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. 2026 Oct 23;97(4):55. doi: 10.1007/s10493-026-01201-3

Ecological impact of steeplebush (Spiraea tomentosa L.) invasion on mite communities (Acari, Mesostigmata) in wet meadow ecosystems

Cezary Urbanowski 1, Jacek Malica 1, Krzysztof Turczański 2, Agnieszka Andrzejewska 3, Przemysław Kurek 4, Blanka Wiatrowska 2,✉
PMCID: PMC13694395  PMID: 42909570

Abstract

Invasive plants are species introduced outside their natural range and often cause ecological or economic harm. By modifying litter quality, root architecture, and soil chemistry, invasive plants can alter many soil processes, including nutrient cycling and edaphic activity. The main aim of our study was to provide the first assessment in Poland of the ecological impact of Spiraea tomentosa L. invasion on the abundance, diversity, and species richness of soil Mesotigmata (Gamasida; Gamasina and Uropodnia) mite communities in wet meadow ecosystems, thereby offering novel insights into the belowground ecological consequences of this invasive species. The research was carried out in a wet meadow complex located in the Lower Silesian Forests in south-western Poland. A total of 104 soil samples were collected from 26 experimental plots (13 invaded by Spiraea tomentosa and 13 uninvaded) in May and October 2024. In total, we identified 2,502 mesostigmatid mites belonging to 71 taxa. The three most abundant mite species across the entire study were Paragamasus vagabundus (Karg) (409 ind.), Zercon gurensis Mihelčič (228), and Trachytes aegrota (C. L. Koch) (216). Mite abundance was negatively associated with soil Fe, while litter pH and litter Pb showed positive but non-significant trends. Species richness was positively associated with soil Mn and litter Pb, and negatively associated with soil Fe and litter Cu. Mite diversity showed a significant overall effect of collection time and site type, although Tukey HSD post hoc tests did not detect significant pairwise differences among treatment combinations. Moreover, Mesostigmata diversity increased with soil Mn and K and decreased with soil Fe, Na, and soil moisture. However, diversity was lower at uninvaded sites than at invaded sites. Overall, our findings show that microhabitat alteration associated with the biological invasion of Spiraea tomentosa has a complex impact on the diversity and composition of mite communities.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1007/s10493-026-01201-3.

Keywords: alien shrub, biodiversity, edaphic fauna, invasive species, soil mites, soil properties

Introduction

The impact of invasive plant species on soil ecosystems and soil fauna can be especially profound (De Almeida et al. 2022; Rusterholz et al. 2014; St. John et al., 2006; Wohlgemuth et al. 2022). By modifying litter quality, root architecture, and soil chemistry, invasive plants can alter nutrient cycling, moisture levels, and microbial activity (Čerevková et al. 2019; Wohlgemuth et al. 2022). These changes cascade down to soil organisms, including mites (Acari), springtails (Collembola), nematodes (Nematoda), and other invertebrates, influencing their abundance, diversity, and species richness (St. John et al. 2006; Rusterholz et al. 2014; Urbanowski et al. 2022a, b). The appearance of an invasive plant species may have various effects on the structure of soil fauna, including mites. Some studies show that invasive plants promote the dominance of eurytopic mite species in studied soil communities after invasion (Skubała 2013; Kohyt and Skubała 2020). One of the most widespread groups of soil microarthropods that play an important role in regulating soil processes are mites, including mesostigmatid mites (Mesostigmata = Gamasida; Koehler 1997, 1999; Wissuwa et al. 2012). Mesostigmatid mites are predominantly free-living predators that occupy the upper levels of soil food webs (Madej 2004; Beaulieu and Weeks 2007). Despite their relatively small size (Skorupski 2010; Urbanowski et al. 2021a), they represent one of the most abundant and important mite groups in the soil and generally exhibit a relatively sedentary lifestyle. For this reason, mesostigmatid mites serve as reliable indicators of soil environmental conditions and overall soil quality (Madej 2004; Beaulieu and Weeks 2007; Madej and Stodółka 2008; Urbanowski et al. 2018, 2022a, b). These predatory mites primarily feed on other soil-dwelling organisms (Koehler 1999; Wissuwa et al. 2012). Shifts in soil fauna communities, including species that serve as their potential prey, may arise not only from environmental changes such as shifts in soil temperature, moisture, or levels of plant organic matter (Koehler 1999; Beaulieu and Weeks 2007; Wissuwa et al. 2012; Hasegawa et al. 2013) but also from predator pressure itself. The abundance and condition of predatory mesostigmatid assemblages are not closely tied only to microhabitat parameters (Huhta and Hänninen 2001; Manu et al. 2019; Urbanowski et al. 2022a, b). Food availability is another key factor shaping the size and composition of mesostigmatid communities, as it directly influences their reproductive capacity (Polis and Strong 1996; Beaulieu and Weeks 2007). Consequently, soil nutrient availability or broader soil conditions may be indirectly mirrored in the structure and number of mesostigmatid mite assemblages (Beaulieu and Weeks 2007; Urbanowski et al. 2021b).

In Europe, one of the most invasive shrub species is steeplebush (Spiraea tomentosa L., Rosaceae) (Dajdok et al. 2011). The species is native to North America, where its natural range extends along the eastern seaboard, but it is also found in Oregon, in the western United States (Flora of North America 2015; WFO – The Plants Database 2025), where it was likely introduced unintentionally and subsequently became established as a weed in cranberry farms (Zika 2003). In its native range, Spiraea tomentosa typically inhabits wetlands, including transitional and raised bogs (Kost et al. 2007; Zimmerman et al. 2012; Edinger et al. 2014), where it often forms dense, monospecific stands (Gille 1950), and in Oregon, it exhibits the potential to spread into natural wetlands (Flora of North America 2015). In Europe, Spiraea tomentosa has been cultivated as an ornamental plant since the 18th century (Symes 1983). It is currently recorded in nine countries (Dajdok et al. 2011; GBIF 2025) – and is considered invasive in five of them: Belgium (BFIS 2025), Denmark (Bruus et al. 2007), Germany (Balkenhol et al. 2018), Sweden (Tyler et al. 2015), and Poland (Tokarska-Guzik et al. 2012). Nevertheless, its distribution within the secondary range appears to be scattered, suggesting that the species’ full dispersal potential in Eurasia may not yet be fully understood (Wiatrowska et al. 2020). In regions of secondary distribution, the shrub frequently forms monodominant stands that markedly alter the structure and composition of wetland ecosystems (Dajdok et al. 2011; Wiatrowska and Danielewicz 2016).

Studies have demonstrated that alien, invasive plants often alter litter quality and decomposition rates, thereby shifting the abundance and diversity of edaphic fauna, such as mites, springtails, and nematodes. For example, invasive plant species such as Fallopia japonica (Houtt.) Ronse Decr. (Reynoutria spp.) and Solidago gigantea Aiton have been shown to significantly affect soil fauna by modifying soil properties and altering resource availability (Čerevková et al. 2019, 2020; De Almeida et al. 2022). In Poland, increasing Spiraea tomentosa cover has been shown to cause a linear decline in plant diversity (Wiatrowska et al. 2023). Furthermore, it negatively affects multiple animal groups, including spiders (Balkenhol et al. 2018), bees, butterflies, and flies, with both their abundance and species richness decreasing significantly and linearly as shrub cover increases (Wiatrowska et al. 2023). Moreover, it has been shown that Spiraea tomentosa can significantly influence the feeding preferences of native phytophagous insects (Wiatrowska et al. 2018), yet it does not appear to affect the abundance or species richness of Uropodina mites within their communities (Wiatrowska et al. 2024). Collectively, the above-mentioned studies highlight that invasive plants, including Spiraea tomentosa, not only transform aboveground vegetation but also exert strong, cascading effects on edaphic communities, including soil mites. Therefore it is justified to study the impact of Spiraea tomentosa on the abundance, diversity and species richness of mesostigmatid mites.

The main aim of our study was to assess the effects of Spiraea tomentosa invasion on soil mesostigmatid mite communities in wet meadow ecosystems. Specifically, we addressed two distinct objectives: (1) to determine the effects of Spiraea tomentosa invasion, sampling time, and their interaction on mesostigmatid mite abundance, taxon richness, diversity, and community composition; and (2) to examine the relationships between soil and litter properties and mesostigmatid mite community responses. We hypothesised that Spiraea tomentosa invasion would alter mesostigmatid mite community composition and reduce mite abundance, taxon richness, and diversity compared with uninvaded wet meadow sites (H1). We further hypothesised that changes in soil and litter properties associated with Spiraea tomentosa invasion would be related to changes in mesostigmatid mite community structure, with higher soil moisture and organic matter content being associated with greater mite abundance, taxon richness, and diversity (H2). Finally, we expected that the effects of Spiraea tomentosa invasion on mesostigmatid mite communities would vary depending on sampling time, resulting in significant interactions between invasion status and sampling time for mite abundance, taxon richness, diversity, and community composition (H3).

Materials and methods

Study Area and Design

The research was carried out in a wet meadow complex located in the Lower Silesian Forests in south-western Poland (51°24’44.88” N; 15°4’4.97” E; altitude: ~150 m a.s.l.). The study area was described in detail by Wiatrowska et al. (2023). In 2024, 13 wet meadow complexes (hereafter referred to as study sites) were randomly selected for subsequent analyses. Within each site, two permanent 100 m² square plots (10 × 10 m) were established: one located in a well-preserved meadow without evidence of invasion (uninvaded plot), and the other in a meadow entirely dominated (the shrub coverage was 85–100%) by the invasive shrub Spiraea tomentosa (invaded plot). To minimize potential interactions between the invaded and uninvaded plots, they were placed ~ 50 m apart. All study sites are strongly influenced by significant seasonal changes in moisture conditions (moist in summer, waterlogged in winter). Groundwater levels in summer drop to 50–60 cm depth. Therefore, upper organic material undergoes significant decomposition. It results in the formation of shallow low-peat deposits with upper murshic material (Dystric Murshic Histosols), as well as mineral soils with gleyic properties and a thin organic layer (Dystric Murshic or Histic Gleysols).

Sampling Design and Methods

Mesostigmatid Mites Sampling

Soil samples for Mesostigmatid mite analysis were collected using a 5 cm diameter metal corer to a depth of approximately 10 cm. A total of 104 soil samples were collected from 26 experimental plots (13 invaded by Spiraea tomentosa and 13 uninvaded). Two samples were collected from each plot in May 2024 and two in October 2024 (26 plots × 2 sampling dates × 2 samples per plot = 104 soil samples). For community-level analyses, the two soil cores collected from the same plot at the same collection time were pooled by summing taxon abundances, resulting in 52 plot × collection-time observations. Thus, each plot represented one community-level observation at each collection time, with May and October constituting repeated observations of the same plot. The 26 plots were distributed across 13 sites, with one invaded and one uninvaded plot at each site, forming paired site-level comparisons. Each sample was carefully labelled and stored in plastic bags. After soil collection, the collected soil samples were processed using Berlese-Tullgren funnels equipped with a 40 W light bulb and a 2 mm mesh strainer, with soil fauna extracted into containers filled with 75% ethanol. The extraction was conducted for at least seven days, until the soil samples were completely dry, at a temperature of 30.0 ± 0.5 °C measured at the sieve. Mesostigmatid mites were then isolated under a binocular stereomicroscope. Species identification and determination of developmental stages were performed using acarological keys (Micherdziński 1969; Karg 1971, 1993; Gwiazdowicz 2007, 2010; Mašán and Halliday 2014), with detailed morphological examination conducted under a Zeiss Axio Scope.A1 compound microscope.

Soil Analyses

Soil and litter samples (~ 200 g) were collected from the centre of each plot to a depth of 5 cm and placed in plastic bags (26 × 2 soil, 26 × 2 litter; 104 in total). In invaded plots, the litter consisted of decaying leaves of Spiraea tomentosa, whereas in uninvaded plots it comprised remnants of meadow vegetation. Sampling was conducted no more than 2 cm from the location where soil samples for mite analysis were collected. After air-drying, soil samples were analysed for pH, total nitrogen (N%), carbon (C%), sulphur (S%), soil organic matter (SOM%), soil texture, and the concentrations of Zn, Mg, Ni, Fe, Mn, K, Na, Ca, Pb, Cu and Cd (mg/kg). Soil pH was measured in distilled water using the potentiometric method with a 1:2.5 soil-to-water ratio. Total nitrogen content was determined using the Kjeldahl’s method with sulphuric acid (VI) and a K₂SO₄:CuSO₄ catalyst, using a „FOSS” autoanalyzer (Nelson and Sommers 1996). Carbon and sulphur contents were determined by oxygen combustion using an Eltra CS-800 analyzer (Ostrowska et al. 1991). Soil organic matter was determined by the loss-on-ignition method (Turczański et al. 2022), and soil texture was determined using the Casagrande’s aerometric method modified by Prószyński (Lityński et al. 1976). Element concentrations were determined after extraction in 1 mol L⁻¹ HCl using atomic absorption spectrometry (AAS; Varian 55B). In addition, 26 intact soil samples (100 cm³) were collected to determine soil bulk density (g/cm³) using the core method and soil moisture (%) using the gravimetric method (Lityński et al. 1976). Moreover, litter samples were analysed for pH, total nitrogen (N%), carbon (C%), sulphur (S%), and the concentrations of Zn, Mg, Ni, Fe, Mn, K, Na, Ca, Pb, Cu and Cd (mg/kg), using the same analytical methods as described above, where applicable. Measurements obtained from samples within each plot were averaged prior to statistical analyses. Sampling was conducted twice, in May and October 2024.

Vegetation Assessment

Plant species composition was assessed twice (24–26 June and 25–26 July 2024) at each of the 26 study plots. Plants were identified at each plot following Rutkowski (2011). Within each plot, all species present were recorded along with their estimated percentage cover. Single individuals of alien species other than Spiraea tomentosa were occasionally observed, but their cover was minimal (0.5–2.0%), and they were therefore considered unlikely to affect mite communities. Community diversity was characterised by species richness.

Data Analysis

For each mesostigmatid mite sample, the number of individuals, species richness, and diversity were determined. Average values and their standard errors (SE) were then calculated for these metrics across the different site types. Species richness was defined as the total count of unique mite taxa recorded within each individual sample. Data visualizations were created using the ggplot2 package (Wickham 2016; version 3.5.2).

Models were built using the packages glmmTMB, lme4, lmerTest, MuMIn, and MASS (Venables and Ripley 2002; Bates et al. 2015; Brooks et al. 2017; Kuznetsova et al. 2017; Bartoń 2025; versions 1.1–13, 1.1–37, 3.1-3, 1.48.11, and 7.3–65, respectively). We modeled three response variables describing mite communities: abundance, richness, and diversity. Abundance and richness were treated as count data and analyzed using negative binomial generalized linear mixed models (glmmTMB) to account for overdispersion, while diversity (continuous) was analyzed using linear mixed-effects models (lmer) with random effects for the combination of collection time and site type. Predictors included soil chemical properties, nutrient concentrations, and vegetation variables. To select the most informative predictors, a stepwise model selection based on Akaike’s Information Criterion (AIC) was applied. Prior to modeling, numeric predictors were scaled, and highly correlated variables (|r| > 0.7) were removed to reduce multicollinearity. Model assumptions were checked through residual diagnostics, and significance of fixed effects was assessed using z-tests for generalized models and Satterthwaite’s t-tests for linear mixed models.

Diversity analyses were conducted using the iNEXT and vegan packages (Hsieh et al. 2024; Oksanen et al. 2025; versions 3.0.1 and 2.7-1, respectively), which allow for the calculation of rarefaction, extrapolation, and asymptotic diversity estimates. Species richness (Chao1), Shannon-Wiener, and Simpson indices were computed to capture different aspects of mite community diversity. Moreover, the sample coverage index (SC) was calculated and measured the representativeness of the sample by estimating the proportion of the total number of mite individuals in the community accounted for by the observed species, thereby assessing how well the sample has reflected the actual biological diversity. Furthermore, the Chao1 estimator, a non-parametric method, was applied to estimate true species richness while accounting for rare taxa, including singletons and doubletons. This approach provides both observed richness (the actual number of species recorded in the samples) and asymptotic richness estimates, reflecting the estimated total number of species. The Shannon-Wiener index (H’) was calculated with the diversity() function. This index reflects the uncertainty involved in predicting the species identity of an individual randomly selected from a sample, taking into account both species abundance and evenness. It is expressed as: H’=−∑piln(pi), where pi represents the proportion of individuals belonging to the i’th species. The Simpson index (D) quantifies the likelihood that two randomly chosen individuals from a sample belong to different species, placing greater emphasis on the more abundant species. It is defined as: D = 1−∑pi2 where pi is the proportion of individuals of the i’th species. Together, the Shannon-Wiener and Simpson indices offer complementary perspectives on community diversity, highlighting aspects of species richness, evenness, and dominance patterns.

Species accumulation curves were produced with the specaccum() function from the vegan package (Oksanen et al. 2025; version 2.7-1). These curves illustrate how the number of recorded species increases as sampling effort increases, providing insight into sampling completeness and the detection of rare species. The curves were constructed individually for each site type.

Non-metric multidimensional scaling (NMDS) based on Bray–Curtis dissimilarities was applied to visualize differences in mite community composition. Prior to community-level analyses, the two soil cores collected from the same plot at the same collection time were pooled by summing taxon abundances, resulting in 52 plot × collection time observations from 26 plots across 13 sites. Samples with zero total abundance were excluded from Bray–Curtis-based analyses, leaving 47 observations. The analysis used a two-dimensional configuration, with the stress value indicating the goodness-of-fit and the adequacy of the reduced-dimensional representation of the data. To test for statistically significant differences in community composition, permutational multivariate analysis of variance (PERMANOVA) was performed, providing a non-parametric assessment of the influence of explanatory factors on assemblage patterns. The effect of invasion status (invaded vs. uninvaded) was tested using permutations restricted within site pairs, reflecting the paired sampling design. The effect of collection time (May vs. October) was tested using permutations restricted within plots to account for repeated measurements. The invasion × collection time interaction was assessed by comparing seasonal changes in community composition between paired invaded and uninvaded plots within sites. Homogeneity of multivariate dispersion among collection time × invasion status groups was assessed using PERMDISP (betadisper) to distinguish differences in community location from differences in within-group dispersion. Mite taxa with the strongest fit to the NMDS ordination were identified to interpret the main drivers of variation within the mite communities.

Differences in mite abundance, species richness, and diversity among site types were evaluated using a two-way analysis of variance (ANOVA). The F-statistic was calculated to assess the magnitude of the tested effect relative to residual variation. To further explore pairwise differences, post-hoc comparisons were carried out with Tukey’s honest significant difference (Tukey HSD) test, enabling the detection of statistically significant contrasts between individual site type categories.

Species site type associations of mesostigmatid mites were analyzed using the bipartite package (Dormann et al. 2008, 2009; Dormann 2011; version 2.21). Interactions between species and site types were visualized with web plots, employing the plotweb() and specieslevel() functions to illustrate both the distribution and strength of species–site type links. To quantify habitat specialization, the species specificity index (SSI) was calculated, with values ranging from 0 for widely distributed taxa to 1 for species occurring exclusively in a single site type. These analyses provided a standardized framework for evaluating whether taxa exhibit generalist or specialist tendencies based on their occurrence across different site types. Statistical analyses were conducted in RStudio (R version 4.5.0; R Core Team 2025).

Results

Influence of Vegetation and Soil Properties on Mite Communities

Across the four treatment combinations, abundance showed higher values recorded in October than in May. Mean abundance ranged from 16.96 ± 5.42 individuals at the invaded sites in May to 31.42 ± 6.09 at the uninvaded sites in October (Fig. 1A). Despite these trends, the ANOVA indicated that differences among treatments were not statistically significant (F = 1.14, p = 0.338). Consistently, Tukey HSD tests detected no significant pairwise contrasts, pairwise p-values > 0.27. Thus, although abundance was highest in October and at uninvaded sites, these differences were not statistically supported. Mite abundance was significantly influenced by soil chemical properties. The negative binomial model indicated that soil iron (Fe) was negatively associated with mite abundance (Estimate = − 0.809, p < 0.001), while litter pH (pH_fl) and litter lead (Pb_fl) showed positive but non-significant associations with abundance (Estimate = 0.261, p = 0.093 and Estimate = 0.244, p = 0.063, respectively; Table 1).

Fig. 1.

Fig. 1

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Comparison of mite communities between invaded (light blue) and uninvaded (light orange) sites in May and October. (A) Mean abundance of individuals per sample, (B) mean species richness per sample, and (C) mean diversity (H′). Boxes represent the interquartile range, horizontal lines indicate the median, whiskers show the range excluding outliers, and white circles indicate the mean values

Table 1.

Summary of the final models for mite community responses. Abundance and richness were analyzed using negative binomial generalized linear mixed models, while diversity was analyzed using a linear mixed-effects model. Shown are the estimated coefficients (Estimate), standard errors (Std. Error), test statistics (z or t), p-values (P_value), and significance levels (* p < 0.05, ** p < 0.01, *** p < 0.001). Predictors include soil chemical properties, nutrient concentrations, and site type. Abbreviations: pH – soil pH; SM – soil moisture; pH_fl – litter pH; Fe – iron in soil; Mn – manganese in soil; Cu – copper in soil; Cu_fl – copper in litter; Pb_fl – lead in litter; K – potassium in soil; Na – sodium in soil; site_type – invaded or uninvaded site type

Response Predictor Estimate Std_Error Statistic P_value Significance
Abundance (Intercept) 2,9879 0,1357 22,01 < 0.001 ***
pH_fl 0,2612 0,1555 1,679 0.093
Fe -0,8088 0,1774 -4,56 < 0.001 ***
Pb_fl 0,2441 0,1311 1,863 0.063
Richness (Intercept) 1,8397 0,1623 11,338 < 0.001 ***
Cu 0,1518 0,1068 1,422 0.155
Mn 0,2944 0,1016 2,897 0.004 **
Fe -0,544 0,1224 -4,443 < 0.001 ***
Cu_fl -0,3578 0,1587 -2,254 0.024 *
Pb_fl 0,3182 0,1071 2,97 0.003 **
site_type: uninvaded -0,4494 0,2783 -1,615 0.106
Diversity (Intercept) 1,45138 0,12429 11,678 < 0.001 ***
pH 0,18401 0,11565 1,591 0.115
pH_fl 0,14312 0,09769 1,465 0.146
SM -0,2476 0,09722 -2,547 0.013 *
Mn 0,21032 0,0848 2,48 0.015 *
Fe -0,36981 0,07926 -4,666 < 0.001 ***
K 0,36753 0,11212 3,278 0.002 **
Na -0,261 0,12126 -2,152 0.034 *
Pb_fl 0,14786 0,07566 1,954 0.054
site_type: uninvaded -0,5996 0,21051 -2,848 0.005 **
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Trends in species richness followed a similar seasonal pattern to those observed for abundance, with higher mean values in October than in May (from 4.42 ± 1.01 at invaded site type (May) to 7.04 ± 0.94 at uninvaded (October); Fig. 1B). The ANOVA, however, revealed no significant treatment effect (F = 1.87, p = 0.14). Tukey HSD post hoc comparisons showed no significant differences among the four treatment combinations. All pairwise comparisons resulted in p-values ≥ 0.21, with confidence intervals encompassing zero, suggesting that the observed increases in mite species richness were not statistically significant. Species richness of mite communities was affected by multiple soil nutrients and site type. Soil manganese (Mn) and lead in litter (Pb_fl) had positive effects on mesostigmatid richness (Estimate = 0.294, p = 0.004 and Estimate = 0.318, p = 0.003, respectively), while soil iron (Fe) and copper in litter (Cu_fl) had negative effects (Estimate = -0.544, p < 0.001 and Estimate = -0.358, p = 0.024, respectively). Soil copper (Cu) showed a positive but non-significant trend, and richness tended to be lower in uninvaded sites compared to invaded ones (Estimate = -0.449, p = 0.106; Table 1).

In contrast, diversity (based on Shannon–Wiener Index) differed significantly among the combinations of collection time and site type (F = 3.21, p = 0.026). Mean diversity was lower in May (0.92 ± 0.17 at invaded site type; 0.90 ± 0.19 at uninvaded) and higher in October (1.43 ± 0.11 at invaded; 1.36 ± 0.15 at uninvaded; Fig. 1C). Despite this significant overall effect, Tukey HSD post hoc tests did not identify any significant pairwise differences among treatments (p-values ≥ 0.08). These results suggest that while diversity generally increased from spring to autumn, the magnitude of these differences was not sufficient for significant pairwise separation in the post hoc analysis. Positive predictors included soil manganese (Mn; Estimate = 0.210, p = 0.015) and potassium (K; Estimate = 0.368, p = 0.002), whereas iron (Fe; Estimate = -0.370, p < 0.001), sodium (Na; Estimate = -0.261, p = 0.034), and soil moisture (SM, Estimate = -0.248, p = 0.013) negatively affected mesostigmatid diversity. Site type also mattered, with lower diversity at uninvaded sites (Estimate = -0.600, p = 0.005). Litter pH (pH_fl) had small positive but non-significant effects; moreover, lead in litter (Pb_fl) showed a positive trend (p = 0.054; Table 1).

The cumulative species richness curves indicated differences in species accumulation among the four treatments. In May, invaded sites exhibited a gradual increase in species richness, reaching a maximum of 38 taxa after 26 soil samples collected from 13 sites. Uninvaded sites accumulated mite taxa more rapidly, reaching a higher maximum richness of 50 taxa after 26 soil samples from 13 sites. In October, invaded sites showed intermediate species richness, with a maximum of 35 taxa at 25–26 samples, whereas uninvaded sites reached 41 taxa, indicating consistently higher richness compared with invaded sites. Across all treatments, the accumulation curves demonstrated that uninvaded sites supported higher cumulative species richness, whereas invaded sites exhibited lower richness and slower species accumulation. (Fig. 2).

Fig. 2.

Fig. 2

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Rarefaction curves showing cumulative species richness across soil samples collected from invaded and uninvaded plots at 13 sites in May and October. Lines represent mean values, and shaded areas/dashed lines indicate variability

Asymptotic Chao estimates suggest that the true number of species may be higher than observed, particularly in invaded sites, where the difference between observed and estimated species richness was greatest. Shannon-Wiener and Simpson indices show that relative diversity and species dominance were also lower at invaded sites, reflecting reduced evenness of species distribution. Sample extrapolation indicates that further increasing the number of mite individuals collected would only slightly raise the estimated mesostigmatid diversity, especially at uninvaded sites. Moreover, uninvaded sites exhibit higher observed and estimated species diversity than invaded sites at both collection times, suggesting an association between invasion status and reduced mesostigmatid taxon richness. The sample coverage index (SC) for all mesostigmatid mite communities was high (> 0.95) in complete samples, indicating that the samples well represent the actual species diversity (Fig. 3).

Fig. 3.

Fig. 3

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Rarefaction and extrapolation curves showing species diversity as a function of the number of individuals sampled under different conditions (The estimated richness of mite taxa in a community based on the Chao estimates, type = 1). Blue lines represent invaded samples (dark blue: October, light blue: May), while orange lines represent uninvaded samples (dark orange: October, light orange: May). Shaded areas indicate confidence intervals

Mesostigmatid Mite Assemblages

In total, we identified 2,502 individuals belonging to 71 taxa. The three most abundant mite species across the entire study were Paragamasus vagabundus (Karg) (409 ind.), Zercon gurensis Mihelčič (228), and Trachytes aegrota (C. L. Koch) (216). In May, the most numerous species on invaded sites were Paragamasus vagabundus (78 ind.) and Zercon gurensis (49), while on uninvaded sites they were Paragamasus vagabundus (75) and Olodiscus minima (Kramer) (51). In October, invaded sites were dominated by Trachytes aegrota (127 ind.) and Paragamasus vagabundus (116), whereas uninvaded sites had the highest abundances of Paragamasus vagabundus (140) and Zercon gurensis (102; Table 2).

Table 2.

Total abundances of the ten most numerous mite species recorded across all samples. Values indicate the number of individuals collected in each collection time (May, October) and site type (invaded, uninvaded), as well as the overall sum across all samples

No. Mite taxa Collection time/Site type Sum
May October
Invaded Uninvaded Invaded Uninvaded
1. Paragamasus vagabundus (Karg, 1968) 78 75 116 140 409
2. Zercon gurensis Mihelčič, 1962 49 42 35 102 228
3. Trachytes aegrota (C. L. Koch, 1841) 36 6 127 47 216
4. Prozercon kochi Sellnick, 1943 7 33 94 57 191
5. Olodiscus minima (Kramer, 1882) 10 51 28 84 173
6. Veigaia nemorensis (C. L. Koch, 1839) 38 23 56 49 166
7. Hypoaspis praesternalis Willmann, 1949 15 25 16 76 132
8. Urodiaspis tecta (Kramer, 1876) 17 2 20 26 65
9. Gamasellus montanus (Willmann, 1936) 0 27 2 32 61
10. Paragamasus runcatellus (Berlese, 1903 sensu Karg 1971) 7 23 0 19 49
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PERMANOVA confirmed that collection time significantly affected Mesostigmata community composition (F = 4.774, R² = 0.102, p = 0.0003), whereas the effect of invasion status was not statistically significant (F = 1.628, R² = 0.069, p = 0.0819). The invasion × collection time interaction was also not significant (mean paired difference in seasonal Bray–Curtis change = 0.0231, p = 0.7526). Multivariate dispersion did not differ among the four collection time × invasion status groups (F = 0.365, p = 0.789) (Table 3). Non-metric multidimensional scaling (NMDS; Fig. 4) based on Bray–Curtis dissimilarities showed variation in mesostigmatid mite community composition across collection times (May and October) and site types (invaded and uninvaded). The two-dimensional NMDS configuration had a stress value of 0.159, indicating a moderate representation of the observed community dissimilarities. Mite taxa fitting the NMDS gradients most strongly included Amblyseius spp., Hypoaspis praesternalis Willmann, and Paragamasus runcatellus (Berlese sensu Karg), highlighting their contribution to the differentiation of mite communities among studied treatments (Table 4).

Table 3.

Results of permutation-based analyses testing the effects of invasion status and collection time on Mesostigmata community composition. Community composition was analysed using Bray–Curtis dissimilarity after square-root transformation of abundance data. Invasion status was tested using 12 complete invaded–uninvaded site pairs (24 plots), with permutations restricted within site. Collection time was tested using 22 complete May–October plot pairs (44 observations), with permutations restricted within plot. The invasion × collection time interaction was assessed separately using a paired sign-permutation test of the difference in seasonal Bray–Curtis change between invaded and uninvaded plots: mean paired difference = 0.0231, p = 0.7526. Multivariate dispersion did not differ among collection time × invasion status groups (PERMDISP: F = 0.365, p = 0.789). A total of 9,999 permutations was used for all permutation tests

Source df Sum of Squares R² F p-value
Invasion status 1 0.348 0.069 1.628 0.0819
Residual 22 4.703 0.931
Total 23 5.052 1.000
Collection time 1 1.234 0.102 4.774 0.0003
Residual 42 10.854 0.898
Total 43 12.088 1.000
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Fig. 4.

Fig. 4

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Non-metric multidimensional scaling (NMDS) plot of mite community composition. Points (samples) are colored by collection time (May, October) and site type (invaded, uninvaded). Ellipses represent 95% confidence intervals around the centroids of each treatment

Table 4.

Top three soil mite taxa contributing to differences among studied treatments, based on NMDS envfit analysis. Values of r² indicate the proportion of variation in the NMDS ordination explained by each species, and p-values represent statistical significance. Only species with p < 0.05 are shown

Mite taxa r² p-value
Amblyseius spp. 0.158 0.044
Hypoaspis praesternalis Willmann, 1949 0.093 0.027
Paragamasus runcatellus (Berlese, 1903 sensu Karg 1971) 0.081 0.044
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The web plot results reveal clear patterns of treatment specificity among mesostigmatid mite taxa. Taxa occurring in only a single site (proportion of studied treatments = 0.25; e.g., Asca aphidioides (Linnaeus), Cheiroseius mutilus (Berlese), Dendrolaelaps spp.) consistently exhibit high species specificity indices (1.000) and elevated d′ values (0.140–0.361), indicating strong specialization. Conversely, taxa distributed across all surveyed sites (proportion of studied treatments = 1.0; e.g., Amblyseius spp., Holoparasitus calcaratus (C. L. Koch), Leptogamasus suecicus Trägårdh) display low specificity indices (0.103–0.472) and minimal d′ values (0.001–0.055), reflecting generalist behaviour. Intermediate cases, where species occupy two or three sites (proportion of studied treatments = 0.50–0.75; e.g., Veigaia cerva (Kramer) or Gamasellus montanus (Willmann)), show moderate d′ (0.109–0.304) and specificity indices (0.544–0.970), suggesting partial specialization with a preference for particular sites. Overall, the plot web data demonstrate a strong correlation between the number of occupied sites and species specificity, with taxa confined to fewer sites exhibiting higher specialization and those widely distributed exhibiting generalist tendencies (Table 5; Fig. 5).

Table 5.

Network statistics for mesostigmatid mite taxa, describing their affiliation to studied treatments and specialization

No. Mite taxa Mite taxa abbreviations Number of studied treatments Proportion of studied treatments Index d′ Species specificity index
1. Alliphis halleri (Canestrini & Canestrini, 1881) All_hal 2 0.50 0.047 0.577
2. Amblyseius spp. Amb_spp 4 1.00 0.055 0.472
3. Arctoseius resinae Karg, 1969 Arc_res 2 0.50 0.078 0.638
4. Asca aphidioides (Linnaeus, 1758) Asc_aph 1 0.25 0.140 1.000
5. Asca bicornis (Canestrini & Fanzago, 1887) Asc_bic 4 1.00 0.043 0.263
6. Cheiroseius borealis (Berlese, 1904) Che_bor 3 0.75 0.090 0.563
7. Cheiroseius mutillus (Berlese, 1916) Che_mut 1 0.25 0.240 1.000
8. Dendrolaelaps spp. Den_spp 1 0.25 0.141 1.000
9. Dinychus perforatus Kramer, 1882 Din_per 1 0.25 0.140 1.000
10. Epicriopsis horridus (Kramer, 1876) Epi_hor 2 0.50 0.121 0.707
11. Eviphis ostrinus (C. L. Koch, 1836) Evi_ost 4 1.00 0.031 0.401
12. Gamasellodes bicolor (Berlsese, 1918) Gam_bic 2 0.50 0.109 0.619
13. Gamasellodes insignis (Hirschmann, 1963) Gam_ins 1 0.25 0.294 1.000
14. Gamasellus montanus (Willmann, 1936) Gam_mon 3 0.75 0.125 0.544
15. Gamasellus vibrissatus (Emberson, 1967) Gam_vib 1 0.25 0.224 1.000
16. unidentified Gamasina Gam 1 0.25 0.051 1.000
17. Geholaspis longispinosus (Kramer, 1876) Geh_lon 2 0.5 0.109 0.638
18. Geholaspis mandibularis (Berlese, 1904) Geh_man 1 0.25 0.183 1.000
19. Geholaspis spp. Geh_spp 3 0.75 0.113 0.577
20. Holoparasitus calcaratus (C. L. Koch, 1839) Hol_cal 4 1.00 0.025 0.339
21. Hypoaspis aculeifer (Canestrini, 1883) Hyp_acu 2 0.50 0.091 0.707
22. Hypoaspis praesternalis Willmann, 1949 Hyp_pra 4 1.00 0.049 0.440
23. Hypoaspis spp. Hyp_spp 1 0.25 0.051 1.000
24. Hypoaspis vacua (Michael, 1891) Hyp_vac 3 0.75 0.069 0.517
25. unidentified Laelapidae Lae 1 0.25 0.092 1.000
26. Lasioseius lawrencei Evans, 1958 Las_law 2 0.50 0.163 0.900
27. Leptogamasus suecicus Trägårdh, 1936 Lep_sue 4 1.00 0.001 0.103
28. Macrocheles montanus (Willmann, 1951) Mac_mon 2 0.50 0.057 0.577
29. Macrocheles sp. 1 Mac_sp_1 1 0.25 0.267 1.000
30. Macrocheles sp. 2 Mac_sp_2 1 0.25 0.183 1.000
31. Macrocheles spp. Mac_spp 3 0.75 0.062 0.408
32. unidentified Macrochelidae Mac 2 0.50 0.177 0.872
33. Neiordensia levis (Oudemans & Voigts, 1904) Nei_lev 3 0.75 0.055 0.503
34. Neiordensia spp. Nei_spp 1 0.25 0.051 1.000
35. Nenteria sp. Nen_sp 1 0.25 0.193 1.000
36. Ololaelaps placentula (Berlese, 1887) Olo_pla 3 0.75 0.101 0.503
37. Olodiscus minima (Kramer, 1882) Olo_min 4 1.00 0.044 0.369
38. Oodinychus ovalis (C. L. Koch, 1839) Ood_ova 2 0.50 0.115 0.638
39. Pachylaelaps furcifer Oudemans, 1903 Pac_fur 3 0.75 0.102 0.458
40. Pachyseius humeralis Berlese, 1910 Pac_hum 3 0.75 0.043 0.408
41. Paragamasus conus (Karg 1971) Par_con 3 0.75 0.133 0.625
42. Paragamasus jugincola Athias-Henriot, 1967 Par_jug 3 0.75 0.178 0.609
43. Paragamasus runcatellus (Berlese, 1903 sensu Karg 1971) Par_run 3 0.75 0.093 0.434
44. Paragamasus spp. Par_spp 4 1.00 0.225 0.473
45. Paragamasus vagabundus (Karg, 1968) Par_vag 4 1.00 0.004 0.153
46. unidentified Parasitidae Par 2 0.50 0.123 0.638
47. Parazercon radiatus (Berlese, 1914) Par_rad 2 0.50 0.212 0.947
48. Pargamasus puerilis Karg, 1963 Par_pue 1 0.25 0.171 1.000
49. Pergamasus barbarus (Berlese, 1904) Per_bar 3 0.75 0.108 0.762
50. Pergamasus crassipes (Linnaeus, 1758) Per_cra 1 0.25 0.051 1.000
51. Pergamasus mediocris Berlese, 1904 Per_med 1 0.25 0.031 1.000
52. Pergamasus quisquiliarum (Canestrini & Canestrini, 1882) Per_qui 1 0.25 0.224 1.000
53. Pergamasus septentrionalis (Oudemans, 1902) Per_sep 1 0.25 0.171 1.000
54. Pergamasus spp. Per_spp 4 1.00 0.061 0.354
55. Prozercon kochi Sellnick, 1943 Pro_koc 4 1.00 0.066 0.387
56. Prozercon traegardhi (Halbert, 1923) Pro_tra 1 0.25 0.361 1.000
57. Rhodacarellus silesiacus Willmann, 1936 Rho_sil 1 0.25 0.089 1.000
58. Rhodacarus coronatus Berlese, 1921 Rho_cor 2 0.50 0.074 0.638
59. Rhodacarus mandibularis Berlese, 1921 Rho_man 1 0.25 0.051 1.000
60. Trachytes aegrota (C. L. Koch, 1841) Tra_aeg 4 1.00 0.127 0.478
61. Trachytes pauperior Berlese, 1914 Tra_pau 3 0.75 0.144 0.693
62. Urodiaspis tecta (Kramer, 1876) Uro_tec 4 1.00 0.046 0.314
63. Uropoda sellnicki (Hirschmann & Zirngielb-Nicol, 1969) Uro_sel 1 0.25 0.051 1.000
64. unidentified Uropodina Uro 4 1.00 0.011 0.243
65. Veigaia cerva (Kramer, 1876) Vei_cer 2 0.50 0.304 0.970
66. Veigaia kochi (Trägårdh, 1901) Vei_koc 4 1.00 0.040 0.351
67. Veigaia nemorensis (C. L. Koch, 1839) Vei_nem 4 1.00 0.015 0.173
68. Vulgarogamasus kraepelini (Berlese, 1904) Vul_kra 1 0.25 0.141 1.000
69. Zercon gurensis Mihelčič, 1962 Zer_gur 4 1.00 0.024 0.268
70. Zercon peltatus C. L. Koch, 1836 Zer_pel 1 0.25 0.296 1.000
71. Zercon triangularis C. L. Koch, 1836 Zer_tri 1 0.25 0.141 1.000
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Fig. 5.

Fig. 5

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Co-occurrence network showing bipartite relationships between mesostigmatid mite taxa (upper boxes) and studied treatments (lower boxes). Box size is proportional to total mite abundance, and ribbon width reflects the strength of co-occurrence. Abbreviations for studied treatments: MI – May, invaded site; MU – May, uninvaded site; OI – October, invaded site; OU – October, uninvaded site. Mite taxa abbreviations correspond to the first three letters of the genus and species names (see Table 5 for full list)

Discussion

Alien and invasive species introduce significant amount of organic matter into the ecosystems they occupy, including litter that decomposes faster than that of native species (Horodecki et al. 2019; Hu et al. 2022). Invasive species – Spirea tomentosa – deposits a significant amount of biomass (Wiatrowska et al. 2022), thereby altering the microhabitat properties of the topsoil in wet meadow ecosystems. At the studied sites, almost twice as much soil organic matter was found at invaded sites compared to uninvaded ones. Thus, the thick layer of litter, as well as the reduced insolation of the topsoil layer caused by the invasive species (Nsikani et al. 2018; Hess et al. 2019), results in higher soil moisture at the sites invaded by Spirea tomentosa. Hence, the presence of this invasive shrub was associated with differences in mesostigmatid mite community structure, although its effects on abundance, taxon richness, and diversity were not consistently negative. In our studies, the diversity of mesostigmatid mite assemblages was influenced by nutrient availability, soil moisture, and invasion status of the studied sites. What is important, sites invaded by Spiraea tomentosa were characterised by higher soil moisture compared to non-invaded sites, both in May and October. Previous studies revealed that soil moisture is a key determinant of activity and survival of mesostigmatid mites (Manu et al. 2021; Kamczyc et al. 2022). For example, Manu et al. (2021) found that soil moisture is a major factor determining the abundance and structure of mesostigmatid communities, suggesting that moisture may limit the occurrence of certain species and shape local species diversity.

Our results did not support the assumption that uninvaded wet meadow sites would consistently exhibit higher mite diversity, abundance, and taxon richness than invaded sites. However, considering that differences in nutrient concentrations on studied sites (uninvaded vs. invaded sites) likely affect the structure and diversity of mesostigmatid mite communities, it is essential to note that microhabitat conditions, including the availability of soil micro- and macroelements, are primarily influenced by the vegetation of a given site (Zhao et al. 2022; Xiong et al. 2024; Xiao et al. 2025). In our study, the presence of Spiraea tomentosa was associated with differences in mesostigmatid mite community characteristics, although these patterns were not consistently negative across all diversity measures. Per-sample abundance and species richness did not differ significantly among the four treatment combinations, and Shannon-Wiener diversity was slightly higher at invaded sites at both collections – May and October. However, species accumulation curves provided a different perspective, showing that uninvaded sites supported higher cumulative species richness and that invaded sites exhibited slower species accumulation. The observed and estimated diversity measures based on species accumulation also indicated higher diversity at uninvaded sites, suggesting that the effects of Spiraea tomentosa may become more apparent when species richness is considered at the level of the accumulated community rather than individual samples. These findings therefore suggest that invasion may be associated with changes in the composition and structure of mesostigmatid mite communities. Furthermore, the lower cumulative richness and slower species accumulation observed at invaded sites are consistent with previous studies showing that invasive plants can modify soil fauna communities, although the direction and magnitude of these effects may depend on the invasive species, habitat, and faunal group considered. For example, De Almeida et al. (2022), studying the impact of invasive Reynoutria spp. on soil mesofauna (Collembola) and microfauna (Nematoda), demonstrated changes in the abundance and species composition of soil fauna, while also emphasizing that the effects differed between forest and meadow habitats. Similarly, Čerevková et al. (2019) reported lower abundance and species richness of soil nematode communities at sites invaded by Fallopia japonica, together with negative effects on particular nematode trophic groups. Rusterholz et al. (2014) found that Impatiens glandulifera invasion was associated with declines in Collembola richness and abundance, as well as reduced Acari abundance in leaf litter and topsoil, accompanied by changes in the composition of soil microarthropod communities. In another study, Čerevková et al. (2020) found that Solidago gigantea invasion increased overall nematode abundance but reduced species diversity, indicating that invasive plants may affect different components of soil fauna communities in contrasting ways. Studies examining the effects of Spiraea tomentosa on other invertebrate groups have also reported changes in community characteristics, including spiders (Balkenhol et al. 2018) and insects (Wiatrowska et al. 2018, 2023). Taken together, these findings support the view that invasive plants can modify soil and litter conditions and consequently influence soil fauna communities, but the direction and magnitude of these responses are not necessarily uniform across taxa or diversity measures. In our study, the lower cumulative richness and slower species accumulation at invaded sites suggest that Spiraea tomentosa may contribute to changes in mesostigmatid mite community diversity, while the absence of consistent differences in per-sample richness and Shannon-Wiener diversity indicates that this relationship is complex and should not be interpreted as a simple suppressive effect of invasion. Overall, our results highlight the multifaceted influence of invasion-related microhabitat modification on mesostigmatid mite assemblages.

Moreover, our findings revealed that the structure and diversity of mesostigmatid mite communities are shaped by soil and litter chemistry, including soil moisture conditions. In particular, we found that higher Fe concentrations in soil and litter (higher values were recorded on invaded sites, both in May and October) were associated with reduced mite abundance. Although Fe is an essential micronutrient, excessive levels may restrict biological activity by limiting the availability of other elements or exerting toxic effects, ultimately creating stressful conditions for soil fauna (Totaro et al. 1992; van Straalen et al. 2001; Fink et al. 2016; Herndon et al. 2019). For example, previous studies show that Fe can negatively affect the availability of other micro- and macroelements, including P in the soil (Fink et al. 2016; Herndon et al. 2019), as well as directly affecting soil fauna by accumulating in the bodies of arthropods (van Straalen et al. 2001; Skubała and Zaleski 2012). Furthermore, slightly more alkaline litter and higher Pb concentrations in the litter showed positive associations with mesostigmatid mite abundance, although these relationships were not statistically significant. This pattern likely reflects complex interactions between litter quality and the mite taxa, especially given the taxon-specific responses of soil mesostigmatid mites to heavy metals. The quality of litter, expressed in terms of the availability of micro- and macroelements, can significantly shape soil fauna communities, including Mesostigmata (Frouz 2008; Frouz et al. 2015; Urbanowski et al. 2022a, b). The substrate–matrix quality interaction (SMI) hypothesis deserves considerable attention in this regard, as it assumes that the rate of decomposition does not depend solely on the quality of the substrate, but rather on its interaction with the quality of the surrounding decomposition matrix. According to the SMI hypothesis, low-quality substrates decompose relatively quickly when they are in a low-quality matrix; however, they decompose more slowly than would be expected in medium- or high-quality matrices (Freschet et al. 2012).

Patterns in species richness further emphasized the role of micronutrient availability in shaping mesostigmatid communities. Soil Fe and litter Cu were negatively associated with mite species richness, indicating that variation in the concentrations of these elements may be related to differences in the number of mite taxa occurring in a given microhabitat. In contrast, soil Mn and litter Pb were positively associated with mesostigmatid mite richness. These opposing effects may reflect differences in species-specific tolerances to metals (Skubała and Zaleski 2012; Chrzan 2016; Manu et al. 2019), as well as indirect influence on microbial and prey communities of other micro- and mesoarthropods that form the trophic base for predatory mesostigmatid mites (Chrzan 2017; Du et al. 2025). Manu et al. (2019) found that, among other factors, Pb concentrations affected the occurrence of mesostigmatid mites, indicating that these mite species respond to metal contamination. Furthermore, according to Skubała and Zaleski (2012), soil mites exhibit tolerance to heavy metals, including Cu, and are capable of accumulating them, suggesting the presence of physiological resistance mechanisms. Similarly, Chrzan (2016) highlighted that heavy metals such as Pb and Cu were detected in soil invertebrates even when their concentrations in the soil did not exceed permissible limits, suggesting a capacity for metal accumulation and potentially a degree of tolerance within soil fauna.

Conclusions

Our study revealed that mesostigmatid mite diversity, species richness, and abundance were associated with soil and litter chemistry, element concentrations, soil moisture, and site characteristics. Among the elements examined, Fe and litter Cu showed negative associations with mite community characteristics, whereas soil Mn and litter Pb were positively associated with selected community metrics, particularly species richness and, in the case of Mn, diversity. These contrasting patterns indicate that elemental composition may contribute to variation in mesostigmatid community structure, although the observed relationships represent statistical associations rather than direct toxic or promotive effects. These contrasting relationships suggest that different elements and environmental compartments may be associated with distinct aspects of mesostigmatid community structure, potentially reflecting species-specific responses to local environmental conditions. However, these patterns should be interpreted as statistical associations rather than direct toxic or promotive effects, as metal bioavailability and toxicity were not directly assessed.In particular, sites invaded by the alien plant Spiraea tomentosa showed lower cumulative species richness, slower species accumulation, and lower observed and estimated diversity than uninvaded sites, suggesting that invasion may be associated with reduced cumulative mesostigmatid mite diversity. These findings are broadly consistent with previous studies showing that invasive plants can modify soil fauna diversity and community structure, although the direction and magnitude of these effects may vary among taxa and environmental conditions. Considering the invasive status of Spiraea tomentosa in Poland and the patterns observed in our study, further comprehensive research is needed to better understand how this plant and its litter influence soil fauna communities.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (57.6KB, docx)

Acknowledgements

We would like to thank Jacek Kamczyc (Department of Game Management and Forest Protection, Poznań University of Life Sciences, Poland) for his enthusiastic assistance and useful comments on the manuscript.

Author contributions

CKU: Conceptualization, Methodology, Investigations, Writing – Original Draft, Writing – Reviewing and Editing, Formal Analysis; JM: Conceptualization, Methodology, Investigations, Writing – Reviewing and Editing; KT: Conceptualization, Methodology, Investigations, Writing – Original Draft, Writing – Reviewing and Editing, Nutrient Analysis; AA: Nutrient Analysis; PK: Conceptualization, Methodology, Investigations; BW: Conceptualization, Methodology, Investigations, Writing – Original Draft, Writing – Reviewing and Editing.

Data availability

All data supporting the findings of this study are available within the paper.

Declaration

Competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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