*
 

iForest - Biogeosciences and Forestry

iForest - Biogeosciences and Forestry
*

Soil fauna communities and microbial activities response to litter and soil properties under degraded and restored forests of Hyrcania

iForest - Biogeosciences and Forestry, Volume 14, Issue 6, Pages 490-498 (2021)
doi: https://doi.org/10.3832/ifor3583-014
Published: Nov 11, 2021 - Copyright © 2021 SISEF

Research Articles

Reforestation has long been the best practice to restore degraded forests due to human interventions. In this paper we investigated the effect of forest degradation (DNF) along with reforestation using 4 endemic species (Alnus subcordata, ASP; Acer velutinum, AVP; Cupressus sempervirens, CSP; Quercus castaneifolia Mey, QCP) on forest’s soil chemical and biological indicators compared to a close-to-virgin natural forest (VNF). For this study, a total of 24 physico-chemical and 25 biological and microbial indicators were measured in soils of all 6 forest stands along with the litter properties. Results showed that the lowest soil quality was observed under DNF, CSP, and QCP which was the result of forest cover degradation in DNF and low litter quality, especially low pH and high C:N, in CSP and QCP. Soil fauna communities were significantly affected by tree species. We found two times higher density of earthworms in VNF compared to ASP, but in DNF the density was 5 times lower than VNF. We found no epigeic earthworms in QCP, CSP and DNF and no endogeic earthworms in DNF. Acarina and Collembola density was high in VNF and ASP, but they showed significant differences (VNF>ASP), and their density sharply decreased in other stands, especially in CSP (3 times lower than VNF) and DNF (8 to 10 times lower than VNF). Nematode density was statistically equal in VNF, ASP, and AVP, but significantly lower in other stands. Protozoa, bacteria and fungi densities were significantly higher in VNF and ASP (VNF>ASP) compared to each other and other forest stands. Basal respiration, substrate induced respiration, microbial biomass N and P, and carbon availability index was also higher in VNF and ASP compared to other stands. Although VNF has the best condition because of old forest cover and high diversity, ASP soil showed significant improvements, demonstrating the importance of litter quality in soil restoration. Restoration effectiveness ranking of the four tested species on soil improvement are therefore ASP>AVP>QCP>CSP. The significant improvement of soil quality under ASP compared to other reforestated stands, only after 3 decades, emphasizes the importance of tree species selection and litter quality on soil chemical and biological restoration.

Forest Restoration, Reforestation, Litter Quality, Soil Biological Activity, Soil Chemical Properties, Soil Fauna

  Introduction 

Reforestation of clear-cut forests and agricultural lands is a well known practice to restore natural forest ecosystems around the world. As soil functional traits (especially carbon sequestration) are affected by forest degradation, the process of restoration can lead to significant increase in the ecological value of the temperate forests and in soil quality ([39]), and improves the microbial community (MC) of the forest soils ([44]). Although reforestation, due to low diversity in tree species, does not lead to the same soil and ecosystem characteristics as of natural forests ([25]), these stands are the most closed ecosystems to the natural ones that human are able to establish, and at some points, the ecosystem and soil characteristics could also be close to natural ones ([39]). Therefore, reforestation has impacts on several aspects, both aboveground and belowground.

At a global scale, reforestation is currently one of the strategies to tackle climate change, as tree plantations can be exploited to sequester atmospheric carbon ([37]). Recent studies found that large scale reforestations, after the bare soil C efflux to the atmosphere, can lead to significant increase in soil carbon pools. This means that the reforestation of clear-cut forests and abandoned agricultural lands are among the best options to increase belowground carbon sequestration ([22]).

At the local scale, forest restoration can lead to significant increase in biodiversity of soil fauna and flora, however, at a lower diversity compared to natural forests. Soil fauna play an important role in soil development and layer formation. Studies have shown that fauna activity have the potential to completely exploit soil organic layer (Oe) and develop a thick organomineral layer (A) in forest soil ([13]). Other studies showed that soil microbial and fauna activities, especially earthworms, can significantly modify chemical and microbial properties of the forest soils through the digestion of organic matter in their guts. Therefore, soil properties can be affected by a combination of tree species and soil fauna activities ([14]). On the other hand, reforestation has significant effects on soil MC structure and consequently, can lead to increased soil C sequestration and soil nitrogen content ([40]). MC of the forest soils is also influenced by the quantity and chemical properties of the input litter. Shao et al. ([40]) found that in the early stages of reforestation, MC have a significant effect on SOC accumulation, however, at the mature stages, these communities will be affected by vegetation diversity and litter quality. Therefore, there is a close mutual relationship between soil MC and tree species characteristics in natural and restored forests ([42]).

Studies also found several influential factors on soil chemical properties under natural restored forests. While soil degradation due to deforestation has significant negative effects on chemical soil properties ([39]), reforestation has a positive effect on soil quality according to the selected tree species ([47]). Tree species not only improve soil chemical quality through litter, but also through root exudates. Several studies have shown that reforestation using coniferous species can significantly increase SOC ([44]), while introducing native broadleaved species will result in higher quality and SOC chemical stability ([47]), with higher enzyme activity in the soil ([10]). Therefore, tree species selection for reforestation and forest restoration is a key influential factor in forest management.

As reforestation using different tree species may not lead to final ecosystem characteristics matching those of the natural ones, monitoring the effect of restoration practices on the ecosystem (especially soil) under different reforestation strategies is crucial. Here we examined soil chemical and biological response to different reforestation species after forest clear-cut. We aimed at determining which of four different tree species can lead to closer soil characteristics to that of a natural forest. The goals of this study are: (i) determining litter quality among different reforestation species; (ii) understanding the relationship between soil quality and biological and microbial soil characteristics in degraded and restored forests; (iii) determining whole species effect on soil chemical and biological characteristics of the restored forest soil; and (iv) disentangling the relationship between soil fauna community changes, soil carbon/nitrogen/phosphorus, and litter quality under different tree species.

  Materials and methods 

Site description

The study site has an area of 1394 ha and is located in the city of Ramsar (Mazandaran province, northern Iran - 36° 48′ 37″ and 36° 51′ 00″ N, 50° 43′ 10″ and 51° 35′ 00″ E). The site has an altitudinal range of 60-2130 m a.s.l. with an average slope of 40%. The climate is classified as seasonal temperate humid with an average annual rainfall of 773 mm and temperature of 12.3 °C. The monthly average temperature ranges from 5.2 in February to 19.5 °C in July, without frost periods. According to the USDA Soil Taxonomy, the forest soils are classified as Loamy-Clay-Sand Alfisols, developed on conglomerate composed of lime stones belonging to the Jurassic period ([41]).

Significant parts of these natural forests have been severely damaged during past decades. After clear cutting in 1988, the Forests and Rangelands Organization of Iran (FROI) performed reforestation practices to restore these habitats. Reforestation were done using Alder (Alnus subcordata C.A. Mey.) in an area of 16 ha, Maple (Acer velutinum Boiss.) 11 ha, Oak (Quercus castaneifolia C.A. Mey.) 4 ha, Mediterranean cedar (Cupressus sempervirens var. horizontalis) 8 ha. Degraded forests dominant species includes hornbeam (Carpinus betulus L.) and ironwood (Parrotia persica C.A. Mey.) species with an area of approximately 6 ha, and close-to-virgin natural forest (VNF) includes Oak (Quercus castaneifolia C.A. Mey.), hornbeam (Carpinus betulus L.) and ironwood (Parrotia persica C.A. Mey.), with an area of approximately 10 ha.

Sampling

Six forest types were selected for the purpose of this study, which includes pure reforestation of Alnus subcordata (ASP), Acer velutinum (AVP), Quercus castaneifolia (QCP), Cupressus sempervirens plantation (CSP), a close-to-virgin natural forest (VNF, oak-hornbeam-ironwood as control) and a degraded forest (DNF, hornbeam and ironwood). All the selected forest types were close to each other with similar soil texture and topographic conditions. A 4 ha area (200 × 200 m) was selected in each site for sampling. In order to reduce the boundary effects, the rows around the sampling area were not considered. Sampling was performed during summer 2019 using four parallel transects (200 m in length and 66 m apart from each other) and five soil and litter samples (25 × 25 cm) were randomly collected at a depth of 0-10 cm (Fig. 1). A total of 60 samples (i.e., 5 litter samples and 5 soil samples type) were transferred to the laboratory for litter and soil analysis ([24]).

Fig. 1 - Locations of the studies forest stands and sampling plots. A 4-ha square was randomly selected in each stand (right panel) and 5 out of 16 subplots were selected at random to measure litter and soil properties.

  Enlarge/Shrink   Download   Full Width  Open in Viewer

Laboratory analyses

Litter thickness was measured using a tape, and then transported in bags to the laboratory, washed gently for 30 seconds to remove mineral soil, and dried at 70 °C for 48 h, and then the dry weight measured. Dried litter samples were finely grounded and analyzed. Total C and N, along with nutrient contents in litter samples were determined using dry combustion with an elemental analyzer (Fisons EA1108, Milan, Italy), calibrated by the BBOT [2.5-bis-(5-tert-butyl-benzoxazol-2-yl) -thiophen] standard (ThermoQuest Italia S.p.A., Rodano, MI, Italy). Litter phosphorus (LP) concentration was determined spectrophotometrically. An atomic absorption spectrophotometer was used to determine total litter potassium (LK), calcium (LCa), and magnesium (LMg) concentration by flame emission.

A portion of soil samples was stored in polyethylene bags for biological analysis at 4 °C until processed. Another portion was air-dried and passed through 2-mm sieve (aggregates were broken to pass through a 2 mm sieve). Bulk density (BD) was measured by the clod method ([38]). Soil texture was determined using the Bouyoucos hydrometric method ([4]). Soil water content was measured by drying soil samples at 105 °C for 24 h. Soil pH and electric conductivity (EC) were measured using an Orion Ionalyzer Model 901 pH meter in a 1:2.5 (soil: water) solution ([43]).

Soil organic C was measured using the Walkey-Black method ([2]) and total N using a semi Micro-Kjeldahl method ([5]). Available P was measured by a spectrophotometer using the Olsen method ([18]), and available K, Ca, and Mg were determined using an atomic absorption spectrophotometer (by ammonium acetate extraction at pH 9). Particulate organic matter carbon (POM-C) and nitrogen (POM-N) were determined by physical fractionation ([17]). Fine roots (diameter < 2 mm) were collected from each soil sample and dried at 70 °C. The earthworms were collected simultaneously with the soil sampling by hand sorting, and identified using the Edwards et al. ([11]) key based on ecological categories (i.e., epigeic, anecic and endogeic) by external characteristics. Earthworm biomass was then determined after drying at 60 °C for 24 h and weighing. Soil Acarina and Collembola were extracted with the help of modified Tullgren funnel, as described by Hutson & Veitch ([19]). Nematodes were extracted from 100 g soil sample (fresh weight) by a modified cotton-wool filter method ([29]). Following the extraction method, soil protozoa population densities were counted under a microscope ([33]). Soil total bacteria and fungi were counted following extraction from soil (10 g fresh weight were mixed with 90 ml distilled water) using the homogenization and centrifugation techniques and culturing as described by Wollum ([50]). Soil basal respiration (BR) was determined by measuring the CO2 evolved in a 3-day incubation experiment at 25 °C ([1]). Substrate induced respiration (SIR), was determined using 1% glucose solution as substrate and the evolved CO2 was measured after 72 h incubation. The evolved CO2 was adsorbed in NaOH and measured by HCI titration ([3]). Carbon, nitrogen and phosphorous in the microbial biomass (MBC, MBN, and MBP, respectively) were measured by fumigation-extraction method ([6]). The soil metabolic quotient (qCO2 = BR/MBC - [3]) and carbon availability index (CAI = BR/SIR -[7]) were calculated based on the values of organic C, BR, substrate induced respiration (SIR) and MBC.

Statistical analysis

The normality of the variables was tested by the Kolmogorov-Smirnov test, and Levene’s test was used to examine the homogeneity of variances. Data are presented as mean ± standard error throughout the text, figures and tables. One-way analysis of variance (ANOVA) was used to compare litter and soil features data among the different land covers. Duncan test was further employed to test for mean differences with α = 0.05. Relationships between the measured parameters was also investigated using Pearson’s correlation. All the statistical analyses were done using the statistical software package SPSS® ver. 20 (IBM, Armonk, NT, USA). PCA is frequently used as an ordination and data reduction technique to distinguish treatments and to determine and characterize the most important parameters ([20]). Multivariate correlations and principal components were used to identify significant relationships among the variables using PC-Ord ver. 5.0 ([34]). To better interpret the data, only the first and second components were considered.

  Results 

Litter and soil properties

Results showed that there was a significant difference in the characteristics of the litter and soil among different forest covers (Fig. 2, Tab. 1). The mean thickness of litter layer was significantly higher (p < 0.05) in CSP stands (3.57 cm) than in other studied forest covers and was more than 3 times compared to that of ASP (1.04 cm). Litter C was significantly higher in CSP (57%) compared to other forest covers. QCP was the second forest cover with the highest litter C (56%) and the lowest litter C was observed in ASP (31.68%). On the contrary, establishment of ASP led to an increase in the concentration of litter N (3.11%), which was significantly higher than other studied forest covers. Litter C/N ratio in different forest covers was significantly different with the lowest value in ASP, which was the closest one to VNF (11.42 and 15.96, respectively). VNF had the highest concentration of Ca, P, K, and Mg, but no significant difference was observed between VNF and ASP (Fig. 2).

Fig. 2 - Mean values of the litter properties across different forest covers. Different letters indicate significant differences (p<0.05) between the means of forest covers after Duncan test. (VNF): close-to-virgin natural forest; (ASP): Alnus subcordata plantations; (AVP): Acer velutinum; (QCP): Quercus castaneifolia; (CSP): Cupressus sempervirens; (DNF): degraded natural forest. Error bars represent the standard error.

  Enlarge/Shrink   Download   Full Width  Open in Viewer

Tab. 1 - Mean (± standard error, SE) of the soil physical, chemical and biological properties analyzed under different forest covers. (VNF): virgin natural forest; (ASP): Alnus subcordata plantation; (AVP:): Acer velutinum; (QCP): Quercus castaneifolia; (CSP): Cupressus sempervirens; (DNF): degraded natural forest. Different letters indicate significant differences (p<0.05) between the means in different forest covers after Duncan test.

Soil Properties Forest cover
VNF ASP AVP QCP CSP DNF F-test P-value
Bulk density (g cm-3) 1.43 ± 0.04b 1.51 ± 0.07b 1.46 ± 0.06b 1.06 ± 0.01c 1.03 ± 0.00c 1.73 ± 0.01a 37.732 <0.001
Sand (%) 25.00 ± 2.36bc 19.80 ± 2.57c 34.20 ± 8.40ab 27.40 ± 2.99abc 29.40 ± 4.61abc 40.20 ± 2.13a 2.603 0.051
Silt (%) 39.40 ± 1.77ab 46.40 ± 4.92a 30.20 ± 3.77b 43.60 ± 2.31a 45.00 ± 4.72a 36.80 ± 2.35ab 2.969 0.032
Clay (%) 35.60 ± 1.24a 33.80 ± 3.39a 35.60 ± 5.67a 29.00 ± 2.04ab 25.60 ± 3.23ab 23.00 ± 0.63b 2.892 0.035
Water content (%) 48.48 ± 3.05a 33.65 ± 3.39bc 38.61 ± 4.92abc 44.73 ± 3.91ab 45.59 ± 3.79a 30.86 ± 2.47c 3.714 0.012
pH (1:2.5 H2O) 7.06 ± 0.11a 7.12 ± 0.06a 6.88 ± 0.16ab 6.32 ± 0.09bc 6.29 ± 0.52bc 5.77 ± 0.09c 5.243 0.002
EC (ds m-1) 0.30 ± 0.01a 0.32 ± 0.03a 0.24 ± 0.01a 0.17 ± 0.01b 0.15 ± 0.04b 0.12 ± 0.00b 9.962 <0.001
Organic C (%) 4.29 ± 0.45bc 3.44 ± 0.24c 3.96 ± 0.43bc 5.09 ± 0.65b 6.62 ± 0.26a 3.91 ± 0.35bc 7.311 <0.001
Total N (%) 0.46 ± 0.05a 0.56 ± 0.11a 0.27 ± 0.02b 0.22 ± 0.03b 0.17 ± 0.04b 0.12 ± 0.01b 9.071 <0.001
C/N ratio 9.29 ± 0.13cd 7.23 ± 1.46d 14.60 ± 0.66cd 23.20 ± 2.37bc 46.48 ± 8.83a 33.84 ± 7.01ab 10.388 <0.001
Available P (mg kg-1) 29.21 ± 0.86a 27.15 ± 2.13a 19.34 ± 3.11b 13.51 ± 1.67c 11.03 ± 2.06c 10.49 ± 0.57c 17.932 <0.001
Available K (mg kg-1) 415.80 ± 16.08a 405.20 ± 52.78a 316.60 ± 21.05b 184.20 ± 13.97c 166.00 ± 19.86c 155.60 ± 18.61c 19.764 <0.001
Available Ca (mg kg-1) 293.80 ± 28.35a 277.20 ± 42.95ab 214.60 ± 16.33b 101.40 ± 16.50c 97.20 ± 7.97c 89.00 ± 6.72c 16.342 <0.001
Available Mg (mg kg-1) 79.00 ± 1.76a 71.60 ± 5.83a 65.60 ± 8.44ab 50.80 ± 7.35bc 38.60 ± 3.23c 42.60 ± 3.35c 8.780 <0.001
POM-C (g kg-1) 3.61 ± 0.20ab 2.13 ± 0.20c 2.75 ± 0.51bc 3.76 ± 0.45a 4.44 ± 0.18a 1.02 ± 0.17d 14.987 <0.001
POM-N (g kg-1) 0.50 ± 0.05b 0.67 ± 0.04a 0.27 ± 0.02c 0.15 ± 0.01 0.12 ± 0.00d 0.07 ± 0.01d 62.340 <0.001

  Enlarge/Reduce  Open in Viewer

Significant difference was also found between different forest covers in terms of soil physical and chemical properties. Bulk density in DNF (1.73 gr cm-3) was significantly higher compared to all other forest covers, and the lowest bulk density was observed in CSP (1.03 gr cm-3) and QCP (1.06 gr cm-3). Soil texture showed significant difference among some forest covers. ASP had the lowest sand content (19.8%) while DNF had the highest (40.20%). Silt content was only significantly different between ASP and AVP, while clay showed significantly lower content in DNF compared to VNF, ASP, and AVP (Tab. 1).

Soil water content in VNF and CSP (48.48% and 45.59%, respectively) was significantly higher than in DNF. Soil pH also showed significantly low value in DNF (5.77), while ASP and VNF showed the highest values. Soil organic carbon content (SOC) and C/N ratio were significantly different among the sites, with CSP having the highest value (6.62% and 46.48%). Soil total N, and available P, K and Mg were significantly higher in VNF and ASP compared to other forest covers, while the highest amount of available soil Ca (293.80 mg kg-1) was observed under VNF. Lowest soil total N, and available P, and K were observed in DNF.

POM-C and POM-N showed similar pattern to that of soil organic C and total N with the highest average POM-C of 4.44 ± 0.18 in CSP and POM-N of 0.67 ± 0.04 in ASP (Tab. 1).

Soil fauna and microbial biomass

The highest fine root biomass was observed in VNF (62.58 g m-2) while the lowest was measured in DNF (11.9 g m-2) soils. According to our data, degradation of natural forests resulted in a significant decrease in soil fauna, to the extent that reforestation could not restore this community close to that of VNF. VNF showed 7.18 mg m-2 biomass of epigeic earthworms, while no biomass was detected in QCP, CSP, and DNF soils; yet, the differences between the stands were non-significant. Anecic earthworm biomass and density were also lower in CSP and AVP compared to other stands and they were absent in DNF, while we detected 9.7 mg m-2 in VNF. Endogeic earthworm biomass was significantly higher in VNF (24.84 mg m-2) compared to AVP (8.63 mg m-2), QCP (4.49 mg m-2), CSP (4.65 mg m-2), and DNF (1.26 mg m-2). Earthworm density and biomass in VNF (3.20 n m-2, and 41.72 mg m-2, respectively) were significantly higher compared to other stands and more than two folds higher than ASP stand. Earthworm population was close to zero in each DNF and we found only one earthworm in 5 m-2 (at a depth of 10 cm) of forest soils in DNF.

Acarina and Collembola density showed three different levels. Both of these organisms showed significantly higher density in VNF compared to other stands with averages of 47.032 and 37.088 organisms per m2, respectively. Also, in ASP the density of both organisms were significantly higher than the remaining stands (39.560 for Acarina and 26.502 for Collembola per m2). Acarina density was similar in AVP, QCP, CSP, and DNF, while Collembola was the lowest in DNF, with 10 times lower density compared to VNF. Soil nematode community was recovered in ASP (250.6 in 100 g of soil) and AVP (220.4 in 100 g of soil) to a level that is not significantly different from VNF (279 in 100 g of soil), while protozoa density was significantly higher in VNF (646.2 ×102 g soil) and then ASP (468.8 ×102 g soil) compared to other stands. Protozoa community was 7 times higher in VNF compared to DNF (89.4 ×102 g soil).

Total bacteria and total fungi in ASP (4.11 and 2.45 ×107 g soil, respectively) were close to VNF (4.87 and 3.63 ×107 g soil, respectively); however, the differences were significant at p < 0.05. Both communities were more than 10 times higher in VNF compared to DNF (0.41 and 0.33 ×107 g soil, respectively). Bacterial community was statistically equal in AVP (1.95 ×107 g soil) compared to QCP (1.88 ×107 g soil), and CSP (0.67 ×107 g soil) compared to DNF (0.41 ×107 g soil), while fungal community was similar in AVP (0.76 ×107 g soil), QCP (0.72 ×107 g soil), CSP (0.57 ×107 g soil), and DNF (0.33 ×107 g soil - Tab. 2).

Tab. 2 - Mean (± SE) of the soil biological properties analyzed under different forest covers. (VNF): virgin natural forest; (ASP): Alnus subcordata plantation; (AVP:): Acer velutinum; (QCP): Quercus castaneifolia; (CSP): Cupressus sempervirens; (DNF): degraded natural forest. Different letters indicate significant differences (p<0.05) between the means in different forest covers after Duncan test.

Soil properties Forest cover
VNF ASP AVP QCP CSP DNF F-test P-value
Fine root biomass (g m-2) 62.58 ± 5.32a 42.72 ± 6.07b 37.57 ± 2.13b 33.59 ± 5.66b 29.57 ± 3.42b 11.09 ± 0.73c 14.936 <0.001
Epigeic density (n m-2) 0.40 ± 0.24a 0.20 ± 0.20a 0.20 ± 0.20a 0.00 ± 0.00a 0.00 ± 0.00a 0.00 ± 0.00a 1.143 0.365
Epigeic biomass (mg m-2) 7.18 ± 4.43a 2.31 ± 2.31a 1.93 ± 1.93a 0.00 ± 0.00a 0.00 ± 0.00a 0.00 ± 0.00a 1.626 0.191
Anecic density (n m-2) 0.80 ± 0.37a 0.20 ± 0.20a 0.20 ± 0.20a 0.40 ± 0.24a 0.20 ± 0.20a 0.00 ± 0.00a 1.425 0.251
Anecic biomass (mg m-2) 9.69 ± 4.86a 2.65 ± 2.65a 2.65 ± 2.65a 7.95 ± 4.87a 2.33 ± 2.33a 0.00 ± 0.00a 1.256 0.315
Endogeic density (n m-2) 2.00 ± 0.31a 1.00 ± 0.31ab 0.80 ± 0.37b 0.60 ± 0.60b 0.40 ± 0.40b 0.20 ± 0.20b 2.711 0.044
Endogeic biomass (mg m-2) 24.84 ± 4.13a 13.18 ± 4.92ab 8.63 ± 3.85b 4.49 ± 4.49b 4.65 ± 4.65b 1.26 ± 1.26b 4.410 0.005
Earthworm density (n m-2) 3.20 ± 0.73a 1.40 ± 0.40b 1.20 ± 0.48b 1.00 ± 0.57b 0.60 ± 0.60b 0.20 ± 0.20b 3.961 0.009
Earthworm biomass
(mg m-2)
41.72 ± 9.73a 18.15 ± 4.79b 13.22 ± 4.73b 12.44 ± 5.10b 6.99 ± 6.99b 1.26 ± 1.26b 5.450 0.002
Acarina density (n×103 m-2) 47.03 ± 5.53a 39.56 ± 0.93b 13.00 ± 2.10c 11.46 ± 0.44c 10.77 ± 0:20c 5.82 ± 1.35c 47.780 <0.001
Collembola density
(n×103 m-2)
37.09 ± 4.03a 26.50 ± 3.76b 16.72 ± 1.77c 13.27 ± 1.30c 12.19 ± 0.27c 3.51 ± 0.68d 23.674 <0.001
Total nematodes
(in 100 g soil)
279.00 ± 28.82a 250.60 ± 42.83a 220.40 ± 31.87a 102.60 ± 15.69b 84.00 ± 7.14b 35.40 ± 9.46b 14.920 <0.001
Protozoa density
(×102 g soil)
646.20 ± 123.42a 468.80 ± 67.10b 249.80 ± 38.66c 163.40 ± 15.59c 110.80 ± 4.65c 89.40 ± 1.43c 13.887 <0.001
Total bacteria (×107 g soil) 4.87 ± 0.23a 4.11 ± 0.12b 1.95 ± 0.19c 1.88 ± 0.28c 0.67 ± 0.11d 0.41 ± 0.09d 92.089 <0.001
Total fungi (×107 g soil) 3.63 ± 0.23a 2.45 ± 0.22b 0.76 ± 0.07c 0.72 ± 0.07c 0.57 ± 0.07c 0.33 ± 0.02c 86.421 <0.001

  Enlarge/Reduce  Open in Viewer

Soil biological activity

Soil basal and substrate induced respiration (BR and SIR) in VNF and ASP were not statistically different, with averages of 0.76 and 1.64 mg CO2 g-1 day-1 for VNF and 0.73 and 1.49 mg CO2 g-1 day-1 for ASP, respectively, but they were significantly higher than those of other forest covers. Soil carbon microbial biomass showed the highest value (600.61 mg kg-1) in CSP, which was significantly higher than those of ASP, AVP and DNF, but statistically equals to VNF and QCP. Nitrogen microbial biomass was significantly higher in ASP (48.07 mg kg-1) and VNF (41.48 mg kg-1), compared to other forest covers, while phosphorus microbial biomass was the highest in VNF (81.40 mg kg-1). Further, microbial biomass indicators were significantly the lowest in DNF.

The ratios MBC/MBN and MBC/MBP were significantly higher in CSP (33.95 and 22.31) compared to other forest covers, while MBN/MBP was similar for all forest covers (p > 0.05). Metabolic quotient ratio in ASP (1.88 μg CO2 - C mg-1 MBC day-1) was significantly higher than in other sites, and five times higher than that of CSP (0.37 μg CO2 - C mg-1 MBC day-1), but not significantly different from DNF. Finally, ASP and VNF showed the highest carbon availability index (CAI), which was significantly higher compared to other forest stands (0.49 and 0.48, respectively - Tab. 3).

Tab. 3 - Mean (± SE) of the soil microbial properties analyzed under different forest covers. (VNF): virgin natural forest; (ASP): Alnus subcordata plantation; (AVP:): Acer velutinum; (QCP): Quercus castaneifolia; (CSP): Cupressus sempervirens; (DNF): degraded natural forest. Different letters indicate significant differences (p<0.05) between the means in different forest covers after Duncan test.

Soil properties Forest cover
VNF ASP AVP QCP CSP DNF F test P-value
Basal respiration
(mg co2 g-1 day-1)
0.76 ± 0.03a 0.73 ± 0.05a 0.35 ± 0.02b 0.26 ± 0.02bc 0.22 ± 0.01c 0.12 ± 0.01d 74.678 <0.001
Substrate induced respiration (mg CO2 g-1 day-1) 1.64 ± 0.14a 1.49 ± 0.10a 1.10 ± 0.04b 1.08 ± 0.01b 1.00 ± 0.05b 0.84 ± 0.07b 13.056 <0.001
Microbial biomass carbon (mg kg-1) 504.02 ± 72.62ab 415.93 ± 57.29b 417.39 ± 54.61b 519.05 ± 69.75ab 600.61 ± 14.63a 111.78 ± 28.7c 9.913 <0.001
Microbial biomass nitrogen (mg kg-1) 41.48 ± 5.66a 48.07 ± 3.77a 26.03 ± 2.48b 21.37 ± 0.66b 18.22 ± 1.30b 6.21 ± 1.02c 25.725 <0.001
Microbial biomass phosphorous (mg kg-1) 81.40 ± 0.97a 60.20 ± 7.78b 37.00 ± 3.84c 30.00 ± 3.17c 27.20 ± 1.35c 11.20 ± 0.86d 43.373 <0.001
MBC/MBN 12.60 ± 1.81cd 8.88 ± 1.37d 16.33 ± 2.22bcd 24.20 ± 3.20b 33.95 ± 3.54a 18.56 ± 4.55bc 8.969 <0.001
MBC/MBP 6.17 ± 0.87c 7.38 ± 1.21c 12.25 ± 2.51bc 17.43 ± 2.23ab 22.31 ± 1.26a 10.24 ± 2.81c 9.978 <0.001
MBN/MBP 0.51 ± 0.06a 0.84 ± 0.11a 0.77 ± 0.17a 0.75 ± 0.08a 0.67 ± 0.05a 0.54 ± 0.06a 1.639 0.188
Metabolic quotient
(μg CO2-C mg-1 MBC day-1)
1.73 ± 0.41ab 1.88 ± 0.27a 0.92 ± 0.16abc 0.56 ± 0.09bc 0.37 ± 0.03c 1.72 ± 0.73ab 3.152 0.025
Carbon availability index 0.48 ± 0.05a 0.49 ± 0.02a 0.32 ± 0.02b 0.24 ± 0.02b 0.23 ± 0.01bc 0.15 ± 0.01c 20.924 <0.001

  Enlarge/Reduce  Open in Viewer

The studied forest sites and their litter and soil characteristics were then analyzed using PCA. The first and second axes accounted for 52.43% and 14.55% of the total variance, respectively. The first PC axis discriminates fairly well two groups of forest covers (sites). The first group reflects good litter quality, accumulation of nutrients, and more biological and microbial activity, and is mainly formed by VNF and ASP sites. While the second group along the PC1 axis shows low quality of litter and soil, low nutrients and low biological activity at DNF, CSP, and QCP sites (Fig. 3).

Fig. 3 - PCA analysis of the measured variables in different forest covers. The first PCA axis accounted for 52.43% of the total variance, while the second axis explained 14.55%.

  Enlarge/Shrink   Download   Full Width  Open in Viewer

Relationship between soil properties

Correlation analysis of the studied soil and litter properties showed that most of the measured variables have positive correlation, however some key variables (e.g., litter C/N ratio and thickness and soil C/N ratio) have significant negative correlation with most of the measured variables. Litter calcium content is likely a key parameter significantly affecting soil chemical properties and fauna communities in a positive way, while soil potassium and phosphorus have significant correlation with soil microfauna communities. Potassium and phosphorus also have significant correlation with soil biological activity. Acarina, Collembola, Nematodes, Protozoa, Bacteria, and Fungi have significant correlation with soil biological properties, especially soil respiration and CAI. It seems that MBC only has significant correlation with soil carbon content (SOC, POM-C, FRB); however, soil bulk density has significant negative relationship with this parameter. Soil metabolic quotient is strongly correlated with the ratios of MBC/MBN and MBC/MBP (Fig. 4).

Fig. 4 - Correlation heatmap of studied soil properties (N=30). Variable labels are reported in the List of Abbreviations (see below).

  Enlarge/Shrink   Download   Full Width  Open in Viewer

  Discussion 

Litter and soil physicochemical properties

Litter production and decomposition are two important processes for the formation of soil organic matter and the nutrient cycle. Also, the ratio between production and decomposition determines the thickness of the substrate layer on the forest floor ([27]). In this study, litter in CSP and QCP stands have a low degradation rate, which was the result of poor litter quality of CSP and high lignin content of QCP litter ([51]), which creates a thicker organic layer under these species compared to ASP, a nitrogen-fixing species with high litter nitrogen content ([30]). However, a faster decomposition under VNF and AVP forests in this study is probably due to a more attractive source for decomposers due to the lower C/N ratio, which resulted in thinner litter layer ([46]).

Vivanco & Austin ([45]) found that litter decomposition rate is significantly controlled by tree species rather than climate, and not only macronutrients can contribute to litter decomposition, but also micronutrients have significant effect on the process. Along with litter quality, soil texture has also a significant role in litter decomposition process by providing moisture and greater microbial biomass due to higher clay content ([36]). This effect can be seen in VNF, AVP, and ASP as the clay content of the forest floor was higher than other stands. Soil pH and EC are also dependent on the vegetation cover of the soil and the decomposition process. Haghdoost et al. ([16]) found that soil EC depends on the type and properties of vegetation litter. On the other hand, lower pH of CSP can be due to the acidification of coniferous litter ([44]). Our study showed similar results to those of abovementioned studies in case of pH, however, low pH in QCP and DNF can be due to C/N ratio of the leaf litters. Kooijman et al. ([26]) found that low litter quality in beech stands resulted in high pH compared to hornbeam stands. Therefore, we can assume that under highly decomposable leaf litter the pH value is significantly higher, which explains why the litter thickness is higher in the stands with low soil pH in our study ([35]).

We observed that VNF and ASP have significantly higher amounts of available P, K, Ca, and Mg. This might be the result of higher soil moisture and higher biological activity of the soil microorganisms derived by higher pH, which have led to faster decomposition of the litter layer, while keeping pH at suitable levels ([31]).

Nitrogen availability in VNF and ASP can be attributed to the presence of nitrogen fixing tree species like A. subcordata. This species can increase soil nitrogen availability by increasing the activity of nitrogen-fixing microorganisms ([47]). On the other hand, higher C/N ratios, lower pH, and lower nutrient content in coniferous needles compared to broadleaves have previously been reported ([44]). POM-C and POM-N provide a sensitive indicator that reflects the impact of short-term changes in soil surface on soil quality. High levels of litter under CSP and QCP lead to significantly higher amounts of POM-C which can be the result of lower decomposition rate in this stands ([44]). On the other hand, higher amounts of nitrogen in ASP and VNF litter led to higher POM-N which is one of the characteristic of these forests ([23]). Studies have shown that soil properties are affected by the interaction effect of substrate and tree species. In a study on 4 different tree species in Poland, Józefowska et al. ([22]) found that soil C:N ratio depends on tree species as well as soil fauna communities. However, as our stands were close to each other, we found that soil properties are affected directly by tree species.

Soil fauna community

Fine root, earthworm, and other soil organism biomass were significantly higher in VNF and ASP compared to other forest covers. Studies on soil respiration showed that basal respiration (BR) significantly depends on soil mineralization process, which mostly results from chemical and biological reactions of the soil and also root respiration of the forest covers ([48]). Our results showed that both BR and SIR significantly increase from DNF< CSP<QCP<AVP<ASP<VNF, which indicates that low forest cover and also conifer forests have lower soil respiration. These results are in line with the results of Jílková ([21]) who showed that a shift in forest cover from conifers to deciduous forests can significantly increase soil respiration. On the other hand, litter properties have significant effects on soil respiration by providing essential nutrients which are needed by soil microorganisms. In other words, in forests with higher litter quality and litter nutrient content, the rate of soil BR and SIR is significantly higher, as recorded in VNF and ASP in this study ([21]). A sharp decline in soil respiration in both SCP and DNF is the result of low biological activity in both forest covers due to lower soil pH and lower water content ([44], [48]).

Forest degradation and the following decline of the canopy created an unfavorable climate for the activity of soil organisms under the degraded forest. The substrate layer is also essential for soil organisms because it acts as both habitat and food source ([42]), which is scarce under DNF cover.

Increase in vegetation diversity, aboveground biomass, and forest structure tends to increase the abundance of soil organisms ([10]), which is the case of VNF in our study. Indeed, higher litter quality and higher soil organism biomass and activity in VNF compared to other forest cover were observed, which could explain the higher SIR. Soils under natural forest with lower bulk density, higher water and nutrients content, and low C:N ratio, with appropriate soil pH has significantly increased the community of soil organisms ([25]). On the other hand, reforestation of the degraded forests with broadleaved species also increased soil organisms ([9]), which is the case of ASP and AVP in this study.

Earthworms, small arthropods (Acarina and Collembola), nematodes, protozoa, bacteria, and fungi in ASP and AVP were all significantly higher than in DNF and CSP. This can imply a higher nutrient availability of the forest soil and thinner litter layer due to higher biological activity in the soil ([12]). Li et al. ([28]) found that a larger community of soil microorganisms can lead to faster decomposition of litter and thus higher availability of nutrients to vegetation roots. Additionally, in forests with high litter quality and low litter C:N ratio, faunal activity (especially earthworms) is higher, which leads to higher bioturbation and thinner litter layer ([13]).

Among the earthworm groups, only endogeic showed a significant increase in the following order: DNF<CSP<QCP<AVP <ASP<VNF, which may be due to the movement of endogeic groups to different layers of soil where they are able to establish in better conditions ([25]). Acarina and Collembola are microphages and indirectly affect the process of the nutrient cycle by controlling the bacteria and fungi in the soil community ([49]). Small arthropods and protozoa are associated with soil pH in forest ecosystems. The amount of nematodes often shows the highest value in soils with pH 7.0 or higher compared to soils with pH between 5.9 and 6.5 ([32]), which can explain our results.

Soil biological activity

MBN is an indicator of soil quality and is used as a criterion for the availability of nitrogen for plants. MBN in VNF and ASP were significantly higher than that of other forest covers, which also confirms that the tree species has affected the soil microbial community ([10]). Although non-significant, MBN was roughly 20% higher in ASP compared to VNF, which indicates the key role of nitrogen-fixing tree species in providing nitrogen for microbial communities, while VNF could provide significantly more P to microbial communities (and higher MBP). This is contrary to what observed for MBC. CSP showed significantly higher MBC compared to ASP, which is an indicator of difference in litter input quality between the two reforested stands ([23]). The microbial indicators MBC/MBN and MBC/MBP differed significantly across different forest covers. According to Kooch et al. ([23]), such significant changes can be attributed to the input of plant residues with different characteristics of understory (i.e., to the annual vegetation cover on the ground) in the studied sites.

Changes in metabolic rate can indicate that different microbial community structures have occurred according to different tree species ([47]). It also can be affected by soil moisture, soil pH, organic carbon, total nitrogen content and nutritional status of the different forest covers in the study area ([8]). In this regard, ASP with higher soil pH and nitrogen content has the highest metabolic rate. To better understand the state of microbial communities, we also calculated CAI ([15]). This parameter which is the ratio between BR and SIR is significantly lower in DNF compared to other forest stands. Such low value for CAI is the result of significantly lower BR in DNF stand. While SIR in VNF is almost two times higher than that of DNF, BR is 6 times higher. This means that significantly low biological activity in DNF soils resulted in low BR ([44]).

Our results showed significant positive correlation between fauna populations, and soil CAI and respiration. This correlation can demonstrate the key role of soil fauna in litter decomposition, as they help distribute organic materials through different soil layers and help leaching and release of POM ([14]).

  Conclusion 

Our study showed that old grown natural forests are key forests to maintain maximum soil quality and microbial activity, however, some key species like A. subcordata can significantly revive soil quality after a short period of time. In our study, most biological and microbial indicators were significantly higher in close-to-virgin natural forest and A. subcordata plantations (e.g., ecological groups of earthworms, Acarina, Collembola, nematodes, protozoa, bacteria and fungi), along with soil nutrients (N, P, K, Ca, and Mg), which means not only vegetative intactness can contribute to higher soil quality, but also litterfall have a key role in soil activity and fertility. While the activities of soil organisms and soil fertility were at the lowest levels in degraded forests, C. sempervirens plantations, and Q. castaneifolia plantations, they have been significantly increased in A. subcordata plantations only after 3 decades. In addition, soil microbial activities significantly depend on litter N content and C:N ratio along with soil pH, which itself is also a dependent variable to litter quality. A. subcordata, an endemic nitrogen-fixing tree species to these forests, has a good growth rate, and plays an important role in the nutrient cycle and soil-related processes in temperate ecosystems. Therefore, it can be recommended for the restoration of degraded natural forests. Our study clearly shows that the main effect of forest degradation can be directly reflected to soil quality, soil fauna community, and biological activity. On the other hand, species selection for reforestation can significantly contribute to soil restoration, to an extent that planting C. sempervirens, and Q. castaneifolia does not make any difference from a degraded forest.

  List of abbreviations 

Acarina: Acarina density; Ane bio: Anecic biomass; Ane den: Anecic density; ASP: Alnus subcordata; AVP: Acer velutinum; Bacteria: Total bacteria; BD: Bulk density; BR: Basal respiration; CAI: Carbon availability index; Collembola: Collembola density; CSP: Cupressus sempervirens; DNF: Degraded natural forest; Earth bio: Earthworm biomass; Earth den: Earthworm density; Endo bio: Endogeic biomass; Endo den: Endogeic density; Epi bio: Epigeic biomass; Epi den: Epigeic density; FRB: Fine root biomass; Fungi: Total fungi; LC/N: Litter C/N ratio; LCa: Available litter calcium; LK: Available litter potassium; LMg: Available litter magnesium; LN: Litter nitrogen; LOC: Litter carbon; LP: Available litter phosphorus; LThick: Litter thickness; MBC: Microbial biomass of carbon; MBN: Microbial biomass of nitrogen; MBP: Microbial biomass of phosphorus; Nematode: Total nematode; POM-C: Particulate organic matter carbon; POM-N: Particulate organic matter nitrogen; Protozoa: Protozoa density; qCO2: Metabolic quotient; QCP: Quercus castaneifolia; SCa: Available soil calcium; SC/N: Soil C/N ratio; SIR: Substrate induced respiration; SK: Available soil potassium; SMg: Available soil magnesium; SMois: Soil moisture; SN: Soil total nitrogen; SOC: Soil organic carbon; SP: Available soil phosphorus; VNF: Close-to-virgin natural forest.

  Acknowledgments 

M.B. and A.S. carried out the field measurements; Y.K. performed the statistical analysis; V.E. and M.B. performed the laboratory analysis; M.B. conceived the study and helped to draft the manuscript. The authors wish to thank Mr. Jahansouz Foroughi for his helps on field sampling and Mr. Ehsan Khedive for his assistance on writing the manuscript. This work was supported by financial supports of the University of Tehran, Iran.

  References

(1)
Alef K (1995). Estimating of soil respiration. In: “Methods in Soil Microbiology and Biochemistry” (Alef K, Nannipieri P eds). Academic Press, New York, USA, pp. 464-470.
Gscholar
(2)
Allison LE (1965). Organic carbon. In: “Methods of Soil Analysis. Part 2. Chemical and Microbiological Properties” (Page AL, Miller RH, Keeney DR eds). American Society of Agronomy, Soil Science Society of America, Madison, WI, USA, pp. 1367-1378.
CrossRef | Gscholar
(3)
Anderson T-H, Domsch KH (1990). Application of eco-physiological quotients (qCO2 and qD) on microbial biomasses from soils of different cropping histories. Soil Biology and Biochemistry 22: 251-255.
CrossRef | Gscholar
(4)
Bouyoucos GJ (1962). Hydrometer method improved for making particle size analysis of soils. Journal of Agrobiology 56: 464-465.
CrossRef | Gscholar
(5)
Bremner JM, Mulvaney CS (1982). Nitrogen-total. In: “Methods of Soil Analysis. Part 2. Chemical and Microbiological Properties” (Page AL, Miller RH, Keeney DR eds). American Society of Agronomy, Soil Science Society of America, Madison, WI, USA, pp. 595-624.
Gscholar
(6)
Brookes PC, Landman A, Pruden G, Jenkinson DS (1985). Chloroform fumigation and the release of soil nitrogen: a rapid direct extraction method to measure microbial biomass nitrogen in soil. Soil Biology and Biochemistry 17: 837-842.
CrossRef | Gscholar
(7)
Cheng W, Zhang Q, Coleman DC, Carroll CR, Hoffman CA (1996). Is available carbon limiting microbial respiration in the rhizosphere? Soil Biology and Biochemistry 28: 1283-1288.
CrossRef | Gscholar
(8)
Cui Y, Wang X, Zhang X, Ju W, Duan C, Guo X, Wang Y, Fang L (2020). Soil moisture mediates microbial carbon and phosphorus metabolism during vegetation succession in a semiarid region. Soil Biology and Biochemistry 147: 107814.
CrossRef | Gscholar
(9)
De Oliveira Paulucio V, Da Silva CF, Martins MA, Pereira MG, Schiavo JA, Rodrigues LA (2017). Reforestation of a degraded area with Eucalyptus and Sesbania: microbial activity and chemical soil properties. Revista Brasileira de Ciencia do Solo 41: e0160239.
CrossRef | Gscholar
(10)
Diao M, Yang K, Zhu J, Li M, Xu S (2020). Native broad-leaved tree species play key roles on maintaining soil chemical and microbial properties in a temperate secondary forest, Northeast China. Forest Ecology and Management 462: 117971.
CrossRef | Gscholar
(11)
Edwards CA, Arancon NQ, Dumanig F (2021). Biology and ecology of earthworms. Springer Science and Business Media, USA, pp. 422.
Online | Gscholar
(12)
Elie F, Vincenot L, Berthe T, Quibel E, Zeller B, Saint-André L, Normand M, Chauvat M, Aubert M (2018). Soil fauna as bioindicators of organic matter export in temperate forests. Forest Ecology and Management 429: 549-557.
CrossRef | Gscholar
(13)
Frouz J, Livečková M, Albrechtová J, Chronáková A, Cajthaml T, Pizl V, Hánel L, Stary J, Baldrian P, Lhotáková Z, Simáčková H, Cepáková S (2013). Is the effect of trees on soil properties mediated by soil fauna? A case study from post-mining sites. Forest Ecology and Management 309: 87-95.
CrossRef | Gscholar
(14)
Frouz J (2018). Effects of soil macro- and mesofauna on litter decomposition and soil organic matter stabilization. Geoderma 332: 161-172.
CrossRef | Gscholar
(15)
Gorobtsova ON, Gedgafova FV, Uligova TS, Tembotov RK (2016). Ecophysiological indicators of microbial biomass status in chernozem soils of the Central Caucasus (in the territory of Kabardino-Balkaria with the Terek variant of altitudinal zonation). Russian Journal of Ecology 47: 19-25.
CrossRef | Gscholar
(16)
Haghdoost N, Akbarinia M, Hosseini SM, Kooch Y (2011). Conversion of Hyrcanian degraded forests to plantations: effects on soil C and N stocks. Annals of Biological Research 2: 385-399.
Online | Gscholar
(17)
Handayani IP, Coyne MS, Tokosh RS (2010). Soil organic matter fractions and aggregate distribution in response to tall fescue stands. International Journal of Soil Science 5: 1-10.
CrossRef | Gscholar
(18)
Homer CD, Pratt PF (1961). Methods of analysis for soils, plants and waters. Agricultural Sciences Publications, University of California, Berkeley, CA, USA, pp. 309.
Gscholar
(19)
Hutson BR, Veitch LG (1987). Densities of Collembola and Acarina in the soil and litter of three indigenous South Australian forests related to layer, site and seasonal differences. Australian Journal of Ecology 12: 239-261.
CrossRef | Gscholar
(20)
Jiang J-P, Xiong Y-C, Jiang H-M, De-You YE, Ya-Jie S, Feng-Min LI (2009). Soil microbial activity during secondary vegetation succession in semiarid abandoned lands of Loess Plateau. Pedosphere 19: 735-747.
CrossRef | Gscholar
(21)
Jílková V (2020). Soil respiration in temperate forests is increased by a shift from coniferous to deciduous trees but not by an increase in temperature. Applied Soil Ecology 154: 103635.
CrossRef | Gscholar
(22)
Józefowska A, Pietrzykowski M, Cajthaml T, Frouz J (2017). The effects of tree species and substrate on carbon sequestration and chemical and biological properties in reforested post-mining soils. Geoderma 292: 9-16.
CrossRef | Gscholar
(23)
Kooch Y, Tavakoli M, Akbarinia M (2018). Microbial/biochemical indicators showing perceptible deterioration in the topsoil due to deforestation. Ecological Indicators 91: 84-91.
CrossRef | Gscholar
(24)
Kooch Y, Bayranvand M (2019). Labile soil organic matter changes related to forest floor quality of tree species mixtures in Oriental beech forests. Ecological Indicators 107 (6): 105598.
CrossRef | Gscholar
(25)
Kooch Y, Ehsani S, Akbarinia M (2020). Stratification of soil organic matter and biota dynamics in natural and anthropogenic ecosystems. Soil and Tillage Research 200: 104621.
CrossRef | Gscholar
(26)
Kooijman AM, Weiler HA, Cusell C, Anders N, Meng X, Seijmonsbergen AC, Cammeraat LH (2019). Litter quality and microtopography as key drivers to topsoil properties and understorey plant diversity in ancient broadleaved forests on decalcified marl. Science of the Total Environment 684: 113-125.
CrossRef | Gscholar
(27)
León JD, Osorio NW (2014). Role of litter turnover in soil quality in tropical degraded lands of Colombia. The Scientific World Journal 2014: 693981.
CrossRef | Gscholar
(28)
Li X, Dong W, Song Y, Wang W, Zhan W (2019). Effect of soil fauna on home-field advantages of litter mass loss and nutrient release in different temperate broad-leaved forests. Forests 10 (11): 1033.
CrossRef | Gscholar
(29)
Liang W, Lou Y, Li Q, Zhong S, Zhang X, Wang J (2009). Nematode faunal response to long-term application of nitrogen fertilizer and organic manure in Northeast China. Soil Biology and Biochemistry 41: 883-890.
CrossRef | Gscholar
(30)
Llorente M, Glaser B, Turrión MB (2010). Storage of organic carbon and black carbon in density fractions of calcareous soils under different land uses. Geoderma 159: 31-38.
CrossRef | Gscholar
(31)
Margenot AJ, Sommer R, Parikh SJ (2018). Soil phosphatase activities across a liming gradient under long-term managements in Kenya. Soil Biology and Biochemistry 82: 850-861.
CrossRef | Gscholar
(32)
Matute MM (2013). Soil nematodes of Brassica rapa: influence of temperature and pH. Advances in Natural Science 6: 20-26.
Online | Gscholar
(33)
Mayzlish E, Steinberger Y (2004). Effects of chemical inhibitors on soil protozoan dynamics in a desert ecosystem. Biology and Fertility of Soils 39: 415-421.
CrossRef | Gscholar
(34)
McCune B, Mefford MJ (1999). PC-ORD: multivariate analysis of ecological data, version 4. MjM software design, Gleneden Beach, OR, USA.
Gscholar
(35)
Mohr D, Topp W (2005). Hazel improves soil quality of sloping oak stands in a German low mountain range. Annals of Forest Science 62: 23-29.
CrossRef | Gscholar
(36)
Montes-Pulido CR, Ramos-Miras JJ, José-Wery S, María A (2017). Estimation of soil organic carbon (SOC) at different soil depths and soil use in the Sumapaz paramo, Cundinamarca-Colombia. Acta Agronómica 66: 95-101.
CrossRef | Gscholar
(37)
Nave LE, Walters BF, Hofmeister KL, Perry CH, Mishra U, Domke GM, Swanston CW (2019). The role of reforestation in carbon sequestration. New Forests 50: 115-137.
CrossRef | Gscholar
(38)
Plaster EJ (2013). Soil science and management. Cengage Learning, Albany, NY, USA, pp. 544.
Gscholar
(39)
Sanji R, Kooch Y, Rey A (2020). Impact of forest degradation and reforestation with Alnus and Quercus species on soil quality and function in northern Iran. Ecological Indicators 112 (7): 106 132.
CrossRef | Gscholar
(40)
Shao P, Liang C, Lynch L, Xie H, Bao X (2019). Reforestation accelerates soil organic carbon accumulation: evidence from microbial biomarkers. Soil Biology and Biochemistry 131: 182-190.
CrossRef | Gscholar
(41)
Soil Survey Staff (2002). Soil taxonomic maps of northern Iran. Soil and Water Research Institute, Alborz, Iran, pp. 234.
Gscholar
(42)
Tajik S, Ayoubi S, Lorenz N (2020). Soil microbial communities affected by vegetation, topography and soil properties in a forest ecosystem. Applied Soil Ecology 149 (306): 103514.
CrossRef | Gscholar
(43)
Tavakoli M, Kooch Y, Akbarinia M (2018). The effect of degraded and reclaimed forest areas on carbon dioxide gas emissions and soil carbon mineralization in West of Mazandaran. In: Proceedings of the “International Symposium of Climate Change and Dendrochronology in Caspian Ecosystems”. Sari (Iran), 16-17 May 2008, pp. 12.
Gscholar
(44)
Vázquez E, Benito M, Espejo R, Teutscherova N (2020). Response of soil properties and microbial indicators to land use change in an acid soil under Mediterranean conditions. Catena 189: 104486.
CrossRef | Gscholar
(45)
Vivanco L, Austin AT (2019). The importance of macro- and micro-nutrients over climate for leaf litter decomposition and nutrient release in Patagonian temperate forests. Forest Ecology and Management 441: 144-154.
CrossRef | Gscholar
(46)
Walmsley A, Vachová P, Hlava J (2019). Tree species identity governs the soil macrofauna community composition and soil development at reclaimed post-mining sites on calcium-rich clays. European Journal of Forest Research 138: 753-761.
CrossRef | Gscholar
(47)
Wang H, Liu S, Song Z, Yang Y, Wang J, You Y, Zhang X, Shi Z, Nong Y, Ming A, Lu L, Cai D (2019). Introducing nitrogen-fixing tree species and mixing with Pinus massoniana alters and evenly distributes various chemical compositions of soil organic carbon in a planted forest in southern China. Forest Ecology and Management 449: 117477.
CrossRef | Gscholar
(48)
Wang Y, Liu S, Wang J, Chang SX, Luan J, Liu Y, Lu H, Liu X (2020). Microbe-mediated attenuation of soil respiration in response to soil warming in a temperate oak forest. Science of The Total Environment 711 (7): 134563.
CrossRef | Gscholar
(49)
Wardle DA, Lavelle P (1997). Linkages between soil biota, plant litter quality and decomposition. In: “Driven by Nature: Plant Litter Quality and Decomposition” (Gadish G, Giller KE eds). CAB International, Wallingford, UK, pp. 107-124.
Gscholar
(50)
Wollum AG (1983). Cultural methods for soil microorganisms. In: “Methods of Soil Analysis. Part 2. Chemical and Microbiological Properties” (Page AL, Miller RH, Keeney DR eds). American Society of Agronomy, Soil Science Society of America, Madison, WI, USA, pp. 781-802.
CrossRef | Gscholar
(51)
Zarafshar M, Bazot S, Matinizadeh M, Bordbar SK, Rousta MJ, Kooch Y, Enayati K, Abbasi A, Negahdarsaber M (2020). Do tree plantations or cultivated fields have the same ability to maintain soil quality as natural forests? Applied Soil Ecology 151 (12): 103536.
CrossRef | Gscholar

Authors’ Affiliation

(1)
Maryam Bazyari
Department of Forestry and Forest Economy, Faculty of Natural Resources, University of Tehran, Tehran (Iran)
(2)
Vahid Etemad 0000-0001-6560-7653
Anoushirvan Shirvany 0000-0001-9953-3779
Faculty of Natural Resources, University of Tehran, Tehran (Iran)
(3)
Yahya Kooch 0000-0003-3965-9665
Faculty of Natural Resources & Marine Sciences, Tarbiat Modares University, 46417-76489, Noor, Mazandaran (Iran)

Corresponding author

 
Vahid Etemad
vetemad@ut.ac.ir

Citation

Bazyari M, Etemad V, Kooch Y, Shirvany A (2021). Soil fauna communities and microbial activities response to litter and soil properties under degraded and restored forests of Hyrcania. iForest 14: 490-498. - doi: 10.3832/ifor3583-014

Academic Editor

Maurizio Ventura

Paper history

Received: Jul 12, 2020
Accepted: Sep 06, 2021

First online: Nov 11, 2021
Publication Date: Dec 31, 2021
Publication Time: 2.20 months

© SISEF - The Italian Society of Silviculture and Forest Ecology 2021

  Open Access

This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Creative Commons Licence

Breakdown by View Type

(Waiting for server response...)

Article Usage

Total Article Views: 495
(from publication date up to now)

Breakdown by View Type
HTML Page Views: 0
Abstract Page Views: 0
PDF Downloads: 458
Citation/Reference Downloads: 0
XML Downloads: 37

Web Metrics
Days since publication: 76
Overall contacts: 495
Avg. contacts per week: 45.59

Article citations are based on data periodically collected from the Clarivate Web of Science web site
(last update: Nov 2020)

(No citations were found up to date. Please come back later)


 

Publication Metrics

by Dimensions ©

List of the papers citing this article based on CrossRef Cited-by.

 

iForest Similar Articles

 

This website uses cookies to ensure you get the best experience on our website. More info