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Pesticide body burden of the crustacean Gammarus pulex as a measure of toxic pressure in agricultural streams Naeem Shahid, Jeremias Martin Becker, Martin Krauss, Werner Brack, and Matthias Liess Environ. Sci. Technol., Just Accepted Manuscript • Publication Date (Web): 22 Jun 2018 Downloaded from http://pubs.acs.org on June 22, 2018

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Environmental Science & Technology

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Title

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Pesticide body burden of the crustacean Gammarus pulex as a measure of toxic pressure in

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agricultural streams

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Author Affiliation 1. Author: Naeem Shahid *,†,‡,§ ([email protected], phone: 0341 235 1494, fax: +49 341

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235 1494)

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2. Author: Jeremias Martin Becker†,‡

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3. Author: Martin Krauss∥

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4. Author: Werner Brack‡,∥ 5. Author: Matthias Liess*,†,‡ ([email protected], phone: +49 341 235 1578, fax: +49

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341 235 1578)

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†

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Permoserstraße 15, 04318 Leipzig, Germany

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‡

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Germany

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§

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Vehari, Pakistan

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∥UFZ,

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Permoserstraße 15, 04318 Leipzig, Germany

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*

UFZ, Helmholtz Centre for Environmental Research, Department of System-Ecotoxicology,

RWTH Aachen University, Institute for Environmental Research (Biology V), Aachen,

Department of Environmental Sciences, COMSATS Institute of Information Technology,

Helmholtz Centre for Environmental Research, Department Effect-Directed Analysis,

Corresponding author

21 22 23 1 ACS Paragon Plus Environment


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ABSTRACT

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Risk assessments of toxicants in aquatic environments are typically based on the evaluation of

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concentrations in water or sediment. However, concentrations in water are highly variable, while

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the body burden may provide a better time-integrated measure of pesticide exposure and

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potential effects in aquatic organisms. Here, we quantified pesticide body burdens in a dominant

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invertebrate species from agricultural streams, Gammarus pulex, compared them pesticide

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concentrations in water samples, and linked the pesticide contamination with observed ecological

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effects on macroinvertebrate communities.

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In total, 19 of 61 targeted analytes were found in the organisms, ranging from 0.037 to 93.94 ng

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g-1 (wet weight). Neonicotinoids caused the highest toxic pressure among the pesticides detected

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in G. pulex. Using linear solvation energy relationships (LSERs), we derived equivalent pesticide

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concentrations in stream water based on the body burden. These equivalent concentrations

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correlated with the concentrations in water samples collected after run-off (65% of variance

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explained).

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Pesticide pressure significantly affected the aquatic macroinvertebrate community structure,

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expressed as SPEARpesticides, and caused, on average, threefold increased insecticide tolerance in

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G. pulex as a result of adaptation. The toxic pressure derived from body burden and from water

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samples similarly explained the change in community structure (68% and 64%). However, the

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increased tolerance of G. pulex to pesticides was better explained by the toxicity derived from

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body burden (70%) than by the toxicity from water samples (53%). We conclude that the internal

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body burden of macroinvertebrates is suitable to assess the overall pesticide exposure and effects

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in agricultural streams.

2 ACS Paragon Plus Environment

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Abstract Art

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INTRODUCTION

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Pesticides in agricultural streams considerably affect the diversity and species composition of

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aquatic macroinvertebrates1, 2 and, consequently, related ecosystem functions such as leaf litter

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degradation.3-6 Sensitive species show significant reduction even at concentrations more than 2

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orders of magnitude below the acute median lethal concentration (LC50) that kills 50% of the

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individuals of laboratory standard test species.2,

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concentrations include an increased sensitivity of individuals to pesticides under multiple stress

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conditions8 and the culmination of effects from sequential exposure.9 The same low

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concentrations exert a considerable pressure for adaptation, which results in increased pesticide

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tolerance in exposed non-target species, with unknown ecological consequences on their general

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fitness.10,

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regarding the protection from pesticide contamination are necessary. The decision base for such

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measures is a profound quantification and localisation of pesticide exposure and effects through

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monitoring in agricultural streams.

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Reasons for these low environmental effect

To reduce effects of pesticides in the environment, local management measures



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One relevant pathway of pesticides to enter agricultural streams is through run-off from arable

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land after heavy rainfall. Accordingly aquatic organisms are typically exposed to short pulses of

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high pesticide concentrations.12 Thus, relevant measurements of pesticide exposure must include

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run-off events; as such peak concentrations are related to the long-term effects on the

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macroinvertebrate community much better than the low concentrations found in event-

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independent samples.2, 13 However, measuring these short-term pesticide pulses is challenging as

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the peak concentrations depend on the timing and intensity of both the pesticide application and

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the subsequent run-off event, which can vary widely within a spraying season.12, 13

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Currently, a range of passive samplers have been suggested for integrative sampling of various

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pollutants in surface waters14-16 and have also been used to capture short-term pollution events.17,

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linking local toxic pressure to ecological effects on macroinvertebrate communities.3,

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However, the installation of passive samplers is costly and time-consuming. Toxic pressure was

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defined here as a stress exerted by pesticides in streams, using Toxic Unit20 modified by applying

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the highest Toxic Unit - TUmax as a measure for the ecological relevant toxic pressure.2 In the

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present study, we additionally used an alternative quantification of pesticide exposure based on

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pesticide residues in macroinvertebrates sampled from agricultural streams. Organisms

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accumulate pesticides in lipids and proteins from the water phase according to the compound

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properties and therefore, may serve as natural passive samplers.21 Their body burden may

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provide a better time-integrated picture of the overall pesticide exposure than do short-term

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measurements from the water phase. Additionally, an assessment of pesticide exposure based on

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internal concentrations may be more representative because it is the concentration at the site of

Generally, pesticide concentrations determined by passive samplers are highly suitable for


15, 19

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action within organisms, rather than the external concentration in water, that triggers adverse

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effects.22

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We used pesticide residues in the common freshwater shrimp G. pulex to predict the time

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integrated pesticide concentrations in stream water based on equilibrium partitioning23 with a

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linear solvation energy relationship (LSER)24 model. This model estimates the partitioning of

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pesticides into different compartments such as storage and membrane lipids and proteins and

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considers polar and H-bonding interactions rather than hydrophobicity only. The predicted

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equilibrium concentrations in water (Cw,eq) were compared with the pesticide peak

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concentrations in water samples from the same streams following rain events. Additionally, we

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evaluated the ecological effect of the toxic pressure estimated from body burden and from water

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samples on the macroinvertebrate community composition according to the SPEAR approach

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(Species at Risk)2 and on the pesticide tolerance in G. pulex as a consequence of adaptation.

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MATERIALS AND METHODS

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Description of the study sites

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The investigated streams were located in central Germany, including 9 sites from intense

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agricultural landscapes and 6 sites from either undisturbed forested sections or from streams with

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considerable buffer strips and non-agricultural upstream reaches (Supporting Information, Figure

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S1). The second group was considered non-agricultural streams and used as reference sites.

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During macroinvertebrate sampling, different environmental parameters such as water level, pH,

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water temperature, dissolved oxygen and electrical conductivity were measured (Supporting

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Information, Table S1).

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Sampling of Gammarus pulex and water



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Thirty individuals of G. pulex (wet weight 900mg) per site were sampled for the analysis of

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pesticide body burden in May 2016 during the maximum pesticide application period in the

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study area.12, 25 Organisms of different sizes were collected to avoid any bias regarding greater

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bioaccumulation and exposure time. After collection, individuals were transferred to the lab and

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stored at −20°C.

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Water samples from all streams were collected using event-driven samplers (EDS)2 from May to

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July 2016 during the maximum pesticide application period. The number of samples from each

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site varied from 1 to 5, depending on rainfall events. Briefly, two brown glass bottles, each with

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a capacity of 1 L, were installed at each sampling site. Both bottles were attached to a stainless

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rod at heights of 5 and 15 cm from the regular level of the stream water that filled when the

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water level had risen due to heavy rainfall (see Supporting Information, Figure S2 for illustration

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of event-driven samplers). After rainfall events, bottles were collected within 24 h, kept in a cool

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box at 4°C and transported to the laboratory. Subsequently, 1 mL aliquots were transferred into 2

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mL autosampler vials and stored at −20°C for analysis. Results of pesticide concentrations in

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water samples have already been published elsewhere.11 In the present study we compared water

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concentrations (external concentrations) with body burden (internal concentrations) to measure

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the pesticide exposure and potential effects in aquatic organisms.

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Sample preparation and extraction

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Target compounds were extracted from gammarids using the method of Inostroza, et al.26

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(pulverized liquid extraction and a QuEChERS approach with an additional hexane phase).

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Frozen gammarids were transferred to a 10 mL centrifuge tube; 1 mL of LC-MS grade

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acetonitrile, 1 mL of LC-MS grade water and 0.5 mL of LC grade hexane were added; then the

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sample was homogenized using an Ultra-Turrax (IKA, Staufen, Germany) homogenizer.


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Subsequently, 400 mg of MgSO4 and 100 mg of NaCl were added to induce phase separation

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between water and acetonitrile. The top-layer hexane fraction was taken for the analysis of

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pyrethroids via gas chromatography-tandem mass spectrometry (GC-MS/MS), evaporated to

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dryness in a nitrogen stream, reconstituted in 500 µL of ethyl acetate and filtered using a PTFE

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syringe filter (pore size 0.45 µm) into a 2 mL autosampler vial. Permethrin-D6 was added as an

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injection standard. The acetonitrile phase was cleaned up via dispersive solid-phase extraction

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using primary-secondary amine and transferred into a 2 mL glass vial. The sample was

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evaporated to dryness and reconstituted in 450 µL of methanol:water (70:30) for liquid

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chromatography-high-resolution mass spectrometry (LC-HRMS) analysis. A volume of 50 µL of

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internal standard solution containing 40 isotope-labelled compounds (1 µg/mL) was added.

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GC-MS/MS analysis

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The pyrethroid analysis was conducted via GC-MS/MS using an Agilent GC 7890-MS/MS 7000

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system with negative-mode chemical ionization. For separation, we used a HP-5ms UltraInert

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column (30 m x 0.25 mm, 0.25 µm film thickness) equipped with a guard column (5 m x 0.25

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mm). The column oven program started at 100°C, was held for 1 min, increased at 30°C/min to

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150°C, then at 6°C/min to 186°C and at 10°C/min to 300°C, and was then held for 2 min. The

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transfer line temperature was 300°C. Splitless injection of 1 µL was used at an injector

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temperature of 280°C. Pyrethroids were quantified with MassHunter software (Agilent) using

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one quantifier and up to two qualifier MS/MS transitions. A method-matched calibration was

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prepared using water spiked at seven concentration levels ranging from 0.028 to 28 ng (g

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gammarids wet weight)-1, for which the same sample preparation protocol was used as for the

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samples.



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LC-HRMS analysis

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Polar pesticides were analysed using LC-HRMS in positive ion mode (Ultimate 3000 LC system

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coupled to a QExactive Plus MS equipped with a heated electrospray ionization (ESI) source, all

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from Thermo Scientific). Separation was conducted using a methanol:water gradient (eluent A

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and B, respectively, both containing 0.1% formic acid) on a Kinetex C18 EVO column (50 x 2.1

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mm, 2.6 µm particle size, Phenomenex) at a flow rate of 300 µL/min. After eluting for 1 min

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with 5% B, the fraction of B was linearly increased to 100% within 12 min and kept for 11 min.

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Then, the flow was diverted to waste, and the column was rinsed for 2 min using

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isopropanol:acetone 50:50 / eluent B / eluent A (85% / 10% / 5%). Finally, the column was re-

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equilibrated to the starting conditions for 5.7 min. The heated ESI source and the transfer

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capillary were operated at 300°C, the spray voltage was 3.8 kV, the sheath gas flow rate was 45

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a.u., and the auxiliary gas flow rate was 1 a.u. The runs combined a full scan experiment (100-

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1000 m/z) at a nominal resolving power of 70,000 (referenced to m/z 200) and data-independent

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MS/MS experiments at a nominal resolving power of 35,000. For the latter, we acquired the data

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using isolation windows of approximately 50 mass units (i.e., m/z ranges 97-147, 144-194, 191-

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241, 238-288, 285-335, 332-382, 379-429, 426-476) and 280 mass units (i.e., m/z ranges 460-

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740, 730-1010). Eight method-matched calibration standards corresponding to levels of 0.056 to

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27.8 ng (g gammarids wet weight)-1 were prepared the same way as the samples using 1 mL of

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spiked water instead of gammarids. For water analysis, the calibration standards at

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concentrations from 1 to 2000 ng/L were prepared using the same method as that applied to the

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samples using 1 mL of pristine stream water, 25 µL of a methanolic analyte stock solution of the


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appropriate concentration, 25 µL of an internal standard solution and 10 µL of a 2 M NH4-

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formate buffer (pH 3.5).

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For data evaluation, the software Trace Finder 3.2 (Thermo) was used, employing internal

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calibration with the isotope-labelled compound with the closest retention time of each analyte.

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For quantification, we used the full scan extracted ion chromatogram (7 ppm window), while for

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confirmation, one or two diagnostic MS/MS fragments and isotope patterns were compared with

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those of reference standards. Table S2 provides an overview of all analysed pesticides, their

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physicochemical properties, method detection limits and recoveries for the gammarid extraction

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method and Table S3 contains detected pesticides at all study sites.

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Calculation of chemical activity and partition coefficients

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We assumed proteins and lipids as the relevant phases for the absorption of organic pollutants

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and calculated estimated equilibrium concentrations (Cw,eq) in body tissues of G. pulex (Eq. 1).

‫ܥ‬௪,௘௤ =

‫ܥ‬௕,௠ (݂ௌ௅ ‫ܭ‬ௌ௅ௐ + ݂௠௟ ‫ܭ‬ெ௅ௐ + ݂௉ ‫ܭ‬௉ௐ ) × 0.2175

(1)

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where Cb,m is the total measured concentration of contaminants in body tissues, fSL is the storage

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lipid fraction, KSLW represents the storage lipid-water partitioning coefficient, fML is the

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membrane lipid fraction, KMLW represents the membrane lipid-water partition coefficient, fP is the

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protein fraction, and KPW is the protein-water partitioning coefficient. To calculate gammarid-

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water partition coefficients, the composition of G. pulex (per dry weight) was assumed as 47%

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protein,27 4.5% storage lipid and 1.5% membrane lipid.28 The factor 0.217529 was used for the

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conversion of wet weight to dry weight. This assumption is well in agreement with typical values

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from more in-depth studies on the composition of Gammarus pulex,30 although ignoring



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variability between sexes, seasons and individuals. The present experimental setting did not

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allow for measuring individual lipid contents in the specimens used in the experiments.

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Partition coefficients were calculated using linear solvation energy relationships (LSERs, Eq. 2)

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for (1) protein (muscle)–water, (2) storage lipid–water and (3) membrane lipid–water. The

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system parameters (v, e, s, a, b, and c) used for the respective systems were taken from the open-

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access UFZ-LSER database.24 The compound descriptors (V, E, S, A and B) were calculated for

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all compounds using the software ACD/Percepta (ACD labs, v2016; Supporting Information,

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Table S4).

203

log ‫ܭ‬௜ = ‫ ܸݒ‬+ ݁‫ ܧ‬+ ‫ ܵݏ‬+ ܽ‫ ܣ‬+ ܾ‫ ܤ‬+ ܿ

(2)

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Calculation of pesticide exposure

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Measured and estimated equilibrium water concentrations of pesticides were converted into toxic

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units (TU) representing the decadic logarithm of the concentration of a compound divided by its

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LC50 in a 48 h acute toxicity test with a standard reference organism. We estimated the overall

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pesticide-induced toxic pressure in a stream as the maximum toxic unit of all compounds (TUmax;

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Eq. (3)2), quantified either from internal body burdens (TUmax-Int) or from external water samples

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(TUmax-Ext). TUmax works very well for agricultural streams but may not for other ecosystems that

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receive contamination from many different sources.31

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஼௜

௡ TU୫ୟ୶ = Max௜ୀଵ ቂlogଵ଴ ቀ௅஼ ቁቃ ఱబ೔

(3)

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where TUmax is the highest toxic unit of n pesticides at a given site, Ci is the freely dissolved

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pesticide concentration (µg L−1), and LC50i is the median lethal concentration (µg L−1) of that

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pesticide for a reference organism after a 48 h exposure (Supporting Information, Table S5). We

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used Daphnia magna, Chironomus tentans, Chironomus riparius and Hyalella azteca as

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reference organisms; the LC50 values were obtained from the ECOTOX database32 and the 10 ACS Paragon Plus Environment

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Pesticide Properties Database PPDB.33 When data from several organisms were available for the

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same compound, the most sensitive organism was used. For instance, Chironomus riparius and

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Hyalella azteca showed much higher sensitivity to neonicotinoid insecticides as compared to

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Daphnia magna and were therefore considered as reference organisms. In few cases, the name of

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the reference species was not provided in the database and therefore considered unknown

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(Supporting Information, Table S5).

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Characterizing effects of pesticides on the macroinvertebrate community

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At each sampling site, we quantified the long-term effects of pesticides on the macroinvertebrate

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community composition using the bio-indicator SPEARpesticides2 The SPEAR index categorizes

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aquatic macroinvertebrates into vulnerable and non-vulnerable species on the basis of four

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ecological traits: (1) potential for pesticide exposure, (2) physiological sensitivity, (3) generation

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time (pace of recovery), and (4) migration ability (recolonization). We calculated SPEAR using

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the desktop application SPEAR Calculator 0.10.0.2, 34

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Stream macroinvertebrates were sampled from May to July 2016 along with the collection of G.

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pulex. At each site, ten subsamples were collected from different habitats along a 50 m stream

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section. Macroinvertebrates were collected using a 25 × 25 cm kick-net with a 500 µm mesh

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size. The content of the Surber sampler was filtered through a set of four sieves (mesh sizes: 8

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mm, 4 mm, 2 mm, and 500 µm; Retsch GmbH, Haan, Germany) and transferred into plastic trays

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for the identification of taxa (family level) and determination of abundance. When the family of

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an organism could not be identified, the individuals were preserved in ethanol and identified in

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the laboratory with a stereo-microscope (Zeiss Discovery V20; Oberkochen, Germany). The



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abundance of each family was divided by the sampling area to calculate population densities per

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m² used for the calculation of the SPEARpesticides values.

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Identification of insecticide tolerance in Gammarus pulex

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At each study site, 170 individuals of G. pulex within a size range of 6–10 mm were collected

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together with the individuals used for the body burden analysis, using a 25 × 25 cm kick-net with

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a 500 µm mesh size. The individuals were caught with a pipette and transferred into aerated and

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cooled plastic boxes filled with stream water. On the day of collection, the organisms were

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transported to the laboratory and left to acclimate in white trays filled with a mixture of aerated

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ADaM (Artificial Daphnia medium)35 and steam water at 15°C for 24 h.

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Insecticide sensitivity was assessed in acute toxicity tests following the OECD guidelines for the

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testing of chemicals and the guidelines for rapid tests for community-level risk assessment.36, 37

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We selected the neonicotinoid insecticide clothianidin for sensitivity testing that represents the

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most commonly applied class of insecticides in agriculture for more than a decade.38 A 500 mg L-

254

1

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(Spiess-Urania Chemical GmbH, Germany) in distilled water while stirring for 12 h and further

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diluted in ADaM to obtain the required test concentrations. The test organisms were exposed to

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seven different concentrations i.e., 0, 5, 15, 45, 135, 405 and 1215 µg/L for a period of 48 h.

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Only undamaged and active individuals without visible infection with parasites were selected for

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pesticide exposure. Five organisms were placed in a stainless tea strainer, and four tea strainers

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per concentration were placed in a glass beaker containing 1000 mL of the test solution. During

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contamination, the beakers were constantly aerated and the temperature was maintained at 15°C.

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After exposure for 48 h, the organisms were checked for immobility. Organisms were considered

stock solution of the neonicotinoid insecticide clothianidin was prepared with DANTOP®


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immobile when they neither moved their bodies within 30 seconds of undisturbed observation

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nor after probing with a rod (fanning of gills and antenna was not considered body movement).

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Data analysis

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All data analyses were conducted using the open-source application RStudio version 1.1.383 for

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Windows39 and the basic R version 3.4.3 for Windows.40 To compare the insecticide tolerance in

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different populations, we calculated the EC50 (concentration that immobilized 50% of the test

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individuals) and the 95% confidence intervals from the acute toxicity tests using a generalized

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linear model with a quasi-binomial error distribution and a logit link function. The tolerance of

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G. pulex populations from each site was quantified as the ratio of the local EC50/mean EC50 of all

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populations from non-contaminated streams (TUmax-Int < -3). This EC50 ratio allowed us to

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directly compare the tolerance of different populations to neonicotinoids and thus to quantify

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adaptation in contaminated streams.

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Linear regression was applied to analyse the relationships between the toxic pressure calculated

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from pesticide body burden and water concentration (TUmax-Int and TUmax-Ext, respectively) and

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the change in community structure or the neonicotinoid tolerance. To identify the additional

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effect of the local species diversity on the increase in neonicotinoid tolerance, we applied

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multiple linear regression model. The normal distribution and homoscedasticity of residuals were

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confirmed using normal-Q-Q plots, histograms, plots of residuals vs. fitted values, and statistical

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tests for deviations from the normal distribution and from variance homogeneity. To obtain a

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normal distribution, the EC50 ratio was ln(x) transformed before analysis. Agricultural and non-

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agricultural streams were compared with respect to the SPEAR index, EC50 and physicochemical



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parameters using two-sample t-tests and Welch's t-tests. In cases in which the data were not

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normally distributed, Wilcoxon's rank sum test was used.

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RESULTS

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Pesticide residues in individuals of Gammarus pulex

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A total of 19 chemicals of 61 targeted compounds (see Supporting Information, Table S2) were

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quantified in all organisms collected (Supporting Information, Table S3). Concentrations ranged

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from 0.037 to 14.28 ng g-1 (wet weight) for insecticides, 0.08 to 93.94 ng g-1 for herbicides and

293

0.06 to 12.74 ng g-1 for fungicides. The fungicide prothioconazole-desthio was the most

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frequently detected chemical across all sites. The highest concentration was found for the

295

herbicide diflufenican with 93.94 ng g-1 (Supporting Information, Table S3).

296

Generally, insecticides were detected only in organisms from agricultural streams. The only

297

exceptions were low concentrations of the pyrethroid insecticides cyfluthrin and cypermethrin in

298

organisms from a single non-agricultural stream (Non-Agri 6). Additionally, organisms from

299

non-agricultural streams showed only very low concentrations of the fungicide prothioconazole-

300

desthio (0.17-0.28 ng g-1) and the herbicides diflufenican (2.58 ng g-1) and prosulfocarb (2.18 ng

301

g-1).

302 303

Pesticides in the water samples

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A total of 50 chemicals of 55 targeted analytes were detected in the water samples collected from

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the streams. All the agricultural sites were contaminated with most of the analytes, including

306

insecticides, fungicides and herbicides. The fungicide prothioconazole-desthio and herbicides

307

ethofumesate and terbuthylazine were the most frequently detected chemicals in water samples


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across all the streams. The highest concentration was found for the herbicide ethofumesate with

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84.70 µg L-1. In agricultural streams, the most commonly detected insecticides were clothianidin,

310

thiamethoxam and thiacloprid. The highest concentration was determined for thiamethoxam

311

(2.83 µg L-1). In most non-agricultural streams, only certain herbicides and fungicides were

312

detected. Four of these sites were slightly contaminated (3.4 to 12 ng L-1) with the neonicotinoid

313

insecticide clothianidin (Supporting Information, Table S6).

314 315

Estimation of aquatic pesticide exposure

316

The toxicity of the pesticide mixture measured in water samples after runoff events was

317

expressed in toxic units of the most toxic compound (TUmax-Ext, see materials and methods). To

318

derive the pesticide exposure alternatively from the body burden of G. pulex, we converted the

319

internal pesticide concentrations to estimated equilibrium concentrations in water and calculated

320

the maximum toxic unit (TUmax-Int) for invertebrates. The results of both methods are compared

321

in Table 1.

322

In agricultural streams, the TUmax-Int ranged from -1.3 to 0.7 with a median value of 0.4. In all of

323

the agricultural sites, neonicotinoids represented the most toxic compound. The highest toxicity

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was caused by imidacloprid (TUmax-Int at 5 sampling sites; mean TU = 0.6), followed by

325

thiacloprid (TUmax-Int at 4 sampling sites; mean TU = -0.8). In contrast, the non-agricultural

326

streams were contaminated only to a minor extent. In those streams, the TUmax-Int ranged from -

327

6.2 to -4.3 with a median value of -6.0.

328

The TUmax-Ext in agricultural streams ranged from -3.2 to -1.1 and was always lower than the

329

TUmax-Int, with a mean difference of 1.8 TU. In non-agricultural streams, by contrast, the TUmax-

330

Ext

ranged from -3.3 to -5.0 and was always higher than the TUmax-Int (mean difference: 1.5 TU),



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except for one stream in which insecticides were detected in the organisms (Non-Agri 6).

332

However, TUmax-Int and TUmax-Ext were significantly correlated with one another (adjusted R² =

333

0.65, p < 0.001; Fig. 1). Both TUmax-Int and TUmax-Ext showed a clear separation of streams from

334

intense agricultural areas and from reference streams. However, within these groups the TUmax-Int

335

was not clearly associated with the TUmax-Ext, indicating a relevant amount of residual variation.

336

Table 1

337

Pesticide-induced toxic pressure in the investigated streams, expressed as the toxic unit of the most toxic compound

338

(TUmax). The toxic units were calculated from pesticide concentrations in water samples collected after runoff events

339

(TUmax-Ext); they were also derived from internal pesticide concentrations detected in G. pulex, which were converted

340

to equivalent concentrations in water (TUmax-Int). Additionally, the toxic pressure was estimated using the

341

SPEARpesticides bio-indicator based on the macroinvertebrate community composition (SPEAR), and using the

342

increase in tolerance of G. pulex (EC50) to the neonicotinoid insecticide clothianidin as a result of adaptation. Most toxic compound

TUmax

SPEAR

Site ID

EC50 (95% CI)

Ext

Int

In water

In organisms

Agri-1

-1.8

0.4

Thiamethoxam (I)

Imidacloprid (I)

22.3

263(228-302)

Agri-2

-3.2

0.0

Diflufenican (H)

Thiacloprid (I)

24.4

298(227-388)

Agri-3

-1.9

-0.6

Pirimiphos-methyl (I) Thiacloprid (I)

10.2

321(203-506)

Agri-4

-1.8

0.4

Thiamethoxam (I)

Imidacloprid (I)

17.0

178(148-213)

Agri-5

-1.9

-1.3

Clothianidin (I)

Thiacloprid (I)

32.4

191(135-270)

Agri-6

-1.1

0.5

Thiamethoxam (I)

Imidacloprid (I)

7.0

213(171-265)

Agri-7

-1.7

0.4

Thiamethoxam (I)

Imidacloprid (I)

14.7

153(121-192)

Agri-8

-1.7

0.7

Thiamethoxam (I)

Imidacloprid (I)

25.0

190(144-249)

Agri-9

-1.9

-1.3

Clothianidin (I)

Thiacloprid (I)

15.6

160(121-210)

Non-Agri-1

-3.6

-6.2

Pirimicarb (I)

Prothioconazole-desthio (F)

49.7

83(62-110)

Non-Agri-2

-4.8

-6.1

Prosulfocarb (H)


37.7

94(66-132)

Non-Agri-3

-3.3

-6.0

Clothianidin (I)


48.4

67(37-120)


Page 17 of 29


Non-Agri-4

-3.4

-6.1

Clothianidin (I)


34.4

98(85-111)

Non-Agri-5

-3.8

-4.3

Clothianidin (I)

Diflufenican (H)

37.2

93(65-130)

50.3

57(38-83)

Non-Agri-6

-5.0

-4.4

-

Cyfluthrin

(I)

343

I: insecticide; F: fungicide; H: herbicide; Agri: Agricultural sites; Non-Agri: Non-agricultural sites; Ext: external;

344

Int: Internal.

345

346 347

Fig. 1. Relationship between TUmax-Int (TUmax calculated using

348

equivalent pesticide concentrations in water derived from internal

349

pesticide concentrations of G. pulex) and TUmax-Ext (TUmax calculated

350

using pesticide concentrations measured in water). The grey area

351

corresponds to the 95% confidence interval. R² = 0.67, adjusted R² =

352

0.65, F = 26.5, residual d.f. = 13, p < 0.001.

353 354

Ecological effect assessment



Page 18 of 29

355

To compare how both estimations of pesticide exposure (TUmax-Ext and TUmax-Int) could explain

356

the observed ecological effects of pesticides on macroinvertebrate communities, we applied the

357

SPEARpesticides

358

(http://www.systemecology.eu/indicate/). Agricultural streams showed low to medium

359

SPEARpesticides values from 7 to 32.4, which corresponded to a range from bad (< 11) to moderate

360

(< 33) ecological status as defined by Beketov et al.41 In contrast, non-agricultural streams

361

showed significantly higher SPEARpesticides values (Wilcoxon's rank sum test, W = 54, p-value
44) ecological status (Table 1). Of all environmental parameters measured,

364

SPEARpesticides depended only on pesticide exposure and was equally well explained by the

365

toxicity calculated from body burdens (TUmax-Int, Fig. 2a) and by the toxicity in water samples

366

(TUmax-Ext, Fig. 2b).

indicator.2

Calculation

was

performed

using

the

INDICATE

tool

367

368 369

Fig. 2. Relationship between the community composition of macroinvertebrates, expressed as SPEARpesticides, and

370

water contamination, expressed as toxic units. The grey areas correspond to the 95% confidence interval. (a) The

371

calculation of TUmax-Int was based on the equivalent pesticide concentrations in water derived from internal


Page 19 of 29


372

pesticide concentrations in G. pulex; R² = 0.70, adjusted R² = 0.68, F = 30.89, regression d.f. = 1, residual d.f. =

373

13, p < 0.0001. (b) The calculation of TUmax-Ext was based on pesticide concentrations in water samples collected

374

from streams after run-off events; R² = 0.67, adjusted R² = 0.64, F = 26.28, regression d.f. = 1, residual d.f. = 13, p

375

< 0.0001.

376 377

In the next step, we compared the calculated TUmax values and SPEARpesticides with a second

378

indicator of pesticide effects, the insecticide tolerance of G. pulex from agricultural and non-

379

agricultural sites. On average, populations from agricultural streams showed 3-fold higher

380

tolerance to the neonicotinoid insecticide clothianidin than those from non-agricultural control

381

streams (Welch two-sample t-test, p < 0.001, t = -6.39, d.f. = 9.67; Fig. 3).

382 383

Fig. 3. Comparison of the median effective concentrations (EC50) of G.

384

pulex collected from agricultural (red) and non-agricultural streams

385

(green) after 48 h exposure to the neonicotinoid insecticide clothianidin in

386

the laboratory. The boundaries of the central box are the 25th and 75th



387

percentiles; the horizontal line is the median; the blue dot indicates the

388

mean; and whiskers of the boxplot represent the minimum and maximum

389

values. The significance level for the observations is displayed as: *** =

390

p < 0.001.

391

The observed increase in insecticide tolerance was better explained by the pesticide toxicity

392

estimated from internal body burden (TUmax-Int, adjusted R2 = 0.70, Fig. 4a) than that from

393

pesticide concentrations in water samples (TUmax-Ext, adjusted R2 = 0.53, Fig. 4b). The increase in

394

insecticide tolerance with TUmax-Int tended to be stronger when the local species diversity was

395

below average, but the difference was not significant (adjusted R² = 0.72, F = 3.28, d.f. = 1,

396

residual d.f. = 11, p = 0.097 for the interaction toxic unit:Shannon; Supporting Information,

397

Figure S4). The two indicators of pesticide effects, SPEARpesticides and the insecticide tolerance in

398

G. pulex, were closely correlated (adjusted R² = 0.67; Fig. 5).

399 400

Fig. 4. The freshwater shrimp G. pulex adapted to pesticide exposure, resulting in increased tolerance to the

401

neonicotinoid insecticide clothianidin. Pesticide exposure (in toxic units) were calculated from equivalent

402

pesticide concentrations in water derived from the internal body burden of G. pulex (a) or from pesticide

403

concentrations measured in water samples (b). The increase in tolerance was quantified as the ratio of the local


Page 20 of 29

Page 21 of 29


404

EC50/mean EC50 of all populations from non-contaminated streams (TUmax-Int < -3). The grey areas correspond to

405

the 95% confidence interval. (a) R² = 0.72, adjusted R² = 0.70, F = 33.95, residual d.f. = 13, p < 0.001. (b) R² =

406

0.56, adjusted R² = 0.53, F = 16.58, residual d.f. = 13, p = 0.001.

407

408 409

Fig. 5. Relationship between the community composition of

410

macroinvertebrates, expressed as SPEARpesticides, and the increase in

411

tolerance of G. pulex to the insecticide clothianidin, expressed as ratio

412

of the local EC50/mean EC50 of all populations from non-contaminated

413

streams (TUmax-Int < -3). The grey areas correspond to the 95%

414

confidence interval. R² = 0.70, adjusted R² = 0.67, F = 29.74, d.f. = 13,

415

p < 0.001.

416 417

DISCUSSION

418

Pesticide exposure derived from water samples and body burden 21 ACS Paragon Plus Environment


419

Both the pesticide exposure derived from water samples after runoff (TUmax-Ext) and from body

420

burden in G. pulex (TUmax-Int) showed that the agricultural and the non-agricultural streams

421

differed in the level of pesticide contamination (Table 1). All agricultural streams showed a

422

TUmax-Int ≥ -1.3 and a TUmax-Ext ≥ -3.2, whereas all non-agricultural streams showed a TUmax-Int ≤

423

-4.3 and a TUmax-Ext ≤ -3.3. According to Liess and von der Ohe,2 a pesticide contamination of

424

TUmax > -3 results in considerable chronic effects on macroinvertebrates, and such an effect was

425

confirmed by an increased dominance of tolerant macroinvertebrate taxa (low SPEARpesticides

426

values) and an increased insecticide tolerance of G. pulex in agricultural streams. However,

427

within the agricultural and the non-agricultural streams, no clear correlation was observed

428

between TUmax-Int and TUmax-Ext, which presumably results from the uncertainty associated with

429

both methods.

430

The neonicotinoid insecticides provided the highest toxicity (both in organisms and in the stream

431

water), confirming previous pesticide measurements from water samples in the study area.3, 10

432

Inostroza, et al.42 measured the pesticide body burden in G. pulex from the nearby Holtemme

433

River and identified neonicotinoids as the most toxic compounds. Neonicotinoids represent the

434

class of insecticides that is currently sold in the largest quantities in the study area43 and

435

worldwide.44 Although Inostroza, et al.42 reported no pesticide measurements from water

436

samples, the derived toxicity from body burden (TUmax-Int = -2.7 to -0.07) was generally lower

437

than that in our study. This difference might result from the fact that the highest pesticide

438

concentration is typically found in the small upper reaches of streams due to the dilution effects

439

in larger rivers.45, 46

440

In the present study, we found that in agricultural streams, the TUmax-Int calculated from

441

estimated equilibrium water concentrations derived from body burden was approximately one


Page 22 of 29

Page 23 of 29


442

order of magnitude higher than the TUmax-Ext derived from measured water concentrations (Table

443

1). This difference was most likely due to the uncertainty of calculated partition coefficients

444

between G. pulex and the water phase but might also reflect disequilibrium at highly dynamic

445

water concentrations during a peak event. As shown previously, neonicotinoids have a tendency

446

to exhibit a higher bioaccumulation than that estimated with existing partitioning models.26 The

447

availability of effect concentrations and thus TUs based on internal concentrations in G. pulex

448

would largely remove this uncertainty and provide a better basis for the estimation of effects on

449

populations and communities. However, good correlation between body burden and community

450

effect (SPEAR) shows that even with uncertainties resulting from back calculation to water

451

concentrations, internal concentrations are a useful and robust approach in the assessment of

452

pesticide effects.

453 454

Comparison of pesticide exposure with pesticide effects

455

The effects of pesticide exposure on the macroinvertebrate community composition

456

(SPEARpesticides) were similar to the findings of previous studies in which pesticide exposure was

457

quantified from water and sediment samples.3,

458

(TUmax-Int) explained the changes in the community composition equally as effectively as the

459

toxicity derived from water samples (TUmax-Ext). These results suggest that pesticide

460

concentrations in both water samples after run-off and macroinvertebrates can be used for an

461

adequate assessment of long-term pesticide exposure in streams.

462

In contrast, the TUmax-Int explained the increase in tolerance to the insecticide clothianidin in G.

463

pulex considerably better (adj. R² = 0.70) than the TUmax-Ext (adj. R² = 0.53). This difference

464

resulted primarily from a particularly low TUmax-Ext in a single agricultural stream (Agri-2, Table

7, 47

The toxicity derived from body burden



465

1), whereas TUmax-Int, SPEARpesticides and the insecticide tolerance consistently indicated a high

466

pesticide exposure in this stream. This observation suggests that assessing the pesticide exposure

467

of a stream from water samples was in our case less reliable than the assessment based on body

468

burdens or on observable effects on macroinvertebrates. Pesticide peaks in the stream water may

469

be more subject to unpredictable short-term variation caused by the erratic combination of

470

rainfall and pesticide applications than to the other exposure indicators that rely on longer-term

471

processes. However, peak concentrations in the water still provided an adequate measure of

472

pesticide exposure as the difference from the other indicators became obvious only in one of 15

473

streams (Fig. 2b, Fig. 4b).

474

Consistent with an earlier study,10 we observed that the increase in insecticide tolerance of G.

475

pulex with TUmax-Int was less strong when the local species diversity was high. However, this

476

effect was not significant, probably due to the limited number of study sites compared with

477

Becker and Liess.10 In contrast, Russo, et al.48 even reported increased sensitivity to

478

esfenvalerate in G. pulex populations collected from agricultural streams. Pesticides may weaken

479

exposed organisms,49 rendering them more sensitive to subsequent exposure unless they can

480

adapt to the toxicant. Refuge sections can serve as a source of immigrating sensitive organisms

481

and increase the local species diversity, both of which undermine the local adaptation to

482

pesticides.10, 11, 50 The higher availability of refuge sections at the sites studied by Russo et al.48

483

may therefore explain the increased sensitivity instead of adaptation, as opposed to the

484

populations investigated in Shahid et al.11 and the present study. Another study also reported

485

increased sensitivity51 to thiacloprid in G. pulex populations experiencing pre-exposure to

486

wastewater. Organisms subjected to press disturbance are more likely less tolerant to

487

superimposed pulse disturbances.52 Interestingly, with increasing duration of exposure,


Page 24 of 29

Page 25 of 29


488

gammarids subjected to pure wastewater showed a slight increase in EC50 between 4 to 6 weeks.

489

It might be an indication towards evolution of co-tolerance.53

490

The increase in the insecticide tolerance of G. pulex in agricultural streams was closely

491

correlated with changes in the macroinvertebrate community composition. Both effects of

492

pesticide exposure likely represent a selection process for more tolerant organisms that acts

493

simultaneously on the population and the community levels.54 Therefore, as previously suggested

494

by Luoma,55 the local increase in pesticide tolerance of a rather tolerant species such as G.

495

pulex2 can be used as an approximation

496

community composition and vice versa.

497

We conclude that pesticide residues in macroinvertebrates are suitable to assess the overall

498

pesticide exposure and effects in agricultural streams. This method appears to be at least as

499

reliable as the assessment based on pesticide concentrations in stream water after run-off events:

500

Water concentrations underestimated the high toxicity in one stream that was indicated by body

501

burdens and by the adaptation of macroinvertebrates to insecticides at the species and

502

community levels. In contrast, the back-calculation from body burden to water concentration

503

tended to overestimate the pesticide toxicity in highly exposed streams. However, both

504

assessment methods generally provided adequate results that were confirmed by the observed

505

pesticide effects on macroinvertebrates. We suggest that the comparison between body burden

506

and synthetic passive samplers is an important area for future investigation.

of long-term effects on the macroinvertebrate

507 508

ACKNOWLEDGEMENTS

509

We thank Oliver Kaske and Klaus Seyfarth from the Department of System-Ecotoxicology,

510

Helmholtz Centre for Environmental Research GmbH – UFZ, and Stefanie Lippmann from the



511

University of Koblenz-Landau for their support in the collection and identification of

512

macroinvertebrates. We also acknowledge German Academic Exchange Service (Deutscher

513

Akademischer Austauschdienst, DAAD) for financially supporting to N.S through doctoral

514

fellowship. A free academic license for JChem, InstantJChem and the Calculator Plugins was

515

kindly provided by ChemAxon (Budapest, Hungary).

516 517

AUTHOR CONTRIBUTIONS

518

N.S. collected the water samples and the invertebrates for the chemical analysis and SPEAR

519

calculation, conducted the sensitivity tests, analyzed the data and wrote the ﬁrst draft of the

520

manuscript. J.M.B. contributed to the data analyses, interpretation of results and writing of the

521

manuscript. M.K. and W.B. performed the chemical analyses and contributed to the writing of

522

the manuscript. M.L. conceived the concept of research and contributed to the interpretation of

523

results and writing of the manuscript.

524

SUPPORTING INFORMATION

525

Figures showing the study map, illustration of event-driven water sampler (EDS), comparison of

526

the ecological status of streams, and effect of species diversity on the adaptation to pesticides.

527

Tables showing physicochemical properties of investigated streams, compound descriptors, list

528

of median lethal concentrations and reference organisms used in TU calculation, and lists of

529

pesticides analysed and detected in water and G. pulex (PDF).

530 531

REFERENCES

532 533

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