Studies on ecological successions have a long tradition and have strongly
contributed to the understanding of community assembly, niche theory, and
ecosystem structure and functionality. Reports on ecological successions are
however mostly restricted to one or two taxonomic groups, neglecting the
mutual influences and dependencies between multiple taxonomic groups that
are the building blocks of diverse communities. We introduce the Alpine
research platform Ödenwinkel to promote observational and experimental research on the
emergence of multidiversity and ecosystem complexity. We established n= 140 permanent plots along the successional gradient of the forefield of the
Ödenwinkelkees glacier at the end of the Stubachtal valley in the Hohe Tauern
range (Hohe Tauern National Park, Land Salzburg, Austria). In summer 2019 we
completed a first full inventory of biotic and abiotic characteristics of
these plots covering the diversity and composition of vascular plants,
bryophytes, arthropods, and other animals, bacteria and fungi as well as some
geomorphologic properties. In this paper we introduce the design of the
research platform and show first results. While focusing on the diversity
and composition of vascular plants along the successional gradient, we also
provide data on the diversity of animals, bacteria, and fungi. The
Ödenwinkel platform will be available as a long-term ecological research site where
researchers from various disciplines can contribute to the accumulation of knowledge
on ecological successions and on how interactions between various taxonomic
groups structure ecological complexity in this Alpine environment.
Introduction
Biodiversity is of indispensable value for ecosystem functioning and
stability and provides multiple direct and indirect benefits crucial for
human well-being (Gaston and Spicer, 2004). Anthropogenic alterations
of the environment such as climate warming and habitat conversion have
fueled recent scientific and social interest in the causes and consequences
of biodiversity decline (Allan et al., 2014; Fischer et al., 2010; Gascon
et al., 2015; Soliveres et al., 2016). Compared to those studies, research
on the emergence of biodiversity and ecosystem complexity (i.e., the increase
in biodiversity, ecological successions) has a longer tradition and is of
continued interest as the ecological processes involved in community
development are strongly relevant for the restoration of anthropogenically
altered landscapes (Meiners et al., 2015). Prime examples of succession
are recent deglaciations that create virtually uninhabited substrates
waiting for initial colonization and the assembly of multidiverse
communities consisting of microbes, plants, and animals. Such natural
experiments (Diamond, 1983) provide an excellent opportunity to track
transformations in community composition and diversity over time.
Additionally, next to a purely biotic consideration, abiotic changes along a
chronosequence can help us to understand biogeomorphic feedbacks that shape
landscapes (Eichel, 2019; Matthews, 1992; Miller and Lane, 2019).
Whereas some large-scale studies of biodiversity decline consider a number
of taxonomic groups such as plants, animals, and microbes (Fischer et
al., 2010), most information on succession has been gathered in studies
focusing on plants. Consequently, concepts of community establishment have
mostly been based on plant succession (Eichel et al., 2013; Fierer et
al., 2010; Mueller-Dombois, 1995). However, successional processes are
regulated by the concerted appearance of plants, animals, and microbes. For
instance, herbivores affect plant succession (Milchunas and Vandever,
2014), plants and microbes reciprocally affect their establishment
(Tscherko et al., 2005), and studies have shown that the community
composition and diversity of plants, animals, and microorganisms cannot be
viewed in isolation because of mutual influences (Humphrey et al., 2014;
Mikkelson et al., 2016; Samuni-Blank et al., 2014). Furthermore, local soil
microbial communities have the potential to determine the local abundance of
plant species (Klironomos, 2002) and to affect tritrophic interactions
between plants, herbivores, and parasitoids (Benítez et al., 2017).
These examples demonstrate that overall diversity and composition of
communities emerge from interdependent taxa that affect each other's
presence or absence and abundance (Junker et al., 2019; Manning et al.,
2015). Thus, the different ecological roles of plants, animals, and
microorganisms as well as their interactions demand ecosystem-wide
comprehensive assessments of the diversity of several taxonomic groups
(Humphrey et al., 2014; Manning et al., 2015). This cumulative diversity
of a number of taxonomic groups is defined as multidiversity (Allan et
al., 2014). Studies that comprehensively track the successional increase in
multidiversity in the field are lacking, and thus little is known about the
mechanisms of the successional establishment of multidiversity and
ecological complexity under natural conditions. However, this knowledge is
crucial for understanding ecosystem processes and designing conservation and
restoration efforts.
Successional processes on glacier forefields are excellent study systems for
groundbreaking insights into the fundamentals of the generation of
multidiversity and the interactions between various trophic levels as well
as feedbacks between biotic and abiotic factors. In order to facilitate
research addressing these issues, we introduce the research platform
Ödenwinkel, recently established along a glacier forefield of the Ödenwinkelkees in
the Austrian Alps, that will serve as a long-term field site to study
processes towards the emergence of multidiversity and ecological complexity.
The permanent plots (n= 140) representing a chronosequence of
∼ 170 years of community establishment will be used to
accumulate plot-specific information on biotic and abiotic characteristics
as well as to monitor long-term changes therein. Information on the precise
location, geomorphological characteristics and previous ecological
data gathered at the plots will be made available to colleagues interested
in addressing their research questions within the framework of
Ödenwinkel.
Description of the research platform ÖdenwinkelGeomorphology and plot location
In summer 2019 we established n= 140 permanent plots along the
successional gradient of the forefield of the Ödenwinkelkees glacier
(Austria). The Ödenwinkelkees is a low-lying, highly sheltered valley
glacier at the end of the Stubachtal valley in the Hohe Tauern range. The field site
is located in the Hohe Tauern National Park and is part of the Natura 2000
areas. The glacier initiates below a northern exposed cirque headwall (more
than 600 m in height) formed by the peaks of Hoher Kasten (3189 m a.s.l.),
Eiskögele (3423 m a.s.l.), and Johannisberg (3453 m a.s.l.). The headwall
is a buildup of two distinct, stacked lithological units with an upper layer
of Penninic mica schists and amphibolites underlain by sub-Penninic granitic
gneisses of the Tauern Window. The Ödenwinkelkees glacier has an area of
1.4 km2 (2015) and is about 2.6 km long. The current ice
surface spreads between 2880 and 2190 m a.s.l. and is mainly nourished by
avalanches from the headwall. The majority of the plots (n= 135) were
established within the glacier forefield, i.e., the area that was covered by
ice at the latest glacial maximum in the Little Ice Age (LIA; around 1850).
Glacier melt released an area of 0.9 km2 in a typically
u-shaped valley. The flat valley floor that hosts the plots has an area of
0.35 km2 at elevations between 2070 and 2170 m a.s.l. The glacier
forefield is characterized by a variable pattern of sedimentary deposits
originating from the glacier. Besides subglacial and glaciofluvial
sediments, coarse boulders, deposited as supraglacial debris from rockfall
activity dominate the surface material composition. An additional n= 5 plots
were established outside the glacier forefield in areas unaffected by recent
geomorphological influences (i.e., age ≫ 170 years).
The plots within the glacier forefield were evenly distributed between the
LIA glacier maximum and the current extent of the glacier (glacier tongue)
to represent a chronosequence of succession with high temporal resolution.
Locations of the plots were preselected to guarantee longevity in this
highly geomorphodynamic environment. Therefore, five main criteria were
operationalized using a 2007 digital elevation model (5m×5m by the state of
Salzburg, Austria) and ARC GIS 10.4 to predefine potential areas with the
following characteristics: (1) outside of the main river bed and episodic
fluvial rills, (2) outside of the recent bed load exchange reach, (3) situated
on the terrace with a low curvature, (4) plot slope angle ≤ 10 %, (5) distance to the river at least 2 m. Afterwards, the n= 135 plots located in the glacier forefield were predefined in order to
evenly cover the temporal succession. The predefined locations were located
in the field using a GPS device (Geo 7X, Trimble, Sunnyvale, US) and
permanently marked using ground anchors (FENO-BODENANKER, FENO-Bodenanker,
Faynot, Thilay, France). Each ground anchor was marked with a running number
with plot_001 as the youngest plot close to the glacier
tongue and plot_135 as the oldest plot within the glacier
forefield close to the glacial maximum of 1850. Plot_136–plot_140 are located outside of the glacier forefield.
Locations of the plots can be found in Fig. 1 and Supplement 1a
both as coordinates and as what3words addresses, facilitating the location of
the plot using any smartphone and the what3words app
(https://what3words.com, last access: January 2020). Additional plots that will be located in future
deglaciated areas following expected glacial retreat will have negative
numbers.
The Ödenwinkel platform is registered as a long-term ecological research
site at DEIMS-SDR (Dynamic Ecological Information Management System – site
and dataset registry;
https://deims.org/activity/fefd07db-2f16-46eb-8883-f10fbc9d13a3, last access: June 2020).
Plot design
The location of each plot is defined by a square with 1 m side
length and the ground anchor as the center of the plot, resulting in an area of
1 m2. One tip of the square is pointing towards north, allowing the alignment
of the plots to a defined orientation. Each plot is divided into 100 grid cells
(A1–J10, 0.1 m × 0.1 m; Fig. 2a). In summer 2019, we buried one
thermologger (MF1921G iButton, Fuchs Elektronik, Weinheim, Germany) per
plot, took soil samples in two grid cells, and located pitfall traps in the
same cells where possible. Because of the heterogenous distribution of rocks
and sediments within each of the plots, it was not possible to take these
measurements in the same grid cells in all the plots. However, we recorded
the grid cells for each of the measurements per plot, allowing the
rediscovery of the exact positions in future monitoring events (see
Supplement 1).
Plot design. In summer 2019 we established n=140 plots along the
successional gradient of the Ödenwinkelkees glacier in Land Salzburg,
Austria. The majority of the plots (n= 135) were established within the
glacier forefield, i.e., the area that was covered by ice at the latest
glacial maximum. Another n= 5 plots were established outside the glacier
forefield in areas unaffected by geomorphological influences (i.e., age ≫ 170 years). Location of plots can be found in Fig. 1 and Supplement 1a. Each plot is defined by a square with 1 m
side length and a ground anchor as the center of the plot, resulting in an area
of 1 m2. One tip of the square is pointing towards north (red arrow).
Each plot is divided into 100 grid cells (A1–J10). These grid cells allow
the rediscovery of the position of samples taken within the plot. Note that the
locations of the measurements (thermologger, soil samples, and pitfall
traps) are located at different positions and grid cells in each plot (see
Supplement 1), accounting for the heterogenous distributions of
rocks and soil in each of the plots. An early-succession (b,
plot_002) and an old-succession plot (c, plot_123) are shown.
Inventory in 2019
From July to September 2019 we conducted a full inventory of the n= 140 plots considering abiotic and biotic factors. In order to reduce the
covariation between seasonality of the organisms with plot age due to
sampling of plots in the same order as they are located in the study area,
we randomized the order in which every survey was performed. For that
purpose, we assigned the 140 plots to 10 sections. Sections 1–9 include 15 plots each (plots 1 to 135 within the glacier forefield), and Sect. 10
includes the remaining 5 plots outside the glacier forefield (136–140).
These sections were visited in a random order for the inventory.
Plot survey
For each plot, we recorded the abiotic and biotic properties
(Supplement 1), including percentage of bare ground, rock and
scree as abiotic features, cover of litter, lichens on rocks, lichens on
soil, bryophytes on rocks, bryophytes on soil, and vascular plants as biotic
features. Abiotic features were measured with a resolution of a quarter of a
grid cell and biotic features with a resolution of a tenth of a grid cell,
representing 0.25 % and 0.1 % of total possible plot cover,
respectively. We recorded the predominant lithological property and the
geomorphological process that deposited material on each plot. Additional
information, such as exact coordinates, elevation, aspect, slope, distance
to closest stream, distance to and plot height above main riverbank, and
surface roughness index, were either directly exported from the GPS device or
later retrieved from a digital elevation model (1 m LIDAR DEM, Land
Salzburg). Additionally, we took up to two soil cores (7.5 cm diameter, 5 cm
depth, grid location of soil cores corresponds to location of Berlese traps
in Supplement 1) from the northern and southern part of every
plot for potential analyses of soil properties such as pH value or grain
size.
Temperature of plots
On each plot we installed a temperature logger at a depth of 3 cm below
ground, located 10 cm north of the plot center. The thermologgers were set
to start on 13 August between 09:00 and 09:10 MESZ (UTC+2). Temperature will be
recorded every 255 min at a resolution of 0.5 ∘C, and loggers
will be collected after 2048 measurements (362 d) in 2020. These data
will allow modeling of plot microclimatic heterogeneity, which has been
shown to affect plant species diversity, composition, and interactions
between plants and other organisms (Ohler et al., 2020).
Plant diversity survey
On each plot we identified all vascular plant species, selected moss
species (Niphotrichum canescens, Grimmaceae; Polytrichum piliferum, Polytrichaceae; and Dicranoweisia crispula, Dicranaceae), and estimated
their cover with a resolution of 0.1 %. In addition, we identified and
recorded the presence of all bryophyte species on each plot. Two vegetation
surveys were conducted in early July (covering all plots) and early
September 2019 (covering one plot per section).
Plant trait measurement
For all plant species that occurred on at least 10 of the 140 plots, we
recorded plant traits including plant height, leaf area, dry leaf weight, and
specific leaf area. We measured plant height in the field and collected the
youngest fully developed leaf of plant individuals. For the three
intensively studied plant species (defined below), we phenotyped up to three
individuals on every plot on which they occurred. In addition, we phenotyped every
plant species that was present on at least 10 plots throughout the
successional gradient three times. For these n= 45 species, up to five
individuals were phenotyped on the youngest, the oldest, and the intermediate
plot where they occurred. If an insufficient number of individuals occurred
within the plot, we chose the closest-growing individuals outside of the
plot. Collected leaves were scanned using an HP ScanJet 200 (HP Inc.,
Wilmington, USA) along with a 1 cm2 square as a calibration
reference. We used image analysis software ImageJ
(Schneider et al., 2012) to calculate leaf area
from the digital leaf scans. After scanning, leaves were dried for 3 d at 60 ∘C and weighed. Specific leaf area was calculated as
leaf area divided by dry leaf weight. Field data were complemented with
phenotypic data (e.g., life form, type of reproduction, pollen vector)
available in online databases such as BiolFlor (Klotz et al.,
2002).
Definition of intensively studied plant species
Based on the vegetation survey, we identified three plant species that
will be intensively studied. We chose one plant species that occurs
throughout the succession and one plant species each for
early and late succession. For each plant species, we determined the total
number of plots where the plant species occurred, the youngest and oldest
plot of occurrence, and the mean plot number. Additionally, we used the
machine learning algorithm “random forest” for regression (Breiman, 2001),
using the R package randomForest to identify plant species that are representative of
young, intermediate, or old parts of the chronosequence or those that
increase or decrease with successional age. We used plot number (proxy for
time since deglaciation) as a dependent variable and the cover of each plant
species per plot as explanatory variables. For random forest analysis we
chose the number of plant species divided by 3≈36=mtry as
the number of variables randomly sampled as candidates at each split, and we grew
a total of ntree= 10 000 trees. For each plant species we extracted
variable importance (%IncMSE), informing about the degree to which the
chronosequence of community establishment is reflected by the cover of each
plant species. Low %IncMSE values indicate species that either occur on
few randomly distributed plots or have an even cover on plots throughout the
gradient. High %IncMSE values indicate species that either increase (or
decrease) in cover along the gradient or are present only in a given range
within the chronosequence. Thus, the first of the intensively studied plant
species that occurs throughout the succession needs an occurrence ranging
from young to old plots and a low %IncMSE value. The other two
intensively studied plant species should be restricted to either younger or
older plots and have a high %IncMSE value. Information on these
characteristics for all plant species is given in Supplement 2.
Animal (mostly arthropod) diversity
Arthropod diversity was surveyed with pitfall traps. To catch
aboveground arthropods, we used plastic boxes (8.1 cm × 10.8 cm) as pitfall
containers and filled them with 150 mL of a 30 % ethylene glycol–water
mixture. A wire grid with 1.3 cm × 1.3 cm mesh size was put on every trap to
avoid catching larger animals. A hole was punched in containers (1.5 cm
below the rim) to allow drainage. Traps were active for a total of 14 d and were emptied twice after 7 d each. Samples were stored in
70% ethanol. Two pitfall traps were installed on each plot (north and
south). If the topography of a plot did not allow a north–south orientation,
we placed them in east–west direction and noted the exact location of the
traps on the grid (location noted in Supplement 1a). The
abundance of all arthropods, excluding Collembola and Acari, larger than 3
mm was counted. The abundance of Collembola and Acari and of animals smaller
than 3 mm was estimated based on random samples of aliquots of the total
sample. All arthropods and other animals are currently identified to the
order level, but samples are stored for reidentification.
Diversity and plant cover at the n= 140 study plots. (a) The
estimated year of deglaciation decreases with plot number. For each plot the
plant cover (circle size) and diversity (circle color) are given. The upper bar on the top of the panel informs about the lithological category of each
plot, the lower bar about the geomorphological process responsible for the
deposition of material on the plots. Plot age increases with plot number. (b) Plant Shannon diversity (green) and d′ (blue) as a function of plot age (old
plots are on the right, young plots on the left). d′ informs about the degree
of uniqueness of each plot regarding the composition of plant species. Low
d′ values indicate that plots share all or most plant species with many other
plots; high d′ values indicate that plots are vegetated with plant species
that are unique or shared with few plots only.
Sampling of microbial communities associated with plants and soil
We sampled microorganisms (bacteria and fungi) inhabiting the
phyllosphere of plants and the soil of each plot. Samplings were performed
within 11 d during the main vegetative period (31 July–10 August 2019).
Collected samples were directly transferred to ZR BashingBeads Lysis tubes
containing 750 µL of ZymoBIOMICS lysis solution (ZymoBIOMICS DNA
Miniprep Kit; Zymo Research, Irvine, California, USA). Leaf and soil samples
were collected using sterilized forceps (dipped into 70 % ethanol and
flamed) to avoid contamination. We sampled bacterial and fungal communities
in the phyllospheres of three intensively studied plants (see above) on every plot where they
occurred. Furthermore, we took three samples of vascular plant species that
occurred on 10 or more plots. In these cases, we took samples on the
oldest, the youngest, and the intermediate plot containing them. Soil
microbiome was sampled in two locations within each plot (coinciding with
the location of pitfall traps if possible) whenever there was enough soil to
proceed. With a bulb-planting device, we took soil cores with an approximate
depth of 5 cm, from which we took soil samples at 3 cm depth. In plots where
it was not possible to take soil cores, we lifted boulders and took
microbiome samples from sediments that were underneath these rocks.
Additionally, we took microbiome samples of the three selected bryophyte
species (see above) and rocks associated with them on 10 plots along the
successional gradient. Rock microbiome was sampled by using a sterile cotton
swab moistened with a lysis solution. The swab was swiped over the rock
surface, and the tips were cut with sterile scissors. Within 8 h after collection of microbial samples, ZR BashingBeads Lysis tubes were
sonicated for 7 min to detach microorganisms from the surfaces. In the case
of plant leaves and bryophyte tissues, we removed them from tubes next to a
flame with sterile forceps after the sonication to decrease the amount of
plant DNA in the samples. Subsequently, all microbial samples were shaken
using a ball mill. In cases where we were able to fully remove plant tissues
from collection tubes and soil samples, tubes were shaken for 9 min with a
frequency of 30.0 s-1. In some cases it was not possible to fully remove
plant tissues from tubes, and samples were shaken for 5 min at 20.0 s-1.
Microbial DNA was extracted using the ZymoBIOMICS DNA Miniprep Kit following the
manufacturer's instructions. Next-generation sequencing and microbiome
profiling of isolated DNA samples were performed by Eurofins Genomics
(Ebersberg, Germany). Further details on the procedure can be found in
Supplement 3. Prior to the statistical analysis of bacterial and
fungal communities associated with leaf surfaces, we performed a cumulative
sum scaling (CSS) normalization (R package metagenomeSeq v1.28.2) on the
count data. In this paper we report on the diversity of bacteria and
fungal communities associated with soil samples.
Test for spatial autocorrelation
Many glacier forefields are characterized by an overall steady retreat
of the glacier since the Little Ice Age, which results in an environmental
and temporal gradient that affects the assembly of communities. In such a
setting, these gradients are also spatially correlated, which may interfere
with the conclusions concerning abiotic and biotic effects on community
composition and species interactions (Hawkins, 2012; Kühn and
Dormann, 2012). In order to document the spatial autocorrelation (SA), we tested
for SA in our dataset using Moran's
I (Moran, 1948) based on geographic distance between plots using the R
package spdep (Bivand and Wong, 2018), assessing significance levels
with 1000 permutational steps.
First findings and discussion
The n= 140 plots established in 2019 follow the successional gradient of
the glacier forefield in the south–north direction for about 1500 m within an
elevational range of 119.6 m. The five plots outside of the glacier
forefield are located about 500 m northeast of the glacier forefield in an
area that has been unaffected by glacial activities for at least 170 years (Fig. 1,
Supplement 1a). In total, we identified n= 107 plant species
on the n= 140 plots; plot_001 and plot_006
were unvegetated. We assigned the plots to predominant geomorphological
processes that deposited material on the plots: n= 34 plots are located
on basal till, 60 plots are composed of supraglacial debris, 38 plots are by
glaciofluvial deposits, 2 plots are covered by two or more sediment types,
and for one plot no processes could be determined. The five plots outside of the
glacier forefield were treated as a separate group (Fig. 3a, Supplement 1a). Furthermore, the plots could be assigned to four lithological
categories: schist (n= 18 plots), gneiss (30 plots), fine material (57 plots), and mixed lithology (34 plots); one plot could not be assigned to
either of the categories (Fig. 3a, Supplement 1a).
Plant cover linearly increased with increasing time since deglaciation
(Pearson's product-moment correlation: t138=9.31, p<0.001,
r2=0.39; Fig. 3a). As expected, plant cover was highest in the five
plots outside of the glacier forefield (analysis of variance, ANOVA: F3,134=11.96, p<0.001; Tukey multiple comparisons of means: p≤0.007 in
comparison to the other processes); the geomorphological processes that
deposited material onto the plots otherwise mostly did not explain variation
in plant cover except a significant difference between the plant cover on
plots composed of supraglacial and basal deposits (Tukey multiple
comparisons of means: p<0.01), with higher plant cover on plots
covered by basal till (Fig. 3a, Supplement 1a and b). In
contrast, plant cover was strongly affected by lithology (ANOVA:
F3,136=17.31, p<0.001), with significant differences between
all the categories except for gneiss and mix (according to Tukey multiple
comparisons of means: fine > gneiss = mix > schist;
Fig. 3a, Supplement 1a and 1b). The high plant cover on plots
with fine material may represent a biogeomorphic feedback where plants
support the establishment of soils and soils the establishment of further
plants (Borin et al., 2010; D'Amico et al., 2014; Schmidt et al., 2008),
which may deserve future investigations.
Plant Shannon diversity peaked on plots that have been deglaciated for roughly
110 years (Fig. 3b; quadratic model: F2,135=31.08, p<0.001,
r2=0.31), showing a clear increase in plant diversity in the first
110 years after deglaciation but a decrease after succession proceeds, i.e.,
the five plots outside the glacier forefield had a lower plant diversity
than the ∼ 110-year-old plots. Plant diversity was largely
unaffected by surface material origin (depositional processes; ANOVA:
F3,134=3.52, p=0.017), with a slightly higher
diversity on plots covered by basal till on average compared to those composed of
supraglacial debris (Tukey multiple comparisons of means: p=0.042; all
other pairwise comparisons were nonsignificant; Fig. 3a and b, Supplement 1a and 1b). Plant diversity was affected by lithology, with the lowest
diversity on plots with schist (ANOVA: F3,136=8.23, p<0.001;
Tukey multiple comparisons of means between schist and all other lithologies:
p≤0.004; Supplement 1a and 1b). Schist was also the
predominant lithology on younger plots (i.e., few years after deglaciation).
Next to diversity indices, we adopted bipartite network analysis to inform
about the plots' plant composition by defining plant species occurring on
the plots as well as the plots themselves as nodes; the edges inform about
the presence or absence of the plant species on each of the plots, weighted
by the plant cover of each species on each plot. We calculated d′ for each
plot, which is a network index informing about the complementary
specialization of the plots (Blüthgen et al., 2006). In other
words, d′ informs whether plots are characterized by a unique set of plant
species (i.e., specialization, high d′ values) or by plant species that occur
on many other plots (i.e., generalization, low d′ values). The d′ values had a
bimodal distribution, with higher values at very young (few years after
deglaciation) and very old plots (many years after deglaciation). Plots
that deglaciated about 90 years ago were most generalized in the plant species
composition, which means that these middle-aged plots share the occurrence
of plant species with many other plots (Fig. 3b; quadratic model:
F2,135=52.9, p<0.001, r2=0.43). These results
suggest that pioneer species present on plots a few years after deglaciation
are replaced by plant species that are representative of Alpine successions
early in community assembly. Plots outside the glacier forefield were
vegetated with species that cannot be found on many plots within the glacier
forefield or that have a low coverage on these plots. The d′ of plots was
negatively correlated to Shannon diversity of plots (Pearson's
product-moment correlation: t136=-10.30, p<0.001,
r2=0.44), indicating that plant communities are most diverse after a
few decades of succession and also share a core of plant species that can be
found on many of the plots. This notion is also supported by nonmetric
multidimensional scaling (NMDS) based on Bray–Curtis distances of vegetation
composition (cover per species), where recently deglaciated (young) plots and
some late successional plots as well as those outside of the glacier
forefield occupy peripheral positions in the ordination (Fig. 4). Most other
plots that were deglaciated between 30 and 145 years ago are clustered at
the center of the ordination (Fig. 4). Accordingly, the position of each
plot within the ordination, measured as the Euclidean distance of each plot to
the origin of the ordination, strongly and positively correlated to d′
(Pearson's product-moment correlation: t136=18.39, p<0.001, r2=0.71) and negatively to the diversity of the plots
(t136=-14.00, p<0.001, r2=0.59). This again
supports the notion that a typical successional vegetation is formed a few
decades after deglaciation that is slowly replaced by a climax vegetation.
This continuous shift from early to late to climax vegetation is also
evidenced by the clear signal of plot age (and overall plant cover) in the
ordination (fit of vectors to ordination, age: p<0.001, r2=0.40; cover: p<0.001, r2=0.64; Fig. 4). The distribution of
plant species and plant diversity is in line with the intermediate disturbance hypothesis (IDH) stated by
Connell (1978) and also suggested as an explanation for
plant diversity patterns along glacier forefields (Raffl et al., 2006).
Based on our findings and the IDH, we hypothesize that the low plant cover
in young and intermediate-aged plots offers a large number of niches to be
filled by diverse plant species that do not directly compete for space and
nutrients. With increasing plant cover, competition for these resources
increases, too, which may lead to the dominance of few species, resulting in
reduced plant diversity. Therefore, the Ödenwinkel research platform
allows the testing of this and further ecological hypotheses and the comparison the
assembly of diverse communities of various trophic levels and thus the
establishment of multidiversity and ecosystem complexity.
Similarity of plots regarding quantitative plant species composition
based on Bray–Curtis distances considering the cover of each plant species
on each plot. The closer plots appear in the nonmetric multidimensional scaling, the more similar their plant species composition is. With increasing plot
age, the color of the circles becomes darker. Arrows point towards increasing age
and increasing total plant cover.
Intensively studied plant species
Based on the criteria described above, we identified Oxyria digyna (Polygonaceae,
occurred on 27 plots) as representative of early successions, Trifolium badium (Fabaceae,
50 plots) as representative of late succession, and Campanula scheuchzeri (Campanulaceae, 116
plots) as the most widespread species along the successional gradient (Fig. 5).
C. scheuchzeri individuals became larger (plant height) with plot age (Pearson's
product-moment correlation: t114=5.63, p<0.001, r2=0.22); the other two focus species did not show a response in plant height
to plot age (t≤1.78, p≥0.08, r2≤0.06). Leaf area of
all three species varied independently of plot age (t≤1.39, p≥0.18, r2≤0.07). However, in all species we detected a strong
positive correlation between leaf area and plant height (t≥3.12, p≤0.005, r2≥0.28). Both plant traits were unrelated to cover (t≤1.12, p≥0.23, r2≤0.01), except for the plant height
of O. digyna, which was positively correlated to cover (t25=4.87, p<0.001, r2=0.49). C. scheuchzeri and T. badium displayed a unimodal abundance distribution
along the successional gradient (quadratic model: F≥4.61, p≤0.015, r2≥0.12), both peaking at intermediate locations of their
distribution range (Fig. 5). Abundance of O. digyna also peaked at intermediate
locations of the species' distribution range, but we did not find a
significant correlation with age (Fig. 5). These results suggest that the
three selected focus species find ideal conditions in different stages of the
succession and that these conditions become less well suited at the margins
of their distribution ranges or are unsuited at parts of the succession in
O. digyna and T. badium. This scenario provides the opportunity to study the biotic and abiotic
factors that shape the realized niche (Junker et al., 2019) and
therefore the ability of the species to grow and reproduce.
Distribution of the three intensively studied plant species Oxyria digyna
(Polygonaceae; red), Trifolium badium (Fabaceae; green), and Campanula scheuchzeri (Campanulaceae; blue). The
abundance of each plant species given as log(cover+1) is given for the
three plant species on each plot. Missing circles indicate that the species
are absent from a given plot. Lines show significant quadratic regression
models with plot number as an explanatory variable and cover as a dependent
variable; the dashed line indicates a statistically nonsignificant association.
The size of the circles is proportional to relative mean height of the species on
each plot (plant height values are standardized for each species between 0
and 1); the darkness of the circles is proportional to mean leaf area, with light
colors indicating relatively small leaves within a species and dark colors
indicating larger leaves.
Diversity of animals, bacteria, and fungi
Next to plants, animals and microorganisms colonize glacier forefields
in a successional manner (Brown and Jumpponen, 2014; Doblas-Miranda et
al., 2008; Tscherko et al., 2003). On the Ödenwinkel platform, animal
Shannon diversity peaked on plots that have been deglaciated for roughly 150 years (Fig. 6a; quadratic model: F2,82=16.50, p<0.001,
r2=0.27) and therefore lagged behind the plant species diversity
that peaked about 40 years earlier (see above). In contrast to plants and
animals, diversity of microorganisms was not correlated to the successional
gradient (Fig. 6a; F2,132≤2.14, p≥0.121, r2≤0.031). The diversities of plants, animals, bacteria, and fungi did not vary
independently from each other: we found positive relationships between the
diversities of arthropods and plants, fungi and plants, and bacteria and
fungi (Fig. 6b). These results confirm concerted changes in the diversity of
different taxa along ecological successions, e.g., between microbes and
plants (Brown and Jumpponen, 2014) or plants and invertebrates
(Albrecht et al., 2010). Based on the results presented here, we
cannot discriminate between the effects of mutual influences among the taxa,
environmental factors, and random processes. Likewise, the direction of
these effects cannot be directly assessed, i.e., whether microbes affect the
composition of plant communities (Klironomos, 2002) or vice versa and whether
plant–microbe interactions subsequently affect plant–animal interactions
(Benítez et al., 2017; Karamanoli et al., 2020; Peters et al.,
2017). However, the available data allow more detailed analyses to generate
hypotheses to be tested in field or lab experiments.
Shannon diversity of plants, animals, fungi, and bacteria as a
function of plot age. (a) As with plant diversity (see also Fig. 3b), animal
diversity increases in the first years of the succession but slightly
decreases on the oldest plots. In contrast, bacterial and fungal diversities
vary independently of plot age. (b) Correlations between plant, animal,
bacterial, and fungal diversity across the study plots. Significant
correlations are marked by asterisks.
Spatial autocorrelation
As usual in many studies taking advantage of space-for-time substitution
and environmental gradients (Baker and Barmuta, 2006; Pickett, 1989), we
detected a strong spatial signal in the variation in abiotic (including
lithology and geomorphic processes; mean Moran's I=0.42±0.08; p≤0.001) and biotic parameters (including bacteria
diversity, fungi diversity, plant diversity, animal diversity, and plant cover;
mean ± SD Moran's I=0.30±0.13; p≤0.01) that
characterize the n= 140 plots. From a devil's advocate perspective, the
reported patterns may therefore be attributed to the spatial distribution of
the plots, reducing the impact of the ecologically relevant parameters.
However, as spatial autocorrelation, i.e., the phenomenon that adjacent
locations are more similar in biotic and abiotic parameters
(Kühn and Dormann, 2012), usually affects only the few
closest locations (Dormann, 2007), the trends observed across the
n= 135 plots within the glacier forefield may be mostly attributed to
successional changes in the biotic and abiotic environment and not merely to
the spatial distribution. Nonetheless, the high spatial resolution of the
plots may allow the discrimination between spatial, random, and ecological
effects on community establishment in future analyses and experiments. The
five plots outside of the glacier forefield represent potential outcomes of the
community establishment, but given the close proximity of the five plots,
spatial effects may be more pronounced. This spatial clustering should be
kept in mind in future analysis, and the plots should potentially be
supplemented by further sampling events in climax communities.
Concluding remarks and outlook
The glacier forefield of the Ödenwinkelkees features a largely linear
increase in plot age and therefore also community age, with little
covariation with elevation (119.6 m differences in elevation between the
lowest and the highest plot) or other abiotic factors. Therefore, together
with the excellent infrastructure that allows comparably effortless access
to the glacier forefield, these characteristics make the glacier forefield of
the Ödenwinkelkees an ideal study system to investigate the successional
increase in multidiversity and ecosystem complexity. In the field campaign
in summer 2019, where we performed a first inventory of plant, arthropod,
bacteria, and fungi diversity and composition as well as a first
geomorphological assessment of n= 140 plots, we built the foundation for
future observational and experimental investigations using the
Ödenwinkel platform. Furthermore, the exact documentation of the location of the plots
and even the within-plot position of sampling events (traps, thermologgers,
and soil samples) will allow a precise replication of the inventory in
coming years to track the successional progress over years.
We are hoping that the research platform Ödenwinkel, with all the pre-existing
knowledge on the plot level, will also be useful for colleagues in diverse
disciplines who can use this platform to build upon existing data and
contribute to the accumulation of data on multidiversity and ecosystem
complexity along the successional gradient. The assessment
of additional taxonomic groups such as viruses, protists, and lichens as well as a
more detailed documentation of the geomorphology that shaped the plots and
the whole glacier forefield to study biogeomorphic feedbacks at high
temporal and spatial resolution is conceivable. Additionally, investigations on the
population dynamics, phenotypic plasticity, or local adaptation of the
intensively investigated plant species are conceivable to disentangle the
biotic and abiotic contributions to these processes. Additionally, this
research platform provides ideal conditions for long-term monitoring of
biotic and abiotic processes and the responses of plant, animal, and microbe
communities to climate warming and time since deglaciation. Thus, we invite
colleagues to make use of the research platform Ödenwinkel for diverse research
questions and offer assistance in the design of future studies.
Data availability
The data used in this study are provided in the Supplement.
The supplement related to this article is available online at: https://doi.org/10.5194/we-20-95-2020-supplement.
Author contributions
RRJ conceived the platform; RRJ, MH, XH, VRH, JCO, SK, KB, FG, LMO, and WT
designed the platform; MH, XH, VRH, and JCO performed field work; RRJ, MH, XH,
VRH, JCO, and SK drafted the first version of the manuscript; all authors
contributed to the final version.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank the Hohe Tauern National Park Salzburg administration and the
Rudolfshütte for organizational and logistic support, the governing
authority Land Salzburg for the permit to conduct our research (permit no. 20507-96/45/7-2019), and Anna Vojtkó for help in the field. We also
thank all participants of the symposium on interdisciplinary views on
ecological complexity and biodiversity at Salzburg University in March 2019.
Financial support
This research has been supported by the Austrian Science Fund (FWF), which provided funding to Robert R. Junker (grant no. Y1102).
Review statement
This paper was edited by Sonja Knapp and reviewed by Mark Frenzel and one anonymous referee.
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