DOI: 10.5061/dryad.x95x69pmw
title: Data from: Environmental change alters nitrogen fixation rates and microbial parameters in a subarctic biological crust

description: Together, Biological Soil Crust (BSC) and other cryptogamic groundcovers
 can contribute up to half of the global nitrogen (N) fixation. BSC also
 stabilizes the soil (reducing erosion and dust emissions), fixes carbon
 (C), retains moisture, and acts as a hotspot of microbial diversity and
 activity. Much of the knowledge about how climate change is affecting the
 composition and functioning of BSC comes from hot arid and semiarid
 regions. The comparatively smaller body of research on BSC from cold and
 mesic environments has been primarily observational, for example along
 chronosequences after a glacier retreat. Few studies have experimentally
 investigated the effects of the environment on BSC from high latitudes.
 Such experiments allow unraveling of relationships at a resolution that
 can only be achieved by controlling for confounding factors. We measured
 short-term (2-4 days) responses of a liverwort-based (Anthelia juratzkana)
 BSC from the south of Iceland to a range of temperature, moisture and
 light conditions. Warming increased N fixation rates, especially when
 moisture was at a saturation level, and only when light was not limiting.
 A correlation analysis suggests that increases in N fixation rates were
 linked to cyanobacterial abundance on the BSC surface and to the rates of
 their metabolic activity. Warming and moisture changes also induced
 compositional and structural modification of the bacterial community, with
 consequences at the functional level. In contrast to many observations on
 BSC from hot drylands, the BSC from our cold and mesic study site is more
 limited by low temperature and light than by moisture. Our findings show
 possible ways in which BSC from cold and mesic ecosystems can respond to
 short-term manifestations of climate change, such as increasingly frequent
 heat waves.
description: We designed a controlled laboratory experiment to investigate the
 responses of a subarctic liverwort-based (Anthelia juratzkana) BSC from
 the south of Iceland to different levels of temperature, moisture and
 light. We studied how these environmental factors affect the capacity of
 subarctic BSC to fix N, and whether these responses were linked to changes
 in the abundance of N fixers and/or to structural changes in the BSC
 microbial communities. 1. Sample collection
 In September 2018 we collected BSC from a site adjacent to the
 Climate Research Unit at Subarctic Temperatures (CRUST) experiment
 (Salazar et al., in progress), near Landmannahellir, Iceland (64°02'
 N, 19°13' W; 590 m.a.s.l.). Mean annual temperature and precipitation
 at the site are ca. 5 °C and 1500 mm, respectively. Surface cover in this
 area is primarily liverwort-based BSC (ca. 50%), followed by mosses (ca.
 30%) and Salix herbacea dwarf willow (ca. 20%), on an andosol/vitrisol
 substratum.  We randomly collected eight BSC blocks
 (i.e. replicates) of 13x16 cm2 and ca. 5 cm deep (Figure S1a in article).
 Blocks were separated by at least 10 meters. Since the focus of this study
 is on BSC, patches of moss or vascular plants were avoided. We transported
 (approx. 5 h) the BSC blocks in coolers with ice packs and stored them in
 a dark room at 5 °C for 2 to 5 weeks while we performed the analyses
 described below. We kept wet paper towels inside the coolers to prevent
 desiccation. We subsampled BSC disks of 5 cm diameter and 1.5 cm depth out
 of the 13x16x5 cm3 BSC blocks (Figure S1c) for N fixation analyses
 (section 3). Then, we subsampled BSC disks of 1.5 cm diameter, 1.5 cm
 depth from each 5 cm diameter BSC disk, for Chl a (section 4) and
 cyanobacteria and liverwort cover (section 5) analyses and for DNA
 extractions (section 6).   2. Experimental design and
 environmental treatments We studied the effects of
 temperature, moisture and light on N fixation and the microbial community
 structure. For this, we conducted a factorial experiment (4 x 2 x 2) with
 four levels of temperature: 10, 15, 20 and 25 °C; two levels of moisture:
 ca. 75% (close to moisture at the moment of sampling) and 100%
 (saturated); and two levels of light ca. 2 μmol m-2 s-1 (low intensity)
 and ca. 90 μmol m-2 s-1 (high intensity; Figure S2 in article). Light was
 available all the time (i.e. we did not set day/night cycles), to simulate
 conditions similar to those in the sampling site during the summer.
 Temperature and light treatments were set in a growth chamber (Termaks
 series 8000, Bergen, Norway), and monitored hourly with temperature/light
 loggers (HOBO Pendant® MX Temperature/Light Data Logger, MX2202, Onset,
 Bourne, MA, USA). Levels of these environmental variables were selected
 within ranges commonly experienced by BSC at the sampling site (between
 ca. &gt;0 and 25°C; 0 and &gt;100 μmol m-2 s-1; and between
 dryness for short periods of time during the summer, and saturation e.g.
 after the winter snow is melted; unpublished observations) and comparable
 ecosystems (e.g. a mesic-dry heath in Greenland; Rousk et al., 2018). We
 compared ambient vs. saturation moisture levels because mean annual
 precipitation in subarctic and arctic regions is projected to increase in
 the coming decades (IPCC, 2021). The maximum temperatures in our
 experimental design were selected based on peaks of warming (measured at
 the soil surface) recorded during previous growing seasons (unpublished
 data). In this sense, our high temperature treatment should simulate BSC
 responses to heat waves at the study site, under different moisture and
 light conditions.    Average temperature and light
 intensity inside the jars were 11.1 ± 0.7, 16.5 ± 0.7, 21.5 ± 0.7 and 26.6
 ± 0.9 °C (2 loggers x 2 light levels; n = 4) and 2.3 ± 0.04 and 88.0 ± 1.6
 μmol m-2 s-1 (2 loggers x 4 temperature levels; n = 8) respectively
 (Figure S2a and b in article). Temperature levels inside the jars were
 slightly higher than temperatures set in the growing chamber due to a
 greenhouse effect. To create a saturation level in the
 moisture treatment, we wetted each sample with an excess of deionized
 water and waited for approximately one minute until it stopped dripping.
 Moisture was maintained between analyses by placing wet towels in the
 coolers stored in the cold, dark room. After environmental treatments and
 N fixation measurements (see following section), we oven dried (60 °C, 24
 h) BSC disks to estimate the dried weight of the samples, and to prepare
 them for chlorophyll a analysis and DNA extraction. Average moisture
 content was 75.5 ± 2.4 and 107.2 ± 2.3 % (Figure S3c in
 article). 3. N fixation under controlled temperature,
 moisture and light conditions We estimated N fixation
 rates with the Acetylene Reduction Assay (ARA; Hardy et al., 1968). We
 used eight 5-cm subsampled disks (i.e. replicates) per combination of
 temperature and moisture treatments. Thus, each temperature-specific ARA
 analysis was composed of a total of 16 samples with two levels of
 moisture, eight saturated and eight unsaturated, plus controls with
 acetylene, ethylene and air. The BSC disks were weighed (for further water
 content analysis) and placed in 350 mL glass jars with rubber septa in the
 lids (Figure S1c in article). These jars were then placed in an
 environmental chamber (Termaks series 8000, Bergen, Norway) at fixed
 temperature and light conditions. We acclimated the samples to each
 combination of temperature and light for 24 h. We then manually aerated
 the jars for a few seconds, closed the jars tightly and replaced 10% of
 the headspace with acetylene (except in jars used as ethylene and air
 controls). We incubated the jars at the set temperature and light
 conditions for 24 h. Then, we collected 22 mL of gas from each jar and
 analyzed it using a Clarus 400 gas chromatograph (PerkinElmer Ltd.,
 Beaconsfield, UK) equipped with an automatic split/splitless injector and
 a flame ionization detector (FID), and an Elite-Alumina column (30 m, 0.53
 mm; PerkinElmer Ltd., Beaconsfield, UK). At the end of
 each 48 h acclimation-incubation period, we manually aerated the samples
 and started a new acclimation-incubation at a different light (but same
 temperature) condition. To control for a possible effect of the storing
 time in the cold room, we randomized the order of the temperatures for the
 incubations. We incubated first samples (8 replicates at ca. 75% and 8 at
 100% moisture content) at 20°C, then at 10, 25 and 15 °C. Also, to control
 for a possible cumulative effect between light levels, we switched the
 order of the light levels for each temperature treatment. For example, for
 samples incubated at 20°C we measured ethylene production first at low
 light (48 h) and then at high light (48 h). For the next quarter of the
 samples, incubated at 10°C, we measured ethylene production first at high
 light (48 h) and then at low light (48 h), and so on, for the other two
 temperature treatments. Since ARA is a non-destructive method, we were
 able to estimate N fixation rates on the same sample at different light
 treatments. For the rest of the analysis, based on destructive methods
 (see details below), we measured BSC responses to moisture and
 temperature.      4. Cyanobacteria and liverwort cover
 on BSC We estimated the cover of cyanobacteria and
 liverwort (Anthelia juratzkana; Figure S1b in article) on the BSC surface
 by epifluorescence microscopy (Figure S3 in article; similar to Lan et
 al., 2019). After ARA measurements, BSC samples were stored in a dark room
 at 5 °C for 1 to 4 days. Plant and cyanobacterial growth was assumed to be
 minimal under these conditions. From each 5 cm diameter BSC disk (Figure
 S1c), we subsampled a 1.5 cm diameter BSC disk and imaged the plant
 (liverwort) chlorophyll using a Leica DM6000B fluorescent microscope
 (Leica, Heerbrugg Switzerland) equipped with an I3 filter cube (Ex 450/90,
 Di 510, Em 515), and the cyanobacterial phycocyanin with a TX2 filter cube
 (Ex 560/40, Di 595, Em 630/30). Multiple fields of view were measured
 using both filter cubes and stitched together to form an image of 1x1 cm
 of BCS surface (Figure S3) using the Leica software. Images were analyzed
 in ImageJ/Fiji (Collins, 2007; Schindelin et al., 2012), and estimates of
 cyanobacterial and plant covers calculated as percentage of BSC surface
 cover. We did not subsample BSC disks between light
 levels, but rather used samples that were exposed to low light for 48 h
 (24 h acclimation plus 24 h ARA) and then to high light for another 48 h,
 or vice versa. Therefore, the treatments in this part of our analysis
 include temperature and moisture, but not light. 5.
 Chlorophyll a We estimated Chl a content as an
 indicator of net photosynthetic rate in BSC (Yan-Gui et al., 2011).
 Similar to our BSC cover analysis, we subsampled a 1.5 cm diameter, 1.5 cm
 depth BSC disk from each 5 cm diameter BSC disk (Figure S1c in article)
 used for ARA analysis. We dried subsamples at 60 °C for 24 h, extracted
 Chl a using DMSO (65 °C, 90 min) and then estimated Chl a content by
 spectrophotometry (665 and 750 nm; Genesys 20, Thermo Scientific, Waltham,
 MA), as in Caesar et al. (2018):  
 Chl a µg = (11.9035 × (A665 − A750)) ×
 S                                                (1)
 Chl a [mg × m−2] = Chl a [µg] / (AR ×
 1000)                                               (2)
   Where S is volume of solvent, AR is area (in
 m-2) and A665 and A750 are absorbances at 665 and 750 nm,
 respectively.           As for BSC cover, treatments in
 this part of our analysis included temperature and moisture, but not
 light.   6. DNA extraction and analysis
 Immediately after the fluorescence microscopy measurements
 (section 4), we dried (60 °C, 24 h) and ground (1 min, Mini bead beater
 16; Biospec products) the 1.5 cm diameter, 1.5 cm depth BSC disks used for
 the cyanobacteria/liverwort cover analysis and stored them at -80 °C for
 up to four months for DNA extraction. We pooled together replicates in
 pairs, combining them in equal weight parts (125 mg each for a total of
 250 mg). We used the PowerSoil® DNA extraction kit (MOBIO/Qiagen), and
 shotgun sequencing approaches and analyses via the alignment-free fast
 taxonomic annotation tool Kraken2 (Wood and Langmead, 2019) with the
 Kraken2 Refseq Standard plus protozoa and fungi database and the web-based
 pipeline Kaiju (Menzel et al., 2016). We estimated relative abundance of
 microbial groups using Kraken2 and fungal:bacteria ratios based on Kaiju
 taxonomic assignments (see sections below). After quality filtering the
 raw reads using Trim Galore microbial metagenome functional profiling was
 performed using HUMAnN 3 (Beghiji et al., 2021). For the functional
 annotation, UniRef50 (Suzek et al., 2015), KEGG (Kanehisa and Goto, 2000),
 and BioCyc databases (Karp et al., 2019) were used. As for BSC cover and
 Chl a, treatments for this part of our analysis included temperature and
 moisture but not light. We characterized microbial communities only at two
 temperature levels: 10 and 20 °C, which showed significant differences in
 N fixation and cyanobacterial cover (see Results in article) 
 7. Fungal:bacterial ratios Fungi and bacteria
 decompose organic matter at different rates, which affects the N and C
 biogeochemistry of substrates like BSC. To study potential effects of the
 environment on the biogeochemistry of BSC via differential effects on
 fungi and bacteria, we estimated fungal:bacterial ratios. We calculated
 fungal:bacterial ratios based on numbers of gene copies assigned to each
 group by Kaiju. 8. Microbial community and statistical
 analyses Microbial community analyses were performed
 using the microeco package in R (version 3.5.0). We first investigated the
 most important Orders for classifying samples into different treatments
 using a random forest approach. We then conducted an ANOVA test followed
 by a Tukey’s HSD test, α&lt;0.05, as well as Pearson correlations and
 PERMANOVA analyses between the Bray–Curtis dissimilarity score and
 moisture content. Finally, we conducted a Distance-based redundancy
 analysis (dbRDA) to assess the effects of the abiotic treatments on the
 top most abundant bacterial orders. To identify distinctive molecular
 pathways between treatments, we performed a linear discriminant analysis
 (LDA) effect size (LEfSe) analysis as implemented in the microeco package,
 then we selected the functions with a LDA score ≥ 3.5.
 We used a mixed model (lmer function in R, version 3.6.1) to
 analyze the fixed effects of environmental manipulations on N fixation,
 while accounting for the random effect of measurements on the same sample
 at two light levels. For the other response variables, which varied in
 response to temperature and moisture but not light, we used fixed models
 (lm function in R, version 3.6.1). We compared models based on the
 Bayesian Information Criterion (BIC; Figure S4 in article).
description: We used phyloseq and metaphlan2 to open tsv files.Other options
 include:   
 QIIME MG-RAST PICRUSt Mothur phyloseq MEGAN VAMPS metagenomeSeq Phinch RDP Classifier USEARCH PhyloToAST EBI Metagenomics GCModeller MetaPhlAn 2 More about this in https://biom-format.org/