agricultural land‐use history and restoration impact soil ... · and restoration tree thinning...

J Appl Ecol 2020001ndash12 wileyonlinelibrarycomjournaljpe emsp|emsp 1copy 2020 British Ecological Society

Received 5 August 2019emsp |emsp Accepted 19 January 2020

DOI 1011111365-266413591

R E S E A R C H A R T I C L E

Agricultural land-use history and restoration impact soil microbial biodiversity

Nash E Turley12 emsp| Lukas Bell-Dereske3emsp| Sarah E Evans234emsp| Lars A Brudvig12

1Department of Plant Biology Michigan State University East Lansing MI USA2Program in Ecology Evolutionary Biology and Behavior Michigan State University East Lansing MI USA3Kellogg Biological Station Michigan State University East Lansing MI USA4Department of Integrative Biology Michigan State University East Lansing MI USA

CorrespondenceNash E TurleyEmail nashuagoatsgmailcom

Present addressNash E Turley Department of Biology University of Central Florida Orlando FL USA

Funding informationDepartment of Agriculture Forest Service Savannah River GrantAward Number DE-EM0003622

Handling Editor Gaowen Yang

Abstract1 Human land uses such as agriculture can leave long-lasting legacies as ecosystems

recover As a consequence active restoration may be necessary to overcome land-use legacies however few studies have evaluated the joint effects of agricultural history and restoration on ecological communities Those that have studied this joint effect have largely focused on plants and ignored other communities such as soil microbes

2 We conducted a large-scale experiment to understand how agricultural history and restoration tree thinning affect soil bacterial and fungal communities within longleaf pine savannas of the southern United States This experiment contained 64 pairs of remnant (no history of tillage agriculture) and post-agricultural (refor-ested following abandonment from tillage agriculture gt60 years prior) longleaf pine savanna plots Plots were each 1 ha and arranged into 27 blocks to mini-mize land-use decision-making biases We experimentally restored half of the remnant and post-agricultural plots by thinning trees to reinstate open-canopy savanna conditions and collected soils from all plots five growing seasons after tree thinning We then evaluated soil bacterial and fungal communities using metabarcoding

3 Agricultural history increased bacterial diversity but decreased fungal diversity while restoration increased both bacterial and fungal diversity Both bacterial and fungal richness were correlated with a range of environmental variables includ-ing above-ground variables like leaf litter and plant diversity and below-ground variables such as soil nutrients pH and organic matter many of which were also impacted by agricultural history and restoration

4 Fungal and bacterial community compositions were shaped by restoration and agricultural history resulting in four distinct communities across the four treatment combinations

5 Synthesis and applications Past agricultural land use has left persistent legacies on soil microbial biodiversity even over half a century after agricultural abandon-ment and after intensive restoration activities The impacts of these changes on soil microbe biodiversity could influence native plant establishment plant produc-tivity and other aspects of ecosystem functioning following agricultural abandon-ment and during restoration

2emsp |emsp emspenspJournal of Applied Ecology TURLEY ET aL

1emsp |emspINTRODUC TION

The conversion of natural ecosystems for human land uses is a lead-ing threat to biodiversity (Foley et al 2005 Newbold et al 2015) and can leave long-lasting legacies on ecosystems (Flinn amp Vellend 2005 Foster et al 2003) For example former agricultural lands can support altered soils plant communities and other properties for decades to millennia following farm abandonment relative to lsquoremnantrsquo ecosystems with no history of agricultural use (Bellemare Motzkin Foster amp Forest 2002 Dupouey Dambrine Laffite amp Moares 2002 Flinn amp Marks 2007) As a consequence active res-toration may be necessary to overcome land-use legacies in many ecosystems (Perring et al 2015 2016 Suding 2011) Yet our un-derstanding of land-use legacies and the role of active restoration for mitigating legacy effects remains unresolved for several reasons

First incomplete understanding results from taxonomic biases in studies of land-use legacies and restoration Plants have been a strong focus in both agricultural legacy and restoration research (Brudvig 2011 Flinn amp Vellend 2005 Hermy amp Verheyen 2007) Yet taxa respond differently to legacies and restoration activities (eg Jones et al 2018) and many taxa remain poorly investigated Here we focus on responses of soil microbial communities which can be strongly affected by land-use legacies and restoration prac-tices (eg Barber Chantos-Davidson Amel Peralta Sherwood amp Swingley 2017 Freschet Oumlstlund Kichenin amp Wardle 2014 Hui et al 2018 Jangind et al 2011 Ma De Frenne Boon et al 2019 Xue Carrillo Pino Minasny amp McBratney 2018) but are poorly studied taxa in these fields (eg Brudvig 2011)

Understanding soil microbial responses to agricultural legacies and restoration is especially important because these groups can have major impacts on plant diversity and productivity as well as res-toration success (van der Bij et al 2018 van der Heijden Bardgett amp Straalen 2008 Wubs Putten Bosch amp Bezemer 2016) For ex-ample because of host preferences and their roles in mutualistic and antagonistic relationships soil microbes can limit plant species distributions and alter plant community interactions (Kardol Martijn Bezemer amp Putten 2006 Wubs et al 2016) In turn inoculation of former agricultural fields with mycorrhizal fungi or whole soils from remnant ecosystems can affect plant establishment and community assembly dynamics during restoration (Koziol et al 2018 Wubs et al 2016) Thus how agricultural legacies and restoration affect soil microbes may have broad-reaching implications for ecosystem recovery

Second studies of land-use legacies face study design challenges (De Palma et al 2018) including biases introduced through decisions made in the past about where and how land was used by humans For example temperate forests on level ground near roads and with higher pH soils are more likely to be converted to agricultural fields

(Flinn Vellend amp Marks 2005) In turn fields on steep slopes located far from roads and with lower pH soils are more likely to be aban-doned from agriculture (Flinn et al 2005) As a consequence under-lying site properties might be mistakenly interpreted as agricultural legacy effects when in fact they are simply consequences of the land-use decision-making process Controlling for these land-use biases is particularly important for resolving how soil microbial communities respond to land-use legacies given soil microbes re-sponsiveness to soil conditions (Fierer amp Jackson 2006 Lauber Strickland Bradford amp Fierer 2008 Ma De Frenne Vanhellemont et al 2019 Ma et al 2018 Xue et al 2018) Here we control for land-use biases through a study design where post-agricultural plots and remnant plots with no known history of agriculture are paired in space resulting in no bias in underlying soil types (Brudvig Grman Habeck Orrock amp Ledvina 2013)

Third studies are needed to explicitly consider how restoration affects land-use legacies and in turn how land-use legacies affect restoration outcomes Systems with a history of intensive human land use such as agriculture are a common focus of restoration ef-forts and yet whether and how restoration can overcome the lega-cies of past land uses remains unclear (Jones et al 2018 Meli et al 2017) Moreover because land-use history can affect numerous system attributes legacies alter the template onto which restoration acts As a consequence restoration outcomes may differmdashperhaps substantiallymdashfor locations with differing land-use histories (Brudvig amp Damschen 2011 Turley amp Brudvig 2016) In other instances however restoration may have clear effects that are broadly similar to areas with and without a particular history (eg Breland Turley Gibbs Isaacs amp Brudvig 2018) What is needed are controlled repli-cated experiments to draw strong inferences about how restoration and land-use legacies interact Yet experiments manipulating resto-ration treatments across areas differing in land-use history are rare Ideally such investigations would be coupled with measurements of key environmental variables hypothesized to affect the taxa of inter-est including those affected by agricultural history and restoration For example within our focal system bee responses to restoration may be mediated by the increase in flower cover resulting from res-toration (Breland et al 2018) Therefore determining mechanisms of legacy and restoration effects likely requires coupled measure-ments of key environmental variables along with the focal taxa

We overcame these limitations through a replicated restoration experiment in longleaf pine savannas Our experiment included a factorial manipulation of restoration (overstorey tree thinning) and agricultural history whereby plots with and without a history of till-age agriculture received restoration or were left as unrestored con-trols We arranged plots into blocks to control for land-use biases and considered how agricultural history restoration thinning and their interaction affect soil bacteria and fungi

K E Y W O R D S

agricultural history bacteria community ecology fungi land-use legacy metabarcoding restoration soil microbe biodiversity

emspensp emsp | emsp3Journal of Applied EcologyTURLEY ET aL

Previous work within our experiment has shown how agricultural history and restoration affect abiotic and biotic conditions in ways that we expect to influence soil microbial communities and diver-sity In particular compared to remnants post-agricultural savannas support soils that are more compacted support elevated phospho-rus and reduced organic matter content and water holding capac-ity as well as lower tree canopy cover and altered plant community composition (but comparable plant species richness Brudvig et al 2013) The restoration thinning treatment decreased tree canopy cover and litter accumulation increased near-ground temperatures and sunlight reaching ground level increased plant species richness and altered plant community composition (Hahn amp Orrock 2015 Stuhler amp Orrock 2016 Turley amp Brudvig 2016) Based on these past findings we suspected that soil microbial diversity and commu-nity composition will also be affected by both land-use history and restoration thinning

We asked the following questions related to both soil bacteria and soil fungi from our field experiment

1 What are the effects of agricultural land-use history and res-toration on soil microbe diversity and composition

2 Does the effect of restoration on diversity and composition de-pend on agricultural land-use history (ie do agricultural history and restoration interact)

3 Do environmental variables correlate with microbial biodiversity4 Do correlations between microbe diversity metrics and environ-

mental variables help explain the impacts of restoration and land-use history on soil microbe biodiversity

2emsp |emspMATERIAL S AND METHODS

21emsp|emspStudy location and experimental design

Our research took place at the Savannah River Site (SRS) an ~80000 ha National Environmental Research Park located on the upper coastal plain in South Carolina (3320degN 8140degW) This area

historically supported fire-maintained longleaf pine savanna in the sandy uplands (Kilgo amp Blake 2005)mdashan ecosystem characterized by sparse canopies dominated by longleaf pine trees Pinus palustris and a dense understorey plant layer of graminoids forbs and shrubs (Noss et al 2015) By the mid-20th century most of the SRS uplands had been converted to tillage agriculture primarily for cotton and corn (Kilgo amp Blake 2005) In 1951 the US government obtained SRS and began converting agricultural fields to longleaf loblolly Pinus taeda and slash pine Pinus elliottii plantations (Kilgo amp Blake 2005) Following acquisition (and likely for decades prior to this) fire was excluded from ecosystems within SRS until initiation of prescribed burning in the early 21st century (Kilgo amp Blake 2005)

At SRS we conducted a factorial experimental manipulation of agricultural history and restoration tree thinning across 126 1-ha plots arranged into 27 blocks (Figure 1) Each block was focused around a fragment of remnant longleaf pine savanna with no known history of tillage agriculture adjacent to a former agricultural field supporting closed-canopy pine (longleaf where possible) plantation at the initiation of the study (Brudvig et al 2013) We determined land-use histories for each plot using historical aerial photos taken in 1951 at the time of SRSs creation (Brudvig et al 2013) Remnant and post-agricultural plots within blocks supported similar soil types and topographies (Brudvig et al 2013) suggesting that the blocked experimental design adequately controlled for non-random land-use decision-making

In 2011 prior to the start of the growing season we applied a tree thinning treatment to restore open-canopy savanna structure to half of the remnant and post-agricultural plots (Turley amp Brudvig 2016) This reduced tree densities from an average of 650 treesha to 10 treesha All plots have subsequently been managed with one or more prescribed fires The frequency of prescribed surface fire did vary among the 26 blocks since the initiation of the experiment however all plots and thus all four treatment combinations within a block were always burned together Although fire could be an im-portant factor shaping soil microbes within longleaf pine savannas (Semenova-Nelsen Platt Patterson Huffman amp Sikes 2019) look-ing at this is beyond the scope of this study

F I G U R E 1 emsp Diagram showing the experimental sites and soil sampling locations within the Savannah River Site in South Carolina Each of 27 sites has 1-ha experimental plots in remnant and post-agricultural areas Half of the 1-ha plots in each land-use type had restoration tree thinning in 2011 to restore open-canopy savanna conditions Soil samples were collected across all 1-ha plots in 2015

Remnant Post-agricultural

Soil sample locationVegetation transect

Thinned

Thinned Control

20 km

100 m

1 m

Single soil probe

South Carolina

Savanna River Site

1 of 27 experimental sites

Control


22emsp|emspSoil sampling and processing

In Fall 2015 we collected ~12 L of soil from each of the 126 1-ha plots Each soil sample was an aggregate of 30 16 cm wide by 20 cm deep soil probes collected along two 50 m transects through the middle of each plot (Figure 1) The soil sampling transects ran on both sides of our already-present vegetation sampling transects (Turley amp Brudvig 2016 Figure 1) Before each probe the leaf lit-ter duff and sticks were brushed aside To minimize contamina-tion we used one soil probe for all remnant sites and another for all post-agricultural sites and between each plot we rinsed the in-side and outside of the probe with a 10 bleach solution and then water Aggregate soil samples were mixed thoroughly and split up for different purposes About 50 ml was stored in a minus20degC freezer for microbial analysis and two other subsamples were used for en-vironmental sampling

For microbial analysis we extracted soil DNA using MoBio PowerSoil Extraction Kit following the manufacturers instructions We submitted DNA to the Michigan State University Core Genomics Facility for Illumina sequence library construction Following their standard protocols bacterial 16S V4 (515f806r) and ITS (ITS-FITS2) Illumina compatible libraries were prepared using primers containing both the target sequences and the dual indexed Illumina compatible adapters The 16S and ITS1 amplicon pools were se-quenced independently in a 2 times 250bp paired end format using in-dependent v2 500 cycle MiSeq reagent cartridges

The first of the soil subsamples was analysed by Brookside Laboratories Inc for soil texture (percent sand clay and silt) pH organic matter and nutrients and minerals On the second subsam-ple we measured soil water holding capacity (proportionate differ-ence between saturated wet and oven dry weight) and gravimetric soil moisture using the same methods as Brudvig and Damschen (2011) Soil pH water holding capacity organic matter and several soil nutrients all decreased with agricultural history while soil phos-phorus was strongly increased (see Table S1)

23emsp|emspEnvironmental data collection

We measured a set of environmental variables within each experi-mental plot at 10 m intervals along the 100 m vegetation transects (Figure 1) during the 2015 growing season In 1 times 1 m plots we visu-ally estimated the percent cover of leaf litter down woody debris bare ground and understorey vegetation At each of these plots we also measured the depth of leaf litter and canopy cover of oversto-rey trees using a spherical densiometer In 1 times 1 m and 10 times 10 m plots we recorded all plant species and calculated plant species richness For all these environmental variables we averaged the 10 measurements across each transect to get one value per 1-ha plot Restoration thinning resulted in strong declines in leaf litter and canopy cover and large increases in vegetation cover and under-storey plant richness (Table S1) Units and methods for measuring all of our environmental variables are available in Table S6

24emsp|emspBioinformatics

We processed and clustered bacterial and fungal reads into operational taxonomic units (OTUs) Reads from the bacterial community were chimera checked quality filtered and merged using Trimmomatic and Pandaseq (Bolger Lohse amp Usadel 2014 Masella Bartram Truszkowski Brown amp Neufeld 2012) Processed reads were clustered into OTUs at 97 identity level using UCLUST61 with the default settings (Edgar 2010) Singletons were removed and contigs were screened using QIIME 191 (Caporaso et al 2010) with the default parameters OTUs classified to chloroplast mitochondria or with less than four reads across all samples were filtered out to avoid over splitting (Thieacutery Moora Vasar Zobel amp Oumlpik 2012) and sequencing errors (Dickie 2010) The resulting community was composed of 90103 OTUs and 1650420 reads Fungal reads were quality filtered and merged using the USEARCHv10 pipeline (httpdrive5comusear ch Edgar 2010 2013) Merged sequences were quality filtered to an expected error threshold of 10 fastq_filter (Edgar amp Flyvbjerg 2015) and primer sequences bases were removed The combined reads were clustered into OTUs at 97 identity level then reference-based chimera checked (Edgar 2016) and classified against the UNITE 71 ITS1 chimera and reference da-tabases respectively (Kotildeljalg et al 2013) All non-fungal OTUs and those with less than four reads were filtered from the com-munity matrix The resulting fungal community had 10285 OTUs and 584113 reads

25emsp|emspStatistical analysis

We conducted all analyses in R version 351 We first removed two samples with extremely low reads a bacteria sample with 471 reads and a fungal sample with 78 reads (compared to means of ~69000 and 5000 respectively) For measuring diversity we rarified the community datasets following Weiss et al (2017) using the lsquorrarefyrsquo function (Oksanen et al 2010) We set the minimum value in the rarefaction to the lowest observed read number in a sample With those community datasets we calculated richness evenness and inverse Simpsons D Our evenness metric was in-verse Simpsons diversity divided by species richness We focus primarily on inverse Simpsons D as our measure of biodiversity as this is recommended for microbial datasets (Haegeman et al 2013) We evaluated correlations between average plot-level (1-ha) environmental variables and diversity metrics using Pearsons correlations

To test the effects of agricultural history and restoration thin-ning on biodiversity metrics we fit mixed effects models using the lsquolmerrsquo function (Bates Maumlchler Bolker amp Walker 2015) We included restoration thinning agricultural history (both two-level factors) and their interaction as fixed effects Site (a 27-level categorical factor) and land-use history were included as random effects Land-use history was nested within site to account for the pseudoreplication


inherent in the hierarchical experimental design The model syntax was

We used the lsquoANOVArsquo function (Fox amp Weisberg 2018) to calcu-late p-values using Type 2 sums of squares We used Type 2 sums of squares because our models had non-significant interaction terms and this allowed us to interpret the main effects while keeping the inter-action term in the model We determined R2 for the factors using the lsquor2betarsquo function with the standardized generalized variance method (Jaeger 2017) For community composition analyses we transformed the data using the lsquovarianceStabilizingTransformationrsquo function with the lsquolocalrsquo fit type (Love Huber amp Anders 2014 Weiss et al 2017) On the transformed datasets we created a distance matrix using BrayndashCurtis dissimilarity which was abundance weighted by read number We tested the effects of our factors on community composition by fitting PERMANOVA models with the lsquoadonisrsquo function (Oksanen et al 2010) We included the site factor as a lsquostratarsquo term Because nesting is not possible with the lsquoadonisrsquo function the degrees of freedom for these tests are inflated which could artificially reduce p-values We vi-sualized the effects of our treatments on community composition by performing a constrained analysis of principal coordinates using the lsquocapscalersquo function with default parameters then visualizing the ordi-nation using the lsquoordiplotrsquo function (Oksanen et al 2010) We used the lsquoenvfitrsquo function (Oksanen et al 2010) to test for correlations between environmental variables (BrayndashCurtis dissimilarity matrix) and the mi-crobe community ordinations (non-metric multidimensional scaling with BrayndashCurtis dissimilarity) To account for concerns of oversplit-ting due to open reference OTU clustering (Edgar 2017) we ran the same PERMANOVA model on the bacterial Unifrac distance matrix Accounting for phylogeny did not change the results so we only pres-ent the BrayndashCurtis-based results

We explored the relationship among experimental treatments environmental variables and microbial diversity variables using structural equation modelling Because there were many some-times collinear potential environmental variables to include in the analyses (Tables S3 and S4) we simplified the data into two composite variables using a principle components analysis (PCA) We standardized all variables to have a mean of 0 and standard deviation of 1 then fit SEMrsquos using the lsquosemrsquo function (Rosseel

2012) We fit models with PC1 and PC2 as endogenous variables between the treatments and microbe biodiversity metrics To test the importance of the environmental variables (PC1 and PC2) in the models we fit SEMrsquos without them and compared the R2 to the full models with them included

3emsp |emspRESULTS

31emsp|emspQuestion 1 Effects of agricultural history and restoration on soil microbial biodiversity

History of agricultural land use had opposite effects on bacterial and fungal diversity (inverse Simpsons D) and also shaped com-munity composition For bacteria agricultural history increased diversity by 537 (Figure 2a Table 1) whereas for fungi agri-cultural history reduced diversity by 185 (Figure 2b Table 1) These results were driven primarily by changes in evenness for bacteria and richness in fungi (Table S2) Agricultural history also significantly affected microbial composition (Figure 3 Table 1) which explained 25 of bacterial and 39 of fungal community variation

Restoration increased both bacterial and fungal diversity and impacted community composition Restoration increased bacterial diversity by 138 (Figure 2a Table 1) and fungal diversity by 601 (Figure 2b Table 1) These changes in diversity were driven by in-creases in both richness and evenness (Table S2) Restoration thin-ning also shaped bacterial and fungal communities (Figure 3 Table 1) and this factor explained 12 and 26 of variation in communities respectively

32emsp|emspQuestion 2 Effects of agricultural history on restoration effects

Overall there was little evidence that the effects of restoration were dependent on agricultural history There were no significant interactions between restoration and agricultural history for bacte-rial or fungal diversity (Table 1) There was a significant interaction between agricultural history and restoration on fungal community composition explaining 1 of variation

Ysim thinning lowast land use +(1|site∕land use∕thinning

)

F I G U R E 2 emsp Effects of agricultural land-use history and restoration thinning on diversity (inverse Simpsonss D) within a longleaf pine savanna experiment in South Carolina for (a) bacteria and (b) fungi Remnant plots are savannas with no history of agriculture and post-agricultural sites had tillage agriculture that was abandoned over 60 years ago and then managed as pine plantation

(a) (b)


33emsp|emspQuestion 3 Correlations between environmental variables and soil microbial biodiversity

Many environmental variables were correlated with soil microbial di-versity richness and evenness (Tables S3 and S4) A PCA collapsed this variation into two composite variables The first axis from this

analysis (PC1) was associated mostly with below-ground variables Negative values were associated with sand soil Fe and soil P while positive values were associated with a wide range of soil micronutri-ents soil organic matter soil water holding capacity silt and soil pH (Figure 4 Table S5) PC2 was associated mostly with above-ground variables related to canopy density Positive values of PC2 were as-sociated with canopy cover leaf litter and soil S whereas negative values were associated with cover bare ground (Figure 4 Table S5) Plant richness vegetation cover and leaf litter were associated with both axes with PC1 positively associated with plant richness and PC2 negatively associated with plant richness (Figure 4 Table S5)

The principle components of environmental variables pre-dicted soil microbial richness and evenness and diversity The strongest correlations were between PC1 and richness (Table 2)

TA B L E 1 emsp Results of models for soil bacteria and fungal Simpsons diversity and community composition from longleaf pine savannas Data are from an experiment with 126 1-ha plots factorially manipulating agricultural land-use history and restoration tree thinning Inverse Simpsons diversity results are from mixed effects models and community results are from multivariate PERMANOVA models

ddf F p r2

Bacteria

Inverse Simpsons D

Land use 23 14895 lt001 390

Restoration 46 1625 lt001 048

Land use times rest 45 022 639 002

Community

Land use 121 314 lt001 025

Restoration 121 148 002 012


Fungi

Inverse Simpsons D

Land use 25 544 028 003



Community

Land use 121 513 lt001 039



Note DDF denominator degrees of freedom Values with p lt 05 are bolded

F I G U R E 3 emsp Effects of agricultural land-use history and restoration thinning on (a) bacteria community composition and (b) fungal community composition from longleaf pine savanna soils

CAP1

CA

P2

2 1 0 1 2

21

01

2

CAP1

CA

P2

03 01 01 03

04

02

00

02

Post-agcontrolPost-agthinned

RemnantcontrolRemnantthinned

Bacteria Fungi(a) (b)

F I G U R E 4 emsp Ordination of a principle components analyses of all environmental variables collected from each of the 126 1-ha plots The location of each variable along each axis indicates how strongly associated the variable is with that axis PC1 is strongly associated with various below-ground variables such as nutrients soil texture and soil moisture PC2 is most associated with above-ground variables like tree canopy cover leaf litter and bare ground However both axes are strongly associated with plant richness and percent cover of vegetation

PC1

PC

2

Pveg

Plitter

Pwood

Ptreetrunk

Pbareground

PcanopycoverLitterdepth

Duffdepth

richness1 times 1

richness10 times 10

PclayPsilt

Psand

pH

OM

S

P

Ca MgKNa

FeMn

Cu

Al

PmoisturePWHC

ndash10 ndash05 00 05 10 15

ndash05

00

05

10

TA B L E 2 emsp Pearsons correlations between soil microbe biodiversity metrics and principle component axes of soil and vegetation environmental parameters (see Figure 4) All variables were measured within 126 1-ha longleaf pine savanna

Variable 1 Variable 2

Bacteria Fungi

r p r p

Richness PC1 46 lt001 66 lt001

Evenness PC1 minus18 05 11 21

Simpsons D PC1 minus02 78 37 lt001

Richness PC2 minus21 02 minus14 13

Evenness PC2 minus35 lt001 minus33 lt001

Simpsons D PC2 minus37 lt001 minus31 lt001

Note Values with p lt 05 are bolded


Locations with wetter more nutrient rich and basic soils and with greater plant species richness supported greater soil mi-crobial richness and this relationship was stronger in fungi than in bacteria (Figure 5) PC1 was somewhat negatively correlated with bacterial evenness and not significantly correlated with di-versity (Table 2) PC1 had no relationship with fungal evenness and was positively correlated with fungal richness (Table 2) PC2 was negatively correlated with all measures of fungal and bac-terial biodiversity (richness evenness and Simpsons diversity) with the exception of fungal richness (Table 2) Thus plots with greater tree canopy cover and leaf litter had reduced soil mi-crobial diversity whereas plots with more bare ground under-storey vegetation and plant richness supported greater levels of microbial biodiversity

Microbial community composition was also correlated with a wide range of environmental variables (Tables S3 and S4) Bacterial communities were correlated with most below-ground variables such as soil pH nutrients texture and water holding capacity (Table S3) but not with above-ground variables (with the exception of one measure of plant richness) Fungal communities were also correlated with below-ground variables similar to bacteria but were also correlated with above-ground variables such as plant richness leaf litter and tree canopy cover (Table S4) Overall environmental

variables had significant correlations with community ordination for bacteria (Mantel test r = 21 p = 001) and fungi (Mantel test r = 23 p = 001)

34emsp|emspQuestion 4 Do environmental variables help explain effects of treatments on microbe biodiversity

Our structural equation models (SEMrsquos) showed that agricultural land-use history and restoration treatments impacted microbial di-versity (inverse Simpsons D) and evenness mostly independently of the environmental variables we measured while microbial richness was mostly predicted by environmental variables and not the treat-ments The SEMrsquos showed that agricultural history and restoration thinning impacted both of the environmental PC axes (Figure 6) and the direct effects of the treatments on environmental variables are summarized in Table S1 Agricultural history was the strongest pre-dictor of bacterial diversity but the environmental variables were also significant (Figure 6a) The model overall explained 57 of the variation in bacterial diversity (Figure 6a) A SEM fit without the environmental variables as intermediates between the treatments and diversity still explained 53 of variation in bacterial diversity The fungal diversity SEM had restoration thinning as a significant

F I G U R E 5 emsp Relationship between the first principle component axis of environmental variables (see Figure 4) on (a) bacterial richness and (b) fungal richness Richness was calculated from a rarefied community dataset Negative values of PC1 are associated with sand Fe P leaf litter while positive values are associated with a wide range of soil micronutrients soil organic matter soil water holding capacity vegetation cover and plant richness

3000

3500

4000

4500

ndash1 0 1 2

Environmental PC1

Ric

hnes

s

200

300

400

500

600

ndash1 0 1 2

Environmental PC1

Ric

hnes

s



(a) Bacteria (b) Fungi

r = 46 p lt 001 r = 66 p lt 001

F I G U R E 6 emsp Structural equation model path diagrams showing the main treatment effects at the top principle component axis of environmental variables in the middle and inverse Simpsons diversity at the bottom for (a) bacteria and (b) fungi The width of the arrows is proportional to the magnitude of the path coefficient Black arrows are positive correlations grey arrows are negative correlations and dashed arrows are non-significant paths

(a) (b)


predictor along with the environmental PC axes (Figure 6b) which explained a total of 30 of the variation in diversity This model without the environmental variables explained 24 of variation in fungal diversity The models for evenness (both for bacterial and fungal) showed similar patterns to those of diversity with the envi-ronmental variables explaining minimal variation (lt2) in evenness (Figure S4)

SEM explained little variation in microbial richness when environ-mental variables were excluded The full model for bacterial richness explained 44 of variation in richness (Figure S3) but without environ-mental variables explained only 4 Similarly for fungal richness the full model explained 48 of variation in richness (Figure S3) while the model without environmental variables explained only 17

4emsp |emspDISCUSSION

Soil bacteria and fungi biodiversity were both affected by agricultural history restoration thinning and environmental variables Our results point to four major conclusions (a) agricultural history increased bac-terial diversity while reducing fungal diversity (b) restoration thin-ning increased fungal and bacterial diversity (c) agricultural history and restoration thinning resulted in four distinct bacterial and fungal communities across the four plot types and (d) environmental varia-bles were important predictors of microbial diversity mostly through their impacts on microbial richness

41emsp|emspPossible explanations for changes in bacterial and fungal biodiversity

Agricultural land-use history increased bacterial diversity similar to findings from other studies (Delgado-Baquerizo et al 2017 Dong Huai-Ying De-Yong amp Huang 2008 Hartman Richardson Vilgalys amp Bruland 2008 Jesus Marsh Tiedje amp Moreira 2009 Rodrigues et al 2013 Upchurch et al 2008) Soil nutrients (Delgado-Baquerizo et al 2017 Lauber et al 2008) and soil pH (Jesus et al 2009 Rodrigues et al 2013) may be important fac-tors mediating land-use history effects on microbial diversity Similarly we found a suite of variables that correlated with bacte-rial diversity (Figure 6) and richness (Figure 5) that were also im-pacted by agricultural history In our system post-agricultural sites had decreased soil organic matter micronutrients (S Ca Mg Al and K) moisture and water holding capacity and increased soil P (Table S1) Given collinearities among these variables (Figure 4) it is difficult to say which of those that correlated with measures of bacterial metrics of biodiversity (Table S3) mechanistically influ-enced diversity However we did find a strong pattern that envi-ronmental variables especially below-ground variables were the most important predictors of bacterial richness greatly increas-ing our predictive power of the effects of treatments on richness (Figure S3) However environmental variables explained much less variation in diversity (Figure 6) and almost none at all for evenness

(Figure S4) This suggests that microbial evenness and richness are responding to fundamentally different environmental gradients in this system and illustrates the importance of considering multiple biodiversity measures when evaluating responses to disturbance and management

In contrast to bacteria fungal diversity was lower in post- agricultural plots although the magnitude of this response was rel-atively small (Figure 2) Other studies have also found that agricul-tural land use lowers fungal diversity (Ding et al 2013 Oehl et al 2003 Wagg Dudenhoumlffer Widmer amp Heijden 2018) and our anal-yses suggest that the above-mentioned environmental variables associated with bacteria could also be important factors shaping fungal diversity It is also possible that post-agricultural recovery was limited by dispersal from remnant to post-agricultural plots for fungi as we see for plants (Turley Orrock Ledvina amp Brudvig 2017) or that fungi are relatively slower growing than bacterial and thus slower to recover following disturbance

Restoration increased both bacterial and fungal diversity al-though the effect was stronger for fungi (Figure 2) Decreases in canopy cover and leaf litter along with increases in vegetation cover and plant richness may help explain the increased bacterial richness and diversity in thinned plots as PC2 was a strong pre-dictor of bacterial diversity (Figure 6a) and richness (Figure S3) However this was less for fungi (Figure 6b Figure S3) Restoration greatly increased plant species richness (Table S1 Turley amp Brudvig 2016) which may mediate the effects of restoration thinning on soil microbial communities by increasing the number of suitable plant hosts for host-specific microbes (Peay Baraloto amp Fine 2013 Prober et al 2015) although it is also possible that microbial diversity enhanced plant richness Finally restoration thinning in savanna ecosystems can increase the variability in biota and en-vironmental gradients (Brudvig amp Asbjornsen 2009) thereby in-creasing the number of potential niches within a site for microbes of diverse life histories (Curd Martiny Li amp Smith 2018) Such en-hancement of heterogeneity may be particularly important when restoring post-agricultural ecosystems like in our study given re-ductions in heterogeneity that can persist for decades or longer following agricultural abandonment (Flinn amp Marks 2007)

42emsp|emspCommunity composition in response to agricultural history and restoration

Our results illustrate how agricultural legacies are long-lasting for soil microbial communities persisting over half a century after ag-ricultural abandonment despite post-agricultural and remnant plots being adjacent in our experiment These findings add to a grow-ing body of literature showing varying effects of land-use legacies on soil microbes (Fichtner Oheimb Haumlrdtle Wilken amp Gutknecht 2014 Hartman et al 2008 Hui et al 2018 Jangind et al 2011 Lauber et al 2008 Upchurch et al 2008) although some studies show no impacts of land-use history on soil bacteria (Ma De Frenne Boon et al 2019 Ma De Frenne Vanhellemont et al 2019) Our


community analyses show that both fungal and microbial communi-ties cluster into four distinct community types (Figure 3 Table 1) which is very similar to how plant communities have responded to our treatments (Turley amp Brudvig 2016) This means that restoration did not result in post-agricultural communities being more similar to remnant communities Similarly Strickland et al (2017) found that restored forests in Mississippi had soil microbial communities dis-tinct from agricultural fields and from nearby remnant forests They conclude that above-ground restoration focused on forest structure does little to drive microbial communities towards the remnant ref-erence state or perhaps that these changes will happen very slowly or be contingent on restoration of plant community composition Alternatively agricultural legacies could be due to priority effects where chance events early in community assembly results in differ-ent community outcomes that persist even with the recovery of en-vironmental conditions (Keiser Strickland Fierer amp Bradford 2011)

43emsp|emspImplications for management

We found little evidence that the effects of restoration thinning for soil microbes differed between remnant and post-agricultural plots This finding suggests that agricultural history and restora-tion are independently operating on different groups of microbial species with some species either dispersal limited or affected by altered environmental gradients following agricultural abandon-ment (eg elevated soil phosphorus) and a second group promoted by restoration thinning This presents a mixed message for the prospects of soil microbial recovery during restoration On the one hand restoration can increase the diversity of soil fungi and bac-teria in plots within either land-use history On the other hand restoration does not mitigate the legacies of historical agricultural land use Thus successful soil microbial restoration may require coupling of structural habitat manipulation to reinstate appropri-ate environmental conditions for a diverse suite of microbes with active reintroduction of soil microbes that do not recover passively following agricultural land use (eg Koziol et al 2018 Wubs et al 2016) In turn active reintroduction of soil microbes may be im-portant for re-establishing certain plant species during restoration (Harris 2009 Kardol amp Wardle 2010) Evidence to date from our experiment does not support this however with a suite of under-storey herbs actually establishing better in post-agricultural plots and performing similarly when grown in soils inoculated with soil microbes from remnant and post-agricultural plots (Barker Turley Orrock Ledvina amp Brudvig 2019)

Whether and how soil microbial communities recover following human land use and active restoration efforts remains an open ques-tion (Harris 2009) and our study adds to accumulating evidence that restoration actions manipulating ecosystem structure and plant di-versity (directly or indirectly) also affect soil microbial communities (Banning et al 2011 Barber et al 2017 Dickens Allen Santiago amp Crowley 2015 Potthoff et al 2006) We further illustrate the po-tential for restoration to benefit soil microbes across sites supporting

different land-use histories Given the consequences of microbial communities for ecosystem dynamics during restoration (Kardol amp Wardle 2010) soil microbial differences resulting from land-use legacies and restoration actions may have broad-reaching implica-tions for ecosystem recovery and restoration outcomes in degraded ecosystems

ACKNOWLEDG EMENTSWe are indebted to John Blake Andy Horcher Ed Olson and the prescribed fire crew at the USDA Forest Service-Savannah River for their assistance with creating and maintaining the Remnant Project experiment We thank Sabrie Breland Joe Ledvina and John Orrock for their help with coordinating the Remnant Project experiment Selina Pradhan for laboratory assistance and Will West (Evans Lab) for assistance with bioinformatics This work was supported by funds provided to the Department of Agriculture Forest Service Savannah River under Interagency Agreement DE-EM0003622 with the Department of Energy Aiken SC

AUTHORS CONTRIBUTIONSNET and LAB conceived the research idea and wrote the paper NET collected the field samples and analysed the data LB-D and SEE conducted laboratory work and bioinformatics All the authors edited the paper

DATA AVAIL ABILIT Y S TATEMENTAll raw sequence data from this study are available through the NCBI Sequence Read Archive under project PRJNA551504 and SRAs SRR9609456 - SRR9609568 Data available via the Dryad Digital Repository httpsdoiorg105061dryadx3ffb g7fd (Turley Brudvig Bell-Dereske amp Evans 2020)

ORCIDNash E Turley httpsorcidorg0000-0001-7318-8786

R E FE R E N C E SBanning N C Gleeson D B Grigg A H Grant C D Andersen G L

Brodie E L amp Murphy D V (2011) Soil microbial community suc-cessional patterns during forest ecosystem restoration Applied and Environmental Microbiology 77 6158ndash6164 httpsdoiorg101128AEM00764-11

Barber N A Chantos-Davidson K M Amel Peralta R Sherwood J P amp Swingley W D (2017) Soil microbial community composition in tallgrass prairie restorations converge with remnants across a 27-year chronosequence Environmental Microbiology 19 3118ndash3131 httpsdoiorg1011111462-292013785

Barker C A Turley N E Orrock J L Ledvina J A amp Brudvig L A (2019) Agricultural land-use history does not reduce woodland un-derstory herb establishment Oecologia 89 1049ndash1060 httpsdoiorg101007s00442-019-04348-6

Bates D Maumlchler M Bolker B amp Walker W (2015) Fitting linear mixed-effects models using lme4 Journal of Statistical Software 67(1) 1ndash48 httpsdoiorg1018637jssv067i01

Bellemare J Motzkin G Foster D R amp Forest H (2002) Legacies of the agricultural past in the forested present An assessment of historical land-use effects on rich mesic forests Journal of


Biogeography 29 1401ndash1420 httpsdoiorg101046j1365-2699 200200762x

Bolger A M Lohse M amp Usadel B (2014) Trimmomatic A flexible trimmer for Illumina sequence data Bioinformatics 30 2114ndash2120 httpsdoiorg101093bioin forma ticsbtu170

Breland S Turley N E Gibbs J Isaacs R amp Brudvig L A (2018) Restoration increases bee abundance and richness but not pollination in remnant and post-agricultural longleaf pine woodlands Ecosphere 9 e02435

Brudvig L A (2011) The restoration of biodiversity Where has research been and where does it need to go American Journal of Botany 98 549ndash558 httpsdoiorg103732ajb1000285

Brudvig L A amp Asbjornsen H (2009) The removal of woody encroach-ment restores biophysical gradients in Midwestern oak savannas Journal of Applied Ecology 46 231ndash240 httpsdoiorg101111 j1365-2664200801590x

Brudvig L A amp Damschen E I (2011) Land-use history historical con-nectivity and land management interact to determine longleaf pine woodland understory richness and composition Ecography 34 257ndash266 httpsdoiorg101111j1600-0587201006381x

Brudvig L A Grman E Habeck C W Orrock J L amp Ledvina J A (2013) Strong legacy of agricultural land use on soils and un-derstory plant communities in longleaf pine woodlands Forest Ecology and Management 310 944ndash955 httpsdoiorg101016jforeco201309053

Caporaso J G Kuczynski J Stombaugh J Bittinger K Bushman F D Costello E K hellip Knight R (2010) QIIME allows analysis of high-throughput community sequencing data Nature Methods 7 335ndash336 httpsdoiorg101038nmethf303

Curd E E Martiny J B H Li H amp Smith T B (2018) Bacterial di-versity is positively correlated with soil heterogeneity Ecosphere 9 httpsdoiorg101002ecs22079

De Palma A Sanchez-Ortiz K Martin P A Chadwick A Gilbert G Bates A E hellip Purvis A (2018) Challenges with inferring how land-use affects terrestrial biodiversity Study design time space and synthesis Advances in Ecological Research 58 163ndash199

Delgado-Baquerizo M Reich P B Khachane A N Campbell C D Thomas N Freitag T E hellip Singh B K (2017) It is elemental Soil nutrient stoichiometry drives bacterial diversity Environmental Microbiology 19 1176ndash1188

Dickens S J M Allen E B Santiago L S amp Crowley D (2015) Extractable nitrogen and microbial community structure respond to grassland restoration regardless of historical context and soil compo-sition AoB Plants 7 httpsdoiorg101093aobpl aplu085

Dickie I A (2010) Insidious effects of sequencing errors on perceived diversity in molecular surveys New Phytologist 188 916ndash918 httpsdoiorg101111j1469-8137201003473x

Ding G-C Piceno Y M Heuer H Weinert N Dohrmann A B Carrillo A hellip Smalla K (2013) Changes of soil bacterial diversity as a consequence of land use in a semi-arid ecosystem PLoS ONE 8 e59497

Dong X U E Huai-Ying Y A O De-Yong G E amp Huang C-Y (2008) Soil microbial community structure in diverse land use systems A com-parative study using Biolog DGGE and PLFA analyses Pedosphere 18 653ndash663 httpsdoiorg101016S1002-0160(08)60060-0

Dupouey J L Dambrine E Laffite J D amp Moares C (2002) Irreversible impact of past land use on forest soils and biodi-versity Ecology 83 2978ndash2984 httpsdoiorg1018900012-9658(2002)083[2978IIOPL U]20CO2

Edgar R C (2010) Search and clustering orders of magnitude faster than BLAST Bioinformatics 26 2460ndash2461 httpsdoiorg101093bioin forma ticsbtq461

Edgar R C (2013) UPARSE Highly accurate OTU sequences from mi-crobial amplicon reads Nature Methods 10 996ndash998 httpsdoiorg101038nmeth2604

Edgar R C (2016) UCHIME2 Improved chimera prediction for amplicon sequencing BioRxiv 074252

Edgar R C (2017) Accuracy of microbial community diversity estimated by closed- and open-reference OTUs PeerJ 5 e3889 httpsdoiorg107717peerj3889

Edgar R C amp Flyvbjerg H (2015) Error filtering pair assembly and error correction for next-generation sequencing reads Bioinformatics 31 3476ndash3482 httpsdoiorg101093bioin forma ticsbtv401

Fichtner A Von Oheimb G Haumlrdtle W Wilken C amp Gutknecht J L M (2014) Effects of anthropogenic disturbances on soil micro-bial communities in oak forests persist for more than 100 years Soil Biology and Biochemistry 70 79ndash87 httpsdoiorg101016jsoilb io 201312015

Fierer N amp Jackson R B (2006) The diversity and biogeography of soil bacterial communities Proceedings of the National Academy of Sciences of the United States of America 103 626ndash631 httpsdoiorg101073pnas05075 35103

Flinn K M amp Marks P L (2007) Agricultural legacies in forest envi-ronments Tree communities soil properties and light availability Ecological Applications 17 452ndash463 httpsdoiorg10189005- 1963

Flinn K M amp Vellend M (2005) Recovery of forest plant communities in post agricultural landscapes Frontiers in Ecology and the Environment 3 243ndash250 httpsdoiorg1018901540-9295(2005)003[0243 ROFPC I]20CO2

Flinn K M Vellend M amp Marks P L (2005) Environmental causes and consequences of forest clearance and agricultural abandonment in central New York USA Journal of Biogeography 32 439ndash452 httpsdoiorg101111j1365-2699200401198x

Foley J A DeFries R Asner G P Barford C Bonan G Carpenter S R hellip Snyder P K (2005) Global consequences of land use Science 309 570ndash574 httpsdoiorg101126scien ce1111772

Foster D Swanson F Aber J Burke I Brokaw N Tilman D amp Knapp A (2003) The importance of land-use legacies to ecology and conservation BioScience 53 77ndash88 httpsdoiorg101641 0006-3568(2003)053[0077TIOLU L]20CO2

Fox J amp Weisberg S (2018) An R companion to applied regression London UK Sage Publications

Freschet G T Oumlstlund L Kichenin E amp Wardle D A (2014) Above and belowground legacies of native Sami land use on boreal forest in northern Sweden 100 years after abandonment Ecology 95 963ndash977

Haegeman B Hamelin J Moriarty J Neal P Dushoff J amp Weitz J S (2013) Robust estimation of microbial diversity in theory and in practice The ISME Journal 7 1092ndash1101 httpsdoiorg101038ismej201310

Hahn P G amp Orrock J L (2015) Spatial arrangement of canopy struc-ture and land-use history alter the effect that herbivores have on plant growth Ecosphere 6 193 httpsdoiorg101890ES15- 000361

Harris J (2009) Soil microbial communities and restoration ecol-ogy Facilitators or followers Science 325 573ndash574 httpsdoiorg101126scien ce1172975

Hartman W H Richardson C J Vilgalys R amp Bruland G L (2008) Environmental and anthropogenic controls over bacterial communi-ties in wetland soils Proceedings of the National Academy of Sciences of the United States of America 105 17842ndash17847 httpsdoiorg101073pnas08082 54105

Hermy M amp Verheyen K (2007) Legacies of the past in the present-day forest biodiversity A review of past land-use effects on forest plant species composition and diversity Ecological Research 22 361ndash371 httpsdoiorg101007s11284-007-0354-3

Hui N Liu X Jumpponen A Setaumllauml H Kotze D J Biktasheva L amp Romantschuk M (2018) Over twenty years farmland reforesta-tion decreases fungal diversity of soils but stimulates the return of


ectomycorrhizal fungal communities Plant and Soil 427 231ndash244 httpsdoiorg101007s11104-018-3647-0

Jaeger B (2017) r2glmm Computes R squared for mixed (multilevel) models R package version 012 Retrieved from httpsCRANR-proje ctorgpacka ge=r2glmm

Jangind K Williams M A Franzluebbers A J Schmidt T M Coleman D C amp Whitman W B (2011) Land-use history has a stronger impact on soil microbial community composition than abo-veground vegetation and soil properties Soil Biology amp Biochemistry 43 2184ndash2193 httpsdoiorg101016jsoilb io201106022

Jesus E D C Marsh T L Tiedje J M amp Moreira F M D S (2009) Changes in land use alter the structure of bacterial communities in Western Amazon soils The ISME Journal 3 1004ndash1011 httpsdoiorg101038ismej200947

Jones H P Jones P C Barbier E B Blackburn R C Rey Benayas J M Holl K D hellip Moreno Mateos D (2018) Restoration and repair of Earths damaged ecosystems Proceedings of the Royal Society B Biological Sciences 285(1873) 2017ndash2577 httpsdoiorg101098rspb20172577

Kardol P Martijn Bezemer T amp Van Der Putten W H (2006) Temporal variation in plantndashsoil feedback controls succession Ecology Letters 9 1080ndash1088 httpsdoiorg101111j1461-0248200600953x

Kardol P amp Wardle D A (2010) How understanding aboveground- belowground linkages can assist restoration ecology Trends in Ecology amp Evolution 25 670ndash679 httpsdoiorg101016jtree201009001

Keiser A D Strickland M S Fierer N amp Bradford M A (2011) The effect of resource history on the functioning of soil microbial com-munities is maintained across time Biogeosciences 8(6) 1477ndash1486 httpsdoiorg105194bg-8-1477-2011

Kilgo J C amp Blake J I (2005) Ecology and management of a forested landscape Fifty years on the Savannah River Site Washington DC Island Press

Kotildeljalg U Nilsson R H Abarenkov K Tedersoo L Taylor A F S Bahram M hellip Larsson K-H (2013) Towards a unified paradigm for sequence-based identification of fungi Molecular Ecology 22 5271ndash5277 httpsdoiorg101111mec12481

Koziol L Schultz P A House G L Bauer J T Middleton E L amp Bever J D (2018) The plant microbiome and native plant resto-ration The example of native mycorrhizal fungi BioScience 68 996ndash1006 httpsdoiorg101093biosc ibiy125

Lauber C L Strickland M S Bradford M A amp Fierer N (2008) The influence of soil properties on the structure of bacterial and fungal communities across land-use types Soil Biology amp Biochemistry 40 2407ndash2415 httpsdoiorg101016jsoilb io200805021

Love M I Huber W amp Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 Genome Biology 15 550 httpsdoiorg101186s13059-014-0550-8

Ma S De Frenne P Boon N Brunet J Cousins S A O Decocq G hellip Verheyen K (2019) Plant species identity and soil characteris-tics determine rhizosphere soil bacteria community composition in European temperate forests FEMS Microbiology Ecology 95 fiz063 httpsdoiorg101093femse cfiz063

Ma S De Frenne P Vanhellemont M Wasof S Boeckx P Brunet J hellip Verheyen K (2019) Local soil characteristics determine the microbial communities under forest understorey plants along a lat-itudinal gradient Basic and Applied Ecology 36 34ndash44 httpsdoiorg101016jbaae201903001

Ma S Verheyen K Props R Wasof S Vanhellemont M Boeckx P hellip De Frenne P (2018) Plant and soil microbe responses to light warming and nitrogen addition in a temperate forest Functional Ecology 32 1293ndash1303 httpsdoiorg1011111365-243513061

Masella A P Bartram A K Truszkowski J M Brown D G amp Neufeld J D (2012) PANDAseq Paired-end assembler for illumina sequences BMC Bioinformatics 13 31 httpsdoiorg1011861471-2105- 13-31

Meli P Holl K D Rey Benayas J M Jones H P Jones P C Montoya D amp Mateos D M (2017) A global review of past land use climate and active vs passive restoration effects on forest recovery PLoS ONE 12 e0171368

Newbold T Hudson L N Hill S L L Contu S Lysenko I Senior R A hellip Purvis A (2015) Global effects of land use on local terres-trial biodiversity Nature 520 45ndash50 httpsdoiorg101038natur e14324

Noss R F Platt W J Sorrie B A Weakley A S Means D B Costanza J amp Peet R K (2015) How global biodiversity hotspots may go un-recognized Lessons from the North American coastal plain Diversity and Distributions 21 236ndash244 httpsdoiorg101111ddi12278

Oehl F Sieverding E Ineichen K Maumlder P Boller T amp Wiemken A (2003) Impact of land use intensity on the species diversity of arbus-cular mycorrhizal fungi in agroecosystems of Central Europe Applied Environmental Microbiology 69 2816ndash2824 httpsdoiorg101128AEM6952816-28242003

Oksanen J Blanchet F G Kindt R Legendre P Orsquohara R B Simpson G L hellip Wagner H (2010) Vegan community ecology package R package version 117-4 Retrieved from httpcranr-proje ctorggt

Peay K G Baraloto C amp Fine P V A (2013) Strong coupling of plant and fungal community structure across western Amazonian rainforests The ISME Journal 7 1852ndash1861 httpsdoiorg101038ismej201366

Perring M P De Frenne P Baeten L Maes S L Depauw L Blondeel H hellip Verheyen K (2016) Global environmental change effects on ecosystems The importance of land-use legacies Global Change Biology 22 1361ndash1371 httpsdoiorg101111gcb13146

Perring M P Standish R J Price J N Craig M D Erickson T E Ruthrof K X hellip Hobbs R J (2015) Advances in restoration ecol-ogy Rising to the challenges of the coming decades Ecosphere 6 131 httpsdoiorg101890ES15-001211

Potthoff M Steenwerth K L Jackson L E Drenovsky R E Scow K M amp Joergensen R G (2006) Soil microbial community composi-tion as affected by restoration practices in California grassland Soil Biology and Biochemistry 38 1851ndash1860 httpsdoiorg101016 jsoilb io200512009

Prober S M Leff J W Bates S T Borer E T Firn J Harpole W S hellip Fierer N (2015) Plant diversity predicts beta but not alpha diver-sity of soil microbes across grasslands worldwide Ecology Letters 18 85ndash95 httpsdoiorg101111ele12381

Rodrigues J L M Pellizari V H Mueller R Baek K Jesus E D C Paula F S hellip Nusslein K (2013) Conversion of the Amazon rain-forest to agriculture results in biotic homogenization of soil bacte-rial communities Proceedings of the National Academy of Sciences of the United States of America 110 988ndash993 httpsdoiorg101073pnas12206 08110

Rosseel Y (2012) lavaan An R package for structural equation model-ing Journal of Statistical Software 48(2) 1ndash36 Retrieved from httpwwwjstat softorgv48i02

Semenova-Nelsen T A Platt W J Patterson T R Huffman J amp Sikes B A (2019) Frequent fire reorganizes fungal communities and slows decomposition across a heterogeneous pine savanna landscape New Phytologist 224 916ndash927 httpsdoiorg101111nph16096

Strickland M S Callaham Jr M A Gardiner E S Stanturf J A Leff J W Fierer N amp Bradford M A (2017) Response of soil microbial community composition and function to a bottomland forest resto-ration intensity gradient Applied Soil Ecology 119 317ndash326 httpsdoiorg101016japsoil201707008

Stuhler J D amp Orrock J L (2016) Historical land use and present-day canopy thinning differentially affect the distribution and abun-dance of invasive and native ant species Biological Invasions 18 1813ndash1825 httpsdoiorg101007s10530-016-1122-5

Suding K N (2011) Toward and era of restoration ecology Successes failures and opportunities ahead Annual Review of Ecology Evolution and Systematics 42 465ndash487


Thieacutery O Moora M Vasar M Zobel M amp Oumlpik M (2012) Inter- and intrasporal nuclear ribosomal gene sequence variation within one isolate of arbuscular mycorrhizal fungus Diversispora sp Symbiosis 58 135ndash147 httpsdoiorg101007s13199-012-0212-0

Turley N E amp Brudvig L A (2016) Agricultural land-use history causes persistent loss of plant phylogenetic diversity Ecology 97 2240ndash2247 httpsdoiorg101002ecy1443

Turley N Brudvig L Bell-Dereske L amp Evans S (2020) Data from Agricultural land-use history and restoration impact soil microbial biodiversity Dryad Digital Repository httpsdoiorg105061dryadx3ffb g7fd

Turley N E Orrock J L Ledvina J A amp Brudvig L A (2017) Dispersal and establishment limitation slows plant community recovery in post-agricultural longleaf pine savannas Journal of Applied Ecology 54 1100ndash1109

Upchurch R Chiu C Y Everett K Dyszynski G Coleman D C amp Whitman W B (2008) Differences in the composition and diver-sity of bacterial communities from agricultural and forest soils Soil Biology and Biochemistry 40 1294ndash1305

van der Bij A U Weijters M J Bobbink R Harris J A Pawlett M Ritz K hellip van Diggelen R (2018) Facilitating ecosystem assembly Plant-soil interactions as a restoration tool Biological Conservation 220 272ndash279 httpsdoiorg101016jbiocon2018 02010

van der Heijden M G A Bardgett R D amp van Straalen N M (2008) The unseen majority Soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems Ecology Letters 11 296ndash310 httpsdoiorg101111j1461-0248200701139x

Wagg C Dudenhoumlffer J H Widmer F amp Van Der Heijden M G (2018) Linking diversity synchrony and stability in soil micro-bial communities Functional Ecology 32 1280ndash1292 httpsdoiorg1011111365-243513056

Weiss S Xu Z Z Peddada S Amir A Bittinger K Gonzalez A hellip Knight R (2017) Normalization and microbial differential abun-dance strategies depend upon data characteristics Microbiome 5 27 httpsdoiorg101186s40168-017-0237-y

Wubs E R J van der Putten W H Bosch M amp Bezemer T M (2016) Soil inoculation steers restoration of terrestrial ecosystems Nature Plants 2 16107 httpsdoiorg101038nplan ts2016107

Xue P P Carrillo Y Pino V Minasny B amp McBratney A B (2018) Soil properties drive microbial community structure in a large scale transect in south eastern Australia Scientific Reports 8 11725

SUPPORTING INFORMATIONAdditional supporting information may be found online in the Supporting Information section

How to cite this article Turley NE Bell-Dereske L Evans SE Brudvig LA Agricultural land-use history and restoration impact soil microbial biodiversity J Appl Ecol 2020001ndash12 httpsdoiorg1011111365-266413591


1emsp |emspINTRODUC TION

The conversion of natural ecosystems for human land uses is a lead-ing threat to biodiversity (Foley et al 2005 Newbold et al 2015) and can leave long-lasting legacies on ecosystems (Flinn amp Vellend 2005 Foster et al 2003) For example former agricultural lands can support altered soils plant communities and other properties for decades to millennia following farm abandonment relative to lsquoremnantrsquo ecosystems with no history of agricultural use (Bellemare Motzkin Foster amp Forest 2002 Dupouey Dambrine Laffite amp Moares 2002 Flinn amp Marks 2007) As a consequence active res-toration may be necessary to overcome land-use legacies in many ecosystems (Perring et al 2015 2016 Suding 2011) Yet our un-derstanding of land-use legacies and the role of active restoration for mitigating legacy effects remains unresolved for several reasons

First incomplete understanding results from taxonomic biases in studies of land-use legacies and restoration Plants have been a strong focus in both agricultural legacy and restoration research (Brudvig 2011 Flinn amp Vellend 2005 Hermy amp Verheyen 2007) Yet taxa respond differently to legacies and restoration activities (eg Jones et al 2018) and many taxa remain poorly investigated Here we focus on responses of soil microbial communities which can be strongly affected by land-use legacies and restoration prac-tices (eg Barber Chantos-Davidson Amel Peralta Sherwood amp Swingley 2017 Freschet Oumlstlund Kichenin amp Wardle 2014 Hui et al 2018 Jangind et al 2011 Ma De Frenne Boon et al 2019 Xue Carrillo Pino Minasny amp McBratney 2018) but are poorly studied taxa in these fields (eg Brudvig 2011)

Understanding soil microbial responses to agricultural legacies and restoration is especially important because these groups can have major impacts on plant diversity and productivity as well as res-toration success (van der Bij et al 2018 van der Heijden Bardgett amp Straalen 2008 Wubs Putten Bosch amp Bezemer 2016) For ex-ample because of host preferences and their roles in mutualistic and antagonistic relationships soil microbes can limit plant species distributions and alter plant community interactions (Kardol Martijn Bezemer amp Putten 2006 Wubs et al 2016) In turn inoculation of former agricultural fields with mycorrhizal fungi or whole soils from remnant ecosystems can affect plant establishment and community assembly dynamics during restoration (Koziol et al 2018 Wubs et al 2016) Thus how agricultural legacies and restoration affect soil microbes may have broad-reaching implications for ecosystem recovery

Second studies of land-use legacies face study design challenges (De Palma et al 2018) including biases introduced through decisions made in the past about where and how land was used by humans For example temperate forests on level ground near roads and with higher pH soils are more likely to be converted to agricultural fields

(Flinn Vellend amp Marks 2005) In turn fields on steep slopes located far from roads and with lower pH soils are more likely to be aban-doned from agriculture (Flinn et al 2005) As a consequence under-lying site properties might be mistakenly interpreted as agricultural legacy effects when in fact they are simply consequences of the land-use decision-making process Controlling for these land-use biases is particularly important for resolving how soil microbial communities respond to land-use legacies given soil microbes re-sponsiveness to soil conditions (Fierer amp Jackson 2006 Lauber Strickland Bradford amp Fierer 2008 Ma De Frenne Vanhellemont et al 2019 Ma et al 2018 Xue et al 2018) Here we control for land-use biases through a study design where post-agricultural plots and remnant plots with no known history of agriculture are paired in space resulting in no bias in underlying soil types (Brudvig Grman Habeck Orrock amp Ledvina 2013)

Third studies are needed to explicitly consider how restoration affects land-use legacies and in turn how land-use legacies affect restoration outcomes Systems with a history of intensive human land use such as agriculture are a common focus of restoration ef-forts and yet whether and how restoration can overcome the lega-cies of past land uses remains unclear (Jones et al 2018 Meli et al 2017) Moreover because land-use history can affect numerous system attributes legacies alter the template onto which restoration acts As a consequence restoration outcomes may differmdashperhaps substantiallymdashfor locations with differing land-use histories (Brudvig amp Damschen 2011 Turley amp Brudvig 2016) In other instances however restoration may have clear effects that are broadly similar to areas with and without a particular history (eg Breland Turley Gibbs Isaacs amp Brudvig 2018) What is needed are controlled repli-cated experiments to draw strong inferences about how restoration and land-use legacies interact Yet experiments manipulating resto-ration treatments across areas differing in land-use history are rare Ideally such investigations would be coupled with measurements of key environmental variables hypothesized to affect the taxa of inter-est including those affected by agricultural history and restoration For example within our focal system bee responses to restoration may be mediated by the increase in flower cover resulting from res-toration (Breland et al 2018) Therefore determining mechanisms of legacy and restoration effects likely requires coupled measure-ments of key environmental variables along with the focal taxa

We overcame these limitations through a replicated restoration experiment in longleaf pine savannas Our experiment included a factorial manipulation of restoration (overstorey tree thinning) and agricultural history whereby plots with and without a history of till-age agriculture received restoration or were left as unrestored con-trols We arranged plots into blocks to control for land-use biases and considered how agricultural history restoration thinning and their interaction affect soil bacteria and fungi

K E Y W O R D S

agricultural history bacteria community ecology fungi land-use legacy metabarcoding restoration soil microbe biodiversity

















Thinned

Thinned Control

20 km

100 m

1 m

Single soil probe

South Carolina

Savanna River Site


Control


















3emsp |emspRESULTS







)


(a) (b)







ddf F p r2

Bacteria

Inverse Simpsons D

Land use 23 14895 lt001 390



Community

Land use 121 314 lt001 025



Fungi

Inverse Simpsons D

Land use 25 544 028 003



Community

Land use 121 513 lt001 039





CAP1

CA

P2

2 1 0 1 2

21

01

2

CAP1

CA

P2

03 01 01 03

04

02

00

02





PC1

PC

2

Pveg

Plitter

Pwood

Ptreetrunk

Pbareground


Duffdepth

richness1 times 1

richness10 times 10

PclayPsilt

Psand

pH

OM

S

P

Ca MgKNa

FeMn

Cu

Al

PmoisturePWHC

ndash10 ndash05 00 05 10 15

ndash05

00

05

10



Bacteria Fungi

r p r p















3000

3500

4000

4500

ndash1 0 1 2

Environmental PC1

Ric

hnes

s

200

300

400

500

600

ndash1 0 1 2

Environmental PC1

Ric

hnes

s




r = 46 p lt 001 r = 66 p lt 001


(a) (b)


































































































































Thinned

Thinned Control

20 km

100 m

1 m

Single soil probe

South Carolina

Savanna River Site


Control


















3emsp |emspRESULTS







)


(a) (b)







ddf F p r2

Bacteria

Inverse Simpsons D

Land use 23 14895 lt001 390



Community

Land use 121 314 lt001 025



Fungi

Inverse Simpsons D

Land use 25 544 028 003



Community

Land use 121 513 lt001 039





CAP1

CA

P2

2 1 0 1 2

21

01

2

CAP1

CA

P2

03 01 01 03

04

02

00

02





PC1

PC

2

Pveg

Plitter

Pwood

Ptreetrunk

Pbareground


Duffdepth

richness1 times 1

richness10 times 10

PclayPsilt

Psand

pH

OM

S

P

Ca MgKNa

FeMn

Cu

Al

PmoisturePWHC

ndash10 ndash05 00 05 10 15

ndash05

00

05

10



Bacteria Fungi

r p r p















3000

3500

4000

4500

ndash1 0 1 2

Environmental PC1

Ric

hnes

s

200

300

400

500

600

ndash1 0 1 2

Environmental PC1

Ric

hnes

s




r = 46 p lt 001 r = 66 p lt 001


(a) (b)



































































































































3emsp |emspRESULTS







)


(a) (b)







ddf F p r2

Bacteria

Inverse Simpsons D

Land use 23 14895 lt001 390



Community

Land use 121 314 lt001 025



Fungi

Inverse Simpsons D

Land use 25 544 028 003



Community

Land use 121 513 lt001 039





CAP1

CA

P2

2 1 0 1 2

21

01

2

CAP1

CA

P2

03 01 01 03

04

02

00

02





PC1

PC

2

Pveg

Plitter

Pwood

Ptreetrunk

Pbareground


Duffdepth

richness1 times 1

richness10 times 10

PclayPsilt

Psand

pH

OM

S

P

Ca MgKNa

FeMn

Cu

Al

PmoisturePWHC

ndash10 ndash05 00 05 10 15

ndash05

00

05

10



Bacteria Fungi

r p r p















3000

3500

4000

4500

ndash1 0 1 2

Environmental PC1

Ric

hnes

s

200

300

400

500

600

ndash1 0 1 2

Environmental PC1

Ric

hnes

s




r = 46 p lt 001 r = 66 p lt 001


(a) (b)
























































































































ddf F p r2

Bacteria

Inverse Simpsons D

Land use 23 14895 lt001 390



Community

Land use 121 314 lt001 025



Fungi

Inverse Simpsons D

Land use 25 544 028 003



Community

Land use 121 513 lt001 039





CAP1

CA

P2

2 1 0 1 2

21

01

2

CAP1

CA

P2

03 01 01 03

04

02

00

02





PC1

PC

2

Pveg

Plitter

Pwood

Ptreetrunk

Pbareground


Duffdepth

richness1 times 1

richness10 times 10

PclayPsilt

Psand

pH

OM

S

P

Ca MgKNa

FeMn

Cu

Al

PmoisturePWHC

ndash10 ndash05 00 05 10 15

ndash05

00

05

10



Bacteria Fungi

r p r p















3000

3500

4000

4500

ndash1 0 1 2

Environmental PC1

Ric

hnes

s

200

300

400

500

600

ndash1 0 1 2

Environmental PC1

Ric

hnes

s




r = 46 p lt 001 r = 66 p lt 001


(a) (b)

























































































































3000

3500

4000

4500

ndash1 0 1 2

Environmental PC1

Ric

hnes

s

200

300

400

500

600

ndash1 0 1 2

Environmental PC1

Ric

hnes

s




r = 46 p lt 001 r = 66 p lt 001


(a) (b)


















































































































agricultural land‐use history and restoration impact soil ... · and restoration tree thinning...

Documents