Differences in spatiotemporal dynamics between soil macrofauna and mesofauna communities in forest ecosystems: The significance for soil fauna diversity monitoring

Geoderma 337 (2019) 266–272

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Differences in spatiotemporal dynamics between soil macrofauna and mesofauna communities in forest ecosystems: The significance for soil fauna diversity monitoring

T

⁎

Pengfei Wu , Changting Wang Institute of Qinghai-Tibetan Plateau, Southwest Minzu University, Chengdu 610041, China

A R T I C LE I N FO

A B S T R A C T

Handling Editor: Yvan Capowiez

Soil fauna including macrofauna and mesofauna play important roles in the material cycle and energy flow of ecosystems. The spatiotemporal dynamics of the soil macrofauna and mesofauna have been studied in different ecosystems. However, the differences in spatiotemporal variability between soil macrofauna and mesofauna communities in the same ecosystem are still unknown. Soil macrofauna and mesofauna were investigated in April, August and November of 2008 in five forests and a subalpine meadow at elevations from 2659 to 3845 m. Results showed that the community composition of the soil macrofauna was more sensitive to habitat variation than that of soil mesofauna across the seasonal changes, but the community composition of soil mesofauna was more sensitive to seasonal changes than that of soil macrofauna across all the six habitats. Abundance, richness and Shannon index varied significantly between the six habitats for soil macrofauna but had no obvious spatial pattern for soil mesofauna. Moreover, the differences in abundances and diversity index between sampling months were not significant for soil macrofauna, but were significant for soil mesofauna. The spatial distributions of soil macrofauna were more easily affected by the changes in plant community and soil properties than those of the soil mesofauna, while the temporal dynamics of the soil mesofauna were more sensitive to the changes in climatic factors across sampling months than those of the soil macrofauna. These findings indicate that the soil macrofauna and mesofauna communities respond differently to spatiotemporal changes in environmental factors. The effects of plant communities are greater on soil macrofauna than on mesofauna and the effects of season are greater on soil mesofauna. These results also imply that differences from habitats and seasons should be respectively focused on soil macrofauna and mesofauna when monitoring soil fauna diversity in forest ecosystems.

Keywords: Soil macrofauna Soil mesofauna Spatial variation Temporal variation Forest ecosystems

1. Introduction Spatiotemporal variability is important for understanding the structure and dynamics of populations, communities and ecosystems, and exerts important influences on the ecosystem structure, material cycling, energy flow and other processes (Jones et al., 1996). It can be caused by spatial heterogeneity and seasonal or annual changes (McCann et al., 2005; Stewart et al., 2000).The analysis of spatiotemporal dynamics is particularly important in the soil habitat due to the diverse soil biota resulted from the fine-grained spatial and temporal differences. The analysis is also difficult because the soil structure itself is opaque. The soil fauna, as an important component of the soil biota, is extremely rich in most ecosystems and comprises a high proportion of the total biodiversity (Anderson, 1975). Given that soil fauna drives many key ecosystem processes and functions (Lavelle and Spain, ⁎

2001; Wu et al., 2015), it is important to identify the spatiotemporal dynamics in the soil fauna when analyzing the key functions and processes of ecosystems. The soil fauna includes macrofauna, mesofauna and microfauna. Macrofauna is distinguished by having a body size larger than 2 mm, whereas mesofauna has a body size of 100 μm to 2 mm. The spatial distributions of the soil macrofauna and mesofauna communities are affected by abiotic and biotic factors both above- and below-ground, including plant community (Wissuwa et al., 2012), soil properties (Ammer et al., 2006) and other factors (Huerta and van der Wal, 2012). The community composition of the soil macrofauna and mesofauna also displays temporal variability (Zhao et al., 2014) with climatic factors (Kardol et al., 2011). However, there is no comparable literature data on the spatiotemporal dynamics of macrofauna and mesofauna. It is unclear whether soil macrofauna and mesofauna inhabiting the same

Corresponding author. E-mail address: [email protected] (P. Wu).

https://doi.org/10.1016/j.geoderma.2018.09.031 Received 25 June 2018; Received in revised form 8 September 2018; Accepted 16 September 2018 0016-7061/ © 2018 Elsevier B.V. All rights reserved.

Geoderma 337 (2019) 266–272

P. Wu, C. Wang

and November of 2008. Thus, a total of 108 subplots (6 habitats × 2 plots × 3 subplots × 3 sampling months) were sampled. Soil macrofauna were collected from the soil layers (0–15 cm depth) of each subplot. The soil was sampled with a flat shovel. The macrofauna were hand-sorted and preserved in a 75% alcohol solution. Soil cores (height: 52 mm, radius: 35 mm) were then collected from the 0–5, 5–10 and 10–15 cm soil layers in each subplot. Thus, a total of 324 soil samples were collected (6 habitats × 2 plots × 3 subplots × 3 layers × 3 sampling months). In the laboratory, soil mesofauna were extracted from the soil samples using a Tullgren funnel extractor for 48 h at 38 °C and were then preserved in a 75% alcohol solution. The macrofauna were counted and identified to the family or genus level under a microscope. The mesofauna were counted under a microscope and grouped by genus or family level under a binocular microscope with a magnification of 400×. The reference used for the identification of soil fauna was the Pictorial Keys to Soil Animals of China (Yin, 2000). The macrofauna and mesofauna data were converted into the number of individuals per square meter (ind. m−2) for each subplot. The measurements of environmental characteristics are presented in the references (Wu et al., 2012; Wu et al., 2014).

ecosystem respond differently or similarly to spatiotemporal dynamics in environmental factors. This is of great importance when monitoring soil fauna diversity in the field, especially monitoring soil macrofauna and mesofauna simultaneously. Previously, the spatiotemporal dynamics of soil macrofauna and mesofauna in the Miyaluo forests, which are located in the northern Hengduan Mountains as a global biodiversity hotspot, has been separately analyzed (Wu et al., 2012; Wu et al., 2014). In this paper, we used some data from the two studies and compared the spatiotemporal dynamics in the structure, abundance and diversity of the soil macrofauna and mesofauna communities because those data of macrofauna and mesofauna was collected from the same ecosystems simultaneously. Overall, the mesofauna has a shorter living cycle, and can presumably respond faster to environmental changes than macrofauna. Therefore, we hypothesized that 1) the spatial distribution of mesofauna communities are more sensitive to vegetation and soil variation than those of macrofauna during the same period; and 2) mesofauna communities display greater temporal sensitivities to seasonal changes than macrofauna in the same ecosystem. 2. Materials and methods

2.3. Statistical analysis 2.1. Study site Principal components analysis (PCA) (using Canoco for Windows 4.5) was used to evaluate the effects of habitat (SSF, BSF, PAP, ABF, ALF and SM) and sampling month (April, August and November) on the taxonomic composition of the soil macrofauna and mesofauna (Braak and Smilauer, 2002). Abundance data (ind. m−2) for each taxon in each subplot were log(x + 1) transformed before they were subjected to PCA. The factor scores of the first two axes of the PCA were further analyzed with one-way ANOVA to evaluate the spatial and temporal dynamics of the macrofauna and mesofauna community structure (using IBM SPSS Statistics 22). The abundance (ind. m−2), taxonomic richness (mean number of taxa per habitat) and Shannon index at the community level for each habitat and sampling month were calculated to estimate the spatiotemporal dynamics of the macrofauna and mesofauna. Repeated-measures ANOVA was performed to evaluate the effects of habitat (SSF, BSF, PAP, ABF, ALF and SM) and sampling month (April, August and November) and their interactions on the soil macrofauna and mesofauna communities (using IBM SPSS Statistics 22). The abundance, richness, and Shannon index of the soil macrofauna or mesofauna communities monitored in April, August and November were averaged for each of the six subplots within each habitat. The factor scores of PC1 and PC2 for April, August and November were also averaged. The averaged values were then used in a multiple stepwise regression to test the relationships in the spatial distributions among the soil fauna and the environmental variables related to the altitude, plant community and soil properties of the sampled subplots (using IBM SPSS Statistics 22). Because temperature and moisture are important climatic variables influencing the soil fauna (Irmler, 2006; Wiwatwitaya and Takeda, 2005), a multiple regression analysis was also used to test the relationships in temporal dynamics between the soil fauna and the climatic factors of the mean monthly temperature and precipitation from 2003 to 2008, which were obtained from the Miyaluo meteorological station.

This study was carried out in the Miyaluo forest (31.24°-31.55° N, 102.35°-103.4° E), which is located in the northern Hengduan Mountains. The area is considered to be a global biodiversity hotspot (Myers et al., 2000; Sechrest et al., 2002). Detailed information regarding the study site is presented in the references (Wu et al., 2012; Wu et al., 2014). 2.2. Experimental design Six habitats, including secondary shrub forest (SSF), Betula albosinensis forest (BSF), Picea asperata plantation (PAP), Abies fabri and B. albosinensis mixed forest (ABF), A. fabri and Larix kaempferi (Lamb.) Carrière 1856 mixed forest (ALF) and subalpine meadow (SM), which are present in the same valley, were selected along an altitudinal gradient in the Miyaluo forest. The main plant species of each habitat type are listed by ascending elevation and are described below. Secondary shrub forest (SSF): The dominant species in this community are Kalopanax septemlobus (Thunb.) Koidz. 1925, Juglans regia L. 1753, Acer davidii Frarich. 1886, Acer oliverianum Pax 1897, Tilia intonsa Wils. 1916 and Tilia tuan Szyszyl. 1890. The understory is composed of B. henryana, R. sweginzowii and other shrubs. B. albosinensis forest (BSF): This community is dominated by B. albosinensis. A. faxoniana and Acer spp. are also prominent in the arboreal layer, with Fargesia denudata Yi. 1985 and B. henryana occurring in the shrub layer. P. asperata plantation (PAP): P. asperata is the only species in the arboreal layer of this community. There are few shrubs and no herbaceous layer in the understory. A. fabri and B. albosinensis mixed forest (ABF): This community is dominated by A. fabri and B. albosinensis. The understory includes B. henryana, R. sweginzowii, Lonicera spp. and other shrubs. A. fabri and Larix kaempferi (Lamb.) Carrière 1856 mixed forest (ALF): This community is dominated by A. fabri and L. kaempferi. The understory is mainly composed of R. sweginzowii and B. henryana. Subalpine meadow (SM): This community is dominated by P. oederi, Taraxacum lugubre Dahlst. 1926, R. cordifolia, P. tanakae, A. tomentosa, C. tangutica, Polygonum viviparum L. 1753 and other herbaceous species. Two permanently marked plots (50 m × 50 m), spaced approximately 500 m apart, were established in each of the six habitat types. Within each plot, three subplots (50 cm × 50 cm) were randomly placed at 10 m intervals during each sampling month in April, August

3. Results 3.1. Spatiotemporal variation in community structure The PCA results showed that the soil macrofauna community composition differed between the six habitats in April, and especially in August and November, while the composition of soil mesofauna communities of the six habitats overlapped in all three months (Fig. 1). The results of the one-way ANOVA (Table 1) showed that the factor scores 267

Geoderma 337 (2019) 266–272

April

1.5

1.4

August

SM

PC 2 (23.6%)

PC 2 (22.4%)

BSF

ALF ABF PAP

SM

PAP ABF

SSF

BSF ABF PAP ALF

ALF

SSF

SM

November

PC 2 (19.0%)

1.3

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SSF

PC 1 (27.4%)

April

-1.2 -1.2

1.3

1.4

PC 1 (29.4%)

PC 1 (31.0%)

1 .5

November

SSF

BSF

SM

SM

PC 2 (26.8%)

PC 2 (21.9%)

-1.2

SSF

ABF

SSF

1.2

August

ALF PAP

BSF

PC 2 (15.5%)

1.3

-1.0

1.5

-1.0

-1.0

BSF

ALF ABF

PAP

PAP ALF

SM

BSF

-1.1

BSF

PAP

PC 1 (24.9%)

1.5

ABF

ALF

PC 1 (44.3%)

-1.0

SM

1 .9

-1.3

SSF

-1 . 5

-1.3

ABF

-1.0

PC 1 (30.7%)

1.8

Fig. 1. Principal component analysis for soil macrofauna (above) and soil mesofauna (below) in April, August and November, with the studied habitats as an overlay.

of the first two axes for the soil macrofauna differed significantly among habitats in the three sampling months (P < 0.001), but significant differences among habitats were only recorded in the first-axis factor scores for mesofauna in August and November (P < 0.01 and 0.05). Considering the temporal dynamics of community structure, the soil macrofauna communities of the SSF and BSF habitats in April, August and November were clearly separated from one another on the PC1 and PC2 axes, but this was not the case for the PAP, ABF, ALF and SM habitats (Fig. 2). Significant differences among the sampling months were found in the first two axes for BSF (P < 0.01 and 0.001), but only in the first axis for SSF, PAP, ALF and SM (P < 0.05 and 0.001) (Table 2). The soil mesofauna communities in April, August and November were clearly separated from one another for the SSF, BSF, PAP, ABF and ALF habitats but not for SM (Fig. 3). The factor scores significantly differed among sampling months for the first two axes of BSF, PAP, ABF and ALF (P < 0.05, 0.01 and 0.001) and only for the first axis of SSF and SM (P < 0.001) (Table 2).

diversity differed between the soil macrofauna and mesofauna. The abundance, richness and Shannon index values for the soil macrofauna in PAP and ABF were relatively lower than those in other habitats (P < 0.001), with abundance also affected by the interactive effects between habitat and sampling month (P < 0.01) (Fig. 4, Table 3). Abundance and diversity did not significantly differ among sampling months for the soil macrofauna as a whole, although significant differences in abundances were observed for SSF, BSF and PAP (Table 3, Fig. 4). The abundance and diversity of the soil mesofauna showed no obvious spatial tendencies, although diversity varied significantly among habitats (P < 0.01 or 0.05) but was significantly lower in August than in April and November (P < 0.001, 0.01 or 0.05) (Fig. 4, Table 3). 3.3. Relationships between the spatiotemporal dynamics of the soil fauna and environmental factors The results of the multiple regression analysis showed that the factor score for the first axis for the soil macrofauna was positively correlated with litter mass and SOC (P < 0.001 and 0.05) and that the factor score for the second axis correlated with plant community coverage and SOC (P < 0.001 and 0.01) (Table 4). The abundance,

3.2. Spatiotemporal variation in community abundance and diversity The patterns in the spatiotemporal dynamics of abundance and

Table 1 The results of the One-way ANOVA for the effects of habitat type on the factor scores for the first two axes in the three sampling months. The statistically significant (P < 0.05) results are in boldface. Soil fauna

Macrofauna Mesofauna

April

F P F P

August

November

PC1

PC2

PC1

PC2

PC1

PC2

8.88 < 0.001 77.00 0.576

37.80 < 0.001 1.12 0.369

30.94 < 0.001 18.66 0.002

19.64 < 0.001 1.02 0.426

20.37 < 0.001 2.87 0.031

21.60 < 0.001 0.80 0.560

268

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SSF

1.5

1.0

BSF

November

PC 2 (17.5%)

PC 2 (18.4%)

April

August

November

November

PC 1 (49.0%)

1.2

-1.2

1.5

-1.0

1.0

1.2

PC 1 (36.0%)

ABF

April

-1.2

-1.0

-0.9 -0.8

August

August

April

1.5

PAP

PC 2 (14.3%)

0.9

P. Wu, C. Wang

ALF

PC 1 (47.4%)

1.5

SM

-1.0

April

-1.2

August

PC 1 (39.6%)

1.3

PC 2 (20.1%)

August April

August

November

November

-1.0

November

August

-1.1

PC 2 (16.0%)

November

-1.2

PC 2 (18.0%)

April April

PC 1 (38.8%)

1.4

-1.3

PC 1 (34.8%)

1.3

Fig. 2. Principal component analyses of the temporal dynamics in the community structure of the soil macrofauna in SSF, BSF, PAP, ABF, ALF and SM.

habitats and the Shannon index for most habitats were negatively correlated with air temperature, with the richness of some habitats negatively correlating with precipitation (P < 0.001, 0.01 and 0.05) (Table 5).

richness and Shannon index values for the soil macrofauna were positively correlated only with plant species richness (P < 0.001 and 0.01) (Table 4). Regarding the soil mesofauna, the first axis was only correlated with litter mass, and the relationship was negative (P < 0.01); the taxonomic richness was negatively correlated with soil bulk density (P < 0.01), and the Shannon index was correlated with plant community coverage (P < 0.01) (Table 4). The elevation and soil TN and TP had no significant effects on the soil macrofauna and mesofauna. The results of the multiple regression analysis of the relationships between the soil fauna and climatic factors showed that the factor scores of PC1 or PC2 for the soil macrofauna and mesofauna in each habitat were significantly positively or negatively correlated with air temperature (P < 0.001, 0.01 and 0.05), but the PC1 score of soil mesofauna for BSF and ABF were also correlated with precipitation (P < 0.05) (Table 5). The soil macrofauna abundances for SSF and BSF were positively correlated with air temperature (P < 0.001 and 0.05), and the richness and Shannon index values were positively correlated with precipitation only for PAP (P < 0.01 and 0.05) (Table 5). However, the abundance and richness of the soil mesofauna in all six

4. Discussion 4.1. Differences in spatial distributions between soil macrofauna and mesofauna During all the sampling months, soil macrofauna displayed more remarkable spatial differences than mesofauna. These differences may be attributed to the relationships between soil fauna and environmental factors. The multiple regression showed that the community structure of macrofauna was affected by changes in plant coverage, litter mass and SOC, which has great spatial heterogeneity, whereas that of mesofauna was only affected by litter mass (Table 4). This implied that the macrofauna community composition was more sensitive to spatial changes in environmental factors than mesofauna.

Table 2 The results of the One-way ANOVA of the effects of the month of sampling on the factor scores for the first two axes across six habitats. The statistically significant (P < 0.05) results are in boldface. Soil fauna

Macrofauna Mesofauna

SSF

F P F P

BSF

PAP

ABF

ALF

SM

PC1

PC2

PC1

PC2

PC1

PC2

PC1

PC2

PC1

PC2

PC1

PC2

48.76 < 0.001 10.32 0.002

3.39 0.061 3.10 0. 074

79.96 < 0.001 44.80 < 0.001

10.69 0.001 18.35 < 0.001

4.87 0.025 48.85 0.001

2.18 0.147 7.69 0.005

3.65 0.051 42.34 < 0.001

2.94 0.083 19.36 < 0.001

19.63 < 0.001 58.63 < 0.001

0. 45 0. 646 18.53 < 0.001

5.64 0.015 22.38 < 0.001

2.06 0.162 4.07 0.039

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SSF

1.1

1.3

1.2

P. Wu, C. Wang

BSF

PAP

April

November

PC 2 (27.2%)

PC 2 (24.0%)

PC 2 (31.3%)

August

November

August

August

November

1.4

PC 1 (48.9%)

1.2

-1.2

ALF

PC 1 (48.1%)

-1.0

1.2

SM

April

PC 2 (20.5%)

August

November

November

PC 2 (26.0%)

April

PC 2 (24.8%)

1.4

1.5

PC 1 (41.7%)

ABF

-1.1

-1.3

-1.2

April

-1.0

1.5

April

August

November

April

August

PC 1 (47.6%)

1.5

November

-1.2

April

-1.5

-1.1

-1.0

August

PC 1 (64.7%)

-1.1

1.2

PC 1 (43.8%)

-1.2

1.5

Fig. 3. Principal component analyses of the temporal dynamics in the community structure of the soil mesofauna in SSF, BSF, PAP, ABF, ALF and SM.

mainly feed on litter and are affected directly by the amount and quantity of litter (Loranger-Merciris et al., 2007; Scherber et al., 2010), which vary with plant species (Loranger-Merciris et al., 2007; Maharning et al., 2009). Thus, the macrofauna can be dependent on vegetation type. Overall, the spatial distribution of soil macrofauna is clearer in their

The abundance and diversity (including species richness and the Shannon index) of the macrofaunal community also varied significantly among habitats and were mainly affected by plant species richness, coverage and litter mass. This could be firstly explained by the result that diverse plant species provide macrofauna with favorable microhabitats (Nachtergale et al., 2002). Additionally, the soil macrofauna

August

AB A BC

C

c

a

a b

1.0

b

0.5

4.0

40

3.5

35

3.0 2.5 2.0 1.0

a a b

c b

0.5

a b

a

c b

a

a

c

c b

A

SSF

BSF

PAP ABF Habitats

ALF

SM

A

ab b

1.0

a a

b

c

a

a ab a b

b

c

a

c

b b

5

A A

c

a

b

2.0

A

AB

a

2.5

c b

B

a ab

b a

b

ab

1.5

A

a

a

a a b

b

1.0 0.5 0.0

0

0.0

A

a

B

1.5

3.0

AB

C

BC

10

b

A

AB

25 15

November A

0.0 3.5

30 20

August

0.5

2

0 50

a

C

4

45

April A A

B

6

4.5

a

2.0

8

0.0 5.0

1.5

AB

A

Shannon index

2.0

a

November A

10

Richness/sample

ab

a

August

12

A

2.5

2.5 April

A

November

Shannon index

April b

3.0

1.5

14

A

3.5

Richness/sample

Abundance˄h104 ind.m-2˅

Abundance˄×102 ind.m-2˅

4.0

SSF

BSF

PAP ABF Habitats

ALF

SM

SSF

BSF

PAP ABF Habitats

ALF

SM

Fig. 4. Spatiotemporal variations in abundance, richness and Shannon diversity for the soil macrofauna (above) and mesofauna (below) (mean ± SE). Capital letters indicate spatial differences between habitats at the P < 0.05 level, while lower-case letters indicate temporal differences within habitats at the P < 0.05 level.

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Table 3 Repeated-measures ANOVA results for the effects of habitat, sampling month and their interaction on the abundance and diversity of soil macrofauna and mesofauna. Statistically significant (P < 0.05) results are in boldface (n = 36). Source

df

Habitat Sampling month Habitat × sampling month

Soil macrofauna

5, 30 2, 60 10, 60

Soil mesofauna

Abundance

Richness

F

P

F

5.77 0.61 2.74

0.001 0.547 0.008

9.50 2.83 1.50

Shannon index

Abundance

Richness

P

F

P

F

P

F

P

F

P

< 0.001 0.067 0.163

6.00 2.21 1.39

0.001 0.119 0.209

2.28 90.21 2.50

0.072 < 0.001 0.014

4.27 133.45 2.69

0.005 < 0.001 0.009

3.11 18.43 3.58

0.022 < 0.001 < 0.001

community composition, abundance and diversity than mesofauna. Vegetation also exerted stronger effects on the soil macrofauna than on the mesofauna communities. These findings contradict our first hypothesis, and suggest more attention should be focused on the soil macrofauna rather than the mesofauna communities in different habitats when monitoring soil fauna diversity.

Table 4 Partial correlation coefficients from the multiple regression analysis between soil fauna and the spatially-based environmental factors related to plants and soil (n = 36). Soil fauna

Plant community Species richness

Soil macrofauna PC1 PC2 Abundance 0.45⁎⁎ Richness 0.58⁎⁎⁎ Shannon index 0.48⁎⁎ Soil mesofauna PC1 PC2 Abundance Richness Shannon index

Soil properties Coverage

Litter mass

Bulk density

0.54⁎⁎⁎

SOC

4.2. Differences in temporal variability between soil macrofauna and mesofauna

0.33⁎ 0.39⁎⁎

⁎⁎⁎

0.56

Temporal variation in soil macrofauna (Rossi and Blanchart, 2005) and mesofauna (Berg et al., 1998; Wiwatwitaya and Takeda, 2005) communities has been reported in many ecosystems. In the present study, the community structure of the soil macrofauna and mesofauna in all six habitats differed markedly among sampling months (Figs. 2 and 3) and was affected by temperature, with the soil mesofauna in BSF and ABF also influenced by precipitation (Table 5). However, the mesofauna community structure exhibited more pronounced temporal patterns than that of macrofauna. Moreover, the abundance and diversity of macrofauna did not significantly differ among the sampling months but those of mesofauna showed significant temporal

−0.43⁎⁎

−0.48⁎⁎

−0.49⁎⁎

⁎⁎⁎ ⁎⁎ ⁎

Shannon index

Indicates significance at the 0.001 level. Indicates significance at the 0.01 level. Indicates significance at the 0.05 level.

Table 5 Partial correlation coefficients from the multiple regression analysis between soil fauna and the temporal environmental factors of climate (n = 18). The data in parentheses indicate the coefficients between PC2 and climatic factors. The air temperature and precipitation data are from the Miyaluo meteorological station. Habitats

Soil macrofauna

Soil mesofauna

Air temperature PC1 (PC2)

Abundance

Richness

Shannon index

SSF BSF PAP ABF ALF SM SSF BSF PAP ABF ALF SM SSF BSF PAP ABF ALF SM SSF BSF PAP ABF ALF SM

Precipitation

⁎⁎⁎

⁎

Precipitation

⁎

0.91 −0.88⁎⁎⁎ −0.50⁎ 0.50⁎ 0.80⁎⁎⁎ 0.68⁎⁎ 0.52⁎ 0.76⁎⁎⁎

0.52⁎

0.60⁎⁎

−0.52 −0.85⁎⁎⁎ (0.72⁎⁎⁎) −0.85⁎⁎⁎ 0.93⁎⁎⁎ −0.86⁎⁎⁎ −0.48⁎ −0.74⁎⁎⁎ −0.62⁎⁎ −0.76⁎⁎⁎ −0.73⁎⁎⁎ −0.80⁎⁎⁎ −0.72⁎⁎⁎ −0.85⁎⁎⁎ −0.71⁎⁎⁎ −0.76⁎⁎⁎ −0.82⁎⁎⁎ −0.77⁎⁎⁎ −0.85⁎⁎⁎ −0.69⁎⁎⁎ −0.77⁎⁎⁎ −0.58⁎

⁎⁎⁎ ⁎⁎

Air temperature

Indicates significance at the 0.001 level. Indicates significance at the 0.01 level. Indicates significance at the 0.05 level.

271

−0.49⁎ −0.51⁎

−0.56⁎ −0.51⁎ −0.57⁎

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differences. These differences indicated that the soil mesofauna communities were more easily affected by seasonal changes than macrofauna. The reason might be that soil mesofauna taxa have smaller body sizes and shorter living cycles and therefore respond more quickly to seasonal changes than macrofauna (Glime, 2013; Pianka, 1972). The temporal patterns of the macrofauna abundance and diversity varied among habitats. At the same time, the abundance and diversity of mesofauna was the least in August in each habitat. The relationships between soil fauna and climatic factors indicated stronger effects of variations of temperature and precipitation on the abundance and diversity of the soil mesofauna than those of macrofauna (Table 5). Previous study has also found that temperature and precipitation are the critical factors determining soil mesofauna abundance (Irmler, 2006). Considering the effects of temperature and precipitation on community structure, abundance and diversity of soil fauna, we found that climatic factors have a stronger effect on the soil mesofauna than on macrofauna communities. As a result, our second hypothesis was supported. The results suggest more attention should be focused on the soil mesofauna rather than the macrofauna communities in different seasons when monitoring soil fauna diversity.

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5. Conclusions The community composition, abundance and diversity of soil macrofaunal communities were more sensitive to habitat variation than soil mesofauna. Nevertheless, those of soil mesofauna were more sensitive to seasonal changes, as compared to soil macrofauna. Plant community and soil properties were the main factors affecting the spatial distribution of soil macrofauna communities, and the seasonal changes of mesofaunal communities were affected by climatic factors. These results indicate differential responses of soil macrofauna and mesofauna within the same ecosystem to spatiotemporal changes at community level, as different results of environmental effects. This study implied that differences in habitats and seasons should be the emphasized when monitoring forest soil macrofauna and mesofauna, respectively. Acknowledgments We thank all of the people who helped during the fieldwork portion of this study, especially Daxing Yang, Xiaofei Yu, Jingen Xu and Peng Yin. Special thanks to Yujing Yang for the language improvement on earlier drafts. This work was supported by the National Natural Science Foundation of China (41371270), Key Projects of Applied Basic Research Program in Sichuan (2018JY0556) and Innovative Team of Sichuan Provincial Educational Office of China (14TD0049). References Ammer, S., Weber, K., Abs, C., Ammer, C., Prietzel, J., 2006. Factors influencing the distribution and abundance of earthworm communities in pure and converted Scots pine stands. Appl. Soil Ecol. 33 (1), 10–21. Anderson, J., 1975. The enigma of soil animal species diversity. In: Progress in Soil

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