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Characterization of Natural and Affected Environments

Factors influencing neonicotinoid insecticide concentrations in floodplain wetland sediments across Missouri Kyle J Kuechle, Lisa Webb, Doreen Mengel, and Anson Main Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.9b01799 • Publication Date (Web): 14 Aug 2019 Downloaded from pubs.acs.org on August 20, 2019

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Environmental Science & Technology

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7/19/2019 Kyle Kuechle Missouri Cooperative Fish and Wildlife Research Unit, University of Missouri

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Title:

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Factors influencing neonicotinoid insecticide concentrations in floodplain wetland sediments

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across Missouri

302 Anheuser-Busch Building, Columbia, Missouri, 65211

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Author Affiliation:

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*KYLE J. KUECHLE, Missouri Cooperative Fish and Wildlife Research Unit, School of Natural

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Resources, University of Missouri, Columbia, MO 65211 ELISABETH B. WEBB, U.S. Geological Survey, Missouri Cooperative Fish and Wildlife Research Unit, Columbia, MO 65211 DOREEN MENGEL, Missouri Department of Conservation, Resource Science Division, Columbia, MO 65201 ANSON R. MAIN, Missouri Cooperative Fish and Wildlife Research Unit, School of Natural Resources, University of Missouri, Columbia, MO 65211

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*Current contact information:

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Ducks Unlimited, Inc. 2525 River Road Bismarck, ND 58503 [email protected]

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ACS Paragon Plus Environment


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Abstract Art

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Abstract

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Widespread use of neonicotinoid insecticides in North America has led to frequent detection of

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neonicotinoids in surface waters. Despite frequent surface water detections, few studies have

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evaluated underlying sediments for presence of neonicotinoids. Thus, we sampled water and

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sediments for neonicotinoids during a one-year period at 40 floodplain wetlands throughout

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Missouri. Analyzed for six common neonicotinoids, sediment samples consistently (63% of

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samples) contained neonicotinoids (e.g. imidacloprid, clothianidin) in all sampling periods. Mean

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sediment and aqueous neonicotinoid concentrations were 1.19 μg kg-1 (range: 0 to 17.99 μg kg-1)

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and 0.03 μg L-1 (0 to 0.97 μg L-1), respectively. We used Boosted Regression Tree analysis to

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explain sediment neonicotinoid concentrations and ultimately identified six variables that

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accounted for 31.6% of concentration variability. Efforts to limit sediment neonicotinoid

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contamination could include reducing agriculture within a wetland below a threshold of 25%

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area planted to limit contamination. Also, prolonging periods of overlying water >25cm deep

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when water temperatures reach/exceed 18°C could promote conditions favorable for

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neonicotinoid degradation. Results of this study can be useful in determining potential routes and

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levels of neonicotinoid exposure experienced by non-target benthic aquatic invertebrates as well

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as potential means to mitigate neonicotinoid concentrations in floodplain wetlands.

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Introduction A demand for increased agricultural production has led to the conversion of a diverse

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array of wetlands and historic landscapes to monocrop agriculture.1,2 Remaining wetlands that

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are surrounded by agricultural crop are more susceptible to pesticide contamination with

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concentrations of herbicides (e.g., glyphosate) four times greater than wetlands in grassland

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landscapes.3–5 However, the implications of increased chemical loads are typically not widely

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considered when evaluating environmental impacts of agricultural production.6

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The advent of modern hydrophilic systemic insecticides in the 1980s reduced the need for

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targeted application; instead, prophylactic use of seed treatment technologies, especially

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fungicides and insecticides, has increased exponentially.7–9 Since the early 21st century, seed

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treatments have increasingly contained the neonicotinoid class of insecticides, especially for corn

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(79 to 100% hectares treated) and soybeans (34 to 44% hectares treated) grown in the mid-

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latitude United States.9,10 Ubiquitous use of seed treatment insecticides in conjunction with high

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environmental mobility of neonicotinoids, has resulted in frequent detections of imidacloprid,

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clothianidin, and thiamethoxam in surface waters across North America.11–13 Studies monitoring

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surface waters for pesticides have reported a range of neonicotinoid detection rates (16 to 98% of

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samples) and maximum concentrations (0.17 to 6.9 µg L-1) from a diversity of landscapes.14

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During periods of greatest detection frequency, neonicotinoid concentrations can exceed U.S.

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Environmental Protection Agency imidacloprid benchmarks for acute exposure (0.385 μg L-1),

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and many detections surpass the chronic (0.01 μg L-1) aquatic life benchmark.15 Therefore,

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aquatic invertebrates in North American surface waters may be exposed to neonicotinoids at

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concentrations that are deleterious, particularly through long-term or repeated (chronic)


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exposure. Toxicity is especially likely for more sensitive aquatic insects found in the orders

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Ephemeroptera, Trichoptera, and Diptera.16

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Although a range of surface waters have been analyzed for neonicotinoids,11–13 a

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knowledge gap exists related to intensively-managed riverine floodplain wetlands. Remaining

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floodplain wetlands are typically located within a highly altered landscape in which processes of

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erosion and deposition no longer function in a manner that maintains an equilibrium between

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wetland creation, destruction and maintenance.2 Instead, managed wetlands are frequently

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equipped with a water distribution system and accompanying water control structures to

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manipulate wetland hydrology and promote the growth of annual moist-soil vegetation valuable

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to wetland dependent wildlife.17 In addition, annual planting of agricultural crops within

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wetlands is another management tool used to emulate lost processes and functions. Planting

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agricultural crops when wetlands are dry not only provides additional food resources for wildlife

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but also resets vegetative succession through soil disturbance.18 An unforeseen consequence of

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planting agricultural crops in floodplain wetlands is the potential for direct application of

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neonicotinoids through use of seed treatments. Application of neonicotinoid treated seeds when

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wetlands are dry may result in hydrophilic compounds persisting in sediments, similar to how

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these compounds perform in arable soil.19,20

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While numerous studies have quantified neonicotinoid concentrations in wetland surface

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water, there is limited information on neonicotinoid concentrations in wetland sediments.11,12,21

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In Canada’s Prairie Pothole Region (PPR), neonicotinoids were detected infrequently in wetland

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sediments (6% of samples) and in concentrations less than or equal to 20 μg kg-1.11 However,

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PPR wetlands are different in structure and function than mid-continent floodplain wetlands and

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may exhibit different patterns of neonicotinoid contamination and persistence.22 Further, PPR



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wetlands that have not been hydrologically modified are generally less susceptible to direct

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planting of agricultural crops compared to floodplain wetlands, a factor which may influence

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neonicotinoid concentrations in sediment.23

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In this study, we evaluated the occurrence of neonicotinoid insecticides in wetland

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sediments and overlying water within managed floodplain wetlands located on state-owned

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Conservation Areas (CAs) in Missouri. Our first objective was to quantify the detection

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frequency and concentrations of neonicotinoids in water and sediment collected from Missouri

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floodplain wetlands throughout the year. Second, we evaluated the relationship between

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neonicotinoid concentrations and agricultural, aquatic, soil, and vegetation variables in

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floodplain wetlands using Boosted Regression Tree (BRT) modeling. Our central hypothesis was

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that neonicotinoid concentrations would be most associated with agricultural land use at varying

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spatial scales from the wetland to the watershed level. Using these objectives, our goal was to

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inform future decisions regarding the use of neonicotinoid seed treatments as a management

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activity in public wetlands, which may pose a risk to wetland ecosystems.

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Materials and Methods

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Study wetlands (n=40) were located on ten state managed CAs throughout Missouri that

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were selected from 22 CAs that met our initial criteria for inclusion. We selected study wetlands

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based on data from the United States Department of Agriculture’s Cropland Data Layer (CDL),

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which we used to calculate the amount of land under agriculture production for each local sub-

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watershed (HUC 12) in Missouri.24 We then plotted the distribution of CAs across the gradient of

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percentage watershed planted to agricultural row crops (e.g. corn [Zea mays], soybean [Glycine

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max]) and randomly selected two CAs from within each of the upper and lower quartiles of


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percentage watershed planted, and six CAs from the middle quartiles (Figure S1). To further

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stratify study sites, we selected four individual wetlands within each CA based on agricultural

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planting history in the previous five years. Within each CA, we selected two wetlands including

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one that, to the area manager’s knowledge, had never been planted with neonicotinoid treated

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seed and a second wetland that had been planted with treated seed annually for the past five

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years. The two remaining wetlands selected on each CA had received treated seed in one to four

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of the previous five years. When there were insufficient wetlands in a treatment category, a

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wetland was chosen randomly from among all available wetlands within the CA to be included

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in the study.

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Sampling Methods: We initiated wetland water and sediment sample collection in spring 2016

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and collected samples during four time periods through spring 2017. Neonicotinoid

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concentrations were thought to be more dependent on timing of management activities rather

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than Julian date.12 Thus, we based sample collection timing on phenology of agricultural and

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wetland management practices at each CA. We collected sediment and surface water (when

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available) from each of the 40 wetlands; prior to agricultural crop planting on the CA (spring

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2016), post-planting (summer 2016), post-autumn inundation (autumn 2016), and one year from

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the original sample date (spring 2017). Additionally, to assess the potential for neonicotinoid

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exposure through wetland water source, water used to inundate wetlands prior to autumn

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sampling was analyzed for neonicotinoids. Water and sediment sampling methods were adapted

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from Main et al. 2014; specific methodological details can be found in the Supporting

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Information.11 Our sampling dates differed slightly among CAs because varying temperatures

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and precipitation dictated when management activities such as crop planting and spring water-



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level drawdown occurred. However, all study wetlands within a CA were sampled within 24

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hours and all study sites were sampled within 30 days during each sampling period.

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Neonicotinoid sample extraction and analysis was performed at the University of

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Nebraska Lincoln Water Sciences Laboratory. Liquid chromatography tandem mass

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spectrometry (LC-MS/MS) was used to quantify concentrations of the six most common

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neonicotinoids (acetamiprid, clothianidin, dinotefuran, imidacloprid, thiacloprid, and

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thiamethoxam). Methodological details associated with LC-MS/MS analysis are described in the

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neonicotinoid analysis section of the SI. Method detection limits (MDL) for all neonicotinoids

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were 0.020 μg L-1 and 0.200 μg kg-1 for water and sediment samples, respectively. Instrument

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derived concentration values were provided by the laboratory for samples that had detectable

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concentrations but were below the MDL. Although there is lower statistical confidence in values

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reported below the MDL, we opted to use the instrument derived concentrations for statistical

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analysis as inclusion of these data is a less biased method compared to other substitution

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methods for regression type analyses.25,26

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Wetland variables: Concurrent with water and sediment sample collection, we also recorded

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basic water quality parameters (e.g., temperature, pH, and conductivity) at each wetland using a

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handheld multi-parameter instrument (YSI®, Pro Plus 2030, 2003 –Pro Series Galvanic

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Dissolved Oxygen Sensor) and pH meter (Hanna instruments, pHep®, HI73127 pH electrode).

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Water quality variables were collected at the three locations within a wetland where composite

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water and sediment samples were also collected and then averaged across a wetland within each

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sampling period. At each sampling location we also measured water depth (cm) from the benthic

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surface to the top of the water column. We assessed vegetation community structure in each


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wetland during peak growing season (August) of 2016 by establishing twenty 1 m2 quadrats

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located along the elevational gradient within the wetland. We identified plant species within each

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quadrat and categorized dominant vegetation as one of five major vegetation community types:

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1) agricultural crop: annually planted by managers (% wetland planted); 2) moist-soil: all annual

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herbaceous plants including grass and forbs (% moist-soil); 3) persistent-emergent: perennial

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wetland plant species (% emergent); 4) woody: all trees or shrubs (% woody); or 5) open water

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or bare soil (% open water). The frequency of quadrats characterized to each dominant

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vegetation type was used to estimate cover (%) for each vegetation community within a

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wetland.27,28 Excess soil from composite samples collected in spring 2017 was retained for soil

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particle size and organic matter analysis because these metrics were identified in previous studies

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as potential factors influencing soil sorption and transport of neonicotinoids.29,30 We determined

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particle size using a hydrometer method and estimated percent organic matter using mass loss-

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on-ignition.31,32

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We quantified agricultural activity for each study wetland at three spatial scales; wetland,

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CA, and the HUC 12 watershed level. Watershed agricultural intensity was estimated using 2014

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CDL land use data, which we used to classify areas with annually planted crops as under

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production (USDA National Agricultural Statistics Service, 2014). Wetland- and CA-scale

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agricultural planting information were obtained both directly from land managers as well as

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through our vegetation surveys. Conservation area managers provided information on type of

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crop planted, planted acreage per crop, and whether a neonicotinoid seed treatment was used.

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Production agriculture is planted on CAs under the terms of an agricultural crop permit between

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the administrating state agency and a permittee farmer. As such, CA managers provided

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information on type of crop planted, planted acreage per crop, and whether a neonicotinoid seed



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treatment was used, but typically lacked information on specific treatment varieties and planting

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rates used by the farmers. For our model, we used the percent wetland area planted to agriculture

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(% wetland planted) and considered the entire wetland “treated” if any of the crop had a

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neonicotinoid seed treatment or untreated for wetlands where no seed treatment was planted. For

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each CA we calculated an overall agriculture percentage (treated and untreated seed) as well as

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the percent of each CA planted with neonicotinoid treated seed. Both CA agriculture terms were

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highly correlated and therefore only the term for neonicotinoid treated crops was used for

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modeling purposes.

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Boosted Regression Trees: Data exploration revealed non-linear relationships between

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neonicotinoid concentrations and numerous independent variables, indicating potential threshold

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values and/or interactions.34 Boosted Regression Trees (BRTs) are a machine learning modeling

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technique that can be used to analyze ecological data with complex interactions and non-linear

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thresholds as well as account for missing values.35–37

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The relatively small sample size of sediment data (n=157) necessitated limiting the

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number of independent variables and interactions included in each model. First, we totaled

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neonicotinoid concentrations detected within a sample to account for the different seed treatment

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brands and formulations used by permittee farmers; totals were then log-transformed to account

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for the zero-inflated data distribution.11 We included twenty independent variables, simplified

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across three categories of a priori models, which we later used to hierarchically select a single

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composite model.38 Initial models were developed individually for variables associated with

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three broad categories; agricultural, abiotic wetland factors, and wetland plant community (Table

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1). The abiotic, agricultural and plant community models initially included seven, six and eight


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independent variables, respectively. All models were fit using a learning rate of 0.001, tree

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complexity = 2, and bag fraction = 0.5 in order to maximize predictive deviance with the proper

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number of trees.35 We performed variable selection for each model category using “gbm

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simplify” in the R package Dismo, which continuously removed variables through 10-fold cross-

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validation until the change in predicted deviance exceeded the original deviance’s standard

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error.39 Top variables retained in each model were combined into a final composite model and

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ranked based on their variable importance (VI) scores, which represented the frequency at which

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a variable was included in the model through all iterations weighted by model performance.35,37

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All BRT models were evaluated using percent deviance explained, where % deviance explained

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= (mean total deviance – cross validated deviance)/ mean total deviance. Our study design of

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repeated measurements collected from wetlands nested within a CA presented the potential for

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spatial and temporal autocorrelation.40 We addressed these potential experimental design issues

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by including a season and CA grouping term in the final composite model and examined the

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change in predicted deviance (SI, BRTs).

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Results

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Neonicotinoid concentrations: Across the four discrete sampling periods, we collected a total of

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160 composite sediment samples from all study wetlands (n =40). However, three samples broke

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during shipment and were excluded from further chemical analysis. Across all sampling periods,

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at least one neonicotinoid was detected in 55 (Spring 2017) to 76% (spring 2016) of collected

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sediment samples. (Table 2). We observed the greatest detection frequency in sediments during

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spring 2016 (76%), largely driven by clothianidin, which was detected in 55% of samples. Total

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mean sediment concentrations ranged from 0.71 μg kg-1 in spring 2016 (pre-planting) to 1.97 μg

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kg-1 in autumn 2016 (post-inundation) with a maximum total neonicotinoid concentration of



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17.99 μg kg-1 (autumn 2016). The most commonly detected neonicotinoids in wetland sediments

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were clothianidin (43% of samples) and imidacloprid (40%) with thiamethoxam (4%) detected

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less frequently. Clothianidin and imidacloprid had similar annual means (0.56 and 0.60 μg kg-1,

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respectively), however clothianidin had greater variation in seasonal means (range: 0.19 – 1.12

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μg kg-1) than imidacloprid (0.26 – 0.85 μg kg-1). Mean sediment concentration for imidacloprid

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peaked during summer sampling (0.85 μg kg-1) whereas clothianidin did not reach peak mean

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concentration until autumn (1.12 μg kg-1).

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Analysis of 149 water samples resulted in detection of at least one neonicotinoid in 60%

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of samples across all sampling periods (Table 3). Detection frequency was greatest in summer

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2016 (77%) and lowest in spring 2017 (28%). Mean total aqueous neonicotinoid concentrations

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ranged from 0.003 to 0.11 μg L-1, with a maximum total concentration of 0.97 μg L-1. Similar to

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sediment concentrations, clothianidin (52%) and imidacloprid (28%) were detected most

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frequently, followed by a lower detection frequency of thiamethoxam (4%). Source water

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samples also most often contained clothianidin (80%) and imidacloprid (60%) with a total mean

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concentration of 0.02 μg L-1.

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Water and sediment concentrations varied and were influenced by a site treatment

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history, with the greatest concentrations (2.18 μg kg-1) occurring in wetland sediments that

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experienced a neonicotinoid treatment annually over the last five years. Notably, among

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wetlands not planted with neonicotinoid treated seed in the previous five years, neonicotinoids

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were present in 50 and 65% of sediment and water samples, respectively (Figure S2; treatment

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history = 0). Wetlands planted with neonicotinoid treated seed in two to four of the previous five

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years exhibited varying concentrations in sediment (mean: 0.54, range:0-10.65 μg kg-1) and


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water (mean: 0.04, range:0-0.15 μg L-1); however, there were insufficient wetlands in these

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categories to establish a definitive trend.

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Across the four sampling periods, neonicotinoids were detected with similar frequency in

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water (60%) and sediment (63%; Tables 2, 3). However, average sediment concentrations (1.19

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μg kg-1) when compared to overlying water concentrations (0.03 μg L-1), indicated that study

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wetland neonicotinoid concentrations may not be in equilibrium. Using published Koc values

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and field measured soil organic carbon content we determined a greater proportion of

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neonicotinoids are retained in the sediment than would be predicted (SI).41 Due to the relative

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novelty of neonicotinoid detections in wetland sediments, we modeled only sediment

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concentrations using BRTs to evaluate environmental factors associated with sediment

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neonicotinoid concentrations.

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BRT Models: The top plant/soil model explained the greatest predictive deviance (27.4%)

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among a priori models. Silt content of the sediment cores (VI=51.9%) and moist-soil vegetation

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cover (VI=48.1%) were retained in the top model after six less informative predictors were

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removed. By comparison, the top agricultural model had the second greatest predictive deviance

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(17.6%) among a priori models. Percent wetland area planted to agriculture (VI=67.3%) and %

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CA planted with treated seed (VI=32.7%) were the best predictors of sediment neonicotinoids.

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Finally, the abiotic water quality model predicted less deviance (16.4%) than the other models

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and retained two predictors (water temperature [VI=52.9%] and water depth [VI=47.1%]) after

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variable selection. Through hierarchical variable selection we identified six variables (Table 1)

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with the most influence from each original model. Variable selection for all a priori models

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contributed two predictors to the composite model. After combining influential variables from



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the individual models into a final model (Figure 1), we found the composite model explained

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31.6% of variation in sediment neonicotinoid concentrations.

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Final composite model variables were ranked by their relative influence with water

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temperature (VI=31.8%) as the most important predictor. Water temperature exhibited a negative

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quadratic relationship with neonicotinoid concentrations, with concentrations peaking between

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15 and 20° C (Figure 2). The next most influential variables were % wetland planted (VI=23.5%)

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followed by water depth (VI=18.3%), which when combined with water temperature accounted

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for approximately 74% of modeled neonicotinoid variation. Wetland area planted had a positive

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effect on sediment neonicotinoid concentrations after a threshold of 25% of the wetland planted,

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while water depth had an overall negative influence on sediment neonicotinoid concentrations,

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particularly >25cm. The three remaining terms, % silt (VI=10.1%), % moist-soil (VI=8.8%) and

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% CA treated (VI=7.4%) accounted for the remaining 26% of model variation. The final

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composite model was evaluated using two forms of cross-validation to evaluate leverage of both

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random subsets of data and individual CAs (Figures S3 & S4in addition to being tested for

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spatial and temporal autocorrelation

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Discussion To our knowledge these results are among the first to demonstrate frequent persistent

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concentrations of neonicotinoid insecticides in wetland sediments in addition to concentrations in

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associated water. Across a one-year time period, three commonly used neonicotinoids

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(clothianidin, imidacloprid and thiamethoxam) were frequently detected in water and sediment

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samples collected in Missouri floodplain wetlands. Based on their Groundwater Ubiquity Score

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(GUS), neonicotinoids have greater leaching potential than other common agricultural chemicals


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(e.g. glyphosate, chlorpyrifos, azoxystrobin). The GUS is measured based on persistence in soil

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and soil organic-carbon adsorption coefficient (Koc).42 Neonicotinoids have long soil half-lives

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(50-545 days) coupled with Kocs of 56-225 L kg-1 resulting in their classification of high to very-

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high leaching potential.43 In addition to high GUS leaching potential, neonicotinoids are water

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soluble (184 - 4,100 mg L-1) resulting in their aqueous horizontal transport to surface waters.44

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Consequently many previous studies of North American aquatic ecosystems have focused on

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neonicotinoid concentrations in water, with less emphasis on quantifying sediment

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concentrations.12,13,45,46 One study that did assess neonicotinoid sediment concentrations reported

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detectable levels in only a small percentage (6%) of prairie pothole wetlands.11 However,

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Missouri wetlands differ substantially from prairie potholes in hydrology, management and

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landscape position.22 Therefore, sediment neonicotinoid contamination in the PPR may not be

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predictive of patterns of contamination found in riverine floodplain wetlands that experience

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annual drawdowns and soil disturbance. Currently, and likely due in part to the infrequent

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detection of neonicotinoids in wetland sediments, US Environmental Protection Agency

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regulations focus on aqueous concentrations. If neonicotinoids are frequently detected in

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sediments across a wider distribution it may be important to consider sediment as an additional

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exposure pathway.

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Seasonal drying and planting of crops in our study wetland impoundments may have led

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to neonicotinoids binding to wetland sediment (similar to agricultural field soil) rather than

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dissolving or binding to suspended sediments in the water column (similar to prairie

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potholes).11,20 Using published Koc values for imidacloprid and clothianidin as well as estimated

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organic carbon content of our sediments, we determined overlying water concentrations to be

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lower than expected, indicating water and sediment values may not have been in equilibrium.47,48



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In Missouri floodplain wetlands, the mean total aqueous neonicotinoid concentration (0.03 μg L-

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1

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waters.12,13,15 Aqueous neonicotinoid concentrations in Missouri wetlands may be influenced by

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the underlying sediment in multiple ways: (1) Concentrations may be lessened through wetland

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sediments retaining a greater proportion of neonicotinoids; (2) Instances where sediment

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concentrations are greater than equilibrium, benthic sediments may act as a source of

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neonicotinoids to the water column through desorption;29,49 and, (3) Neonicotinoids degrade

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much slower in wetland sediments compared to neonicotinoids undergoing photolysis in the

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water column.50 For these reasons aqueous concentrations are likely more temporally dynamic

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than concentrations in sediments, a process that may not be fully captured across our four

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sampling periods.

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) was an order of magnitude less than those reported for other North American surface-

Previous attempts to model neonicotinoid concentrations in surface waters have related

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neonicotinoid concentrations to land use, precipitation events, and wetland variables.12,37,51

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Specifically, Main et al. (2015) similarly used BRTs to model neonicotinoid fate in the PPR of

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Canada and found wetland plant communities, agricultural crop type, and water depth best

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explained aqueous neonicotinoid concentrations. Because neonicotinoid concentrations in

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sediment have not frequently been measured or detected in previous studies, factors associated

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with sediment neonicotinoids remain relatively understudied and therefore, less well-understood.

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As neonicotinoids have been identified as harmful to aquatic invertebrates, the addition of

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sediment neonicotinoid concentrations may present an additional route of exposure that

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currently, is not often quantified.16 Also, if sediment neonicotinoids are not in equilibrium with

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overlying water, sediment concentrations may represent a long term source of neonicotinoids to

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the water column.


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The top variable in the BRT model came from the abiotic water model. Water

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temperature (VI=31.8%) had a negative quadratic relationship with neonicotinoid concentrations

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which could be explained by sediment temperature mediating neonicotinoid sorption and

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degradation processes. It is possible wetland sediment temperatures were correlated with the

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overlying water temperature, and water temperature was a surrogate for this relationship in our

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model. Warmer soil temperatures can lead to increased sorption to soil particles.52 Increasing soil

374

temperatures also increase degradation rates and may have resulted in lower sediment

375

neonicotinoid concentrations observed at greater temperatures.53,54 It is also possible that water

376

temperature was a surrogate measure of season as water temperatures were more dependent on

377

season than wetland. Water temperatures were greatest where water remained in isolated pools

378

during the summer sampling season, however including season as a model covariate did not

379

improve the final model. Water temperatures varied greatly (0.3 – 37.1 ºC), but most temperature

380

values (mean: 16.7 ± 8.8 ºC) fell within the positive portion of the quadratic curve, and therefore

381

inference may be limited to these data.

382

The % area of agricultural crops planted within each wetland during spring 2016

383

(VI=23.5%) had an overall positive relationship with sediment neonicotinoid concentrations and

384

this relationship was strongest in wetlands with >25% area planted. Although wetlands with

385

>25% area planted to agriculture had the greatest sediment concentrations, they were typically

386

less common on the landscape. Most study wetlands (73%) had 8 cm) to shield neonicotinoids

404

from photolysis.59 Further, microbial degradation of clothianidin increases in anoxic

405

conditions.53 Greater water depths slow the diffusion of oxygen to wetland sediments and can

406

create anoxic conditions needed to enhance neonicotinoid degradation. Water depth in floodplain

407

wetlands is often related to hydroperiod and inundated area, with low lying areas typically

408

holding water for the longest duration.17 An estimation of percent wetland inundation among

409

study wetlands showed a similar relationship as water depth, however percent inundation was not

410

retained in the final model (VI=9.1%). Retention of water depth as a negative factor in the model

411

further supports our hypothesis that managed drying in floodplain wetlands increases

412

neonicotinoid sorption and persistence in wetland sediments.


Page 18 of 37

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413


The final BRT model identified silt content (VI=10.6%) as potentially informative,

414

displaying a weak positive quadratic relationship. Previous laboratory tests of both early and

415

later generation neonicotinoids indicated sorption in soils is governed by organic matter, soil

416

particle size, and minerology; however, greater sorption is typically associated with clay and

417

organic carbon, making our findings an outlier.29,30,60,61 It is possible the occurrence of silt in our

418

final model is a reflection of sediments measured or perhaps an indicator of ecological factors

419

(e.g. soil and plant biogeochemical interactions) not measured in this study. Percentage of moist-

420

soil vegetation (VI=8.8%), a vegetation community for which floodplain wetlands are often

421

managed, was negatively associated with neonicotinoid sediment concentrations.17 The

422

mediating presence of native vegetation on neonicotinoid concentrations may be ranked lower in

423

the final model because it was generally inversely related to the wetland area planted to crop, the

424

second most informative variable in the model.

425

Finally, the % CA treated was retained in the final model and indicated a similar

426

threshold type relationship as wetland area planted, however this variable exhibited a weaker

427

relationship at the larger spatial scale. The increase in sediment neonicotinoid concentrations

428

occurring at 25% treated area of the CA is similar to a threshold identified for pesticide

429

(including neonicotinoids) contamination in small streams where a threshold of 28% watershed

430

in agriculture resulted in concentrations exceeding regulatory limits.62 Thirty percent of our

431

randomly selected study CAs included at least 25% treated area of the CA. Therefore, we

432

suggest up to one-third of CAs in Missouri that actively manage floodplain wetlands are likely

433

susceptible to neonicotinoid contamination when accounting for other management variables.

434 435

Our BRT modeling efforts to understand the variation in sediment neonicotinoid concentration data explained 31.6% of environmental variability with six variables. Although



436

soil and plant variables were identified in the literature as informative factors explaining aqueous

437

neonicotinoid concentrations, in the case of Missouri managed wetlands, effects of these

438

variables were not as great as the influence of overlying water variables and agricultural

439

management practices.37,44,63 While acknowledging our BRT model only accounts for one-third

440

of environmental variability, we believe presenting the six most influential variables is important

441

in starting to elucidate the drivers of sediment neonicotinoid concentrations and in helping guide

442

future research questions. BRT model results are often used to identify associations among

443

variables but given their often exploratory nature, these models do not provide insight into the

444

mechanistic explanations of these relationships. Therefore, future experiments are necessary to

445

discern causal relationships between sediment neonicotinoid concentrations and environmental

446

variables.

447

A central hypothesis to our study was that neonicotinoid concentrations would be

448

influenced by wetland management activities, especially use of neonicotinoid treated seed in

449

wetlands. Contrary to this hypothesis, wetland level neonicotinoid treatment variables within

450

models were removed at the original stage of variable selection. No measures of neonicotinoid

451

seed treatment use in 2015 (VI=6.0%), 2016 (VI=1.7%) or the five-year wetland treatment

452

history (VI=1.6%) were selected for inclusion in the composite model. The influence of % CA

453

treated reflects neonicotinoid treatment at a broader scale, however similar results can be

454

obtained using the % CA in agriculture, regardless of neonicotinoid seed treatment. Additionally,

455

because the majority of corn and soybeans planted in the Midwestern United States is treated

456

with neonicotinoids, a simple measure of area planted may be a more important than treatment.9

457

As CAs often rely on permittee farmers to plant agricultural crops, we were not able to control or


Page 20 of 37

Page 21 of 37


458

account for application rate of treated seeds, which may have influenced environmental

459

neonicotinoid concentrations and, ultimately, which variables were included in the final model.

460 461 462

Implications for wetland management Neonicotinoids are detrimental to non-target wildlife populations including pollinators,

463

aquatic invertebrates, and vertebrate taxa at environmentally relevant concentrations.64,65 For

464

aquatic invertebrates, one primary uncertainty is how sediment neonicotinoid concentrations

465

affect wetland invertebrates lethally (i.e. survival), or sub-lethally (growth, behavior, emergence

466

timing). Lethal and sub-lethal effects have been demonstrated for terrestrial invertebrates at soil

467

neonicotinoid concentrations as low as 20 μg kg-1, however effect concentrations can range

468

orders of magnitude greater than this.66,67 Invertebrates common to our study wetlands that

469

interact with benthic sediments and overlying water (e.g. family chironomidae) may be exposed

470

to neonicotinoids multiple times through sediment and water; it remains unclear how these

471

multiple exposure pathways may impact organisms. Further, study wetlands exhibited a range of

472

sediment organic matter (1.6-16.5%) which, while not a predictor of neonicotinoid

473

concentrations, may influence neonicotinoid toxicity in the sediment. Future studies are

474

important to determine environmental levels and toxicity of neonicotinoids in floodplain wetland

475

sediment at broader geographic ranges and additional wetland types to further determine if

476

sediment contamination is regionally or ecosystem specific.

477

Our modeling efforts indicate neonicotinoid sediment concentrations are at least partially

478

influenced by factors manipulated as part of wetland management decisions (e.g. water depth

479

and crop planting). If chemical inputs negatively impact certain management objectives (e.g.

480

invertebrate production), managers may face the decision of whether to plant treated seed to



481

meet other management goals. Maintaining greater water depths in wetlands for longer time

482

periods could have multiple effects on sediment neonicotinoid concentrations. Deeper water

483

levels maintained later in the spring or earlier in the fall could achieve both longer periods of

484

anoxia in wetland sediments and greater temperatures as seasonal light intensity increases,

485

ultimately resulting in faster degradation of neonicotinoids. In wetlands where neonicotinoids are

486

not directly applied, water temperature and depth may be an especially important mitigation tools

487

as managers are unable to directly control the neonicotinoid source, but could instead manipulate

488

local environmental conditions to facilitate neonicotinoid degradation.

489 490

Acknowledgements

491

We would like to thank W. Boys and J. Murray for assisting with field work on this project and

492

D. Tillit, K. Goyne, and R. Blakey who provided technical guidance throughout the study.

493

Additionally, we thank the University of Nebraska’s Water Science Laboratory and D. Snow for

494

assistance with chemical data and methodology. This work was funded through a cooperative

495

agreement with the Missouri Department of Conservation and a grant from Ducks Unlimited

496

Canada’s Institute for Wetland and Waterfowl Research. The Missouri Cooperative Fish and

497

Wildlife Research Unit is jointly sponsored by the MDC, the University of Missouri, the U.S.

498

Fish and Wildlife Service, the U.S. Geological Survey, and the Wildlife Management Institute.

499

Use of trade, product, or firm names is for descriptive purposes only and does not imply U.S.

500

Government endorsement. The authors declare no competing interests.

501 502

Supporting information

503

Detailed information on sample methods, equilibrium calculation, statistical analysis,


Page 22 of 37

Page 23 of 37


504

neonicotinoid analysis, a table listing chemical standards and analytes (Table S1), table of LC-

505

MS/MS retention times (Table S2), a table of LC-MS/MS instrument sensitivity, a map of study

506

sites (Figure S1), neonicotinoid concentrations stratified by treatment history (Figure S2), results

507

of BRT cross-validation (Figures 3&4), an Excel file of detailed sediment and aqueous

508

neonicotinoid concentrations. This information is available free of charge via the Internet at

509

http://pubs.acs.org.

510 511 512

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728 729 730 731 732


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733 734 735 736


Table 1: Variables included in each a priori model, variables bolded were selected based on their variable importance score in their respective models for use in the final Boosted Regression Tree final composite model to estimate sediment neonicotinoid concentrations in Missouri wetlands. model name

737 agriculture

739

variables wetland area planted % CA treated 2016 treatment 2015 treatment Treatment history

variable description Crop planted 2016 (%) Conservation Area planted with treated seed (%) Neonicotinoid treated seed planted 2016 (Y/N) Neonicotinoid treated seed planted 2015 (Y/N) 738 Numbers of years treated in previous 5 years (years)

% watershed in agriculture

Watershed area under agricultural tillage (%)

depth temperature turbidity pH wetland inundation water source

Water depth (cm) Water temperature (°C) Water turbidity (NTU) Water pH Amount of wetland footprint inundated (%) Wetland water source (well, surface water, precipitation) Total neonicotinoid concentration from water source fall 2016 (μg L-1)

740 741 742 743

abiotic

744

water concentration

745

plant/soil

% moist-soil % silt Litter depth % persistent emergent vegetation % open water % woody % loss on ignition % Clay

Amount of annual herbaceous plants (%) Silt fraction of sediment core (%) Depth of organic debris (cm) Amount of persistent emergent or aquatic vegetation (%) Amount of open water or bare ground (%) Amount of trees or shrubs (%) Amount of organic matter lost on ignition (%) Clay fraction of sediment core (%)



746 747 748

Sample Period

Page 34 of 37

Table 2: Mean and max sediment neonicotinoid concentrations (μg kg-1) in Missouri wetlands for the three most commonly detected active ingredients and the sum of those three ingredients (Total) for four sampling periods in 2016 and 2017. Clothianidin Detection Mean Max frequency μg μg % kg-1 kg-1

Imidacloprid Detection Mean Max frequency μg μg % kg-1 kg-1

Thiamethoxam Detection Mean Max frequency μg μg % kg-1 kg-1

Total Detection Mean frequency μg % kg-1

Max μg kg-1

Spring 2016 n=38

55

0.44

9.37

37

0.26

2.45

0

0.00

0.00

76

0.71

9.61

Summer 2016 n=39

31

0.39

7.85

44

0.85

9.77

13

0.07

0.90

62

1.33

10.66

Autumn 2016 n=40

45

1.12

11.93

35

0.74

7.83

5

0.03

1.09

60

1.97

17.99

Spring 2017 n=40

40

0.19

2.96

45

0.54

10.19

0

0.00

0.00

55

0.73

11.27

Overall n=157

43

0.56

11.93

40

0.60

10.19

4

0.03

1.09

63

1.19

17.99

749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764


Page 35 of 37

765 766 767 Sample Period Spring 2016 n=38 Summer 2016 n=39 Autumn 2016 n=40 Spring 2017 n=40 Overall n=157


Table 3: Mean and max aqueous neonicotinoid concentrations (μg L-1) in Missouri wetlands for the three most commonly detected active ingredients and the sum of those three ingredients (Total) for four sampling periods in 2016 and 2017. Clothianidin Detection Mean frequency μg % L-1

Max μg L-1

Imidacloprid Detection Mean frequency μg % L-1

Max μg L-1

Thiamethoxam Detection Mean Max frequency μg μg % L-1 L-1

Detection frequency %

Total Mean μg L-1

Max μg L-1

67

0.01

0.04

3

0.001

0.03

5

0.01

0.12

67

0.01

0.13

55

0.02

0.10

45

0.09

0.97

0

0.00

0.00

77

0.11

0.97

65

0.01

0.03

48

0.01

0.03

3

0.001

0.05

70

0.02

0.06

21

0.002

0.02

18

0.001

0.01

8

0.001

0.01

28

0.003

0.02

52

0.01

0.10

28

0.02

0.97

4

0.002

0.12

60

0.03

0.97

768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784



785

786 787

_______________________________________________________________________

788 789 790 791

Figure 1: Variable importance (VI) scores for variables retained in the final Boosted Regression Tree model explaining sediment neonicotinoid concentrations. Variables retained in the final model were selected through hierarchical model selection. Model ranked VI scores sum to 100, representing the individual contribution of a predictor variable to the overall model.

792 793 794 795 796 797 798 799 800


Page 36 of 37

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801 802

_______________________________________________________________________

803 804 805

Figure 2: Partial dependency plots for: (A) water temperature, (B) % wetland planted, (C) water depth, (D) % silt, (E) % moist-soil, and (F) % conservation area (CA) treated. Plots represent variable effects on log total sediment neonicotinoid concentrations.


Factors influencing neonicotinoid insecticide ... - ACS Publications

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