Below-ground biodiversity in organic agroforestry with free-range pigs in Denmark

Below-ground biodiversity in organic agroforestry with free-range pigs in Denmark | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Below-ground biodiversity in organic agroforestry with free-range pigs in Denmark Rumakanta Sapkota, Anne Grete Kongsted, Lea Ellegaard-Jensen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9473179/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Integrating biodiversity into agricultural systems, particularly through agroforestry, is increasingly recognized as an important step to achieve sustainable land management and soil health. Free-range farming, where animals are allowed to interact directly with their environment, may add benefits to soil ecosystems. In this study, we investigated how free-range pig farming, combined with agroforestry elements, influences belowground biodiversity, including microbial communities comprising prokaryotic, fungal and invertebrate communities. Prokaryotic, fungal, and invertebrate diversity were assessed using environmental DNA (eDNA), while earthworm density was measured via conventional method of hand sorting to obtain abundance measures. Soil samples were collected from two organic pig farms with differing agroforestry tree compositions and different periods with pigs in the fields. Our results showed that the presence of pigs was associated with the shifts in soil biodiversity, with contrasting responses observed across prokaryotes, fungi, and invertebrates, suggesting that free-range pig systems integrated with agroforestry practices can have complex, taxon specific effects on soil ecological diversity. In addition, tree age, species composition, and tree richness significantly impacted the soil microbial and invertebrate community composition. Earthworm abundance was generally negatively affected by the pig production system, and our results indicate that periods of undisturbed grass-clover cover, without pig activity, had a positive effect on the earthworm community. environmental DNA fauna Earthworms soil microbes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Animal welfare remains a key concern among pork consumers, particularly in western countries (Denver et al. 2023 ). Outdoor pig production systems are widely regarded as enhancing animal welfare, as they generally facilitate the expression of a broad spectrum of species-specific behaviors (Lund 2006 ). This welfare potential is further amplified when outdoor systems are integrated with woody crops in agroforestry settings. Such environments not only provide animals with shade and shelter but also support animal well-being through environmental complexity and resource availability (Smith et al. 2013 ). Agroforestry is generally recognized as enhancing biodiversity compared to conventional cropland on a global scale (Udawatta et al. 2019 ), as well as within Europe (Mupepele et al. 2021 ; Beule et al. 2022 ). Consequently, the conversion of monoculture cropland to agroforestry systems is expected to promote biodiversity. Specifically, both silvoarable and silvopastoral agroforestry systems have been shown to increase microbial biodiversity relative to cropland and pasture systems (Mupepele et al. 2021 ). This enhancement applies to both microbial abundance and diversity (Ndlela et al. 2021 ; Beule et al. 2022 ). Trees play a critical role in shaping microbial communities, with several studies reporting a decline in microbial abundance from tree rows toward the open field in alley cropping systems (Beule and Karlovsky 2021 ; Mupepele et al. 2021 ). Additionally, trees have been observed to influence bacterial species composition (Banerjee et al. 2016 ). Agroforestry has also been found to positively affect arthropod diversity in Europe, across both silvoarable and silvopastoral systems (Mupepele et al. 2021 ). More specifically, in a dehesa ecosystem in Italy, the proximity to oak trees significantly influenced the composition of soil-dwelling arthropods, particularly Collembola (Rossetti et al. 2015 ). Similarly, in Belgium, the distance to trees in an alley cropping system was found to affect the distribution of detritivores such as millipedes and woodlice. These findings suggest that trees in European alley cropping systems can positively influence the diversity and abundance of microorganisms, Collembola, millipedes, and woodlice. Earthworms are widely recognized to benefit from agroforestry systems worldwide, including under temperate climatic conditions (Tsonkova et al. 2012 ), and this pattern is particularly well documented in Europe (Cardinael et al. 2019 ; Vaupel et al., 2023 ). Tree rows, in particular, have been identified as highly favorable habitats for earthworm communities in both France (Cardinael et al. 2019 ) and Germany (Vaupel et al. 2023 ). In contrast, the integration of pigs into agroforestry systems is relatively uncommon. The oak-based silvopastoral systems of Italy, Spain (Dehesa), and Portugal (Montado) represent the most prominent examples of such combinations. However, scientific data on the effects of pigs on soil biodiversity—specifically microorganisms, mesofauna, and earthworms—within these systems are scarce. Consequently, the influence of pigs on soil faunal communities in agroforestry remains largely uncharacterized. Free-range pigs in silvopastoral systems may modify habitat conditions through nutrient enrichment via dung deposition and physical disturbance caused by rooting and trampling. Both mechanisms are expected to affect soil biota. It remains unknown how free-ranging pigs interact with hedgerows of different ages and different tree species composition to shape soil communities. This study reports findings from our investigation of the factors shaping belowground biodiversity, including microbial, Collembolan, and earthworm communities—at two organic farms keeping free-range lactating sows. Microbial and mesofaunal diversity were assessed primarily using eDNA, while earthworm populations were additionally quantified through hand-sorting of soil samples to obtain density estimates. 2. Material and methods 2.1 Study Sites The study was conducted at two commercial organic farms in Denmark. Farm 1 is in central‑western Jutland, and Farm 2 is situated in southern Funen. Both farms are characterized by sandy soil. Organic outdoor pig production has been practiced at Farm 1 since 2011 and at Farm 2 since 2007. At both farms, the production system followed a two‑year rotational cycle alternating between keeping lactating sows on grass-clover pasture and cultivating spring barley with an undersown clover–grass ley. After harvest the spring barley fields remained under clover–grass cover (either untouched or cut) until the following year, when paddocks were established, and sows late in pregnancy were introduced. In some cases, fields remained in clover–grass for several additional months before being used for pigs. At both sites, the sows were accompanied by piglets up to10 weeks of age. In accordance with standard on-farm practice, the sows were fitted with nose-rings to prevent deep-rooting behavior and thereby reduce vegetation damage. At Farm 1, each 1,000 m 2 single paddock hosted three to four consecutive lactating sows over the course of the year. The paddocks were arranged in rows adjacent to hedgerows dominated by poplar trees up to 6 or 16 m in height. In 8 of these rows, pigs had direct access to the hedgerows, which were sufficiently mature to tolerate sow and piglet activity. In 10 additional rows, sows, but not piglets, were excluded from accessing the hedgerows. Owing to the rotational system, some hedgerows were bordered by spring barley fields and were therefore not accessible to pigs during the study period in 2024. At Farm 2, the lactating sows were kept together in larger community paddocks with approximately 300 m 2 per sow. Each paddock was only used for one group of sows over the course of the year. After sow occupation, these areas were either left fallow or cut depending on the regrowth of the clover–grass. At Farm 2, there were two hedgerows consisting of newly established (planted in 2021) single rows of mixed tree species, including oak, maple, plum, and dog rose. These hedgerows were bordered by spring barley in the study period. The remaining paddocks were situated on clover-grass fields and did not include hedgerows. 2.2 Hedgerow characteristics At Farm 1, the hedgerows consisted of either three or five tree rows of poplar trees and were arranged in double rows, separated by either a farm track or an electric fence integrated into the paddock system. The hedgerows varied in length from 234 m to 609 m. Some hedgerows included rows of mature oak ( Quercus rubra ) trees (reaching up to 15 m in height), while others contained scattered individuals of several species, including cherry plum (P runus cerasifera ), sitka spruce ( Picea sitchensis ), chokeberry ( Aronia melanocarpa ), and hazel ( Corylus avellana ). In eight hedgerows, the poplar trees were planted in 2014 and reached a height of 16 m, while in other ten hedgerows, the poplar trees had been planted in 2021 and reached heights of about 6 m. At the elder hedgerows the pig paddocks were integrated into the hedgerows, while they were situated adjacent to the hedgerows in the younger ones. At Farm 2, the two hedgerows consisted of recently planted, low‑growing up to 1½ m high trees located alongside the spring barley fields. At another field at Farm 2 five paddocks without integrated or adjacent hedgerows were also sampled for earthworms and only three for the eDNA soil samples. The characteristics of the hedgerows have been summarized in supplementary table 1. 2.3 Sampling system Samples were taken in pig paddocks (in some cases without pigs) or in barley fields next to the hedgerows in four evenly spaced randomly chosen replicates from each hedgerow. In the paddock without hedgerows at Farm 2, sampling sites were positioned at the corners of the paddocks, 10 m from each side. 2.4 Soil Sampling for eDNA Analysis Soil samples for eDNA analyses were taken at Farm 1 at the older and younger hedgerows 11 and 20 June 2024, respectively, and at Farm 2 the 13 June 2024. All samples were collected as composite samples, each consisting of nine subsamples. Subsamples were taken using a one‑piece soil corer measuring 20 cm in length and 2 cm in diameter. At Farm 1, subsamples were collected along the hedgerows following a zigzag pattern, with every second subsample taken between the trees. At Farm 2 the same pattern was used in the paddocks adjacent to hedgerows, and in the paddocks without adjacent hedgerows the zigzag transects were established from the corners of the paddocks toward their centers. Between samples, the soil corer was rinsed for remaining soil, sterilized thoroughly in a sodium hypochlorite solution, and finally rinsed in sterilized water. Composite samples were kept in a cooled container during transport and subsequently stored at −18 °C until eDNA extraction 2.5 eDNA Extraction from soil Soil samples collected in 50 mL Falcon tubes were first lyophilized using a freeze dryer (Scanlaf Model Coolsafe 55, Lynge, Denmark) for a minimum of 72 h. The dried samples were then homogenized in a bead‑mill homogenizer (Bead Ruptor Elite, Omni International) using 2.4 mm sterile metal beads (Metal Bead Media, Omni International, USA) at 4 m s⁻¹ for 30 s. This homogenization step was repeated three times to ensure thorough mixing and grinding of approximately 40 g of soil per tube. Soil DNA extractions were performed using the DNeasy PowerLyzer PowerSoil DNA Isolation Kit (Qiagen, Denmark) (PS).For DNA extraction, 0.25 g subsamples of the homogenized soil were used following the manufacturer’s protocol, with the exception that the bead‑mill homogenizer was applied as described above (30 s at 4 m s⁻¹) instead of the homogenizer recommended by the supplier. The DNA concentrations of the extracts were quantified using the Qubit 1X dsDNA High Sensitivity Assay Kit with a Qubit 4.0 Fluorometer (Invitrogen, Oregon, USA). 2.6 Library preparation We used three primer sets targeting prokaryotes, fungi, and invertebrates, respectively (Table 1) as described earlier (Sapkota et al. 2025; Iturbe-Espinoza et al. 2025). In brief, a two‑step PCR dual‑indexing strategy was applied for Illumina MiSeq sequencing. In the first PCR (PCR1), amplicons were generated using a reaction mix consisting of 0.25 µL HiFi polymerase (PCR Biosystems), 5 µL HiFi buffer, 0.5 µL of each 10 µM forward and reverse primer, 0.5 µL BSA, 3 µL template DNA, and nuclease‑free water to a final volume of 25 µL. PCR1 thermal program were specific to primer sets. Prokayotic libaries were prepared using the thermal program with an initial step at 95°C for 2 min; followed by 33 cycles of 15 s at 95°C, 15 s at 55°C, and 40 s at 68°C; concluding with a final step at 68°C for 4 min. For fungal PCR, the initial step was at 94°C for 5 min; followed by 33 cycles of 30 s at 94°C, 30 s at 57°C, and 30 s at 72°C; and a final step of 10 min at 72°C. For COI, the PCR1 thermocycling program began with an initial denaturation at 95 °C for 5 min, followed by 10 cycles of 94 °C for 30 s, 50 °C for 30 s, and 72 °C for 1 min; then 10 additional cycles of 94 °C for 30 s, 52 °C for 30 s, and 72 °C for 1 min; and finally 15 cycles of 94 °C for 30 s, 54 °C for 30 s, and 72 °C for 1 min, with a final elongation step at 72 °C for 5 min (Sapkota et al. 2025). For dual indexing, the second PCR (PCR2) was performed using 3 µL of PCR1 product and 2 µL of primers containing Illumina adaptors and index sequences, with the remaining components as described above. The PCR2 thermocycling program consisted of an initial denaturation at 98 °C for 5 min, followed by 13 cycles of 98 °C for 10 s, 55 °C for 20 s, and 68 °C for 40 s, and a final elongation at 68 °C for 5 min. Successful amplification was confirmed by electrophoresis on 1.5% agarose gels stained with SYBR Green. PCR2 products were purified to remove primer dimers using 20 µL HighPrep™ magnetic beads (MagBio Genomics Inc., Gaithersburg, Maryland, USA) following the manufacturer’s instructions and eluted in 25 µL TE buffer. During the library preparation, negative controls (no-template controls, NTCs) were included to monitor potential contamination. DNA concentrations were then quantified using a Qubit 4.0 Fluorometer (Thermo Fisher Scientific) with the High‑Sensitivity DNA Assay Kit. Samples were subsequently pooled in equimolar amounts and sequenced on an Illumina MiSeq platform using the 500‑cycle V2 reagent kit (Department of Environmental Science, Aarhus University). Table 1 List of markers and primers used for metabarcoding in this study Marker Primer Sets Reference 16S V3-V4 region 341F/806R (Klindworth et al. 2013)(Yu et al. 2005) ITS2 fITS7/ITS4 (Ihrmark et al. 2012) COI mlCOlintF / jgHCO2198 (Leray et al. 2013) 2.7 Bioinformatics and analysis of eDNA data Sequencing data from the Illumina MiSeq platform were processed using QIIME2 version 2020.10.0 (Bolyen et al., 2019). Prior to downstream analysis, reads were trimmed to remove primer sequences and truncated at 230 bp for both forward and reverse reads to eliminate low‑quality bases. Reads were filtered, denoised, merged, chimera‑checked, and dereplicated using the DADA2 plugin in QIIME2 with default parameters (Callahan et al. 2016). For prokaryotic and fungal communities, taxonomic classification was carried out using the SILVA v138 database (Quast et al. 2013) for 16S rRNA ASVs and the UNITE v 8 database (Abarenkov et al. 2010) for ITS2 ASVs. COI taxonomic assignment was carried out as described earlier (Sapkota et al. 2026). In brief, ASVs were manually blasted using the sequence‑id tool on www.gbif.org against the BOLD database (v.2024‑07‑19) (Ratnasingham and Hebert 2007). Taxonomic assignments were curated using identity and coverage thresholds: >97% for species level, >95% for genus, >90% for family, >85% for order, and >80% for class level. All ASVs assigned to Animalia within the phyla Annelida, Arthropoda, Nematoda, Tardigrada, Mollusca, and Rotifera were retained; all others were discarded. Resulting taxonomy and ASV tables were exported to R and statistical analyses of the eDNA data were performed in R version 2025.5.1.513 (R Core Team, 2025). Data wrangling and diversity analyses were conducted using the vegan package version 2.5‑7 (Oksanen et al. 2020) and the phyloseq package version 1.3 (McMurdie et al. 2013). Samples containing fewer than 1000 reads were excluded, resulting in the removal of two samples. For prokaryotic and fungal communities, alpha diversity was estimated using observed richness and the Shannon index. Rarefaction curves of observed ASV richness were generated using the ggrarecurve() function from the MicrobiotaProcess R package to assess whether sequencing depth was sufficient to capture the majority of microbial diversity. Beta diversity was calculated using Bray–Curtis distance matrices and visualized via principal coordinate analysis (PCoA). PERMANOVA tests were conducted using the adonis function in vegan to assess the effects of farm, pig access, and hedgerow composition on community structure. Correlation between beta diversity and tree composition (presence/absence) was tested using Mantel’s method with Bray–Curtis and Jaccard distances and 999 permutations. For invertebrates, alpha diversity was estimated using species richness, while beta diversity was calculated using Sørensen dissimilarity matrices. The relationship between tree species richness and fungal alpha diversity (Observed ASVs and Shannon index) was assessed using non-parametric Spearman rank correlations. Differential abundance analysis (Log 2 -fold change) was carried out using DESeq2 v 1.40.2 r package. 2.8 Earthworm Sampling Sampling took place at Farm 1 on the 21 and 22 October 2024, and at Farm 2 on the 4 November 2024. At both farms earthworms were sampled by excavating a 25 × 25 × 25 cm soil block, including the turf layer, using a spade, at each paddock. The soil was placed on a plastic sheet and hand‑sorted to extract all earthworms. Collected individuals were transferred to plastic containers and kept at room temperature for 24 hours to allow gut clearance. Thereafter, earthworms were identified to species or genus level and weighed. 2.9 Statistical Analysis of earthworm samples All statistical analyses and visualizations of earthworm data were performed in R, using the following packages: boral for multivariate modelling, dplyr for data manipulation, Hmisc for correlation tests, and ggplot2 for graphical output. Packages within the tidyverse, including dplyr and ggplot2, were cited following standard conventions. Patterns in earthworm community composition were analysed using a Bayesian multivariate framework implemented in the boral package. The model was fitted to abundance data using a negative binomial distribution to account for overdispersion. Two independent latent variables were included to capture unmeasured ecological gradients, and a random row effect was added to account for site‑level variability (Hui et al. 2015). Hedgerow characteristics (Table 1) were appended to the latent variable scores for subsequent analysis. In boral, latent variable scores represent the estimated positions of sites along underlying unmeasured ecological gradients that explain residual variation in species composition beyond observed environmental variables. Group centroids and dispersion ellipses were calculated for sites with and without pigs to assess community clustering in latent variable space. Group centroids reflect the average position of sites within each group, while dispersion ellipses—calculated from the standard deviation of distances from each site to its centroid—illustrate the spread of sites around their group means. To evaluate the influence of environmental predictors on earthworm community structure, Pearson correlation coefficients were computed between the latent variables and selected environmental variables. Significant correlations were visualized in an ordination plot generated with ggplot2, where environmental variables were displayed as vectors originating from the origin. Vector lengths were scaled for clarity, and labels were added to aid interpretation. To complement the multivariate analysis and obtain more robust estimates of environmental effects on earthworm abundance, we fitted a generalized linear mixed‑effects model (GLMM). The model used a Poisson distribution with a log‑link function and was fitted using the glmer() function in the lme4 package (Bates et al. 2015). Locality was included as a random effect to account for spatial variation among sampling sites, and the hedgerow characteristics were included as fixed effects (predictors). All continuous predictors were mean‑centred and scaled prior to analysis. The categorical variable Locality was converted to a factor to ensure correct specification of random effects. The final dataset contained 87 observations (Farm 1: 64; Farm 2: 28). Model selection was performed using the dredge() function in the MuMIn package (Bartoń 2023), which evaluates all possible submodels of the full model. Models were ranked using Akaike’s Information Criterion corrected for small sample size (AICc). The best‑supported model (lowest AICc) was extracted using get.models(). To incorporate model uncertainty, model averaging was applied across all models with ΔAICc < 4 using model.avg(). Model residuals were inspected visually for normality using Q–Q plots of standardized residuals. Fixed‑effect estimates and their 95% confidence intervals from the best‑supported model were visualized using the plot_model() function in the sjPlot package (Lüdecke 2023). Coefficients were presented on the natural (untransformed) scale, with statistical significance indicated by associated p‑values. Estimates were plotted with point estimates and 95% confidence intervals and styled using ggplot2‑compatible theming. 3. Results 3.1 Microflora, microfauna, and mesofauna Amplicon sequencing was performed on 82 soil samples collected from the two farms (62 from farm 1 and 20 from farm 2), targeting prokaryotes, fungi, and soil invertebrates. After quality control, the eDNA dataset contained 508,219 reads assigned to 15,081 ASVs for prokaryotes; 3,476,648 reads assigned to 4,892 ASVs for fungi; and 405,880 reads assigned to 3,942 ASVs for soil fauna (phyla Annelida , Arthropoda , Nematoda , Tardigrada , Mollusca , Rotifera ). Rarefaction curves approached plateaus for most samples, indicating that sequencing depth was sufficient to capture the majority of ASV diversity (Supplementary Fig, SF 1). 3.1.1 Prokaryotic, fungal and invertebrate community structures Soil prokaryotic, fungal, and invertebrate communities were significantly influenced by the presence, composition, and age of the hedgerows (Table 2). The two farms included in the study differed in their microbial community structures; however, samples from paddocks with hedgerows clustered together across both farms (Supplementary Fig. SF 2). Because Farm 1 contained several paddocks that varied in hedgerow tree composition, we examined this farm in greater detail to assess the importance of tree communities. The age of the hedgerows was associated with distinct community patterns in the PCoA plots: paddocks with older hedgerows (7 years) formed clearly separated clusters compared with those containing younger hedgerows (3 years) (Fig. 1). Consistently, PERMANOVA results indicated that hedgerow age was a significant driver of soil microbial and invertebrate community structures (Table 2). For Farm 1, we observed significant correlations between tree composition and beta diversity, with Mantel r values of r = 0.2 (p = 0.001) for invertebrates, r = 0.34 (p = 0.001) for fungi, and r = 0.60 (p = 0.001) for bacteria. These results indicate that community structure is weakly to strongly associated with the tree compositions depending on group of taxa. Table 2 Permutational analysis of variance (PERMANOVA) using the ‘adonis2’ test on Bray-Curtis distance matrices for bacterial and fungal community dissimilarity assessment using 1000 permutations. For invertebrate community, beta diversity was calculated using Sørensen dissimilarity matrices. Tree species richness on community composition was performed only for Location 1, as sufficient data were available; no such analysis was possible for Location 2 due to lack of data. Dataset Factors Prokaryotic (R 2 ) Fungal (R 2 ) Invertebrate (R 2 ) Whole Location 0.10 *** 0.12*** 0.11 *** Hedgerow and tree age 0.08 *** 0.06*** 0.05 *** Farm 1 Hedgerow and tree age 0.19 *** 0.16*** 0.10 ** Tree Richness 0.11 *** 0.13 *** *** 0.12 *** Pig Access 0.03** 0.03** 0.03** Farm 2 Hedgerow and tree age 0.32 ** 0.27*** 0.24 *** Farm 1 old hedgerows Pig Access 0.10*** 0.12*** 0.08*** Farm 2 young hedgerows Pig Access 0.11** -- 0.09* Significance of test indicated as *** for p0.01, *p<0.05 and R 2 for proportion of variation explained. Based on the prokaryotic community structure at Farm 1, we observed that paddocks with active pig presence formed a distinct cluster within the plots containing 7‑year‑old hedgerows. Moreover, pig presence significantly affected the soil prokaryotic community structure (Table 2). 3.1.2 Alpha diversity across tree age and richness Paddocks with older hedgerows were associated with higher alpha diversity in both prokaryotic and fungal communities. For both the Shannon and Simpson indices, diversity was significantly higher in soils from older hedgerows, whereas observed richness did not differ significantly between age groups (Fig. 2). In contrast, within invertebrate communities, neither the Shannon nor the Simpson index showed significant differences. Interestingly, soil samples from paddocks with young trees at Farm 1 exhibited higher invertebrate richness compared with those from older hedgerows (Fig. 2). Fungal alpha diversity (ASV richness) tends to increase with tree species richness, with Spearman rank correlations showing a positive association (rho = 0.20, and P =0.06). Similarly, invertebrate richness was positively correlated with the tree richness (rho = 0.23, and P =0.03), However, prokaryotic community diversity showed non-significant correlation with the tree richness. Opposite to hedgerow age, we found that the presence of pigs in paddocks affected alpha diversity in the soil samples. For prokaryotes, pigs presence reduced alpha diversity, whereas the opposite trend was observed for fungi and invertebrates (Fig. 3, Table 3). Across all community types examined—prokaryotic, fungal, and invertebrate—hedgerow age and hedgerow tree‑species richness consistently emerged as two of the strongest predictors of community structure, explaining the largest proportion of variance in the PERMANOVA analyses (Table 2). Interestingly, this pattern remained robust regardless of the beta‑diversity metric applied (Bray–Curtis for prokaryotic and fungal communities; Sørensen for invertebrate communities). Notably, location—commonly considered a major determinant of microbial and macrofaunal community assembly in ecological studies—did not rank among the top explanatory variables in our dataset. This suggests that hedgerow‑associated tree characteristics exert a stronger influence on soil biodiversity than broader spatial factors. In addition to the dominant drivers, the presence of individual tree species was also significantly associated with variation in community structure ( p < 0.05). Although each tree species explained only a small proportion of the total variance, their combined effects may still be ecologically meaningful, reflecting the multifactorial nature of soil community assembly processes. Based on the enrichment analysis, we found distinct and mixed ecological responses across taxonomic groups. For prokaryotes, no clear direct association with dung-related taxa was observed; however, several enriched genera were linked to soil disturbance and organic matter turnover such as Acidothermus, Glutamicibacter, Streptosporangium, Terrisporobacter among others. For fungi, responses were mixed, with enriched taxa largely reflecting general soil organic matter dynamics, while hedgerow age showed a clearer pattern, including a strong association of ectomycorrhizal taxa such as Cortinarius with older hedgerows, indicating increasing ecosystem maturity. For soil fauna, responses to both pig access and hedgerow age were less pronounced, with only limited and inconsistent taxonomic enrichment patterns (Supplementary figure 3 and 4). In total, we detected 10 ASVs assigned to the family Lumbricidae , representing earthworms, using eDNA metabarcoding. Earthworm ASVs were found in 13 samples and were assigned to the genera Allolobophora and Aporrectodea . The Spearman correlation between eDNA‑based ASV counts and total earthworm abundance measured using conventional methods showed a weak positive relationship (ρ = 0.35, p > 0.01). 3.2 Earthworm community At Farm 1, 189 earthworms of only three species, Lumbricus rubellus/terrestris (68), Aporrectodea tuberculata (35), A. caliginosa (6) were found. Furthermore, 80 Aporrectodea juveniles were found of which most of them were supposed to be A. tuberculate due to the captures of the adults Aporrectodea . At Farm 2 173 specimens were found from 6 different species of which L. terrestris/rubellus (35) and A. tuberculata were the most abundant ones. Also, here we found relative many juvenile Aporrectodea sp. The average density was highest at Farm 2 with 120.3 earthworms per m2 and lowest at Farm 1 with 47.3 earthworms per m2. Posterior estimates from the boral statistical models indicated that environmental variables—rather than pig presence alone—were the primary drivers of variation in earthworm community composition, as reflected in the latent‑variable structure of the model (Fig. 4). Latent‑variable site scores showed less distinct grouping by pig presence than in the model without covariates (Fig. 4). When environmental variables were included, the difference in standard deviations—represented by the circle sizes in Fig. 4—was reduced between treatment groups, indicating that much of the variation initially attributed to pig presence was better explained by associated habitat characteristics such as vegetation type and structural complexity (Fig. 4B). Furthermore, the dispersion ellipses in Fig. 4 demonstrate that earthworm community composition was more variable in paddocks where pigs were present, suggesting that pig activity may increase small‑scale spatial heterogeneity in earthworm assemblages. Environmental vectors projected into the ordination space showed that only the variables grass cover and spring barley aligned with the latent variables, suggesting that these factors contributed only weakly to the observed patterns in earthworm community structure Regarding Lumbricus abundance, the generalized linear mixed model (GLMM) included spring barley as a fixed effect and a random intercept for replicate nested within locality. Spring barley was identified as a significant negative predictor of Lumbricus abundance, with an estimated log‑mean effect of −0.49 (SE = 0.12, z = −3.95, p < 0.001). The model intercept was not significant (estimate = 0.06, p = 0.697). The random‑effects structure indicated a variance of 0.108 (SD = 0.33) for the replicate‑within‑locality grouping. The model was fitted to 87 observations across 8 replicate groups. Scaled residuals ranged from −1.44 to 4.11, indicating an overall acceptable model fit. For Allolobophora abundance, a generalized linear model (GLM) without an intercept was fitted to estimate mean abundances for each land‑use category. The model identified Grass (without pigs) as a significant positive predictor, with an estimated mean abundance of 1.43 (SE = 0.55, t = 2.59, p = 0.018). Plots with pigs had a mean abundance of 0.88 (SE = 0.52, p = 0.105), while Spring barley plots did not differ from zero (estimate ≈ 0, p = 1.000). The model was based on 24 observations from Farm 2, explained 32.4% of the variance (adjusted R² = 0.223), and yielded an overall F‑statistic of 3.20 ( p = 0.046), indicating moderate explanatory power. The final GLMM for Aporrectodea abundance included hedgerow age, grass cover, pig presence, and tree‑species richness as fixed effects, with a random intercept for replicate nested within locality. All predictors were significant, whereas the model intercept was not. Grass cover showed a strong negative association with Aporrectodea abundance (log‑mean effect = −0.59, SE = 0.11, z = −5.36, p < 0.001), and pig presence was likewise negatively associated (log‑mean effect = −0.51, SE = 0.11, p < 0.001). In contrast, hedgerow age and tree‑species richness had positive effects on abundance (hedgerow age: log‑mean effect = 0.38, SE = 0.13, p = 0.004; tree richness: log‑mean effect = 0.27, SE = 0.11, p = 0.010). The model was fitted to 87 observations across 8 replicate groups. The random‑effects term showed a variance of 0.54 (SD = 0.74) for the replicate‑within‑locality grouping, indicating moderate site‑level variation Total earthworm abundance was analyzed using a generalized linear model (GLM) with a Poisson distribution and log link (Fig. 5). The model identified hedgerow age, grass cover, and pig presence as significant predictors. Hedgerow age showed a negative association with abundance (log‑mean effect = −0.19, SE = 0.06, p = 0.001), indicating lower earthworm densities in plots with older hedgerows. Grass cover also exhibited a strong negative effect (log‑mean effect = −0.45, SE = 0.07, p < 0.001), and pig presence was similarly associated with reduced earthworm abundance (log‑mean effect = −0.28, SE = 0.07, p < 0.001). The intercept was positive and highly significant (estimate = 1.33, SE = 0.06, p < 0.001). The model was fitted to 87 observations and provided a reasonable fit to the data (residual deviance = 346.8 on 83 degrees of freedom). Overall, no single element of the production system or hedgerow design consistently enhanced all components of biodiversity or earthworm abundance (Table 3). However, several clear patterns emerged from the analyses: 1) Pig presence increased the diversity of fungi and soil invertebrates but reduced earthworm density, particularly within the genus Aporrectodea, 2) Spring barley in the crop rotation promoted prokaryotic diversity but was associated with lower abundance of Lumbricus spp., 3) Grass cover had contrasting effects: it was negative for fungal diversity and Aporrectodea abundance, yet positive for Allolobophora abundance, 4) Tree‑species richness in hedgerows positively influenced fungal diversity and the abundance of Aporrectodea, and 5) Hedgerow age was positively associated with prokaryotic and fungal diversity, as well as earthworm abundance, especially within Aporrectodea . In summary, factors that increased earthworm abundance were primarily those associated with the hedgerows, whereas all three production‑related factors generally had negative effects—except for the positive response of Allolobophora to grass cover. For microorganisms and soil invertebrates, pig presence enhanced fungal diversity, prokaryotes benefited most during the spring barley phase, and grass cover provided limited benefits beyond supporting Allolobophora . 3.3 Key results A key conclusion from this study is that, although the rotational production system exhibits both positive and negative effects on belowground biodiversity, the presence of hedgerows and a diverse tree community can substantially enhance biodiversity in outdoor sow production systems. This effect is particularly evident for fungal and prokaryotic diversity, as well as for the abundance of earthworms in the genus Aporrectodea . These findings highlight the potential of targeted habitat features—especially structurally diverse and mature hedgerows—to support soil biodiversity within pig‑based agroecosystems. Table 3 An overview of the results of the statistical analyses. 1) Farm 1 only, 2) Only when pigs were not present in the paddocks. Signs +/- indicates increase of decrease in alpha diversity, and their significance with * p < 0.05, ** p < 0.01, *** p < 0.001, and ns for non-significant Rotation/Production system Hedgerows Community Diversity measure/ Family Pigs present Spring barley Grass cover Tree spec. richness Age Prokaryote diversity Sp. Richness Shannon Simpson ns ns * (-) 1) ns * (+) 1) *** (+) 1) ns ns ns ns ns ** (-) 1) ns ** (+) 1) *** (+) 1) Fungal diversity Sp. Richness Shannon Simpson *** (+) 1) * (+) 1) ns *** (-) 1) * (-) 1) ns **(-) 1) ** (-) 1) * (-) 1) ns * (+) 1) *** (+) 1) ns * (+) 1) *** (+) 1) Soil invertebrate diversity Sp. Richness Shannon Simpsons *(+) 1) ns ns ns ns ns ns ns ns ns * (-) ns ns Earthworm abundance All earthworms *** (-) ns *** (-) ns ** (-) Lumbricidae ns *** (-) ns ns ns Aporrectodea *** (-) ns *** (-) * (+) ** (+) Allolobophora ns ns * (+) 2) ns ns 4. Discussion In this study, we investigated hedgerow age, tree composition, and pig access may affect the soil below ground diversity using two commercial farms. Our study shows that significant differences between two participating farms with outdoor sow production system, referred here as location or sites, as the sites showed distinct pattern in the microbial, invertebrate and earthworm community structure. In the Farm 1, where a higher number of plots were used in this study, it was possible to evaluate how plot associated characteristics such as hedgerow tree age and tree species contributed to belowground biodiversity. Our results showed that the presence of hedgerows age along with the tree community structure (both composition and the tree richness) significantly affected soil biodiversity. Pig access to plots showed significant effect on the soil prokaryotic, fungal and invertebrate community structure. However, such effect was smaller than that of hedgerow age and tree richness. Overall, hedgerow age was found to be strongest driver of soil biodiversity. Together, these findings highlight the central role of structural vegetation features in shaping biodiversity in outdoor pig production systems. 4.1 Hedgerow age as the strongest driver of below ground diversity In agroforestry systems, hedgerows are known to support complex and diverse soil microbial communities, particularly in old and established system (Zagatto et al. 2026). However, studies explicitly addressing the effect of hedgerow age are limited, and here we compared hedgerows of two different ages, and the effect of hedgerow age on soil biodiversity indicates that woody vegetation is a vital driver of below ground communities. Older hedgerow contribute to higher organic matter inputs, promote stable microhabitats- which potentially promote the functionally diverse microbial and invertebrate communities. Previous work in agroforestry and traditional hedgerow systems also point out the importance of trees for soil microbial diversity aligning well with existing knowledge on their positive effects on soil microorganisms (both bacteria and fungi) (Beule et al. 2022). The observed positive effect on fungal diversity can be attributed partly to the absence of tillage, which is known to favour fungi relative to bacteria and other prokaryotes (Banerjee et al 2024), and partly to the continuous input of organic matter in the form of fallen leaves, dead roots, and root exudates. In additional to increased soil carbon input, maturity of tree dominated structures also modify microclimates, and promote connectivity across these microhabitats. In addition, many tree species form species‑specific associations with ectomycorrhizal fungi, which likely contributes to the increase in fungal diversity with greater tree‑species richness in the hedgerows (Tederso et al. 2024). 4.2 Tree richness and community composition In addition to age, in Farm 1, tree species richness and community composition were also found here as significant contributors to below ground biodiversity, explaining 11-13 % of variation, which makes second influential driver after hedgerow age. Furthermore, significant Mantel correlations reveal a strong association between tree community composition and patterns of microbial and invertebrate beta diversity. Such association between above ground plant diversity to below ground soil communities is also highlighted earlier (De Deyn and Van Der Putten 2005), indicating that diverse tree communities may promote niche differentiation, varied litter inputs both temporally and spatially, complementary rooting patterns. In agroforestry and forest systems, similar linkages between trees community composition and soil diversity have been documented (Vaupel et al. 2025; Awazi et al. 2025), emphasizing the role of plant–soil feedback in structuring below‑ground communities. 4.3 Effects of pig access Although pig access had a weaker effect compared to the trees related factors, it is still significantly influenced by the soil prokaryotic, fungal and invertebrate community structure across two farms. The presence of pigs also increased fungal diversity in our agroforestry system with free-range sows and piglets. This effect can be attributed to pig manure, which has been shown to contain high concentrations of both fungi and heterotrophic bacteria (Salau and Olowe 2024). Pig manure is also known to introduce numerous exogenous microorganisms, many of which are copiotroph bacteria specialized in degrading animal dung (Lia et al. 2022). In addition, manure inputs have been reported to increase the relative abundance of Basidiomycota (Zheng et al. 2024), a fungal group that plays an important role in lignin and cellulose degradation. These dung‑associated bacteria and fungi were therefore likely favored by the deposition of pig droppings in the paddocks. Our enrichment analysis showed mixed reactions which could be linked to disturbance in soil. Foraging activities of pigs also involve disturbances in the soil, which might enhance or lead to enriching certain groups of microbial taxa which were observed in this study. Earlier study using wild boar has shown foraging and disturbance in semidry grassland increases species diversity of grassland vegetation (Horčičková et al. 2019). 4.4 Earthworms dynamics Earthworm responses were more complex and not uniformly aligned with the patterns observed for soil microorganisms. Grass cover and pig presence both reduced total earthworm abundance, suggesting that rooting disturbance and dense swords may alter soil structure through compaction, reduced aeration, or shifts in organic matter dynamics (Kerschbaumer et al. 2024). Functional strategies among earthworm groups help explain the contrasting genus level responses. Lumbricus species, which build deep permanent burrows (Capowiez et al. 2015; Potvin and Lilleskov 2016), are partly buffered from surface disturbance such as pig rooting (Cole 2013). In contrast, endogeic Aporrectodea , which occupy the upper mineral soil and create horizontal burrows (Earthworm Society of Britain 2023), were more strongly reduced—consistent with findings from wild boar disturbances (Bueno and Jiménez 2014). The absence of Allolobophora chlorotica from pigoccupied plots further supports the sensitivity of shallow dwelling taxa to rooting pressure (Capowiez et al. 2015). The negative correlation between pig presence and earthworm abundance is most likely attributable to predatory foraging by pigs. Although the extent to which domestic pigs actively consume earthworms is not well documented, wild boars are known to feed extensively on earthworms (Baubet et al. 2003). Such predation occurs during rooting activities but is particularly pronounced during so‑called “worm nights,” when earthworms emerge in masse at the soil surface. Worm nights are triggered by specific weather conditions, typically occurring at temperatures around 10 °C following at least moderate precipitation (Baubet et al. 2003). The negative association between earthworm abundance and pig presence in our system suggests that domestic pigs may also consume earthworms when the opportunity arises, potentially benefiting from similar environmental conditions that facilitate surface activity Despite these disturbances, earthworm densities remained moderate to high relative to European arable fields. This likely reflects the inclusion of clover–grass phases in the crop rotation, which are known to support high earthworm populations (Phillips 2020). At Farm 2, pigs occupied paddocks for only a short farrowing period, leaving long undisturbed periods that likely allowed earthworm populations to recover. At Farm 1, pigs occupied paddocks for much longer, reducing opportunities for recolonization—consistent with lower densities observed there. Stand age effects on earthworms were mixed. Older hedgerows may accumulate more recalcitrant litter and develop denser soil horizons, which can reduce earthworm abundance (Crow et al. 2009). However, tree species richness had a positive effect on Aporrectodea , likely due to improved litter heterogeneity, nutrient cycling, and soil structure—mechanisms previously linked to tree identity and richness effects on soil fauna (Schelfhout et al. 2017). 4.5 Implications For soil prokaryotic, fungal, and invertebrate communities overall, our findings indicate that tree structure and composition—particularly tree cover and tree‑species richness—were the primary drivers of belowground biodiversity. Although pig presence had significant effects when examined within specific clusters, the broader patterns clearly showed that hedgerow characteristics exerted the strongest influence on soil microbial and invertebrate community structure. Our results for earthworms further demonstrate that earthworm communities are shaped not only by vegetation complexity but also by disturbance regimes that interact with species‑specific life‑history strategies. Anecic species, with their deep and relatively stable vertical burrows, can continue to function as ecosystem engineers even under moderate disturbance. In contrast, endogeic and epigeic species, which occupy mineral topsoil and surface litter layers respectively, contribute more to aggregate formation and litter decomposition near the soil surface and are therefore more sensitive to grazing pressure and rooting activity (Capowiez et al. 2015; Earthworm Society of Britain 2023). Designing silvopastoral systems that balance tree diversity with controlled ground cover and minimal rooting disturbance is thus essential for maintaining soil biodiversity and ecosystem functioning. Declarations Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, in interpretation of data, in the writing of the manuscript, or in the decision to publish the results. Author Contribution RS, JA, REH and AGK designed the experiment. RS, JA, REH and AGK conducted laboratory and field work and analyzed the data. RS, JA and REH wrote the first draft. LEJ supervised library preparation and performed amplicon sequencing. JA secured the funding and led the study. All authors read, revised, and approved the manuscript. Acknowledgements The project “Supporting biodiversity and animal welfare in organic pig production” is part of the Organic RDD 9 programme, which is coordinated by International Centre for Research in Organic Food Systems (ICROFS) in collaboration with the Green Growth and Development programme (GUDP) under the Danish Ministry of Food, Agriculture and Fisheries. 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Agric Ecosyst Environ 400:110238. https://doi.org/10.1016/J.AGEE.2026.110238 Additional Declarations No competing interests reported. Supplementary Files Supplementaryfigurestables.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 15 May, 2026 Reviewers agreed at journal 09 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers invited by journal 04 May, 2026 Editor assigned by journal 22 Apr, 2026 Submission checks completed at journal 22 Apr, 2026 First submitted to journal 20 Apr, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-9473179","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":637470248,"identity":"6f16de57-e5e3-4797-b9e9-965b49c84f22","order_by":0,"name":"Rumakanta Sapkota","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApElEQVRIiWNgGAWjYFAC5gNwpmQDcVrYEkjWwmNAohbz/jMfHxe21THwz0hgvDmDGC0yN3I3G89sO8wgcSOB2XIDMVokJHi3SfOcOcDAcCOBTfIBUVr4zzz/zXOmjkGeeC0MOWzMPBXMDAYgLUQ6LM1YmqfiMI/hmYfNlkR5X4L/8MPPPAZ1cnLHkw/e7CFGCwzwMDAwNpCiYRSMglEwCkYBPgAARZssjZF2cygAAAAASUVORK5CYII=","orcid":"","institution":"Aarhus University","correspondingAuthor":true,"prefix":"","firstName":"Rumakanta","middleName":"","lastName":"Sapkota","suffix":""},{"id":637470249,"identity":"de67a556-23b2-455f-9862-3ae8cc283949","order_by":1,"name":"Anne Grete Kongsted","email":"","orcid":"","institution":"Aarhus University","correspondingAuthor":false,"prefix":"","firstName":"Anne","middleName":"Grete","lastName":"Kongsted","suffix":""},{"id":637470250,"identity":"845c2b32-fe1c-4937-93d4-7bdcc8d81356","order_by":2,"name":"Lea Ellegaard-Jensen","email":"","orcid":"","institution":"Aarhus University","correspondingAuthor":false,"prefix":"","firstName":"Lea","middleName":"","lastName":"Ellegaard-Jensen","suffix":""},{"id":637470251,"identity":"0fe9d9b3-7cda-4d35-9178-7014349779c9","order_by":3,"name":"Rikke Eisner Hansen","email":"","orcid":"","institution":"Aarhus University","correspondingAuthor":false,"prefix":"","firstName":"Rikke","middleName":"Eisner","lastName":"Hansen","suffix":""},{"id":637470252,"identity":"771d4355-602a-4cb7-9887-673b8ee270fa","order_by":4,"name":"Jørgen Axelsen","email":"","orcid":"","institution":"Aarhus University","correspondingAuthor":false,"prefix":"","firstName":"Jørgen","middleName":"","lastName":"Axelsen","suffix":""}],"badges":[],"createdAt":"2026-04-20 13:53:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9473179/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9473179/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109205206,"identity":"aed4e8b5-0e12-433b-87a6-2a76629c56c9","added_by":"auto","created_at":"2026-05-13 15:03:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":168288,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of soil prokaryotic beta diversity\u003cstrong\u003e.\u003c/strong\u003ePrincipal coordinate analysis (PCoA) of soil prokaryotic, fungal, and invertebrate communities based on Bray–Curtis dissimilarity. Pig access to paddocks is indicated by different point shapes, and hedgerow age is represented by color.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9473179/v1/51f7eee18fc3a3ecd55ebc55.png"},{"id":109139800,"identity":"97d346c5-2d6f-4021-9560-a1f393591086","added_by":"auto","created_at":"2026-05-13 02:11:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":144260,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of soil prokaryotes, fungal, and invertebrate alpha diversity. Boxplots showing three alpha‑diversity metrics for each biological group. Pairwise differences were assessed using Tukey’s HSD test; adjusted p‑values are indicated as follows: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 (\u003cem\u003e), p \u0026lt; 0.01 (), and p \u0026lt; 0.001 (\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9473179/v1/4addbafecb0e6cbfbd563468.png"},{"id":109205991,"identity":"0fb08eae-26be-4625-a9ec-adbcdce9babc","added_by":"auto","created_at":"2026-05-13 15:10:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":185407,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of soil prokaryotic, fungal, and invertebrate diversity in paddocks with (Yes) and without (No) pig presence across two hedgerow age classes (Farm 1 A: 7‑year‑old hedgerows; Farm 1 B: 3‑year‑old hedgerows). Pairwise comparisons were performed using the Wilcoxon test; adjusted \u003cem\u003ep\u003c/em\u003e-values are indicated as follows: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 (*), and \u003cem\u003ens\u003c/em\u003e for non‑significant differences.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9473179/v1/c4d5dcdf956e455b4c49dcb6.png"},{"id":109222317,"identity":"8ec0eba3-9215-4c18-8a50-fdf19478c29f","added_by":"auto","created_at":"2026-05-13 21:07:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":72675,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Ordination derived from a model fitted with environmental covariates, including Shrubs, Spring barley, age of the trees, Grass without pigs, Grass cover, and Tree species richness. In this constrained ordination, pig-based groupings are less distinct, indicating that most variation in earthworm community structure is better explained by environmental habitat features rather than pig presence alone (B). Ordination based on a latent variable model without environmental covariates. Sites are plotted in the space defined by the first two latent variables (LV1 on the x-axes and LV2 on the y-axes), with ellipses representing 1 standard deviation around group centroids (pig vs. no-pig sites). Environmental variables were post hoc correlated with the latent variables and are displayed as arrows from the origin, with direction and length indicating the strength and direction of their association with community structure.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9473179/v1/28bffc3d16340bd35c87e6b8.png"},{"id":109139802,"identity":"09b151e7-e122-466c-9575-d534f66eb209","added_by":"auto","created_at":"2026-05-13 02:11:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":131966,"visible":true,"origin":"","legend":"\u003cp\u003eEstimates including confidence intervals of the models. * Indicate the significance level of model terms, and red signifies a negative correlation, and blue signifies a positive correlation between earthworm abundances and the model term. Age = hedgerow age.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9473179/v1/ad4b8e780ef22cf4470c6bd3.png"},{"id":109252426,"identity":"c5234503-908b-4e34-b9fa-5daa5586ae1c","added_by":"auto","created_at":"2026-05-14 09:26:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":903339,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9473179/v1/571b894e-ada5-4600-af75-bc0d37257810.pdf"},{"id":109205218,"identity":"2cf9ec61-e89d-457f-8317-68bf0f1aec56","added_by":"auto","created_at":"2026-05-13 15:03:47","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":468748,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigurestables.docx","url":"https://assets-eu.researchsquare.com/files/rs-9473179/v1/46e14add2f0a1afbfa109fed.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Below-ground biodiversity in organic agroforestry with free-range pigs in Denmark","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAnimal welfare remains a key concern among pork consumers, particularly in western countries (Denver et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Outdoor pig production systems are widely regarded as enhancing animal welfare, as they generally facilitate the expression of a broad spectrum of species-specific behaviors (Lund \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This welfare potential is further amplified when outdoor systems are integrated with woody crops in agroforestry settings. Such environments not only provide animals with shade and shelter but also support animal well-being through environmental complexity and resource availability (Smith et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAgroforestry is generally recognized as enhancing biodiversity compared to conventional cropland on a global scale (Udawatta et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), as well as within Europe (Mupepele et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Beule et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, the conversion of monoculture cropland to agroforestry systems is expected to promote biodiversity. Specifically, both silvoarable and silvopastoral agroforestry systems have been shown to increase microbial biodiversity relative to cropland and pasture systems (Mupepele et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This enhancement applies to both microbial abundance and diversity (Ndlela et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Beule et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Trees play a critical role in shaping microbial communities, with several studies reporting a decline in microbial abundance from tree rows toward the open field in alley cropping systems (Beule and Karlovsky \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mupepele et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, trees have been observed to influence bacterial species composition (Banerjee et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAgroforestry has also been found to positively affect arthropod diversity in Europe, across both silvoarable and silvopastoral systems (Mupepele et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). More specifically, in a \u003cem\u003edehesa\u003c/em\u003e ecosystem in Italy, the proximity to oak trees significantly influenced the composition of soil-dwelling arthropods, particularly Collembola (Rossetti et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Similarly, in Belgium, the distance to trees in an alley cropping system was found to affect the distribution of detritivores such as millipedes and woodlice. These findings suggest that trees in European alley cropping systems can positively influence the diversity and abundance of microorganisms, Collembola, millipedes, and woodlice.\u003c/p\u003e \u003cp\u003eEarthworms are widely recognized to benefit from agroforestry systems worldwide, including under temperate climatic conditions (Tsonkova et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and this pattern is particularly well documented in Europe (Cardinael et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Vaupel et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Tree rows, in particular, have been identified as highly favorable habitats for earthworm communities in both France (Cardinael et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Germany (Vaupel et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, the integration of pigs into agroforestry systems is relatively uncommon. The oak-based silvopastoral systems of Italy, Spain (Dehesa), and Portugal (Montado) represent the most prominent examples of such combinations. However, scientific data on the effects of pigs on soil biodiversity\u0026mdash;specifically microorganisms, mesofauna, and earthworms\u0026mdash;within these systems are scarce. Consequently, the influence of pigs on soil faunal communities in agroforestry remains largely uncharacterized.\u003c/p\u003e \u003cp\u003eFree-range pigs in silvopastoral systems may modify habitat conditions through nutrient enrichment via dung deposition and physical disturbance caused by rooting and trampling. Both mechanisms are expected to affect soil biota. It remains unknown how free-ranging pigs interact with hedgerows of different ages and different tree species composition to shape soil communities.\u003c/p\u003e \u003cp\u003eThis study reports findings from our investigation of the factors shaping belowground biodiversity, including microbial, Collembolan, and earthworm communities\u0026mdash;at two organic farms keeping free-range lactating sows. Microbial and mesofaunal diversity were assessed primarily using eDNA, while earthworm populations were additionally quantified through hand-sorting of soil samples to obtain density estimates.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study Sites\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted at two commercial organic farms in Denmark. Farm 1 is in central‑western Jutland, and Farm 2 is situated in southern Funen. Both farms are characterized by sandy soil.\u003c/p\u003e\n\u003cp\u003eOrganic outdoor pig production has been practiced at Farm 1 since 2011 and at Farm 2 since 2007. At both farms, the production system followed a two‑year rotational cycle alternating between keeping lactating sows on grass-clover pasture and cultivating spring barley with an undersown clover\u0026ndash;grass ley. After harvest the spring barley fields remained under clover\u0026ndash;grass cover (either untouched or cut) until the following year, when paddocks were established, and sows late in pregnancy were introduced. In some cases, fields remained in clover\u0026ndash;grass for several additional months before being used for pigs. At both sites, the sows were accompanied by piglets up to10 weeks of age. In accordance with standard on-farm practice, the sows were fitted with nose-rings to prevent deep-rooting behavior and thereby reduce vegetation damage.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt Farm 1, each 1,000 m\u003csup\u003e2\u003c/sup\u003e single paddock hosted three to four consecutive lactating sows over the course of the year. The paddocks were arranged in rows adjacent to hedgerows dominated by poplar trees up to 6 or 16 m in height. In 8 of these rows, pigs had direct access to the hedgerows, which were sufficiently mature to tolerate sow and piglet activity. In 10 additional rows, sows, but not piglets, were excluded from accessing the hedgerows. Owing to the rotational system, some hedgerows were bordered by spring barley fields and were therefore not accessible to pigs during the study period in 2024.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt Farm 2, the lactating sows were kept together in larger community paddocks with approximately 300 m\u003csup\u003e2\u003c/sup\u003e per sow. Each paddock was only used for one group of sows over the course of the year. After sow occupation, these areas were either left fallow or cut depending on the regrowth of the clover\u0026ndash;grass. At Farm 2, there were two hedgerows consisting of newly established (planted in 2021) single rows of mixed tree species, including oak, maple, plum, and dog rose. These hedgerows were bordered by spring barley in the study period. The remaining paddocks were situated on clover-grass fields and did not include hedgerows.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Hedgerow characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt Farm 1, the hedgerows consisted of either three or five tree rows of poplar trees and were arranged in double rows, separated by either a farm track or an electric fence integrated into the paddock system. The hedgerows varied in length from 234 m to 609\u0026nbsp;m. Some hedgerows included rows of mature oak (\u003cem\u003eQuercus rubra\u003c/em\u003e) trees (reaching up to 15\u0026nbsp;m in height), while others contained scattered individuals of several species, including cherry plum (P\u003cem\u003erunus cerasifera\u003c/em\u003e), sitka spruce (\u003cem\u003ePicea sitchensis\u003c/em\u003e), chokeberry (\u003cem\u003eAronia melanocarpa\u003c/em\u003e), and hazel (\u003cem\u003eCorylus avellana\u003c/em\u003e). In eight hedgerows, the poplar trees were planted in 2014 and reached a height of 16 m, while in other ten hedgerows, the poplar trees had been planted in 2021 and reached heights of about 6 m. At the elder hedgerows the pig paddocks were integrated into the hedgerows, while they were situated adjacent to the hedgerows in the younger ones.\u003c/p\u003e\n\u003cp\u003eAt Farm 2, the two hedgerows consisted of recently planted, low‑growing up to 1\u0026frac12; m high trees located alongside the spring barley fields. At another field at Farm 2 five paddocks without integrated or adjacent hedgerows were also sampled for earthworms and only three for the eDNA soil samples. The characteristics of the hedgerows have been summarized in supplementary table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Sampling system\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSamples were taken in pig paddocks (in some cases without pigs) or in barley fields next to the hedgerows in four evenly spaced randomly chosen replicates from each hedgerow. In the paddock without hedgerows at Farm 2, sampling sites were positioned at the corners of the paddocks, 10 m from each side.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Soil Sampling for eDNA Analysis\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Soil samples for eDNA analyses were taken at Farm 1 at the older and younger hedgerows 11 and 20 June 2024, respectively, and at Farm 2 the 13 June 2024. All samples were collected as composite samples, each consisting of nine subsamples. Subsamples were taken using a one‑piece soil corer measuring 20 cm in length and 2 cm in diameter. At Farm 1, subsamples were collected along the hedgerows following a zigzag pattern, with every second subsample taken between the trees. At Farm 2 the same pattern was used in the paddocks adjacent to hedgerows, and in the paddocks without adjacent hedgerows the zigzag transects were established from the corners of the paddocks toward their centers. Between samples, the soil corer was rinsed for remaining soil, sterilized thoroughly in a sodium hypochlorite solution, and finally rinsed in sterilized water. Composite samples were kept in a cooled container during transport and subsequently stored at \u0026minus;18 \u0026deg;C until eDNA extraction\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 eDNA Extraction from soil\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSoil samples collected in 50 mL Falcon tubes were first lyophilized using a freeze dryer (Scanlaf Model Coolsafe 55, Lynge, Denmark) for a minimum of 72 h. The dried samples were then homogenized in a bead‑mill homogenizer (Bead Ruptor Elite, Omni International) using 2.4 mm sterile metal beads (Metal Bead Media, Omni International, USA) at 4 m s⁻\u0026sup1; for 30 s. This homogenization step was repeated three times to ensure thorough mixing and grinding of approximately 40 g of soil per tube. Soil DNA extractions were performed using the DNeasy PowerLyzer PowerSoil DNA Isolation Kit (Qiagen, Denmark) (PS).For DNA extraction, 0.25 g subsamples of the homogenized soil were used following the manufacturer\u0026rsquo;s protocol, with the exception that the bead‑mill homogenizer was applied as described above (30 s at 4 m s⁻\u0026sup1;) instead of the homogenizer recommended by the supplier. The DNA concentrations of the extracts were quantified using the Qubit 1X dsDNA High Sensitivity Assay Kit with a Qubit 4.0 Fluorometer (Invitrogen, Oregon, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Library preparation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used three primer sets targeting prokaryotes, fungi, and invertebrates, respectively (Table 1) as described earlier (Sapkota et al. 2025; Iturbe-Espinoza et al. 2025). In brief, a two‑step PCR dual‑indexing strategy was applied for Illumina MiSeq sequencing. In the first PCR (PCR1), amplicons were generated using a reaction mix consisting of 0.25\u0026nbsp;\u0026micro;L HiFi polymerase (PCR Biosystems), 5\u0026nbsp;\u0026micro;L HiFi buffer, 0.5\u0026nbsp;\u0026micro;L of each 10\u0026nbsp;\u0026micro;M forward and reverse primer, 0.5\u0026nbsp;\u0026micro;L BSA, 3\u0026nbsp;\u0026micro;L template DNA, and nuclease‑free water to a final volume of 25\u0026nbsp;\u0026micro;L. PCR1 thermal program were specific to primer sets. Prokayotic libaries were prepared using the thermal program with an initial step at 95\u0026deg;C for 2 min; followed by 33 cycles of 15 s at 95\u0026deg;C, 15 s at 55\u0026deg;C, and 40 s at 68\u0026deg;C; concluding with a final step at 68\u0026deg;C for 4 min. For fungal PCR, the initial step was at 94\u0026deg;C for 5 min; followed by 33 cycles of 30 s at 94\u0026deg;C, 30 s at 57\u0026deg;C, and 30 s at 72\u0026deg;C; and a final step of 10 min at 72\u0026deg;C. For COI, the PCR1 thermocycling program began with an initial denaturation at 95\u0026nbsp;\u0026deg;C for 5\u0026nbsp;min, followed by 10 cycles of 94\u0026nbsp;\u0026deg;C for 30\u0026nbsp;s, 50\u0026nbsp;\u0026deg;C for 30\u0026nbsp;s, and 72\u0026nbsp;\u0026deg;C for 1\u0026nbsp;min; then 10 additional cycles of 94\u0026nbsp;\u0026deg;C for 30\u0026nbsp;s, 52\u0026nbsp;\u0026deg;C for 30\u0026nbsp;s, and 72\u0026nbsp;\u0026deg;C for 1\u0026nbsp;min; and finally 15 cycles of 94\u0026nbsp;\u0026deg;C for 30\u0026nbsp;s, 54\u0026nbsp;\u0026deg;C for 30\u0026nbsp;s, and 72\u0026nbsp;\u0026deg;C for 1\u0026nbsp;min, with a final elongation step at 72\u0026nbsp;\u0026deg;C for 5\u0026nbsp;min (Sapkota et al. 2025).\u003c/p\u003e\n\u003cp\u003eFor dual indexing, the second PCR (PCR2) was performed using 3\u0026nbsp;\u0026micro;L of PCR1 product and 2\u0026nbsp;\u0026micro;L of primers containing Illumina adaptors and index sequences, with the remaining components as described above. The PCR2 thermocycling program consisted of an initial denaturation at 98\u0026nbsp;\u0026deg;C for 5\u0026nbsp;min, followed by 13 cycles of 98\u0026nbsp;\u0026deg;C for 10\u0026nbsp;s, 55\u0026nbsp;\u0026deg;C for 20\u0026nbsp;s, and 68\u0026nbsp;\u0026deg;C for 40\u0026nbsp;s, and a final elongation at 68\u0026nbsp;\u0026deg;C for 5\u0026nbsp;min. Successful amplification was confirmed by electrophoresis on 1.5% agarose gels stained with SYBR Green.\u003c/p\u003e\n\u003cp\u003ePCR2 products were purified to remove primer dimers using 20 \u0026micro;L HighPrep\u0026trade; magnetic beads (MagBio Genomics Inc., Gaithersburg, Maryland, USA) following the manufacturer\u0026rsquo;s instructions and eluted in 25 \u0026micro;L TE buffer. During the library preparation, negative controls (no-template controls, NTCs) were included to monitor potential contamination. DNA concentrations were then quantified using a Qubit 4.0 Fluorometer (Thermo Fisher Scientific) with the High‑Sensitivity DNA Assay Kit. Samples were subsequently pooled in equimolar amounts and sequenced on an Illumina MiSeq platform using the 500‑cycle V2 reagent kit (Department of Environmental Science, Aarhus University).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e List of markers and primers used for metabarcoding in this study\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eMarker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003ePrimer Sets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e16S V3-V4 region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e341F/806R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e(Klindworth et al. 2013)(Yu et al. 2005)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eITS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003efITS7/ITS4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e(Ihrmark et al. 2012)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eCOI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003emlCOlintF / jgHCO2198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e(Leray et al. 2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Bioinformatics and analysis of eDNA data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSequencing data from the Illumina MiSeq platform were processed using QIIME2 version 2020.10.0 (Bolyen et al., 2019). Prior to downstream analysis, reads were trimmed to remove primer sequences and truncated at 230 bp for both forward and reverse reads to eliminate low‑quality bases. Reads were filtered, denoised, merged, chimera‑checked, and dereplicated using the DADA2 plugin in QIIME2 with default parameters (Callahan et al. 2016). For prokaryotic and fungal communities, taxonomic classification was carried out using the SILVA v138 database (Quast et al. 2013) for 16S rRNA ASVs and the UNITE v 8 database (Abarenkov et al. 2010) for ITS2 ASVs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCOI taxonomic assignment was carried out as described earlier (Sapkota et al. 2026). In brief, ASVs were manually blasted using the sequence‑id tool on www.gbif.org against the BOLD database (v.2024‑07‑19) (Ratnasingham and Hebert 2007). Taxonomic assignments were curated using identity and coverage thresholds: \u0026gt;97% for species level, \u0026gt;95% for genus, \u0026gt;90% for family, \u0026gt;85% for order, and \u0026gt;80% for class level. All ASVs assigned to Animalia within the phyla Annelida, Arthropoda, Nematoda, Tardigrada, Mollusca, and Rotifera were retained; all others were discarded.\u003c/p\u003e\n\u003cp\u003eResulting taxonomy and ASV tables were exported to R and statistical analyses of the eDNA data were performed in R version 2025.5.1.513 (R Core Team, 2025). Data wrangling and diversity analyses were conducted using the vegan package version 2.5‑7 (Oksanen et al. 2020) and the phyloseq package version 1.3 (McMurdie et al. 2013). Samples containing fewer than 1000 reads were excluded, resulting in the removal of two samples. For prokaryotic and fungal communities, alpha diversity was estimated using observed richness and the Shannon index. Rarefaction curves of observed ASV richness were generated using the\u0026nbsp;ggrarecurve()\u0026nbsp;function from the MicrobiotaProcess R package to assess whether sequencing depth was sufficient to capture the majority of microbial diversity. Beta diversity was calculated using Bray\u0026ndash;Curtis distance matrices and visualized via principal coordinate analysis (PCoA). PERMANOVA tests were conducted using the adonis function in vegan to assess the effects of farm, pig access, and hedgerow composition on community structure. Correlation between beta diversity and tree composition (presence/absence) was tested using Mantel\u0026rsquo;s method with Bray\u0026ndash;Curtis and Jaccard distances and 999 permutations. For invertebrates, alpha diversity was estimated using species richness, while beta diversity was calculated using S\u0026oslash;rensen dissimilarity matrices. The relationship between tree species richness and fungal alpha diversity (Observed ASVs and Shannon index) was assessed using non-parametric Spearman rank correlations. Differential abundance analysis (Log\u003csub\u003e2\u003c/sub\u003e-fold change) was carried out using DESeq2 v 1.40.2 r package.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8 Earthworm Sampling\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Sampling took place at Farm 1 on the 21 and 22 October 2024, and at Farm 2 on the 4 November 2024. At both farms earthworms were sampled by excavating a 25 \u0026times; 25 \u0026times; 25 cm soil block, including the turf layer, using a spade, at each paddock. The soil was placed on a plastic sheet and hand‑sorted to extract all earthworms. Collected individuals were transferred to plastic containers and kept at room temperature for 24 hours to allow gut clearance. Thereafter, earthworms were identified to species or genus level and weighed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9 Statistical Analysis of earthworm samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses and visualizations of earthworm data were performed in R, using the following packages: boral for multivariate modelling, dplyr for data manipulation, Hmisc for correlation tests, and ggplot2 for graphical output. Packages within the tidyverse, including dplyr and ggplot2, were cited following standard conventions.\u003c/p\u003e\n\u003cp\u003ePatterns in earthworm community composition were analysed using a Bayesian multivariate framework implemented in the boral package. The model was fitted to abundance data using a negative binomial distribution to account for overdispersion. Two independent latent variables were included to capture unmeasured ecological gradients, and a random row effect was added to account for site‑level variability (Hui et\u0026nbsp;al. 2015).\u003c/p\u003e\n\u003cp\u003eHedgerow characteristics (Table 1) were appended to the latent variable scores for subsequent analysis. In boral, latent variable scores represent the estimated positions of sites along underlying unmeasured ecological gradients that explain residual variation in species composition beyond observed environmental variables. Group centroids and dispersion ellipses were calculated for sites with and without pigs to assess community clustering in latent variable space. Group centroids reflect the average position of sites within each group, while dispersion ellipses\u0026mdash;calculated from the standard deviation of distances from each site to its centroid\u0026mdash;illustrate the spread of sites around their group means.\u003c/p\u003e\n\u003cp\u003eTo evaluate the influence of environmental predictors on earthworm community structure, Pearson correlation coefficients were computed between the latent variables and selected environmental variables. Significant correlations were visualized in an ordination plot generated with ggplot2, where environmental variables were displayed as vectors originating from the origin. Vector lengths were scaled for clarity, and labels were added to aid interpretation.\u003c/p\u003e\n\u003cp\u003eTo complement the multivariate analysis and obtain more robust estimates of environmental effects on earthworm abundance, we fitted a generalized linear mixed‑effects model (GLMM). The model used a Poisson distribution with a log‑link function and was fitted using the glmer() function in the lme4 package (Bates et\u0026nbsp;al. 2015). Locality was included as a random effect to account for spatial variation among sampling sites, and the hedgerow characteristics were included as fixed effects (predictors). All continuous predictors were mean‑centred and scaled prior to analysis. The categorical variable Locality was converted to a factor to ensure correct specification of random effects. The final dataset contained 87 observations (Farm 1: 64; Farm 2: 28).\u003c/p\u003e\n\u003cp\u003eModel selection was performed using the dredge() function in the MuMIn package (Bartoń 2023), which evaluates all possible submodels of the full model. Models were ranked using Akaike\u0026rsquo;s Information Criterion corrected for small sample size (AICc). The best‑supported model (lowest AICc) was extracted using get.models(). To incorporate model uncertainty, model averaging was applied across all models with \u0026Delta;AICc \u0026lt;\u0026nbsp;4 using model.avg().\u003c/p\u003e\n\u003cp\u003eModel residuals were inspected visually for normality using Q\u0026ndash;Q plots of standardized residuals. Fixed‑effect estimates and their 95% confidence intervals from the best‑supported model were visualized using the plot_model() function in the sjPlot package (L\u0026uuml;decke 2023). Coefficients were presented on the natural (untransformed) scale, with statistical significance indicated by associated p‑values. Estimates were plotted with point estimates and 95% confidence intervals and styled using ggplot2‑compatible theming.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e\u0026nbsp;3.1 Microflora, microfauna, and mesofauna\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmplicon sequencing was performed on 82 soil samples collected from the two farms (62 from farm 1 and 20 from farm 2), targeting prokaryotes, fungi, and soil invertebrates. After quality control, the eDNA dataset contained 508,219 reads assigned to 15,081 ASVs for prokaryotes; 3,476,648 reads assigned to 4,892 ASVs for fungi; and 405,880 reads assigned to 3,942 ASVs for soil fauna (phyla \u003cem\u003eAnnelida\u003c/em\u003e, \u003cem\u003eArthropoda\u003c/em\u003e, \u003cem\u003eNematoda\u003c/em\u003e, \u003cem\u003eTardigrada\u003c/em\u003e, \u003cem\u003eMollusca\u003c/em\u003e, \u003cem\u003eRotifera\u003c/em\u003e). Rarefaction curves approached\u0026nbsp;plateaus for most samples, indicating that sequencing depth was sufficient to capture the majority of ASV diversity (Supplementary Fig, SF\u0026nbsp;1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1.1 Prokaryotic, fungal and invertebrate community structures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSoil prokaryotic, fungal, and invertebrate communities were significantly influenced by the presence, composition, and age of the hedgerows (Table 2). The two farms included in the study differed in their microbial community structures; however, samples from paddocks with hedgerows clustered together across both farms (Supplementary Fig. SF 2). Because Farm 1 contained several paddocks that varied in hedgerow tree composition, we examined this farm in greater detail to assess the importance of tree communities. The age of the hedgerows was associated with distinct community patterns in the PCoA plots: paddocks with older hedgerows (7 years) formed clearly separated clusters compared with those containing younger hedgerows (3 years) (Fig. 1). Consistently, PERMANOVA results indicated that hedgerow age was a significant driver of soil microbial and invertebrate community structures (Table 2). For Farm 1, we observed significant correlations between tree composition and beta diversity, with Mantel r values of r = 0.2\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(p = 0.001) for invertebrates,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003er = 0.34\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(p = 0.001) for fungi, and r = 0.60 (p = 0.001)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003efor bacteria. These results indicate that community structure is weakly to strongly associated with the tree compositions depending on group of taxa.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Permutational analysis of variance (PERMANOVA) using the \u0026lsquo;adonis2\u0026rsquo; test on Bray-Curtis distance matrices for bacterial and fungal community dissimilarity assessment using 1000 permutations. For invertebrate community, beta diversity was calculated using S\u0026oslash;rensen dissimilarity matrices. Tree species richness on community composition was performed only for Location 1, as sufficient data were available; no such analysis was possible for Location 2 due to lack of data.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDataset\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProkaryotic (R\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFungal (R\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInvertebrate (R\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;Whole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.10 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.12***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.11 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eHedgerow and tree age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.08 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.06***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.05 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 90px;\"\u003e\n \u003cp\u003eFarm 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eHedgerow and tree age\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.19 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.16***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.10 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eTree Richness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.11 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.13\u0026nbsp;***\u0026nbsp;***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.12 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003ePig Access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.03**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.03**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.03**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eFarm 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eHedgerow and tree age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.32 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.27***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.24 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eFarm 1 old hedgerows\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003ePig Access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.10***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.12***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.08***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eFarm 2 young hedgerows\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003ePig Access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e0.11**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.09*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSignificance of test indicated as *** for p\u0026lt;0.001, ** p \u0026gt;0.01, *p\u0026lt;0.05 and R\u003csup\u003e2\u003c/sup\u003e for proportion of variation explained.\u003c/p\u003e\n\u003cp\u003eBased on the prokaryotic community structure at Farm 1, we observed that paddocks with active pig presence formed a distinct cluster within the plots containing 7‑year‑old hedgerows. Moreover, pig presence significantly affected the soil prokaryotic community structure (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1.2 Alpha diversity across tree age and richness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePaddocks with older hedgerows were associated with higher alpha diversity in both prokaryotic and fungal communities. For both the Shannon and Simpson indices, diversity was significantly higher in soils from older hedgerows, whereas observed richness did not differ significantly between age groups (Fig. 2). In contrast, within invertebrate communities, neither the Shannon nor the Simpson index showed significant differences. Interestingly, soil samples from paddocks with young trees at Farm 1 exhibited higher invertebrate richness compared with those from older hedgerows (Fig. 2). Fungal alpha diversity (ASV richness) tends to increase with tree species richness, with Spearman rank correlations showing a positive association (rho = 0.20, and P =0.06). Similarly, invertebrate richness was positively correlated with the tree richness (rho = 0.23, and P =0.03), However, prokaryotic community diversity showed non-significant correlation with the tree richness.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOpposite to hedgerow age, we found that the presence of pigs in paddocks affected alpha diversity in the soil samples. For prokaryotes, pigs presence reduced alpha diversity, whereas the opposite trend was observed for fungi and invertebrates (Fig. 3, Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcross all community types examined\u0026mdash;prokaryotic, fungal, and invertebrate\u0026mdash;hedgerow age and hedgerow tree‑species richness consistently emerged as two of the strongest predictors of community structure, explaining the largest proportion of variance in the PERMANOVA analyses (Table 2). Interestingly, this pattern remained robust regardless of the beta‑diversity metric applied (Bray\u0026ndash;Curtis for prokaryotic and fungal communities; S\u0026oslash;rensen for invertebrate communities). Notably, location\u0026mdash;commonly considered a major determinant of microbial and macrofaunal community assembly in ecological studies\u0026mdash;did not rank among the top explanatory variables in our dataset. This suggests that hedgerow‑associated tree characteristics exert a stronger influence on soil biodiversity than broader spatial factors. In addition to the dominant drivers, the presence of individual tree species was also significantly associated with variation in community structure (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Although each tree species explained only a small proportion of the total variance, their combined effects may still be ecologically meaningful, reflecting the multifactorial nature of soil community assembly processes.\u003c/p\u003e\n\u003cp\u003eBased on the enrichment analysis, we found distinct and mixed ecological responses across taxonomic groups. For prokaryotes, no clear direct association with dung-related taxa was observed; however, several enriched genera were linked to soil disturbance and organic matter turnover such as \u003cem\u003eAcidothermus, Glutamicibacter, Streptosporangium, Terrisporobacter\u003c/em\u003e among others. For fungi, responses were mixed, with enriched taxa largely reflecting general soil organic matter dynamics, while hedgerow age showed a clearer pattern, including a strong association of ectomycorrhizal taxa such as \u003cem\u003eCortinarius\u003c/em\u003e with older hedgerows, indicating increasing ecosystem maturity. For soil fauna, responses to both pig access and hedgerow age were less pronounced, with only limited and inconsistent taxonomic enrichment patterns (Supplementary figure 3 and 4).\u003c/p\u003e\n\u003cp\u003eIn total, we detected 10 ASVs assigned to the family \u003cem\u003eLumbricidae\u003c/em\u003e, representing earthworms, using eDNA metabarcoding. Earthworm ASVs were found in 13 samples and were assigned to the genera \u003cem\u003eAllolobophora\u003c/em\u003e and \u003cem\u003eAporrectodea\u003c/em\u003e. The Spearman correlation between eDNA‑based ASV counts and total earthworm abundance measured using conventional methods showed a weak positive relationship (\u0026rho; = 0.35, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.2 Earthworm community\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt Farm 1, 189 earthworms of only three species, \u003cem\u003eLumbricus rubellus/terrestris\u003c/em\u003e (68), \u003cem\u003eAporrectodea tuberculata\u003c/em\u003e (35), \u003cem\u003eA. caliginosa\u003c/em\u003e (6) were found. Furthermore, 80 \u003cem\u003eAporrectodea\u003c/em\u003e juveniles were found of which most of them were supposed to be A. tuberculate due to the captures of the adults \u003cem\u003eAporrectodea\u003c/em\u003e. At Farm 2 173 specimens were found from 6 different species of which \u003cem\u003eL. terrestris/rubellus\u003c/em\u003e (35) and \u003cem\u003eA. tuberculata\u003c/em\u003e were the most abundant ones. Also, here we found relative many juvenile \u003cem\u003eAporrectodea sp.\u003c/em\u003e The average density was highest at Farm 2 with 120.3 earthworms per m2 and lowest at Farm 1 with 47.3 earthworms per m2.\u003c/p\u003e\n\u003cp\u003ePosterior estimates from the \u003cem\u003eboral\u003c/em\u003e statistical models indicated that environmental variables\u0026mdash;rather than pig presence alone\u0026mdash;were the primary drivers of variation in earthworm community composition, as reflected in the latent‑variable structure of the model (Fig. 4).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Latent‑variable site scores showed less distinct grouping by pig presence than in the model without covariates (Fig. 4). When environmental variables were included, the difference in standard deviations\u0026mdash;represented by the circle sizes in Fig. 4\u0026mdash;was reduced between treatment groups, indicating that much of the variation initially attributed to pig presence was better explained by associated habitat characteristics such as vegetation type and structural complexity (Fig. 4B). Furthermore, the dispersion ellipses in Fig. 4 demonstrate that earthworm community composition was more variable in paddocks where pigs were present, suggesting that pig activity may increase small‑scale spatial heterogeneity in earthworm assemblages.\u003c/p\u003e\n\u003cp\u003eEnvironmental vectors projected into the ordination space showed that only the variables \u003cem\u003egrass cover\u003c/em\u003e and \u003cem\u003espring barley\u003c/em\u003e aligned with the latent variables, suggesting that these factors contributed only weakly to the observed patterns in earthworm community structure\u003c/p\u003e\n\u003cp\u003eRegarding \u003cem\u003eLumbricus\u003c/em\u003e abundance, the generalized linear mixed model (GLMM) included spring barley as a fixed effect and a random intercept for replicate nested within locality. Spring barley was identified as a significant negative predictor of \u003cem\u003eLumbricus\u003c/em\u003e abundance, with an estimated log‑mean effect of \u0026minus;0.49 (SE = 0.12, \u003cem\u003ez\u003c/em\u003e = \u0026minus;3.95, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). The model intercept was not significant (estimate = 0.06, \u003cem\u003ep\u003c/em\u003e = 0.697). The random‑effects structure indicated a variance of 0.108 (SD = 0.33) for the replicate‑within‑locality grouping. The model was fitted to 87 observations across 8 replicate groups. Scaled residuals ranged from \u0026minus;1.44 to 4.11, indicating an overall acceptable model fit.\u003c/p\u003e\n\u003cp\u003eFor \u003cem\u003eAllolobophora\u003c/em\u003e abundance, a generalized linear model (GLM) without an intercept was fitted to estimate mean abundances for each land‑use category. The model identified Grass (without pigs) as a significant positive predictor, with an estimated mean abundance of 1.43 (SE = 0.55, \u003cem\u003et\u003c/em\u003e = 2.59, \u003cem\u003ep\u003c/em\u003e = 0.018). Plots with pigs had a mean abundance of 0.88 (SE = 0.52, \u003cem\u003ep\u003c/em\u003e = 0.105), while Spring barley plots did not differ from zero (estimate \u0026asymp; 0, \u003cem\u003ep\u003c/em\u003e = 1.000). The model was based on 24 observations from Farm 2, explained 32.4% of the variance (adjusted R\u0026sup2; = 0.223), and yielded an overall F‑statistic of 3.20 (\u003cem\u003ep\u003c/em\u003e = 0.046), indicating moderate explanatory power.\u003c/p\u003e\n\u003cp\u003eThe final GLMM for \u003cem\u003eAporrectodea\u003c/em\u003e abundance included hedgerow age, grass cover, pig presence, and tree‑species richness as fixed effects, with a random intercept for replicate nested within locality. All predictors were significant, whereas the model intercept was not. Grass cover showed a strong negative association with \u003cem\u003eAporrectodea\u003c/em\u003e abundance (log‑mean effect = \u0026minus;0.59, SE = 0.11, \u003cem\u003ez\u003c/em\u003e = \u0026minus;5.36, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and pig presence was likewise negatively associated (log‑mean effect = \u0026minus;0.51, SE = 0.11, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). In contrast, hedgerow age and tree‑species richness had positive effects on abundance (hedgerow age: log‑mean effect = 0.38, SE = 0.13, \u003cem\u003ep\u003c/em\u003e = 0.004; tree richness: log‑mean effect = 0.27, SE = 0.11, \u003cem\u003ep\u003c/em\u003e = 0.010). The model was fitted to 87 observations across 8 replicate groups. The random‑effects term showed a variance of 0.54 (SD = 0.74) for the replicate‑within‑locality grouping, indicating moderate site‑level variation\u003c/p\u003e\n\u003cp\u003eTotal earthworm abundance was analyzed using a generalized linear model (GLM) with a Poisson distribution and log link (Fig. 5). The model identified hedgerow age, grass cover, and pig presence as significant predictors. Hedgerow age showed a negative association with abundance (log‑mean effect = \u0026minus;0.19, SE = 0.06, \u003cem\u003ep\u003c/em\u003e = 0.001), indicating lower earthworm densities in plots with older hedgerows. Grass cover also exhibited a strong negative effect (log‑mean effect = \u0026minus;0.45, SE = 0.07, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and pig presence was similarly associated with reduced earthworm abundance (log‑mean effect = \u0026minus;0.28, SE = 0.07, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). The intercept was positive and highly significant (estimate = 1.33, SE = 0.06, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). The model was fitted to 87 observations and provided a reasonable fit to the data (residual deviance = 346.8 on 83 degrees of freedom).\u003c/p\u003e\n\u003cp\u003eOverall, no single element of the production system or hedgerow design consistently enhanced all components of biodiversity or earthworm abundance (Table 3). However, several clear patterns emerged from the analyses: 1) Pig presence increased the diversity of fungi and soil invertebrates but reduced earthworm density, particularly within the genus \u003cem\u003eAporrectodea,\u003c/em\u003e 2) Spring barley in the crop rotation promoted prokaryotic diversity but was associated with lower abundance of \u003cem\u003eLumbricus\u003c/em\u003e spp., 3) Grass cover had contrasting effects: it was negative for fungal diversity and \u003cem\u003eAporrectodea\u003c/em\u003e abundance, yet\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003epositive for \u003cem\u003eAllolobophora\u003c/em\u003e abundance, 4) Tree‑species richness in hedgerows positively influenced fungal diversity and the abundance of \u003cem\u003eAporrectodea, and 5)\u0026nbsp;\u003c/em\u003eHedgerow age was positively associated with prokaryotic and fungal diversity, as well as earthworm abundance, especially within \u003cem\u003eAporrectodea\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eIn summary, factors that increased earthworm abundance were primarily those associated with the hedgerows, whereas all three production‑related factors generally had negative effects\u0026mdash;except for the positive response of \u003cem\u003eAllolobophora\u003c/em\u003e to grass cover. For microorganisms and soil invertebrates, pig presence enhanced fungal diversity, prokaryotes benefited most during the spring barley phase, and grass cover provided limited benefits beyond supporting \u003cem\u003eAllolobophora\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Key results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA key conclusion from this study is that, although the rotational production system exhibits both positive and negative effects on belowground biodiversity, the presence of hedgerows and a diverse tree community can substantially enhance biodiversity in outdoor sow production systems. This effect is particularly evident for fungal and prokaryotic diversity, as well as for the abundance of earthworms in the genus \u003cem\u003eAporrectodea\u003c/em\u003e. These findings highlight the potential of targeted habitat features\u0026mdash;especially structurally diverse and mature hedgerows\u0026mdash;to support soil biodiversity within pig‑based agroecosystems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eAn overview of the results of the statistical analyses. \u003csup\u003e1)\u003c/sup\u003e Farm 1 only, \u003csup\u003e2)\u0026nbsp;\u003c/sup\u003eOnly when pigs were not present in the paddocks. Signs +/- indicates increase of decrease in alpha diversity, and their significance with * p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001, and ns for non-significant\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"647\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 244px;\"\u003e\n \u003cp\u003eRotation/Production system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eHedgerows\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eCommunity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eDiversity measure/\u003c/p\u003e\n \u003cp\u003eFamily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003ePigs present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eSpring barley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eGrass cover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eTree spec. richness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eProkaryote diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eSp. Richness\u003c/p\u003e\n \u003cp\u003eShannon\u003c/p\u003e\n \u003cp\u003eSimpson\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003cp\u003e* (-)\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003cp\u003e* (+)\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e*** (+)\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003cp\u003e** (-)\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003cp\u003e** (+)\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e*** (+)\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFungal diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eSp. Richness\u003c/p\u003e\n \u003cp\u003eShannon\u003c/p\u003e\n \u003cp\u003eSimpson\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e*** (+)\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e* (+)\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e*** (-)\u003csup\u003e1)\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e* (-)\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e**(-) \u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e** (-)\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e* (-)\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003cp\u003e* (+)\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e*** (+)\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003cp\u003e* (+)\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e*** (+)\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eSoil invertebrate diversity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eSp. Richness\u003c/p\u003e\n \u003cp\u003eShannon\u003c/p\u003e\n \u003cp\u003eSimpsons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e*(+) \u003csup\u003e1)\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e* (-)\u003c/p\u003e\n \u003cp\u003ens\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ens\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eEarthworm abundance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eAll earthworms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e*** (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e*** (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e** (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eLumbricidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e*** (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eAporrectodea\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e*** (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e*** (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e* (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e** (+)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eAllolobophora\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e* (+) \u003csup\u003e2)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we investigated hedgerow age, tree composition, and pig access may affect the soil below ground diversity using two commercial farms. Our study shows that significant differences between two participating farms with outdoor sow production system, referred here as location or sites, as the sites showed distinct pattern in the microbial, invertebrate and earthworm community structure. In the Farm 1, where a higher number of plots were used in this study, it was possible to evaluate how plot associated characteristics such as hedgerow tree age and tree species contributed to belowground biodiversity. Our results showed that the presence of hedgerows age along with the tree community structure (both composition and the tree richness) significantly affected soil biodiversity. Pig access to plots showed significant effect on the soil prokaryotic, fungal and invertebrate community structure. However, such effect was smaller than that of hedgerow age and tree richness. Overall, hedgerow age was found to be strongest driver of soil biodiversity. Together, these findings highlight the central role of structural vegetation features in shaping biodiversity in outdoor pig production systems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1 Hedgerow age as the strongest driver of below ground diversity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn agroforestry systems, hedgerows are known to support complex and diverse soil microbial communities, particularly in old and established system (Zagatto et al. 2026). However, studies explicitly addressing the effect of hedgerow age are limited, and here we compared hedgerows of two different ages, and the effect of hedgerow age on soil biodiversity indicates that woody vegetation is a vital driver of below ground communities. Older hedgerow contribute to higher organic matter inputs, promote stable microhabitats- which potentially promote the functionally diverse microbial and invertebrate communities. Previous work in agroforestry and traditional hedgerow systems also point out the importance of trees for soil microbial diversity aligning well with existing knowledge on their positive effects on soil microorganisms (both bacteria and fungi) (Beule et al. 2022). The observed positive effect on fungal diversity can be attributed partly to the absence of tillage, which is known to favour fungi relative to bacteria and other prokaryotes (Banerjee et al 2024), and partly to the continuous input of organic matter in the form of fallen leaves, dead roots, and root exudates. In additional to increased soil carbon input, maturity of tree dominated structures also modify microclimates, and promote connectivity across these microhabitats. In addition, many tree species form species‑specific associations with ectomycorrhizal fungi, which likely contributes to the increase in fungal diversity with greater tree‑species richness in the hedgerows (Tederso et al. 2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTree richness and community composition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn addition to age, in Farm 1, tree species richness and community composition were also found here as significant contributors to below ground biodiversity, explaining 11-13 % of variation, which makes second influential driver after hedgerow age. Furthermore, significant Mantel correlations reveal a strong association between tree community composition and patterns of microbial and invertebrate beta diversity. Such association between above ground plant diversity to below ground soil communities is also highlighted earlier (De Deyn and Van Der Putten 2005), indicating that diverse tree communities may promote niche differentiation, varied litter inputs both temporally and spatially, complementary rooting patterns. In agroforestry and forest systems, similar linkages between trees community composition and soil diversity have been documented (Vaupel et al. 2025; Awazi et al. 2025), emphasizing the role of plant\u0026ndash;soil feedback in structuring below‑ground communities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Effects of pig access\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough pig access had a weaker effect compared to the trees related factors, it is still significantly influenced by the soil prokaryotic, fungal and invertebrate community structure across two farms. The presence of pigs also increased fungal diversity in our agroforestry system with free-range sows and piglets. This effect can be attributed to pig manure, which has been shown to contain high concentrations of both fungi and heterotrophic bacteria (Salau and Olowe 2024). Pig manure is also known to introduce numerous exogenous microorganisms, many of which are copiotroph bacteria specialized in degrading animal dung (Lia et al. 2022). In addition, manure inputs have been reported to increase the relative abundance of Basidiomycota (Zheng et al. 2024), a fungal group that plays an important role in lignin and cellulose degradation. These dung‑associated bacteria and fungi were therefore likely favored by the deposition of pig droppings in the paddocks. Our enrichment analysis showed mixed reactions which could be linked to disturbance in soil. Foraging activities of pigs also involve disturbances in the soil, which might enhance or lead to enriching certain groups of microbial taxa which were observed in this study. Earlier study using wild boar has shown foraging and disturbance in semidry grassland increases species diversity of grassland vegetation (Horčičkov\u0026aacute; et al. 2019).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Earthworms dynamics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEarthworm responses were more complex and not uniformly aligned with the patterns observed for soil microorganisms. Grass cover and pig presence both reduced total earthworm abundance, suggesting that rooting disturbance and dense swords may alter soil structure through compaction, reduced aeration, or shifts in organic matter dynamics (Kerschbaumer et al. 2024). Functional strategies among earthworm groups help explain the contrasting genus level responses. \u003cem\u003eLumbricus\u003c/em\u003e species, which build deep permanent burrows (Capowiez et al. 2015; Potvin and Lilleskov 2016), are partly buffered from surface disturbance such as pig rooting (Cole 2013). In contrast, endogeic \u003cem\u003eAporrectodea\u003c/em\u003e, which occupy the upper mineral soil and create horizontal burrows (Earthworm Society of Britain 2023), were more strongly reduced\u0026mdash;consistent with findings from wild boar disturbances (Bueno and Jim\u0026eacute;nez 2014). The absence of \u003cem\u003eAllolobophora chlorotica\u003c/em\u003e from pigoccupied plots further supports the sensitivity of shallow dwelling taxa to rooting pressure (Capowiez et al. 2015).\u003c/p\u003e\n\u003cp\u003eThe negative correlation between pig presence and earthworm abundance is most likely attributable to predatory foraging by pigs. Although the extent to which domestic pigs actively consume earthworms is not well documented, wild boars are known to feed extensively on earthworms (Baubet et al. 2003). Such predation occurs during rooting activities but is particularly pronounced during so‑called \u0026ldquo;worm nights,\u0026rdquo; when earthworms emerge in masse at the soil surface. Worm nights are triggered by specific weather conditions, typically occurring at temperatures around 10\u0026nbsp;\u0026deg;C following at least moderate precipitation (Baubet et al. 2003). The negative association between earthworm abundance and pig presence in our system suggests that domestic pigs may also consume earthworms when the opportunity arises, potentially benefiting from similar environmental conditions that facilitate surface activity\u003c/p\u003e\n\u003cp\u003eDespite these disturbances, earthworm densities remained moderate to high relative to European arable fields. This likely reflects the inclusion of clover\u0026ndash;grass phases in the crop rotation, which are known to support high earthworm populations (Phillips 2020). At Farm 2, pigs occupied paddocks for only a short farrowing period, leaving long undisturbed periods that likely allowed earthworm populations to recover. At Farm 1, pigs occupied paddocks for much longer, reducing opportunities for recolonization\u0026mdash;consistent with lower densities observed there.\u003c/p\u003e\n\u003cp\u003eStand age effects on earthworms were mixed. Older hedgerows may accumulate more recalcitrant litter and develop denser soil horizons, which can reduce earthworm abundance (Crow et al. 2009). However, tree species richness had a positive effect on \u003cem\u003eAporrectodea\u003c/em\u003e, likely due to improved litter heterogeneity, nutrient cycling, and soil structure\u0026mdash;mechanisms previously linked to tree identity and richness effects on soil fauna (Schelfhout et al. 2017).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5 Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor soil prokaryotic, fungal, and invertebrate communities overall, our findings indicate that tree structure and composition\u0026mdash;particularly tree cover and tree‑species richness\u0026mdash;were the primary drivers of belowground biodiversity. Although pig presence had significant effects when examined within specific clusters, the broader patterns clearly showed that hedgerow characteristics exerted the strongest influence on soil microbial and invertebrate community structure.\u003c/p\u003e\n\u003cp\u003eOur results for earthworms further demonstrate that earthworm communities are shaped not only by vegetation complexity but also by disturbance regimes that interact with species‑specific life‑history strategies. Anecic species, with their deep and relatively stable vertical burrows, can continue to function as ecosystem engineers even under moderate disturbance. In contrast, endogeic and epigeic species, which occupy mineral topsoil and surface litter layers respectively, contribute more to aggregate formation and litter decomposition near the soil surface and are therefore more sensitive to grazing pressure and rooting activity (Capowiez et al. 2015; Earthworm Society of Britain 2023).\u003c/p\u003e\n\u003cp\u003eDesigning silvopastoral systems that balance tree diversity with controlled ground cover and minimal rooting disturbance is thus essential for maintaining soil biodiversity and ecosystem functioning.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, in interpretation of data, in the writing of the manuscript, or in the decision to publish the results.\u003c/p\u003e \u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRS, JA, REH and AGK designed the experiment. RS, JA, REH and AGK conducted laboratory and field work and analyzed the data. RS, JA and REH wrote the first draft. LEJ supervised library preparation and performed amplicon sequencing. JA secured the funding and led the study. All authors read, revised, and approved the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe project \u0026ldquo;Supporting biodiversity and animal welfare in organic pig production\u0026rdquo; is part of the Organic RDD 9 programme, which is coordinated by International Centre for Research in Organic Food Systems (ICROFS) in collaboration with the Green Growth and Development programme (GUDP) under the Danish Ministry of Food, Agriculture and Fisheries.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe sequence data has been deposited in the NCBI Sequence Read Archive (SRA) database and can be accessed under the BioProject ID PRJNA1438128\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbarenkov K, Henrik Nilsson R, Larsson K-H, et al (2010) The UNITE database for molecular identification of fungi\u0026ndash;recent updates and future perspectives. 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Agric Ecosyst Environ 400:110238. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/J.AGEE.2026.110238\u003c/span\u003e\u003cspan address=\"10.1016/J.AGEE.2026.110238\" 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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"agroforestry-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agfo","sideBox":"Learn more about [Agroforestry Systems](http://link.springer.com/journal/10457)","snPcode":"10457","submissionUrl":"https://submission.nature.com/new-submission/10457/3","title":"Agroforestry Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"environmental DNA, fauna, Earthworms, soil microbes","lastPublishedDoi":"10.21203/rs.3.rs-9473179/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9473179/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntegrating biodiversity into agricultural systems, particularly through agroforestry, is increasingly recognized as an important step to achieve sustainable land management and soil health. Free-range farming, where animals are allowed to interact directly with their environment, may add benefits to soil ecosystems. In this study, we investigated how free-range pig farming, combined with agroforestry elements, influences belowground biodiversity, including microbial communities comprising prokaryotic, fungal and invertebrate communities. Prokaryotic, fungal, and invertebrate diversity were assessed using environmental DNA (eDNA), while earthworm density was measured via conventional method of hand sorting to obtain abundance measures. Soil samples were collected from two organic pig farms with differing agroforestry tree compositions and different periods with pigs in the fields. Our results showed that the presence of pigs was associated with the shifts in soil biodiversity, with contrasting responses observed across prokaryotes, fungi, and invertebrates, suggesting that free-range pig systems integrated with agroforestry practices can have complex, taxon specific effects on soil ecological diversity. In addition, tree age, species composition, and tree richness significantly impacted the soil microbial and invertebrate community composition. Earthworm abundance was generally negatively affected by the pig production system, and our results indicate that periods of undisturbed grass-clover cover, without pig activity, had a positive effect on the earthworm community.\u003c/p\u003e","manuscriptTitle":"Below-ground biodiversity in organic agroforestry with free-range pigs in Denmark","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-13 02:11:37","doi":"10.21203/rs.3.rs-9473179/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-15T19:58:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"647800858208922168203021466724460775","date":"2026-05-09T18:43:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203010787514681975903362101714051217435","date":"2026-05-05T08:30:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"257118344227371400835856583622629492539","date":"2026-05-04T18:49:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-04T18:42:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-22T15:01:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-22T11:01:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Agroforestry Systems","date":"2026-04-20T13:41:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"agroforestry-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agfo","sideBox":"Learn more about [Agroforestry Systems](http://link.springer.com/journal/10457)","snPcode":"10457","submissionUrl":"https://submission.nature.com/new-submission/10457/3","title":"Agroforestry Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"83c5760d-cfcc-4fb5-8d6a-2b404dfed7ec","owner":[],"postedDate":"May 13th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-15T19:58:29+00:00","index":19,"fulltext":""},{"type":"reviewerAgreed","content":"647800858208922168203021466724460775","date":"2026-05-09T18:43:58+00:00","index":17,"fulltext":""},{"type":"reviewerAgreed","content":"203010787514681975903362101714051217435","date":"2026-05-05T08:30:05+00:00","index":15,"fulltext":""},{"type":"reviewerAgreed","content":"257118344227371400835856583622629492539","date":"2026-05-04T18:49:33+00:00","index":14,"fulltext":""},{"type":"reviewersInvited","content":"8","date":"2026-05-04T18:42:26+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T02:11:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-13 02:11:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9473179","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9473179","identity":"rs-9473179","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}