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Article

Response of Soil Fauna Diversity to Agricultural Landscape Het-Erogeneity in the Middle and Lower Reaches of the Yellow River—A Case Study in Gongyi City, China

by
Panpan Zhang
1,2,
Chenchen Zhang
1,2,
Shunping Ding
3,
Ziqi Bian
1,2,
Peikun Li
1,2,
Jian Zhang
1,2 and
Shengyan Ding
1,2,*
1
Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Ministry of Education, Kaifeng 475001, China
2
College of Geography and Environmental Science, Henan University, Kaifeng 475001, China
3
Horticulture and Crop Science Department, California Polytechnic State University, San Luis Obispo, CA 93407, USA
*
Author to whom correspondence should be addressed.
Diversity 2022, 14(8), 602; https://doi.org/10.3390/d14080602
Submission received: 19 June 2022 / Revised: 25 July 2022 / Accepted: 27 July 2022 / Published: 28 July 2022
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Abstract

:
Soil fauna contribute to important ecological functions such as improving soil structure and promoting nutrient circulation. They are the common environmental indicators in agricultural landscape. Therefore, this study took Gongyi City, Henan Province, China, located in the middle and lower reaches of the Yellow River, as the study area, to explore the impact of multi-scale landscape heterogeneity on soil fauna diversity and the response of soil fauna to it. Our results showed that patch types and degree of fragmentation in the study area increased significantly with the increase of spatial scale. The diversity indices of soil fauna in different habitats showed that the Shannon-Wiener diversity index, Simpson’s diversity index, Pielou’s evenness index, and Margalef richness index were the lowest in farmland habitat. Diversity indices of artificial forests were higher than those of natural forests. Diversity indices of soil fauna under different geomorphic conditions showed that Pielou’s evenness index and Margalef richness index had significant differences under different geomorphic conditions (p < 0.05). The effects of multi-scale landscape heterogeneity on soil fauna diversity were different. In the 150 m buffer zone, soil fauna community composition and diversity indices were strongly correlated with patch richness index, patch richness density, and other landscape indices (p < 0.05). Meanwhile, the contribution rate of landscape index to soil fauna community composition were 45.05%, 32.5%, and 42% in farmland, plantation, and natural forest, respectively. Therefore, the 150 m buffer zone could be used as the characteristic response scale of soil fauna diversity. The multi-scale interaction of landform, habitat, and landscape also had a significant impact on soil fauna diversity.

1. Introduction

Soil faunas are commonly used as environmental indicators in agricultural landscapes, and they contribute to important ecological functions such as biological control and nutrient circulation [1]. Protecting soil fauna diversity is the key to ensuring soil fertility and ecological health of cultivated land, and is conducive to promoting sustainable utilization of cultivated land [2,3]. In recent years, studies on soil biodiversity have been carried out widely in different climates and ecosystems. However, there are still considerable inconsistencies in the results [4,5,6]. For example, in Changbai Mountain, soil fauna diversity is the highest in shrubbery and the lowest in cultivated land [7]; the diversity index of soil fauna in the middle reaches of the Yangtze River was higher than that in other habitats, while the diversity index of soil fauna in the lower reaches of the Yangtze River was the lowest [5]; the diversity index of soil fauna in farmland boundary of purple soil hilly area was higher than that in orchard, farmland, and woodland [4]; the diversity index of soil fauna in the natural ecosystem of Haikou tropical urban area is higher than that in the urban greenbelts [6]. In addition, the influencing factors and driving factors of soil fauna diversity in the Yellow River in eastern Henan were not clear [8]. Therefore, this study focused on exploring the factors affecting soil fauna diversity by studying the middle and lower reaches of the Yellow River.
Landscape heterogeneity refers to the variation degree of landscape and its attributes, which is the basic attribute of landscape and determines the diversity of spatial pattern [9,10] and has always been one of the basic issues of landscape ecology [11,12]. Current studies on landscape heterogeneity mostly focus on landscape heterogeneity and its ecological effects, specifically, landscape heterogeneity will directly affect a variety of attributes of the ecosystem, such as animal migration, population maintenance, interaction between species, dynamics, and basic functions of the ecosystem [13,14]. Meanwhile, landscape scale has become one of the core issues in modern ecology. Multi-scale spatial pattern analysis is the basis for scale effect analysis and cross-scale deduction, and researches on ecological patterns and processes are often meaningless at uncertain scales [15,16]. So far, most studies on soil fauna diversity are focused on different plots, and there are few studies on the effects of multi-scale landscape heterogeneity on soil fauna community [17,18]. Therefore, this study has important theoretical significance to explore the effects of landform, landscape, and habitat on soil fauna diversity at different scales.
The middle reaches of the Yellow River are dominated by farming-pastoral ecotone landscape and agroforestry landscape, while the lower reaches are dominated by agricultural landscape, which is composed of farmland, forest, wetland, and orchard, with high landscape heterogeneity. It is important to carry out biodiversity research in the Yellow River Basin [15,16,17,18,19]. Therefore, this study took Gongyi City, a typical agricultural landscape in the middle and lower reaches of the Yellow River, as the study area. The aims of this study were to (1) explore the change trend of landscape indices in the study area with the increase of buffer zone; (2) determine the species composition and diversity of soil fauna in different habitats, landforms, and landscapes; and (3) explore the multi-scale interaction of landscape heterogeneity on soil fauna diversity and the response of soil fauna to it. We anticipated that the community composition and diversity of soil fauna in the study area would be different under different habitats, landforms, and landscape conditions; that the composition and diversity characteristics under natural conditions were better than those under semi-natural conditions; and that the effects of multi-scale landscape interaction on soil fauna diversity were more obvious than those of single landscape scale.

2. Materials and Methods

2.1. Study Area

The study area is located in Gongyi City, Central and Western of Henan Province, the middle and lower reaches of the Yellow River in China (34°31′–34°52′ N, 112°49′–113°28′ E) (Figure 1a,b). Gongyi City covers an area of 1042 km2 and is characterized by a warm temperate continental monsoon climate. The average annual rainfall is 593.23 mm, and the average annual temperature is 16 °C. The soil types are mainly divided into brown soil, cinnamon soil, and moisture soil. The terrain is high in the south and low in the north, with complex and diverse landforms (Figure 1c). According to the difference in altitude, the study can be divided into three geomorphic types: mountain area, hilly area, and plain area. Mountain areas are mainly distributed in the southeast and south of Gongyi City, usually low mountains, with an elevation of 400–1000 m, and a small number of mountains above 1000 m, accounting for about 18% of the area. Due to the higher elevation and larger slope, the areas of farmland in the mountainous areas are relatively small. Hilly areas are located in the middle and northeast of Gongyi City, with an elevation of 200–500 m. It is a transitional landform from mountain to river plain, accounting for about 39% of the area. Plain areas are located in the south of the Yellow River, southwest of Gongyi City, and north of the Yellow River, accounting for about 43% of the area. The region is rich in biological resources and diverse in flora and fauna. In addition, relying on the advantaged industrial conditions, the economy of Gongyi City has developed rapidly, but it also leads to high intensity and large-scale land use, resulting in landscape fragmentation and landscape simplification.

2.2. Experimental Design

The study area was divided into three landform types: mountain (MA), hill (HI), and plain (PL). A total of 24 sampling sites were selected from the study area (Figure 2), with 7 sampling sites selected in mountainous landform, 12 in hilly landform, and 5 in plain landform. One or two habitats were studied in each sampling site with a total of 42 habitats were studied (Table A1). Three plots were selected for each habitat and three soil samples were randomly collected from each plot as replicates. A total of 378 soil samples were collected by using a 200 cm3 ring knife. Then took it back to the lab as soon as possible. Soil meso-micro faunas were collected for 24 h by Tullgren method, and stored in 75% alcohol for indoor classification and identification. Classification was carried out under Leica M125 binocular stereomicroscope, and identification was carried out by referring to Chinese Soil Fauna Retrieval Guide [20,21] and General Entomology [22]. Due to their different ecological functions, the number of groups and individuals of soil faunas was counted separately.

2.3. Data Analysis

2.3.1. Landscape Indices

To understand the impact of landscape characteristics at different scales on soil fauna diversity in the study area, 13 buffer radii (25 m, 50 m, 75 m, 100 m, 125 m, 150 m, 175 m, 200 m, 225 m, 250 m, 500 m, 750 m, and 1000 m) were selected as buffers. Referring to the setting of buffer zone by Liu [23], considering the short migration distance of soil faunas, we divided the radius of buffer zone into closer and finer ones.
The landscape indices include patch size and density index, patch shape index, proximity and similarity index, matrix index, and diversity index (Table 1). The patch size and density indices were selected as follows: total area (TA), mean patch size (AREA_MN), mean radius of gyration (GYRATE_MN), radius of gyration (GYRATE_CV), fractal dimension index (FRAC_CV), perimeter area ratio (PARA_CV), mean shape index (SHAPE_MN), euclidean nearest neighbor distance (ENN_CV), contagion (CONTAG), patch richness (PR), patch richness density (PRD), Shannon’s diversity index (SHDI), and Shannon’s evenness index (SHEI).

2.3.2. Biodiversity Indices

Biodiversity indices can quantitatively measure and compare biodiversity differences of different communities. α diversity refers to the species diversity in a specific region or ecosystem. To comprehensively reflect the richness and uniformity of soil fauna in the region, Shannon-Wiener diversity index (H’), Simpson’s diversity index (D), Pielou’s evenness index (E), and Margalef richness index (R) were used to represent α diversity [24,25,26,27]. The diversity profiles analysis was conducted through the online software NEXT-4steps to quantify sample completeness and compare diversities among assemblages [28,29].
Redundancy analysis (RDA) was performed using the vegan package of statistical software R 4.1.3. Soil fauna community data were selected as response variables and landscape diversity data as explanatory variables to analyze the relative impact of landscape diversity on soil fauna community composition. Before the RDA, Hellinger transformation was performed for species abundance data and data standardization was performed. By calculating the biplot scores for constraining variables of each landscape index, several landscape index factors showing large explanatory volume under each habitat condition were selected to conduct the subsequent RDA ranking analysis.
ArcGIS 10.2 was used for landscape data processing and spatial analysis. Fragstats 4.2.1 was used for landscape pattern index calculation, and diversity characteristics of soil fauna were calculated in R 4.1.3. One-way ANOVA and least significant difference method (LSD) were used to compare soil fauna differences among different landforms and habitats. Correlation analysis and variance analysis were conducted in SPSS 24, and charts were drawn by Origin 2021.

3. Results

3.1. Characteristics of Landscape Heterogeneity in the Study Area

The landscape indices of the region changed regularly with the change of buffer radius (Table 1). The values of TA, AREA_MN, GYRATE_MN, GYRATE_CV, SHAPE_MN, FRAC_CV, and SHDI all increased gradually with the expansion of buffer radius, while PRD index decreased gradually with the expansion of buffer radius. The PR index value was the largest at 750 m buffer radius and the smallest at 25 m buffer radius. SHDI reached its maximum value when the buffer zone radius was 1000 m, showed that with the increase of landscape scale, the richer land use was, the higher the degree of fragmentation was. CONTAG index values gradually decreased with the increase of scale, showed that certain dominant patch types within the buffer radius of 25 m form good connectivity. However, in the radius of 1000 m buffer zone, the landscape was a dense pattern with multiple elements, with a high density of patches, poor connectivity, and serious fragmentation in the landscape. When the SHEI index value is 0, there is no diversity in the landscape. When the SHEI index value is 1, the overall area ratio of different patch types in the landscape is consistent, presenting a completely uniform state; the landscape homogenization degree was the highest in the buffer zone of 25 m and there was no obvious dominant patch type. When the SHEI index value is 500 m, dominant patch type dominates the landscape.

3.2. Community Composition of Soil Fauna in the Study Area

A total of 378 soil samples were collected in Gongyi city, including 90 soil samples collected from mountain landform, 198 soil samples collected from hilly landform and 90 soil samples collected from plain landform. We demonstrated that the number of soil samples collected for each landform type was sufficient by plotting the species cumulative curves (Figure A1). The soil fauna isolated from soil samples represented the actual situation under their respective landforms, and the sample size was sufficient to reflect the community composition of soil fauna.
A total of 51,699 soil faunas, 21 orders, 65 families were captured in the field (Figure 3a), including 18,150 soil faunas in mountainous areas, 22,871 soil faunas in hilly areas, and 10,678 soil faunas in plain areas, belonging to 62 taxa. The dominant individuals (populations with abundance greater than 10% of the total number of captured) were Prostigmata (34,769 individuals, accounting for 67.25% of the total number of captured), Oribatida (7000, 13.54%), Isotomidae (6314, 12.21%). Others were rare individuals (populations with an abundance less than 1% of the total catch). The quantity distribution of different species in the community was not uniform, dominant individuals accounted for a large proportion of all soil faunas collected. The ecological functions of the dominant organisms, such as the Prostigmata, the Oribatida, and the Isotomidae, were prominent.
A total of 18,150 soil faunas individuals were sampled in four habitats in the mountain landforms (Figure 3b), including 7257 in farmland, 7130 in natural forest, 1116 in artificial forest, and 2647 in shrub. Prostigmata (5166, 71.19%) and Isotomidae (914, 12.59%) were observed as dominant groups in farmland habitat. Prostigmata (781, 69.98%) and Oribatida (194, 18.06%) were observed as dominant groups in artificial forest. Prostigmata (4638, 65.05%), Isotomidae (1182, 16.58%), and Oribatida (764, 10.72%) were observed as dominant groups in natural forest. Prostigmata (1444, 54.55%) and Oribatida (782, 29.54%) were observed as dominant groups in shrub.
A total of 22,871 soil faunas individuals were sampled in four habitats in the hilly landforms (Figure 3c), including 6730 in farmland, 179 in natural forest, 14,921 in artificial forest, and 1041 in shrub. Prostigmata (4729, 70.27%), Oribatida (780, 11.59%), and Isotomidae (863, 12.82%) were observed as dominant groups in farmland. Prostigmata (9665, 64.77%), Oribatida (2292, 15.36%), and Isotomidae (1776, 11.90%) were observed as dominant groups in artificial forest. Prostigmata (102, 56.98%), Oribatida (25, 13.97%), and Isotomidae (36, 20.11%) were observed as dominant groups in natural forest. Prostigmata (737, 70.80%) and Oribatida (181, 17.39%) were observed as dominant groups in shrub.
A total of 10,678 soil faunas individuals were captured in two habitats of plain landforms (Figure 3d), including 6730 in farmland and 4308 in artificial forest. Prostigmata (4429, 69.53%), Isotomidae (1004, 15.76%), and Oribatida (649, 10.19%) were observed as dominant groups in farmland habitat. Prostigmata (3078, 71.45%) and Oribatida (671, 15.58%) were observed as dominant groups in artificial forest.

3.3. Comparative Analysis of Soil Fauna Diversity in Different Landforms and Habitats

To analyze the diversity of soil fauna in different habitat types in 2017, five biodiversity indices including Shannon-Wiener diversity index (H’), Simpson’s diversity index (D), number of soil fauna groups (S), Pielou’s evenness index (E), and Margalef richness index (R) were selected (Table 2).
The performance of soil fauna diversity indices under different habitat conditions is as follows (Figure 4). Shannon-Wiener diversity indices (H’) under different landforms was shown as follows. In four habitats of mountain landforms, Shannon-Wiener diversity index (H’) was highest in shrub, followed by in artificial forest, natural forest, and farmland (p < 0.01), Shannon-Wiener diversity index in artificial forest was significantly higher than that in farmland. In four habitats of hill landform, it was highest in artificial forest, followed by in natural forest, shrub, and farmland (p < 0.01), Shannon-Wiener diversity index of artificial forest was significantly higher than that of farmland. In two habitats of plain landform, it was artificial forest higher than farmland. Simpson’s diversity indices (D) under different landform was shown as follows. In four habitats of mountain landform, Simpson’s diversity index (D) was highest in shrub, followed by in artificial forest, natural forest, and farmland (p < 0.05), Simpson’s diversity index of artificial forest was significantly higher than that of farmland. In four habitats of hill landforms, it was highest in artificial forest, followed by in natural forest, shrub, and farmland (p < 0.05). In the two habitats of plain landforms, it was artificial forest higher than farmland. Pielou’s evenness indices (E) under different landforms was shown as follows. In four habitats of mountain landforms, Pielou’s evenness index (E) was highest in shrub, followed by in artificial forest, natural forest, and farmland, but there was no significant difference among the four habitats. In four habitats of hill landform, Pielou’s evenness index (E) was highest in natural forest, followed by in shrub, artificial forest, and farmland (p < 0.01), the Pielou’s evenness index of natural forest was significantly higher than that of shrub, artificial forest and farmland. Between the two habitats in plain landform, it was artificial forest higher than farmland, but there was no significant difference. Margalef richness indices (R) under different landforms was shown as follows. In four habitats of mountain landforms, Margalef richness index (R) was highest in farmland, followed by in artificial forest, natural forest, and shrub (p < 0.05). The Margalef richness index (R) of farmland was significantly higher than that of artificial forest and natural forest. Among the four habitats in the hill landform, it was highest in artificial forest, followed by in shrub, natural forest, and farmland (p < 0.001), the Margalef richness index of artificial forest was significantly higher than that of farmland. Between the two habitats in plain landform, it was artificial forest higher than farmland, but there was no significant difference.
The performance of the biodiversity indices under different landforms is as follows (Figure 4). Shannon-Wiener diversity indices (H’) under different landforms was shown as follows. Among the three landforms under farmland habitat, Shannon-Wiener diversity index (H’) was highest in mountain, followed by in plain and hill. In the three landforms of artificial forest, it was highest in hill, followed by in mountain and plain. In the natural forest of the two landforms, it was in hill higher than in mountain; In the two landforms of shrub, it was in mountain higher than in hill. Simpson’s diversity indices (D) under different landforms was shown as follows. Among the three landforms under farmland habitat, Simpson’s diversity index (D) was highest in hill, followed by in plain and mountain. Among the three landforms under the artificial forest, it was highest in hill, followed by in mountain and plain; in the natural forest of the two landforms, it was in hill higher than in mountain; in the shrub of the two landforms, it was in hill higher than in mountain. Pielou’s evenness indices (E) under different landforms was shown as follows. Among the three landforms under farmland habitat, it was highest in plain, followed by in hill and mountain (p < 0.05). In the three landforms of artificial forest, it was highest in plain, followed by in mountain and hill (p < 0.05). In the natural forest of the two landforms, it was in hill higher than in mountain (p < 0.05). In the shrub of the two types of landforms, it was in mountain higher than in hill. Margalef richness indices (R) under different landforms was shown as follows. Among the three landforms under farmland habitat, it was highest in mountain, followed by in plain and hill (p < 0.05), mountain landform was significantly higher than plain and hill. In the three landforms of artificial forest, it was highest in mountain, followed by in hill and plain. In the natural forest of the two landforms, it was in mountain higher than in hill (p < 0.05). In the shrub of the two types of landforms, it was in mountain higher than in hill.
The soil fauna diversity under different landforms was analyzed by the diversity profile analysis (Figure 5, Table 3). Our sample completeness analysis shows that the undetected species richness within the hill, mountain, and plain data are, respectively, at least 7.56 (≥12.09%), 13.5 (≥23.48%) and 4.08 (≥12.09%). In every landform, almost all the abundant and highly abundant soil faunas are found. For Shannon diversity (q = 1) and Simpson diversity (q = 2), there was no significant difference in the diversity of the three landforms. For soil fauna richness, inference and significance testing can be performed up to a standardized coverage value of Cmax = 100% (the minimum coverage of two samples extrapolated to double the size of the reference sample). Under a standardized coverage of 100%, the richness of soil fauna varied greatly among mountain, hill and plain. Pielou’s evenness measure shows that the evenness among soil fauna abundances in the mountain is highest, followed by in hill and plain. Thus, the major difference between the three landforms lied in rare species. The profile, however, reveals that the evenness values for the three landforms were very close for any order q between 0 and 2.

3.4. RDA Analysis of Landscape Indices and Soil Fauna Composition

At landscape scale, RDA was used to analyze the relative effects of landscape indices on soil fauna community composition (Figure A2, Figure A3 and Figure A4). The data of the different habitats were sorted by RDA. Due to the limitation of natural conditions, there were too few samples selected in shrub habitats, so the RDA sorting analysis of shrub habitats was no longer carried out.
The results showed that the contributions of the two ordination axes to the interpretation of soil fauna community composition were different under different habitat conditions(Table 4). In the farmland habitat, when the buffer radii were 25 m, 50 m, 75 m, and 150 m, the interpretation values of the two ordination axes were larger than those of other radii. SHDI, PR, PRD, or PD were negatively correlated with Prostimata, while TA was less correlated with soil fauna. In the artificial forest, when the buffer radius was 150 m or 1000 m, the interpretation values of the two ordination axes were larger. Isotomidae was strongly correlated with landscape indices. In the natural forest habitats, when the buffer radius was 125 m, 250 m, or 500 m, the RDA ranking interpretation values of landscape index to soil fauna community were larger than that of other radii. Isotomidae showed strong correlation with CONTAG index and PR index.Next, the correlation between seven landscape indices and soil fauna communities at different buffer radii was analyzed by RDA. It was found that the correlation between Isotomidae and buffer radius was strong under PR, PRD, PD, SHDI, SHEI, CONTAG and TA landscape indices (Figure A5).

3.5. Effects of Landscape Scale on Soil Fauna Diversity

Based on the correlation analysis of soil fauna diversity indices and landscape indices (Table 5), soil fauna diversity index was significantly correlated with landscape index at 150 m scale and was strongly correlated with patch density index (PD), patch richness index (PR) and patch richness density (PRD). Therefore, patch density index (PD), patch richness index (PR), and patch richness density (PRD) at the 150 m scale were selected as the characteristic response scale indexes of soil fauna.
Using PRD index in the buffer range of 150 m as a reference, the 24 sampling sites were divided into different levels of landscape heterogeneity, with high landscape heterogeneity (Sites 5, 9, 15, 17, and 23), medium landscape heterogeneity (Sites 1, 2, 3, 7, 10, 11, 12, 13, 14, 16, 18, 19, 20, and 22) and low landscape heterogeneity (Sites 4, 6, 8, 21, and 24).

3.6. Multi-Scale Analysis of the Impact of Agricultural Landscape Heterogeneity on Soil Fauna Diversity

Based on the multivariate analysis of landscape heterogeneity and species diversity indices at landform, landscape, and habitat scales, the differences of landform, habitat, and landscape scale all had significant effects on the individual numbers of soil fauna (p < 0.001). The interaction between landform and landscape, habitat and landscape, and habitat had a significant influence on Shannon-Wiener diversity index (p < 0.05). The interaction of any two landforms, habitat, and landscape had a significant influence on Simpson’s diversity index (p < 0.05). Landform, habitat, landscape, the interaction between landform and habitat, the interaction between landform and landscape all had significant effects on Pielou’s evenness index (p < 0.01). The effect of landform, and the interaction between landscape and landform had significant effects on Margalef richness index (p < 0.01) (Table 6).

4. Discussion

4.1. Comparison of Soil Fauna Diversity under Different Landforms and Habitats

This study found that Shannon-Wiener diversity index, Simpson’s diversity index, and Pielou’s evenness index were the lowest in farmland. This could be the result of long-term human interference in farmland. Farmland human disturbances are manifested in long-term tillage, fertilization, and pesticide spraying. Studies have found that long-term tillage [30] and fertilization [31,32,33] would affect soil structure, weaken the formation of soil aggregates, change soil physical and chemical properties and organic matter content, and then affect the community characteristics of soil faunas [15,16,19,34,35,36,37], leading to instability of soil fauna community. Insecticides and herbicides also have an impact on soil faunas, which is manifested in the decrease of soil fauna species and number [38,39,40,41]. Studies have confirmed that the Prostigmata is very sensitive to the reaction of pesticide spraying [42,43]. As the dominant group in this study area, Prostigmata accounts for a large proportion of the population, and its change may have an important impact on the diversity of soil faunas in the whole region. The middle and lower reaches of the Yellow River are important grain-producing areas in China. Human activities have a profound impact on the composition, structure, and function of landscape patterns in this region, which affects regional biodiversity. In addition, there were differences in aboveground vegetation among different habitat types. Studies have proved that vegetation differences can significantly affect the composition and structure of subsurface biodiversity, and the distribution of soil faunas is closely related to soil humus and vegetation types [23]. Similarly, biodiversity and landscape heterogeneity can be used as indicators to improve the ecological multifunction of forestland in agricultural landscapes [44,45].
In addition, this study found that the biodiversity indices of mountain landforms were in artificial forests higher than in natural forest. Since artificial forests are mostly monocultures or mixtures of a few tree species, the species diversity and richness of artificial forest are expected to be lower than that of natural forest. It is generally believed that artificial forest forests are “ecological deserts”, and the role of artificial forests in biodiversity protection is often one-sided denied, or ignored [46]. However, studies have confirmed that artificial forest has higher understory biodiversity than natural forest when it has some habitat characteristics [47,48,49,50,51]. Artificial forests can promote the natural regeneration of understory vegetation and thus promote biodiversity conservation.

4.2. Effects of Landscape Scale on Soil Fauna Diversity

It was found that the influence of landscape scale on the composition and diversity of soil fauna communities was obvious in the 150 m buffer radius. Studies have shown that the impact of landscape heterogeneity on biodiversity may depend on the size of geographical range of species, thus reflecting the impact of species characteristics and land cover [49] The habitat heterogeneity at landscape scale has a positive contribution to soil fauna diversity by providing different niches, as well as passive diffusion and population patch dynamics [50]. Studies have shown that the correlation between landscape indices and diversity of large arthropods were the strongest within the buffer range of 200 m−300 m [52,53,54], and the correlation between diversity indices of pollinators and landscape indices was the highest within the buffer radius of 500 m, which was larger in spatial range of horizontal distance and vertical height than that of large arthropods [55,56,57,58]. Compared with large arthropods and pollinators, small and medium-sized soil faunas separated by Tullgren method were studied in this study. Small and medium-sized soil faunas were small in individual and had weak migration ability, mainly relying on short distance migration, so the buffer radius was relatively small [59].
In different habitats, the dominant species Isotomidae and Prostigmata were greatly affected by landscape heterogeneity and landscape scale. This is especially true in the farmland habitat, where this strong effect was well reflected. We think it was because of their large numbers in farmland habitats. Studies have shown that the numbers of Prostigmata individuals in different habitats are quite different; that the number of Prostigmata in farmland is greater, which is related to the food source in the habitat; and that they endanger roots, stems, leaves, or seedlings on cultivated crops [52,60]. In addition, due to the limitation of natural conditions, we selected the largest number of samples in farmland, which is also one reason.
In addition, this study found that PD, PR, and PRD indices were strongly correlated with soil fauna diversity indices, and landscape spatial heterogeneity was divided into landscape composition heterogeneity and landscape configuration heterogeneity [61]. Landscape composition heterogeneity can be measured by PR, while configuration heterogeneity can be measured by PRD [62]. This is consistent with the results obtained in this paper, which more fully proves the impact of landscape spatial heterogeneity on soil fauna diversity. The diversity of agricultural and non-agricultural habitats, such as farmland, natural forest, artificial forest, and shrub, caused the heterogeneity of landscape composition, and the difference of spatial configuration and combination of different landscape composition types under the same geomorphic type caused the heterogeneity of landscape architecture. Therefore, to preserve the diversity of soil fauna, it is necessary to consider the appropriate landscape pattern and spatial and temporal scale. In addition, the response scale of different biological groups to landscape heterogeneity is inconsistent, which can be further analyzed and studied on this basis.

4.3. Multi-Scale Analysis of Effects of Agricultural Landscape Heterogeneity on Soil Fauna Diversity

Landscape heterogeneity has a strong scale effect on biodiversity [13]. This study found that soil fauna diversity varies at different spatial scales (landscape, landform, and habitat), which is consistent with relevant research results [63]. In addition, this study also analyzed the interaction of different spatial scales and found that for some biodiversity indices, the impact of multi-scale spatial interaction was more significant. To protect biodiversity and improve ecosystem services, we should not only pay attention to the changing characteristics of soil fauna at different spatial scales but also consider the impact of multi-scale interactions on soil fauna. In addition, interactions are not only reflected in multi-scale landscape patterns. In future studies, interactions between soil faunas and other organisms, as well as ecological relationships between aboveground and underground, should be taken into consideration.

5. Conclusions

In this study, we explored the response of soil fauna diversity to agricultural landscape heterogeneity in the middle and lower reaches of the Yellow River.
The results showed that the biodiversity indices were the lowest in farmland, followed by natural forest, and the biodiversity index of artificial forest was the highest of the three. Therefore, we concluded that, in agricultural landscapes, moderate anthropogenic disturbances have positive effects on soil fauna.
Within the buffer radius of 150 m, the impact of landscape scale on soil fauna community composition and diversity was obvious. The main reason could be that the Soil meso-micro faunas were small and had weak migration ability, mainly relying on short distance migration. The dominant groups Isotomidae and Prostigmata were greatly affected by landscape heterogeneity and landscape scale, mainly due to their strong population advantage and feeding habits.
Multi-scale spatial interaction was observed to have a more significant effect on soil fauna diversity. In future agricultural landscape construction and biodiversity conservation, attention should be paid to the multi-scale interaction of landscape heterogeneity. Rational deployment of the agricultural landscape is crucial to maintain the high diversity of soil fauna in the region.

Author Contributions

Conceptualization, P.Z. and S.D. (Shengyan Ding); methodology, Z.B.; software, P.L.; validation, P.Z., Z.B. and S.D. (Shengyan Ding); formal analysis, P.Z.; investigation, C.Z.; resources, S.D. (Shengyan Ding); data curation, J.Z.; writing—original draft preparation, P.Z.; writing—review and editing, Z.B., P.L. and S.D. (Shunping Dingand); visualization, J.Z.; supervision, S.D. (Shengyan Ding); project administration, P.Z.; funding acquisition, S.D. (Shengyan Ding). All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Nature Science Foundation of China (#42171091) and (#41371195).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

We thank Zhendong Hong, Zihan Geng, and Pengwei Qiu for help in the Research idea. We also thank Jingzhen Liu, An Wang, Yaru Jing, and Mengwen Lu for help in the data processing.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

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Figure A1. Species cumulative curves of the study area. The green line represents the species cumulative curve of mountainous landform, the red line represents the species cumulative curve of hilly landform, and the blue line represents the species cumulative curve of plain landform.
Figure A1. Species cumulative curves of the study area. The green line represents the species cumulative curve of mountainous landform, the red line represents the species cumulative curve of hilly landform, and the blue line represents the species cumulative curve of plain landform.
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Figure A2. Ordination diagram for the RDA analysis for soil fauna distribution and landscape indices in farmland. (a) 25 m buffer radius. (b) 50 m buffer radius. (c) 75 m buffer radius. (d) 100 m buffer radius. (e) 125 m buffer radius. (f) 150 m buffer radius. (g) 175 m buffer radius. (h) 200 m buffer radius. (i) 225 m buffer radius. (j) 250 m buffer radius. (k) 500 m buffer radius. (l) 750 m buffer radius. (m) 1000 m buffer radius.
Figure A2. Ordination diagram for the RDA analysis for soil fauna distribution and landscape indices in farmland. (a) 25 m buffer radius. (b) 50 m buffer radius. (c) 75 m buffer radius. (d) 100 m buffer radius. (e) 125 m buffer radius. (f) 150 m buffer radius. (g) 175 m buffer radius. (h) 200 m buffer radius. (i) 225 m buffer radius. (j) 250 m buffer radius. (k) 500 m buffer radius. (l) 750 m buffer radius. (m) 1000 m buffer radius.
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Figure A3. Ordination diagram for the RDA analysis for soil fauna distribution and landscape indices in artificial forest. (a) 25 m buffer radius. (b) 50 m buffer radius. (c) 75 m buffer radius. (d) 100 m buffer radius. (e) 125 m buffer radius. (f) 150 m buffer radius. (g) 175 m buffer radius. (h) 200 m buffer radius. (i) 225 m buffer radius. (j) 250 m buffer radius. (k) 500 m buffer radius. (l) 750 m buffer radius. (m) 1000 m buffer radius.
Figure A3. Ordination diagram for the RDA analysis for soil fauna distribution and landscape indices in artificial forest. (a) 25 m buffer radius. (b) 50 m buffer radius. (c) 75 m buffer radius. (d) 100 m buffer radius. (e) 125 m buffer radius. (f) 150 m buffer radius. (g) 175 m buffer radius. (h) 200 m buffer radius. (i) 225 m buffer radius. (j) 250 m buffer radius. (k) 500 m buffer radius. (l) 750 m buffer radius. (m) 1000 m buffer radius.
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Figure A4. Ordination diagram for the RDA analysis for soil fauna distribution and landscape indices in natural forest. (a) 25 m buffer radius. (b) 50 m buffer radius. (c) 75 m buffer radius. (d) 100 m buffer radius. (e) 125 m buffer radius. (f) 150 m buffer radius. (g) 175 m buffer radius. (h) 200 m buffer radius. (i) 225 m buffer radius. (j) 250 m buffer radius. (k) 500 m buffer radius. (l) 750 m buffer radius. (m) 1000 m buffer radius.
Figure A4. Ordination diagram for the RDA analysis for soil fauna distribution and landscape indices in natural forest. (a) 25 m buffer radius. (b) 50 m buffer radius. (c) 75 m buffer radius. (d) 100 m buffer radius. (e) 125 m buffer radius. (f) 150 m buffer radius. (g) 175 m buffer radius. (h) 200 m buffer radius. (i) 225 m buffer radius. (j) 250 m buffer radius. (k) 500 m buffer radius. (l) 750 m buffer radius. (m) 1000 m buffer radius.
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Figure A5. RDA analysis of landscape indices composition with soil fauna in different buffer zones. (a) CONTAG. (b) PD. (c) PR. (d) PRD. (e) SHDI. (f) SHEI. (g) TA.
Figure A5. RDA analysis of landscape indices composition with soil fauna in different buffer zones. (a) CONTAG. (b) PD. (c) PR. (d) PRD. (e) SHDI. (f) SHEI. (g) TA.
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Table
Table A1. The type of habitat contained in each sampling site.
Table A1. The type of habitat contained in each sampling site.
LandformSampling SiteHabitat
Plain1Farmland
Artificial forest
2Farmland
Artificial forest
4Farmland
Artificial forest
5Farmland
Artificial forest
7Farmland
Artificial forest
Hill3Artificial forest
6Farmland
Artificial forest
8Farmland
Artificial forest
9Farmland
Artificial forest
10Farmland
Artificial forest
11Natural forest
Shrub
13Artificial forest
18Farmland
Artificial forest
21Farmland
Artificial forest
22Farmland
Artificial forest
23Farmland
Artificial forest
24Farmland
Artificial forest
Mountain12Natural forest
14Farmland
Shrub
15Farmland
16Farmland
Natural forest
17Artificial forest
Shrub
19Natural forest
20Natural forest

References

  1. Lemic, D.; Pajač Živković, I.; Posarić, M.; Bažok, R. Influence of Pre-Sowing Operations on Soil-Dwelling Fauna in Soybean Cultivation. Agriculture 2021, 11, 474. [Google Scholar] [CrossRef]
  2. Zhang, W.X.; Shen, Z.F.; Shao, Y.H.; Shi, L.L.; Liu, S.J.; Shi, N.N.; Fu, S.L. Soil biota and sustainable agriculture: A review. Acta Ecol. Sin. 2020, 10, 3183–3206. [Google Scholar] [CrossRef]
  3. Culliney, T.W. Role of Arthropods in Maintaining Soil Fertility. Agriculture 2013, 4, 629–659. [Google Scholar] [CrossRef] [Green Version]
  4. Wu, Y.H.; Cai, Q.N.; Lin, C.W.; Zhao, X.; Cheng, X. Community structure of soil mesofauna under different land use patterns in purple soil hilly area of Sichuan Basin. Chin. J. Ecol. 2009, 28, 277–282. [Google Scholar]
  5. Wang, G.L.; Wang, Y.; Han, L.L.; Zhang, M.W.; Li, B. Soil animal communities of variously utilized in the Dongting Lake region. Acta Ecol. Sin. 2005, 25, 2629–2636. [Google Scholar]
  6. Fan, Y.Q. Analysis on Ecogeographical Characteristics of Soil Animal Groups of Tropical Urban Greentbelt in Haikou; Hainan Normal University: Hainan, China, 2010. [Google Scholar]
  7. Li, H.Y.; Yin, X.Q.; Ma, C.; Guo, Y.M. Ecological Distribution of Soil Fauna Communities in Hilly Regions of the Changbai Relative to Land Use Mountains. Acta Pedol. Sin. 2017, 54, 1018–1028. [Google Scholar] [CrossRef]
  8. Zhu, X.Y.; Hu, Y.C.; Hou, R.H. Soil Macrofauna Community Compositions and Diversities in Different Wetlands of Old Riverway of the Yellow River in Eastern Henan. Soils 2016, 48, 1131–1138. [Google Scholar] [CrossRef]
  9. Turner, M.G. Landscape Ecology in North America: Past, Present, and Future. Ecology 2005, 86, 1967–1974. [Google Scholar] [CrossRef] [Green Version]
  10. Xiao, D.N.; Bu, R.C.; Li, X.Z. Spatial Ecology and Landscape Heterogeneity. Acta Ecol. Sin. 1997, 17, 435–461. [Google Scholar]
  11. Graziano, M.P.; Deguire, A.K.; Surasinghe, T.D. Riparian buffers as a critical landscape feature: Insights for riverscape conservation and policy renovations. Diversity 2022, 14, 172. [Google Scholar] [CrossRef]
  12. Xiao, D.N.; Li, X.Z. Development and prospect of contemperary landscape ecology. Sci. Geogr. Sin. 1997, 17, 355–364. [Google Scholar]
  13. Kauffman, M.J.; Varley, N.; Smith, D.W.; Stahler, D.R.; Macnulty, D.R.; Boyce, M.S. Landscape Heterogeneity Shapes Predation in a Newly Restored Predator-prey System. Ecol. Lett. 2007, 10, 690–700. [Google Scholar] [CrossRef] [PubMed]
  14. Fraterrigo, J.M.; Pearson, S.M.; Turner, M.G. Joint Effects of Habitat Configuration and Temporal Stochasticity on Population Dynamics. Landsc. Ecol. 2009, 24, 863–877. [Google Scholar] [CrossRef]
  15. Shi, F.; Liu, S.; Sun, Y.; An, Y.; Zhao, S.; Liu, Y.; Li, M. Ecological Network Construction of the Heterogeneous Agro-Pastoral Areas in the Upper Yellow River Basin. Agric. Ecosyst. Environ. 2020, 302, 107069. [Google Scholar] [CrossRef]
  16. Wang, Z.; Qin, Y.; Li, W.; Yang, W.; Meng, Q.; Yang, J. Microplastic contamination in freshwater: First observation in Lake Ulansuhai, Yellow River Basin, China. Environ. Chem. Lett. 2019, 17, 1821–1830. [Google Scholar] [CrossRef]
  17. Izadi, M.; Habashi, H.; Waez-Mousavi, S.M. Variation in Soil Macro-Fauna Diversity in Seven Humus Orders of a Parrotio-Carpinetum Forest Association on Chromic Cambisols of Shast-Klateh Area in Iran. Eurasian Soil Sci. 2017, 50, 341–349. Available online: https://esajournals.onlinelibrary.wiley.com/servlet/linkout?suffix=null&dbid=16&doi=10.1002%2Fecs2.3075&key=10.1134%2FS106422931703005X (accessed on 22 July 2022). [CrossRef]
  18. Fu, S.L. Biodiversity conservation along the Yellow River should emphasize the complex spatial heterogeneity. Biodivers. Sci. 2020, 28, 1445–1446. [Google Scholar] [CrossRef]
  19. Barnett, J.; Webber, M.; Wang, M. Ten key questions about the management of water in the yellow river basin. Environ. Manag. 2006, 38, 179–188. [Google Scholar] [CrossRef]
  20. Yin, W.Y.; Hu, S.H.; Shen, Y.F.; Ning, Y.Z.; Sun, X.D.; Wu, J.H.; Yang, D.R. Pictorical Keys to Soil Animals of China; Science Press: Beijing, China, 1998; Available online: https://scholar.google.com/scholar_lookup?title=Pictorial+Keys+to+Soil+Animals+of+China&author=Yin,+W.Y.&publication_year=1998 (accessed on 22 July 2022).
  21. Zhu, X.Y.; Zhu, B. Diversity and abundance of soil fauna as influenced by long-term fertilization in cropland of purple soil, China. Soil Tillage Res. 2015, 146, 39–46. [Google Scholar] [CrossRef]
  22. Cai, W.Z.; Pang, X.F.; Hua, B.Z. General Entomology, 2nd ed.; China Agricultural University Press: Beijing, China, 2011. [Google Scholar]
  23. Liu, X.B. Effects of Agricultural Landscape Structure and Habitat Characteristics Ground-Dwelling Spider Typical Regions of the Lower Reaches of the Yellow River; Henan University: Henan, China, 2015. [Google Scholar]
  24. Hill, M.O. Diversity and evenness: A unifying notation and its consequences. Ecology 1973, 54, 427–432. [Google Scholar] [CrossRef] [Green Version]
  25. Simpson, E.H. Measurement of diversity. Nature 1949, 163, 688. Available online: https://onlinelibrary.wiley.com/servlet/linkout?suffix=null&dbid=16&doi=10.1002%2Fece3.2798&key=10.1038%2F163688a0 (accessed on 22 July 2022). [CrossRef]
  26. Margalef, R. Information theory in ecology. Gen. Syst. 1958, 3, 36–71. Available online: http://scholar.google.com/scholar_lookup?hl=en&volume=3&publication_year=1958&pages=36-71&journal=General+Systems&author=R.+Margalef&title=Information+theory+in+ecology (accessed on 22 July 2022).
  27. Magurran, A.E. Measuring Biological Diversity; Blackwell Publishing: Oxford, UK, 2004. [Google Scholar]
  28. Chao, A.; Kubota, Y.; Zelený, D.; Chiu, C.; Li, C.; Kusumoto, B.; Yasuhara, M.; Thorn, S.; Wei, C.; Costello, M.J.; et al. Quantifying sample completeness and comparing diversities among assemblages. Ecol. Res. 2020, 35, 292–314. [Google Scholar] [CrossRef]
  29. Chao, A.; Ricotta, C. Quantifying evenness and linking it to diversity, beta diversity, and similarity. Ecology 2019, 100, e02852. [Google Scholar] [CrossRef] [PubMed]
  30. Ingty, T. Pastoralism in the highest peaks: Role of the traditional grazing systems in maintaining biodiversity and ecosystem function in the alpine Himalaya. PLoS ONE 2021, 16, e0245221. [Google Scholar] [CrossRef] [PubMed]
  31. Zheng, H.; Liu, W.; Zheng, J.; Luo, Y.; Li, R.; Wang, H.; Qi, H. Effect of long-term tillage on soil aggregates and aggregate-associated carbon in black soil of Northeast China. PLoS ONE 2018, 13, e0199523. [Google Scholar] [CrossRef]
  32. Renard, D.; Tilman, D. National Food Production Stabilized by Crop Diversity. Nature 2019, 571, 257–260. [Google Scholar] [CrossRef]
  33. Zhao, Z.B.; He, J.Z.; Geisen, S.; Han, L.L.; Wang, J.T.; Shen, J.P.; Wei, W.X.; Fang, Y.T.; Li, P.P.; Zhang, L.M. Protist communities are more sensitive to nitrogen fertilization than other microorganisms in diverse agricultural soils. Microbiome 2019, 7, 33. [Google Scholar] [CrossRef]
  34. Yin, X.; Song, B.; Dong, W.; Xin, W.; Wang, Y. A Review on the Eco-Geography of Soil Fauna in China. J. Geogr. Sci. 2010, 20, 333–346. [Google Scholar] [CrossRef]
  35. Vera-aviles, D.; Suarez-capello, C.; Llugany, M.; Poschenrieder, C.; Santis, P.D.; Cabezas-guerrero, M. Arthropod Diversity Influenced by Two Musa -based Agroecosystems in Ecuador. Agriculture 2020, 10, 235. [Google Scholar] [CrossRef]
  36. Hättenschwiler, S.; Tiunov, A.V.; Scheu, S. Biodiversity and Litter Decomposition in Terrestrial Ecosystems. Annu. Rev. Ecol. Evol. Syst. 2005, 36, 191–218. [Google Scholar] [CrossRef]
  37. Wang, X.; Fan, J.; Xing, Y.; Xu, G.; Wang, H.; Deng, J.; Wang, Y.; Zhang, F.; Li, P.; Li, Z. The effects of mulch and nitrogen fertilizer on the soil environment of crop plants. Adv. Agron. 2019, 153, 121–173. Available online: https://scholar.google.com/scholar_lookup?title=The+effects+of+mulch+and+nitrogen+fertilizer+on+the+soil+environment+of+crop+plants&author=Wang,+X.&author=Fan,+J.&author=Xing,+Y.&author=Xu,+G.&author=Wang,+H.&author=Deng,+J.&author=Wang,+Y.&author=Zhang,+F.&author=Li,+P.&author=Li,+Z.&publication_year=2019&journal=Adv.+Agron.&volume=153&pages=121%E2%80%93173 (accessed on 22 July 2022).
  38. Ye, L.; Zhao, X.; Bao, E.; Li, J.; Zou, Z.; Cao, K. Bio-organic fertilizer with reduced rates of chemical fertilization improves soil fertility and enhances tomato yield and quality. Sci. Rep. 2020, 10, 177. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  39. Rasheed, T.; Bilal, M.; Nabeel, F.; Adeel, M.; Iqbal, H.M. Environmentally-related contaminants of high concern: Potential sources and analytical modalities for detection, quantification, and treatment. Environ. Int. 2019, 122, 52–66. [Google Scholar] [CrossRef] [PubMed]
  40. Alengebawy, A.; Abdelkhalek, S.T.; Qureshi, S.R.; Wang, M.Q. Heavy metals and pesticides toxicity in agricultural soil and plants: Ecological risks and human health implications. Toxics 2021, 9, 42. [Google Scholar] [CrossRef] [PubMed]
  41. Frampton, G.K.; Jänsch, S.; Scott-Fordsmand, J.J.; Römbke, J.; Van den Brink, P.J. Effects of Pesticides on Soil Invertebrates in Laboratory Studies: A Review and Analysis Using Species Sensitivity Distributions. Environ. Toxicol. Chem. 2006, 25, 2490–2501. [Google Scholar] [CrossRef]
  42. Markus, K.; Benjamin, P.; Carsten, B.; Arnon, D.; Isaac, Z.; Jawad, S.; Gabriele, S. Effects of Olive Mill Wastewater on Soil Microarthropods and Soil Chemistry in Two Different Cultivation Scenarios in Israel and Palestinian Territories. Agriculture 2015, 5, 857–878. [Google Scholar] [CrossRef] [Green Version]
  43. Kong, J.M.; Zheng, R.Q.; Gu, L.; Zhou, L.Q. Effects of Acetochlor on Biodiversity of Mesofauna Community in Soil. J. Agro-Environ. Sci. 2005, 24, 576–580. [Google Scholar]
  44. Wardle, D.A.; Bardgett, R.D.; Klironomos, J.N.; Setälä, H.; van der Putten, W.H.; Wall, D.H. Ecological Linkages Between Aboveground and Belowground Biota. Science 2004, 304, 1629–1633. [Google Scholar] [CrossRef]
  45. Wenninger, E.J.; Inouye, R.S. Insect community response to plant diversity and productivity in a sagebrush-steppe ecosystem. J. Arid Environ. 2008, 72, 24–33. [Google Scholar] [CrossRef]
  46. Baum, S.; Weih, M.; Busch, G.; Kroiher, F.; Bolte, A. The impact of Short Rotation Coppice plantations on phytodiversity. Landbauforsch. Volkenrode 2009, 3, 163–170. Available online: https://scholar.google.com/scholar_lookup?title=The+impact+of+Short+Rotation+Coppice+plantations+on+phytodiversity.+Landbauforsch&author=Baum,+S.&author=Weih,+M.&author=Busch,+G.&author=Kroiher,+F.&author=Bolte,+A.&publication_year=2009&journal=Vti+Agric.+For.+Res.&volume=3&pages=163%E2%80%93170 (accessed on 22 July 2022).
  47. Brockerhoff, E.G.; Jactel, H.; Parrotta, J.A.; Quine, C.P.; Sayer, J. Plantation forests and biodiversity: Oxymoron or opportunity? Biodivers. Conserv. 2008, 17, 925–951. [Google Scholar] [CrossRef]
  48. Mthimunye, T.A.; Munyai, T.C. Can Monoculture Timber Plantations Conserve More Ant Communities Than Adjacent Natural Forests? Diversity 2022, 14, 430. [Google Scholar] [CrossRef]
  49. Katayama, N.; Amano, T.; Naoe, S.; Yamakita, T.; Komatsu, I.; Takagawa, S.-I.; Sato, N.; Ueta, M.; Miyashita, T. Landscape Heterogeneity–Biodiversity Relationship: Effect of Range Size. PLoS ONE 2014, 9, e93359. [Google Scholar] [CrossRef]
  50. Vanbergen, A.J.; Watt, A.D.; Mitchell, R.; Truscott, A.-M.; Palmer, S.C.F.; Ivits, E.; Eggleton, P.; Jones, T.H.; Sousa, J.P. Scale-specific correlations between habitat heterogeneity and soil fauna diversity along a landscape structure gradient. Oecologia 2007, 153, 713–725. [Google Scholar] [CrossRef]
  51. Phalan, B.; Onial, M.; Balmford, A.; Green, R.E. Reconciling Food Production and Biodiversity Conservation: Land Sharing and Land Sparing Compared. Science 2011, 333, 1289–1291. [Google Scholar] [CrossRef] [PubMed]
  52. Wang, Y.T.; Ding, S.Y.; Liang, G.F. Multi-scale effects analysis for landscape structure and biodiversity of seminatural habitats and cropland in a typical agricultural landscape. Prog. Geogr. 2014, 33, 1704–1716. [Google Scholar] [CrossRef]
  53. Jeanneret, P.; Schüpbach, B.; Pfiffner, L.; Walter, T. Arthropod Reaction to Landscape and Habitat Features in Agricultural Landscapes. Landsc. Ecol. 2003, 18, 253–263. [Google Scholar] [CrossRef]
  54. Mitchell, M.G.; Bennett, E.M.; Gonzalez, A. Agricultural landscape structure affects arthropod diversity and arthropod-derived ecosystem services. Agric. Ecosyst. Environ. 2014, 192, 144–151. [Google Scholar] [CrossRef]
  55. Steffan-Dewenter, I.; Potts, S.G.; Packer, L. Pollinator diversity and crop pollination services are at risk. Trends Ecol. Evol. 2005, 20, 651–652. [Google Scholar] [CrossRef] [PubMed]
  56. Holzschuh, A.; Steffan-dewenter, I.; Tscharntke, T. Agricultural Landscapes with Organic Crops Support Higher Pollinator Diversity. Oikos 2008, 117, 354–361. [Google Scholar] [CrossRef]
  57. Theobald, E.J.; Gabrielyan, H.; HilleRisLambers, J. Lilies at the limit: Variation in plant-pollinator interactions across an elevational range. Am. J. Bot. 2016, 103, 189–197. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  58. Lazarina, M.; Devalez, J.; Neokosmidis, L.; Sgardelis, S.P.; Kallimanis, A.S.; Tscheulin, T.; Tsalkatis, P.; Kourtidou, M.; Mizerakis, V.; Nakas, G.; et al. Moderate fire severity is best for the diversity of most of the pollinator guilds in Mediterranean pine forests. Ecology 2019, 100, e02615. [Google Scholar] [CrossRef]
  59. Gao, M.; Liu, D.; Lin, L.; Wu, D. The small-scale structure of a soil mite metacommunity. Eur. J. Soil Biol. 2016, 74, 69–75. [Google Scholar] [CrossRef]
  60. Ma, H.; Liu, H. Characteristics of soil fauna communities in different land use patterns in the Lhasa River basin. J. Northeast Norm. Univ. (Nat. Sci. Ed.) 2012, 44, 84–90. [Google Scholar]
  61. Deutschewitz, K.; Lausch, A.; Kühn, I.; Klotz, S. Native and alien plant species richness in relation to spatial heterogeneity on a regional scale in Germany. Glob. Ecol. Biogeogr. 2003, 12, 299–311. [Google Scholar] [CrossRef] [Green Version]
  62. Fahrig, L.; Baudry, J.; Brotons, L.; Burel, F.G.; Crist, T.O.; Fuller, R.J.; Sirami, C.; Siriwardena, G.M.; Martin, J.L. Functional landscape heterogeneity and animal biodiversity in agricultural ecosystems. Ecol. Lett. 2011, 14, 101–112. [Google Scholar] [CrossRef]
  63. Gabriel, D.; Sait, S.M.; Hodgson, J.A.; Schmutz, U.; Kunin, W.E.; Benton, T.G. Scale matters: The impact of organic farming on biodiversity at different spatial scales. Ecol. Lett. 2010, 13, 858–869. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Geographical location of the study area. (a) Geographical location of Henan Province, China. (b) Geographical location of Gongyi City, Henan Province, China. (c) Land use types of Gongyi City.
Figure 1. Geographical location of the study area. (a) Geographical location of Henan Province, China. (b) Geographical location of Gongyi City, Henan Province, China. (c) Land use types of Gongyi City.
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Figure 2. Distribution of 24 sampling sites in the studied area.
Figure 2. Distribution of 24 sampling sites in the studied area.
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Figure 3. Composition of soil fauna under different landform types. (a) overall. (b) mountain landforms. (c) hilly landforms. (d) plain landforms.
Figure 3. Composition of soil fauna under different landform types. (a) overall. (b) mountain landforms. (c) hilly landforms. (d) plain landforms.
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Figure 4. Effects of landform and habitat type on soil fauna diversity. (a) Shannon-Wiener diversity indices under different geomorphic and habitat conditions; (b) Simpson’s diversity indices under different geomorphic and habitat conditions; (c) Pielou’s evenness indices under different geomorphic and habitat conditions; (d) Margalef richness indices under different geomorphic and habitat conditions. Error bars in the figure are standard deviations. The lowercase a and b on top of the columns represent the results of variance analysis in different habitats of the same landform. Different letters represent significant differences between the two (p < 0.05). The upper-case A and B on top of the columns represent the results of variance analysis under different geomorphic conditions of the same habitat, and different letters represent significant differences between them (p < 0.05).
Figure 4. Effects of landform and habitat type on soil fauna diversity. (a) Shannon-Wiener diversity indices under different geomorphic and habitat conditions; (b) Simpson’s diversity indices under different geomorphic and habitat conditions; (c) Pielou’s evenness indices under different geomorphic and habitat conditions; (d) Margalef richness indices under different geomorphic and habitat conditions. Error bars in the figure are standard deviations. The lowercase a and b on top of the columns represent the results of variance analysis in different habitats of the same landform. Different letters represent significant differences between the two (p < 0.05). The upper-case A and B on top of the columns represent the results of variance analysis under different geomorphic conditions of the same habitat, and different letters represent significant differences between them (p < 0.05).
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Figure 5. (a) The sampling plots of estimated sample completeness curves as a function of order q between 0 and 2 in the hill landform (Sobs = 55, n = 22,871), mountain landform (Sobs = 44, n = 18,150), and plain landform (Sobs = 41, n = 10,678). (b) Size-based rarefaction (solid lines) and extrapolation (dashed lines) curves up to double the reference sample size. (c) The asymptotic estimates of diversity profiles (solid lines) and empirical diversity profiles (dotted lines); numerical values refer to the estimated asymptotic diversities. (d) Coverage-based rarefaction (solid lines) and extrapolation (dashed lines) curves up to the corresponding coverage value for a doubling of each reference sample size. (e) Evenness profile as a function of order q, 0 < q ≤ 2, based on the normalized slope of Hill numbers. Solid dots and triangles denote observed data points. All shaded areas in (a–d) denote 95% confidence bands obtained from a bootstrap method with 100 replications.
Figure 5. (a) The sampling plots of estimated sample completeness curves as a function of order q between 0 and 2 in the hill landform (Sobs = 55, n = 22,871), mountain landform (Sobs = 44, n = 18,150), and plain landform (Sobs = 41, n = 10,678). (b) Size-based rarefaction (solid lines) and extrapolation (dashed lines) curves up to double the reference sample size. (c) The asymptotic estimates of diversity profiles (solid lines) and empirical diversity profiles (dotted lines); numerical values refer to the estimated asymptotic diversities. (d) Coverage-based rarefaction (solid lines) and extrapolation (dashed lines) curves up to the corresponding coverage value for a doubling of each reference sample size. (e) Evenness profile as a function of order q, 0 < q ≤ 2, based on the normalized slope of Hill numbers. Solid dots and triangles denote observed data points. All shaded areas in (a–d) denote 95% confidence bands obtained from a bootstrap method with 100 replications.
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Table
Table 1. Landscape indices in different buffer radius.
Table 1. Landscape indices in different buffer radius.
Buffer Radius (m)TAAREA_MNGYRATE_MNGYRATE_CVSHAPE_MNFRAC_CVPARA_CVENN_CVCONTAGPRPRDSHDISHEI
259.001.0050.000.001.000.000.0011.52100.00555.561.300.81
5027.001.1750.908.301.011.448.7094.0868.41725.931.530.79
7545.271.3345.7217.321.011.2922.4869.9553.271022.091.580.69
10074.142.0052.7624.061.052.1728.8748.4648.11912.141.600.73
125114.162.2459.2330.891.103.1932.0084.0850.11119.641.550.65
150162.712.8165.2338.401.143.2433.8173.7345.71106.151.560.68
175224.392.8064.3744.901.133.2931.95112.8144.81125.351.670.67
200290.483.4671.8249.861.203.9233.29108.1545.08113.791.610.67
225363.103.6372.3954.191.183.5633.4297.3546.98143.861.720.65
250455.214.1877.3858.851.203.8734.30108.1047.87132.861.640.64
5001814.106.9292.2986.681.284.6434.43184.5149.13180.991.850.64
7504077.417.2489.12102.671.264.7430.10239.3747.51190.471.920.65
10007249.168.1492.72112.041.274.8929.71203.3045.56180.251.940.67
Note: total area (TA), mean patch size (AREA_MN), mean radius of gyration (GYRATE_MN), radius of gyration (GYRATE_CV), fractal dimension index (FRAC_CV), perimeter area ratio (PARA_CV), mean shape index (SHAPE_MN), Euclidean nearest neighbor distance (ENN_CV), contagion (CONTAG), patch richness (PR), patch richness density (PRD), Shannon’s diversity index (SHDI), and Shannon’s evenness index (SHEI).
Table
Table 2. Biodiversity indices in the study area.
Table 2. Biodiversity indices in the study area.
LandformHabitatH’DERS
MountainFarmland2.551.850.421.4332.00
Artificial forest2.892.130.551.3721.00
Natural forest2.711.990.461.3533.00
Shrub3.112.320.571.2828.00
HillFarmland2.182.180.490.9736.00
Artificial forest2.952.950.511.3554.00
Natural forest2.792.790.771.1010.00
Shrub2.412.410.541.1117.00
PlainFarmland2.531.940.541.1434.00
Artificial forest2.572.020.601.1536.00
Note: H’, Shannon-Wiener diversity index; D, Simpson’s diversity index; E, Pielou’s evenness index; R, Margalef richness index; S, Number of soil fauna groups.
Table
Table 3. The numerical values for the three special cases of q = 0, 1 and 2 for abundance-based soil faunas collected.
Table 3. The numerical values for the three special cases of q = 0, 1 and 2 for abundance-based soil faunas collected.
Step 1. Sample Completeness Profiles (Panel a in Each Figure)
Completenessq = 0q = 1q = 2
Hill87.91%99.95%1
Mountain76.52%99.95%1
Plain87.91%99.95%1
Step 2. Asymptotic analysis (panels b and c in each figure)
Diversityq = 0q = 1q = 2
Hill
Asymptotic62.563.312.09
Empirical553.302.09
Undetected7.560.010
Mountain
Asymptotic57.503.292.11
Empirical44.003.282.11
Undetected13.50.010
Plain
Asymptotic45.082.901.91
Empirical41.002.891.91
Undetected4.080.010
Step 3. Non-asymptotic coverage-based rarefaction and extrapolation (panel d in each figure) Maximum standardized coverage Cmax = 1
Diversityq = 0q = 1q = 2
Hill58.563.312.09
Mountain50.573.292.11
Plain43.502.891.91
Step 4: Evenness among species abundances (panel e in each figure)
EvennessPielou J’q = 1q = 2
Hill0.290.040.02
Mountain0.300.050.02
Plain0.280.040.02
Table
Table 4. Explanation of two axis of RDA analysis under different buffer radius.
Table 4. Explanation of two axis of RDA analysis under different buffer radius.
Buffer Unit (m)FarmlandArtificial ForestNatural Forest
First AxisSecond AxisFirst AxisSecond AxisFirst AxisSecond Axis
2546.9%8.76%9.18%7.53%26%8.86%
5041.3%6.86%15.6%9.07%26%21.7%
7539%6.85%17.5%9.41%25.4%15.9%
10032.7%10.1%19.5%8.29%51%16.6%
12533.3%10.4%20.8%9.33%53%19%
15035.8%9.25%19.4%13.1%29.8%12.4%
17533.1%6.45%16.4%9.3%33.1%12.4%
20033.2%5.91%16.2%8.47%32.8%15.9%
22526.2%6.5%20.5%12.8%31.1%15.1%
25037.4%6.15%16.8%9.55%51.9%25%
50028.2%6.99%16%12.5%53.9%24.8%
75018.3%7.85%16.9%10.5%48.7%14.4%
100011.1%4.17%21%14.4%42.6%17.4%
Table
Table 5. Correlation coefficients between landscape indices of different buffer radius and species diversity indices.
Table 5. Correlation coefficients between landscape indices of different buffer radius and species diversity indices.
Buffer UnitBiodiversity IndicesLandscape Indices
TAPDCONTAGPRPRDSHDISHEI
25H’0.2320.2570.2360.3540.3190.2880.26
D0.150.2980.240.3620.3450.270.216
E−0.0350.2080.0850.2290.2450.2040.124
R0.2050.3260.2560.435 *0.408 *0.3970.343
50H’0.0960.2010.040.2550.2550.2810.207
D−0.0610.1780.0820.2480.2480.2630.206
E−0.2740.1890.2530.2870.2870.1730.096
R0.2880.27−0.0010.3520.3520.3860.224
75H’0.0890.320.010.2710.2710.290.2
D−0.0770.2640.0040.2550.2550.2810.238
E−0.3090.2210.2230.3520.3520.2850.238
R0.3070.507 *0.1270.449 *0.449 *0.3510.106
100H’0.3780.3860.2510.3540.2330.3340.24
D0.3870.30.2230.350.2270.3110.229
E0.2870.20.3290.434 *0.340.30.203
R0.2750.567 **0.2780.430 *0.340.411 *0.267
125H’0.1160.3450.3410.2550.2550.2970.287
D−0.0590.2350.2910.2380.2380.2710.28
E−0.3050.1540.434 *0.415 *0.415 *0.2740.2
R0.3320.665 **0.2640.486 *0.485 *0.3930.253
150H’0.1120.3050.3660.2560.2560.3130.342
D−0.0620.2380.3320.2480.2480.2790.306
E−0.3030.2470.495 *0.417 *0.417 *0.2850.211
R0.3290.523 **0.230.415 *0.415 *0.444 *0.377
175H’0.1070.382−0.3970.10.10.3040.375
D−0.0670.25−0.3790.0680.0680.2650.36
E−0.3110.126−0.2560.220.220.2680.248
R0.3260.616 **−0.3880.3250.3250.456 *0.369
200H’0.1180.363−0.310.1420.1420.3050.289
D−0.0580.245−0.2770.1490.1490.2670.259
E−0.3050.031−0.1730.2810.2810.2550.169
R0.3330.676 **−0.3360.3690.3690.460 *0.318
225H’0.1120.362−0.3550.1550.1550.2890.325
D−0.0610.252−0.3020.0760.0760.2130.276
E−0.3050.089−0.1790.1170.1180.1920.173
R0.330.640 **−0.3990.2490.2490.420 *0.372
250H’−0.2740.238−0.436 *0.0330.2120.2310.309
D−0.3020.115−0.3820.0090.2080.1980.286
E−0.148−0.021−0.2310.1550.2470.2090.214
R−0.2050.583 **−0.476 *0.2660.3890.3990.354
500H’−0.2750.164−0.157−0.149−0.0260.0690.158
D−0.3030.061−0.135−0.165−0.0290.0440.149
E−0.149−0.057−0.14−0.079−0.0120.0770.159
R−0.2060.413 *−0.3090.0520.1290.3220.294
750H’0.1160.037−0.165−0.284−0.284−0.0060.16
D−0.059−0.052−0.136−0.243−0.2430.0040.156
E−0.305−0.093−0.043−0.136−0.1360.0020.078
R0.3320.193−0.226−0.161−0.1610.10.193
1000H’0.116−0.136−0.076−0.187−0.187−0.0490.083
D−0.059−0.261−0.055−0.156−0.156−0.0240.086
E−0.305−0.2180.075−0.036−0.036−0.053−0.036
R0.3320.108−0.148−0.127−0.1280.0220.117
Note: * p < 0.05; ** p < 0.01. H’, Shannon-Wiener diversity index; D, Simpson’s diversity index; E, Pielou’s evenness index; R, Margalef richness index.
Table
Table 6. Multivariate analysis of variance of the effects of different scales (landform, habitat, landscape) on soil faunas.
Table 6. Multivariate analysis of variance of the effects of different scales (landform, habitat, landscape) on soil faunas.
H’DERS
Landform0.3920.9640 ***0.003 **0 ***
Habitat0.046 *0.1290.001 **0.1690 ***
Landscape0.8220.9250.008 **0.7380 ***
Landform*habitat0.0810.02 *0 ***0.2800 ***
Landform*landscape0.01 *0.027 *0.002 **0 ***0 ***
Habitat*landscape0.033 *0.012 *0.0590.023 *0 ***
Landform*habitat*landscape0.0730.0820.1010.011 *0.617
Note: * p < 0.05; ** p < 0.01; *** p < 0.001. H’, Shannon-Wiener diversity index; D, Simpson’s diversity index; E, Pielou’s evenness index; R, Margalef richness index; S, number of soil fauna groups.
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Zhang, P.; Zhang, C.; Ding, S.; Bian, Z.; Li, P.; Zhang, J.; Ding, S. Response of Soil Fauna Diversity to Agricultural Landscape Het-Erogeneity in the Middle and Lower Reaches of the Yellow River—A Case Study in Gongyi City, China. Diversity 2022, 14, 602. https://doi.org/10.3390/d14080602

AMA Style

Zhang P, Zhang C, Ding S, Bian Z, Li P, Zhang J, Ding S. Response of Soil Fauna Diversity to Agricultural Landscape Het-Erogeneity in the Middle and Lower Reaches of the Yellow River—A Case Study in Gongyi City, China. Diversity. 2022; 14(8):602. https://doi.org/10.3390/d14080602

Chicago/Turabian Style

Zhang, Panpan, Chenchen Zhang, Shunping Ding, Ziqi Bian, Peikun Li, Jian Zhang, and Shengyan Ding. 2022. "Response of Soil Fauna Diversity to Agricultural Landscape Het-Erogeneity in the Middle and Lower Reaches of the Yellow River—A Case Study in Gongyi City, China" Diversity 14, no. 8: 602. https://doi.org/10.3390/d14080602

APA Style

Zhang, P., Zhang, C., Ding, S., Bian, Z., Li, P., Zhang, J., & Ding, S. (2022). Response of Soil Fauna Diversity to Agricultural Landscape Het-Erogeneity in the Middle and Lower Reaches of the Yellow River—A Case Study in Gongyi City, China. Diversity, 14(8), 602. https://doi.org/10.3390/d14080602

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