DOI: 10.5061/dryad.bcc2fqzgr
title: Scrub encroachment promotes biodiversity in temperate European wetlands under eutrophic conditions

description: Wetlands are important habitats, often threatened by drainage,
 eutrophication and suppression of grazing. In many countries, considerable
 resources are spent combatting scrub encroachment. Here, we hypothesize
 that encroachment may benefit biodiversity – especially under eutrophic
 conditions where asymmetric competition among plants compromises
 conservation targets. We studied the effects of scrub cover, nutrient
 levels and soil moisture on richness of vascular plants, bryophytes, soil
 fungi and microbes in open and overgrown wetlands. We also tested the
 effect of encroachment, eutrophication and soil moisture on indicators of
 conservation value (red-listed species, indicator species and uniqueness).
 Plant and bryophyte species richness peaked at low soil fertility, whereas
 soil fertility promoted soil microbes. Soil fungi responded negatively to
 increasing soil moisture. Lidar-derived variables reflecting degree of
 scrub cover had predominantly positive effects on species richness
 measures. Conservation value indicators had a negative relationship to
 soil fertility and a positive to encroachment. For plant indicator
 species, the negative effect of high nutrient levels was offset by
 encroachment, supporting our hypothesis of competitive release under
 shade. The positive effect of soil moisture on indicator species was
 strong in open habitats only. Nutrient poor mires and meadows host many
 rare species and require conservation management by grazing and natural
 hydrology. On former agricultural lands, where restoration of infertile
 conditions is unfeasible, we recommend rewilding with opportunities for
 encroachment towards semi-open willow scrub and swamp forest, with the
 prospect of high species richness in bryophytes, fungi and soil microbes
 and competitive release in the herb layer.
description: <strong>Study sites</strong> As
 part of the present study, we conducted field inventories at 44 wetland
 sites. The data collection was designed to supplement data collected in
 the Biowide project, a nation-wide survey of biodiversity in Denmark
 (Brunbjerg et al. 2019). Biowide included a total of 130 study sites (40 m
 × 40 m), of which we included all 58 sites evaluated as moist or wet based
 on plant species composition and soil moisture measurements. The Biowide
 sites varied in woody species cover from open vegetation over
 heterogeneous scrub to closed-canopy forest. The Biowide sites also varied
 in nutrient status from infertile to fertile soils, but were selected to
 foremost include natural and semi-natural habitats. The additional 44
 sites were chosen to increase data coverage of former agricultural land,
 semi-natural meadows and agriculturally improved meadows, as well as
 different levels of scrub encroachment. We did a stratified selection of
 sites according to succession/light availability (open, tall herb, shrubs,
 closed canopy), nutrient status (high/fertilized, mid, low/natural) and
 soil moisture (moist, wet). Sites were located across Denmark, preferably
 with a minimum distance of 500 m between sites (except two set of sites,
 where distances were 252 m and 491 m, Fig. 1a). The geographic dispersal
 of sites ensured sites from both carbon rich soils, clay and sand. Each
 site (40 m × 40 m) consisted of four 20 m x 20 m quadrants, each with a 5
 m circular plot in the centre (Fig. 1b).  To illustrate
 the coverage of the soil moisture, nutrient and encroachment gradients
 covered by the combined data, we compared site mean Ellenberg F, N and L
 values (Ellenberg et al. 1992) for all 5 m circle plots with a reference
 data set from national monitoring, using identical 5 m circular plots
 (59,227 sites from agricultural, semi-natural, natural open and forest
 vegetation, http://www.naturdata.dk) (Nygaard et al. 2017). Mean Ellenberg
 values were calculated for plots with more than five species present. In
 scatterplots of plot-mean Ellenberg values, 95 percentile convex hull
 polygons were drawn for the reference data set as well as the Biowide and
 wetland data set to visually evaluate the representativity of our data
 (Appendix A). <em>Biowide data
 collection</em> Collection of vascular plants and
 bryophyte presence/absence data: Vascular plants and bryophytes were
 inventoried by a trained botanist and exhaustive lists for the four 5 m
 circle plots were made for each site. In addition, all additional species
 in the quadrat, but outside the 5 m circles, were recorded. The inventory
 was done in summer 2014 with a few early spring vascular plant species
 added in May 2015 (Brunbjerg et al. 2019). We aggregate the four 5 m
 circle plots and additional species list to obtain a site species list for
 analyses. Collection of soil eDNA data: As alternative
 measures of biodiversity, we used the richness of operational taxonomic
 units, i.e. OTUs (Blaxter et al. 2005) of fungi and (eukaryotic) soil
 microbes obtained from metabarcoding of soil-extracted eDNA (Frøslev et
 al. 2017, Frøslev et al. 2019). We collected soil samples from all sites
 for the eDNA inventory. Samples were taken in October-November 2014. At
 each site, we sampled 81 equally distant soil samples from the top c. 15
 cm and pooled them after removal of coarse litter. We homogenized the soil
 by mixing with a drilling machine mounted with a mixing paddle. A
 subsample of soil for DNA extraction and metabarcoding was taken from the
 homogenized sample. Soil moisture: soil moisture was
 measured using a FieldScout TDR 300 Soil Moisture Meter. Sixteen equally
 distanced measurements were taken in each 40 × 40 m site in May 2016
 (spring/wet period). To cover the temporal variation in moisture the
 measurements were repeated in August 2016 (summer/dry period) (Brunbjerg
 et al. 2020). <em>Wetland sites data
 collection</em> All additional data collection
 specifically for the present study was done according to Biowide protocols
 (Brunbjerg et al. 2019), with the exception of the following: 1)
 presence/absence of early-spring plants species was not recorded on a
 separate visit, 2) soil samples were collected during the plant inventory
 in July-August 2018, i.e. not in November. Soil moisture was measured as
 in the Biowide project in July-August 2018. The present
 dataset spans 102 sites covering most types of wetlands including
 agriculturally improved meadows, natural meadows, fens, bogs, reed swamps,
 meadows dominated by large herbaceous perennials, open wetlands with
 scattered willows and birches, willow thickets, birch forests and swamp
 forests (Appendix B). <em>DNA extraction and
 metabarcoding</em> For Biowide and wetland soil
 samples DNA was extracted and subjected to eDNA metabarcoding through DNA
 extraction, PCR amplification of genetic marker regions (DNA barcoding
 regions) and massive parallel sequencing on the Illumina MiSeq platform as
 described in Brunbjerg et al. (2019). For this study, we used
 high-throughput sequencing data from marker genes amplified with primers
 targeting eukaryotes (mainly soil microbes) (18S) and fungi (ITS2). OTU
 tables were constructed following the overall pipeline in Frøslev et al.
 (2017). For both fungi and eukaryotes, this consisted of an initial
 processing with DADA2 (ver. 1.8) (Callahan et al. 2016) to identify exact
 amplicon sequence variants (ESVs) including removal of chimeras. The
 preparation of the Biowide-fungal (ITS2) and Biowide-soil microbe (18S)
 eDNA datasets have been published in Fløjgaard et al. (2019) and Frøslev
 et al. (2019) respectively, although the fungal dataset was re-sequenced
 for this study (a detailed description of the sequencing data can be seen
 in Appendix C). <em>Lidar-based
 measures</em> We calculated 23 lidar variables to
 represent encroachment in the 102 sites. We used the same procedure as in
 Valdez et al. (2021). The calculations were based on the National
 lidar-based point cloud (recorded leafs-off, springs and autumns
 2014-2015; light wavelength: 1550 nm; point density = 4-5 points/m2,
 vertical uncertainty: 5–10 cm) that is freely available from
 www.dataforsyningen.dk. The lidar point cloud was converted to a canopy
 height model by subtracting the terrain model (DTM) from the surface model
 (DSM). The final set of variables had a resolution of 1.5 m (except one,
 see below). For all lidar processing and calculation, we used the OPALS
 tools (Pfeifer et al. 2014) version 2.3.1 in a Python 2.7 environment, and
 we used the supplier classification of points into terrain and vegetation
 that came with the dataset originally. The means and standard deviations
 of the following lidar variable were calculated for a 30 m radius circle
 centered in each study site to reflect actual levels and their variability
 within sites and their immediate surroundings. For further details on
 calculation of lidar variables see Valdez et al. (2021). The set of lidar
 variables encompassed: potential solar radiation (mean and std), adjusted
 solar radiation (i.e., solar radiation adjusted for vegetation cover; mean
 and std), amplitude (uncalibrated, but corrected for aircraft type and
 seasonality, see Valdez et al. 2021), vegetation height (mean and std),
 vegetation cover (mean, std), mean vegetation density at 0-100 cm, 1 -3 m,
 3 -10 m and 10 -50 m, canopy openness (mean, std), terrain openness (mean,
 std), terrain slope (mean, std),  echo ratio (i.e., canopy complexity;
 mean, std), heat load (std) and mean fine-scale (0.5m) terrain roughness
 (Appendix D). <strong><em>Response
 variables</em></strong> <em>OTU
 richness</em> As alternative measures of
 biodiversity, we used the richness of operational taxonomic units, i.e.
 OTUs (Blaxter et al. 2005) of fungi and soil microbes from metabarcoding
 of soil eDNA (Frøslev et al. 2017, Frøslev et al. 2019). Classical data
 collection of fungi is time consuming and OTU richness has been found to
 resemble classical observed species richness at least for groups of
 macrofungi that are feasible to include in field inventories (Frøslev et
 al. 2019). We used OTU richness of fungi and soil microbes as response
 variables to reflect diversity of species groups not monitored otherwise
 in this project. <em>Red-listed
 species:</em> Site richness of red-listed species (belonging to the
 categories <em>Critically endangered</em>,
 <em>endangered</em>, <em>vulnerable</em>,
 <em>near-threatened</em> and <em>data
 deficient</em>) was calculated for vascular plants and bryophytes
 based on the current national red list (Moeslund et al. 2019a,
 Redlist.au.dk). <em>Indicator species:</em>
 Indicator species include vascular plant species considered moderately to
 very sensitive to habitat alteration (Fredshavn et al. 2010, see Appendix
 E). The list of indicator species (Fredshavn et al. 2010) was developed to
 indicate favorable conservation status according to the Habitats Directive
 (European Commission 2007). Common to these indicator species is a
 preference for infertile habitats (low Ellenberg N and high Grime’s S
 values, Grime 1979). <em>Biotic uniqueness:
 </em>Uniquity is a scale-dependent metric of biodiversity reflecting
 how unique the biodiversity of a given site is compared to the gamma
 diversity across the containing region or collection of sampled sites
 (Ejrnæs et al. 2018). Uniquity can be calculated based on both
 observational data as well as non-annotated DNA-data (e.g., OTUs) and
 hence can reflect both species uniqueness and genetic uniqueness. Contrary
 to other biodiversity metrics, uniquity accounts for sampling bias and
 spatial scale. Due to the built-in weighting method, uniqueness of
 non-annotated DNA-data can be calculated corresponding to e.g. red-listed
 species (Ejrnæs et al. 2018). Here, we calculated fungi and soil microbe
 uniquity according to Ejrnæs et al. (2018) in order to reflect the unique
 site contribution of fungi and soil microbe DNA to the gamma diversity of
 the collection of sites. Uniquity calculations were based on fungal and
 soil microbe OTU matrices, site habitat classes and weights from the full
 Biowide data set (n=130, Brunbjerg et al. 2019) combined with the wetland
 data set (n=44). The parameter X was set to 1000.
 <em><strong>Explanatory
 variables</strong></em> <em>Soil
 moisture: </em>The trimmed mean of 16 measures per site was used to
 reflect site soil moisture. For Biowide sites, we used the trimmed mean in
 August. We detected a systematic discrepancy between moisture in Biowide
 sites (measured in 2016) and wetland sites (measured in 2018), which could
 be accounted for by the summer of 2018 being extremely dry. We therefore
 interpolated the soil moisture trimmed mean values for wetland sites using
 the predicted values from a k nearest neighbor regression using soil
 moisture trimmed mean in Biowide sites (n=130, Brunbjerg et al. 2019) as
 response variable, Ellenberg F values as explanatory variable and k =
 11. <em>Soil fertility: </em>Good and
 reliable field-based measures of nutrient availability are difficult to
 obtain, as nutrient availability is extremely variable across time and
 space (Andersen et al. 2013 and references herein). In contrast, the
 nutrient ratio (mean site Ellenberg N/mean site Ellenberg R, Ellenberg et
 al. 1992) has been found to reflect eutrophication in wetlands and be
 highly correlated with the number of typical species in fens (Andersen et
 al. 2013). For each site we cal­culated mean Ellenberg N and Ellenberg R
 values (plant-based bioindication of nutrient status and soil pH,
 respectively) (Ellenberg et al. 1992). The Ellenberg nutrient ratio was
 used to reflect eutrophication and the idea of the ratio is to account for
 the fact that natural nutrient availability in wetlands increases with pH.
 To avoid circularity in analyses, plant species included in the
 plant-based conservation indicators (red-listed plants, typical plants)
 were removed before calculating the nutrient ratios for each model in
 question. <em>Encroachment: </em>We made a
 rough classification of sites into two groups (open vegetation and
 scrub/forest vegetation) based on field photos. The encroachment variable
 was coarse i.e. ‘open’ represented mainly open and herb dominated
 vegetation but sites with scattered small shrubs were also categorized as
 ‘open’ plots as long as the shrubs did not dominate the plot. The two
 level factor variable was used as explanatory variable.
 References: Andersen, D. K., B. Nygaard, J. R.
 Fredshavn, and R. Ejrnæs. 2013. Cost-effective assessment of conservation
 status of fens. Applied Vegetation Science 16:491-501.
 Blaxter, M., J. Mann, T. Chapman, F. Thomas, C. Whitton, R.
 Floyd, and E. Abebe. 2005. Defining operational taxonomic units using DNA
 barcode data. Philosophical Transactions of the Royal Society B:
 Biological Sciences 360:1935-1943. Brunbjerg, A. K., H.
 H. Bruun, L. Brøndum, A. T. Classen, L. Dalby, K. Fog, T. G. Frøslev, I.
 Goldberg, A. J. Hansen, M. D. D. Hansen, T. T. Høye, A. A. Illum, T.
 Læssøe, G. S. Newman, L. Skipper, U. Søchting, and R. Ejrnæs. 2019. A
 systematic survey of regional multi-taxon biodiversity: evaluating
 strategies and coverage. BMC Ecology 19:43. Brunbjerg,
 A. K., H. H. Bruun, L. Dalby, A. T. Classen, C. Fløjgaard, T. G. Frøslev,
 O. L. Pryds Hansen, T. T. Høye, J. E. Moeslund, J.-C. Svenning, and R.
 Ejrnæs. 2020. Multi-taxon inventory reveals highly consistent biodiversity
 responses to ecospace variation. Oikos 129:1381-1392.
 Callahan, B. J., P. J. McMurdie, M. J. Rosen, A. W. Han, A. J. A.
 Johnson, and S. P. Holmes. 2016. DADA2: High-resolution sample inference
 from Illumina amplicon data. Nat Meth 13:581-583.
 Ejrnæs, R., T. G. Frøslev, T. T. Høye, R. Kjøller, A. Oddershede,
 A. K. Brunbjerg, A. J. Hansen, and H. H. Bruun. 2018. Uniquity: A general
 metric for biotic uniqueness of sites. Biological conservation
 225:98-105. Ellenberg, H., H. E. Weber, R. Düll, V.
 Wirth, W. Werner, and D. Paulissen. 1992. Zeigerwerte von pflanzen in
 Mitteleuropa. Scripta Geobotanica 18:1-248. European
 Commission. 2007. Interpretation manual of European Union habitats
 EUR27. Fløjgaard, C., T. G. Frøslev, A. K. Brunbjerg,
 H. H. Bruun, J. Moeslund, A. J. Hansen, and R. Ejrnæs. 2019. Predicting
 provenance of forensic soil samples: Linking soil to ecological habitats
 by metabarcoding and supervised classification. Plos One
 14:e0202844. Fredshavn, J., R. Ejrnæs, and B. Nygaard.
 2010. Teknisk anvisning for kortlægning af terrestriske naturtyper. TA-N3,
 Version 1.04. Fagdatacenter for Biodiversitet og Terrestriske Naturdata,
 Danmarks Miljøundersøgelser. Frøslev, T. G., R.
 Kjøller, H. H. Bruun, R. Ejrnæs, A. K. Brunbjerg, C. Pietroni, and A. J.
 Hansen. 2017. Algorithm for post-clustering curation of DNA amplicon data
 yields reliable biodiversity estimates. Nature communications
 8:1188. Frøslev, T. G., R. Kjøller, H. H. Bruun, R.
 Ejrnæs, A. J. Hansen, T. Læssøe, and J. Heilmann-Clausen. 2019. Man
 against machine: Do fungal fruitbodies and eDNA give similar biodiversity
 assessments across broad environmental gradients? Biological conservation
 233:201-212. Grime, J. P. 1979. Plant strategies and
 vegetation processes. John Wiley & Sons, New York.
 Moeslund, J. E., B. Nygaard, R. Ejrnæs, N. Bell, L. D. Bruun, R.
 Bygebjerg, H. Carl, J. Damgaard, E. Dylmer, M. Elmeros, K. Flensted, K.
 Fog, I. Goldberg, H. Gønget, F. Helsing, M. Holmen, P. Jørum, J. Lissner,
 T. Læssøe, H. B. Madsen, J. Misser, P. R. Møller, O. F. Nielsen, K. Olsen,
 J. Sterup, U. Søchting, P. Wiberg-Larsen, and P. Wind. 2019a. Den danske
 Rødliste. Aarhus Universitet, DCE – Nationalt Center  for Miljø og Energi.
 www.redlist.au.dk. Nygaard, B., C. Damgaard, K. E.
 Nielsen, J. Bladt, and R. Ejrnæs. 2017. Terrestriske Naturtyper 2004 –
 2015. NOVANA. Aarhus Universitet, DCE – Nationalt Center  for Miljø og
 Energi. www.novana.au.dk. Pfeifer, N., G. Mandlburger,
 J. Otepka, and W. Karel. 2014. OPALS – A framework for Airborne Laser
 Scanning data analysis. Computers, Environment and Urban Systems
 45:125-136. Valdez, J. W., A. K. Brunbjerg, C.
 Fløjgaard, L. Dalby, K. K. Clausen, M. Pärtel, N. Pfeifer, M. Hollaus, M.
 H. Wimmer, R. Ejrnæs, and J. E. Moeslund. 2021. Relationships between
 macro-fungal dark diversity and habitat parameters using LiDAR. Fungal
 Ecology 51:101054.
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