Pesticide dose : effects on the environment and target and non-target organisms 9780841232099, 0841232091, 9780841232112

Table of contents :
Content: Pesticide Dose – A Parameter with Many Implications / Duke, Stephen O. / Herbicide Dose: What Is a Low Dose? / Kudsk, Per, Department of Agroecology, Aarhus University, Forsoegsvej 1, DK-4200 Slagelse, Denmark
Moss, Stephen, Stephen Moss Consulting, Harpenden, Herts AL5 5SZ, United Kingdom / Dose, Drift, and Non-Target Organisms / Streibig, Jens C., Department of Plant and Environmental Sciences, University of Copenhagen, Hoejbakkegaard Allé 13, DK-2630 Taastrup, Copenhagen, Denmark
Green, Jerry M., Green Ways Consulting LLC, Landenberg, Pennsylvania 19350, United States / Variations in Pesticide Doses under Field Conditions: Pesticide Dose Variation / Velini, E. D., Department of Crop Science, São Paulo State University (Universidade Estadual Paulista “Júlio de Mesquita Filho” UNESP), College of Agricultural Sciences (Faculdade de Ciências Agronômicas), Rua Dr. José Barbosa de Barros 1780, 18.610-307 Botucatu/SP, Brazil
Carbonari, C. A., Department of Crop Science, São Paulo State University (Universidade Estadual Paulista “Júlio de Mesquita Filho” UNESP), College of Agricultural Sciences (Faculdade de Ciências Agronômicas), Rua Dr. José Barbosa de Barros 1780, 18.610-307 Botucatu/SP, Brazil
Trindade, M. L. B., Bioativa Pesquisa e Compostos Bioativos, Botucatu/SP, Brazil
Gomes, G. L. G. C., Department of Crop Science, São Paulo State University (Universidade Estadual Paulista “Júlio de Mesquita Filho” UNESP), College of Agricultural Sciences (Faculdade de Ciências Agronômicas), Rua Dr. José Barbosa de Barros 1780, 18.610-307 Botucatu/SP, Brazil
Antuniassi, U. R., Department Rural Engineering, São Paulo State University, College of Agricultural Sciences, Rua Dr. José Barbosa de Barros, 1780, 18610307 Botucatu/SP, Brazil / Catch 22: All Doses Select for Resistance. When Will This Happen and How To Slow Evolution? / Gressel, Jonathan / Reduced Fungicide Dose in Cereals: Which Parameters To Consider? / Jørgensen, Lise Nistrup, Department of Agroecology, Aarhus University, Forsøgsvej 1, Flakkebjerg, 4200 Slagelse, Denmark
Ørum, Jens Erik, University of Copenhagen, Food and Resource Economics Institute, Rolighedsvej, Copenhagen, Denmark / Perspectives on Hormesis and Implications for Pesticides / Calabrese, Edward J. / Occurrence and Significance of Insecticide-Induced Hormesis in Insects / Cutler, G. Christopher, Department of Plant, Food, and Environmental Sciences, Faculty of Agriculture, Dalhousie University, P.O. 550, Truro, Nova Scotia, Canada, B2N 5E3
Guedes, Raul N. C., Department of Entomology, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil, 36570-000 / Chemical Hormesis on Plant Pathogenic Fungi and Oomycetes / Pradhan, Sumit, Department of Entomology and Plant Pathology, Oklahoma State University, Stillwater, Oklahoma 74078 United States
Flores, Francisco J., Department of Live Sciences and Agriculture, Universidad de las Fuerzas Armadas-ESPE, Sangolquí, Ecuador
Melouk, Hassan, Department of Entomology and Plant Pathology, Oklahoma State University, Stillwater, Oklahoma 74078 United States
Walker, Nathan R., Department of Entomology and Plant Pathology, Oklahoma State University, Stillwater, Oklahoma 74078 United States
Molineros, Julio E., Department of Entomology and Plant Pathology, Oklahoma State University, Stillwater, Oklahoma 74078 United States
Garzon, Carla D., Department of Entomology and Plant Pathology, Oklahoma State University, Stillwater, Oklahoma 74078 United States / Herbicide-Mediated Hormesis / Belz, Regina G., Agroecology Unit, Hans-Ruthenberg-Institute, University of Hohenheim, Stuttgart 70593, Germany
Duke, Stephen O., National Center for Natural Products Research, Agricultural Research Service, United States Department of Agriculture, Oxford, Mississippi, United States / Effects of Herbicides on Non-Target Terrestrial Plants / Strandberg, Beate, Department of Bioscience, Aarhus University, Vejlsøvej 25, 8600 Silkeborg, Denmark
Boutin, Céline, Environment Canada, Science & Technology Branch, 1125 Colonel By Drive, Raven Road, Carleton University, Ottawa, Ontario K1A 0H3, Canada
Mathiassen, Solvejg K., Department of Agroecology, Aarhus University, Forsøgsvej 1, 4200 Slagelse, Denmark
Damgaard, Christian, Department of Bioscience, Aarhus University, Vejlsøvej 25, 8600 Silkeborg, Denmark
Dupont, Yoko L., Department of Bioscience, Aarhus University, Vejlsøvej 25, 8600 Silkeborg, Denmark
Carpenter, David J., Environment Canada, Science & Technology Branch, 1125 Colonel By Drive, Raven Road, Carleton University, Ottawa, Ontario K1A 0H3, Canada
Kudsk, Per, Department of Agroecology, Aarhus University, Forsøgsvej 1, 4200 Slagelse, Denmark / Low Dose Effects of Pesticides in the Aquatic Environment / Cedergreen, Nina, Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg, Denmark
Rasmussen, Jes J., Department of Bioscience, Aarhus University, Vejlsøvej 25, 8600 Silkeborg, Denmark / Editors’ Biographies /

Citation preview

Pesticide Dose: Effects on the Environment and Target and Non-Target Organisms

ACS SYMPOSIUM SERIES 1249

Pesticide Dose: Effects on the Environment and Target and Non-Target Organisms Stephen O. Duke, Editor Agricultural Research Service U.S. Department of Agriculture Oxford, Mississippi, United States

Per Kudsk, Editor Aarhus University Slagelse, Denmark

Keith Solomon, Editor University of Guelph Ontario, Canada

Sponsored by the ACS Division of Agrochemicals

American Chemical Society, Washington, DC Distributed in print by Oxford University Press

Library of Congress Cataloging-in-Publication Data Names: Duke, Stephen O., 1944- editor. | Kudsk, Per, editor. | Solomon, Keith R., editor. | American Chemical Society. Division of Agrochemicals. Title: Pesticide dose : effects on the environment and target and non-target organisms / Stephen O. Duke, editor, Agricultural Research Service, U.S. Department of Agriculture, Oxford, Mississippi, United States, Per Kudsk, editor, Aarhus University, Slagelse, Denmark, Keith Solomon, editor, University of Guelph, Ontario, Canada ; sponsored by the ACS Division of Agrochemicals. Description: Washington, DC : American Chemical Society, [2017] | Series: ACS symposium series ; 1249 | Includes bibliographical references and index. Identifiers: LCCN 2017028809 (print) | LCCN 2017029478 (ebook) | ISBN 9780841232099 (ebook) | ISBN 9780841232112 Subjects: LCSH: Pesticides--Environmental aspects. | Pesticides and wildlife. Classification: LCC QH545.P4 (ebook) | LCC QH545.P4 P47835 2017 (print) | DDC 363.738/498--dc23 LC record available at https://lccn.loc.gov/2017028809

The paper used in this publication meets the minimum requirements of American National Standard for Information Sciences—Permanence of Paper for Printed Library Materials, ANSI Z39.48n1984. Copyright © 2017 American Chemical Society Distributed in print by Oxford University Press All Rights Reserved. Reprographic copying beyond that permitted by Sections 107 or 108 of the U.S. Copyright Act is allowed for internal use only, provided that a per-chapter fee of $40.25 plus $0.75 per page is paid to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA. Republication or reproduction for sale of pages in this book is permitted only under license from ACS. Direct these and other permission requests to ACS Copyright Office, Publications Division, 1155 16th Street, N.W., Washington, DC 20036. The citation of trade names and/or names of manufacturers in this publication is not to be construed as an endorsement or as approval by ACS of the commercial products or services referenced herein; nor should the mere reference herein to any drawing, specification, chemical process, or other data be regarded as a license or as a conveyance of any right or permission to the holder, reader, or any other person or corporation, to manufacture, reproduce, use, or sell any patented invention or copyrighted work that may in any way be related thereto. Registered names, trademarks, etc., used in this publication, even without specific indication thereof, are not to be considered unprotected by law. PRINTED IN THE UNITED STATES OF AMERICA

Foreword The ACS Symposium Series was first published in 1974 to provide a mechanism for publishing symposia quickly in book form. The purpose of the series is to publish timely, comprehensive books developed from the ACS sponsored symposia based on current scientific research. Occasionally, books are developed from symposia sponsored by other organizations when the topic is of keen interest to the chemistry audience. Before agreeing to publish a book, the proposed table of contents is reviewed for appropriate and comprehensive coverage and for interest to the audience. Some papers may be excluded to better focus the book; others may be added to provide comprehensiveness. When appropriate, overview or introductory chapters are added. Drafts of chapters are peer-reviewed prior to final acceptance or rejection, and manuscripts are prepared in camera-ready format. As a rule, only original research papers and original review papers are included in the volumes. Verbatim reproductions of previous published papers are not accepted.

ACS Books Department

Contents Preface .............................................................................................................................. ix 1.

Pesticide Dose – A Parameter with Many Implications ....................................... 1 Stephen O. Duke

2.

Herbicide Dose: What Is a Low Dose? ................................................................ 15 Per Kudsk and Stephen Moss

3.

Dose, Drift, and Non-Target Organisms .............................................................. 25 Jens C. Streibig and Jerry M. Green

4.

Variations in Pesticide Doses under Field Conditions ........................................ 47 E. D. Velini, C. A. Carbonari, M. L. B. Trindade, G. L. G. C. Gomes, and U. R. Antuniassi

5.

Catch 22: All Doses Select for Resistance. When Will This Happen and How To Slow Evolution? ....................................................................................... 61 Jonathan Gressel

6.

Reduced Fungicide Dose in Cereals: Which Parameters To Consider? ........... 73 Lise Nistrup Jørgensen and Jens Erik Ørum

7.

Perspectives on Hormesis and Implications for Pesticides ................................. 83 Edward J. Calabrese

8.

Occurrence and Significance of Insecticide-Induced Hormesis in Insects ...... 101 G. Christopher Cutler and Raul N. C. Guedes

9.

Chemical Hormesis on Plant Pathogenic Fungi and Oomycetes ..................... 121 Sumit Pradhan, Francisco J. Flores, Hassan Melouk, Nathan R. Walker, Julio E. Molineros, and Carla D. Garzon

10. Herbicide-Mediated Hormesis ............................................................................ 135 Regina G. Belz and Stephen O. Duke 11. Effects of Herbicides on Non-Target Terrestrial Plants ................................... 149 Beate Strandberg, Céline Boutin, Solvejg K. Mathiassen, Christian Damgaard, Yoko L. Dupont, David J. Carpenter, and Per Kudsk 12. Low Dose Effects of Pesticides in the Aquatic Environment ........................... 167 Nina Cedergreen and Jes J. Rasmussen

vii

Editors’ Biographies .................................................................................................... 189

Indexes Author Index ................................................................................................................ 193 Subject Index ................................................................................................................ 195

viii

Preface This book is the outcome of the symposium Pesticide Dose: Effects on the Environment and Target and Non-Target Organisms that was part of the Agrochemical Division of ACS program for the annual meeting held in Boston, Massachusetts in the summer of 2015. The symposium was organized by us (Stephen Duke and Per Kudsk) and Keith Solomon. Pesticide dose is a parameter that is central to pesticide efficacy, effects of pesticides on non-target organisms, evolution of pesticide resistance, and nonintended pesticide effects such as hormesis (the stimulatory effect of a sub-toxic dose of a toxin). Yet, to our knowledge, pesticide dose had never been the topic of a symposium or a book. This first book on the topic is not comprehensive, but covers most of what was discussed in the symposium. We thank the Agrochemical and Environmental Divisions of ACS for cosponsoring the symposium upon which this book is based. We also thank Dow AgroSciences for partial financial support of the symposium. December, 2016

Stephen O. Duke USDA, ARS, NPURU Thad Cochran Research Center University of Mississippi Oxford, Mississippi 38677, United States

Per Kudsk Aarhus University Department of Aroecology Flakkebjerg DK-4200 Slagelse, Denmark

Keith Solomon University of Guelph School of Environmental Sciences, Centre for Toxicology Guelph, Ontario N1G, Canada

ix

Chapter 1

Pesticide Dose – A Parameter with Many Implications Stephen O. Duke* National Center for Natural Products Research, Agricultural Research Service, United States Department of Agriculture, University of Mississippi, Oxford, Mississippi 38677, United States *E-mail: [email protected].

Like pharmaceuticals, pesticides can have unintended effects, even when used at the proper dose. For pesticides, the possible effects are even more diverse, because the chemicals are released immediately into the environment and the dose reaching the intended target(s) and unintended targets can vary widely, even when applied properly. Unlike most pharmaceuticals, pesticides are usually applied to kill more than one pest species with different levels of susceptibility. So, the recommended dose must kill the most tolerant targeted pest species. Subtoxic doses can cause stimulatory effects on many physiological and growth processes (hormesis). The dose used can influence the mechanism of evolved resistance to the pesticide, with high doses favoring target site resistance and low doses favoring other mechanisms. Controlled release formulations of pesticides can alter the kinetics and dose of exposure to targeted and non-targeted species. Technologies such as smart spray systems for weed management have the potential to greatly reduce the amount of herbicides used. These are only a small sampling of the complex and far-ranging effects and implications of pesticide dose.

© 2017 American Chemical Society

Pesticide dose is clearly a parameter with many implications. The dose applied should be within the recommendations for management of the pest(s) targeted. Many considerations go into these recommendations and into whether the pesticides have the desired effect. As discussed below, relatively little of the applied pesticide usually reaches the intended pest(s), even when properly applied, and an even smaller amount reaches the target site after the pesticide reaches the pest. The maximal dose in the environment of the targeted pest(s) occurs at the time of application, and many factors reduce the dose available to the pest thereafter. A significant amount of pesticide can move or drift off site, where non-target organisms can be exposed to the pesticide and its metabolites, sometimes resulting in toxic or other (e.g., hormetic) effects. Even within the habitat of the pest, the doses that reach the intended pest(s) can be highly variable. Methods of application (e.g., soil applied) and type of formulations (e.g., controlled release) of pesticides can alter the kinetics of dose exposure to targeted and non-targeted species. Technologies such as smart spray systems for weed management have the potential to greatly reduce the amount of herbicides used per unit of land area. Since the amount of herbicide applied dwarfs the use of other pesticides in agriculture, such technologies can have significant impacts on total pesticide dose per unit area. These are but a sample of the complex and far-ranging implications of pesticide dose. This introductory chapter summarizes some of what is discussed in the rest of the chapters of this volume. As most pesticides are utilized in agricultural fields, this type of use will be the emphasized in this chapter.

Recommended Pesticide Doses Determination of recommended doses (rates) of pesticides is not a trivial endeavor. Pesticide dose (or rate) recommendations found on pesticide labels are based on the dose that effectively kills the most tolerant pest for which the product is recommended. Thus, the recommended rate is often one that is much higher than needed for effective management of some the more susceptible target species. This difference can influence the type of selection pressure of the same pesticide for different pests, resulting in differences in evolved mechanisms of resistance (1). Furthermore, to ensure that the least sensitive species for which the product is recommended is killed or greatly suppressed, the recommended dose should be one that is effective under a range of environmental conditions that can be expected in the settings for which the pesticide is recommended. For many pesticides, pest developmental stage (e.g., size of susceptible weeds) during which the recommended rate is effective is generally stated on the label. A major consideration of the label is to avoid recommended use rates that would cause unacceptable environmental contamination. Therefore, most pesticide use labels provide instructions on proper application, including places and environmental conditions in which the product should not be applied. There can be liability to a company, both due to unacceptable non-target effects and due to lack of pesticide efficacy when used as directed. So, the balance between 2

the maximum recommended rate and unacceptable non-target effects requires careful deliberation and negotiation with regulatory agencies. Many of the issues considered by those writing the pesticide label can be reduced to the doses that reach the pest as well as those that reach non-target organisms. Effective doses of pesticides can change after introduction of a pesticide due to evolved resistance and/or accelerated degradation of the pesticide in soil caused by changes in soil microbes.

Dose Reaching the Target In most cases, very little of the pesticide that is sprayed into an agricultural field or any other space inhabited by pests actually reaches the pests for which it is intended. Based on insecticide data, Pimentel (2) stated that less than 0.1% of applied pesticides are estimated to reach their target pests. For a dense infestation of weeds, this percentage can be much higher, and for some fungicides, the percentage might be lower. For example, at a dose of 1 kg/ha, 0.003% of a carbaryl insecticide applied to collards was consumed by targeted cabbage white butterfly caterpillars (3), and Joyce (4) reported only 0.0000001% of DDT applied for Heliothis spp. control reached the insects. Obviously, almost all of pesticides applied do not reach the intended pests and are dispersed through the environment, their concentrations changing as they disperse and degrade. Non-target organisms, including humans, inhabit the environments subjected to these ever changing doses of pesticides. Figure 1 depicts some of the factors that are responsible for the very small fraction of applied pesticides reaching targeted pests. These include drift of the pesticide away from the sprayed area and volatilization of the pesticide into the atmosphere. Some of applied pesticides degrade by both biotic and abiotic processes before they reach the pest. For sufficient efficacy, a pesticide must have a sufficiently long environmental half-life to reach the pest at a lethal dose, but the half-life should not be so long as to become a toxicological issue. Depending on when the application is made, much of the pesticides in agriculture are initially deposited on the ground and/or on the crop. For insects and pathogens, placement on the crop is usually desired, but for herbicides, interception by the crop is undesirable, except in the case of parasitic weeds. When sprayed, a certain amount of the pesticide is lost from the pest area due to spray droplet drift and/or volatilization (5). This can be a serious problem, especially when the pesticide is active at very low concentrations and is volatile. For example, movement of highly volatile herbicides such as 2,4-D from tolerant crops where they are applied to highly susceptible crops or other vegetation of value outside the treated field can create serious liability and/or environmental issues (6). Proper application methods (e.g., nozzle type) (7) and formulation (8), as well as adherence to environmental restrictions on the label can reduce this problem.

3

Figure 1. Factors that influence the percentage of a sprayed pesticide reaching the target pests. (see color insert) The nature and density of the pests influences the amount of pesticide reaching the pests. For example, with a high weed density before the crop emerges, a relatively large proportion of a herbicide will reach the weeds. But, even in this situation, emerging weed seedlings can be sheltered by older weeds, resulting in relatively little interception of an applied herbicide by some members of a weed population. After crop emergence, weeds can be shadowed by crops, and these weeds receiving the least herbicide are the most competitive for resources due to their proximity to the crop. Weeds and crops also shelter insects and plant pathogens from direct interception of insecticides and fungicides. Even without vegetation, direct interception of insecticides by insects that represent an extremely tiny fraction of the surface area reached directly by an insecticide application (e.g., interception of contact insecticides by mosquitoes) is generally much less efficient at directly reaching the pest than herbicides. Application to surfaces over which insects moves or, better yet, are consumed by the insect increases the efficiency of delivery. Similarly, fungicides are generally applied to the crops that are or can be infested by plant pathogens. With herbicides, directed application (e.g., sprayer configurations that shield the crop) (9) or rubbing or wiping the pesticide on the pest (e.g., rope wick technologies) (10) can make the application process more efficient, as can recirculating sprayers that recycle spray that is not intercepted by weeds (9). Application to soil or soil incorporation of pesticides can improve efficiency of delivery, especially with systemic pesticides (11), provided they are not too mobile in the soil. Chemigation, the application of pesticides by dissolving them in irrigation water, is a more efficient method of delivery of some pesticides (12). Seed treatment with the pesticides (mainly insecticides, nematicides, and fungicides) can also greatly reduce off target pesticide movement, although drift of pesticide-laden dust during planting has been a perceived problem (13). 4

After the pesticide reaches the pest, it must move to the molecular target site of the particular pesticide. The amount of pesticide reaching the molecular target can be assumed to be a very small percentage of the pesticide reaching the pest. Data on this aspect of the inefficacy of pesticides is generally lacking. Some of the parameters that limit pesticide movement to the target, once in contact with the pest, are delineated in Figure 2. Usually, only a small fraction of the pesticide intercepted by the pest moves into the pest, due to retention or degradation on the pest surface, or wash off or volatilization before it can move into the pest. Once in the pest, the pesticide can be metabolized (a common mechanism of tolerance and evolved resistance) or sequestered where it can do no harm (another mechanism tolerance and evolved resistance).

Figure 2. Factors that influence the amount of a sprayed herbicide that reaches the pest that arrives at the molecular target site. (see color insert) To be effective in multicellular pests, most pesticides must reach certain cell types where the proper functioning of the molecular target is essential, so that movement or translocation to these cells is required. For example, mitotic inhibitor herbicides must move to meristematic cells to be effective. Similarly, insecticides that affect the insect nervous system must reach nerve cells. Once inside the proper cell type, the pesticide must reach the cellular compartment of the molecular target (e.g., cytoplasm, chloroplast, or mitochondrion), where the pesticide should have a preferential binding affinity for its target. For example, the target enzyme of the herbicide glyphosate, 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS), has high affinity for glyphosate when shikimate-3-phosphate is present (14). Significant binding to any other molecule in the cell would dilute the concentration of the pesticide available to the target molecule. A sufficient percentage of the target enzyme or receptor must be bound (inhibited) for the pesticide to have a deadly effect. We can surmise that this percentage will depend on many factors. 5

We know very little about the percent of target site that must be bound for a lethal effect for various pesticide modes of action. However, essential enzymes that that are in high abundance (e.g., RUBP carboxylase in plants) have generally not been chosen as target sites for pesticides, presumably because such target sites would require many kilograms per hectare for efficacy, as there are many kilograms per hectare of the target site in a field of weeds. In some cases of pesticide resistance, organisms have evolved extra copies of genes for the pesticide target (gene amplification), so that the recommended dose of the pesticide does not inhibit a sufficient percentage of the pesticide’s target molecule to kill the pest. For example, much more glyphosate is required for control of glyphosate-resistant palmer amaranth (Amaranthus palmeri) that has evolved resistance by increasing the copy number of genes encoding EPSPS (15). Thus, at recommended field rates, insufficient glyphosate arrives in the cell to inhibit a sufficient proportion of the EPSPS molecules to cause a lethal herbicidal effect. Another important aspect of particular pesticide targets is what percentage of the target in the cell must be bound by the pesticide for lethality. Unfortunately, this is unknown for most pesticides. For some herbicide targets, inhibition of only a small percentage of the target can lead to accumulation of a toxic compound. For example, when the enzyme protoporphyrinogen oxidase (PPO) is inhibited, the substrate, protoporphyrinogen IX accumulates in cell compartments where it would normally not be found (e.g., the cytoplasm or plasma membrane) and is oxidized to protoporphyrin IX (PPIX) by more promiscuous oxidases (16). PPIX is a photosensitizer, which generates singlet oxygen in the presence of light and molecular oxygen, resulting in membrane lipid oxidation and loss of membrane function. Thus, inhibition of only a small fraction of PPO can result in formation of enough PPIX to cause cellular damage. Probably because of this, some PPO inhibitor herbicides are among the lowest use rate herbicides (only a few grams per hectare). At such low application rates, the amount of herbicide actually interacting PPO in the field is minute. Another group of very low application rate herbicides are some of the acetolactate synthase (ALS) inhibitors. ALS is an enzyme involved in synthesis of branched chain amino acids. One of the substrates for this enzyme is α-ketobutyric acid, a phytotoxic compound. Although excellent inhibitors of other enzymes in the branched chain amino acid pathway, such as ketoacid reductoisomerase (KARI), exist, they are not effective herbicides at economical field rates (17). The fact that KARI and enzymes of the branched chain amino acid pathway other than ALS have not materialized as good herbicide targets may be at least partly because none of their substrates are phytotoxic. Accumulation of toxic intermediates or other toxins due to inhibition is likely to reduce the effective dose of an enzyme inhibitor. The binding affinity or the pesticide for the binding site of molecular target influences the effective dose of the pesticide. For example, the slow, but tight, essentially irreversible binding of many ALS inhibitors (18) is another factor that explains the very low recommended doses of many of these herbicides. The tighter the binding, the less pesticide needed. In most cases, pesticides are metabolically modified to some extent by the target pest. So, to be effective, the rate of metabolism must be sufficiently slow for 6

the pesticide to reach the molecular target site at an adequate dose to kill the pest. Xenobiotic chemicals commonly induce expression of a range genes encoding detoxifying enzyme (e.g. (19),). So, the race between metabolic detoxification of the pesticide and getting to the target site is critical. Crops metabolize certain herbicides so rapidly that the herbicide is crop selective. The rate of metabolism can be influenced by other chemicals, both synergists and antagonists. Crop safeners are used to induce rapid metabolism of some herbicides in the crop to allow use of those herbicides with crops (20). In some cases, the pesticide can be compartmentalized into cellular components, such as vacuoles (e.g. (21),) or cell types (e.g., trichomes) that do not contain the pesticide target (e.g. (22),). These factors will increase the dose required for pest control. The small fraction of pesticide that reaches the pest that actually reaches the molecular target reduces the fraction of what is applied that reaches the target site to a miniscule fraction. Clearly, there are a multitude of factors that can be improved to increase the amount of pesticide reaching the molecular target.

Dose Variations within the Treated Area As mentioned earlier, the actual concentration at a particular point in a treated field soon after spraying can vary considerably, depending on many factors. For example, the size of the target can be a source of variation. Tofoli (23) found that the variation in deposition (maximum deposition divided by minimum deposition) of sprayed material decreased three fold as the target size was increased from 0.3 cm to 10 cm in diameter. Another chapter in this volume provides examples of such variations (24). An important parameter in determining variation is the size of the measured point in the field. Even with a simple two dimensional surface, there can be biologically meaningful variation. Sampling square meters will give more uniform results than sampling square centimeters. In a field with no vegetation, at a smaller scale, there will be some points with no pesticide and others with a high dose. The treated area is actually three dimensional because of vegetation (crops and weeds), non-uniform soil surface, and other factors. As the three dimensional properties of the treated area becomes more complex, variation in dose can become more pronounced. Other factors that can influence the uniformity of pesticide coverage include application equipment, applicator competency, pesticide formulation, and weather conditions. Different diluent spray volumes of the same dose of pesticide per unit area can influence uniformity of the pesticide within a treated area. This parameter, along with nozzle type, and whether or not the spray system is air assisted affects droplet size and, in turn, deposition and drift patterns, both which affect the uniformity of application. Efficacy of the same dose of herbicides can vary significantly with spray volume (25). Formulation, the characteristics of the surface on which a pesticide droplet is deposited, humidity, temperature, and other factors will influence spread of the droplet. If a pesticide is systemic within treated plants, it will be further diluted after celluar uptake from the droplet deposition site. The concentrations within 7

the different plant parts and cell can become highly variable, depending how the pesticide is translocated. Spatial variations in pesticide concentrations deposited on soil can be reduced by rainfall and other processes that dissipate the chemical. Thus, variations in pesticide concentrations found immediately after application are reduced over time by a multitude processes. Nevertheless, individuals within pest populations are clearly exposed to variable pesticide doses. Crop architecture can obviously greatly complicate the variation in deposition of pesticides in a crop, especially in more mature crops or vine or tree crops. Spray boom movements can also be a source of variation. Ooms et al. (26) found horizontal movements of the spray boom to be the main source of variations in spray deposits, although wind and horizontal movements also contributed. This was the case with five different sprayers and three different crops. With backpack sprayers, the variation will be even greater, due to operator inconsistencies.

Methods of Reducing Dose While Maintaining Pest Management For environmental and toxicological safety reasons, high efficacy with the lowest dose possible is desirable. Proper formulation of a pesticide can increase the efficacy of a pesticide markedly. Formulations can do this by affecting droplet size and flow rate from the sprayer, by increasing spread of pesticide droplet on the pest or the crop, by increasing adhesion of the spray droplet to the pest, by delaying drying of the droplet on the pest or crops, and by increasing uptake of the pesticide by the crop or pest (27, 28). The effects of a formulations on these and other factors that influence pesticide efficacy can be complex. In some cases, formulation ingredients can be toxic to the target pest and/or non-target organisms. Likewise, water quality and tank-mix partners in a formulation can reduce the efficacy of a pesticide, thus increasing the effective dose of active ingredient. For example, divalent metal cations such a Ca++ reduce the efficacy of the glyphosate anion due to chelation that prevents uptake by the target weed (reviewed in (29)). Chemical synergists can reduce the needed dose of a pesticide. For example, inhibitors of P450 enzymes that metabolically degrade insecticides in target insects, such as piperonyl butoxide, can substantially increase insecticide activity (reviewed in (30)). Conversely, herbicide safeners that enhance crop metabolism of herbicides by induction of a number different detoxification enzymes can increase the dose of herbicide needed for control of some weed species (discussed in (31)). Different agrochemicals can interact either antagonistically or synergistically to change the dose of a pesticide needed. For example, organophosphate insecticides such as malathion can inhibit plant P450 enzymes in weeds to make them more susceptible to some herbicides (discussed in (32)). Some fungicides can increase the efficacy of some insecticides. For example, fungicides that inhibit ergosterol synthesis by inhibiting P450 enzymes involved in ergosterol synthesis can decrease the LD50 of lambda cyalotrhin (33) and neonicotinoid insecticides (34) in honey bees. One would expect to see the same phenomenon with insect pests. Unfortunately, such potentially beneficial interactions have not been the focus of those interested in “integrated pest 8

management”, in part because of the lack of interest among weed scientists, entomologists, and plant pathologists in each others’ fields of study. For insecticides, doses per unit area can be dramatically reduced by confining the pesticide to bait stations that lure the insects with an attractant to a substrate or surface containing a killing insecticide. Such a strategy reduces the amount of insecticide per unit area without changing the amount needed to control the insect. An example of this are attract and kill combinations marketed under the Specialized Pheromone and Lure Application Technology (SPLAT®) tradename (35). Just as attract and kill technologies can reduce the amount of insecticide per unit area, precision agriculture, such as technologies that selectively apply herbicide to weeds, can do the same. Several of these, such as the rope wick applicator, are mentioned earlier in this chapter. Spray systems that detect and only apply herbicide to weeds can also accomplish this. Machine-vision systems that directs a non-selective herbicide only on weeds or even differentiates between crop and different weed species, directing the proper herbicide to appropriate species could greatly reduce the amount of herbicides used (36, 37). There are other methods and approaches and that can improve efficacy of a pesticide, and thus reduce the dose needed for the desired effect. Some of these are improvement of application technology operator training, improved pesticide shelf life (especially important for biopesticides), and avoiding contaminants in tank mixes.

Implications of Low Doses and Preconditioning Doses A low dose is one that is significantly lower than the recommended field application rate. As mentioned earlier, the recommended application rate is one that should control or kill the most tolerant species for which the pesticide is recommended. So, a low dose for the most tolerant target species may often be quite effective on many other targeted species. Also, in many cases, less than the recommended rate of a pesticide can effectively manage all of the targeted pests under most conditions, and farmers can save money and reduce environmental impacts by applying such rates. For example, an extensive, multiyear study of several postemergence herbicides use in soybeans found that half the recommended rates provided similar weed management results to the full, recommended rates (38). However, in general, low pesticide application rates favor evolution of multigenic, non-target site resistance (1). As a result of increasing widespread evolution of pesticide resistance, strong recommendations that farmers use the full, recommended application rate are being made (e.g. (39),). Nevertheless, even with recommended rates, targeted pests often receive doses lower than those needed for pest mortality or even for toxicity symptoms (24). Lower than toxic doses of toxicants can result in stimulation of growth and other pest parameters such as fecundity or longevity, a phenomenon known as hormesis (40). The topics of hormetic effects of fungicides (41), insecticides (42), and herbicides (43) are dealt with in other chapters of this volume. I will not cover 9

the many details of this topic covered in these other chapters, but will mention a some of the findings that suggest important implications of this phenomenon. Belz and Sinkkonen (44, 45) recently showed that, in addition to there being variable doses delivered to a pest, there are subpopulations of a species with different responses to a toxicant in the pest population. This finding has strong implications for the mechanism of low doses in causing the evolution of “creeping” resistance based on multigenic factors. Due to the range of doses at different sites in a field when used at the recommended rate (24), hormetic and non-hormetic sublethal doses occur, both of which can influence selection by the pesticide on the pest population. A prior exposure to a sublethal dose of a toxicant can often reduce the response in the same individual to subsequent exposure, a phenomenon known as preconditioning or adaptive stress response (40). The basis of this in many cases is induction of protective enzyme systems. There are relatively few data describing this in the pesticide literature, although there are many studies in the non-pesticide literature that describe this phenomenon (46). Recent examples with pesticides are the increase in hormesis caused by glyphosate in plants preconditioned with a hormetic dose of glyphosate (47) and decreased efficacy of the fungicide trifloxystrobin on Sclerotinia sclerotiorum in pretreated fungi (48). Cutler and Guedes discuss examples of this phenomenon with insecticides (43). In cases in which there are multiple applications of pesticides, this phenomenon is expected to reduce efficacy of subsequent applications to the same individual pests and influence selection for multigenic resistance.

Conclusions This chapter was meant to give the reader an appreciation for some of the many implications of pesticide doses. Even in the most efficient cases, a very small proportion of applied pesticides actually reaches their molecular targets in the intended pest species. There are many strategies such as improved formulation to more directed application systems that can improve this proportion. Several aspects of the molecular target site in the pest influence the dose needed for efficacy. From the setting of recommended doses by pesticide manufacturers to the implications for evolution of pesticide resistance and non-target toxicological impacts, the ramifications of pesticide doses are complex.

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19. Baerson, S. R.; Sánchez-Moreiras, A.; Pedrol-Bonjoch, N.; Schulz, M.; Kagan, I. A.; Agarwal, A. K.; Reigosa, M. J.; Duke, S. O. Detoxification and transcriptome response in Arabidopsis seedlings exposed to the allelochemical benzoxazolin-2(3H)-one (BOA). J. Biol. Chem. 2005, 280, 21867–21881. 20. Rosinger, C.; Bartsch, K.; Schulte, W. Safeners for herbicide. In Modern Crop Protection Compounds, Vol. 1, Kraemer, W., Ed.; Wiley-VCH Verlag GmbH & Co., KGaA, Weinheim, Germany, 2012, pp. 371-397. 21. Ge, X.; d’Avignon, D. A.; Ackerman, J. J. H; Sammons, R. D. Rapid vacuolar sequestration: the horseweed glyphosate resistance mechanism. Pest Manag. Sci. 2010, 66, 345–348. 22. Stegink, S. J.; Vaughn, K. C. Norflurazon (SAN-9789) reduces abscisic acid levels in cotton seedlings: A glandless isoline is more sensitive than its glanded counterpart. Pestic. Biochem. Physiol. 1988, 31, 269–275. 23. Tofoli, G. R. Irregularity of pesticide spray deposition as affected by nozzle type and target size. Master Thesis, Faculdade de Ciências Agronômicas / Universidade Estadual Paulista, Botucatu, SP, Brazil, 2001. 24. Velini, E. D.; Carbonari, G. A.; Trinity, M. L. B.; Gomes, G. L. I. C. Variation in pesticide doses under field conditions. Am. Chem. Soc. Symp. Ser. 2017, 1249, 47–60. 25. Knoche, M. Effect of droplet size and carrier volume on performance of foliage applied herbicides. Crop Prot. 1994, 34, 221–239. 26. Ooms, D.; Ruter, R.; Lebeau, F.; Destain, M.-F. Impact of the horizontal movements of sprayer boom on the longitudinal spray distribution in field conditions. Crop Prot. 2003, 22, 813–820. 27. Zabkiewicz, J. A. Adjuvants and herbicidal efficacy – present status and future prospects. Weed Res. 2000, 40, 139–149. 28. Zabkiewicz, J. A. Spray formulation efficacy- holistic and futuristic perspectives. Crop Prot. 2007, 26, 312–319. 29. Duke, S. O. Glyphosate. In Herbicides: Chemistry Degradation, and Mode of Action; Kearney, P. C., Kaufman, D. D., Eds.; Marcel Dekker, Inc.: New York, 1988; Vol. 3, pp 1−70. 30. Feyereisen, R. Insect P450 enzymes. Annu. Rev. Entomol. 1999, 44, 507–533. 31. Abu-Qare, A. W.; Duncan, H. J. Herbicide safeners: uses limitations, metabolism, and mechanisms of action. Chemosphere 2002, 48, 965–974. 32. Werck-Reichhart, D.; Hehn, A.; Didierjean, L. Cytochromes P450 for engineering herbicide tolerance. Trends Plant Sci. 2000, 5, 1360–1385. 33. Pilling, E. D.; Jepson, P. C. Synergism between EBI fungicides and a pyrethroid insecticide in the honeybee (Apis mellifera). Pestic. Sci. 1993, 39, 293–297. 34. Iwasa, T.; Motoyama, N.; Ambrose, J. T.; Roe, R. M. Mechanism for the differential toxicity of neonicotinoid insecticides in the honey bee, Apis mellifera. Crop Prot. 2004, 23, 371–378. 35. Mafra-Neto, A.; Fettig, C. J.; Munson, A. S.; Rodriguez-Saona, C.; Holdcraft, R.; Faleiro, J. R.; El-Shafie, H.; Reinke, M.; Bernardi, C.; Villigran, K. M. Development of specialized pheromone and lure application 12

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Chapter 2

Herbicide Dose: What Is a Low Dose? Per Kudsk*,1 and Stephen Moss2 1Department

of Agroecology, Aarhus University, Forsoegsvej 1, DK-4200 Slagelse, Denmark 2Stephen Moss Consulting, Harpenden, Herts AL5 5SZ, United Kingdom *E-mail: [email protected].

In the wake of the steadily increasing number of cases of non-target site resistance (NTSR) in recent years, the discussion of low versus high herbicide doses (‘the dose discussion’) has attracted renewed attention. Several studies have concluded that low doses can select for NTSR phenotypes and this has led to a general recommendation to farmers not to apply doses lower than the recommended dose. The objective of this paper is to discuss whether this simple, and easy to convey, recommendation is justified. It is concluded that the term ‘low dose’ is misleadingly over-simplistic as it makes no reference to herbicide efficacy. Lower doses often provide the same, or very similar, level of control as the recommended dose and therefore can be applied without jeopardizing the sustainability of cropping systems. Some herbicides (e.g. ACCase and ALS inhibitors), some weeds (e.g. Lolium spp.) and some agronomic practices (e.g. repeated application of herbicides with the same site of action) pose a much greater resistance risk than others. Compared to these risk factors, herbicide dose is relatively insignificant. It is recommended 1) that more focus is devoted to generating information on herbicide dose response relationships, 2) that terms like ‘low’ or ‘reduced’ doses are omitted and there is more focus on efficacy levels and 3) that diversity in management and avoidance of ‘high risk’ practices becomes the key points in the advice to farmers, rather than herbicide dose.

© 2017 American Chemical Society

Introduction Herbicide resistance is one of the major threats to effective weed management in the future. Currently 472 unique cases of herbicide resistance have been reported globally, and weeds have developed resistance to 23 of the 26 known herbicide site of actions (1). In most cases resistance is due to a major gene mutation causing resistance to a specific site of action referred to as target site resistance (TSR) but, in recent years, an increasing number of cases of non-target site resistance (NTSR) conferring resistance to several herbicide sites of action have been found (2). In most cases the cause of NTSR is the enhanced ability of the weed to metabolise the herbicide. In contrast to TSR, NTSR is believed to be a polygenic trait. NTSR is widespread in grass species like Alopecurus myosuroides and Lolium rigidum. Evolution of herbicide resistance is primarily determined by the intensity of herbicide selection. Frequent use of herbicides with the same mode of action has proven to be the single most important cause of selection for herbicide resistance, but herbicide dose is also important. It has long been acknowledged that the use of high herbicide doses will promote the evolution of TSR. The reason is that most TSR phenotypes exhibit a very high level of resistance and only resistant plants are likely to survive an application with a high herbicide dose. The resistance level observed in NTSR phenotypes is often much lower than in TSR phenotypes, and the role of herbicide dose on the evolution of NTSR was for a long time unknown and resulted in the so-called ‘dose rate debate’ (3). Recent years have seen a number of publications concluding that the use of low herbicide doses can lead to the rapid development of NTSR in out-crossing species (e.g. (4–7)), whereas the role of dose is less evident in a self-crossing species like Avena fatua (8). The underlying assumption is that recombination between surviving plants will lead to an accumulation of genes conferring NTSR. The fact that both high and low doses select for herbicide resistance, albeit different types, has been described as a ‘Catch 22’ situation (9). As NTSR is considered a bigger threat to weed management than TSR, at least in grass weeds, recommendations on herbicide dose have changed markedly in recent years, promoting the use of high doses and discouraging the use of low doses. In the discussion of the role of herbicide dose on the evolution of herbicide resistance it is often assumed that a ‘high dose’ is a dose equal to, or higher than, the recommended dose while a ‘low dose’ is anything less than the recommended dose. We find this to be a grossly and misleading simplification, not taking into account some important aspects of chemical weed control. Herbicides vary in their efficacy against individual weed species, so a reduced rate (‘low dose’) of a highly active herbicide may be more effective than the full rate, or even double rate (‘high dose’), of a less active herbicide. This is the reality in many practical situations worldwide making the generalized concept of greater selection for resistance at low doses meaningless. Sometimes, there is a naïve assumption that the full rate of a herbicide will kill all weeds, whereas a reduced rate (‘low dose’) will allow survivors. This is rarely the case in real field situations - if it were, many weed species would be eliminated once the residual seed bank in the soil had been exhausted. Ag-chem companies are keen to promote the idea that 16

reduced rates encourage resistance for obvious and understandable commercial reasons. However, we are not aware that any commercial company has generated its own data to support this position in relation to herbicides. Higher herbicide doses are associated with an increased cost to the farmers but, besides the link to the costs of weed management, the choice of herbicide dose also has practical significance in parts of the world, like Europe, where pesticide legislation attempts to reduce pesticide use and farmers are encouraged to minimize doses. The purpose of this paper is to clarify and broaden the discussion on herbicide dose and to call in question the assumption that the recommended dose is an appropriate reference when assessing the risk of NTSR to herbicides.

Intra- and Inter-Specific Variation In the ongoing discussion on high- and low-dose selection, focus has been solely on the intra-specific variation in herbicide susceptibility, i.e. the variation within a population of one weed species. This is illustrated in Figure 1 (3). The intra-specific variation in the dose required to kill an individual is represented by the normal distribution of resistance phenotypes. The broken arrow illustrates the applied dose. Applying a high dose will kill all individuals in the population. Only TSR phenotypes exhibiting a very high level of resistance (located far to the right of the broken arrow) will survive. Where a lower dose is applied, the least susceptible individuals in the population will survive (grey shading area) and, in outcrossing species, it is envisaged that recombination can result in the accumulation of genes conferring NTSR in the offspring. This can, over time, reduce the overall susceptibility of the population (an increasing proportion of the population will survive the low dose, i.e. the normal distribution curve will gradually shift to the right).

Figure 1. A conceptual model illustrating (a) high-dose and (b) low-dose herbicide resistance selection. The variation in susceptibility within a population is represented by the normal distribution curve. The mean resistance phenotype is equivalent to the LD50 value for the population. (Reproduced with permission from reference Neve et al. (2014).Copyright John Wiley and Sons Inc.) 17

In contrast to pest and disease control, where often only one pest species is targeted at a time, with weeds the farmer will normally be applying herbicides to a weed flora consisting of several weed species with some being more abundant or important than others. Major differences are observed in the susceptibility of different weed species ranging from no to full effect at a given dose, as illustrated in Figure 2 for two herbicides and five weed species. Little information is available on the magnitude of the intra-specific variation but it is definitely significantly lower than the inter-specific variation.

Determining the ‘Recommended Dose’ In the EU, as in most countries, an applicant seeking authorization of a herbicide will have to provide a ‘Biological Assessment Dossier’ (BAD) which includes information on the efficacy and selectivity of the herbicide, effects on succeeding and adjacent crops and risk of herbicide resistance. The information is used by the applicant to justify the label claims and support the proposed recommendations of correct use. One part of the biological assessment dossier is a justification of the so-called ‘minimum effective dose’. Typically the minimum effective dose is justified by providing data on the effect of two to four doses on a limited range of weed species with varying susceptibilities. The ‘minimum effective dose’, that will later become the label or recommended dose, is determined largely by the response of the least susceptible weed species.

Figure 2. Efficacy of chlorsulfuron and ioxynil+bromoxynil applied to five weed species in spring barley at the recommended dose (1 N) and 50, 25, 12.5 and 6.25% of the recommended dose (1 N = 4 g a.i./ha chlorsulfuron and 200+200 g/ha ioxynil +bromoxynil). Data from two field trials conducted by Aarhus University. 18

By increasing the dose beyond the ‘minimum effective dose’ the applicant may be able to target more weed species but crop tolerance could be lost or it may no longer be possible to meet the human health and/or environmental criteria that pesticides have to fulfill to be authorized. Hence, from a legislative point of view, it is more correct to talk about a maximum registered dose. Having justified the ‘minimum effective dose’ the remaining part of the BAD only includes data from experiments where the recommended dose was applied, i.e. very little dose response information can be deduced from the BAD. Anyway, the data contained in the BAD is confidential and can only be shared with consultants and farmers with the consent of the applicant. In practice, this means that dose response information is typically generated post-authorization as part of the trials conducted by public institutes, advisory services etc. In many countries no, or little, information is available in the public domain for farmers to make decisions on the appropriate dose. In Denmark herbicides have, for many years, been examined at lower, as well as the recommended dose in the efficacy trials conducted by Aarhus University. Hence, we have access to a unique database on dose response of commercial herbicides. The database forms the backbone of the Danish web-based decision support system ‘Crop Protection Online – Weeds’ (CPO-Weeds). The data have been used to generate the doseresponse curves simulating more than 50,000 combinations of herbicides, weed species, crops and seasons (10, 11). We believe that the data available to farmers in Denmark can serve to illustrate that the term ‘the recommended dose’ makes good sense when considering the spectrum of weed species that can be controlled, but makes little sense when it comes to determining what is a ‘low’ and a ‘high’ dose.

Classification of Dose Depends on the Inherent Activity of the Herbicide Figure 3 shows the dose response curves included in CPO-Weeds for the herbicide mesotrione applied in maize to weeds at the 3 to 4-leaf stage. Of the 45 weed species included (plus three sulfonylurea resistant phenotypes of Alopecurus myosuroides, Papaver rhoeas and Tripleurospermum inodorum), 21 weed species are listed on the label as being effectively controlled by the recommended product dose of 1.5 L/ha (1 N in Figure 3). It is obvious, however, that several of these weed species can be controlled effectively by lower doses. Several weed species would be controlled satisfactorily at e.g. 50% of the recommended dose (1/2 N in Figure 2), whereas doses lower than 50% of the recommended dose will only control a few species. Applying the recommended dose without considering the composition of the weed flora could result in overdosing that, besides being an additional cost to the farmer, is not in compliance with either good agricultural practice or IPM.

19

Figure 3. Simulated dose response curves for the herbicide mesotrione (Callisto, 100 g/L mesotrione) applied in maize to weeds at the 3-4 leaf-stage. Dose response curves were generated based on data from field trials conducted by Aarhus University and data supplied by Syngenta Crop Protection. The signatures are predicted and not observed values

Another example is shown in Figure 4 for the herbicide prosulfocarb. Prosulfocarb is primarily used for grass weed control in winter cereals and is recommended for use in rotation with ALS and ACCase herbicides to reduce the risk of evolution of resistance to these resistance-prone herbicide groups (Heap, 2016). Clearly, the most common grass weed species in Denmark respond very differently to prosulfocarb and applying the maximum recommended product dose of 5 L/ha (1 N in Figure 4) to all grass species would not be sustainable either from an economic or agronomic point of view. Used against Lolium multiflorum the recommended dose should be applied to ensure satisfactory control but against Apera spica-venti dose reductions are possible due to the very flat dose response around the recommended dose. This should not increase the risk of selection of NTSR phenotypes and thus jeopardizing the long-term sustainability of the cropping system.

20

Figure 4. Simulated dose response curves for the herbicide prosulfocarb (Boxer EC, 800 g/L prosulfocarb) applied in winter wheat in the autumn to weeds at the 0-2 leaf-stage. Dose response were generated based on data from field trials conducted by Aarhus University and data supplied by Syngenta Crop Protection, the company holding the authorization of this prosulfocarb product.

Returning to the conceptual model for ‘high’ and ‘low’ dose herbicide selection (Figure 1), the results in Figures 2, 3 and 4 reveal that, in the field, three scenarios are possible depending on the weed species and herbicide. Firstly, a dose reduction has no impact because reduced doses, as well as the recommended dose, provide full control of the whole population (Figure 5A). Secondly, where full doses give full control, a reduced dose will select for the least susceptible phenotypes in the population which may result in a build up of NTSR (Figure 5B = Figure 1). Thirdly, a herbicide will select for the least susceptible phenotypes, irrespective of dose applied, in situations where even the recommended dose does not provide full control (Figure 5C). Knowing which scenario applies is pertinent in a political environment where farmers not only have to address agronomic issues like herbicide resistance, but also have to meet a public demand to reduce the adverse impact of pesticide use on the environment and human health. 21

Figure 5. A modified conceptual model illustrating three possible scenarios of high-dose and low-dose herbicide resistance selection. (The original figure was reproduced with permission from reference Neve et al. (2014).Copyright John Wiley and Sons Inc.)

Conclusions and Recommendations The examples provided here clearly illustrate that no unequivocal relationship exists between reductions in herbicide dose and reductions in herbicide efficacy -maximum control can often be achieved with less than the full recommended dose. This is by no means novel information but, nonetheless, it seems not to be widely reflected either on herbicide labels or in the ongoing discussion on ‘high’ versus ‘low’ doses. We conclude that the term ‘low dose’ is misleadingly over22

simplistic, as it makes no reference to herbicide efficacy. The dose rate debate would benefit from omitting the terms ‘low’ or ‘reduced’ doses and should focus instead on efficacy levels. We acknowledge that detailed information on dose response relationships is not available in all countries but, as highlighted in this paper, such information is important for decision making, not only in relation to the risk of evolution of herbicide resistance, but also to integrated weed management (IWM). Adjusting doses to the conditions in the field is an important component of IWM. It is clear that some herbicides (e.g. ACCase and ALS inhibitors), some weeds (e.g. Alopecurus myosuroides, Lolium spp.) and some agronomic practices (e.g. repeated application of herbicides with the same mode of action) pose a much greater resistance risk than others. Compared to these risk factors, herbicide dose appears relatively insignificant. It is recommended: 1) that more focus is devoted to generating information on herbicide dose response relationships, 2) that terms like ‘low’ or ‘reduced’ doses are omitted and there is more focus on efficacy levels and 3) that encouraging diversity in management and avoidance of ‘high risk’ practices become the key points in the advice to farmers, rather than herbicide dose. Although anecdotal, it is interesting that in the Scandinavian countries, where the use of lower than recommended doses has been promoted more than in any other country in Europe due to long-standing political pressure to reduce pesticide use, herbicide resistance is causing less problems than in most other European countries. In addition, we know of no evidence to support the view that reduced rates of herbicides have been a significant factor in the widespread evolution of herbicide resistance in Europe. We therefore view the dose rate debate as an unfortunate distraction from more important aspects of herbicide resistance evolution.

References 1. 2.

3.

4.

5.

6.

Heap, I. The International Survey of Herbicide Resistant Weeds; 2016. Available at www.weedscience.org. Yu, Q; Powles, S. B. Metabolism-based herbicide resistance and cross-resistance in crop weeds: A threat to herbicide sustainability and global crop production. Plant Physiol. 2014, 166, 1106–1118. Neve, P; Busi, R.; Renton, M.; Vila-Auib, M. Expanding the ecoevolutionary context of herbicide resistance research. Pest Manage. Sci. 2014, 70, 1385–1393. Neve, P.; Powles, S. B. Recurrent selection with reduced herbicide rates results in rapid evolution of herbicide resistance in Lolium rigidum. Theor. Appl. Genet. 2015, 110, 1154–1166. BusiR.; PowlesS. B. Evolution of glyphosate resistance in a Lolium rigidum population by glyphosate selection at sublethal doses. Heredity 2009, 103, 318–325. Manalil, S.; Busi, R.; Renton, M.; Powles, S. B. Rapid evolution of herbicide resistance by low herbicide dosages. Weed Sci. 2011, 59, 210–217. 23

7.

Renton, M.; Diggle, A.; Manalil, S.; Powles, S. B. Does cutting herbicide rates threaten the sustainability of weed management in cropping systems? J. Theor. Biol. 2011, 283, 14–27. 8. Busi, R.; Girotto, M.; Powles, S. B. Response to low dose herbicide selection in self-pollinated Avena fatua. Pest Manage. Sci. 2016, 72, 603–608. 9. Gressel, J. Creeping resistance: the outcome of using marginally effective or reduced rates of herbicides. Proceedings Brighton Crop Protection Conference – Weeds 2005, 587–592. 10. Rydahl, P. A web-based decision support system for integrated management of weeds in cereals and sugar beet. Bulletin OEPP 2003, 33, 455–460. 11. Sønderskov, M.; Kudsk, P.; Mathiassen, S. K.; Bøjer, O. M.; Rydahl, P. Decision support system for optimized herbicide dose in spring barley. Weed Technol. 2014, 28, 19–27.

24

Chapter 3

Dose, Drift, and Non-Target Organisms Jens C. Streibig*,1 and Jerry M. Green2 1Department of Plant and Environmental Sciences, University of Copenhagen, Hoejbakkegaard Allé 13, DK-2630 Taastrup, Copenhagen, Denmark 2Green Ways Consulting LLC, Landenberg, Pennsylvania 19350, United States *E-mail: [email protected].

This chapter deals with dose-response models to describe the relationship between a dose and its effect on target and non-target organisms, often after the dose has been diluted by drift. We define pesticide drift and describe the endeavor to link effects to an arbitrary point outside the field. Lastly, we analyze data from published papers on non-target plants to determine how they contribute to understand the biological effect of herbicide drift. The research bottleneck is in the drift model and the way non-target plants are affected. The variation in determining EDx (Effective Dose at a response level x) among species is often so large that sensitivity of species cannot be unraveled. In particular, when herbicides with large potency differences are included in a study, the effect of herbicides usually stands out, while other factors are not significant. An aspect currently in the news is the novel use of auxin herbicides on genetically modified auxin herbicide-tolerant crops and the problems that can occur when large areas are sprayed nearby very sensitive non-target plants. One additional aspect is that when using “wild species” as test plants, EDx levels will vary much more than when using crops and weed species that are genetically more uniform.

© 2017 American Chemical Society

Introduction The most common reason for off-target pesticide movement is the drift of very small or fine spray particles caused by wind and poorly calibrated application equipment, but drift can also occur because of chemical volatility. Environmental conditions can influence both types of off-target movement and must always be taken into account when applying pesticides. The effect of a certain dose at origin of spray is the basis for a diluted dose at a given drift distance on non-target plants (Figure 1). It requires knowledge of spraying technique, the wind direction, the behavior of the drift fog and the action of the effective dilution on non-target plant at a distance of interest. According to Figure 1, the horizontal displacement of a dose is a function of the travel distance. Eventually, at some distances the effect of a diluted dose would be so small that it would reach the No Observable Effect Level (NOEL) or Lowest Observable Effect Level (LOEL).

Figure 1. A presumed schematic relationship between a spraying at the origin (dotted line) and the dilutions of the dose-response curve caused by drift. Arrows show wind direction. This chapter deals with the dose-response models to describe the relationship between a dose and its effect on target and non-target organisms. Subsequently, we define pesticide drift and link the effect to an arbitrary point outside the field. Lastly, we review published papers on non-target plants to illustrate how they are affected by herbicide dose. 26

Dose-Responses Pesticides are designed to control insects, fungi, and weeds. The level of effect or potency can be defined by ED50, the dose that is required to affect the organism 50% relative to a maximum value. In principle, at very high doses, herbicides will severely injure crops, weeds, and non-target plants, while small doses can have no effect (1). The old axiom of Paracelsus does apply:

At recommended doses, insecticides and fungicides generally do not directly affect plants. Basic studies of susceptibility/tolerance require knowledge of the relationship between dose and plant response from no effect levels at low doses to complete kill at high doses. In this context, the study of herbicides with doseresponse curves is the very same as in general toxicology and pharmacology. General dose response curves are shown in Figure 2. With a wide dose-range, the response goes from zero effect to complete kill. Mathematically the response, y, is a function of the dose, x, described by the log-logistic curve below:

Figure 2. Log-logistic dose-response curves. The continuous biomass response has no finite upper limit whilst that of quantal response could be alive/dead, affected/not affected etc and has a range between 0 to 1. 27

For continuous responses (e.g. biomass, seed yield, height, enzyme) the upper limit, d, is a parameter close to the untreated control (Figure 2). The lower limit at high doses, c, can be different from zero. The ED50 is the dose that halves the responses between the upper and lower limit. The log-logistic model is just one of numerous models that can be used (1, 2). With quantal responses that are defined by either dead/alive or affected/ nonaffected, the upper limit cannot exceed all organisms being affected, which make the proportion 1.0, and the lower limit no organisms affected (usually defined as zero). Dose-response can be described by two parameters, relative slope, b, and Lethal Dose (LD50) (Figure 2). This kind of data are denoted quantal response and requires a regression fit by assuming a binomial distribution of data (3, 4). Working with quantal response data makes interpretation easy, because the restriction of the response range is between 0 and 1. Quantal responses are often converted to response as a percent of control. It may not affect the parameter estimates, but may have a huge effect on the standard errors of parameters, say LD50; therefore, this scaling should be discouraged. There are cases where the curves in Figure 2 do not apply; if very low doses increase, say biomass relative to the untreated control. This phenomenon, called hormesis, can be difficult to reproduce when experiments are done independently (5). The log-logistic dose-response is a symmetric curve around the inflection point, which coincides with the ED50. Other curves could be asymmetric (1), where the ED50 is not a “natural” parameter, but can be derived from the curve fit (2). In toxicology, the World Health Organization classifies a compound by its LD50 on the basis of mortality studies of feeding rats, guinea pigs or hamsters (3, 6). One of the reasons for choosing the 50% level is, it is the most precise estimate of any LDx level (Figure 2) mortality or seedling demise; and it is perhaps a “best way” of identifying resistance, compared to biomass. However, in ecotoxicology and non-target species identification, ED50 or LD50 is not the best response level to operate on, as the ED10 or LD10 is much more informative (Figure 2), but the variation is wide compared to LD50/ED50, as seen in Figure 3. Whatever the analytical problems, the fitting of a dose-response curve should be the first step when defining drift hazards.

Experinental Design Knowing the activity of a herbicide is helpful when designing an experiment. Box et al. (6) stated, “The choice of experimental design depends on the current knowledge hypothesis. The chosen design should explore the shadowed region of the present knowledge, whose illumination is currently believed to be important to progress.” The effect of experimental error can be reduced by proper experimental design. We want to have small standard errors that give narrow confidence intervals for regression parameters and steep relative slopes of the curves. The higher the relative slope, b, the more precise would be the ED50. 28

Figure 3. Dose-response fit and 95% confidence interval for a four parameter log-logistic model, The most precise effective dose level is ED50 as the confidence band is narrower than at other effective dose-levels.

In bioassay, the slope, b, of a dose-response curve can be more complex. Knowing the mode of action of a herbicide can help. For example ALS inhibitors often have shallow slopes whilst photosystem I and photosystem II inhibitors have steep slopes. Contact herbicides may have more variable slopes among otherwise similar experiments (see Table 2 in the section Non-Target Plants). Fitting a four parameter log-logistic regression model to continuous data requires at least four doses (7). Two doses to characterize the middle part of the curve to estimate ED50 and relative slope, b. The other two doses are necessary to estimate the upper limit, d, and the lower limit, c. However, to get proper regression fit we need more than four doses. With biomass assays or other complex endpoints, assay to assay 29

variation in ED50 sometimes make it virtually impossible to delimit dose range within the steepest part of the curve with so few doses. Intuitively, the number of doses for a single response curve should be around six to eight. The most straight forward way to do an assay is to find the ED50 in preliminary experiments and then reduce and increase the doses on either side (Table 1).

Table 1. Four Herbicides Are Being Tested in an Assay. Herbicide # 1 and #2 Have Approximately the Same ED50 while Herbicide #3 Is More Potent and Herbicide #4 Is Even More Potent than Herbicide #3. The Knowledge of ED50 Could Be Based upon Either Preliminary Experiments or Literature (3) Herbicide Dose

1

2

3

4

0

X

X

X

X

1/236

X

1/128

X

X

1/64

X

X

X

X

1/32

X

X

X

X

1/16

X

X

X

X

1/8

X

X

X

X

¼

X

X

X

½

X

X

X

1

X

X

By distributing the doses within the respective herbicides, the ED50 would be determined with high precision. In Table 1, the doses are being increased or diluted by a factor 2 giving the same distance between doses. However, this rather straight forward way of choosing dose-ranges on the basis of assumed ED50 does not apply when it comes to ED10 or ED90, because the variation of the upper and lower part of the regression is larger than the one at ED50 (Figure 3).

Drift Spray drift is defined as the movement of pesticide dust or droplets through the air at the time of application or soon after, to any site other than the area intended. Pesticide drift is most commonly the result of very small or fine droplets produced by spray nozzles moving off-target by wind. Other factors including chemical volatility can also cause pesticide drift under certain conditions. Pesticide drift can contaminate non-target areas and negatively affect human health, the environment and contaminate organic produce. With herbicides, the 30

most common drift problem is contamination and subsequent injury to non-target plants, which include endangered species, crops, and garden plants. The following factors influence chemical spray drift: 1. 2. 3. 4. 5. 6. 7. 8.

Droplet size (nozzle choice) Boom height Speed of application Air-assistance, shielding Dose Crop development, adjacent crops, shelter buffer zones Wind speed Temperature and humidity

The first five factors relate to the technique. Droplet size is the most influential factor and is influenced by the choice of nozzle, spray pressure and spray mixture. Drift is increased when speed is increased due to turbulence. Concerning biological efficacy, a neutral or positive influence of air-assistance is seen dependent on the type of application. There are a number of ways to mitigate spray drift including adjusting spray application parameters, using anti-drift nozzles and drift mitigation adjuvants. Regulatory agencies such as the U.S. Environmental Protection Agency (EPA) are getting more involved in mitigating spray drift. The EPA is mandating that new herbicide labels including the use of 2,4-D and dicamba in auxin herbicide-tolerant crops have very specific application requirements and EPA has initiated a star rating program to evaluate and rank spray Drift Reduction Technologies (DRT). The star rating DRT program is already being used in the United Kingdom (8). The EPA and the public are increasingly intolerant of any herbicide contamination on any non-target organism. Historically, visual plant damage was the most common way to identify herbicide spray drift with follow-up chemical detection in the laboratory to confirm presence of unwanted pesticides. Chemical analysis techniques continue to improve and are now often more sensitive than are bioassays. Depending of the agricultural area, drift becomes a problem when the arable land makes up a large proportion of the landmass. In Denmark, 65% of the area is cultivated, and drift is a serious problem for sensitive crops or habitats that are receiving pesticide load on an annual basis. This is problematic for organic growers, scattered within the conventional farming area. Auxin herbicides are already used on over 200 million ha globally, but the use of auxin herbicide-resistant crops will likely greatly expand their use in new application scenarios more vulnerable to causing drift problems. The increased use of the auxinic herbicides dicamba and 2,4-D in their respective resistant crops has the potential of injuring other non-target crops and reducing biodiversity in field margins and nearby non-crop habitat if unmanaged (9). Off-target movement of auxin herbicides can occur via spray particle and vapor drift. Particle drift is usually more problematic and should be managed with application techniques, drift control adjuvants, and correct decisions as to when, where, and how to apply. 31

Particular troublesome for auxin herbicides would be drift onto highly sensitive crops such as soybeans (Glycine max L. Merr), cotton (Gossypium hirsutum L.), and grapes (Vitis vinifera L.). Interestingly, 2,4-D is safer than dicamba on soybeans, and dicamba is safer than 2,4-D on cotton (10). As little as 0.01% of labeled rate of dicamba can injure soybeans, and 0.001% of labeled rate of 2,4-D butyl ester can injure tomatoes (Lycopersicon esculentum Mill.) and lettuce (Lactuca sativa L.) (11). Of more concern to environmentalists will be any drift onto potentially more sensitive species that are considered endangered or threatened (12). New formulations with less volatile salts and drift control adjuvants will help to reduce off-target movement and could even help to reinvent auxin herbicide technology (13, 14). For this technology to be successful, growers will need to follow label directions and make correct decisions on when and how to apply, based on temperature, wind velocity, droplet size, release height, buffer zones and drift reduction technologies. The illegal use of dicamba and widespread drift complains in 2016 casts serious doubt that growers will follow such directions (15).

Figure 4. Data from Ganzelmeiriers (1995) being subject to the model y= A*xB (19), It is virtually the same as given by De Jong who used inverse polynomials (18). 32

Numerous models have been suggested to predict the effect and extend of pesticide drifts. One of the classical models in Europe has been developed for various crops. Ganzelmeire (cited by Rautmann et al. (16, 17)) has collected data and developed models to predict deposition as function of distances given the wind speed. This has been somewhat extended by de Jong (18). A closer look at the models seems as if the models are virtually identical. The graph in Figure 4 shows a rather good relationship between observed and predicted percent drift. EFSA (European Food Safety Authority) has given model parameters for crops including vegetable below the height of 50 cm. On the basis of data and model fits, recommendations were given, e.g. 75% reduction was already achieved at 0.7 -3 m. Taking into consideration the confidence intervals of the regression the safety margin is in fact close to 3 m.

Non-Target Plants According to the US EPA: “Non-target organism is any organism for which the pesticide was not intended to control. On pesticide labels the intended target organism must be specified. When pesticides can affect other than intended targets, warning must appear of possible contamination of these non-target organism.”. EPA makes rather detailed description of how to test the ecotoxicology of nontarget plants, and suggests species to be used, which include off field species, as well as weed and crop species (20). In contrast to EPA, EFSA has not gone into that much detail but have recently published an opinion paper on “the state of the science on risk assessment of plant protection products for Non-Target Terrestrial Plants (NTTPs) (21). This paper tries defining off-field, in-field, and endangered species. Specific Protection Goals (SPGs) are included and closely linked to ecosystem services and functions, and include maintaining provision of water regulation, food web support, aesthetic values, genetic resources and biodiversity. However, within fields there are rather few plant species competing with crops that are considered non-target for farmers, and, therefore, the EFSA opinion paper is not very operational in practice because too many SPGs are included. The paper also includes almost the same suggestions as to analysis of data and how to present the results, as do the documents from EPA. The Organisation for Economic Co-operation and Development (OECD) also has defined non-target plants. In contrast to EPA and EFSA, non-target plants are outside the target area for crop protection products. Because of this explicitly expressed limitation of what constitutes a non-target plant, it seems operational in practice. All three institutions operate with NOEC and LOEC that often are derived from ANOVA. There are also detailed description of species to be used and the statistical model used. The definition of non-target plants depends upon where they grow. Sometime non-target plants in natural habitats are acting as weeds on arable land and the same applies to specific weeds, because in some situations they are crops. However, EPA defines non-target organism as any organism for which the pesticide was not intended to control. A literature search for the effect of herbicides on non-target 33

plants yielded about 300 references (August 2016, ISI Web of Knowledge). The major part of those was on aquatic plant species. The desired control levels of weeds in the field are currently beyond ED90 and, as mentioned before, the uncertainty at this response level is rather high. In some special cases such as cultivation of GMO crops there is a decided but unapproachable zero weed tolerance, ED100. The same applies for say ED10, which is an effective dose but more variable than is the ED50. Researchers can create conditions in greenhouse and growth chamber bioassays that make herbicides less potent, but usually indoor studies will show more sensitive response levels when carried out under field conditions. Pesticide companies strive at a recommended rate to ensure “necessary” control levels on target pests under a range of conditions so as to be protected against the uncertainties of stage of development of weeds, weather and temperature. Non-target plants growing adjacent to the target area are not expected to ever receive the full recommended rate (Figure 4).

Log Logistic Regression Slope and EDx The majority of papers for the last 10 to15 years have used dose-response analyses to give EDx-levels and sometimes together with associated standard errors or confidence intervals. On the basis of six papers on terrestrial species, the selection criteria were that EDx was associated with standard error or confidence interval; and the regression model was defined with documentation of experimental design. The stage of development at spraying, duration of the experiment either in days or at defined stage of development at harvest time must be stated, as well as defined environment, greenhouse or field. Lastly, the research questions must be explicitly explained. The table data, extracted from the six papers, were subjected to weighted ANOVA of EDx values by using either the standard errors of the EDx or deriving the standard error from 95% confidence intervals. The ANOVA of the table data from the papers were only considered significant if the p-value was below 1%. It was a precaution of not reporting significance by chance. All papers, except one, have listed ED50. In some instances one can adjust to ED10 by using the assumed relative slope of the log-logistic curves on the basis of the mode of action of the herbicides. Table 2 shows ranges of relative regression slopes from various herbicide bioassays with biomass as endpoints. There is no systematic published analysis of the distribution of the relative slopes around the ED50. However, there is some research in the differences among relative slopes in the Danish Crop Protection Online (CPO) (22, 23). The relative slopes in CPO range between 1 and 3. This was based upon the log-logistic model defined by Finney (4), whilst we use the log-logistic model defined by Ritz et al. (24) It means the CPO slopes have to be multiplied with 2 and the relative slopes would be approximately 1-6. In our own lab we have experienced relative slopes below 1, particularly for ALS inhibitors. If the relative slope exceeds 8 it indicates that the responses may not be optimally distributed. 34

Table 2. Experience of Range of Relative Slope of Log-Logistic Dose-Response Curve of Biomass for Various Herbicide Mode of Action Groups. For the Two Photosystem Inhibitors the Slopes Are General Steeper for Contact than for Systemic Herbicides, but Uncertainty for Contact Herbicides Is Much Wider When Repeating Experiments. Mode of action

Relative slope, b

Photosystem I and II

4-6

ALS-inhibitors

1-2

Auxins

1-3

Glyphosate

2-4

Irrespective of estimates of relative slope, b, the upper limit, d, and lower limit, c, of response curves are just a matter of scale range, when comparing various EDx given in the literature (25). On the basis of the slope of the curves and the specific ED50, defined as 1.00, it is possible to predict any other EDx values as shown in Figure 5. The shallower the curves the larger difference between EDx. The slopes, as mentioned earlier, are in fact often the most variable due to environmental variation (26). ANOVA is still being used to find the species sensitivity to herbicides (27–29). The problem with ANOVA is that it does not capture the dose-response relationship so one only can determine a NOEL at pre-defined dose in the ANOVA. It means that the NOEL cannot be compared among papers when different preset doses have been used.

Data Analysis from Published Papers Fletcher et al. (28) studied the effect of chlorsulfuron on non-target crops. The results were analyzed by ANOVA and the lowest dose yielding responses significantly lower than the control was identified. It means that the pre-defined doses, next to the significant dose is determining NOEL. The experiments were done in the greenhouse, and the canola and soybeans were sprayed at various stages of development with chlorsulfuron. By using dose-response curves, the differences at say ED50 and at any other EDx were determined by the entire dose-response curve and not only at a preset dose relative to an untreated control. Obviously, the pre-flower stage of development would, by visual estimate, stand out as significantly different from the rest, but was not in Figure 6, because observations at the lower part of the curve were missing. The use of the ED50 is a well-defined response level and even though it does not give the NOEL; ED25 or ED10 would be more appropriate. But no ED10s were significantly different from zero. The ED10 were in both instances about 0.03-0.05 g ai/ha. The ANOVA NOEL was around 0.1g ai/ha. The ED25 values for canola were all significantly different from zero, but not significantly different from each other. 35

Figure 5. The influence of the relative slope, b, on the differences in EDx values, when the ED50 is 1.00. The most common relative slopes are between 2 to 6.

The purpose of herbicide efficacy trials is to demonstrate effective control on a wide range of weed species, i.e. the effect level observed in the trials with the recommended rate tends to be close to 100%. Inclusion of the two lower dosages (25 and 50% of the recommended dosage) makes it possible to classify weed species according to sensitivity. Even the lowest rate very often produces effects in the range between 75 and 100%. In Denmark, efficacy experiments are carried out in different years and at different locations, i.e. experimental conditions can vary significantly. Consequently, the standard errors of the ED90 values make comparison among herbicide and weeds difficult. However, due to the high number of efficacy trials the results can give indications of differences in sensitivity of weeds and non-target plants between and within plant families (22). 36

Figure 6. Canola sprayed at various stages of development. Neither ED50 nor ED10 were significant different form each other. For Soybean ED50 for Preflower and Preflower +13d were significantly different form each other. At ED10 there were no difference among stage of development. Data from Fletcher et al. (28) Strandberg et al. (30) presented bioassay results of 38 annual weed species that occurred in one or more of the Danish field efficacy trials. ED90 values could be estimated for 5 and 10 weed species, respectively, for metsulfuron-methyl and mecoprop-P applied to winter cereals in the autumn or in the spring (Tables 4.1 and 4.2 (30)). An ANOVA of ED90 for the two herbicides in Figure 7 shows that neither species nor cereal type were significant different, only the effect of herbicides was significant (Figure 7). It is notable that the ED90 seems to be realistic from an efficacy point of view. For metsulfuron-methyl the recommend rate was well above the ED90. For mecoprop-P the recommended rate was not enough to keep the efficacy control level above 90%. In greenhouse and growth chambers, the EDx values are usually smaller than in the field, but greenhouse and growth chamber conditions can be managed to make species less responsive. When the change in potency is separated in different locations, there is usually a correlation (31, 32). Strandberg et al. (30) also published ED50 values for three annual and three perennial species in greenhouses. Biomass was the end point and plants were sprayed at either the vegetative or reproductive stage of development (Figure 8). The ED50 is much lower than the recommended field rates, and there was no difference among species (Figure 8). As some of the species are important to the flora outside the arable land, we could look at the sensitivity if the species were exposed to 1% of the recommended field by using the ED50 to estimate the ED10 (Table 2 and Figure 5). On the basis of Table 2 and the relationship between the EDx and ED50 at different slopes in Figure 4, we have calculated the ED10 and also the 1% of recommended rate. For metsulfuron-methyl, the species in Table 3 were not protected from a 1% of recommended rate. 1% is a fair reduction close to the sprayed field (Table 3). 37

Figure 7. Field assays with two herbicides. Note the y-axis is on a logarithmic scale in order to make a proper separation of box-plots. The horizontal lines with the same color as the boxplot denote recommended rate of the two herbicides (data from Strandberg et al (30), Tables 4.1 and 4.2). The recommended rates seemed to be able to control most of the weed at 90%.

38

Figure 8. Boxplot of ED50 of various herbicides. Note the y-axis is on a logarithmic scale in order to make a proper separation of box-plots. The horizontal lines with the same color as the boxplot denote recommended rate of the three herbicides in Denmark (Strandberg et al. (30) Table 4.4)

Table 3. Theoretical ED10 and 1% of Recommended Rate Herbicide

ED10 g ai/ha

1% of Recommended rate

Glyphosate

50

13

Mecoprop-P

111

30

Metsulfuron-methyl

0.05

0.1

Carpenter and Boutin (33) published greenhouse dose response analyses of glufosinate ammonium and derived the ED50 for biomass. They used the common log-logistic curve, Gompertz curve, and a linear interpolation method for sublethal toxicity built into the program, ICPIN.exe, which is basically an ANOVA and thus not compatible with the log-logistic and Gompertz curve. 39

Species were classified as monocots or dicots and as crops or wild species. The species were harvested early, 21 days after spraying. At the late harvest time, plants were transplanted into larger pots to prevent stress in the small pots. The ED50 was derived from the dose- response curves. We used a factorial ANOVA model to analyze the differences of the treatments. The differences between monocots and dicots were of course significant due to selectivity of the compound controlling monocots in dicot crops, as was the choice of regression model as well as their interactions (Figure 9).

Figure 9. Interactions between treatments, (LT= harvesterd three weeks after application and ST=harvested later, depending of stage of development) and regression models. The broken line is the recommended field rate (data from Table 3) (33)

The late harvested plants (LT) had a higher ED50 than did early harvested plants (ST). What is interesting is that different models estimated different ED50 values (Figure 9). The Gompertz curve gave significantly lower ED50 than did the log-logistic; and the lowest was that of ICPIN. However, for some of the Gompertz regression runs, the biomass was square root transformed and thus one cannot rule out that the Gompertz curve was not the proper regression, because transformation changes the functional relationship (34, 35). The ICPIN approximation is basically an ANOVA and the resulting ED50 values were the smallest. Generally, it shows that it is difficult to relate EDx derived from different models to each other, if there is not a proper test for the model fit. 40

Rotchés-Ribalta et al. (36) tested tribenuron-methyl and 2,4-D in a greenhouse study on species being classified as rare or common. The duration of tests was either called short or long term as mentioned before (The late harvest was around 48 days after treatment) (33). The specific dose-response model was a log-logistic model. On the basis of an ANOVA on ED25, there were no differences between duration of the stage of development or the classification of common and rare species. All variation was overruled by the two herbicides with an ED25 of 195 g ai/ha for 2.4-D and 3 g ai/ha for tribenuron-methyl, respectively. One of the interesting results of the two last papers is comparinson between short term and long term bioassays in a confined environment. There were no differences in EDxs by Rotchés-Ribalta et al. (36), a non-obvious result. This is against common experience in the field where younger plants are more sensitive than older plants. The recommended rate for 2,4-D in common crops is around 1000 g ai/ha, whilst that of tribenuron-methyl is 24 g ai/ha. On the basis of the predicted slopes (Figure 4 and Table 2) and ED25, the predicted ED10 would be 330g ai/ha and 3g ia/ha for 2,4-D and tribenuron-methyl, respectively. For 2,4-D this seems to be a fair protection with a ED10 of 330 g, but not so convincingly for tribenuron-methyl.

Figure 10. The classification on the x-axis is a conglomerate of various other treatments that could not be analyzed (Table 2 (37)). Horizontal line is the recommended field rate of tepraloxydim. 41

Riemens et al. (37) published a rather complicated experimental design to assess the effect of tepraloxydim on biomass of non-crop grasses (Table 2 of ref. (37)).They used a log-logistic dose-response model with tepraloxidim on four grasses exposed to eight levels of treatments of which some were in the field and some in the greenhouse. The grasses are both serious weeds, but also growing in natural habitats outside the arable land. Unfortunately, it was not possible to analyze the data on the basis of the treatments, which confounded field or greenhouse with spraying time and harvest time. Redefining the treatment as either a field or a greenhouse experiment showed that there was a pronounced interaction between species and treatment (Figure 10). The results in the field and greenhouse were similar except for Poa annua, which stood out in the greenhouse. In fact getting consistent dose-response results with P. annua is difficult due to the plasticity of the species.

Figure 11. Boxplot for the various EDx estimates. Note very few of the EDxs were significantly different form zero. The recommended rates of the herbicides have the same color as the boxplots (Egan et al. (38))

Egan et al. (38) compared herbicide tolerance of rare and common species in the agricultural landscape of Pennsylvania to atrazine, dicamba and glyphosate over two years in the greenhouse. On the basis of surveys, five genera representing both common and rare species were selected. The dose range for the herbicides constituted of 5 doses. A three parameter log-logistic model was fitted and the ED05, ED25 and ED50 were derived. Whatever the EDx of species, the confidence intervals were mostly overlapping zero and thus the EDx values were 42

not significantly different from zero. Consequently, there was no way to do a weighted ANOVA, and as anticipated the ordinary ANOVA did not find any significance between abundance (rare or common) and the herbicides. Figure 11 illustrates the ED05, ED25, ED50. In the Supplement to the paper, the dose response curves were shown and it appears strange that so many of the EDx values (81% for ED05, 86% for ED25, 64% for ED50) were not significantly different from zero. The results perhaps show an experience encountered by many researchers. The “wild species” selected in natural habitats are much more variable than are weeds and crops being selected for generations to adapt to a rather homogenous arable environment.

Concluding Remarks The study of non-target organism response begins with the Paracelsus principle: size of dose determines if a herbicide has an effect or not. The dose at the origin of spray will sometimes drift and and cause unwanted effects on non target species. Our understanding of factors that influence drift and the effect of dose needs to be utilized to mitigate any off-target effects of pesticide application. The use of dose-response curves and EDx values are are indispensable in this process.

References 1.

Ritz, C. Toward A Unified Approach to Dose-Response Modeling in Ecotoxicology. Environ. Toxicol. Chem. 2010, 29, 220–229. 2. Ritz, C.; Baty, F.; Streibig, J. C.; Gerhard, D. Dose-Response Analysis Using R. PLoS One 2015, 10. 3. Finney, D. J. Probit Analysis, 3rd ed.; Griffin: London, 1971. 4. Finney, D. J. Statistical Method in Biological Assay, 2nd ed.; Charles Griffin & Company Ltd: London, 1978. 5. Cedergreen, N.; Ritz, C.; Streibig, J. C. Improved empirical models describing hormesis. Environ. Toxicol. Chem. 2005, 24, 3166–3172. 6. Box, G. E. P.; Hunter, W. G.; Hunter, J. S. In Statistics for Experimenters. An Introduction to Design, Data Analysis, and Model Building; John Wiley & Sons: New York, 1978. 7. Abdelbasit, K. M.; Plackett, R. L. Experimental design for joint action. Biometrics 1982, 38, 171–179. 8. Ferguson, J. C.; Hewitt, A. J.; Eastin, J. A.; Connell, R. J.; Roten, R. L.; Kruger, G. R. Developing a comprehensive drift reduction technology risk assessment scheme. J. Plant Prot. Res. 2014, 54, 85–89. 9. Bowe, S. Environmental characteristics of dicamba formulations. Proc. Weed Sci. Soc. Am. 2016, 50, 155. 10. Sciumbato, A. S.; Chandler, J. M.; Senseman, S. A.; Bovey, R. W.; Smith, K. L. Determining exposure to auxin-like herbicides. II. Practical application to quantify volatility. Weed Technol. 2004, 18, 1135–1142. 43

11. Vanrensburg, E.; Breeze, V. G. Uptake and Development of Phytotoxicity Following Exposure to Vapor of the Herbicide C-14 2,4-D Butyl by Tomato and Lettuce Plants. Environ. Exp. Bot. 1990, 30 (4), 405–414. 12. Egan, J. F.; Mortensen, D. A. Quantifying vapor drift of dicamba herbicides applied to soybean. Environ. Toxicol. Chem. 2012, 31, 1023–1031. 13. Wilson, S.; Downer, B.; Kennedy, A.; Li, M.; Ouse, D. Formulation innovation for limiting off-target movement. Proc. Weed Sci. Soc. Am. 2012, 52, 358. 14. Xu, W.; Cannan, T. M.; Finch, C. W.; S., G. Advancements in dicamba formulation. Proc. Weed Sci. Soc. Am. 2012, 52, 329. 15. Kaskey, J.; Mulvany, L. Monsanto seeds unleash unintended consequences across U.S. farms. Bloomberg 2016. http://www.bloomberg.com/news/ articles/2016-09-01/a-soybean-killing-pesticide-spreads-across-america-sfarm-belt (11/4/16). 16. Rautmann, D.; Terry, H.; Winkler, R. New basic drift values in the authorization procedure for plant protection products. Mitt. Biol. Bundesanst. Land- Forstwirtsch., Berlin-Dahlem 2001, 383, 133–141. 17. Wang, M.; Rautmann, D. A Simple Probabilistic Estimation of Spray DriftFactors Determining Spray Drift and Development of A Model. Environ. Toxicol. Chem. 2008, 27, 2008–2626. 18. de Jong, F. M. W.; de Snoo, G. R.; van de Zandec, J. C. Estimated nationwide effects of pesticide spray drift on terrestrial habitats in the Netherlands. J. Environ. Manage. 2008, 86, 721–730. 19. Rautmann, D.; Streloke, M.; Winkler, R. New basic drift values in the authorization procedure for plant protection products. Mitt. Biol. Bundesanst. Land- Forstwirtsch., Berlin-Dahlem 2001, 383, 133–141. 20. Green, J. M.; Owen, M. D. K. Herbicide-Resistant Crops: Utilities and Limitations for Herbicide-Resistant Weed Management. J. Agric. Food Chem. 2011, 59, 5819–5829. 21. N.N Scientific Opinion addressing the state of the science on risk assessment of plant protection products for non-target terrestrial plants. EFSA 2016. https://www.efsa.europa.eu/en/efsajournal/pub/3800 (11/4/16). 22. Sonderskov, M.; Kudsk, P.; Mathiassen, S. K.; Bojer, O. M.; Rydahl, P. Decision Support System for Optimized Herbicide Dose in Spring Barley. Weed Technol. 2014, 28, 19–27. 23. Sonderskov, M.; Fritzsche, R.; de Mol, F.; Gerowitt, B.; Goltermann, S.; Kierzek, R.; Krawczyk, R.; Bojer, O. M.; Rydahl, P. DSSHerbicide: Weed control in winter wheat with a decision support system in three South Baltic regions - Field experimental results. Crop Prot. 2015, 76, 15–23. 24. Ritz, C.; Streibig, J. C. Bioassay Analysis using R. J. Statistical Software 2005, 12, 1–22. 25. Cedergreen, N.; Streibig, J. C. Can the choice of endpoint lead to contradictory results of mixture-toxicity experiments? Environ. Toxicol. Chem. 2005, 24, 1676–1683. 26. Ritz, C.; Cedergreen, N.; Jensen, J. E.; Streibig, J. C. Relative potency in nonsimilar dose-response curves. Weed Sci. 2006, 54, 407–412. 44

27. Power, E. F.; Kelly, D. L.; Stout, J. C. The impacts of traditional and novel herbicide application methods on target plants, non-target plants and production in intensive grasslands. Weed Res. 2013, 53, 131–139. 28. Fletcher, J. S.; Pfleeger, T. G.; Ratsch, H. C.; Hayes, R. Potential impact of low levels of chlorsulfuron and other herbicides on growth and yield of nontarget plants. Environ. Toxicol. Chem. 1996, 15, 1189–1196. 29. Semenov, A. V.; van Elsas, J. D.; Glandorf, D. C. M.; Schilthuizen, M.; de Boer, W. F. The use of statistical tools in field testing of putative effects of genetically modified plants on nontarget organisms. Ecol. Evol. 2013, 3, 2739–2750. 30. Strandberg, B.; Mathiassen, S. K.; Bruus, M.; Kjaer, C. Effect of herbicides on non-target plants: how do effects in standard plant test relate to effect in naturl habitats? Moljoestyrelsen: Copenhagen, 2012. 31. Streibig, J. C.; Green, J. M. How to compare glasshouse and field dose responses. Comparing Glasshouse and Field Pesticide Performance II, BCPC Monograph No 59 1994; pp 173−180. 32. Green, J. M.; Foy, C. L. Adjuvants: Test design, interpretation, and presentation of results. Weed Technol. 2000, 14, 819–825. 33. Carpenter, D.; Boutin, C. Sublethal effects of the herbicide glufosinate ammonium on crops and wild plants: short-term effects compared to vegetative recovery and plant reproduction. Ecotoxicology 2010, 19, 1322–1336. 34. Ritz, C.; Baty, F.; Streibig, J. C.; Gerhard, D. Dose-Response Analysis Using R. Plos One 2015, 10 (12). 35. Van der Vliet, L. Statistics for Analyzing Ecotoxicity Test Data. In Encyclopedia of Aquatic Ecotoxicology, 1st ed.; Springer: Berlin Heidelberg, 2013.. 36. Rotches-Ribalta, R.; Boutin, C.; Blanco-Moreno, J. M.; Carpenter, D.; Sans, F. X. Herbicide impact on the growth and reproduction of characteristic and rare arable weeds of winter cereal fields. Ecotoxicology 2015, 24, 991–1003. 37. Riemens, M. M.; Dueck, T.; Kempenaar, C.; Lotz, L. A. P.; Kropff, M. J. J. Sublethal effects of herbicides on the biomass and seed production of terrestrial non-crop plant species, influenced by environment, development stage and assessment date. Environ. Pollut. 2009, 157, 2306–2313. 38. Egan, J. F.; Graham, I. M.; Mortensen, D. A. A comparison of the herbicide tolerances of rare and common plants in an agricultural landscape. Environ. Toxicol. Chem. 2014, 33, 696–702.

45

Chapter 4

Variations in Pesticide Doses under Field Conditions Pesticide Dose Variation E. D. Velini,*,1 C. A. Carbonari,1 M. L. B. Trindade,2 G. L. G. C. Gomes,1 and U. R. Antuniassi3 1Department of Crop Science, São Paulo State University (Universidade Estadual Paulista “Júlio de Mesquita Filho” UNESP), College of Agricultural Sciences (Faculdade de Ciências Agronômicas), Rua Dr. José Barbosa de Barros 1780, 18.610-307 Botucatu/SP, Brazil 2Bioativa Pesquisa e Compostos Bioativos, Botucatu/SP, Brazil 3Department Rural Engineering, São Paulo State University, College of Agricultural Sciences, Rua Dr. José Barbosa de Barros, 1780, 18610307 Botucatu/SP, Brazil *E-mail: [email protected].

Pesticide doses are most commonly expressed by the volume or weight of the pesticide applied to an area (usually onehectare). However, many plants, insects or microbes can complete their life cycles in environments of only a few cm² or mm². Pesticide doses are not uniform in the field and, on such a small scale, some target organisms survive because they do not receive enough pesticide. Highly variable doses within a field can also contribute to the selection of resistant biotypes, and some target organisms receive doses low enough to show hormesis. The information available consistently shows highly variable pesticide deposition or concentrations in individual leaves, plants (crops or weeds) and soil samples. Because of dose variability in the field, higher mean doses are necessary to achieve satisfactory control levels. Weeds compete for spray droplets, and higher weed populations can reduce the individual doses deposited. The presence of heterogeneous amounts of

© 2017 American Chemical Society

straw on the soil can contribute to increased variation in the doses of pre-emergence herbicides and the possibility of other classes of pesticides being transported to the soil by rain water.

Introduction Pesticide doses under field conditions are not uniform. The dose applied corresponds to the mean dose observed in the field in the absence of losses due to drift. The few studies that have addressed variation in pesticide doses under field conditions have shown that the deposition of pesticides in plants, leaves or individually assessed targets can vary by orders of magnitude. The consequence of this variation is the establishment of a myriad of combinations of doses and selection pressures after the application of a pesticide at a previously selected dose that should be uniform (1). Even when a sublethal dose of an herbicide is applied, some plants will receive very high doses that are above the expected mean dose. Similarly, the use of high doses is not sufficient to ensure that plants are not subjected to sublethal doses of herbicides. A high dose of herbicide results in high mortality in the plants that receive it, but it can select rare resistance genes capable of producing a high-level of resistance; in contrast, low doses of herbicide (many plants die, but some survive) select all possible resistance genes, including genes for both high- and low-level resistance (2). Previous studies have shown that vertical and horizontal movements of the spray boom (3–6), protection by mulch (7) and protection by weeds or cultivated plants (8–10) can cause variation in single pesticide doses under field conditions. Gazziero et al. (9) and Souza et al. (10) assessed the variation in the doses deposited in early weeds in the soybean crop. The analysis of the data indicated that the variation was sufficient for some weeds to receive sufficiently low doses for hormetic effects to occur. Hormesis is the stimulation of growth by low levels of inhibitors (11–13). Among pesticides, herbicide hormesis has been studied more frequently. Several papers have discussed the hormetic effects of glyphosate (14–17) or herbicides in general (18, 19). For most pesticides applied to plants, their actions do not depend on deposition alone because they need to be absorbed to be toxic to the plants, insects or pathogens. The limited information available in the literature does not allow any conclusions about the effects of deposition and absorption processes in making pesticide concentrations in plant tissues more or less uniform.

Variation of Doses under Field Conditions Nation (4) studied the variation of doses applied with the use of new sprayers using targets with a 25-cm² surface. The author observed that the deposition coefficients of the spray deposits ranged from 27 to 98%. The variation between the largest and smallest deposits for each application ranged between 3.9 and 28-fold; the variation could not be determined for two applications for which the 48

minimum deposit was null. The lowest deposition expressed as the percentage of the mean for each application was 0%, whereas the maximum deposition was 253%. The author concluded that the spray boom movements were the main factor responsible for the variation in doses, that the boom movements primarily occurred due to the transmission of motion of the sprayer, and that the variation could be considerably reduced using spray boom stabilizers. Notably, 25-cm² targets can be quite large when considering the size of the soil area required for a seed or a fungal spore to germinate. A similar study (6) concluded that the horizontal movements of the spray boom were the most relevant factors in terms of dose variation and that the variation was larger when flat fan nozzles were used compared to the use of hollow cone jets. The coefficients of deposit variation estimated for the different operating conditions ranged from 6.7 to 64%. Increasing the boom height from 0.5 m to 0.9 m reduced the coefficients of variation of the deposits. The vertical movements of the spray boom also led to variation in pesticide doses (5). Considering the 22 experimental conditions evaluated, the coefficients of variation of the deposits ranged from 1.84 to 116.22% as a result of those movements. Under the condition with the lowest coefficient of variation, the doses ranged between 98 and 102% of the mean. Under the condition with the highest coefficient of variation, the doses ranged between 0 and 760% (5) Keenes et al. (20) studied the effects of pendulum, simple and double trapezium suspensions in reducing the vertical movements of the spray boom and concluded that the three models could be very effective. Boom stabilizers have been widely used in modern sprayers, especially those that operate at higher speeds and use longer booms. Tofoli (21) studied the uniformity of the spray deposition at ground level. The application speed was 4 km/h. Two similar sprayer sets were used with booms 12 m wide and nozzles spaced at 0.5-m intervals. At the time of spray application, the temperature was 25 °C and the relative humidity was 46%. The Conic Jet Nozzle model JA 1.5 and Fan Jet Nozzle model API11002 nozzle types were evaluated under normal operating conditions. Round targets made of Formica® were used to evaluate the amount of spray deposited in areas with diameters of 0.3175, 0.635, 1.27, 2.54, 5.08 and 10.16 cm. A total of 117 targets of each size were used for each nozzle type. The main results obtained are presented in Table 1. For the two smaller target sizes (0.3175 and 0.635 cm), the variation in spray deposition was very high. For the smallest target size (0.3175 cm), the spray deposition ranged between 38.25 and 249.65% and between 14.32 and 392.71% of the mean for the Fan Jet API 11002 and Conic Jet JA 1.5 nozzles, respectively. The results obtained for the remaining target sizes (1.27 to 10.16 cm) were similar. Higher coefficients of variation were observed for the Fan Jet API 11002 nozzle, but the largest spray deposition was obtained using the Conic Jet JA 1.5 nozzle. Even for the largest targets tested, the spray deposition ranged between 68.04 and 128.26% and between 38.68 and 154.66% of the mean for the Fan Jet API 11002 and the Conic Jet JA 1.5 nozzles, respectively.

49

Table 1. Minimum and Maximum Deposition, Deposition Amplitude and Coefficient of Variation of the Deposition Observed for Different Target Sizes and Nozzle Types (21). Nozzle type

Fan Jet API11002

Conic Jet JA 1.5

Target size (cm)

50

0.3175

0.635

1.27

2.54

5.08

10.16

Minimum deposition (% of the mean)

38.25

41.42

50.09

62.95

60.35

68.04

Maximum deposition (% of the mean)

249.65

187.36

135.34

128.72

135.39

128.26

Deposition amplitude (% of the mean)

211.40

145.94

85.25

65.77

75.04

60.22

Maximum deposition/Minimum deposition

6.53

4.52

2.70

2.04

2.24

1.89

Coefficient of Coefficient of variation (%)

78.76

49.17

22.36

19.24

20.89

18.01

Minimum deposition (% of the mean)

14.32

33.81

36.94

47.27

44.12

38.68

Maximum deposition (% of the mean)

392.11

304.46

157.42

153.92

154.22

154.66

Deposition amplitude (% of the mean)

377.79

270.66

120.48

106.66

110.10

115.98

Maximum deposition/Minimum deposition

27.38

9.01

4.26

3.26

3.50

4.00

Coefficient of Coefficient of variation (%)

59.27

45.89

25.95

25.11

26.31

28.56

Silva (8) studied the effects of Cyperus rotundus population densities and operational characteristics, including nozzle types, in the deposition of spray solution on the plants and acrylic plates simulating the soil of infested areas. The evaluated C. rotundus population densities are representative of those that occur in agricultural areas. The results presented in Table 2 were obtained using XR 11002 nozzles at a 0.5 m height from the targets spaced 0.5 m apart and operating at 2.5 bar and 3.2 km/h with a flow rate of 193 L/ha. With an increase in the population density from 300 to 1200 plants/m², the amount of spray solution deposited in each plant was reduced by 29% (from 14.57 to 10.32 µL/plant). Because C. rotundus population densities are not uniform in the field, the results indicate that the variations in population density can lead to variations in the doses received by each plant, which is quite relevant when the herbicide applied acts exclusively at the post-emergence stage and has no activity on the soil. The results indicate that isolated plants tend to receive higher doses than plants on highly infested patches, which can contribute to the patchiness of C. rotundus populations. The percent deposition of spray solution in the plants increased with the increasing population density of the weed, even with the reduction of deposits in each individual plant. The results indicate that the doses of post-emergence herbicides received by each plant and the probability of survival depend on the number of plants around it (i.e., the population density).

Table 2. Effects of the Cyperus rotundus L. Population Densities on the Deposition of the Spray Solution (8). Plants/m²

Deposition

Percent of total deposition in the targets

µL/plant

Soil

Plant

300

14.57

79.07

20.93

600

12.67

45.35

54.65

900

13.23

45.04

54.96

1200

10.32

38.75

61.25

Gazziero et al. (9) evaluated the deposition of a glyphosate solution in soybean and wild poinsettia plants (Euphorbia heterophylla). Glyphosate was applied 17, 24, 31, 38 and 45 days after soybean emergence. Glyphosate application was performed using XR 110-015 nozzles pressurized at 2.07 bar and consuming 150 L of spray solution/ha. For the evaluations at 17 and 31 days after soybean emergence, the frequencies as a function of the depositions expressed in µL/cm² or µL/plant are shown in Figures 1 and 2. Although spray deposition expressed as µL/plant increased with the crop age, the spray deposition on soybean or wild poinsettia plants expressed in µL/cm² decreased progressively with crop growth, with possible negative effects on the efficacy of herbicides 51

directed at the weeds or fungicides and insecticides directed to the crop. Later applications produced less uniform spray depositions on the soybean and wild poinsettia plants, possibly contributing to the lack of control on plants that received lower doses.

Figure 1. Spray deposition on soybean and wild poinsettia plants (µL/plant) 17 and 31 days after crop emergence (DAE) (9).

Figure 2. Spray deposition on soybean and wild poinsettia plants (µL/cm²) 17 and 31 days after crop emergence (DAE) (9). Souza et al. (10) observed that the crop itself could capture drops of the spray solution, thereby reducing the amount available for weeds. The authors found that Brachiaria plantaginea plants located in the soybean planting row received 34% less herbicide than plants in the inter-rows and that in the same location the 52

spray deposits were proportional to the foliar areas of the weed. Therefore, similar to the results obtained by Gazziero et al. (9), Souza et al. (10) found that larger soybean plants received higher spray depositions if expressed in µL/plant but lower depositions when the data were expressed in µL/cm². Carbonari and Velini (22) studied the uniformity of herbicide deposition in 30 commercial applications of pre-emergence herbicides in sugarcane. The herbicide depositions were individually evaluated with 15 to 30 glass plates (10 cm x 20 cm) positioned on the soil or glass surface. For the 30 applications, the number of sampled points was 635. Notably, 0.62% and 3.94% of the targets received less than 30% and less than 50% of the planned dose, respectively (Figure 3). Only 0.94% of the targets received more than 120% of the planned dose. When the deposition was expressed as the percentage of the mean value observed for each application (Figure 4), the deposition was between 80 and 120% for 82.83% of the targets and more than 50% for all targets. The spray deposition was more than 150% of the mean deposition observed in the respective application for only three targets (0.47%). The variation in spray deposition observed by the authors might be quite significant and contribute to weed control failures or crop damage under field conditions. In agreement with the previous study (23–26), the authors observed that the amount of straw in raw cane, the time interval between the herbicide application and the first rainfall event and the rainfall depth could change the amount of herbicide transported to the soil. The increased amount of straw reduced the transport of herbicides to the soil primarily when the initial rains were scarce (less than 20 mm). Because the distribution of straw in the field is not uniform, the presence of this crop waste can increase the variation in the single doses that reach the soil.

Figure 3. Deposition of herbicides applied to sugarcane fields expressed as the percentage of the planned dose (22). 53

Figure 4. Deposition of herbicides applied to sugarcane fields expressed as the percentage of the mean deposition for each application (22).

Figure 5. Deposition of spray solutions on individual leaves of adult orange trees (27). Concerning variations in pesticide doses in fruit trees, Palladini (27) evaluated the deposition of spray solutions with surface tensions of 0.0728 N/m (equivalent to the surface tension of water) and 0.0365 N/m (achieved with the addition of surfactant) in mature orange trees. The spray volume was 1.830 L/ha or 5.1 L/plant. In the areas tested, 600 leaves were collected from 12 different positions on the plants. The databases built contained 600 data points regarding deposition on individual leaves for each of the two treatments evaluated. Figure 5 shows the accumulated frequencies as a function of the deposits expressed in µL/cm². The deposition data are summarized in Table 3. Regardless of the surface tension, the deposits in µL/cm² were very heterogeneous, with certain 54

leaves receiving doses significantly above or below the observed mean. The relationships between the minimum and maximum deposits were 55.0 and 32.9 and the coefficients of variation were 64.37% and 58.67% for the treatments with surface tensions of 0.0728 N/m and 0.0365 N/m, respectively. The non-uniform deposition in individual leaves can reduce the effectiveness of fungicides and insecticides applied in orange trees or demand larger doses for the pesticide dose to be sufficient for the control of the target organism, even in leaves with a lower spray deposition. The results obtained by Antuniassi et al. (28) provided evidence of the variation in fungicide deposition in individual leaves of adult peach trees. The authors assessed fungicide deposition in six applications performed under different operating conditions corresponding to distinct combinations of sprayer speed and wind speed. The consumption of the spray solution was measured at the end of each application and ranged from 825 to 927 L/ha. After application, 300 leaves were collected from five different positions in the plants. Therefore, databases were built containing 300 data points regarding the deposition of the spray solution in individual leaves. The results were expressed as µL/cm² (Figure 6) or as percentage of the mean deposits observed (Figure 7). The data obtained are summarized in Table 4. The range between the minimum and maximum deposits was 11.2, 9.7, 33.4, 119.9, 13.2 and 13.9 for applications 1 to 6, respectively. The coefficients of variation were between 37.1% and 53.6%, and the minimum and maximum values were observed for applications 2 and 4, respectively. When the deposition is represented as the percentage of the mean observed for each application, the results obtained for the six sets of operating conditions evaluated were very similar. The variation in the deposition of the spray solution in the leaves of adult peach trees was significant and could compromise the effectiveness of fungicide treatments applied to the crop under the evaluated operating conditions.

Figure 6. Deposition of spray solution on individual leaves of adult peach trees expressed as µL/cm² (28). 55

Table 3. Minimum, mean, and Maximum Deposition and Coefficient of Variation of Spray Deposition in Individual Leaves of Adult Orange Trees (27). Surface Tension

µL/cm²

µL/cm²

µL/cm²

% of the mean

% of the mean

Coefficient of

(N/m)

Minimum

Mean

Maximum

Minimum

Maximum

variation (%)

0.0728

0.116

1.505

6.358

7.67

422.33

64.37

0.0365

0.165

1.636

5.443

10.12

332.80

58.67

56

Table 4. Minimum, Mean, and Maximum Deposition and Coefficient of Variation of Spray Deposition in Individual Leaves of Adult Peach Trees (28). Applications

µL/cm²

µL/cm²

µL/cm²

% of the mean

% of the mean

Coefficient of

Minimum

Mean

Maximum

Minimum

Maximum

variation (%)

1

0.32

1.28

3.58

24.9

278.8

47.5

2

0.38

1.47

3.73

26.2

253.6

37.1

3

0.14

1.41

4.60

9.8

327.5

52.0

4

0.04

1.85

4.75

2.1

256.1

53.6

5

0.34

1.55

4.46

21.8

287.1

46.6

6

0.24

1.30

3.38

18.7

260.0

46.1

57

Figure 7. Deposition of spray solution on individual leaves of adult peach trees expressed as the percentage of the mean for each application (28).

Conclusion The first point worth noting is the apparent inconsistency between the results presented on variations of pesticide doses in the field and the consistency of the control exerted by these products under normal application conditions. Low-effectiveness applications are of little use to farmers and high effectiveness can be achieved even in non-uniform applications by increasing the mean dose applied. As C. rotundus population densities are not uniform in the field, variations in population density can lead to variations in the doses received by each plant, which is quite relevant when the herbicide applied acts exclusively at the post-emergence stage and has no activity on the soil (8). The research was limited to C. rotundus, but similar results might be obtained for any weed species or species association. The soybean crop itself reduced the deposition of spray solution in Brachiaria plantaginea plants (10). The data shown in this chapter are apparently inconsistent with the uniformity of the spray flow rate and deposition along spray booms observed in standardized tests conducted in patternators. The standard tests are carried out under static conditions and over long periods of time for the data to be stable and representative. In contrast, under field conditions, individual targets, plants or leaves are exposed to the application droplets for very short periods of time. For example, in an application with a flat fan nozzle at 5 km/h in which 5 cm is considered the thickness of the cloud drops, targets (plant, leaf or soil area) with a 1 cm or 10 cm length would be exposed to drops for only 0.144 s and 0.0792 s, respectively. At this time scale, the effects of the horizontal and vertical movements of the spray boom can lead to large individual variations in pesticide deposition. 58

Many of the studies discussed here refer to the application of herbicides, but the data presented (9, 10) may help better illustrate the variations in the deposition of other pesticide classes in crops. The studies by Antuniassi et al. (28) and Palladini (27) assessed the variation in fungicide, insecticide or fertilizer deposition in individual leaves of adult orange and peach trees using air-carrier sprayers. Additionally, the doses used in these studies were very heterogeneous, indicating that high variability was not restricted to boom sprayers. The presence of heterogeneous amounts of straw on the soil can contribute to increased variation in the doses of pre-emergence herbicides and the possibility of other classes of pesticides being transported to the soil by rain water, thereby impacting their effectiveness and environmental dynamics. Highly variable individual doses under field conditions can be compatible with high effectiveness if the average planned dose is high enough for the most of the target organisms to receive lethal doses. In order to achieve acceptable efficacy levels, the less uniform the individual doses, the higher must be the average planned dose. Different doses or spatial arrangements of doses in the field can exert different selection pressures, possibly contributing to the selection of biotypes resistant to the different pesticide classes.

References 1.

Velini, E. D.; Carbonari, C. A.; Gomes, G. L. G. C.; Tropaldi, L. Perspectivas na evolução de novos casos de resistência no Brasil: Novos conceitos sobre a resistência de plantas daninhas a herbicidas. In Aspectos de Resistência de Plantas Daninhas a Herbicidas, 4th ed.; Christoffoleti, P. J., Nicolai, M., Eds.; HRAC-BR: Piracicaba, SP, 2016; pp 251−262. 2. Powles, S. B.; Yu, Q. Annu. Rev. Plant Biol. 2010, 61, 317–347. 3. Speelman, L.; Jansen, J. W. J. Agric. Eng. Res. 1974, 19, 117–129. 4. Nation, H. J. J Agr Eng Res. 1982, 27, 61–70. 5. Langenakens, J. J.; Clijmans, L.; Ramon, H.; De Baerdemaker, J. J. Agric. Eng. Res. 1999, 74, 281–291. 6. Ooms, D.; Ruter, R.; Lebeau, F.; Destain, M. F. Crop Prot. 2003, 22, 813–820. 7. Tokura, L. K. Effect of nozzles types and of pearl millet mulch on the spraying deposition of application solution in initial postemergence of weeds. Ph.D. Thesis, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista, Botucatu, SP, Brazil, 2006. 8. Silva, M. A. Herbicide deposition in soil and in plants of Cyperus rotundus L. as affected by the application technology. Ph.D. Thesis, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista, Botucatu, SP, Brazil, 2000. 9. Gazziero, D. L. P.; Maciel, C. D. G.; Souza, R. T.; Velini, E. D.; Prete, C. E. C.; Oliveira Neto, W. Planta Daninha 2006, 24, 173–181. 10. Souza, R. T.; Velini, E. D.; Palladini, L. A. Planta Daninha. 2007, 25, 195–202. 11. Calabrese, E. J.; Blain, R. Toxicol. Appl. Pharm. 2005, 202, 289–301. 59

12. Calabrese, E. J. Environ. Toxicol. Chem. 2008, 27, 1451–1474. 13. Calabrese, E. J. Hum. Exp. Toxicol. 2010, 29, 249–261. 14. Velini, E. D.; Alves, E.; Godoy, M. C.; Meschede, D. K.; Souza, R. T.; Duke, S. O. Pest. Manage. Sci. 2008, 64, 489–496. 15. Belz, R. G.; Leberle, C. Julius-Kühn-Arch. 2012, 434, 427–434. 16. Cedergren, N.; Olesen, C. F. Pestic. Biochem. Physiol. 2010, 96, 140–148. 17. Silva, F. M. L.; Duke, S. O.; Dayan, F. E.; Velini, E. D. Weed Res. 2016, 56, 124–136. 18. Belz, R. G.; Duke, S. O. Pest Manage. Sci. 2014, 70, 698–707. 19. Cedergreen, N.; Felby, C.; Porter, J. R.; Streibig, J. C. Field Crop Res. 2009, 114, 54–57. 20. Kennes, P.; Ramon, H.; De Baerdemaeker, J. J. Agric. Eng. Res. 1999, 72, 217–229. 21. Tofoli, G. R. Irregularity of pesticide spray deposition as affected by nozzle type and target size. Master Thesis, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista, Botucatu, SP, Brazil, 2001. 22. Carbonari C. A.; Velini, E. D. 2016. Faculdade de Ciências Agronômicas / Universidade Estadual Paulista. Drift and deposition of herbicides applied to sugarcane fields. Botucatu / SP – Brazil. Unpublished dataset, cited with permission. 23. Carbonari, C. A.; Gomes, G. L. G. C.; Trindade, M. L. B.; Silva, J. R. M.; Velini, E. D. Weed Sci. 2016, 64, 201–206. 24. Cavenaghi, A. L.; Rossi, C. V. S.; Negrisoli, E.; Costa, E. A. D.; Velini, E. D.; Toledo, R. E. B. Planta Daninha 2007, 25, 831–837. 25. Tofoli, G. R.; Velini, E. D.; Negrisoli, E.; Cavenaghi, A. L.; Martins, D. Planta Daninha 2009, 27, 815–821. 26. Rossi, C. V. S.; Velini, E. D.; Luchini, L. C.; Negrisoli, E.; Correa, M. R.; Pivetta, J. P.; Costa, A. G. F; Silva, F. M. L. Planta Daninha 2013, 31, 223–230. 27. Palladini, L. Methodology for the evaluation of spraying deposits. Ph.D. Thesis, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista, Botucatu, SP, Brazil, 2000. 28. Antuniassi, U. R.; Velini, E. D.; Martins, D. Spray deposition and drift evaluation of air carrier peach orchard sprayer. In International Conference on Agricultural Engineering, 1996, Madrid; Universidade Politécnica de Madrid: Madrid, Spain, 1996; pp 279−280.

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Chapter 5

Catch 22: All Doses Select for Resistance. When Will This Happen and How To Slow Evolution? Jonathan Gressel* Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, Israel *E-mail: [email protected].

High dose rates of pesticides rapidly select for monogenic target site resistance, especially when the chemical persists in the field or the farmer persists in continuous application, but preclude the evolution of minor gene quantitative resistance. Near sub-lethal low rates select for incrementally creeping quantitative resistance, and is exacerbated by having large pest populations in the field where economic threshold treatment strategies were used. Pest evolution can be delayed by practices that keep populations very low, such as crop and pesticide rotations using mixtures and rotating dose regimes, sanitation, crop breeding and genetic engineering for resistance as well as non-chemical cultivation.

Catch 22: (definition) “a difficult circumstance from which there is no escape because of mutually conflicting, exclusive or dependent conditions”, as originated by Heller (1). A botanical example with such a dual catch is the Australian Calamus muelleri, called lawyer vine because it has spines as well as snagging sharp-hooked tendrils pointed in all directions and‘gets you whether you are coming or going’. When ancestral humans domesticated cultivated agriculture, they created new ecological niches that pests (weeds, arthropods, pathogens) rapidly filled. As different cultural practices were developed to deal with the pests, evolution countered by evolving resistance to the practice, or by filling the new niche

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with naturally resistant pests. Pests did not readily evolve resistance to the earliest pesticides, such as sulfuric acid to kill weeds, arsenicals to kill insects and mercurials against pathogens. Their multi-target toxicity, targeting multiple cellular sites in both pests and humans resulted in a quest for more selective pesticides: herbicides selective between weeds, crops and humans; insecticides and fungicides selective between pest arthropods and pathogens and beneficial insects, fungi and bacteria, and less toxic to humans.

Target Site Resistance to Residual Pesticides Most of the modern (past half century) pesticides commercialized inhibited a single enzyme in a key metabolic pathway. Initially the preferred ones were effective for long durations, providing season long control. The early philosophy of growers was ‘the only good pest is a dead pest’, so the highest registered rates for each pesticide were used. If a pesticide did not have the desired residual effect, it was applied repeatedly during a growing season, with the highest possible selection pressure. Such practices were abetted by advice from industry and extension, each for their own interests. The lack of persistence of some pesticides to provide season long control was overcome by the persistence of farmers to repeatedly apply the same pesticide throughout the season. An inaccurate version of Occum’s razor was invoked: “The simplest solution to a problem is most likely the correct one”, or more cynically “KISS=keep it simple, stupid”. While this extremely strong selection pressure was being exerted, some began constructing models based on simple population genetics and dynamics that indicated that the evolution of resistant arthropods (2), pathogens (3), and weeds (4), was an inevitable consequence. The models were published at about the same time as the first resistances appeared. The models could not be realistically tested in the laboratory with organisms larger than microorganisms, and there was a good possibility laboratory data might not predict field results. For example, the chromosome-inherited target site resistance to penicillin that is easily selected for on petri-plates has yet to be reported to appear in a hospital patient. Plasmid inherited antibiotic inactivation commonly appeared in hospital wards many decades ago, but was not reported to evolve in any petri-plate selections. Pesticide resistance field experiments would require huge areas and immensely large populations as the initial mutation frequencies are very low, and many generations of evolution, well beyond typical budgets as well as time spans for student dissertations. Because of this inability to perform laboratory or field experiments, epidemiology should be used to validate model predictions and elucidate best practices, by analyzing the practices where resistance has evolved, and where it has not. Many competent scientists initially denied the possibility of widespread resistance ‘because we have yet to see it’ or ‘it has only occurred in limited areas’. All the models showed that under constant selection pressure resistance individuals are enriched in an exponential manner from a very low initial mutation frequency. As this number is typically (but not always) less than one in a million, it is generations before resistant individuals become a noticeable fraction of the 62

population. Resistance was slowly becoming enriched in pest populations while the deniers denied. This is depicted in Figure 1, both as actual measured field data (Figure 1A) as well as how that would appear as population distributions (Figure 1B).

Figure 1. Seemingly sudden appearance of major monogenic target site resistance (A,B) vs. incremental creep of quantitative resistance (C,D). A. Field data from a monoculture maize field treated annually with a high dose of highly persistent atrazine. As the frequency of resistance in the field was exponentially enriched from a very low number, the area covered by Amaranthus was exceedingly low until the area covered suddenly jumped from 9,000 dose responses, from the broad range of biological and biomedical literature. Of this total, 597 were classified as pesticidial agents. Of the 597 dose responses 60.1% of those were derived from in vivo studies. Approximately 80% of the hormetic dose responses have at least two doses below the estimated threshold or zero equivalent point (ZEP) (Figure 2). About 30% of the dose responses for in vivo (29.5%) and in vitro (33.1%) studies had four or more doses below the ZEP (Figure 2). The maximum stimulatory responses for inverted U-shaped dose responses were generally modest with 82% for both in vivo/in vitro studies having a stimulatory response less than twice the control group values (Figure 3). The width of the stimulatory range was less than 100-fold below the ZEP for 87.5% (in vivo) and 75.3% (in vitro) of the studies (Figure 4). These overall results are consistent with findings reported in the Hormetic Data Base for a broad range of chemical agents, demonstrating the broad generality of the hormesis dose-response characteristics and that pesticide responses, are consistent with the dose response pattern of all other chemical classes as well as ionizing radiation.

Figure 2. Percent in vivo and in vitro experiments with pesticides for the number of doses below the zero equivalent point (ZEP) (each total added to 100%). 95

Figure 3. Percent of experiments in the Hormesis Data Base by maximum stimulatory response relative to the control group of pesticide studies.

Figure 4. Percent of experiments in the Hormesis Data Base by width (dosage) of stimulatory range of pesticides studies. 96

Discussion Hormesis is a fundamental biological concept that represents how cells, organs and organisms adapt to exogenous and endogenous stressor agents and how it is mediated in dose-response patterns. The hormetic dose response integrates both evolutionary “strategies” of adaptation and biological resource allocation “tactics” to achieve biological performance goals that enhance survival. The past three decades of research in this area have revealed that the biological and biomedical communities overlooked this fundamental concept by which adaptive responses are mediated due to an unusual clustering of circumstances involving historical conflicts between homeopathy and traditional medicine that affected the capacity to objectively evaluate biphasic dose responses, the use of only a few very high doses for hazard assessment, the failure of regulatory communities to validate dose-response models (e.g., threshold) in regulation and the unique quantitative features of the hormetic dose response that requires rigorous study designs, heightened statistical power for proper evaluation, and a commitment to replicate findings. This clustering condition led to the failure to recognize a key biological principle having profound implications for medicine, agriculture, pharmaceutics, environmental risk assessment and numerous other areas dependent on dose-response relationships. That hormesis would be widely reported for pesticides, therefore, would not be surprising. While the past has witnessed the adoption of research strategies that have ignored or even denied the possibility of the hormetic dose response, there is now convincing evidence that hormesis needs to become an integral component in the education of biomedical scientists but also part of their functional experimental strategies and tactics that can lead to improved identification of biological responses in the low dose zone for both risk assessment and response efficacy purposes. Failure to integrate the hormesis concept within the hazard and risk assessment process has been a serious and long-term failure of the scientific and regulatory communities, compromising environmental and public health goals and resulting in invalid cost/benefit analyses that result in flawed governmental policies as well as poor decisions by individuals on personal health matters.

Acknowledgments Research activities in the area of dose response have been funded by the United States Air Force and ExxonMobil Foundation over a number of years. However, such funding support has not been used for the present manuscript. The views and conclusions contained herein are those of the author and should not be interpreted as necessarily representing policies or endorsement, either expressed or implied. Sponsors had no involvement in study design, collection, analysis, interpretation, writing and decision to submit.

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Chapter 8

Occurrence and Significance of Insecticide-Induced Hormesis in Insects G. Christopher Cutler*,1 and Raul N. C. Guedes2 1Department

of Plant, Food, and Environmental Sciences, Faculty of Agriculture, Dalhousie University, P.O. 550, Truro, Nova Scotia, Canada, B2N 5E3 2Department of Entomology, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil, 36570-000 *E-mail: [email protected]. Phone: 902-896-2471. Fax: 902-893-1404.

High amounts of stress are harmful to organisms, but in low amounts may stimulate certain biological processes. This biphasic response to a stressor, termed ‘hormesis’, has been seen in many insect taxa following mild exposure to stressors, including insecticides. Insecticide-induced hormesis in arthropods is most often observed as stimulated reproduction, although stimulatory effects on other physiological and behavioral processes have also been reported. Given that insect pests in agricultural settings are often exposed to sub-lethal doses of insecticide, the ramifications of insecticide-induced hormesis for pest outbreaks and insecticide resistance development may be significant. On the other hand, there may be opportunities to use hormetic principles to improve commercial production of insects, or to better understand how beneficial insects like pollinators respond to low doses of insecticide. Keywords: sublethal insecticide exposure; pest outbreaks; preconditioning; insecticide resistance; insect behavior; insect rearing; bees

© 2017 American Chemical Society

Introduction Insecticides remain important tools in modern crop protection. For example, in the United States an average of more than 90,000 tonnes of insecticidal active ingredient per year were used during the period 1992-2007 (1). This means that insects inhabiting these cropping systems will often be exposed to insecticides. In addition to being exposed to lethal doses of insecticide, exposure to lower doses may lead to any number of sublethal effects on insect physiology and behavior. Traditionally, study of insect response to sublethal doses of insecticide has emphasized inhibitory effects following exposure, such as reduced fecundity, delayed development, decreased longevity, compromised locomotion or learning, and so on (2, 3). However, low-dose pesticide stress can also have modest stimulatory effects on insects. That is, the very chemicals that kill and sub-lethally harm insects at high doses, can at lower doses stimulate certain biological processes. This biphasic dose-response, characterized by high-dose inhibition and low-dose stimulation during or following exposure to a toxicant is termed ‘hormesis’ (4). Hormesis is not a biological phenomenon restricted to insects; it has been observed in a wide range of single-cell and multicellular species, and across multiple levels of biological organization. Hormetic responses are also not limited to chemical stressors such as pesticides, having been widely reported following exposure to low amounts of, for example, temperature stress, ionizing radiation, or heavy metals (4, 5). Such responses are typically depicted as inverted U-shaped curves that reflect increased normal function at certain low doses (e.g., stimulated growth, fecundity), or J-shaped curves that indicate reduced dysfunction at low doses (e.g., reduced carcinogenesis, mutagenesis, disease incidence) (Figure 1). Study of insecticide-induced hormesis – where low doses of insecticide stimulate certain biological processes – and hormesis in general has rapidly increased (6, 7). T.D. Luckey coined the term “hormoligosis” in the 1950s, which he used to describe situations where minute quantities of a stressing agent would stimulate an organism already maintained under stress (e.g., high salt diet, as in Luckey’s study), whereas larger quantities of stressing agent would be harmful to the organism (8, 9). The term has in large part been superseded by “hormesis”, although some entomologists continue to use “hormoligosis” when describing stimulatory responses in insects following insecticide exposure. Irrespective of terminology, it is clear that insect toxicologists are becoming more familiar with the hormesis phenomenon. The data in Table 1 show exponential growth in citations related to hormesis, insects, and pesticides. Table 1 is not exhaustive, as in many instances biological stimulation due to low doses of insecticide is not reported as “hormesis” or “hormoligosis”, and searches based on taxonomic rank of order (e.g., Coloptera, Diptera, etc.) may miss relevant citations that refer only to family or species names. In addition to not being restricted to an particular group of insects, it is also clear that hormesis can be expressed through a number of biological endpoints, life stages, and kinds of insecticidal active ingredients (6).

102

Figure 1. (A) inverted U-shape (e.g. stimulated growth, fecundity) and (B) J-shaped (e.g. carcinogenesis, mutagenesis, disease incidence) hormetic dose-response curves.

103

Table 1. Citationsa of Hormoligosis and Hormesis with Insect- or Pesticide-Related Terms. Adapted with permission from Cutler 2013b. Copyright (2013) SAGE Publishing. Search Terms

Citations per years indicated

104

pre-1960

1960-69

1970-79

1980-89

1990-99

2000-09

2010-16

Totals

Hormoligosisc

0

3

1

2

11

17

17

51

Hormesis

6

1

1

0

66

238

2290

2602

Hormesis AND Coleoptera

0

0

0

1

1

4

12

18

Hormesis AND Diptera

0

0

0

1

3

27

42

73

Hormesis AND Hemiptera

0

0

0

0

0

4

7

11

Hormesis AND Heteroptera

0

0

0

0

0

6

3

9

Hormesis AND Homoptera

0

0

0

0

2

3

12

17

Hormesis AND Hymenoptera

0

0

0

0

1

1

8

10

Hormesis AND Lepidoptera

0

0

0

0

0

5

22

27

Hormesis AND Insect

0

0

0

2

5

52

93

152

Hormesis AND Insecticide

0

0

0

2

5

21

61

89

Hormesis AND Pesticide

0

0

0

2

11

38

95

146

Hormesis AND Drosophilad

0

0

0

2

3

87

93

185

Totals

6

4

2

12

108

503

2755

3390

From the Thomson Reuters Web of Science database arthropods d Predominantly biomedical research.

a

b

Cutler 2013 (6)

c

Use of this term has generally, but not exclusively, been confined to insects and

Although most work on insecticide-induced hormesis has been done in the laboratory, it is of interest to field entomologists, ecotoxicologists, and pest management practitioners because the dose of insecticide to which insects are exposed in the field will tend to vary greatly due to many biotic and abiotic processes: spray drift, volatilization of spray droplets, density and growth of plant canopies, differential exposure of leaf surfaces, and degradation of the insecticide over time from e.g. microbes and UV radiation, will spatially and temporally change the doses of pesticide to which insect populations are exposed in the field. Though initial insecticide concentrations will usually be lethal to target pests, invariably the aforementioned factors dictate that sublethal concentrations of insecticide will be encountered, and such concentrations could be in the hormetic dose range (Figure 2). It is still early in the study of insecticide-induced hormesis. To date, most work has focussed on documenting evidence of stimulated reproduction and population growth, although an increasing number of studies have examined behavioral effects, and molecular and biochemical mechanisms related to insecticide-induced hormesis (Figure 3). In the following sections we emphasize the practical importance of the phenomenon, while highlighting recent activity and areas of future study we feel will be of interest going forward. Overlap of themes unavoidably occurs in several instances; e.g., discussion of hormetic effects of insecticides on natural enemies or bees may also refer to changes in behavior. Also, in some cases where hormesis is of significance for insects, the stressor may not be an insecticide, and therefore we sometimes discuss hormetic responses stemming from other forms of mild stress, such as heat shock or caloric restriction.

Pest Resurgence and Outbreaks The resurgence of pest species following application of insecticides – whether they be the primary target pest, or secondary pests – has traditionally been attributed to reduced competition from natural enemies of the pest. That is, application of the insecticide reduces populations of both the pest and its natural enemies, but the delayed recovery of the natural enemy population allows the recovering pest population to rapidly resurge (10, 11). Although natural enemy disturbance from insecticides is probably a cause of pest resurgence and outbreaks in many instances, insecticide-induced hormesis is also probably an important driver of this phenomenon. For example, Morse (12) originally observed that some insecticides applied for control of citrus thrips resulted in increased fruit damage, and subsequently showed in the laboratory that specific sublethal doses of these same insecticides or exposure to field-weathered insecticide-treated foliage stimulated thrips reproduction (13). In experiments with brown planthopper, Nilaparvata lugens, Chelliah and Heinrichs (14, 15) found that field resurgences of the pest following application of decamethrin and methyl parathion were most likely due to stimulated reproduction following exposure to insecticides, and not due to effects of the insecticides on predators of N. lugens. 105

Figure 2. General responses of insects to high and low doses of insecticide that vary across time and space.

Figure 3. Results of search terms with “hormesis” + “insecticide” in the Thomson Reuters Web of Science database (9 July 2016). Several field studies have demonstrated that exposure to certain insecticides can cause outbreaks of aphids. Lowery and Sears (16, 17) reported strong surges in populations of green peach aphid, Myzus persicae, on field potatoes following 106

application of azinphosmethyl. Through concurrent laboratory bioassays they showed this was due to direct action of the insecticide on reproduction of aphids rather than from changes in the host potato plants induced by the insecticide. Similarly, field applications of carbaryl against filbert aphid, Myzocallis coryli, in hazelnut orchards resulted in initial population reductions, but populations later resurged to levels that sometimes eclipsed aphid population levels on untreated trees. The effect was not seen with other insecticides that were equally toxic to natural enemies, and laboratory tests showed that aphids directly exposed to low doses of carbaryl had increased reproduction, suggesting exposure to low-dose foliar residues of cabaryl stimulated M. coryli populations. More recently, the potential of insecticides to stimulate M. persicae population growth have been demonstrated in greenhouse studies, where exposure to low concentrations of systemically applied imidacloprid on whole plants for three weeks that spanned multiple generations resulted in a doubling of the aphid population (18, 19). Evidence of insecticide-induced hormesis as mechanism of pest outbreaks is not restricted to insects. As pointed out by Cohen (20), there are many examples of insecticide applications inducing outbreaks of phytophagous mites. Although such outbreaks of pest mites may sometimes be due to effects on natural enemies or plant physiology (21, 22), insecticides can also directly stimulate mite reproduction and population growth (23–25). A demonstration of how insecticide-induced hormesis and not natural enemy disruption probably is the cause of mite outbreaks was provided by Cordeiro et al. (26) On Brazilian coffee farms, applications of deltamethrin against coffee leaf miner, Leucoptera coffeella, are often followed by outbreaks of another pest, the southern red mite, Oligonychus ilicis. Though selectivity favoring O. ilicis over its natural enemies, such as the phytoseiid predator Amblyseius herbicolus, is generally assumed to be the cause of such outbreaks, Cordeiro et al. (26) showed that the predator (A. herbicolus) does not avoid deltamethrin and is three times more tolerant to deltamethrin than its prey. At the same time, field-relevant doses of deltamethrin stimulated population growth of O. ilicis but not A. herbicolus, suggesting that deltamethrin-induced hormesis is a likely cause of the reported red mite outbreaks. Pests like aphids, mites, and leafhoppers reproduce rapidly, and with life cycles that can be as short as 1-3 weeks can have many overlapping generations per year. The occurrence in the field of resurgences and outbreaks due to stimulatory effects of low doses of insecticide suggests that insecticide-induced hormesis does not necessarily reduce the biological fitness of insects. This is not to suggest that fitness trade-offs will not occur, as they most certainly can. Trade-offs can occur within individuals in the form of, for example, increased egg production or pupation following mild insecticide or heavy metal exposure being offset by reduced size, emergence, or survival of individuals (27, 28), or reduced reproduction following stimulated growth of individuals (29). Trade-offs may also occur across generations, such that exposure concentrations that stimulate processes in early generations may be inhibitory in later generations. This was observed by Ayyanath et al. who found that concentrations of insecticides that initially stimulated reproduction or longevity of aphids caused reduced reproduction and longevity in later generations (Figure 4) (18, 30). Nevertheless, insecticide-induced outbreaks do occur and can quantifiably increase the growth 107

rate of insect populations (18, 26, 31, 32), indicating that fitness can remain high or exceed baseline levels, a least temporarily, in spite of any fitness trade-offs that might occur. Although field observations of pest resurgences following insecticide applications were probably the main impetus for initial studies of insect hormoligosis and hormesis, we still lack definitive demonstrations of insecticideinduced hormesis for many pest-crop complexes. More greenhouse and field experiments are required to elucidate the extent to which insecticide-induced hormesis occurs in normal crop production operations, how this plays off other drivers of pest resurgence (such as natural enemy disturbance), and the economic consequences of resulting pest resurgences or outbreaks (6, 7).

Figure 4. Multigenerational (G0, G1, G2, G3) effects of continuous exposure to sublethal concentrations of imidacloprid on the longevity of adult green peach aphid. Reproduced with permission from Reference (18).

Insecticide Resistance Insecticide resistance continues to be a major concern for insect pest management. The Insecticide Resistance Action Committee (IRAC) reports that as of 2015, 14,644 cases of pesticide resistance have been reported in 597 species of arthropods, involving 336 different compounds (33). There are at least three different scenarios that would be of interest and should be explored related to insecticide-induced hormesis and potential implications for insecticide resistance. 108

First, if field rates of insecticide fail to kill and suppress an insecticide resistant population, then these sublethal doses might be in the “hormetic zone” for that population. If they are, they might stimulate reproduction and population growth of the resistant population, thereby increasing the frequency of resistance alleles. This was demonstrated by Guedes et al. (31) in experiments with maize weevil, Sitophilus zeamais. They showed that field-relevant concentrations of deltamethrin that were lethal to a susceptible strain of the weevil resulted in significant increases in population growth of a deltamethrin-resistant strain, indicating that field doses of insecticide that do not kill resistant individuals may actually boost their population growth through insecticide-induced hormesis. Second, insecticide-induced hormesis might coincide with induction of detoxification enzymes that render insects less susceptible to insecticides. If increased expression of detoxification enzymes during insecticide-induced hormesis was a heritable epigenetic process, the combination of stimulated population growth via hormesis with detoxification gene induction could augment pest pressure and resistance development (7). This does not suggest that insecticide-induced hormesis per se would cause induction of detoxification enzymes, but that both events could co-occur in time and space to exacerbate pest problems. For example, Rix et al. (19) found that exposure to low doses of imidacloprid significantly increased the instantaneous rate of population growth of M. persicae, and that this same treatment also altered expression of the detoxification genes E4-esterase and cytochrome P450-CYP6CY3 within and across generations. Whether or not such changes in detoxification gene expression functionally translate into reduced susceptibility or resistance to insecticides over generations is unclear. Third, Gressel (34) pointed out that pests which survive exposure to sublethaldoses of pesticide can nonetheless be highly stressed by such exposures, and that stress is a general enhancer of mutation rates. Survivors of sublethal pesticide exposure are thus likely to have more mutations than normal, and some of these mutations might confer pesticide resistance, both for multifactorial and major gene resistance. It is plausible that insects that survive sublethal insecticide stress may respond hormetically (e.g., stimulated reproduction and population growth), while at the same time developing mutations in genes that render the population resistant to the insecticide. So far as we are aware, this hypothesis has not been tested.

Preconditioning Hormesis Preconditioning hormesis occurs where hormetic doses of a stressor stimulate adaptive responses that condition and subsequently protect the organism (or specific tissues) against a second, higher dose of the same or different agents (35). This area of study is rapidly expanding in many areas of biology (35, 36), and preconditioning hormesis in insects exposed to low doses of insecticide has been reported. This is significant for pest insects in that it could present a “double whammy” given that low (hormetic) doses of insecticide could stimulate their reproduction, while also conditioning or priming the pests or their offspring to be better cope with subsequent stressors in the environment. 109

For example, the instantaneous rate of increase and total reproductive output of green peach aphids developing for three weeks on potato plants treated systemically with imidacloprid was significantly greater than that on control plants, and this same treatment improved aphid survival when subsequently deprived of food and water (Figure 5); this may have been from observed upregulation of heat shock proteins, which allow organism to survive many biotic and abiotic stresses, including dehydration (19). Interestingly, however, the same preconditioning challenge on imidacloprid-treated plants resulted in aphids that had reduced survival when exposed to an LC20 concentration of spirotetramat, a lipid biosynthesis inhibitor insecticide, suggesting that the success of insecticide-induced preconditioning hormesis depends on the nature of the subsequent stress. In another study, exposure of an insecticide susceptible strain of western flower thrips, Frankliniella occidentalis, to an LC25 concentration of spinosad negatively affected development time, fecundity, and population growth of the parental generation, but for F1 offspring the negative effects of such exposure were reduced (37), suggesting that exposure of the parental generation to the insecticide conditioned offspring to better cope with pending insecticide exposure.

Figure 5. Box plots showing effects of subsequent stress (no food/water) on 72-h survival of third-generation green peach aphids from potato plants systemically treated with 0.25 μg imidacloprid L-1. Reproduced with permission from Reference (19). Copyright 2016 Springer. 110

Though induction of hormesis from insecticide exposure is the emphasis of this chapter, it is important to remember that the principles of preconditioning hormesis – and hormesis in general – in insects are not restricted to insecticides. Studies with Drosophila have shown that, for example, preconditioning with mild heat shock enhances locomotor and synaptic performance during subsequent hyperthermia (38), and that mild endoplasmic reticulum preconditioning stress results in subsequent neuroprotection and triggering of autophagy (natural, regulated disassembly of unnecessary or dysfunctional cellular components) which inhibited caspase (protease enzymes with essential roles in programmed cell death) activation and apoptosis (39). Wang et al. (40) provided evidence from a set of experiments that food deprivation during development of the honey bee (Apis mellifera) resulted in a shift in phenotypes to better cope with nutritional stress. They found that mild starvation of larvae gave rise to adult honey bees that were more resilient toward starvation. Although this was accompanied by reduced ovary size, elevated glycogen stores and juvenile hormone (JH) titers, and decreased sugar sensitivity in those adults, the results suggests mild starvation stress preconditioned the colony to anticipate and cope with poor environmental conditions (40). Further discussion of how preconditioning hormesis may be beneficial for mass culturing of insects is provided below. Another excellent example of the utility of preconditioning hormesis for applied entomology is through rearing of pest insects for use in sterile insect technique (SIT). This technique is founded on the mass rearing and sterilization of particular pest species, with the release of an overwhelming number of those sterile individuals – usually males – into an area containing the pest. Success of SIT requires that sterile males, which are usually produced through gamma irradiation (a stressful event), retain physiological and behavioral fitness so that they are able to compete successfully with wild (fertile) males for mates. To this end, López-Martínez and co-authors carried out a series of experiments to determine if the fitness of Caribbean fruit fly, Anastrepha suspense, destined for irradiation could be improved through anoxic preconditioning. Indeed, challenge with anoxia stress (1 h) prior to irradiation treatments and adult emergence resulted in a hormetic response that conferred increased antioxidant enzyme activity, which resulted in lower lipid and protein oxidative damage, higher adult emergence rates, lower mortality rates, longer lifespan, greater flight ability, and overall enhanced male sexual performance (41–43). These results suggest that preconditioning hormetic treatments, such as those designed to enhance antioxidant activity prior to sterilization can result in more competitive sterile males, thereby improving the efficacy and economy of SIT programs (42).

Stimulatory Effects on Insect Behavior The potential for stress to affect behavior in a biphasic/hormetic manner has been considered across multiple fields. For example, hormetic stress in mice through short-term caloric-restriction can elicit antidepressant-like responses in maze and forced swim tests (44), enhance learning and consolidation processes 111

(45), and improve remote contextual fear memory (46). Likewise, several authors have recently explored potentially stimulatory effects of low doses of insecticide on the behavior of insects or other arthropods, including orientation ability, mating behavior, and predatory behavior. Low-dose exposure to toxic insecticide has been shown to stimulate orientation behavior and mating success in pest moths. Rabhi et al. (47) found that low doses of clothianidin induced a biphasic hormetic-like effect on sex pheromone-guided behavior of the black cutworm moth Agrotis ipsilon, a wide-ranging pest that attacks many vegetable and grain crops. Although high and low doses of clothianidin had no effect or inhibited orientation behavior, a LD20 dose of clothianidin improved orientation of male moths. However, this LD20 dose also reduced the survival rate and flight capacity of moths, potentially negating any putative effects of improved flight orientation. Rabhi et al. subsequently showed that this same dose of clothianidin significantly increased antennal lobe sensitivity in male moths, such that 100-fold lower pheromone dose was required to elicit a response in neurons after intoxication compared with control moths (48). Similarly, Lalouette et al. (49) found exposure of cotton leafworm Spodoptera littoralis to a dose equivalent to one-tenth the LD50 of deltamethrin resulted in improved mating success, which was associated with a modified olfactory receptor neuron response to pheromonal stimulation (faster signal termination), and changes to antennal detoxification or stress response. Insecticide-induced hormetic effects on mating behavior have been seen in other insects. Oriental fruit fly, Bactrocera dorsalis, responded biphasically to cyantraniliprole, an anthranilic diamide insecticide; whereas a concentration of 3.27 μg g-1 in adult diet significantly lowered mating competiveness, exposure to diet treated with 1.30 μg g-1 improved the mating frequency and competitiveness of treated males, while resulting in more oviposition in exposed female flies (50). Other work showed that exposure of the neotropical brown stink bug Euschistus heros to low doses of imidacloprid (which is toxic to the insect at high doses) resulted in greater mating frequencies when at least one member of the couple was exposed. Mating duration was shortened when only females were exposed to imidacloprid, and exposed males showed increased walking activity, lower respiration rates, and induced higher fecundity rates when mated to unexposed females, indicating that male E. heros can have increased sexual fitness following insecticidal stress in early adulthood (51). Among non-insect arthropods, when the wolf spider Pardosa pseudoannulata was exposed to high concentrations (25-200 mg L-1) of the neonicotinoid insecticide imidacloprid, inhibitory effects on survival, reproduction, and predatory behavior were observed, but exposure to a low concentration (12.5 mg L-1) of imidacloprid stimulated predation ability (52). Niedobová et al. (53) examined effects of agrochemical surfactants on the foraging behavior of Pardosa agrestis. Although some treatments adversely affected short-term predatory activity, male spiders in one particular surfactant treatment killed significantly more flies than did those in the control group.

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Hormesis in “Beneficial” Insects Most work on insecticide-induced hormesis has focused on pest insects (6, 7). However, given that the term “pest” is anthropocentric, insecticide-induced hormesis is just as likely to occur in “beneficial” insects. Culture and sale of beneficial insects for scientific inquiry, biological control, pollination, textiles (e.g. silk), and food for pets, livestock, poultry, fish, or humans is worth billions of dollars globally. Given the burgeoning interest in applying hormetic principles to benefit human health (54), it seems we should also examine opportunities to harness potential benefits of hormesis when rearing or maintaining beneficial insects. Contamination that results in competition, parasitism, predation, or disease is one of the most serious problems in mass production of insects. Insect pathogens are perhaps the most pervasive and difficult problem to contend with, and efforts to supress their impacts can be costly and labour intensive (55). Exploitation of hormetic responses may be beneficial during insect rearing to help reduce susceptibility to pathogens. For example, Galleria melonella is an important insect model in many lines of research, and is also reared as food for captive animals in terraria. G. melonella, like many insects, is susceptible to infection by the fungus Beauveria bassiana, but Wojda et al. (56) found that exposure of G. melonella larvae to mild heat shock (38 °C for 30 min), before infection of the fungus, extended the insect’s lifespan, which the authors suggested was due to higher expression of antimicrobial peptides and higher antifungal and lysozyme activities in the heat-shocked animals. It was subsequently shown that G. melonella larvae exposed to heat shock or heat-killed pathogens were more resistant to infection by entomopathogenic bacteria like Bacillus thuringiensis and Photorhabdus luminescens, with corresponding increases in amounts of anti-microbial peptides, and increased transcription of genes encoding antimicrobial peptides (57–59). This priming response to mild stress (preconditioning hormesis) seems to be conserved across insect taxa (60), and thus could be explored with many insects that are commonly cultured. In addition to improving the hardiness of insects in culture, there might be applications for hormetic stress to increase reproductive outputs of insect cultures. Several laboratory studies have shown that exposure to low doses of insecticide can boost reproduction or oviposition of natural enemies, such as the Heteroptera predators Podisus distinctus (32) (Figure 6) and Supputius cincticeps (61), the lacewing Chrysopa californica (62), the ladybird beetles Harmonia axyridis (63) and Coleomegilla maculate (64), and the parasitoid Bracon hebetor (65). Insecticide-induced hormesis might also benefit natural enemy behavior. For example, exposure of Leptopilina heterotoma, a parasitoid of Drosophila, to a LC20 concentration of chlorpyrifos significantly increased oviposition probing (with or without conditioning of a stimulatory banana odor) 1 h after conditioning. After 24 h, the stimulation produced by chlorpyrifos was no longer significant, but nonetheless remained higher than that of non-conditioned female wasps, suggesting that sublethal insecticide exposure could increase parasitoid efficiency without compromising odor memory (66). In other experiments with L. heterotoma, LC20 concentrations of chlorpyrifos and deltamethrin significantly 113

increased the arrestment of parasitoids by kairomones, which increased their residence time on the kairomone infested area, a behavioral change that could be advantageous for parasitoids by increasing their host-finding (67). Stimulatory effects of low-dose insecticide exposure on bee learning are described below.

Figure 6. Effect of sublethal doses of permethrin on the mean (+/- standard error) net reproductive rate of the predatory bug Podisus distinctus. Adapted with permission from reference (32). Adapted with permission from Reference (32). Copyright 2009 Oxford University Press.

Although it would probably be difficult to achieve practical applications or repeatability in most field situations, there is also some evidence that insecticide-induced hormesis can affect natural enemy behavior in the field. Mills et al. (68) examined effects of different pesticides used in orchard pest management on eight different natural enemies. Though inhibitory effects of insecticides were found, exposure to some of the insecticides resulted in significant increases in performance among survivors. For example, life table analysis found that Deraeocoris brevis juveniles exposed to chlorantraniliprole and cyantraniliprole gave more adult females relative to controls, and Hippodamia convergens adults exposed to spinetoram and copper+mancozeb had increased daily fecundity. To date, experiments showing hormetic responses in beneficial insects seem to have been limited to observations within a single generation. Whereas some single and multi-generational studies with pest species suggests insects can have hormetic 114

responses to mild stress without fitness consequences, others studies show that there can be fitness costs associated with hormesis in insects (see above). Thus, it is not yet clear if and how hormetically induced changes in vigor or reproduction can be satisfactorily maintained in beneficial insects across multiple generations, or if such stimulations are temporary and costly for the population. This will be an important line of inquiry for the discipline going forward.

Insecticide-Induced Hormesis in Bees Given mounting concerns of declines of managed and wild pollinator populations, study of potential effects of insecticides on pollinators has increased rapidly; a Thomson Reuters Web of Science search (12 July 2016) of the terms “bee” and “insecticide” generates 771 hits since the year 1950, but 479 of these articles have been published since 2010. The occurrence and significance of insecticide-induced hormesis in bees has recently been discussed (69), but some authors who detect insecticide-induced hormesis in bees are clearly still not familiar with the phenomenon. For example, Moffat et al. (70) found that chronic exposure to field-relevant concentrations of the neonicotinoid insecticide clothianidin had no significant effects on any measure of bumblebee colony health (no. live bees, no. brood cells, change in nest mass, proportion of females) except queen production, where clothianidin-treated colonies produced 266% more queens than control colonies (P = 0.005). Unfortunately, the authors paid little attention to this result and make no reference to hormesis. Given that pesticide-induced hormesis occurs across so many diverse taxa of insects, it is not surprising that insecticide-induced hormesis in bees will occur (69). Fortunately, more researchers are becoming aware of this and are reporting their findings in the literature. For example, bumble bees (Bombus terrestris) exposed to low, sublethal doses of clothianidin in the laboratory had slightly, but not significantly, faster learning compared to other treatments (71). This result is similar to findings with nicotine and caffeine, which though lethally toxic to honey bees (Apis mellifera) (72), were also shown to improve learning and memory when administered at low doses (73, 74), as was a combination of imidacloprid and the miticide coumaphos (75). Stimulating effects of low-dose insecticide exposure have even been seen in field studies with bees. For example, B. terrestris workers from colonies chronically exposed to 10 ppb thiamethoxam had increased visitation to Lotus corniculatus flowers, spent less time learning to forage, and collected more pollen than control bees (76). Similarly, chronic exposure to 2.4 ppb thiamethoxam (a field-realistic exposure concentration) resulted in B. terrestris workers visiting a higher number of apple flowers than bees from control colonies (77), even though thiamethoxam and other neonicotinoid insecticides (e.g. imidacloprid, clothianidin) are highly toxic to bees under certain experimental conditions (78, 79). Others have shown that exposure to sublethal doses of nicotine, thiamethoxam, and biopesticide can improve survival of honey bees and bumble bees (80–82). 115

These results suggest that bees are not passive organisms when it comes to pesticides exposure. Rather, like all organisms, they have evolved to adapt to stress, and such adaptations may result in hormetic (stimulatory) responses. Increased awareness of insecticide-induced hormesis in bees will hopefully lead to new mechanistic and evolutionary insights into how bees adapt to chemical stress, and how different doses of insecticide interact with target sites in bees, with potential implications for pollinator risk assessment (69).

Conclusions The occurrence of insecticide-induced hormesis is now well supported experimentally, both in the laboratory and in the field. The potential significance of the phenomenon for pest outbreaks is clear, but more study is required to clarify the most likely pest-crop-insecticide scenarios for its development, as well as its consequences for pest management. In addition, hormesis might have applications for mass culture of insects, and may produce new perspectives during characterization of risk of pesticides to bees and other beneficial insects. Although there has been progress into basic molecular, biochemical, and physiological underpinnings of insecticide-induced hormesis (19, 30, 40, 51, 83, 84), there is need for far more work in this area. This will give insight into mechanisms responsible for specific events, while also increasing our overall understanding of the phenomenon.

Acknowledgments The authors acknowledge the Natural Sciences and Engineering Research Council of Canada (NSERC), the National Council of Scientific and Technological Development (CNPq), the CAPES Foundation (Brazilian Ministry of Education), and the Minas Gerais State Foundation of Research Aid (FAPEMIG) for financial support of their research on insecticide-induced hormesis.

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Chapter 9

Chemical Hormesis on Plant Pathogenic Fungi and Oomycetes Sumit Pradhan,1 Francisco J. Flores,2 Hassan Melouk,1 Nathan R. Walker,1 Julio E. Molineros,1 and Carla D. Garzon*,1 1Department

of Entomology and Plant Pathology, Oklahoma State University, Stillwater, Oklahoma 74078 United States 2Department of Live Sciences and Agriculture, Universidad de las Fuerzas Armadas-ESPE, Sangolquí, Ecuador *E-mail: [email protected].

Hormetic effects of fungicides on fungi and oomycetes include moderate increase in growth rate, secondary metabolite production, and disease severity by plant pathogens. Hormesis has been documented in plant pathogens for exposure to low-doses of fungicides with both high and low fungicide resistance risk. Exposure to sub-inhibitory doses of fungicides can occur from improper use (e.g., over use of an active ingredient, poor application technique, or dilution of fungicide solutions in recirculation systems) or from natural degradation of the active ingredient, which may also lead to selection of fungicide resistant strains. One of the main challenges of chemical hormesis research is reproducibility. Systematic and meticulous screening and selection of target organisms, optimal mycelial age, chemical stressors and appropriate endpoints are required elements in experimental design for hormesis studies. Furthermore, multiple replicates and assay repetitions are necessary to ensure reproducibility and accuracy. Attempts to elucidate the mechanisms behind chemical hormesis have been fruitless so far. However, recent research suggests that increased mutation rates may result from exposure to sublethal doses of fungicides in fungi. Awareness of the risks associated to hormetic stimulation of fungi and oomycetes © 2017 American Chemical Society

among scientists, educators, growers and the general public is necessary to prevent aggravated damages and crop losses that may result from accidental stimulation of pathogens.

Introduction As stated in other chapters in this volume, hormesis is the toxicological phenomenon where biological systems exhibit opposed responses to high and low doses of a stressor, characterized by low-dose stimulation and high-dose inhibition (1). Hormetic responses are consistent and independent of biological system, stressor, endpoint or mechanisms studied. Calabrese (1) highlighted that one of the most distinctive features of hormetic dose responses is the display of modest stimulatory responses at doses that are below and contiguous with the no observed adverse effect level (NOAEL) of the threshold dose-response model. Such modest stimulatory responses represent percentages rather than fold increments, often peaking at ranges between 10 to 60%, even though stressor doses may vary 10 to 20 fold, and in exceptional cases even 1000 fold (1–4). Recent studies on the hormetic effects of fungicides on fungi and oomycetes are consistent with the expectations derived from numerous case studies in so many other biological systems. Accidental exposures to subinhibitory doses of fungicides often derive from over use of fungicide active ingredients in disease management plans without proper fungicide rotation plans, leading to selection of fungicide resistant fungal strains. Most reports of fungicide hormesis are related to fungicides with high risk of resistance development, such as phenylamides, quinone inside inhibitors (QiI), and methyl benzimidazole carbamates (MBC) (4–8). However, hormetic responses to subtoxic doses of propamocarb, a low to medium risk fungicide, have also been observed (4). Furthermore, non-resistant isolates also display hormetic responses to subinhibitory doses of fungicides (4), but their hormetic dose ranges may be several degrees of magnitude lower than those of resistant isolates. Poor application technique, dilution of fungicide solutions in recirculation systems, natural degradation processes, among others, may also result in accidental exposure of fungal plant pathogens to subinhibitory doses of fungicides (Figure 1). Phytopathological literature reviews reveal numerous examples of plant pathogens stimulated by subtoxic doses of fungicides (4, 9–18). Most of them were accidental discoveries that were described as unexpected outcomes in fungicide efficacy studies and often reported as a curiosities. Unfortunately, much of this evidence has been overlooked and often explained as experimental error, deemed not relevant to the main objectives of research, or as anecdotal.

A Historical Perspective The earliest documented study of biphasic dose responses in fungi was reported by Hugo Schulz in 1888 (19). Schultz detected increased efficiency of yeast fermentation in presence of low doses of multiple chemicals, followed 122

by inhibition at higher doses. Although the unique biological responses were soon after referred to as the Arndt-Schultz law, because of their overlapping similarities with the observations of the homeopathic physician Rudolph Arndt, the low-dose stimulation concept was undermined at the time due to a lack of understanding of the mechanisms underlying such effects, and because of its needless association with homeopathy, a discipline that was widely criticized and deemed a pseudoscience. Several years later, Branham realized Schulz’s findings had been overlooked in spite of their validity. Stimulated by this realization, Branham studied the sub-lethal dose effects on CO2 production by yeasts of numerous chemical stressors, including mercuric chloride, mercurochrome, metaphen, hexylresorcinol, chloramine-T, iodine, and sodium hypochlorite (20). Exposure to sublethal doses of those chemicals resulted in an outburst of gas production during different phases of fermentation (20). Southam and Ehrlich (21) observed growth stimulation of Fomes officinalis, when multiple strains of the wood decaying fungus were grown on media amended with different concentrations of red-cedar extracts. Southam and Ehrlich used the term “hormesis” (derived from Greek word “hormôn” meaning to stimulate or induce) to describe their observations. After the exciting observations by Southam and Ehrlich, research on chemical hormesis of fungi was halted for decades. Nonetheless, numerous accounts of stimulation of fungi and oomycetes (fungus-like organisms of the Kingdom Stramenopila) by exposure to subtoxic doses of antifungal chemicals can be found in the literature (9–16).

Figure 1. How subinhibitory pesticide doses occur in the environment. 123

Hormesis in Plant Pathogenic Oomycetes Although not defined as hormetic responses in the original articles, several studies have reported biphasic dose responses in oomycetes exposed to subinhibitory doses of antifungal chemicals. Fenn and Coffey (10) mentioned growth stimulation of up to 28% and 91% over the control at low doses of H3PO3 for Pythium ultimum and Pythium myriotylum, respectively. Likewise, Kato et al. (11) reported the stimulation of Phytophthora undulata linear growth by low doses of hymexazol. Evidence of growth stimulation of Phytopthora infestans by metalaxyl, a commonly used fungicide to control oomycetes, was demonstrated by Zhang et al. (13). In that study, three metalaxyl resistant isolates displayed vigorous growth stimulation with increased aerial biomass when grown on a media amended with metalaxyl (20 µl/ml). An interesting observation was that one of the three isolates grew more in metalaxyl amended media containing fewer nutrients. Based on these observations, metalaxyl was inferred to be beneficial to resistant isolates of Phytopthora infestans under low nutrition conditions. Moorman and Kim (16) observed increased radial growth of some strains of Pythium aphanidermatum, Pythium irregulare (recently re-named Globisporangium irregulare) and P. ultimum (G. ultimum) that were resistant to both propamocarb and mefenoxam. Stimulation of isolates of the three species was observed on media amended with propamocarb (1 µg/ml). Furthermore, a resistant isolate of P. aphanidermatum was stimulated by an unusually high dose of propamocarb (1000 µg/ml). In recent years, a renewed interest on fungicide hormesis emerged in response to casual grower reports of increased disease incidence in ornamental crops after fungicide applications (22). Garzon et al. (5) reported stimulatory effects of low doses of mefenoxam on radial growth of mycelium of mefenoxam and propamocarb resistant isolates of Pythium aphanidermatum and P. cryptoirregulare (currently Globisporangium cryptoirregulare), and increased severity of Pythium damping-off of geranium seedlings. Modest average mycelial radial growth stimulation of up to 10% was observed in vitro, with significant increments in damping-off of geranium seedlings of up to 61 % (Figure 2). Since in vitro growth stimulation is consistent but often modest in chemical hormesis assays, it can be challenging to obtain reproducible stimulation at specific fungicide doses, which might not be detected using traditional, and inappropriate statistical analyses (i.e. ANOVA). In an attempt to improve the accuracy of hormetic estimate, Flores and Garzon (4) reported standardized protocols in vitro for detection and assessment of such effects using radial growth as an endpoint. A statistical analysis capable of differentiating small responses to stimuli from background noise was used. The Brain-Cousens non-linear regression model (23) describes dose-response relationships where stimulation at low doses is followed by inhibition at high doses. Using the standardized protocols, significant growth stimulation of a mefenoxam and propamocarb resistant isolate of P. aphanidermatum was observed when it was exposed to multiple doses of ethanol and the fungicides propamocarb and cyazofamid (Figure 3). Pradhan et al. (18) reported significant hormetic responses in mefenoxam and propamocarb resistant isolates of Globisporangium ultimum and G. irregulare when exposed 124

to subinhibitory doses of mefenoxam. These results further support hormesis as a common phenomenon in oomycetes.

Figure 2. Area under the disease progress curves (AUDPC) of damping-off of geranium seedlings caused by a Pythium aphanidermatum. Exposure of a mefenoxam and propamocarb resistant isolate to subtoxic doses of mefenoxam resulted in increased disease levels (a). Significant differences are represented by non-overlapping notches. (5). Reproduced with permission from reference Garzon, C. D., Molineros, J. E., Yánez, J. M., Flores, F. J., Jiménez-Gasco, M. D. M., and Moorman, G. W. 2011. Sublethal doses of mefenoxam enhance Pythium damping-off of geranium. Plant Disease, 95: 1233–1238. (Reproduced with permission from APS Press, 2011) One of the main challenges faced during assessment of hormetic responses in microbiological systems is the reproducibility of stimulatory responses at specific doses. Reproducibility can be a challenge due to technical issues during fungicide solution preparation, as well as obtaining accurate endpoint measurements. A relatively simple solution to minimize experimental error is the careful and consistent preparation of fresh fungicide stock solutions to be used on the same day or soon after. This practice is fundamental to ensure result reproducibility in chemical hormesis studies, but in particular those focused on fungicide formulations at high concentrations, since the small volumes needed to prepare stock solutions increase the risk of random errors in experimental measurements (8, 24). Endpoint measurement accuracy can be affected by intrinsic properties of the biological systems under study, physiological changes due to environmental variability, as well as by technical difficulties during data collection. Systematic and meticulous screening and selection of target organisms, optimal mycelial age, chemical stressors and appropriate endpoints are required elements in experimental design for hormesis studies. It is not uncommon in fungicide hormesis studies to observe radial growth stimulation of 10% to 25% at doses under the NOAEL, with maximum stimulation dose varying from repetition to repetition, which can result in statistically non-significant stimulation across repetitions (5, 24). Pradhan et al. (18) obtained improved result reproducibility using alternative endpoints, like total mycelial dry mass weight 125

and total growth area. In particular, the calculation of total growth area using image measuring software minimized measurement biases due to experimental error and optimized reproducibility of results in the study, thus this endpoint and measurement protocol were recommended for future studies of fungicide hormesis for assessment of growth stimulation.

Figure 3. Observed values of radial growth (% control) in vitro of P. aphanidermatum in response to subtoxic doses of cyazofamid (ln of ppb), and modeled curve using the Brain-Cousens non-linear regression model (4).Reproduced with permission from reference Flores, F.J., and Garzon, C.D. 2013. Detection and assessment of chemical hormesis on the radial growth in vitro of oomycetes and fungal plant pathogens. Dose-Response 11:361-373. (Reproduced with permission from SAGE Publishing, 2013).

Hormesis in Plant Pathogenic Fungi The first evidence of hormesis on a fungal pathogen was reported by Southam and Ehrlich (21), who demonstrated growth stimulation in Fomes officinalis cultured in vitro at low doses of red-cedar heartwood extract. Nearly a decade later, Hessayon (9) examined the production on different soils of an antifungal compound produced by Trichothecium roseum, and found that sublethal doses of the studied trichothecene induced mycelial growth in Fusarium oxysporum. Baraldi et al. (15) studied 41 thiabendazole (TBZ) resistant isolates of Penicillium expansum and observed improved conidia germination in seven isolates when compared to the control, and proposed that such stimulatory response might have resulted from metabolization of the fungicide as a nutrient compound. A 2014 study of plant extracts as potential alternatives for filamentous fungi control found that although extracts of Acalipha subviscida and Echeveria acutifolia had excellent potential for control of Fusarium oxisporum and Alternaria alternata, they had biphasic dose effects on A. alternata, with growth inhibition at high 126

doses and growth stimulation at low doses (25). Noguerol-Pato et al. (26) reported significant increases in ethanol production in the yeast Saccharomyces cerevisiae exposed to the maximum residual level of dimethomorph (3.0 mg/kg) established by the European legislation in wine grapes. Studies designed to assess hormetic responses in fungi to sublethal doses of antifungal agents are few and recent. Nonetheless, these studies have confirmed the potential for hormetic responses to chemical stressors in basidiomycetes and ascomycetes (4, 7, 8, 24, 27, 28). Flores and Garzon (4) reported radial growth stimulation in Rhizoctonia spp. at subtoxic doses of ethanol. Audenaert et al. (27) observed stimulatory effects of subtoxic doses of the fungicide prothioconazole on Fusarium graminearum production of the mycotoxin deoxynivalenol (DON) in vitro and in planta. Low doses of prothioconazole+fluoxastrobin increased the production of the hydrogen peroxide, which enhanced DON production (Figure 4).

Figure 4. Production of deoxynivalenol (DON) by Fusarium graminearum during exposure to serial dilutions of prothioconazole+fluoxastrobin. DON production increased 48 h after fungicide treatment. (Audenaert et al. (27)). Conidia (106 conidia/ml) were challenged with tenfold dilutions of prothioconazole+fluoxastrobin starting from the field rate of 0.5 g/l + 0.5 g/l, in absence of 1000 U/ml catalase. The experiment included two repetitions and was replicated twice. Statistical analysis with a Kruskall-Wallis and Mann-Whitney test with a sequential Bonferroni correction for multiple comparisons found significant differences between treatments as reflected by different letters over the bars. (Modified from Audenaert et al. (27)). Reproduced with permission from reference Audenaert, K., Callewaert, E., Höfte, M., De Saeger, S., Haesaert, G. 2010. Hydrogen peroxide induced by the fungicide prothioconazole triggers deoxynivalenol (DON) production by Fusarium graminearum. BMC Microbiology 10:112. DOI: 10.1186/1471-2180-10-112. (Reproduced with permission from Springer Nature, 2010). 127

Figure 5. Infection progress time course of Sclerotinia sclerotiorum isolates (AH-17 and LJ-86) on detached rapeseed leaves after treatment with subtoxic doses of carbendazim (0.2 and 1 ug/ml respectively) (Di et al. (8)) . Reproduced with permission from reference Di, Y.L., Lu, X.M., Zhu, Z.Q., and Zhu F.X. 2016. Time course of carbendazim stimulation on pathogenicity of Sclerotinia sclerotiorum indicates a direct stimulation mechanism. Plant Dis. 100:1454-1459. (Reproduced with permission from APS Press, 2016).

Zhou et al. (6) observed hormetic effects of dimethachlon fungicide on different isolates of Sclerotinia sclerotiorum. Eighteen out of 58 isolates had increased growth rates compared to the non-treated control when grown on media amended with 0.5 - 4 µg/ml dimethachlon. They also found increased virulence on detached leaves of oilseed rape after spraying plants with dimethachlon at a concentration of 2 µg/ml. Di et al. (7) reported statistically significant stimulation of the virulence of two carbendazim resistant S. sclerotiorum isolates on rapeseed detached leaves (up to 31% virulence increase) and on potted plants (23% increase) at doses of carbendazim between 0.2 and 5 µg/ml. Although this 128

study attempted to elucidate the potential pathogenicity mechanisms involved in stimulation, hormetic doses failed to increase oxalic acid production or isolate tolerance to hydrogen peroxide, two known pathogenicity mechanisms of S. sclerotiorum. In a subsequent study, Di et al. (8) assessed the time course of virulence stimulation by subtoxic doses of carbendazim. They observed larger lesion diameters caused by two S. sclerotiorum isolates on detached leaves treated with 0.2 and 1 µg/ml of carbedazim, compared to the non-treated control, after 12, 18 and 48 hours post inoculation (Figure 5). They also reported that virulence stimulation was significant from one to seven days after application (DAA) of 400 µg/ml of carbendazim on potted plants, with a reduction in stimulation magnitude at 10 DAA, and no significant stimulation observed 14 days DAA. Mycelium grown on PDA medium amended with 400 µg/ml of carbendazim lost its enhanced virulence on detached rapeseed leaves two days after being transferred to non-amended medium. However, a new attempt to elucidate the pathogenicity mechanism enhanced at hormetic doses failed when no significant effects were observed on cell-wall- degrading enzymes.

Unknown Risks, Challenges, and Future Directions Our current dependence on chemical management of plant diseases to maintain the current levels of agricultural productivity can serve as an indirect measure of the relevance of fungicide hormesis to agriculture and of the unaccounted economic losses occurring due to biphasic dose effects on fungal plant pathogens, derived from the lack of awareness about hormetic dose responses among growers, extension educators, plant scientists and plant pathologists in general. The study of fungicide hormesis is an emerging area of science with tremendous challenges ahead but also with imminent beneficial impact on agriculture and agriculture based economies. Although hormesis has been generalized as a universal phenomenon, scientists studying chemical hormesis face multiple challenges during the design and execution of experimental assays. Field, greenhouse and landscape observations of enhanced disease incidence and severity after application of certain fungicides often help to identify biological systems with potential hormetic responses, however, if the endpoint selected for assessment of biphasic dose responses is not appropriate, potential fruitful and high impact research can come to a halt. Too frequently, casual reports of increased virulence of fungal pathogens in the field offer opportunities to examine potential hormetic responses, but no evident stimulation is observed in vitro, even if multiple endpoints are examined (24, 28). Identifying an appropriate endpoint to evaluate the effects of subtoxic doses of inhibitory chemicals is not an intuitive task, and most of the time it is a trial and error discovery process. The selection of endpoint depends on the variability of the parameters observed and the resources available. Growth stimulation in vitro is the most frequently used endpoint in the fungicide hormesis literature (6–8, 18) since it is relatively simple to measure and it is often the most evident hormetic response. Growth parameters vary from study to study, 129

ranging from linear growth, to radial growth, to growth area and total dry mass (18). Important factors when selecting endpoints for a study are reproducibility and magnitude of stimulation (29). Reproducibility can be optimized by using standardized protocols that minimize experimental conditions variability (4), and using quantification tools that allow unbiased measurement of the target endpoints (18). In some biological systems, certain fungicides at low doses can produce consistently mild growth stimulation in vitro in response to a range of hormetic doses, but the absence of a maximum stimulation dose can result in a lack of statistical significance using standard statistical analyses (5). In those cases, the application of statistical tools capable of discriminating low but significant stimulation from background noise is necessary. Several statistical methods are available for assessment of hormetic responses, including parametric, non-parametric, and model based methods (17, 29–32). Pathogenicity, often measured in terms of disease severity quantified as area under the disease progress curve (AUDPC), is another important endpoint for assessment of fungicide hormesis in plant pathogenic oomycetes and fungi. Hormetic responses often result in highly significant increments in disease incidence and disease severity, which can easily be assessed for statistical significance using standard statistical methods (5–8). However, pathogenicity is a complex trait that fundamentally describes the ability of an organism to cause disease in its host. Pathogenicity can be modified by extrinsic factors, such as host genotype, environmental factors affecting pathogen and host metabolism, fungicide formulations and toxicity on the host, etc. Hence, to demonstrate statistically that changes in pathogenicity and disease severity were caused by subtoxic doses of fungicides, requires solid experimental design, strictly standardized experimental protocols with appropriate negative controls, extremely careful execution of methods and procedures, and multiple replicates and repetitions (4). Precise attention to detail during experiment execution is critical for reproducibility, which cannot be accomplished without careful data records, and critical observation analysis. Without these strict experimental conditions, seemingly minor methodological changes and small experimental errors can result in variability that will render extensive data sets, collected over several weeks or months, nearly if not completely unpublishable (5). Fungicide hormesis research builds scientific character. Although enhanced growth and increments in disease severity are relatively easy to detect and quantify, elucidating the mechanisms behind hormetic stimulation in fungal pathogens is elusive (7, 8). Attempts to identify mechanisms underlying hormesis in fungi and oomycetes have been so far fruitless ((7, 8), Garzon et al. unpublished). Pathogenicity factors such as production of oxalic acid, pectinase, polygalacturonidase, and cellulase, as well as tolerance to oxidative stresses have been examined as potential hormetic mechanisms in Sclerotinia sclerotiorum, however, no evidence has been found of their stimulation during exposure to sublethal doses of fungicides (7, 8). Nonetheless, since enhanced virulence was detected less than 24h after inoculation, Di et al. (8) concluded that direct stimulation and not adaptive compensation mechanisms may be involved in this hormetic response of S. sclerotiorum to sublethal doses of carbendazim. 130

Hormetic responses result in modest endpoint increases, some peaking at over 60% of the control. Such increases reflect direct stimulation or overcompensation responses to mild injury caused by a stressor. Systematic search and critical analysis of hormesis literature, provides evidence that increments above hundred percent of the control are rare. Suggesting hormetic responses may result from allometrically based integrative biological processes (1). The impact of fungicide hormetic dose-responses of fungal plant pathogens on their host may be predicted within allometric parameters. Accordingly, disease severity increments of up to 60% were reported for Pythium damping off of geranium seedlings due to exposure to sublethal doses of mefenoxam (5); while disease severity increments of 20 to 30% were described for Sclerotinia sclerotiorum necrotic lesion expansion on rapeseed leaves due to exposure to low doses of carbendazim (8). This property of hormetic dose responses needs to be considered in future studies assessing the risk of deliberate and accidental use of fungicides at subinhibitory doses. Awareness of the risk associated to fungicide hormesis among growers, scientists and educators, is necessary to bring attention to preventable losses that may result from accidental stimulation of plant pathogens. Furthermore, a recent study suggested that exposure of Sclerotinia sclerotiorum to sublethal doses of fungicides may increase mutation rates in this clonal fungus, which could result in new phenotypes, potentially leading to the emergence of more virulent and fungicide resistant strains (33). Currently, there is a complete lack of information regarding crop losses due to fungicide hormesis, but a rough estimate of 30 to 60% increment on disease severity, can provide an indirect assessment of the potential economic impact of this toxicological phenomenon. Fungicide hormesis research is currently widely underrepresented in research initiatives and is underfunded. Hopefully, the increasing evidence of fungicide hormesis accumulating in the phytopathological and toxicological literature will help to remediate the challenges we presently face.

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22. Garzon, C. D.; Moorman, G. W.; Yánez, J. M.; Molineros, J. E.; Leonard, R. C.; Jimenez-Gasco, M. Low-doses of fungicides have a stimulatory effect on Pythium spp. in vitro and in planta. Phytopathology 2008, 98, S58. 23. Brain, P.; Cousens, R. An equation to describe dose responses where there is stimulation of growth at low doses. Weed Res. 1989, 29, 93–96. 24. Flores, F. J. 2010. Effect of Low Doses Of Pesticides on Soilborne Pathogens an Approach to the Hormetic Response. Master of Science Thesis, Oklahoma State University, Stillwater, OK. 25. Lira-DeLeón, K.; Ramírez-Mares, M. V.; Sánchez-López, V.; RamírezLepe, M.; Salas-Coronado, R.; Santos-Sánchez, N. F.; Valadez-Blanco, R.; Hernández-Carlos, B. Effect of crude plant extracts from some Oaxacan flora on two deleterious fungal phytopathogens and extract compatibility with a biofertilizer strain. Front. Microbiol. 2014, 5, 383.1–383.10. 26. Noguerol-Pato, R.; Torrado-Agrasar, A.; González-Barreiro, C.; CanchoGrande, B.; Simal-Gándara, J. Influence of new generation fungicides on Saccharomyces cerevisiae growth, grape must fermentation and aroma biosynthesis. Food Chem. 2014, 146, 234–241. 27. Audenaert, K.; Callewaert, E.; Höfte, M.; De Saeger, S.; Haesaert, G. Hydrogen peroxide induced by the fungicide prothioconazole triggers deoxynivalenol (DON) production by Fusarium graminearum. BMC Microbiology 2010, 10, 112DOI:10.1186/1471-2180-10-112. 28. Pradhan, S. 2015. Fungicide-Induced Hormetic Effects in Plant Pathogenic Fungi and Oomycetes. Master of Science Thesis. Oklahoma State University, Stillwater, OK. 29. Deng, C. Q.; Graham, R.; Shukla, R. Detecting and estimating hormesis using a model-based approach. Hum. Ecol. Risk Assess. 2001, 7, 849–866. 30. Deng, C.; Zhao, Q.; Shukla, R. Detecting hormesis using a non-parametric rank test. Hum. Exp. Toxicol. 2000, 19, 703–708. 31. Schabenberger, O.; Tharp, B. E.; Kells, J. J.; Penner, D. Statistical tests for hormesis and effective dosages in herbicide dose response. Agron. J. 1999, 91, 713–721. 32. Cedergreen, N.; Ritz, C.; Streibig, J. C. Improved empirical models describing hormesis. Environ. Toxicol. Chem. 2005, 24, 3166–3172. 33. Amaradasa, B. S.; Everhart, S. E. Effects of Sublethal Fungicides on Mutation Rates and Genomic Variation in Fungal Plant Pathogen, Sclerotinia sclerotiorum. PLoS ONE 2016, 11, e0168079. DOI:10.1371/ journal.pone.0168079.

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Chapter 10

Herbicide-Mediated Hormesis Regina G. Belz*,1 and Stephen O. Duke2 1Agroecology

Unit, Hans-Ruthenberg-Institute, University of Hohenheim, Stuttgart 70593, Germany 2National Center for Natural Products Research, Agricultural Research Service, United States Department of Agriculture, Oxford, Mississippi, United States *E-mail: [email protected].

Hormesis is the stimulatory effect of a subtoxic level of a toxin. This phenomenon is common with most herbicides on most plant species, although the effect is generally difficult to quantitatively repeat, even under laboratory conditions. The magnitude of and the dose required for hormesis is influenced by many biological and environmental parameters. Hormesis with glyphosate seems to be more consistent than with most other herbicides, perhaps due to its unique mode of action as a herbicide. However, little is known of the mode of action of any herbicide-mediated hormesis. Herbicide-induced hormesis may play a role in the evolution of herbicide resistance. Although subtoxic levels of herbicides are sometimes used to stimulate certain desired crop responses (e.g., sucrose accumulation in sugarcane), the unpredictability of hormesis makes it too risky for general crop production. A better understanding of plant hormetic responses to herbicides is needed.

© 2017 American Chemical Society

Hormesis, the stimulatory effect of a subtoxic dose of a toxin or a stress, is a relatively simple concept, yet it is poorly understood (1, 2). Hormesis is observed with almost all toxicants and on almost all organisms. The earliest synthetic herbicides, the auxinic herbicides, have been known to stimulate plant growth at subtoxic levels for many years (e.g., (3)). Meanwhile, hormesis has been reported with almost all herbicide classes and modes of action. These studies have been summarized in several previous reviews (4–11). Hormesis is also common in insects exposed to low doses of insecticides (12) and plant pathogens exposed to low fungicide doses (13). Hormesis is a common phenomenon, and we know that weeds and non-target plants are commonly exposed to a range of herbicide doses, including those that will cause hormesis (14). However, little consideration is given to what the environmental, ecological, and evolutionary implications of hormesis could be. This short chapter will provide a summary of previous work on herbicide hormesis and bring the reader up to date with more recent findings related to this topic.

The General Phenomenon The doses of a toxicant required for hormesis are generally found in a narrow range just before the negative effects of the toxin begin. Hormesis is represented by a biphasic dose-response curve. Most dose-response studies of herbicides have not captured this phenomenon because the concentrations used have not been sufficiently low or have not had concentrations in the often narrow hormetic range. For most herbicides, the effect is 15 to 30% stimulation in the laboratory and slightly less under field conditions (10), although there are cases in which there is no detectable or significant hormesis and others with which the effect is much more than 30% (see ref. (11) and glyphosate section below). Whether one finds hormesis or not and the magnitude of the effect found depends on many factors. One of these is the parameter endpoint measured. For example, the same concentration of a phytotoxin can have no effect on root growth, while increasing shoot growth (15). The hormetic dose can vary for different parameters. Velini et al. (15) reported 2 g ha-1 of glyphosate to stimulate root growth of Pinus caribaea Morelet maximally, whereas for leaf growth, the optimal hormetic dose was 20 g ha-1 (Figure 1). The plant growth stage at the time of herbicide exposure can also have a profound influence on hormesis. For example, De Carvalho et al. (16) found glyphosate hormesis in coffee (Coffea arabica L.) plants exposed at 35 days after transplanting, but not in plants exposed at 10 days (Figure 2). The time after treatment with the herbicide can also be a critical factor in detecting hormesis, since the phenomenon represents a dynamic process. For example, hormesis was easily detectable in barley (Hordeum vulgare L.) treated with a range of glyphosate doses up to seven weeks after spraying, but the effect disappeared thereafter, with no effect on growth or grain yield at doses that had been hormetic to growth of the plants earlier (17) (Figure 3). These dynamics further imply that the hormetic dose range can change with time after treatment. For example, the glyphosate dose that caused increases in growth rate of Bracharia brizantha (Hochst.), was higher at 30 days after application than at 15 days (18). 136

Figure 1. Dose-response curves for glyphosate effects on growth of different organs of Pinus caribea. Reproduced with permission from Reference (15). Copyright 2008 John Wiley & Sons.

Figure 2. Coffee (Coffea arabica) plant height at 60 days after being exposed to glyphosate applied to plants at 10 days (A) and 45 days (B) after transplanting. Reproduced from Reference (16) and used under a Creative Commons Attribution-NonCommercial 3.0 license. Published 2013 by Academia Brasileira De Ciencias. 137

Figure 3. Barley (Hordeum vulgare) plant weights at different times after spraying a range of glyphosate doses. The one week results show results of a second experiment. Reproduced with permission from Reference (17). Copyright 2008 Elsevier Applied Science Publishers. Nutrient status can affect herbicide-induced hormesis. Cedergreen et al. (19) found that nitrogen, but not phosphorus limitation, can allow and/or enhance glyphosate-mediated hormesis in hydroponically-grown barley. In duckweed (Lemna minor L.), glyphosate-mediated hormesis was seen on both nitrogen- and phosphorus-deficient plants, and the authors hypothesized that glyphosate might act as a source of phosphorus. Atmospheric CO2 levels, light intensity, and temperature can have profound effects on hormesis (11). Pre-stressing a plant with another chemical or with the same chemical can influence hormesis as well. For example, glyphosate hormesis was only found in lettuce root length in plants that had been pre-stressed with a pelargonic acid or methanol exposure (20). Pre-exposure to a hormetic dose of glyphosate affects a later dose-response curve. Silva et al. (21) found a greater stimulation of growth by a hormetic dose of glyphosate in soybean plants that had received a preconditioning hormetic dose of glyphosate 14 days earlier. Calabrese (22) reported that such preconditioning effects on hormesis are common in nonherbicide studies of hormesis. We have mentioned only a few of the many parameters, both environmental and plant aspects that can influence whether herbicide-mediated hormesis is found and the magnitude of the effect. Hormesis is influenced by almost every parameter that has been tested, which makes it a phenomenon that is sometimes difficult to reproduce with even slight changes in an experiment.

What Causes Herbicide Hormesis? The mechanism(s) of hormesis is (are) poorly understood. If the mode of action of the stimulation at lower doses is unrelated to that of the inhibition at higher doses, one could expect a relatively wide hormetic peak if the stimulatory effect occurs at a sufficiently low dose. But, the stimulatory peak is generally a rather narrow dose range just before inhibition begins, which 138

supports the hypothesis that hormesis is caused by responses to very mild stress caused by a toxin, presumably as a secondary effect of the toxin affecting its target. In animals, hormesis has been associated with the production of cellular increases in cytoprotective and restorative proteins such as growth factors and antioxidants (23). If all of this is the case, the phenomenon of hormesis is due to secondary and tertiary effects of mild stress resulting from the target site of the toxicant being affected. There is some evidence for this “mild stress” hypothesis in the case of glyphosate, a case in which we can easily see a biomarker for the herbicide affecting its target site. Shikimic acid accumulates to high levels in glyphosate-treated plants as a result of inhibition of 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS), a key enzyme of the shikimate pathway (24, 25), and even at hormetic doses, shikimate levels increase in most plants (i.e., (15)). Neither hormesis nor shikimate was found in glyphosate-resistant soybean at glyphosate doses that cause hormesis in glyphosate-susceptible soybean (15). Thus, glyphosate hormesis seems to be tied to the stress caused by the herbicide at the herbicide target site, and not to an effect by some other mechanism. In rare cases, a herbicide can be transformed to a plant growth-stimulating compound, so that, under some conditions, one might mistake the effect for a direct hormetic effect of the herbicide. For example, sulcotrione can be phototransformed to a compound that is further converted to a mimic of salicylic acid (26). This compound is a root growth enhancer. So, this process might explain the mechanism of some of the sulcotrione-mediated hormesis.

Glyphosate-Mediated Hormesis – A Special Case? Glyphosate is the most important herbicide in history (27). Thus, any aspect of its biological activity has importance. It is a relatively high use rate herbicide that is normally recommended to be used at 0.29 to 2.16 kg ha-1, depending on the weed species. Early in its use, it was found to enhance sucrose levels in sugarcane at low (40 to 180 g ha-1) doses (28), but these doses are normally quite phytotoxic, if not lethal to other plant species. Hormetic growth effects of glyphosate on sugarcane were observed with doses of 7 to 36 g ha-1 (29). Hence, the glyphosate-mediated sucrose accumulation may be in part or entirely caused by reduced sucrose movement to metabolic sinks that have reduced growth because of glyphosate toxicity. There have been more reported cases of hormesis with glyphosate than any other herbicide, with hormetic doses of 0.2 to 20 g ha-1 (11, 15, 21). The magnitude of the effect is sometimes much higher than that found with other herbicides. For example, in Commelina benghalensis L., a 100% increase was reported for shoot dry weight in response to ca. 0.2 g ha-1 glyphosate (15). In addition to the list of cases of glyphosate hormesis in our earlier review (14), there are several new reports. Cochavi et al. (30) found that glyphosate significantly enhanced Egyptian broomrape-infested carrot growth at ca. 100 g ha-1, apparently due to killing the broomrape. However, as it involves the interaction with another organism for an effect, it could be argued that this is not true hormesis. Pokhrel and Karsai (31) found 1% of the recommended 139

dose of commercially formulated glyphosate to promote biomass of Bryophyllum pinnatum (Lam.) Oken more than 50%. The magnitude of stimulation of some woody plants under certain environmental conditions and growth stages is quite high. For example, root dry weights of Eucalyptus grandis and Pinus caribea were increased to 210 and 153%, respectively, of the control value by ca. 2 g ha-1 glyphosate (15). However, the response of woody plants to glyphosate depends on many factors, and this magnitude of hormetic response is not always found. For example, Schrübbers et al. (32) found that although coffee plant height was increased by as much as 35% by 10-86 g ha-1 of glyphosate, no hormesis was found with leaf area and total biomass. The 86 g ha-1 dose caused increases in shikimic acid. But, this study did not include lower doses that might have maximized hormesis. Could glyphosate-mediated hormesis have additional direct mechanisms that add to the “mild stress” hypothesis? Hormetic doses of glyphosate can enhance photosynthesis (21, 33), but there is no evidence as to whether this is a direct effect or a secondary effect of mild stress from inhibition of the herbicide target site. Cedergreen et al. (19) speculated that glyphosate might be a plant phosphorous source in some cases, but this is unlikely to occur unless glyphosate is rapidly metabolized, which may not be the case in most plant species (34). Another hypothesis is that at hormetic doses, all or part of the carbon in the shikimate that accumulates would have been channeled into lignin, a product of the shikimate pathway. If so, stiffening of cell walls would be delayed, resulting in larger cells before growth stops, as suggested by Velini et al. (15). This could account for taller plants or longer roots, but it would not necessarily account for increased dry weight. Because the magnitude of the hormetic effect of glyphosate is generally greater than with most other herbicides, the question of whether sub-toxic doses of glyphosate can increase yield arises. Abbas et al. (35) reported that potted chickpea (Cicer arietinum L.) grain yield was increased 34% by a glyphosate dose of 7.2 g ha-1 applied at 4 weeks after seedling emergence. Number of seeds per plant was increased in four weed species (Chenopodium album L., Rumex dentalus L., Coronopus didymus (L.) Smith, and Lathyrus aphaca L.) by low (8-31 g ha-1) doses of glyphosate (36).

Can Herbicide Hormesis Be Harnessed? Considering population growth and global climate change, there is a dire need to improve crop productivity and stress tolerance (37). Low doses of many herbicides or other plant toxins are known to increase harvestable crop yield or other desired plant traits such as stress tolerance, and the use of herbicide hormesis for desired agronomic effects has been proposed in publications and patents. Furthermore, the potential for crop improvement by herbicide hormesis has been considered to be similar or greater than what can be achieved by breeding or modern biotechnology (10). Despite this tempting potential to enhance plant productivity and health and, thus, to provide a significant economic benefit for a farmer, harnessing herbicide hormesis has succeeded in only a few 140

cases. Examples of commercial exploitation of hormesis are the use of low doses of glyphosate and other herbicides as ripeners in sugarcane to enhance sugar production (38), the use of herbicide safeners to induce tolerance in crops to a followup herbicide treatment (39), and the use of low doses of several auxins and antiauxins as bioregulators for yield improvement (40–42). Furthermore, plant stimulatory responses to certain commercial biostimulants may also represent a hormetic response since some of these low-toxicity compounds may be phytotoxic at higher doses. This may especially apply for hormone containing products or substances extracted from plants (43). A fairly new effort in this regard is the restoration of crop yield losses caused by herbicides using such plant growth biostimulants. First reports show recovery rates of nearly 100% from herbicide injury causing yield losses up to 30% (44). Applications targeting preconditioning hormesis and, thus, the induction of hormesis before plants are exposed to stress may also be envisioned, e.g. young transplants or during bloom in order to protect highly stress sensitive growth stages. However, none of these few actual or proposed uses are labelled under the term (herbicide) hormesis, and few of the compounds mentioned are actually registered as a herbicide. What is it that makes the herbicide hormesis phenomenon so difficult to use commercially for general crop production? According to the current state of knowledge, its unpredictability makes it simply very risky. The examples given above and discussed in our earlier reviews (10, 11) show that hormesis varies quantitatively with many environmental and plant aspects and with time. Furthermore, since hormesis is at the low end of the dose-response curve and is followed by the negative effect as the dose increases, only a slight change in the dose reaching the molecular target site(s) can dramatically change the resulting response from no effect to a considerable negative effect if the dose range for hormetic responses is narrow. Figure 4 illustrates this practical constraint in the form of a spray application of the phytotoxin parthenin, a metabolite of the invasive weed Parthenium hysterophorus L., in two independent experimental runs conducted under semi-natural conditions (45). A parthenin dose of 0.23 kg ha-1 lead to a maximum stimulation of 38% over control in leaf area of Sinapis arvensis L. in one experiment, while the same dose applied in another experiment inhibited leaf area growth by 21%. The reasons for this may be manifold, however, looking at the untreated controls it is indicated, that the absence of hormesis in the one experiment may have been partly caused by unfavourable climatic conditions preventing S. arvensis plants from enhanced growth (45). Such an interference of hormesis formation with growth factors seems to widely apply in plant biology such that hormesis is unlikely to manifest itself under conditions preventing plants from enhanced growth, i.e. conditions leading to retarded/no plant growth or optimum growth (46). Plants under field conditions are inevitably exposed to multiple stressors at once or in sequence and, therefore, a low-dose application rate that reproducibly elicits a stimulatory response under all circumstances is highly unlikely. Or vice versa, the final outcome of a hormetic application under the conditions encountered in the field is hard to predict (10). Hence, finding the ‘ideal application window’ and predicting a reliable hormetic dose for a desired increase in final yield of economically relevant plant parts under particular field conditions seem to be the major reasons for the previous 141

failure to harness hormetic herbicides for crop growth and/or stress tolerance improvement (37). At the moment it seems unfeasible to overcome these practical problems in a field situation and, therefore, herbicide hormesis may continue to be economically worthwhile only in rare cases or under the more controlled conditions of a greenhouse or a floriculture setting of higher value crops.

Figure 4. Dose-response relationships for the effect of the phytotoxin parthenin applied as spray application under seminatural conditions on leaf area of wild mustard (Sinapis arvensis) in two independent experiments. Adapted from Reference (45) and used under a Creative Commons Attribution-NonCommercial 3.0 license. Published 2008 by Sage Publications. As it may never be possible to widely use the hormetic phenomenon as hitherto intended, Gressel and Dodds (37) proposed to change the strategy from direct field applications of hormetic herbicides to unraveling the genetic processes triggering and regulating hormesis and using this knowledge for transgenic breeding of improved crops as well as for screening more reliable hormetic compounds.

Influence on Evolution of Herbicide Resistance? Herbicide hormesis can also be associated with undesirable effects in an agricultural context and of particular importance seems to be the promotion of weeds by regular applications of herbicides for which they have evolved resistance (10, 11). Reports of growth stimulation in herbicide-resistant weeds by the herbicide to which they have evolved resistance are available for target-site (TSR) and non-target-site resistant (NTSR) biotypes. For example, at doses that severely damage the sensitive biotype, growth of acetyl-CoA carboxylase (ACCase)-TSR biotypes of Alopecurus myosuroides Huds. was stimulated 54% over the control by ACCase-inhibiting herbicides (10, 47), growth of acetolactate synthase (ALS) TSR biotypes of Matricaria inodora L. and Lolium perenne L. 142

were stimulated 31% (Figure 5) and 64%, respectively, by ALS-inhibitors (48, 49), and a psbA-TSR biotype of Chenopodium album L. showed a 37% maximum stimulation by terbuthylazine, a PSII inhibitor (Figure 5) (48). Furthermore, growth of triazine-NTSR annual bluegrass (Poa annua L.) was enhanced by 25 and 95% at simazine doses of 2.24 and 1.12 kg ha-1, respectively (50), and growth of glyphosate-resistant Eleusine indica (L.) Gaertn. proved much more responsive to low-dose stimulation by glyphosate than the wildtype (Figure 5) (48). The dose causing maximum stimulation of resistant weeds does not always match the field rate, but resistant weeds may still be considerably promoted by regular herbicide applications (10). This does not directly cause a selection pressure for evolution of resistance, but may indirectly promote the development of resistance by making hormetically enhanced resistant weeds more competitive, more resistant to a second weed control measure, or even more reproductive (10, 11). While early growth stimulation by herbicide hormesis is well documented for resistant weeds, an impact on competitive ability, repeated herbicide applications, or reproduction especially under practical field conditions is still to investigate. For glyphosate hormesis, however, results with sensitive plants have shown hormetic effects on reproduction (36, 39) as well as enhanced hormesis with a sequential low glyphosate dose (21), both of which could facilitate the evolution of resistance.

Figure 5. Dose-response relationships for sensitive and herbicide-resistant weed biotypes in germination assays. A: sensitive and glyphosate resistant Eleusine indica exposed to glyphosate; D: sensitive and ALS target-site resistant (TSR) biotypes of Matricaria inodora exposed to the ALS-inhibitors iodo-/mesosulfuron; I: sensitive and PSII-TSR Chenopodium album exposed to the PSII-inhibitor terbuthylazine. Reproduced from Reference (48) and used under a Creative Commons Attribution-ShareAlike 4.0 International license. Published 2014 by Julius-Kühn-Institut. Besides these obvious effects that herbicide hormesis may have on resistance evolution, Cutler and Guedes (51) hypothesized in connection with insecticide-induced hormesis in insects that hormetic doses may also be able to induce mutations that might support resistance or tolerance. Such an increase in mutation frequencies was assumed by Gressel (52) for sublethal pesticide doses and so it may also be conceivable that low-dose herbicide hormesis is a driver of resistance or tolerance endowing mutations. The phenomenon has so far been neither addressed in insects (51) nor weeds. Moreover, selective effects of low herbicide doses on individuals within a weed population may 143

be a further long-term aspect impacting herbicide sensitivity or resistance by undesired changes in population composition. Studies investigating herbicide hormesis usually consider the population level in the form of mean values without taking into account possible differences in responses of individual plants (e.g., (6, 7, 16, 45)). A weed population within an agricultural field, however, can be a very heterogeneous group of individual plants of the same species due to genetic variation or non-genetic variability. This variation substantiates the ubiquitous ability of a population to adapt to local stress factors, including herbicide exposures (53). It is, thus, likely that by ‘selective hormesis’ parts of a population are hormetically boosted, while others are not affected or are adversely affected by herbicides, which may lead to changes in population composition and ultimately in herbicide sensitivity. A very good example to demonstrate this ‘selective hormesis’ hypothesis are TSR populations of diploid species where the resistance appears heteroand homozygous. For example, a TSR field population of blackgrass (A. myosuroides) typically represents a mixed population of sensitive, heteroand homozygous resistant individuals with varying sensitivity to the selecting herbicide (54). A preliminary study with an ACCase-TSR blackgrass biotype indicated that splitting-up the monophasic response of the entire population to an ACCase-inhibitor into selective responses of individual genotypes gives considerable differences in efficacy and hormesis (Figure 6). An application rate of 25 g a.i. ha-1 clodinafop for instance completely controlled the sensitive individuals, while the heterozygous individuals were stimulated by 69% over control, and the homozygous individuals remained unaffected.

Figure 6. Dose-response relationships for the ACCase-inhibitor clodinafop applied as spray application in a greenhouse study on the aboveground biomass of the entire population of an ACCase target-site resistant biotype of Alopecurus myosuroides as well as the sensitive, hetero- and homozygous-resistant sub-populations. Data are from Reference (54). 144

‘Selective hormesis’ was, furthermore, observed in sensitive populations if exposed to low doses of herbicides. This may be relevant whenever low herbicide doses appear in practice, e.g. drift deposition, run-off, errors in application, leaf contact of treated and untreated plants, protection by taller plants or mulch, or absorption of low doses from soil (10, 28). For a sensitive, high-density population of M. inodora exposed to the ALS-inhibitors iodo-/mesosulfuron (55) as well as for a model-population of lettuce (Lactuca sativa L.) exposed to the antiauxin PCIB [2-(p-chlorophenoxy)-2-methylpropionic acid] (53), selective stimulation of individual plants depending on the individual growth rate could be observed (Figure 7). The slow-growing individuals within the population (represented by the ≤10% percentile) were more sensitive and were more strongly stimulated than the total population, while the fast-growing individuals (represented by the ≥90% percentile) were less responsive both at being stimulated and inhibited. Hence, depending on the dose, such selective effects will change the size distribution within an exposed population. Figure 7 illustrates this selectivity for root and shoot length responses. Very low doses of PCIB of around 0.1 mM selectively promoted root elongation of the more sensitive slow-growing individuals with a maximum of 134% stimulation and, thus, lead to a decrease in overall size variation. The same PCIB dose selectively enhanced shoot elongation at a maximum of 53% stimulation, although shoot length was not promoted by PCIB at the population level and at the fast-growing individuals. At higher PCIB doses of around 0.5 mM root length of the less sensitive fast-growing individuals was promoted at most, while root growth of the slow-growing individuals was already severely inhibited. The same PCIB dose also severely inhibited shoot growth of the slowgrowing individuals, while shoot growth of the fast-growing individuals remained unaffected (Figure 7). Especially doses that selectively promote less sensitive, fast-growing individuals or leave them unaffected may shift a population towards less sensitive individuals which may in the long-run affect the herbicide sensitivity of a population.

Figure 7. Dose-response relationships for the antiauxin PCIB [2-(p-chlorophenoxy)-2-methylpropionic acid] in a germination assay on root length of the 10 and 90% percentile (%ile) of a population of Lactuca sativa (Left) and on shoot length of the 10 and 90%ile (Right). Data are from Reference (53). 145

The relevance and ecological implications of selective effects of low doses of herbicides under field conditions are yet unknown. However, the examples point to the possibility that herbicide hormesis may have the potential for a hitherto underestimated factor in the development of weed resistance.

Parting Thoughts The implications of herbicide hormesis are many. Almost any plant growth or development parameter can be stimulated or enhanced by a low dose of almost any herbicide under the right conditions. The phenomenon is, however, too difficult to predict in the field for its use in yield or crop quality improvement for most crop/herbicide combinations. Nevertheless, unpredictable herbicide hormesis almost certainly occurs in crops. The economic impact of this is unknown. New information indicates that the variable hormesis parameters for subpopulations of the same weed species within a field results in selection for and against certain subpopulations by low herbicide doses. This phenomenon deserves more study to understand its full impact on the evolution of weed resistance to herbicides.

Acknowledgments The authors acknowledge the German Research Foundation for funding RG Belz’s research on herbicide-induced hormesis (Individual grant BE4189/1-2).

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Chapter 11

Effects of Herbicides on Non-Target Terrestrial Plants Beate Strandberg,*,1 Céline Boutin,2 Solvejg K. Mathiassen,3 Christian Damgaard,1 Yoko L. Dupont,1 David J. Carpenter,2 and Per Kudsk3 1Department

of Bioscience, Aarhus University, Vejlsøvej 25, 8600 Silkeborg, Denmark 2Environment Canada, Science & Technology Branch, 1125 Colonel By Drive, Raven Road, Carleton University, Ottawa, Ontario K1A 0H3, Canada 3Department of Agroecology, Aarhus University, Forsøgsvej 1, 4200 Slagelse, Denmark *E-mail: [email protected].

Over the past decades declining biodiversity has been a major concern across the world; however, preventing biodiversity declines in agricultural areas has been ineffective. Failure to adequately assess and properly regulate herbicide effects can have important implications for species richness and overall biodiversity within natural and semi-natural habitats adjacent to agricultural fields. While the use of agrochemicals including both fertilizers and pesticides has been considered of great concern for the observed biodiversity declines, this paper primarily focusses on herbicide effects on non-target terrestrial plants. Although not explicitly addressed in current pesticide regulations, environmental protection, in particular, should include considerations of the impacts on non-target species, biodiversity and ecosystems. However, current toxicity tests are not optimised in terms of the endpoints assessed, the species tested, or the importance of pesticide effects on community composition, ecosystem functioning and biodiversity and thus, do not adequately fulfil the requirements of the EU pesticide legislation for environmental protection.

© 2017 American Chemical Society

Introduction Rapid world population growth is driving an unprecedented demand for food production resulting in wide scale destruction and ecosystem degradation of natural areas for the production of farmlands (1). This leads to large declines in native biodiversity and threatens to disrupt ecosystem services vital to human well-being (2). This degradation is much more pronounced in agricultural areas than in natural habitats outside production (3, 4). Currently, several studies have identified that the biodiversity within European agricultural areas is declining (1, 5–14). These long-term studies all point at agricultural intensification as a main cause of the observed decline. Although it is well known that herbicides and fertilisers each independently decrease biodiversity (15–19), the prevention of biodiversity declines in agricultural areas is still failing (10, 11, 20) and the need to protect semi-natural habitats from the potential effects of pesticide drift is thus becoming more pivotal. Herbicide effects on non-target terrestrial plants (NTTPs) have received increasing interest over the last 15 years. In 2014, the European Food and Safety Agency (EFSA) published a scientific opinion including a literature review on this topic (21). This paper will provide an updated review on this subject that will be further illustrated by some of our own data. Within the EFSA opinion, NTTPs are defined as all plants growing outside of fields, as well as those growing within fields that are not intended herbicide targets. In the present review, we will use this definition but our focus will be on the off-field conditions.

Exposure of NTTPs When herbicides are used to control weeds, NTTPs may unintentionally be exposed and affected. Off-field plants may be exposed through multiple routes. Spray drift, i.e. drift during application, is currently considered the main route of exposure for off-field plants, although other types of exposures such as overspray, vapour drift and dust, can still be relevant (21). Spray drift deposition rates vary due to a number of factors including crop type and stage, climatic conditions during and immediately after spraying, spray equipment, surface characteristics, and boom height. Generally, spray drift deposition, given as a percentage of the field application rate, is estimated using drift models, e.g. Ganzelmeier et al (22). Currently, the values published by Rautmann et al. (22) are favoured in the EU (21). Based on these algorithms, Schmitz and co-workers have calculated exposure rates for field margins adjacent to cereal fields to be approximately 30% of the field rate in the vicinity of the field, followed by a rate of 15% at 0.76 m and thereafter rapidly decreasing to 2.77% at a distance of 1 m to the edge of the field (24, 25).

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Regulations and Standard Phytotoxicity Testing as Safeguard for the Environment Herbicide effects on NTTPs are not explicitly addressed under the existing EU regulations but Regulation (EC) No1107/2009 (26) requires that “substances or products produced or placed on the market do not have any harmful effect on human or animal health or any unacceptable effects on the environment”. With respect to the environment this, in particular, includes considerations of impacts on non-target species and biodiversity and on the ecosystem (21). Although the test guidelines for phytotoxicity testing (27–31) includes a list of 52 non-crop species in addition to 32 crop species, tests provided by applicants for risk assessment are mostly done using crop species. Over the years, it has been debated whether phytotoxicity data on crops represent an adequate safeguard for effects on non-target species, biodiversity and ecosystems and this question has been addressed in a number of studies (e.g. (16, 32–37)). These studies demonstrated that crop species do not consistently differ from wild species in herbicide sensitivity when testing effects according to the standard test guidelines, i.e. testing effects on seedling emergence and growth (biomass) of seedlings and young plants. Additionally, numerous studies have shown that no plant species is consistently the most or the least sensitive (e.g. (16, 33, 38, 39)). Overall, species sensitivity is dependent on the efficacy spectrum of the herbicide and thus the observed sensitivity differences between crops and non-crops are likely influenced by the specific test conditions themselves, as opposed to solely being driven by whether or not the species is classified as a crop or non-crop (16). One rationale for choosing crops as test species rather than NTTPs for phytoxicity tests is that crops generally have faster and more uniform germination rates as compared to wild species. Crop seeds are often large, have no particular germination requirements and germinate at a consistent rate. This makes it unproblematic to meet the requirement of 70% seedling emergence for the OECD seedling emergence test (27). However, White et al. (40), who tested the germination of 29 NTTPs including terrestrial and wetland species, find that 23 species attained the 70% emergence requirement within 14 days or less. In a subsequent experiment, germination variability among populations obtained from various sources is observed in eight species when two to four populations per species are tested (Figure 1). Nevertheless, the 70% threshold is reached in the majority of the populations (Figure 1). Populations obtained from Germany are particularly poor performers, which may be due to immature seed lots, old seed stocks or other quality control assessment issues from the source. Given this discrepancy, germination rates needs to be verified prior to the beginning of phytotoxicity test experiments. Additionally, many phytotoxicity studies have been conducted successfully with NTTPs, although germination characteristics have not been included (16, 33, 41–45). Therefore, it seems feasible to include more NTTPs in the phytotoxicity testing.

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Figure 1. Percent germination (mean of three replicates with standard errors) of eight species tested in Petri dishes filled with soil and placed under greenhouse or growth chamber conditions (Refer to Boutin et al. (46)) for complete materials and methods). Two to four populations per species originating from different parts of the world were tested: WNA: Western North America, UK: United Kingdom, GER: Germany, ENA: Eastern North America, MID: American Midwest, ON: Ontario, Canada, FLO: Florida, US. A line indicating the 70% germination level is shown. Assessing herbicide effects on crop species as the only representative for NTTPs, biodiversity and ecosystems seems inadequate. No tests are conducted on cryptogams e.g. ferns, mosses, and lichens; and among the flowering plants perennial and woody species are neglected even though these taxa and lifeforms often dominate the vegetation of natural areas. Furthermore, the short-term duration of standard tests, usually 21-28 days, and the selection of seed germinability and aboveground biomass as the only end-points is insufficient to cover the potential herbicide effects that may occur over the full life cycle of NTTPs. In the coming sections we will review studies that have elucidated the importance of taxa, lifeforms and end-points for understanding the responses of individual species to herbicides. Additionally, we will also review the few studies that have addressed the effect of herbicides on communities, food webs and ecosystems.

Sensitivity of Different Taxa and Lifeforms Species selection for regulatory testing is usually limited to a narrow taxonomic range of annual crop species. This raises the question as to whether the risk assessment will be representative of other taxa and lifeforms. Vegetation within natural and semi-natural habitats is composed of species belonging to many different taxa and is often dominated by perennial species although annual and biennial species may also be present. A number of studies testing the sensitivity of NTTPs to herbicides have included both annual and perennial species. Strandberg et al. (16) compare the herbicide sensitivity of two groups of plants, one group composed of annual species, the other composed of perennial 152

herbs that are taxonomically and morphologically similar to the annuals, to three herbicides (glyphosate, metsulfuron-methyl, mecoprop-P). They find no consistent differences in sensitivity, in terms of effects on biomass and seed production, among the tested annuals and perennials. Other studies support this finding, drawing further attention to the recovery potential of plants following herbicide exposure and how this may differ for species with different lifespans (33, 37, 44, 45, 47). A long-term experimental study being conducted in a grassland (http://bios.au.dk/faciliteter/long-term-experimental-plot/ ) has shown that vegetation exposed yearly to low doses of glyphosate (0-360 g a.i./ha) in the spring (late May) has a higher proportion of stress tolerant species in plots receiving the highest dose compared to control as calculated by Grimes S (16, 19, 48). Additionally, this long-term experiment shows that continuous exposure to glyphosate decreases the proportion of annual and biennial species in the community over the years when compared to non-sprayed control plots (16). This suggests that species that have to complete their entire life cycles in shorter timeframes (i.e. within one to two years) in order to persist in the vegetation will exhibit a level of vulnerability that is not necessarily manifested in toxicity testing. Only a few studies have tested herbicide effects on woody species and no consistent pattern regarding sensitivity has been found. Marshall (49) finds that the herbicides: mecoprop, fluroxypyr, chlorsulfuron, metsulfuron-methyl and glyphosate significantly decrease aboveground biomass of four shrub species. Boutin et al. (47) find that the sensitivity of seven woody species to a commercial herbicide formulation containing mecoprop (61.6%), 2,4 D (32.5%) and dicamba (5.8%), is within the range of sensitivities of 13 crop species that are tested in parallel to the woody species (Figure 2). A number of other studies have investigated fruit production of woody species exposed to herbicides. These studies show that even low doses of the tested herbicides, 0.2% and 2.5% of the label rates of chlorsulfuron and metsulfuron-methyl, respectively, reduce fruit production of Prunus avium (50, 51) and Crataegus monogyna, respectively (52, 53). The finding that reproductive endpoints are particularly sensitive to herbicides has not only been documented for woody species, but also for herbaceous species, as will be discussed below. We have little knowledge about the sensitivity of ferns, horsetails, lichens and mosses to herbicides. Nonetheless, some limited data does indicate that these taxa are indeed quite sensitive to exposure (47, 54–57).

Selection of Endpoints Relative to Phenological Stage at Time of Exposure According to the guidelines for standard phytotoxicity tests (27–31), plants must be sprayed with herbicides at the two- to six leaf stage, and effects recorded 14-28 days after exposure when plants are still in the vegetative stage. The most frequently recorded parameter, by far, is aboveground biomass, and many studies have documented that the biomass response, including that of NTTPs, to herbicides is most pronounced at earlier life stages (21, 58). However, when the 153

duration of the experiments is extended to record seed production, some studies have found that seed production is a more sensitive endpoint than biomass (e.g. (16, 42–44, 58)), independent of the life stage at the time of exposure (16). Boutin et al. (58) summarize dose-response data from studies in which plants have been exposed at different growth stages and where effects on biomass as well as on reproduction (seed production or measurable equivalent) are recorded. The data include 59 cases (different combinations of plant species and herbicides). Among these, reproductive endpoints are more sensitive than biomass in 58% of all cases, biomass is more sensitive than reproduction endpoints in 32% of the cases, and the two endpoints are equally sensitive in the remaining 10%.

Figure 2. Sensitivity of twenty species including seven woody species (text to the left of the points and in bold) and 13 crops to an herbicide formulation containing mecoprop (61.6%), 2,4 D (32.5%) and dicamba (5.8%). Plants were grown under greenhouse conditions and sprayed at the four- to six-leaf stage and the aboveground biomass was harvested 28 days after spray. Species below the dashed line were affected at doses 10% below the recommended label rate of 1848 g a.i./ha. (from Boutin et al. (47)). 154

NTTPs growing in natural and semi-natural habitats adjacent to agricultural fields are expected to be exposed to herbicide drift at different growth stages. In a typical agricultural landscape in Denmark, 10% to 44% of the plant species composing the hedgerow ground flora will be in flower during the normal spray application periods in May, June and September, as well as in July where flowering naturally peaks and herbicides are occasionally applied for crop desiccation (58). Depending on the growth stage, effects may be observed in any of four ways: Plants sprayed at the seedling stage 1) will have their vegetative parts affected or 2) will be affected on their future reproductive parts, and plants sprayed later during their reproduction 3) may have their seed production (or other reproductive endpoints, see below) affected, or 4) germination or vegetative parts of the F1 generation may be impacted (58). This highlights the importance of taking endpoints other than biomass into consideration, as biomass may not necessarily be the most sensitive endpoint in all cases. Whereas crop species seem to be suitable surrogates for wild species when plants are tested at the juvenile stage, the selection of test species needs to be reconsidered when other endpoints are to be evaluated. Among the reproductive endpoints, effects on seed production have received the most attention until now; however, others endpoints still remain relevant for proper environmental assessments. Some recent studies have documented effects on plant flowering that may not only be of great importance to plant reproduction and dispersal, but may also be of high concern due to the potential effects on higher trophic levels, including the loss of food resources for local pollinators. In a two-year field study, Schmitz et al. (24) observe a significant reduction in flowering intensity of Ranunculus acris that is exposed to the sulfonylurea herbicide mesosulfuron-methyl+iodosulfuron-methyl-sodium at a rate of 30% of recommended field rate (12+2.4 g a.i./ha, respectively). The herbicide is applied once a year in April approximately one to two weeks before onset of flowering. The documented reduction in flower intensity potentially affects different insects including hoverflies and solitary bees, which visit the flowers of R. acris for pollen and/or nectar. Another Danish study assesses the effects of fluroxypyr on the flowering of two perennial species, Taraxacum vulgare and Trifolium pratense, which are both important to flower-visiting insects (58). In the study, 16 pots of each species, having the same number of flower buds and the same overall size, are exposed to fluroxypyr at four doses: 0, 5, 25 and 100% of the label rate of 144 g a.i./ha, in a standard spray chamber. Flowering onset and the number of flowers are recorded daily for the week following exposure and thereafter weekly until no more flowers bloom and no more flower buds are formed. All herbicide doses cause sub-lethal visible effects even at the 5% dose. The average cumulative number of flowers produced by T. pratense is severely impaired at all doses, whereas T. vulgare experiences effects at higher doses (> 5% of label rate). In addition, onset of flowering is significantly delayed in both species except at the 5% dose (Figure 3).

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Figure 3. Average number of days following herbicide application until flowering onset (histogram) and average cumulative number of flowers (lines) in a) Trifolium pratense and b) Taraxacum vulgare exposed at the bud stage to fluroxypyr at 0, 5, 25 and 100% label rate of 144 g a.i./ha. Significant differences between doses were tested using Kruskall-Wallis non-parametric tests applying the Conover-Inman test for post hoc comparisons. Differences between doses are presented with capitals in histograms for number of days after exposure for onset of flowering, and with small letters above lines for cumulative numbers of flowers. (photo: B. Strandberg, from Boutin et al. (58)). (Reproduced with permission from reference (58). Copyright 2014 Elsevier).

Floral abundance and onset of flowering are also significantly impaired for two Asteraceae species, Tanacetum vulgare and Leucanthemum vulgare, in another study conducted in Denmark. In an experimentally established grassland, plots 7 by 7 meters are exposed to low doses of glyphosate (0, 14.4, 72, or 360 g a.i./ ha) and nitrogen (0 or 100 kg N/ha) (19). A reduction in floral density is more severe in the early-summer flowering L. vulgare, that starts flowering shortly after glyphosate and fertilizer application, and flowering plants are completely absent in many of the treated plots. However, even in the late-summer flowering T. vulgare, which is exposed two months prior to its natural flowering period, the floral density within the glyphosate treated plots is also significantly reduced. In addition to the measured reduction in density, flowering time of both species is significantly delayed in the glyphosate treated plots (Figure 4). Median flowering date of T. vulgare is delayed by 7.5 to 40 days for glyphosate doses of 5 to 25%, respectively (19). Flowering of L. vulgare was too sparse to give a trustful estimate of the delay. Flowers of T. vulgare are visited by a broad spectrum of insects belonging to the Diptera, Hymenoptera, Lepidoptera and Coleoptera taxa. However, in October, which corresponds to the peak flowering period within the 25% dose treatments, only a few species of Diptera are found visiting the flowers. In late-flowering species such as T. vulgare, high spray drift doses may hence postpone flowering to the autumn, leading to a mismatch with the activity period of important flower visiting insects and thus leading to a lack of pollination. 156

Figure 4. Flowering density (number of flowering stems) of Tanacetum vulgare per plot (6.5 x 6.5 m, excluding a buffer zone of 0.5 m on all sides), from 10 July 2013 (week no. 30) to 13 November 2013 (week no. 44). The upper graph depicts the flowering curves of plots treated with different doses of glyphosate without added nitrogen (0% is the untreated control), while the lower graph shows the flowering curves for the highest and lowest doses of glyphosate combined with 100 kg N/ha. (from Damgaard et al. (19)).

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Furthermore, both disk diameter (T. vulgare and L. vulgare) and ray floret length (L. vulgare) decrease with increasing glyphosate concentration (Figure 5). Ray length is known to affect pollination success in L. vulgare (59). However, natural variation among individuals in flower size greatly exceede the treatment effects (19).

Figure 5. Photo: Leucanthemum vulgare growing at the edge of a plot receiving glyphosate (360 g a.i./ha) at a long-term experimental plot in Denmark (http://bios.au.dk/faciliteter/long-term-experimental-plot/). The photo demonstrates the changes in length of the ray florets. Graph: Length of ray florets in flower heads of L. vulgare in plots treated with four different doses of glyphosate (0 – 360 g a.i./ha) and no fertilizer, as well as the highest dose of glyphosate with added nitrogen fertilizer (100 kg N/ha). (photo: Beate Strandberg, data from Damgaard et al. (19)).

Herbicide Effects on Plant Interactions and Community Composition Effects of sub-lethal doses of herbicides on plant community dynamics and indirect effects on other trophic level organisms are poorly understood. As reviewed above, herbicide effects vary with the activity spectrum of the herbicide, life forms of plants present and the life stages of the plants at the time of exposure. However, population ecological mechanisms underlying these effects on natural plant communities have been addressed in only a few studies. 158

Dalton and Boutin (60) initiate two experiments aimed at comparing singlespecies toxicity tests with the outcome of tests with the same species growing in microcosms placed in both the greenhouse and outdoors. One experiment includes nine terrestrial species, while the second includes seven wetland species. For terrestrial species, results generally show that species perform better in singlespecies tests compared to tests in greenhouse microcosms, indicating that singlespecies tests do not reflect the worst-case scenario. The outcome of the experiment with wetland plants is less conclusive. Similarly, Strandberg et al. (16) find that the application of glyphosate at spray drift-relevant doses (14.4, 72, and 360 g a.i./ha equal to 1, 5, and 25% of the labelled rate of 1440 g a.i./ha respectively) decrease species diversity compared to the untreated controls and change species composition of an experimentally established grassland. Overall, species cover decreases with increasing glyphosate dose, with the only exceptions being the covers of Festuca ovina and Euphorbia esula, which increase with increasing glyphosate dose due to their tolerance to glyphosate. Strandberg et al. (61) compare the outcomes of three different experiments to assess the applicability of sensitivity recorded in standard phytoxicity tests for NTTPS growing in natural plant communities: i) dose-response of single-species; ii) dose response of two-species undergoing competitive interactions (Agrostis capillaris and F. ovina); and iii) performance of the three dominant grasses: A. capillaris, F.ovina and Elytrigia repens, within the before-mentioned multi-species grassland experiment, in plots receiving increasing glyphosate doses (0, 14.4, 72 and 360 g a.i./ha) and fertilizer (100 kg N/ha). In single-species tests, that are conducted in accordance with standard guidelines, F. ovina (ER10 = 35.1 a.i./ha, ER50 = 114.4 g a.i./ha) is less sensitive to glyphosate than A. capillaris (ER10 = 19.0 a.i./ha, ER50 = 37.5 g a.i./ha) calculated as the herbicide rate resulting in 10 and 50 % effect on biomass, respectively. In the two-species competition experiment, A. capillaris shows little intraspecific competition, i.e. when no F. ovina is present, ER10 is comparable to the ER10 estimated in the single-species test. However, when A. capillaris grows together with F. ovina in a 1:1 mixture of varying densities of both species, the ER10 for A. capillaris is 16% lower than estimated in the single-species test. This indicates that the sensitivity of A. capillaris to glyphosate is further affected by the presence of the less sensitive grass species, F. ovina. In the grassland field experiment, glyphosate significantly reduces the cover, as well as biomass (Figure 6), of A. capillaris and most of the other species. In contrast, the cover and biomass of F. ovina increases in the glyphosate treated plots. The comparison of these studies highlights that effects of herbicide exposure in the field might not easily be deduced from standard tests. When analysing the observed growth of F. ovina and A. capillaris under field conditions in a competition model, it is found that the competitive effect of F. ovina on the growth of A. capillaris increases with the glyphosate level (62, 63). Furthermore, it is observed that glyphosate affects the demography of these two perennial species, i.e., the relative importance of survival compared to colonisation increases with the level of glyphosate for the glyphosate sensitive A. capillaris and decreases for the glyphosate tolerant F. ovina (64).

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Figure 6. Plant biomass of the three dominant grasses, Agrostis capillaris, Festuca ovina and Elytrigia repens, as a function of glyphosate dose (g a.i./ha) within a grass fields experiment with plots receiving glyphosate doses (0, 14.4, 72 and 360 g a.i./ha) and fertilizer (100 kg N/ha). From Strandberg et al. (2007) (61).

In the grassland experiment, glyphosate reduces both the cover and the biomass of most of the plant species (61). In accordance with these results, a study on plant composition of woodland margins adjacent to fields receiving high-inputs of herbicides also shows species composition to be affected, with herbicide-sensitive species being less abundant within these habitats (65). Furthermore, Gove et al. (65) find that short-term greenhouse studies underestimate herbicide toxicity. Very often, habitats adjacent to agricultural fields will be exposed not only to one herbicide but also to a suite of other agrochemicals including other herbicides, fungicides, insecticides, growth regulators and fertilizers. Little is known about the combined effects of these different agrochemicals on plant communities and how the effect of the specific herbicide in question may be separated from the potential confounding effects caused by the presence of additional agrochemicals. Strandberg et al. (16) test if the effect of repeated exposure of Silene noctiflora and S. vulgaris to sub-lethal herbicide dosages differs from the Additive Dose Model (66, 67). Metsulfuron-methyl or glyphosate is applied at the 3-4 leaf stage followed by either glyphosate, metsulfuron or mecoprop-P at the 6-8 leaf stage of the plants. The results indicate that staggered herbicide treatments, i.e. treatments given with a period of time between exposures to the different herbicides, are synergistic, i.e. plants treated by a low metsulfuron-methyl or glyphosate dosage are more sensitive to later herbicide treatments than expected from the Additive Dose Model. This contrasts with finding of Cedergren et al. (68) that shows additive effects of mixtures of herbicides with the same mode of action and same target site. However, Kudsk and Mathiassen (67) also report mixtures of 160

metsulfuron-methyl and glyphosate or glyfosinate to be synergistic and suggest that formulation constituents in the spray solution may lead to a higher activity than expected. The above-mentioned studies all indicate that the response of multi-species plant communities to herbicides cannot be predicted based solely on standard phytotoxicity tests. For an accurate risk assessment, the experimental design should be able to accommodate the variability of natural plant communities (69, 70).

Conclusions In current risk assessment, herbicide effects on plants are mostly assessed as effects on germination and growth of crop species three to four weeks after exposure. We find that seedling growth of NTTPs does not consistently differ from crop species and therefore crops may be adequate surrogates for wild species when only early growth is considered. However, if the goal of risk assessment is to consider all relevant impacts of herbicides on NTTPs, including plant reproduction, biodiversity and ecosystem functioning, it is apparent that the current approach is deficient. Biomass, the most frequently assessed endpoint, is not necessarily the most sensitive. Using other endpoints, especially reproductive endpoints such as plant flowering, seed production and germination of the offspring, appears more relevant in the assessment of effects during the entire life cycle of a plant. In addition, effects on reproductive endpoints may also be highly relevant for understanding the effects on other trophic levels such as pollinators and seed and fruit consuming birds.

Acknowledgments Writing of the manuscript is financed by the project, Pesticide Effects on NonTarget Terrestrial Plants at individual, population and ecosystem levels (PENTA) under the Pesticide Research Programme, Danish EPA.

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58. Boutin, C.; Strandberg, B.; Carpenter, D.; Mathiassen, S. K.; Thomas, P. Herbicide impact on non-target plant reproduction: what are the toxicological and ecological implications? Environ. Pollut. 2014, 185, 295–306. 59. Andersson, S. Pollinator and nonpollinator selection on ray morphology in Leucanthemum vulgare (oxeye daisy, Asteraceae). Am. J. Bot. 2008, 95, 1072–1078. 60. Dalton, R. L.; Boutin, C. Evaluation of phytotoxicty testing: Comparing the effects of herbicides on non-target plants grown singly and in microcosms. Environ. Toxicol. Chem. 2008, 29, 2304–2315. 61. Strandberg, B.; Mathiassen, S. K.; Damgaard, C.; Bruus, M. Testing nontarget effects of herbicide spray drift; Poster presentation SETAC Europe 17th Annual Meeting, Porto, Portugal, May 20−24, 2007. 62. Damgaard, C.; Strandberg, B.; Mathiassen, S. K.; Kudsk, P. The combined effect of nitrogen and glyphosate on the competitive growth, survival and establishment of Festuca ovina and Agrostis capillaris. Agric. Ecosyst. Environ. 2011, 142, 374–381. 63. Damgaard, C.; Strandberg, B.; Mathiassen, S. K.; Kudsk, P. The effect of glyphosate on the growth and competitive effect of perennial grass species in semi-natural grasslands. J. Environ. Sci. Health, Part B 2014, 49, 897–908. 64. Damgaard, C.; Strandberg, B.; Mathiassen, S. K; Kudsk, P. The effect of nitrogen and glyphosate on survival and colonisation of perennial grass species in an agro-ecosystem: does the relative importance of survival decrease with competitive ability? PLoS One 2013, 8, e60992. 65. Gove, B.; Power, S. A.; Buckley, G. P.; Ghazoul, J. Effects of herbicide spray drift and fertilizer overspread on selected species of woodland ground flora: comparison between short-term and long-term impact assessments and field surveys. J. Appl. Ecol. 2007, 44, 374–384. 66. Morse, P. M. Some comments on the assessment of joint action in herbicide mixtures. Weed Sci. 1978, 26, 58–71. 67. Kudsk, P.; Mathiassen, S. K. Joint-action of aminoacid biosynthesis inhibiting herbicides. Weed Res. 2004, 47, 252–261. 68. Cedergren, N.; Kudsk, P.; Mathiassen, S. K.; Streibig, J. C. Combination effects of herbicides on plants and algae: do species and test systems matter? Pest Manage. Sci. 2007, 63, 282–295. 69. Cousens, R.; Marshall, E. J. P.; Arnold, G. M. Problems in the interpretation of effects of herbicides on plant communities. BCPC Monograph no. 40. Field Methods for the Study of Environmental Effects of Pesticides; British Crop Protection Council, 1988. 70. Damgaard, C.; Mathiassen, S. K.; Kudsk, P. Modelling effects of herbicide drift on the competitive interactions between weeds. Environ. Toxicol. Chem. 2008, 27, 1302–1308.

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Chapter 12

Low Dose Effects of Pesticides in the Aquatic Environment Nina Cedergreen*,1 and Jes J. Rasmussen2 1Department

of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg, Denmark 2Department of Bioscience, Aarhus University, Vejlsøvej 25, 8600 Silkeborg, Denmark *E-mail: [email protected]. Telephone: +45 35 33 33 97.

In contrast to the terrestrial environment, organisms in the aquatic environment are exposed to more complex mixtures of pesticides with high concentrations occurring in pulses associated with spray and rain events. To quantify the effect of these complex mixtures, mixture toxicity models have to be used. The standard is to use Concentration Addition (also called Dose Addition), where all co-occurring pesticides are transformed into one common unit, which is summed and used as the joint exposure concentration. Using this approach, both herbicides and insecticides can reach joint concentrations during storm flow that can cause measurable effects in the plant, algae and invertebrate communities. Fungicide concentrations rarely reach the same level of predicted environmental effects, but this is likely owed to the fact that the traditional ecotoxicological tests do not reflect aquatic fungi communities, which are likely the most sensitive to these pesticides. Except for extreme incidents, pesticide occurrences in agricultural catchments will rarely lead to acute extermination of the majority of species. There is no doubt, however, that peak pesticide concentrations can affect the species communities on sub-lethal endpoints such as growth, emergence times, feeding and drift behaviour which ultimately may translate into altered community structure and function. In addition to the low dose effects of pesticides, other stressors of both chemical and physical nature are also stressing © 2017 American Chemical Society

the aquatic communities. Therefore, ensuring a diverse aquatic community, requires a broad focus including both combatting excessive concentrations of pesticides and other chemicals and improving the physical habitat on a local scale as well as on a catchment scale. Only holistic approaches, including all stressors of the aquatic ecosystem, will sufficiently safeguard aquatic ecosystems and potentially recreate lost habitats, as well as increase populations of threatened species.

Introduction Pesticides used for plant protection, vector control and urban use often end up in the aquatic environment, where they can be detected in both water and sediments. The primary transport routes for pesticides to surface waters in agricultural catchments are surface runoff and tile drainage (1–4). The magnitude of pesticide transport is governed by different climatic and geological factors (e.g. amount and intensity of rainfall, hydrology, field slopes and soil types). In vertical crops such as vine and fruit plantations which are intensively sprayed, spray drift can also be an important route of pesticide transport (5). Pesticide occurrences in urban catchments are mainly related to the size of paved areas (6). Often the pesticide concentrations occurring in surface waters are low, being in the ng/L range. Depending on the physico-chemical properties of the pesticides they will sorb to sediments and/or be degraded within days to weeks or months. A wide range of pesticides is used in conventional agriculture and in urban environments (active ingredients primarily used as biocides) facilitating the possible transport to surface waters. Hence, aquatic communities may be exposed to a broad array of different mixtures of varying complexity. In this chapter, we discuss the occurrence and possible environmental effects of these low doses of pesticides on aquatic communities based on the research we have been involved in, within the past ten to fifteen years.

Characterizing Exposure There is a clear link between the applied quantity and application frequency of pesticides in surface water catchments and the magnitude and occurrence frequency of detectable concentrations of the same pesticides in the adjacent surface water bodies (7, 8). Consequently, pesticides are more often detected in surface waters located in agricultural and urban catchments compared to uncultivated catchments, and countries with higher environmental regulatory requirements for pesticide usage are generally characterized by lower pesticide pollution compared to countries with less strict regulatory requirements (9). However, pesticide concentrations in surface waters are in general characterized by substantial variation in time and space governed in part by climatic factors (especially rain events) and differing seasonal usage, generating a discontinuous 168

and complex occurrence pattern in receiving surface waters (1, 10) (Figure 1). Herbicides and fungicides have relatively slow modes of action in both target and non-target organisms and are used during most of the growing season in temperate climate agriculture, which will be the focus of this paper. In contrast, insecticides often have rapid modes of action and target pest organisms that occur in more narrow temporal windows (11).

Figure 1. An example of the variability of pesticide concentrations over time in a small Danish stream, sampled by event triggered sampling. Data were collected by the Danish National Environmental Research Institute and details of sampling is published in Kronvang et al. (87). The data presented are the three herbicides occurring at the highest concentrations from the Nedstrøms, Lillebæk measuring station. In addition to these three herbicides, another ten herbicides and fungicides were also detected following a similar pattern of occurrence peaks during the growth season following rain events. Insecticides were not monitored for in this study. Data were kindly provided by Merete Styczen.

Hence, herbicides and fungicides are generally characterized by higher application frequencies (in part prophylactic treatments), whereas insecticides are generally characterized by comparably lower application frequencies specifically timed with the co-occurrence of insect pest outbreaks. These inherent differences in application patterns between pesticide groups lead to some generic differences in occurrence patterns in surface waters with herbicides and fungicides being more or less continuously detectable in agricultural streams during the crop growing seasons and occurrence of insecticides being rather restricted to the temporal window containing pest insect outbreaks (12–14). 169

Multiple studies have documented that pesticide concentrations increase in water bodies during precipitation events, with storm water concentrations exceeding those of base flow conditions by a factor of 10-100 (4, 5, 12, 13, 15). The magnitude of concentration increase depends on a series of geological (e.g. soil type), topographic (e.g. slope of soil surface adjacent to the water body) and climatic factors (e.g. precipitation depth and frequency of significant rain events) (see review by Schulz, 2004 (5)). Moreover, the physicochemical properties of pesticides are of significance with more water soluble pesticides being more prone to baseflow leaching. Pesticides of low water solubility, on the other hand, are being associated with stronger but shorter increases in concentrations in receiving water bodies, probably due to their association with small washed-out particles (e.g. Kronvang et al., 2004 (16)). Consequently, in agricultural and urban landscapes the vast majority of the seasonal sum of pesticide flux from catchments to streams occurs during rain events (7). As the acute effect of pesticides in the aquatic environment is most often associated with the peak concentrations, it is worth noting that small water bodies are characterized by higher pesticide concentrations compared to larger water bodies, most likely due to higher connectivity between land and water and a lower dilution potential in the smallest water bodies (17). Larger streams are, on the other hand, more often dominated by larger numbers of different pesticides collected from the larger catchment area. Pesticides and residues occurring in surface waters will, strongly depending on the physicochemical properties of the pesticides, partition between water and organic and inorganic surfaces in the aquatic environment (18–20). The vast majority of pesticides with high Kow (e.g. pyrethroid insecticides) will rapidly adsorb to such organic surfaces and dissipate from the water phase (21). The relative adsorption potential is generally higher for the smallest of sediment particles (18), meaning that the majority of pesticides adsorbed to sediment particles reside in the upper few mm of sediments which additionally is the most mobile fraction (22). Pesticide half-life may be significantly prolonged when adsorbed to aquatic sediments and may therefore serve as a fingerprint of both past and present use (4, 23). The concentration ranges detected are usually in the ng/L range, but can get into the low µg/L range during storm flow and during spray seasons in heavily sprayed areas (4, 7, 12, 17, 24). We have given examples of base flow and storm flow concentrations from a Danish and Californian study in Table 1, representing cultivated catchments and have included an example from measurements in German orchard ditches, which we consider as being representative for very intense peak concentrations.

170

Table 1. Examples of Concentration Ranges of Representative Pesticides Measured in Surface Waters under Base Flow and Storm Flow Events in Three Countries with Different Topography and Agricultural Practices. the Data Are from Rasmussen et al. (4), Lorenz et al. (17) and Smalling and Orlando (12). For All Catchments the Number of Water Samples Measured, the Mean Concentrations of All Measured Pesticides > Limit of Detection (LOD), the Median Concentration and the Number of Pesticides Detected >LOD in Each Sample Are Given ±stdev. Country Location description (n sampling sites)

Base flow

Storm flow

n

Mean±stdev (µg/L)

Median (µg/L)

Detected pesticides

n

Mean±stdev (µg/L)

Median (µg/L)

Detected pesticides

Agricultural catchment (n = 10)

10

0.192±0.099

0.077

7.10±3.96

37

1.845±0.339

1.001

21.29±8.43

Control catchments (n = 9)

9

0.033±0.014

0.015

5.00±5.83

28

0.277±0.088

0.075

7.74±4.66

12

3.505±5.11

1.375

-

12

13.91±15.15

6.84

-

Pajaro estuary (n = 4)

37

0.037±0.059

0.018

4.16±3.43

20

0.447±2.691

0.054

9.70±2.27

Salinas estuary (n = 4)

32

0.022±0.033

0.013

4.31±1.92

16

0.140±0.476

0.041

6.56±3.32

Santa Maria estuary (n = 4)

27

0.273±1.016

0.058

10.3±2.2

12

0.239±0.330

0.112

12.4±6.1

Denmarka

171

Germanyb Orchard ditches (n = 2) Californiac

The Danish base flow samples were collected in August 2012 in 19 streams of agricultural dominated catchment and control catchments. The storm flow samples were collected by event triggered sampling in the period May-June 2012, and 70 pesticides and pesticide metabolites were analyzed for. b The German study investigated the difference between weekly integrated samples (base flow) and samples taken within 1-2 days after a spray event (“storm flow”) in ditches in an orchard region. Results from six pesticides were reported. c The Californian study investigated three catchments during 4 event triggered storm flow events during 2008 and 2009, including 11 samplings during base flow. They monitored 68 pesticides and pesticide metabolites. The data was kindly provided by Kelly Smalling. a

How Do We Deal with Mixtures? As mentioned above, the exposure scenario in the aquatic environment is characterized by its high complexity where co-exposure to at least 10-20 pesticides is the rule rather than the exception (3, 4, 25). Within the field of aquatic toxicology there has therefore been a large focus on how to predict the joint effect of mixtures. Basically there are two competing concepts for predicting mixture effects: Concentration Addition (also called Dose Addition), assuming that the compounds have a similar mode of action, and Independent Action (also called Response Multiplication, Response Addition and Effect Addition), assuming binary endpoints and dissimilar mode of action (For a review of the concepts and their use, see Cedergreen et al, 2013 (26)). A range of studies performed during the past two decades have shown Concentration Addition (CA) to explain binary mixtures of pesticides within a two-fold error for approximately 90% of the tested mixtures, disregarding the mode of action of the pesticide combination (27, 28). Also, comparing the two concepts showed relatively little difference between model predictions for a range of test systems, with CA most often being the conservative model (29). In addition, several studies have shown that increasing the number of chemicals in a mixture usually decrease the deviation from the reference model (30, 31). Hence, for risk assessment purposes, there is broad agreement concerning the use of CA as a reference model. The basic concept of CA is that all chemicals act in a similar way, and less toxic chemicals simply act as a dilution of the more potent chemicals. If you have a mixture, you can therefore convert all chemical concentrations to the same unit, the toxic unit (TU), by dividing their concentration by an EC-value from a specific test. Often the EC50 of the species believed to be most sensitive is used. For joint risk assessment of pesticides, using CA as a reference model is particularly convenient, as there are available EC-values for at least algae, daphnids and fish for all registered compounds. The toxic units of a specific mixture are then summed up (∑TU), giving a measure of the joint concentration of the mixture. Benchmark concentrations for ∑TU for different groups of organisms have been found (32), and the risk of a particular water sample toward the aquatic community can then be evaluated. Importantly also, using the toxic unit principle opens the possibility of investigating which chemicals in the mixture contributes the most to the joint toxicity. This is done by investigating the fraction by which each chemical contributes to the ∑TU. Using this approach we could for example estimate how big a fraction of the toxicity of pesticides measured in 19 Danish streams could be attributed to pesticides no longer allowed for use in Denmark, compared to the currently used pesticides (4). Also, the use of the TU-approach has shown that in most cases, even when many pesticides are detected in a water or sediment sample, toxicity is usually driven by relatively few compounds (4, 25).

Do Synergists Play a Role? We stated above that approximately 90% of pesticide mixtures could be described by CA. But this still leaves 10% deviating from the model. Of these, 172

the mixtures that interact synergistically, thereby inducing an effect that is higher than predicted by CA, are of the largest concern in a risk assessment perspective. In a recent review we evaluated all available mixture studies on pesticides on aquatic organisms to identify, which pesticides might act as synergists (27). A synergistic mixture was defined as a mixture where the observed EC50 was less than two-fold smaller than the EC50 predicted by CA. The study showed that for 95% of the 69 cases where synergy was observed, either azole fungicides or acetylcholinesterase inhibitors were part of the mixture. Azole fungicides are known to inhibit cytochrome P450 enzymes active in phase I metabolism of xenobiotics, and acetylcholinesterase inhibitors, such as organophosphates and carbamates inhibit esterases, which are likewise involved in phase I metabolism. Hence, for pesticides in the aquatic environment it seems that most synergies involve interactions on xenobiotic metabolism. The size of the synergy was rarely above 10-fold. Extrapolating the time of the experiments where synergy could be observed beyond the usual 48 hours used for many aquatic tests could, however, increase synergy ratios to 40-60-fold decrease in EC50 compared to mixtures without the synergist (33). To get an insight in the probability of synergy occurring under more natural conditions than those obtained in laboratory studies, experiments were conducted in mesocosms, where the sorption and degradation of the pesticides could occur under more natural conditions and effects on aquatic communities could be studied. The pyrethroid esfenvalerate was added to the mesocosms in concentrations corresponding to 5, 10 and 25% spray drift events on a 30 cm water column, and the azole fungicide prochloraz was added at a concentration of 90 µg/L, corresponding to a severe run-off event (34). An 8 to 14- fold enhancement of the esfevalerate toxicity was observed for a range of pelagic macroinvertebrate species during the four weeks of observation, while others were unaffected or even increased in population size due to the decreased competition from more sensitive species (34, 35). The study was criticized for using too high concentrations of the synergists, as the µg/L concentrations used are far from the base-flow concentrations usually being in the ng/L range (See Table 1). During the next years, we therefore investigated how low the concentrations of synergists, such as the azole fungicides prochloraz, propiconazole and epoxiconazole, should be to induce a significant synergistic effect (>two- fold). We did so in different test setups using different species and endpoints (24, 36, 37). The conclusion was that the lower threshold for synergy was in the range of 6.4±0.8, 58±7.5 and 40±15 µg/L, for the three azoles, respectively, which is above the base-flow concentration measured and may be achieved only during severe storm-flows (17). How low concentrations that can induce synergy within the acetylcholinesterase inhibitors are less well investigated. Studies on salmon and acetylcholinesterase activity in their brain tissue, however, showed that diazinon and chlorpyriphos were synergistic when combined at 7.3 and 0.1µg/L (38). These results were related to measured aquatic concentrations of diazonon and chlorpyriphos of 6.0 and 0.5 µg/L (38). Hence, it seems that organophosphate insecticides can act as synergists at environmentally realistic concentrations, even though the reported concentrations in the µg/L range is also in the high end of more frequently occurring concentration ranges. 173

Recently, with the new regulation of Plant Protection compounds in Europe, potential effects of adjuvants and known synergists have come into focus (32). The known pesticide synergist piperonyl butoxide (PBO) have received attention in terms of its potential to induce synergy when occurring at environmentally realistic concentrations (39, 40). So far, however, no severe synergistic interactions have been observed. In the study by Giddings et al (2016), where synergy between PBO and pyrethrins in the sensitive amphipod Hyallella azteca was systematically investigated, synergy was found at PBO concentrations > 4µg/L. The size of the synergistic interactions, however, never exceeded two fold (39). Hence, in a regulatory perspective the synergy was very small. The lack of increase in effect of formulation products under aquatic conditions was also found for ten herbicides representing seven modes of action being tested as both formulated and technical compounds on Lemna minor and algae (41). The only herbicide where larger toxicity was found for the formulated herbicide compared to the technical was for glyphosate, where the glyphosate formulation containing polyethoxylated tallow amine POEA was used. POEA is known to be toxic in itself (42, 43), hence, it is no surprise that the mixture had a higher toxicity than the technical compound. The reason that none of the other formulation compounds increased the herbicide effect is most likely that they are primarily surfactants and penetration oils that need to be present in high concentrations in the spray droplet to enhance herbicide uptake. When diluted in the aquatic environment their uptake enhancing effect disappears. For synergists as PBO, which interact with the detoxification enzymes inside the organisms in a similar way as the azole fungicides, the lower threshold for synergy will occur at a concentration where the fraction of enzymes being affected is too small to play a significant effect for pesticide detoxification. Hence, it can be concluded that even though synergies are interesting from a scientific point of view and may be of importance in certain cases, from a risk assessment perspective, including all pesticides present in surface waters in a cumulative risk assessment is of greater importance if the aim is to catch the full toxic potential of pesticides in a water sample.

Low Dose Effects of Herbicides Aquatic plants and algae are the groups of organisms most sensitive to herbicides. A literature study on herbicidal activity towards aquatic algae including > 120 herbicides, showed no specific herbicidal mode of action to be particularly toxic to aquatic plants and algae (41). In reality, however, combining occurrence and toxicity, photosystem inhibitors are among the herbicides most often found to contribute to effects on submerged plants and algae. In North America, atrazine has been a big issue (44), while European studies on environmental samples, applying the toxic unit approach to identify which herbicides contribute most to the joint toxicity towards algae, also find PSII inhibitors such as isoproturon, diuron, linuron, simazine and terbuthylazine to top the list (4, 45). The question is, whether the concentrations monitored have an effect on the aquatic community. 174

To study pesticide effects on aquatic algae and macrophyte communities, three basic approaches are available: creating species sensitivity distributions (SSD’s) based on laboratory derived EC-values for a range of species, conducting microcosm studies or analyzing field data trying to isolate the impact of pesticides on populations (46). Using the SSD-approach, the sensitivity of a range of species is tested, and under the assumption of the tested species being representative of all species, and that their sensitivity distribution is log- normally distributed, the aquatic concentration protecting 95% of the species (The five percent hazard concentration: HC5) can be calculated (Figure 2). In a study on ten species of aquatic macrophytes and an epiphyte community on terbuthylazine and metsulfuron- methyl, we found the HC5 (based on EC50) to be 11 and 39 µg/L for terbuthylazine and 0.031 and 0.014 µg/L metsulfuron-methyl at two different irradiance regimes (47). For terbulhylazine the HC5 values range in the high end compared to SSD’s for another five photosystem II inhibitors of 1.8-10 µg/L (48), while the metsulfuron-methyl HC5 is similar to the ALS-inhibitor HC5 of 0.018 µg/L presented in Giddings et al. (2014) (49). SSD’s do, however, not take species interactions into account, as do microcosm experiments.

Figure 2. The figure shows an example of a Species Sensitivity Distribution (SSD) for aquatic macrophytes. The sensitivity of the species, here given as EC50, is ranked and the distribution is described by a log-logistic curve. The 5% Hazard Concentration (HC5) describes the concentration below which 95% of the species are below their EC50. The data are from Cedergreen et al. (47)). 175

A larger study comparing the SSD-approach with microcosm studies for nine herbicides largely concludes that HC5 values can be used to set benchmark concentrations for environmental effects based on results from microcosm studies (48). To see if this is also the case for mixtures, Knauer and Homme (2013) tested a mixture of three PSII inhibiting herbicides in microcosms at concentrations jointly summing up to HC5 and HC30 for algae SSD’s. They found no measurable effects on algae and macrophyte growth or photosynthetic activity in the HC5 treatments, while significant effects were found in the HC30 treatments (50). They therefore conclude that HC-values in combination with mixture models can be used to derive benchmark concentrations protecting the aquatic algae and macrophyte community. Using the European benchmark concentration for sums of toxic units (∑TU) for algae of 0.1 (32) and applying it to pesticide monitoring data from 19 Danish streams (10 streams with high agricultural pressure), showed no exceedance for the 19 base flow measurements, while 81% of the measurements conducted in the nine agricultural stream during storm-flow exceeded the benchmark of 0.1 (n= 37). Results from four agricultural streams in Sweden during nine years also showed that the low baseflow concentrations only rarely exceeded the benchmark for algae (