likewise, the mean values of CO2 effluxes obtained in our study were similar to those reported by Yáñez-Díaz et al. [28], who found CO2 effluxes of 3.86 and 4.33 µmol m−2 s−1 in Eucalyptus camaldulensis and E. microtheca forest plantations in Northeastern Mexico, respectively.
The observed CO2 efflux patterns showed no significant differences between forest types. The fact that no differences in CO2 efflux were identified might be a result of the low variability in species diversity in such forests [29] and the observed IVI values of some species (Figure 2). Our results come from three forest types defined by the dominance of species belonging to genus Pinus and Quercus. However, the three forest types are mixed pine–oak forests, even the one named pine forest. In addition, since the studied forests are located close to each other under same edaphic and climatic conditions, Tsoil and Msoil showed little differences among them; we may expect low differentiation in the microbial community, both in total microbial biomass and community structure (fungi, bacteria, and actinomycetes) due to the similar amount of litterfall and mixtures in forest floor litter, and therefore, low differences in soil CO2 efflux since soil microorganisms are highly important in affecting this process [30]. Furthermore, the absence of significant differences in CO2 efflux patterns suggests that certain dominant species, such as Quercus siredoxyla in the pine–oak and oak–pine forests and Pinus durangensis and Pinus cooperi in the pine forest, along with P. durangensis, which is common to all three forest types, may strongly influence the autotrophic respiration processes. Consequently, soil respiration rates could be remarkably similar across the different forest types. The statement by Peng et al. [31] supports our findings, since they point out that the differences in soil respiration among stands also result from the prevailing tree species.
Despite the lack of statistical differences among the three forest types, the highest CO2 efflux was found in the forest with the highest aboveground biomass (AGB) stocks (oak–pine forest). Such dynamics of soil respiration in this forest type are due to the fact that mixed broadleaved forests produce more readily decomposable litter than conifer-dominated forests [32], and that mixed forests have higher net primary productivity than pine [4], which means that the higher AGB in the oak–pine forest is associated with increased microbial soil respiration. These results are reasonable and support the assumption that autotrophic respiration is closely related to aboveground productivity, due to the C input from plants functioning as a substrate for root respiration [33], whereby AGB positively conditions soil respiration.
Although many studies have reported different soil respiration rates, our findings are in line with prior research reporting that CO2 effluxes do not significantly differ among forest types. For instance, Borken et al. [2] found that soil respiration was not significantly different among Fagus sylvatica, Picea abies, and Pinus sylvestris forest stands in Germany. Similarly, Hibbard et al. [23] reported that soil CO2 fluxes did not differ between deciduous broadleaf and conifer forests, and Kim et al. [34] observed no statistical differences in CO2 efflux among deciduous, conifer, and mixed forests in South Korea. The above means that other factors such as soil moisture, rainfall intensity and frequency, temperature, litter decomposition rate, and the amount of carbon derived from organic matter regulate CO2 effluxes in forest ecosystems [35].
While the main aim of this study was to explore the effect of forest types, we observed that seasonal variation could be an important aspect to consider in understanding carbon dynamics in the forests evaluated. Soil respiration was constant and relatively low during the first months of the study period (January–April). This may have been due to the fact that during the winter season and the first months of spring, the CO2 efflux is affected by low temperatures, although Msoil remains relatively high. From March to mid-May, soil Msoil decreased due to the increase in environment temperature, lack of precipitation, and the beginning of physiological activities of plants, drastically reducing the CO2 efflux. From mid-May onwards, the CO2 efflux gradually increased, reaching a peak in June. Normally in this region of Mexico, at the end of May, the first rainfall favours the biological activities in the soil profile, reactivating microbial activity. This is the factor suggesting the high soil moisture and nutrient supply throughout the growing season led to the highest CO2 efflux in June [5].
Soil respiration was positively related to species richness; consequently, the results highlight the importance of maintaining species diversity and aboveground biomass stocks in order to preserve the ecosystem processes driving soil respiration. The highest species diversity was observed in the oak–pine forest (Table 2), which corresponded to the highest CO2 efflux. This positive relationship between species richness and CO2 efflux can be understood under the assumption that more diverse sites are more productive in the multiplicity of ecosystem processes, due to species complementarity [36]. In this context, Bréchet et al. [37] highlighted that tree species are one of the most important factors in the variation in soil respiration. Our results are consistent with those reported by Hirota et al. [38], who pointed out that vegetation properties, including plant biomass and species richness, determined the small-scale heterogeneity of soil CO2 fluxes. Likewise, litter decomposition rate is directly related to diversity, a process that is also directly related to a higher diversity of decomposer microorganisms [39]. Authors such as Li et al. [40] found that soil CO2 fluxes are directly influenced by species diversity, as a result of facilitation in soil microbiota, which, by decomposing leaf litter, increase soil respiration. Similarly, Salahuddin et al. [41] highlighted that plants invest more energy in intraspecific competition for belowground resources and, therefore, autotrophic respiration shows an increasing trend with decreasing plant diversity. Moreover, aboveground biomass influences CO2 efflux due to the amount of litter and nutrients incorporated into the system, both in broadleaf forest soils [42] and in conifer forest soils [15]; consequently, a higher litter input favours the interaction of microorganisms and increases soil respiration [43].
We observed a positive correlation between CO2 efflux and Tsoil. This finding is consistent with those reported in other studies, in which Tsoil was the main factor controlling soil respiration [30,44,45]. Furthermore, Cantú-Silva et al. [46] reported that soil CO2 fluxes are directly influenced by Tsoil, where microorganisms responsible for the decomposition of soil organic matter are involved. Nevertheless, Sugasti et al. [47] point out that, although there is a positive correlation between CO2 efflux and Tsoil, variations in this variable are not the only factor driving soil CO2 fluxes; therefore, each particular forest depends on a balance of conditions such as soil moisture content, belowground biomass and microorganisms, which play an important role in CO2 production and transport to the soil surface.
Based on the aforementioned statement, we identified a correlation between soil water content, measured through Msoil, and CO2 efflux (r = 0.45). This is consistent with the findings reported by Li et al. [48] and Zhang et al. [49], who emphasize that soil moisture is a key factor controlling soil respiration since it inhibits the diffusion of CO2. The lowest soil CO2 fluxes were found in March, April, and May, when the Msoil reached its minimum values of 9.23, 9.05, and 8.78%. This finding is congruent with the observations by Darenova and Čater [3], who highlight that soil moisture is not sufficient to explain the variability of soil respiration, but it is one of main drivers for temporal CO2 efflux variation in forests. Similar conclusions have been reached by authors such as Kosugi et al. [50] and Darenova et al. [51].
Soil respiration was positively correlated with canopy cover, assessed by tree density (trees ha−1) and basal area. This correlation is explained in a study by Tscharntke et al. [52], who described the shading effect caused by forest structures, particularly the upper canopy species, leading to favourable microclimatic conditions with reduced temperatures in the understory. Consequently, plants in the canopy filter and absorb a portion of the solar radiation, mitigating its impact and thermally regulating the soil surface while maintaining adequate water availability. These optimal temperature conditions support the proliferation of soil microorganisms and facilitate their metabolic processes, including respiration.
The developed Generalized Linear Model (GLM) was designed to consider the effect of Tsoil, Msoil, stand-related factors and diversity metrics on CO2 efflux, and it showed good performance. The fitted model explained more than 48% of the variability of the soil respiration in the three forest types evaluated. The explained CO2 efflux variance was higher than the reported in similar studies, such as the one conducted by Yan et al. [4] (R2 = 0.38), who used the soil temperature as an explanatory variable. Similarly, in studies such as those by Chen et al. [53], Wang et al. [54], and Tian et al. [55], soil temperature alone accounted for about 40% of the seasonal fluctuations in soil respiration. However, these models often rely on single-factor associations, which can be confounded by other factors. In contrast, our model significantly enhanced its performance by including species richness, Simpson’s diversity index, stand quadratic diameter, and aboveground biomass as explanatory variables. These additions contributed approximately 10% to explaining the variation in CO2 efflux data.
This is of particular importance, because it indicates that soil temperature and soil moisture are not necessarily the sole controlling factors over soil respiration in forest ecosystems. This perspective is consistent with Reichstein et al. [56], who formulated nonlinear regression models that encompassed, in addition to soil temperature and soil moisture, factors such as leaf area index, leaf biomass, leaf productivity, aboveground biomass and soil carbon stock, as well as precipitation and air temperature, achieving acceptable model performance.

5. Conclusions

Soil respiration showed consistent rates across forest types, suggesting uniformity in CO2 efflux processes. The absence of significant differences in CO2 efflux patterns could be explained by the presence of one or two dominant species common to the three forest types, which may strongly influence the autotrophic respiration processes. Within the temperate forests of Northwestern Mexico, fluctuations in CO2 efflux over time seem to be controlled mainly by soil temperature and soil moisture. Nevertheless, this study also highlighted the significance of not only such biophysical controls, but also other ecological parameters, such as diversity metrics (e.g., species diversity and richness), structural stand indicators such as the quadratic mean diameter and basal area, as well as aboveground biomass. GLM regression analysis yielded suitable estimates of CO2 efflux, providing information that can be used as a basis for soil CO2 emissions. Although the focus of the study was to identify differences in CO2 fluxes among forest types, the results also may establish a baseline for future research to assess the impact of human (e.g., forest management practices) and natural disturbances on CO2 dynamics within the soil–atmosphere relationship of these temperate forests. This holds particular importance due to the direct influence of forest management practices, such as harvesting and thinning, on regional and global carbon balance.

Author Contributions

Data collection and curation, J.A.M.-R.; funding acquisition, conceptualization, supervision, formal analyses, and writing original draft preparation, B.V.-L.; methodology, G.Á.-P.; manuscript review, F.J.H. and J.O.L.-M.; project administration, C.G.A.-C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Tecnológico Nacional de México (Grant: 11366.21-P).

Data Availability Statement

The datasets generated during the current study are available from the corresponding author upon reasonable request.

Acknowledgments

We appreciate the anonymous reviewers for their valuable comments and suggestions on the manuscript.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Lal, R. Sequestration of atmospheric CO2 in global carbon pools. Energy Environ. Sci. 2008, 1, 86–100. [Google Scholar] [CrossRef]
  2. Nielsen, U.N.; Ayres, E.; Wall, D.H.; Bardgett, R.D. Soil biodiversity and carbon cycling: A review and synthesis of studies examining diversity-function relationships. Eur. J. Soil Sci. 2011, 62, 105–116. [Google Scholar] [CrossRef]
  3. Darenova, E.; Čater, M. Effect of spatial scale and harvest on heterogeneity of forest floor CO2 efflux in a sessile oak forest. CATENA 2020, 188, 104455. [Google Scholar] [CrossRef]
  4. Yan, W.D.; Xu, W.M.; Chen, X.Y.; Tian, D.L.; Peng, Y.Y.; Zhen, W.; Zhang, C.; Xu, J. Soil CO2 flux in different types of forests under a subtropical microclimatic environment. Pedosphere 2014, 24, 243–250. [Google Scholar] [CrossRef]
  5. Finderup, N.T.; Rand, R.N.; Michelsen, A. Increased CO2 efflux due to long-term experimental summer warming and litter input in subarctic tundra—CO2 fluxes at snowmelt, in growing season, fall and winter. Plant Soil 2019, 444, 365–382. [Google Scholar] [CrossRef]
  6. Chase, J.M.; Leibold, M.A. Spatial scale dictates the productivity-biodiversity relationship. Nature 2002, 416, 427–430. [Google Scholar] [CrossRef]
  7. Bohdalková, E.; Toszogyova, A.; Šímová, I.; Storch, D. Universality in biodiversity patterns: Variation in species–temperature and species–productivity relationships reveal a prominent role of productivity in diversity gradients. Ecography 2021, 44, 1366–1378. [Google Scholar] [CrossRef]
  8. Van Ruijven, J.; Berendse, F. Positive effects of plant species diversity on productivity in the absence of legumes. Ecol. Lett. 2003, 6, 170–175. [Google Scholar] [CrossRef]
  9. Lellei-Kovács, E.; Kovács-Láng, E.; Botta-Dukát, Z.; Kalapos, T.; Emmett, B.; Beier, C. Thresholds and interactive effects of soil moisture on the temperature response of soil respiration. Eur. J. Soil Biol. 2011, 47, 247–255. [Google Scholar] [CrossRef]
  10. Smith, K.; Ball, T.; Conen, F.; Dobbie, K.; Massheder, J.; Rey, A. Exchange of greenhouse gases between soil and atmosphere: Interactions of soil physical factors and biological processes. Eur. J. Soil Sci. 2003, 54, 779–791. [Google Scholar] [CrossRef]
  11. Luo, Y.; Zhou, X. Soil Respiration and the Environment; Elsevier: Berkeley, CA, USA, 2006. [Google Scholar] [CrossRef]
  12. Adachi, M.; Ito, A.; Yonemura, S.; Takeuchi, W. Estimation of global soil respiration by accounting for land-use changes derived from remote sensing data. J. Environ. Manag. 2017, 200, 97–104. [Google Scholar] [CrossRef] [PubMed]
  13. Acosta, M.; Darenova, E.; Krupková, L.; Pavelka, M. Seasonal and inter-annual variability of soil CO2 efflux in a Norway spruce forest over an eight-year study. Agric. For. Meteorol. 2018, 256–257, 93–103. [Google Scholar] [CrossRef]
  14. De Carlo, N.D.; Oelbermann, M.; Gordon, A.M. Carbon dioxide emissions: Spatiotemporal variation in a young and mature riparian forest. Ecol. Eng. 2019, 138, 353–361. [Google Scholar] [CrossRef]
  15. Jílková, V. Soil respiration in temperate forests is increased by a shift from coniferous to deciduous trees but not by an increase in temperature. Appl. Soil Ecol. 2020, 154, 103635. [Google Scholar] [CrossRef]
  16. Merino, A.; Edeso, J.M.; González, M.J.; Marauri, P. Soil properties in a hilly area following different harvesting management practices. For. Ecol. Manag. 1997, 103, 235–246. [Google Scholar] [CrossRef]
  17. Challenger, A. Use and Conservation of Mexico’s Terrestrial Ecosystems: Past, Present and Future; Comisión Nacional para el Conocimiento y Uso de la Biodiversidad/Instituto de Biología, UNAM-Agrupación Sierra Madre: Mexico City, Mexico, 1998. [Google Scholar]
  18. García, E. Modificaciones al Régimen de Clasificación Climática de Köppen; Instituto de Geografía, UNAM: Mexico City, Mexico, 1988. [Google Scholar]
  19. Instituto Nacional de Estadística Geográfica e Informática (INEGI). Uso del Suelo y Vegetación Escala 1:250 000 Serie V, Información Vectorial; INEGI: Mexico City, Mexico, 2012.
  20. Borken, W.; Xu, Y.J.; Davidson, E.A.; Beese, F. Site and temporal variation of soil respiration in European beech, Norway spruce, and Scots pine forests. Glob. Change Biol. 2002, 8, 1205–1216. [Google Scholar] [CrossRef]
  21. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2020. [Google Scholar]
  22. Vargas-Larreta, B.; López, S.C.; Corral-Rivas, J.J.; López, M.J.; Aguirre, C.C.; Álvarez-González, J.G. Allometric equations for estimating biomass and carbon stocks in the temperate forests of North-Western Mexico. Forests 2017, 8, 269. [Google Scholar] [CrossRef]
  23. Hibbard, K.A.; Law, B.E.; Reichstein, M.; Sulzman, J. An analysis of soil respiration across northern hemisphere temperate ecosystems. Biogeochemistry 2005, 73, 29–70. [Google Scholar] [CrossRef]
  24. Adachi, M.; Bekku, Y.; Konuma, A.; Kadir, W.; Okuda, T.; Koizumi, H. Required sample size for estimating soil respiration rates in large areas of two tropical forests and of two types of plantations in Malaysia. For. Ecol. Manag. 2005, 210, 455–459. [Google Scholar] [CrossRef]
  25. Se-Hoon, J.; Dong-Joo, K.; Chan-Woo, P.; Seong-Deog, K. Appropriate sampling points and frequency of CO2 measurements for soil respiration analysis in a pine (Pinus densiflora) forest. Anim. Cells Syst. 2015, 19, 332–338. [Google Scholar] [CrossRef]
  26. Pantani, O.L.; Fioravanti, F.; Stefanini, F.M.; Berni, R.; Certini, G. Accounting for soil respiration variability—Case study in a Mediterranean pine-dominated forest. Sci. Rep. 2020, 10, 1787. [Google Scholar] [CrossRef] [PubMed]
  27. Barba, J.; Yuste, J.C.; Martínez-Vilalta, J.; Lloret, F. Drought-induced tree species replacement is reflected in the spatial variability of soil respiration in a mixed Mediterranean forest. For. Ecol. Manag. 2013, 306, 79–87. [Google Scholar] [CrossRef]
  28. Yáñez-Díaz, M.I.; Cantú-Silva, I.; González-Rodríguez, H.; Marmolejo-Monsiváis, J.G.; Jurado, E.; Gómez-Meza, M.V. Soil respiration in four land use systems. Rev. Mex. Cienc. For. 2017, 8, 123–149. [Google Scholar]
  29. Čater, M.; Darenova, E.; Simončič, P. Harvesting intensity and tree species affect soil respiration in uneven-aged Dinaric Forest stands. For. Ecol. Manag. 2021, 480, 118638. [Google Scholar] [CrossRef]
  30. Zhang, Y.; Zou, J.; Meng, D.; Dang, S.; Zhou, J.; Osborne, B.; Ren, Y.; Liang, T.; Yu, K. Effect of soil microorganisms and labile C availability on soil respiration in response to litter inputs in forest ecosystems: A metaanalysis. Ecol. Evol. 2020, 10, 13602–13612. [Google Scholar] [CrossRef]
  31. Peng, Y.; Thomas, S.C.; Tian, D. Forest management and soil respiration: Implications for carbon sequestration. Environ. Rev. 2008, 16, 93–111. [Google Scholar] [CrossRef]
  32. Wang, C.; Yu, G.; Zhon, G.; Yan, J.; Zhang, L.; Wang, X.; Tang, X.; Sun, X. CO2 flux elevations over the evergreen coniferous and broadleaved mixed forest in Ginghushan, China. Sci. China Ser. D Earth Sci. 2006, 49 (Suppl. S2), 127–138. [Google Scholar] [CrossRef]
  33. Wertin, T.M.; Belnap, J.; Reed, S.C. Experimental warming in a dryland community reduced plant photosynthesis and soil CO2 efflux although the relationship between the fluxes remained unchanged. Funct. Ecol. 2017, 31, 297–305. [Google Scholar] [CrossRef]
  34. Kim, D.G.; Mu, S.; Kang, S.; Lee, D. Factors controlling soil CO2 effluxes and the effects of rewetting on effluxes in adjacent deciduous, coniferous, and mixed forests in Korea. Soil Biol. Biochem. 2010, 42, 576–585. [Google Scholar] [CrossRef]
  35. Yan, T.; Qu, T.; Song, H.; Sun, Z.; Zeng, H.; Peng, S. Ectomycorrhizal fungi respiration quantification and drivers in three differently-aged larch plantations. Agric. For. Meteorol. 2019, 265, 245–251. [Google Scholar] [CrossRef]
  36. Tilman, D.; Knops, J.; Wedin, D.; Reich, P.; Ritchie, M.; Siemann, E. The Influence of functional diversity and composition on ecosystem processes. Science 1997, 277, 1300–1302. [Google Scholar] [CrossRef]
  37. Bréchet, L.; Ponton, S.; Roy, J.; Freycon, V.; Coûteaux, M.; Bonal, D.; Epron, D. Do tree species characteristics influence soil respiration in tropical forests? A test based on 16 tree species planted in monospecific plots. Plant Soil 2009, 319, 235–246. [Google Scholar] [CrossRef]
  38. Hirota, M.; Zhang, P.; Gu, S.; Shen, H.; Kuriyama, T.; Li, Y.; Tang, Y. Small-scale variation in ecosystem CO2 fluxes in an alpine meadow depends on plant biomass and species richness. J. Plant Res. 2010, 123, 531–541. [Google Scholar] [CrossRef] [PubMed]
  39. Gessner, M.O.; Swan, C.M.; Dang, C.K.; McKie, B.G.; Bardgett, R.D.; Wall, D.H.; Hättenschwiler, S. Diversity meets decomposition. Trends Ecol. Evol. 2010, 25, 372–380. [Google Scholar] [CrossRef]
  40. Li, Q.; Tian, Y.; Zhan, X.; Xu, X.; Wang, H.; Kuzyakov, Y. Labile carbon and nitrogen additions affect soil organic matter decomposition more strongly than temperature. Appl. Soil Ecol. 2017, 114, 152–160. [Google Scholar] [CrossRef]
  41. Salahuddin, R.B.; Razaq, M.; Li, J.; Khan, F.; Jie, Z. Root order-based traits of Manchurian walnut & larch and their plasticity under interspecific competition. Sci. Rep. 2018, 8, 9815. [Google Scholar] [CrossRef]
  42. Kammer, A.; Schmidt, M.W.; Hagedorn, F. Decomposition pathways of 13C-depleted leaf litter in forest soils of the Swiss Jura. Biogeochemistry 2012, 108, 395–411. [Google Scholar] [CrossRef]
  43. Shaver, G.R.; Johnson, L.C.; Cades, D.H.; Murray, G.; Laundre, J.A.; Rastetter, E.B.; Nadelhoffer, K.J.; Giblin, A.E. Biomass and CO2 flux in wet sedge tundras: Responses to nutrients, temperature, and light. Ecol. Monogr. 1998, 68, 75–97. [Google Scholar] [CrossRef]
  44. Reth, S.; Reichstein, M.; Falge, E. The effect of soil water content, soil temperature, soil pH-value and the root mass on soil CO2 efflux—A modified model. Plant Soil 2005, 268, 21–33. [Google Scholar] [CrossRef]
  45. Wei, W.; Weile, C.; Shaopeng, W. Forest soil respiration and its heterotrophic and autotrophic components: Global patterns and responses to temperature and precipitation. Soil Biol. Biochem. 2010, 42, 1236–1244. [Google Scholar] [CrossRef]
  46. Cantú-Silva, I.; González-Rodríguez, H.; Gómez-Meza, M.V. CO2 efflux in vertisol under different land use systems. Trop. Subtrop. Agroecosystems 2010, 12, 389–403. [Google Scholar]
  47. Sugasti, L.; Eng, B.; Pinzón, R. Medición continúa de flujo de CO2 en suelo en una parcela de bosque tropical en Isla Barro Colorado, Canal de Panamá. In Proceedings of the 16th LACCEI International Multi-Conference for Engineering, Education, and Technology: Innovation in Education and Inclusion, Lima, Peru, 19–21 July 2018. [Google Scholar] [CrossRef]
  48. Li, Y.L.; Otieno, D.; Owen, K.; Zhang, Y.; Tenhunen, J.; Rao, X.Q.; Lin, Y.B. Temporal variability in soil CO2 emission in an orchard forest ecosystem. Pedosphere 2008, 18, 273–283. [Google Scholar] [CrossRef]
  49. Zhang, P.; Tang, Y.; Hirota, M.; Yamamoto, A.; Mariko, S. Use of a regression method to partition sources of ecosystem respiration in an alpine meadow. Soil Biol. Biochem. 2009, 41, 663–670. [Google Scholar] [CrossRef]
  50. Kosugi, Y.; Mitani, T.; Itoh, M.; Noguchi, S.; Tani, M.; Matsuo, N.; Takanashi, S.; Ohkubo, S.; Nik, A.R. Spatial and temporal variation in soil respiration in a Southeast Asian tropical rainforest. Agric. For. Meteorol. 2007, 147, 35–47. [Google Scholar] [CrossRef]
  51. Darenova, E.; Pavelka, M.; Macalkova, L. Spatial heterogeneity of CO2 efflux and optimization of the number of measurement positions. Eur. J. Soil Biol. 2016, 75, 123–134. [Google Scholar] [CrossRef]
  52. Tscharntke, T.; Clough, Y.; Bhagwat, S.A.; Buchori, D.; Faust, H.; Hertel, D.; Hölscher, D.; Juhrbandt, J.; Kessler, M.; Perfecto, I.; et al. Multifunctional shade-tree management in tropical agroforestry landscapes—A review. J. Appl. Ecol. 2011, 48, 619–629. [Google Scholar] [CrossRef]
  53. Chen, X.Y.; Eamus, D.; Hutley, L.B. Seasonal patterns of soil carbon dioxide efflux from a wet-dry tropical savanna of northern Australia. Aust. J. Bot. 2002, 50, 43–51. [Google Scholar] [CrossRef]
  54. Wang, C.K.; Yang, J.Y.; Zhang, Q.Z. Soil respiration in six temperate forests in China. Glob. Chang. Biol. 2006, 12, 2103–2114. [Google Scholar] [CrossRef]
  55. Tian, D.L.; Peng, Y.Y.; Yan, W.D.; Fang, X.; Kang, W.X.; Wang, G.J.; Chen, X.Y. Effects of thinning and litter fall removal on fine root production and soil organic carbon content in Masson pine plantations. Pedosphere 2010, 20, 486–493. [Google Scholar] [CrossRef]
  56. Reichstein, M.; Rey, A.; Freibauer, A.; Tenhunen, J.; Valentini, R.; Banza, J.; Casals, P.; Cheng, Y.; Grünzweig, J.; Irvine, J.; et al. Modeling temporal and large-scale spatial variability of soil respiration from soil water availability, temperature and vegetation productivity indices. Glob. Biogeochem. Cycles 2003, 17, 1104. [Google Scholar] [CrossRef]
Forests 14 01763 g001
Figure 1. Study area, distribution of the permanent sampling plots, and sampling design.
Figure 1. Study area, distribution of the permanent sampling plots, and sampling design.
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Figure 2. Importance value index (IVI) for the species identified in the three forest types.
Figure 2. Importance value index (IVI) for the species identified in the three forest types.
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Figure 3. Monthly soil respiration (A), soil moisture (B), and soil temperature (C) for the three forest types evaluated.
Figure 3. Monthly soil respiration (A), soil moisture (B), and soil temperature (C) for the three forest types evaluated.
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Figure 4. Relationships between soil respiration and soil temperature (A), soil moisture (B), aboveground biomass (C), stand variables (D,E), and diversity metrics (F–H). The solid line represents the fitted linear regression.
Figure 4. Relationships between soil respiration and soil temperature (A), soil moisture (B), aboveground biomass (C), stand variables (D,E), and diversity metrics (F–H). The solid line represents the fitted linear regression.
Forests 14 01763 g004
Table 1. Description of stand variables of the three forest types evaluated.
Table 1. Description of stand variables of the three forest types evaluated.
Forest TypeDq
(cm)		DBH
(cm)		BA
(m2 ha−1)		Density
(Stems ha−1)		GS
(m3 ha−1)		AGB
(Mg ha−1)		GeneraSpeciesElevation (m.a.s.l)
Pine22.119.120.63543191.1154.1372707
Pine–oak20.218.123.01719203.5130.76142690
Oak–pine20.717.824.91737202.6160.17162687
Dq = quadratic mean diameter, BA = basal area, GS = growing stock, AGB = aboveground biomass.
Table 2. Monthly mean CO2 efflux, soil temperature and soil moisture, and Tukey’s test of soil CO2 efflux by month.
Table 2. Monthly mean CO2 efflux, soil temperature and soil moisture, and Tukey’s test of soil CO2 efflux by month.
MonthSoil CO2 Efflux
(µmol m−2 s−1) †		Soil Temperature
(°C)		Soil Moisture (%)
January2.81 ± 1.24 a8.81 ± 1.2859.2 ± 10.9
February2.49 ± 1.46 ab9.05 ± 1.9829.9 ± 13.7
March2.46 ± 1.42 ab11.6 ± 1.9520.5 ± 5.66
April2.28 ± 1.47 ab12.7 ± 1.7222.1 ± 9.69
May4.15 ± 2.57 ac13.8 ± 2.1622.8 ± 10.1
June7.97 ± 2.94 d15.1 ± 1.8554.8 ± 23.1
July8.79 ± 1.81 de16.1 ± 1.8068.4 ± 25.8
August8.07 ± 1.47 def14.2 ± 2.2166.4 ± 16.5
October7.64 ± 1.94 dfg12.7 ± 1.7065.7 ± 18.2
November6.38 ± 1.74 fgh10.7 ± 1.4364.4 ± 16.6
December5.53 ± 1.86 h9.8 ± 0.8251.7 ± 11.5
† Mean ± standard deviation. Means labelled with the same letter (in the same column) are not significantly different (p > 0.05).
Table 3. Mean values and Tukey’s test of soil CO2 efflux, soil temperature, and soil moisture in the three forest types evaluated.
Table 3. Mean values and Tukey’s test of soil CO2 efflux, soil temperature, and soil moisture in the three forest types evaluated.
Forest TypeSoil CO2 Efflux
(µmol m−2 s−1) †		Soil Temperature
(°C)		Soil Moisture (%)
Pine5.03 ± 2.96 a12.2 ± 2.71 a42.4 ± 23.9 a
Pine–oak4.81 ± 2.83 a11.9 ± 2.82 a45.2 ± 24.9 a
Oak–pine5.08 ± 3.40 a11.7 ± 3.27 a43.7 ± 22.3 a
† Mean ± standard deviation. Means labelled with the same letter (in the same column) are not significantly different (p < 0.05).
Table 4. Estimates and goodness-of-fit of the GLM procedure for estimating CO2 efflux in temperate forests.
Table 4. Estimates and goodness-of-fit of the GLM procedure for estimating CO2 efflux in temperate forests.
Cross-Validation
ParameterEstimateStd. Err.t(262)p-ValueRMSER2
b10.10300.007513.6992<0.00002.4830.483
b20.01660.001116.5048<0.0000
b30.05990.01903.1613<0.0000
b4−1.25490.2572−4.8799<0.0000
b5−0.03280.0055−5.9732<0.0000
b60.00390.00075.2337<0.0000
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Martínez-Rivas, J.A.; Vargas-Larreta, B.; López-Martínez, J.O.; Aguirre-Calderón, C.G.; Hernández, F.J.; Ángeles-Pérez, G. Understanding Soil Respiration Dynamics in Temperate Forests in Northwestern Mexico. Forests 2023, 14, 1763. https://doi.org/10.3390/f14091763

AMA Style

Martínez-Rivas JA, Vargas-Larreta B, López-Martínez JO, Aguirre-Calderón CG, Hernández FJ, Ángeles-Pérez G. Understanding Soil Respiration Dynamics in Temperate Forests in Northwestern Mexico. Forests. 2023; 14(9):1763. https://doi.org/10.3390/f14091763

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Martínez-Rivas, José Alexis, Benedicto Vargas-Larreta, Jorge Omar López-Martínez, Cristóbal Gerardo Aguirre-Calderón, Francisco Javier Hernández, and Gregorio Ángeles-Pérez. 2023. "Understanding Soil Respiration Dynamics in Temperate Forests in Northwestern Mexico" Forests 14, no. 9: 1763. https://doi.org/10.3390/f14091763

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