Are nutrients or salinity the drivers of macroinvertebrate community composition in wetlands of the desert Southwest?

doi:10.21203/rs.3.rs-1397118/v1

The relatively rare freshwater ecosystems in the arid southwestern United States serve as biodiversity hotspots, yet they remain among the most threatened systems in the world due to human impacts and climate change. Water quality and macroinvertebrate data were collected over 2 years from 14 different wetland and riparian sites spanning across West Texas, New Mexico and Arizona. This project aims to shed light on the factors that drive macroinvertebrate community composition of wetlands of the US desert Southwest. Principal Component Analysis (PCA) indicated that salinity related variables such as chloride, sulfate and conductivity were the greatest drivers of environmental variance (32%) among sampled desert wetlands. Nutrients such as nitrate and phosphate described a second axis, with 22% of variation in environmental data explained, where we found a clear distinction between wastewater and non-wastewater wetlands. Subsequently, nutrients were shown to have the greatest impact on macroinvertebrate communities with wetlands of higher nutrient loads showing more uneven functional diversity and lower Simpson Index scores. Increased salinity levels were shown to correlate with lower Simpson Index scores and a higher abundance of tolerant macroinvertebrates such as coleopterans and amphipods. Overall, these data may be crucial in forecasting whether high nutrient levels lead to homogenization of macroinvertebrate communities within wastewater wetlands.

Wetlands

arid region

macroinvertebrates

salinity

nutrients

desert

US Southwest

The loss of global biodiversity is occurring at an exceedingly rapid rate due to climate change and overexploitation by humans (Dawson et al 2011). While terrestrial ecosystems are often in the spotlight, aquatic ecosystems surpass their rate in decline in biodiversity due to decline in water quality, changes in nutrient availability and increasing temperatures (Van De Waal et al 2010; Woodward et al 2010; Vörösmarty et al 2010). Arid region wetlands are especially vulnerable due to their reliance on seasonal rainfall, which is now becoming more unpredictable due to climate change (Burkett and Kusler 2000; Taylor et al 2013). In addition, many sites are threatened by declining groundwater flow due to overuse (Taylor et al 2013; Richey et al 2015). As biodiversity hotspots, these oases are habitat for many organisms and provide critical habitat connectivity within the desert landscape (Dinerstein et al 2001; Bogan et al 2014; Drake et al 2017). Due to the rapid loss of habitat, there has been a recent push to protect and restore these rare freshwater ecosystems.

In the southwest United States, many wetlands have been restored or created to replace those that were lost, sometimes with the use of effluent water from wastewater treatment plants as a water source. These habitats are often created wetlands used to further purify effluent water through the uptake of nutrients (i.e. nitrogen and phosphorus) and contaminants by wetland macrophytes and microalgae before replenishing ground water sources (Whitton et al 2016; Matamoros et al 2017; Zhuang et al 2019). Other sites use the aggregation of wastewater to mitigate wetlands areas that were previously lost due to river channelization or agricultural use (O’Geen et al 2010; Rodriguez and Lougheed 2010). While studies have shown these wetlands to be effective at reducing excess nutrients and contaminants from wastewater, the initial presence of these byproducts has lasting effects on wetland biota. In some non-arid created wetlands, the increased nutrients cause shifts in community composition with an increase in pollution-tolerant macroinvertebrate taxa (Pinto et al 2014). It remains to be seen whether wastewater wetlands in arid region follow the same patterns as those in non-arid regions, and whether they differ from arid-region wetlands receiving water from other sources.

While freshwater habitats are known to support ~10% of all species, there have been drastic declines in the range and abundance of freshwater species (Strayer and Dudgeon 2010). As the home to many endangered and endemic species, desert wetlands are some of the most biologically diverse ecosystems in the world (Walsh et al 2009; Hershler and Liu 2010; Minckley et al 2013; Stanislawczyk et al 2018). Recent studies have highlighted the idea that there may be cryptic taxa within these wetlands and taxonomic richness may be underestimated (Wallace et al 2005; Stanislawczyk et al 2018). However, arid region wetland ecosystems worldwide are understudied and under-recognized when it comes to wetland ecology and conservation (Murphy et al 2013; Nieto et al 2017).

While little is known about macroinvertebrate community diversity in arid regions, macroinvertebrates have historically been used as indicators of water quality and wetland health (Hilsenhoff 1987; Mandaville 2002). As bioindicators, aquatic macroinvertebrates serve as a low cost and useful tool for monitoring wetland health and function due to their constant contact with water and sediment (Hilsenhoff 1987; Cairns and Pratt 1993; Bartell 2006; Siddig et al 2016; McIntosh et al 2019). By monitoring the abundance, diversity, and reproductive success of these organisms we can determine habitat response to change or disturbance (Foote and Rice Hornung 2005; Siddig et al 2016; Wu et al 2017).

When examining wastewater and non-wastewater wetlands in non-arid regions the difference in macroinvertebrate community composition has often been attributed to vegetation community composition (Balcombe et al 2005; Stewart and Downing 2008; Becerra Jurado et al 2009; Swartz et al 2019). However, due to variables relatively unique to arid regions (i.e. extreme heat, irregular and rare precipitation) it is unknown if macroinvertebrates in arid wastewater wetlands respond the same way as those in non-arid regions. Studies assessing desert ponds and springs in the Chihuahuan desert have pointed to hydroperiods and desiccation cycles as the driving factor of macroinvertebrate community composition (Esposito 2012). Similar studies in the Sonoran Desert directly linked community composition to wetland isolation and dispersal limitations (Stanislawczyk et al 2018).

In this study, we describe gradients in water quality in desert wetlands and relate macroinvertebrate metrics and diversity indices to these physiochemical characteristics to identify the main drivers of community composition within these rare habitats. In doing so, we can further assess any differences in macroinvertebrate diversity related to water source (i.e. wastewater sites vs. non-wastewater sites) and any possible lingering effects of exposure to wastewater. Furthermore, we aim to distinguish any drivers of community composition unique to arid region wetlands.

Study Sites

We sampled wetland sites throughout the southwest United States, primarily in the Chihuahuan Desert. Due to scarcity sites within the region, we extended sites into the neighboring Sonoran desert (Table 1). Sites were grouped by water sources: either wastewater (effluent water from treatment plants) or non-wastewater (i.e. river, spring or storm water). Most sites were sampled at least twice, in two different years, however, Cattail Falls and Manzanita Springs were only sampled once. Some sites, such as the Rio Bosque, were sampled in more than one area, as indicated in the Code Names in Table 1 (i.e. RB1, RB2). Different areas sampled within one wetland were usually associated with separate ponded areas.

Macroinvertebrate Sampling

Macroinvertebrate samples were collected from most sites once a year during the summer months (June through August) of 2018 and 2019. Macroinvertebrate samples were collected using a 250µm d-frame kick net from all representative aquatic habitats in each site. Three successive dips were performed through all dominant habitats, for a total of three sweeps per habitat. Contents from the sweeps were pooled into 1 composite sample. Macroinvertebrates collected with the net were counted in the field with some specimens kept for further identification in the lab. Specimens were preserved in 70% ethanol, stored at room temperature, and identified to the lowest possible taxonomic level. Many groups were identified to the genus level with some being identified to species names (Merritt and Cummins 1996; Smith 2001). Because all sites were sampled with the same effort, abundances are reported as catch per unit effort (CPUE) and are directly comparable.

Using these data, a variety of metrics of macroinvertebrate community composition were calculated, including those that summarized taxonomic richness, abundance, composition, and functional feeding groups (Table 2). Using abundance data, Simpson’s Diversity Index (λ) was calculated for each sampling visit as a measurement of macroinvertebrate diversity (Simpson 1949). Both λ scores and the percentages of functional feeding groups were computed for each site visit, then averaged for sites that were sampled more than once (Anderson and Davis 2013).

Water Quality Sampling

Physiochemical conditions such as pH, conductivity, dissolved oxygen (DO) and temperature were collected in the field using a YSI® 556 multi-probe (YSI Incorporated Yellow Springs, OH, USA). Dissolved organic carbon (DOC) and total dissolved nitrogen (TDN) samples were filtered through pre-ashed GF/F filters and stored in precombusted amber glass bottles at 4°C until analysis. Both were determined using a Shimadzu TOC-L analyzer with TMN module. Water samples for additional water chemistry were collected from an open water location using acid washed HDPE bottles. Anion concentrations (Cl⁻, SO₄²⁻, NO₃⁻, PO₄³⁻) were measured on a Dionex 2100 ion chromatograph. Alkalinity was measured using a Mettler Toledo G20 auto-titrator. Turbidity was measured in triplicate using a Hach 2100 turbidimeter. Percent organic matter was determined using a “loss on ignition” method in which a subsample of the sediment was dried at 100^°Cfor one hour. The sample was then weighed and heated in a muffle furnace at 550^°C for fifteen minutes and reweighed. Percent organic matter was calculated from the mass lost after ashing.

Chlorophyll-a concentration, as an estimate of algal biomass, was quantified for both phytoplankton and periphyton. To measure phytoplankton, a known volume of water (between 150-1000 mL) was collected from open water and filtered through a GF/C filter to collect algae floating in the water column. Filters were frozen until analysis. Periphyton was collected from pond sediment surfaces at three haphazard locations in each pond using a spatula and an inverted petri dish. All three periphyton samples were combined into one composite sample. Algae were separated from the sediment by rinsing with distilled water, pouring off and retaining the algal-rich supernatant solution and repeating ten times, at which point the solution typically became clear. A subsample of the resulting algal suspension was stored in a test tube, wrapped in foil and frozen until the analysis for chlorophyll. Chlorophyll a (CHLa) was extracted into 90% acetone for 24 h in the freezer. Absorbance of the extract was measured with a Genesis 10 UV spectrophotometer. Concentrations were calculated on a volumetric basis for phytoplankton (µg L^-1) and by area sampled for periphyton (µg cm^-2). Phytoplankton CHLa was corrected for turbidity and phaeopigments by acidification (Wetzel and Likens 2002); total CHLa refers to uncorrected CHLa values.

Data Analysis

A Principal Component Analysis (PCA) was used to describe underlying gradients in the environmental data. All environmental data, including physiochemical properties and algal biomass were entered into the analysis. Correlations were performed to relate environmental data, macroinvertebrate metrics, PCA scores and, and Simpsons Index scores for all sites. For the PCA and correlations, data were log10 transformed, as required, to approximate a normal distribution. All statistical analysis and graphing were performed in R (Version 4.1.2).

Environmental Gradients

Environmental conditions ranged from nutrient-poor (non-detectable levels of N&P) to nutrient rich, with extremely high levels of water column chlorophyll (maximum 352 ug/L), DOC (maximum 75ppm) and nutrients (Table 3). There was also a large gradient of salinity related variables such as Cl⁻ and SO₄²⁻ranging from non-detectable amounts to 828.5 and 5309 ppm, respectively. Water clarity ranged from clear (1.8 NTU) to highly turbid (208.3 NTU). Sites generally had largely inorganic sediments with the highest percentage of organic matter containing only 9%.

The PCA yielded two dimensions explaining more than 50% of variation in the environmental data: PCA 1 accounted for 31.9% of the variability, and PCA 2 accounted for 22.1%. For PCA1, DOC was the greatest driver of variance, along with salinity related variables such as Cl⁻, SO₄²⁻, alkalinity and conductivity. Both total and corrected phytoplankton CHLa were also related to this axis (Figure 1a; Table 4). This axis contrasted urban ponds with high salinity, such as Keystone and Crossroads, to more pristine sites, such as Manzanita Springs and Cattail Falls, with relatively low salinity levels. Nutrients such as NO₃⁻, PO₄³⁻ and TDN, as well as soil organic matter, were the greatest drivers of variance along PCA 2 (Figure 1a). This axis contrasted sites flooded with effluent water (Rio Bosque, Sweet Water, Tres Rios and BJ Bishop) to all other sites. Sites flooded with water from the Rio Grande (Rio Grande sites, Las Palomas, La Mancha) were shown to have relatively low levels of nutrients. Differences based on sites flooded with wastewater versus those flooded with non-wastewater is especially apparent, as they occupied distinct groups on the PCA plot (Figure 1b).

Macroinvertebrate Metrics

In total, 13,760 macroinvertebrate individuals were collected during the 2018 and 2019 sampling seasons. Total abundances ranged from 15 to more than 1000 per unit effort, the latter being sites that were dominated by mostly Ostracods and Cladocera, while the number of taxa found at each site ranged from 2 to 10, depending on the site.

When grouped by water type, many metrics were significantly higher in sites that were fed with wastewater, including greater abundance (in CPUE) of Ostracoda, Amphipoda and Zygoptera (Table 5). The percentage of Filterers was also significant higher in wastewater sites, along with more families, orders and total taxa in these sites. Within sites flooded with non-wastewater, metrics related to more senstitive orders (% Ephemeroptera, % EOT, % Collector-gatherers) were found in greater numbers. The percentage of Predators and Collector-gatherers were also found to be significantly higher in the non-wastewater sites. When correlated with the PCA axes, macroinvertable metrics tended to be correlated either with PCA1 (salinity) or PC2 (nutrients) but never both (Table 6). PCA1 showed a negative significant relationship with: % Amphipoda and % Coleoptera (Table 6). PCA2 had significant positive relationships with more sensitive invertebrate metrics such as %EOT , % Odonota and % Predators, but a negative relationship with % Filterers and % Ostracoda (Figure 2).

λ scores also significantly positively related with both PCA1 (r² = 0.08, p < 0.05) and PCA 2 (r² = 0.13, p < 0.05). Both increased salinity and nutrient levels have negative effects on macroinvertebrate community diversity and evenness. When comparing the λ scores of wastewater sites and non-wastewater sites, there was a significant difference with non-wastewater sites displaying higher macroinvertebrate diversity scores (Figure 3).

Salinity

Wetlands in this study tended to vary along a gradient of either salinity or nutrient enrichment, with salinity appearing to explain more among-site variability. The salinity gradient contrasted permanent and isolated spring sites such as Cattail Falls and Manzanita Springs, with low chloride, sulfate and conductivity levels, to known highly saline sites within El Paso city limits, such as Keystone and Crossroads. The relatively high levels of salinity within these two sites are likely due their location, the sites being within two miles of each other. They are in an arid region, do not have a regular source of water (Crossroads being flooded by rainwater and Keystone by spring and ground water) and are highly dependent on the water table to maintain water levels. This irregular influx of water and rising temperatures could lead to high evaporative conditions with could be responsible for the high levels of salinity within these sites (Jolly et al 2008; Borrok and Engle 2014). Keystone also receives groundwater, which is known to have high levels of salts and sulfate in the region (Nielsen et al 2003; Chaudhuri and Ale 2014).

DOC and chlorophyll-a were also shown to vary along the salinity gradient. Microbes that take up DOC may be sensitive to high levels of salinity. Sites that are highly saline have been shown to have suppressed microbial activity, which may lead to higher levels of available DOC within these sites (Straathof et al 2014; Yang et al 2018). In some studies, the increase in chlorophyll-a levels within highly saline sites was related to SO₄²⁻andsalt-induced aggregation of suspended matter, which can lead to increase light penetration of the water column and thus, high rates of photosynthesis (Donnelly et al 1997; Nielsen et al 2003). However, given we saw no effect of water clarity in our study, this is unlikely.

Sites that were higher in salinity tended to have a lower Simpson Index Scores, thus lower diversity and evenness. This remains consistent with similar studies showing negative relationships between macroinvertebrate taxonomic richness and functional evenness with increasing levels of salinity and related parameters (Kefford et al 2004; Chemers et al 2011; Ordonez et al 2011; Cuthbert et al 2020; Muresan et al 2020). The percent of Amphipoda and Coleoptera increased significantly with salinity. Several species of Amphipoda found in the Chihuahuan desert are known to be adapted to high levels to salinity (Chemers et al 2011; Cuthbert et al 2020). Similarly, Coleopterans are known to be highly tolerant of low water quality and high salinity levels within freshwater ecosystems (Lancaster and Scudder 1987; Garrido and Munilla 2008; Sharma et al 2019). At low salinity sites, there were trends showing higher percentages of Predators and Scrapers, though these were not significantly correlated with salinity gradient.

Nutrients

Not surprisingly, there was a distinct difference in physiochemical features between sites flooded with wastewater and those flooded with non-wastewater. The sites flooded with wastewater were significantly higher in nutrients such as NO₃⁻, PO₄³⁻, and TDN, typical of effluent water (Zhuang et al 2019)(Table 7). Periphyton was also significantly higher in the wastewater sites, likely due to the high levels of nutrients, which are often a limiting factor of benthic algal communities (Power 1992; Francoeur et al 1999).

Sites with lower nutrient levels had more diverse and even macroinvertebrate communities. Lougheed et al (2008) also found that wetlands in less developed, nutrient poor locations had increased diversity of multiple taxonomic groups. This remains consistent with multiple studies finding homogenization of macroinvertebrate communities with increased nutrient levels, some stating total phosphorus as the main driver of decline in diversity (Spieles and Mitsch 2000; Hsu et al 2011; Ouyang et al 2018; Qu et al 2019). Along the nutrient gradient, we saw a clear contrast in macroinvertebrate community structure between wastewater sites and non-wastewater sites (Figure 4). Within the non-wastewater sites (low nutrients), we found multiple taxa with relatively even percent abundances (10-15%), including Ephemeroptera, Odonata, Hemiptera, Coleoptera. The finding is consistent with findings of increased evenness in non-wastewater or low nutrient sites compared to wastewater wetlands, specifically with the increase in more sensitive taxa such as Ephemeropterans (Becerra Jurado et al 2009; Hsu et al 2011). The percent Odonata, EOT and Predators also increased significantly within non-wastewater sites, likely due to their sensitivity to anthropogenic impacts (Kutcher and Bried 2014). The increase in Predators may also be explained by increased presense of macroinvertebrates such as corixidae within the Rio Grade water column (Burdett et al 2015). Since there were multiple sites flooed with river water and two sites at the river itself, this may have led to an increase in Predators sampled from these sites. Furthermore, the functional groups were evenly represented in the community, with each forming approximately one-third of the composition. In contrast, Filterers (ostracods in particular) dominated the community in wastewater sites, representing more than 60% of the total abundance. This remains consistent with other studies relating declines in Predators and Scrapers functional feeding groups as a result of increased nutrients and anthropogenic disturbances (Fu et al 2016; Zhang et al 2019). One possibility for the increase in filter feeder percentages in high nutrient sites could be due to increased periphyton algae levels within these sites (Hillebrand and Kahlert 2001).

Other studies indicated plant diversity as being the main driver of diversity and habitat selection in macroinvertebrates (Hsu et al 2011; Perron and Pick 2020; Perron et al 2021). Although we did not quantitatively evaluate plant species richness, there appeared to be a similar trend with macroinvertebrate richness increasing within sites that tended to have higher plant diversity, many of which are non-wastewater sites. Alternatively, ostracods and filter feeders were inversely related to the nutrient gradient, indicating that sites with non-wastewater had smaller percentages of these macroinvertebrates.

While salinity may be the greatest driver of environmental variation amongst desert wetlands, nutrient load appears to be the greatest driver of variation within macroinvertebrate communities. This follows the trajectory of other studies listing anthropogenic disturbances as greater drivers of community composition over salinity loads (Moreno et al 2010). Increased levels of nutrients, such as those found in wastewater from treatment sites, has shown to lead to changes in functional feeding groups, specifically leading to communities dominated by filter feeders. Results from this investigation could be an important consideration for maintaining or restoring biodiversity to macroinvertebrates in wastewater wetlands and desert wetlands. More research is needed to confirm whether prolonged nutrient inclusion leads to further homogenization of macroinvertebrate communities, or whether this becomes an alternative stable state for these sites. Further investigation is required to determine if other trophic levels are equally impacted by salinity and nutrient levels within these arid wetland ecosystems.

Acknowledgements

This research was funded by STEMGrow (Dept of Education P031C160235) and the USDA (2015-68007-23130). The National Park Service granted access to sample at Big Bend National Park (BIBE-2019-SCI-0030) and Guadalupe Mountains National Park (GUMO-2019-SCI-0019). Thank you to Dr. Elizabeth Walsh for site location suggestions, to Dr. Lixin Jin for processing water samples, and to John Sproul and all other wetland managers for allowing us to collect at their sites. This project would not have been possible without the help from the STEMGrow undergraduate research assistants: Viridiana Orona, Christy Adame, Jovannie Gomez, Bryan Yu, Alyssa Calderon and Carlos Vargas. Special thanks to volunteer field assistant, Emmanuel Jacquez.

Funding

This work was supported by Department of Education Hispanic Serving Institution STEMGROW (P031C160235) and the United States Department of Agriculture Water Resources Project Summer Internship (2015-68007-23130).

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Author Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Anna Elisa Piña. The first draft of the manuscript was written by Anna Elisa Piña and Vanessa Lougheed commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Data Availability

The datasets generated and analyzed during the current study are available in the Environmental Data Initiative repository, https://doi.org/10.6073/pasta/f42ad76f0562e27544d1bb0b56f19fb6

Anderson JT, Davis CA (2013) Wetland techniques volume 2: Organisms. Wetland Techniques: Volume 2: Organisms. doi: 10.1007/978-94-007-6931-1
Balcombe CK, Anderson JT, Fortney RH, Kordek WS (2005) Aquatic macroinvertebrate assemblages in mitigated and natural wetlands. Hydrobiologia 541:175–188. doi: 10.1007/s10750-004-5706-1
Bartell SM (2006) Biomarkers, Bioindicators, and Ecological Risk Assessment—A Brief Review and Evaluation. Environmental Bioindicators 1:60–73. doi: 10.1080/15555270591004920
Becerra Jurado G, Callanan M, Gioria M, et al (2009) Comparison of macroinvertebrate community structure and driving environmental factors in natural and wastewater treatment ponds. Hydrobiologia 634:153–165. doi: 10.1007/s10750-009-9900-z
Bogan MT, Noriega-Felix N, Vidal-Aguilar SL, et al (2014) Biogeography and conservation of aquatic fauna in spring-fed tropical canyons of the southern Sonoran Desert, Mexico. Biodiversity and Conservation 23:2705–2748. doi: 10.1007/s10531-014-0745-z
Borrok DM, Engle MA (2014) The role of climate in increasing salt loads in dryland rivers. Journal of Arid Environments 111:7–13. doi: 10.1016/j.jaridenv.2014.07.001
Burdett AS, Fencl JS, Turner TF (2015) Evaluation of freshwater invertebrate sampling methods in a shallow aridland river (Rio Grande, New Mexico). Aquatic Biology 23:139–146. doi: 10.3354/ab00616
Burkett V, Kusler J (2000) Climate change: potential impacts and interactions in wetlands of United States. Journal Of The American Water Resources Association 36:313–320. doi: 10.1111/j.1752-1688.2000.tb04270.x
Cairns J, Pratt JR (1993) A history of biological monitoring using benthic macroinvertebrates. Freshwater Biomonitoring and Benthic Macroinvertebrates 10–27.
Chaudhuri S, Ale S (2014) Temporal evolution of depth-stratified groundwater salinity in municipal wells in the major aquifers in Texas, USA. Science of the Total Environment 472:370–380. doi: 10.1016/j.scitotenv.2013.10.120
Chemers M, Zurbriggen E, Syed M, et al (2011) The Role of Efficacy and Identity in Science Career Commitment Among Underrepresented Minority Students. Journal of Social Issues 67:469–491. doi: 10.1111/j.1540-4560.2011.01710.x
Cuthbert RN, Kotronaki SG, Dick JTA, Briski E (2020) Salinity tolerance and geographical origin predict global alien amphipod invasions. Biology Letters. doi: 10.1098/rsbl.2020.0354
Dawson TP, Jackson ST, House JI, et al (2011) Beyond predictions: Biodiversity conservation in a changing climate. Journal of Vegetation Science 332:53–58. doi: 10.1126/science.1200303
Dinerstein E, Olson D, Atchley J, et al (2001) Ecoregion-Based Conservation in the Chihuahuan Desert. A Biological Assessment. Nature 376.
Donnelly TH, Grace MR, Hart BT (1997) Algal blooms in the Darling-Barwon River, Australia. Water, Air, and Soil Pollution 1997 99:1 99:487–496. doi: 10.1007/BF02406888
Drake JC, Griffis-Kyle K, McIntyre NE (2017) Using nested connectivity models to resolve management conflicts of isolated water networks in the Sonoran Desert. Ecosphere 8:e01652. doi: 10.1002/ecs2.1652
Esposito GR (2012) What is the role of isolation and disturbance on the diversity of aquatic invertebrates in former ranchlands of the northern Chihuahuan Desert? doi: 10.1017/CBO9781107415324.004
Foote AL, Rice Hornung CL (2005) Odonates as biological indicators of grazing effects on Canadian prairie wetlands. Ecological Entomology 30:273–283. doi: 10.1111/j.0307-6946.2005.00701.x
Francoeur SN, Biggs BJF, Smith RA, Lowe RL (1999) Nutrient limitation of algal biomass accrual in streams: Seasonal patterns and a comparison of methods. Journal of the North American Benthological Society 18:242–260. doi: 10.2307/1468463
Fu L, Jiang Y, Ding J, et al (2016) Impacts of land use and environmental factors on macroinvertebrate functional feeding groups in the Dongjiang River basin, southeast China. Journal of Freshwater Ecology 31:21–35. doi: 10.1080/02705060.2015.1017847
Garrido J, Munilla I (2008) Aquatic Coleoptera and Hemiptera assemblages in three coastal lagoons of the NW Iberian Peninsula: Assessment of conservation value and response to environmental factors. Aquatic Conservation: Marine and Freshwater Ecosystems 18:557–569. doi: 10.1002/aqc.883
Hershler R, Liu H-P (2010) Two new, possibly threatened species of Pyrgulopsis (Gastropoda: Hydrobiidae) from southwestern California. Zootaxa 2343:1–17.
Hillebrand H, Kahlert M (2001) Effect of grazing and nutrient supply on periphyton biomass and nutrient stoichiometry in habitats of different productivity. Limnology and Oceanography 46:1881–1898. doi: 10.4319/lo.2001.46.8.1881
Hilsenhoff WL (1987) An improved biotic index of organic stream pollution. The Great Lakes Entomologist 20:31–39.
Hsu C Bin, Hsieh HL, Yang L, et al (2011) Biodiversity of constructed wetlands for wastewater treatment. Ecological Engineering 37:1533–1545. doi: 10.1016/j.ecoleng.2011.06.002
Jolly ID, McEwan KL, Holland KL (2008) A review of groundwater-surface water interactions in arid/semi-arid wetlands and the consequences of salinity for wetland ecology. Ecohydrology 1:43–58. doi: 10.1002/eco.6
Kefford BJ, Papas PJ, Metzeling L, Nugegoda D (2004) Do laboratory salinity tolerances of freshwater animals correspond with their field salinity? Environmental Pollution 129:355–362. doi: 10.1016/j.envpol.2003.12.005
Kutcher TE, Bried JT (2014) Adult Odonata conservatism as an indicator of freshwater wetland condition. Ecological Indicators 38:31–39. doi: 10.1016/j.ecolind.2013.10.028
Lancaster J, Scudder GGE (1987) Aquatic Coleoptera and Hemiptera in some Canadian saline lakes: patterns in community structure. Canadian Journal of Zoology 65:1383–1390. doi: 10.1139/z87-218
Lougheed VL, Mcintosh MD, Parker CA, Stevenson RJ (2008) Wetland degradation leads to homogenization of the biota at local and landscape scales. Freshwater Biology 53:2402–2413. doi: 10.1111/j.1365-2427.2008.02064.x
Mandaville SM (2002) Benthic Macroinvertebrates in Freshwaters - Taxa Tolerance Values, Metrics, and Protocols. Soil & Water Conservation Society of Metro Halifax 85–98.
Matamoros V, Rodríguez Y, Bayona JM (2017) Mitigation of emerging contaminants by full-scale horizontal flow constructed wetlands fed with secondary treated wastewater. Ecological Engineering 99:222–227. doi: 10.1016/j.ecoleng.2016.11.054
McIntosh L, Fierro-Cabo A, Benavides JA (2019) Macroinvertebrate assemblages from two sampling methods similarly discriminated freshwater wetlands with different ecosystem status in south Texas. Aquatic Ecosystem Health and Management 22:65–76. doi: 10.1080/14634988.2018.1505142
Merritt RW, Cummins KW (1996) An Introduction to the Aquatic Insects of North America. doi: 10.2307/1467288
Minckley TA, Turner DS, Weinstein SR (2013) The relevance of wetland conservation in arid regions: A re-examination of vanishing communities in the American Southwest. Journal of Arid Environments 88:213–221. doi: 10.1016/j.jaridenv.2012.09.001
Moreno JL, Angeler DG, De las Heras J (2010) Seasonal dynamics of macroinvertebrate communities in a semiarid saline spring stream with contrasting environmental conditions. Aquatic Ecology 44:177–193. doi: 10.1007/s10452-009-9251-x
Muresan AN, Gaglio M, Aschonitis V, et al (2020) Structural and functional responses of macroinvertebrate communities in small wetlands of the Po delta with different and variable salinity levels. Estuarine, Coastal and Shelf Science 238:106726. doi: 10.1016/j.ecss.2020.106726
Murphy NP, Adams M, Guzik MT, Austin AD (2013) Extraordinary micro-endemism in Australian desert spring amphipods. Molecular Phylogenetics and Evolution 66:645–653. doi: 10.1016/j.ympev.2012.10.013
Nielsen DL, Brock MA, Rees GN, Baldwin DS (2003) Effects of increasing salinity on freshwater ecosystems in Australia. Australian Journal of Botany 51:655–665. doi: 10.1071/BT02115
Nieto C, Ovando XMC, Loyola R, et al (2017) The role of macroinvertebrates for conservation of freshwater systems. Ecology and Evolution 7:5502–5513. doi: 10.1002/ece3.3101
O’Geen AT, Budd R, Gan J, et al (2010) Mitigating nonpoint source pollution in agriculture with constructed and restored wetlands. Advances in Agronomy. doi: 10.1016/S0065-2113(10)08001-6
Ordonez C, Lougheed VL, Gardea-Torresdey JL, Bain LJ (2011) Impact of metals on macroinvertebrate assemblages in the forgotten stretch of the Rio Grande. Archives of Environmental Contamination and Toxicology 60:426–436. doi: 10.1007/s00244-010-9557-7
Ouyang Z, Qian SS, Becker R, Chen J (2018) The effects of nutrients on stream invertebrates: a regional estimation by generalized propensity score. Ecological Processes. doi: 10.1186/s13717-018-0132-x
Perron MAC, Pick FR (2020) Stormwater ponds as habitat for Odonata in urban areas: the importance of obligate wetland plant species. Biodiversity and Conservation 29:913–931. doi: 10.1007/s10531-019-01917-2
Perron MAC, Richmond IC, Pick FR (2021) Plants, water quality and land cover as drivers of Odonata assemblages in urban ponds. Science of the Total Environment 773:145467. doi: 10.1016/j.scitotenv.2021.145467
Pinto U, Maheshwari BL, Morris EC (2014) Understanding the Relationships among Phytoplankton, Benthic Macroinvertebrates, and Water Quality Variables in Peri-Urban River Systems. Water Environment Research 86:2279–2293. doi: 10.2175/106143014x13975035526220
Power ME (1992) Top-down and bottom-up forces in food webs: do plants have primacy? Ecology 73:733–746. doi: 10.2307/1940153
Qu W, Li J, Han G, et al (2019) Effect of salinity on the decomposition of soil organic carbon in a tidal wetland. Journal of Soils and Sediments 19:609–617. doi: 10.1007/s11368-018-2096-y
Richey AS, Thomas BF, Lo MH, et al (2015) Quantifying renewable groundwater stress with GRACE. Water Resources Research 51:5217–5237. doi: 10.1002/2015WR017349
Rodriguez R, Lougheed VL (2010) The potential to improve water quality in the middle Rio Grande through effective wetland restoration. Water Science and Technology 62:501–509. doi: 10.2166/wst.2010.323
Sharma S, Sharma G, Pir FA (2019) Diversity and habitat selection of aquatic beetles (Coleoptera). IOSR Journal Of Pharmacy And Biological Sciences (IOSR-JPBS) e-ISSN 14:31–37. doi: 10.9790/3008-1401013137
Siddig AAH, Ellison AM, Ochs A, et al (2016) How do ecologists select and use indicator species to monitor ecological change? Insights from 14 years of publication in Ecological Indicators. Ecological Indicators 60:223–230. doi: 10.1016/j.ecolind.2015.06.036
Simpson EH (1949) Measurement of diversity. Nature 163:688. doi: 10.1038/163688a0
Smith DG (2001) Fresh-water invertebrates of the United States, 4th edn. doi: 10.1016/0016-0032(54)91072-8
Spieles DJ, Mitsch WJ (2000) Macroinvertebrate community structure in high and low nutrient constructed Wetlands. Wetlands 20:716–729. doi: 10.1672/0277-5212(2000)020[0716:MCSIHA]2.0.CO;2
Stanislawczyk K, Walters AD, Haan TJ, et al (2018) Variation among macroinvertebrate communities suggests the importance of conserving desert springs. Aquatic Conservation: Marine and Freshwater Ecosystems 28:944–953. doi: 10.1002/aqc.2894
Stewart TW, Downing JA (2008) Macroinvertebrate communities and environmental conditions in recently constructed wetlands. Wetlands 28:141–150. doi: 10.1672/06-130.1
Straathof AL, Chincarini R, Comans RNJ, Hoffland E (2014) Dynamics of soil dissolved organic carbon pools reveal both hydrophobic and hydrophilic compounds sustain microbial respiration. Soil Biology and Biochemistry 79:109–116. doi: 10.1016/j.soilbio.2014.09.004
Strayer DL, Dudgeon D (2010) Freshwater biodiversity conservation: Recent progress and future challenges. Journal of the North American Benthological Society 29:344–358. doi: 10.1899/08-171.1
Swartz LK, Hossack BR, Muths E, et al (2019) Aquatic macroinvertebrate community responses to wetland mitigation in the Greater Yellowstone Ecosystem. Freshwater Biology 64:942–953. doi: 10.1111/fwb.13276
Taylor RG, Scanlon B, Döll P, et al (2013) Ground water and climate change. Nature Climate Change 3:322–329. doi: 10.1038/nclimate1744
Tsiripidis I, Bergmeier E, Fotiadis G, Dimopoulos P (2009) A new algorithm for the determination of differential taxa. Journal of Vegetation Science 20:233–240. doi: 10.1111/j.1654-1103.2009.05273.x
Van De Waal DB, Verschoor AM, Verspagen JMH, et al (2010) Climate-driven changes in the ecological stoichiometry of aquatic ecosystems. Frontiers in Ecology and the Environment 8:145–152. doi: 10.1890/080178
Vörösmarty CJ, McIntyre PB, Gessner MO, et al (2010) Global threats to human water security and river biodiversity. Nature 467:555–561. doi: 10.1038/nature09440
Wallace RL, Walsh EJ, Arroyo ML, Starkweather PL (2005) Life on the edge: Rotifers from springs and ephemeral waters in the Chihuahuan Desert, Big Bend National Park (Texas, USA). Hydrobiologia. Springer, pp 147–157
Walsh EJ, Schröder T, Wallace RL, Rico-Martinez R (2009) Cryptic speciation in Lecane bulla (Monogononta: Rotifera) in Chihuahuan Desert waters. SIL Proceedings, 1922-2010 30:1046–1050. doi: 10.1080/03680770.2009.11902298
Wetzel RG, Likens GE (2002) Composition and Biomass of Phytoplankton. Limnological Analyses. Springer, pp 147–174
Whitton R, Le Mével A, Pidou M, et al (2016) Influence of microalgal N and P composition on wastewater nutrient remediation. Water Research 91:371–378. doi: 10.1016/j.watres.2015.12.054
Woodward G, Perkins DM, Brown LE (2010) Climate change and freshwater ecosystems: Impacts across multiple levels of organization. Philosophical Transactions of the Royal Society B: Biological Sciences 365:2093–2106. doi: 10.1098/rstb.2010.0055
Wu H, Guan Q, Lu X, Batzer DP (2017) Snail (Mollusca: Gastropoda) assemblages as indicators of ecological condition in freshwater wetlands of Northeastern China. Ecological Indicators 75:203–209. doi: 10.1016/j.ecolind.2016.12.042
Yang J, Zhan C, Li Y, et al (2018) Effect of salinity on soil respiration in relation to dissolved organic carbon and microbial characteristics of a wetland in the Liaohe River estuary, Northeast China. Science of the Total Environment 642:946–953. doi: 10.1016/j.scitotenv.2018.06.121
Zhang Y, Cheng L, Li K, et al (2019) Nutrient enrichment homogenizes taxonomic and functional diversity of benthic macroinvertebrate assemblages in shallow lakes. Limnology and Oceanography 64:1047–1058. doi: 10.1002/lno.11096
Zhuang LL, Yang T, Zhang J, Li X (2019) The configuration, purification effect and mechanism of intensified constructed wetland for wastewater treatment from the aspect of nitrogen removal: A review. Bioresource Technology 293:122086. doi: 10.1016/j.biortech.2019.122086

Table 1: Sample sites, location of site, and water source. Sites 1 – 12 were visited in 2018 and 2019. Code names appear in Fig. 1b. Sites 13-14 were added in 2019 help elucidate patterns.
#	Name	Location	Code Name	Water Source
1	Tres Rios Wetlands	Phoenix, AZ	TR1, TR2	wastewater
2	Sweetwater Wetlands	Tucson, AZ	SW1, SW2	wastewater
3	Las Palomas Marsh	Caballo Lake, NM	LP	non-wastewater
4	Rio Grande	Las Palomas, NM	RG1	non-wastewater
5	La Mancha Wetlands	Las Cruces, NM	LM	non-wastewater
6	Rio Grande	Las Cruces, NM	RG2	non-wastewater
7	Keystone Wetlands	El Paso, TX	KS	non-wastewater
8	Crossroads pond	El Paso, TX	CR	non-wastewater
9	Ascarate Lake	El Paso, TX	AS	wastewater
10	Rio Bosque Wetlands	El Paso, TX	RB1, RB2	wastewater
11	Sandia Springs	Balmorhea, TX	SS1, SS2, SS3	non-wastewater
12	BJ Bishop Wetlands	Presidio, TX	BJ	wastewater
13	Cattail Falls	Big Bend National Park, TX	CF	non-wastewater
14	Manzanita Springs	Guadalupe Mountains National Park, TX	MS	non-wastewater

*Table 2: Classification of wetland invertebrate metrics used in this study. EOT: Ephemeroptera, Odonata, Tricoptera**
Assemblage characteristic	Metric	Assemblage characteristic	Metric
Richness and abundance	Total macroinvertebrate abundance	Composition	% Ephemeroptera
Richness and abundance	No. of macroinvertebrate families		% Odonata
	No. of macroinvertebrate orders		% Amphipoda
	Total macroinvertebrate taxa		% Gastropoda
	Ostracoda abundance		% Hemiptera
	Cladocera abundance		% Coleoptera
	Ephemeroptera abundance		% Diptera
	Hemiptera abundance		% Chironomidae
	Amphipoda abundance		% Cladocera
	Gastropoda abundance		% Decapoda
	Anisoptera abundance		% Ostracoda
	Zygoptera abundance		% Platyhelminthes
	Decapoda abundance		% Tricoptera
	Diptera abundance		% EOT*
	Chironomidae abundance	Functional Feeding Groups	% Predators
	Platyhelminthes abundance	Functional Feeding Groups	% Scrapers
	Coleoptera abundance		% Filterers
	Tricoptera abundance		% Collector-gatherers
Diversity	Simpson Diversity Index - λ		% Shredders

Table 3. Median, standard deviation and range of water physio-chemical variables for all sites.
	Median	SD	Min	Max
Conductivity (mS/cm)	3.30	3.89	0.21	16.40
DOC (ppm)	13.84	17.46	0.29	75.04
Alkalinity (meq/L)	200.11	130.80	21.98	457.62
Corrected CHL (µg L^-1)	21.82	60.63	0.00	146.68
Cl⁻(ppm)	281.84	290.23	0.00	828.53
SO₄²⁻ (ppm)	536.38	1073.27	0.00	5309.00
Total CHL (µg L^-1)	17.58	34.91	0.16	352.28
TDN (ppm)	2.84	3.85	0.00	7.00
NO₃⁻ (ppm)	1.62	2.79	0.00	9.00
PO₄³⁻(ppm)	2.63	4.97	0.00	26.00
Periphyton (µg cm^-2)	0.00	0.01	0.00	0.02
Organic Matter %	1.29	2.93	0.00	8.97
pH	7.40	0.76	6.25	9.29
Turbidity (NTU)	24.29	38.26	1.80	208.30

Table 4: Correlation coefficients (r) of water physiochemical parameters with PCA1 and PCA2 scores. Significance: p<0.0001, p<0.01, p<0.05**
	PCA1	PCA2
Conductivity (mS/cm)	-0.8250***	0.1259
DOC (ppm)	-0.8855***	-0.1917
Alkalinity (meq/L)	-0.6148***	-0.2793
Corrected CHL (µg L^-1)	-0.6981**	-0.1450
Cl⁻(ppm)	-0.8052***	-0.0884
SO₄²⁻ (ppm)	-0.8586***	0.1127
Total CHL (µg L^-1)	-0.5972**	-0.2161
TDN (ppm)	-0.4484	-0.7393***
NO₃⁻ (ppm)	0.2392	-0.8511***
PO₄³⁻(ppm)	0.0374	-0.8458***
Periphyton (µg cm^-2)	0.0318	-0.5853**
Organic Matter %	0.2686	-0.5627**
pH	-0.5632**	0.5456**
Turbidity (NTU)	-0.2529	0.1949
Longitude	-0.0366	0.3313*

Table 5. Means and Standard Error of macroinvertebrate metrics grouped by water type. Significance difference between groups *p<0.0001, p<0.01, *p<0.05, + <0.10, without asterisks indicate non- significance.
	Non-Waste	Waste
No. of families	6.76 (0.42)	8.50 (0.81)+
No. of orders	5.10 (0.34)	6.5 (0.59)+
Total taxa	7.16 (0.46)	9.78 (1.12)*
Ostracoda abundance	45.60 (15.53)	770.71 (269.58)*
Amphipoda abundance	9.36 (4.19)	108.21 (39.86)*
Zygoptera abundance	8.76 (2.19)	21.42 (5.27) *
Chironomidae abundance	4.38 (1.23)	1.70 (0.57)*
% Ephemeroptera	13.56 (3.16)	3.17 (1.56)**
% Odonata	10.86 (2.40)**	3.67 (1.12)
% Hemiptera	11.05 (3.59)*	1.55 (0.93)
% Coleoptera	12.54 (4.10)**	0.30 (0.15)
% Diptera	5.75 (1.30)*	2.00 (0.60)
% Ostracoda	20.33 (5.62)	61.75 (7.64)**
% EOT	24.46 (3.93)**	6.85 (2.34)
% Predators	32.51 (4.98)***	5.53 (1.43)
% Filterers	25.77 (6.00)	74.02 (5.78)***
% Collector-gatherers	29.38 (4.58)+	15.93 (5.35)

Table 6: Correlation coefficients (r) of macroinvertebrate metrics with PC1 axis scores (salinity gradient) and PC2 axis scores (nutrient gradient). Significance: p<0.0001, p<0.01, p<0.05**
Metric		PC1	PC2
Total Abundance	0.133		-0.342*
Simpson Diversity Index	0.333*		0.402**
% Odonata	0.190		0.405*
% Amphipoda	-0.379*		-0.075
% Coleoptera	-0.333*		0.083
% Diptera	0.036		0.331*
% Ostracoda	-0.093		-0.498**
% EOT	0.253		0.451**
% Predators	0.274		0.470*
% Filterers	-0.076		-0.520**

Table 7. Means and Standard Error of water quality parameters grouped by water type. Significance difference between groups *p<0.0001, p<0.01, *p<0.05, + <0.10, without asterisks indicate non- significance.
	Non-wastewater	Wastewater
Conductivity (mS/cm)	4.76 (0.98)	2.28 (0.49)
DOC (ppm)	15.45 (4.65)	7.75 (1.23)
Alkalinity (meq/L)	223.65 (24.55)	245.60 (32.32)
Corrected CHL (µg L^-1)	28.76 (15.94)	21.28 (11.72)
Cl⁻ (ppm)	358.51 (71.29)	155.86 (23.76)
SO₄²⁻(ppm)	951.65 (285.07)	122.31 (12.97)
Total CHL (µg L^-1)	14.55 (6.10)	22.54 (11.58)
TDN (ppm)	1.39 (0.40)	5.20 (1.33) **
NO₃⁻ (ppm)	0.23 (0.06)	4.64 (0.89) ***
PO₄³⁻ppm)	0.15 (0.05)	7.34 (1.72) ***
Periphyton (µg cm^-2)	0.001 (0.0003)	0.008 (0.002) **
Organic Matter %	0.02 (0.003)	0.05 (0.005) **
pH	7.74 (0.15) **	6.89 (0.14)
Turbidity (NTU)	30.52 (9.61)	14.05 (4.39)

Are nutrients or salinity the drivers of macroinvertebrate community composition in wetlands of the desert Southwest?

Archived Versions:

Version 1

Abstract

Figures

Introduction and Background