Soils and plants in forest ecosystems are tightly linked, due especially to the long-term influence of forest stands on the soil. Soil conditions determine plant growth to some extent, which is why plants are widely used as indicators or for estimating soil and site properties [1
]. Plants, though, strongly affect soil properties, including the characteristics of communities of soil biota. Trees affect soil via both above- and belowground resource-based mechanisms (e.g., litter input on the soil surface, belowground deposition of root exudates, dead roots), and by altering abiotic conditions (e.g., by leaf fall, shading, frost protection, transpiration) [3
]. Trees can strongly affect the physical structure of the soil, water flow, soil pH, and the contents of soil organic matter and nutrients [4
]. Changes in soil properties can consequently affect the quality of living conditions for soil biota, leading to changes in their abundance, biomass, activity, and community structure [6
European beech (Fagus sylvatica
L.) and Norway spruce (Picea abies
(L.)) are dominant species in forests in Central Europe, covering 30% of the forested area [9
]. European beech forests have been exploited more intensively than Norway spruce forests and have been mostly converted to age class systems. Pure stands of Norway spruce have often been cultivated outside its natural range at the expense of European beech throughout the last 200 years [12
]. Norway spruce outperforms most other species when water is abundant, and bark beetles, windthrow, and ice breakage are rare [11
]. Natural unmanaged forests represent unevenly aged forests characterised by a fine-grained mosaic of different developmental stages of trees due to frequent small-scale disturbances and the presence of large amounts of dead wood and decaying trees [14
]. After harvesting, mixed mountain forests were often left in a more natural state due to the difficulty of access. High heterogeneity within unevenly aged stands increases biodiversity and stand stability, and such forests can more successfully fulfil their functions than forests with more traditional evenly age stands favoured by policy makers [16
]. Studies in the last decade, however, have confirmed that biodiversity is not necessarily higher in forests without human influence [17
]. This inconsistency has led to ongoing discussions of biodiversity in managed and unmanaged forests [19
]. Studies have especially been concerned with plants, birds, or beetles, but only a few studies have investigated soil nematode communities.
Nematodes in forest soil are ubiquitous, abundant, functionally diverse, and very sensitive to environmental changes. They participate in all major trophic levels of the soil food web and are responsible for several processes vital for the correct functioning of soil ecosystems [20
]. The analysis of the composition of nematode fauna serves as a basis for the ecological assessment of soil [22
]. Nematode communities are made up of diverse species that, according to their feeding habits, can be classified into five major groups: plant parasites, bacterial and fungal feeders, predators, and omnivores [23
]. The most abundant nematode taxa in forest soil feed on bacteria and, together with fungivorous nematodes, play a key role in the decomposition of organic matter and the cycling of nutrients in the soil [23
]. Herbivorous nematodes feed on and damage plant roots and can have a negative impact on plant growth. An increase in their abundance or a strong dominance of herbivore species can occur in biotopes with characteristics similar to those in long-term monocultures and is usually associated with soil degradation [23
]. Omnivorous and predatory nematodes represent the highest trophic level amongst soil microfauna. An increase in the abundance of these groups may be an indication of the naturalness of the environment [23
]. Analysis of nematode communities provides information on succession, changes in the pathways of decomposition in soil food webs, nutrient status, fertility, soil acidity, and the effects of soil contaminants [26
]. Nematodes are useful indicators of soil conditions due to their ecological importance and sensitivity to environmental changes [21
In our study, we focused on soil nematode communities in managed (pure beech and pure spruce) stands and an unmanaged old-growth forest, all representing the most typical and broadly distributed forests in mountains of the temperate zone in Europe. The goals of our study were to determine (i) whether and to what extent the management of forest stands affects the abundance and structure of nematode communities compared to unmanaged forests, (ii) whether and how the stands of different age classes alter soil nematode communities, and (iii) whether or not the patterns of nematode responses associated with the stand age and management are the same in spruce and beech ecosystems. We hypothesised that nematode communities would generally be more diverse in the unmanaged forest due to the more heterogeneous stand structure. Environmental conditions and understory vegetation change during the lifecycle of a stand, however; therefore, we expected that the succession of developmental stages would affect nematode communities, and that the pattern of responses would not necessarily be uniform for tree species such as beech and spruce, representing broadleaves and conifers.
2. Materials and Methods
2.1. Site Description
This study was carried out on Mount Poľana (48°37′ N 19°30′ E) in the Western Carpathian Mountains in central Slovakia (Europe). Mount Pol’ana is one of the highest European former volcanoes, with an altitude of 1458 m a.s.l., formed mainly of andesite and andesite tuffs rich in soil nutrients. Eutric Cambisol is the most widespread soil type at lower altitudes, with Dystric Cambisols transitioning to Andosols at higher altitudes, where the presence of allophane has led to a higher capacity of the soil to accumulate organic matter [27
]. Soils are deep, with variable contents of rock fragments depending on the parental material. Soils developed from andesite are characterised by a high stone content in the profile, but soils developed from andesite tuffs contain much fewer rock fragments. Mean annual temperature ranges from 2 to 4 °C, and mean annual precipitation ranges between 900 and 1200 mm [28
], typical for a humid continental climate. The vegetation of the area is very diverse, with >1200 taxa of higher vascular plants. Forests represent the most typical and broadly distributed example of temperate forests in European mountains [29
]. The species composition of the native trees has been affected by management practices, but natural forests in a part of the area have been protected as natural reserves since 1972 and as Biosphere Reserves since 1990. Plots were established at sites with similar altitudes of 950 to 1250 m a.s.l. on slopes with southern aspects (ESE to SE).
2.2. Sampling Procedure
This study was performed in three types of forest ecosystems: a managed beech forest (BEE), a managed spruce forest (SPR), and an unmanaged mixed forest (UNM).
The managed forests (BEE and SPR) were developed from natural forests, clearcut, and characterised by a single crown layer typical for evenly aged stands. In BEE, European beech (Fagus sylvatica L.) from natural regeneration is the dominant tree species, but Acer spp. originating from natural regeneration can also occur. In SPR, Norway spruce (Picea abies (L.) H. Karst.) trees were planted. The plots in BEE and SPR were stratified by stand age into three age classes: 0–20, 40–60, and 100–120 years of age.
UNM represents an old-growth forest in an untouched area, characterised by diverse spatial structures, heights, and diameters, and composed of mixed tree species dominated by F. sylvatica
, Abies alba
Mill., and Acer pseudoplatanus
L., with occasional Fraxinus excelsior
L., P. abies
, and Ulmus glabra
Our study had a total of 45 plots, 15 plots for each forest type (5 plots for each age class in each forest type (BEE and SPR) and 15 plots in UNM). In each plot, five average soil samples, which consisted of four sub-samples from a 1 m2 area at a depth of 10–15 cm, were collected in August 2019 using a hand spade. The samples were thoroughly mixed, transferred to the laboratory in plastic bags, and stored at 5 °C until processing.
2.3. Analyses of Soil Properties
The soil moisture content of the fresh samples was estimated gravimetrically by oven drying at 105 °C overnight to a constant weight. The chemical soil properties were analysed in air-dried soil samples. Soil pH was measured potentiometrically in a water suspension using a digital pH meter at a 1:2.5 soil/water ratio. The contents of total carbon (C) and nitrogen (N) were determined using a Vario MACRO Elemental Analyzer (CNS Version; Elementar, Hanau, Germany).
2.4. Analyses of Nematode Communities
Nematodes were isolated from 100 g of soil using a modified Baermann technique with a set of two cotton-propylene filters for 24 h at room temperature (20 °C), and extracted nematodes were heat killed, fixed in Ditlevsen’s solution, and mounted in glycerin [30
]. Isolated nematodes were identified at the genus level using a light microscope (Nikon Instruments Europe BV, The Netherlands), original species descriptions, and several taxonomic keys [31
The nematodes were divided into five trophic groups: bacterivores, fungivores, herbivores, omnivores, and predators [25
]. The maturity index (MI) for free-living taxa and the plant parasite index (PPI) for plant-parasitic taxa [39
] were calculated using the coloniser-persister (cp) value based on life history trails following Bongers and Bongers [40
]. The enrichment index (EI), the structure index (SI), the channel index (CI) [41
], and the basal index (BI) [42
] were calculated. The CI is calculated as the percentage of bacterivores relative to their number plus that of fungivores. The EI, SI, CI, and BI differ in that their calculation involves a weighing system for nematode functional guilds, and in that they are used to infer the food web complexity and the main pathways of organic matter decomposition [41
]. The nematodes were assigned to trophic groups [23
]. All indices (MI, PPI, EI, SI, BI, CI) and nematode biomass were calculated using the online programme ‘NINJA: An automated calculation system for nematode-based biological monitoring’ [43
]. The Shannon–Weaver index was used for calculating generic diversity: H’gen = −∑(Pi
), where Pi
is the proportion of the genus divided by the total nematode abundance in the sample [44
]. The dominance of the nematode genera (D, %) was calculated as: D
) × 100, where n
is the total abundance of nematode genera, and s
is the total abundance of nematode genera per sample. The frequency of occurrence (F, %) was calculated as: F
) × 100, where ni
is the number of samples containing genus i
, and s
is the total number of samples.
2.5. Statistical Analysis
The data were analysed in two ways. All samples were first analysed by forest type (BEE, SPR, and UNM), but samples for only two stands were analysed by both forest type (BEE and SPR) and age class (0–20, 40–60, and 100–120 years of age) due to the unstructured nature of UNM.
Soil moisture content, pH, total C, total N content, and the C/N ratio were analysed untransformed because they satisfied the assumptions of the parametric tests. In contrast, nematode characteristics (total abundance, H’gen, total biomass, and abundance per trophic group) and basic ecological characteristics (MI, PPI, EI, SI, BI, and CI) did not meet the assumptions of the parametric tests; therefore, a Box–Cox transformation was applied using maximum likelihood and a golden search iterative procedure prior to the tests.
One-way ANOVA was used for samples analysed by forest type. Two-way ANOVA with mixed effect and main effect ANOVA (if a mixed effect was not confirmed) was used for samples analysed by both forest type and age class. Each type of forest was analysed separately if a mixed effect of forest type × age was significant (only for H’gen, PPI, and omnivores). Fisher’s LSD post hoc test was used to identify differences amongst the age classes. All statistical analyses were performed using Statistica Cz, version 12.0 [45