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Article

Taxonomic Diversity and Abundance of Soil Macrofauna in Temperate Forests Under Different Types of Forest Management: A Case Study in European Russia

by
Daniil I. Korobushkin
1,*,
Nina A. Pronina
1,
Ruslan A. Saifutdinov
1,
Polina A. Guseva
1,
Sergey M. Tsurikov
1 and
Ksenia V. Dudova
1,2
1
A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Leninsky Pr., 33, Moscow 119071, Russia
2
Faculty of Biology, M.V. Lomonosov Moscow State University, Leninskie Gory, 1, Moscow 119991, Russia
*
Author to whom correspondence should be addressed.
Diversity 2025, 17(3), 216; https://doi.org/10.3390/d17030216
Submission received: 19 February 2025 / Revised: 15 March 2025 / Accepted: 16 March 2025 / Published: 18 March 2025
(This article belongs to the Section Biodiversity Loss & Dynamics)
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Abstract

:
Soil fauna perform a plethora of vital ecological functions and are often used as indicators of ecosystem disturbances. Investigating their taxa, functional diversity, and abundance is essential to assess ecosystem resilience, detect environmental stress, and guide conservation efforts. In this study, we investigated the taxonomic richness, diversity, and total and functional group abundance of soil macrofauna, as well as the environmental parameters of five model forests with different types of forest management (referred to as the “forest type”) within a temperate region of European Russia. These model forest types were subject to various types of forest management and were located in and around the Central Forest State Nature Biosphere Reserve (Tver Oblast, Russia): zonal forest (hereinafter referred to as the “zonal forest” treatment), forest disturbed by recreation (“recreational forest”), spruce forest monoculture (“monoculture”), secondary birch forest (“secondary forest”), and clear-cut site (“clear-cut”). We found that there was a significant difference in the total and average taxonomic richness of the macrofauna between the studied model forests, but no difference in mean abundance. The greatest difference was observed between the recreational (26 taxa, 11.2 ± 1.3 per site), monocultural (12 taxa, 4.8 ± 1.9 per site), and zonal (13 taxa, 4.5 ± 1.3 per site) forest types, while the macrofauna taxonomic composition was similar between the monocultural and control forests and significantly differed from that in the recreational and secondary forests and clear-cuts. Mobile taxa, mainly predators, were prevalent in the clear-cuts, while saprophages and phytophages dominated in the zonal forests and monocultures. The most important environmental factors influencing the macrofauna communities were the depth, mass, and composition of the litter, which depended on the presence of spruce (Picea abies), but not on soil parameters, the projective vegetation cover, or the abundance of microorganisms. Our study showed that anthropogenic disturbance in natural forests may not significantly alter the total abundance of the macrofauna, but it can impact the taxonomic composition and diversity of soil invertebrates. Therefore, greater attention should be given to analyzing functional and taxonomic diversity rather than relying solely on abundance data. Our findings highlight the importance of studying both the roles and diversity of soil species, not just their abundance, to better understand and protect natural ecosystems in the face of human impact.

1. Introduction

Studying the consequences of changes in natural ecosystems under the influence of global change and anthropogenic factors is an extremely relevant task in modern ecology [1,2]. In the temperate climatic zone of European Russia, forest ecosystems are a natural vegetation type that occupy the largest areas [3,4]. They perform a wide range of ecosystem functions (e.g., oxygen production and carbon sequestration, purifying water and climate regulation, and providing habitats for wildlife) and provide various ecosystem services (e.g., timber and berry production and recreation areas) for humans [5,6]. The quantity and quality of these functions are directly and indirectly linked to the high biological diversity of forest ecosystems [6,7], a significant proportion of which is supported by the soil biota. Soil organisms constitute approximately 60% of the total biodiversity [8] and actively participate in the biogenic element cycle and soil fertility formation by decomposing and transforming organic matter, thereby influencing the physical and chemical properties of soils [9,10]. Invertebrates, in particular, play a significant role in soil functioning, regulating microbial biomass, reintegrating biogenic elements, breaking down plant residues for further decomposition, and aerating and mixing substrates [11].
Forest ecosystems in European Russia face the greatest anthropogenic pressure near urbanized areas [12,13]. However, studies on the transformation of these ecosystems usually focus on vegetation succession, changes in flora, and transformation and pollution of soil cover but rarely address the soil fauna, including the communities of soil macrofauna [14]. These organisms are also sensitive to terrestrial ecosystem deformation, which can lead to significant disruptions in forest ecosystem functioning [15,16].
The most commonly used indicators of soil community disturbance are changes in biodiversity and animal abundance [17,18]. Studies on the effects of human impact are typically conducted along gradients of distance from sources of impact or are related to different stages of vegetation succession [14]. For example, proximity to industrial facilities, such as metal and chemical plants and linear infrastructure like highways and power lines, often leads to degradation of soil and vegetation cover, exceeding permissible levels of chemical compounds in the soil and reducing the biodiversity and abundance of soil fauna [19,20]. Anthropogenic transformations in ecosystems can also positively affect invertebrate communities. For instance, the managed recreational parks in urban areas [21,22] and organic farming [23,24] often show an increase in the abundance and diversity of soil invertebrates.
In the temperate zone of European Russia, comprehensive studies on soil fauna diversity in transformed forests are quite rare [25,26]. These studies often focus on a limited number of taxa or even single taxa [27,28,29] and are oriented towards examining the biodiversity of agricultural lands rather than forested areas [30]. However, the same forest ecosystem can be changed by various management practices, such as recreational use, local logging, or forest crop planting. Thus, soil fauna communities may also change in response to the type of forest management practice, but the studies on soil communities are still fragmented. This lack of research is especially notable because transformed forest ecosystems occupy a significant area within the biomes of the southern taiga, mixed forests, and deciduous forests [31].
The aim of this study is to investigate the soil macrofauna communities in forest ecosystems that have been altered by different forest management practices. For that, we have selected zonal natural forests and several types of transformed forest ecosystems that are typical of the temperate zone of European Russia, including zonal forests under recreational pressure, clear-cuts, coniferous plantations, and secondary forests that have replaced zonal forests. In addition to assessing the taxonomic composition and abundance of the soil macrofauna, we aimed to determine key environmental parameters and evaluate the relationships between changes in the macrofauna communities, forest types, and environmental factors.

2. Materials and Methods

2.1. Study Area and Samples Collection

Fieldwork was conducted in forests of the Tver Oblast, European Russia, including the Central Forest State Nature Biosphere Reserve and its surroundings. Soil samples were collected from 11–16 August 2023 (Supplementary Materials, Table S1) in forests with different types of forest management: zonal undisturbed spruce–broadleaved forest (hereinafter referred to as the “zonal forest” treatment); recreational forest, similar in structure to those in protected areas (hereinafter referred to as the “recreational forest” treatment); clear-cuts previously covered with zonal forest (hereinafter referred to as the “clear-cut” treatment); secondary forest dominated by birch and aspen (hereinafter referred to as the “secondary forest” treatment); and monocultural spruce planting in the place of the zonal forest (hereinafter referred to as the “monoculture” treatment). Each treatment was replicated three times. In total, 15 sites were selected. The distance between sites within a replicate was at least two kilometers. Within each site, two 20 × 20 m sampling plots were chosen (microsite), located 20–50 m apart, and pseudo-replicates were further considered to form a mean value per site. At each sampling plot, we collected the following: one mixed soil sample using a 15 × 15 cm frame and a depth of 15 cm to assess the abundance and diversity of the soil macrofauna; one soil sample using a corer of 2.5 cm in diameter and down to the depth of 10 cm, to analyze the total microbial biomass (these samples were immediately frozen and stored before processing); one soil sample using a corer with a diameter of 5 cm and a sampling depth of 15 cm, for subsequent determination of soil parameters, such as pH, litter weight and depth, total carbon, and nitrogen soil content; a geobotanical description following the method of Dylis [32]; the forest age was determined by coring ten trees (mostly spruce) within each site using a Pressler increment borer, followed by a counting of the annual rings; and a morphological description of the soil profile, classified according to IUSS WRB [33] guidelines.
The zonal forests were located under the protected regime of the reserve and were covered by nemoral spruce–deciduous trees (Picea abies, Tilia cordata, Acer platanoides, Quercus robur, Populus tremula) with hazel (Corylus avellana) and included elements of boreal flora. The average total projective vegetation cover was 95% (according to the methodology by Rabotnov [34]). An average of 27 vascular plant species was observed per each site. The mean age of the zonal forest was 94 ± 2 years. The abundance of deadwood was approximately 20% of the projective cover. The soils were Fragic Albic Retisols (Loamic).
The vegetation of the recreational forests was represented by spruce–birch–linden (P. abies, Betula pendula, T. cordata) forests with a predominance of hazel in the shrub stratum. According to visual analysis, these forests were affected by recreational activities such as hiking and the picking of mushrooms and berries, and there were trails, campfires, and garbage. The mean age was 46 ± 10 years, the average total projective vegetation cover was 82%, with an average of 27 vascular plant species per site. The abundance of deadwood was about 5% of the total projective cover. The soils were Plaggic Gleyic Albic Retisols (Loamic).
The secondary forests were located on the place of the cut zonal forest, which was cut down approximately 35 years ago. The tree canopy was formed predominantly by birch (B. pendula) and aspen (P. tremula) with an undergrowth of spruce. The forest age was 52 ± 3 years, the average total projective vegetation cover was approximately 88%, and an average of 21 vascular plant species was observed per site. The soils were Albic Retisols (Loamic).
The vegetation of the monoculture was represented by spruce (P. abies) plantings with isolated birch (B. pendula) and willow (Salix caprea) trees. The grass stratum was sparse or absent. The mean forest age was 46 ± 5 years, the average total projective vegetation cover was 88%, and there was an average of 21 vascular plant species per site. The soils were Fragic Folic Albic Retisols (Loamic).
The vegetation of the clear-cuts was represented by young (8 years old) birch and aspen (B. pendula, P. tremula) with grass communities. All of the sampled clear-cuts were located within previously cut zonal forests. The average total projective cover was about 96%, and an average of 33 vascular plant species was observed per site. The soils were Gleyic Albic Luvisols.

2.2. Animal Identification

The extraction of macrofauna from all the collected soil samples was performed using Tullgren extractors with a heating lamp positioned above each funnel into a 96% alcohol, along with preliminary hand-sorting of the samples to collect spiders and earthworms. To improve the drying of the soil and facilitate the extraction of invertebrates, each soil sample (15 × 15 × 15 cm) was divided into four parts and placed into separate funnels. The extracted invertebrates from these parts were combined within each sample and counted together. The extraction lasted for seven days, which was sufficient time for the soil to reach air-dry conditions. All the taxa were predominantly identified to a family level, and some groups (Aranea, Oniscidea, Chilopoda, and Diplopoda) were further identified to a genera or species level. If the identification of juveniles was impossible, they were assigned as “juv.”. The abundance of the soil macrofauna was expressed in individuals per square meter (ind./m2). Taxonomic identification was conducted using the appropriate identification keys [35,36,37,38,39]. All the animals were allocated to different functional traits based on published data [40,41] according to their feeding preferences (saprophages, predators, and herbivores), predominant vertical distribution (aboveground and belowground), and mobility (mobile and predominantly resident).

2.3. Environmental Parameters Analysis

The pH of the soil solution was determined for each soil sample by mixing it with distilled water in a 1:5 ratio. Measurements were taken in five replicates using a pH meter (M500T, MT Measurement, Moscow, Russia), following standard methods [42]. The total soil and litter carbon (C, %) and nitrogen (N, %) content was determined using the elemental analyzer (CN802, VELP Scientifica, Usmate, Italy). Prior to analysis, all the samples were oven-dried at 50 °C for 120 h. Then, soil and litter were homogenized to a state of powder using a ball mill (MM200, Retsch GmbH, Haan, Germany) and were weighed to 100 ± 5 mg and 200 ± 1 mg, respectively, using a microbalance (MX6, Mettler Toledo, Gießen, Germany). All the analyses were performed at the Joint Usage Center «Instrumental methods in ecology», A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Moscow, Russia.
The assessment of total microbial biomass (Cmic) in the soil was determined by CO2 emission using the substrate-induced respiration method [43]. A suspension of unhomogenized soil (19 g) was placed in 200 mL vials and incubated for 72 h at 25 °C. Then 5 g of glucose per 100 mL of distilled water was added, sealed with rubber stoppers, and incubated for 4 h at 25 °C. A portable infrared gas analyzer, (EGM-5, PP-Systems, Amesbury, MA, USA), was used to measure CO2 emission. The resulting CO2 emission rate was expressed as mg C/g dry soil.

2.4. Statistical Analysis

Statistical analysis was performed using R ver. 4.3.3 [44] in the RStudio software environment ver. 2023.12.1 [45]. Before statistical processing, data on the macrofauna abundance and environmental parameters obtained from microsites within each site were averaged. The data are presented as mean values (n = 3) ± standard error of the mean (SE). Differences with a p-value < 0.05 were considered statistically significant.
The effects of the model forest type on the environmental parameters and quantitative macrofauna parameters were evaluated using the one-way ANOVA with main factor “forest type”. Prior to analysis, all the dependent variables were tested for normality using the Shapiro–Wilk test and quantile–quantile (Q-Q) plots. If necessary, the dependent variables were transformed to meet a normal distribution by logarithmization. Tukey’s test was applied as a post hoc test using the multcomp package ver. 1.4-25 [46].
Taxonomic similarity of the studied macrofauna communities was analyzed using non-metric multidimensional scaling (NMDS) and permutation analysis of variance (PERMANOVA). For this purpose, a matrix with the mean abundance of taxa per site was generated and transformed by square root extraction. The Bray–Curtis index was chosen as a measure of similarity. Prior to PERMANOVA analysis, the homogeneity of the multivariate variance of the data was assessed using betadisper and permutest functions. The analysis was performed using vegan package ver. 2.6-4 [47].
The influence of environmental parameters on the soil macrofauna communities in the studied forests was assessed using canonical correspondence analysis (CCA). The dependent variable was a matrix of macrofauna species with abundance across all sites, transformed by the Hellinger method. The independent variables were environmental parameters: plant litter weight and depth; soil pH; total soil carbon, nitrogen, and their ratio; and Cmic in the soil. The independent variable “forest type” was converted to a dummy variable to represent the categorical data as binary numerical values. The matrix with environmental parameters was then also transformed using the Hellinger method. To identify the most significant independent variables explaining the distribution of macrofauna communities in the studied ecosystems, the forward stepwise method was applied. Two models were built: an “empty” model including only the macrofauna species matrix and the free term, and a “full” model including all independent variables. The best model was selected using the ordistep function based on 9999 permutation tests. The analysis was performed using the vegan package ver. 2.6-4 [47].
Descriptive statistics of the data were determined using the psych package vers. 2.4.1 [48]. The results were visualized using the ggplot2 package ver. 3.5.0 [49].

3. Results

A total of 621 individuals of the soil macrofauna belonging to 37 taxa from 14 orders were collected (Supplementary Materials, Table S2). In the zonal forests, the macrofauna were represented by 13 taxa belonging to 11 families and eight orders, dominated by Thysanoptera, Cecidomyiidae larvae, Diaspididae, and Staphylinidae. The secondary forest was inhabited by 17 taxa from 13 families and nine orders. A high abundance of several taxa (e.g., Curculionidae, Elateridae, Staphylinidae) was found here, while no pronounced dominant taxa were identified. Myriapoda were represented by centipedes Lithobius curtipes and millipedes by juvenile Polydesmus sp. Among spiders, only the families Linyphiidae and Theridiidae were collected. Within the monoculture, 12 taxa from 10 families and nine orders were found. The highest abundance was shown by Curculionidae, Diaspididae, and Cecidomyiidae larvae. The Myriapoda was only represented by the Julidae family, and the spiders belonged to the Robertus genus. The recreational forest was inhabited by 26 taxa from 17 different families and 11 orders, dominated mainly by Cantharidae, Chilopoda, Limoniidae, and Cecidomyiidae dipterans. Overall, the abundance and diversity of predators was high, as evidenced by the highest taxonomic richness of predatory Coleoptera (seven taxa) and Araneae (seven taxa) found in this forest type. In the clear-cuts, we collected 25 taxa of macrofauna belonging to 19 families and 11 orders, with Araneae (seven taxa) and Myriapoda (six taxa) being the most diverse and abundant.
The taxonomic composition of the soil macrofauna communities differed significantly in the multidimensional space, depending on the model forest type (PERMANOVA: F = 2.04, p < 0.005). The communities in the monocultural forests were the most similar to those in the zonal forest along both NDMS axes (Figure 1). The communities from the other three forest types were taxonomically close to each other, and at the same time, they strongly differed from the communities found in the zonal and monocultural forests.
The statistical analysis (Table 1) also showed significant differences in the mean number of taxa between forest types. Recreational forests had the highest value, followed by clear-cuts and secondary forests, while the zonal forests and monocultures showed the lowest average and total taxonomic richness. We also observed a high diversity of predators and mobile invertebrates in disturbed forest types, while saprotrophic invertebrates predominated in zonal forests.
The average abundance of the macrofauna was not significantly (Table 1) different between the studied forest types. However, it was slightly higher in the recreational forests compared to the zonal forests and monocultures; this was mainly due to the high values of Curculionidae. Among the functional groups, mobile invertebrates showed significantly higher abundance in recreational forests compared to the zonal and monocultural forests; this was predominantly due to high abundance of predators. A comparison of the abundance of the other functional groups between the forest types did not reveal significant differences. However, the abundance ratio of non-predatory taxa (the combined abundance of saprophages and phytophages) to predatory taxa showed a predominance of the latter in the clear-cuts (non-predatory/predatory taxa = 0.5 ± 0.1), recreational forests (non-predatory/predatory taxa = 0.7 ± 0.3), and secondary forests (non-predatory/predatory taxa = 0.5 ± 0.2). Conversely, non-predatory invertebrates predominated in the zonal forests (non-predatory/predatory taxa = 2.5 ± 0.4) and monocultures (non-predatory/predatory taxa = 3.1 ± 1.6).
The “forest type” factor significantly (Table 2) affected litter depth, its weight, and nitrogen content, in addition to the average number of taxa. Litter weight was significantly higher in zonal forests compared to all other forest types except secondary forests. The highest litter depth was also measured in the zonal forests, as well as in the monocultures. However, there were no significant differences between forest types in terms of soil acidity and microbial biomass, which showed minor variations within narrow ranges (5.07 to 5.66 pH and 0.93 to 1.23 mgC/g dry soil, respectively). According to the CCA, a model with litter depth as a single independent variable has been found to be the most effective in explaining the distribution of macrofauna communities in the studied forests (F = 1.6, p < 0.006). Litter depth emerged as a significant determinant in distinguishing between taxonomically similar macrofauna communities in the zonal forests and monocultures compared to the other three forest types (Figure 2). In particular, the increase in litter depth and, consequently, its thickness was strongly correlated with an increase in the abundance of Myriapoda, Thysanoptera, Diptera larvae (Limoniidae, Tipulidae), Diaspididae, and Curculionidae.

4. Discussion

Our study revealed that despite the notably insignificant variation in total macrofauna abundance and soil environmental parameters (measured soil properties, microbial biomass, and total projective cover of plants), forest transformation has a significant impact on the taxonomic structure of soil macrofauna communities. A common feature of macrofauna communities in forest types managed by humans, except for the monocultures, is unpredictable spatial distribution of the abundance of dominant and subdominant taxa, even between sites within the same forest type. This heterogeneity of invertebrate communities results in higher taxonomic richness compared to zonal forests and leads to an increase in the number of potential dominant taxa. Compared to the zonal forest, the managed forest types demonstrated an alteration in the trophic group ratio and an increase in mobile taxa, especially the abundance of predators. This indicates trophic imbalances in the soil macrofauna communities and indicates the instability of the ecosystem, as primary consumers are typically dominant in undisturbed ecosystem conditions [50]. Similar results with decreasing saprophagous macroinvertebrate abundance ratios were shown for fire-induced ecosystems [40,51,52] and those disturbed by windstorms [15,53]. These findings suggest that saprophagous invertebrates generally have lower resilience than other trophic groups of epigeic invertebrates [40,54]. In our research, we also observed lower litter depth values in the managed forests, which may be due to the higher mineralization rate of the deciduous litter by biota and the trampling and blowing of leaves in recreational forests, as well as the absence of old trees and the unstable soil moisture level in clear-cuts and secondary forests. Zonal forests and spruce monocultures, which had experienced minimal anthropogenic impact in our research, usually show less pronounced fragmentation and compaction of litter particles [55]. These results are consistent with previous observations that showed that the thickness, quality, and quantity of litter are important drivers of the formation of soil invertebrate communities [10,52,56].
The highest total and taxonomic richness of the macrofauna was observed in the recreational forests and clear-cuts. Similar findings have been reported in the park-like forests of the Urals, where higher densities of soil organisms were noted compared to undisturbed or intermittently disturbed ecosystems [50,57]. This can be explained by the potentially higher structural complexity in moderately disturbed forests contributing to a higher diversity of microhabitat conditions (e.g., contrasting microhabitats in clear-cuts with dry areas, puddles, and areas with coniferous wood residues and birch–aspen litter) and resources compared to more homogeneously transformed (e.g., monocultures) or undisturbed forests [15,58]. In the case of lower litter depth, the increase in the abundance of highly mobile invertebrates seems logical. This functional group primarily consists of surface-dwelling predators that scavenge for prey across habitats [59]. Another reason for the increase in the abundance of predators in disturbed forest types compared to the zonal forests may be related to proximity to artificial water bodies near recreational forests and the significant soil waterlogging that is usual during the post-logging period in clear-cuts [60]. Such conditions facilitate an increase in the abundance of spiders and hydrophilous beetles due to emerging of amphibious insects, which can make up a significant part of their diets [40], and may also explain the high abundance of hygrophilous Limoniidae dipterans in studied clear-cuts [38]. Overall, the changes in predatory species composition (especially arachnids) under ecosystem disturbance was quite expected [15,50].
We did not find significant differences in almost any of the studied environmental parameters, including microbial biomass and soil pH, between the model forest types. This uniformity may explain why these parameters did not significantly influence the distribution of macrofauna among forest types. Our results are also consistent with previous studies, which have shown that soil acidity and forest stand composition have a weaker effect on soil communities [61,62,63]. The measured values of soil acidity corresponded well with the data provided by Karavanova et al. [64] for various soils of the same area, which allows us to exclude the possibility of an instrumental error. Nevertheless, we determinate a significantly higher total nitrogen content in the soils of the zonal forests compared to clear-cuts and monocultures. This strongly depended on soil type and, in particular, on the higher humic substance content of the upper horizon in the soils of zonal forests [65], compared with the dry and acidic upper soil horizon of the monocultures and over-moistened and dense upper soil horizon of the clear-cuts. In this regard, we expected differences in microbial biomass between the forest types, as total nitrogen soil content, vegetation, and leaf litter varied between treatments [66]. But the total C/N ratio did not differ significantly between forest types, indicating that there were no significant differences in the litter quality and activity of its mineralization by microorganisms [67]. While changes in the composition of microbial communities may occur depending on the dominant litter species [66,68], the total abundance of microorganisms and their correlation with the soil invertebrate abundance and community structure have not been confirmed. We also assumed higher Cmic values in the studied area, based on the study by Ananyeva and colleagues [69] for similar soil types. But the abiotic conditions seemed to differ during our research compared to previously published data. Since the Cmic value is a highly variable parameter, it can be influenced by differences in sampling season, temperature, precipitation amount, soil humidity, litter composition and amount, etc. [68,70]. The next important issue seems to be the measurement of the C/N values in the leaf litter of different plant species, which will provide information on the quality of the dominant litter and its availability to soil microorganisms and soil animals.
We expected a sharp decrease in the total and average taxonomic richness and abundance of soil invertebrates in the monocultural forests compared to the zonal forests, due to the known reduction in the number and complexity of microhabitats [71]. Conversely, however, our study revealed the most similarities in taxonomic composition between these forest types using both NMDS and CCA analyses. The revealed separation of studied forest types into two clusters can be explained based on the ratio of deciduous to coniferous tree species. Zonal forests and monocultures were dominated by spruce, which acted as the main forest-forming species and formed most of coniferous litter. The high amount of coniferous litter is an important factor in reducing the occurrence of many saprotrophic invertebrates that prefer deciduous leaf litter [72,73], directly influencing the abundance and diversity of woodlice and millipedes in studied forests. This is confirmed by the low abundance of woodlice in the zonal and monocultural coniferous forests and the high abundance found in the secondary and recreational forests with a high content of birch and hazel leaves in the litter layer.
Surprisingly, low densities of Carabidae beetles in zonal forests and monocultures have been confirmed by previous studies conducted close to our study area, including the Central Forest State Nature Biosphere Reserve [74]. This research suggests that most species of ground beetles near our study area prefer more open habitats, such as meadows, glades, and sparse forests, while carabids are rare in mixed and deciduous forests around the reserve [74]. Studying such an ecologically and taxonomically diverse family as ground beetles clearly requires additional seasonal research on its spatial distribution and species diversity, applying different sampling methods, such as pitfall trapping and hand sorting.
Despite the results obtained, the pilot status of this research provides certain limitations. Firstly, the geographically small sample size (only the eastern part of the biome) limited the ability to fully assess macrofauna and soil parameters on a biome-wide scale and meant that its extrapolation to other biomes was impossible. Secondly, we also recognize some limitations in the sampling methodology. The number of soil samples and the size of the soil frame used (15 × 15 cm) may not fully reflect the actual taxonomic richness and abundance of the active and large-sized taxa, the diversity of which is often estimated using a pitfall trap method [75]. The assessment of earthworm abundance was also not carried out during the research, since we used the Tullgren extraction method based on thermotaxis. The method is generally the best for extracting soil macrofauna, but ineffective for earthworm abundance assessment [18,75], for which the hand-sorting protocol of Tropical Soil Biology and Fertility (TSBF) method is typically used [76]. Thus, additional detailed research is required to assess the response of Coleoptera and earthworm abundance and diversity to ecosystem change, as well as the species level of taxonomic identification of some soil invertebrate taxa. Further research on soil mesofauna abundance (e.g., springtails, oribatid mites, and enchytraeids) is also urgently needed in order to fully understand the consequences of community changes. Because the soil mesofauna plays an important role in litter decomposition and the overall ecosystem functioning [10], it also serves as a main food source for small predators, like hunting spiders and linyphiids [77].

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/d17030216/s1, Table S1: GPS coordinates of the sampling plots, Table S2: List of macrofauna taxa, total and average taxonomic richness, average in the studied forest types.

Author Contributions

Conceptualization, D.I.K. and K.V.D.; methodology, D.I.K., R.A.S., S.M.T. and P.A.G.; software, D.I.K. and R.A.S.; formal analysis, D.I.K., N.A.P. and R.A.S.; investigation, D.I.K., N.A.P., P.A.G., S.M.T. and K.V.D.; data curation, R.A.S. and K.V.D.; writing—original draft preparation, N.A.P., D.I.K., R.A.S. and P.A.G.; writing—review and editing, D.I.K., R.A.S. and S.M.T.; visualization, R.A.S.; supervision, D.I.K.; project administration, K.V.D.; funding acquisition, K.V.D. and N.A.P. All authors have read and agreed to the published version of the manuscript.

Funding

The research was supported by the Russian Science Foundation (Project No. 23-74-01143).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The specimens described in this study are stored and available at the A.N. Severtsov Institute of Ecology and Evolution RAS, Moscow, Russia. The data on soil macrofauna are available as electronic Supplementary Materials.

Acknowledgments

We are grateful to K.B. Gongalsky and A.S. Zaitsev for their valuable assistance and helpful comments. We also thank A.A. Panchenkov, A.S. Dudova, P.A. Gusev, and G.D. Korobushkin for their help during fieldwork. Special thanks to A.V. Popova for her assistance with soil sample processing and CO2 emission measurements.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Ordination diagram of taxonomic similarity of macrofauna communities of the studied forest types, obtained by non-metric multidimensional scaling (NMDS) analysis. The Bray–Curtis index was used as a measure of similarity. Each forest type is represented by 3 true replicates (n = 3), and each point is represented by the mean of two microsite pseudo-replicates. Numbers in the diagram illustrate taxa: 1—Hemiptera sp., 2—Curculionidae sp., 3—Staphylinidae juv., 4—Staphylinidae ad., 5—Carabidae ad., 6—Lithobius curtipes, 7—Lithobius sp. juv., 8—Polyzonium germanicum, 9—Polydesmus denticulatus, 10—Polydesmus sp. juv., 11—Julidae sp. juv., 12—Leptoiulus proximus, 13—Lumbricidae ad., 14—Hahniidae sp. juv., 15—Ozyptila sp. juv., 16—Xysticus sp. sad., 17—Stylommatophora sp., 18—Linyphiidae sp. ad., 19—Opiliones ad., 20—Linyphiidae sp. juv., 21—Robertus sp. juv., 22—Theridiidae sp., 23—Ligidium hypnorum, 24—Trachelipus rathkii, 25—Thysanoptera ad., 26—Diaspididae ad., 27—Lepidoptera juv., 28—Alleculidae juv., 29—Trochosa sp. ad., 30—Alopecosa sp. juv., 31—Pardosa sp. juv., 32—Lycosidae sp. juv., 33—Elateridae juv., 34—Cantharidae juv., 35—Tipulidae juv., 36—Cecidomyiidae juv., 37—Limoniidae juv., 38—Coleoptera sp. The stress value of the analysis is 0.163.
Figure 1. Ordination diagram of taxonomic similarity of macrofauna communities of the studied forest types, obtained by non-metric multidimensional scaling (NMDS) analysis. The Bray–Curtis index was used as a measure of similarity. Each forest type is represented by 3 true replicates (n = 3), and each point is represented by the mean of two microsite pseudo-replicates. Numbers in the diagram illustrate taxa: 1—Hemiptera sp., 2—Curculionidae sp., 3—Staphylinidae juv., 4—Staphylinidae ad., 5—Carabidae ad., 6—Lithobius curtipes, 7—Lithobius sp. juv., 8—Polyzonium germanicum, 9—Polydesmus denticulatus, 10—Polydesmus sp. juv., 11—Julidae sp. juv., 12—Leptoiulus proximus, 13—Lumbricidae ad., 14—Hahniidae sp. juv., 15—Ozyptila sp. juv., 16—Xysticus sp. sad., 17—Stylommatophora sp., 18—Linyphiidae sp. ad., 19—Opiliones ad., 20—Linyphiidae sp. juv., 21—Robertus sp. juv., 22—Theridiidae sp., 23—Ligidium hypnorum, 24—Trachelipus rathkii, 25—Thysanoptera ad., 26—Diaspididae ad., 27—Lepidoptera juv., 28—Alleculidae juv., 29—Trochosa sp. ad., 30—Alopecosa sp. juv., 31—Pardosa sp. juv., 32—Lycosidae sp. juv., 33—Elateridae juv., 34—Cantharidae juv., 35—Tipulidae juv., 36—Cecidomyiidae juv., 37—Limoniidae juv., 38—Coleoptera sp. The stress value of the analysis is 0.163.
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Figure 2. Ordination diagram illustrating the results of canonical correspondence analysis (CCA), with a matrix of macrofauna taxa (dependent variable) and environmental parameters (independent variables). The model with a single independent variable (plant fall depth) was selected as the best model based on the Akaike criterion and the highest total explained variance using the ordistep function of the vegan package. Before analysis, both matrices were transformed using the Hellinger method. Each forest type is represented by 3 true replicates (n = 3). The numbers in the diagram illustrate the taxa: 1—Hemiptera sp., 2—Curculionidae sp., 3—Staphylinidae juv., 4—Staphylinidae ad., 5—Carabidae ad., 6—Lithobius curtipes, 7—Lithobius sp. juv., 8—Polyzonium germanicum, 9—Polydesmus denticulatus, 10—Polydesmus sp. juv., 11—Julidae sp. juv., 12—Leptoiulus proximus, 13—Lumbricidae ad., 14—Hahniidae sp. juv., 15—Ozyptila sp. juv., 16—Xysticus sp. sad., 17—Stylommatophora sp., 18—Linyphiidae sp. ad., 19—Opiliones ad., 20—Linyphiidae sp. juv., 21—Robertus sp. juv., 22—Theridiidae sp., 23—Ligidium hypnorum, 24—Trachelipus rathkii, 25—Thysanoptera ad., 26—Diaspididae ad., 27—Lepidoptera juv., 28—Alleculidae juv., 29—Trochosa sp. ad., 30—Alopecosa sp. juv., 31—Pardosa sp. juv., 32—Lycosidae sp. juv., 33—Elateridae juv., 34—Cantharidae juv., 35—Tipulidae juv., 36—Cecidomyiidae juv., 37—Limoniidae juv., 38—Coleoptera sp.
Figure 2. Ordination diagram illustrating the results of canonical correspondence analysis (CCA), with a matrix of macrofauna taxa (dependent variable) and environmental parameters (independent variables). The model with a single independent variable (plant fall depth) was selected as the best model based on the Akaike criterion and the highest total explained variance using the ordistep function of the vegan package. Before analysis, both matrices were transformed using the Hellinger method. Each forest type is represented by 3 true replicates (n = 3). The numbers in the diagram illustrate the taxa: 1—Hemiptera sp., 2—Curculionidae sp., 3—Staphylinidae juv., 4—Staphylinidae ad., 5—Carabidae ad., 6—Lithobius curtipes, 7—Lithobius sp. juv., 8—Polyzonium germanicum, 9—Polydesmus denticulatus, 10—Polydesmus sp. juv., 11—Julidae sp. juv., 12—Leptoiulus proximus, 13—Lumbricidae ad., 14—Hahniidae sp. juv., 15—Ozyptila sp. juv., 16—Xysticus sp. sad., 17—Stylommatophora sp., 18—Linyphiidae sp. ad., 19—Opiliones ad., 20—Linyphiidae sp. juv., 21—Robertus sp. juv., 22—Theridiidae sp., 23—Ligidium hypnorum, 24—Trachelipus rathkii, 25—Thysanoptera ad., 26—Diaspididae ad., 27—Lepidoptera juv., 28—Alleculidae juv., 29—Trochosa sp. ad., 30—Alopecosa sp. juv., 31—Pardosa sp. juv., 32—Lycosidae sp. juv., 33—Elateridae juv., 34—Cantharidae juv., 35—Tipulidae juv., 36—Cecidomyiidae juv., 37—Limoniidae juv., 38—Coleoptera sp.
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Table 1. Average macrofauna abundance (individuals per square meter ± standard error, n = 3), total and average taxonomic richness and environmental parameters (mean ± standard error, n = 3) in different forest types. Different letters indicate significant differences between the mean values according to Tukey’s HSD test, p < 0.05.
Table 1. Average macrofauna abundance (individuals per square meter ± standard error, n = 3), total and average taxonomic richness and environmental parameters (mean ± standard error, n = 3) in different forest types. Different letters indicate significant differences between the mean values according to Tukey’s HSD test, p < 0.05.
ParameterZonal ForestSecondary ForestMonocultureRecreational ForestClear-Cut
Total abundance, ind. m−2503.7 ± 233.2 a792.6 ± 216.3 a592.6 ± 301 a1844.4 ± 607.1 a800.0 ± 66.7 a
Predators, ind. m−2140.7 ± 39.2 b437.0 ± 137.2 ab185.2 ± 141.3 b1163.0 ± 543.2 a533.3 ± 67.9 ab
Saprophages, ind. m−281.5 ± 26.7 a103.7 ± 19.6 a140.7 ± 39.2 a259.3 ± 83.5 a111.1 ± 12.8 a
Phytophages, ind. m−2281.5 ± 172.3 a229.6 ± 85.4 a266.7 ± 135.2 a303.7 ± 99.7 a118.5 ± 37 a
Unknown trophic position, ind. m−2 *0 ± 022.2 ± 12.80 ± 0118.5 ± 51.937.0 ± 26.7
Mobile, ind. m−2459.3 ± 211.7 b688.9 ± 167.8 ab577.8 ± 299.0 b1666.7 ± 560.9 a688.9 ± 33.9 ab
Resident, ind. m−244.4 ± 22.2 ab103.7 ± 48.6 ab14.8 ± 7.4 b177.8 ± 46.3 a118.5 ± 26.6 ab
Aboveground, ind. m−2355.6 ± 215.8 a311.1 ± 115.5 a200.0 ± 109.6 a474.1 ± 94.6 a318.5 ± 26.7 a
Belowground, ind. m−2148.1 ± 19.6 a481.5 ± 232.8 a392.6 ± 194.3 a1370.4 ± 531.5 a488.9 ± 58.8 a
Total number of taxa **1317122625
Average taxonomic richness ***4.5 ± 1.3 b7.7 ± 1.6 ab4.8 ± 1.9 b11.2 ± 1.3 a9.5 ± 0.6 ab
Litter weight, g11.69 ± 2 a8.93 ± 19.2 ab4.76 ± 0.25 bc5.27 ± 0.77 bc1.88 ± 0.24 c
Litter depth, cm4.42 ± 0.17 a0.95 ± 0.15 b3.67 ± 0.3 a1.58 ± 0.22 b1.13 ± 0.07 b
pH5.51 ± 0.27 a5.07 ± 0.15 a5.4 ± 0.13 a5.66 ± 0.2 a5.19 ± 0.15 a
C, %7.2 ± 1.23 a3.93 ± 0.49 a3.75 ± 0.48 a3.93 ± 0.9a3.95 ± 0.5 a
N, %0.58 ± 0.06 a0.3 ± 0.04 ab0.27 ± 0.03 b0.3 ± 0.08 ab0.28 ± 0.04 b
C/N11.9 ± 0.67 a13.1 ± 0.21 a13.8 ± 0.85 a13.04 ± 0.25 a14.2 ± 1.41 a
Cmic, mg C/g dry soil1.23 ± 0.21 a1.01 ± 0.05 a1.05 ± 0.27 a0.93 ± 0.14 a0.93 ± 0.02 a
* Invertebrates with unknown trophic position were excluded from the ANOVA test; ** Staphylinidae gen. sp. juv. and Linyphiidae gen. sp. juv. were excluded when counting the total number of taxa per site. *** Juveniles specimens were excluded from the taxonomic richness if species belonging to the same taxon were present at the site.
Table 2. Results of one-factor analysis of variance (ANOVA) illustrating the effect of the type of forest studied on environmental parameters, mean abundance, and taxonomic richness of soil macrofauna. Dependent variables for which statistically significant differences were found at p < 0.05 are marked in bold.
Table 2. Results of one-factor analysis of variance (ANOVA) illustrating the effect of the type of forest studied on environmental parameters, mean abundance, and taxonomic richness of soil macrofauna. Dependent variables for which statistically significant differences were found at p < 0.05 are marked in bold.
VariableSum of SquaresdfMean SquareFp-Value
Litter weight176.2444.18.80.003
Litter depth5.941.545.10.001
pH0.740.21.60.2
Cmic0.240.050.50.7
N, %1.340.34.60.03
C, %0.940.23.10.07
C/N0.0540.011.30.3
Total macrofauna abundance3.940.972.30.1
Average taxonomic richness301.7475.46.80.01
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Korobushkin, D.I.; Pronina, N.A.; Saifutdinov, R.A.; Guseva, P.A.; Tsurikov, S.M.; Dudova, K.V. Taxonomic Diversity and Abundance of Soil Macrofauna in Temperate Forests Under Different Types of Forest Management: A Case Study in European Russia. Diversity 2025, 17, 216. https://doi.org/10.3390/d17030216

AMA Style

Korobushkin DI, Pronina NA, Saifutdinov RA, Guseva PA, Tsurikov SM, Dudova KV. Taxonomic Diversity and Abundance of Soil Macrofauna in Temperate Forests Under Different Types of Forest Management: A Case Study in European Russia. Diversity. 2025; 17(3):216. https://doi.org/10.3390/d17030216

Chicago/Turabian Style

Korobushkin, Daniil I., Nina A. Pronina, Ruslan A. Saifutdinov, Polina A. Guseva, Sergey M. Tsurikov, and Ksenia V. Dudova. 2025. "Taxonomic Diversity and Abundance of Soil Macrofauna in Temperate Forests Under Different Types of Forest Management: A Case Study in European Russia" Diversity 17, no. 3: 216. https://doi.org/10.3390/d17030216

APA Style

Korobushkin, D. I., Pronina, N. A., Saifutdinov, R. A., Guseva, P. A., Tsurikov, S. M., & Dudova, K. V. (2025). Taxonomic Diversity and Abundance of Soil Macrofauna in Temperate Forests Under Different Types of Forest Management: A Case Study in European Russia. Diversity, 17(3), 216. https://doi.org/10.3390/d17030216

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