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THESIS TITLE: DIFFERENCES IN ECOSYSTEM CARBON AND NITROGEN STORAGE BETWEEN AN ARTIFICIAL WETLAND AND REFERENCE WETLANDS IN SOUTHERN CALIFORNIA

AUTHOR: Jacob Maziarz

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Differences in Ecosystem Carbon and Nitrogen Storage of an Artificial Wetland and Local Wetlands in Southern California

Jacob Maziarz Thesis Adviser: Dr. Vourlitis

California State University San Marcos Department of Biological Sciences

Abstract Wetland ecosystems are among the most biologically productive ecosystems on Earth, yet they are being lost and degraded at an alarming rate. A growing trend to offset this loss is to construct artificial wetlands to replace the destroyed natural wetlands, yet our understanding of how to construct artificial wetlands, and make them functional, is vastly underdeveloped. This study sampled stocks of nitrogen (N) and carbon (C) in an artificial wetland on the California

State University San Marcos (CSUSM) campus and compared them to stocks from local wetlands. Samples were taken from both biomass and soil to assess the stocks of C and N throughout the wetlands. Plant cover was also measured at each site via a line-intercept method.

We hypothesized that C and N stocks would be significantly lower in the artificial wetland than the natural wetlands due to the young age of the artificial wetland while plant cover and biodiversity (species richness) would be similar across all wetlands. The study revealed a large degree of variation among the three wetlands and that soil and vegetation C and N stocks of the

CSUSM wetland appeared to plateau after 11 years. However, the CSUSM wetland C and N stocks and plant cover were higher than, or comparable to, the two local wetlands. Plant species composition was unique for each wetland, with only five species shared among two or more sites. The local wetlands showed a great deal of variation between themselves. Batiquitos Maziarz 2 showed very low soil and plant C and N stocks, while the site at Cannon Road had much higher stocks, especially in its plant biomass. While this study demonstrated that CSUSM was functioning at least on par with local wetlands, it also raises a number of implications regarding the functional status of wetlands in California. The two local wetlands have a history of anthropogenic disturbance and alteration. California wetlands, both those sampled in this study and those from other studies, showed high variability amongst themselves. In some comparisons, artificial wetlands seem to sequester nutrients even better than a natural wetland, but in other comparisons, failed to match nutrient stocks. This high variability can originate from differences in wetland hydrology, plant abundance and species composition, differences in surrounding terrain, and anthropogenic alterations. California wetlands had much lower nutrient stocks compared to wetlands in other parts of the U.S., which is presumably caused by regional differences in hydrology.

Introduction Wetlands possess some of the highest levels of biodiversity on Earth (Ramsar 2013), and provide a number of useful benefits to human communities. Despite their biological significance, wetlands make up less than 9% of the Earth’s total surface (Zedler & Kercher 2005). A wetland is primarily characterized by permanent, or semi-permanent, soil saturation without being completely submerged in water, but there are many variations and distinctions pertaining to salinity, plant species composition, and water regimes that are important for wetland designation

(Cowardin et al. 1979). Wetlands have long been considered a valuable resource to humans through both active exploitation and passive roles. Wetlands provide food, freshwater, Maziarz 3 agriculture, waste disposal and tourism through active usage as well as nutrient removal, flood control, and shoreline stability (Meli et al. 2014).

Wetlands sequester available carbon (C) and nutrients (such as nitrogen compounds from the environment) and lock them into less accessible pools such as vegetation and soil organic matter. For example, CO2 sequestered from photosynthesis is converted into other stable forms such as plant biomass and peat (Kayranli et al. 2010), minimizing its release into the atmosphere.

While wetlands make up < 9% of the global surface area (Zedler & Kercher 2005) they are responsible for up to 15% of the total global net primary production and > 50% of the total global soil C (Schlesinger & Bernhardt 2013). Nitrogen (N) primarily enters wetlands through runoff, atmospheric deposition, and biological N fixation, where it undergoes many biogeochemical processes, predominantly in the hyporheic zone (the bottom of the water body where sediment, ground water and freshwater mix) that transforms N from one form to another

(Zhou, Zhao & Shen 2014). For example, among the most important N transformations in

- wetlands include denitrification, which is a microbial process that converts nitrate (NO3 ) into gaseous N (Carlson & Ingraham 1983), and anaerobic ammonium oxidation, which are

- - performed by chemolithoautotrophic or mixotrophic bacteria that use nitrite (NO2 ) or NO3 as electron acceptors to produce N gas (Strous & Jetten 2004, Kartal et al. 2008). N storage primarily occurs through plant and microbial uptake, or as deposits in the soil sediment

(Vymazal 2007). Wetlands also take up and store numerous types of heavier elements such as lead, zinc, and copper (Karpiscak et al. 2001).

Nutrient removal is an especially useful benefit for human society, as it prevents eutrophication of downstream coastal waters. Eutrophication is when an excess of normally limited nutrients, such as N or phosphorus (P), causes a bloom in algae in a body of water. Maziarz 4

(Ryther & Dunstan 1971). The sudden increase in algal biomass results in an increase in decaying biomass, a process that consumes dissolved oxygen in the water. These blooms commonly result in a decrease in biodiversity and changes in the composition of the remaining species, which can lead to disastrous consequences to communities that rely on these species and ecosystems (Anderson, Glibert, & Burkholder, 2002). Particularly severe blooms can lead to hypoxic conditions that create what are known as dead zones, areas in the ocean where no aerobic life can exist. As human activity increases, the amount of anthropogenic sources of nutrients also increase (Anderson, Glibert, & Burkholder, 2002), increasing the frequency and potency of these algal blooms. Degradation of wetlands also reduces their ability to sequester carbon. In degraded wetlands, organic C, previously locked away in sediment is frequently exposed to aerobic conditions. Oxidation of newly exposed C by microorganisms results in a release of CO2 into the atmosphere, which accelerates global warming (Huang et al. 2010).

Loss of wetlands is largely attributed to anthropogenic activities that result in either complete destruction or degradation of natural wetlands (Zedler & Kercher 2005). Agriculture development is the largest contributing factor to wetland loss, through direct conversion into farmland and through indirect means, such as increased drainage and runoff (Niu et al. 2012).

Other negative influences on wetlands include conversion to fully flooded and submerged habitat, which can also be triggered by human influences (Coleman, Huh, & Braud Jr. 2008).

Though wetlands make excellent buffers against eutrophication, an excessive influx of nutrients, primarily from human sources, can cause wetland eutrophication and degradation (Vymazal

2011). Wastewater discharge, while typically filled with nutrients, may also contain harmful substances, such as heavy metals (Karpiscak et al. 2001) and pathogens (Mulling et al. 2013) that can damage natural wetlands. Surveys found that most of the world’s major river systems, and Maziarz 5 their associated wetlands, have been negatively impacted since the early 1980s (Coleman, Huh,

& Braud Jr. 2008). The most significant losses have occurred in rapidly developing and industrializing nations such as China (Niu et al. 2012). As a result, the creation of artificial wetlands, and the restoration of degraded wetlands, are increasingly used to mitigate these losses.

Artificial Wetlands

Artificial wetlands offer a tantalizing solution to the loss of natural wetlands. Creating artificial wetlands is increasingly common across the globe, first becoming widespread in

Western Europe (Vymazal 2005) and then spreading to the world at large (Moreno-Mateos et al.

2012; Niu et al. 2012). Artificial wetlands fill a number of roles similar to their natural counterparts, with one of the largest being wastewater treatment. Natural wetlands have fulfilled this role for many centuries, but many of these wetlands have since become degraded from excessive volumes of nutrient-rich wastewater. Artificial wetlands can be created and specifically designed as a means of treating wastewater from various sources, often in a more specialized capacity than natural wetlands by emphasizing different aspects of nutrient cycling.

Artificial wetlands have been successful in this role, even more so than natural wetlands

(Vymazal 2011). Artificial wetlands use a variety of construction techniques to facilitate wastewater treatment and emphasize different aspects of nutrient removal, these aspects are: hydrology, plant composition, and flow path (Vymazal 2011). These construction techniques can also be merged, creating constructed wetlands that utilize more than one construction design.

These types of wetlands help prevent eutrophication in coastal areas downstream from the wetlands. The benefits that wetlands provide to animal and human societies remains true in artificial wetlands as much as their natural counterparts. While artificial wetlands have excelled at wastewater treatment, there are still several shortcomings. Even in the field of wastewater Maziarz 6 treatment, mismanagement of wetlands can cause them to become sources of C and other nutrients, such as N (Zhou, Zhao & Shen 2014; Kayranli et al. 2010), rather than sinks.

Other applications of artificial wetlands include their use as habitat by many different species, including birds (Rajpar & Zakaria 2013, Li et al. 2013), insects and (Gucel et al.

2012), amphibians (Denton & Richter 2013), and bats (Sirami, Jacobs, & Cumming 2013). In addition to replacing habitat, artificial wetlands (and other artificial habitats) can greatly increase the connectivity between habitats (Lookingbill et al. 2010). Other studies found that even century old artificial wetlands failed to equal the biodiversity of natural wetlands (Tourenq et al. 2001).

However, it should be noted that the artificial wetlands (the rice fields) compared by Tourenq et al. (2001) were not intended or created to act as replacement habitat.

Artificial wetlands can also be created under the concept of mitigation banking when faced with the loss of natural wetlands. Mitigation banking is defined as the protection, restoration, or creation of habitat to offset habitat that is being damaged or destroyed (Levrel et al. 2017). In order to effectively replace a natural ecosystem, the restored or constructed ecosystem must function ecologically as the natural ecosystem (Simenstad & Thom 1996).

However, more and more evidence suggests that many restored or artificially built wetlands do not meet the same level of functionality as a previously existing natural wetland (Zedler 2004).

Mitigation projects can fail for several reasons. Some mitigation policies follow a misguided ideal that surviving wetlands can be enhanced to offset the area loss in wetland habitat (Zedler

2004). Mitigation banking projects sometimes produce wetlands of a different type than what was destroyed, and in many cases, long term monitoring is sporadic or nonexistent (Zedler

2004). Mitigation banking can also be accomplished by preserving a similar, often larger-sized ecosystem (Walsh 2009) when a natural ecosystem is destroyed as opposed to trying to rebuild it. Maziarz 7

Most federal and state laws require some form of mitigation banking when natural habitats are destroyed or damaged. The California State University San Marcos (CSUSM) artificial wetland, which was created in 2002 (completed in 2004) to replace a natural wetland that was destroyed to construct on-campus housing, is an example of mitigation banking.

On an even smaller scale, artificial wetlands tend to lack a similar level of biogeochemical cycling that unaltered natural wetlands possess. Studies have found that degraded wetlands begin to lose C (Luan et al. 2014) and N (Norton et al. 2011). Even if restoration of degraded wetlands occurs, many of these wetlands fall short of full recovery, and continue to display lower nutrient levels, suggesting that recovery is a very slow process, or that the wetland itself has developed into an alternate state that differs from its previous natural state

(Moreno-Mateos et al. 2012). In most cases, such alternative states, even if they are recovering, can have on average 25% less soil C than a natural wetland (Moreno-Mateos et al. 2012). Such changes can occur naturally, but are also brought about by anthropogenic influence (Holling

1973). In artificial wetlands, it has long been established that there is no rapid recovery or common model to follow (Zedler & Callaway 1999). Wetlands display a large amount of interannual variability that makes establishing a clear trajectory difficult, and even if such patterns exist, wetland development often proceeds far slower than anticipated (Zedler &

Callaway 1999). Studies have shown that nutrient stocks can remain around 50% of the original reference wetland stock even 50 years after construction or restoration of a wetland (Moreno-

Mateos et al. 2012, Yu et al. 2017). Southern California wetlands can be especially difficult to track, as they are historically subject to periodic flooding that can provide a large influx of nutrients that lead to temporary increases in productivity (Zedler 1983). These shortcomings in Maziarz 8 our understanding artificial wetlands need to be addressed if they are going to act as an effective tool for ecological restoration.

Research Significance and Rationale

A large gap in our understanding of artificial wetlands stems from the lack of long term studies on artificial wetlands and how they compare to natural wetlands in terms of nutrient levels and ecological maturation (Zedler & Callaway 1999). This problem is further exacerbated by a lack of interest in monitoring artificial wetlands and documenting their maturation in the long term, especially by those who are responsible for mitigating their loss (Zedler & Callaway

1999). Restoration projects can also fall short of mimicking natural wetlands due to a lack of understanding of the functions in natural ecosystems and insufficient time spent monitoring the artificial wetland (Lichko & Calhoun 2003; Denton & Richter 2013; Simenstad & Thom 1996).

Thus, the purpose of this study is to compare restoration efforts at the CSUSM artificial wetland to natural wetlands in the southern California area and assess the following: (1) are soil and plant

C and N stocks and (2) levels of plant abundance (cover) and biodiversity (species richness) similar between the constructed and other local wetlands, and (3) what is the rate of C and N storage over time in the CSUSM artificial wetland, and how does it compare to storage rates in other wetlands? Soil and plant C and N stocks were studied because accumulation of C and N is considered to be a hallmark of wetland ecosystems with functional hydrological regimes (Yu et al. 2017). Given the young age of the artificial wetland, it was hypothesized that vegetation and soil C and N storage will be lower for the CSUSM wetland than in presumably older, more established local wetlands. However, because the CSUSM wetland was planted by Dudek &

Associates (an environmental and engineering consulting firm) to mimic natural wetlands in Maziarz 9 southern California, it is predicted that plant cover and species richness will be similar to older, more established local wetlands.

Methods Site Selection

The site at which the CSUSM wetland is located was previously a disturbed “old-field” dominated by non-native forbs. Two storm drain inlets created small stream beds, but these did not support any wetland vegetation or proper wetland nutrient retention (Joshi and Walsh 2001).

The artificial wetland at CSUSM started construction in 2002 and was completed in 2004.

During the construction phase, non-native vegetation was removed, and the soil was graded to be compatible with existing vegetation and the groundwater elevation (Joshi and Walsh 2001). Top soil was salvaged from development areas for placement in excavated areas. A five-year maintenance and monitoring period followed, with irrigation, weeding, and plant maintenance.

Plans to add fertilizer were considered if soil nutrients became too low, but were ultimately deemed unnecessary (Joshi and Walsh 2001). Irrigation ceased in late 2006, as per regulations, and monitoring continued until 2009 when maintenance and monitoring by Dudek & Associates ended and the wetland received certification as a functional wetland per California standards.

The wetland was designed to replace a loss of wetland habitat, and features 2.4 acres of artificial habitat and an additional 1.04 acres of coastal sage scrub buffer habitat around the perimeter

(Walsh 2009). The site itself is located along Barham Drive, in the northwest corner of the

CSUSM campus (Fig 1.).

Two natural wetlands were selected in the City of Carlsbad, CA to act as comparisons, one located in eastern Batiquitos Lagoon (BL) near the mouth of the San Marcos Creek and Maziarz 10 another that is located at the intersection of Cannon Road (CR) and El Camino Real that is part of Agua Hedionda Creek (Fig. 1). Selection of natural wetlands was quite limited, due to the heavy urbanization in southern California. These limitations were considered during site selection and during the data analysis. Sites were also chosen based on similarities to the artificial wetland at CSUSM, such as the presence of emergent sedges and riparian vegetation, freshwater, and presence of vegetation in the genus , which is the dominant species in the

CSUSM wetland. The National Wetlands Inventory was used to determine wetland habitat type.

Sampling at CSUSM was done in in February 2015, while the comparison wetlands were sampled in February 2016. Data from CSUSM in 2015 data was compiled with data collected from Dr. Vourlitis in 2009 (immediately after the wetland was certified by the State of

California) and in 2012 to assess temporal variations in soil and vegetation C and N storage.

Vegetation sampling and analysis

The CSUSM wetland had five-50 m long transects (spanning east to west diagonally across the wetland) that were previously installed by Dudek & Associates during their 5-year monitoring period, and all vegetation cover measurements were made along these permanent transects using the same methods to permit comparison between their results and ours.

Additionally, plant C and N stocks were measured from these transects in 2009 and 2012. For the

2015 soil and plant sampling, eight new transects were laid out every 10 m perpendicular to the length of the wetland. These eight transects extended 30 m into the wetland basin and soil samples were collected at a random point every 10 m. At BL and CR, four-30 m transects were laid out. Transects were randomly chosen along a length of measuring tape that was laid out perpendicular to the wetland of interest at both sites. The 30 m transects were sampled every 10 m for soil. Random number tables were used to select all random points. Maziarz 11

At all sites, plant cover for each species was sampled every 0.5 m using the point- intercept method (Heady et al. 1959; Jonasson 1988). At each point, a metal rod was lowered through the canopy to the soil surface, and each plant that contacted the rod was recorded.

Percent cover of each plant species encountered was calculated as (Σli/n) x 100, where Σli is the sum of all intercepts per species (i) and n is the total number of points per transect (n = 100/ transect for CSUSM and 60/transect for BL and CR).

Leaf area index (LAI = area leaf/ground area) was measured using a par-ceptometer

(AccuPAR LP-80, Decagon Devices, Inc., Pullman, WA, USA). LAI was measured every 10 m along each transect at a random points that were chosen within each 10 m interval (n = 5 measurements/transect at CSUSM and 3 at BL and CR). LAI was calculated as a function of the photosynthetically active radiation (PAR) measured above and below the canopy, and the par- ceptometer software was used to calculate LAI, as only the leaves will absorb radiation, while stems and non-photosynthetic material does not (Decagon 2001).

Leaf tissue samples were collected along each transect from any plant that was intercepted by the transect. Additionally, tissue samples were collected in a way that allowed an estimate of one-sided leaf area or volume depending on whether the leaf was flat or cylindrical

(except for transect 4 at Cannon Road, due to time constraints). For plants with flat leaves, a hole-puncher was used to collect same-sized discs that could be easily converted to one-sided area (d2/4; where d = diameter of the leaf disk), while for plants with cylindrical leaves, 10 cm long stems were collected and measured for volume (l × d2/4; where l = leaf length = 10 cm).

These estimates of leaf volume and area were used to calculate specific leaf mass (SLM = leaf dry mass/leaf area), which was needed to convert the leaf N and C concentration to a pool size

(described below). All samples were separated by species, dried at 70˚C and then ground into a Maziarz 12 fine powder for C and N analysis. C and N concentration was measured using an elemental analyzer (ECS 4010, Costech Analytical Technologies, Inc., Valencia, CA, USA). C and N vegetation pool size (gN or C/m2 ground area) are calculated as SLM (gdw/m2 leaf area) × LAI

(m2 leaf area/m2 ground area) × [C or N] (gN or C/gdw).

Soil sampling and analysis

At CSUSM, soil samples were collected at 2 randomly chosen points every 10 m along a

20 m long transect (n = 8 transects), while at BL and CR, soil samples were collected at one randomly chosen point every 10 m along a 30 m long transect (n = 4 transects/site). Soil samples at each site were collected from the upper 0-10 cm and 10-20 cm soil layers in 2015 (CSUSM) and 2016 (BL and CR), while in previous years at CSUSM (2009 and 2012), soil samples were only taken from the upper 0-10 cm soil layer.

Soil samples were primarily collected using a 5 cm diameter × 10 cm deep bucket auger or a 5 cm diameter × 10 cm deep impact auger. The bucket auger collected disturbed soil samples that were used for soil physical and chemical analyses while the impact auger was able to obtain undisturbed soil samples for physical and chemical analyses and an estimate of soil bulk density (gdw/soil volume) that was needed to convert the soil C and N concentrations to soil

C and N pools sizes (described below).

Soil samples were passed through a 2 mm sieve to remove rocks and other mineral and organic debris. Percent soil moisture was measured gravimetrically as [(Mf-Md)/Md] × 100, where Mf = fresh soil mass and Md is the mass of soil after oven drying at 105˚C for a week.

Soil particle size distribution was analyzed using the Bouyoucos hydrometer method

(Gee and Bauder 1986) to see if there were significant differences in soil texture among the sample sites. Four composite samples/site were analyzed for soil texture for each sampling Maziarz 13 depth. Each composite sample consisted of two randomly chosen samples/site. Soil texture was analyzed using 80 g of dried and sieved soil, which was mixed with 100 mL of distilled water and 100 mL of sodium metaphosphate (Gee and Bauder 1986). The solution was mixed for 10 minutes, transferred to a graduated cylinder, and inverted 15 times to further mix the solution.

The cylinder was set out and temperature recorded. After 40 seconds a hydrometer reading was taken, which corresponds to the amount of silt + clay suspended in the solution, and after 2 h, which corresponds to the amount of clay in the suspension (Gee and Bauder 1986).

Total soil C and N are measured from 18-20 mg of oven-dried soil using an elemental analyzer (ECS 4010, Costech Analytical Technologies, Inc., Valencia, CA, USA). Soil C and N pool size for each sampling depth (gN or C/m2 ground area) was calculated as Bulk density

(gdw/m3 soil volume) × 0.1 m or 0.2 m (depth of the soil core depending on site or sample date)

× [C or N] (gN or C/gdw).

CSUSM monitoring

The CSUSM wetland had been monitored and samples collected every three years, in

2009, 2012, and 2015. The CSUSM wetland data was analyzed for changes in soil C and N pool size, vegetation C and N pool size, and changes in species composition and coverage.

Statistical Analysis

Data (soil C and N, plant C and N, cover, and soil moisture) from the three wetlands were analyzed by a 2-way analysis of variance (ANOVA) using R statistical software with accompanying Tukey post-hoc tests where significant differences were found. A repeated measures (RM) ANOVA was used to compare the three different sampling years (soil C and N, plant C and N, and cover) for the CSUSM wetland data. Transect 5 at CSUSM was sampled only once in 2009 before it became overgrown and inaccessible, so data from this transect was Maziarz 14 dropped from the data analysis when comparing 2009 to 2012 and 2015. Significant RM

ANOVAs were then compared using paired t-tests. All ANOVAs where checked to identify any significant statistical interactions between explanatory variables or violations of assumption using R’s diagnostics. Data sets with outliers were retested with these outliers dropped. If the result did not change, the data set passed. RM ANOVAs were also tested for spherecity. Most data passed, but one set of data did not. The Plant N pool data for the three sites had outliers

(both high and low) that violated homogeneity of variance and a Breusch-Pagan test in R (p

<0.001) confirmed the violation. For these data, a log transformation was attempted first, but failed to impact the data. A randomization test using R’s LMPerm package was then used to identify any significant differences, followed by Tukey-post hocs using subsets of the data. A canonical correspondence analysis was also conducted between the three sites to compare species composition between sites.

Results Comparison Sites

Soil texture analysis showed that there was no significant difference in texture between the two sampling depths, but there were significant differences in sand and silt concentrations between the different sampling sites (Fig. 2). Batiquitos Lagoon (BL) had significantly less silt in its soil composition than the other two sites, while Cannon Road (CR) had significantly less sand in its soil composition (Fig. 2). There were no significant differences in clay, and no significant interaction between site and depth.

Total soil C and N pools were significantly higher in the upper 10 cm of soil than in the lower 10-20 cm layer for all sites (Fig. 3 and Fig. 4). However, when each individual site’s Maziarz 15 sample depths were compared using Student’s t-tests, only CSUSM had significantly higher N in the 0-10cm soil layer than in the 10-20 cm soil layer (Fig. 4). An ANOVA of the soil C pools showed a significant difference between sites, with CSUSM having the highest soil C, followed by CR, and then BL with the lowest soil C pool (Fig. 3). An ANOVA of soil N pools showed a significant difference between the CSUSM site and the other two sites (Fig. 4). There were no significant interactions between site and depth for either soil C or soil N. Overall, the CSUSM wetland had the highest total soil C and N pools of all three wetlands.

Data for plant C and N pools showed that the CR had the largest plant C pool compared to BL. A randomization test and accompanying post-hocs showed that CR had the largest plant N pool, followed by CSUSM with the second largest, and BL with the smallest (Fig. 5).

Batiquitos had significantly lower plant coverage than the other two sites (Fig. 6). Each site had a distinct species composition (Table 1), with many species occurring only in one site and only a small number of plant species were present at multiple sites. CSUSM showed the highest number of plant species (14 total), followed by CR (9 total), then BL (5 total). A canonical correspondence analysis (CCA) confirmed the unique distribution of species in the three sites (p = <0.001) (Fig. 7). Many of the encountered species were unique or common to one site, and thus, were major drivers for the differences among these sites. The CCA 1 axis separates CSUSM’s transects from CR’s transects. Looking at the species composition at these sites, there were only two shared species (Schoenoplectus californica and Juncus acutus) and the majority of them were observed in CSUSM. Many other plant species were exclusive to one site or the other (Table 1). The CCA 2 axis separates BL from the other two sites, while CCA 1 places BL closer to CSUSM. Three species observed at BL (Artemisia douglasiana, Baccharis pilularis, and Juncus acutus) were also observed at CSUSM. B. pilularis also had very similar Maziarz 16 measures of absolute cover between these two sites. The large observed population of A. douglasiana at BL is the likely driver for the site’s separation on CCA 2. Conversely, many of the unique species observed at CR (Table 1) separated it from both CSUSM and BL on CCA 1.

Differences in soil moisture were statistically significant between site and sampling depth. Tukey tests comparing each sampled site at a specific depth showed that CR had more soil moisture than CSUSM, and more soil moisture than BL at the 0-10cm depth (Fig. 8). While tukey post- hocs could be used to compare all possible combinations of sample site and depth, this would have resulted in a large decrease in statistical power. To avert this, only the three sites were compared to each other at the two different sampling depths (CSUSM’s 0-10cm depth to

Batiquitos’ 0-10cm to Cannon Road’s 0-10cm, and the same for the 10-20cm depth). As two different sampling depths were being compared, an adjusted alpha level of 0.025 was used.

CSUSM Overtime Monitoring

Repeated Measures ANOVAs showed that there were few significant differences over the course of the three sampling years. Change in soil C pool over the three sampling years was detected as significant, though only barely (p = 0.045) (Fig. 9). Paired t-tests between the sampling years showed no significant differences. Again, low statistical power due reduced our ability to detect significant differences. There was no significant change in soil N (Fig. 9). There were no significant changes in plant carbon or nitrogen pools (Fig. 10). Plant coverage did not change significantly over the three sampling years (Fig. 11), though it does show a distinct decline over time. Maziarz 17

Discussion Among the three sampled sites, each site showed remarkable uniqueness. CSUSM had the highest soil C and N pools, the highest species richness, while other parameters were also very high (plant C and N pools and cover) compared to the two local wetlands. However, the

CSUSM wetland displayed the lowest soil moisture among the three sites as it receives no regular source of water (Joshi and Walch 2001). Studies have shown that wetland function in constructed wetlands tends to recover much faster with higher plant richness, even more so than the effect of any single species (Means et al. 2017). With 14 observed species at CSUSM, the high species richness could be playing key role in wetland function. High nutrient content in the soil (such as N) can also affect plant communities, with higher N correlating to increased litter production, which in turn, introduces more nutrients into the soil (Song et al. 2013). Members of the genus Juncus have also been known to produce large quantities of litter (Ritson et al. 2017), which appeared to be true for Juncus acutus. The soil results do not support the initial hypothesis that CSUSM would have the lowest C and N stocks of the three sampled wetlands, and instead

CSUSM matches or exceeds the other wetlands in many criteria. These data could indicate that the CSUSM wetland is functioning at a similar level to the other two local wetlands. However, the significant difference between the two local wetlands at Batiquitos and Cannon Road make direct comparisons difficult. In some measurements, such plant C and N pools, CSUSM more closely aligns with Cannon Road, while soil moisture measurements suggest that CSUSM is more similar to Batiquitos. Additionally, the two sites chosen to act as comparison wetlands, while they are natural wetlands, have themselves a history of anthropogenic influence in the past few decades. The heavy level of development in Southern California make finding a truly pristine wetland exceptionally difficult (Bradshaw et al. 1976), and other sites such as one at Los

Peñasquitos, were passed by for showing even more severe signs of degradation. Maziarz 18

Batiquitos Lagoon had the lowest measured soil C, plant cover and C and N storage, and species richness of the three sites. Only the soil N was comparable to Cannon Road, and both those sites had less soil N than at CSUSM. At Batiquitos, anthropogenic developments have been altering the lagoon and its watershed for the past century (Mudie et al. 1976, Gayman 1978a).

The most profound impact, however, was the completion of the Batiquitos Lagoon Enhancement project in 1996, which transformed much of the lagoon into open water habitat year round

(Beller et al. 2014). The creation of the lagoon could mark a period of habitat transition for the surrounding wetland habitats. While the site sampled was still classified as an emergent freshwater wetland, and there were no outward signs of tidal influence (no wrack was present), it is possible that the soil is becoming more saline or wetter. Changes in soil hydrology have been shown to impact the production of soil C (Liu et al. 2017). Batiquitos’ low species richness (only five observed species) could have a negative effect on the wetland’s functions (Means et al.

2017). It is possible that BL is comparable to other freshwater wetlands that are close to estuarine wetlands, but not to a pure freshwater wetland. Data obtained from BL more closely matched data from similar wetland studied by Langis et al. 1991.

At Cannon Road (CR), the wetland showed the highest plant C and N pools, and was comparable to CSUSM in other factors, such as plant cover. It was also the wettest site sampled, and the only site that had standing water. Like Batiquitos, the area around the sampling site has experienced anthropogenic alterations (Bradshaw et al. 1976). The CR site experienced heavy residential development as early as 1986, which continued through to 2007 (Tetratech 2007).

Increased human development introduces sources of urban runoff from sewer effluent, storm water discharge, and various other anthropogenic sources. Additionally, these developments result in a steady loss of water permeable surfaces, causing more water and dissolved particles to Maziarz 19 be funneled into the watershed (Murphy et al. 2015). As a result, many southern California lagoons and their associated watersheds now have surface flow year round where there was once only seasonal flow. These increases in water flow result in an increase in nutrient, sediment, heavy metal and pollution deposition (Tang et al. 2005), which could artificially increase the amount of C and N measured at the site. One study showed that larger influxes of nutrients in the water tended to result in increased biomass in members of the genus Typha and Schoenoplectus

(Adhikari et al. 2011), which were present in the CR wetland. The inundated state at CR, coupled with a heavy increase in residential developments could have resulted in a large increase in plant biomass as these species take up excess nutrients. By comparison, CSUSM and Batiquitos were quite dry and the influx of nutrients from overland flow would be quite low. The specific site at

CR was also bordered on two sides by roads (elevated several feet higher than the basin itself), creating a pool for water to collect in.

The CSUSM wetland showed a distinct peak in 2012, and though an RM ANOVA highlighted a significant difference between the sampling years for soil C, no differences among sampling years could be identified in post-hoc analysis. The other variables measured (soil N, plant C and N, and cover) were not significantly different from one another based on the RM

ANOVA. Future sampling done should include a larger sample size to increase statistical power and better avoid type II errors. The data could suggest a plateau in the wetland’s capacity to store both soil C and N as well as plant C and N, or that recovery is proceeding very slowly.

The data are compared with studies conducted in the US (Table 2), including another study conducted in San Diego County (Langis et al. 1991). Overall, the wetlands sampled in this study had much lower soil and plant C and N than those observed elsewhere, but were comparable to the San Diego Bay wetland sampled by Langis et al. (1991). Differences among Maziarz 20 states are likely caused by differences in hydrology, as Southern California receives very little rainfall compared to the rest of the United States. Hydrological differences in wetlands, can in turn affect plant composition (Euliss Jr. et al. 2004). Some sites, like BL, had very low C and N stocks compared to other wetlands in the country, while others, like CR, more closely matched other wetlands, or even exceed them in some cases. These results conflict with much of the established scientific literature, which indicate that artificial wetlands usually struggle to replicate a natural wetland. Wetland studies generally indicate that artificial and restored wetlands tend to lag behind natural wetlands in their C and N stocks by has much as 50%, even after decades of existence (Edwards and Proffitt 2003, Yu et al. 2017, Sánchez-González et al.

2017). C and N stocks can be particularly low in cases where an entirely new wetland is constructed, as opposed to restoration of a degraded one. In such cases, the disruption and removal of soil can remove a great deal C and N that was previously locked away, as well as disrupt plant communities and impact their ability to sequester C and N (Sánchez-González et al.

2017). The final Dudek report indicated a native plant absolute cover of 135% and a high species diversity (Walsh 2009), which matches the data collected during this study. However, the plant cover at CSUSM also shows a distinct decline over the sampling years.

These data indicates that the CSUSM wetland’s ability to sequester and store C and N is comparable to the reference wetlands sampled as well as other wetlands in San Diego County.

What studies are available, however, suggest that even decade old sites (including CSUSM) in

California lag behind their natural counterparts (Langis et al. 1991, Zedler and Callaway 1999).

Additionally, there has been a distinct lack of studies conducted in California that track nutrient sequestration in wetlands, especially over a large period of time. However, given the wide disparity between the two local wetlands sampled in this study (BL and CR), there are major Maziarz 21 implications for the state and functionality of California’s wetlands, including at CSUSM. The functionality and success of an artificial wetland is measured by its ability to function like a natural wetland (Simenstad & Thom 1996). Comparison wetlands were chosen based on their similarity to the CSUSM wetland with respect to plant community and water source (freshwater), but were in many cases more different from each other than to CSUSM. This makes assessing the functionality of CSUSM difficult. CSUSM seems to perform very well compared to BL, but is only on par when compared to CR. This result itself is also not supported by the scientific literature, which suggests that artificial wetlands can take decades to reach a nutrient stock level similar to a natural wetland (Edwards and Proffitt 2003, Yu et al. 2017, Sánchez-González et al.

2017), though it is possible that CSUSM is an exception. Background information on these two sites (BL and CR) suggest a history of anthropogenic activities which have steadily altered these wetlands into what was observed in this study, and would suggest that many California wetlands are heavily impacted by urban development. This in turn, affects the ecosystem function of these wetlands, altering nutrient stocks and releasing previously sequestered nutrients back into the greater environment, creating additional sources of carbon dioxide (Bridgham et al. 2006). This lack of attention to nutrient dynamics in wetlands hampers our ability to accurately construct and assess artificial wetlands, especially when attempting to mimic natural wetlands.

Maziarz 22

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Figures

Fig. 1 Map of San Diego County (top right) with the three wetland sites indicated. CSUSM is at the bottom right, Batiquitos Lagoon at the bottom left, and Cannon Road at the top left. County map is from Enter San Diego (http://www.entersandiego.com/), the other three are from google maps. Maziarz 28

Fig 2. Mean (+ se; n = 2 for all sites/soil textures) soil texture percentages between the three sampling sites. (S) and (D) are site comparisons and depth comparisons, respectively. (SD) is the site and depth interaction. (***) indicates p < 0.001 Different letters indicate sites that are significantly different from each other. Maziarz 29

Fig. 3. Mean (+ se; n = 8 for CSUSM and 4 for Batiquitos and Cannon Road) total soil carbon pools between the three sample sites at two different sampling depths (0-10cm and 10-20cm). (S) and (D) are the site and depth comparisons, respectively. (SD) is the interaction between site and depth. (***) indicates p < 0.001, (*) indicates p < 0.05. Sites with differing letters are significantly different from each other.

Maziarz 30

Fig. 4. Mean (+ se; n = 8 for CSUSM, n = 4 for Batiquitos and Cannon Road) total nitrogen pools between three sites at two different sampling depths (0-10cm and 10-20cm). (S) and (D) are the site and depth comparisons, respectively. (SD) is the interaction between site and depth (***) indicates p <0.001, (**) indicates p <0.01. Sites with differing letters are significantly different from each other. There was significantly more N in the upper 10 cm * of soil, but for each individual site, only CSUSM had significantly more N in its upper 10cm of soil (t1,7 = 2.630 ).

Maziarz 31

Fig 5. Mean (+se; n = 8 for CSUSM, 4 for Batiquitos and 3 for Cannon Road) plant C (bottom panel) and N pools (top panel) and F statistic between the three sampling sites. (*) indicates p < 0.05 and (***) indicates p < 0.001. N pool data was analyzed with a randomization test and the F-statistic provided for that test. Differing letters indicate sites that were significantly different from each other. Maziarz 32

Fig. 6. Mean (+ se, n = 4 for all sites) absolute cover between the three sampling sites. (***) indicates p < 0.001. Sites that had significant differences in plant cover are noted with different letters.

Maziarz 33

Table 1. Average absolute cover for each species encountered along the transects at the three sites. Species are sorted by their species scores from a CCA. Only four species (Artemisia douglasiana, Baccharis pilularis, Juncus acutus, and Schoenoplectus californicus) were encountered in more than one site. Only Juncus acutus was encountered in all three sites. All other species were unique to a specific site. Species CSUSM Batiquitos Cannon Road Artemisia douglasiana 1.8 37.7 Baccharis pilularis 2.0 2.5 Baccharis salicifolia 2.8 Iva heyesiana 36.3 Juncus acutus 52.8 7.0 3.3 Juncus mexicanus 73.0 Leymus condesatus 17.0 Populus fremontii 0.8 Salix lasiolepis 3.3 Schoenoplectus californicus 5.8 0.4 Sonchus oleraceuous 0.3 Typha latifolia 2.0 Brassica rapa 1.5 Encelia californica 0.8 Malosma laurina 0.8 Sambucus mexicanus 2.0 Distichlis spicata 2.5 Salicornia pacifica 5.7 30.7 Lythrum californicum 11.9 Tamarix ramosissima 25.0 Cuscuta californica 0.4 Jaumea carnosa 8.2

Maziarz 34

Fig. 7. CCA of counts of plant species using the three sites as predictors. Species present on the graph include the five species found at multiple sites: Artemisia douglasiana (A.d.), Baccharis pilularis (B.p), Juncus acutus (J.a), Salicornia californica (Sa.c), and Schoenoplectus californicus (Sc.c). The most common species that were unique to each specific site were plotted as well (Iva heyesiana (I.h.) for CSUSM, Distichlis spicata (D.s) for Batiquitos, and Juncus mexicanus (J.m) for Cannon Road). I.h and Sc.c overlap on the graph. Other species were not plotted to reduce clutter (see Table 1 for the full list). p-value < 0.001.. The first two CCAs (CCA 1 and CCA 2) are plotted as they account for most of the differences among sites. CCA 1 identifies differences between CSUSM and CR, while CCA 2 identifies differences from BL. Individual points represent the transects at each site (12 total).

Maziarz 35

Fig. 8. Mean (+ se, n = 8 for CSUSM, n = 4 for Batiquitos and Cannon Road) soil moisture as %water across the three sample sites and two sampling depths. (S) and (D) are site and depth comparisons, respectively. (SD) is the interaction between site and depth. (***) indicates p < 0.001 and (*) indicates p < 0.05. Tukey tests were conducted with an adjusted α (0.025) due to the two sampling depths. CSUSM and CR showed significant differences throughout the 10-20 cm soil column (p = <0.005 for both depths) and there was a significant difference between BL and CR at 10-20cm depth (p = 0.016).

Maziarz 36

Fig. 9. Mean (+se; n = 8) soil C (top-panel) and N (bottom-panel) pools in the upper 0-10 cm soil layer at the CSUSM wetland between 2009 and 2015. (*) indicates a p-value of <0.05. Although the RM ANOVA was significant, paired t-tests produced no significant differences among any of the sampling years.

Maziarz 37

Fig. 10. Mean (+ se, n = 4) plant carbon (top panel) and nitrogen pools (bottom panel) over the course of three sampling years. There were no significant differences among the three years (p > 0.05 for both C and N). Maziarz 38

Fig. 11. Change in CSUSM mean (+ se, n = 4) plant cover over the course of three sampling years. There were no significant differences (p >0.05).

Maziarz 39

Table 2. Comparison of this study’s findings to other findings in the scientific literature. (*) Indicates dry biomass data which was converted to a C and N pool respectively using %C and %N data from CSUSM 2015 sampling year. Data reported from this study uses the average of the two depths sampled (0-10cm and 10-20cm). Mean (+sd) values do not include those reported here. FW-E = freshwater wetland with emergent vegetation; FW-F = freshwater wetland with forest; FW-S = freshwater wetland with shrubs; E = estuary. Wetland status is defined as: Natural (no major alterations or impacts by anthropogenic activity. All wetlands are assumed to be natural wetlands unless otherwise stated by the study), or Artificial (a man-made wetland where none existed previously). % g/m2 (%) Source Soil C Soil N Plant C Plant N Location Type Status This Study 1.4 0.1 292 11 CSUSM, CA (2015) FW-E Artificial Batiquitos Lagoon, This Study 0.4 0.04 84 7 FW-E Natural CA This Study 1.8 0.1 794 34 Cannon Road, CA FW-E Natural Sweet Hall Marsh, Doumlele 1981 327* 12* FW-F Natural VA Flemer et al. 1978 598* 23* Patuxent River, MD E Natural Flemer et al. 1978 378* 14* Parker Creek, MD E Natural Stolt et al. 2000 1.4 1.6 Cub Creek, VA FW-F Artificial Stolt et al. 2000 1.4 1.6 Cub Creek, VA FW-E Natural Stolt et al. 2000 1.0 0.9 Western Freeway, VA FW-F Artificial Stolt et al. 2000 6.3 4.4 Western Freeway, VA FW-E Natural Stolt et al. 2000 4.0 0.3 Duntons Mill, VA FW-S Artificial Stolt et al. 2000 3.6 2.5 Duntons Mill, VA FW-S Natural Sweetwater Marsh, Langis et al. 1991 2.1 0.2 191* 6 E Natural CA Sweetwater Marsh, Langis et al. 1991 1.1 0.1 81* 2 E Artificial CA Mean (+sd) Natural 3.5+2.2 2.2+1.8 373+169 13.8+6.9 Mean (+sd) Artificial 1.9+1.4 0.7+0.7 81 2.1