from forest to open bog1586699/... · 2021. 8. 21. · peat subsidence in the restored area and...

From forest to open bog

A status report from a forest-to-open bog-restoration,8 years later

Axel Hjelm

Degree project in biology, Master of science (2 years), 2021Examensarbete i biologi 60 hp till masterexamen, 2021Biology Education CentreSupervisor: Gustaf GranathExternal opponent: Liam Heffernan

1

Abstract

Peatlands are associated with a range of ecosystem services such as long-term carbon storage and sequestration, biodiversity, and potential water reservoirs, mitigating floods and droughts. However, in the 20th century, large peatland areas in the northern temperate and boreal regions were drained by ditching, primarily to enhance forest and agricultural production and peat harvesting. Drainage of peatland is associated with a reduction in wet tolerant peatland species, reduced long term carbon sequestration and increased carbon emissions, conflicting with the goals of the Convention on Biological Diversity (CBD) and the Paris Agreement. Today, there is a big ongoing effort from society to rewet and restore drained peatlands in Sweden, but post-restoration monitoring to evaluate success are often scarce. Here, I examine the recovery of a restored drained and afforested bog in Southern Sweden, using a pristine area of the bog as the reference target. The aim was to ascertain to what extend the restoration had succeed and its potential to fully recover. During one year pore water quality, water level and peat surface level were monitored. In the fall peat cores were collected to investigate differences in peat physical and elemental and the microbial biomass and composition. I found that there are still considerable differences between restored and reference area, most marked by the deeper water level in the restored area, but this was not due to a reduced capacity of peat oscillation (i.e. the peats ability to expand and shrink to follow the water table. However, the restoration had raised the water table closer to the surface when compared to other drained areas in Sweden. The study also found considerable higher quantities and higher aromaticity of dissolved organic matter (e.g. DOC) in the porewater of the restored area and an overall lower total amount of microbial biomass with altered community composition, with higher relative amounts’ of fungi and G-negative bacteria’s in the restored area. The nutrient profile in the porewater (inorganic N,P,K) were similar to what was found in the reference area. In conclusion, both the hydrological and porewater chemistry status are currently most likely sufficient for wet-dwelling peat mosses to establish. Here, I argue that the restoration effect is noticeable but complete recovery is yet far away and there is a risk of recession towards afforestation if peat mosses fails to re-establish.

Keywords: Peatland restoration, Porewater chemistry, PLFA, Peat properties, Peat oscillation

2

Innehållsförteckning

Abstract ............................................................................................................................. 1

INTRODUCTION .................................................................................................................. 4

Background ................................................................................................................................. 4

Water table and peat characteristics ........................................................................................... 5

Peat soil ...................................................................................................................................... 5

Microbial community composition .............................................................................................. 6

Porewater chemistry ................................................................................................................... 6

Aims ............................................................................................................................................ 7

Methods ............................................................................................................................ 8

Study site .................................................................................................................................... 8

Experimental design .................................................................................................................... 8

Height above water table and peat oscillation ........................................................................... 10

Soil properties ........................................................................................................................... 11 Bulk density ................................................................................................................................................. 11 Elemental analysis Carbon, Nitrogen and Phosphorus ................................................................................ 12 PLFA ............................................................................................................................................................. 12

Porewater chemistry ................................................................................................................. 13 Carbon and Nitrogen ................................................................................................................................... 13 Ions .............................................................................................................................................................. 14 pH ................................................................................................................................................................ 14 DOM quality ................................................................................................................................................ 14

Statistical analysis ..................................................................................................................... 15

Results ............................................................................................................................. 16

Water table and peat oscillation ............................................................................................... 16

Soil properties ........................................................................................................................... 18 Bulk density ................................................................................................................................................. 18 Elementals in soil ......................................................................................................................................... 18 Microbial composition ................................................................................................................................. 20

Porewater chemistry ................................................................................................................. 24

Discussion ........................................................................................................................ 29

Heigh above water table ........................................................................................................... 29

Bulk density .............................................................................................................................. 29

Peat oscillation .......................................................................................................................... 30

Elements in the peat ................................................................................................................. 30

Bacterial composition ................................................................................................................ 31

Porewater chemistry ................................................................................................................. 32

Conclusions ...................................................................................................................... 33

Acknowledgements .......................................................................................................... 34

3

References ....................................................................................................................... 35

Appendix .......................................................................................................................... 38

4

INTRODUCTION Background Northern peatlands are associated with a range of ecosystem services such as long-term carbon storage and sequestration, biodiversity, and potential water reservoirs, mitigating floods and droughts. Northern peatlands are estimated to store about 15-30% or 445Gt (Gorham 1991) but may be as high as 1055Gt (Nichols and Peteet 2019) of all terrestrial organic carbon, despite only covering 3% of the Earth's land surface (Gorham, 1995). In temperate and boreal peatlands, this accumulation of carbon is primarily driven by peat mosses (genus Sphagnum), which dominate and engineer these peatlands to be wet, nutrient-poor, and have a low pH. In the 20th century, large peatland areas in the northern boreal and subarctic regions were drained by ditching, primarily to enhance forest and agricultural production and prepare for peat harvesting. In Sweden, where peatland drainage have been used for enhanced production and peat harvesting for over one and a half centuries, about 15% is affected by afforestation (Vasander et al. 2017). Restoring these wetlands is essential for suppressing greenhouse gas emissions and conserving biodiversity in a changing climate. Drainage of peatland is associated with a reduction in wet tolerant peatland species (Laine et al., 1995) and reduced long term carbon sequestration and increased C loss in the form of in situ emission or runoff (Jauhiainen et al. 2019), conflicting with the goals of the Convention on Biological Diversity (CBD) and the Paris Agreement. Today, there are many actions taken for restoration of drained peatland to regain functional ecosystems and halt these effects (Kareksela et al. 2015). Peatland restoration has been addressed as a cost-effective and relatively easy venture to prevent future- and reducing present carbon emission (Bain et al. 2011). The most popular restoration measure is to rewet the peatland by damming old ditches and cut trees and shrubs to elevate the water table and to promote wet-dwelling vegetation to reestablish, respectively (Granath et al. 2016). This can for example be done to initiate a "forest to open bog restoration". Drainage and following vegetation changes result in a significant shift of the ecosystem structure and function. Restoration aims to reverse the ecosystem into a previous state, which ideally is functionally similar. However, legacy effects of drained peatland are complex, and the result of restoration depends on how damaged the system have been, including factors such as (1) the severity and duration of the ditching (press perturbation), (2) the system's capacity to retain its ecological characteristics during a perturbation (ecological stability) and (3) the system's capacity to fully recover into a pristine, reference state once the press perturbation lets go (recovery) (Van Meerbeek, Jucker, and Svenning 2021). An example of such press perturbation is peatland drainage. Where drainage is successful and the water table is lowered and followed by tree establishment, positive hydrological and biogeochemical feedbacks often reduce the ecological stability and the perturbation increases. In particular, this is driven by increased decomposition and subsidence of peat soil above the water table, decreasing the pore space and hence the amount of water that can be stored within the peat. Further, growing trees becomes a nutrient sink, immobilizing excess nutrient mineralized due to higher aeration (Laiho, Sallantaus, and Laine 1999), and tree canopies shade the ground vegetation, and enhanced evapotranspiration by the trees and lowering the water table even further. As a result, trees outcompete the typical peatland vegetation such as peat mosses, who are the foundation of the initial higher water level and characteristics of a peatland. The transition in vegetation also implicates a shift in the quality of litter deposited, which further

5

affects the microbial composition and decomposition rates (R. Andersen, Chapman, and Artz 2013). Water table and peat characteristics Key domain of attraction in terms of ecosystem services in peatland includes its role as a net carbon sink. The reduced redox environment, in combination with low litter quality produced by peatland-associated vegetation such as Sphagnum species facilitating net carbon sequestration by a higher production (biomass) than decomposition (mineralization of plant residues). This is maintained by a high water table (near the surface) that creates a near anoxic environment. Because the diffusion-rate of oxygen is slow between the air-water interface, the consumption of oxygen by decomposers is higher than the supply from the atmosphere. Thus, below the water table, it becomes a shortage of oxygen, leading to a reduced redox environment. In these more reduced conditions other, less effective, electron acceptors with lower redox potential are used in the metabolic processes resulting in slower decomposition. Oxygen, on the other hand, is the most effective electron acceptor (highest redox-potential) used in metabolic processes. Thus, oxidized conditions caused by a lower water table lead to faster decomposition of peat soil. (Rydin and Jeglum 2015) Peat bogs have an incredible capacity to regulate their surface elevation to stay in close relationship to the water table. The phenomenon is called mire breathing or surface oscillation and is caused by a range of factors (Howie and Hebda 2018). Briefly; When the water table is high, the peat expands by absorbing water; simultaneously, increased buoyancy on peat that normally is above the water table helps to lose weight from underlying peat, which has elastic features and expands during reduced pressure. During a drought however, when the water table is low, water absorbed in macropores of the peat evaporates, making the pores collapse and the peat volume decrease, simultaneously, increased pressure on underlying layers of peat due to less floating forces gets compressed which withholds the peat surface in proximity to the lower water table. Consequently, the peat surface height above the water table (HWT) is dependent on the water table and peat elevation. Since long-term drainage and afforestation are associated with altered soil properties as increased decomposition altering pore structure within it, this might be a long-lasting effect on the hydrology and the ability of self-regulation through mire breathing. Such a reduction would possibly halt the recovery and impact of restoration. Peat soil Physical properties of peat soil are important for the hydrology in peatlands. Peat bulk density (BD), a measure of dry mass per unit volume, is an indicator of how decomposed the peat is as well as its hydrological properties. Fresh, poorly decomposed peat formed by sphagnum species are associated with a high content of macropores, which facilitate an effective absorption of, and can store large amounts of rainwater. However a high content of macropores deteriorates the peats capacity to retain water due to less capillary forces within the medium. A prolonged period of a low water table affects the peats hydrological characteristics by reducing macropores in three ways, 1) buoyancy forces decreases and macropores collapse 2) increased decomposition weakens the integrity of the peat and 3) large trees increases the weigh load on the peat causing further reduction of macropores. This subsidence reduces the peats capacity of absorbing rainwater but increasing moisture retention time. (Waddington et al. 2015) suggesting that the increased moisture retention time in more decomposed peat induces a negative feedback and by lowering the redox potential in soil (due to higher moisture) prevent further decomposition. On the other hand, according to (Noble et al. 2017), increased water retention in top layers of the peat hampers the growth of sphagnum who are less able to obtain the tightly bound water due to its lack of rooting system.

6

Peat elemental composition is a product of external input, litter quality, decomposition rates and plant assimilation, whereof many affected by water table height. Gravimetric composition of C, N and P and their interrelationship are closely linked to the ecosystem type, peat type and its trophic gradient in nutrient richness (Rydin and Jeglum 2015), and thus important for controlling growing plants, microbial composition and activity, and biogeochemical processes. In general, peat mosses produces litter of lower quality as substrate with higher C:N ratios than vascular plants, and increased decomposition tend to decrease the C:N ratio as C is lost to the atmosphere as gaseous CO2 and methane, while N is recirculated by the microorganisms (Wang et al. 2014). Microbial community composition Changes in redox environment and litter quality further induces a change in microbial community composition (Lahio, 2006; Trinder et al 2008). Different microbial groups produce different enzymes that interacts and completing each other in carbon cycling processes. For example, fungi are specialists in decomposing large recalcitrant organic matter (OM) using exo-enzymes and may outcompete certain bacteria's but benefit Gram-negative bacteria's who are promoted by labile monomers produced by fungi (R. Andersen, Chapman, and Artz 2013). Ultimately, an altered composition of the microbial community induces changes in the cycling processes of OM and thus affects the carbon cycle, nutrient availability and pore water chemistry. Previous studies have shown that persistent water level drawdown significantly alter microbial composition and that fungi benefits from lower water table in mesotrophic fens but disadvantage in ombrotrophic bogs (Jaatinen et al. 2007). Porewater chemistry The pore water in a peat bog is from where vascular plants assimilate nutrients and the chemical composition of pore water is therefore a major predictor of vegetation species composition. Sphagnum mosses, who are essential for recovery of a peatland, lacking root system and cannot compete with vascular plants in nutrient assimilation from the porewater. Hence they are facing a disadvantage under high nutrient concentrations, especially the combination of excess potassium (K), nitrogen (N) and phosphorus (P) in the pore water (Bubier, Moore, and Bledzki 2007). Chemical composition of peat pore water is also of importance for downstream water bodies and altered concentrations as well as quality of dissolved organic matter (DOM) constitute a risk of altering watercourse and lake systems by acidification, browning effects and eutrophication. This is described in (Kritzberg et al. 2020), briefly: Terrestrial DOM, origin from plants is in general more refractory to their nature, characterized as heavier compounds with higher aromaticity than aquatic DOM origin from decay of phytoplankton. DOM with higher aromaticity absorbs sunlight in the same spectra as photoautotrophic organisms (PAR) and may restrict light penetration in the benthic zone, inducing a shift towards phytoplankton dominated primary production over macrophytes and periphyton. Enhanced DOM concentration in lake water does also provide a C source for bacterial production and when mineralized, releasing carbon emission and inorganic nutrients, the latter initiating eutrophication and driving a shift in community composition in lakes. Peatland restoration have shown to decrease DOC concentration in porewater compared to unrestored, drained sites over time (10 years post restoration) (T. Haapalehto et al. 2014). However, other studies have shown increased concentrations of DOC immediately after restoration (1 year post restoration) (Gaffney et al. 2018). The controls of DOC are complex and controlled by factors as vegetation, decomposition rate, and solubility, which are all in turn affected by hydrology and mainly the water table height and fluctuations in water table

7

(Peacock et al. 2014). For instance, a shift in vegetation composition, caused by altered water regime, from high abundance of Sphagnum to higher abundance of vascular plants, increases the root exudates and stimulates decomposition and thus, more DOC are produced. Two widely used techniques for getting insights in the composition of DOM are absorbance and fluorescence spectroscopy. Absorbance spectroscopy is based on that molecules of different structures absorbs radiations in different ways. Indices of absorption at specific wavelengths have been developed to generalize properties of DOM such as the E2:E3 ratio which correlates negatively with molecular weight and size (Peuravuori and Pihlaja 1997), which in turn has been shown to act as more labile substrate for bacteria's. SUVA is a measure of the specific absorbance at 254nm and has a positive correlation with aromaticity, where higher values indicating a less fresh, less labile substrate for bacteria with lower bioavailability. E xcitation-emission matrices (EEM) are obtained by measuring the fluorescence intensity emitted from the sample at different emission wavelengths at a fixed excitation wavelength, called λEm. Additionally, by simultaneously measuring the fluorescence intensity emitted from the sample at a fixed emission wavelength when scanning the sample over a range of excitation wavelengths λEx is obtained. EEMs are then produces as a 3 dimensional dataset with fluorescence intensity at different λEm and λEx (Lakowicz 2006) and indices are used to generalize about the molecular structure in DOM. Fluorescence index (FI) is used to determine the origin of DOM, and distinguish if DOM origins from extracellular release from bacteria's or by terrestrial plants. Freshness index (Fr) provides information of how recently produced the DOM is and how well decomposed it is. Lastly, Humification index (HIX) indicates the extent of humification in the DOM (Fellman, Hood, and Spencer 2010). Peak picking from EEMs is done by identifying the highest fluorescence intensity at a certain region of λEm and λEx (Hess et al. 2002), and provides information about composition of humic like substances (peak A, C and M) and protein like substances (peak B and T) (Fellman, Hood, and Spencer 2010). The composition of peaks can be used to reveal subtle difference in DOM and provides a unique fingerprint for DOM of different origin (Tfaily et al. 2015). Aims By considering a range of factors that are important features in a successful ‘forest-to-open-bog-restoration’, my study aims to quantify how similar a restored drained forested bog is compared to pristine adjacent (100m away) open bog, 8 years post-restoration. I provide a one year snapshot of major properties of hydrology and pore water chemistry, and a one time-point characterisation of peat soil, microbial composition to specifically address three main questions.

1) Has the height above water table recovered into a pristine state and if not; is this caused by a reduced capacity of peat oscillation?

2) Are there are legacy effects in peat physical properties, elemental composition and

bacterial composition from afforestation in the restored area that may impede recovery?

3) Does the pore water chemistry differ between areas in terms of nutrient and DOC concentrations and DOM quality, that may impede recovery of peatland specialist species and constitute a risk of altering downstream water bodies?

8

Methods Study site The study site, Anderstorp Stormosse (not to be confused with the nearby peat bog Store Mosse) is an ombrotrophic peatland located in the boreal-nemoral zone of southern Sweden with an annual precipitation of 800-1000 mm per year. In the 1940s, central parts of the bog were drained by ditching for peat harvesting and affected parts became tree covered during the second half of the century. Anderstorp Stormosse was restored under the EU financed project 'Life to (ad)mire' in 2013. The measures included in the restoration was to fill and block ditches from the main ditch which remained unfilled but blocked, clear cut the area and removal of the trees stems, with the aim to establish the water table near the peat surface and allow for ground vegetation associated to open bogs, to reestablish (Figure 1). The vegetation cover of the two areas shows obvious differences where the pristine area was dominated by vegetation typical of peat bogs such as Sphagnum sp., Eriophorum vaginatum, Erica tetralix, Carex sp. and small pines, while the restored area has a vegetation cover of forest and peatland species, with higher abundance of shrubs (Calluna vulgaris, Betula nana), young birch trees and dwarf birch. Additionally to differences in vegetation cover, a fairly large amount of branches from the clearcutting remains on ground. Total peat depth is roughly 2 m in both areas.

Figure 1. Anderstorp Stormosse before restoration (left) and after restoration (right). Tree-stems have been removed and drainage-ditches have been blocked and infilled. Water mirrors in the right picture shows where peat harvesting have occurred. Samples were taken in the western-most part, where drainage but no peat harvesting has taken place. Photo: Bergslagsbild AB Experimental design Samples from the restored area was collected in the western parts of the bog where ditching but no peat harvesting have occurred. Reference samples, from pristine undisturbed parts of the bog have been collected just south from the restored area with no history of drainage or peat harvesting (see Figure 2. For schematic figure see Appendix. Figure 1). A 400 meters long main duckboard was built to run through the restored area into the pristine reference area. The duckboard was placed in parallel to old, infilled ditches with a distance of approximately 15

N

Before After

9

meters to the nearest ditch in both directions. Perpendicular from the main duckboard, in total, 12 sample platforms were placed, 6 for each area to facilitate sampling without disturbing the peat surface (Figure 1). Each sampling platform was equipped with two wells (slotted tubes inserted to a depth of approximately 1.5-2 meters in the peat) making a total of 24 wells for collection of pore water and water table measurements. The two wells at each sampling platform were placed to cover microtopography structures, i.e. hummock and hollows, resulting in four sampling categories, Restored hummock (R.hum), Restored hollow (R.hol), Pristine hummock (P.hum) and Prisitne hollow (P.hol) (Figure 2) with a sample size of 6 for each group. This experimental setup was chosen to provide a higher resolution of the study since if microtopography are not accounted for, important dimensions of peatland dynamics might be overlooked.

Figure 2. Study Site. Main platform runs from north to south, parallel with the infilled ditch in the restored area. Perpendicular to the main platform, facing south is the sampling platforms, 6 for each area. Scalebar corresponds to the overview image, and magnified image are enlarged by x2. Arrow indicates north. Photos were taken in August 29 2020. During the study period, May 2020 to April 2021, pore water was collected and analyzed monthly. Porewater analysis included concentrations of dissolved organic carbon (DOC), total dissolved nitrogen (TDN), Major cations; Sodium (Na+), Ammonium (NH4+), Magnesium (Mg+2), Potassium (K+) and Calcium (Ca2+) and anions; Fluoride (F-), Chloride (Cl-), Nitrate (NO3-), Phosphate (PO43-) and Sulphate (SO42-). Additionally, optical properties including absorbance and fluorescence indexes, and emission-excitation spectra (EEMs) with "peak picking" was examined to reveal insight of the quality of dissolved organic matter (DOM). Soil samples were collected in proximity to the wells to not disturb the peat from where pore water was collected. Here, as for porewater measurement, samples representing both hummock and hollows were collected to match the groups I.e. R.hum, R.hol, P.hum and P.hol so that in total, 3 plots from each group. I acknowledge this setup does not provide basis for directly comparisons between peat and pore water samples, however the aim of this project was not to link peat and pore water chemistry but rather to investigate disparity in peat and pore water from a pristine and restored area.

Pristine x2

Restored x2

Pristine Restored

10

Figure 3. Example picture of vegetation cover of the 4 groups. Photos were taken in August 27 2020.

Height above water table and peat oscillation Peat surface height above the water table (HWT) was manually measured monthly as the distance (cm) between peat surface and water surface in the wells. HWT measurements were collected at the same time as pore water samples. To examine the peat's vertical movement and response to water table fluctuations, two homebuilt devices (figure 4) was constructed and installed as described by (Evans et al. 2021), one in the pristine and one in the restored area. Here, we aimed to install the devices on flat surface, i.e. not defined hummock or hollow structures. The device was built with a stool as foundation, equipped with ground anchors screwed into the peat, where the elevation change of the ground will affect the elevation of the stool. The stool has a time-lapse camera mount on it (Wingscapes Timelapse Cam Pro WCB-00121, Alabaster, Alabama, United States), programmed to take pictures with a set interval (every 4 hours). The camera takes continuous photos of a ruler attached to a fixed pipe that is pushed down through the peat layer and fixed in the underlying clay. The camera moves with the peat's movement while the fixed pipe with the ruler remains unaffected by the peat movement. Additionally, a well (slotted tube) was installed beside the fixed pipe. A float, made from a plastic pipe and foam was placed in the well, so that the device provided data of both peat oscillation and water level. The pictures were analyzed with a special software developed for the purpose by Zak Mitchell. The software automatically detected movement of an assigned region of interest in the picture and by identifying and compare a unique combination of pixels from each picture with a reference picture, the movement compared to the reference value could be assessed. However, due to poor image quality during wet or/and cold weather condition, causing incorrect readings, all pictures were controlled visually as well. For longer periods (weeks) of poor image quality at least one measurement every 20 hours were recorded. During the cold months (December, January and February), water level measurement became unreliable due to ice formation in the wells why these months were removed prior to analysis. Precipitation data was downloaded from SMHI, station Hestra D-73230, located 20 km north of Anderstorp Stormosse and used to graphically inspect how the peat surface and water table oscillation responded to precipitation.

11

Figure 4. Peat elevation measurement device installed in the restored area (left). Blue pipe is the fixed reference point. White pipe is the float, measuring water level. The platform is anchored in the peat by ground anchors screwed into the peat. Right picture shows an example picture from the device.

Soil properties Soil samples for analyzing peat bulk density (BD), elemental composition (C, N and P), and Phospho lipid fatty acids (PLFAs) was collected on August 3, 2020 and additionally samples for PLFAs were collected in September 27, 2020. In total, 24 soil samples were taken, where twelve 0-50cm cores (BD, elements, SOM, PLFA) and twelve 10-30cm cores (PFLA). For each depth interval I sampled three cores from each group (i.e. P.hol, P.hum, R.hol, R.hum). The cores were taken in proximity to where pore water samples were collected. A cylinder of thin sheet metal with a diameter of 10 cm and a length of 10 cm was carefully pushed into the soil. The bottom was cut with a knife. and the cylinder was pulled up, cut into two 5 cm pieces and stored separately in plastic bags. In total, ten 5cm section were sampled from the twelve deep cores while for the twelve additional samples (PLFA), two longer sections were kept (10-20cm and 20-30cm). Caution was taken to not compress the peat while sampling. The peat samples were stored in a freezer (-18°C) for 90 days and thawed in 4°C for 120h before handling. In lab, thawed samples were homogenized by hand and during this step larger roots (>2mm), sticks and bark chunks was removed. All samples was then dried in an oven (65°c) until completely dried (constant weight) for about 96 hours. For samples where PLFAs was to be analyzed (depth interval 10-20 and 20-30), additionally, equal amount of fresh samples from 10-15, 15-20 and 20-25, 25-30 was mixed to be freeze dried. Bulk density The dry mass of each dry sample was used to calculate the bulk density of each sample as in equation 1. To compensate the volume of the samples for removed roots, sticks, and bark chunks the volume of removed material was defined as the rise in water table in a glass cylinder when the material was submerged. The volume of remaining sample in those samples where some peat was taken for PLFAs analysis was calculated as original volume times

12

weight fraction of remaining sample. Dry samples were stored in freezer until further analysis.

𝐵𝐷 =!!"#%&'()*

"+*,%&'()* Eq. 1

Elemental analysis Carbon, Nitrogen and Phosphorus Oven-dried samples from BD measurement was grinded in a planetary ball mill (Retsch PM 100) to a fine powder. This was done to ensure a homogenized and representative part from the peat since very small amounts was used for C, N and P analysis. Total C and N content were determined by Elemental analyzer. Samples were prepared by placing ~ 3 mg dried peat into thin foil capsules. Standard samples with a known concentration of C and N was used to affirm a correct reading from the machine every tenth sample. Total P was measured with wet-chemical analysis by transferring organically bound phosphorus to orthophosphate trough ignition of approximately 0.2g grinded peat at 550°C in a muffle furnace and then boiling the residue in a known volume of 1M hydrochloric acid. Orthophosphate was then measured from a dilution of the solution by the molybdate reactive phosphorus (MRP) -method where the absorbance (A) of the molybdate phosphorus complex is measured at 882nm. The absorbance reading for each sample was then related to a reading from a standard sample containing a known concertation of P. This method was first described by Andersen (1976). PLFA Determination of Phospholipid fatty acids (PLFA) is a well-used method for revealing patterns in composition of microorganism groups in soil. It is based on the idea that different groups of microorganisms produce specific types of PLFAs and if a certain PLFA is found it can therefore be associated with a specific microorganism group. Since PLFA, to their nature are relatively easy degraded, the method is selective to living or recently dead microorganisms. Phospholipids were extracted as described in detail by (Å Frostegård, Tunlid, and Bååth 1991), briefly, by mixing approximately 0.4 g of milled, freeze dried peat in 10mL of Blight and Dyer reagent mix (chloroform : methanol : citrate buffer 1:2:0.8 v/v/v) in a 50mL eppendorf tube. After two hours, the samples were centrifuged at 4000rpm for 10 minutes and the supernatant was decanted to a new tube. This extraction step was repeated with 5mL of reagent mix to ensure a complete extraction. 4 mL of chloroform and citrate buffer was then added to the supernatant and after phase separation the lower phase containing lipids were transferred into a glass test tube and the organic solvent was evaporated under a flow of nitrogen gas. Separation of neutral lipids, glygolipids and phospholipids was done by first dissolve the lipids in chloroform and add the lipid containing solution onto a silica SPE-column. Neutral lipids were then removed by rinsing the column with chloroform, glycolipids were removed by rinsing with acetone and last, the phosplipids were collected by rinsing with methanol. Again, the organic solvent was evaporated under a flow of nitrogen. A non-natural occurring standard (19:0) was then added to all sample tubes containing phospholipids to allow for quantification of PLFAs from the GC-chromatograms. Samples were methylated by incubation at 37°c for 15 minutes with a solution of 2 molar KOH in methanol. Lastly hexan:chloroform (1:4), mQ-water and acetic acid (1M) were added to the solution with methylated PLFAs and after phase separation, the organic phase containing PLFAs were collected and the solvent was evaporated under nitrogen. Prior to analysis in GC-MS, the dry sample of PLFAs were dissolved in hexane.

13

Identification of PLFAs was done manually by comparing the retention time and order of peaks of two standard solution containing known PLFAs (BAME and FAME) with peaks from the samples. Quantification of PLFAs were done by comparing the peak intensity of the internal standard with every other peak as mol PLFA. To determine not only the amount of individual PLFAs but also their relative contribution of total amount PLFAs in the soil, the percentage of each PLFA to the total amount was calculated. Iso and anteiso -branched PLFAs (BrFA) are associated with Gram-positive bacteria's (G+) (Mpamah et al. 2017). Five BrFAs were identified in the samples including i15:0, a15:0, i16:0, i17:0 and a17:0, these were summed up to represent the contribution of PLFAs from G+ bacteria. Four PLFAs associated with Gram-negative (G-) bacterias were identified and summed up to represent the contribution of PLFA from G- bacterias, including the mono-unsaturaded PLFA 16:1w9c, the cyclopropyl PLFAs cy17:0 and cy19:0 and the hydroxy-substituted PLFA 14:0 3OH (Mpamah et al. 2017). Methyl-branced fatty acids are associated with actinomycetes and sulfate reducing bacteria’s (Roxane Andersen et al. 2010), and two of these, 10Me16:0 and 10Me18:0 were identified and summed up. Three Straight-chain PLFAs (14:0, 15:0, and 17:0) where identified and used as general bacteria markers (A. Frostegård and Bååth 1996; Lindahl et al. 1997), together with two mono-unsaturated (18:1w9t and 18:1w7c) which co-eluted and were thus inseparable and reported as “18:1w9t + 18:1w7c”. 18:1w9t and 18:1w7 have both been suggested to correlate with G+ (Kao-Kniffin and Zhu 2013) but (Bahn et al. 2013) suggests that they correlate with G-, why 18:1w9t+18:1w7t was reported as general bacteria marker. PLFA biomarkers found in the samples associated with fungi includes 16:1w5c as Arbuscular mycorrhiza (AMF fungi) (Å Frostegård, Bååth, and Tunlio 1993), 18:2w6c and 18:1w9c (Saprophytic fungi) (A. Frostegård and Bååth 1996; Kao-Kniffin and Zhu 2013). However, 18:1w9c are not exclusively produced from Saprophytic fungi but can also origin from plants (Åsa Frostegård, Tunlid, and Bååth 2011) why a decision was made to exclude this PLFA entirely. The sum of PLFA groups belong to bacterias and fungis were calculated to represent total bacterial and total fungi PLFAs and the ratio was calculated for fungi:bacteria (F:B-ratio). Porewater chemistry Porewater samples were collected monthly from wells (slotted tubes inserted approximately to a depth of 2 meters in the peat) using a handheld liquid transfer pump from a depth of approximately 1 m from the peat surface. Before sampling, the well was emptied and left for an hour to allow fresh pore water to enter the well. This setup was used to get a representative sample from the entire pore water column, and is not assigned to a specific depth. Approximately 250 mL sample was collected from each well and stored in glass containers during transport. Filtering was conducted within 24h after collection for the most, however, due to practical reasons, samples collected in June 2020 was stored in a dark cold room for 21 days prior to filtering and samples collected in December 2020 were filtered after 7 days. Filtered samples were then analyzed for major ions, carbon and nitrogen content, and its optical characteristics. Carbon and Nitrogen Pore water samples were analyzed for Non Purgeble Organic Carbon (NPOC) and Total Nitrogen (TN) using a Shimadzu TOC-L analyzer with automatic acidification and sparging treatment to remove inorganic C. Prior to analysis, all samples were filtered with a GF/F 0,7 um filter to remove particular matter, meaning that only dissolved matter are analyzed and

14

hereafter referred to as DOC and DN. Filters were ignited at 450 C for 4 hours to remove excess carbon on the filter strips prior to filtering. Ions Cations; Sodium(Na+), Ammonium(NH4+), Magnesium(Mg+2), Potassium(K+) and Calcium(Ca2+) and anions, Fluoride(F-), Chloride(Cl-), Nitrate(NO3-), Nitrite(NO2-) Phosphate(PO43-) and Sulphate(SO42-), were analyzed using ion chromatography with a conductivity detector. Samples were passed through a prerinsed 0,2um filter to protect the column from excess polar organic matter in the samples prior to analysis. To protect samples from atmospheric NH4+, cation-samples were covered with a penetrable cap during analysis. Peaks in the ion chromatogram was integrated manually in the software MagICnet and quantified from a calibration curve based on standard samples of known concentrations. Detection limit was 10ug/L and concentrations below detection limit are reported as 10 ug/L. Inorganic nitrogen (N-NH4+, N-NO3- and N-NO2-) was summed to get the concentration of total inorganic nitrogen. NH4+ N was calculated as [NH4+] * 0.77649, NO3- N was calculated as [NO3-] * 0.22590. NO2- was not detected and thus not included in calculations of inorganic N. pH The pH was measured in field on September 29 by insertion of the pH electrode/temperature sensor (wtw pH320 with a SenTix®41 sensor) in subsamples of each porewater sample collected that date. The electrode were left in the sample for one minute before the reading of pH were taken to allow the electrode to stabilize and the electrode were rinsed with tap water between each sample. DOM quality All samples used for analysis of absorbance and fluorescens properties were after filtration, prior to analysis, diluted with mQ-water to a DOC concentration of approximately 10mg/L. This was done to avoid readings above the linear spectra. There was relatively high differences in DOC concentration between samples why the dilution factor ended up being up to 5 times higher in samples with the most DOC. pH may alter the structure of DOM and thus affect the fluorescence properties, however the change in pH between the extreme samples where tested and did not exceed 1 pH unit. Temperature of the sample may also affect the optical properties, thus all samples were left to reach room temperature prior to analysis. Absorbance Absorbance was measured on a Lambda 40 UV-visible spectrophotometer (Perkin Elmer, Waltham, USA), using a 1 cm quartz cuvette. Absorbance spectra was measured at a wavelength interwall of 1nm between the wavelengths 200 and 600 nm. The measured absorbance at each wavelength were first subtracted by the readings from a blank sample (Milli-Q filtered as samples) and then multiplied by the dilution factor for correction. The absorbance spectra was inspected graphically but a discission was made to use established absorbance indices (table 1) instead of the whole spectra for interpretation. SUVA (C normalized Absorbance (A) at 254nm) has a positive correlation with aromaticity. Low aromaticity may indicate a fresher, more labile substrate with higher bioavailability. The ratio of absorbance at 254nm and 365nm (A254/A365) have shown to be negatively correlated with the molecular weight and aromaticity (Table 1). (Peuravuori and Pihlaja, 1997; Helms et al., 2008) Table 1. Absorbance indicies, names and interpretation.

15

Index Interpretation Calculation Reference

E2:E3 Molecular size/ Eq. 1 (Peuravuori and Pihlaja 1997)

molecular weight

SUVA Aromaticity Eq.2 (Weishaar et al. 2003)

𝑆𝑈𝑉𝐴#$% =&-./[()*]

∗ 100 Eq.3

𝐸2: 𝐸3 = &-./

&01. Eq.4

Fluorescence A fluorescence spectrophotometer (SPEX FluoroMax-4, Horiba Jobin Yvon) with a 10*10 mm quartz cuvette was used to analyze Excitation-emission matrices (EEMs). Excitation and emission wavelength ranged from 250-445 by 5nm increments and 300-600 nm by 4nm increments respectively, integration time was set to 0.1 s, scan width to 5nm and sample emission to lamp reference (S/R) mode was used. All samples were multiplied with its dilution factor, subtracted with milli-Q matrices and inner filter effects was corrected for with data from absorbance measurements using the MatLab -script 'FDOMcorrect.m (ver. 1.5: 2011-09-19)' as described in Murphy et al. (2010). Indices was calculated as described by (Fellman, Hood, and Spencer 2010) where, FI was calculated as the ratio of emission wavelength at 472 and 520 nm obtained at excitation 370 nm. Freshness index (Fr) was calculated from the ratio of emission intensity obtained at excitation 310 nm for emission at 380 nm and the maximum emission observed between 420 and 436 nm. Humification index (HIX) was calculated as ratio of maximum emission between emission 436-438 nm and 300-344 nm at excitation 255nm. Peak picking was performed for peaks A, C, M, T and B from the EEMs using excitation and emission maxima as tabulated in table 2, (Fellman, Hood, and Spencer 2010). Table 2. Peak names and corresponding excitation and emission maxima used for "peak picking" with description of interpretation. Modified from (Fellman, Hood, and Spencer 2010).

Peak Excitation(ex) and Emission(em) maxima (nm) Reference Description

A ex 260, em 400-460 (Coble, Del Castillo, and Avril 1998)

Aromatic, high molecular weight

C ex 320-360, em 420-460 (Coble, Del Castillo, and Avril 1998)

Humic, high molecular weight

M ex 290-310, em 370-410 (Coble, Del Castillo, and Avril 1998) Low molecular weight

T ex 275, em 340-350 (Coble, Del Castillo, and Avril 1998) Amino acids

B ex 270, em 305 (Coble et al. 1990) Amino acids

Statistical analysis Differences in HWT between area, topography and month was tested as for pore water chemistry. Additionally, to test differences in fluctuation of HWT between areas over the sampling period, differences in coefficient of variance (CV) was tested using the asymptotic

16

test function from R package “cvequality”. To evaluate the response of peat oscillation to oscillation in the water table, I performed a Pearson’s correlation analysis (function “cor”) between water table and peat oscillation expressed as deviation from start. Soil cores were grouped by area (pristine or restored) and topography (hummock or hollow) to represent four groups (P.hol=, P.hum =, R.hol =, and R.hum =) for each stratum so that there were 3 soil cores of 10 depth-layers a' 5cm from each group. To test differences between the restored and the pristine reference area, I first fitted a linear mixed effect models (LMMs) using the lmer function from R package lme4, where Area, Topography and Depth was used as fixed factors and peat core number as random factor. The linear model was evaluated by analysis of variance (ANOVA) using the anova function from R package car. To examine trends across peat depth in the groups, the mean of each variable for every group was plotted as a depth profile and inspected graphically. The depth profile showed larger differences in the uppermost layers and was more similar in deeper layers why samples was further grouped in depth intervals of 0-10, 10-20, 20-30 and 30-50, i.e. P.hol 10-20, P.hol 20-30, and so on. To test which groups at which depth that significantly differed from each other I used the glht function from package ‘multcomp’ (Hothorn et al. 2008) to do pairwise comparison with a TukeyHSD test. Differences in amount of total PLFAs, PLFA-groups and F:B-rato, between the restored and pristine area (for each topography and depth layer) were tested by fitting LMMs followedf by ANOVAs. Individual soil core was used as random factor. To test differences in composition of PLFAs between plots and depth, a multivariate permutational analysis of variance (PERMANOVA) was performed using “adonis” from the vegan package (Oksanen et al. 2020), on the relative contribution of every PLFA to the total amount detected. Porewater samples, as well as HWT data were grouped as P.hol, P.hum, R.hol and R.hum with a sample size of n = 6 per group per month. ANOVA was performed on a linear mixed effect model with area, topography and month as fixed factors and individual well number as random factor to account for that the same wells were sampled every month. Pairwise comparison with TukeyHSD test was performed on data from the whole sampling period using the glht function in R to test which groups who significantly differed from each other over the time frame of sampling. For data variables HWT, C, N, SUVA and E2:E3, 10 months were sampled, for ions there are 7 months of sampling, and for fluorescence data there is 6 month of sampling data.

Results Water table and peat oscillation The distance between the peat surface and the water table (HWT) was significantly higher in the restored area than in the pristine area (Figure 5). Hollows in the pristine area (P.Hol) had the lowest mean HWT (Mean±SD: 3.4 ± 4.9 cm), followed by P.hum, R.hol and R.hum (Mean±SD: 14.5±6.8, 17.1±9.4, 22.5±9.3 cm respectively) (figure 12a). Mean HWT was different from each other between all groups (TukeyHSD, p < 0.05 ) with the exception of P.hum and R.hol (p = 0.182). However, during the dry period in August, HWT increased more in R.hol then it did in P.hum (figure 10a). Overall, HWT dropped more pronouncedly in the restored area during the dry month of August and September. There were larger fluctuations (asymptotic test: p <0.001) in HWT in the restored area compared to the pristine area.

17

Peat surface of the restored area showed larger oscillation than peat of the pristine area (figure 6), with a oscillation of the peat surface by 4.6 and 1.8 cm from the lowest to the highest elevation point, for restored and pristine area, respectively. Peat elevation showed strong correlation with water level in both areas (figure 7), (Restored: r = 0.94, p <0.001, Pristine: r = 0.88, p <0.001). Generally, both water level and peat elevation were more responsive to precipitation in the restored area with a more pronounced decline during periods of no precipitation and more pronounced increase during precipitation.

Figure 5. Heigh above water table (HWT) for each group (n = 66). Black vertical line represents the median, the box covers the 1st and 3rd quantile and whiskers covers the lowest and highest value. Each individual sampling point is represented as grey dots.

Figure 6. a) Temporal development of Peat surface oscillation (solid line) and water table oscillation (dotted line) expressed as difference from start. b) Daily precipitation during time of measurement.

0

10

20

30

40

Pristine hollow Pristine hummock Restored hollow Restored hummock

HW

T [c

m]

Area pristine restored

−20

−10

0

10

Aug Sep Okt Nov Dec

Peat

ele

vatio

n m

m (S

olid

), W

ater

tabl

e cm

(Dot

s)

Anderstorp store mosse

0

5

10

15

Aug Sep Okt Nov DecDate

Day

ly p

resi

pita

tion

(mm

)

A

B

18

Figure 7. Linear correlation between water level and peat level expressed as difference from median for the restored area (left) and the pristine area (right). Each dot represents one measurement and the color gradient indicates time from start, moving from darker to lighter. Time between each sampling point is for the most 4 hours, sampling period starting August 4 ends November 27, 2020.

Soil properties Bulk density The highest bulk densities were found in the upper layers of the restored area and there were no clear tendency of differences in BD between hummock and hollows in the restored area over the whole depth profile (figure 6a). The lowest bulk density was found in the top layer of P.hum, this layer contained almost intact, recently dead Sphagnum in an very early state of degradation and even if there was no significant difference between hummock and hollows in the pristine area at the top 10 cm layer, the soil profile between these two areas showed differences. While BD decreases with depth from 0 – 20 cm in P.hol, it increases in P.hum. In the depth interval between 10 – 30 cm, P.hol was instead the area with highest BD, significantly different from P.hol (appendix. table 1). There was a difference between topography in the pristine area but not in the restored area. Elementals in soil C, N and P contents (%; figure 8b,c and d) were highest in the surface layer of R.hol, followed by R.hum, P.hum and P.hol. Hummock and hollows in the restored area resembling each other over the depth profile for C, N and P while significant differences were revealed between topography in the pristine area in the 10-20 stratum for N and P (appendix. Table 1). P.hum showed a peak at the 12.5 cm layer, in N and P that was not present for any other group. For C, N and P, there was an interaction effect between Area and Depth where C decreased with depth in the restored area but increased in the pristine area and N and P decreased with depth in both areas but the decrease was more pronounced in the restored area (table 3). There was an interaction effect of area and depth for C:N ratios where the increment was larger in the restored

Time from start

R = 0.94, p < 2.2e−16

−2

−1

0

1

−10 0 10 20Water level cm (diff. from median)

Peat

leve

l cm

(diff

. fro

m m

edia

n)

Restored

R = 0.88, p < 2.2e−16

−1.0

−0.5

0.0

0.5

−5 0 5Water level cm (diff. from median)

Peat

leve

l cm

(diff

. fro

m m

edia

n)

Pristine

19

area. In the pristine area, there was a significance difference in C:N ratio in the 10-20 stratum with higher ratio in P.hol (figure 6).

Figure 8. Mean values of soil properties per group and stratum (n = 3). Horizontal error lines indicates ± 1 SE.

Table 3. ANOVA table from the linear mixed effect models evaluating the effect of Area (Pristine, Restored), Topography (Hummock, Hollow), Depth (0-50cm) and their interaction effects on soil properties. Significant effects (P < 0.05) are highlighted by *.

P.Hol P.Hum R.Hol R.Hum

−50

−40

−30

−20

−10

0

0.06 0.08 0.10 0.12 0.14 0.16 Bulk density (g cm−3)

Dep

th

−50

−40

−30

−20

−10

0

1.0 1.5 2.0N%

−50

−40

−30

−20

−10

0

0.02 0.04 0.06P%

−50

−40

−30

−20

−10

0

48 50 52C%

Dep

th

−50

−40

−30

−20

−10

0

20 40 60 80C:N

−50

−40

−30

−20

−10

0

2000 4000 6000C:P

NumDF DenDF F value Pr(>F) NumDF DenDF F value Pr(>F)

Bd C

Site 1 4 2.363 0.199 1 4.009 0.207 0.673

Topo 1 76 0.934 0.337 1 73.02 5.655 0.02*

Depth 9 76 7.712 <0.001* 9 73.039 3.071 0.004*

Site:Topo 1 76 7.678 0.007* 1 73.021 5.173 0.026*

Site:Depth 9 76 5.813 <0.001* 9 73.05 2.971 0.005*

Topo:Depth 9 76 0.808 0.611 9 73.021 0.976 0.467

Site:Topo:Depth 9 76 3.509 0.001* 9 73.022 0.684 0.721

N P

Site 1 4.046 0.705 0.448 1 4 3.873 0.12

Topo 1 73.073 2.42 0.124 1 76 4.934 0.029*

Depth 9 73.119 44.267 <0.001* 9 76 45.169 <0.00*1

Site:Topo 1 73.077 26.138 <0.001* 1 76 27.534 <0.001*

Site:Depth 9 73.145 4.905 <0.001* 9 76 15.856 <0.001*

Topo:Depth 9 73.075 1.688 0.107 9 76 1.197 0.31

Site:Topo:Depth 9 73.078 1.369 0.218 9 76 2.791 0.007*

C:N ratio

Site 1 4.019 1.399 0.302

Topo 1 73.03 0.786 0.378

Depth 9 73.049 28.016 <0.001*

Site:Topo 1 73.031 23.76 <0.001*

Site:Depth 9 73.059 2.857 0.006*

Topo:Depth 9 73.031 0.93 0.504

Site:Topo:Depth 9 73.032 0.753 0.66

20

Microbial composition The average concentration of total PLFAs was significantly higher in the pristine area (table 5). Highest concentration was found in P.hol where the amount of PLFA increased in the deeper stratum (Figure 9a). The higher total amount of PLFA was mainly driven by PLFAs associated to bacteria's (figure 9c). Gram-positive (G+) bacteria was most abundant in P.hol and highest abundance was found in the 25 cm layer of P.hol which were significantly higher than in all other areas (figure 10). In the pristine area, G+ bacteria increased with depth, while in the restored area there was no differences between depths and G+ abundance. There were less difference in total amount of Gram-negative (G-) bacteria's between areas which resulted in a relative higher G- abundance in the restored area. Actinomycetes specific PLFAs was overall higher in the pristine area and increased with depth in P.hol but no differences in depth was found for P.hum, R.hol or R.hum. Other bacteria, bacteria specific PLFAs that are produced by a range of bacteria’s and can therefore not be assign to a specific group, was higher in the pristine area and no differences was found between depths.

Figure 9. Mean concentrations (n = 6) (nmol PLFA g-1 dry peat ± standard error) of total PLFA detected (A), total PLFA associated with Bactria (C) and fungi (D). B shows the molar ratio of fungi and bacteria associated PLFAs.. Lighter color shade represents sample from the 10-20 cm layer and darker color shades represent samples from the 20-30 cm layer. Significant differences in mean concentrations and ratio (pairwise comparison TukeyHSD, p < 0.05) are shown by different letters. P = pristine, R = restored, hol = hollow, hum = hummock, 15 = sample collected between the depth of 10-20 cm. 25 = sample collected at the depth of 20 – 30 cm. No differences in total amount was found between groups or depths for Saprophytic fungi and AMF fungi (figure 10e,10f). However, the amount of the PLFA 18:1w9 detected was hugely in all samples but yet more in samples from the pristine area where they made out more than 50% of the total amount of PLFAs. To validate the plausibility of this PLFA to actually origin from saprophytic fungi, I examined the correlation between 18:1w9 and the other saprophytic associated PLFA 18:2w6c. There was no correlation, why the contribution of 18:1w9 likely origin from plants and are therefore not included in data presented here.

GroupP.Hol.15

P.Hol.25

P.Hum.15

P.Hum.25

R.Hol.15

R.Hol.25

R.Hum.15

R.Hol.25

AA A A

BBB

C

0

2000

4000

6000

P.Hol.15

P.Hol.25

P.Hum.15

P.Hum.25

R.Hol.15

R.Hol.25

R.Hum.15

R.Hol.25

Tota

l PLF

A A AA

AAAA A

0.000

0.025

0.050

0.075

0.100

P.Hol.15

P.Hol.25

P.Hum.15

P.Hum.25

R.Hol.15

R.Hol.25

R.Hum.15

R.Hol.25

F:B

ratio

AA A A

BB

B

C

0

1000

2000

3000

4000

P.Hol.15

P.Hol.25

P.Hum.15

P.Hum.25

R.Hol.15

R.Hol.25

R.Hum.15

R.Hol.25

Bact

eria

PLF

A

A

A A AAA A

A

0

50

100

150

P.Hol.15

P.Hol.25

P.Hum.15

P.Hum.25

R.Hol.15

R.Hol.25

R.Hum.15

R.Hol.25

Fung

i PLF

A

Pristine Restored Pristine Restored

A B

C D

21

The two main principal components (PC) PC1 and PC2 together explained 54% of the variation in relative contribution of each PLFA in the peat, (figure 11). Gram-negative bacteria's and fungi made out a larger relative amount of total PLFA in both restored areas (R.hol and R.hum) compared to peat from the pristine area. P.hum was separated from other groups in a cluster driven by a high fraction of general bacteria markers and P.hol distinguished from others by a high fraction of general bacteria PLFAs, which became more pronounced in the deeper layer. There was a clear ordinal separation of areas (table 4) as well as of the interaction effect from area and topography (PERMANOVA p < 0.001), but not between areas and depth, (PERMANOVA p = 0.19), however there are tendencies of such ordinal separation.

Figure 10. Mean concentrations ( n= 6) (nmol PLFA g-1 dry peat ± standard error) of PLFA assigned to the functional groups Gpos, Gneg, actinomycetes, other bacteria, saprophytic fungi and arbuscular mycorrhiza fungi (AMF). Lighter color shades represent the depth layer 15 and darker color shades represents the depth layer 25. Significant differences in mean concentrations and ratio (ANOVA, alpha = 0.05) are shown by different letters. P = pristine, R = restored, hol = hollow, hum = hummock, 15 = sample collected between the depth of 10-20 cm. 25 = sample collected at the depth of 20 – 30 cm.

P.Hol.15

P.Hol.25

P.Hum.15

P.Hum.25

R.Hol.15

R.Hol.25

R.Hum.15

R.Hol.25

A A A A

ABB

B

C

0

500

1000

1500

P.Hol.15

P.Hol.25

P.Hum.15

P.Hum.25

R.Hol.15

R.Hol.25

R.Hum.15

R.Hol.25

Gra

m−p

ositiv

e PL

FA

A A AB AB ABABB

C

0

250

500

750

P.Hol.15

P.Hol.25

P.Hum.15

P.Hum.25

R.Hol.15

R.Hol.25

R.Hum.15

R.Hol.25

Gra

m−n

egat

ive

A AAA

B BC BCC

0

100

200

300

400

P.Hol.15

P.Hol.25

P.Hum.15

P.Hum.25

R.Hol.15

R.Hol.25

R.Hum.15

R.Hol.25

Actin

omyc

etes

AA A A

BB

B B

0

500

1000

1500

P.Hol.15

P.Hol.25

P.Hum.15

P.Hum.25

R.Hol.15

R.Hol.25

R.Hum.15

R.Hol.25

Oth

er b

acte

ra AAA A

AA

A A

0

25

50

75

100

P.Hol.15

P.Hol.25

P.Hum.15

P.Hum.25

R.Hol.15

R.Hol.25

R.Hum.15

R.Hol.25

Sapr

ophy

tic fu

ngi

A A AA A A

AA

0

25

50

75

100

P.Hol.15

P.Hol.25

P.Hum.15

P.Hum.25

R.Hol.15

R.Hol.25

R.Hum.15

R.Hol.25

AMF

fung

i

Pristine Restored Pristine Restored Pristine Restored

22

Figure 11. Principal component analysis (PCA) of identified PLFAs (mol% of total PLFA). Points that are closer together in space are more similar in PLFA composition. Each point/triangle represent an individual sample from the 15 cm and 25 cm layer respectively. The direction of arrows shows the correlation between each variable while the length of the arrow represent the contribution of a variable to the principal component. Ellipses represent the core area for each area with a confidence interval of 68% (n = 6). PC1 (x axis) explains 36% of the variance and PC2 (y axis) explains 18% so that in total, 54% of the variance is explained by the diagram. P = pristine, R = restored, hol = hollow, hum = hummock, 15 = sample collected between the depth of 10-20 cm. 25 = sample collected at the depth of 20 – 30 cm.

Table. 4 Three-way factorial (Area, Topography and Depth) analysis (PERMANOVA) explaining the variation in relative abundance of each PLFA between areas (Pristine, Restored), Topo (Hummock, Hollow) and Depth (15, 25 cm) and their interaction effects.

Df Sums Of Sqs Mean Sqs F.Model R2 Pr(>F)

Area 1 0.266 0.266 75.238 0.576 0.001*

Type 1 0.015 0.015 4.507 0.034 0.008*

Depth 1 0.007 0.007 2.174 0.016 0.001*

Area:Topo 1 0.016 0.016 4.729 0.036 0.017*

Area:Depth 1 0.006 0.006 1.816 0.013 0.134

Type:Depth 1 0.004 0.004 1.369 0.010 0.214

Area:Topo:Depth 1 0.002 0.002 0.75 0.005 0.430

Residuals 40 0.141 0.003 0.306 Total 47 0.463 1.000

14:0 (bacteria)i15:0 (G+)

a15:0 (G+)

15:0 (bacteria)

14:0 3OH (G

−)

i16:0 (G+)

16:1w

9c (G−)

16:1 (bacteria)16:1w5c (AMF)

16:0 (GENERAL)10Me16:0 (actinomycetes)

i17:0

(G+)

a17:0 (G+)

17:1

(bac

teria)

cy17:0 (G−)

17:0 (bacte

ria)

18:2w6c(saprophytic fungi)

18:1w9t + 18:1w7c (bacteria)

18:0 (GENERAL)

10Me1

8:0 (a

ctino

mycete

s)

cy19:0 (G−)

20:0 (GENERAL)

−2

−1

0

1

2

−2 −1 0 1 2PC1 36%

PC2

18%

Depth15

25

GroupsP.Hol.15

P.Hol.25

P.Hum.15

P.Hum.25

R.Hol.15

R.Hol.25

R.Hum.15

R.Hum.25

23

Table 5. ANOVA table from the linear mixed effect models evaluating the effect of Area (Pristine, Restored), Topography (Hummock, Hollow), Depth (0-50cm) and their interaction effects on absolute amount of PLFAs. Significant effects (P < 0.05) are highlighted by *.

NumDF DenDF F value Pr(>F) NumDF DenDF F value Pr(>F)

Total PLFA F:B ratio

Site 1 9.865 216.982 <0.001* 1 40 2.217 0.144

Topo 1 29.953 10.033 0.003* 1 40 0.902 0.348

Depth 1 32.441 7.35 0.01* 1 40 0.165 0.687

Site:Topo 1 29.953 17.967 <0.001* 1 40 0.402 0.53

Site:Depth 1 32.508 6.819 0.013* 1 40 1.432 0.238

Topo:Depth 1 29.953 1.113 0.299 1 40 1.325 0.257

Site:Topo:Depth 1 29.953 1.562 0.22 1 40 0.003 0.959

Bacteria Fungi

Site 1 9.871 218.435 <0.001* 1 10.028 1.852 0.186

Topo 1 29.967 10.959 0.002* 1 30.145 0.817 0.373

Depth 1 32.647 7.82 0.008* 1 33.348 0.16 0.692

Site:Topo 1 29.967 17.789 <0.001* 1 30.145 <0.001 0.984

Site:Depth 1 32.72 7.921 0.008* 1 33.435 1.162 0.289

Topo:Depth 1 29.967 0.677 0.417 1 30.145 1.74 0.197

Site:Topo:Depth 1 29.967 1.312 0.26 1 30.145 0.329 0.57

G-positive G-negative

Site 1 10.022 78.53 <0.001* 1 10.051 19.338 <0.001*

Topo 1 30.157 12.197 0.002* 1 30.117 23.729 <0.001*

Depth 1 33.809 9.4 0.004* 1 31.979 14.212 0.001*

Site:Topo 1 30.157 16.342 <0.001* 1 30.117 33.837 <0.001*

Site:Depth 1 33.908 8.759 0.006* 1 32.029 10.166 0.003*

Topo:Depth 1 30.157 1.953 0.173 1 30.117 1.679 0.205

Site:Topo:Depth 1 30.157 2.246 0.145 1 30.117 2.296 0.14

Actinomycetes Other bacteria

Site 1 9.924 160.122 <0.001* 1 9.881 193.917 <0.001*

Topo 1 30.057 0.213 0.648 1 29.953 1.298 0.263

Depth 1 33.658 2.248 0.143 1 32.015 0.633 0.431

Site:Topo 1 30.057 0.189 0.667 1 29.953 3.744 0.061

Site:Depth 1 33.756 10.615 0.003 1 32.071 0.623 0.435

Topo:Depth 1 30.057 1.678 0.205 1 29.953 0.158 0.693

Site:Topo:Depth 1 30.057 3.902 0.057 1 29.953 0.012 0.913

Saprophytic fungi AMF fungi

Site 1 10.052 3.025 0.094 1 40 0.132 0.719

Topo 1 30.153 0.44 0.512 1 40 0.758 0.389

Depth 1 32.946 0.07 0.793 1 40 0.15 0.701

Site:Topo 1 30.153 1.624 0.212 1 40 1.827 0.184

Site:Depth 1 33.022 0.372 0.546 1 40 1.476 0.232

Topo:Depth 1 30.153 0.595 0.447 1 40 2.205 0.145

Site:Topo:Depth 1 30.153 1.368 0.251 1 40 0.069 0.795

24

Porewater chemistry DOC concentration was significantly higher in the restored area but no differences were found between topography within areas (figure 12), (table 6). The highest mean DOC concentration was measured in late September for all groups. This peak was followed by a decrease in DOC for all groups to reach the lowest concentration in March. This reduction of DOC occurred earlier and more pronounced in the late autumn in P.hol and during the winter months (Nov, Dec, Jan, Mar) there were lower difference in concentration of DOC between topography within the pristine area (P.hum and P.hol) than between topography in the restored area. Concentrations of total dissolved nitrogen (TDN) were highest in the restored area with no significant differences in neither yearly average nor seasonal development between R.hum and R.Hol, they both showed significantly higher concentration than P.hol which had the lowest concentration of all groups. Highest concentration of TDN was measured in late August for all groups. NH4+ constituted the major part of the dissolved inorganic nitrogen (DIN) pool in pore water in both areas, with low NO3- concentrations and NO2- concentrations below the detection limit in all samples. Furthermore, DIN accounted for 37, 46, 45 and 33% of the TDN pool in pore water for P.hol, P.hum, R.hol and R.hum respectively. There was no significant difference in NH4+ concentration between areas, but between P.hol and R.hol, and almost significant (anova, p = 0.083), between areas with tendencies of higher NH4+ concentrations in the restored area.

25

Figure 12. Monthly mean (n = 6) (left) and total mean (n = 66) of the entire sampling period (right) for each group. In the time series, point shows mean and error bar shows 1 SE. For total mean, error bar shows +/- SE and different letters indicate significant differences between groups. P.hol = Pristine hollow, P.hum = Pristine hummock, R.hol = Restored hollow, R.hum = Restored hummock. HWT are shown as negative values to better illustrate the distance between water level and peat surface.

P.hol P.hum R.hol R.hum

−40

−30

−20

−10

0

May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr

−HW

T [c

m] A

B BCC

−40

−30

−20

−10

0


0

25

50

75

100

125


DO

C (m

g L−

1 )

A A

BB

0

25

50

75

100

125


0

1

2

3


DN

(mg

L−1 )

AAB

BB

0

1

2

3


0

1

2

3

4

5


SUVA

AB

C C

0

1

2

3

4

5


3.0

3.5

4.0

4.5

5.0


A2A3 AA

B B

3.0

3.5

4.0

4.5

5.0


A

B

C

D

E

26

pH measurements from September showed significant (Welch Two Sample t-test, p <0.001) higher pH in the pristine area (Mean±SD: pH = 4.105 ± 0.088) compared to the restored area (Mean±SD: 3.958 ± 0.091). (Appendix. Figure 1) Significant differences in ion concentration between areas was found for Na, K, Mg, Ca and Cl (figure 13). Concentrations of Na, K and Cl was higher in the pristine area than in the restored area where instead concentrations of Mg and Ca was highest. Concentrations of PO43- in pore water was close to, or below detection limit in all water samples except from samples collected in the beginning of July, where low concentrations (40ug/L) were detected in all samples but P.hum. Concentrations of SO4 showed a complex pattern over the sampling period between groups but no main effect of area was found.

Figure 13. Montlhy mean (n = 6) (time series) and total mean (n = 46) of the entire sampling period (bar plot) for each group. In the time series, points show mean and error bar shows ± 1 SE. For total mean, error bar shows ± 1 SE and different letters indicate significant differences of total mean between groups.

SUVA (measure of aromaticity and molecular size) was significantly higher in the restored area (figure 10d) and there was no differences between R.hol and R.hum. In the pristine area, SUVA was significantly higher in P.hum compared to P.hol. SUVA remained rather constant over the sampling period with the exception of SUVA for P.hum and P.hol in May and an increase in P.hol from October to January. Readings in May are extremely low and should be interpreted with caution. However, removing readings from this month did not change the significant differences between groups. The ratio E2:E3 (measure of molecular size) was significantly higher in the pristine area compared to the restored with no differences between topography from the same area (Figure 10e). In the restored area, E2:E3 remained constant during the whole sampling period, while in the pristine area E2:E3 was high in early and late summer months and decreased during autumn and winter.


0500100015002000

May Jun Jul Aug Sep Oct Nov Dec

NH4

µg L−

1

AABAB

B

0500100015002000

P.holP.humR.holR.hum0

250

500

750

May Jun Jul Aug Sep Oct Nov DecCa

µg L−

1

AA AA

0

250

500

750


0

2000

4000

6000


Cl µg L−

1

A AB B

0

2000

4000

6000



F µg L−

1

A AA A

0255075100125


0

300

600

900


K µg L−

1

AABABB

0

300

600

900

P.hol P.hum R.hol R.hum0

250

500

750

1000


Mg

µg L−

1

A AAB B

0

250

500

750

1000

P.holP.humR.holR.hum

0

2000

4000

6000


Na

µg L−

1

A ABBC C

0

2000

4000

6000



NO3

µg L−

1

AAA A

050100150200250


0

20

40

60


PO4

µg L−

1

A A AA

0

20

40

60

P.hol P.hum R.hol R.hum0

200

400

600


SO4

µg L−

1

AA AA

0

200

400

600


27

Table 6. ANOVA table from the linear mixed effect models evaluating the effect of Area (Pristine, Restored), Topography (Hummock, Hollow), Month (for HWT, DOC, DN, SUVA and E2:E3 May 2020 – April 2021, for ions, fluorescence indices and peaks May 2020 – December 2021) and their interaction effects on HWT (cm), porewater mass concentration, fluorescence indices and Peaks (relative intensity to total). Significant effects (P < 0.05) are highlighted by *.

Fluorescence measurements showed no differences in DOM composition between areas. Fluorescence intensities of peaks was higher in the restored area for all peaks, reflecting the quantity of DOM rather than quality. Here, fluorescence measurements were not used to compare quantity of DOM but the quality. Hence, instead, each peaks relative contribution to the total intensity of all peaks was compared, (figure 14). This revealed no differences in peak contribution between areas with the exception of peak B where the intensity was higher in the restored areas. Freshness index, Fluorescence index and humification index resembled each other over the sampling periods for all groups and temporal differences was revealed but no differences between areas (figure 15).

NumDF DenDF F value Pr(>F) NumDF DenDF F value Pr(>F) NumDF DenDF F value Pr(>F)HWT DOC Peak A

Area 1 19.45 55.037 <0.001* 1 20.46 33.835 <0.001* 1 20 1.007 0.328 AreaTopo 1 23.64 29.919 <0.001* 1 26.4 4.041 0.055 1 20 1.699 0.207 TopoMonth 10 190.035 114.938 <0.001* 10 191.027 26.802 <0.001* 6 120 23.011 <0.001* MonthArea:Topo 1 23.686 2.479 0.129 1 26.466 0.508 0.482 1 20 0.352 0.559 Area:TopoArea:Month 10 190.383 31.42 <0.001* 10 191.405 2.98 0.002* 6 120 1.154 0.336 Area:MonthTopo:Month 10 190.759 1.618 0.104 10 191.768 4.291 <0.001* 6 120 1.581 0.159 Topo:MonthArea:Topo:Month 10 190.667 1.985 0.037* 10 191.59 0.641 0.777 6 120 1.033 0.407 Area:Topo:Month

DN SUVA Peak CArea 1 20.562 13.716 <0.001* 1 17.291 93.639 <0.001* 1 20 0.98 0.334 AreaTopo 1 28.279 0.551 0.464 1 18.924 9.538 0.006* 1 20 2.098 0.163 TopoMonth 10 191.025 35.951 <0.001* 10 185.539 54.352 <0.001* 6 120 5.551 <0.01* MonthArea:Topo 1 28.37 0.771 0.387 1 18.953 8.746 0.008* 1 20 0.191 0.666 Area:TopoArea:Month 10 191.422 1.036 0.415 10 185.764 23.297 <0.001* 6 120 1.168 0.328 Area:MonthTopo:Month 10 191.77 4.4 <0.001* 10 186.161 2.411 0.01* 6 120 0.603 0.727 Topo:MonthArea:Topo:Month 10 191.53 1.457 0.158 10 186.186 1.438 0.166 6 120 0.686 0.661 Area:Topo:Month

E2:E3 Na Peak TArea 1 19.474 63.329 <0.001* 1 20.044 19.617 <0.001* 1 20 0.985 0.333 AreaTopo 1 22.802 0.062 0.806 1 22.301 2.696 0.115 1 20 0.289 0.597 TopoMonth 10 188.049 24.396 <0.001* 7 130.713 27.042 <0.001* 6 120 37.299 <0.001* MonthArea:Topo 1 22.841 0.202 0.658 1 22.356 0 0.992 1 20 0.134 0.718 Area:TopoArea:Month 10 188.355 15.884 <0.001* 7 130.937 2.08 0.05* 6 120 0.599 0.731 Area:MonthTopo:Month 10 188.718 2.052 0.03* 7 131.368 0.964 0.46 6 120 0.355 0.906 Topo:MonthArea:Topo:Month 10 188.685 1.636 0.099 7 131.407 0.346 0.931 6 120 0.587 0.74 Area:Topo:Month

NH4 K Peak BArea 1 20.285 3.323 0.083 1 20.268 5.935 0.024* 1 20 5.713 0.027* AreaTopo 1 27.946 0.231 0.634 1 22.697 1.868 0.185 1 20 0.819 0.376 TopoMonth 7 130.833 49.697 <0.001* 7 130.922 43.384 <0.001* 6 120 4.889 <0.001* MonthArea:Topo 1 28.075 4.558 0.042* 1 22.752 0.606 0.444 1 20 0.001 0.981 Area:TopoArea:Month 7 131.265 3.161 0.004* 7 131.156 4.262 <0.001* 6 120 1.332 0.248 Area:MonthTopo:Month 7 131.616 1.389 0.215 7 131.571 2.394 0.024* 6 120 0.919 0.484 Topo:MonthArea:Topo:Month 7 131.575 2.677 0.013* 7 131.612 1.204 0.305 6 120 0.614 0.719 Area:Topo:Month

Mg Ca Peak MArea 1 19.811 13.306 0.002* 1 19.407 4.981 0.038* 1 20 1.031 0.322 AreaTopo 1 24.261 2.34 0.139 1 20.967 0.307 0.585 1 20 0.117 0.736 TopoMonth 7 130.504 10.167 <0.001* 7 130.203 10.659 <0.001* 6 120 7.162 <0.001* MonthArea:Topo 1 24.333 0.398 0.534 1 21.03 0.359 0.555 1 20 0.027 0.872 Area:TopoArea:Month 7 130.869 0.66 0.705 7 130.37 1.385 0.217 6 120 0.741 0.617 Area:MonthTopo:Month 7 131.248 2.985 0.006* 7 130.953 0.499 0.834 6 120 1.262 0.28 Topo:MonthArea:Topo:Month 7 131.266 1.419 0.203 7 130.954 1.19 0.313 6 120 0.304 0.934 Area:Topo:Month

Cl NO3 FIArea 1 19.851 64.517 <0.001* 1 150 0.09 0.764 1 20 0.052 0.822 AreaTopo 1 22.606 0.551 0.466 1 150 2.37 0.126 1 20 0.021 0.885 TopoMonth 7 130.515 21.449 <0.001* 7 150 23.43 <0.001* 6 120 21.085 <0.001* MonthArea:Topo 1 22.661 0.335 0.568 1 150 1.155 0.284 1 20 0.014 0.906 Area:TopoArea:Month 7 130.782 4.892 <0.001* 7 150 1.503 0.17 6 120 0.469 0.83 Area:MonthTopo:Month 7 131.185 0.514 0.823 7 150 0.751 0.629 6 120 1.186 0.318 Topo:MonthArea:Topo:Month 7 131.229 0.947 0.473 7 150 4.34 <0.001* 6 120 0.481 0.821 Area:Topo:Month

PO4 SO4 HIXArea 1 150 0.793 0.375 1 19.735 2.309 0.144 1 20 1.472 0.239 AreaTopo 1 150 1.071 0.302 1 21.117 0.467 0.502 1 20 0.026 0.873 TopoMonth 7 150 6.318 <0.001* 7 130.746 14.605 <0.001* 6 120 43.73 <0.001* MonthArea:Topo 1 150 1.547 0.215 1 21.199 0.009 0.924 1 20 0.069 0.796 Area:TopoArea:Month 7 150 0.803 0.586 7 130.884 2.82 0.009* 6 120 0.642 0.697 Area:MonthTopo:Month 7 150 0.429 0.883 7 131.639 1.254 0.278 6 120 0.047 1 Topo:MonthArea:Topo:Month 7 150 1.242 0.283 7 131.577 2.906 0.007* 6 120 0.283 0.944 Area:Topo:Month

F FrArea 1 19.666 3.198 0.089 1 20 0.005 0.946 AreaTopo 1 21.017 1.507 0.233 1 20 0.391 0.539 TopoMonth 7 130.818 50.106 <0.001* 6 120 9.696 <0.001* MonthArea:Topo 1 21.11 0.351 0.56 1 20 0.023 0.88 Area:TopoArea:Month 7 130.95 6.712 <0.001* 6 120 0.625 0.71 Area:MonthTopo:Month 7 131.814 0.694 0.677 6 120 0.27 0.95 Topo:MonthArea:Topo:Month 7 131.705 0.223 0.979 6 120 0.285 0.943 Area:Topo:Month

28

Figure 14. Relative contribution(%) (n = 42) of total intensity from peaks A, C, M, T and B for each group.

Figure 15. Fluorescence indices for each group. points shows monthly mean (n = 6) and error bar represents ± 1 SE.

0

25

50

75

100

P.hol P.hum R.hol R.humGroup

Rel

ative

con

tribu

tion

% PeakA

C

M

T

B

Group P.hol P.hum R.hol R.hum

0.34

0.36

0.38

Jul Oct Jan

Fres

hnes

s in

dex

1.20

1.25

Jul Oct Jan

Fluo

resc

ence

Inde

x

8

10

12

14

Jul Oct Jan

Hum

ifica

tionI

ndex

29

Discussion Heigh above water table The 'forest-to-open-bog' restoration at Anderstorp Stormosse have 8 years after restoration not achieved to establish a water table position similar to a pristine state. The greater height above the water table (HWT) in the restored area can pose a risk for drying and a succession towards forest species dominance (T. O. Haapalehto et al. 2011). HWT in the restored area was similar to that in hummocks in the pristine area but during dryer periods there was a more pronounced HWT increase in both hummock and hollows in the restored area. There was an overall higher fluctuation in water table in the restored area, fluctuations in HWT have been shown to impede Sphagnum growth (Kim et al. 2021). The mean HWT measured in the restored area suggests suitable conditions for a range of Sphagnum species (Bobrov, Charman, and Warner 2002). However, no Sphagnum was observed in the restored area except for in scattered water pools. The proximity to healthy populations of Sphagnum assures dispersion of diaspores but the combination of a mean high HWT, fluctuations in HWT, and a ground cover of vascular plants and forest mosses is likely to impede Sphagnum re-establishment. HWT in this study is in line with what others have found from restored bogs. For example, Gaffney et al. (2018) found a temporal step-wise recovery of a shallow water table over a period of 17 years after restoration of a blanket bog, but the HWT remained, on average, 8 cm higher, and occasionally HWT was more than 25 cm to 40 cm greater than in the reference area. Haapalehto et al (2011) found a similar stepwise temporal decline in HWT in restored areas when comparing to a unrestored the first 3 years post restoration, but which stopped and reversed the following 5 years, suggesting a cease in reclamation. At Anderstorp Stormosse, it remains unknown how HWT have developed since the restoration measures. But, based on 11 studies covering 37 Swedish mires compiled in Granath et al. (2016), the mean difference in HWT was 24 cm (17 versus 41 cm) between drained and undrained mires. At Anderstorp Stormosse, measurements show a difference in mean HWT of 9.4 cm (20.9 versus 11.5). This suggests that that the restoration have indeed brought the water table closer to the peat surface. Thus, even though the restoration has not yet reduced the HWT into a pristine-like regime, the restoration measures have most likely reduced the HWT, and are as for now closer to a pristine state than pre-restoration. Bulk density Bulk density (BD) was highest in the top layers (0-10 cm) in the restored area. In an experimental study, Noble et al. (2017) found that an increase in BD by 0.04 g cm-3, from 0.13 to 0.17 g cm-3, had a negative impact on Sphagnum. This is because less macropores and more micropores in more decomposed peat increases the peats’ ability to hold water and, thus limits the water accessibility for Sphagnum. In Anderstorp Stormosse, BD was 0.05 g cm-3 higher in the restored area on average in the surface layer (0-10 cm), 0.085 vs 0.135 g cm-3 which can impede Sphagnum re-establishment and growth in the restored area. Below the 10 cm stratum there was no difference in BD between peat from restored and pristine area over the depth profile, which indicates that the subsidence and alteration of the soil due to afforestation have only affected the surface layer. More decomposed peat in the upper layer of the restored area may be responsible for the altered hydrological patterns, as indicated by HWT and peat oscillation measurements. According to T. Haapalehto et al. (2014), subsidence is most likely to be even more pronounced closer to old ditches, this makes up for a sloping ground towards old ditches and increases the lateral flow and thus generates a more effective drainage with a higher HWT as result. However, a higher HWT towards the main ditch (wich remains open but

30

blocked) was not detected here, but the study design was not optimal to reveal such pattern sine the infilled ditches are running in parallel with old ditches. Peat oscillation Peat oscillation were more pronounced in the restored area compared to the pristine area. This was somewhat contradictory to the general understanding, namely that more decomposed peat would have a lower ability to oscillate due to less pore space, but similar results have been obtain by others (Morton and Heinemeyer 2019, Fritz 2016). This is probably best explained by that peat oscillation is mainly caused by oscillations in water table (Howie and Hebda 2018), since the water level oscillated significantly more in the restored area. Further, peat oscillation correlated strongly with water level in both areas, which indicates that peat oscillation acts to lower HWT fluctuations in both areas. I installed the peat oscillation measurement devices on lawns, i.e. neither hollow nor hummock structures, which resulted in that the HWT between the devices was 10.1 cm on the date of installation (HWT = 19.4 and 9.3 cm for restored and pristine area, respectively) and below the depth of 30 cm in the peat there was no clear differences in BD. Assuming that the lawn peat BD profile compose a mean of hummock and hollows, there is basically no difference in peat BD in depth interval where the water table fluctuated in the two areas. Overall, peat oscillation measurement revealed that during the sampling period, "bog breathing" had impact on HWT in both the pristine and restored area and mitigated, based on highest and lowest water peat elevations, 14.6% of the water fluctuations in the pristine area and 13.1% in the reference area. Fritz (2016) found a mean “mitigation effect” of 21% when compiling 16 bogs globally. Further, despite failing to catch water fluctuations at cold weather (persistent below freezing point) the low-cost camera solution used here, managed to capture peat elevation with high resolution over long periods with minimal maintenance. The higher HWT in the restored area could not be explained by a reduction in the capacity of peat oscillation. Elements in the peat Peat elemental concentrations varied for all variables (C, N, P) between the two areas. Differences were detected in the upper surface (0-20 cm) layer between areas, and among hummocks and hollows in the pristine area. This reflects the larger difference in HWT between hummock and hollows in the pristine area, which controls the plant community composition and consequently litter production, nutrient assimilation, and biogeochemical processes related to decomposition and redox conditions. Differences in C:N ratios in the pristine area are in line with (Diamond et al. 2020), who found higher C:N ratios in hollows compared to hummocks when analyzing peat from approximately 15-25 cm below the peat surface. I did not observe such differences in the restored area. High P concentrations in the top layer of the restored area is probably due to constant high HWT and high redox environment, which allows for adsorption of P by iron complex, while in the wetter, pristine area, iron is used instead of oxygen as an electron acceptor during anaerobic respiration, which reduces the amount of iron complex where P can be retained to in the restored, wetter area (Kjaergaard et al. 2012). High P concentrations in the peat soil also compose a risk for increased P leachate during floods. High concentration of N alone, without corresponding high P concentrations, may induce a positive feedback, driven by increased mineralization of N when microorganisms has to mineralize more organic matter to satisfy their P demand. Such consequences have been observed by others post restoration, but is probably not the case here since the higher N content are accompanied by higher P content. Because there is a lack of information about severity of subsidence of the peat, as well as peat accumulation between the restored and pristine area, and thus, which of the soil stratum in the

31

restored area corresponds to the pristine, prior to afforestation, (I did not date the layers), comparisons about loss of soil organic matter and changes in BD cannot be made with this dataset. However, the pattern of uniform soil properties in deeper peat indicates prerequisites for a pristine-like functionality and C-cycling in the deeper anaerobic part of the peat. Bacterial composition There was a higher amount of total PLFA in the pristine area than in the restored area and the highest amount was detected in the most submerged peat layer, i.e. the deepest layer in pristine hummock. This was true even when excluding 18:1w9 and in line with Mpamah et al. (2017) who found total PLFA biomass to be higher in a untouched wetter area compared to a drained area within the same peat bog in Finland. Mpamah et al. (2017) further highlighted that PLFA, despite being selective for living or recently dead microbes, it does not distinguish between active and non-active cells and that lower redox environment may act to prolong the turnover rate of dead cells. The high amount of 18:1w9 detected, especially in the pristine area where the redox environment is poor, due to a water table close to the peat surface, supports the alternative explanation of poorly selectiveness of active cells, and would provide bias towards higher amounts of PLFA in wetter areas. However, Mitchell et al. (2003) did observe lower amount of total microbial biomass in dryer samples of sphagnum growing in wet conditions (compared to dryer conditions), when using other identification techniques but PLFA, and the high amount of 18:1w9 observed here, in the pristine area, could just as likely, be explained by difference in vegetation cover between the two areas. Even though the PLFA 18:1w9 has been widely used as an indicator of saprophytic fungi, it is also produced by plants and should therefore only be used if there is a positive correlation with 18:2w6 (Åsa Frostegård, Tunlid, and Bååth 2011), which was not the case here. Consequently, I excluded 18:1w9 in my analyses and this reinforces that while some PLFAs serve as good markers for microbial groups in some soils they are not useful for all soil types. The relationship between fungi and bacteria (F:B ratio) did not show any significant differences between the areas, neither did the absolute amount of fungi. There was however a tendency of a higher F:B ratio in the restored area (but weak statistical support, P=0.14), which was also seen in the PCA where samples from the restored area were more influenced by fungi-markers than samples from the pristine area. These tendencies are in line with Mitchell et al. (2003) who found a positive correlation between F:B ratio and HWT, i.e. higher relative fungi abundance with more aerated peat. Despite their relative low amount in comparison to bacteria, fungi are the main decomposer in the oxic layer of peat (Jaatinen et al. 2007) and the similarities in absolute amount may infer that decomposition rate in the restored area is similar to that of the pristine in the studied layers (10-30 cm) despite differences in HWT. However, Myers et al. (2012) suggests that the total biomass of microbes, rather than F:B ratio is most important in carbon cycling which suggests higher decomposition in the pristine area. Moreover, other studies have reported F:B ratios in the same magnitude as found in Anderstorp Stormosse, where for example (Sundh, Nilsson, and Borgå 1997) reported a median of F:B = 0.04 from 2 pristine boreal peat bogs in Sweden. Jaatinen et al. (2007) studied PLFA composition under different HWT regimes in a ombrotrophic peat bog in Finland and reported generally higher F:B ratios in hummocks compared to lawns but differences was small between areas and in the same magnitude as reported here below the depth of 10 cm. Gram-negative associated PLFAs made out a larger relative proportion of the PLFAs in the restored area. This is what could be expected considering that fungi competes other bacteria's

32

but benefits Gram-negative bacterias (R. Andersen, Chapman, and Artz 2013). This reasoning does also apply on the relative lower abundance of other bacteria in the pristine area. Further, Dhandapani et al. (2019) found that the relative abundance of G+ and G- positively correlated with CO2 and CH4 emission respectively, which may imply higher potential of CH4 emissions from the restored area. However, CH4 fluxes are mainly dominated by water table height (M. Strack and Zuback 2013). Lastly, the PCA and PERMANOVA revealed significant differences on microbial community composition between the two areas. It is uncertain what consequences this might have on carbon and nutrient cycling, and, ultimately, carbon emission, nutrient leachate and vegetation composition. However, loss of microbial diversity, by replacement of specialists with generalists have been linked to increased DOC concentrations in porewater (R. Andersen, Chapman, and Artz 2013). This may offer an alternative explanation as to why DOC concentrations were higher in porewater of the restored area. Porewater chemistry There was a higher concentration of dissolved organic carbon (DOC), and total dissolved nitrogen (TDN) in the restored area, i.e. higher concentrations of dissolved organic matter (DOM). Elevated DOM concentrations in restored areas have been observed in many studies and may constitute a risk for increased brownification and nutrient flushing for downstream water bodies (T. Haapalehto et al. 2014), (Senar, Creed, and Trick 2021). Mineralization rate, pH and ionic strength are the main influencers of DOM in peatlands, controlling production and solubility respectively (Clark et al. 2005). The higher HWT and thus deeper layer of aerated peat are likely to increase mineralization rate and production of DOM. Furthermore, pH was 0.14 units higher in the pristine area and the solubility of DOC and pH are known to have a positive correlation. Ionic strength in turn, having a negative correlation with solubility of DOC, are closely associated with SO4-2 concentrations, which did not show any significant difference between the areas. Hence it seems likely that higher DOM concentration in the restored area is coupled to higher decomposition rates and different vegetation rather than differences in pH and ionic strength. Differences in dissolved inorganic nitrogen (DIN) was not as pronounced as for TDN between areas. The bulk of DIN pool consisted of NH4+ , where the lowest concentration was found in the hollows in the pristine area which also had the lowest HWT, i,e, the wettest group. This is most likely because of the lower redox potential and slower mineralization rates. High concentration of inorganic N accompanied by P and K have been showed to benefit vascular plants over Sphagnum significantly more than excess N alone (Bubier, Moore, and Bledzki 2007). In Anderstorp Stormosse, inorganic phosphorus (PO4+) was scarce in both areas and might be the limiting nutrient for plants in both areas as high atmospheric N-deposition in the area has led to an increase in N content of plant litter i.e. lower C:N ratios and increase decomposition rate in both areas (Aerts et al. 2001). However, I found significant higher gravimetrical P in the upper layer of peat soil from the restored area, which indicates higher P accessibility for plants and the low concentration of inorganic P may be due to a higher uptake of inorganic P in the restored area. K concentrations were lower in the restored area, this is probably due to a higher demand of K in vascular plants (Wang and Moore 2014), which were more abundant in the restored area. To conclude, main inorganic nutrients for influencing plant species composition in the restored area are similar to those in the pristine and should not hinder Sphagnum from the pristine area to reestablish in the restored area. However, existing plants may still pose a competition of light and space why a clearance of existing shrubs and birches may be necessary and worth consider to facilitate for Sphagnum to reestablish.

33

SUVA was significantly higher in the restored area indicating DOM with higher aromaticity and molecular size than in the pristine area. DOM with higher aromaticity are in turn more potent for inducing browning effects in lakes (Senar, Creed, and Trick 2021), but being less labile may counteract export since DOC with higher lability are more prone for export. Ritson et al. (2017) found that different plants produced DOM of different SUVA values, where Sphagnum produced DOM with lowest SUVA values. Further, according to Clark et al., (2012) pH and SUVA have a positive correlation in porewater of peatlands. Indeed, the vegetation cover differed between areas, with less Sphagnum in the restored area, but higher pH. Further, increased organic matter decomposition, especially heterotrophic soil respiration, have been shown to decrease the aromaticity of DOM (Maria Strack, Munir, and Khadka 2019). This would predict lower aromaticity in the restored area, which had a deeper layer of aerated peat where heterotrophic respiration could occur. However, this prediction was not supported and an explanation may be that the restored area did not have a higher amount of microbial biomass. Thus, the higher aromaticity in the pristine area is probably a consequence of different vegetation composition. E2:E3 correlates negatively with molecular size and was lower in the restored area, giving further support for higher aromaticity and molecular size of DOM in the restored area. However, Fe-ions, are known to obstruct absorbance measurements and may lead to an overestimation of SUVA and an underestimation of E2:E3 (Poulin, Ryan, and Aiken 2014). Fe concentrations were not measured here and were thus not corrected for, however (T. Haapalehto et al. 2014) found significant (2.204 ± 1.051 mg/L) higher Fe concentrations in porewater of a peat bog 10 years after restoration compared to a pristine reference area, quantities that are most likely to affect absorbance measurements (Poulin, Ryan, and Aiken 2014). In contrast to absorbance measurements, the fluorescence data (indices and peak contribution) did not show any significant difference in quality of DOM between areas. All variables resembled each other during the sampling period. While Fe concentrations indeed affects fluorescence measurements by lowering the fluorescence intensity, it is unilinear and hard to predict (Poulin, Ryan, and Aiken 2014) and it might be that different Fe concentrations between area causes this contradiction between the two techniques. With that said, only a small fraction of the total DOM generates fluorescence (Fellman, Hood, and Spencer 2010), and differences in DOM composition may still be present but not detected by this technique. Further, high humification index (HIX), values greater than 10 represents strongly humified, plant derivate DOM while lower values (<4) indicates a high fraction of microbial produced DOM (Tfaily et al. 2015). Unsurprisingly, in Anderstorp Stormosse, HIX values varied between 9 and 14, suggesting that plant derived humic DOM, rather than extracellular substances produced by microorganisms are the main contributor of the DOM pool in both areas. Relative contribution of total intensity from each peak, as well as indices was similar to those reported by (Tfaily et al. 2015) from open peat bogs in Canada.

Conclusions This study aimed to give a status report on a ‘forest-to-open-bog’ restored area and provide insights to its recover process. By comparing the restored area to the conditions of a pristine, reference area, I provided a snapshot of major properties of hydrology, peat, microbial composition and pore water chemistry. I found that there are still considerable differences in distance between the peat surface and water table, and that this is not caused by a reduction in the peats capacity of following the

34

water table elevation i.e. bog-breathing, but more likely due to altered flow patterns caused by peat subsidence in the restored area and still ongoing drainage. I also found that peat in the restored area had higher bulk density in the uppermost layer that makes it more prone to retain water. The altered hydrology i.e. water table further away from the peat surface and higher water holding capacity may impede the recolonization of Sphagnum, which is essential for regaining key domain of attraction associated with pristine peatlands. Likely due to differences in plant community and altered bacterial community, porewater in the restored area had higher concentrations of DOM, with higher aromaticity and molecular size. This constitutes a risk for enhanced GHG emission and altering downstream watercourses and lakes systems. However, fluorescence measurement showed no differences in DOM composition and the differences observed in SUVA and E2:E3 may be due to altered concentration of Fe-ions. Differences in soil elemental concentrations was found in the uppermost stratum with higher concentrations of N and P in the restored area. This together with more aerated peat, it was thought to result in a higher abundance of microbes, especially fungi. However, this was not the case and instead there were higher amounts of total PLFA fungi in the pristine area. Further, there were signs of higher relative abundance of fungi in the resorted area, which was accompanied by relative higher abundance of Gram-negative bacteria’s and overall different composition of microbes based on PLFA. The consequences this will have on the restores area progress towards a pristine state remains unclear, but it may affect the carbon cycle by increasing the amount of DOC leachate and enhanced CH4 emissions. The nutrient profile of porewater, concerning the most important nutrients for influencing vegetation composition (N, P and K) did not show any essential differences and would thus be suitable for Sphagnum growth in the restored area. Furthermore, the water table depth, despite being higher, would be, as for now, bearable for a range of Sphagnum species. However, the re-establishment of Sphagnum has not yet begun but instead the vegetation cover is influenced by forest species. It might be a matter of time for Sphagnum to re-establish, but if follow-up actions are to be taken, I would consider to clear the present vegetation to remove competition from vascular plants, alternatively improve the existing water barriers and deteriorate the habitat for forest species. Since Sphagnum, to its nature, engineers’ peatlands to be wet and nutrient-poor, The re-establishment of Sphagnum is fundamental as it engineers’ peatlands to be wet and nutrient-poor and would further drive the restored area into a pristine-like state.

Acknowledgements I want to thank the following people for their help during this thesis process: Christoffer Bergvall for all your help in lab. Dolly Kothawala for assisting with finishing fluorescens data. Yvonne Meyer-Lucht for helping with setting up PLFA analysis. Sören Andersson for order and soldering pipes for peat oscillation measurement devices. Jovani Sancho for expert assistance with getting started with peat oscillation measurements. My supervisor Gustaf Granath, for introducing me to the intriguing world of peatlands and for guiding me through this process. And finally, Anna, for your support, brainstorming and the many frisbee breaks.

35

References Aerts, Rien, Bo Wallén, Nils Malmer, and Hannie De Caluwe. 2001. “Nutritional Constraints

on Sphagnum-Growth and Potential Decay in Northern Peatlands.” Journal of Ecology 89(2): 292–99.

Andersen, Jens M. 1976. “An Ignition Method for Determination of Total Phosphorus in Lake Sediments.” Water Research 10(4): 329–31. Andersen, Roxane., Stephen. J. Chapman, and Rebekka. R.E. Artz. 2013. “Microbial

Communities in Natural and Disturbed Peatlands: A Review.” Soil Biology and Biochemistry 57: 979–94. http://dx.doi.org/10.1016/j.soilbio.2012.10.003.

Andersen, Roxane et al. 2010. “Changes in Microbial Community Structure and Function

Following Sphagnum Peatland Restoration.” Soil Biology and Biochemistry 42(2): 291–301. http://dx.doi.org/10.1016/j.soilbio.2009.11.006.

Bahn, Michael et al. 2013. “Responses of Belowground Carbon Allocation Dynamics to

Extended Shading in Mountain Grassland.” New Phytologist 198(1): 116–26. Bobrov, A. A., D. J. Charman, and B. G. Warner. 2002. “Ecology of Testate Amoebae from

Oligotrophic Peatlands: Specific Features of Polytypic and Polymorphic Species.” Biology Bulletin 29(6): 605–17.

Bubier, Jill L., Tim R. Moore, and Leszek A. Bledzki. 2007. “Effects of Nutrient Addition on

Vegetation and Carbon Cycling in an Ombrotrophic Bog.” Global Change Biology 13(6): 1168–86.

Clark, Joanna M., Pippa J. Chapman, John K. Adamson, and Stuart N. Lane. 2005. “Influence of Drought-Induced Acidification on the Mobility of Dissolved Organic Carbon in Peat Soils.” Global Change Biology 11(5): 791–809.

Coble, Paula G., Carlos E. Del Castillo, and Bernard Avril. 1998. “Distribution and Optical Properties of CDOM in the Arabian Sea during the 1995 Southwest Monsoon.” Deep-Sea Research Part II: Topical Studies in Oceanography 45(10–11): 2195–2223.

Coble, Paula G., Sarah A. Green, Neil V. Blough, and Robert B. Gagosian. 1990. “Characterization of Dissolved Organic Matter in the Black Sea by Fluorescence Spectroscopy.” Nature 348(6300): 432–35.

Dhandapani, Selvakumar et al. 2019. “Are Secondary Forests Second-Rate? Comparing Peatland Greenhouse Gas Emissions, Chemical and Microbial Community Properties between Primary and Secondary Forests in Peninsular Malaysia.” Science of the Total Environment 655: 220–31.

Diamond, Jacob S., Daniel L. McLaughlin, Robert A. Slesak, and Atticus Stovall. 2020. “Microtopography Is a Fundamental Organizing Structure of Vegetation and Soil Chemistry in Black Ash Wetlands.” Biogeosciences 17(4): 901–15.

Evans, Chris D. et al. 2021. “A Novel Low-Cost, High-Resolution Camera System for Measuring Peat Subsidence and Water Table Dynamics.” Frontiers in Environmental Science 9(March): 1–18.

Fellman, Jason B., Eran Hood, and Robert G.M. Spencer. 2010. “Fluorescence Spectroscopy Opens New Windows into Dissolved Organic Matter Dynamics in Freshwater Ecosystems: A Review.” Limnology and Oceanography 55(6): 2452–62.

Fritz, C. (2006). Surface oscillation in peatlands: How variable and important is it?. Masters thesis. University of Waikato, New Zealand.

Frostegård, A., and E. Bååth. 1996. “The Use of Phospholipid Fatty Acid Analysis to Estimate Bacterial and Fungal Biomass in Soil.” Biology and Fertility of Soils 22(1–2):

36

59–65. Frostegård, Å, E. Bååth, and A. Tunlio. 1993. “Shifts in the Structure of Soil Microbial

Communities in Limed Forests as Revealed by Phospholipid Fatty Acid Analysis.” Soil Biology and Biochemistry 25(6): 723–30.

Frostegård, Å, A. Tunlid, and E. Bååth. 1991. “Microbial Biomass Measured as Total Lipid Phosphate in Soils of Different Organic Content.” Journal of Microbiological Methods 14(3): 151–63.

Frostegård, Åsa, Anders Tunlid, and Erland Bååth. 2011. “Use and Misuse of PLFA Measurements in Soils.” Soil Biology and Biochemistry 43(8): 1621–25. http://dx.doi.org/10.1016/j.soilbio.2010.11.021.

Gorham, Eville. 1991. “Northern Peatlands : Role in the Carbon Cycle and Probable Responses to Climatic Warming Author ( s ): Eville Gorham Published by : Wiley on Behalf of the Ecological Society of America Stable URL : Http://Www.Jstor.Org/Stable/1941811 JSTOR Is a Not-for-Pr.” 1(2): 182–95.

Granath, Gustaf, Paul A. Moore, Maxwell C. Lukenbach, and James M. Waddington. 2016. “Mitigating Wildfire Carbon Loss in Managed Northern Peatlands through Restoration.” Scientific Reports 6(June): 1–9. http://dx.doi.org/10.1038/srep28498.

Haapalehto, T., J. S. Kotiaho, R. Matilainen, and T. Tahvanainen. 2014. “The Effects of Long-Term Drainage and Subsequent Restoration on Water Table Level and Pore Water Chemistry in Boreal Peatlands.” Journal of Hydrology 519(PB): 1493–1505.

Haapalehto, Tuomas O. et al. 2011. “The Effects of Peatland Restoration on Water-Table Depth, Elemental Concentrations, and Vegetation: 10 Years of Changes.” Restoration Ecology 19(5): 587–98.

Hess, Samuel T., Shaohui Huang, Ahmed A. Heikal, and Watt W. Webb. 2002. “Biological and Chemical Applications of Fluorescence Correlation Spectroscopy: A Review.” Biochemistry 41(3): 697–705.

Howie, Sarah A., and Richard J. Hebda. 2018. “Bog Surface Oscillation (Mire Breathing): A Useful Measure in Raised Bog Restoration.” Hydrological Processes 32(11): 1518–30.

Oksanen, J. F., Guillaume Blanchet, Michael Friendly, Roeland Kindt, Pierre Legendre, Dan McGlinn, Peter R. Minchin, R. B. O'Hara, Gavin L. Simpson, Peter Solymos, M. Henry H. Stevens, Eduard Szoecs and Helene Wagner (2020). vegan: Community Ecology Package. R package version 2.5-7. https://CRAN.R-project.org/package=vegan Jaatinen, Krista, Hannu Fritze, Jukka Laine, and Raija Laiho. 2007. “Effects of Short- and

Long-Term Water-Level Drawdown on the Populations and Activity of Aerobic Decomposers in a Boreal Peatland.” Global Change Biology 13(2): 491–510.

Jauhiainen, Jyrki et al. 2019. “Reviews and Syntheses: Greenhouse Gas Exchange Data from Drained Organic Forest Soils-A Review of Current Approaches and Recommendations for Future Research.” Biogeosciences 16(23): 4687–4703.

Kao-Kniffin, Jenny, and Biao Zhu. 2013. “A Microbial Link between Elevated CO2 and Methane Emissions That Is Plant Species-Specific.” Microbial Ecology 66(3): 621–29.

Kim, Jinhyun et al. 2021. “Water Table Fluctuation in Peatlands Facilitates Fungal Proliferation , Impedes Sphagnum Growth and Accelerates Decomposition.” 8(April): 1–9.

Kjaergaard, Charlotte, Lisa Heiberg, Henning S. Jensen, and Hans Christian B. Hansen. 2012. “Phosphorus Mobilization in Rewetted Peat and Sand at Variable Flow Rate and Redox Regimes.” Geoderma 173–174: 311–21. http://dx.doi.org/10.1016/j.geoderma.2011.12.029.

Kritzberg, Emma S. et al. 2020. “Browning of Freshwaters: Consequences to Ecosystem Services, Underlying Drivers, and Potential Mitigation Measures.” Ambio 49(2): 375–90.

37

Lakowicz, Joseph R. 2006. Principles of Fluorescence Spectroscopy General Features of Protein Fluorescence.

Lindahl, Viggo, Åsa Frostegård, Lars Bakken, and Erland Bååth. 1997. “Phospholipid Fatty Acid Composition of Size Fractionated Indigenous Soil Bacteria.” Soil Biology and Biochemistry 29(9–10): 1565–69.

Mitchell, E. A.D. et al. 2003. “Structure of Microbial Communities in Sphagnum Peatlands and Effect of Atmospheric Carbon Dioxide Enrichment.” Microbial Ecology 46(2): 187–99.

Morton, Phoebe A., and Andreas Heinemeyer. 2019. “Bog Breathing: The Extent of Peat Shrinkage and Expansion on Blanket Bogs in Relation to Water Table, Heather Management and Dominant Vegetation and Its Implications for Carbon Stock Assessments.” Wetlands Ecology and Management 27(4): 467–82. https://doi.org/10.1007/s11273-019-09672-5.

Mpamah, Promise Anyalewachi et al. 2017. “The Impact of Long-Term Water Level Draw-down on Microbial Biomass: A Comparative Study from Two Peatland Sites with Different Nutrient Status.” European Journal of Soil Biology 80: 59–68.

Myers, Britney, Kara L. Webster, Jim W. Mclaughlin, and Nathan Basiliko. 2012. “Microbial Activity across a Boreal Peatland Nutrient Gradient: The Role of Fungi and Bacteria.” Wetlands Ecology and Management 20(2): 77–88.

Nichols, Jonathan E., and Dorothy M. Peteet. 2019. “Rapid Expansion of Northern Peatlands and Doubled Estimate of Carbon Storage.” Nature Geoscience 12(11): 917–21. http://dx.doi.org/10.1038/s41561-019-0454-z.

Noble, Alice et al. 2017. “Impacts of Peat Bulk Density, Ash Deposition and Rainwater Chemistry on Establishment of Peatland Mosses.” Plant and Soil 419(1–2): 41–52.

Peacock, Mike et al. 2014. “UV-Visible Absorbance Spectroscopy as a Proxy for Peatland Dissolved Organic Carbon (DOC) Quantity and Quality: Considerations on Wavelength and Absorbance Degradation.” Environmental Sciences: Processes and Impacts 16(6): 1445–61.

Peuravuori, Juhani, and Kalevi Pihlaja. 1997. “Molecular Size Distribution and Spectroscopic Properties of Aquatic Humic Substances.” Analytica Chimica Acta 337(2): 133–49.

Poulin, Brett A., Joseph N. Ryan, and George R. Aiken. 2014. “Effects of Iron on Optical Properties of Dissolved Organic Matter.” Environmental Science and Technology 48(17): 10098–106.

Ritson, Jonathan P. et al. 2017. “The Effect of Drought on Dissolved Organic Carbon (DOC) Release from Peatland Soil and Vegetation Sources.” Biogeosciences 14(11): 2891–2902.

Rydin, Håkan, and John K. Jeglum. 2015. The Biology of Peatlands The Biology of Peatlands.

Senar, Oscar E., Irena F. Creed, and Charles G. Trick. 2021. “Lake Browning May Fuel Phytoplankton Biomass and Trigger Shifts in Phytoplankton Communities in Temperate Lakes.” Aquatic Sciences 83(2): 1–15. https://doi.org/10.1007/s00027-021-00780-0.

Strack, M., and Y. C.A. Zuback. 2013. “Annual Carbon Balance of a Peatland 10 Yr Following Restoration.” Biogeosciences 10(5): 2885–96.

Strack, Maria, Tariq M. Munir, and Bhupesh Khadka. 2019. “Shrub Abundance Contributes to Shifts in Dissolved Organic Carbon Concentration and Chemistry in a Continental Bog Exposed to Drainage and Warming.” Ecohydrology 12(5): 1–12.

Sundh, Ingvar, Mats Nilsson, and Peter Borgå. 1997. “Variation in Microbial Community Structure in Two Boreal Peatlands as Determined by Analysis of Phospholipid Fatty Acid Profiles.” Applied and Environmental Microbiology 63(4): 1476–82.

Tfaily, Malak M. et al. 2015. “Utilization of PARAFAC-Modeled Excitation-Emission Matrix

38

(EEM) Fluorescence Spectroscopy to Identify Biogeochemical Processing of Dissolved Organic Matter in a Northern Peatland.” Photochemistry and Photobiology 91(3): 684–95.

Vasander, H. et al. 2017. “Status and Restoration of Peatlands in Northern Europe . Wetlands Ecology and Management , 11 ,.” Wetlands Ecology and Management 11(February 2003): 51–63. http://doi.org/10.1023/A.

Waddington, J. M. et al. 2015. “Hydrological Feedbacks in Northern Peatlands.” Ecohydrology 8(1): 113–27.

Wang, Meng, and Tim R. Moore. 2014. “Carbon, Nitrogen, Phosphorus, and Potassium Stoichiometry in an Ombrotrophic Peatland Reflects Plant Functional Type.” Ecosystems 17(4): 673–84.

Wang, Meng, Tim R Moore, Julie Talbot, and John L Riley. 2014. “Global Biogeochemical Cycles in Peat Formation.” Global Biogeochemical Cycles: 113–21.

Weishaar, James L. et al. 2003. “Evaluation of Specific Ultraviolet Absorbance as an Indicator of the Chemical Composition and Reactivity of Dissolved Organic Carbon.” Environmental Science and Technology 37(20): 4702–8.

Appendix

Appendix Figure 1. Experimental setup. Red rectangles represents sampling platforms, numbers indicating individual wells (1-12 Restored, 13-24 Pristine). Black dots showing were soil samples were collected. The black rectangle, perpendicular to sampling platforms shows the main duckboard. In the restored area (light green), in parallel to the main duckboard old ditches are marked out, as well as the main ditch (remains open) but old ditches are filled and blocked to prevent water to reach the main ditch. Black triangles shows the position of peat oscillation measuring device.

39

Appendix Figure 2. pH for each group (n=6) sampled on September 29 2021. Black vertical line represents the median, the box covers the 1st

and 3rd quantile and whiskers covers the lowest and highest value. Each individual sampling point is represented as grey dots.

Appendix Table 1. Mean values of soil properties for stratum (0-10, 10-20, 20-30 and 30-50 cm). Significant differences in mean (pairwise comparison TukeyHSD, p < 0.05) are indicated by different letters.

Depth 0 - 10 10 - 20 20 - 30 30-50

Bd mean code mean code mean code mean code

P.Hol 0.1 AB 0.08 A 0.07 A 0.07 A

P.Hum 0.07 A 0.11 B 0.09 B 0.08 B

R.Hol 0.13 B 0.1 AB 0.08 AB 0.08 AB

R.Hum 0.14 B 0.08 AB 0.07 AB 0.08 AB

Depth 0 - 10 10 - 20 20 - 30 30-50

C mean code mean code mean code mean code

P.Hol 48.5 A 49.1 A 49.4 A 49.5 A

P.Hum 49.4 AB 49.4 A 49.8 A 48.8 A

R.Hol 50.5 B 49.6 A 49.1 A 48.8 A

R.Hum 49.9 AB 49.0 A 48.4 A 48.0 A

Depth 0 - 10 10 - 20 20 - 30 30-50

N mean code mean code mean code mean code

P.Hol 1.44 A 1.02 A 0.98 A 0.93 A

P.Hum 1.55 AB 1.70 B 1.18 A 1.01 A

3.8

3.9

4.0

4.1

4.2

4.3

P.hol P.hum R.hol R.humGroup

pH

40

R.Hol 1.85 AB 1.25 AB 0.97 A 0.86 A

R.Hum 2.01 B 1.15 AB 0.88 A 0.74 A

Depth 0 - 10 10 - 20 20 - 30 30-50

P mean code mean code mean code mean code

P.Hol 0.021 A 0.014 A 0.014 A 0.012 A

P.Hum 0.028 A 0.038 A 0.019 A 0.014 A

R.Hol 0.060 B 0.029 A 0.018 A 0.014 A

R.Hum 0.053 B 0.023 A 0.018 A 0.012 A

Depth 0 - 10 10 - 20 20 - 30 30-50

C:N mean code mean code mean code mean code

P.Hol 35.29 A 48.79 B 50.43 B 54.16 AB

P.Hum 33.13 A 30.29 A 42.40 A 48.85 AB

R.Hol 25.51 A 40.30 B 52-70 AB 58.99 A

R.Hum 27.20 A 43.32 B 57.49 AB 67.54 B

Depth 0 - 10 10 - 20 20 - 30 30-50

C:P mean code mean code mean code mean code

P.Hol 2951 C 3526 B 3683 B 4725 B

P.Hum 1664 B 1705 A 2779 A 3680 A

R.Hol 855.4 A 1795 AB 2988 AB 3615 AB

R.Hum 955.7 AB 2249 AB 2741 AB 3999 AB

Appendix Table 2. Pairwise comparison of mean (TukeyHSD with holm correction of p values) for differences means between groups over the sampling period. Significant differences (p<0.05) are indicated with *. WTH (cm), DOC and DN (mg/L), SUVA and E2:E3 indicies.

1 P.hum R.hol R.hum R.hol R.hum R.hum

2 P.hol P.hol P.hol P.hum P.hum R.hol

wth Mean diff. (1-2) 10.700 13.476 18.897 2.776 8.198 5.421

SE 2.048 2.081 2.084 2.081 2.084 2.094

p <0.001* <0.001* <0.001* 0.182 <0.001* 0.019*

DOC Mean diff. (1-2) 7.219 31.854 47.152 24.638 39.932 15.294

SE 7.540 8.409 8.412 8.369 8.372 8.656

p 0.338 <0.001* <0.001* 0.010* <0.001* 0.154

DN Mean diff. (1-2) 0.316 0.948 0.921 0.632 0.605 -0.027

SE 0.244 0.284 0.284 0.283 0.283 0.296

p 0.392 0.005* 0.006* 0.102 0.102 0.927

SUVA Mean diff. (1-2) 0.456 1.017 1.035 0.560 0.579 0.018

SE 0.120 0.120 0.120 0.121 0.121 0.121

p <0.001* <0.001* <0.001* <0.001* <0.001* 0.879

41

E2-E3 Mean diff. (1-2) 0.029 -0.509 -0.521 -0.538 -0.551 -0.012

SE 0.089 0.093 0.093 0.093 0.093 0.095

p 1 <0.001* <0.001* <0.001* <0.001* 1

from forest to open bog1586699/... · 2021. 8. 21. · peat subsidence in the restored area and...

Documents