DOI: 10.5061/dryad.x3ffbg7fd
title: Data from: Agricultural land-use history and restoration impact soil microbial biodiversity

description: 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. 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 (reforested following abandonment from
 tillage agriculture &gt;60 years prior) longleaf pine savanna plots.
 Plots were each 1-ha and arranged into 27 blocks to minimize 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. 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 including aboveground variables
 like leaf litter and plant diversity, and belowground variables such as
 soil nutrients, pH, and organic matter, many of which were also impacted
 by agricultural history and restoration. Fungal and bacterial community
 compositions were shaped by restoration and agricultural history resulting
 in four distinct communities across the four treatment combinations. Past
 agricultural land use left persistent legacies on soil microbial
 biodiversity, even over half a century after agricultural abandonment and
 after intensive restoration activities. The impacts of these changes on
 soil microbe biodiversity could play important roles in the functioning of
 ecosystems following agricultural abandonment and during restoration.
description: In Fall 2015 we collected ~1.2 L of
 soil from each of the 126 1-ha plots. Each soil sample was an aggregate of
 30 1.6 cm wide by 20 cm deep soil probes collected along two 50 m
 transects through the middle of each plot (Fig. 1). The soil sampling
 transects ran on both sides of our already-present vegetation sampling
 transects (Turley and Brudvig 2016; Fig. 1). Before each probe the leaf
 litter, duff, and sticks were brushed aside. To minimize contamination we
 used one soil probe for all remnant sites and another for all
 post-agricultural sites and between each plot we rinsed the inside 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. 50
 mL was stored in a -20 C freezer for microbial analysis, and two other
 subsamples were used for environmental sampling. For
 microbial analysis, we extracted soil DNA using MoBio PowerSoil Extraction
 Kit following the manufacture’s instructions. We submitted DNA to the
 Michigan State University Core Genomics Facility for Illumina sequence
 library construction. Following their standard protocols, bacterial 16S V4
 (515f/806r) and ITS (ITS-F/ITS2) 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 sequenced independently in a 2x250bp paired end format using
 independent v2 500 cycle MiSeq reagent cartridges. 
 The first of the soil subsamples was analyzed by Brookside
 Laboratories Inc. (New Bremen, Ohio, USA) for soil texture (percent sand,
 clay, and silt), pH, organic matter, and nutrients and minerals. On the
 second subsample we measured soil water-holding capacity (proportionate
 difference 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 phosphorus was strongly
 increased (Table S1). <i>Environmental data
 collection</i> We measured a set of environmental
 variables within each experimental plot at 10 m intervals along the 100 m
 vegetation transects (Fig. 1) during the 2015 growing season. In 1 × 1 m
 plots we visually estimated the percent cover of leaf litter, down woody
 debris, bare ground, and understory vegetation. At each of these plots we
 also measured the depth of leaf litter and canopy cover of overstory trees
 using a spherical densiometer. In 1 × 1 m and also 10 × 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 understory plant richness (Table S1). Units and
 methods for measuring all of our environmental variables are available in
 supplementary material (Table S6).
 <i>Bioinformatics </i> 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
 (Masella et al. 2012, Bolger et al. 2014). Processed reads were clustered
 into OTUs at 97% identity level using UCLUST6.1 with the default settings
 (Edgar 2010). Singletons were removed and contigs were screened using
 QIIME 1.9.1  (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 (Thiéry et
 al. 2012) and sequencing errors (Dickie 2010). The resulting community was
 composed of 90,103 OTUs and 1,650,420 reads. Fungal reads were quality
 filtered and merged using the USEARCHv10 pipeline
 (http://drive5.com/usearch/; Edgar 2010, 2013). Merged sequences were
 quality filtered to an expected error threshold of 1.0 fastq_filter (Edgar
 and 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 7.1 ITS1
 chimera and reference databases, respectively (Kõljalg et al. 2013). All
 non-fungal OTUs and those with less than four reads were filtered from the
 community matrix. The resulting fungal community had 10,285 OTUs and
 584,113 reads.
description: See read me file
 named: turley_etal_jae_2020_read_me_explinations_of_data_files.txt