Divergent national-scale trends of microbial and animal biodiversity revealed across diverse temperate soil ecosystems.

Soil biota accounts for ~25% of global biodiversity and is vital to nutrient cycling and primary production. There is growing momentum to study total belowground biodiversity across large ecological scales to understand how habitat and soil properties shape belowground communities. Microbial and animal components of belowground communities follow divergent responses to soil properties and land use intensification; however, it is unclear whether this extends across heterogeneous ecosystems. Here, a national-scale metabarcoding analysis of 436 locations across 7 different temperate ecosystems shows that belowground animal and microbial (bacteria, archaea, fungi, and protists) richness follow divergent trends, whereas β-diversity does not. Animal richness is governed by intensive land use and unaffected by soil properties, while microbial richness was driven by environmental properties across land uses. Our findings demonstrate that established divergent patterns of belowground microbial and animal diversity are consistent across heterogeneous land uses and are detectable using a standardised metabarcoding approach.

Introduction

Soil n class="Species">biotapan>, including bacteria, archaea, protists, funpan>gi, and animals, unpan>derpin globally important ecosystem funpan>ctions. Funpan>damental funpan>ctions of soil communpan>ities include nutrient and hydrological cycling, decomposition, pollution mitigation, and supporting terrestrial primary production, which are inextricably linked to global food security, climate regulation, and other ecosystem services[1,2]. Nevertheless, unpan>til recently, characterising soil biodiversity (popularly referred to as a ‘black box’) has been constrained by our inability to identify typically intractable levels of diversity using either traditional or molecular approaches. High-throughput sequencing has however resulted in a step change, facilitating the characterisation of bacteria[3-7], archaea[6-8], funpan>gi[9,10], protists[11-13], and animals[14] within the belowgrounpan>d biosphere. Inpan>creasingly, efforts have been made to investigate the total biodiversity of the soil biosphere across large ecological[15-17] and taxonomic scales[15,16,18,19].

Understanding the response of the total soil biosphere to changes in land use and environmental drivers has become an important research focus in regional soil monitoring programmes[15,16,19] and in small-scale field[20,21] and mesocosm experiments[18,20]. Yet despite the move towards unified study of soil biota, funpan>damenpan>tal challenpan>ges of technpan>ique and scale remainpan>. Oftenpan>, such studies require the comparisonpan> of soil biota metrics captured through both traditional and modern molecular techniques[15,19-21]. To our knowledge, relatively few studies have attempted to assess all components of belowground communities using a multi-marker metabarcoding approach[22].

There is mounting evidence that the microbial and animal fractions of soil communities may respond differentially to land use change. Microbial richness increases[15], whereas richness of soil fauna declines inpan> responpan>se to more inpan>tenpan>se land use[15,23,24]. However, these finpan>dinpan>gs come from relatively homogenpan>ous landscapes, such as grasslands[15]. It is unpan>clear whether the differenpan>tial responses of soil microbes and faunpan>a extenpan>d across heterogenpan>eous land uses. For example, across heterogenpan>eous landscapes of Wales, UK, α-diversity of mesofaunpan>a is both lowest inpan> agricultural and bog systems, which are the most- and least-inpan>tenpan>sively managed systems inpan> the counpan>try, respectively[23]. Changes inpan> soil properties may further dictate declinpan>es of commonpan> soil faunpan>a inpan> low-inpan>tenpan>sity land uses. Therefore, it is critical to assess whether the positive effect of inpan>creasinpan>g land use inpan>tenpan>sity onpan> microbial richnpan>ess is conpan>sistenpan>t across regionpan>s made up of markedly diverse ecosystems and land uses. Similarly, the importance of inpan>dividual soil properties inpan> shapinpan>g belowgrounpan>d communpan>ities has also provenpan> difficult to disenpan>tangle. Many studies have demonpan>strated the conpan>sistenpan>t dominpan>ance of pH inpan> shapinpan>g belowgrounpan>d communpan>ity compositionpan> at nationpan>al[23,25-28] and global scales[4,5,9,29]. However, climatic factors[9,30] and other soil properties, inpan>cludinpan>g organic matter, nitrogen (N) availability, and the carbon (C)-to-N ratio[9], are also recognised as important drivers of belowground community composition yet consistent trends remain elusive[30]. Therefore it is unclear whether the total soil biosphere responds to changes in land use and soil properties in the same manner across heterogeneous landscapes.

Here, we sought to assess whether divergent responses to land use and soil properties in the microbial and animal fractions of soil communities persist across heterogeneous systems at the national-scale using a standardised metabarcoding approach. We present a national-scale analysis of soil biodiversity across Wales, UK, from the micro-to-macro scale including all major groups of soil microbes in addition to animals, from 436 sites over 2 years across a diverse array of oceanic-temperate ecosystems, including grasslands, forests, bogs, and managed systems. Biotic metrics come from high-throughput sequencing of prokaryotic, fungal, microbial eukaryotic, and soil animal communities using 16S, ITS, and 18S rRNA marker genes; these are complemented by an extensive suite of co-located abiotic soil properties and vegetation cover data. Specifically, we investigate how richness and β-diversity of all major fractions of subterranean life respond to land use type and prevailing soil properties (e.g. n class="Disease">organic matter, pH, and N) to explore which linpan>eages play a demonpan>strable role inpan> determinpan>inpan>g belowgrounpan>d communpan>ity structures across large and complex ecological gradienpan>ts. Our results demonpan>strate that across a gradienpan>t of heterogenpan>eous land uses, richnpan>ess of soil animals is governpan>ed more by land use regime rather than inpan>trinpan>sic soil properties. Inpan> conpan>trast, microbial richnpan>ess is drivenpan> by soil properties and demonpan>strates a largely linpan>ear trenpan>d of decreasinpan>g richnpan>ess alonpan>g a productivity gradienpan>t of land use based onpan> decreasinpan>g soil nutrienpan>t availability.

Results

Sequencing results

Illumina sequencing and environmental data were collected from across Wales as part of the Glastir Monitoring and Evaluation Programme (n class="Chemical">GMEP)[31]. Sample sites were categorised inpan>to Aggregate Vegetationpan> Classes (AVCs) based onpan> plant species assessmenpan>ts usinpan>g established criteria (see Supplemenpan>tary Note 1). An explanationpan> of the composition of AVCs is described in Supplemenpan>tary Table 1. Briefly, the 7 AVCs used inpan> the currenpan>t study were established by clusterinpan>g samples based onpan> an assessmenpan>t of vegetationpan> data usinpan>g a detrenpan>ded corresponpan>denpan>ce analysis[32]. The ordinpan>ationpan> of the detrenpan>ded corresponpan>denpan>ce analysis has shown that the land use categories follow a gradienpan>t of soil nutrienpan>t conpan>tenpan>t[32] from which soil productivity and managemenpan>t inpan>tenpan>sity can also be inpan>ferred (see Supplemenpan>tary Note 1 and Supplemenpan>tary Table 1). The AVCs inpan> descenpan>dinpan>g order of productivity are crops/weeds, fertile grassland, inpan>fertile grassland, lowland wood, upland wood, moorland grass-mosaic, and heath/bog.

In total, 29,690 bacterial and 156 archaeal operational taxonomic units (OTUs) were identified from 16S reads. Overall, the most abundant class was Alphaproteobacteria (Fig. 1a). Proportional abundances (OTU n/total × 100) of Acidobacteria increased in less-productive land use types from its lowest in crops/weeds to its highest in heath/bog AVCs. In contrast, abundances of Actinobacteria followed the exact opposite trend, as did Spartobacteria and Bacilli (Fig. 2a). For archaea, Nitrososphaeria was the most abundant class overall (Fig. 1d); however, the proportion of Thermoplasmata became dominant in less productive AVCs (Fig. 2d).

Fig. 1

Sankey diagrams of proportional abundances of OTUs from all samples for major soil biota groups. Arms denote proportions of OTUs at the class-level for a bacteria; b fungi; of major lineages of c protists; class-level for d archaea; and at the phylum-level for e animals. For information on how this figure was created, please see Supplementary Methods

Fig. 2

Proportionate abundances of OTUs for major soil biota groups within each Aggregate Vegetation Class. Land uses are ordered from most (crops/weeds) to least (heath/bog) using the same divisions as Fig. 1 for a bacteria; b fungi; c protists; d archaea; and e animals

Sankey diagrams of proportional abundances of OTUs from all samples for major soil n class="Species">biota groups. Arms denpan>ote proportionpan>s of OTUs at the class-level for a bacteria; b funpan>gi; of major lineages of c protists; class-level for d archaea; and at the phylum-level for e animals. For inpan>formationpan> onpan> how this figure was created, please see Supplemenpan>tary Methods

Proportionate abundances of OTUs for major soil n class="Species">biota groups withinpan> each Aggregate Vegetationpan> Class. Lanpan>d uses are ordered from most (crops/weeds) to least (heath/bog) using the same divisionpan>s as Fig. 1 for a bacteria; b funpan>gi; c protists; d archaea; and e animals

There were 7582 OTUs recovered from ITS1 sequences. Agaricomycetes were the most abundant class of fungi overall. There was also a large proportion of Sordariomycetes (Fig. 1b). Proportionate abundances of Sordariomycetes and Agaricomycetes followed contrasting trends, with the dominance of the former replaced by the later in lower productivity AVCs (Fig. 2b).

In total, 8683 protist OTUs were recovered from the 18S reads. Chloroplastida (green algae) was by far the most abunpan>dant protist group, followed by Rhizaria, n class="Species">Stramenopiles, and then n class="Species">Alveolates (Fig. 1c). Green algae, largely comprised of unidentified sequences (Supplementary Fig. 1a), were least abundant in crops/weed and heath/bog sites (Fig. 2c). Proportions of Rhizaria were relatively constant across AVCs (Fig. 2c) and entirely comprised of Cercozoa (Supplementary Fig. 1b). Among Stramenopiles, proportions of Ochrophyta were also largely consistent, while those of Oomycetes and Bicosoecida followed contrasting trends across the productivity gradient of AVCs, declining and increasing, respectively (Supplementary Fig. 1c). Ciliates were the most common Alveolates in most AVCs; however, the proportion of Apicomplexa was greater in the lowland wood and grassland AVCs (Supplementary Fig. 1d). The proportion of Amoebozoa was surprisingly low (Fig. 1c), potentially due to primer bias in our study when compared to other studies[12,15]. Across AVCs Tublulinea was consistently dominant among the Amoebozoa, though divergent trends in Gracilipodida and Discosea can be seen along the productivity/intensity gradient (Supplementary Fig. 1e).

In the animal dataset, 1138 OTUs were recovered. Nematode OTUs were the most abundant animal group across all samples (Fig. 1e). Annelids and arthropods followed opposing trends in proportionate abundance, increasing and decreasing respectively, across the productivity gradient. Proportions of Platyhelminthes and Tardigrades also increased in less-productive AVCs (Fig. 2e).

Effect of land use on belowground richness

We found significant differences in biodiversity trends across land use types. There was a marked shift along the productivity gradient of crops/weeds-to-heath/bog in all organismal groups, except animals (Fig. 3). Significant differences in the mean richness of bacterial OTUs were prominent (F6,264 = 78.47, p < 0.0001) following ANOVA. Bacterial richness decreased in AVCs across the productivity gradient with highest values in the most productive crops/weeds and grasslands and lowest in the low productivity land uses (i.e. moorland grass-mosaic, heath/bog) (Fig. 3a). The same trend was also observed in fungi (F6,248 = 48.98, p < 0.001; Fig. 3b), and protists (F6,249 = 59.86, p < 0.001; Fig. 3c). For individual pair-wise comparisons see Supplementary Note 4. Richness of archaeal OTUs had an opposing trend to that of other microbial groups. Archaeal OTU richness was significantly lower (F6,185 = 24.37, p < 0.001) in higher-productivity AVCs and highest in the least-productive land-use types (Fig. 3d). In the crops/weeds, AVC richness of archaeal OTUs was significantly lower than upland wood (p = 0.01), moorland grass-mosaic (p = 0.005), and heath/bog sites (p < 0.001) based on Tukey’s post hoc tests, with the remaining land uses displaying intermediate OTU richness values.

Fig. 3

Boxplots of OTU richness for each organismal group. Richness of a bacteria; b fungi; c protists; d archaea; e animals are plotted against Aggregate Vegetation Class ordered from most (crops/weeds) to least (heath/bog) productive. Boxes are bounded on the first and third quartiles; horizontal lines denote medians. Black dots are outliers beyond the whiskers, which denote 1.5× the interquartile range. Source data are provided as a Source Data file

Animal OTU richness did not follow the trends observed in microbial communities. Differences observed with ANOVA were significant (F6,244 = 6.25, p < 0.001) but plateaued after the grassland AVCs, as opposed to the sloped trend of microbial groups across the productivity gradient (Fig. 3e). Richness in the infertile grasslands was significantly greater than in crops/weeds (p = 0.008), heath/bog (p = 0.003), and upland wood (p = 0.02) based on Tukey’s post hoc tests. Richness was lowest in the most intensively management crops/weeds sites and was shown to be significantly lower than richness of lowland woods (p = 0.04) with Tukey’s test. Collectively, the results demonstrate a strong divergence between the richness of animal and microbial communities across all AVCs.

Relationships of richness between organismal groups

Bacterial richness from the total dataset was significantly correlated with all other organismal groups (Supplementary Table 2). Such relationships were positive between bacterial richness and richness of fungi, protists, and animals. Similarly, there was a positive relationship between protistan richness and both fungal and animal richness. However, n class="Disease">archaeal richness demonpan>strated signpan>ificant, but negative correlationpan>s with all organisms except animals. Inpan>deed animal richnpan>ess (measured by metabarcodinpan>g) was onpan>ly signpan>ificantly correlated with animals (measured by taxonpan>omic assessmenpan>t; Table 1) and protists (Supplemenpan>tary Table 2).

Table 1

Results of partial least squares regressions for soil biota against soil properties for richness

Soil and environmental variables	Taxon
	Bacteria	Archaea	Fungi	Protists	Animals
Total Ca	1.14 (R2 = 0.44***)	1.21 (R2 = 0.13***)	0.44	1.3 (R2 = 0.35***)	0.9
Total Na	0.93	0.89	0.93	0.8	1.18
C:N ratiob	1.45 (R2 = 0.41***)	1.31 (R2 = 0.09***)	1.64 (R2 = 0.28***)	1.67 (R2 = 0.35***)	0.1
Total P (mg kg−1)b	0.35	0.59	0.7	0.85	0.67
Organic matter (% LOI)a	1.47 (R2 = 0.5***)	1.27 (R2 = 0.14***)	1.13 (R2 = 0.29***)	1.27 (R2 = 0.35***)	1.08
pH (CaCl2)	1.98 (R2 = 0.51***)	1.68 (R2 = 0.25***)	1.52 (R2 = 0.23***)	1.56 (R2 = 0.33***)	0.9
Soil water repellencya,c	1.31 (R2 = 0.2***)	0.9	1.23 (R2 = 0.13***)	0.93	0.98
Volumetric water content (m3 m3 −1)	0.36	1.33 (R2 = 0.13***)	0.6	0.41	0.4
Soil bound water (g water g dry soil−1)	1.25 (R2 = 0.41***)	0.83	1.08 (R2 = 0.26***)	1.23 (R2 = 0.31***)	0.63
Rock volume (mL)	0.25	0.61	0.64	0.27	1.3
Bulk density (g cm3 −1)	1.39 (R2 = 0.44***)	1.43 (R2 = 0.18***)	1.41 (R2 = 0.29***)	1.5 (R2 = 0.35***)	1.39
Clay content (%)d	0.85	1.19 (R2 = 0.1***)	0.84	1.14 (R2 = 0.09***)	0.05
Sand content (%)d	0.45	0.16	0.6	0.51	0.78
Elevation (m)	1.66 (R2 = 0.42***)	1.7 (R2 = 0.27***)	1.68 (R2 = 0.22***)	1.65 (R2 = 0.36***)	0.57
Mean annual precipitation (mL)	1.08 (R2 = 0.25***)	1.75 (R2 = 0.3***)	1.44 (R2 = 0.18***)	1.48 (R2 = 0.27***)	0.46
Temperature (°C)	0.51	0.5	0.56	0.58	0.35
Collembolae	0.34	0.06	0.41	0.17	1.14 (R2 = 0.03***)
Mitese	0.49	0.2	1.17 (R2 = 0.03***)	0.23	1.74 (R2 = 0.08***)
Total mesofaunae	0.44	0.1	1.03 (R2 = 0.01*)	0.15	1.71 (R2 = 0.08***)

Positive relationships are written in bold and negative relationships are written in italics

aLog10-transformation

bSquare-root-transformation

cSoil water repellency was derived from median water drop penetration times (s)

dAitchison’s log-ratio transformation

eLog10 plus 1 transformation

***p < 0.001; **0.001 > p < 0.01; *0.01 > p < 0.05, and blank indicates p > 0.05

Results of partial least squares regressions for soil papan class="Chemical">n class="Species">biota againpan>st soil properties for richness

Positive relationships are writtepan class="Chemical">n in bold and negative relationships are written in italics

aLog10-transformatiopan class="Chemical">n

bSquare-root-transformatiopan class="Chemical">n

cSoil n class="Chemical">waterpan> repellency was derived from median n class="Chemical">water drop penetration times (s)

dAitchison’s log-ratio trapan class="Chemical">nsformation

eLog10 plus 1 transformatiopan class="Chemical">n

***p < 0.001; **0.001 > p < 0.01; *0.01 > p < 0.05, and blapan class="Chemical">nk indicates p > 0.05

Relationships between richness and environmental variables

Partial least squares (n class="Disease">PLS) regressions demonpan>strated that the divergenpan>ce observed betweenpan> animal and microbial communpan>ities may be due to the effects of soil properties. No soil properties were signpan>ificantly correlated with richnpan>ess of soil animal OTUs (Table 1). Conpan>versely, there were stronpan>g relationpan>ships betweenpan> microbial richnpan>ess and a range of soil properties. However, although microbes were inpan>fluenpan>ced by the same enpan>vironpan>menpan>tal variables, there were distinpan>ct patternpan>s withinpan> each group. For example, while pH was the best predictor of bacterial richnpan>ess, it was ranked as seconpan>d for funpan>gi and protists and third for archaea. Bulk denpan>sity and C:N ratio were also major drivers of richnpan>ess across all microbial groups. Elevationpan> (here closely linpan>ked with precipitationpan> and organic matter content) was the most important environmental variable in relation to archaea and protist richness. Organic matter and bulk density were strong predictors of fungal OTU richness. All environmental properties that had positive relationships with OTU richness of bacteria, fungi, and protists had negative relationships with archaea.

Community structure (β-diversity) across land uses

Non-metric multidimensional scaling (NMDS) using Bray–Curtis distances showed consistent differences in β-diversity between AVCs across all organismal groups. Plots show tight clustering of the crops/weeds, fertile grassland, and infertile grassland AVCs, whereas the other AVCs form a more dispersed organismal assemblage (Fig. 4 for bacteria and Supplementary Figs. 2–5). Results of PERMANOVAs were significant across all groups and analyses of dispersion were also significant (Fig. 4 for bacteria and Supplementary Figs. 2–5) for all groups except for the dispersion of animals (F6,401 = 0.67, p = 0.68) owing to the wide range of sample numbers within each AVC (Supplementary Fig. 5). We also found that this clustering was present using constrained canonical analyses of principle components (CAP) ordinations for each organismal group (Supplementary Figs. 6–10).

Fig. 4

Plot of the non-metric dimensional scaling ordination (stress = 0.06) of bacterial community composition across GMEP sites. Samples are coloured by Aggregate Vegetation Class. Results of PERMANOVA (F6,427 = 30.76, p = 0.001) and dispersion of variances of groups (F6,427 = 10.97, p = 0.001) were significant

Plot of the nopan class="Chemical">n-metric dimensional scaling ordination (stress = 0.06) of bacterial community composition across n class="Chemical">GMEP sites. Samples are coloured by Aggregate Vegetation Class. Results of PERMANOVA (F6,427 = 30.76, p = 0.001) and dispersion of variances of groups (F6,427 = 10.97, p = 0.001) were signpan>ificant

pH was the best predictor of β-diversity from linear fitting for all soil organisms (Table 2 and Supplementary Tables 3–6). The carbon-to-nitrogen (C:N) ratio was the second most important variable in all major groups except animals. Mean C:N values were higher in the crops/weeds and grassland AVCs and lower in the remaining land use types (Supplementary Table 6). Mean pH values and C:N ratios (Supplementary Table 6) reflect the distribution of points in NMDS plots, with tight groupings observed in the crops/weeds and grasslands AVCs and increasingly more spread out groupings in all other AVCs as pH values decreased and became more varied (Fig. 4 for bacteria and Supplementary Figs. 2–5). Across all groups, all or nearly all variables were significant following linear fitting; however, most were only weakly correlated with β-diversity values. Other important variables varied in their ranked importance, including elevation, mean annual precipitation, organic matter content, total C, bulk density, volumetric water content, and clay content of soil (Table 2 and Supplementary Tables 3–6). The results of linear model fitting for CAP ordinations, though not identical (Supplementary Tables 7–11), were highly related to those of the NMDS ordinations (Supplementary Fig. 11).

Table 2

Summary of relationships amongst environmental factors and bacteria communities

Soil and environmental variables	R 2	Correlation
		Axis 1	Axis 2	Axis 3
pH (CaCl2)	0.71***	−	−	+
C:N ratioa	0.52***	+	−	+
Volumetric water content (m3 m3 −1)	0.49***	+	−	+
Bulk density (g cm3 −1)	0.47***	−	+	−
Organic matter (% LOI)b	0.46***	+	−	+
Elevation (m)	0.45***	+	−	−
Mean annual precipitation (mL)	0.43***	+	−	−
Total Cb	0.39***	+	−	+
Clay content (%)c	0.33***	−	+	−
Soil bound water (g water g dry soil−1)	0.31***	+	−	+
Soil water repellencyb,d	0.27***	+	−	−
Total N (%)b	0.26***	+	−	+
Sand content (%)c	0.21***	+	+	+
Collembolae	0.09***	−	+	−
Mitese	0.06***	+	+	−
Total P (mg kg−1)a	0.06***	−	−	−
Total mesofaunae	0.06***	+	+	−
Rock volume (mL)	0.05**	−	+	−
Temperature (°C)	0.03*	+	+	−

+/− signify the direction of association between each variable and respective NMDS axes

aSquare-root-transformation

bLog10-transformation

cAitchison’s log-ratio transformation

dSoil water repellency was derived from median water drop penetration times (s)

eLog10 plus 1 transformation

***p < 0.001; **0.001 > p < 0.01; *0.01 > p < 0.05, and blank indicates p > 0.05

Summary of relationships amopan class="Chemical">ngst environmental factors and bacteria communities

+/− signify the directiopan class="Chemical">n of association between each variable and respective NMDS axes

aSquare-root-transformatiopan class="Chemical">n

bLog10-transformatiopan class="Chemical">n

cAitchison’s log-ratio trapan class="Chemical">nsformation

n class="Chemical">dSoil waterpan> repellency was derived from median n class="Chemical">water drop penetration times (s)

eLog10 plus 1 transformatiopan class="Chemical">n

***p < 0.001; **0.001 > p < 0.01; *0.01 > p < 0.05, and blapan class="Chemical">nk indicates p > 0.05

Discussion

High-throughput sequencing of the biosphere amongst heterogeneous soils revealed both expected and novel relationships between soil organisms and environmental drivers. The richness of microbes and animals had notable contrasting trends across land use types. The richness of microbial communities was strongly influenced by both land use and environmental variables, especially pH, C:N ratio, elevation, n class="Disease">organic matter, and annual precipitationpan>. Conpan>versely, we founpan>d no signpan>ificant associationpan>s betweenpan> measured enpan>vironpan>menpan>tal variables and animal richnpan>ess, which was negatively impacted by higher inpan>tenpan>sity land use, suggestinpan>g that richnpan>ess patternpan>s of microbial and macrobial life fractionpan>s adhere to differenpan>t ecological determinpan>ants. For β-diversity, pH was by far the most important enpan>vironpan>menclass="Chemical">pan>tal variable in shapinpan>g communpan>ity compositionpan> of all organismal groups, yet other drivers were attributable for inpan>fluenpan>cinpan>g patternpan>s of α-diversity.

Our findings demonstrate that diverging trends between soil microbes and fauna extend across distinct, heterogeneous land uses. Furthermore, we build on the work of Gossner et al.[15] by demonstrating that microbial richness, with the exception of archaea, increases with greater land use intensity across heterogeneous ecosystems at the national-scale. The divergence between microbes and animals at this scale is supported by previous findings from French soils[17,25]. Across France, bacterial richness[17] and biomass[25] were strongly linked to belowground environmental properties but largely unaffected by aboveground climatic variables, which commonly influence animal and plant biogeography[25,30]. Our findings show that richness of fungi and protists also follow this trend—whereas archaea follow an opposing trend to all other groups.

There are several mechanisms that may explain the relationship between higher microbial richness and intensifying anthropogenic disturbance. One explanation is that consistent nutrient inputs from fertilisers and disturbance under tillage stimulate high α-diversity in these areas[16]. Indeed higher α-diversity has been observed in cropping systems than in forest or grassland sites for both bacteria[16,17] and fungi[16]. Interestingly, high microbial richness in more productive land use types (e.g. arable) may illustrate the intermediate disturbance hypothesis (IDH) within soil ecosystems. Under the IDH, as outlined by Connell[33], diversity reaches its highest levels where succession has been interrupted by intermittent disturbance events. In our sites, microbial richness was highest in AVCs concurrent to disturbances (augmented by nutrient inputs) from agricultural interventions such as fertilisation, tilling, clearing, and the cultivation of livestock. However, it is also possible that the high diversity observed in the grassland and especially in agricultural land uses stems from organisms that have entered a dormant state after disturbance-induced changes to their environment[13,34]. Disturbance pressures can also lead to high bacterial diversity through the reduction in dominant OTUs, which are replaced by a wide range of weaker competitors. It has been demonstrated that α-bacterial diversity is greater in the phyllosphere of ivy in urban habitats associated with more anthropogenic stressors than in less disturbed sites[35]. Our findings suggest that the phenomenon of greater species richness resulting from the addition of nutrients and non-equilibrium dynamics induced by disturbance may extend to across all microbial groups, with the possible exception of archaea.

Richness of all microbial groups, except archaea, followed the land use productivity/management intensity gradient[32] with higher richness in the highly productive and more disturbed grasslands and arable sites and lower richness in the least productive, relatively undisturbed upland heath/bog sites. Changes within bacterial and fungal communities reflected expected within-community changes following the shift in soil nutrient quality across land uses. Actinobacteria[36] and Sordariomycetes[37] are known to dominate bacterial and fungal communities in high productivity grasslands as witnessed here. In contrast, Acidobacteria increased in proportion in low productivity, highly acidic AVCs as expected based on previous studies from the UK[27] and across the globe[7]. Likewise, the greater proportion of Agaricomycetes OTUs in low productivity AVCs is intuitive as many Agaricomycete fungi are common in bogs and related low-productivity habitats across Wales[38].

Protists have been chronically overlooked in European soil monitoring programmes (but see ref. [28]), as extracting trends of protist diversity across land uses is difficult. For example, Gossner et al.[15] were not able to show changes in richness across all protists with land use intensification. We demonstrate that protistan richness follows the trends of bacteria and fungi across land uses, with the highest richness levels in arable land. As with other microbes, there is evidence of increased protist richness at the mesocosm[39] and field[40] level, in response to fertiliser addition. Furthermore, in German grassland soils, protist richness has been shown to increase with land use intensity[41]. Our results show that an association between intensification and protistan richness extends across the national-scale over multiple land uses.

Unlike other microbes, n class="Disease">archaeal richness was greatest in low productivity AVCs and lowest inpan> highly productive sites (Fig. 3d). Furthermore, our unpan>derstandinpan>g of the extenpan>t of soil archaeal diversity and its funpan>ctionpan>al capabilities is conpan>tinpan>ually inpan>creasinpan>g[6-8]. Recenpan>t research has revealed many linpan>eages of Thaumarchaeota are crucial linpan>ks inpan> the N cycle and methanogenpan>esis inpan> soils[7,8]. Archaeal richness was highest in the moorland grass-mosaic and heath/bog AVCs, likely due to the specialised nature of acidophilic lineages. In particular, the Thaumarchaeota[42] and Thermoplasmata[43] are known to proliferate (Fig. 2d) under reduced competition from bacteria.

Animal richness did not change linearly with land use and was not strongly influenced by environmental variables. Our molecular analysis of soil eDNA supports recent findings by George et al.[23] based on morphological assessments of coincident soil mesofauna. Both the present work and George et al.[23] demonstrated that animal richness and abundance were lowest in land uses associated with more intensive management. Animal richness peaked in infertile grasslands and was lowest in crops/weeds sites (Fig. 3e). Agricultural disturbance negatively affects soil n class="Disease">faunal richness and diversity across large geographic scales[14,23,24]. However, inpan> the low-productivity land uses, although proportionpan>al abunpan>dances of arthropod taxa declinpan>ed similarly to the finpan>dinpan>gs of George et al.[23], overall richnpan>ess was not as stronpan>gly affected due to an inpan>crease inpan> fractionpan>s of Annelids, Platyhelminpan>thes, and Tardigrades. Such an inpan>crease inpan> the peat-rich, low-disturbance, higher elevationpan> sites is rather inpan>tuitive sinpan>ce Annelids, Platyhelminpan>thes, and Tardigrades are susceptible to desiccationpan> and require moist habitats to be active componpan>enpan>ts of the soil communpan>ity[44,45]. As soil animals still exhibited expected lower diversity trenpan>ds inpan> more inpan>tenpan>sively managed land uses[15,23,24], there are further opportunpan>ities for research inpan>to unpan>derstanpan>ding the mechanisms unpan>derlyinpan>g the divergenpan>t richnpan>ess trenpan>ds betweenpan> microscopic animals and the rest of soil communpan>ities.

Soil pH, as evidenced by ordination results, was the most important environmental variable in our study for β-diversity and in most cases richness as has been previously observed across the UK[27,28] and at larger national[25,26] and continental scales[4-6]. pH has been implicated with driving n class="Disease">richness of soil Archaea[42,43] and is the most important driver of protist communpan>ities inpan> the UK[28]. However, pH onpan>ly plays a marginpan>al role inpan> shapinpan>g soil protist communpan>ities globally[11]. Likewise, pH is a poor predictor of global funpan>gal biogeography, yet is a good predictor of ectomycorrhizal funpan>gal richnpan>ess[9], which may conpan>tribute to the Agaricomycetes OTUs observed inpan> the presenpan>t study. Nevertheless, it is important to acknowledge the inpan>conpan>sistenpan>t nature of correlations betweenpan> microbial biodiversity and pH, potenpan>tially due to variationpan>s inpan> soil properties occurrinpan>g at scales that do not alignpan> with large-scale soil surveys[30].

We also observed a strong effect of C:N ratio in determining richness of microbes and β-diversity of all organismal groups, as has been observed in bacterial[27] and protistan[28] β-diversity across Britain and some fungi globally[9]. Yet C:N ratio is often co-correlated with other soil properties including bulk density, total C, organic matter, elevationpan>, and mean annual precipitationpan>. Disenpan>tanglinpan>g such related variables is difficult; despite usinpan>g PLS analyses[46] we could not disentangle co-correlated soil properties. For example, AVCs such as moorland grass-mosaic and heath/bog generally had higher elevation, mean annual precipitation, C:N ratio, and both total C and N (Supplementary Table 12) owing to their less-disturbed, upland location, and often peat-rich soils. Higher C:N ratios are indicative of lower-quality soils[47] and have historically been associated with a shift in microbial biomass from bacterial to fungal dominance[48]. Our results suggest that, with the exception of archaea, microbial richness is equally susceptible to the effect of soil quality degradation. According to our results, archaea, on the contrary, appear to be well adapted to habitats with lower nutrient quality.

We observed strong relationships between soil properties and microbial, but not animal richness. We suspect this is due to the direct effects of soil properties on microbes. For example, shifts in pH towards either a more alkaline or acidic condition inhibit the ability of most non-specialised bacteria to uptake nutrients from their environment[26]. In addition the quality of soil nutrients, as discussed previously, was likely a strong determinant of available nutrient resources and therefore total richness of microbes. We also found strong relationships between soil properties and β-diversity and across all organismal groups. These relationships between Bray–Curtis dissimilarities and soil properties demonstrate that more dissimilar belowground communities correlate positively with indicators of better quality soils across the breadth of soil n class="Species">biota (Supplemenpan>tary Table 6). However, associationpan>s betweenpan> nutrienpan>t quality and animal communpan>ity compositionpan> are likely the result of nutrienpan>ts inpan>fluenpan>cinpan>g the compositionpan> of the abovegrounpan>d plant communpan>ity[49] rather than direct inpan>teractionpan>s with animals. Furthermore, animals are more vagile than microbes and can actively seek out microhabitats with better resources[50], limitinpan>g the direct impact of soil properties onpan> animal richnpan>ess.

Using an extensive soil sampling programme and metabarcoding, we present perhaps the most comprehensive assessment of the belowground diversity in Europe. Despite uncertainties on the ability of environmental DNA methods using small soil volumes to accurately characterise communities of larger organisms[51], we were still able to detect key differences in larger organisms (i.e. animals) across land uses. Our results highlight the complexity of belowground ecology by demonstrating a divergence of patterns of richness between soil fauna and microorganisms at a national-level. We show that microbial richness is strongly influenced by soil properties in a near-uniform manner, whereas animal richness is not. Rather, animal richness is likely driven by changes in aboveground communities that stem from intensive land use management, while microbial richness was affected by soil properties in addition to land use. A particularly interesting outcome of our analyses is the near-uniform trend of declining microbial richness along a gradient of decreasing land use productivity/management intensity. The data therefore suggest that soil properties strongly affect bacteria, fungi, and protists in a similar manner, whereby n class="Disease">richness decreases with soil quality; whereas archaea showed an opposinpan>g trenpan>d with inpan>creasinpan>g richnpan>ess as productivity declinpan>ed. The richnpan>ess of animal OTUs, onpan> the conpan>trary, was not affected by soil properties although β-diversity was. Although oftenpan> conpan>sidered as ecological ‘black boxes’, soils conpan>tinpan>ue to provide unpan>ique and coherenpan>t inpan>sights inpan>to the differenpan>ces betweenpan> inpan>terconpan>nected microbial and macrobial assemblages. Our finpan>dinpan>gs also highlight the importance of the dynamics betweenpan> biotic and abiotic processes that drive the organisationpan> of belowgrounpan>d biological diversity.

Methods

Sampling

Soil samples were collected between late spring and early autumn in 2013 and 2014 as part of n class="Chemical">GMEP (Supplemenpan>tary Note 2), established to monpan>itor the Welsh Governpan>menpan>t’s agri-enpan>vironment scheme, Glastir. The scheme covered an area of 3263 km2 with 4911 landowners[31]. Briefly, surveyors collected samples from randomly selected 1 km2 squares with up to 3 locationpan>s withinpan> squares, followinpan>g protocols established by the UK Counpan>tryside Survey[52]. As described previously, habitat withinpan> plots was classified usinpan>g plant species assessmenpan>ts inpan>to onpan>e of sevenpan> AVCs[32]: crops/weeds (n = 9), fertile grassland (n = 98), inpan>fertile grassland (n = 162), lowland wood (n = 17), upland wood (n = 44), moorland-grass mosaic (n = 54), and heath/bog (n = 52) (Supplemenpan>tary Note 1; Supplemenpan>tary Table 1). Soil type was derived from the Nationpan>al Soil Map[53] (Supplemenpan>tary Note 3; Supplemenpan>tary Table 13). n class="Disease">Organic matter content was classified by loss-on-ignition (LOI) following the protocols of the 2007 Countryside Survey[51].

A total of 436 cores were collected from 1 km2 squares, with up to 3 samples coming from an individual square based on a randomised sampling design. Cores were transported to the Centre for Ecology and Hydrology, Bangor, UK, and stored at −80 °C until DNA extraction. Soil physical and chemical properties were taken from 4 cm diameter by 15 cm deep cores co-located with the high-throughput sequencing cores. These included total C (%), N (%), P (mg kg−1), organic matter (% LOI), pH (measured inpan> 0.01 M CaCl2), mean soil water repellency (median water drop penetration time in seconds), bulk density (g cm3 −1), volume of rocks (cm3), soil bound water (g water g dry soil−1), volumetric water content (m3 m3 −1), as well as clay and sand content (%) of soil. Abundances of mesofauna collected as part of GMEP were taken from George et al.[23] and geographic data including grid eastings, northings, and elevation were also included in our analyses. For complete details on chemical analyses, see Emmett et al.[51]. Temperature (°C) and mean annual precipitation (mL) were extracted from the Climate Hydrology and Ecology research Support System dataset[54]. Mean values for each variable were recorded for each AVC (Supplementary Table 12) and soil properties were normalised where appropriate.

Soil texture data were measured by laser granulometry with a LS320 13 analyser (Beckman-Coulter). We subsampled approximately 0.5 g of soil taken from 15 cm cores by manual quartering and removed organic C using n class="Chemical">H2O2 and thenpan> transferred the sample inpan>to 250 mL bottles, added 5 mL of 5% Calgonpan>® and shook overnpan>ight at 240 rpm. Bottles were emptied manually into the laser diffraction inpan>strumenpan>t for measurinpan>g particle size distributionpan>. Full Mie theory was used to obtainpan> a particle size distributionpan> from the raw measuremenpan>t data, with the real refractive inpan>dex set to 1.55 and the absorptionpan> coefficienpan>t at 0.1 as inpan> Özer et al.[55]. The cut-off poinpan>ts for clay, silt, and sand were 2.2, 63, and 2000 μm, respectively. Clay and sand percenpan>tages were selected for subsequenpan>t analyses and normalised usinpan>g Aitchisonpan>’s log-ratio transformationpan>.

DNA extraction

Soils were homogenised by passing through a sterilised 2 mm stainless steel sieve. Sieves were sterilised betweenpan> samples by rinsing unpan>der the tap water using high flow, applying Vircon laboratory disinfectant and UV-treating each side for 5 min DNA was extracted by mechanical lysis and the homogenisation step performed in triplicate from 0.25 g of soil per sample using a PowerLyzer PowerSoil DNA Isolation Kit (MO-BIO). Pre-treatment with 750 μL of 1 M CaCO3 following Sagova-Mareckova et al.[56] was performed as it was shown to improve PCR performances, especially for acidic soils. Extracted DNA was stored at −20 °C until amplicon library preparation began. To check for contamination in sieves 3 negative control DNA extractions were completed and an additional 2 negative control kit extractions were performed using the same technique but without the CaCO3 solution.

Primer selection and PCR protocols for library preparation

Amplicon libraries were created using primers for rRNA marker genes, specifically for the V4 region of the 16S rDNA gene targeting bacteria and archaea (515F/806R)[57], ITS1 targeting fungi (ITS5/5.8S_fungi)[58], and the V4 region of the 18S rDNA gene (TAReuk454FWD1/TAReukREV3)[59] targeting a wide range of, but not all, eukaryotic organisms. We used a two-step PCR following protocols devised in conjunction with the Liverpool Centre for Genome Research. Amplification of amplicon libraries was run in triplicate on DNA Engine Tetrad® 2 Peltier Thermal Cycler (BIO-RAD Laboratories) and thermocyclinpan>g parameters for each PCR started with 98 °C for 30 s and terminpan>ated with 72 °C for 10 minpan> for finpan>al extenpan>sionpan> and held at 4 °C for a finpan>al 10 minpan> For the 16S locus, first-rounpan>d PCR amplificationpan> followed 10 cycles of 98 °C for 10 s; 50 °C for 30 s; 72 °C for 30 s. For ITS1, there were 15 cycles of 98 °C for 10 s; 58 °C for 30 s; 72 °C for 30 s. For 18S there were 15 cycles at 98 °C for 10 s; 50 °C for 30 s; 72 °C for 30 s. Twelve μL of each first-rounpan>d PCR product was mixed with 0.1 μL of exonpan>uclease I, 0.2 μL thermosenpan>sitive alkalinpan>e phosphatase, and 0.7 μL of water and cleaned in the thermocycler with a programme of 37 °C for 15 min and 74 °C for 15 min and held at 4 °C. Addition of Illumina Nextera XT 384-way indexing primers to the cleaned first-round PCR products were amplified following a single protocol which started with initial denaturation at 98 °C for 3 min; 15 cycles of 95 °C for 30 s; 55 °C for 30 s; 72 °C for 30 s; final extension at 72 °C for 5 min and held at 4 °C. Twenty-five μL of second-round PCR products were purified with an equal amount of AMPure XP beads (Beckman Coulter). Library preparation for 2013 samples was conducted at Bangor University. Illumina sequencing for both years and library preparation for 2014 samples were conducted at the Liverpool Centre for Genome Research.

Bioinformatics

Bioinformatics analyses were performed on the Supercomputing Wales cluster. A total of 130,219,260, 104,276,828, and 98,999,009 raw reads were recovered from the 16S, ITS1, and 18S sequences, respectively. Illumina adapters were trimmed from sequences using Cutadapt[60] with 10% level mismatch for removal. Sequences were then de-multiplexed, filtered, quality-checked, and clustered using a combination of USEARCH v. 7.0[61] and VSEARCH v. 2.3.2[62]. Open-reference clustering (97% sequence similarity) of OTUs was performed using VSEARCH; all other steps were conducted with USEARCH. Sequences with a maximum error greater than 1 and shorter than 200 bp were removed following the merging of forward and reverse reads for 16S and ITS1 sequences. A cut-off of 250 bp was used for 18S sequences, according to higher quality scores. There were 15,202,313 (16S), 7,242,508 (ITS1), and 9,163,754 (18S) cleaned reads left at the end of these steps. Sequences were sorted and those that only appeared once in the dataset were removed. Briefly, filtered sequences were matched first against a number of different reference databases: Greengenes 13.8[63], UNITE 7.2[64], and n class="Disease">SILVA 128[65] for 16S, ITS1, 18S, respectively. Tenpan> percenpan>t of sequenpan>ces that failed to match were clustered de novo and used as a new referenpan>ce database for failed sequenpan>ces. Sequenpan>ces that failed to match with the de novo database were subsequenpan>tly also clustered de novo. All clusters were collated and chimeras were removed usinpan>g the uchime_ref command inpan> VSEARCH.

Chimera-free clusters and taxonomy assignment were used to create an OTU table with QIIME v. 1.9.1[66] using RDP[67] methodology with the GreenGenes database for 16S and UNITE database for ITS1 data. Taxonomy was assigned to the 18S OTU table using BLAST[68] against the n class="Disease">SILVA database and OTUs appearinpan>g onpan>ly onpan>ce or inpan> onpan>ly 1 sample were removed from each OTU table.

Newick trees were constructed for the 16S and 18S tables using 80% identity thresholds. The trees were combined with their respective OTU tables as part of analyses using the R package phyloseq[69], removinpan>g OTUs that did not appear inpan> both the tree and OTU table. OTUs idenpan>tified as eukaryotes inpan> the 16S OTU table, non-fungi OTUs inpan> the ITS OTU table, as well as OTUs idenpan>tified as funpan>gi, plants, and nonpan>-soil animals were removed from the 18S OTU table. Read counpan>ts from each group were normalised usinpan>g rarefactionpan>. The OTU tables were rarefied 100 times usinpan>g phyloseq[69] (as justified by Weiss et al.[70]) and the resulting mean richness was calculated for each sample. The read depth used for rarefaction varied for each group (Supplementary Table 14). Samples with lower read counts than this cut-off were removed before rarefaction. A summary of number of replicates per AVC is included in Supplementary Table 1.

Statistical analyses

All statistical analyses were run using R v. 3.3.3[71] using the rarefied data sets for each organismal group. The vegan package[72] was used to assess β-diversity via NMDS and CAP ordinations based on Bray–Curtis dissimilarities. A linear model for each environmental variable was fit separately to the ordination using the envfit function, the results are presented ranked according to goodness-of-fit. Results of goodness-of-fit for each variable from both ordination methods were compared using regression analyses to look for congruence. The values of all variables were plotted against NMDS scores to determine if there were positive or negative relationships with each NMDS axis. Differences in β-diversity amongst AVCs were calculated with PERMANOVA. The assumption of homogeneity of dispersion was verified using the betadisper function.

Linear mixed models were constructed using package nlme[73] to test the differences in α-diversity amongst AVCs for each organismal group. Model selection was performed using AVC, soil type, LOI classification, and sample year as fixed factors; sample square identity was the random factor. To determine the best possible model, predictors other than AVC were dropped to find the lowest AIC scores using the AICcmodavg package[74]. For each model, significant differences were assessed by ANOVA and pairwise differences were identified with Tukey’s post-hoc tests from the multcomp package[75].

n class="Disease">PLS regressions founpan>d inpan> package pls[76] were used to identify the most important environmental variables for richness. Such analysis is ideal for data where there are many more explanatory variables than sample numbers or where extreme multicollinearity is present[46]. As in Lallias et al.[46], we used the variable importance in projection (VIP) approach[77] to sort the original explanatory variables by order of importance; variables with VIP values > 1 were considered most important. Relationships between important variables and richness values for each group of organisms were investigated by linear regression. Richness was normalised before regression when necessary. Pearson’s correlation coefficient was used to directly compare richness of organismal groups.