Managing soil to support soil biodiversity in protected areas agroecosystems. A comparison between arable lands, olive groves, and vineyards in the Conero Park (Italy)

Managing soil to support soil biodiversity in protected areas agroecosystems. 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A comparison between arable lands, olive groves, and vineyards in the Conero Park (Italy) Martina Coletta, Marco Monticelli, Aldo D’Alessandro, Celeste Gentili, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4946545/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Jan, 2025 Read the published version in Environmental Monitoring and Assessment → Version 1 posted 12 You are reading this latest preprint version Abstract Sustainable soil management is essential to conserve soil biodiversity and its provision of vital ecosystem services. The EU Biodiversity Strategy for 2030 highlights the key role of organic farming and land protection in halting biodiversity loss, including edaphic biodiversity. To assess the effectiveness of the proposed measures, a study was conducted to determine the soil quality of three organically managed agroecosystems: arable lands, olive groves and, vineyards in the Conero Park, using the arthropod-based Biological Soil Quality Index (QBS-ar). Soil microarthropods are sensitive indicators of the impact of agricultural practices on soil quality. Given the diversity of the agronomic practices applied in these agroecosystems, the study aimed to compare the soil quality and identify the system with the least impact on soil biodiversity conservation, with the ultimate goal of laying the basis for identifying soil quality benchmarks within each system to be used in monitoring activities in land protected areas. Results showed that organic farming combined with land protection had a positive impact on soil quality. Overall soil quality was excellent, with the highest levels found in arable lands. This is consistent with the Intermediate Disturbance Hypothesis (IDH), which states that slightly disturbed habitats (i.e. arable land with minimum tillage) tend to have higher organism diversity than stable ones. The composition of microarthropod communities in arable land differed from those in stable arboreal crops. Olive groves showed a higher abundance and diversity of microarthropods compared to vineyards, which showed lower values. Promoting the use of QBS-ar, identifying benchmarks for prevalent agroecosystems and ensuring continuous monitoring of protected areas is thus a crucial issue. QBS-ar Soil health Soil microarthropods Organic farming Protected areas Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Soil is the foundation of food production. According to the Food and Agriculture Organization (FAO) (2015), 95% of our food comes directly or indirectly, from soil. Soil fertility is essential for food security, and it is guaranteed by soil biodiversity. Soil organisms play a crucial role in delivering vital soil processes; they release nutrients from soil organic matter, form and maintain soil structure, contribute to soil water entry, storage, and transfer, enhance nutrient cycling, and promote plant growth and health (Lavelle & Spain, 2001 ). The soil biota is fundamental in ensuring the long-term functioning of soil. In fact, soil health is defined as the continued capacity of soil to function as a vital living ecosystem that sustains plants, animals, and humans (Bünemann et al., 2018 ). However, the application of unsustainable agricultural practices poses a threat to soil biodiversity and the provisioning of vital ecosystem services it provides (Köninger et al., 2022 ). The dominant agricultural production model currently in use, generally referred to as “conventional agriculture”, is characterised by monocultures, excessive tillage, heavy applications of agrochemicals, such as pesticides and chemical fertilisers (Jiang et al., 2021 ). This type of farming is estimated to be the main driver of biodiversity loss (Benton et al., 2021 ). To reverse this trend, and to counteract the loss of biodiversity, the European Biodiversity Strategy for 2030 has set a specific objective of increasing the percentage of the EU's agricultural area under organic farming to 25% and that of protected areas to 30% (Montanarella & Panagos, 2021 ). The adoption of nature- and biodiversity-friendly farming practices, such as organic farming, has been shown to enhance biodiversity in agricultural landscapes (Bengtsson et al., 2005 ). Additionally, the expansion of protected areas is crucial as these high-quality environmental habitats are critical reservoirs for biodiversity, including soil biodiversity (Mantoni et al., 2021 ). To test the effectiveness of the proposed solutions on soil fauna, soil microarthropods can be used, as they are considered a sensitive and promising bioindicator. Soil microarthropods have been used for evaluating the impact of human activities on soil functions, including agriculture. They proved to respond sensitively to land management practices and soil disturbances (Parisi et al., 2005 ; Parisi & Menta, 2008 ; Reis et al., 2016 ; Joimel et al., 2017 ; Yin et al., 2020 ; Jiang et al., 2021 ; Ashwood et al., 2022). Nonetheless, the Biological Soil Quality Index based on arthropods (QBS-ar) proved to reliably assess soil health using soil mesofauna (Parisi, 2001 ; Parisi et al., 2005 ; Menta et al., 2018 ). The method has been applied for over 20 years in Italy, as well as in European and non-European countries (Menta et al., 2018 ). The QBS-ar is based on the identification of soil microarthropods’ main functional groups, and the assignment of an Eco-morphological Index (EMI) based on morphological characters of adaptation to soil habitat, thus avoiding the need for taxonomic identification to the species level. The QBS-ar method describes soil biological quality using numerical values. For example, natural habitats such as forest soils, meadows and pastures generally have a QBS-ar of over 120, and sometimes even 200. In contrast, more disturbed environments such as agroecosystems have an average QBS-ar of 94 (Parisi, 2001 ; Menta et al., 2011 ; 2018 ). Numerical ranges and thresholds for QBS-ar can vary considerably depending on the environment (Parisi, 2001 ; Menta et al., 2011 , 2018 ). Most of the QBS-ar collected data concerns agricultural soils and forests in Italy (Menta et al., 2018 ). However, variations in the QBS-ar values were observed in agroecosystems, depending on the type of crop and management method (e.g. conventional or organic). Specific quality ranges have not yet been concretely defined for the different agroecosystems (e.g. arable land, grassland, orchards, olive groves, etc.). This is necessary since the agricultural practices adopted in each system vary widely. To make a proper comparison, it is necessary to develop quality ranges that are distinct, depending on the agroecosystem considered. Further, extending the determination of QBS-ar intervals is crucial for soil in which crops that are widespread and particularly valuable in Italy are grown, such as olive groves and vineyards. Moreover, an integrated approach, including chemical-physical characterisation, is needed for evaluating the overall agroecosystem status (Tabaglio et al., 2009 ). Monitoring the health of these soils is essential for preserving their condition and ensuring highly profitable markets for the Italian economy. In fact, QBS-ar is increasingly used in biological soil quality assessments and soil monitoring programs in Italy, including the MOnitoring SYstem of Soil at multiScale MOSYSS Project, Dunbar et al., 2014) that was conducted in Marche Region and Emilia Romagna, or the Excalibur ( https://excaliburh2020.eu/en/overview/ ) and Minotaur projects ( https://ejpsoil.eu/soil-research/minotaur ). The objectives of the study were: (i) to assess and compare, for the first time, soil health in three organically managed agroecosystems (arable lands, vineyards and olive groves) in the Conero Park (Italy) using the QBS-ar index; (ii) to characterise the soil microarthropod communities of the different organic agroecosystems and to relate them to the agricultural practices applied and to the chemical-physical characteristics of the soil; (iii) to identify the system with the least impact on the conservation of soil biodiversity, while promoting the most sustainable agricultural practices to maintain soil health, with the ultimate aim of laying the basis for identifying soil quality benchmarks within each system to be used in monitoring activities in protected areas. 2. Materials and methods 2.1 Study area The Conero Park (Italy) covers an area of 6011 hectares and includes the municipalities of Ancona, Camerano, Sirolo, and Numana ( https://www.parks.it/parco.conero/Eindex.php ). It represents the boundary between the Mediterranean and the temperate sub-Mediterranean bioclimate, on the eastern side of the Italian peninsula. Within the Park three different organic agroecosystems (arable lands, olive groves, and vineyards), and four sites for each, were selected, resulting in a total of twelve sampling sites (Fig. 1 , Table 1 ). Sites were previously identified by the Park Authority in the framework of the project Agro-environmental Area Agreement for water protection (RDP Marche 2014/2020). The project has the overall aim of promoting environmentally sustainable agricultural practices capable of minimising the impact of agriculture on soil, freshwater and coastal marine ecosystem of the Park. The samplings were conducted in one season: in June and July 2022. Data related to field management information and agricultural practices applied (use of herbicides, pesticides, and fertilisers, crop rotation, irrigation, ploughing/harrowing/subsoiling depth, grubbing, shredding, pruning, hoeing, and sowing) were collected through in-depth interviews with field owners (Tab. S1). Geographic coordinates and environmental parameters were recorded (Table 1 ). Due to the limited extension of the area, the influence of climate was assumed to be homogeneous across all sites (Fig. S1 ). Table 1 Sampling sites characteristics. Number Site code Agroecosystem Locality Sampling date Latitude Longitude Altitude (m asl) Slope (%) Exposure Herbaceous cover (%) Herbaceous cover height (cm) 1 BIA Arable land Numana (AN) 13/06/2022 43°28'32.95"N 13°36'27.60"E 6 m 1.7 E 81–100 > 60 2 NIC Arable land Camerano (AN) 13/06/2022 43°32'55.45"N 13°33'27.60"E 86 m 4.3 N-W 81–100 31–60 3 POL Arable land Ancona (AN) 21/06/2022 43°33'13.08"N 13°32'54.89"E 61 m 18.7 S-E 61–80 16–30 4 PET Arable land Ancona (AN) 13/06/2022 43°34'1.59"N 13°33'15.80"E 141 m 15.4 S-E 81–100 > 60 5 MAR Vineyard Ancona (AN) 05/07/2022 43°31'37.48"N 13°35'0.20"E 140 m 70.4 S-O 81–100 31–60 6 GUA Vineyard Ancona (AN) 05/07/2022 43°34'57.63"N 13°33'2.46"E 132 m 6.8 N-W 81–100 16–30 7 MOD Vineyard Sirolo (AN) 05/07/2022 43°31'54.16"N 13°36'28.69"E 195 m 27.2 S-E < 1 1–15 8 ZAZ Vineyard Camerano (AN) 21/06/2022 43°30'48.82"N 13°33'59.16"E 84 m 12.2 W/W-S 41–60 31–60 9 SEL Olive grove Castelfidardo (AN) 05/07/2022 43°27'52.19"N 13°35'45.09"E 54 m 3.9 N-W 81–100 1–15 10 FAT Olive grove Massignano (AN) 28/06/2022 43°32'16.78"N 13°34'56.94"E 189 m 3.4 S 81–100 > 60 11 LUC Olive grove Ancona (AN) 28/06/2022 43°33'50.67"N 13°33'0.82"E 126 m 24.9 E 81–100 > 60 12 PIA Olive grove Ancona (AN) 28/06/2022 43°33'0.52"N 13°34'31.98"E 247 m 0 N-W 81–100 > 60 2.2 Soil sampling and QBS-ar determination Sampling and extraction of soil microarthropods followed the methodology described by Parisi et al. (2001, 2005 ) and Menta et al. ( 2020 ). In each site, a QBS-ar sample, consisting of three subsamples (A, B, C) with a volume of about 800 cm 3 each, was collected using a cylindrical soil corer. Each subsample was immediately placed in a plastic bag and transported to the laboratory, within the following 24 hours. A homogeneous area was selected in each site, excluding disturbed areas such as flattened vegetation and tractor tracks. The herbaceous cover was removed prior to collecting the soil sample. In arable lands, subsamples were collected at equal intervals (about 10 m) along a diagonal line, in the central part of the field. In vineyards and olive groves, samples were collected in the interrow. A total of 36 soil sub-samples were collected. Arthropod extraction was carried out in the laboratory using the Berlese-Tüllgren funnel, equipped with 25 W light bulbs and a 2-mm sieve mesh. The extraction process lasted for 7 days, and the extracted specimens were preserved in a solution of 75% ethyl alcohol and 25% glycerol by volume. The collected specimens underwent a stereoscopic analysis to determine biological form (BF) and Eco-Morphological Index (EMI) (1 to 20), according to Parisi et al. ( 2005 ). Total QBS-ar and partial QBS-ar values were calculated for each sample. The total QBS-ar total was obtained by summing the highest EMI values attributed to each BF in the three subsamples (A, B, C). The partial QBS-ar was derived from the evaluation of the BFs found in the single subsample. A quantitative approach was used, unlike the original protocol (Parisi et al., 2001, 2005 ), where the number of individuals present for each BF was counted in each subsample, as well as the number of euedaphic forms (EFs). Additionally, total microarthropod abundance and density (ind/m 2 and ind/m 3 ), microarthropod community composition, the Acari/Collembola ratio (Bachelier, 1986) and the percentage of Oribatid mites out of total mites (Aoki, 1967 ; Aoki et al., 1977 ) were determined. 2.3 Soil chemical-physical analysis Soil samples for chemical-physical analyses were collected in accordance with the official methods for soil analysis of the Italian legislation DM 13/09/1999 SO No. 185 ( Approvazione dei “Metodi Ufficiali di analisi chimica del suolo ”). In each site, 10 soil sub-samples were collected in each site by using a pedological auger, and then mixed and reduced to a single composite sample (1 kg). The samples were analysed by the laboratory of the “Agenzia per l'Innovazione nel Settore Agroalimentare e della Pesca "Marche Agricoltura Pesca" (AMAP)”, Jesi (Italy). The analysed parameters were three fraction soil texture (sand, silt, clay; g/kg), total organic carbon (g/kg), organic matter (g/kg), pH, C/N ratio, total nitrogen (g/kg), assimilable phosphorus (mg/kg), exchangeable potassium (mg/kg), field water capacity (%), wilting point (%), total limestone (g/Kg), active limestone (g/Kg), carbon of humic and fulvic acids (g/kg). 2.4 Statistical analysis To test for differences between the three systems (arable lands, vineyards, olive groves), soil biological indicators (QBS-ar, number of BFs and EFs, abundance, Acari/Collembola ratio, percentage of Oribatid mites out of total mites) total and partial data (obtained from each subsample) were considered. The data were first checked for normality of distribution (Shapiro-Wilk test) and homoscedasticity (Levene's or Bartlett's test, depending on the distribution). Differences between management systems were evaluated through One-way Anova or Kruskal Wallis Test, depending on the distribution, and post hoc tests (Tukey's honestly significant difference or Dunn test). A p-value < 0.05 has been considered significant. Statistical analysis was performed using R software, version 4.2.0 (R Development Core Team, 2021 ). Soil chemical and physical properties of the different agroecosystems were statistically analysed, as described above for biological data, and, further, correlations between the different parameters have been evaluated by using “ggcorrplot“ R package. Non-metric multidimensional scaling (nMDS), based on Bray-Curtis dissimilarity index was performed to visualise the grouping of arthropod communities. To associate the community composition with soil characteristics and evaluate the associations between nMDS site scores, the “envfit” function was used. Permutational multivariate analysis of variance (PERMANOVA) was used to test for differences in assemblages among the different patterns visualised with nMDS. Ordination analysis, PERMANOVA, and SIMPER were performed with “vegan” and “pairwiseAdonis” R packages, while “ggplot2” was used to generate the plots and the nMDS ordination diagram. The heatmaps of the log 10 -transformed total abundance of soil microarthropods were generated using Heatmapper web server ( www.heatmapper.ca ) (Babicki et al., 2016 ). 3. Results 3.1 QBS-ar application The average total QBS-ar value of the agroecosystems within the Conero Regional Park was 167. The higher average total QBS-ar was found in arable lands, with a corresponding value of 190 (Table 2 ). Within arable sites, the QBS-ar ranged from 155 to 222. Olive groves had an average total QBS-ar value of 176, ranging from 144 to 231, while vineyards showed a value of 136, ranging from 84 to 184. No significant differences were found when comparing the total QBS values among the systems. However, when comparing partial QBS-ar values, soil quality in arable lands was significantly higher than in vineyards (p-value = 0.042, TukeyHSD) (Fig. 2 ). Table 2 The table shows partial and total QBS-ar values for each site, and average QBS-ar values for each agroecosystem. Number Site code Agroecosystem Partial QBS-ar values (A, B, C) Average partial QBS-ar values (A, B, C) of the system Total QBS-ar values Average total QBS-ar values of the system 1 BIA Arable land 149, 146, 172 135 219 190 2 NIC Arable land 182, 177, 156 222 3 POL Arable land 89, 109, 122 155 4 PET Arable land 126, 56, 132 163 5 MAR Vineyard 72, 56, 48 99 84 136 6 GUA Vineyard 99, 86, 138 169 7 MOD Vineyard 154, 170, 138 184 8 ZAZ Vineyard 75, 82, 75 105 9 SEL Olive grove 83, 94, 114 117 144 176 10 FAT Olive grove 124, 109, 115 155 11 LUC Olive grove 139, 101, 154 231 12 PIA Olive grove 104, 135, 132 175 3.2 BFs, EFs and abundance Overall, 24 BFs were found in the agroecosystems considered within the Conero Regional Park. The highest number of BFs was found in olive groves (23), followed by arable lands (20) and vineyards (19) (Table 3 ). As for BFs, the highest abundance and density were found in olive groves, followed by arable lands and vineyards (Fig. 3 ). On the other hand, the total number of EFs was the same (9) in the three systems. Any significant differences, on partial and total data, was found in either BFs, EFs or abundance. Table 3 The table shows the number of total biological (BFs) and eu-edaphic forms (EFs), and average abundance and density (ind/m and ind/m 2 ) for each site and average total abundance for each agroecosystem. Agroecosystem Total BFs Average BFs Average EFs Total EFs Average abundance Average microarthropod density (ind/m 3 ) Average microarthropod density (ind/m 2 ) Arable land 19 16 7 9 1515 504917 58036 Vineyard 20 14 5 9 1419 473083 54377 Olive grove 23 17 6 9 1656 551833 63429 3.3 Soil microarthropods community composition The composition of the soil microarthropod community is shown by the heatmap (Fig. 4 ). 18 BFs were found in all systems: Pseudoscorpions, Araneidae, Isopoda, Acari, Collembola, Diplopoda, Symphyla, Chilopoda, Diplura, Psocoptera, Hemiptera, Thysanoptera, Coleoptera, Diptera, Hymenoptera, Coleoptera and Diptera larvae and other Holometabola. Among these, Acari, Collembola and Hymenoptera, due to their ubiquitous presence, are not represented in the heatmap of Fig .4 because their high abundance tends to mask and unify the presence of BFs with lower abundance. Data referred to these groups will be discussed separately. The presence of Hemiptera, Psocoptera, Thysanoptera, Diptera and other Holometabola was not so relevant in determining soil quality, as they do not contribute significantly (EMI = 1) to the QBS-ar value. However, all agroecosystems were characterised by the presence of 9 EFs (EMI = 20). Acari, Collembola, Pseudoscorpions, Diplopoda (Julidae), Pauropoda, Symphyla, Chilopoda, Diplura, Coleoptera were found in arable lands and olive groves. The same EFs were found in vineyards, except for Pauropoda, which were not recorded. On the contrary, Protura were found solely in vineyards. Similarly, Hymenoptera larvae (Formicidae) were found in olive groves and vineyards but not in arable lands. Zygentoma, Embioptera and Lepidoptera larvae were registered only in olive groves. Notably, this latter system was characterised by a high number of Diplopoda and Hemiptera, compared to arable lands and vineyards. 3.4 Most abundant soil microarthropod groups (Acari, Collembola and Hymenoptera) Focusing on the most abundant groups (Acari, Collembola, Hymenoptera), the highest number of Acari was found in arable lands (p-value = 0.030, Kruskal-Wallis) and it resulted significantly higher compared to vineyards (p-value = 0.037, Dunn) (Fig. 5 ). The average Acari density in arable land was 49847 ind/m 2 , while 33304 ind/m 2 in vineyards and 37405 ind/m 2 in olive groves (Tab. S2). On the contrary, Collembola resulted to be the least abundant group in arable lands, compared to both vineyards and olive groves, which showed similar values. Hymenoptera abundance showed a similar trend to Collembola, with lower abundance on arable lands (Fig. 5 ). The number of the BFs other than Acari, Collembola and Hymenoptera, resulted significantly higher in olive groves (p-value = 0.040, Kruskal-Wallis), and particularly higher compared to vineyards (p-value = 0.040, Dunn). 3.5 Acari/Collembola ratio and percentage of oribatid mites out of total mites The Acari/Collembola ratio resulted significantly higher in arable lands (p-value = 0.015, Kruskal-Wallis), particularly compared to olive groves (p-value = 0.011, Dunn) (Table 4 ). Notably, olive orchards and vineyards were found to have the same Acari/Collembola ratio (3.6), while arable lands were characterised by a higher value (19.2). As for Acari abundance and Acari/Collembola ratio, Oribatid mites were significantly higher in arable lands (p-value = 0.0003, Kruskal-Wallis) compared to both vineyards (p-value = 0.006, Dunn) and olive groves (p-value = 0.0005, Dunn). Notably, the density of Oribatid mites in arable land was 19636 ind/m 2 , while 6006 ind/m 2 in olive groves, and 7816 ind/m 2 in vineyards (Fig. 6 ; Tab. S2). Whereas the percentage of Oribatid mites out of total mites was 39,4% in arable land, 16,1% in olive groves, and 23,5% in vineyards (Tab. S2). Table 4 The table shows the total Acari/Collembola ratios for each site and average Acari/Collembola ratio for each agroecosystem. Number Site code Agroecosystem Total Acari/Collembola ratios of each site Average Acari/Collembola ratio of the system 1 BIA Arable land 32.1 19.2 2 NIC Arable land 13.8 3 POL Arable land 28.7 4 PET Arable land 13.8 5 MAR Vineyard 0.9 3.6 6 GUA Vineyard 2.0 7 MOD Vineyard 5.0 8 ZAZ Vineyard 129.7 9 SEL Olive grove 7.0 3.6 10 FAT Olive grove 5.9 11 LUC Olive grove 1.8 12 PIA Olive grove 8.1 3.6 Soil chemical-physical parameters and soil microarthropods community structure The agroecosystems showed a similar soil texture; arable lands and vineyards had a silty clay loam texture, whereas olive groves had a clay loam texture (Table 5 ). The main differences were related to sand and silt contents. However, there were no significant differences in terms of soil particle composition (Fig. S2). All soils were calcareous with a high percentage of active limestone, soil pH was generally slightly alkaline in all systems. The results were similar in vineyards and arable lands and then in olive orchards. The carbon to nitrogen ratio (C/N ratio) was low in all agroecosystems. Olive groves have the highest levels of organic matter (SOM), total organic carbon (TOC), total nitrogen (TN), and C from humic and fulvic acids. In contrast, vineyards have higher levels of assimilable P and exchangeable K. The wilting point (WP) was similar across all systems, while the highest field water capacity (FWC) was found in vineyards (31.9%) and the lowest in arable lands (21.7%) (Table 5 , Fig. S2). Statistical analysis showed that FWC was the only parameter that differed significantly between the three systems (p-value = 0.024, Kruskal-Wallis) (Table 5 , Fig. S2). Through correlation analysis (Pearson coefficient), several variables were found to be positively highly correlated, including TN, TOC, SOM, and the carbon content of humic and fulvic acids. Additionally, TL, AL, WP and FWC exhibited a slight positive correlation, as well as TL, C/N and clay. While negative correlations were found between the pH and several parameters, particularly TOC and SOM; carbon of humic and fulvic acids and silt; clay with sand and TL (Fig. 7 ). The composition of the soil microarthropod community was related to the soil chemical-physical properties in a Non-Metric Multidimensional Scaling (nMDS) plot based on Bray Curtis dissimilarity index (stress = 0.12) (Fig. 8 ). The microarthropod community of arable land differed from the other two systems, while the microarthropod communities in vineyards and olive groves tended to overlap. The results showed that soil textural parameters, such as sand and clay contents, seemed to have the greatest impact on microarthropod community composition. Additionally, K and FWC were found to be important variables in community assemblages, although their variation was not statistically significant. However, PERMANOVA analyses revealed no statistically significant differences in microarthropod community assemblage between the agroecosystems (p-value = 0.31, PERMANOVA). The SIMPER analysis revealed that the sand and clay contents, as well as K and total limestone (TL), were the parameters that drove the most differences between community compositions, although not significantly. Table 5 The table shows the soil chemical-physical properties of the three agroecosystems. Total organic carbon, TOC; soil organic matter, SOM; total nitrogen, TN; assimilable phosphorus, P; exchangeable potassium, K; field water capacity, FWC; wilting point WP; total limestone, TL; active limestone, AL; carbon/nitrogen ratio, C/N ratio; carbon of humic and fulvic acids; pH; sand, silt and clay particles content, and soil texture. Site Agroecosystem pH C/N SOM (g/kg) TOC (g/kg) TN (g/kg) P (mg/kg) K (mg/kg) FWC (%) WP (%) TL (g/kg) AL (g/kg) C of humic and fulvic acids (g/kg) Sand (g/kg) Silt (g/kg) Clay (g/kg) Soil texture BIA Arable lands 8.03 8 26.9 15.6 2.35 17.5 316 22.6 17.5 237 159 7.4 58 486 456 Silty-Clay NIC Arable lands 8.08 7.3 24.5 14.2 2.35 11.8 117 22.1 16.9 383 176 7.3 155 526 319 Silty-Clay-Loam POL Arable lands 8.09 8.1 16.7 9.7 1.2 11.3 159 20.15 14.9 158 84 6.5 159 449 392 Silty-Clay-Loam PET Arable lands 8.13 8.1 19.5 11.3 1.4 7.5 165 21.9 17.5 265 155 6.1 120 517 363 Silty-Clay-Loam SEL Olive groves 7.56 7.5 25.7 14.9 2 13.7 140 26.7 16.1 325 139 7.6 324 450 226 Loam FATi Olive groves 7.56 8.6 23.7 13.8 2 10.6 147 27.6 18.6 412 171 7.15 219 454 327 Clay-Loam LUC Olive groves 7.55 7.5 32.3 18.7 2.5 6.5 226 24.5 16.9 345 153 8.1 222 443 335 Clay-Loam PIA Olive groves 7.28 9.4 67.4 39.1 4.15 15.2 185 41.7 28.2 578 183 19 159 449 392 Silty-Clay-Loam MAR Vineyards 7.37 7.5 33.1 19.2 2.55 14.3 179 46.1 26.3 582 194 8.4 267 473 260 Loam GUA Vineyards 8.23 7.9 23.1 13.4 2.1 7.6 245 23.5 14.5 300 125 6.5 184 495 321 Silty-Clay-Loam MOD Vineyards 8.21 6.8 17.6 10.2 1.5 16.9 179 29 17.1 599 169 4.9 182 544 274 Silty-Clay-Loam ZAZ Vineyards 8.28 8.3 25.6 14.9 2.2 29.2 646 27.6 19.3 256 146 9.15 88 448 464 Silty-Clay 4. Discussion 4.1 Biological Soil quality in the Conero Park The overall soil quality of the agroecosystems in Conero Park resulted to be excellent. The average total QBS-ar value (167) exceeded the threshold set for high biological quality in agricultural soils (93.7) (Parisi 2001 ; Menta et al., 2018 ). The application of organic farming, in conjunction with land protection, proved to contribute positively to the overall soil quality of the agroecosystems. According to Mantoni et al. ( 2021 ), land protection can significantly affect the soil microarthropods community by “rescue effects”, resulting in a positive influence on the QBS-ar index in agricultural fields. Similarly, the QBS-ar values obtained in the three agroecosystems were excellent. In several sites, the QBS-ar values exceeded the threshold assigned to natural environments (Table 2 ), which is approximately 150 (Parisi, 2001 ; Menta et al., 2018 ). The agroecosystems had very similar soil chemical-physical compositions. However, the management practices applied differed greatly between them. Considering this, specific soil quality thresholds and reference values to discriminate between high and low soil quality for the prevalent agroecosystems need to be determined (Parisi, 2001 ; Menta et al., 2011 ; 2018 ; Fusco et al., 2023 ). Arable lands had the highest soil quality. The system underwent more frequent tillage (15–20 cm), compared to the others. These constant and light mechanical operations might create numerous suitable ecological niches for soil microarthropods, which are maintained for a short period, equivalent to the crop's growth time. As supported by the Intermediate Disturbance Hypothesis (IDH), disturbance plays a crucial role in maintaining biodiversity and ecosystem stability (Grime 1973 , Connell 1978 ). In several studies concerning arthropods (Know et al., 2013; Wang et al., 2019; Swart et al., 2019 ; Eötvös et al., 2020 ; Muscolo et al., 2021 ; Solano-Barquero et al., 2022 ; Guan et al., 2023 ), including Isopods (Hassall et al., 2006 ), Formicidae (Graham et al., 2009 ), Araneae (Desales-Lara et al., 2013 ), Diplopoda (Bogyó et al., 2015 ), and Coleoptera (Fattorini et al., 2020 ) has been observed that slightly disturbed habitats brought greater benefits for organism diversity than stable ones. Further, arable lands were characterised by a short agricultural cycle (forage crops), while olive groves and vineyards had a long cycle, as stable arboreal crops. Vineyards had the lowest average QBS-ar value (136). Generally, the inter-row vegetation cover was poor, the management of the inter- and under-row was more intense, and the use of Plant Protection Products (PPPs) was frequent. As the QBS-ar intervals resulted similar to those obtained in previous studies in Italy in the same agroecosystems (Gagnarli et al., 2015 ; Costantini et al., 2015 ; Ghiglieno et al., 2019 ; Vignozzi et al. 2019 ) a review of the existing literature of QBS-ar application at national scale might be very helpful in generating soil quality reference values for the most common agroecosystems. Nonetheless, to obtain a reliable soil quality trend over time, systematic soil biomonitoring is necessary. 4.2 Microarthropod overall abundance and number of BFs in the different agroecosystems Olive groves were characterised by a higher soil microarthropod abundance and number of BFs, compared to arable lands and vineyards (Table 3 ). Particularly, only in olive groves, bioindicators of stable environments (Menta, 2008), such as Zygentoma, Embioptera and Lepidoptera larvae were found (Fig. 4 ). On the contrary, vineyards showed the lowest number of BFs and abundance. Protura were found only in vineyards, although with a very low abundance. They are generally associated with soil stability (Menta & Remelli, 2020 ), and their population density is known to be influenced by soil physicochemical characteristics (Gonçalves et al., 2021 ). On the other hand, Pauropoda were not found in vineyards. This group is related to low-input management systems, and favourable abiotic soil conditions (Gonçalves et al., 2021 ). Results suggested that soil microarthropods in vineyards were more disturbed by the agronomic practices adopted, compared to the other systems. Presumably, mowing, and a frequent under-row and inter-row tillage, copper and sulphur addition (widely used as PPPs in vineyards) negatively affected soil quality, diversity and abundance of soil mesofauna in the present system (Outlaf et al., 2022). Soil quality in vineyards was found to be good, although lower than other systems, due to the higher number of inputs. In general, Symphyla occurrence highlighted the presence of non-compacted soils, as they can only use existing soil cracks and tunnels to settle in (Gonçalves et al., 2021 ; Menta & Remelli, 2020 ) (Fig. 4 ). Nonetheless, the presence of generalist predators, such as Pseudoscorpions, Araneidae, Coleoptera, Formicidae, Diplura ( Japix ), and Chilopoda in all systems, indicated a well-developed trophic food web (Menta & Remelli, 2020 ). Furthermore, the ubiquitous presence of Isopoda, Diplopoda, Chilopoda, Diplura, Coleoptera and Diptera larvae underlined a high environmental quality, and confirmed the application of non-intensive agricultural practices (Gonçalves et al., 2021 ). The occurrence of several groups of organisms well-adapted to soil habitats has demonstrated the sustainability of soil management practices employed in all the monitored agroecosystems of the Park. 4.3 Most abundant soil microarthropod groups (Acari, Collembola and Hymenoptera) and other groups in the different agroecosystems In olive groves, the higher abundance of microarthropods belonging to groups other than Acari, Collembola and Hymenoptera was registered (Fig. 5 ). Presumably, the presence of cover crops positively influenced the abundance of microarthropod groups characterised by a lower number of individuals (Carpio et al., 2018 ). On the contrary, vineyards were the system with the lowest abundance of microarthropods belonging to groups other than Acari, Collembola and Hymenoptera, compared to the other agroecosystems (Fig. 5 ). The use of increased treatments in vineyards probably resulted in a negative effect on the soil community. Notably, Acari abundance resulted significantly higher in arable lands compared to vineyards and olive groves (Fig. 5 ). Results are partially consistent with Joimel et al. ( 2017 ), in which a strong dominance of Acari was reported in arable lands. In the present study the average Acari density was 49847 ind/m 2 (Tab. S2). Contrary to Joimel et al. ( 2017 ), a similar density was not found in vineyards which showed a lower Acari density (33305 ind/m 2 ), close to that found in olive groves (37404 ind/m 2 ) (Tab. S2). The burial of crop residues on arable land may have increased the abundance of mites through an increase in the proportion of recalcitrant organic matter. For instance, Oribatids are strongly associated with recalcitrant organic matter (Gonçalves et al., 2021 ). Further, a high presence of Collembola was reported in olive groves, which is consistent with the findings reported in other studies (Gonçalves & Pereira, 2012 ; Gkisakis et al., 2014 ; Carpio et al., 2018 ). Vineyards and olive groves showed similar values for Collembola abundance, while in arable lands lower values were registered (Fig. 5 ). Collembola presence was related to a high content of organic matter, which was found in both olive orchards and vineyards, and to the presence of herbaceous cover crops. Collembola are considered prey to generalist predators, and they could enhance predator densities and their impact in biological control (Gonçalves & Pereira, 2012 ). Focusing on Hymenoptera, Formicidae was the most recorded family. Ants, being social insects with stationary nesting behaviour, are not typically associated with specific farming practices. Their presence is more frequently linked to the presence of a nest in proximity to the sampling area (Menta & Remelli, 2020 ). There were no differences in Hymenoptera abundance between the systems. 4.4 Acari/Collembola ratio and % of Oribatid mites out of total mites The Acari/Collembola ratio is an indicator of environmental stability and complexity (Bachelier 1963 ). High ratios are associated with high soil quality, although there are conflicting opinions (Latella and Gobbi, 2015 ; Angelini et al., 2002). In the study, arable lands exhibited the highest Acari/Collembola ratio. The system was characterised by a lower prevalence of Collembola in comparison to a high density of Acari. This difference could be attributed to the agricultural practices applied and the nature of the system (in terms of overall stability of the environment). In fact, in the case of vineyards and olive groves, the ratio showed similar values. Both are stable systems compared to arable land. On the other hand, the percentage of Oribatid mites out of total mites was significantly higher in arable land than in the other systems (Tab. S2). Almost half of the mites present in arable land were Oribatids (39%), with an average density of 19636 ind/m 2 (Fig. 6 , Tab. S2). Interestingly, the average Oribatid density in vineyards (7816 ind/m 2 ) was similar to the one found in other Italian vineyards by Nannelli and Simoni ( 2002 ). According to Sommaggio and Paoletti ( 2007 ), the percentage of oribatid mites is high in undisturbed soils and decreases in soils with high anthropogenic pressure. Results are consistent with the observation. In olive orchards, where human intervention is minimal, the lowest percentage of Oribatid mites was found (16.1%). In vineyards, where the agricultural practices applied are quite frequent and intense, the percentage of Oribatid mites is average (23.5%). Similarly, in arable lands, the Oribatid mite percentage is high (39.4%). In this latter case, the frequent burial of crop residues, spread across the whole field, could probably increase the Oribatid mite proportion by increasing the organic matter recalcitrant fraction. To summarise, the percentage of oribatid mites observed in the different systems may be indicative of the degree of human intervention in the overall stability of the system. In conclusion, the interpretation of the results obtained from this index proved to be challenging, as was the case with the evaluation of the Acari/Collembola ratio, as previously stated in several studies (Jacomini et al., 2000 ; Santorufo et al., 2012 ; Joimel et al., 2017 ; Huang et al., 2020). 4.5 Soil microarthropod community, management and soil chemical-physical parameters The chemical-physical parameters of the soil were found to be similar across sites and agroecosystems. The composition of the microarthropod community resulted to be more influenced by management than soil characteristics. The only soil chemical-physical parameter that differed significantly among systems was FWC, with arable land having the lowest percentage. This parameter is an indicator of soil water holding capacity at saturation and is highly related to agricultural management, as well as soil texture and structure. It can be increased by increasing the SOM content. The nMDS plot in Fig. 7 , showed that the microarthropod community of arable lands was different from that of vineyards and olive groves, which tended to partially overlap. This result was supported by the IDH (Grime 1973 , Connell 1978 ). In arable lands tillage is more frequent, and the soil is subject to constant, although lighter, mechanical operations. This situation can create, in a short period of time, numerous ecological niches that may be beneficial to a wide range of arthropods. In fact, several potential positive effects of shallow tillage have been put forth, including improved habitat conditions, reduced disturbance, more abundant and diverse prey, decreased risk of drought and increased food sources. In accordance with this, the application of tillage for certain cultivations accelerates the decomposition of crop residue, which favours some microarthropod groups, particularly springtails and mites (Betancur-Corredor et al., 2022 ). Further, reduced tillage can prevent soil compaction and depth stratification of nutrients and organic matter, which could influence the density of soil arthropods in comparison to no-tillage (Xin et al., 2018 ). Olive orchards represent a more stable environment because of the different management practices applied. On the other hand, vineyards require a more frequent and deeper inter-row tillage compared to olive groves, as well as a higher number of treatments (e.g. fertilisation and application of PPPs), although there were no marked differences in the textural classes, soil texture resulted in an influence on soil microarthropod community assemblages. Different percentages of soil particles can affect pore size and consequently the habitability of the soil. Additionally, the composition of microarthropod communities appeared to be influenced by pH and carbonate content. This is consistent with previous studies that have shown that pH determines soil microarthropods abundance and species composition (Guo & Siepel, 2020 ). Furthermore, the varying impacts of K and P contents, as well as FWC on the community may be more closely linked to management practices, such as fertiliser use and grassing. In fact, it is worth noting that the nutrient content is higher in the vineyards (Table 5 ), where more fertiliser was applied (Tab. S1). 4.6 A first overview on the agricultural practices applied in the different agroecosystems, with a view to improving soil quality. According to our results, in arable land and olive groves soil biodiversity and soil health resulted to be maintained by means of sustainable agricultural practices, such as grassing, use of combined plant varieties, low inputs, and low tillage depth. On the contrary, vineyards were the system with the lowest soil quality, and it was characterised by the lower percentage of vegetation cover, high inputs, and deep inter-row tillage depth. In this agroecosystem, to enhance the abundance of soil arthropods and the number of BFs, the growth of spontaneous ground cover or the sowing of mixtures of selected plants (legumes) can be easily implemented. In this way, the welfare of the crop would be also enhanced by the provision of several ecosystem services by soil arthropods, including pests control, organic matter decomposition and soil structure implementation (Gonçalves et al., 2020). However, it is important to note that vineyard management, even if under organic management, involves the use of copper-hydroxide and sulphur, which can seriously reduce soil invertebrate populations (Outlaf et al., 2022). In this sense, it will be of paramount importance to find alternative solutions in fighting vine pathogens, to preserve soil biodiversity. Notably, in olive orchards, predatory soil microarthropods provide biological control of the olive fly ( Bactrocera oleae , Rossi 1790), contributing in increasing Tephritidae pupae mortality (Gkisakis et al., 2014 ). Despite this, the soil microarthropod community is still poorly studied in olive orchards compared to vineyards and arable fields. To our knowledge, Vignozzi et al. ( 2019 ) is currently the only study in which QBS-ar has been applied in olive orchards. In olive groves, the high soil microarthropod abundance and number of BFs seemed to be favoured by the wide variety of plant species used for grassing, which generated different habitats, and provided several food sources. To implement microarthropod diversity, both in arable land and vineyards, the inclusion of ecological infrastructures, such as grassy hedgerows and strips, could be recommended. These natural zones prove to be reservoirs of biodiversity, able to replenish the microarthropods community overtime (Mantoni et al., 2021 ). On the other hand, in arable lands the low soil disturbance applied (e.g. low depth tillage) proved to positively affect the soil microarthropod community, according to IDH (Grime 1973 , Connell 1978 ). 5. Conclusion For the first time the QBS-ar index was applied within Conero Park (Italy). Constantly monitoring biological soil health in protected areas is crucial to take prompt action to preserve soil biodiversity as soon as deteriorating processes start (Fusco et al., 2023 ). Nowadays, in land protected areas, farmers no longer have the unique role of primary goods producers, they also have the ecological role of biodiversity conservation and environmental improvement. In pursuing this latter objective, the application of sustainable agricultural practices is mandatory. An increased awareness of protected area managers is necessary to preserve and restore this 'hidden' yet functionally essential component of terrestrial biodiversity. Preserving, restoring, and implementing soil biodiversity means maintaining and enhancing the ecosystem services it provides (Robinson et al., 2024 ). This, in turn, contributes to the health and well-being of plants, animals, and humans. At the European level, it is crucial to achieve sustainable soil management to meet the objectives of the Green Deal, which also states that at least 30% of the EU's land should be protected by 2050 (Montanarella & Panagos, 2021 ). This target can only be achieved and be effective for land conservation if soil-dwelling organisms, as well as less charismatic but functionally important organisms, are included in the conservation effort, particularly in protected areas (Zeiss et al., 2023). It is important to note that implementing sustainable agricultural management practices, such as organic farming, can lead to a resilient and economically productive system that satisfies the production needs, and the protection of the agroecosystem and its functional biodiversity. Furthermore, this study provides knowledge towards an informed use of microarthropods as bioindicators of soil health. It emphasises the importance of monitoring biological soil health to establish thresholds for the prevalent agroecosystems, characterised by different agronomic practices. This is crucial for promoting the use of the QBS-ar index in national and international soil health monitoring. Declarations Conflicts of interest/Competing interests ALT has received research support from the Conero Park, Italy. Other authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethics approval Not applicable. Competing Interests A.L.T. has received research support from the Conero Park, Italy. Other authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This work was supported by the project Agro-environmental Area Agreement for water protection "Coltiviamo la qualità delle acque del Conero" ( https://www.parcodelconero.org/progetti/tutti-i-progetti/ ) - RDP Marche 2014/2020 [Grant numbers BVC102024] to ALT. Author Contribution M.C. participated to material and data collection, wrote the manuscript text, and prepared Tables and Figures; A.D.A., M.M, N.W., A.T. and participated to material and data collection; C.G. participated to material and data collection and wrote the manuscript; A.L.T. participated to material and data collection, wrote the manuscript, and coordinated the whole research project, was scientific manager and obtained the funding. Acknowledgement We would like to thank Ente Parco Regionale del Conero, Dott. Valerio Ballerini, the landowners, and the farmers for giving us access to their agricultural fields. 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P., 2016. The use of a functional approach as surrogate of Collembola species richness in European perennial crops and forests. Ecological Indicators, 61, 676–682. Robinson, J.M., Liddicoat, C., Muñoz-Rojas, M., Breed, M.F., 2024. Restoring soil biodiversity, Current Biology, 34, 9, 393–398, ISSN 0960–9822, https://doi.org/10.1016/j.cub.2024.02.035 Santorufo, L., Van Gestel, C. A., Rocco, A., & Maisto, G., 2012. Soil invertebrates as bioindicators of urban soil quality. Environmental pollution, 161, 57–63. Babicki, S., Arndt, D., Marcu, A., Liang, Y., Grant, G.R., Maciejewski, A., Wishart, D.S.,. 2016. Heatmapper: web-enabled heat mapping for all. Nucleic Acids Research. doi: 10.1093/nar/gkw419 Seniczak, A., Seniczak, S., Garcia-Parra, I., Ferragut Pérez, FJ., Xamani Monserrat, P., Graczyk, R., Meseguer-Cervera, E., 2018. Oribatid mites of conventional and organic vineyards in Valencian Community, Spain. Acarologia. 58(S): 119–133. https://doi.org/10.24349/acarologia/20184281 Soil BON. URL: https:// monitor.soilbon.org/ Solano-Barquero, A.,Sibaja-Cordero, J.A., Cortés, J., 2022. Macrofauna Associated With a Rhodolith Bed at an Oceanic Island in the Eastern Tropical Pacific (Isla del Coco National Park, Costa Rica). Frontiers in Marine Science, 9, 858416 Sommaggio, D. and Paoletti, M.G., 2007. Gli invertebrati come bioindicatori di un paesaggio sostenibile. Gli invertebrati come bioindicatori di un paesaggio sostenibile. libreriauniversitaria.it, Limena (PD). ISBN 9788862929349 Swart, R.C., Pryke, J.S., Roets, F., 2019. The intermediate disturbance hypothesis explains arthropod beta-diversity responses to roads that cut through natural forests. Biological Conservation, 236, 243–251. Tabaglio, V., Gavazzi, C., Menta, C., 2009. Physico-chemical indicators and microarthropod communities as influenced by no-till, conventional tillage and nitrogen fertilisation after four years of continuous maize. Soil and Tillage Research, 105, 1, 135–142, ISSN 0167–1987. https://doi.org/10.1016/j.still.2009.06.006 Tiberi, M, Ciabocco, G, Bernacconi, C, Bampa, F, Dunbar, M, Montanarella, L., 2013. MOSYSS Project - Monitoring SYstem of Soils at multiScale. Monitoring system of physical, chemical and biological soil parameters in relation to forest and agricultural land management. EUR 26386. Luxembourg (Luxembourg): Publications Office of the European Union. JRC86066. https://dx.doi.org/10.2788/48824 Vignozzi, N., Agnelli, A.E., Brandi, G., Gagnarli, E., Goggioli, D., Lagomarsino, A.,Pellegrini, S., Simoncini, S., Simoni, S., Valboa, G., Caruso, G., Gucci, R., 2019. Soi ecosystem functions in a high-density olive orchard managed by different soil conservation practices. Applied Soil Ecology. 134, 64–76. Wang, C. and Tang, Y., 2019. A global meta-analyses of the response of multi-taxa diversity to grazing intensity in grasslands. Environmental Research Letters, 14(11), 114003 Yin, R., Kardol, P., Thakur, M.P., Gruss, I., Wu, G.V., Eisenhauer, N., Schädler, M., 2020. Soil functional biodiversity and biological quality under threat: Intensive land use outweighs climate change. Soil Biology and Biochemistry, 147, 107847, ISSN 0038–0717 Xin, X., Yang, W., Zhu, Q., Zhang, X., Zhu, A., & Zhang, J., 2018. Abundance and depth stratification of soil arthropods as influ-enced by tillage regimes in a sandy loam soil. Soil Use and Management, 34, 286–296. Zeiss, R., Briones, M. J. I., Mathieu, J., Lomba, A., Dahlke, J., Heptner, L., Salako, G., Eisenhauer, N., Guerra, C. A., 2024. Effects of climate on the distribution and conservation of commonly observed European earthworms. Conservation Biology, 38, e14187. https://doi.org/10.1111/cobi.14187 Additional Declarations Competing interest reported. A.L.T. has received research support from the Conero Park, Italy. Other authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 27 Jan, 2025 Read the published version in Environmental Monitoring and Assessment → Version 1 posted Editorial decision: Revision requested 29 Sep, 2024 Reviews received at journal 29 Sep, 2024 Reviews received at journal 26 Sep, 2024 Reviewers agreed at journal 06 Sep, 2024 Reviewers agreed at journal 03 Sep, 2024 Reviews received at journal 01 Sep, 2024 Reviewers agreed at journal 01 Sep, 2024 Reviewers agreed at journal 01 Sep, 2024 Reviewers invited by journal 31 Aug, 2024 Editor assigned by journal 29 Aug, 2024 Submission checks completed at journal 29 Aug, 2024 First submitted to journal 20 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4946545","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":359518111,"identity":"cbfecf0f-fdd6-48bf-89b7-7a365bffe598","order_by":0,"name":"Martina Coletta","email":"","orcid":"","institution":"School of Biosciences and Veterinary Medicine, University of Camerino","correspondingAuthor":false,"prefix":"","firstName":"Martina","middleName":"","lastName":"Coletta","suffix":""},{"id":359518113,"identity":"725d90ac-3741-47a2-b1a6-e2918c6b74d6","order_by":1,"name":"Marco Monticelli","email":"","orcid":"","institution":"School of Biosciences and Veterinary Medicine, University of Camerino","correspondingAuthor":false,"prefix":"","firstName":"Marco","middleName":"","lastName":"Monticelli","suffix":""},{"id":359518115,"identity":"97c4ba58-e709-434a-8c57-d7338cbee729","order_by":2,"name":"Aldo D’Alessandro","email":"","orcid":"","institution":"School of Biosciences and Veterinary Medicine, University of Camerino","correspondingAuthor":false,"prefix":"","firstName":"Aldo","middleName":"","lastName":"D’Alessandro","suffix":""},{"id":359518117,"identity":"6aaa431a-8da4-41eb-8e69-58bbe257eece","order_by":3,"name":"Celeste Gentili","email":"","orcid":"","institution":"School of Biosciences and Veterinary Medicine, University of Camerino","correspondingAuthor":false,"prefix":"","firstName":"Celeste","middleName":"","lastName":"Gentili","suffix":""},{"id":359518120,"identity":"f7fd1286-3308-4e67-ab0d-e1b095eb0fbd","order_by":4,"name":"Aurora Torresi","email":"","orcid":"","institution":"School of Biosciences and Veterinary Medicine, University of Camerino","correspondingAuthor":false,"prefix":"","firstName":"Aurora","middleName":"","lastName":"Torresi","suffix":""},{"id":359518122,"identity":"a7921b1e-89d7-443f-a45b-ea6acd7b3a1f","order_by":5,"name":"Natasha Waris","email":"","orcid":"","institution":"School of Biosciences and Veterinary Medicine, University of Camerino","correspondingAuthor":false,"prefix":"","firstName":"Natasha","middleName":"","lastName":"Waris","suffix":""},{"id":359518123,"identity":"caedc2c7-e366-4702-9a13-db12f1ed8291","order_by":6,"name":"Antonietta La Terza","email":"data:image/png;base64,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","orcid":"","institution":"School of Biosciences and Veterinary Medicine, University of Camerino","correspondingAuthor":true,"prefix":"","firstName":"Antonietta","middleName":"La","lastName":"Terza","suffix":""}],"badges":[],"createdAt":"2024-08-20 16:36:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4946545/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4946545/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10661-025-13658-7","type":"published","date":"2025-01-27T15:57:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":65464139,"identity":"5247cce4-2ade-448b-a716-24688d8a9e76","added_by":"auto","created_at":"2024-09-27 18:55:41","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1165620,"visible":true,"origin":"","legend":"\u003cp\u003eSatellite images (Google Earth, map data ©2024 Google) of the sites considered in the study. All sites are located within the Conero Regional Park, Italy. The drawing pin represents the central point of each site. In blue arable lands, in red olive groves and in green vineyards.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4946545/v1/f214fb34cc493f76c5e8c22d.jpeg"},{"id":65463801,"identity":"0e7fdea7-99c6-487e-baaa-5bd62a6f852d","added_by":"auto","created_at":"2024-09-27 18:47:41","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":81526,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots show the distribution of the partial QBS-ar values of the agroecosystems. The median (horizontal line), mean (red dot), error bars (dashed line) and outliers (isolated black dots) are reported. Lowercase letters represent significant differences between farming systems by Post-hoc Tukey’s HSD tests at p-value \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4946545/v1/5bc9a8ee5690e2caa410883d.jpeg"},{"id":65463804,"identity":"bb0e632a-bcaf-4cd9-a255-75ef6a703191","added_by":"auto","created_at":"2024-09-27 18:47:41","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":93978,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots show the distribution of the partial abundance of microarthropods in the three different agroecosystems. The median (horizontal line), mean (red dot), error bars (dashed line) and outliers (isolated black dots) are reported.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4946545/v1/8409282e6fb0d097a5528f13.jpeg"},{"id":65464140,"identity":"463bca5b-5186-42b6-b595-86e76d4ffae4","added_by":"auto","created_at":"2024-09-27 18:55:41","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":407893,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of the log10 transformed total abundance of each BFs in each agroecosystem. Darker shades indicate high abundance, light shades indicate low abundance or absence (white). Zygentoma (A), Embioptera (B) and Lepidoptera larvae (C) were found only in olive orchards, while Protura (D) were found only in vineyards. Data concerning Acari, Collembola and Hymenoptera are not reported. The heatmap was generated using Heatmapper web server (www.heatmapper.ca).\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4946545/v1/48f713c032852bdb15e05abe.jpeg"},{"id":65463803,"identity":"e730cb7a-0818-4e2d-9cdd-67db539939f0","added_by":"auto","created_at":"2024-09-27 18:47:41","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":322723,"visible":true,"origin":"","legend":"\u003cp\u003eThe bar plots show the average partial abundance of the soil microarthropods, Acari, Collembola, Hymenoptera, and other biological forms (BFs) apart from Acari, Collembola and Hymenoptera, in each agroecosystem. Numbers represent the average partial abundance and black lines represent error bars. Lowercase letters represent significant differences between the partial average number of individuals (across farming systems) by Post-hoc Tukey’s HSD or Dunn tests at p-value \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4946545/v1/ae56b4f53d6a4749b0e6b922.jpeg"},{"id":65463808,"identity":"2683fe41-8dc2-4b10-a429-21ccea4098fa","added_by":"auto","created_at":"2024-09-27 18:47:41","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":213174,"visible":true,"origin":"","legend":"\u003cp\u003eThe bar plots show the average partial number of non-oribatid mites and Oribatida in each agroecosystemconsidered (arable lands, olive groves, and vineyards). Numbers represent the average partial abundance values for each management system and black lines represent error bars. Lowercase letters represent significant differences between the partial average number of individuals (across management systems) by Post-hoc Dunn tests at p-value \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4946545/v1/93a649dc6891133e62b57949.jpeg"},{"id":65463806,"identity":"220c2fc3-b10b-48ff-a445-f936dae30c45","added_by":"auto","created_at":"2024-09-27 18:47:41","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":332128,"visible":true,"origin":"","legend":"\u003cp\u003eThe plot presents the results of the correlation analysis (Pearson coefficient). Some variables were found to be positively highly correlated (blue), including TN, TOC, SOM, and the carbon content of humic and fulvic acids. Additionally, TL, AL, WP and FWC exhibited a slight positive correlation, as well as TL, C/N and clay. While negative correlations (red) were found between the pH and several parameters, particularly TOC and SOM; carbon of humic and fulvic acids and silt; clay with sand and TL.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4946545/v1/1f9580bdd658ff3343afe402.jpeg"},{"id":65463807,"identity":"997825e3-d31b-49cd-8404-22a1d597df2d","added_by":"auto","created_at":"2024-09-27 18:47:41","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":336477,"visible":true,"origin":"","legend":"\u003cp\u003eNon-metric Multidimensional Scaling (nMDS) based on Bray-Curtis dissimilarity index of soil microarthropod community composition and soil chemical-physical properties in three agroecosystems. Total organic carbon, TOC; soil organic matter, SOM; total nitrogen, TN; assimilable phosphorus, P; exchangeable potassium, K; field water capacity, FWC; wilting point WP; total limestone, TL; active limestone, AL; carbon/nitrogen ratio, C/N ratio; carbon of humic and fulvic acids; pH; soil texture (sand, silt, clay).\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4946545/v1/1839afae39f178e9fc8b37ac.jpeg"},{"id":75351424,"identity":"774adf3c-4332-4338-94f0-dc780754c0cc","added_by":"auto","created_at":"2025-02-03 16:11:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4638595,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4946545/v1/be00ffa4-7274-492c-9510-f5cd9c397f27.pdf"},{"id":65463809,"identity":"27c05ae5-1ba0-4bd2-acc3-c6a54d1b7e7b","added_by":"auto","created_at":"2024-09-27 18:47:41","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":5620938,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4946545/v1/ccbb2f5a647f7c4db5e92d3f.docx"}],"financialInterests":"Competing interest reported. A.L.T. has received research support from the Conero Park, Italy. Other authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","formattedTitle":"Managing soil to support soil biodiversity in protected areas agroecosystems. A comparison between arable lands, olive groves, and vineyards in the Conero Park (Italy)","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSoil is the foundation of food production. According to the Food and Agriculture Organization (FAO) (2015), 95% of our food comes directly or indirectly, from soil. Soil fertility is essential for food security, and it is guaranteed by soil biodiversity. Soil organisms play a crucial role in delivering vital soil processes; they release nutrients from soil organic matter, form and maintain soil structure, contribute to soil water entry, storage, and transfer, enhance nutrient cycling, and promote plant growth and health (Lavelle \u0026amp; Spain, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The soil biota is fundamental in ensuring the long-term functioning of soil. In fact, soil health is defined as the continued capacity of soil to function as a vital living ecosystem that sustains plants, animals, and humans (B\u0026uuml;nemann et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, the application of unsustainable agricultural practices poses a threat to soil biodiversity and the provisioning of vital ecosystem services it provides (K\u0026ouml;ninger et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The dominant agricultural production model currently in use, generally referred to as \u0026ldquo;conventional agriculture\u0026rdquo;, is characterised by monocultures, excessive tillage, heavy applications of agrochemicals, such as pesticides and chemical fertilisers (Jiang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This type of farming is estimated to be the main driver of biodiversity loss (Benton et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To reverse this trend, and to counteract the loss of biodiversity, the European Biodiversity Strategy for 2030 has set a specific objective of increasing the percentage of the EU's agricultural area under organic farming to 25% and that of protected areas to 30% (Montanarella \u0026amp; Panagos, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The adoption of nature- and biodiversity-friendly farming practices, such as organic farming, has been shown to enhance biodiversity in agricultural landscapes (Bengtsson et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Additionally, the expansion of protected areas is crucial as these high-quality environmental habitats are critical reservoirs for biodiversity, including soil biodiversity (Mantoni et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To test the effectiveness of the proposed solutions on soil fauna, soil microarthropods can be used, as they are considered a sensitive and promising bioindicator. Soil microarthropods have been used for evaluating the impact of human activities on soil functions, including agriculture. They proved to respond sensitively to land management practices and soil disturbances (Parisi et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Parisi \u0026amp; Menta, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Reis et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Joimel et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yin et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ashwood et al., 2022). Nonetheless, the Biological Soil Quality Index based on arthropods (QBS-ar) proved to reliably assess soil health using soil mesofauna (Parisi, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Parisi et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Menta et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The method has been applied for over 20 years in Italy, as well as in European and non-European countries (Menta et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The QBS-ar is based on the identification of soil microarthropods\u0026rsquo; main functional groups, and the assignment of an Eco-morphological Index (EMI) based on morphological characters of adaptation to soil habitat, thus avoiding the need for taxonomic identification to the species level. The QBS-ar method describes soil biological quality using numerical values. For example, natural habitats such as forest soils, meadows and pastures generally have a QBS-ar of over 120, and sometimes even 200. In contrast, more disturbed environments such as agroecosystems have an average QBS-ar of 94 (Parisi, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Menta et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Numerical ranges and thresholds for QBS-ar can vary considerably depending on the environment (Parisi, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Menta et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Most of the QBS-ar collected data concerns agricultural soils and forests in Italy (Menta et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, variations in the QBS-ar values were observed in agroecosystems, depending on the type of crop and management method (e.g. conventional or organic). Specific quality ranges have not yet been concretely defined for the different agroecosystems (e.g. arable land, grassland, orchards, olive groves, etc.). This is necessary since the agricultural practices adopted in each system vary widely. To make a proper comparison, it is necessary to develop quality ranges that are distinct, depending on the agroecosystem considered. Further, extending the determination of QBS-ar intervals is crucial for soil in which crops that are widespread and particularly valuable in Italy are grown, such as olive groves and vineyards. Moreover, an integrated approach, including chemical-physical characterisation, is needed for evaluating the overall agroecosystem status (Tabaglio et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Monitoring the health of these soils is essential for preserving their condition and ensuring highly profitable markets for the Italian economy. In fact, QBS-ar is increasingly used in biological soil quality assessments and soil monitoring programs in Italy, including the MOnitoring SYstem of Soil at multiScale MOSYSS Project, Dunbar et al., 2014) that was conducted in Marche Region and Emilia Romagna, or the Excalibur (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://excaliburh2020.eu/en/overview/\u003c/span\u003e\u003cspan address=\"https://excaliburh2020.eu/en/overview/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Minotaur projects (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ejpsoil.eu/soil-research/minotaur\u003c/span\u003e\u003cspan address=\"https://ejpsoil.eu/soil-research/minotaur\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe objectives of the study were: (i) to assess and compare, for the first time, soil health in three organically managed agroecosystems (arable lands, vineyards and olive groves) in the Conero Park (Italy) using the QBS-ar index; (ii) to characterise the soil microarthropod communities of the different organic agroecosystems and to relate them to the agricultural practices applied and to the chemical-physical characteristics of the soil; (iii) to identify the system with the least impact on the conservation of soil biodiversity, while promoting the most sustainable agricultural practices to maintain soil health, with the ultimate aim of laying the basis for identifying soil quality benchmarks within each system to be used in monitoring activities in protected areas.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe Conero Park (Italy) covers an area of 6011 hectares and includes the municipalities of Ancona, Camerano, Sirolo, and Numana (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.parks.it/parco.conero/Eindex.php\u003c/span\u003e\u003cspan address=\"https://www.parks.it/parco.conero/Eindex.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e It represents the boundary between the Mediterranean and the temperate sub-Mediterranean bioclimate, on the eastern side of the Italian peninsula. Within the Park three different organic agroecosystems (arable lands, olive groves, and vineyards), and four sites for each, were selected, resulting in a total of twelve sampling sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Sites were previously identified by the Park Authority in the framework of the project Agro-environmental Area Agreement for water protection (RDP Marche 2014/2020). The project has the overall aim of promoting environmentally sustainable agricultural practices capable of minimising the impact of agriculture on soil, freshwater and coastal marine ecosystem of the Park. The samplings were conducted in one season: in June and July 2022. Data related to field management information and agricultural practices applied (use of herbicides, pesticides, and fertilisers, crop rotation, irrigation, ploughing/harrowing/subsoiling depth, grubbing, shredding, pruning, hoeing, and sowing) were collected through in-depth interviews with field owners (Tab. S1). Geographic coordinates and environmental parameters were recorded (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Due to the limited extension of the area, the influence of climate was assumed to be homogeneous across all sites (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSampling sites characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSite code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgroecosystem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLocality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSampling date\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAltitude (m asl)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSlope (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHerbaceous cover (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eHerbaceous cover height (cm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArable land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumana (AN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13/06/2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43\u0026deg;28'32.95\"N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u0026deg;36'27.60\"E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e81\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArable land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCamerano (AN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13/06/2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43\u0026deg;32'55.45\"N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u0026deg;33'27.60\"E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e86 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eN-W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e81\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e31\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArable land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAncona (AN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21/06/2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43\u0026deg;33'13.08\"N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u0026deg;32'54.89\"E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e61 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eS-E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e61\u0026ndash;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e16\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArable land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAncona (AN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13/06/2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43\u0026deg;34'1.59\"N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u0026deg;33'15.80\"E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e141 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eS-E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e81\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVineyard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAncona (AN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e05/07/2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43\u0026deg;31'37.48\"N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u0026deg;35'0.20\"E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e140 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e70.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eS-O\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e81\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e31\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVineyard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAncona (AN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e05/07/2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43\u0026deg;34'57.63\"N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u0026deg;33'2.46\"E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e132 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eN-W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e81\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e16\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVineyard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSirolo (AN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e05/07/2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43\u0026deg;31'54.16\"N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u0026deg;36'28.69\"E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e195 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e27.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eS-E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZAZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVineyard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCamerano (AN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21/06/2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43\u0026deg;30'48.82\"N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u0026deg;33'59.16\"E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e84 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eW/W-S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e41\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e31\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOlive grove\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCastelfidardo (AN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e05/07/2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43\u0026deg;27'52.19\"N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u0026deg;35'45.09\"E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e54 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eN-W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e81\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOlive grove\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMassignano (AN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28/06/2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43\u0026deg;32'16.78\"N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u0026deg;34'56.94\"E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e189 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e81\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOlive grove\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAncona (AN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28/06/2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43\u0026deg;33'50.67\"N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u0026deg;33'0.82\"E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e126 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e81\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOlive grove\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAncona (AN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28/06/2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43\u0026deg;33'0.52\"N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u0026deg;34'31.98\"E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e247 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eN-W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e81\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Soil sampling and QBS-ar determination\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSampling and extraction of soil microarthropods followed the methodology described by Parisi et al. (2001, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and Menta et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In each site, a QBS-ar sample, consisting of three subsamples (A, B, C) with a volume of about 800 cm\u003csup\u003e3\u003c/sup\u003e each, was collected using a cylindrical soil corer. Each subsample was immediately placed in a plastic bag and transported to the laboratory, within the following 24 hours. A homogeneous area was selected in each site, excluding disturbed areas such as flattened vegetation and tractor tracks. The herbaceous cover was removed prior to collecting the soil sample. In arable lands, subsamples were collected at equal intervals (about 10 m) along a diagonal line, in the central part of the field. In vineyards and olive groves, samples were collected in the interrow. A total of 36 soil sub-samples were collected. Arthropod extraction was carried out in the laboratory using the Berlese-T\u0026uuml;llgren funnel, equipped with 25 W light bulbs and a 2-mm sieve mesh. The extraction process lasted for 7 days, and the extracted specimens were preserved in a solution of 75% ethyl alcohol and 25% glycerol by volume. The collected specimens underwent a stereoscopic analysis to determine biological form (BF) and Eco-Morphological Index (EMI) (1 to 20), according to Parisi et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Total QBS-ar and partial QBS-ar values were calculated for each sample. The total QBS-ar total was obtained by summing the highest EMI values attributed to each BF in the three subsamples (A, B, C). The partial QBS-ar was derived from the evaluation of the BFs found in the single subsample. A quantitative approach was used, unlike the original protocol (Parisi et al., 2001, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), where the number of individuals present for each BF was counted in each subsample, as well as the number of euedaphic forms (EFs). Additionally, total microarthropod abundance and density (ind/m\u003csup\u003e2\u003c/sup\u003e and ind/m\u003csup\u003e3\u003c/sup\u003e), microarthropod community composition, the Acari/Collembola ratio (Bachelier, 1986) and the percentage of Oribatid mites out of total mites (Aoki, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1967\u003c/span\u003e; Aoki et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1977\u003c/span\u003e) were determined.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Soil chemical-physical analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSoil samples for chemical-physical analyses were collected in accordance with the official methods for soil analysis of the Italian legislation DM 13/09/1999 SO No. 185 (\u003cem\u003eApprovazione dei \u0026ldquo;Metodi Ufficiali di analisi chimica del suolo\u003c/em\u003e\u0026rdquo;). In each site, 10 soil sub-samples were collected in each site by using a pedological auger, and then mixed and reduced to a single composite sample (1 kg). The samples were analysed by the laboratory of the \u0026ldquo;Agenzia per l'Innovazione nel Settore Agroalimentare e della Pesca \"Marche Agricoltura Pesca\" (AMAP)\u0026rdquo;, Jesi (Italy). The analysed parameters were three fraction soil texture (sand, silt, clay; g/kg), total organic carbon (g/kg), organic matter (g/kg), pH, C/N ratio, total nitrogen (g/kg), assimilable phosphorus (mg/kg), exchangeable potassium (mg/kg), field water capacity (%), wilting point (%), total limestone (g/Kg), active limestone (g/Kg), carbon of humic and fulvic acids (g/kg).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo test for differences between the three systems (arable lands, vineyards, olive groves), soil biological indicators (QBS-ar, number of BFs and EFs, abundance, Acari/Collembola ratio, percentage of Oribatid mites out of total mites) total and partial data (obtained from each subsample) were considered. The data were first checked for normality of distribution (Shapiro-Wilk test) and homoscedasticity (Levene's or Bartlett's test, depending on the distribution). Differences between management systems were evaluated through One-way Anova or Kruskal Wallis Test, depending on the distribution, and post hoc tests (Tukey's honestly significant difference or Dunn test). A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 has been considered significant. Statistical analysis was performed using R software, version 4.2.0 (R Development Core Team, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Soil chemical and physical properties of the different agroecosystems were statistically analysed, as described above for biological data, and, further, correlations between the different parameters have been evaluated by using \u0026ldquo;ggcorrplot\u0026ldquo; R package. Non-metric multidimensional scaling (nMDS), based on Bray-Curtis dissimilarity index was performed to visualise the grouping of arthropod communities. To associate the community composition with soil characteristics and evaluate the associations between nMDS site scores, the \u0026ldquo;envfit\u0026rdquo; function was used. Permutational multivariate analysis of variance (PERMANOVA) was used to test for differences in assemblages among the different patterns visualised with nMDS. Ordination analysis, PERMANOVA, and SIMPER were performed with \u0026ldquo;vegan\u0026rdquo; and \u0026ldquo;pairwiseAdonis\u0026rdquo; R packages, while \u0026ldquo;ggplot2\u0026rdquo; was used to generate the plots and the nMDS ordination diagram. The heatmaps of the log\u003csub\u003e10\u003c/sub\u003e-transformed total abundance of soil microarthropods were generated using Heatmapper web server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://excaliburh2020.eu/en/overview/\" target=\"_blank\"\u003ewww.heatmapper.ca\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.heatmapper.ca\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e (Babicki et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 QBS-ar application\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe average total QBS-ar value of the agroecosystems within the Conero Regional Park was 167. The higher average total QBS-ar was found in arable lands, with a corresponding value of 190 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Within arable sites, the QBS-ar ranged from 155 to 222. Olive groves had an average total QBS-ar value of 176, ranging from 144 to 231, while vineyards showed a value of 136, ranging from 84 to 184. No significant differences were found when comparing the total QBS values among the systems. However, when comparing partial QBS-ar values, soil quality in arable lands was significantly higher than in vineyards (p-value\u0026thinsp;=\u0026thinsp;0.042, TukeyHSD) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe table shows partial and total QBS-ar values for each site, and average QBS-ar values for each agroecosystem.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSite code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgroecosystem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePartial QBS-ar values (A, B, C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAverage partial QBS-ar values (A, B, C) of the system\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal QBS-ar values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAverage total QBS-ar values of the system\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArable land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149, 146, 172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArable land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e182, 177, 156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArable land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89, 109, 122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArable land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126, 56, 132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVineyard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72, 56, 48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVineyard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99, 86, 138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVineyard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e154, 170, 138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZAZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVineyard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75, 82, 75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOlive grove\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83, 94, 114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOlive grove\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124, 109, 115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOlive grove\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139, 101, 154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOlive grove\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104, 135, 132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 BFs, EFs and abundance\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eOverall, 24 BFs were found in the agroecosystems considered within the Conero Regional Park. The highest number of BFs was found in olive groves (23), followed by arable lands (20) and vineyards (19) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). As for BFs, the highest abundance and density were found in olive groves, followed by arable lands and vineyards (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). On the other hand, the total number of EFs was the same (9) in the three systems. Any significant differences, on partial and total data, was found in either BFs, EFs or abundance.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe table shows the number of total biological (BFs) and eu-edaphic forms (EFs), and average abundance and density (ind/m and ind/m\u003csup\u003e2\u003c/sup\u003e) for each site and average total abundance for each agroecosystem.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgroecosystem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal BFs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage BFs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage EFs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal EFs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAverage abundance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAverage microarthropod density (ind/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAverage microarthropod density (ind/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArable land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e504917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e58036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVineyard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e473083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e54377\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOlive grove\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e551833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e63429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Soil microarthropods community composition\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe composition of the soil microarthropod community is shown by the heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). 18 BFs were found in all systems: Pseudoscorpions, Araneidae, Isopoda, Acari, Collembola, Diplopoda, Symphyla, Chilopoda, Diplura, Psocoptera, Hemiptera, Thysanoptera, Coleoptera, Diptera, Hymenoptera, Coleoptera and Diptera larvae and other Holometabola. Among these, Acari, Collembola and Hymenoptera, due to their ubiquitous presence, are not represented in the heatmap of Fig .4 because their high abundance tends to mask and unify the presence of BFs with lower abundance. Data referred to these groups will be discussed separately. The presence of Hemiptera, Psocoptera, Thysanoptera, Diptera and other Holometabola was not so relevant in determining soil quality, as they do not contribute significantly (EMI\u0026thinsp;=\u0026thinsp;1) to the QBS-ar value. However, all agroecosystems were characterised by the presence of 9 EFs (EMI\u0026thinsp;=\u0026thinsp;20). Acari, Collembola, Pseudoscorpions, Diplopoda (Julidae), Pauropoda, Symphyla, Chilopoda, Diplura, Coleoptera were found in arable lands and olive groves. The same EFs were found in vineyards, except for Pauropoda, which were not recorded. On the contrary, Protura were found solely in vineyards. Similarly, Hymenoptera larvae (Formicidae) were found in olive groves and vineyards but not in arable lands. Zygentoma, Embioptera and Lepidoptera larvae were registered only in olive groves. Notably, this latter system was characterised by a high number of Diplopoda and Hemiptera, compared to arable lands and vineyards.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Most abundant soil microarthropod groups (Acari, Collembola and Hymenoptera)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFocusing on the most abundant groups (Acari, Collembola, Hymenoptera), the highest number of Acari was found in arable lands (p-value\u0026thinsp;=\u0026thinsp;0.030, Kruskal-Wallis) and it resulted significantly higher compared to vineyards (p-value\u0026thinsp;=\u0026thinsp;0.037, Dunn) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The average Acari density in arable land was 49847 ind/m\u003csup\u003e2\u003c/sup\u003e, while 33304 ind/m\u003csup\u003e2\u003c/sup\u003e in vineyards and 37405 ind/m\u003csup\u003e2\u003c/sup\u003e in olive groves (Tab. S2). On the contrary, Collembola resulted to be the least abundant group in arable lands, compared to both vineyards and olive groves, which showed similar values. Hymenoptera abundance showed a similar trend to Collembola, with lower abundance on arable lands (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The number of the BFs other than Acari, Collembola and Hymenoptera, resulted significantly higher in olive groves (p-value\u0026thinsp;=\u0026thinsp;0.040, Kruskal-Wallis), and particularly higher compared to vineyards (p-value\u0026thinsp;=\u0026thinsp;0.040, Dunn).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Acari/Collembola ratio and percentage of oribatid mites out of total mites\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe Acari/Collembola ratio resulted significantly higher in arable lands (p-value\u0026thinsp;=\u0026thinsp;0.015, Kruskal-Wallis), particularly compared to olive groves (p-value\u0026thinsp;=\u0026thinsp;0.011, Dunn) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Notably, olive orchards and vineyards were found to have the same Acari/Collembola ratio (3.6), while arable lands were characterised by a higher value (19.2). As for Acari abundance and Acari/Collembola ratio, Oribatid mites were significantly higher in arable lands (p-value\u0026thinsp;=\u0026thinsp;0.0003, Kruskal-Wallis) compared to both vineyards (p-value\u0026thinsp;=\u0026thinsp;0.006, Dunn) and olive groves (p-value\u0026thinsp;=\u0026thinsp;0.0005, Dunn). Notably, the density of Oribatid mites in arable land was 19636 ind/m\u003csup\u003e2\u003c/sup\u003e, while 6006 ind/m\u003csup\u003e2\u003c/sup\u003e in olive groves, and 7816 ind/m\u003csup\u003e2\u003c/sup\u003e in vineyards (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e; Tab. S2). Whereas the percentage of Oribatid mites out of total mites was 39,4% in arable land, 16,1% in olive groves, and 23,5% in vineyards (Tab. S2).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe table shows the total Acari/Collembola ratios for each site and average Acari/Collembola ratio for each agroecosystem.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSite code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgroecosystem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Acari/Collembola ratios of each site\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAverage Acari/Collembola ratio of the system\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArable land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArable land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArable land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArable land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVineyard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVineyard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVineyard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZAZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVineyard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e129.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOlive grove\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOlive grove\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOlive grove\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOlive grove\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Soil chemical-physical parameters and soil microarthropods community structure\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe agroecosystems showed a similar soil texture; arable lands and vineyards had a silty clay loam texture, whereas olive groves had a clay loam texture (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The main differences were related to sand and silt contents. However, there were no significant differences in terms of soil particle composition (Fig. S2). All soils were calcareous with a high percentage of active limestone, soil pH was generally slightly alkaline in all systems. The results were similar in vineyards and arable lands and then in olive orchards. The carbon to nitrogen ratio (C/N ratio) was low in all agroecosystems. Olive groves have the highest levels of organic matter (SOM), total organic carbon (TOC), total nitrogen (TN), and C from humic and fulvic acids. In contrast, vineyards have higher levels of assimilable P and exchangeable K. The wilting point (WP) was similar across all systems, while the highest field water capacity (FWC) was found in vineyards (31.9%) and the lowest in arable lands (21.7%) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig. S2). Statistical analysis showed that FWC was the only parameter that differed significantly between the three systems (p-value\u0026thinsp;=\u0026thinsp;0.024, Kruskal-Wallis) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig. S2). Through correlation analysis (Pearson coefficient), several variables were found to be positively highly correlated, including TN, TOC, SOM, and the carbon content of humic and fulvic acids. Additionally, TL, AL, WP and FWC exhibited a slight positive correlation, as well as TL, C/N and clay. While negative correlations were found between the pH and several parameters, particularly TOC and SOM; carbon of humic and fulvic acids and silt; clay with sand and TL (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The composition of the soil microarthropod community was related to the soil chemical-physical properties in a Non-Metric Multidimensional Scaling (nMDS) plot based on Bray Curtis dissimilarity index (stress\u0026thinsp;=\u0026thinsp;0.12) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The microarthropod community of arable land differed from the other two systems, while the microarthropod communities in vineyards and olive groves tended to overlap. The results showed that soil textural parameters, such as sand and clay contents, seemed to have the greatest impact on microarthropod community composition. Additionally, K and FWC were found to be important variables in community assemblages, although their variation was not statistically significant. However, PERMANOVA analyses revealed no statistically significant differences in microarthropod community assemblage between the agroecosystems (p-value\u0026thinsp;=\u0026thinsp;0.31, PERMANOVA). The SIMPER analysis revealed that the sand and clay contents, as well as K and total limestone (TL), were the parameters that drove the most differences between community compositions, although not significantly.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe table shows the soil chemical-physical properties of the three agroecosystems. Total organic carbon, TOC; soil organic matter, SOM; total nitrogen, TN; assimilable phosphorus, P; exchangeable potassium, K; field water capacity, FWC; wilting point WP; total limestone, TL; active limestone, AL; carbon/nitrogen ratio, C/N ratio; carbon of humic and fulvic acids; pH; sand, silt and clay particles content, and soil texture.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"18\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgroecosystem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC/N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSOM (g/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTOC (g/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTN\u003c/p\u003e \u003cp\u003e (g/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003e(mg/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eK\u003c/p\u003e \u003cp\u003e(mg/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFWC\u003c/p\u003e \u003cp\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eWP\u003c/p\u003e \u003cp\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eTL\u003c/p\u003e \u003cp\u003e(g/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eAL\u003c/p\u003e \u003cp\u003e(g/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eC of humic and fulvic acids (g/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eSand\u003c/p\u003e \u003cp\u003e (g/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eSilt\u003c/p\u003e \u003cp\u003e(g/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003eClay (g/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003eSoil texture\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArable lands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003eSilty-Clay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArable lands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003eSilty-Clay-Loam\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArable lands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003eSilty-Clay-Loam\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArable lands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003eSilty-Clay-Loam\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlive groves\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003eLoam\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFATi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlive groves\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003eClay-Loam\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlive groves\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003eClay-Loam\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlive groves\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e41.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e28.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003eSilty-Clay-Loam\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVineyards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e46.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e26.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003eLoam\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVineyards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003eSilty-Clay-Loam\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVineyards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003eSilty-Clay-Loam\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZAZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVineyards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e29.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e19.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e9.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003eSilty-Clay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Biological Soil quality in the Conero Park\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe overall soil quality of the agroecosystems in Conero Park resulted to be excellent. The average total QBS-ar value (167) exceeded the threshold set for high biological quality in agricultural soils (93.7) (Parisi \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Menta et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The application of organic farming, in conjunction with land protection, proved to contribute positively to the overall soil quality of the agroecosystems. According to Mantoni et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), land protection can significantly affect the soil microarthropods community by \u0026ldquo;rescue effects\u0026rdquo;, resulting in a positive influence on the QBS-ar index in agricultural fields. Similarly, the QBS-ar values obtained in the three agroecosystems were excellent. In several sites, the QBS-ar values exceeded the threshold assigned to natural environments (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which is approximately 150 (Parisi, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Menta et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The agroecosystems had very similar soil chemical-physical compositions. However, the management practices applied differed greatly between them. Considering this, specific soil quality thresholds and reference values to discriminate between high and low soil quality for the prevalent agroecosystems need to be determined (Parisi, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Menta et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Fusco et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Arable lands had the highest soil quality. The system underwent more frequent tillage (15\u0026ndash;20 cm), compared to the others. These constant and light mechanical operations might create numerous suitable ecological niches for soil microarthropods, which are maintained for a short period, equivalent to the crop's growth time. As supported by the Intermediate Disturbance Hypothesis (IDH), disturbance plays a crucial role in maintaining biodiversity and ecosystem stability (Grime \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1973\u003c/span\u003e, Connell \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1978\u003c/span\u003e). In several studies concerning arthropods (Know et al., 2013; Wang et al., 2019; Swart et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; E\u0026ouml;tv\u0026ouml;s et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Muscolo et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Solano-Barquero et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Guan et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), including Isopods (Hassall et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), Formicidae (Graham et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), Araneae (Desales-Lara et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), Diplopoda (Bogy\u0026oacute; et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and Coleoptera (Fattorini et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) has been observed that slightly disturbed habitats brought greater benefits for organism diversity than stable ones. Further, arable lands were characterised by a short agricultural cycle (forage crops), while olive groves and vineyards had a long cycle, as stable arboreal crops. Vineyards had the lowest average QBS-ar value (136). Generally, the inter-row vegetation cover was poor, the management of the inter- and under-row was more intense, and the use of Plant Protection Products (PPPs) was frequent. As the QBS-ar intervals resulted similar to those obtained in previous studies in Italy in the same agroecosystems (Gagnarli et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Costantini et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ghiglieno et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Vignozzi et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) a review of the existing literature of QBS-ar application at national scale might be very helpful in generating soil quality reference values for the most common agroecosystems. Nonetheless, to obtain a reliable soil quality trend over time, systematic soil biomonitoring is necessary.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Microarthropod overall abundance and number of BFs in the different agroecosystems\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eOlive groves were characterised by a higher soil microarthropod abundance and number of BFs, compared to arable lands and vineyards (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Particularly, only in olive groves, bioindicators of stable environments (Menta, 2008), such as Zygentoma, Embioptera and Lepidoptera larvae were found (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). On the contrary, vineyards showed the lowest number of BFs and abundance. Protura were found only in vineyards, although with a very low abundance. They are generally associated with soil stability (Menta \u0026amp; Remelli, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and their population density is known to be influenced by soil physicochemical characteristics (Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). On the other hand, Pauropoda were not found in vineyards. This group is related to low-input management systems, and favourable abiotic soil conditions (Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Results suggested that soil microarthropods in vineyards were more disturbed by the agronomic practices adopted, compared to the other systems. Presumably, mowing, and a frequent under-row and inter-row tillage, copper and sulphur addition (widely used as PPPs in vineyards) negatively affected soil quality, diversity and abundance of soil mesofauna in the present system (Outlaf et al., 2022). Soil quality in vineyards was found to be good, although lower than other systems, due to the higher number of inputs. In general, Symphyla occurrence highlighted the presence of non-compacted soils, as they can only use existing soil cracks and tunnels to settle in (Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Menta \u0026amp; Remelli, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Nonetheless, the presence of generalist predators, such as Pseudoscorpions, Araneidae, Coleoptera, Formicidae, Diplura (\u003cem\u003eJapix\u003c/em\u003e), and Chilopoda in all systems, indicated a well-developed trophic food web (Menta \u0026amp; Remelli, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, the ubiquitous presence of Isopoda, Diplopoda, Chilopoda, Diplura, Coleoptera and Diptera larvae underlined a high environmental quality, and confirmed the application of non-intensive agricultural practices (Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The occurrence of several groups of organisms well-adapted to soil habitats has demonstrated the sustainability of soil management practices employed in all the monitored agroecosystems of the Park.\u003c/p\u003e \u003cp\u003e \u003cb\u003e4.3 Most abundant soil microarthropod groups (Acari, Collembola and Hymenoptera) and other groups in the different agroecosystems\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn olive groves, the higher abundance of microarthropods belonging to groups other than Acari, Collembola and Hymenoptera was registered (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Presumably, the presence of cover crops positively influenced the abundance of microarthropod groups characterised by a lower number of individuals (Carpio et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). On the contrary, vineyards were the system with the lowest abundance of microarthropods belonging to groups other than Acari, Collembola and Hymenoptera, compared to the other agroecosystems (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The use of increased treatments in vineyards probably resulted in a negative effect on the soil community. Notably, Acari abundance resulted significantly higher in arable lands compared to vineyards and olive groves (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Results are partially consistent with Joimel et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), in which a strong dominance of Acari was reported in arable lands. In the present study the average Acari density was 49847 ind/m\u003csup\u003e2\u003c/sup\u003e (Tab. S2). Contrary to Joimel et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), a similar density was not found in vineyards which showed a lower Acari density (33305 ind/m\u003csup\u003e2\u003c/sup\u003e), close to that found in olive groves (37404 ind/m\u003csup\u003e2\u003c/sup\u003e) (Tab. S2). The burial of crop residues on arable land may have increased the abundance of mites through an increase in the proportion of recalcitrant organic matter. For instance, Oribatids are strongly associated with recalcitrant organic matter (Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Further, a high presence of Collembola was reported in olive groves, which is consistent with the findings reported in other studies (Gon\u0026ccedil;alves \u0026amp; Pereira, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Gkisakis et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Carpio et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Vineyards and olive groves showed similar values for Collembola abundance, while in arable lands lower values were registered (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Collembola presence was related to a high content of organic matter, which was found in both olive orchards and vineyards, and to the presence of herbaceous cover crops. Collembola are considered prey to generalist predators, and they could enhance predator densities and their impact in biological control (Gon\u0026ccedil;alves \u0026amp; Pereira, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Focusing on Hymenoptera, Formicidae was the most recorded family. Ants, being social insects with stationary nesting behaviour, are not typically associated with specific farming practices. Their presence is more frequently linked to the presence of a nest in proximity to the sampling area (Menta \u0026amp; Remelli, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). There were no differences in Hymenoptera abundance between the systems.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Acari/Collembola ratio and % of Oribatid mites out of total mites\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe Acari/Collembola ratio is an indicator of environmental stability and complexity (Bachelier \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1963\u003c/span\u003e). High ratios are associated with high soil quality, although there are conflicting opinions (Latella and Gobbi, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Angelini et al., 2002). In the study, arable lands exhibited the highest Acari/Collembola ratio. The system was characterised by a lower prevalence of Collembola in comparison to a high density of Acari. This difference could be attributed to the agricultural practices applied and the nature of the system (in terms of overall stability of the environment). In fact, in the case of vineyards and olive groves, the ratio showed similar values. Both are stable systems compared to arable land. On the other hand, the percentage of Oribatid mites out of total mites was significantly higher in arable land than in the other systems (Tab. S2). Almost half of the mites present in arable land were Oribatids (39%), with an average density of 19636 ind/m\u003csup\u003e2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Tab. S2). Interestingly, the average Oribatid density in vineyards (7816 ind/m\u003csup\u003e2\u003c/sup\u003e) was similar to the one found in other Italian vineyards by Nannelli and Simoni (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). According to Sommaggio and Paoletti (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), the percentage of oribatid mites is high in undisturbed soils and decreases in soils with high anthropogenic pressure. Results are consistent with the observation. In olive orchards, where human intervention is minimal, the lowest percentage of Oribatid mites was found (16.1%). In vineyards, where the agricultural practices applied are quite frequent and intense, the percentage of Oribatid mites is average (23.5%). Similarly, in arable lands, the Oribatid mite percentage is high (39.4%). In this latter case, the frequent burial of crop residues, spread across the whole field, could probably increase the Oribatid mite proportion by increasing the organic matter recalcitrant fraction. To summarise, the percentage of oribatid mites observed in the different systems may be indicative of the degree of human intervention in the overall stability of the system. In conclusion, the interpretation of the results obtained from this index proved to be challenging, as was the case with the evaluation of the Acari/Collembola ratio, as previously stated in several studies (Jacomini et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Santorufo et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Joimel et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Huang et al., 2020).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Soil microarthropod community, management and soil chemical-physical parameters\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe chemical-physical parameters of the soil were found to be similar across sites and agroecosystems. The composition of the microarthropod community resulted to be more influenced by management than soil characteristics. The only soil chemical-physical parameter that differed significantly among systems was FWC, with arable land having the lowest percentage. This parameter is an indicator of soil water holding capacity at saturation and is highly related to agricultural management, as well as soil texture and structure. It can be increased by increasing the SOM content. The nMDS plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, showed that the microarthropod community of arable lands was different from that of vineyards and olive groves, which tended to partially overlap. This result was supported by the IDH (Grime \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1973\u003c/span\u003e, Connell \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1978\u003c/span\u003e). In arable lands tillage is more frequent, and the soil is subject to constant, although lighter, mechanical operations. This situation can create, in a short period of time, numerous ecological niches that may be beneficial to a wide range of arthropods. In fact, several potential positive effects of shallow tillage have been put forth, including improved habitat conditions, reduced disturbance, more abundant and diverse prey, decreased risk of drought and increased food sources. In accordance with this, the application of tillage for certain cultivations accelerates the decomposition of crop residue, which favours some microarthropod groups, particularly springtails and mites (Betancur-Corredor et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Further, reduced tillage can prevent soil compaction and depth stratification of nutrients and organic matter, which could influence the density of soil arthropods in comparison to no-tillage (Xin et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Olive orchards represent a more stable environment because of the different management practices applied. On the other hand, vineyards require a more frequent and deeper inter-row tillage compared to olive groves, as well as a higher number of treatments (e.g. fertilisation and application of PPPs), although there were no marked differences in the textural classes, soil texture resulted in an influence on soil microarthropod community assemblages. Different percentages of soil particles can affect pore size and consequently the habitability of the soil. Additionally, the composition of microarthropod communities appeared to be influenced by pH and carbonate content. This is consistent with previous studies that have shown that pH determines soil microarthropods abundance and species composition (Guo \u0026amp; Siepel, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, the varying impacts of K and P contents, as well as FWC on the community may be more closely linked to management practices, such as fertiliser use and grassing. In fact, it is worth noting that the nutrient content is higher in the vineyards (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), where more fertiliser was applied (Tab. S1).\u003c/p\u003e\u003cp\u003e\u003cb\u003e4.6 A first overview on the agricultural practices applied in the different agroecosystems, with a view to improving soil quality.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e According to our results, in arable land and olive groves soil biodiversity and soil health resulted to be maintained by means of sustainable agricultural practices, such as grassing, use of combined plant varieties, low inputs, and low tillage depth. On the contrary, vineyards were the system with the lowest soil quality, and it was characterised by the lower percentage of vegetation cover, high inputs, and deep inter-row tillage depth. In this agroecosystem, to enhance the abundance of soil arthropods and the number of BFs, the growth of spontaneous ground cover or the sowing of mixtures of selected plants (legumes) can be easily implemented. In this way, the welfare of the crop would be also enhanced by the provision of several ecosystem services by soil arthropods, including pests control, organic matter decomposition and soil structure implementation (Gon\u0026ccedil;alves et al., 2020). However, it is important to note that vineyard management, even if under organic management, involves the use of copper-hydroxide and sulphur, which can seriously reduce soil invertebrate populations (Outlaf et al., 2022). In this sense, it will be of paramount importance to find alternative solutions in fighting vine pathogens, to preserve soil biodiversity. Notably, in olive orchards, predatory soil microarthropods provide biological control of the olive fly (\u003cem\u003eBactrocera oleae\u003c/em\u003e, Rossi 1790), contributing in increasing \u003cem\u003eTephritidae\u003c/em\u003e pupae mortality (Gkisakis et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Despite this, the soil microarthropod community is still poorly studied in olive orchards compared to vineyards and arable fields. To our knowledge, Vignozzi et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) is currently the only study in which QBS-ar has been applied in olive orchards. In olive groves, the high soil microarthropod abundance and number of BFs seemed to be favoured by the wide variety of plant species used for grassing, which generated different habitats, and provided several food sources. To implement microarthropod diversity, both in arable land and vineyards, the inclusion of ecological infrastructures, such as grassy hedgerows and strips, could be recommended. These natural zones prove to be reservoirs of biodiversity, able to replenish the microarthropods community overtime (Mantoni et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). On the other hand, in arable lands the low soil disturbance applied (e.g. low depth tillage) proved to positively affect the soil microarthropod community, according to IDH (Grime \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1973\u003c/span\u003e, Connell \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1978\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFor the first time the QBS-ar index was applied within Conero Park (Italy). Constantly monitoring biological soil health in protected areas is crucial to take prompt action to preserve soil biodiversity as soon as deteriorating processes start (Fusco et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Nowadays, in land protected areas, farmers no longer have the unique role of primary goods producers, they also have the ecological role of biodiversity conservation and environmental improvement. In pursuing this latter objective, the application of sustainable agricultural practices is mandatory. An increased awareness of protected area managers is necessary to preserve and restore this 'hidden' yet functionally essential component of terrestrial biodiversity. Preserving, restoring, and implementing soil biodiversity means maintaining and enhancing the ecosystem services it provides (Robinson et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This, in turn, contributes to the health and well-being of plants, animals, and humans. At the European level, it is crucial to achieve sustainable soil management to meet the objectives of the Green Deal, which also states that at least 30% of the EU's land should be protected by 2050 (Montanarella \u0026amp; Panagos, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This target can only be achieved and be effective for land conservation if soil-dwelling organisms, as well as less charismatic but functionally important organisms, are included in the conservation effort, particularly in protected areas (Zeiss et al., 2023). It is important to note that implementing sustainable agricultural management practices, such as organic farming, can lead to a resilient and economically productive system that satisfies the production needs, and the protection of the agroecosystem and its functional biodiversity. Furthermore, this study provides knowledge towards an informed use of microarthropods as bioindicators of soil health. It emphasises the importance of monitoring biological soil health to establish thresholds for the prevalent agroecosystems, characterised by different agronomic practices. This is crucial for promoting the use of the QBS-ar index in national and international soil health monitoring.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of interest/Competing interests\u003c/h2\u003e \u003cp\u003eALT has received research support from the Conero Park, Italy. Other authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics approval\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cp\u003eA.L.T. has received research support from the Conero Park, Italy. Other authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the project Agro-environmental Area Agreement for water protection \"Coltiviamo la qualit\u0026agrave; delle acque del Conero\" (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.parcodelconero.org/progetti/tutti-i-progetti/\u003c/span\u003e\u003cspan address=\"https://www.parcodelconero.org/progetti/tutti-i-progetti/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e - RDP Marche 2014/2020 [Grant numbers BVC102024] to ALT.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.C. participated to material and data collection, wrote the manuscript text, and prepared Tables and Figures; A.D.A., M.M, N.W., A.T. and participated to material and data collection; C.G. participated to material and data collection and wrote the manuscript; A.L.T. participated to material and data collection, wrote the manuscript, and coordinated the whole research project, was scientific manager and obtained the funding.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to thank Ente Parco Regionale del Conero, Dott. Valerio Ballerini, the landowners, and the farmers for giving us access to their agricultural fields.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAngelini, P., Fenoglio, S., Isaia, M., Jacomini, C., Migliorini, M., Morisi, A., 2002 Tecniche di biomonitoraggio della qualit\u0026agrave; del suolo. ARPA Piemonte. Gruppo ALZANI, Pinerolo (TO) ISBN 88-7479-003-1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAoki, J.I., 1967. Microhabitats of Oribatid Mites on a Forest Floor. Bulletin of the National Science Museum 10 (2): 133\u0026ndash;138.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAoki, J.I., Harada, H., Miyawaki, A., 1977. Relation between Fauna of Soil Mites (Oribatei) and Human Impacts in Four Main Natural Forest Regions in Kanagawa Prefecture, Central Japan. Bulletin of the Inst. of Env. 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Effects of climate on the distribution and conservation of commonly observed European earthworms. Conservation Biology, 38, e14187. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/cobi.14187\u003c/span\u003e\u003cspan address=\"10.1111/cobi.14187\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"QBS-ar, Soil health, Soil microarthropods, Organic farming, Protected areas","lastPublishedDoi":"10.21203/rs.3.rs-4946545/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4946545/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSustainable soil management is essential to conserve soil biodiversity and its provision of vital ecosystem services. The EU Biodiversity Strategy for 2030 highlights the key role of organic farming and land protection in halting biodiversity loss, including edaphic biodiversity. To assess the effectiveness of the proposed measures, a study was conducted to determine the soil quality of three organically managed agroecosystems: arable lands, olive groves and, vineyards in the Conero Park, using the arthropod-based Biological Soil Quality Index (QBS-ar). Soil microarthropods are sensitive indicators of the impact of agricultural practices on soil quality. Given the diversity of the agronomic practices applied in these agroecosystems, the study aimed to compare the soil quality and identify the system with the least impact on soil biodiversity conservation, with the ultimate goal of laying the basis for identifying soil quality benchmarks within each system to be used in monitoring activities in land protected areas. Results showed that organic farming combined with land protection had a positive impact on soil quality. Overall soil quality was excellent, with the highest levels found in arable lands. This is consistent with the Intermediate Disturbance Hypothesis (IDH), which states that slightly disturbed habitats (i.e. arable land with minimum tillage) tend to have higher organism diversity than stable ones. The composition of microarthropod communities in arable land differed from those in stable arboreal crops. Olive groves showed a higher abundance and diversity of microarthropods compared to vineyards, which showed lower values. Promoting the use of QBS-ar, identifying benchmarks for prevalent agroecosystems and ensuring continuous monitoring of protected areas is thus a crucial issue.\u003c/p\u003e","manuscriptTitle":"Managing soil to support soil biodiversity in protected areas agroecosystems. A comparison between arable lands, olive groves, and vineyards in the Conero Park (Italy)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-27 18:47:36","doi":"10.21203/rs.3.rs-4946545/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-29T16:06:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-29T10:06:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-26T21:08:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73030760441965439645372436727446633267","date":"2024-09-06T10:52:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168751419416509904372172012413695886362","date":"2024-09-03T09:34:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-01T10:47:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"218071255202740861433865158323514885052","date":"2024-09-01T10:30:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3278051896948683946977080902280964627","date":"2024-09-01T05:33:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-01T03:09:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-29T06:38:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-29T06:37:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2024-08-20T16:35:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"89cf5ac1-d7f8-44c6-ba78-7be2b26a1ad1","owner":[],"postedDate":"September 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-03T16:05:32+00:00","versionOfRecord":{"articleIdentity":"rs-4946545","link":"https://doi.org/10.1007/s10661-025-13658-7","journal":{"identity":"environmental-monitoring-and-assessment","isVorOnly":false,"title":"Environmental Monitoring and Assessment"},"publishedOn":"2025-01-27 15:57:22","publishedOnDateReadable":"January 27th, 2025"},"versionCreatedAt":"2024-09-27 18:47:36","video":"","vorDoi":"10.1007/s10661-025-13658-7","vorDoiUrl":"https://doi.org/10.1007/s10661-025-13658-7","workflowStages":[]},"version":"v1","identity":"rs-4946545","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4946545","identity":"rs-4946545","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}