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Ecosystem Functioning of Great Salt Lake Wetlands
Article in Wetlands · July 2020
DOI: 10.1007/s13157-020-01333-1
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Wetlands
https://doi.org/10.1007/s13157-020-01333-1
ECOSYSTEM SERVICES OF WETLANDS
Ecosystem Functioning of Great Salt Lake Wetlands
Maya C. Pendleton 1 & Samuel Sedgwick 1 & Karin M. Kettenring 1 & Trisha B. Atwood 1
Received: 25 February 2020 / Accepted: 19 June 2020
# Society of Wetland Scientists 2020
Abstract
Like many wetlands worldwide, Great Salt Lake (GSL) wetlands have been declining. Yet, little is known about the ecosystem
functions provided by the different GSL wetland plant species. This knowledge gap hinders predictions of the effects of species
loss and restoration practices on ecosystem functioning. To better understand how the loss of different habitat types affects the
provisioning of ecosystem functions, we quantified eight functions and multifunctionality (the support of multiple functions
simultaneously) across seven dominant GSL wetland habitat types. Habitats varied greatly in their capacity to perform functions.
However, no single habitat type supported all eight functions even at 20% of the maximum value for each individual function.
We found that native plants Typha latifolia and Schoenoplectus acutus and invasive Phragmites australis had the highest levels
of multifunctionality. Although these three species were able to support more functions, we found that a diversity of habitats are
required to maintain the breadth of ecosystem functions examined. This study supports the idea that habitat heterogeneity is
critical in supporting a multifunctional environment, and that habitat homogenization may cause a reduction in functioning
provided by GSL wetlands.
Keywords Habitat diversity . Phragmites australis . Ecosystem services . Multifunctionality . Scales of biodiversity
Introduction
Wetlands cover only approximately 5% of the Earth’s surface,
yet they contribute nearly 40% of the ecosystem functions
(i.e., sizes of compartments of materials and rates of processing) and services provided by ecosystems (Zedler and Kercher
2005). Despite their importance, wetlands are some of the
most endangered ecosystems globally. In the twentieth century, urbanization and the expansion of agriculture accelerated
wetland loss, culminating in a 50–87% loss of global wetlands
(Zedler and Kercher 2005, Davidson 2014). This loss paired
with the functional and economic value of wetlands inspired
the U.S. government to create a no-net-loss of wetlands policy
(Mitsch and Gosselink 2000). However, current wetland restoration and mitigation practices often fail to produce wetlands that provide functions and services similar to natural
Electronic supplementary material The online version of this article
(https://doi.org/10.1007/s13157-020-01333-1) contains supplementary
material, which is available to authorized users.
* Trisha B. Atwood
trisha.atwood@usu.edu
1
Department of Watershed Sciences and the Ecology Center, Utah
State University, Logan, UT, USA
wetlands (Zedler and Callaway 1999; Moreno-Mateos et al.
2012). The inability to restore or maintain natural levels of
wetland functioning may be due to a lack of understanding
of how biodiversity in wetlands influences functions (Zedler
2000; Meli et al. 2014).
In addition to habitat loss, the structure and species composition of North American wetlands have been changing as a
result of the spread of invasive species (Zedler and Kercher
2004). One of the most prolific invaders across North
American wetlands is Phragmites australis (Cav.) Trin. ex
Steud. (common reed; Chambers et al. 1999). Although
P. australis is native to parts of North America and was historically widespread, it was not a dominant species (i.e., high
cover relative to other plants) in wetlands (Saltonstall 2003).
However, an invasive lineage comprised of multiple haplotypes introduced from Eurasia has resulted in the aggressive
spread of P. australis, and the homogenization of North
American wetlands (Saltonstall 2003; Lambertini et al.
2012; Chambers et al. 1999; Silliman and Bertness 2004;
Kettenring et al. 2012). Accordingly, P. australis control is a
top priority for managers across the continent (Hazelton et al.
2014).
Although biodiversity-ecosystem functioning research has
helped scientists make predictions about the potential outcome
of invasive species on ecosystem functions in many systems
Wetlands
(Charles and Dukes 2008), its applicability to wetland systems
has been relatively limited until recently (Meli et al. 2014). Early
biodiversity-ecosystem functioning research predicts that a decline in species diversity, in general, should lead to a decline in
ecosystem functioning (Cardinale et al. 2006; Hector and
Bagchi 2007). However, many wetland systems are naturally
species-poor (Zedler and Kercher 2005), at least at the patchlevel, with a mosaic of habitats where plants grow in large
monotypic stands. Furthermore, early biodiversity-ecosystem
functioning research focused on the effects of diversity on the
provisioning of single ecosystem functions (i.e., functions are
analyzed independently of one another) (Hooper et al. 2005;
Tilman, et al. 2012). The focus on independent ecosystem functions, however, does not account for synergies and trade-offs
among functions. As a result, the applicability of independent
ecosystem functioning studies to the conservation and management of wetlands is limited because most wetlands are managed
for a diversity of stakeholders that value different ecosystem
functions (Maltby 1991; Zedler 2000; Euliss et al. 2008).
Two advances in biodiversity-ecosystem functioning research has made this discipline more meaningful for understanding the ecology of wetlands and for informing wetland
management. First, biodiversity-ecosystem functioning research has expanded the spatial scale over which diversity is
measured. In addition to species diversity, biodiversityecosystem functioning studies are beginning to investigate
the relationship between habitat- or landscape-level diversity
and ecosystem functioning, making the results more applicable to systems that have low alpha diversity but high habitatlevel diversity (Pasari et al. 2013; van der Plas et al. 2016;
Alsterberg et al. 2017). Second, biodiversity-ecosystem
functioning research has extended the study of ecosystem
functioning to multifunctionality. Multifunctionality is the
provisioning of multiple ecosystem functions simultaneously, and studies have suggested that even higher levels
of biodiversity are required to support multifunctionality
compared to the delivery of a single function (Hector and
Bagchi 2007; Mouillot et al. 2011; Zavaleta et al. 2010;
Maestre et al. 2012a; Lefcheck et al. 2015; Alsterberg et al.
2017). Despite the above advances in biodiversity-ecosystem functioning research, rarely have multifunctionality or
habitat/landscape-diversity studies been completed in wetland
systems and even less so to P. australis invasion of wetlands.
We chose to focus this study on wetland ecosystem functioning in Great Salt Lake (GSL) wetlands located in northern
Utah, USA. Wetlands in the GSL are both internationally and
locally important ecosystems. In Utah, the GSL wetlands cover >160,000 ha, which constitutes approximately 75% of all of
Utah’s wetlands (Petrie et al. 2013; UWAPJT 2015; USFWS
2018), and the site acts as an important stopover for millions
of migratory birds (Paul and Manning, 2002; Aldrich and
Paul, 2002). In 1983, flooding of the GSL disturbed the native
wetland vegetation, allowing P. australis to spread across the
landscape (Kettenring et al. 2012). As of 2012, P. australis
covered an estimated ~92 km2 of GSL, and its capacity to outcompete native plants is continuing to homogenize the landscape (Long et al. 2017; Kettenring et al. 2012).
The goal of this study was to determine how wetland ecosystem functioning varies by different habitat types and how the
spread of invasive P. australis may be changing wetland ecosystem functions and ecosystem multifunctionality. The primary objectives of the study were to: 1) determine how independent ecosystem functions vary across different wetland habitat
types. 2) determine how multifunctionality varies across different wetland habitat types. To meet these objectives, we chose to
focus on eight ecosystem functions that have been identified as
important or of growing interest to GSL wetland managers and
stakeholders, as well as other wetlands, because they are generally known to support aspects of climate mitigation, water
quality, primary production, and habitat provisioning for wildlife (Zedler and Kercher 2005; Petrie et al. 2013; Downard et al.
2014; Rohal et al. 2018). These functions include below- and
above-ground carbon storage, below- and above-ground nitrogen storage, above-ground biomass (a proxy for primary production), heavy metal/trace element accumulation, seed nutrient
content, and avian diversity and richness.
Methods
Study Area
This study focused on wetlands in the Bear River Migratory
Bird Refuge (hereafter the Refuge) and The Nature
Conservancy Great Salt Lake Shorelands Preserve (hereafter
the Preserve) both located on the eastern side of the GSL
(Fig. 1). The Refuge and the Preserve were selected for this
study because they contain large stands of both native and
non-native wetland plants. Within these two locations, we
characterized ecosystem functions on five native vegetated
habitat types: broadleaf cattail (Typha latifolia L.), alkali bulrush (Bolboschoenus maritimus (L.) Palla), threesquare bulrush (Schoenoplectus americanus (Pers.) Volkart ex Schinz &
R. Keller), hardstem bulrush (S. acutus (Muhl. ex Bigelow) Á.
Löve & D. Löve), and pickleweed (Salicornia rubra (A.
Nelson), one non-native vegetated habitat type (P. australis),
and one unvegetated habitat type (playa) (Table 1). These
habitats represent some of the most dominant habitat types
across the GSL wetland landscape (Downard et al. 2017).
Using field surveys, we identified areas that were at least
400 m2 of continuous, unmixed vegetation for each of the
seven habitat types. This minimum plot size was selected to
reduce any interactive effects on functions from neighboring
plant species. In total, we had 68 plots: T. latifolia (8 plots),
B. maritimus (10 plots), S. americanus (10 plots), S. acutus (9
Wetlands
Bear River
Migratory Bird
Refuge
WA
ND
MT
SD
ID
OR
Great Salt Lake
Shorelands
Preserve
WY
NE
Great Salt Lake
NV
UT
CO
KS
CA
AZ
NM
OK
TX
0
2.5
5
10 Miles
Fig. 1 Map of study site locations. The Bear River Migratory Bird Refuge and The Great Salt Lake Shorelands Preserve are outlined in white
Wetlands
Table 1 Descriptions and growth parameters of the 7 different habitat
types investigated in this study. Habitats are grouped into two wetland
types which can be defined as emergent (shallowly flooded seasonally or
semi-permanently; fresh to brackish water) and playa (periodically
flooded; mostly dry, highly saline and alkaline soils) wetlands.
Parameters are from Downard et al. 2017 and the USDA Plant Guide
https://plants.usda.gov/java/factSheet
Wetland Type
Habitat
Water Depth (cm)
Salt Tolerance
pH
Plant Height (m)
Emergent
Bolboschoenus maritimus
Phragmites australis
Schoenoplectus acutus
S. americanus
5–15
5–50
5–30
10–30
High
High
Low
High
6–9
4.5–8.7
5.2–8.5
6.5–8.5
0.2–1.5
1–4
1–3
0.5–1.5
Low
Very High
–
5.5–8.7
6.5–9.4
7.5–9.5
1–3
0.1–0.3
–
Playa
Typha latifolia
Salicornia rubra
Playa
5–30
0–5
0–5
plots), S. rubra (10 plots), P. australis (10 plots), and playa
(11 plots).
Individual Function Measurements
Below-Ground Carbon and Nitrogen Storage
Below-ground carbon and nitrogen stocks were quantified
using methods from Howard et al. (2014). We collected one,
15 cm composite soil core from the center of each habitat and
transported them to the lab in an upright position (to reduce
mixing within the core) where they were immediately processed or frozen. Because of different soil types and soil moisture we used two types of corers to extract sediments: a PVC
push corer (5 cm diameter * 15 cm height) and an AMS Inc.
(American Falls, ID) hand corer (2.2 cm * 15 cm height). Soils
were subsampled from each core at 5 cm intervals, dried to a
constant weight, and homogenized into a fine powder. Soil
subsamples were analyzed for percent carbon and nitrogen
content at the University of Hawaii at Hilo’s analytical lab
using a Costech elemental analyzer.
We calculated below-ground carbon and nitrogen stocks by
combining percent content with dry bulk density. To calculate
dry bulk density, we divided the sample dry weight by the
core volume (supplementary Fig. S1). We used the following
equation to calculate below-ground carbon and nitrogen
stocks for each plot: Mg carbon ha −1 or Mg nitrogen
ha−1 = (1 Mg/1,000,000 g) * (100,000,000 cm2/1 ha) * subsection thickness (cm) * bulk density (g/cm3)* (% carbon or
nitrogen/100) (Howard et al. 2014). All subsections within
each core were summed to determine the total carbon/
nitrogen stock per hectare of habitat.
Above-Ground Biomass and above-Ground Carbon
and Nitrogen Storage
We collected plant biomass as a proxy for primary production
(above-ground biomass production m−2) using established
methods for herbaceous wetland species (Howard et al.
2014). Plant biomass samples were collected at peak production in August and September 2018. Within each vegetated
plot, we randomly established three, 0.5 m2 subplots using a
PVC frame. Within each 0.5 m2 subplot, we destructively
harvested all rooted material (i.e., not wrack) that was both
living and dead by cutting the plant at the soil-atmosphere
interface. Plant samples were dried to a constant weight at
60o C and weighed.
We used subsamples from the above-ground biomass samples to analyze the percent carbon and nitrogen of stems and
leaves. Samples were dried at 60o C to a constant weight (~
72 h) and homogenized to a fine powder. Percent carbon and
nitrogen were analyzed at the University of Hawaii at Hilo’s
analytical lab using a Costech elemental analyzer. The percent
carbon and nitrogen content for each species were averaged
across plots to develop a carbon and nitrogen conversion factor for each species (supplementary Table S1). Above-ground
carbon and nitrogen stocks were calculated by multiplying the
carbon or nitrogen content by the above-ground biomass for
each plot.
Heavy Metal/Trace Element Accumulation in above-Ground
Biomass
Within each plot, we collected 5 g of stem biomass for
T. latifolia, B. maritimus, S. americanus, S. acutus,
P. australis, and the entire above-ground structure for
S. rubra. We chose stems because the leaves of
S. americanus and S. acutus are often highly reduced and
we wanted to standardize the structure of the plant that was
collected, as heavy metal/trace element distributions within
plant structures can vary (Stoltz and Greger 2002).
However, because S. rubra’s stems are very small and not
very metabolically active, we chose to collect the entire
above-ground biomass. Plant samples were dried to a constant
weight, ground to a fine powder, and analyzed for copper,
arsenic, selenium, lead, mercury, and cadmium at the
Wetlands
University of Hawaii at Hilo’s analytical lab using a Varian
Vista MPX ICP-OES Spectrometer. Heavy metal/trace element extractions were done using methods from Hue et al.
(2000). Briefly, plant samples were dried at 55 °C, ground
to a fine powder, and then ashed at 500 °C using a muffle
furnace. The ash from samples were then digested in a 1 M
HCl solution and the resulting slurry analyzed for heavy
metals/trace elements. Heavy metal/trace element uptake by
each plant species per m2 was calculated by multiplying the
concentration of each metal by the plot above-ground biomass
(see methods above).
Seed Nutritive Value
We calculated the amount of seed nutrient content per m2 by
multiplying seed mass by the seed nutrient composition for
each plant species. Seeds were collected from the plots in
2018 after seeds had ripened: June–July for S. acutus and
S. americanus, late August–September for B. maritimus,
September for T. latifolia, late September–October for
P. australis, and October for S. rubra. Following this collection, we used slightly different methods to determine seed
densities for the bulrush species, P. australis, T. latifolia,
and S. rubra. For the bulrushes, we first counted the total
number of seed heads in each 0.5 m2 subplot. We then collected three seed heads from each subplot for a total of 90 seed
heads for each bulrush species. In the lab, we counted the
number of seeds in 15–20 seed heads and developed an allometric equation that described the species-specific relationship between seed head weight and the number of seeds per
seed head (supplementary Table S2). We then weighed the
remaining seed heads, applied the allometric equation to each
head that was not hand-counted, and averaged all 90 seed
heads to determine the average number of seeds per seed head.
To calculate the seed density in each subplot, we multiplied
the average number of seeds per head by the total number of
seed heads counted in each subplot. Finally, we calculated the
total dry mass of seeds per plot for each species by multiplying
the seed density by the average dry weight of an individual
seed. For P. australis and T. latifolia, because counting the
total number of seeds per seed head was impractical, we made
three subsections of each seed head and counted the number
of seeds per subsection. We then developed species-specific
allometric equations that described the relationship between
seed abundance and seed head weight (supplementary
Table S2) and used this equation to estimate the number of
seeds per seed head for P. australis and T. latifolia. We then
used the same methods as for the bulrush species to calculate
total seed density and total dry seed mass for each plot of
P. australis and T. latifolia. Salicornia rubra does not produce
easily identifiable flowering heads and the seeds germinate
within the parent plant. Because no established methods for
collecting seeds from S. rubra exist and several species of
birds are known to consume the fleshy tips of S. rubra
(Zedler 1982), we estimated the nutrient content by weighing
the entire stem-free above-ground biomass within each
subplot.
To complete the seed nutrient content analysis, we collected mass amounts of each seed type from 5 plots for each
species. Seeds were cleaned to ~95% pure seed. In the case
of S. rubra, where seeds were not collected, the tips of the
plants were used for nutrient analyses. Seeds and S. rubra tips
were sent to a Certified National Testing Forage Center at Bar
Diamond Lab in Parma, Idaho for analysis of apparent metabolizable energy (AME) for waterfowl, which is the gross energy of the seed minus the gross energy in the excrement.
AME has been widely used in quantifying the energy of feed
stuffs for birds (Miller and Reinecke, 1984). In general, the
quantification of AME requires lengthy feeding trials with live
birds. However, Bar Diamond Lab has developed equations
that are used to estimate AME of unusual feed ingredients. See
supplementary Table S3 for AME equations.
Although quantifying AME through proximate analysis
may under or overestimate the true AME value of a food
source (which limits comparison of the raw values to other
studies), because all seeds are analyzed in the same way it still
allows for a relative comparison of seeds of the different study
species. In addition to AME, the lab also provided information
on crude protein, crude fat, ash, acid detergent fiber, neutral
detergent fiber, and general energy (supplementary Table S3).
Total AME per m2 of habitat (kcal/m2) was calculated by
multiplying the average AME for each seed species by the
total dry seed biomass or stem-free above-ground biomass
for S. rubra for each plot.
Avian Diversity and Richness
We calculated bird diversity (Shannon diversity) for each habitat type using point counts. Point counts for each plot were
conducted from the center point of each plot. Point counts
were chosen because they have been used in other wetland
surveys to link avian species with habitat use and are effective
in dense vegetation such as P. australis and T. latifolia (Benoit
and Askins 1999; Conway 2011; Wiest et al. 2016). Surveys
started at sunrise and were concluded 3 h after sunrise.
Surveys were conducted during Spring, Summer, and Fall of
2018 to capture the diversity of bird communities across the
year. Surveys within each plot lasted 5 min and were done
with two observers. It is important to note that most of the
plots consisted of continuous habitat that was far greater than
the minimum 400 m2 required for site selection. All birds
observed and heard anywhere in the target habitat type were
counted (for a list of observed birds, see supplementary
Table S4). We also counted birds flushing from the habitat
patch as we approached the plots to take into account birds
that were disturbed by our presence. However, we excluded
Wetlands
fly-overs that were not specifically using a habitat type.
Following the five-minute counting period, we played the
calls of two secretive marsh birds, the Virginia rail, and the
Sora, to detect their possible presence. Shannon diversity
scores and avian richness were calculated using the “vegan”
package in R version 3.4.4. We also calculated the Shannon
diversity index for overall bird diversity and richness by combining data from each plot for spring, summer, and fall to give
one overall index score per habitat type.
indicated the average number of metals contributing to each
habitat’s threshold score to visualize if metal accumulation
was the main driver behind high threshold scores.
After calculating maximum values for each function, we
determined functional performance using a sensitivity analysis with thresholds at 20%, 40%, 60%, and 80% of the calculated maximum value for each function. The number of functions reaching each threshold was summed for each plot, with
a higher sum indicating that more functions were being provided at that threshold.
Multifunctionality Measurements
Data Analysis
To evaluate multifunctionality within each habitat type, we
used two methods. The first is known as the “averaging” approach (Byrnes et al. 2014). Using the individual function
data, we developed a multifunctionality index (MI) for each
of the seven habitat types. We calculated an MI by first taking
the mean and standard deviation of each ecosystem function
to create a Z-score for each observation of that function
(Maestre et al. 2012). For functions that were measured in
subplots, the values were averaged together to create one value per plot, which was then Z-transformed. Z-scores are a
common method for standardizing functions that have been
measured in different, non-comparable units (Byrnes et al.
2014). We weighted all functions equally by adjusting the
Z-scores of functions that contained multiple independent
measures of that function. For example, heavy metal/trace
element accumulation is a single function, however, we measured the accumulation of six different metals, which would
up-weight this function if we included a Z-score for each
metal independently in the MI. To equally weight the functions, we calculated a Z-score for each independent measure
of that function and then took the average Z-score as the final
score. To calculate the overall MI of each plot, we averaged
the weighted Z-scores for all the functions (Byrnes et al.
2014), with a higher MI indicating a higher level of
multifunctionality.
In addition to the average MI, we also evaluated
multifunctionality performance using the “threshold” approach (Byrnes et al. 2014). This approach allowed for the
investigation of how many functions are being maintained
above a set of desired thresholds (Byrnes et al. 2014;
Gamfeldt et al. 2008; Zavaleta et al. 2010). For this approach,
the threshold was based upon the averaged maximum observed value for each function. This maximum observed value
was calculated by averaging the top eight scores [this averaging number was determined by using the smallest sample size
of the functions measured (Byrnes et al. 2014)]. For bird diversity, we used only the overall annual Shannon diversity
index as opposed to individual seasonal indices. Since habitat
types varied in their capacity to store different metals/trace
elements, we included each independent metal in the threshold
approach. Because we did not combine the metals, we
We investigated the effect of habitat type (fixed effect) on the
response variables below-ground carbon and nitrogen stocks,
using an analysis of variance (ANOVA). Differences among
habitat types for below-ground carbon and nitrogen stocks were
analyzed using a Tukey’s posthoc test. All analyses were done
using the “stats” “lme4” and “multcomp” packages in R version 3.4.4. To determine the effect of habitat type (fixed effect)
on the response variables above-ground carbon and nitrogen
stocks, heavy metal/trace element accumulation, plant biomass,
seed AME, MI and MI thresholds, we used a linear mixedeffect model (LME) with subplots as the random effect. An
LME was chosen over a linear model to account for the variation that may have occurred among subplots. All analyses were
done using the “lme4” and “multcomp” packages in R version
3.4.4. Differences between habitat types for all ecosystem functions were further analyzed using Tukey’s post-hoc test.
Results
Carbon Storage
Post-hoc Tukey’s analyses indicated there was only one significant difference between the habitat types for below-ground
carbon storage. Schoenoplectus americanus (63.89 ± 8.70 Mg
C ha−1) had significantly lower below-ground carbon stocks
than playa (85.69 ± 6.72 Mg C ha−1; P = 0.04).
Bolboschoenus maritimus (82.84 ± 6.17 Mg C ha −1 ),
P. australis (61.40 ± 3.64 Mg C ha−1), S. rubra (76.99 ±
5.67 Mg C ha−1), S. acutus (80.67 ± 2.89 Mg C ha−1), and
T. latifolia (80.50 ± 3.95 Mg C ha−1) were not significantly
different from S. americanus or playa (Fig. 2a; P < 0.01).
Unlike below-ground carbon stocks, there was a significant
difference between habitat types in above-ground carbon
stocks (Fig. 2b). Tukey’s tests indicated that P. australis
(9.47 ± 0.74 Mg C ha−1) and T. latifolia (7.00 ± 0.55 Mg C
ha−1) stored significantly more above-ground carbon than the
other habitat types (P < 0.01), with the exception of S. acutus
(5.90 ± 0.80 Mg C ha−1) which was not significantly different
from T. latifolia (P = 0.89) but was less than P. australis (P =
P = 0.006
A
AB
AB
AB
B
AB
AB
BOMA
PLAYA
SCAC
TYLA
PHAU
SARU
SCAM
4
3
2
1
0
3000
2500
2000
1500
1000
500
0
c
A
A
C
BC
C
P < 0.001
AB
C
BOMA
PLAYA
SCAC
TYLA
PHAU
SARU
SCAM
e
P < 0.001
A
AB
B
C
C
C
BOMA PHAU SARU SCAC SCAM TYLA
Above−ground C Stock
(Mg ha-1)
a
Above−ground N Stock
(Mg ha-1)
100
80
60
40
20
0
AME (kcal m-2)
Above−ground Biomass
(g m-2)
Below−ground N Stock
(Mg ha-1)
Below−ground C Stock
(Mg ha-1)
Wetlands
12
10
8
6
4
2
0
0.5
0.4
0.3
0.2
0.1
0.0
600
500
400
300
200
100
0
P < 0.001
AB
A
b
B
C
C
C
BOMA PHAU SARU SCAC SCAM TYLA
P < 0.001
A
d
B
C
C
C
C
BOMA PHAU SARU SCAC SCAM TYLA
f
P < 0.001
B
B
B
A
B
B
BOMA PHAU SARU SCAC SCAM TYLA
Habitat
Habitat
Fig. 2 Average (± s.e.) individual ecosystem functions: a below-ground
carbon (C) stocks, b above-ground C stocks, c below-ground nitrogen (N)
stocks, d above-ground N stocks, e above-ground biomass (g m−2), and f
apparent metabolizable energy (AME) among Great Salt Lake wetland
habitat types. Letters above bars show significant differences between
habitat types. (BOMA = Bolboschoenus maritimus, PHAU =
Phragmites australis, PLAYA = playa, SARU = Salicornia rubra,
SCAC = Schoenoplectus acutus, SCAM = S. americanus, and TYLA =
Typha latifolia)
0.007). Bolboschoenus maritimus (1.21 ± 0.14 Mg C ha−1),
S. rubra (0.34 ± 0.04 Mg C ha−1), and S. americanus (2.81
± 0.35 Mg C ha−1) were not significantly different than one
another but had 50–96% less above-ground carbon than
S. acutus, P. australis and T. latifolia (Fig. 2b).
Above-ground nitrogen stocks also significantly differed
among habitat types (Fig. 2d; P < 0.001). However, the patterns in above-ground nitrogen stocks varied greatly from
below-ground nitrogen stocks. Phragmites australis (0.43 ±
0.03 Mg N ha−1) stored significantly more above-ground nitrogen than all other habitat types (P < 0.01). Schoenoplectus
acutus (0.26 ± 0.04 Mg N ha−1) stored the second most aboveground nitrogen and was significantly different from all other
habitat types (all P < 0.01). Bolboschoenus maritimus (0.02 ±
0.00 Mg N ha −1 ), S. rubra (0.02 ± 0.00 Mg N ha −1 ),
T. latifolia (0.09 ± 0.00 Mg N ha−1), and S. americanus
(0.10 ± 0.01 Mg N ha−1) stored 77–97% less above-ground
nitrogen than P. australis but were not significantly different
from each other (Fig. 2d).
Nitrogen Storage
We found a significant difference among habitat types in
below-ground nitrogen stocks (Fig. 2c; P < 0.001). Typha
latifolia (2.65 ± 0.22 Mg N ha −1 ), S. acutus (3.07 ±
0.18 Mg N ha−1), and S. americanus (3.38 ±
0.33 Mg N ha−1) all stored significantly more below-ground
nitrogen than the other habitats (all P < 0.001), with the exception of T. latifolia, which did not differ from P. australis
(1.99 ± 0.19 Mg N ha−1). The other habitat types did not differ
in their capacity to store below-ground nitrogen (Fig. 2c;
P > 0.05), but in some cases stored less than 50% of the
below-ground nitrogen than the highest storing species (i.e.,
T. latifolia, S. acutus, and S. americanus).
Above-Ground Biomass
Our results indicate that P. australis (2404.77 ± 188.77 g m−2)
had significantly more above-ground biomass than all other
habitat types (Fig. 2e; all P < 0.001), with the exception of
Wetlands
T. latifolia (1738.69 ± 137.67 g m−2; P = 0.09).
Schoenoplectus acutus (1478.29 ± 200.70 g m−2) was not significantly different from T. latifolia (P = 0.92). Bolboschoenus
maritimus (306.75 ± 35.20 g m −2), S. rubra (134.91 ±
17.55 g m−2), and S. americanus (698.04 ± 86.73 g m−2) had
66–93% less biomass than P. australis, T. latifolia, and
S. acutus, but were not significantly different from each other.
Seed Nutrient Content
We found a significant difference in seed nutrient content
values among habitat types (Fig. 2f; P < 0.001). A post-hoc
Tukey’s analysis revealed that the AME of T. latifolia
(381.60 ± 84.38 kcal m−2) greatly exceeded the nutrient contents per area (> 60%) compared to all other species (P <
0.001). No other habitats were significantly different from
each other.
Heavy Metal Accumulation in above-Ground Biomass
With the exception of arsenic (Fig. 3a; P = 0.052), metal accumulation differed significantly among habitat types (all
P < 0.05). However, the patterns in metal accumulation
among the different habitat types varied depending on the
metal analyzed. For copper, P. australis stored significantly
more than the other habitats (Fig. 3b; 4446.36 ± 1764.35 μg
m−2; P < 0.05) with the exception of T. latifolia (2113.97 ±
641.60 μg m−2) and S. americanus (1813.19 ± 463.79 μg
m−2). However, T. latifolia and S. americanus did not differ
significantly from B. maritimus (292.90 ± 115.50 μg m−2),
S. rubra (768.49 ± 198.20 μg m−2), or S. acutus (806.83 ±
173.05 μg m−2) (all P > 0.05). Phragmites australis also
stored significantly more mercury compared to the other habitat types (Fig. 3c; 76.54 ± 43.93 μg m−2; all P < 0.05).
Schoenoplectus acutus stored significantly more selenium
(176.06 ± 52.85 μg m−2; up to 67% more) and lead (340.98
± 36.76 μg m−2; up to 70% more) compared to any other
habitat type (Fig. 3d and e; all P < 0.01). None of the other
habitat types significantly differed from one another in selenium or lead storage (all P > 0.05). Finally, S. rubra stored up to
90% more cadmium than any other habitat types (Fig. 3f;
180.72 ± 45.39 μg m −2 ; all P < 0.001). Typha latifolia
(20.83 ± 13.64 μg m−2), S. americanus (0.00 ± 0.00 μg m−2),
P. australis (6.15 ± 6.15 μg m−2), and S. acutus (0.00 ±
0.00 μg m−2) did not differ from one another in cadmium
storage (all P > 0.05).
Avian Species Diversity and Richness
The Shannon diversity indices (DI) for habitat type varied by
season (Table 2). Schoenoplectus acutus had the highest diversity index score in spring (DI = 2.47), T. latifolia had the
highest index score in fall (DI = 2.08), and B. maritimus had
the highest score in summer, as well as the combined season
score (DI = 2.02 and 2.58, respectively). Phragmites australis
had the lowest diversity scores across all categories with the
exception of fall.
Species richness also varied by season (Table 2).
Salicornia rubra had the highest richness in spring (R = 19),
T. latifolia had the highest richness for fall (R = 14) and
B. maritimus had the highest richness for summer and the
overall combined score (R = 21 and 29, respectively).
Schoenoplectus americanus consistently had the lowest species richness across all categories.
Multifunctionality
Overall, we found that different habitat types varied greatly in
their functional capacities and not one habitat scored consistently high in all functional categories (Fig. 4). There was a
significant difference in the multifunctionality of the different
habitat types when analyzed using the standardized averaging
approach (Figs. 4 and 5; P < 0.001). Typha latifolia (0.41 ±
0.14 MI), P. australis (0.41 ± 0.12 MI), and S. acutus (0.38 ±
0.11 MI) all had a multifunctionality index 1.5–2 times greater
than all other habitats (P < 0.001) but were not significantly
different from each other. Bolboschoenus maritimus (−0.37 ±
0.04 MI), S. rubra (−0.45 ± 0.06 MI), and S. americanus
(−0.18 ± 0.11 MI) were not significantly different from each
other. Playa (−0.08 ± 0.05 MI) while not significantly different from B. maritimus or S. americanus, was significantly
higher than S. rubra (P = 0.04).
The multifunctionality threshold tallies the number of functions that are above a threshold, which in this study were
defined as 20%, 40%, 60%, and 80%. We found a significant
difference between habitat multifunctionality at all four (20%,
40%, 60% and 80%) functional thresholds (all P < 0.001;
Fig. 6). Typha latifolia, P. australis, and S. acutus could maintain more functions at the 20% (~8 functions), 40% (~6 functions), and 60% (~4.5 functions) thresholds compared to the
other habitats (all P < 0.001), with the exception of
S. americanus which was not significantly different from the
three habitats at the 20% threshold. Typha latifolia, P.
australis, and S. acutus still maintained more functions than
B. maritimus, S. rubra, S. americanus, and playa (all P < 0.05)
at every threshold. At the 20% threshold, there was a significant difference between playa (~3 functions) and S. rubra (~5
functions) (P < 0.01) but neither were significantly different
from B. maritimus (P > 0.05). Finally, at the 80% threshold,
S. acutus and P. australis maintained the greatest number of
functions (~3). Typha latifolia could perform ~2 functions at
the 80% threshold but was not significantly different from any
of the other habitat types. We also found that for the 80%
threshold to be reached for all functions simultaneously, at
least six of the habitat types (T. latifolia, S. americanus,
S. acutus, S. rubra, P. australis, playa) would be required to
Wetlands
Arsenic concentration (ug m-2)
a
A P = 0.053
150
A
100
A
A
A
A
50
0
4000
P = 0.039
A
120
100
80
60
40
B
20
B
B
B
B
0
BOMA PHAU SARU SCAC SCAM TYLA
A
e
P < 0.001
300
200
B
100
B
B
B
B
0
Habitat
Table 2 Shannon diversity index scores (DI) and richness (R) calculated for birds among different Great Salt Lake wetland habitat types.
Calculations were done for each season as well as all seasons combined.
(BOMA = Bolboschoenus maritimus, PHAU = Phragmites australis,
PLAYA = playa, SARU = Salicornia rubra, SCAC = Schoenoplectus
acutus, SCAM = S. americanus, and TYLA = Typha latifolia)
Summer
Fall
AB
3000
AB
2000
B
1000
B
B
0
BOMA PHAU SARU SCAC SCAM TYLA
Selenium concentration (ug m-2)
Lead concentration (ug m-2)
400
c
P = 0.009
A
5000
Cadmium concentration (ug m-2)
Mercury concentration (ug m-2)
140
b
6000
BOMA PHAU SARU SCAC SCAM TYLA
BOMA PHAU SARU SCAC SCAM TYLA
Spring
7000
Copper concentration (ug m-2)
200
Fig. 3 Average (± s.e.) aboveground heavy metal/trace element
accumulation (μg m−2) in different Great Salt Lake wetland habitats: a arsenic, b copper, c mercury, d selenium, e lead, and f
cadmium. Letters above bars
show significant differences between habitat types. (BOMA =
Bolboschoenus maritimus,
PHAU = Phragmites australis,
SARU = Salicornia rubra,
SCAC = Schoenoplectus acutus,
SCAM = S. americanus, and
TYLA = Typha latifolia)
250
d
A
P < 0.001
200
150
100
B
50
B
0
B
B
B
BOMA PHAU SARU SCAC SCAM TYLA
250
f
P < 0.001
A
200
150
100
B
50
B
B
B
B
0
BOMA PHAU SARU SCAC SCAM TYLA
Habitat
be present. In contrast, fewer habitat types and different combinations of habitat types could be used to meet the 20–60%
thresholds for all functions simultaneously.
Discussion
Combined
Habitat
DI
R
DI
R
DI
R
DI
R
BOMA
PHAU
PLAYA
SARU
SCAC
2.13
1.68
2.14
2.14
2.47
13
11
13
19
14
2.02
0.49
1.82
1.61
1.38
21
11
9
10
9
1.76
1.10
1.16
1.02
1.00
13
7
8
9
7
2.58
1.14
2.38
2.38
2.22
29
16
23
27
19
SCAM
TYLA
1.81
2.17
10
18
1.03
1.81
5
12
0.97
2.08
5
14
1.99
2.40
14
27
In this study, we tested the capacity of different GSL wetland
habitat types to provide multifunctionaly, as well as a variety
of independent ecosystem functions that related to carbon
storage, nitrogen storage, primary production, heavy metal/
trace element accumulation, and avian habitat. We found that
the different habitat types varied in their capacity to support
independent ecosystem functions and multifunctionality.
Considering that wetland plants along the GSL and elsewhere
grow in large monotypic stands, the results indicate that
habitat-level diversity is critical for maintaining a wide range
of ecosystem functions. The results on the importance of
Wetlands
Fig. 4 A comparison of each
wetland plant species (colored
lines) and their performance for
each ecosystem function as
calculated by a multifunctionality
index (standardized Z-scores).
The outer grid line represents the
highest Z-score achieved (1.6),
and the inner grid line represents
the lowest Z-score achieved
(−1.03). Playa was not included in
this figure. Above.N = aboveground nitrogen, Bird.Div =
Shannon diversity for birds,
Metals = heavy metal/trace element accumulation, AME = apparent metabolizable energy of
seeds, Soil.C = below-ground
carbon, Soil.N = below-ground
nitrogen, Biomass = aboveground biomass, Above.C =
above-ground carbon).
(BOMA = Bolboschoenus
maritimus, PHAU = Phragmites
australis, SARU = Salicornia
rubra, SCAC = Schoenoplectus
acutus, SCAM = S. americanus,
and TYLA = Typha latifolia)
BOMA
TYLA
SCAC
PHAU
SCAM
SARU
Above.N
Bird.Div
Above.C
Biomass
Metals
Soil.N
AME
Soil.C
Average Multifunctionality Index (MI)
habitat diversity are consistent with another study that focused
on multifunctionality at a landscape-level within highly managed, monotypic stands of forests (van der Plas et al. 2016),
and those from smaller-scale studies on grasslands (Pasari
et al. 2013; van der Plas et al. 2016).
Overall, we found that the GSL wetlands were highly functioning. Below-ground carbon storage for GSL habitats
ranged between 64 Mg C ha−1 and 86 Mg C ha−1 in the top
15 cm. This carbon storage is generally greater than that of
1.0
P < 0.001
A
0.0
BC
A
A
0.5
B
C
BC
−0.5
BOMA
PHAU
PLAYA
SARU
SCAC
SCAM
TYLA
Habitat
Fig. 5 Average (± s.e.) multifunctionality index (MI) score among Great
Salt Lake wetland habitat types. Letters above bars show significant differences between the habitat types. (BOMA = Bolboschoenus maritimus,
PHAU = Phragmites australis, PLAYA = playa, SARU = Salicornia
rubra, SCAC = Schoenoplectus acutus, SCAM = S. americanus, and
TYLA = Typha latifolia)
coastal wetlands (seagrass, tidal marsh, and mangroves),
which range between 21 Mg C ha−1 and 43 Mg C ha−1 if
you rescale their storage to 15 cm (Liao et al. 2007, Engle
2011, Duarte et al. 2013, Atwood et al. 2017; Serrano et al.
2019). Above-ground biomass estimates for S. acutus, T.
latifolia, P. australis in the present study were generally above
previous studies on these species in different location (~500–
1890 g m −2; Neill 1990; Windham and Lathrop 1999;
Maddison et al. 2009; Grisey et al. 2012), as well as above
those for coastal tidal marshes (Serrano et al. 2019). Avian
diversity was also relatively high, especially in T. latifolia and
B. maritimus, which had Shannon indices within the range of
other riparian habitats located in Utah (Shannon indices = 2.2–
3.4, White 2011). Although heavy metal storage in plant biomass was relatively low compared to past studies (Behrends
et al. 1996; Stoltz and Greger 2002; Aksoy, Duman and Sezen
2005; Grisey et al. 2012; Rycewicz-Borecki et al. 2016; Klink
2017), this result was likely due to the relatively low concentrations of metals found in the soils underlying the sites (S.
Sedgwick, unpublished data).
In terms of individual functions, T. latifolia supported the
highest level of functioning for seven of the functions measured. Typha latifolia did particularly well for functions related to carbon and nitrogen storage, primary production, and
some aspects of bird habitat provisioning. Typha latifolia
had the highest seed AME. Although not considered a preferred food of wetland birds nor a priority species of wetland
managers (Stevens and Hoag 2006; Rohal et al. 2018), Typha
8
a
P < 0.001
A
A
A
8
6
A
B
BC
4
C
2
0
BOMA PHAU PLAYA SARU SCAC SCAM TYLA
c
P < 0.001
A
6
A
A
4
2
B
B
B
B
0
BOMA PHAU PLAYA SARU SCAC SCAM TYLA
Habitat
seeds have been found in the gut contents of American avocets, Northern shoveler, Common goldeneye, and Greenwinged teal occupying GSL wetlands (Vest and Conover
2011, Roberts 2013). Furthermore, T. latifolia consistently
had some of the highest avian diversity and richness scores
throughout the year, second only to B. maritimus. These findings are consistent with another study that focused on avian
habitat preferences in remediated wetlands, which found that
birds preferred Typha spp. dominated wetlands because they
offered ample cover without becoming too dense to prohibit
movement (Comin et al. 2001). The invasive plant P. australis
had the second highest overall functioning with regard to primary production, above-ground nitrogen, and carbon storage.
However, it performed poorly in terms of bird habitat, which
is the primary focus of management efforts at the Refuge
(Downard et al. 2014; Rohal et al. 2018). Schoenoplectus
acutus came in third and performed well for sequestering
below-ground carbon and nitrogen as well as selenium and
lead. The lowest-performing habitats were playa and
S. rubra, likely due to their lack of above-ground biomass,
an important characteristic for many of the ecosystem functions we focused on in this study. Although playa scored low
on many functions, it was one of the best performing habitats
for bird diversity and below-ground carbon storage.
Salicornia rubra was also low scoring in most functions but
scored well for bird diversity, and was the only habitat that
accumulated the heavy metal cadmium. In fact, heavy metal/
trace element accumulation required the largest diversity of
habitat types, with copper primarily accumulating in
S. americanus, P. australis, and T. latifolia, selenium and lead
primarily accumulating in S. acutus, mercury primarily accumulating in P. australis, cadmium primarily accumulating in
S. rubra, and arsenic accumulating in all plant species equally.
These results reinforce the idea that habitat heterogeneity is
Average Functions (40%)
10
Average Functions (80%)
Average Functions (20%)
Fig. 6 Average (± s.e.) number of
ecosystem functions performed
by different Great Salt
Lakewetland habitats at four
different thresholds of the
maximum functional value: a
20%, b 40%, c 60%, and d 80%.
Letters above bars show
significant differences between
habitat types. Black solid lines
within bars indicate the average
number of metals contributing to
the threshold index. (BOMA =
Bolboschoenus maritimus,
PHAU = Phragmites australis,
PLAYA = playa, SARU =
Salicornia rubra, SCAC =
Schoenoplectus acutus, SCAM =
S. americanus, and TYLA =
Typha latifolia)
Average Functions (60%)
Wetlands
8
b
P < 0.001
A
A
A
6
B
B
4
B
B
2
0
BOMA PHAU PLAYA SARU SCAC SCAM TYLA
4
d
P < 0.001
A
A
3
AB
2
1
B
B
B
B
0
BOMA PHAU PLAYA SARU SCAC SCAM TYLA
Habitat
needed to support a diversity of ecosystem functions (Pasari
et al. 2013; van der Plas et al. 2016; Alsterberg et al. 2017).
We found that no single wetland species can support, at a
high level, all eight of the ecosystem functions measured. In
fact, not a single species can support all eight functions even at
the 20% threshold. At the 80% threshold, only S. acutus, T.
latifolia, and P. australis were able to provide more than a
single function, however, S. acutus’ high threshold score was
largely the result of its capacity to accumulate several types of
metals which has also been seen in another study focusing on
macrophytes and heavy metal/trace element uptake
(Rycewicz-Borecki et al. 2016). We also found that
T. latifolia, S. acutus, and P. australis, provided the highest
level of multifunctionality through the averaging approach.
The high levels of multifunctionality provisioned by
P. australis suggest that this aggressive invasive plant is capable of supporting multiple services related to nutrient storage and heavy metal/trace elements uptake, which supports its
current use in many constructed wetlands for remediation
(Calheiros et al. 2009; Kiviat 2013). Unfortunately,
P. australis’ capacity to support nutrient storage and heavy
metal/trace elements uptake appears to come at the cost of
providing bird habitat for GSL avian fauna, a finding consistent with its impacts on avian habitat in other regions (Benoit
and Askins 1999; Chambers et al. 1999; Robichaud and
Rooney 2017). Native T. latifolia and S. acutus had comparable multifunctionality to P. australis and provided better bird
habitat, suggesting that a trade-off between functions that support bird habitat and those that support nutrient and heavy
metal/trace element accumulation may not be necessary.
The three native bulrush species (B. maritimus,
S. americanus, S. acutus) are the focus of most of the restoration efforts for the GSL (Marty and Kettenring 2017; Rohal
et al. 2018; Kettenring et al. 2019). Schoenoplectus acutus
Wetlands
performed the most individual functions of the three species
and had one of the highest multifunctionality indices of any
wetland species. Specifically, S. acutus had high belowground carbon and nitrogen storage, and also high lead and
selenium accumulation. Bolboschoenus maritimus had one of
the lowest multifunctionality indices and generally performed
poorly for heavy metal/trace element accumulation and nutrient storage. However, B. maritimus had some of the highest
bird diversity of any of the habitats. Schoenoplectus
americanus also had a low multifunctionality index, similar
to that of habitats with lower above-ground biomass such as
S. rubra and playa, but performed well for below-ground nitrogen storage. Despite that bulrush seeds are thought to be an
important component of the diet of migrating waterfowl in
GSL (Petrie et al. 2013), all three bulrushes had similar or
lower seed AME compared to the other wetland species.
However, the seeds of all the wetland species examined in this
study tend to ripen at different times of the year and may be
chosen by different types of birds (generalists vs. specialists)
and have different seasonal importance in avian diets throughout the year (Swanson et al. 1985; Beerens et al. 2011; Petrie
et al. 2013). Overall, these results suggest that to maintain a
diversity of functions in the GSL, wetland restorations should
focus on maintaining a high level of habitat diversity.
Although T. latifolia and P. australis showed high
multifunctionality, a more thorough examination of these species and the results is warranted for wetland managers. First, it
should be noted that both T. latifolia and P. australis are
considered aggressive by managers and spread rapidly
(Kettenring et al. 2012; Rohal et al. 2018). Planting these
species or reducing the control of them in wetlands may cause
further habitat degradation through habitat homogenization,
which would be detrimental to the overall functioning and
health of the ecosystem as functions unique to other habitats
would be lost. For example, playa, S. rubra and B. maritimus
are critical habitat for migratory shorebirds (a major priority
for the Refuge; Downard et al. 2017; USFWS 2020) and this
function cannot be replaced or mitigated with other, more
multifunctional, habitats such as T. latifolia. Second, although
playa, S. rubra, and B. maritimus had low multifunctionality,
this was in part due to the fact that the functions chosen to be
measured in this study heavily relied on above-ground biomass. Playa, S. rubra, and B. maritimus had the lowest aboveground biomass which would drive lower values for heavy
metal/trace element accumulation, aboveground biomass,
and primary production. Third, each species grows best in
different physical conditions (i.e., water levels and salinities;
Table 1) and these differences likely play into their unique
functional roles in the ecosystem (Downard et al. 2017).
When developing revegetation goals and restoration plans,
different factors including unique functional capacities,
multifunctionality, abiotic tolerances, and growth characteristics of different habitats need to be considered.
Certain limitations arose from this study because the
Refuge and the Preserve are highly disturbed wetlands (impacted by humans, livestock, invasive species, excess nutrient
loading, etc.), and some aspects of wetland management techniques may have had an impact on the results. One obstacle
these wetlands face is a lack of access to water during the
growing season due to their junior water rights within prior
appropriation western water law (Downard et al. 2014; Frank
et al. 2016). Because these wetlands are located at the bottom
of the watershed, they are not always supplied with adequate
water to support plant growth (Downard et al. 2014). The
water received is diverted and heavily controlled by wetland
managers who decide which areas to flood or drain (Downard
et al. 2014). This kind of water manipulation could have affected several of the ecosystem functions such as plant biomass and seed production. These wetland systems are also
exposed to cattle grazing to remove P. australis (Duncan
et al. 2019). Although none of the areas were actively grazed
during the collection of the ecosystem functions for this study,
historical grazing could have resulted in legacy effects on
some of the functions (Davidson et al. 2017). Finally, in some
areas of the Refuge, P. australis was heavily treated with
herbicides, which could have had legacy effects on its biomass
and seed production (Rohal et al. 2019a, b). Although these
limitations and disturbances may have affected the expression
of the ecosystem functions in this study, many wetlands across
the USA and elsewhere are heavily managed and have similar
management practices and disturbances to those in the Refuge
and the Preserve (Brinson and Malvárez, 2002).
Wetlands are highly dynamic in nature and face many
threats including invasive species, pollution, urban encroachment, and water loss (Zedler and Kercher 2005). By developing an understanding of the different functions offered by the
different wetland habitats, we can make more informed decisions about restoration efforts. The results of the present study
suggest that if GSL managers want to maintain a diversity of
ecosystem functions, they will need to incorporate a diversity
of plant species into revegetation efforts. However, some wetland managers may preferentially target specific functions. In
these cases, the results from this study help managers identify
potential functional synergies and trade-offs that may occur
because of their management decisions. Additionally, this
study may extend to other wetland systems given the broad
distributions of many of the focal native species (plants.usda.
gov) and provide new information to land managers across
North America that are also facing P. australis invasions.
The results from this study also further the understanding
of multifunctionality at a landscape-scale and the importance
of maintaining diversity, in this case, habitat diversity, even in
relatively species-poor ecosystems. Although the study supports the importance of habitat diversity for functioning, the
focus was on a single wetland system in the GSL basin. To
further the understanding of the dynamic and complex nature
Wetlands
of wetland multifunctionality, future efforts should focus on
different wetland systems in multiple settings. Understanding
the interplay between landscape-level diversity and ecosystem
functioning can provide the tools to better manage the wetland
resources, plan for future needs, and meet restoration goals in
the face of a changing planet (Zedler 2000, Zedler and
Kercher 2005; Finlayson et al. 2019).
Acknowledgments This research was funded by a Utah Division of
Forestry, Fire & State Lands Great Salt Lake Technical Team grant to
TBA and KMK, a Utah State University Extension grant (EX00029), and
a Utah State University Ecology Center grant to MCP. We would like to
thank Aubie Douglas, Emily Leonard, Emily Tarsa, Adam Brewerton,
and the Kettenring Wetland Ecology and Restoration Lab for assistance.
Finally, we would also like to thank Shane Sterner for assistance with
seed cleaning. All bird surveys were done with the approval of the USU
IACUC office (2856).
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