The influence of environmental variables on soil mite communities (Acari: Mesostigmata) from overgrazed grassland ecosystems – Romania

Abstract

In 2013–2014, the soil mite communities from six overgrazed grassland ecosystems located in the Trascău Mountains, Romania, were investigated. Forty-six species were identified, with 645 individuals. Some abiotic factors from soil were measured (soil temperature – T, soil water content – H, soil acidity – pH, carbon content – C, total nitrogen – Nt, and C/Nt ratio). Significant statistical differences were obtained between environmental factors. Using a canonical correspondence analysis of mite species abundance, environmental variables and habitats, strong relationships between investigated factors were established.

Keywords:

• Grassland
• mite
• community
• structure
• regression

Introduction

Soil is an important habitat for different groups of mesofauna (i.e. nematodes, mites, springtails, proturans). Soil invertebrates have an important regulatory control over the soil food web and have substantial effects on soil pedogenesis [the distribution of soil particles, the soil’s water-holding capacity and water infiltration rate, the lability of organic compounds, mineralisation, immobilisation, the availability of nitrogen (N) and other nutrients, the transport of compounds and the composition, abundance, dispersal and activity of bacteria and fungi; Hassink et al. 1993; Klironomos & Kendrik 1995; Wolters 2000; Cole et al. 2004; Lützow et al. 2006; Gutiérrez et al. 2010].

Mites (Acari) are one of the most abundant invertebrate groups within soil. Many studies have demonstrated that they are sensitive to modifications of the different physical (soil water content, temperature) and chemical [soil nutrients, organic matter, pH, N and carbon (C)] environmental factors (Ruf & Beck 2005; Chikoski et al. 2006; Gulvik 2007; Nielsen et al. 2010, 2012; Kardol et al. 2011).

In Europe, studies concerning the influence of such abiotic factors on predator soil mite communities (Acari: Mesostigmata) have been conducted mainly in forest ecosystems, revealing that external factors, such as those caused by climate change, can have a direct effect on below-ground communities (Salmane 2000; Hasegawa 2001; Huhta & Hänninen 2001; Huhta & Räty 2005; Ruf & Beck 2005; Malmström 2006; Nielsen et al. 2010, 2012; Kardol et al. 2011; Xu et al. 2012). Studies that highlight the correlations between abiotic factors and soil mite communities from other types of terrestrial ecosystems, such as grassland, are few (Cole et al. 2005, 2006, 2008; Briones et al. 2009; Wissuwa et al. 2012).

In Romania, the situation is similar. Studies that demonstrated the influence of abiotic (soil temperature, soil water content and pH) and biotic (type of habitat vegetation) factors on soil mite communities were made only in forest, cliff and shrub ecosystems (Manu 2008, 2011a, 2011b; Manu et al. 2013). Ecological investigations on the edaphic mite communities of Romanian grassland ecosystems have highlighted only the quantitative and qualitative modifications of their population in correlation with vegetation type (Huţu et al. 1991; Călugăr 2006a, 2006b).

In this context, the aim of the present investigation was to determine whether soil environmental variables (temperature, soil water content, pH, C, Nt (total nitrogen), C/Nt (carbon content/total nitrogen)) influence the structure of predator soil mite communities (Acari: Mesostigmata) within different types of overgrazed grasslands, a less studied ecosystem in Romania, and indeed more widely in Europe.

Materials and methods

The mite fauna was sampled in July and September 2013, in six grassland ecosystems. Fifty cores were sampled per ecosystem, to 10 cm depth, with a MacFadyen 5-cm corer. The 50 cores were located within an area of 2500 m². A total of 300 samples were taken from all investigated grasslands. The samples were taken randomly. The mites were extracted with a modified Berlese–Tullgren funnel, in ethyl alcohol, clarified in lactic acid and identified to species level, using identification keys (Ghiliarov & Bregetova 1977; Karg 1993; Masan 2003, 2007; Masan & Fenda 2004; Masan & Halliday 2010, 2014). A total of 645 mites from 49 species were extracted from the 300 cores.

The main abiotic factors (soil temperature – T, soil water content – H%, pH, carbon content – C, total nitrogen – Nt, and C/Nt ratio) were quantified. Twenty-five soil samples from each ecosystem were investigated. The pH was measured with a C532 Jasco Consort pH meter. Soil water content and temperature were measured with a mobile soil hygrometer MST 3000+ and a bimetallic soil thermometer, respectively. Organic carbon and total nitrogen determinations were made by the dry combustion method, using a LECO Truspec CN automatic analyser.

The study was made in six grassland ecosystems within the Trascău Mountains, situated in the south-eastern part of the Apuseni Mountains. Administratively, the Trascău Mountains are allocated to the counties of Cluj (only their northern part, including the Turda and Tureni Gorges, and a small part of the Arieş River valley) and Alba, representing at the same time the mountain unit with the largest area in Alba County (Lazăr 2011). The location and characteristics of the six overgrazed ecosystems/grasslands (G1–G6) are as follows:

• Ecosystem G1 is situated at 45°06ʹ28.79”N and 23°15ʹ60.82”E, at an altitude of 530 m, with a northern exposure and a slope of 20°. The soil is sandy, with accelerated erosion phenomena. The vegetation is dominated by the following species: Agrostis capillaris, Cynodon dactylon, Juncus inflexus, Rumex acetosella and Thymus sp., with a combined coverage of 75%;

• Ecosystem G2 is situated at 45°06ʹ54.35”N and 23°15ʹ51.62”E, at an altitude of 608 m, with a south-west exposure and a slope of 20°. The soil is sandy, while the vegetation is dominated by Agrostis capillaris, Pilosella officinarum and Rumex acetosella, with a combined coverage of 80%;

• Ecosystem G3 is situated at 45°06ʹ51.53”N and 23°15ʹ36.49”E, at an altitude of 519 m, with a south-west exposure and a slope of 20°. The soil is sandy. The vegetation is dominated by Achillea millefolium, Frangula alnus, Rubus caesius and Rumex acetosella, with combined coverage of 100%;

• Ecosystem G4 is situated at 45°07ʹ55.89”N and 23°15ʹ13.68”E, at an altitude of 875 m, with a south-east exposure and a slope of 24.5°. The soil is sandy, on the calcareous substrate. The vegetation is dominated by the following species: Centaurea triumfetti, Plantago lanceolata, Minuartia verna, Nardus stricta and Potentilla erecta, with a combined coverage of 85%;

• Ecosystem G5 is situated at 45°07ʹ47.77”N and 23°15ʹ24.63”E, at an altitude of 808 m, with a southern exposure and a slope of 24°. The soil is sandy, and the vegetation is dominated by Agrostis capillaris, Achillea millefolium, Pilosella officinarum, Nardus stricta, Potentilla erecta, Sanguisorba minor, Thymus sp. and Trifolium arvense, with a combined coverage of 90%;

• Ecosystem G6 is situated at 45°08ʹ90.65”N and 23°15ʹ30.99”E, at 958 m altitude, with a northern exposure and a slope of 20°. The soil is sandy, and the vegetation is dominated by Agrostis capillaris, Achillea millefolium, Pilosella officinarum, Lotus corniculatus, Medicago lupulina, Nardus stricta, Thymus sp. and Trifolium pratense, with a combined coverage of 95% (Figure 1).

Figure 1. Geographical position of the investigated grassland ecosystems.

Prior to analysis, to fit normality and homoscedasticity assumptions, we used the log(x+1) transformation where x is the total abundance and species richness, respectively. To test whether the total abundance and the species richness are related with the environmental variables we used generalised linear mixed models (GLMM) with a Gaussian distribution of error terms and an identity link function. GLMMs allow taking into account the correlated data among the grasslands. In these models, the environmental variables were introduced as fixed factors and the grasslands were used as random factors. The best-supported models were selected using the Akaike information criterion corrected for small sample sizes (AICc) and the corresponding AICc weights (Johnson & Omland 2004). GLMMs were conducted in R with additional functions provided by the R packages lme4 (“lme”, Bates & Maechler 2009) and MuMIn (“dredge”, Barton 2009).

A canonical correspondence analysis (CCA) was used to study mite community responses to the environmental variables. The permutation procedure (based on 9999 cycles) was used to test the significance of constraints (environmental variables) in CCA for all eigenvalues (Johnson & Omland 2004). Prior to analysis, the environmental variables were Z-transformed. CCA was performed using the vegan package (Johnson & Omland 2004).

Results

The soil temperature varied between 8.41°C in G4 and 32.58°C in G2; soil water content varied between 48.44% in G2 and 85.48% in G4. The maximum value of the soil pH was obtained in G5 (5.77) and the minimum in G2 (4.51; Table I).

Table I. Abiotic factors from the grassland ecosystems (G1–G6) from Trascău Mountains (± standard deviation). T, Temperature; H, Soil water content; pH, Soil acidity; C, Carbon content; Nt, Total nitrogen; C/Nt, Carbon content/total nitrogen.

Abiotic factors	G1	G2	G3	G4	G5	G6
T°	31.22 (± 5.59)	32.58 (± 10.40)	29.24 (± 0.43)	8.41 (± 0.21)	20.56 (± 0.30)	12.93 (± 0.13)
H%	50.88 (± 7.16)	48.44 (± 7.87)	60.28 (± 5.41)	85.48 (± 2.63)	74.56 (± 3.72)	72 (± 3.12)
pH	6.16 (± 0.94)	4.51 (± 0.13)	4.90 (± 0.42)	5.36 (± 0.20)	5.77 (± 0.20)	5.53 (± 0.07)
C%	1.75 (± 0.80)	2.36 (± 0.35)	2.15 (± 0.43)	4.32 (± 1)	5.66 (± 1.24)	4.62 (± 0.80)
Nt%	0.14 (± 0.05)	0.19 (± 0.03)	0.19 (± 0.03)	0.33 (± 0.06)	0.38 (± 0.05)	0.33 (± 0.04)
C/Nt	14.54 (± 1.68)	14.43 (± 1.13)	12.82 (± 1.09)	15.06 (± 2.49)	17.15 (± 1.45)	16.18 (± 1.55)

The soil carbon content was highest in G6 (4.62%) and lowest in G1 (1.75%). In the other ecosystems, the values oscillated between these two limits. The nitrogen from soil varied from 0.14% in G1 to 5.66% in G5. If we take into consideration the C/N ratio, the highest value of this variable was recorded in G5 (17.15) and the lowest in G3 (12.82).

In the six grassland ecosystems, 46 species of Mesostigmata mites were identified, with 645 individuals. However, the number of species was heavily correlated with sampling effort as suggested by the individual-based accumulation curve (Figure 2).

Figure 2. Individual-based accumulation curve for species richness of mite community. The shaded area represents the 95% confidence intervals.

Taking into account the numerical abundances, the highest value of this structural parameter was recorded in G6 (267 individuals), in comparison with the other ecosystems, such as G2, where the lowest value was obtained (28 individuals; Table II). Dominant species were: Hypoapis oblonga, Asca bicornis, Hypoaspis vacua, Hypoaspis aculeifer, Hypoaspis praesternalis, Rhodacarellus silesiacus and Rhodacarus denticulatus. Mesostigmata mites with lower numerical abundances (Zercon berlesei, Zercon hungaricus, Trachytes irenae, Prozercon kochi and Parazercon radiatus) are species which prefer natural ecosystems, with habitats rich in organic matter (litter, humus, etc.; Masan & Fenda 2004; Manu et al. 2013).

Table II. Numerical abundance of the mesostigmatid mites identified from investigated grassland ecosystems (G1–G6).

No.	Species	G1	G2	G3	G4	G5	G6	Total
1	Amblyseius sp.					3		3
2	Androlaelaps casalis (Berlese, 1887)						1	1
3	Arctoseius semiscissus (Berlese, 1892)		1					1
4	Asca bicornis (Canestrini & Fanzago, 1887)	17	3		8	2	11	41
5	Cheroseius bryophilus Karg, 1969						5	5
6	Eviphis ostrinus (C.L. Koch, 1836)			1				1
7	Gamasellodes bicolor (Berlese, 1918)	2						2
8	Hypoaspis claviger (Berlese, 1882)					1	2	3
9	Hypoaspis vacua (Michael, 1891)	4	5	4	13	22	7	55
10	Hypoaspis astronomica (C.L. Koch, 1839)					1	1	2
11	Hypoaspis austriaca Sellnick, 1935				1			1
12	Hypoaspis aculeifer (Canestrini, 1883)	58	1	6		4	59	128
13	Hypoaspis angusta Karg, 1965	2						2
14	Hypoaspis oblonga (Halbert, 1915)				4	22	31	57
15	Hypoaspis preasternalis (Wilmann, 1949)	11		16	24	51	93	195
16	Hypoaspis sp.			3	2	1	5	11
17	Hypoaspis karawaiewi (Berlese, 1903)	2			1		2	5
18	Hypoaspis miles (Berlese, 1882)				1			1
19	Iphidonopsis pulvisculus (Berlese, 1921)				1		1	2
20	Iphidonopsis sp.						1	1
21	Lasioseius berlesei (Oudemans, 1938)					1		1
22	Lasioseius sp.					4	2	6
23	Leioseius insignis (Hirschmann, 1963)				2			2
24	Lysigamasus misellus (Berlese, 1904)				2	1	7	10
25	Macrocheles recki Bregetova & Koroleva, 1960		1				2	3
26	Ololaelaps placentulus (Berlese, 1887)		1	2			1	4
27	Ololealaps sellnicki Bregetova & Koroleva, 1964						4	4
28	Oplitis minutissima (Berlese, 1903)				3	1	7	11
29	Pachylaelaps furcifer Oudemans, 1903			1				1
30	Pachylaelaps pectinifer (G. & R. Canestrini, 1881)	1					1	2
31	Parazercon radiatus (Berlese, 1910)					1		1
32	Proctolaelaps pygmaeus (Muller, 1860)	1						1
33	Protogamasellus mica (Athias-Henriot, 1961)			6				6
34	Prozercon kochi Sellnick, 1943			1				1
35	Rhodacarellus perspicuus Halaskova, 1958	1	2		3		3	9
36	Rhodacarellus silesiacus Willmann, 1953	8	3	3	2	3	10	29
37	Rhodacarus coronatus Berlese, 1921						2	2
38	Rhodacarus denticulatus Berlese, 1921		11	6	4			21
39	Rhodacarus roseus Oudemans, 1902						1	1
40	Trachytes irenae Pecina, 1969			1				1
41	Trachytes pauperior Berlese, 1914						2	2
42	Uropoda orbicularis (O.F. Muller, 1776)				1		3	4
43	Veigaia exigua (Berlese, 1916)						2	2
44	Zercon berlesei Sellnick, 1958			1				1
45	Zercon hungaricus Sellnick, 1958			1				1
46	Zerconopsis remiger (Kramer, 1876)				1		1	2
	Total Acari	107	28	52	73	118	267	645

The best-supported models by the data for both abundance and species richness included Nt and H (Table III). For both abundance and species richness the best model included Nt, and for species richness it was followed closely by the second-best-supported model that included Nt and H.

Table III. Model selection results. Models are ranked in a decreasing Akaike weight order. For clarity, models with Akaike weight < 0.04 are not shown. Statistics include the log likelihood (LL), the number of estimated parameters (K), the second-order Akaike information criterion corrected for small sample sizes (AICc), AIC difference (ΔAICi) and Akaike weights (w_i). Nt = Total nitrogen; Nt + H = Total nitrogen + Soil water content; Nt + T = Total nitrogen + Temperature; Nt + H + C = Total nitrogen + Soil water content + Carbon content; Nt + C = Total nitrogen + Carbon content; Nt + H + T = Total nitrogen + Soil water content + Temperature; Nt + pH = Total nitrogen + Soil acidity.

	Abundance					Species richness
Model	LL	K	AICc	∆AIC	wi	LL	K	AICc	∆AIC	wi
Nt	−187.43	4	383.7	0	0.09	−95.4	4	197.54	0	0.1
Nt + H	−189.71	5	384.34	0.64	0.07	−98.68	5	197.57	0.03	0.1
Nt + T	−188.18	6	384.78	1.08	0.05	−98.18	5	197.92	0.38	0.08
Nt + H + C	−190.72	5	385.08	1.38	0.05	−100.12	6	199.42	1.88	0.04
Nt + C	−192.51	4	385.39	1.69	0.04	−96.89	5	199.46	1.91	0.04
Nt + H + T	−188.35	5	385.47	1.77	0.04	−101.89	6	199.55	2	0.04
Nt + pH	−188.3	5	385.62	1.92	0.04	−96.96	5	199.65	2.11	0.04

The CCA of the association between mite abundance and the environmental variables and habitat types are shown in Figures 3 and 4. The first two canonical axes accounted for 42.23% (CCA1 = 21.53%; CCA2 = 20.69%) of the total variation in the original matrix. The first canonical axis was highly correlated with pH (−0.62) whereas the second canonical axis was correlated with N_t (−0.72), temperature (0.62), C (−0.69) and soil water content (−0.554). Species from the upper left quadrant were associated with temperature. The right lower quadrant contains species associated with N_t, C and soil water content. Species associated with pH in particular but also with C/N are placed in the lower left quadrant.

Figure 3. Canonical correspondence analysis bi-plot of abundance mite species and environmental variables. Length and direction of arrows indicate the relative importance and direction of change in the environmental variables (Ar. se. – Arctoseius semiscissus; As.bi. – Asca bicornis; Am.sp. – Amblyseius sp.; Ch. br. – Cheroseius bryophilus; Ga.bi. – Gamasellodes bicolor; Hy.ac. – Hypoaspis aculeifer; Hy.as. – Hypoaspis astronomica; Hy.ka. – Hypoaspis karawaiewi; Hy.ob. – Hypoapis oblonga; Hy.pr. – Hypoaspis preasternalis; Hy.va. – Hypoaspis vacua; Ev.os. – Eviphis ostrinus; Ip.pu. – Iphidonopsis pulvisculus; Ol.se. – Ololaelaps sellnicki; Pa.pe. – Pachylaelaps pectinifer; Pr. ko. – Prozercon kochi; Pr. py. – Protogamasellus pygmaeus; Pr.mi. – Protogamasellus mica; Rh.de. – Rhodacarus denticulatus; Rh.si. – Rhodacarellus silesiacus; Tr.ir. – Trachytes irenae; Tr. pa. – Trachytes pauperior; Ze.re. – Zerconopsis remiger).

Figure 4. Canonical correspondence analysis bi-plot of abundance of mite species and habitat types (Am.sp. – Amblyseius sp.; Ch. br. – Cheroseius bryphilus; Ga.bi. – Gamasellodes bicolor; Hy.ac. – Hypoaspis aculeifer; Hy.ka. – Hypoaspis karawaiewi; Hy.pr. – Hypoaspis preasternalis; Hy.sp.1 – Hypoaspis sp.; Hy.va. – Hypoaspis vacua; La. sp. – Laioseius sp.; Le.in. – Leioseius insignitus; Ol. pl. – Ololaelaps placentulus; Pr.mi. – Protogamasellus mica; Rh de – Rhodacarus denticulatus; Rh.pe. – Rhodacarellus perspicuus; Rh.si. – Rhodacarellus silesiacus).

The third canonical axis explained 12.98% of the variability and the fourth canonical axis 10.60% of the variability, and the axes were associated with habitat types. Species of habitats G2 and G3 were separated from species of habitats G5 and G6, whereas species of habitats G4 and G1 were separated from all the others.

Discussion

In the investigated grassland ecosystems 46 species were identified, with 645 individuals. The majority of the identified species prefer meadow-grasslands, the most dominant ones being: Asca bicornis, Hypoaspis vacua, Hypoaspis praesternalis, Hypoapis aculeifer, Lasioseius berlesei, Rhodacarellus silesiacus, Lysigamasus misselus and Protogamasellus mica (Călugăr 2006a; Gwiazdowicz 2007; Salmane & Brumelis 2010; Wissuwa et al. 2012).

In terms of species diversity, as well as the abundance of the mite fauna, the best conditions for the development of a rich soil Mesostigmata fauna appear to be in ecosystems G4 (34.69% of the total number of observed species), G5 (30.61%) and G6 (57.14%). These ecosystems are characterised by the highest values of soil water content (over 70%), C (over 4.3%) and Nt (over 0.33%).

Considering the CCA, the length of the vectors of the given variable indicates the importance of the variable on the CCA plot. Species plotted close to the vectors have a strong relationship with them. Species located near the origin do not show a strong relationship with any of the variables. Taking account of these relationships, species as Arctoseius semiscissus, Hypoaspis karawaiewi, Trachytes irenae, Hypospis aculeifer and Protogamasellus mica are influenced by the soil temperature. Studies have revealed that temperatures between 30 and 32°C were the maximum tolerated before significant decreases in numbers were detected in Mesostigmata mites (Huhta & Hänninen 2001; Malmström 2006, 2008; Manu 2008, 2011a, 2011b).

Species such as Trachytes pauperior, Pachylaelaps pectinifer, Asca bicornis, Gamasellodes bicolor are closed linked to the pH and C/N ratio. Similar results have been reported by other authors, concluding that a pH close to neutral is optimal for mesostigmatids (Bedano et al. 2005). In contrast, some other research has found that these invertebrates prefer a lower pH, which is favourable for the development of fungi, the food source for other soil invertebrates which in turn form the prey for mesostigmatids (Chikoski et al. 2006; Manu 2011a; Birkhofer et al. 2012). Acidity and dominance of sandy soils (such as those described from the six grassland ecosystems in the present study) are known to contribute to low actinomycete biomass (Kooijman et al. 2009; Birkhofer et al. 2012).

The soil water content, C and Nt are in strong relationship with species such as Ololalepas sellnicki, Hypoapsis astronomica, Hypoaspis preasternalis, Zerconopsis remiger and Iphidonopsis pulvisculus. Previous studies from Europe have highlighted that these species are characteristic of grasslands or meadows (Salmane 2000; Călugăr 2006b; Salmane & Brumelis 2010).

Huhta and Hänninen (2001) found that the numerical abundance of mesostigmatids is constant at varying soil moisture; other researchers have contradicted this statement (Bedano et al. 2005; Kamczyc 2006; Manu 2008, 2011a, 2011b). Salmane (2000) stated that the soil relative humidity must be recognised as a limiting factor and even where soil moisture is sufficient, the decisive limiting factor is soil temperature.

Studies on predator mite communities from natural grassland ecosystems revealed that mites have been reported as most abundant in soils with low pH values and nitrate concentrations (Hasegawa 2001; Bedano et al. 2005; Birkhofer et al. 2012; Miller et al. 2014). Different studies have shown the relationship between soil mesofauna and nitrate content in grasslands to be very variable, with no, weak negative, strong negative or even positive correlations, depending on land-use type (Jandl et al. 2003; Lindberg & Persson 2004; Cole et al. 2005; Fountain et al. 2008; Birkhofer et al. 2012).

A similar trend was described for the C/Nt ratio. Studies have revealed that soil arthropods require more N (due to N mineralisation) than C for their development (Hassink et al. 1993; Chikoski et al. 2006). Carbon to nitrogen ratios vary with climatic conditions, primarily temperature and humidity. Researches have revealed that N mineralisation decrease with decreasing soil moisture and increasing soil temperature (Persson 1989; Hassink et al. 1993; Chikoski et al. 2006). Therefore, it is not surprising that our results showed a pattern in C/N in overgrazed grasslands, with a lower C/N ratio where soil water content varied between 48.44 and 60.28% and soil temperature fluctuated between 29.24 and 32.58°C.

A CCA bi-plot of abundance of mite species and habitat types revealed that some mesostigmatids such as Protogamasellus mica, Ololaelaps placentulus, Rhodacarellus perspicuus, Rhodacarellus silesiacus and Rhodacarus denticulatus occurred in G2 and G3, ecosystems characterised by close average values of soil acidity, C and Nt content. Other species such as Hypoaspis vacua, Leioseius insignitis occurred in G4, the most humid ecosystem, and Hypoaspis aculeifer, Hypoaspis karawaievi, Gamasellodes bicolor in G1, where the soil temperature recorded the highest values and the soil contents of C and N had the lowest percentage.

Hypoaspis oblonga, Cherosieus bryophilus and Hypoaspis praesternalis were found in G5 and G6, ecosystems characterised by close average values, but increased humidity, decreased temperature, more acid soils and higher content of C and Nt, in comparison with the other ecosystems.

Studies from Europe revealed that some species (Rhodacarellus silesiacus, Arctoseius semiscissus, Proctolaelaps pygmaeus and Protogamasellus mica) were characteristic of a range of xerothermophilous habitats (natural or anthropogenic in origin) such as meadows, dunes, urban parks, arable fields, and derelict industrial and mining areas. Other species (e.g. Hypoaspis aculeifer, Veigaia nemorensis and Asca bicornis) have a wide ecological plasticity, being found in different forest habitats and in meadows, as well as in polluted areas (Salmane 2000; Gwiazdowicz 2007; Salmane & Brumelis 2010; Manu 2011a, 2011b; van Leeuwen et al. 2015).

The soil mite communities from the investigated grassland ecosystems, in the present study, are characterised mainly by typical meadow species. Ecosystems defined by the highest values of soil water content, carbon and Nt are characterised by the highest number of mite species and overall numerical abundance. In contrast, those grassland ecosystems with the highest soil temperature and most decreased pH have the lowest species diversity and numerical abundance.

The statistical analysis applied in this study revealed that the environmental variables influence the soil mite communities, and also that the occurrence of certain sensitive species depends on these abiotic factors.

Acknowledgements

This work was supported by the Institute of Biology, Romanian Academy, under Grant RO1567-IBB01/2015, and by the Executive Agency for Higher Education, Research, Development and Innovation Funding, Romania (UE-FISCDI), under Grant 50/2012 ASPABIR.

The authors wish to thank Dr. Peter Mašán, Institute of Zoology, Slovak Academy of Sciences, for confirming the identification of some of the Mesostigmata mites, and Owen Mountford (NERC (National Environment Research Council) Centre for Ecology and Hydrology, UK) for advice on the English text. We thank Prof. Radu Lăcătuşu, from Institute of Soil Science and Agrochemistry, Bucharest, for kindly providing the general soil characterisation in the sampling plots.

We also thank Simona Plumb and Rodica Iosif for their assistance in the laboratory and the field.