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Article

Retention Levels and Years-After-Harvesting Influence over Soil Microbial Activity and Biomass in Southern Patagonian Forests

by
Santiago Toledo
1,
Guillermo Martínez Pastur
2,*,
Julián Rodríguez-Souilla
2 and
Pablo L. Peri
1,3
1
Instituto Nacional de Tecnología Agropecuaria (INTA), Mahatma Gandhi 1322, Río Gallegos 9400, Argentina
2
Centro Austral de Investigaciones Científicas (CADIC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Houssay 200, Ushuaia 9410, Argentina
3
National Institute of Agricultural Technology (NIAT), Universidad Nacional de la Patagonia Austral (UNPA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Lisandro De La Torre 860, Río Gallegos 9400, Argentina
*
Author to whom correspondence should be addressed.
Land 2024, 13(11), 1963; https://doi.org/10.3390/land13111963
Submission received: 20 September 2024 / Revised: 14 November 2024 / Accepted: 15 November 2024 / Published: 20 November 2024
(This article belongs to the Special Issue Sustainable Forest Landscape Management towards Ecosystem Services and Biodiversity)
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Abstract

:
Variable retention harvesting (VRH) was designed for timber purposes and biodiversity conservation in natural forests. This system was globally tested, but few studies are related to soil microbial components. The objective was to evaluate different retention types (aggregated and dispersed retention) considering different years-after-harvesting (6, 9, 16 YAH) on soil microbial community attributes compared with unmanaged primary forests (PF) in Nothofagus pumilio forests of Tierra del Fuego (Argentina). This study also evaluated the influence of climate, soil, and understory vegetation. Results showed that aggregated retention increased microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), and soil basal respiration (SBR) compared to dispersed retention, but with similar values than PF. However, harvested areas decreased MBC/MBN values compared with PF. The results showed an overall decrease in microbial biomass and activity in 9 YAH stands, with a positive recovery at 16 YAH. Soil pH, mean annual temperature, and understory vegetation cover showed a positive relationship with MBC, MBN, and SBR. The recovery after 16 YAH reached to different microbial communities. Therefore, the maintenance of retention components in managed stands for longer periods is needed. The results highlight some advantages of VRH as a tool for conservation of forest-dwelling soil microorganisms, including microbial biomass and activity.

1. Introduction

There is an increasing need for sustainable forest management practices to maintain and enhance economic, ecological, and social values of native forests for future generations [1,2]. Thus, forest ecosystems provide many services for society, such as wood production, supporting biodiversity maintenance and conservation (above- and below-ground), as well as carbon (C) sequestration to mitigate climate change [3,4]. Although management modifies many components of forest ecosystems, such as vegetation structure and composition, soil properties, litter, and microclimate, most of the research has been focused on above-ground components [5,6,7,8,9,10,11]. Recent studies emphasized that forest harvesting impacts on below-ground soil biology, and changed the interactions among microorganisms, plants, and soil components [12,13,14,15,16]. These studies are crucial to understand the long-term impact of harvesting on soil microbial properties, because microorganisms are directly related to productivity and sustainability of natural forest ecosystems [13,14,16,17].
In forests and other terrestrial ecosystems, active soil living microorganisms represent only 0.1–2.0% of the total soil volume [18], but they are involved in nearly 90% of soil ecosystem functions [17]. Certainly, soil microorganisms provide multiple functions, and regulating and supporting ecosystem services, such as biogeochemical cycles, mineralization of organic matter, plant growth, CO2 fluxes to the atmosphere, and play key roles in the development and maintenance of soil structure and fertility [19,20]. Additionally, the characterization of soil microbial variables is commonly used to determine soil biological health, including the response of soils to environmental stress such as climate change, anthropogenic disturbances, and as indicator of ecosystem resilience [21,22,23]. Therefore, soil microbial variables could act as one useful indicator of forest sustainability, ecosystem health, and conservation status under different management practices. The magnitude of forest management depends on direct and indirect effects of soil properties such as bulk density, soil nutrients, C cycling, soil fertility, understory plant species, substrate (e.g., litterfall and rhizodeposition) for the soil microorganisms, and microclimate conditions that affect microorganism activity and abundance [12,13,14,24].
Variable retention harvesting (VRH) is considered an effective tool to support stand-level biodiversity and used as an indicator for sustainable forest monitoring, e.g., STEMS in British Columbia (Canada), and DEMO in Washington, Oregon (USA) [14,16,25,26,27]. Several regeneration methods have been proposed for Southern Patagonian Nothofagus pumilio (lenga) forests based on the natural regeneration [28]. These forests have been mainly managed from selection cuts to clear-cuts, including shelterwood cuts and VRH [1,5,6,28]. In Tierra del Fuego (Argentina), VRH proposes to leave 30% of the forest area as aggregated retention and 15–20% of the original basal area as dispersed retention [8]. The main goals of VRH are to maintain the economic values of the harvesting, facilitate the in situ conservation and connectivity for biodiversity, and provide faster post-harvest forest recovery [9,11]. Although not well documented, there are reasons to believe that VRH will provide similar benefits to below-ground biodiversity and function components. The aggregate retention may facilitate soil microbes by maintaining below-ground plant–soil microorganism interactions [20,29,30,31]. Furthermore, retained trees can provide refugia for above- and below-ground biota being important to maintain carbon and nutrient cycling dynamics [32,33,34]. Additionally, retained trees may maintain physical and chemical soil properties and supply C through symbiotic interactions, litterfall, and rhizodeposition than the surrounding harvest areas [15,16]. Also, aggregates in VRH buffer environmental extremes microclimatic conditions by providing shade and maintaining higher soil pH values [13,17,32]. Nevertheless, the impact of VRH and years-after-harvesting (YAH) on soil microorganisms was less documented, where soil microbial biomass, fungi and bacteria richness, microbial community structure, and microbial activity may be reduced, increased, or unaffected in openings in harvested areas relative to retention patches [13,16,35,36,37,38,39].
Below-ground plant–soil interactions have been increasingly recognized as key factor for ecosystem functioning [8,9,14,29], and this is considered a knowledge gap for retention forestry [30,33]. Indeed, the soil microbial community in ecosystem forests may influence post disturbance rates of nutrient cycling and, ultimately, plant growth and regeneration success [5,9,11,31,34]. Therefore, it is important to understand how harvesting influences soil microbial community in stands with forest managed by VRH across the YAH. To accomplish this, an experiment was conducted in pure N. pumilio forests in Tierra del Fuego (Argentina). This species is a cold-tolerant deciduous tree, which occurs at lower (e.g., sea level) and higher elevations (e.g., treeline in mountains), with a remarkable commercial importance, making it an ideal species to evaluate the impacts of forest management. To our knowledge, few studies have evaluated the response of soil microbial variables, such as biomass and soil respiration, and soil microbial coefficients for VRH across the YAH. Based on further studies describing impacts of VRH on N. pumilio forests, the hypothesis is that soil microbial variables greatly impacted in dispersed retention and conserved at higher levels of retention (e.g., aggregates), and both recover after harvesting but following different trends (e.g., dispersed retention do not reach the same values of aggregates across the years during the recovery). Therefore, the objective was to analyze the influence of different retention types in VRH (aggregated and dispersed retention) across the first 16 YAH on soil microbial community attributes, considering microbial biomass (carbon and nitrogen), soil basal respiration, and microbial indexes, and compare them with control unmanaged primary forests. We also evaluated how climate, soil, and vegetation factors drive the different soil microbial variables by determining the relationships between microbial biomass and soil basal respiration with the main environmental factors. This study addresses the following research questions: (i) Does aggregated retention generate an increased microbial biomass, activity, and communities compared with primary forests? (ii) Does variable retention harvesting generate a microbiome recovery (biomass and activity) after harvesting? And (iii), what are the key environmental drivers that influence microbial response to harvest in these austral forests? Such knowledge would allow a better understanding of the anthropic, biotic, and abiotic factors that modulate the biomass and function of the soil microorganisms, and ultimately, the soil fertility and conservation of forest ecosystems.

2. Materials and Methods

2.1. Study Area and Experimental Design

The study was carried out in three pure natural N. pumilio forests in one east–west gradient that cover different environmental conditions in Tierra del Fuego (Argentina): (i) Lenga Patagonia S.A. (−54.482° S, −66.802° W); (ii) Campo Chico ranch (−54.581° S, −66.911° W); and (iii) Los Cerros ranch (−54.381° S, −67.862° W). These forests have a rolling topography ranging in elevation from 157 to 265 m.a.s.l. A two-factor random design was followed, with VRH and YAH as the main factors. The two retention treatments were: (i) aggregated retention (AR) consisting in one patch of 30 m radius per hectare, where original forest structure was retained, and (ii) dispersed retention (DR) that left remaining trees after harvesting distributed between the aggregates (10–15 m2 ha−1 basal area). YAH factor had three levels: 6 YAH (Lenga Patagonia S.A.), 9 YAH (Campo Chico ranch), and 16 YAH (Los Cerros ranch). At each forest landscape, eight stands were selected (>2 ha each): four harvested stands where the two retention types were applied (AR and DR), and four control stands, defined as primary unmanaged forests (PF) (Figure 1).
Control stands were selected for comparisons, considering that they have a good representation of the original soil microbial biomass and activity before harvesting. The final design included 36 stands (3 YAH × 3 forest treatments × 4 replicates). AR and DR were located at least 20 m apart from the aggregate edge, and less than 500 m from PF. Soil samples (n = 4) were randomly collected within the aggregates, DR and PF.

2.2. Characterization of Environmental and Vegetation Variables

The climate parameters (1960–2010) for each area were derived from the WorldClim 2 data set [40], including mean annual precipitation (MAP) and mean annual temperature (MAT) at a resolution of 0.008333° (near 1 km). Also included was elevation (m.a.s.l.) derived from Farr et al. [41] due to their direct influence in the regional climate. The Tierra del Fuego climate is characterized by short and cool summers and long snowy winters. MAP varied from 419 to 438 mm yr−1, and MAT fluctuated from 4.0 to 4.4 °C at Campo Chico ranch, and from 4.7 to 5.1 °C at Los Cerros ranch (Table 1). Only three months are free of mean daily air temperatures below 0 °C, being the growing season of approximately five months (November to March) [28].
Forest structure was characterized by two sub-plots, using the point sampling method (BAF = 1–5) [42] with a Criterion RD-1000 (Laser Technology, New York, NY, USA). At each sampling point we measured: (i) DH: dominant height of the stand, using a TruPulse 200 laser clinometer and distance rangefinder (Laser Technology, New York, NY, USA) by averaging the height of the two taller trees per transect, (ii) TD: tree density, (iii) basal area (BA), and (iv) FG: forests growth, volume annual growth at stand level. Associated with these forest structures, tree regeneration was also measured: (v) REC: recruitment (seedlings) in two plots of 1 m2, (vi) I-R: initial regeneration including seedlings and saplings < 1.3 m height in two plots of 1 m2, and (vii) A-R: advanced regeneration including saplings with more than 1.3 m height and <5.0 cm diameter at breast height in two plots of 5 m2. For methodology and modeling, see Martínez Pastur et al. [43] and Chaves et al. [31]. To evaluate the understory vegetation, the point-intercept method was used [44] with 50 intercept points (every 1 m) along the same transects at each sampling plot. Vascular plants (dicots, monocots, ferns) were recorded, including tree regeneration less than 1.30 m height, identifying dominant species (DS). The presence data of the species occurring on transects but that were not intercepted by the points was also recorded. These data were used to determine richness and forest floor covers, following Moore [45] and Correa [46]. COV: Cover data were sorted by vascular plant species; BS: bare soil, cover, bryophyte (mosses and liverworts), woody debris (>1 cm diameter), and litter; SR: Species richness was calculated as the total number of vascular plant species identified at each plot. Understory vegetation biomass (BIO) was determined at the peak of the growing season by clipping up to the soil surface all above-ground biomass into two quadrats of 0.2 × 0.5 m. Then, samples were dried (60 °C) for 48 h to obtain dry matter plant biomass (total, green, and dead components) expressed in kg dry matter (DM) ha−1.
Nothofagus forest soils in Tierra del Fuego have been classified as podzols according to IUSS Working Group WRB [47]. The soil in the study area is characterized by an organic uppermost layer up to 2 cm thick (O horizon), followed by a mineral layer of less than 40 cm depth where most roots develop (mostly A horizon), with a variable proportion of stony material. The first soils layers in N. pumilio forests (O horizons at <5 cm and A horizons < 30 cm) account for most of the soil fertility [11,34,48]. At each plot, four composite soil samples (0–10 cm) were randomly collected using soil auger cores (5.6 cm diameter) after removing the litter layer. Soil samples were placed in plastic bags and stored in refrigerated containers until transported to the laboratory. The four samples were used for laboratory analysis, including: (i) soil pH determined in 1:2.5 water soil extract; (ii) soil organic carbon (SOC) concentrations were derived from the dry combustion (induction furnace) method, using a conversion factor of 0.58 [49]; (iii) Kjeldahl digestion was used to determine soil total nitrogen (SN) [50]; and (iv) Olsen methodology was employed to determine available soil phosphorus (SP) [51].

2.3. Soil Microbial Biomass and Activity

Soil microbial variables were sampled and determined during February 2022 (coincident with the peak plant growth). A subsample of 600 g of soil was used, which were sieved (2 mm mesh) and then refrigerated at 4 °C during a week until microbial variables analyses. Microbial biomass carbon (MBC) was estimated using the chloroform–fumigation extraction method [52] calculated as: MBC = (OCf − OCnf)/Kec, where OCf = organic C extracted from fumigated samples, OCnf = C extracted from non-fumigated samples, and kEC = fumigation efficiency constant (0.45 MBC expressed as mg C kg−1 dry soil). Microbial biomass nitrogen (MBN) was determined by a modification of the fumigation–incubation method [53]. Liquid chloroform (1 mL) was added directly to 30 g moist soil samples, stirred, and left for 20 h in sealed beakers located in desiccators. Chloroform was then removed using a vacuum pump. Fumigated and non-fumigated samples were incubated at field capacity for 10 days at 25 °C.
After incubation, samples were extracted with 2 M KCl and analyzed for NH4-N by the Berthelot reaction. MBN was calculated as the difference in N between fumigated and non-fumigated samples, divided by a correction factor (kN). Non-extractable amount of microbial N was compensated for by a correction factor of kN = 0.54. MBN was expressed as mg N kg−1 dry soil. Soil basal respiration (SBR) was estimated by quantifying the carbon dioxide (CO2) released in the process of basal respiration (heterotrophic) during 7 days of incubation at 25 °C [54]. This was performed by placing 75 g fresh soil (moistened to 60% of field capacity) into 1.5 L capacity glass containers with hermetic lids, together with a smaller flask containing 20 mL 0.2 M NaOH to capture the released CO2. The released CO2 was determined by titration with 0.1 M HCl, after precipitation of the barium carbonate formed by adding barium chloride (BaCl2) aqueous solution to the NaOH solution, utilizing phenolphthalein as an indicator.
The mean soil respiration (accumulated SBR/7) during the incubation time was expressed as mg C kg−1 soil day−1. Soil microbial metabolic quotient (qCO2) is defined as soil basal respiration per unit of biomass and was calculated as the C-CO2 evolution (SBR) per unit MBC and per unit time. Finally, the ratio of MBC to total C (qMC = MBC/SOC) and MBC/MBN ratio were calculated to indicate shifts in the fungi/bacteria [55,56,57,58].

2.4. Statistical Analyses

Data were tested for normal distribution (Shapiro–Wilk test) and homogeneity of variances (Levene’s test). The microbial variables (MBC, MBN, MBC/MBN, SBR, qCO2, qMC) were analyzed with a two-way analysis of variance (ANOVA) using forest treatments (AR, DR, PF) and YAH (6, 9, 16) as main factors. Significant differences were compared with Tukey test with a significance level of p < 0.05.
A principal component analysis (PCA) was used to compare and quantify the similarity among forest treatments (AR, DR, and PF) with different YAH (6, 9, and 16) using microbial (MBC, MBN, SBR, MBC/MBN ratio, qCO2), climatic and topographic (MAP, MAT, elevation), soil physical–chemical characteristics (soil pH, SOC, soil N, soil P), and vegetation variables (species richness, understory vegetation, biomass, initial and advanced regeneration, dominant overstory height, forest growth, vegetation cover, bare soil, tree density, regeneration recruitment).
Simple linear regressions were carried out among microbial (MBC, MBN, SBR, MBC/MBN), soil (SOC, soil pH, soil N), climatic and topographic (MAT, elevation, MAP) and vegetation variables (species richness, vegetation cover, forest growth) to determine correlations between forest treatments and YAH. The dependent variables were soil, topographic, climatic, and vegetation, while independent variables were MBC, MBN, SBR and MBC/MBN. All the statistical analyses were performed using INFOSTAT software (version 2014).

3. Results

3.1. Environmental and Vegetation Variables of the Studied Forest Areas

Vegetation cover was higher at 9 YAH stands compared to 6 and 16 YAH stands (Table 1). In addition, vegetation cover in PF were 78.5 to 107.5%, which contrasted with AR and DR treatments that showed values of 141.5% (6 YAH), 209.0% (9 YAH), and 189.0% (16 YAH), being higher in AR than DR. Bare soil fluctuated from 2.5 to 37.5% in DR and PF (Table 1). The studied forests presented a species richness between 13 and 27, which varied according to YAH and forest treatments. The dominant and most representative plant species were Osmorhiza depauperata, Poa pratensis associated with Viola magellanica, Schizeilema ranunculus, Galium aparine, and Taraxacum officinale that varied among YAH and forest treatments. Understory vegetation biomass was higher in DR compared to PF. AR had similar understory vegetation biomass values to PF, except at 9 YAH stands, which showed an increase in the biomass due to tree regeneration. The soil pH values were acidic, decreasing from 5.2 in 16 YAH stands to 4.2 in 6 YAH stands. In contrast, SOC and soil N contents were higher at 9 YAH stands. Additionally, soil P varied among areas and forest treatments (Table 1).

3.2. Response of Soil Microbial Variables to Retention Types and Years-After-Harvesting

Soil microbial variables varied (p < 0.05) depending on YAH and forest treatments (Table 2). AR significantly increased MBC and MBN (15–35% and 37%, respectively) compared to DR and PF. In addition, DR significantly decreased SBR (25%) and the MBC/MBN ratio (30%) compared to PF. No effects of forest treatments on qCO2 and qMC were detected. Regarding YAH, MBN was significantly higher at 16 YAH stands compared to 6 YAH (57%) and 9 YAH (43%). On the other hand, SBR and MBC/MBN were significantly different between 9 and 16 YAH. No effects of YAH on MBC and qCO2 were detected. In contrast, qMC decreased (~50%) at 9 YAH compared to 6 YAH and 16 YAH, where significant interactions were detected between factors for MBC and SBR (Table 2).
The interaction between forest treatments and YAH for MBC showed that 6 YAH and 16 YAH stands did not present statistical differences, while at 9 YAH the DR decreased in MBC compared with AR and PF. However, the interaction between forest treatments and YAH for SBR, determined that AR had higher values at 6 and 9 YAH compared with DR and PF. In addition, the magnitude of change in SBR was higher at 9 YAH than 6 YAH (DR < PF < AR), but without differences at 16 YAH.

3.3. Relationships of Soil Microbial Variables with Environmental Factors

Principal component analyses (PCA) determined that the first two axes explained 47–56% of the total variance of the samples (30–37%, and 18–19% for PC1 and PC2, respectively). There was a clear split between DR and AR or PF (Figure 2A). This pattern was explained because DR presented higher species richness, vegetation cover, understory vegetation biomass, and advance regeneration, while AR or PF exhibited higher tree density, stand forest growth, initial regeneration, SBR, and MBC. In addition, PCA showed a clear separation between 9 and 16 YAH stands (Figure 2B), being strongly correlated with SOC, soil N, and elevation for 9 YAH stands, and MBN, SBR, and MAT for 16 YAH stands.
There was a separation between forest treatments (see Appendix A Figure A1A) where AR presented higher MAT, soil pH, MBC, MBN, and SBR values, and DR were related to areas with higher elevation. In the analyses, there was also a separation between 16 YAH related to MBC, MBN, SBR, and qCO2, and 6 with 9 YAH related to MBC/MBN, vegetation cover, species richness, and understory vegetation biomass (see Appendix A Figure A1B). To better visualize the relationship between soil microbial and environmental variables (soil physicochemical, climate, understory vegetation), we analyzed correlations by using the most significant variables obtained from PCA. MBC was positively correlated with the SOC, soil pH, and N (Figure 3A–C). In contrast, MBN was negatively correlated with the SOC and N (Figure 3D,F), while MBN showed positively correlated with soil pH (Figure 3E). On the contrary, SBR showed a weak and not significant response to SOC and N (Figure 3G,I), whereas SBR was positively correlated with soil pH (Figure 3H).
MBC, MBN, and SBR were positively correlated with the MAT (Figure 4A,D,G) and negatively correlated with elevation (Figure 4B,E,H). MBN showed a negative correlation with MAP (Figure 4F), while MBC and SBR showed a weak and not significant response to MAP (Figure 4C,I). MBC, MBN, and SBR declined significantly with the increased species richness (Figure 5A,D,G). In addition, MBC, MBN, and SBR were shown to be positively related to vegetation cover (Figure 5B,E,H). Finally, MBC and SBR were shown to be positively correlated with forest growth (Figure 5C,I). The MBC/MBN ratio was positively correlated with SOC and soil N. In addition, the MBC/MBN ratio was negatively correlated with the MAT, while being positively correlated with elevation and MAP. However, the MBC/MBN ratio showed a weak response to soil pH. In contrast, the MBC/MBN ratio was positively correlated with species richness, but negatively correlated with vegetation cover. Finally, the MBC/MBN ratio was positively correlated with forest growth (see Appendix A Figure A2).

4. Discussion

The results of this study determined that variable retention harvesting in N. pumilio forests caused shifts on soil microbial biomass and activity. Overall, the results showed that aggregated retention increased MBC, MBN, and SBR compared to DR, but conserved similar values to PF (except for MBN). Results showed that the responses of MBC, MBN, and SBR were strongly dependent on soil, climatic, and vegetation variables. Soil pH, MAT and vegetation cover showed a positive relationship with MBC, MBN, and SBR compared with elevation and species richness that showed a negative relationship. Soil microbial biomass and activity is closely related to soil fertility, being one of the most sensitive indicators to disturbances and ecosystems management [21,22]. In this study, retention of living trees in N. pumilio forests as aggregates was successful in conserving and increasing the soil microbial biomass (MBC, MBN, and SBR) compared to primary forests. Although microbial biomass was unaffected by dispersed retention, there was a shift in MBC and MBN caused by aggregated retention (with a trend to decrease). This was consistent with other studies in other forest ecosystems [12,16]. However, there are antecedents that showed a negative effect of timber harvesting on soil microbial biomass [24,37,59], and other studies that did not find changes in MBC and MBN [17,26,37]. This inconsistency might be caused by differences in soil type, litter quantity and quality, and climate conditions among forest ecosystems, such as MAP, MAT, and elevation compared with our forest growing at these latitudes [59,60].
Another explanation for the differences found in the present study can be related to the understory vegetation [61,62,63], N. pumilio forests increased the understory species richness and vegetation cover in the dispersed retention treatments compared to aggregate retention. Thus, understory vegetation may restrict soil microbial activity via competition for soil available nutrients. This study found a negative relationship between species richness and microbial biomass (MBC and MBN) and activity (SBR). The competitive dynamic between plants and soil microbes for nutrients has been demonstrated in the forest and other ecosystems [60,63,64,65]. Furthermore, the dispersed retention increased understory vegetation cover and biomass, while soil microorganism biomass and activity decreased. However, aggregate retention in N. pumilio forests did not modify the understory structure (richness, biomass, vegetation cover), but changes in soil biomass and microbial activity occurred for this forest treatment. In this study, soil microbial growth and activity during late summer (soil sampling in February) might be limited by soil available nutrients due to high understory plant nutrients uptake. Lencinas et al. [9] and Toro Manríquez et al. [11] found that variable retention modified original understory plant assemblages with greater effects in DR than in AR, and where plant distribution was driven by microclimatic conditions. On the other hand, the impacts of harvesting could be influenced by below-ground resources, since roots contribute with similar amounts of litter than above-ground litter as a source of organic matter in forest soils. Similarly, a global meta-analysis demonstrated that root litter accounted for 48% of annual plant-litter inputs in forests, compared to 41% from leaf litter [66]. Soil microbial communities may be enhanced within patches of living trees after forest harvesting [14,16,27], and fungal communities [25,26,39], with the decomposition of fine roots, ref. [26] maintained their levels in retention patches, but not in the dispersed retention. Thus, the results highlight some advantages of aggregated retention as a tool for conservation of forest-dwelling soil microorganisms [67]. In Douglas-fir forests, Simard et al. [68] found smaller reductions in forest-floor C one year after harvesting in areas where living trees were retained either as single trees (25 large stems ha−1) or in 30 m diameter patches, compared with harvested areas. Martínez Pastur et al. [30,43] showed the importance of preserving N. pumilio forest connectivity to maintain both above-and belowground forest ecosystem functioning. The response of soil microbial communities may be the result of tree–soil-microorganism interactions, where N. pumilio forests with high tree density, regeneration, and growth rate, may increase SOC (aggregated retention) and, therefore, improve microbial biomass and activity (MBC, MBN, SBR). This is consistent with previous study, where microbial biomass (bacterial and fungal) increased with SOC, soil N and soil pH [20,60]. The effects of these three factors are strongly linked to each other, since SOC and soil N are the major soil resource components, and soil pH influences the availability of these nutrients [69]. In N. pumilio forests, soil microbial biomass increased with soil pH (4.2 to 5.2). Differences in soil pH can arise from many factors, including vegetation type, soil characteristics, and climate variables [23,69,70]. In addition, the results demonstrated that the retention treatments tended to decrease the MBC/MBN values compared with the primary unmanaged forests. Soil microbial C/N stoichiometry can reflect microbial community structures. Changes in MBC/MBN ratio indicated shifts in the fungi/bacteria ratio [56,57,58]. In the study, low MBC/MBN values were determined when the forests were harvested (AR and DR), which suggest an increase in bacteria or a decrease in soil fungi [56]. Because fungi (mycorrhizae) are dependent on tree photosynthates for carbon, they are expected to be particularly susceptible to harvesting [16,29,37]. Girdling studies have shown that removal of below-ground photosynthates reduces the fungal, and to a lesser extent of bacterial communities [14,39,65,71]. There are antecedents of a positive relationship between tree cover and the soil mycobiota in N. pumilio forests [29]. Therefore, although aggregate patches cover a smaller area (0.28 ha), it appears that dispersed trees (80 tree ha−1) maintain the conditions necessary for persistence of soil fungi in early stand development stage.
Forest management significantly affects the above- and below-ground structure of the primary N. pumilio forest over time [9,11,28]. The canopy opening allows a rapid growth of forest regeneration, due to the increase in light intensity at the understory level and soil water availability [6,72]. However, these microclimatic changes produce an impact over the natural cycles and soil microorganisms [11,16,30,31,34]. An overall decreasing trend of microbial biomass (MBN) and activity was found in 9 YAH stands; however, some evidence of a positive recovery was found in 16 YAH stands, suggesting that forest management after 16 years maintained greater soil microbial biomass and activity possible positive effects on microbially mediated processes that feed back into forest productivity. Webster et al. [73] found that herbaceous litter, labile needle, and bark residues, serve as an important short-term substrate for microbial respiration, while coarser residues (e.g., large branches and stumps) provide substrate availability for soil microorganism in the medium term (5–15 years), bridging a gap until the regenerating forest can produce substantial quantities of litter [31,34,62]. In austral forests, the initial establishment phase is largely governed by microclimatic conditions, followed by an accelerated growth phase associated with crown development and understory, reflecting increased nutrient demand and greater competence with soil microorganisms.
In Southern Patagonian forests, the increase in bacterial dominance (lower MBC/MBN) in 16-YAH stands was likely related to accelerated microbial turnover and SOC cycling rate, which may increase both soil N level and microbial-derived C accumulation [32,74,75]. Moreover, a lower MBC/MBN ratio is indicative of bacterial dominance (copiotrophs) over fungi (oligotrophs) [58]. The possible increase in soil bacteria due to treatments has been reported in other studies conducted in different forest types and time intervals. For example, bacterial abundance was observed 1–2 years post-harvest in Norway Spruce stands [24], 4 YAH in oak/hickory stands [76], and 8 YAH in Douglas-fir stands [77]. Thus, the increase in bacterial dominance (lower MBC/MBN ratios) in N. pumilio forests may likely be related to accelerated microbial turnover and SOC cycling rate, which may increase both in situ organic N level and microbial-derived C [16,67,68,69,70,71,72,73,74,75]. Philpott et al. [26] found similar species compositions in the 13 and 70 years-old Douglas-fir stands, indicating that VRH has only marginal short-term effects on β-diversity in the fungi community. Indeed, they did not find enough evidence to support the hypothesis that ectomycorrhizal fungi were more abundant under either AR or DR or even in uncut stands. Therefore, these results may imply a dominance of bacterial community and soil N regulation and driving soil C accumulation in the N. pumilio forests [67]. Moretto and Martínez Pastur [78] indicated that this phenomenon could be explained by microorganism immobilization due to the scarce N soil availability, which was evident in this study. In addition, this study determined an increase in the microbial quotient (qMC) 16-YAH compared to 6 (15%) and 9 (56%) YAH. Thus, the contribution of MBC to SOC increased up to 56% over seven years (from 9 to 16 YAH), suggesting that MBC serves as an important indicator of ecosystem recovery in austral forest ecosystems [21]. Indeed, the recovery of soil microbial biomass following harvesting may be controlled by the recovery of forest primary productivity and SOC accumulation. Therefore, maintenance of >10 years forest harvested stands (AR and DR) is essential for soil microbial biomass and activity recovery in N. pumilio forests under management.
In this study, significant reductions in soil microbial biomass had been observed in dispersed retention at the site 9 YAH. However, it seems that microbial biomass and activity recovered 16 YAH. Thus, forest soils of 16 YAH stands supported a greater microbial biomass and activity but different microbial communities (as indicated by MBC/MBN) to that of 9 YAH or 6 YAH forest soils. Spake et al. [79] reported that 90 years was the average time for recovery of soil microorganisms’ richness of old-growth forest. However, Holden and Treseder [80] found that microbial responses were negative for the first 15 years following disturbance in boreal forests. It is possible that the efforts to reduce soil disturbance and the facilitation of natural regeneration and biodiversity of N. pumilio forests preserved the resilience of the forest floor microbial communities to disturbance [2,8,9]. If soil microbial biomass and activity do not recover after severe harvested N. pumilio forests, limited nutrient availability could limit the ecosystem’s productivity. The present study showed that forest growth was related to MCB and SBR. Therefore, future forest productivity will depend on the ability of soil microbial community to recover activity previous harvesting.

5. Conclusions

The results of this research showed that aggregated retention increased MBC, MBN, and SBR compared to dispersed retention, but decreased MBC/MBN values compared with the primary unmanaged forests. The research found an overall decreasing trend of microbial biomass and activity in dispersed retention at the site 9 years after being harvested, but some evidence of a positive recovery was observed in the sites at 16 YAH. Thus, forest soils of 16 YAH stands supported a greater microbial biomass and activity, but a different microbial community than 9 or 6 YAH forest soils. Therefore, more than 10 years are needed without forest intervention to recuperate the microbial soil ecology (oil microbial biomass and activity) in N. pumilio forests under management (aggregated and dispersed retention forest). Also, nutrient retention in microbial biomasses would help in ecosystem recovery following disturbance. Therefore, the results highlight some advantages of aggregated retention forest management as a tool for conservation of forest-dwelling soil microorganisms. Such knowledge would allow a better understanding of the anthropic, biotic, and abiotic factors that modulate the growth and function of the soil microorganisms and, ultimately, the soil fertility and conservation of forest ecosystems.

Author Contributions

S.T.: Investigation, Methodology, Formal analysis, Writing—review draft, Writing—review and editing; G.M.P.: Investigation, Project administration, Methodology and Writing—review and editing; J.R.-S.: Data curation, Project administration, Investigation, Methodology and Resources; P.L.P.: Conceptualization, Investigation, Writing—review and editing; S.T., G.M.P., J.R.-S., P.L.P.: Drafted the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Institute of Agricultural Technology (INTA) of Argentina.

Data Availability Statement

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

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A

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Figure A1. Principal components analyses (PCA) including microbial variables (MBC = microbial biomass carbon, MBN = microbial biomass nitrogen, SBR = soil basal respiration, MBC/MBN ratio, qCO2 = metabolic quotient), climate and topographic variables (MAP = mean annual precipitation, MAT = mean annual temperature, elevation), soil characteristics (soil pH, SOC = soil organic carbon, N = soil nitrogen, P = soil phosphorus) and understory vegetation variables (species richness, understory vegetation biomass, advanced regeneration, dominant height, recruitment, initial regeneration, forest growth, bare soil, tree density) in N. pumilio forests of Tierra del Fuego. (A) Forest treatments including primary unmanaged forests (PF, green), aggregated retention (AR, blue) and dispersed retention (DR, red), and (B) Years-after-harvesting including 6 YAH (white), 9 YAH (gray), and 16 YAH (black).
Figure A1. Principal components analyses (PCA) including microbial variables (MBC = microbial biomass carbon, MBN = microbial biomass nitrogen, SBR = soil basal respiration, MBC/MBN ratio, qCO2 = metabolic quotient), climate and topographic variables (MAP = mean annual precipitation, MAT = mean annual temperature, elevation), soil characteristics (soil pH, SOC = soil organic carbon, N = soil nitrogen, P = soil phosphorus) and understory vegetation variables (species richness, understory vegetation biomass, advanced regeneration, dominant height, recruitment, initial regeneration, forest growth, bare soil, tree density) in N. pumilio forests of Tierra del Fuego. (A) Forest treatments including primary unmanaged forests (PF, green), aggregated retention (AR, blue) and dispersed retention (DR, red), and (B) Years-after-harvesting including 6 YAH (white), 9 YAH (gray), and 16 YAH (black).
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Figure A2. Relationships among MBC/MBN ratio and soil variables, climatic variables, and vegetation variables in N. pumilio forests of Tierra del Fuego. (A) MBC/MBN and soil organic carbon (SOC), (B) MBC/MBN and soil pH, (C) MBC/MBN and soil nitrogen (N), (D) MBC/MBN and mean annual temperature (MAT), (E) MBC/MBN and elevation, (F) MBC/MBN and mean annual precipitation (MAP), (G) MBC/MBN and species richness, (H) MBC/MBN and understory vegetation cover, and (I) MBC/MBN and forests growth. Dots were classified as primary unmanaged forests (green), aggregated retention (blue) and dispersed retention (red) considering different years-after-harvesting (6 = triangles, 9 = squares, 16 = circles). Lines showed the linear regression, where r2 = adjusted parameter, and p = probability.
Figure A2. Relationships among MBC/MBN ratio and soil variables, climatic variables, and vegetation variables in N. pumilio forests of Tierra del Fuego. (A) MBC/MBN and soil organic carbon (SOC), (B) MBC/MBN and soil pH, (C) MBC/MBN and soil nitrogen (N), (D) MBC/MBN and mean annual temperature (MAT), (E) MBC/MBN and elevation, (F) MBC/MBN and mean annual precipitation (MAP), (G) MBC/MBN and species richness, (H) MBC/MBN and understory vegetation cover, and (I) MBC/MBN and forests growth. Dots were classified as primary unmanaged forests (green), aggregated retention (blue) and dispersed retention (red) considering different years-after-harvesting (6 = triangles, 9 = squares, 16 = circles). Lines showed the linear regression, where r2 = adjusted parameter, and p = probability.
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References

  1. Peri, P.L.; Martínez Pastur, G.; Schlichter, T. Uso Sustentable del Bosque: Aportes Desde la Silvicultura Argentina; Ministerio de Ambiente y Desarrollo Sostenible de la Nación Argentina: Buenos Aires, Argentina, 2021. Available online: http://hdl.handle.net/20.500.12123/10343 (accessed on 15 March 2024).
  2. Soler, R.M.; Lorenzo, C.; González, J.; Carboni, L.; Delgado, J.; Díaz, M.; Huertas Herrera, A. The politics behind scientific knowledge: Sustainable forest management in Latin America. For. Pol. Econ. 2021, 131, e102543. [Google Scholar] [CrossRef]
  3. Muys, B. Forest ecosystem services. In Life on Land: Encyclopedia of the UN Sustainable Development Goals; Leal Filho, W., Azul, A.M., Brandli, L., Lange Salvia, A., Wall, T., Eds.; Springer: Cham, Switzerland, 2021. [Google Scholar] [CrossRef]
  4. Peri, P.L.; Martínez Pastur, G.; Nahuelhual, L. Ecosystem Services in Patagonia: A Multi-Criteria Approach for an Integrated Assessment; Springer Nature: Charm, Switzerland, 2021. [Google Scholar] [CrossRef]
  5. Lencinas, M.V.; Martínez Pastur, G.; Gallo, E.; Cellini, J.M. Alternative silvicultural practices with variable retention to improve understory plant diversity conservation in southern Patagonian forests. For. Ecol. Manag. 2011, 262, 1236–1250. [Google Scholar] [CrossRef]
  6. Martínez Pastur, G.; Cellini, J.M.; Lencinas, M.V.; Barrera, M.; Peri, P.L. Environmental variables influencing regeneration of Nothofagus pumilio in a system with combined aggregated and dispersed retention. For. Ecol. Manag. 2011, 261, 178–186. [Google Scholar] [CrossRef]
  7. Gallo, E.A.; Lencinas, M.V.; Martínez Pastur, G. Site quality influences over understory plant diversity in old-growth and harvested Nothofagus pumilio forests. For. Syst. 2013, 22, 25–38. [Google Scholar] [CrossRef]
  8. Soler, R.M.; Schindler, S.; Lencinas, M.V.; Peri, P.L.; Martínez Pastur, G. Why biodiversity increases after variable retention harvesting: A meta-analysis for southern Patagonian forests. For. Ecol. Manag. 2016, 369, 161–169. [Google Scholar] [CrossRef]
  9. Lencinas, M.V.; Sola, F.J.; Martínez Pastur, G. Variable retention effects on vascular plants and beetles along a regional gradient in Nothofagus pumilio forests. For. Ecol. Manag. 2017, 406, 251–265. [Google Scholar] [CrossRef]
  10. Huertas Herrera, A.; Cellini, J.M.; Barrera, M.D.; Lencinas, M.V.; Martínez Pastur, G. Environment and anthropogenic impacts as main drivers of plant assemblages in forest mountain landscapes of Southern Patagonia. For. Ecol. Manag. 2018, 430, 380–393. [Google Scholar] [CrossRef]
  11. Toro Manríquez, M.D.; Cellini, J.M.; Lencinas, M.V.; Peri, P.L.; Peña Rojas, K.; Martínez Pastur, G. Suitable conditions for natural regeneration in variable retention harvesting of southern Patagonian Nothofagus pumilio forests. Ecol. Process. 2019, 8, 18. [Google Scholar] [CrossRef]
  12. Foote, J.A.; Boutton, T.W.; Scott, D.A. Soil C and N storage and microbial biomass in US southern pine forests: Influence of forest management. For. Ecol. Manag. 2015, 355, 48–57. [Google Scholar] [CrossRef]
  13. Lewandowski, T.E.; Forrester, J.A.; Mladenoff, D.J.; D’Amato, A.W.; Palik, B.J. Response of the soil microbial community and soil nutrient bioavailability to biomass harvesting and reserve tree retention in northern Minnesota aspen-dominated forests. Appl. Soil Ecol. 2016, 99, 110–117. [Google Scholar] [CrossRef]
  14. Lewandowski, T.E.; Forrester, J.A.; Mladenoff, D.J.; D’Amato, A.W.; Fassnacht, D.S.; Padley, E.; Martin, K.J. Do biological legacies moderate the effects of forest harvesting on soil microbial community composition and soil respiration. For. Ecol. Manag. 2019, 432, 298–308. [Google Scholar] [CrossRef]
  15. Singh, S.B.; Saha, S.; Dutta, S.K.; Singh, A.R.; Boopathi, T. Impact of secondary forest fallow period on soil microbial biomass carbon and enzyme activity dynamics under shifting cultivation in North Eastern Hill region, India. Catena 2017, 156, 10–17. [Google Scholar] [CrossRef]
  16. Churchland, C.; Bengtson, P.; Prescott, C.E.; Grayston, S.J. Dispersed variable-retention harvesting mitigates N losses on harvested sites in conjunction with changes in soil microbial community structure. Front. For. Glob. Change 2021, 3, e609216. [Google Scholar] [CrossRef]
  17. Li, F.; Zi, H.; Sonne, C.; Li, X. Microbiome sustains forest ecosystem functions across hierarchical scales. Eco-Environ. Health 2023, 2, 24–31. [Google Scholar] [CrossRef] [PubMed]
  18. Blagodatskaya, E.; Kuzyakov, Y. Active microorganisms in soil: Critical review of estimation criteria and approaches. Soil Biol. Biochem. 2013, 67, 192–211. [Google Scholar] [CrossRef]
  19. Delgado-Baquerizo, M.; Maestre, F.T.; Reich, P.B.; Jeffries, T.C.; Gaitan, J.J.; Encinar, D.; Singh, B.K. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat. Commun. 2016, 7, e10541. [Google Scholar] [CrossRef]
  20. Gargaglione, V.; Gonzalez Polo, M.; Birgi, J.; Toledo, S.; Peri, P.L. Silvopastoral use of Nothofagus antarctica forests in Patagonia: Impact on soil microorganisms. Agrofor. Syst. 2022, 96, 957–968. [Google Scholar] [CrossRef]
  21. Marcos, M.S.; Olivera, N.L. Microbiological and biochemical indicators for assessing soil quality in drylands from Patagonia. In Biology and Biotechnology of Patagonian Microorganisms; Olivera, N., Libkind, D., Donati, E., Eds.; Springer: Cham, Switzerland, 2016. [Google Scholar] [CrossRef]
  22. Schloter, M.; Nannipieri, P.; Sørensen, S.J.; van Elsas, J.D. Microbial indicators for soil quality. Biol. Fert. Soils 2018, 54, 1–10. [Google Scholar] [CrossRef]
  23. Fierer, N.; Wood, S.A.; de Mesquita, C.P.B. How microbes can, and cannot, be used to assess soil health. Soil Biol. Biochem. 2021, 153, e108111. [Google Scholar] [CrossRef]
  24. Siira-Pietikäinen, A.; Pietikäinen, J.; Fritze, H.; Haimi, J. Short-term responses of soil decomposer communities to forest management: Clear felling versus alternative forest harvesting methods. Can. J. For. Res. 2001, 31, 88–99. [Google Scholar] [CrossRef]
  25. Kranabetter, J.M.; De Montigny, L.; Ross, G. Effectiveness of green-tree retention in the conservation of ectomycorrhizal fungi. Fungal Ecol. 2013, 6, 430–438. [Google Scholar] [CrossRef]
  26. Philpott, T.J.; Barker, J.S.; Prescott, C.E.; Grayston, S.J. Limited effects of variable-retention harvesting on fungal communities decomposing fine roots in coastal temperate rainforests. Appl. Environ. Microbiol. 2018, 84, e02061-17. [Google Scholar] [CrossRef] [PubMed]
  27. Sultaire, S.M.; Benucci, G.; Longley, R.; Kroll, A.; Verschuyl, J.; Bonito, G.; Roloff, G. Using high-throughput sequencing to investigate summer truffle consumption by chipmunks in relation to retention forestry. For. Ecol. Manag. 2023, 549, e121460. [Google Scholar] [CrossRef]
  28. Martínez Pastur, G.; Lencinas, M.V.; Cellini, J.M.; Peri, P.L.; Soler, R. Timber management with variable retention in Nothofagus pumilio forests of Southern Patagonia. For. Ecol. Manag. 2009, 258, 436–443. [Google Scholar] [CrossRef]
  29. Elíades, L.A.; Cabello, M.N.; Pancotto, V.; Moretto, A.; Ferreri, N.A.; Saparrat, M.C.; Barrera, M.D. Soil mycobiota under managed and unmanaged forests of Nothofagus pumilio in Tierra del Fuego, Argentina. N. Z. J. For. Sci. 2019, 49, e7. [Google Scholar] [CrossRef]
  30. Martínez Pastur, G.; Vanha-Majamaa, I.; Franklin, J.F. Ecological perspectives on variable retention forestry. Ecol. Process. 2020, 9, 12. [Google Scholar] [CrossRef]
  31. Chaves, J.E.; Aravena Acuña, M.C.; Rodríguez-Souilla, J.; Cellini, J.M.; Rappa, N.J.; Lencinas, M.V.; Martínez Pastur, G. Carbon pool dynamics after variable retention harvesting in Nothofagus pumilio forests of Tierra del Fuego. Ecol. Process. 2023, 12, 5. [Google Scholar] [CrossRef]
  32. Jerabkova, L.; Prescott, C.E.; Titus, B.D.; Hope, G.D.; Walters, M.B. A meta-analysis of the effects of clearcut and variable-retention harvesting on soil nitrogen fluxes in boreal and temperate forests. Can. J. For. Res. 2011, 41, 1852–1870. [Google Scholar] [CrossRef]
  33. Gustafsson, L.; Baker, S.C.; Bauhus, J.; Beese, W.J.; Brodie, A.; Kouki, J.; Lindenmayer, D.B.; Lõhmus, A.; Martínez Pastur, G.; Messier, C.; et al. Retention forestry to maintain multifunctional forests: A world perspective. BioScience 2012, 62, 633–645. [Google Scholar] [CrossRef]
  34. Martínez Pastur, G.; Aravena Acuña, M.C.; Chaves, J.E.; Cellini, J.M.; Silveira, E.M.; Rodríguez-Souilla, J.; Peri, P.L. Nitrogenous and phosphorus soil contents in Tierra del Fuego forests: Relationships with soil organic carbon, climate, vegetation and landscape metrics. Land 2023, 12, 983. [Google Scholar] [CrossRef]
  35. Bach, L.H.; Grytnes, J.A.; Halvorsen, R.; Ohlson, M. Tree influence on soil microbial community structure. Soil Biol. Biochem. 2010, 42, 1934–1943. [Google Scholar] [CrossRef]
  36. Mitchell, R.J.; Campbell, C.D.; Chapman, S.J.; Cameron, C.M. The ecological engineering impact of a single tree species on the soil microbial community. J. Ecol. 2010, 98, 50–61. [Google Scholar] [CrossRef]
  37. Hartmann, M.; Howes, C.G.; Van Insberghe, D.; Yu, H.; Bachar, D.; Christen, R.; Mohn, W.W. Significant and persistent impact of timber harvesting on soil microbial communities in Northern coniferous forests. ISME J. 2012, 6, 2199–2218. [Google Scholar] [CrossRef]
  38. Teste, F.P.; Lieffers, V.J.; Strelkov, S.E. Ectomycorrhizal community responses to intensive forest management: Thinning alters impacts of fertilization. Plant Soil 2012, 360, 333–347. [Google Scholar] [CrossRef]
  39. Churchland, C.; Grayston, S.J.; Bengtson, P. Spatial variability of soil fungal and bacterial abundance: Consequences for carbon turnover along a transition from a forested to clear-cut site. Soil Biol. Biochem. 2013, 63, 5–13. [Google Scholar] [CrossRef]
  40. Fick, S.E.; Hijmans, R.J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Clim. 2017, 37, 4302–4315. [Google Scholar] [CrossRef]
  41. Farr, T.G.; Rosen, P.A.; Caro, E.; Crippen, R.; Duren, R.; Hensley, S.; Kobrick, M.; Paller, M.; Rodríguez, E.; Roth, L.; et al. The shuttle radar topography mission. Rev. Geophys. 2007, 45, RG2004. [Google Scholar] [CrossRef]
  42. Bitterlich, W. The Relascope Idea: Relative Measurements in Forestry; CAB: London, UK, 1984. [Google Scholar]
  43. Martínez Pastur, G.; Rosas, Y.M.; Toro Manríquez, M.; Huertas Herrera, A.; Miller, J.A.; Cellini, J.M.; Lencinas, M.V. Knowledge arising from long-term research of variable retention harvesting in Tierra del Fuego: Where do we go from here? Ecol. Process. 2019, 8, 24. [Google Scholar] [CrossRef]
  44. Levy, E.G.; Madden, E.A. The point method of pasture analyses. N. Z. J. Agric. 1933, 46, 267–379. [Google Scholar]
  45. Moore, D. Flora of Tierra del Fuego; Anthony Nelson: London, UK, 1983. [Google Scholar]
  46. Correa, M.N. Flora Patagónica; INTA: Buenos Aires, Argentina, 1998. [Google Scholar]
  47. IUSS Working Group WRB. World Reference Base for Soil Resources 2014, Update 2015: International Soil Classification System for Naming Soils and Creating Legends for Soil Maps; World Soil Resources Reports, FAO: Rome, Italy, 2015; n° 106. [Google Scholar]
  48. Peña-Rodríguez, S.; Moretto, A.S.; Pontevedra-Pombal, X.; Oro, N.; García Rodeja Gayoso, E.; Rodríguez-Salgado, I.; Rodríguez-Racedo, J.; Escobar, J.; Nóvoa-Muñoz, J.C. Trends in nutrient reservoirs stored in uppermost soil horizons of subantarctic forests differing in their structure. Agrofor. Syst. 2013, 87, 1273–1281. [Google Scholar] [CrossRef]
  49. Peri, P.L.; Rosas, Y.M.; Ladd, B.; Toledo, S.; Lasagno, R.G.; Martínez Pastur, G. Modelling soil carbon content in South Patagonia and evaluating changes according to climate, vegetation, desertification and grazing. Sustainability 2018, 10, 438. [Google Scholar] [CrossRef]
  50. Bremner, J.M.; Mulvaney, C.S. Nitrogen-total. In Methods of Soil Analysis: Part 2, Chemical and Microbiological Properties; Page, A.L., Ed.; Agronomy Monographs, American Society of Agronomy: New York, NY, USA, 1983. [Google Scholar] [CrossRef]
  51. Olsen, S.R. Estimation of Available Phosphorus in Soils by Extraction with Sodium Bicarbonate; US Department of Agriculture: New York, NY, USA, 1954; n° 939.
  52. Vance, E.D.; Brookes, P.C.; Jenkinson, D.S. An extraction method for measuring soil microbial biomass C. Soil Biol. Biochem. 1987, 19, 703–707. [Google Scholar] [CrossRef]
  53. Joergensen, R.G.; Mueller, T. The fumigation-extraction method to estimate soil microbial biomass: Calibration of the kEN value. Soil Biol. Biochem. 1996, 28, 33–37. [Google Scholar] [CrossRef]
  54. Robertson, G.P.; Wedin, D.; Groffman, P.M.; Blair, J.M.; Holland, E.A.; Nadelhoffer, K.J.; Harris, D. Soil Carbon and nitrogen availability nitrogen mineralization, nitrification and soil respiration potentials. In Standard Soil Methods for Long Term Ecological Research; Robertson, G.P., Coleman, D.C., Bledsoes, C.S., Sollins, P., Eds.; Oxford University Press: New York, NY, USA, 1999. [Google Scholar]
  55. Anderson, T.H.; Domsch, K.H. Application of eco-physiological quotients (qCO2 and qMC) on microbial biomasses from soils of different cropping histories. Soil Biol. Biochem. 1990, 22, 251–255. [Google Scholar] [CrossRef]
  56. Sun, Y.; Liao, J.; Zou, X.; Xu, X.; Yang, J.; Chen, H.Y.; Ruan, H. Coherent responses of terrestrial C:N stoichiometry to drought across plants, soil, and microorganisms in forests and grasslands. Agric. For. Meteorol. 2020, 292, e108104. [Google Scholar] [CrossRef]
  57. Wan, P.; He, R.; Wang, P.; Cao, A. Implementation of different forest management methods in a natural forest: Changes in soil microbial biomass and enzyme activities. For. Ecol. Manag. 2022, 520, e120409. [Google Scholar] [CrossRef]
  58. Wang, M.; Xu, Z.; Huang, Z. Soil carbon accrual under harvest residue retention modulated by the copiotroph-oligotroph spectrum in bacterial community. J. Soils Sediments 2022, 22, 2459–2474. [Google Scholar] [CrossRef]
  59. Das, S.; Deb, S.; Sahoo, S.S.; Sahoo, U.K. Soil microbial biomass carbon stock and its relation with climatic and other environmental factors in forest ecosystems: A review. Acta Ecol. Sin. 2023, 43, 933–945. [Google Scholar] [CrossRef]
  60. Toledo, S.; Bondaruk, V.F.; Yahdjian, L.; Oñatibia, G.R.; Loydi, A.; Alberti, J.; Peri, P.L. Environmental factors regulate soil microbial attributes and their response to drought in rangeland ecosystems. Sci. Total Environ. 2023, 892, e164406. [Google Scholar] [CrossRef] [PubMed]
  61. Kuzyakov, Y.; Xu, X. Competition between roots and microorganisms for nitrogen: Mechanisms and ecological relevance. New Phytol. 2013, 198, 656–669. [Google Scholar] [CrossRef]
  62. Morikawa, Y.; Hayashi, S.; Negishi, Y.; Masuda, C.; Watanabe, M.; Watanabe, K.; Seiwa, K. Relationship between the vertical distribution of fine roots and residual soil nitrogen along a gradient of hardwood mixture in a conifer plantation. New Phytol. 2022, 235, 993–1004. [Google Scholar] [CrossRef] [PubMed]
  63. Zhang, S.; Yang, X.; Li, D.; Li, S.; Chen, Z.; Wu, J. A meta-analysis of understory plant removal impacts on soil properties in forest ecosystems. Geoderma 2022, 426, e116116. [Google Scholar] [CrossRef]
  64. Xiao, R.; Man, X.; Duan, B.; Cai, T.; Ge, Z.; Li, X.; Vesala, T. Changes in soil bacterial communities and nitrogen mineralization with understory vegetation in boreal larch forests. Soil Biol. Biochem. 2022, 166, e108572. [Google Scholar] [CrossRef]
  65. Fernández, M.; Vincent, G.; Dorr, E.; Bakker, S.; Lerch, T.Z.; Leloup, J.; Bazot, S. Does forest stand density affect soil microbial communities? Appl. Soil Ecol. 2024, 195, e105244. [Google Scholar] [CrossRef]
  66. Freschet, G.T.; Cornwell, W.K.; Wardle, D.A.; Elumeeva, T.G.; Liu, W.; Jackson, B.G.; Cornelissen, J.H. Linking litter decomposition of above- and below-ground organs to plant–soil feedbacks worldwide. J. Ecol. 2013, 101, 943–952. [Google Scholar] [CrossRef]
  67. Högberg, M.N.; Briones, M.J.; Keel, S.G.; Metcalfe, D.B.; Campbell, C.; Midwood, A.J.; Högberg, P. Quantification of effects of season and nitrogen supply on tree below-ground carbon transfer to ectomycorrhizal fungi and other soil organisms in a boreal pine forest. New Phytol. 2010, 187, 485–493. [Google Scholar] [CrossRef] [PubMed]
  68. Simard, S.W.; Roach, W.J.; Defrenne, C.E.; Pickles, B.J.; Snyder, E.N.; Robinson, A.; Lavkulich, L.M. Harvest intensity effects on carbon stocks and biodiversity are dependent on regional climate in Douglas-fir forests of British Columbia. Front. For. Glob. Chang. 2020, 3, e88. [Google Scholar] [CrossRef]
  69. Liu, J.; Wang, Q.; Ku, Y.; Zhang, W.; Zhu, H.; Zhao, Z. Precipitation and soil pH drive the soil microbial spatial patterns in the Robinia pseudoacacia forests at the regional scale. Catena 2022, 212, e106120. [Google Scholar] [CrossRef]
  70. Chen, Y.L.; Ding, J.Z.; Peng, Y.F.; Li, F.; Yang, G.B.; Liu, L.; Yang, Y.H. Patterns and drivers of soil microbial communities in Tibetan alpine and global terrestrial ecosystems. J. Biogeogr. 2016, 43, 2027–2039. [Google Scholar] [CrossRef]
  71. Mummey, D.L.; Clarke, J.T.; Cole, C.A.; O’Connor, B.G.; Gannon, J.E.; Ramsey, P.W. Spatial analysis reveals differences in soil microbial community interactions between adjacent coniferous forest and clearcut ecosystems. Soil Biol. Biochem. 2010, 42, 1138–1147. [Google Scholar] [CrossRef]
  72. Rodríguez-Souilla, J.; Cellini, J.M.; Lencinas, M.V.; Roig, F.A.; Chaves, J.E.; Aravena Acuña, M.C.; Martínez Pastur, G. Variable retention harvesting and climate variations influence over natural regeneration dynamics in Nothofagus pumilio forests of Southern Patagonia. For. Ecol. Manag. 2023, 544, e121221. [Google Scholar] [CrossRef]
  73. Webster, K.L.; Wilson, S.A.; Hazlett, P.W.; Fleming, R.L.; Morris, D.M. Soil CO2 efflux and net ecosystem exchange following biomass harvesting: Impacts of harvest intensity, residue retention and vegetation control. For. Ecol. Manag. 2016, 360, 181–194. [Google Scholar] [CrossRef]
  74. Bingham, A.H.; Cotrufo, M.F. Organic nitrogen storage in mineral soil: Implications for policy and management. Sci. Total Environ. 2016, 551, 116–126. [Google Scholar] [CrossRef]
  75. Soares, M.; Rousk, J. Microbial growth and carbon use efficiency in soil: Links to fungal-bacterial dominance, SOC-quality and stoichiometry. Soil Biol. Biochem. 2019, 131, 195–205. [Google Scholar] [CrossRef]
  76. Ponder, F.; Tadros, M. Phospholipid fatty acids in forest soil four years after organic matter removal and soil compaction. Appl. Soil Ecol. 2002, 19, 173–182. [Google Scholar] [CrossRef]
  77. Moore-Kucera, J.; Dick, R.P. PLFA profiling of microbial community structure and seasonal shifts in soils of a Douglas-fir chronosequence. Microb. Ecol. 2008, 55, 500–511. [Google Scholar] [CrossRef]
  78. Moretto, A.S.; Martínez Pastur, G. Litterfall and leaf decomposition in Nothofagus pumilio forests along an altitudinal gradient in Tierra del Fuego, Argentina. J. For. Sci. 2014, 60, 500–510. [Google Scholar] [CrossRef]
  79. Spake, R.; Ezard, T.H.; Martin, P.A.; Newton, A.C.; Doncaster, C.P. A meta-analysis of functional group responses to forest recovery outside of the tropics. Conserv. Biol. 2015, 29, 1695–1703. [Google Scholar] [CrossRef]
  80. Holden, S.R.; Treseder, K.K. A meta-analysis of soil microbial biomass responses to forest disturbances. Front. Microbiol. 2013, 4, e52720. [Google Scholar] [CrossRef]
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Figure 1. (A) Study area in the gradient of N. pumilio forests distribution (green) in Tierra del Fuego (Argentina). (B) Satellite image showing variable retention harvesting design in Los Cerros ranch (AR = aggregated retention, DR = dispersed retention, PF = unmanaged primary forests). (C) Diagram of the experimental design and soil sampling points (stars) at each forest stand (AR = blue circles, DR = red area, PF = green area).
Figure 1. (A) Study area in the gradient of N. pumilio forests distribution (green) in Tierra del Fuego (Argentina). (B) Satellite image showing variable retention harvesting design in Los Cerros ranch (AR = aggregated retention, DR = dispersed retention, PF = unmanaged primary forests). (C) Diagram of the experimental design and soil sampling points (stars) at each forest stand (AR = blue circles, DR = red area, PF = green area).
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Figure 2. Principal components analyses (PCA) including microbial variables (MBC = microbial biomass carbon, MBN = microbial biomass nitrogen, SBR = soil basal respiration, MBC/MBN ratio, qCO2 = metabolic quotient), climate and topographic variables (MAP = mean annual precipitation, MAT = mean annual temperature, elevation), soil characteristics (soil pH, SOC = soil organic carbon, N = soil nitrogen, P = soil phosphorus) and understory vegetation variables (species richness, understory vegetation biomass, advance regeneration, dominant height, recruitment, initial regeneration, forest growth, bare soil, tree density) in N. pumilio forests of Tierra del Fuego. (A) Forest treatments including primary unmanaged forests (PF, green), aggregated retention (AR, blue), and dispersed retention (DR, red). (B) Years-after-harvesting including 6 YAH (white), 9 YAH (gray), and 16 YAH stands (black). The ellipses indicate clustering of treatments: green for PF and 16 YAH, red for DR and 9 YAH, and blue for AR and 6 YAH.
Figure 2. Principal components analyses (PCA) including microbial variables (MBC = microbial biomass carbon, MBN = microbial biomass nitrogen, SBR = soil basal respiration, MBC/MBN ratio, qCO2 = metabolic quotient), climate and topographic variables (MAP = mean annual precipitation, MAT = mean annual temperature, elevation), soil characteristics (soil pH, SOC = soil organic carbon, N = soil nitrogen, P = soil phosphorus) and understory vegetation variables (species richness, understory vegetation biomass, advance regeneration, dominant height, recruitment, initial regeneration, forest growth, bare soil, tree density) in N. pumilio forests of Tierra del Fuego. (A) Forest treatments including primary unmanaged forests (PF, green), aggregated retention (AR, blue), and dispersed retention (DR, red). (B) Years-after-harvesting including 6 YAH (white), 9 YAH (gray), and 16 YAH stands (black). The ellipses indicate clustering of treatments: green for PF and 16 YAH, red for DR and 9 YAH, and blue for AR and 6 YAH.
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Figure 3. Relationships among soil microbial biomass and soil activity variables in N. pumilio forests of Tierra del Fuego. (A) Microbial biomass carbon (MBC) and soil organic carbon (SOC), (B) MBC and soil pH, (C) MBC and soil nitrogen (N), (D) microbial biomass nitrogen (MBN) and SOC, (E) MBN and soil pH, (F) MBN and soil N, (G) soil basal respiration (SBR) and SOC, (H) SBR and soil pH, and (I) SBR and soil N. Dots were classified as primary unmanaged forests (green), aggregated retention (blue) and dispersed retention (red) considering different years-after-harvesting (6 = triangles, 9 = squares, 16 = circles). Lines showed a linear regression, where r2 = adjusted parameter, and p = probability.
Figure 3. Relationships among soil microbial biomass and soil activity variables in N. pumilio forests of Tierra del Fuego. (A) Microbial biomass carbon (MBC) and soil organic carbon (SOC), (B) MBC and soil pH, (C) MBC and soil nitrogen (N), (D) microbial biomass nitrogen (MBN) and SOC, (E) MBN and soil pH, (F) MBN and soil N, (G) soil basal respiration (SBR) and SOC, (H) SBR and soil pH, and (I) SBR and soil N. Dots were classified as primary unmanaged forests (green), aggregated retention (blue) and dispersed retention (red) considering different years-after-harvesting (6 = triangles, 9 = squares, 16 = circles). Lines showed a linear regression, where r2 = adjusted parameter, and p = probability.
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Figure 4. Relationships among soil microbial biomass and activity and climatic variables in N. pumilio forests of Tierra del Fuego. (A) Microbial biomass carbon (MBC) and mean annual temperature (MAT), (B) MBC and elevation, (C) MBC and mean annual precipitation (MAP), (D) microbial biomass nitrogen (MBN) and MAT, (E) MBN and elevation, (F) MBN and MAP, (G) soil basal respiration (SBR) and MAT, (H) SBR and elevation, and (I) SBR and MAP. Dots were classified as primary unmanaged forests (green), aggregated retention (blue) and dispersed retention (red) considering different years-after-harvesting (6 = triangles, 9 = squares, 16 = circles). Lines showed the linear regression, where r2 = adjusted parameter, and p = probability.
Figure 4. Relationships among soil microbial biomass and activity and climatic variables in N. pumilio forests of Tierra del Fuego. (A) Microbial biomass carbon (MBC) and mean annual temperature (MAT), (B) MBC and elevation, (C) MBC and mean annual precipitation (MAP), (D) microbial biomass nitrogen (MBN) and MAT, (E) MBN and elevation, (F) MBN and MAP, (G) soil basal respiration (SBR) and MAT, (H) SBR and elevation, and (I) SBR and MAP. Dots were classified as primary unmanaged forests (green), aggregated retention (blue) and dispersed retention (red) considering different years-after-harvesting (6 = triangles, 9 = squares, 16 = circles). Lines showed the linear regression, where r2 = adjusted parameter, and p = probability.
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Figure 5. Relationships among soil microbial biomass and activity and vegetation variables in N. pumilio forests of Tierra del Fuego. (A) Microbial biomass carbon (MBC) and species richness, (B) MBC and vegetation cover, (C) MBC and forest growth, (D) microbial biomass nitrogen (MBN) and species richness, (E) MBN and understory vegetation cover, (F) MBN and forest growth, (G) soil basal respiration (SBR) and species richness, (H) SBR and understory vegetation cover, and (I) SBR and forest growth. Dots were classified as primary unmanaged forests (green), aggregated retention (blue), and dispersed retention (red) considering different years-after-harvesting (6 = triangles, 9 = squares, 16 = circles). Lines showed the linear regression, where r2 = adjusted parameter, and p = probability.
Figure 5. Relationships among soil microbial biomass and activity and vegetation variables in N. pumilio forests of Tierra del Fuego. (A) Microbial biomass carbon (MBC) and species richness, (B) MBC and vegetation cover, (C) MBC and forest growth, (D) microbial biomass nitrogen (MBN) and species richness, (E) MBN and understory vegetation cover, (F) MBN and forest growth, (G) soil basal respiration (SBR) and species richness, (H) SBR and understory vegetation cover, and (I) SBR and forest growth. Dots were classified as primary unmanaged forests (green), aggregated retention (blue), and dispersed retention (red) considering different years-after-harvesting (6 = triangles, 9 = squares, 16 = circles). Lines showed the linear regression, where r2 = adjusted parameter, and p = probability.
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Table 1. Characterization of the study areas across a regional gradient of the natural distribution of N. pumilio forests in Tierra del Fuego (Argentina). Geographic location, description of climatic variables, soil physicochemical, and understory vegetation in the studied forest treatments (AR = aggregated retention, DR = dispersed retention, PF = primary unmanaged forests) and years-after-harvesting (YAH = 6, 9, 16). MAP = mean annual precipitation (mm yr−1), MAT = mean annual temperature (°C), ELE = elevation (m.a.s.l.), DH = dominant height (m), TD = tree density (n ha−1), BA = basal area (m2 ha−1), FG = forest growth (m3 ha−1 yr−1), I-R = initial regeneration (n 103 ha−1), A-R = advanced regeneration (n 103 ha−1), REC = recruitment (n 103 ha−1), SR = species richness, BIO = biomass (kg ha−1), DS = dominant species, COV = cover (%), BS = bare soil (%), pH = soil acidity, SOC = soil organic carbon (%), SN = soil nitrogen (%), SP = soil phosphorus (µg g−1), FEMA = Festuca magellanica, GAAP = Galium aparine, HIRE = Hierochloë redolens, MAGR = Macrachaenium gracile, OSDE = Osmorhiza depauperata, PHAL = Phleum alpinum, POPR = Poa pratensis, SCRA = Schizeilema ranunculus, TAOF = Taraxacum officinale, VIMA = Viola magellanica. Values shown are mean and standard deviation (±SD).
Table 1. Characterization of the study areas across a regional gradient of the natural distribution of N. pumilio forests in Tierra del Fuego (Argentina). Geographic location, description of climatic variables, soil physicochemical, and understory vegetation in the studied forest treatments (AR = aggregated retention, DR = dispersed retention, PF = primary unmanaged forests) and years-after-harvesting (YAH = 6, 9, 16). MAP = mean annual precipitation (mm yr−1), MAT = mean annual temperature (°C), ELE = elevation (m.a.s.l.), DH = dominant height (m), TD = tree density (n ha−1), BA = basal area (m2 ha−1), FG = forest growth (m3 ha−1 yr−1), I-R = initial regeneration (n 103 ha−1), A-R = advanced regeneration (n 103 ha−1), REC = recruitment (n 103 ha−1), SR = species richness, BIO = biomass (kg ha−1), DS = dominant species, COV = cover (%), BS = bare soil (%), pH = soil acidity, SOC = soil organic carbon (%), SN = soil nitrogen (%), SP = soil phosphorus (µg g−1), FEMA = Festuca magellanica, GAAP = Galium aparine, HIRE = Hierochloë redolens, MAGR = Macrachaenium gracile, OSDE = Osmorhiza depauperata, PHAL = Phleum alpinum, POPR = Poa pratensis, SCRA = Schizeilema ranunculus, TAOF = Taraxacum officinale, VIMA = Viola magellanica. Values shown are mean and standard deviation (±SD).
Factor6-YAH9-YAH16-YAH
ARDRPFARDRPFARDRPF
Latitude−54.483−54.480−54.482−54.580−54.581−54.569−54.382−54.381−54.365
Longitude−66.828−66.823−66.802−66.907−66.907−66.927−67.864−67.862−67.869
ClimateMAP486.6 ± 1.7487.2 ± 1.2488.7 ± 2.1530.0 ± 1.1530.0 ± 1.1520.6 ± 1.3422.4 ± 1.0425.4 ± 1.0427.1 ± 3.6
MAT4.7 ± 0.14.6 ± 0.14.6 ± 0.14.0 ± 0.14.0 ± 0.14.4 ± 0.14.7 ± 0.14.7 ± 0.15.0 ± 0.1
ELE230 ± 11232 ± 12254 ± 16261 ± 10265 ± 8262 ± 21214 ± 24208 ± 19157 ± 3
Forest StructureDH23.6 ± 3.921.9 ± 1.919.1 ± 1.623.3 ± 2.224.4 ± 2.122.4 ± 2.921.7 ± 2.021.7 ± 1.322.7 ± 2.7
TD229 ± 111120 ± 87376 ± 89234 ± 17261 ± 37671 ± 172354 ± 17045 ± 20350 ± 114
BA51.0 ± 10.418.3 ± 6.855.5 ± 5.249.5 ± 7.514.9 ± 3.063.8 ± 11.151.0 ± 11.512.8 ± 3.250.3 ± 9.0
FG3.3 ± 0.81.1 ± 0.42.7 ± 0.63.6 ± 0.41.3 ± 0.45.3 ± 1.53.2 ± 0.70.8 ± 0.23.3 ± 1.0
I-R603 ± 220101 ± 711078 ± 357446 ± 9594 ± 40195 ± 31181 ± 6131 ± 21170 ± 39
A-R0 ± 00 ± 00 ± 02 ± 13.3 ± 2.90 ± 00 ± 04.0 ± 2.70 ± 0
REC198 ± 2721 ± 1356 ± 300 ± 03 ± 10 ± 031 ±180 ± 0138 ± 25
Understory VegetationSR 17 ± 127 ± 415 ± 416 ±323 ± 217 ± 615 ± 116 ± 213 ± 1
BIO406 ± 1313613 ± 178779 ± 2361771 ± 4872493 ± 1216859 ± 184611 ± 2671968 ± 727524 ± 136
DSOSDE, VIMA, POPRGAAP, OSDE, TAOFOSDE, VIMA, TAOFOSDE, SCRA, PHALOSDE, SCRA, VIMAMAGR, SCRA, VIMAGAAP, OSDE, TAOFOSDE, SCRA, HIREGAAP, OSDE, FEMA
COV103.2 ± 39.2141.5 ± 34.078.5 ± 38.1174.2 ± 49.3209.0 ± 24.689.7 ± 56.063.0 ± 14.8189.0 ± 33.6107.5 ± 27.9
BS25.5 ± 13.716.0 ± 7.337.5 ± 17.97.5 ± 2.45.5 ± 2.533.0 ± 22.135.5 ± 6.22.5 ± 1.621.5 ± 10.6
SoilpH4.62 ± 0.444.24 ± 0.174.44 ± 0.254.66 ± 0.214.66 ± 0.234.44 ± 0.445.24 ± 0.285.01 ± 0.374.74 ± 0.27
SOC8.81 ± 5.109.85 ± 2.799.85 ± 3.6219.11 ± 5.9413.63 ± 2.6915.59 ± 5.378.24 ± 1.627.52 ± 0.579.56 ± 1.55
SN0.34 ± 0.180.32 ± 0.040.29 ± 0.060.69 ± 0.170.48 ± 0.130.49 ± 0.300.26 ± 0.030.27 ± 0.030.29 ± 0.04
SP55.9 ± 36.752.1 ± 17.248.3 ± 34.566.7 ± 34.939.0 ± 21.862.3 ± 28.262.9 ± 17.958.4 ± 27.047.5 ± 9.6
Table 2. Two-way ANOVA results for soil biological variables in forest treatments (FT) (PF = primary unmanaged forests, AR = aggregated retention, DR = dispersed retention) and years-after-harvesting (YAH) (6, 9, 16) as main factors in N. pumilio forests of Tierra del Fuego. MBC = microbial biomass carbon (mg kg−1 dry soil), MBN = microbial biomass nitrogen (mg kg−1 dry soil), SBR = soil basal respiration (mg C-CO2 kg−1 dry soil d−1), MBC/MBN = ratio between MBC and MBN, qCO2 = metabolic quotient (µgC-CO2 mg−1 MBC d−1), qMC = microbial quotient (%). Mean values and standard deviation are shown. Values followed by different letters in each column showed significant differences (p < 0.05) among forest treatments (PF, AR, and DR) and years after harvesting (6, 9, and 16) using Tukey’s test. F = Fisher’s test, p = probability.
Table 2. Two-way ANOVA results for soil biological variables in forest treatments (FT) (PF = primary unmanaged forests, AR = aggregated retention, DR = dispersed retention) and years-after-harvesting (YAH) (6, 9, 16) as main factors in N. pumilio forests of Tierra del Fuego. MBC = microbial biomass carbon (mg kg−1 dry soil), MBN = microbial biomass nitrogen (mg kg−1 dry soil), SBR = soil basal respiration (mg C-CO2 kg−1 dry soil d−1), MBC/MBN = ratio between MBC and MBN, qCO2 = metabolic quotient (µgC-CO2 mg−1 MBC d−1), qMC = microbial quotient (%). Mean values and standard deviation are shown. Values followed by different letters in each column showed significant differences (p < 0.05) among forest treatments (PF, AR, and DR) and years after harvesting (6, 9, and 16) using Tukey’s test. F = Fisher’s test, p = probability.
TreatmentsLevelsMBCMBNSBRMBC/MBNqCO2qMC
FTPF865 ± 228 ab66 ± 33 a133 ± 37 b13 ± 9 b0.17 ± 0.07 a0.81 ± 0.29 a
AR1021 ± 206 b104 ± 42 b155 ± 31 b10 ± 4 a0.15 ± 0.02 a1.09 ± 0.60 a
DR658 ± 331 a65 ± 50 a99 ± 46 a10 ± 8 a0.17 ± 0.07 a0.75 ± 0.44 a
F(p)7.50 (<0.05)8.62 (<0.05)12.82 (<0.05)5.58 (<0.05)0.15 (0.86)3.00 (0.06)
YAH6835 ± 226 a75 ± 47 a129 ± 30 ab11 ± 7 ab0.17 ± 0.05 a1.08 ± 0.50 b
9712 ± 378 a55 ± 32 a109 ± 55 a13 ± 9 b0.16 ± 0.06 a0.43 ± 0.26 a
16970 ± 256 a122 ± 39 b141 ± 34 b8 ± 4 a0.15 ± 0.06 a1.26 ± 0.40 b
F(p)1.70 (0.20)11.20 (<0.05)8.28 (<0.05)6.24 (<0.05)0.67 (0.52)9.38 (<0.05)
Interactions FT × YAH
F(p)2.93 (<0.05)0.47 (0.75)5.36 (<0.05)1.79 (0.15)1.62 (0.19)1.25 (0.31)
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Toledo, S.; Martínez Pastur, G.; Rodríguez-Souilla, J.; Peri, P.L. Retention Levels and Years-After-Harvesting Influence over Soil Microbial Activity and Biomass in Southern Patagonian Forests. Land 2024, 13, 1963. https://doi.org/10.3390/land13111963

AMA Style

Toledo S, Martínez Pastur G, Rodríguez-Souilla J, Peri PL. Retention Levels and Years-After-Harvesting Influence over Soil Microbial Activity and Biomass in Southern Patagonian Forests. Land. 2024; 13(11):1963. https://doi.org/10.3390/land13111963

Chicago/Turabian Style

Toledo, Santiago, Guillermo Martínez Pastur, Julián Rodríguez-Souilla, and Pablo L. Peri. 2024. "Retention Levels and Years-After-Harvesting Influence over Soil Microbial Activity and Biomass in Southern Patagonian Forests" Land 13, no. 11: 1963. https://doi.org/10.3390/land13111963

APA Style

Toledo, S., Martínez Pastur, G., Rodríguez-Souilla, J., & Peri, P. L. (2024). Retention Levels and Years-After-Harvesting Influence over Soil Microbial Activity and Biomass in Southern Patagonian Forests. Land, 13(11), 1963. https://doi.org/10.3390/land13111963

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