## White Paper in Support of the Proposed Risk Assessment Process for Bees

Submitted to the FIFRA Scientific Advisory Panel for Review and Comment

September 11

– 14, 2012

Office of Chemical Safety and Pollution Prevention

Office of Pesticide Programs

Environmental Fate and Effects Division

Washington, D. C.

September 11, 2012

Environmental Assessment Directorate

Pest Management Regulatory Agency

Health Canada

Ottawa, Canada K1A 0K9

California Department of Pesticide Regulation

Sacramento, California

### 2 Executive Summary

Background

Over the past 60 years, the number of managed honey bee ( Apis mellifera ) colonies in the U.S.

has been in decline and recent reports have indicated that the numbers of insect pollinators are

in decline both in North America and Europe. A number of factors have been associated with

declines in honey bees in the North America including disease, pests, poor nutrition, loss of

habitat, pesticides and bee management practices; however, none of these factors have been

identified as the cause. Researchers have hypothesized that some of these factors may interact

and result in losses which are attributed to winter kill and Colony Collapse Disorder. Although

pesticides have not been implicated as the singular cause of insect pollinator declines in general

or of declines in honey bees specifically, efforts have been directed at determining the extent to

which pesticides may be affecting bees and ways to mitigate potential effects. Regulatory

authorities in North America and elsewhere are developing improved procedures for evaluating

the potential risks of pesticides to bees.

While many pesticides are applied to foliar surfaces with the intention of affecting insects

through contact, systemic pesticides can be applied to soil, seed or foliar surfaces and be

transported to pollen and nectar where bees can be exposed through ingestion of residues. This

paper considers exposures for foliar applied pesticides that may impact bees through contact

and through the diet. This paper also addresses potential dietary exposures and effects of

systemic pesticides on bees.

Although a number of insects provide pollination services, this white paper focuses on the

honey bee. The honey bee is a social organism that lives in hives which can contain in upwards

of 50,000 bees that are primarily worker bees (females) further subdivided by castes, but all

under the direction of a single queen. The individual bee cannot function independent of the

hive for prolonged periods; therefore, the functional unit of the honey bee is the colony itself.

Given that the hive is composed of thousands of bees, it has been likened to a “superorganism”.

The attributes of the bee colony and its reliance on the queen represent challenges in assessing

the potential risk of pesticides to bees since there can be complex routes of exposure and the

colony can compensate for some losses given the redundancy that is built into the hive. Given

the distance which bees can forage, spatial and temporal scales must be considered in

evaluating potential exposure.

The extent to which regulatory authorities have evaluated the potential risks of pesticides to

bees has varied; however, there are efforts both internationally and domestically to improve the

process. In North America, regulatory agencies have had relatively similar processes. Toxicity

tests used to evaluate potential effects have been tiered and have first examined effects on

individual bees under laboratory conditions while more refined testing is conducted using whole

hives under field conditions. These processes are continuing to evolve to reflect the changing

science.

While EPA has evaluated the potential risks of pesticides to other taxa using a point estimate

‐

based approach, it has historically only qualitatively described the potential hazard to bees.

However, this paper describes a quantitative approach for developing risk quotients for bees.

The proposed process is consistent with that used for other taxa in that it is both iterative and

### 3 tiered and takes into account both foliarly acting pesticides as well as those which are

distributed systemically in plants. Increased levels of refinement focus on areas where specific

risks may exist and are intended to be increasingly representative of actual use/exposure

conditions. As with other taxa, the risk assessment ultimately depends on a collective

understanding of available data which includes information from registrant

‐ submitted studies as

well as information reported in the open literature and any data which may be available through

incident reports.

There are several challenges that exist when integrating the various exposure and effects data

that can be used to assess potential effects of pesticides on honey bees and their colonies. For

instance, different bees are expected to be exposed to pesticides at different magnitudes,

depending upon their function in the colony. In addition, interpreting the impacts of mortality

and sublethal effects on the ultimate survival of the colony is complicated by a lack of definitive

understanding of the linkages between many of these endpoints. Colony

‐ level simulation

models represent a useful tool that may be used to integrate exposure and effects data with the

complexities of the social structure and biology of a honey bee colony.

Consistent with the existing process, the white paper is organized similar to the proposed risk

assessment process and is divided into three sections, i.e

., problem formulation, analysis

(exposure characterization and effects characterization) and risk characterization (estimation

and discussion).

Problem Formulation

Problem formulation represents the initial phase of a risk assessment where management

(protection) goals are defined and the subsequent assessment and measurement endpoints

used in the assessment are articulated. This phase of a risk assessment also presents the

conceptual models where the source of the stressor (pesticide use), routes of exposure, and

effects (attribute changes) are also articulated. For bees, three protection goals are identified:

pollination services, honey production and biodiversity. The primary assessment endpoints are

colony survival, growth and reproduction and the proposed measurement endpoints include

acute lethality to individual bees and measures of impaired survival, growth and reproduction of

the colony itself. Several conceptual models are depicted to account for potential adverse

effects from foliarly applied systemic and non

‐ systemic pesticides and from systemic pesticides

applied to soil, seeds or tree trunks.

Measures of effect are based primarily on honey bees because they are readily available, are

relatively easy to work with under laboratory conditions and their husbandry needs are well

documented both at the level of the individual bee as well as the colony. While the honey bee is

the major focus of the process discussed in this paper, the honey bee is considered as a

surrogate for other non

‐ Apis bees even though many of these other insect pollinators are not

social and have life histories which are substantially different from that of the honey bee.

Consistent with the process used for other terrestrial taxa ( e.g

., birds and mammals), screening

‐

level estimated environmental concentrations used for evaluating exposure to honey bees are

based on an existing tools ( i.e

., T

‐ REX model) as well as approaches for estimating exposure

through ingestion of pollen and nectar containing residues of a systemic pesticide applied to soil

or seeds that have not previously been used for regulatory purposes in North America.

### 4 Characterization of Exposure

The proposed process for estimating exposure is consistent with that used for other taxa in that

at a screening level (Tier I), exposure estimates are intended to be relatively conservative [high

‐ end] while at higher tiers (Tiers II and III), exposure is based on measured values which are

reflective of actual use conditions. While a number of routes of exposure are identified in the

conceptual models depicted in the problem formulation, for pesticides that are applied via foliar

spray, the primary routes of exposure are considered to be through contact and diet ( i.e

., consumption of contaminated pollen or nectar) and for systemic pesticides applied to soil and

seeds, the primary route of exposure is through the diet. With these methods, dose

‐ based

estimates of exposure through the diet are calculated by considering food consumption rates of

larvae and adult worker bees and the estimated upper

‐ bound concentrations in pollen and

nectar. The larval consumption rate is based on daily food consumption rates of worker larvae

during the last two days of the uncapped period, while the adult worker bee food consumption

rate is based on that of nectar foraging worker bees. Due to their relatively high food intake

rates of worker larvae and nectar foraging bees, these two daily food consumption rates

represent the portions of the larval and adult life stages that are expected to receive the

greatest pesticide exposures when considering the entire life cycle of a worker bee.

Different methods are presented to estimate pesticide concentrations in pollen and nectar for

pesticide applications made via foliar spray, soil treatment, seed treatment and tree trunk

applications. In screening

‐ level assessments contact exposure is estimated for pesticides applied

via foliar spray. An upper

‐ bound residue value of chemicals on honey bees based on Koch and

Weisser 1997 is proposed to represent contact exposures. The estimation of dietary exposure to

pesticides applied via foliar spray, soil treatment, or seed treatment, involves a few different

methods. The proposed methods described in this white paper differ in the nature of the

estimated concentrations in pollen and nectar consumed by bees. For foliar spray applications,

the proposed approach involves the use of the tall grass residue value from the T

‐ REX model (v.

1.5) as a surrogate for pesticide concentrations in nectar and pollen. For soil treatments, the

proposed Tier I method for estimating exposure involves the use of the Briggs’ soil

‐ plant uptake

model, which is designed to estimate pesticide concentrations in plant shoots; these estimated

concentrations in plant shoots are used as a surrogate for concentrations in pollen and nectar.

For seed treatments, the proposed Tier I exposure method is based on the International

Commission for Plant

‐ Bee Relationships’ (ICP

‐ BR) 1 mg a.i./kg concentration as an upper

‐ bound

for pesticides in nectar and pollen.

Characterization of Effects

Since regulatory authorities have required pollinator toxicity testing for many years, multiple

standardized tests exist that examine effects to both individual bees and colonies. The

laboratory

‐ based studies have historically focused on measuring effects in young adult bees;

however, only recently have tests been developed to examine the effects of chemicals on larval

bees. The proposed screening

‐ level risk assessment process includes acute toxicity tests with

adult and larval bees. Higher

‐ tier assessments transition away from studies of individual bees to

colony

‐ level studies conducted under semi

‐ field (tent/tunnel) and full field conditions. Toxicity

tests conducted with both individual bees and colonies will continue to collect data on effects

### 5 on survival for acute toxicity studies and on impaired survival, growth and reproduction for

longer

‐ term studies with whole colonies. Although additional endpoints (sublethal effects) may

be measured in these studies, there can be considerable uncertainty in how these measurement

endpoints relate to assessment endpoints and the whole organism adverse (apical) effects on

which regulatory agencies are required to regulate. Until there are sufficient data to establish

plausible adverse outcome pathways with consistent and reproducible linkages between

molecular initiating events and key events across multiple levels of biological organization to an

adverse effect at the whole organism/colony/population level, it is difficult to make use of

sublethal effects other than in qualitatively describing potential adverse effects. In transitioning

to higher tier testing, the resources involved in conducting and reviewing the studies also

increase because the complexity of the study increases. Therefore, there is a need to refine the

scope of these studies to address specific uncertainties which have been identified in lower

‐ tier

studies. As with refinements in exposure estimates, refined effects testing is intended to reflect

increasingly realistic conditions surrounding how the chemical will actually be used. While

relatively standardized test protocols exist for semi

‐ field testing, full field studies are less well

defined and would be requested on a case

‐ by

‐ case basis depending on the nature of

uncertainties. Study design considerations are discussed for the conduct of these studies.

Risk Characterization

Risk characterization is intended to integrate estimates of exposure and effects to provide a

quantitative estimate of potential risk. Consistent with the process used for other taxa, the

proposed process determining risk to bees is based on the risk quotient (RQ) which is the ratio

of point estimates of exposure to point estimates of effects. At a screening level the acute oral

or contact lethal dose to 50% of the organisms tested (LD

50 ) serves as the denominator of the

ratio while the contact or dietary exposure value serves as the numerator. The level of concern

to which the acute RQ value is compared is set to 0.4 and is based on the historic average dose

‐

response relationship for acute toxicity studies with bees and a 10% mortality level. When

acceptable chronic toxicity test designs are available, the proposed process involves developing

chronic RQs using NOAECs and evaluating the RQs using an LOC of 1. As with other taxa, the risk

assessment process is intended to be iterative. Either through the incorporation of possible

changes in use or through more refined estimates of exposure based on measured residue

values, the RQ values can be further refined in the screening level to determine whether they

exceed levels of concern. Further testing under semi

‐ or full field conditions can provide

additional information for qualitative evaluations of effects on the entire colony. These

refinements would be considered in conjunction with any incident data available for the

compound. Therefore, similar to the process used for other taxa, multiple lines of evidence are

considered and integrated in the proposed risk assessment process for bees.

Although this white paper focuses on honey bees, the process described herein applies to both

individual bees and to the colony and thereby is applicable to other insect pollinators, in

particular non

‐ Apis bees. The paper acknowledges the limitations in this assumption. While

some toxicity testing methods may be available for evaluating effects to non

‐ Apis bees, these

tests have not been sufficiently vetted at this time to support their use in quantifying risks to

these other taxa.

### 6 Table of Contents

1 Introduction

.......................................................................................................................... 17

1.1 Overview of pollinator declines and factors associated with declines

........................ 17

1.2 Plant Sugar, Nutrient and Water Transport and the Movement of Systemic

Compounds

............................................................................................................................... 18

1.2.1 Plant Structures that are Attractive to Bees

.......................................................... 21

1.3 Consideration of Honey bee Biology in Ecological Risk Assessment

........................... 22

1.4 International and Domestic Efforts to Refine Effects and Risk Assessment Methods for

Bees

............................................................................................................................... ....... 23

1.5 Current Risk Assessment Process

................................................................................ 26

1.5.1 Overview

................................................................................................................ 26

1.5.2 Limitations

............................................................................................................. 27

1.6 International Risk Assessment Process

........................................................................ 30

1.6.1 Overview

................................................................................................................ 30

1.6.2 Limitations

............................................................................................................. 31

2 Proposed Risk Assessment Process

...................................................................................... 32

2.1 Decision Tree

................................................................................................................ 36

2.2 Problem Formulation

................................................................................................... 41

2.2.1 Management Goals and Assessment Endpoints

................................................... 41

2.2.2 Conceptual Model

.................................................................................................. 46

2.2.2.1 Non

‐ systemic, Foliar Spray Applications

....................................................... 47

2.2.2.2 Systemic, Foliar Spray Applications

............................................................... 48

2.2.2.3 Systemic, Seed Treatment

............................................................................. 50

2.2.2.4 Systemic, Soil Application and Tree Application

........................................... 50

2.2.3 Analysis Plan

.......................................................................................................... 54

2.2.3.1 Measures of Exposure

................................................................................... 54

2.2.3.2 Measures of Effect

......................................................................................... 54

2.2.3.3 Integrating Exposure and Effects

................................................................... 56

3 Characterization of Exposure

................................................................................................ 57

3.1 Tier I Exposure Assessment for Honey Bees

................................................................ 58

3.1.1 Quantifying Pesticide Exposures from Foliar Applications

.................................... 62

3.1.1.1 Estimating exposure through consumption of nectar and pollen

................. 62

3.1.1.1.1 Concentrations in Nectar after Foliar Applications

.................................. 63

3.1.1.1.2 Concentrations in Pollen after Foliar Applications

................................... 65

3.1.1.2 Assumptions and Uncertainties associated with dietary exposure methods

for foliar applications

....................................................................................................... 66

### 7 3.1.2 Estimating exposure through contact for foliar applications

................................ 69

3.1.2.3 Comparison of Koch and Weisser 1997 and T

‐ REX values to empirical data 71

3.1.2.4 Assumptions and uncertainties associated with contact exposure methods

for foliar applications

....................................................................................................... 72

3.1.3 Summary of Proposed Tier I Exposure Assessment Method for Foliar Applications

.

............................................................................................................................... 73

3.1.4 Potential options for characterizing exposure (resulting from foliar applications)

using available data when Tier I screen is failed

.................................................................. 74

3.2 Quantifying Exposure Resulting from Soil Applications

............................................... 78

3.2.1 Proposed method for estimating exposures from soil applications: Briggs’ Model

.

............................................................................................................................... 80

3.2.1.1 Method Description and Evaluation

.............................................................. 80

3.2.1.2 Assumptions and Uncertainties

..................................................................... 84

3.2.2 Alternative method that was considered: EPPO’s 1 ppm default value

................ 85

3.2.2.1 Method Description and Evaluation

.............................................................. 85

3.2.2.2 Assumptions and Uncertainties

..................................................................... 86

3.2.3 Summary of Tier I Method for Soil Applications

.................................................... 87

3.3 Potential options for characterizing exposure using available data when Soil

Treatment Tier I screen is failed

............................................................................................... 88

3.3.1 Terrestrial Field Dissipation Studies for a Cropped Field

....................................... 88

3.3.1.1 Crop Field Trials (magnitude of residue studies)

........................................... 89

3.3.1.2 Nature of Residue in Plants (Plant metabolism studies)

............................... 90

3.3.1.3 Accumulation in Rotational Crops

................................................................. 91

3.4 Quantifying Exposure Resulting from Seed Treatments

.............................................. 93

3.4.1 Method Description and Evaluation

...................................................................... 93

3.4.1.1 Assumptions and Uncertainties

..................................................................... 95

3.5 Quantifying Pesticide Exposure from Tree Injections and Trunk Drenches

................ 96

3.5.1 Method Description and Evaluation

...................................................................... 96

3.5.1.1 Assumptions and Uncertainties

..................................................................... 97

3.6 Discussion of Potential Exposures through Other Routes besides Diet and Direct Spray

............................................................................................................................... ....... 98

3.6.1 Contact with Dust

.................................................................................................. 98

3.6.2 Consumption of Contaminated Drinking Water

.................................................... 99

3.7 Tier II Exposure Assessment for Honey Bees

............................................................. 100

4 Characterization of Ecological Effects

................................................................................. 104

4.1 Tier I Effects Assessment

............................................................................................ 105

4.1.1 Acute Toxicity....................................................................................................... 105

### 8 4.1.1.2 Oral (Dietary) Exposure to Adult Worker Bees

............................................ 107

4.1.1.3 Oral Exposure to Larval Worker Bees

.......................................................... 108

4.1.1.4 Other Acute Toxicity Data

........................................................................... 109

4.1.2 Chronic Toxicity

.................................................................................................... 110

4.2 Tier II Effects Assessment........................................................................................... 116

4.2.1 Toxicity directed to brood

.................................................................................... 118

4.2.1.1 Semi

‐ Field Tunnel Study (OECD 75; EPPO 170)

........................................... 119

4.2.2 Field Feeding design

............................................................................................ 121

4.3 Tier III Effects Assessment.......................................................................................... 127

4.3.1 Refinement of Tiers I and II

.................................................................................. 127

4.3.2 Design of a Field Study for A. mellifera

................................................................ 129

4.3.3 Analysis and Interpretation of Effects

................................................................. 134

4.3.3.1 Exposure

...................................................................................................... 134

4.3.4 Biological Significance

......................................................................................... 136

4.3.5 Experimental Design Weaknesses

....................................................................... 136

4.3.5.1 Level of Exposure

......................................................................................... 137

4.3.5.2 Exposure to Other Pesticides

...................................................................... 142

4.3.5.3 Colony size

................................................................................................... 142

4.3.5.4 Separation of treatment from control plots

................................................ 142

4.3.5.5 Parasites and Pests

...................................................................................... 143

4.3.5.6 Duration of the study

.................................................................................. 144

4.3.6 Suggested modifications...................................................................................... 144

5 Risk Characterization

.......................................................................................................... 147

5.1 Risk Estimation

........................................................................................................... 148

5.1.1 Levels of Concern (LOCs)

...................................................................................... 149

5.2 Risk Description

.......................................................................................................... 153

5.3 Consideration of Non

‐ Apis Bees in Ecological Risk Assessment

................................ 157

5.3.1 Biological and Ecological Considerations

............................................................ 157

5.3.2 Exposure Considerations

...................................................................................... 158

5.3.3 Toxicity Testing Considerations

........................................................................... 159

5.4 Colony Models

........................................................................................................... 161

5.4.1 Description of Desired Characteristics of Models that May be used for Risk

Assessments of Bees

........................................................................................................... 162

5.4.2 Description of Selected Colony

‐ level Models

....................................................... 164

5.4.3 Summary Regarding Colony

‐ level Models

........................................................... 167

### 9 6 Epilogue

.............................................................................................................................. 168

7 References

.......................................................................................................................... 172

Appendix 1. Estimation of food consumption rates for worker larvae and adults

..................... 185

Food consumption of larval worker bees

............................................................................... 186

Nectar (and honey)

............................................................................................................. 186

Pollen (and bee bread)

........................................................................................................ 187

Proposed food consumption rate for larval worker bees

................................................... 187

Discussion

....................................................................................................................... 187

Food consumption of adult worker bees

................................................................................ 188

Nectar

............................................................................................................................... .. 189

Pollen (and bee bread)

........................................................................................................ 193

Proposed food consumption rate for adult worker bees

.................................................... 193

Discussion

........................................................................................................................... 194

Comparison of proposed consumption rates and resulting exposures to other castes

......... 195

Drones

............................................................................................................................... .. 199

Queens

............................................................................................................................... . 200

Appendix 2. Discussion of Potential Pesticide Exposures through Consumption of Contaminated

Drinking Water

............................................................................................................................ 201

Water Uses and Sources

......................................................................................................... 201

Drinking Water Consumption Rates

....................................................................................... 202

Method based on Honey Bee Observations

........................................................................ 203

Method based on Brown Paper Wasp Flux Rate

................................................................ 203

Preferred method for estimating drinking water intake rate

............................................. 207

Estimation of pesticide doses through consumption of different sources of contaminated

water

............................................................................................................................... ........ 207

Summary of Exposure through Drinking Water

......................................................... 216

Appendix 3. Summaries of empirical studies from unpublished, registrant

‐ submitted studies that

were used to evaluate Tier I methods for estimating pesticide exposures

................................ 217

Beedle and Harbin 2011...................................................................................................... 217

Bocksch 2010

...................................................................................................................... 218

Bryne et al. 2011

................................................................................................................. 219

Federal Office of Agriculture (FOAG) 2009

......................................................................... 220

Freeseman and Harbin 2011

............................................................................................... 220

Lückmann et al. 2010

.......................................................................................................... 221

Maus et al. 2004

................................................................................................................. 221

Staedtler 2009

..................................................................................................................... 223

### 10 Study 1

‐ 1

............................................................................................................................. 224

Study 1

‐ 2

............................................................................................................................. 225

Study 1

‐ 3

............................................................................................................................. 226

Study 1

‐ 4

............................................................................................................................. 227

Study 1

‐ 5

............................................................................................................................. 228

Study 1

‐ 6

............................................................................................................................. 230

Study 2

‐ 1

............................................................................................................................. 232

Study 2

‐ 2

............................................................................................................................. 233

Study 2

‐ 3

............................................................................................................................. 234

Study 2

‐ 4

............................................................................................................................. 235

Study 2

‐ 5

............................................................................................................................. 236

Study 2

‐ 6

............................................................................................................................. 237

Study 2

‐ 7

............................................................................................................................. 238

Appendix 4. Summaries of empirical studies from the scientific literature that were used to

evaluate Tier I methods for estimating pesticide exposures

...................................................... 240

Choudhary and Sharma 2008

............................................................................................. 242

Delabie et al. 1985

.............................................................................................................. 246

Dively and Kamel 2012

....................................................................................................... 247

Fries and Wibran 1987

........................................................................................................ 248

Girolami et al. 2009

............................................................................................................ 249

Hanny and Harvey 1982...................................................................................................... 249

Hoffman and Castle 2012

................................................................................................... 251

Koch and Weisser 1997

....................................................................................................... 252

Lord et al. 1968

................................................................................................................... 255

Reetz et al. 2011

................................................................................................................. 257

Škerl et al. 2009

.................................................................................................................. 257

Stoner and Eitzer 2012

........................................................................................................ 258

Tapparo et al. 2011

............................................................................................................. 259

Wallner 2009

...................................................................................................................... 260

Appendix 5. Transpiration Stream Concentration Factors (TSCFs)

............................................. 262

### 11 List of Tables

Table 1. Proposed protection goals and examples of associated assessment and measurement

(population and individual) endpoints for bees.

........................................................................... 44

Table 2. Application rate normalized upper

‐ bound and mean initial residue values incorporated

into T

‐ REX (v. 1.5.1) for various food items.

.................................................................................. 62

Table 3. Comparison of T

‐ REX upper

‐ bound values for plants and measured concentrations

(maximum and mean values) of pesticides in nectar.

................................................................... 64

Table 4. Comparison of T

‐ REX upper

‐ bound values for plants and measured concentrations

(maximum and mean values) of pesticides in pollen measured within 24 hours of application.

. 65

Table 5. Summary statistics describing concentration data for nectar, pollen and tall grass.

..... 67

Table 6. Mean pesticide residues measured on leaves and flowers directly sprayed with

pesticides. Values based on samples collected on the day of the application.

............................ 68

Table 7. T

‐ REX screen compared to residue data for contact with adult forager bees. Exposure

through direct spray on to forager bees measured within 24 hours of application.

.................... 72

Table 8. Tier I exposure values used to estimate high end exposures of honey bees to pesticides

applied via foliar applications.

....................................................................................................... 73

Table 9. Day 0 residue values for imidacloprid (and degradates), and normalized residues to 1 lb

a.i./A

............................................................................................................................... ............... 76

Table 10. Unnamed chemical 1 Day 0 residue values, and normalized residues to 1 lb a.i./A

..... 76

Table 11. Unnamed chemical 1 pollen and nectar data compared to magnitude of residue Day 0

residues.

............................................................................................................................... ......... 77

Table 12. Measured concentrations of pesticides in pollen and nectar of crops treated with

pesticides via soil applications. Concentration units are unnormalized ( i.e.

, expressed as mg

a.i./kg) and normalized to application rate ( i.e.

, expressed as mg a.i./kg per 1 lb a.i./A).

........... 79

Table 13. Comparison of estimated concentrations of pesticides in plant stems (based on

Equation 2) and empirical data measurements in nectar and pollen. Concentration units are mg

a.i./kg

‐ plant matrix normalized to 1 lb a.i./A.

............................................................................... 82

Table 14. Comparison of estimated concentrations of pesticides in plant stems (based on

Equation 2 and 95 th percentile TSCF) and empirical data measurements in nectar and pollen.

Concentration units are mg a.i./kg

‐ plant matrix normalized to 1 lb a.i./A (1.12 kg a.i./ha).

........ 84

Table 15. Sample parent compound residue data for a confined rotational study (860.1850,

MRID 45422618) using soluble concentrate formulation of clothianidin. A single application of

0.293 lbs a.i./A, applied directly to bare soil prior to planting rotation........................................ 93

Table 16. Sample residue data for a confined rotational study (860.1850) of unnamed chemical 1

applied to directly to bare soil using a single application of 0.535 lbs a.i./A.

............................... 93

Table 17. Measured concentrations of pesticides in pollen and nectar of plants where seeds

were treated. Data are not normalized to app rate.

..................................................................... 94

Table 18. Tier I exposure values used to estimate high end exposures of honey bees to pesticides

applied via seed treatments.

......................................................................................................... 95

Table 19. Comparison of Tier II Semi

‐ Field Study Designs

.......................................................... 125

Table 20. Results of a pollen identification analysis (palynology) in a field study on citrus with a

test chemical

– treated plots.

...................................................................................................... 138

Table 21. Results of a pollen identification analysis (palynology) in a field study on citrus with a

test chemical

– control plots.

...................................................................................................... 139

Table 22. Summary of exposure and effect estimates used in deriving risk quotients for Tier I risk

assessments.

............................................................................................................................... . 149

Table 23. Statistics associated with slopes of acute oral and contact toxicity data available in

### 12 EFED’s toxicity database for pesticides.

...................................................................................... 152

Table 24. Levels of concern associated with different slope values and different levels of

mortality in worker bees.

............................................................................................................ 152

Table 25. Comparison of Oral Exposure to Pollen and Nectar for adult Apis and Non

‐ Apis Bees

............................................................................................................................... ...................... 159

Table 26. Comparison of Oral Exposure to Pollen and Nectar for larval Apis and Non

‐ Apis Bees

............................................................................................................................... ...................... 159

List of Figures

Figure 1. Diagram of the iterative risk assessment process consisting of three phases, i.e

., Problem Formulation, Analysis and Risk Characteri zation............................................................ 33

Figure 2. Proposed Tiered Approach for Assessing Risk to Honey Bees from Foliar Spray

Applications.

............................................................................................................................... ... 38

Figure 3. Proposed Tiered Approach for Assessing Risk to Honey Bees from Soil/Seed

Treatments.

............................................................................................................................... .... 39

Figure 4. Generic Conceptual Model of Non

‐ Systemic, Foliar

‐ Applied Pesticides for Honey Bee

Risk Assessment. Dashed lines represent routes of exposure that are not considered to be

major.

............................................................................................................................... ............. 48

Figure 5. Generic Conceptual Model of Systemic, Foliar

‐ Applied Pesticides for Honey Bee Risk

Assessment. Red depicts systemic pathways. Dashed lines represent routes of exposure that are

not considered to be major.

.......................................................................................................... 49

Figure 6. Generic Conceptual Model of Systemic, Foliar

‐ Applied Pesticides for Honey Bee Risk

Assessment. Red depicts systemic pathways. Dashed lines represent routes of exposure that are

not considered to be major.

.......................................................................................................... 51

Figure 7. Conceptual Model of Soil

‐ Applied Systemic Pesticides for Honey Bee Risk Assessment.

Red depicts systemic pathways. Dashed lines represent routes of exposure that are not

considered to be major.

................................................................................................................ 52

Figure 8. Conceptual Model of Trunk

‐ Injected or Trunk

‐ Drench Systemic Pesticides for Honey

bee Risk Assessment. Red depicts systemic pathways.

................................................................ 53

Figure 9. Median and 95 th percentile upper

‐ bound TSCF data fit to Briggs et al. (1982)

empirically

‐ based TSCF data for 18 pesticides.

............................................................................. 83

Figure 10. Schematic of Honey bee Semi

‐ Field Tunnel Study Design (Source: OECD 2007). BFD =

brood fixing day ( i.e

., day colonies are introduced to the tunnel).

............................................. 119

Figure 11. Classification of blooming intensity in a field study with citrus showing differences in

blooming timing.

......................................................................................................................... 141

Figure 12. Adult honey bee mortality following planting of treated seed.

................................. 170

Figure 13. Overall honey bee colony strength (mean number of bees per hive) following planting

of treated seed.

........................................................................................................................... 170

### 13 Prologue

This white paper is intended to provide an introduction to the proposed risk assessment process

for insect pollinators using honey bees ( Apis mellifera ) as a surrogate. The paper is divided into

sections that are intended to be consistent with the risk assessment process described by the

EPA Ecological Risk Assessment Guidelines (USEPA 1998) which is both tiered and iterative. After

an introduction to issues associated with bees and a brief discussion about the biology of plants

and bees relative to potential routes of exposure, the paper discusses a proposed risk

assessment process consisting of screening level (Tier I) exposure and effects assessments

including a description of how risks may be quantified using these data. While EPA has routinely

required effect studies for bees to support its qualitative assessment of potential hazards to

bees, a well

‐ defined tiered process for evaluating exposure does not currently exist; therefore,

considerably greater detail is devoted to the Tier 1 exposure section. The paper also discusses

more refined assessments which transition away from data based on individual organisms,

toward colony

‐ based assessments under controlled test conditions where test colonies are

confined to enclosures (Tier II semi

‐ field studies) and then under less controlled, but more

realistic conditions where bees are allowed to forage freely (Tier III full field studies). In addition,

this paper discusses the potential utility of a colony

‐ level model in integrating exposure and

effects data collected to assess risks at some or all of the three tiers in order to assess the risks

of a pesticide in impacting colony survival, pollination services and honey production. With

increasing levels of refinement, both exposure and effect estimates are intended to be

increasingly representative of how the pesticide is used. These refinements imply that exposure

and effects study methods are directed at addressing specific uncertainties with an increasing

level of realism. The proposed process is intended to enhance the ability of EPA’s Office of

Pesticide Programs, the Canadian Pest Management Regulatory Agency (PMRA) and the

California Department of Pesticide Regulation (CalDPR) to reliably assess lists of chemicals for

direct and indirect effects specifically on managed honey bee colonies as well as other non

‐ Apis

bees using honey bees as a surrogate and/or when appropriate data are available.

### 14 List of Commonly Used Abbreviations and Nomenclature

μ g/kg Symbol for “micrograms per kilogram”

μ g

∙ L ‐ 1 Symbol for “micrograms per liter”

θ soil

‐ water content by volume (cm 3 /cm 3 )

δ soil bulk density (g

‐ dw/cm 3 )

ρ b bulk density (kg/m 3 )

θ sed Sediment porosity (unitless)

a.i. Active Ingredient

AOP Adverse Outcome Pathway

BCF Bioconcentration Factor

BEAD Biological and Economic Analysis Division

BFD Brood Fixing Day

Bw Body Weight

CCD Colony Collapse Disorder

CFR Code of Federal Regulations

CalDPR California Department of Pesticide Regulation

CI Confidence Interval

CL Confidence Limit

C dew(t) Concentration in dew (mg a.i./L)

C soil concentration in soil ( μ g a.i./g soil)

C

stem concentration in stems ( μ g a.i./g plant)

C

w Concentration in water ( μ g a.i./L)

DACO Data Code (Canadian Use Site Category)

d sed Depth of sediment (m)

d

w Depth of water (m)

EC 25 25% Effect Concentration

EC 50 50% (or Median) Effect Concentration

ECOTOX EPA managed database of ECOTOXicology data

EEC Estimated Environmental Concentration

EFED Environmental Fate and Effects Division

EFSA European Food Safety Authority

e.g

. Latin exempli gratia (“for example”)

EIIS Ecological Incident Information System

EPPO European and Mediterranean Organization for Plant Protection

et al. Latin et alii (“and others”)

Etc. Latin et cetera (“and the rest” or “and so forth”)

### 15 EU European Union

FAO Food and Agricultural Organization (United Nations)

FIFRA Federal Insecticide Fungicide and Rodenticide Act

f

oc fraction of organic carbon in soil

g a.i./ha grams of active ingredient per hectare

GENEEC GENeric Estimated Environmental Concentration

Ha hectare

HQ Hazard Quotient

IAPV Israeli Acute Paralysis Virus

ICP

‐ BR International Commission for Plant

‐ Bee Relationships

i.e

. Latin for id est (“that is”)

IEC Individual Effect Chance Model (Version 1.1)

K CW Partitioning coefficient between plant cuticle and water

K d Solid

‐ water Distribution Coefficient

K f Freundlich Solid

‐ Water Distribution Coefficient

Kg Kilogram(s)

km Kilometer(s)

K

oc Organic

‐ carbon Partition Coefficient

K

ow Octanol–water Partition Coefficient

L Liter

L/kg

‐ oc Liters per kilogram organic carbon

lb a.i./A Pound(s) of active ingredient per acre

LC 50 50% (or Median) Lethal Concentration

LD 50 50% (or Median) Lethal Dose

LOAEC Lowest Observable Adverse Effect Concentration

LOC Level of Concern

Log Logarithm

LOQ Level of Quantitation

m meter(s)

m ai ' mass applied per unit area (kg/ha)

mg Milligram(s)

mg/kg Milligrams per kilogram (equivalent to ppm)

mg/L Milligrams per liter (equivalent to ppm)

mi mile(s)

mL milliliter

MOR Magnitude of Residue studies

MRID Master Record Identification Number

### 16 n/a Not applicable

NASS National Agricultural Statistics Service

NOAEC No Observable Adverse Effect Concentration

NPIC National Pesticide Information Center

OCSPP Office of Chemical Safety and Pollution Prevention (formerly

OPPTS)

OECD Organization for Economic Cooperation and Development

OEPP Organisation Européenne et Méditerranéenne pour la Protection

des Plantes (EPPO)

OPP Office of Pesticide Programs

OPPTS Office of Pollution Prevention and Toxic Substances

ORD Office of Research and Development

PCA Percent Cropped Area

PEIP Pesticide Effects on Insect Pollinators (OECD)

pK

a Symbol for the negative logarithm of the acid dissociation constant,

dimensionless

PMRA Pest Management Regulatory Agency (Canada)

ppb Parts per Billion (equivalent to μg/L or μ g/kg)

ppm Parts per Million (equivalent to mg/L or mg/kg)

PPR Plant Protection Products and the Residues (EPPO)

PROC NLIN Non

‐ linear regression procedure (SAS®)

PRZM Pesticide Root Zone Model

RAC Raw Agricultural Commodities

RQ Risk Quotient

SAP FIFRA Scientific Advisory Panel

SAS® Statistical Analysis System

SCFs Stem Concentration Factor(s)

SETAC Society of Environmental Toxicology and Chemistry

T

‐ REX Terrestrial Residue Exposure model

TFD Terrestrial Field Dissipation

TSCFs Transpiration Stream Concentration Factor(s)

USDA United States Department of Agriculture

USEPA United States Environmental Protection Agency

USGS United States Geological SurveyPage 17 of 275 

## 1 Introduction

1.1 Overview of pollinator declines and factors associated with declines

A number of sources have reported declines in certain pollinator species globally. A 2006 report by the

National Academies of Science (NAS 2008) indicated declines in some North American pollinators,

including North America’s most important managed pollinator, the honey bee ( Apis mellifera ). The

report noted that insufficient information existed to determine the causes of those declines. Similar

declines have also been reported in Europe in a report by J. C. Biesmeijer et al. 2006. The decline of

managed honey bees in the United States has also been measured by the U.S. Department of

Agriculture (USDA) National Agricultural Statistics Survey (NASS 2008). NASS data indicate that managed

honey bee colonies have declined from a peak of approximately 6 million colonies in 1947 to roughly 2.5

million in 2006 (USDA 2012). According to USDA, since the introduction of parasitic mites into the U.S. in

the 1980s, typical annual overwintering losses of managed honey bee colonies have been roughly 15%

to 21% for beekeepers; however, annual overwintering colony losses ranging between 31% and 36%

were reported in the United States between 2007 and 2010. In 2006, some commercial beekeepers

reported losses of 30

– 90% of their hives during the wintering months. A portion (approximately 50%)

of these declines were characterized by the disappearance of adult worker bees with few or no dead

bees in the colony, the presence of capped brood with a small cluster of nurse bees, the queen, and the

presence of intact honey and pollen stores; this syndrome has been termed Colony Collapse Disorder

(CCD) (USDA 2007). Trends in overwintering losses in Canada are similar to those reported in the United

States. Prior to the introduction of parasitic mites in Canada, normal long

‐ term overwintering mortality

has been considered to be 15%, while annual overwintering colony losses ranging between 21.0% and

35.0% were reported in Canada between 2006 and 2011 (CAPA 2010, 2011). Symptoms by which CCD is

being characterized in the U.S. have not been diagnosed by professional apiculturists in Canada (CAPA

2010, 2011). Although a number of factors/agents have been hypothesized as potential contributors to

CCD, increased overwintering losses, and to declines in honey bee health in general, at this time, no

factor has been identified as the single cause. Rather, the available science suggests that pollinator

declines are a result of multiple factors which may be acting in various combinations (Pettis and

Delaplane 2010). Research is being directed at identifying the individual and combinations of stressors

that are most strongly associated with pollinator declines.Page 18 of 275 While the exact cause(s) of the general decline in pollinator species and the phenomenon characterized

as CCD have not been determined, potential contributing factors including diseases, habitat

destruction/urbanization, agricultural practices/monocultures, pesticides, nutrition, and bee

management practices (USDA 2007) must be considered. Researchers at the USDA have hypothesized

that CCD may be caused by primary stressors ( e.g

., parasitic varroa mites ( Varroa destructor ), poor bee

management, nutrition and/or pesticides that may in turn cause honey bees to become more

susceptible to disease (Yang and Cox

‐ Foster 2005, USDA 2007, vanEngelsdorp et al

. 2008, vanEngelsdorp

et al

. 2009). Surveys of managed migratory bee colonies indicate that a broad range of pesticides have

been detected in hive products ( e.g

., honey, stored pollen, wax). The most frequently detected

pesticides and the two that occur in the highest quantity are those used by beekeepers to control varroa

mite (coumaphos and fluvalinate). Typically, a combination of pesticides is detected in the same hive

products, with an average of four pesticides detected in the same sample (Mullin et al

. 2011). In spite of

the presence of these compounds in honey bee colonies, at this time, there has been no correlation

between the incidences of CCD or pollinator declines in general, with use of any pesticide or class of

pesticides (vanEngelsdorp et al

. 2009).

Historically, pesticides and in particular insecticides, have been applied via foliar spray in order to kill

target pests on contact. Models are currently available and are already used for regulatory purposes for

estimating exposures of non

‐ target animals to pesticides that have been applied via foliar spray ( i.e

., T

‐

REX). At this time, models are not being used in North America to estimate exposures to non

‐ target

animals when the pesticides are systemic and are applied to the soil, seeds or tree trunks. In considering

the potential for pesticides to affect bees, increased attention has been devoted to dietary exposure of

systemic pesticides to the honey bee. The following section provides a brief overview of how exposure

to bees may occur via movement of systemic pesticides through plants.

1.2 Plant Sugar, Nutrient and Water Transport and the Movement of Systemic Compounds

Plants transport sugars, nutrients and water through specialized vascular tissues called xylem and

phloem. Depending on physical

‐ chemical properties, systemic pesticides can move within these vascular

tissues to untreated tissues of the plant or remain locally distributed through extracellular movement.Page 19 of 275 Therefore, a pesticide may be xylem mobile, phloem mobile, both xylem and phloem mobile, or locally

systemic.

Xylem transports water and mineral nutrients from below

‐ ground sources through the roots and

upwards through the plant to the stems, leaves, flowers, and fruits (Raven et al

. 1992). In order to enter

the xylem located in the center of the root, water (containing minerals and pesticides) must pass

through the endodermis 1

. Because the cell walls and the intercellular space between the endodermis

cells are filled with suberin and lignin, they are impenetrable to nearly all compounds, thus requiring

materials to pass through the living phospholipid membrane of the endodermis cells. Minerals and other

compounds are selectively transported across the membrane by active transport. Once through the

endodermis, the compound can move either within the living cells (protoplastic movement), or between

them (apoplastic movement). The primary mechanism for moving water upward is passive and based on

a negative water potential gradient created by the evaporation of water from stomata 2 that occurs

because of evapotranspiration. As molecules of water leave the stomata, they pull on the entire water

column within the xylem tissues, thus evaporation draws the water upwards. Xylem tissues are

composed of non

‐ living cells and are found throughout the plant, maintaining water pressure and

nutrient flow to all living tissues, including flowers, anthers (pollen containing structures), fruits, and

nectaries (Escalante

‐ Pérez and Heil 2012). Chemicals that are transported through the xylem

(acropetalous systemics) are then transported upwards with the flow of water such that if these

chemicals are applied to the soil, the entire plant will be exposed. Foliar applications of xylem

‐

transported chemicals that are not also transported within the phloem will result in the chemical

remaining in the leaves and will not likely travel to the roots or later developing structures such as

flowers or fruits unless the chemical also comes in contact with the stem.

Phloem is the living tissue that actively transports photosynthate ( i.e.

, sugars) to portions of the plant

that are growing, storing starches, or exuding nectars. Sugar and other compounds transported through

the phloem are mediated by bulk flow from the source in photosynthetic cells to the various sinks.

Phloem transport of chemicals results in the movement of the photosynthate throughout all living

tissues of a vascular plant regardless of the point of contact. Phloem transport generally occurs in a

downward movement from the leaves toward non

‐ photosynthetic tissues such as roots and storage

1 a selective barrier of living cells with an impermeable intercellular space

2 tightly regulated openings in the epidermisPage 20 of 275 organs, but also includes movement to nectaries, and developing above

‐ ground structures such as

flowers, fruits and seeds. To enter the phloem through a soil application, a pesticide would require

transport through the cortex cells and the regulatory endodermis. Once in the central vasculature of the

root, the pesticide would be transported upwards with the water in the xylem; however it would also

accumulate in phloem. Compounds that are likely to be captured or “trapped” by the phloem can

become more concentrated in phloem than in xylem; these compounds would be of particular concern

for nectar exposures. Unlike for xylem

‐ only transported compounds, the foliar application of a phloem

transported compound would result in chemical transport out of the leaf and into the roots, developing

tissues, flowers, nectaries and fruits.

If a compound is unable to pass into the plant vascular system, the chemical could still be locally

systemic. Locally systemic compounds can move to untreated tissues of the plant via apoplastic

movement through diffusion between cells ( e.g.

, translaminar movement from the upper leaf surface to

the underside of the leaf surface). This process would provide relatively low probability of transport out

of the tissues where the chemical made contact.

Depending on their route of transport within the plant, systemic pesticides can move to untreated

tissues of the plant via apoplastic movement, xylem transport, and/or phloem transport. With these

three types of transport mechanisms, systemic pesticides may move within the extracellular matrix,

move unidirectionally upwards through the xylem from the base of the plant to the exposed shoots

(xylem transport), move unidirectionally downwards from the above ground tissues to the roots of the

plant (phloem transport), or move bidirectionally. Bidirectionally transported chemicals, two

‐ way

systemic pesticides (acro/basipetalous transport), move through the xylem and phloem of the plants

resulting in nearly complete integration throughout the plant tissues. The chemical/physical properties

of a pesticide determine whether it is systemic, and the nature of its systemic movement, i.e. whether it

will move from the tissues that were exposed into the roots, stems, leaves, pollen, nectar, fruit and/or

seeds of the plant. Furthermore, pesticide residues in these tissues can be influenced by the growth

stage of the plant, whereby tissue with a high nutrient need or greatest growth potential ( e.g

., buds,

flowers, tubers) will accumulate more sugars and could, by extension, accumulate higher residues of

phloem mobile pesticides.Page 21 of 275 1.2.1 Plant Structures that are Attractive to Bees

Honey bees primarily collect nectar and pollen from plants. In addition, they collect honey dew, plant

waxes, resins and may collect guttation fluid. Because honey bees rely on nectar and pollen to meet the

majority of their nutritional requirements, the focus of the proposed risk assessment method is on these

two plant

‐ based materials. Brief descriptions of the plant structures that house nectar and pollen are

provided below.

Nectaries are diverse in their structural form (Escalante

‐ Pérez and Heil 2012) but can be generally

described as living tissues that release sugars, amino acids and other compounds to the surface for

pollinator or defender attraction. Nectaries are found in flowers, as well as on leaves and stems

(extrafloral nectaries), and release variably viscous mixtures of water, sugars, amino acids, phenolics and

many other compounds. Nectar may be contaminated with pesticides applied via foliar spray when

flowers are open and because extrafloral nectaries are located on the surface of the plant. When

nectaries are producing exudates, the sugars come directly from phloem, resulting in a high likelihood of

bee exposures to those pesticides that are phloem

‐ transported. Potentially high exposure could result

from compounds prone to phloem trapping because of the potential for greater concentrations to be

found in phloem and nectar relative to pesticides within the xylem.

Pollen is contained in the anthers of flowers. Pollen can be contaminated by pesticides applied directly

to open flowers ( i.e., through foliar spray applications). The development and maturity of pollen grains

in the stamen (anthers) of flowering plants depends on nutrients and water transport and could lead to

systemic pesticide exposure to bees. The stamen is comprised of two main structures, the filament and

the anther, both containing xylem and phloem vascular tissues. During the development and maturation

of pollen grains, they are surrounded by a solution of water, proteins, amino acids and many other

chemicals, potentially including systemic pesticides transported by either xylem or phloem. During the

maturation phase of development, the fluid matrix dries out and the materials in the solution are

deposited onto the exterior surface of the pollen grain.Page 22 of 275 1.3 Consideration of Honey bee Biology in Ecological Risk Assessment

A careful consideration of honey bee biology and ecology is necessary for developing ecological risk

assessment methods that facilitate attainment of pollinator protection goals through appropriate risk

management activities. In particular, honey bees possess a number of distinctive attributes which

identify routes of exposure to pesticides. First and foremost, by being comprised of thousands of

individual organisms each with clearly defined functions within and among different castes, the health

and propagation of a colony depends on the collective interactions among bees both inside and outside

the hive. A single hive can contain 50,000 or more bees of which the vast majority are sexually

undeveloped female workers; other castes include male drones, developing larvae and pupae

(collectively referred to as brood), and a single reproducing queen. While the largest number of bees in

the colony are typically represented by female workers who forage for food, younger worker bees are

confined to the hive where they serve in various capacities ( e.g

., nurse bees, mortuary bees, guard

bees). Hence, the term “superorganism” has been devised to describe the interdependent structure and

function of the honey bee colony (Tautz 2008). As a result, the translation of the loss of one or more

individual bees into effects at the whole colony level depends on a number of factors. These factors

include the nature of the interactions (including extensive communication mechanisms), the extent of

loss of various hive functions, and the ability of individuals within the colony to compensate for lost or

reduced functions served by these bees 3

. While the intent of this white paper is to focus on a risk

assessment process for honey bees, the data provided through studies conducted on individual bees

have utility in assessing the potential effects of pesticides on solitary bees which do not form colonies.

A second attribute important to risk assessment pertains to the vast differences in exposure potential to

pesticides in terms of the magnitude, timing and duration, frequency and medium of exposure among

and within different castes of bees. Pollen serves as the main protein source for bees, while plant nectar

serves as the main source of carbohydrates (sugar). Foragers have the most direct contact with foliarly

applied pesticides via spray droplets, inhalation, and contact with foliage. They are also highly exposed

to pesticide residues via pollen and nectar used for food as well as plant resins (propolis) used to seal

hives. In contrast, bees living within the hive ( e.g

., nurse bees, mortuary bees, wax producing bees,

queen bee, larvae) are exposed primarily through consumption and processing of pollen, nectar, wax,

3 Loss of the queen is a notable exception which can lead to the loss of a colony. However, hives have the ability to respond to

queen loss by forming a new queen. Furthermore, loss of older foragers can result in younger brood attending bees taking on

foraging responsibilities to maintain adequate food supplies.Page 23 of 275 honey and propolis. Nurse bees, for example, consume relatively high amounts of bee bread, which is a

fermented mixture of pollen and honey, while developing brood consume protein rich secretions known

as brood food (worker jelly) and royal jelly from paired glands (hypopharyngeal and mandibular glands)

located in the frontal area of the head of young worker bees (nurse bees); nurse bees also distribute the

jelly to nest mates through trophallactic ( i.e

., exchanging secretions between bees) interactions

(DeGrandi

‐ Hoffman et al

. 2010). The queen bee, on the other hand, consumes royal jelly throughout her

life in the hive. Larvae that develop into workers are fed royal jelly early on in their development, after

which time they are fed brood food. In winter, adult workers consume large amounts of stored honey as

an energy source during periods where thermoregulation (via exercising flight muscles) is necessary.

A third important factor to consider in risk assessment is that the entire reproductive capacity of the

colony is critically linked with the queen. Most species have large numbers of reproducing individuals

within any given population, and thus the loss of one reproducing individual may be of little overall

consequence to the population. In the case of honey bees, the queen is the only individual responsible

for laying eggs, which can be as high as 1,200 per day. Thus, the health and overall fate of the queen

becomes a critical component of the success of a colony.

Lastly, it is important to consider both the temporal and spatial scales on which honey bees may be

exposed to pesticides. Workers usually forage within 1

‐ 2 km of the hive, but may reach distances of 7

km (5 miles) in some instances. Thus, the spatial scale of potential pesticide exposure can be much

larger than typical sizes of fields planted in crops and may encompass multiple cropped and non

‐

cropped areas at any given time. Temporally, honey bees may be exposed not just during or near

pesticide application, but potentially over much longer time periods for highly persistent pesticides due

to processing and storage of hive materials ( e.g

., bee bread, honey, wax, and propolis) or through

collecting exudates from extra

‐ floral nectaries when plants are not in bloom.

1.4 International and Domestic Efforts to Refine Effects and Risk Assessment Methods for Bees

A number of efforts have been underway to improve and refine pesticide risk assessment methods for

pollinators. The International Commission for Plant

‐ Bee Relationships (ICP

‐ BR), which is affiliated with

the International Union of Biological Sciences, has been developing harmonized methods for testing the

toxicity of pesticides to bees. The efforts of this organization inform decisions by regulatory authoritiesPage 24 of 275 in Europe, e.g

., the European and Mediterranean Organization for Plant Protection (EPPO) as well as the

Organization for Economic Cooperation and Development (OECD). The ICP

‐ BR Bee Protection Group has

regularly scheduled symposia which draw on expertise of government, industry and academia to inform

scientific/risk assessment efforts aimed at bee protection. The proceedings of the 10 th International

Symposium of the ICP

‐ BR Bee Protection Group, reported on efforts to revise the EPPO risk assessment

scheme (Alix et al

. 2009) for plant protection products ( i.e

., pesticides) and the studies used to inform

that process (Lewis et al

. 2009).

In July 2009, EPA hosted a USDA

‐ sponsored meeting on Pollinator Toxicity Testing. The meeting served

as a forum for researchers to discuss current testing protocols for acute and chronic toxicity studies for

honey bees and to identify uncertainties and limitations in those protocols. The goal of this forum was

to consider refinements and expansion of current test designs toward more comprehensive and

standardized protocols and increase consistency and reproducibility and enhance the utility of these

studies in regulatory decision making (USDA 2009).

In January of 2011, the Society of Environmental Toxicology and Chemistry (SETAC) sponsored a global

Pellston 4 workshop on pollinator risk assessment. The intent of the workshop was to bring together the

best available science regarding pesticide exposure and effects assessment methods for honey bees and

non

‐ Apis bees and to further harmonize risk assessment approaches among North and South America,

Europe, Australia, and Africa for a global improvement of the protection of insect pollinators in cropped

areas. Work at the Pellston was organized to cover four primary components of evaluating risk:

exposure assessment, laboratory effects assessment, semi

‐ field and field effects assessment, and

integrating exposure and effects data to estimate the likelihood and magnitude of potential adverse

effects, i.e

., risk assessment. A fifth area of expertise was identified on non

‐ Apis bees and was charged

with determining the extent to which honey bee exposure, effects and risk assessments could be used

4 The first Pellston Conference was held in 1977 to address the needs and means for assessing the hazards of chemicals to

aquatic life. Since then, many conferences have been held to evaluate current and prospective environmental issues. Each has

focused on a relevant environmental topic, and the proceedings of each have been published as a peer

‐ reviewed or informal

report. These documents have been widely distributed and are valued by environmental scientists, engineers, regulators, and

managers because of their technical basis and their comprehensive, state

‐ of

‐ the

‐ science reviews. The first four Pellston

conferences were initiated before the Society of Environmental Toxicology and Chemistry (SETAC) was effectively functioning.

Beginning with the 1982 conference, however, SETAC has been the primary organizer and SETAC members (on a volunteer

basis) have been instrumental in planning, conducting, and disseminating conference results. Taken from:

http://www.setac.org/node/104Page 25 of 275 to assess risk to non

‐ Apis bees. An executive summary (Fischer and Moriarty 2011) of the workshop was

published on

‐ line by SETAC in September 2011 and the full proceedings will be published in late 2012.

The European Food Safety Authority (EFSA) Panel on Plant Protection Products and their Residues (PPR)

was asked to deliver a scientific opinion on the science behind the development of a risk assessment

process for bees including both Apis and non

‐ Apis bees (EFSA 2012). In the resulting opinion, EFSA

identified specific protection goals based on an ecosystem services approach that included ensuring

pollination services, production of honey bee hive products and biodiversity; these protection goals are

the same as to those identified through the SETAC Pellston (Fischer and Moriarty 2011). The opinion

examined potential routes of exposure to categories/castes of bees ( e.g

., colony foragers and/or larvae

versus solitary bees) and it examined the strengths and limitations of current toxicity testing methods

along with recommendations on how to improve these methods. Recommendations included the need

for an expanded pesticide toxicity testing including contact and inhalation studies with both adult and

larvae and which include sublethal measurement endpoints as well as lethality. The EFSA also noted that

few studies have been conducted thus far on non

‐ Apis bees that consider endpoints such as fecundity,

larval mortality rate, adult longevity and foraging behavior even though micro

‐ colonies of bumble bees

may be suited to measure lethal and sublethal effects from varying durations of exposure. This opinion

is intended to serve as a basis for the development of a guidance document for regulatory authorities on

assessing the risks of pesticides to pollinator insects. As noted earlier, the EFSA opinion has informed the

process outlined in this white paper.

EPA, as well as PMRA, has engaged its partners of the OECD to find areas where cooperation would

advance the science and/or the management around protecting pollinators. To that end, EPA and PMRA

are working with (17) OECD countries on issues relating to pollinator protection. This effort has focused

on collaboration and coordination in the areas of science and policy. In 2010, the OECD published the

results of a survey of member countries on pollinator testing, mitigation and information management

(OECD 2010). As a result of the survey, specific focus areas for additional work included:

1) advancing and harmonizing the science of risk assessment;

2) developing a compendium of risk management approaches;

3) developing tools to share incident information; and,

4) providing an index of research relevant to pollinator health and protection.Page 26 of 275 EPA and PMRA have continued to work with OECD partners and have formed a Pesticide Effects on

Insect Pollinators (PEIP) workgroup to address each of these focus areas.

1.5 Current Risk Assessment Process

1.5.1 Overview

In North America, both EPA and PMRA have developed a tiered system for assessing the effects of

pesticides to non

‐ target organisms. While the goal is to be protective of a broad number of beneficial

terrestrial invertebrates, the regulatory agencies have relied on surrogate species to represent these

assemblages (including insect pollinators). In this testing system, the honey bee serves as the surrogate

for non

‐ target insects and for insect pollinators; however, when there are non

‐ Apis data to evaluate

potential risks to non

‐ target insects ( e.g

., from the scientific literature), those data are considered. In

the US, the Code of Federal Regulations (CFR) 40, Part 158 (abbreviated 40CFR158) defines the data

required to support the registration of a pesticide, while in Canada data requirements are outlined in

Directive DIR2003

‐ 01 (Health Canada 2003), which lists and defines all the Use Site Categories for

conventional chemicals and refers to the data

‐ code tables posted on the PMRA website (DACO tables;

Health Canada 2012). Similar to other taxa for which data requirements exist, the data requirements in

North America are tiered. Initially, studies are conducted on individual organisms under laboratory

conditions where the environment can be readily controlled. Higher tier studies typically transition from

the laboratory to field and focus on larger numbers of organisms under much less controlled conditions.

The first tier of testing in North America consists of an acute contact toxicity test, i.e

., OCSPP Guideline

850.3020 (USEPA 2012 a ) or OECD Guideline 214 (OECD 1998 a ) on adult honey bees that provides a

median Lethal Dose (LD

50 ), i.e.

, the lethal dose that causes death of 50% of the exposed organisms,

along with any sublethal effects ( i.e

., effects which do not result in direct mortality) that may have

occurred as a result of chemical exposure. In addition to a contact toxicity test, an acute oral toxicity test

is also required in Canada that provides information on the oral LD 50 from a single dose of the test

compound, along with any sublethal effects (OECD Guideline 213 (OECD 1998 b ). The acute contact and

oral LD 50 is assessed after 24 and 48 hours. Depending on the outcome of these toxicity tests, pesticides

are classified as practically non

‐ toxic (LD

50 >11 μ g a.i./bee), moderately toxic (LD

50 is 2

‐ 11 μ g a.i./bee), or

highly toxic to bees (LD

50 <2 μ g a.i./bee) on an acute exposure basis. If the acute contact LD 50 is less than

11 μ g active ingredient/bee, additional testing may be required in the form of a foliar residue studyPage 27 of 275 (OCSPP Guideline 850.3030; USEPA 2012 b ) to determine the duration over which field

‐ weathered foliar

residues remain toxic to honey bees. On a case

‐ by

‐ case basis, agencies may also require additional

higher

‐ tiered studies such as hive studies (published methods including Ooman et al

., 1992) and semi

‐

field and field pollinator studies, i.e

., OCSPP Guideline 850.3040 (USEPA 2012 c ), OECD Guidance

Document 75 (OECD 2007), and the EPPO Standards PP1/170 (OEPP/EPPO 2010) if the data from toxicity

studies indicate potential chronic effects or adverse effects on colonies.

In the U.S. and Canada, risk assessment relies on multiple lines of evidence. While pesticide

manufacturers are required to conduct studies in support of the registration of these products,

regulatory authorities are not confined to these studies as the sole source of information regarding the

potential for the use of a pesticide to result in adverse effects on the environment. Risk assessors make

use of information reported in the open literature as well as incident reports. Depending on the level of

detail of information contained in these alternate sources, the information may be used quantitatively,

i.e

., to calculate point estimates of exposure and effect thresholds for use in calculating a risk quotient

(RQ), or qualitatively (to characterize risk identified using RQs) to describe potential adverse effects

resulting from the use of the compound and to develop appropriate label language to mitigate the

potential effects.

1.5.2 Limitations

At this time, the major limitation to the current process has been the lack of a clear, comprehensive and

quantitative process for evaluating pesticide exposure and subsequent risk to bees from different routes

of exposure. Bees may be exposed to pesticides through multiple routes ( e.g

., direct contact with

pesticide residues and/or ingestion of contaminated pollen/nectar). Historically, EPA and PMRA have

not had a means of consistently estimating exposure to bees through either direct contact or via

ingestion of food with pesticide residues, nor have data been routinely available on measured pesticide

residues in pollen, nectar and honey. While residue data have been collected as part of the battery of

tests used to establish pesticide tolerances and/or to determine potential worker exposure, these data

have not typically included matrices ( i.e

., pollen, nectar and honey), which would be informative to

determining potential exposure to bees.Page 28 of 275 The current battery of toxicity tests identified in the 40CFR158.630 and DIR2003

‐ 01 for evaluating the

potential effects of pesticides on honey bees is also limited. The laboratory

‐ based studies provide

information on only young adult worker bees with a short duration of exposure and do not provide

information on other life stages, i.e

., developing larvae/pupae (brood) or caste members (i.e., queens,

and drones. Although acute oral toxicity data with adult worker bees are routinely considered by PMRA,

these data are not required by EPA; however, these data are now more commonly available and are

considered by EPA for risk assessment if available. As mentioned earlier, the laboratory

‐ based studies

focus on individual bees, yet bees must be considered within the context of the larger colony (hive) in

which they live. Although current test guidelines stipulate that sublethal effects must be reported in the

laboratory

‐ based studies, the primary focus of these studies is frank mortality; therefore, the exposure

concentrations used in the studies may mask sublethal effects. Open literature and registrant

‐ submitted

studies have continued to identify an increasing number of sublethal measurement endpoints; however,

there is uncertainty as to how many of these measurement endpoints are related to regulatory

assessment endpoints of impaired growth, survival and reproduction of the hive. Until sufficient linkages

have been developed which enable risk assessors to extrapolate sublethal measurement endpoints to

assessment endpoints, their utility in a regulatory context may continue to be limited.

For those chemicals where there is considerable uncertainty regarding the potential for adverse effects

on the entire colony, brood/hive studies, semi

‐ field, or field pollinator studies may be required.

Although field pollinator studies are meant to represent more realistic conditions, the execution and

interpretation of field

‐ based studies with freely foraging bees can be challenging as it can be difficult to

ensure that test bees will forage in a particular area given that they can forage over 7

‐ km from their

colonies depending on the availability of forage. While honey bees can exhibit some crop fidelity, i.e

.,

foraging on a specific crop, the extent to which this occurs is uncertain and is also difficult to control in

free

‐ foraging bees. In an attempt to account for these factors, protocols for these studies have varied

widely and these studies have frequently been confounded by the presence of other chemicals within

the colony or the effective forage area of the bees. Although field studies are intended to be more

representative of actual conditions of use and exposure, other factors influencing the colony are difficult

to consistently control; therefore, disease, parasites, nutrition, and bee management practices can be

confounding effects on these studies.Page 29 of 275 As noted previously, the current process within EPA is to rely on the battery of toxicity tests to

determine potential hazards to honey bees. Historically, EPA has qualitatively assessed potential hazard

and not risk since toxicity estimates alone were used. Estimates of potential exposure levels have not

been generated and therefore, the traditional quotient

‐ based risk characterization approach used for

other taxa has not been established for honey bees. In addition to using the potential hazard for

determining label language, PMRA has determined a risk quotient based on the method described by

Atkins et al

. 1981 which is discussed later in this paper.

The EPA has developed interim guidance (USEPA 2011) to ensure consistency in requests for pollinator

exposure and toxicity data to inform risk assessments and decision. In case

‐ by

‐ case situations, EPA is

asking for additional non

‐ guideline data to reduce uncertainties related to potential exposure and

effects. These special studies include honey bee acute oral toxicity tests, acute larval toxicity tests, semi

‐

field toxicity tests with honey bee colonies confined to tents/enclosures or fed with an artificial diet, and

full field studies. Additionally, to more realistically characterize exposure, residue studies in pollen and

nectar are considered. This interim guidance also reflects the PMRA practice of requesting additional

pollinator data ( e.g

., field pollinator studies) to inform risk assessments and decisions.

Although toxicity testing is routinely required to understand the potential effects of pesticides on non

‐

target arthropods, e.g

., honey bee toxicity data, and the results from these studies have been used to

determine appropriate label language to reduce potential hazards, an overall decision framework for

quantifying risks from pesticides to bees has been lacking. The interim guidance provided to EPA risk

assessors on both toxicity and exposure data requirements was intended to assist in characterizing

potential hazards to bees as opposed to quantifying potential risks. Only through the development of a

reliable means of quantifying exposure using either screening

‐ level models and/or more refined

measures of actual residues could a process consistent with that used for other taxa be developed for

quantifying risk and for evaluating how mitigation measures may affect risk estimates. Such a process

would also enable the development of a decision framework for better allocating resources to those

chemicals which may require more refined assessments to determine the spatial and temporal scope of

their potential effects on bees. The development of a risk assessment process for bees is the focus of

this white paper.Page 30 of 275 1.6 International Risk Assessment Process

1.6.1 Overview

In the European Union (EU), risk to honey bees from exposure to pesticides (referred to in the EU as

“plant protection products”) is assessed according to the EPPO standards PP 3/10 (2) (OEPP/EPPO ) and

includes a tiered progression of testing described by the guideline No. 170 (OECA/EPPO 2010). This

guideline describes lab tests, semi

‐ field (cage/tunnel) tests, and field tests for evaluating the lethal and

sub

‐ lethal effects of agrochemical products on honey bees. The testing approach in the EU is similar to

that of the EPA and Canada in that it consists of a tiered approach. In contrast to EPA, the EU and

Canada require the acute oral toxicity (LD

50 ) on adult workers in addition to the acute contact toxicity. In

the EU, it is also standard practice to conduct both acute oral and contact laboratory LD 50 studies on

formulated end

‐ use products as well as the active ingredient (a.i.), and Canada also regularly receives

and reviews these acute oral and contact studies on formulated end

‐ use products. EPA also receives

these studies when it is engaged in a global review with its European partners. The EU relies on a three

‐

tier process, with initial studies conducted in the laboratory, followed by semi

‐ field studies, and finally

field studies. The trigger criterion for assessing potential risk to adult bees as a result of foliar spray

treatments and for moving from Tier I (laboratory tests) to Tier II (semi

‐ field tests) and/or Tier III (field

tests) is the Hazard Quotient (HQ), where HQ is the ratio of the field application rate in grams of active

ingredient per hectare (g a.i./ha) to the oral or contact LD 50 (whichever is lower) expressed as

micrograms of active ingredient per bee ( μ g a.i./bee). Comprehensive reviews of pesticide bee kill

incidents show that bee kills have never been observed when the HQ<50. Therefore, a HQ of 50 is used

as a decision criteria to determine whether risk of acute lethality can be precluded ( i.e

., when the

HQ<50) or whether it requires further study ( i.e

., when the HQ>50 indicating the need for higher tier

testing). Higher tier tests like semi

‐ field and field tests under controlled exposure conditions typically

measure effects on adult bee behavior, queen performance, brood development, and overall colony

performance.Page 31 of 275 1.6.2 Limitations

Although the EU’s (lower tier) testing and risk assessment scheme for foliar application is more detailed

than the one used in North America in that it defines measures of exposure as well as effects used in

estimating risk, the EU is looking to expand its risk assessment capability even further. The EU believes

that more standardized tests for measuring effects of plant protection products to brood and for

measuring sublethal effects to honey bees are needed as an intermediate step. Efforts have been made

in Europe to develop more standardized procedures for measuring effects to brood. In addition, the

determination of the acute LD 50 and associated sublethal effects for oral and contact exposures at 24

and 48 hours versus some other length of exposure is somewhat arbitrary and needs to be further

vetted. In the risk assessment scheme of systemic products, due to their exposure scenario, it may be

appropriate to develop an ED 50 or EC 50 (median effect dose or concentration for 50% of the organisms

tested) for chronic exposure similar to the requirements in assessing risk to other taxa ( e.g

., aquatic

invertebrates) when study methods become available.Page 32 of 275 2 Proposed Risk Assessment Process

Although the role of pesticides in pollinator declines has not been well established, global experts from

different disciplines ( e.g

., chemistry, ecotoxicology and entomology) and across various sectors ( e.g

.,

government, academia, non

‐ governmental regulators and industry) agree on the need to advance the

science to better assess potential exposure, hazard and risk to honey bees and other insect pollinators

from pesticides used in agriculture. The need to advance the science has been accentuated with the

development of newer pesticides which are systemically distributed in plants following applications to

soil, seeds and tree trunks. For these applications, pesticide exposure through the diet is expected to be

the major route of exposure. This is in contrast to exposures of bees via both contact and diet that arise

from more traditional pesticides that are applied via foliar spray. The proposed process which the SAP is

being asked to consider reflects a synthesis of domestic and international efforts to develop a means for

quantifying the potential risks of pesticides to bees for use by North American pesticide regulatory

agencies (EPA, CalDPR and PMRA). Therefore, the recent work by the global SETAC Pellston conference

on pollinator risk assessment (Fischer and Moriarty 2011), the scientific opinion by EFSA (EFSA 2012),

and the efforts of EPP0, ICP

‐ BR, and the OECD Pesticide Effects on Insect Pollinators work group have

been used to inform the proposed process.

As with risk assessments for other taxa, the proposed risk assessment methods described in this

document make use of a surrogate species. The ecological risk assessment process consists of a series of

steps which are intended to be iterative. In general, the risk assessment process for evaluating

pesticides entering the market or those undergoing re

‐ evaluation as in the US and Canada, consists of

problem formulation, analysis and risk characterization (USEPA 1998). This generic process is depicted in

Figure 1

. In problem formulation, risk assessors define protection goals based on discussion with risk

managers, identify assessment and measurement endpoints, prepare a conceptual model and develop

an analysis plan for the risk assessment. Based on the conceptual model and its associated risk

hypothesis, the analysis plan articulates how the risk hypothesis will be tested.Page 33 of 275 Figure 1. Diagram of the iterative risk assessment process consisting of three phases, i.e

., Problem Formulation,

Analysis and Risk Characterization.

During the Analysis step, available measures of exposure and of effects are evaluated and the

movement of a stressor ( i.e

., pesticide) through the environment is characterized based on its intended

use pattern(s); this is frequently termed the exposure characterization or exposure profile. Similarly, the

potential effects of a chemical are characterized in what is frequently termed the effects assessment.

Once effects and exposure are characterized and estimates and/or measures are available, the risk

assessment proceeds to the risk characterization step. Typically, the risk characterization consists of two

steps, i.e.

, risk estimation and risk description. In the risk estimation step, the measures of exposure and

measures of effect are integrated to develop risk estimates. These estimates may be based on point

estimates of exposure, e.g

., maximum estimated exposure values based on the maximum applicationPage 34 of 275 rate for a particular use, and point estimates of effect, e.g

. the acute median lethal concentration to

50% of the species tested (LD

50 ). Alternatively, risk quotients could be based on distribution

‐ based

estimates of both exposure ( e.g

., maximum residue concentrations in pollen from several field

monitoring studies based on maximum application rate for a particular use), and effects ( e.g

., species

sensitivity distribution using LD 50 values). Regardless of whether point and/or distribution

‐ based

estimates are used, the integration of exposure and effects data is typically expressed as a ratio

(quotient) of estimated exposure and the effect endpoint; this ratio is considered the "risk estimate".

Deterministic estimates of risk, based on point estimates of exposure and effect endpoints, do not

typically provide information on the magnitude and likelihood of adverse effects; whereas distribution

‐

based estimates of both exposure and effects that lead to joint probability distributions are frequently

used to estimate both the likelihood (probability) and magnitude of an adverse effect. The decision to

move from point

‐ estimate based approaches to distribution

‐ based approaches that may also be

spatially and temporally specific are determined by the availability of data, the extent of variability

associated with exposure and effect estimates, and by the need for additional information to support a

risk management decision.

The second part of risk characterization is the risk description where quantitative estimates of risk are

further described qualitatively using other data as well. Multiple lines of evidence are used to describe

what is known about potential adverse effects resulting from the use of a pesticide. Risk descriptions

include additional information about the variability associated with what has been measured or

estimates/assumed along with associated uncertainties. The effects of potential mitigation measures

and uncertainties on risk assessments may also be described. Therefore, although the risk description

portion of the characterization is described as “qualitative”, it often includes various quantitative

elements ( e.g

., point estimates of exposure or toxicity endpoints. The risk characterization is intended to

provide a transparent, clear, concise and reasonable synthesis of the overall conclusions (USEPA 2000).

Although the risk assessment process is depicted as three distinct steps, each step is intended to be

iterative. As more information (data) becomes available, the process can evolve if needed to further

refine risk management decision making. The risk assessment process is, therefore, intended to take

advantage of multiple lines of evidence, and the problem formulation with its conceptual model and risk

hypothesis may change as more information becomes available. A critical component to this iterative

process is frequent and clear communication with risk managers to ensure that protection goals arePage 35 of 275 adequately accounted for and that the effects of uncertainties and potential mitigation measures on risk

estimates are evaluated.

Consistent with the iterative nature of the risk assessment process, regulatory agencies typically rely on

a tiered process for conducting ecological risk assessments. The preliminary or screening

‐ level (Tier I)

assessments are intended to determine whether potential risks exist while higher

‐ tier assessments

(Tiers II and III) attempt to refine and/or characterize risk estimates to determine the conditions of risk

occurrence and, when relevant, to identify spatially

‐ and temporally

‐ specific risks. The tiered risk

assessment process enables regulatory agencies to identify those chemicals for which higher levels of

resources should be devoted to support a more refined approach.

In a tiered, iterative process, the risk assessor and risk manager work together to evaluate the results of

each step and determine whether mitigation measures can be developed to reduce exposure. Where

such measures are implemented, the need for additional refinements is eliminated.

The proposed quantitative risk assessment process for pollinators adheres to the basic elements

described in the preceding sections and is, therefore, consistent with the process used for other taxa. As

with other taxa, if risks are identified in the screening

‐ level risk assessment and/or if the risk manager

requires additional information to support a regulatory decision, the risk assessment may be further

refined through more detailed analyses of exposure and/or effects. With every iteration, estimates of

exposure and effects are intended to produce more realistic values that are reflective of what may occur

under actual use conditions of the pesticide.

Consistent with the proposed approach vetted at the SETAC Pellston workshop (Fischer and Moriarty

2011) and the EU (Alix et al

. 2009), there is recognition that the potential risk to bees from the use of

pesticides may differ by use or by chemical. For example, pesticides applied via foliar spray may reach

bees through contact and through the diet, whereas dietary exposure would be the major route

expected for pesticides that are systemic and applied to the soil, seed or tree trunks. For seed

treatments of systemic pesticides, pesticide exposures are expected to occur through the diet. In

addition, it is recognized based on incident data from Germany (Pistorius et al

. 2009, Forster 2009) and

through studies reported in the open literature (Krupke et al

. 2012, Tapparo et al

. 2012) that seed

treatments can result in the formation of dusts from abraded treated seed coat during planting and thatPage 36 of 275 these contaminated dusts can serve as a source of contact exposure. Risk assessments may differ on a

chemical

‐ by

‐ chemical basis as well. For instance, in cases where a pesticide has toxic degradates,

additional data may be required for those degradates.

2.1 Decision Tree

Figure 2 illustrates the proposed decision

‐ making process for assessing risks to honey bees associated

with foliar spray applications of pesticides ( e.g

., via ground and aerial methods) while Figure 3 illustrates

the process for soil and seed treatments ( e.g

., soil drench, seed treatments). These decision

‐ making

frameworks are introduced to provide context to the detailed discussions of each of these risk

assessment and decision steps described in the ensuing sections. The overall proposed approach is a

tiered process whereby risks are first assessed using simple and conservative exposure screening models

to generate estimated environmental concentrations (EECs) ( Boxes 3a

, 3b and 3c of Figures 2 and 3 )

coupled with toxicity estimates derived from laboratory studies (Tier I) to calculate risk quotients (RQs)

( Boxes 4a

, 4b and 4c of Figures 2 and 3 ). Results from the Tier I risk assessment process are expected to

be reasonably conservative such that the likelihood of a false negative is low ( i.e

., the chance that no risk

is indicated but risks actually occur), while at the same time ensuring that the likelihood of a false

positive ( i.e

., the chance that risk is indicated when none actually exists) is not unacceptably high. For

example, the initial exposure estimates used in Tier I are generally not chemical

‐ specific, but rather

reflect upper

‐ bound estimates that would encompass exposures across all relevant pesticide uses. If

risks are identified in Tier I ( i.e

., where risk estimates exceed levels of concern; Box 5 of Figures 2 and 3 ),

additional data may be used to refine the results, such as using estimates of exposure derived from

available magnitude of residue or other commonly submitted studies ( Box 6 of Figures 2 and 3 ).

If risks are still identified after refinement with available data ( Box 7 of Figures 2 and 3 ), then

appropriate risk mitigation options would be identified and further evaluated for their impact on risk

estimates ( Box 8 of Figures 2 and 3 ). Alternatively (or in addition), a higher tier assessment may be

necessary (Tier II) and studies providing refined estimates of exposure ( e.g

., field studies quantifying

residues in pollen and nectar; Box 9a of Figures 2 and 3 ) and effects at the colony level ( e.g

., semi

‐ field

tunnel studies or field

‐ level feeding studies; Box 9b of Figures 2 and 3 ) may be requested. Measured

residues in pollen and nectar ( Box 9a of Figures 2 and 3 ) from these studies may be used to refine risk

estimates from Tier I ( Box 6 of Figures 2 and 3 ) and/or for qualitatively evaluating risk at the colony levelPage 37 of 275 associated with pesticide applications ( Box 10 of Figures 2 and 3 ). They may also be used to identify

more targeted risk mitigation options than those that could be identified based on Tier I risk estimates.

Although not specifically depicted in Figure 2 for foliar applications, data from the toxicity of residues on

foliage study are used qualitatively to characterize the length of time that residues remain toxic to bees.

The results of the guideline study may result in precautionary label statements similar to those

discussed in the EPA Label Review Manual (USEPA 2012) or in guidance documents intended to reduce

the potential effects of pesticides on bees (e.g., Riedl et al

. 2006).

If available risk mitigation options ( Box 11 of Figures 2 and 3 ) do not provide for an acceptable reduction

in risk, proceeding to Tier III ( Box 12 of Figures 2 and 3 ) may be necessary to resolve specific

uncertainties identified from Tiers I and II for the proposed uses of the pesticide. For example, effects on

the ability of colonies to successfully emerge in the spring ( e

. g

., produce sufficient brood and adult bees

after over

‐ wintering) may be a concern for some pesticides/uses which are not typically addressed in

earlier tiers.

The risk assessment process depicted in Figures 2 and 3 is intended to be iterative and to rely on

multiple lines of evidence to further refine and characterize potential risk. At a screening level, risk to

individual bees is quantified through the use of RQ values. Where RQ values exceed the LOC, more

refined estimates of exposure may be used to re

‐ evaluate RQ values for individual bees based on

laboratory toxicity estimates. Where RQs still exceed LOCs, higher tier semi

‐ field and full

‐ field studies

may be required to determine whether effects observed under highly controlled conditions extend to

the whole colony under increasingly realistic exposure conditions. As depicted in Figures 2 and 3

, if the

multiple lines of evidence indicate that unacceptable effects on survival, growth or reproduction of the

colony are not likely, then a presumption of minimal risk can be supported. Alternatively, there may be

situations where colony

‐ level effects may be likely, given the proposed use or known mode of action of

a compound. In this case, a presumption of minimal risk cannot be supported, and risk assessors should

attempt to characterize the nature and possible magnitude and duration of the effect. This

characterization should include a discussion of uncertainties which limit the extent to which the possible

magnitude and duration can be estimated. Also, the risk characterization should include any potential

mitigation options for minimizing risk to bees from the proposed use of a pesticide.Page 38 of 275 Figure 2. Proposed Tiered Approach for Assessing Risk to Honey Bees from Foliar Spray Applications.Page 39 of 275 Figure 3. Proposed Tiered Approach for Assessing Risk to Honey Bees from Soil/Seed Treatments.Page 40 of 275 In the following section, the problem formulation of the risk assessment is discussed in greater detail as

it relates to assessing risk to bees. As indicated earlier, problem formulation represents the first step of

risk assessment where management goals are articulated along with the assessment and measurement

endpoints used to evaluate whether those goals are affected. The problem formulation also identifies

conceptual models and risk hypotheses which will be evaluated. Conceptual models serve as a graphic

representation of the structure of the risk assessment. These models specify the stressor, i.e

., the

pesticide applied to a specific site, its source ( e.g

., whether the chemical is applied via foliar spray or

seed/soil treatments), the exposure media ( e.g

., whether exposure is likely through consumption of

contaminated pollen and nectar versus residues on the plant’s surface), biological receptors ( e.g

.,

individual adult bees, larvae, pupae, colonies), and the attribute change ( e.g

., reduced survival,

decreased reproduction of the hive). Multiple conceptual models are depicted since multiple routes of

exposure are possible.Page 41 of 275 

### 2.2 Problem Formulation

Problem formulation serves as the first step of a risk assessment and it provides the foundation for the

entire ecological risk assessment. A problem formulation is intended to identify the objective of the risk

assessment and is intended to produce three products: (1) assessment and measurement endpoints

that adequately reflect management goals and the ecosystem they represent, (2) conceptual models

that describe key relationships between a stressor ( i.e

., pesticide) and assessment endpoint or between

several stressors and assessment endpoints, and (3) an analysis plan (USEPA 1998). In the following

sections, each of these products is discussed in greater detail relative to evaluating the potential risks of

pesticides to bees.

2.2.1 Management Goals and Assessment Endpoints

The mission of OPP is to protect human health and the environment from potential risks from pesticides

and toxic substances based on a strong foundation of science, transparency and the rule of law (USEPA

2010). As identified in its strategic plan in its efforts to ensuring chemical safety, EPA is leveraging

available expertise, information and resources by collaborating with other countries, federal agencies,

states, tribes and the public to improve chemical safety. The intent of this effort is to:

 control risks of new chemicals before they are introduced into commerce;

 evaluate chemicals already in use;

 develop and implement regulatory and other actions to eliminate or reduce identified chemical

risks; and,

 make public the data necessary to assess chemical safety to the extent allowed by law.

Consistent with the Agency’s mission and its 5

‐ year strategic plan, EPA is collaborating with PMRA and

with the CalDPR while taking advantage of work conducted in the EU through the EFSA and through

research conducted by government, academic and industry ( e.g

., ICP

‐ BR and the SETAC global Pellston

Workshop) both domestically and internationally to understand and incorporate state of the art

methods for evaluating potential risks of pesticides to bees.

Ecological risk assessments are typically developed within a risk management context to evaluate

human

‐ induced changes that are considered undesirable (USEPA 1998). Changes often consideredPage 42 of 275 undesirable are those that alter important structural or functional characteristics or components of

ecosystems; therefore, an evaluation of adverse effects may include consideration of the type, intensity,

and scale of the effect as well as the potential for recovery. However, the acceptability of adverse

effects is determined by risk managers after consideration of multiple factors which are used to assess

the risks and benefits of a pesticide.

Risk assessment is intended to provide a basis for comparing, ranking and prioritizing risks such that the

results can be used in cost

‐ benefit and cost

‐ effectiveness analyses which in turn offer additional

opportunities to evaluate alternative management options. Therefore, it is important that risk

assessments consider management goals and objectives as well as scientific issues when developing

assessment endpoints and conceptual models during problem formulation. Depending upon available

data and risk management goals, risk assessments may be based on qualitative descriptions of the

likelihood of adverse effects or quantitative probabilities of the occurrence of adverse effects.

Management goals are statements about the desired condition of ecological values of concern. With

respect to pesticides, management goals are in part defined by the laws which provide the statutory

authority to regulate pesticides. Under the Federal Insecticide, Fungicide and Rodenticide Act (FIFRA 5 ),

EPA must ensure that the use of a pesticide does not cause unreasonable adverse effects on the

environment. FIFRA defines “unreasonable adverse effects on the environment” to mean (Cornell Law

School 2012):

1. any unreasonable risk to man or the environment, taking into account the economic, social, and

environmental costs and benefits of the use of any pesticide, or

2. a human dietary risk from residues that result from a use of a pesticide in or on any food

inconsistent with the standard under FIFRA section 346a of title 21

. The Administrator shall

consider the risks and benefits of public health pesticides separate from the risks and benefits of

other pesticides. In weighing any regulatory action concerning a public health pesticide under

this subchapter, the Administrator shall weigh any risks of the pesticide against the health risks

such as the diseases transmitted by the vector to be controlled by the pesticide.

Therefore, under the FIFRA statute, EPA is required to consider the benefits as well as the risks

associated with the use of any pesticide. In evaluating potential risks to non

‐ target organisms, EPA

examines the potential effects of pesticides on growth, reproduction and survival since these broad

categories of effects are known to impact populations and communities.

5 Summary of the Federal Insecticide, Fungicide, and Rodenticide Act http://www.epa.gov/lawsregs/laws/fifra.htmlSmPage 43 of 275 To address this statutory standard, the Agency has developed regulations and guidance which specify

measures or actions that must (or must not) be taken, rather than establishing a value

‐ based

management goal or desired state. This guidance provides a framework for the scope, focus and

conduct of a risk assessment. Risk assessments are typically designed to determine if a pre

‐ established

decision criterion is exceeded. These criteria often contain inherent assumptions about exposure and

conditions ( e.g

., acute contact exposure) which must be clearly articulated to ensure that management

issues are addressed. As will be discussed later in this paper, the decision criteria are used in a tiered risk

assessment framework to determine how extensive an assessment should be, i.e

., whether additional

refinements may be necessary. In higher tiers, other factors beyond decision criteria may be considered

since the risk management question changes from yes/no questions at the Tier 1 level to questions of

spatial and temporal relevance, duration, frequency, and magnitude of adverse effect at the Tier II and

III levels.

Management or protection goals therefore reflect the statutory and scientific objectives of a regulatory

agency. These goals then inform decisions about the type of data which are considered in terms of both

exposure and effects and they provide a basis for specific criteria which are applied to determine

whether additional refinements are needed and/or whether mitigation should be considered. Consistent

with the results of the SETAC Pellston workshop (Fischer and Moriarty 2011), the specific protection

goals proposed in this white paper include:

 protection of pollination services provided by bees;

 protection of honey production and other hive products; and,

 protection of pollinator biodiversity in terms of an adequate number and diversity of species

that contribute to the health of the environment.

Notably, the recent scientific opinion by EFSA on the science behind the development of a risk

assessment of plant protection products on bees (EFSA 2012) also identified pollination, hive products

(relevant for honey bees) and biodiversity as relevant ecosystem services and values to protect.

Once management/protection goals are identified, specific assessment and measurement endpoints are

developed. As defined in the EPA guidelines (USEPA 1998), assessment endpoints are explicit

expressions of the actual environmental values that are to be protected and they are operationally

defined by an ecological entity and its attributes. The ability of the assessment endpoints to support risk

management decisions is dictated by whether they are measurable ecosystem characteristics that canPage 44 of 275 adequately represent management goals. EPA has defined three criteria for determining the selection of

assessment endpoints; these include:

 ecological relevance,

 susceptibility to known or potential stressors, and

 relevance to management goals (USEPA 1998).

Table 1 provides protection goals and examples of relevant assessment endpoints for bees that were

discussed at the SETAC Pellston (Fischer and Moriarty 2011) and are consistent with those that underlie

the approach proposed in this white paper. In addition to assessment endpoints, it is important to

identify specific endpoints which will be measured in toxicity tests; these measurement endpoints must

have clear linkages to assessment endpoints if they are to be used quantitatively in an assessment.

Table 1. Proposed protection goals and examples of associated assessment and measurement (population and

individual) endpoints for bees.

Protection Goal Assessment Endpoints Measurement Endpoints

(Population level and

higher) Measurement Endpoints

(Individual Level)

1. Provision of Pollination

Services Population size and

stability of managed bees Colony strength and

survival Individual worker survival

Queen fecundity

Brood size

Worker bee longevity

2. Production of Hive

Products Quantity and quality of

hive products Quantity and quality of

hive products; residue

levels on honey/wax Individual worker survival

Queen fecundity

Brood success

3. Contribution to

Pollinator Biodiversity Species richness 1 and

abundance Colony strength and

survival

Species richness and

abundance 1 Individual worker survival

Brood success

Queen fecundity

1 Use of honey bees as a surrogate for other insect pollinators has limitations; however, it is assumed that as with all surrogates, data on

individual organisms as well as colony

‐ level data would provide some relevant information on the potential effects of a pesticide on both

solitary bees as well as eusocial taxa. In addition, protection of honey bees would contribute to pollinator diversity indirectly by preserving the

pollination and propagation of the many plants species pollinated by honey bees, which also serve as food sources for other pollinating insects.

There are an increasing number of measurement endpoints reported in Apis and non

‐ Apis bee toxicity

studies which may be conceptually relevant to the proposed assessment endpoints, but for which

quantitative relationships to assessment endpoints ( e.g

., colony strength and survival) have not been

developed. Although such endpoints may not be used quantitatively to estimate potential effects on

assessment endpoints, they can be used qualitatively to characterize potential effects which may not

have clearly established relationships to colony

‐ level effects and for explaining mechanisms by which

colony

‐ level effects might occur. These effects are frequently referred to as sublethal effects and are

discussed in greater detail in the Tier I effects section of this white paper. As part of those discussions,

the concept of adverse outcome pathways (AOPs) is introduced. Although regulatory authorities havePage 45 of 275 developed standardized suites of in vivo studies intended to provide endpoints which can be directly

related to assessment endpoints, studies reported in open literature contain a broad array of endpoints

which span multiple levels of biological organization. These include alterations to biochemical pathways

(molecular changes), effects on feeding behavior ( e.g

., proboscis extension reflex), and alterations in

immune response of individual bees; however, no information may be available from these studies on

subsequent effects on survival, growth or reproduction and frequently assumptions are made regarding

potential relationships. There is also an increased demand to reduce the number of animals used in

testing and to make greater use of the broader suite of effects particularly as newer measurement

technologies ( e.g

., genomics, proteomics), computational tools ( e.g

., quantitative structure activity

relationships), and an understanding of the underlying molecular pathways evolve. Consistent with the

recommendations of the National Academies of Science on toxicity testing in the 21 st Century (National

Research Council 2007), EPA is committed to making greater use of these evolving tools once there are

sufficient data to translate this information into endpoints meaningful to ecological risk assessments

( i.e.

, effects on survival, development and reproduction) which in turn affect populations. The AOP

concept outlined by Ankley et al

. 2009 represents a conceptual construct which portrays existing

knowledge concerning the linkages between a direct molecular initiating event and other key events

which lead to an adverse outcome at a biological level that is relevant to risk assessment. Establishing

the necessary linkages which will enable the use of measurement endpoints from various levels of

biological organization to predict ecologically relevant effects (adverse outcomes) is an area which will

continue to evolve as more data become available.

Therefore, the proposed assessment endpoints used to support efforts to evaluate potential risk to bees

(some of which are listed in Table 1 ) include impaired survival of individual bees ( e.g

., acute and chronic

mortality of adult and larval bees), survival of the colony, development ( e.g

., relative number of

different brood stages), and reproduction ( e.g

., fecundity of queen). These are consistent with the

assessment endpoints used to evaluate risks to other taxa. These assessment endpoints are also

consistent with the protection goals identified earlier. Measurement endpoints used to reflect these

assessment endpoints will be impaired individual or colony survival following acute exposure and

impaired individual or colony survival, development (growth), or reproduction following chronic

exposure.Page 46 of 275 2.2.2 Conceptual Model

An important element of ecological risk assessment is identifying the exposure pathway(s) by which a

pesticide moves in the environment from its source to an ecological receptor (in this case, honey bee

colonies). For an ecological pathway to be complete, it must have a source, a release mechanism, an

environmental transport medium, a point of exposure for ecological receptors, and a feasible route of

exposure.

A conceptual model provides a written description and visual representation of the predicted

relationships between the stressor (pesticide), potential routes of exposure, and the predicted effects

for the assessment endpoint. A conceptual model consists of two interrelated components: a

conceptual diagram and risk hypotheses (USEPA, 1998). The conceptual model is developed from

information collected and evaluated during the problem formulation process. Risk hypotheses are

developed that reflect specific assumptions about potential adverse effects ( i.e.

, changes in assessment

endpoints) and may be based on theory and logic, empirical data, mathematical models, or probability

models (USEPA 1998).

To illustrate the current understanding of the relationship between pesticide releases to the

environment and changes in the proposed assessment endpoints described earlier, a series of generic

conceptual diagrams and risk hypotheses are presented in this section. These diagrams were informed

by two recent reviews related to pollinator risk assessment (Fischer and Moriarty 2011, EFSA 2012). The

intent of these generic conceptual diagrams and risk hypotheses is two

‐ fold: first, to serve as the

conceptual basis of the proposed risk assessment process for honey bees presented in this white paper

and second, to provide a basis for conceptual models to be used in future EPA and PMRA pesticide risk

assessments. There are many factors that determine the exposure of honey bees to a pesticide,

including methods of application, application rate, crop (some crops are not attractive to bees), and

whether a pesticide is systemic are particularly useful in distinguishing the types of exposure pathways

that must be considered in a risk assessment. As such, the example conceptual models provided below

are organized according to application method and the systemic nature of pesticide uptake by plants.

Because pesticide exposure pathways can differ greatly among different groups of honey bees, separate

consideration is given to specific differences that may be described by age ( i.e

., larvae or adults), task of

adult worker ( e.g

., foragers or nurse bees) and castes ( i.e

., worker, queen, or drone). Importantly, thesePage 47 of 275 conceptual models are considered generic and would be modified to account for specific circumstances

of a pesticide use in a given risk assessment. Furthermore, the examples provided here do not account

for all types of pesticides and/or application methods.

2.2.2.1 Non

‐ systemic, Foliar Spray Applications

For non

‐ systemic pesticides applied via foliar spray, dominant exposure routes of foraging bees include

direct deposition of spray droplets onto bees, deposition onto plant surfaces (leaf, flower, pollen,

nectar, extra

‐ floral nectaries) followed by contact and/or ingestion, and inhalation of gaseous phase

chemical (for highly volatile pesticides; Figure 4 ). Exposure of honey bees via soil residues is not

expected to be a major source of exposure to non

‐ systemic pesticides due to limited interaction of

honey bees with soil and lack of translocation of pesticides into plant tissue; however, this route is likely

important for non

‐ Apis ground

‐ nesting bees. Foraging honey bees may also be exposed to non

‐ systemic

pesticides via consumption of water from dew droplet formation on leaves, puddles, and other surface

water 6

. However, a recent review of exposure routes of honey bees indicated high uncertainty in the

importance of water ingestion relative to other oral ingestion sources of non

‐ systemic and systemic

pesticides ( e.g

., nectar and pollen) due to lack of information on water intake rates by bees and multiple

factors that affect these rates (EFSA 2012). Dominant exposure routes of hive bees ( e.g

., nurse, worker,

drone bees) include ingestion and processing of pollen and nectar and exposure through production and

contact with comb wax. Stored honey is expected to be an important exposure route for over wintering

bees. Processed bee bread, brood food, and royal jelly are major routes of exposure for developing

larvae and the queen. Although for highly volatile pesticides, hive bees may be exposed via inhalation of

gaseous phase chemical, this potential route of exposure is not included in the conceptual model since

this would require hives to be in relatively close proximity to the treated site. For example, while soil

fumigants typically are among the pesticides with the greatest volatility, their application to fields with

bare soil (often under tarped or deep shank injection) suggests that honey bees would not likely be

foraging in the treated fields.

Changes in the assessment endpoints ( e.g

., size and stability of bee colonies, production of hive

products, pollinator species richness and abundance) as a result of the aforementioned pesticide

exposures may occur through various means, including reduction in number of worker bees available for

6 For non

‐ systemic pesticides, exposure via water of guttation and honey dew is expected to be minor relative to other routes

of exposure.Page 48 of 275 foraging or maintaining hive temperature (over wintering), reduction in foraging efficiency via sublethal

effects on workers, decreased number or delayed development of brood either from direct exposure to

pesticide or indirectly from reduced brood feeding and maintenance by hive bees, and reduced

fecundity and survival of queens. Protecting Apis populations from pesticide

‐ related effects would also

be expected to directly protect other arthropod pollinators with similar exposure and effects profiles.

Indirectly, the central role of Apis in the propagation of numerous flowering plants would also

contribute to species diversity of other non

‐ Apis bees. Changes in these assessment endpoints are

directly related to impacts on protection goals of maintaining pollination services, production of hive

products and contribution to pollinator biodiversity.

Figure 4. Generic Conceptual Model of Non

‐ Systemic, Foliar

‐ Applied Pesticides for Honey Bee Risk

Assessment. Dashed lines represent routes of exposure that are not considered to be major.

2.2.2.2 Systemic, Foliar Spray Applications

Foliar applications of systemic pesticides are likely to result in many of the same routes of exposure to

honey bees as described previously for non

‐ systemic, foliar

‐ applied pesticides, with several importantPage 49 of 275 exceptions. First, deposition onto plant surfaces and soil will lead to translocation of the pesticide to

other plant tissues, potentially contributing to higher quantities of pesticide residues in pollen and

nectar. For persistent systemic pesticides, the exposure window could include longer periods of time

(red arrows, Figure 5 ) compared to similar applications of non

‐ systemic pesticides. Second, pesticide

residues in plant exudates (guttation fluid, honey dew) also become a potentially relevant route of

exposure. As discussed in the previous section, uncertainty and variability in the extent to which honey

bees harvest and use guttation fluid (relative to other available sources of water) leads to ambiguity in

the importance of this exposure pathway to honey bees. The translation of exposure into changes in

assessment endpoints is essentially the same as that described for non

‐ systemic pesticides. However,

the timing of exposure to residues in pollen and nectar may persist for significant periods of time after

pesticide application, depending on the systemic and persistence characteristics of the pesticide.

Figure 5. Generic Conceptual Model of Systemic, Foliar

‐ Applied Pesticides for Honey Bee Risk

Assessment. Red depicts systemic pathways. Dashed lines represent routes of exposure that are not

considered to be major.Page 50 of 275 2.2.2.3 Systemic, Seed Treatment

Major exposure routes of honey bees to systemic pesticides used as seed treatments include pollen,

nectar, exudates ( e.g

., guttation fluid), and honey dew resulting from translocation from the seed to

growing plant tissues (Figure 6)

. Another important route of exposure includes contact with abraded

seed coat dust during planting. The latter pathway has been associated with numerous incidents of

honey bee mortality (Pistorius et al

. 2009, Forster et al

. 2009) and is the focus of considerable research

( e.g

., Taparro et al

. 2012, Krupke et al

. 2012). The extent to which honey bees are exposed via contact

with abraded seed coat dust is determined by many factors including the physico

‐ chemical properties of

the seed coating, seed planting equipment, use of seed delivery agents ( e.g

., talc), environmental

conditions (wind speed, humidity), and hive location in relation to sowing. Off

‐ site drift of contaminated

seed coat dust also may contribute to residues on plants, soil, and surface water to which bees may be

exposed through direct contact and ingestion of surface water, pollen, and nectar. One important

attribute of the seed treatment exposure pathway is that exposure to pesticides may occur over a wide

time scale ( e.g

., at seed sowing, during plant growth and flowering, and potentially at plant harvest from

exposure to contaminated plant dust). Furthermore, seeds may be coated with multiple fungicides and

insecticides which results in simultaneous exposure to pesticide mixtures.

2.2.2.4 Systemic, Soil Application and Tree Application

Systemic pesticides are also applied as soil applications ( e.g

., soil drench) ( Figure 7 ) and less commonly,

via trunk drench or injection ( Figure 8 ). Exposure of honey bees to pesticides via these applications are

expected to result primarily from translocation to plant tissues (pollen, nectar, exudates, and honey

dew). For soil applications, there is potential exposure via runoff and subsequent translocation into

plants adjacent to the treated field.Page 51 of 275 Figure 6. Generic Conceptual Model of Systemic, Foliar

‐ Applied Pesticides for Honey Bee Risk

Assessment. Red depicts systemic pathways. Dashed lines represent routes of exposure that are not

considered to be major.Page 52 of 275 Figure 7. Conceptual Model of Soil

‐ Applied Systemic Pesticides for Honey Bee Risk Assessment. Red

depicts systemic pathways. Dashed lines represent routes of exposure that are not considered to be

major.Page 53 of 275 Figure 8. Conceptual Model of Trunk

‐ Injected or Trunk

‐ Drench Systemic Pesticides for Honey bee Risk

Assessment. Red depicts systemic pathways.Page 54 of 275 2.2.3 Analysis Plan

The intent of the problem formulation’s analysis plan of a risk assessment is to summarize what has

been done during the problem formulation and to focus on those risk hypotheses which are considered

most likely to contribute to the actual risk associated with a particular use of a pesticide. The analysis

plan provides a rationale for selecting and omitting risk hypotheses in the actual analysis. As with any

risk assessment process, the analysis plan also articulates data gaps, the methods used to evaluate

existing and anticipated data, and the assumptions that will be made where data may be missing. The

analysis plan also identifies the specific measures of exposure ( e.g

., estimated environmental

concentrations; EECs) and effect ( e.g

., median lethal dose for 50% of the organisms tested; LD 50 ) which

will be used to develop risk estimates. As with other taxa, the measures of exposure and effects are not

likely to be comprehensive and uncertainties associated with the available data and underlying

assumptions must be clearly articulated.

2.2.3.1 Measures of Exposure

Measures of exposure are data which detail a pesticide’s existence and movement in the environment

and its contact or co

‐ occurrence with the assessment endpoint or its associated measurement endpoint

(USEPA 1998). While EPA has routinely generated EECs with increasing levels of refinement for other

terrestrial and aquatic taxa, it has not had a similar tiered process for bees. Therefore, in the section to

follow, greater detail is devoted to describing measures of exposure to support the screening

‐ level risk

assessment process and how these measures can be refined to represent more environmentally realistic

estimates based on the needs of risk managers. The proposed measures of exposure make use of

existing tools such as the T

‐ REX model as well as approaches that have not previously been used for

regulatory purposes in North America for estimating exposure through ingestion of pollen and nectar

containing residues of a systemic pesticide applied to soil or seeds.

2.2.3.2 Measures of Effect

Measures of effect are data which describe the effect of a chemical on individuals, species, populations

and communities in the ecosystem (USEPA 1998). They are intended to reflect consistent and reliable

measurable changes in an attribute on an assessment endpoint or its surrogate in response to a

pesticide to which it is exposed. In the past, risk assessors have relied heavily on laboratory

‐ based

studies with individual bees to evaluate potential hazards to bees and only had limited opportunity toPage 55 of 275 evaluate potential effects at the colony level. While the laboratory studies will continue to play an

important role in the assessment process, the proposed risk assessment process for bees will articulate

additional data which should be considered as well as the triggers that will be used to recommend

additional studies. As discussed in the following sections, some of the additional data may already be

available since these studies are currently required by Canada and the EU.

Effects data used in the assessment are not confined to registrant

‐ submitted studies but include studies

published in the open literature. The open literature studies are identified using EPA’s ECOTOXicology

(ECOTOX) database, which is maintained by the EPA Office of Research and Development and which

employs a literature search engine for locating chemical toxicity data for a range of taxa including bees

(USEPA, 2009 a ). EPA has developed guidance (USEPA 2011) for evaluating ecological toxicity data

reported in open literature studies and this guidance includes specific information related to studies

conducted with bees.

The proposed process for estimating risk to bees focuses on the honey bee, which has historically served

as a surrogate for both Apis and non

‐ Apis bees. Regulatory agencies rely heavily on the use of surrogate

species to evaluate potential adverse effects to taxa since well

‐ defined toxicity tests have been

developed using particular species. The use of these test species is based on well

‐ defined husbandry

conditions which allow the organisms to thrive under laboratory conditions. Additionally, the test

species must be readily available and relatively easy to manipulate. All of these conditions influence the

extent to which a particular organism can be routinely and reliably used to estimate the toxicity of

chemicals without unreasonable confounding effects. The honey bee is readily available, its husbandry

needs are very well defined and given that it has been domesticated for many centuries, both the

individual bee and the more complex bee colony lend themselves to toxicity testing. The honey bee also

serves as a reasonable choice for evaluating the protection goals defined earlier because the species is

responsible for a large proportion of crop pollination in North America and produces honey. The use of

any species as a surrogate is predicated on the understanding that while the organism may thrive under

laboratory and field test conditions, it may not be representative of the most sensitive species or of

species with markedly different life histories and pesticide exposure routes. Although the honey bee

has historically served as a surrogate for Apis as well as non

‐ Apis bees, there are limitations in the use of

honey bees as surrogates and these limitations and potential means of addressing them are discussed

later in this white paper. Also, to the extent that alternative data on the effects of the pesticide on non

‐Page 56 of 275 target arthropod are available, this information can be used to further characterize the extent to which

honey bee data may or may not be adequate for assessing effects to non

‐ Apis bees.

2.2.3.3 Integrating Exposure and Effects

The proposed screening

‐ level (Tier I) risk assessment process will rely on the point estimate

(deterministic) based approach using the ratio of exposure concentration to effects concentration to

derive risk quotients (RQs) that serve as a basis for estimating risk to other species of insect pollinators.

In cases where RQs exceed the Level of Concern (LOC), risks of the pesticide to insect pollinators cannot

be precluded and additional refinements may be necessary. The proposed LOC is set to 0.4 and is based

on the historic average dose

‐ response relationship for acute toxicity studies with bees and a 10%

mortality level in foragers and worker larvae. When acceptable chronic toxicity test designs are

available, the proposed process involves developing chronic RQs using no observed adverse effect

concentrations (NOAECs) and evaluating the RQs using an LOC of 1.

Higher tier assessments will be based on a qualitative approach ( i.e

., RQs not directly calculated) to

refine the risk estimates in the lower tier assessments. Given the protection goals and assessment

endpoints discussed earlier, risk will be assessed through examining the likelihood of acute mortality

and chronic effects on survival, development and reproduction of bees/colonies. Measures of effect will

include the acute contact and/or oral LD 50 from laboratory studies with individual bees using currently

available acute toxicity test guidelines, and the most sensitive chronic NOAEC and lowest observed

adverse effects concentration (LOAEC) for survival, growth or reproduction from chronic toxicity studies

when suitable study designs are developed. Measurement endpoints for screening

‐ level assessments

will be based on individual bees (adults and/or larvae), while higher tier assessments are expected to

provide colony

‐ level NOAEC/LOAEC values. As with other taxa, data from acceptable registrant

‐

submitted and open literature laboratory and field

‐ based studies and the resulting RQ values will be

considered in the context of any ecological incident data ( e.g

., bee kills) which may be available in the

EPA Ecological Incident Information System (EIIS) and other incident databases.

One tool that is being considered for the risk assessment method for bees is a colony

‐ level simulation

model. This tool may be useful for integrating the wealth of biological information on honey bees and

their colonies with the lethal and sublethal effects data for pesticides. A colony

‐ level model would be

useful in evaluating the effects of a chemical on the stated protection goals by evaluating impacts of aPage 57 of 275 chemical on colony survival, pollination services and honey production. Although no specific colony

‐ level

models are proposed for use at this time, they could potentially be developed to integrate exposure and

effects data in the evaluation of risks of a pesticide.

After the Problem Formulation has articulated the protection goals, conceptual model/risk hypothesis

and associated assessment/measurement endpoints, the risk assessment proceeds to the Analysis step

where available exposure and effects data are assessed and characterized. In the following sections, the

two components on Analysis ( i.e.

, exposure characterization and effects characterization) are discussed.

Although EPA has historically evaluated potential hazards to bees using existing registrant

‐ submitted

guideline toxicity studies as well as open literature studies and incident data, estimates of potential

exposure to bees have not been routinely evaluated by EPA. Therefore, a more detailed discussion of

methods to estimate exposure is provided in the exposure characterization section; whereas, the effects

characterization section focuses on study design elements to improve/expand existing guideline studies.



### 3 Characterization of Exposure

This section describes two Tiers that may be used to characterize potential pesticide exposures to honey

bees. The first Tier is intended to efficiently identify those pesticides that do not pose a risk to

pollinators. In regards to exposure, the Tier I screen is based on upper

‐ bound estimates of exposure to

bees through diet or direct spray. In this approach, surrogate data ( i.e., based on other chemicals and

plant matrices besides pollen and nectar) are used to represent potential exposures for assessed

pesticides. The proposed Tier I exposure method is efficient in that it is easy to use, requires few input

parameters, and takes little time to complete.

For chemicals that fail the Tier I screen, prior to moving on to the Tier II assessment, it may be possible

to utilize pesticide

‐ specific data from registrant

‐ submitted studies describing pesticide residues in plants

( e.g., magnitude of residues, OSCPP 860.1500; USEPA 1996 a ).These studies could potentially provide

enough information to allow for adequate characterization of the risks of the pesticide without needing

to move on to Tier II, which would preclude the need to require additional pesticide

‐ specific field

studies. If the existing registrant

‐ submitted studies do not provide sufficient information to characterize

the risks of the pesticide to honey bees, the data could potentially be used to inform the study designs

of the Tier II studies. These refinements may involve gathering and evaluating large amounts of chemicalPage 58 of 275 specific data. This step is not efficient; however, it may provide valuable information that may be used

to characterize the risk of a chemical to honey bees.

Tier II exposure assessments rely upon field measurements of the assessed pesticide in pollen and

nectar of crops that are relevant to the use of the pesticide and the exposure of bees ( e.g.

, crops that

are attractive to bees). In cases where sufficient pesticide residue data for pollen and nectar are

submitted prior to risk assessment, it may not be necessary for the risk assessor to use the Tier I screen

since pesticide

‐ specific residues in pollen and nectar could be used directly to understand potential

exposures to honey bees.

This section describes the proposed Tier I exposure assessment method for honey bees, which includes

a discussion of how registrant

‐ submitted field studies that are submitted to meet identified data needs

not specifically related to honey bees ( i.e.

, for determining potential pesticide exposures to humans),

could potentially be used to characterize risks that could not be precluded by the Tier I screen. The final

component of this section is a discussion of the Tier II exposure options, with a focus on considerations

related to Tier II study designs.

3.1 Tier I Exposure Assessment for Honey Bees

The purpose of this section is to describe a screening level approach that is proposed as a Tier I method

to assess exposures of honey bees to pesticides. The Tier I exposure method is intended to account for

the major routes of pesticide exposure that are relevant to bees ( i.e

., through diet and contact). As

indicated by the conceptual models, the exposure routes for bees are expected to differ based on

application type. For instance, honey bees foraging in a field treated with a pesticide through foliar spray

could potentially be exposed to the pesticide through direct spray as well through consuming

contaminated food. For honey bees foraging in fields treated with a pesticide through direct application

to soil ( e.g.

, drip irrigation), or through seed treatments, direct spray onto bees is not expected. For

these application methods, pesticide exposure through consumption of residues in nectar and pollen are

expected to be the dominant routes. The conceptual models discussed previously in this white paper

acknowledge that foraging honey bees may also be exposed to pesticides via contact with dust from

seed treatments or via consumption of water from surface water, puddles, dew droplet formation onPage 59 of 275 leaves and guttation fluid. As explained below, the proposed Tier I exposure methods do not include

quantification of exposures via these routes.

As discussed in the effects characterization, acute oral toxicity data are necessary for adult and larvae in

order to characterize the risks of a pesticide. Because these toxicity data are expressed on a dose basis

( i.e., μ g a.i./bee), it is necessary to convert estimated concentrations of pesticides in food (expressed as

mg a.i./kg) into doses. Honey bees fulfill their nutritional requirements through consumption of nectar,

honey, pollen and bee bread. In addition to requiring pollen and nectar or honey, bees also require royal

jelly and brood food to fulfill their nutritional requirements. In the proposed approach, pesticide doses

received by bees can be calculated using nectar and pollen consumption rates for larval and adult

worker bees. For larvae, the proposed total food consumption rate is 120 mg/day, which is based on the

total daily consumption of pollen and nectar (based on honey consumption) by larvae during day 5 of

the uncapped larval life stage. For adult worker bees, the proposed food consumption rate is 292

mg/day, based on nectar consumption rates of nectar foraging bees, which are expected to receive the

highest dietary exposures among different types of worker bees. In addition, it is likely that these food

consumption rates are protective of drones and queens. A detailed discussion related to the derivation

of the proposed food consumption rates for larvae and adult workers is provided in Appendix 1

, along

with a comparison of the proposed rates to other types of worker bees and castes.

In the proposed approach for representing food consumption rates of larvae and adults, it is assumed

that exposures through consumption of nectar and pollen are conservative representations of potential

exposures through consumption of honey and bee bread, respectively. This approach is likely to be

conservative because it assumes that pesticides do not degrade while honey and bee bread are stored in

the hive. For bees that consume honey, it is assumed that the estimated pesticide exposures can be

related back to the original concentration in nectar by accounting for the amount of sugar consumed by

bees. It is also assumed that pollen and nectar consumption rates and resulting exposures are protective

of exposures of bees to pesticides through consumption of royal jelly and brood food. This is supported

by work by Davis and Shuel 1988 and Kamel et al

. (unpublished) demonstrating that pesticide

concentrations in food consumed by nurse bees are 2

‐ 4 orders of magnitude higher than concentrations

measured in royal jelly.Page 60 of 275 Methods for estimating dietary exposures to bees differ in the nature of the estimated concentrations in

pollen and nectar potentially consumed by bees. For foliar spray applications, the proposed approach

involves the use of the tall grass residue value from the T

‐ REX model (v. 1.5) as a surrogate for pesticide

concentrations in nectar and pollen. Also discussed below is the potential for deriving a pollen

‐ specific

upper

‐ bound concentration to represent concentrations of pesticides on pollen and nectar of flowers

directly sprayed with pesticides. For soil treatments, the proposed method is based on a modification to

a plant

‐ soil uptake model developed by Briggs et al

. 1982 and 1983, which is designed to estimate

pesticide concentrations in plant shoots; the concentrations in plant shoots are proposed as a surrogate

for concentrations in pollen and nectar (following systemic transport). For seed treatments, the

proposed Tier I exposure method is based on the International Commission for Plant

‐ Bee Relationships’

(ICP

‐ BR) 1 mg a.i./kg concentration to represent an upper

‐ bound concentration in nectar and pollen. For

tree injections and trunk drenches, the proposed method is a simplistic approach that considers the

mass of the pesticide applied to a tree and the mass of the leaves of the tree.

The proposed Tier I methods for foliar spray applications, soil applications, seed treatments and tree

injections/drenches are described in detail below, along with comparisons of estimated values to

empirical data and a discussion of the strengths and limitations of the methods. The model evaluations

described below discuss available empirical measurements of pesticides in pollen, nectar and on bees.

To compare the estimated exposures to the empirical measurements in nectar and pollen and on bees,

all concentrations were normalized to 1 lb a.i./A. In addition, the Tier I exposure estimates are

converted to a dose basis ( μ g a.i./bee) to facilitate the comparison of the exposure value to toxicity

data, the latter of which are expressed on a dose basis. For reference purposes, summaries of the

studies from registrant

‐ submissions and the scientific literature used to evaluate Tier I model

predictions are provided in Appendices 3 and 4

, respectively.

The Tier I method is intended to generate “reasonably conservative” estimates of pesticide exposure to

honey bees, meaning that the estimates of exposure should generally be within one or two orders of

magnitude higher than the “true” environmental exposure. Models were evaluated by comparing

estimated exposures to available empirical data for nectar, pollen and bees to determine whether they

could be considered reasonably conservative. The amounts of data that could be used to evaluate thePage 61 of 275 different methods varied, which lead to differences in the evaluation of the conservativeness of

different methods. For example, for foliar spray applications, the empirical data available to evaluate the

proposed methods for estimating exposures through consumption of pollen and nectar (N>15) were

much larger than the data for estimating contact exposures (N

= 3). In cases where only a few data are

available to evaluate a method, it is preferred that the estimated exposure value has a larger margin of

safety than the methods that can be evaluated using larger empirical data sets. For the methods with

smaller evaluation data sets, there is less confidence in the conservativeness of the method because it is

not certain that the small data set captures upper

‐ bound exposures in the environment. For methods

that have more empirical evaluation data, the margin of safety between the estimated value and the

largest empirical value could be lower as long as the estimated value was conservative for the overall

data set because it is more likely that the upper

‐ bound of environmental exposures is represented.

As discussed in the analysis plan of this white paper, estimated exposure concentrations are integrated

with available toxicity data in order to characterize risks of a pesticide to honey bees. In doing so, Tier I

estimated exposures and toxicity endpoints are compared based on the same exposure routes. For

instance, estimated exposures through direct spray onto foraging bees are combined with toxicity

endpoints from contact toxicity test, while estimated dietary exposures 7 are matched with oral toxicity

data. The effects characterization below discusses both acute and chronic toxicity tests. At the Tier I

level, both acute and chronic exposure estimates are represented by the highest single day exposure

value. Although a time weighted estimated exposure value may be more representative of the exposure

used in a chronic toxicity test, exposure occurring over a single day could potentially be sufficient to

elicit effects. Therefore, in the Tier I approach, chronic exposure is conservatively represented by the

highest single day estimated exposure.

7 Note that the term “dietary exposure” is used throughout the exposure characterization section to describe potential

exposures of bees through consumption of pollen and nectar. This term is often described in other publications as “oral

exposure”; however the term “dietary” is used to distinguish between pesticide exposures through consumption of food and of

drinking water.Page 62 of 275 3.1.1 Quantifying Pesticide Exposures from Foliar Applications

3.1.1.1 Estimating exposure through consumption of nectar and pollen

In an ideal situation, the Tier I exposure estimates for honey bees would be based on residue values

measured directly in nectar and pollen of flowers sprayed with pesticides. This cannot be achieved at

this time because there is an insufficient amount of data that may be used to adequately describe the

distribution of pesticide residues that occur in pollen and nectar relative to pesticide application rate. As

an alternative, the proposed method relies on upper

‐ bound pesticide residue values from EPA’s T

‐ REX

model (version 1.5) (USEPA 2012 e ). T

‐ REX (version 1.5) is a Tier I model currently used by EPA to

determine potential risks of pesticide exposures to non

‐ target animals located in terrestrial

environments, including the treated field or areas receiving spray drift. This model is used for pesticides

that are applied via aerial or ground spray applications. The T

‐ REX model estimates pesticide exposures

to mammals, birds, terrestrial

‐ phase amphibians and reptiles that are consuming dietary items

containing pesticide residues. T

‐ REX is simple and efficient, requiring only a few input parameters

related to the application of the chemical ( i.e.

, application rate, number of applications and interval) and

its fate ( i.e.

, foliar dissipation half

‐ life). This model generates estimates of exposure that can be

expressed as mg a.i./kg

‐ food item and can be related to application rate by multiplying a default residue

value that is normalized to 1 lb a.i./A (1.12 kg a.i./ha) by the application rate. Dietary items included in

the model are grass (short and tall), broadleaf plants, fruit, pods, seeds and arthropods. The normalized

upper

‐ bound and mean residue values for these food items are provided in Table 2

.

Table 2. Application rate normalized upper

‐ bound and mean initial residue values incorporated into T

‐

REX (v. 1.5.1) for various food items.

Terrestrial animal food item Initial residue (mg a.i./kg per 1 lb a.i./A)

Upper

‐ bound Mean

Short grass 240 85

Broadleaf plants 135 45

Tall grass 110 36

Arthropods 94 65

Fruit, pods and seeds 15 7

For the plant dietary items, this model incorporates the Kenega nomograph, as modified by Fletcher et

al. (1994), which is based on a large set of field residue data. The upper

‐ limit values from the

nomograph represent the upper bound of residue values from actual field measurements (Hoerger and

Kenega 1972). The Fletcher et al. 1994 modifications to the Kenega nomograph are based on measuredPage 63 of 275 field residues from 249 published research papers, including information on 118 species of plants, 121

pesticides, and 17 chemical classes.

The T

‐ REX model is of particular interest for the Tier I exposure approach for bees because it is based on

empirical data from field and laboratory studies, represents a conservative approach to deriving

pesticide residue estimates on plants and arthropods, is part of EPA’s standard Tier I ecological risk

assessment method, and can be modified to account for the application rate and number of applications

of a pesticide. Since the T

‐ REX model does not specifically include residue values for pollen and nectar, it

is necessary to select an appropriate plant residue value to act as a surrogate for these matrices. The

sections below compare the residues on different T

‐ REX plant categories to empirically

‐ based pesticide

concentrations in pollen and nectar of flowers that were directly sprayed. As noted in the effects section

below, Tier I level acute oral toxicity data are potentially submitted for two life stages of worker bees,

i.e.

, adults and larvae. Therefore, for the Tier I exposure method, two estimated dietary doses are

generated to represent the two life stages. Since both of these life stages consume nectar and pollen to

varying extents, it is proposed that dietary exposures be assessed as a combination of the two food

sources.

3.1.1.1.1 Concentrations in Nectar after Foliar Applications

Empirical data for pesticide concentrations in nectar from flowers that were directly sprayed with

pesticides were compiled in order to evaluate the potential utility of the T

‐ REX upper

‐ bound residues in

short grass, tall grass, broadleaf plants and fruit, seeds and pods as potential screens for assessing

dietary exposures to honey bees. These data are listed in Table 3 and summaries of the studies used to

generate the data are provided for reference purposes in Appendices 3 and 4

. Empirical measurements

of eight different pesticides in nectar are available from seven studies (Choudhary and Sharma 2008,

Wallner 2009, and 5 unpublished registrant

‐ submitted studies involving chemicals whose names are not

provided here because the chemicals are not currently registered). The highest empirical measurement

from these studies was 13.6 mg/kg

‐ nectar (normalized to 1 lb a.i./A) ( Table 3 ); however, it should be

noted that this value corresponds to a mean measurement of pesticides in nectar, not to a maximum

value. Therefore, it is more appropriate to compare this value to the mean residues used by the T

‐ REX

model, rather than the upper bound. Despite this limitation, the mean value is useful to describe

potential residues of pesticides in nectar.Page 64 of 275 Table 3. Comparison of T

‐ REX upper

‐ bound values for plants and measured concentrations (maximum

and mean values) of pesticides in nectar.

Citation Normalized Concentration

(mg/kg per 1 lb a.i./A) Chemical Crop

Maximum Mean

T

‐ REX (short grass) 240* 85 All All

T

‐ REX (Broadleaf plants) 135* 45 All All

T

‐ REX (Tall grass) 110* 36 All All

Choudhary and Sharma (2008) NA 13.6 Lambda cyhalothrin + Mustard

Choudhary and Sharma (2008) NA 12.5 Lambda cyhalothrin + Mustard

Choudhary and Sharma (2008) NA 7.22 Spiromesifen + Mustard

Choudhary and Sharma (2008) NA 7.03 Spiromesifen + Mustard

T

‐ REX (Fruit, pods and seeds) 15* 7 All All

Choudhary and Sharma (2008) NA 3.90 Endosulfan + Mustard

Choudhary and Sharma (2008) NA 3.45 Endosulfan + Mustard

Wallner (2009) NA 3.2 Boscalid Oilseed

rape

Wallner (2009) NA 3.2 Prothioconazol Oilseed

rape

Study 1

‐ 2+ 2.2 2.1 Unnamed chem #1 Phacelia

Study 1

‐ 2+ 2.1 1.5 Unnamed chem #1 Phacelia

Beedle and Harbin (2011) 1.1 NA Imidacloprid ** Cotton

Study 1

‐ 1 0.83 NA Unnamed chem #1 Cotton

Study 2

‐ 1 0.61 NA Unnamed chem #2 Phacelia

Study 1

‐ 1+ 0.41 NA Unnamed chem #1 Cotton

Study 1

‐ 1+ 0.41 NA Unnamed chem #1 Cotton

Study 2

‐ 2 0.17 NA Unnamed chem #2 Melon

*Value represents upper bound, not maximum.

**Includes parents and degradates.

+This study included two different trials. The results for each trial are presented separately.

NA = not available

When comparing the maximum values from the available empirical data to the T

‐ REX upper

‐ bound

residue values for plants, the T

‐ REX residue estimates exceed the maximum empirical values. When

considering cases where only means were provided in study reports, the T

‐ REX mean values exceed the

empirical means for short grass, broadleaf plants and tall grass. Since several mean values from

empirical studies exceed the mean residue value for seeds, fruit and pods, this T

‐ REX residue value may

not be protective of residue values in nectar. The next highest residue value from T

‐ REX, which is 110

mg/kg normalized to 1 lb a.i./A (1.12 kg a.i./ha), corresponds to an upper bound of pesticides measuredPage 65 of 275 on tall grass. The mean value for short grass exceeds all empirical mean data, and is within a factor of 3

of the highest mean value. This indicates that this value would serve as a screen that is a reasonably

conservative estimate of the pesticide dose received by bees consuming nectar. Therefore, the T

‐ REX

screening value of 110 mg/kg (per 1 lb a.i./A or 1.12 kg a.i./ha) is proposed as a surrogate for nectar.

3.1.1.1.2 Concentrations in Pollen after Foliar Applications

Empirical data for pesticide concentrations in pollen from flowers that were directly sprayed with

pesticides were compiled in order to evaluate the potential utility of the T

‐ REX upper

‐ bound residues in

short grass, tall grass, broadleaf plants and fruit, seeds and pods as potential screens for assessing

dietary exposures to honey bees. The T

‐ REX surrogates are evaluated using empirical measurements of

11 different pesticides in pollen from studies available in the open literature (Choudhary and Sharma

2008, Fries and Wibran 1986, Hanny and Harvey 1982, Škerl et al

. 2009 and Wallner 2009 ) or from

unpublished studies submitted by pesticide registrants. These data are listed in Table 4 and study

summaries are provided in Appendices 3 and 4

.

Table 4. Comparison of T

‐ REX upper

‐ bound values for plants and measured concentrations (maximum

and mean values) of pesticides in pollen measured within 24 hours of application.

Citation Normalized Concentration

(mg/kg per 1 lb a.i./A) Chemical Crop

Maximum Mean

T

‐ REX (short grass) 240* 85 All All

Wallner 2009 NA 58 Boscalid Oilseed rape

T

‐ REX (Broadleaf plants) 135* 45 All All

T

‐ REX (Tall grass) 110* 36 All All

Fries and Wibran 1987 50 NA PP321 Oilseed rape

Fries and Wibran 1987 49 NA Cypermethrin Oilseed rape

Study 2

‐ 1 + 38 NA Unnamed chem #2 Phacelia

Study 1

‐ 1 + 31 NA Unnamed chem #1 Cotton

Study 1

‐ 1 + 17 NA Unnamed chem #1 Cotton

Choudhary and Sharma 2008 + NA 25.0 Lambda cyhalothrin + Mustard

Choudhary and Sharma 2008 + NA 24.1 Lambda cyhalothrin + Mustard

Study 2

‐ 1 + 19 NA Unnamed chem #2 Phacelia

Study 1

‐ 2 + 19 NA Unnamed chem #1 Phacelia

T

‐ REX (Fruit, pods and seeds) 15* 7 All All

Study 1

‐ 2 + 14 NA Unnamed chem #1 Phacelia

Study 1

‐ 1 + 12 NA Unnamed chem #1 Cotton

Choudhary and Sharma 2008 + NA 10.5 Spiromesifen + MustardPage 66 of 275 Study 1

‐ 1 + 10 NA Unnamed chem #1 Cotton

Choudhary and Sharma 2008 + NA 9.09 Spiromesifen + Mustard

Choudhary and Sharma 2008 + NA 4.75 Endosulfan Mustard

Choudhary and Sharma 2008 + NA 4.54 Endosulfan Mustard

Hanny and Harvey 1982 NA 3.1 Carbaryl ** Corn

Hanny and Harvey 1982 NA 1.6 Carbaryl ** Corn

Škerl et al

. 2009 1.5 NA diazinon Apple

Study 2

‐ 2 1.21 NA Unnamed chem #2 Melon

Study 2

‐ 3 1.29 NA Unnamed chem #2 Tomato

Škerl et al

. 2009 1 NA Thiacloprid Apple

*Value represents upper bound, not maximum.

**Two different experiments were conducted using different formulations. The results of the two formulations are presented separately.

+This study included two different trials (for each pesticide). The results for each trial are presented separately.

NA = not available

When comparing the empirical data to the T

‐ REX upper

‐ bound residue values for plants ( Table 4 ), the T

‐

REX residues values for short grass, tall grass and broadleaf plants exceed the maximum empirical data

for pollen. The highest maximum value is within a factor of 2 of the tall grass upper

‐ bound value. The

upper

‐ bound residue for seeds, fruit and pods (15 mg/kg normalized to 1 lb a.i./A or 1.12 kg a.i./ha) is

lower than several values from empirical data set for pollen, suggesting that this value is not protective

for pollen. When considering the mean empirical data, there is one mean value that exceeds the mean

values for tall grass and broadleaf plants; however it is still within a factor of 2 of both of these values.

Given that all but one of the mean or maximum empirical data are below the T

‐ REX tall grass mean or

upper

‐ bound values (respectively) and that the empirical dataset is composed of a large number of data

points ( i.e.

, N = 23) for pollen, it appears that the T

‐ REX upper

‐ bound residue value for tall grass

represents a conservative screening estimate of the pesticide dose received by bees consuming pollen.

Therefore, the T

‐ REX screening value of 110 mg/kg (per 1 lb a.i./A) will be proposed as a surrogate for

pollen.



### 3.1.1.2 Assumptions and Uncertainties associated with dietary exposure methods for foliar

applications

As indicated previously, the Tier I dietary exposure estimates would ideally be based on residue values

measured directly in nectar and pollen of flowers sprayed with pesticides. Due to the limited availability

of empirical measures of pesticides in nectar and pollen ( Table 3 and Table 4

, respectively), there is

limited confidence in the upper

‐ bound estimates that would be generated using these data. In addition,

the available data are not necessarily diverse enough to represent the major variables that are expectedPage 67 of 275 to lead to differences in pesticide concentrations among treatment sites. For example, there may be

crop specific factors, such as flower structure or orientation that may impact the magnitude of pesticide

concentrations in pollen and nectar of different types of crops. There are an insufficient number studies

available for each type of crop that may be used to evaluate the potential influences of these factors. In

addition, the available data represent a mixture of maximum and maximum values (where the means

are often based on a low number of replicates) making it difficult to combine these data into a unified

data set that can be used to reliably estimate 95 th percentile concentrations in nectar and pollen. If 95 th

percentile values were calculated using the maximum values available for nectar and pollen, these

values would be 2.2 (N=8) and 49 mg/kg per 1 lb a.i./A (N

= 14), respectively ( Table 5 ). When compared

to the T

‐ REX upper

‐ bound residue value for tall grass ( i.e., 110 mg a.i./kg per 1 lb a.i./A), the 95 th

percentile value for pollen is only a factor of 2 less. Given the limited amount of data available for

pollen, it is likely that if the amount of data is increased, the higher concentrations in the data set may

be closer to the tall grass upper

‐ bound value. Although the value for nectar appears to be an order of

magnitude lower compared to pollen, in some studies where pollen and nectar were collected from the

same flowers ( e.g., Choudhary and Sharma 2008), the concentrations in the two matrices were almost

identical, suggesting that pesticide concentrations in pollen and nectar may be equivalent in some cases.

Table 5. Summary statistics describing concentration data for nectar, pollen and tall grass.

Parameter Concentration in nectar

(mg/kg per 1 lb a.i./A) Concentration in pollen

(mg/kg per 1 lb a.i./A) Concentration in tall grass

(mg/kg per 1 lb a.i./A) *

Maximum Mean Maximum Mean Maximum

Maximum 2.2 14 50 58 197

Average 1.0 5.8 19 16 36.0

Standard deviation 0.8 4.3 17 18 40.6

N 8 10 14 9 46

Upper 95 th percentile 2.2 13 49 45 110 **

Number of plant species 5 8 6

Number of chemicals 8 11 20

*From Fletcher et al. 1994.

**This value represents an approximation of the 95 th percentile.

There is uncertainty regarding the extent to which the residues incorporated into T

‐ REX for plants,

specifically tall grass, represent biologically relevant surrogates for nectar and pollen. Perhaps a more

biologically relevant surrogate for pollen and nectar would be whole flowers. There are some data to

suggest that pesticide residues on leaves are similar to those on whole flowers when applied directly to

the foliage and flowers via spray ( Table 6 ), which supports the use of tall grass as a surrogate for pollen,Page 68 of 275 despite questions as to the biological relevance of leaves. In the future, as additional pesticide

concentration data for nectar and pollen are available, it will be possible to base upper

‐ bound values for

nectar and pollen directly on empirical data, thus eliminating the need for the tall grass foliar residue

surrogate.

Table 6. Mean pesticide residues measured on leaves and flowers directly sprayed with pesticides.

Values based on samples collected on the day of the application.

Citation Mean (SD) Normalized Concentration

(mg/kg per 1 lb a.i./A) Chemical Crop

Leaves Flowers

Menkene et al., 1989 46.4 ± 15.5 49.3 ± 17.9 methamidophos Sugar beets

Perrit et al., 1990 132 ± 80 32 ± 18 methamidophos Cotton

Delabie et al

. 1985 44 33 cypermethrin Oilseed rape

It should be noted that the T

‐ REX model is designed to derive EECs following foliar spray applications of

pesticides. Since the data set used to derive the T

‐ REX residue values (Fletcher et al

. 1994) is based on

various spray application methods ( e.g., ground boom, air blast, aerial spray), it is assumed that these

residues are applicable to any foliar spray application.

Although the data set used to derive the initial residue values of T

‐ REX likely contained systemic

pesticides, the residue values are based on measurements that were taken within 24 hours of the

application. This leaves little time to allow for transport throughout the plant. Therefore this approach is

not applicable to derive EECs representing systemic transport of pesticides after soil or seed treatments.

In using the T

‐ REX residue value, it is assumed that direct spray onto foliage represents a conservative

(upper bound) estimate of concentrations in pollen and nectar for both systemic and non

‐ systemic

pesticides. Although movement of systemic transport of pesticides to pollen and nectar are expected to

occur, concentrations in nectar and pollen that occur due to systemic transport are not expected to

exceed the concentrations from direct spray. This is because systemic transport takes time and not all of

the mass of the pesticide that is applied to the plant will move to pollen and nectar due to dissipation of

the pesticide and transport to other portions of the plant. This assumption is supported by data

available for unnamed chemical #1 (study 1

‐ 1) and dinotefuran and thiamethoxam (Dively and Kamel

2012). In Study 1

‐ 1, cotton blossoms that were directly sprayed with the chemical had higher

concentrations in pollen collected from plants (by 2 orders of magnitude) compared to concentrations inPage 69 of 275 pollen of blossoms that opened after the application, which represents systemically

‐ transported

chemical. Concentrations of the pesticide in nectar collected by foraging bees were similar ( i.e

., within 1

order of magnitude) when comparing flowers that were directly sprayed and flowers that opened after

the application, with one exception. In this one case, the pesticide was measured in nectar (from flowers

that were not directly sprayed) at a level that was one order of magnitude above the nectar

concentration in flowers that were directly sprayed for reasons that are not fully understood. Since the

tall grass residue value appears to be higher than available residue data for nectar, the tall grass residue

value is expected to still be conservative in cases where systemic transport to nectar may occur. Dively

and Kamel 2012 applied dinotefuran and thiamethoxam to pumpkin leaves via foliar spray and

measured their residues in pollen and nectar of unsprayed flowers several weeks after the applications.

In nectar, maximum residues were 0.015

‐ 0.080 mg a.i./kg per 1 lb a.i./A. In pollen, maximum residues

were 0.082

‐ 0.95 mg a.i./kg per 1 lb a.i./A. These values are several orders of magnitude below the tall

grass upper

‐ bound residue value ( i.e., 110 mg a.i./kg per 1 lb a.i./A), which suggests that this residue

value is protective for foliar sprays, including cases where pesticides may be systemically transported to

pollen and nectar.

3.1.2 Estimating exposure through contact for foliar applications

This section discusses two methods that were considered for the Tier I method to estimate exposure to

bees through contact. The proposed upper

‐ bound residue value for estimating exposures to honey bees

is based on the maximum residue value reported by Koch and Weisser 1997 ( i.e., 2.7 μ g a.i./bee per 1 lb

a.i./A). When developing the proposed Tier I method for quantifying contact exposure, the upper

‐

bound arthropod residue value from T

‐ REX ( i.e., 12 μ g a.i./bee per 1 lb a.i./A) was also considered. Both

methods are useful in deriving upper bound concentrations of chemicals on insect pollinators, and are in

general agreement with each other (they differ by a factor of 5) and with the limited empirical data from

other studies reporting pesticide concentrations on bees. Although the T

‐ REX arthropod residue value is

relevant to quantifying upper

‐ bound residue values on insects, the utility of this residue value to

represent exposures to honey bees is uncertain because the underlying data set does not include

measured residues on honey bees. Since the focus of the Tier I risk assessment method is on honey bees

(which represent surrogates for non

‐ Apis bees), the proposed upper bound residue value for estimating

exposures to honey bees is based on the maximum residue value reported by Koch and Weisser 1997

because this value is based on measured concentrations of pesticides on honey bees. In cases where it isPage 70 of 275 necessary to assess exposures to non

‐ Apis bees, the T

‐ REX residue value may be considered more

appropriate.

3.1.2.1 Proposed method: Koch and Weisser (1997)

Koch and Weisser 1997 investigated potential contact based exposures of bees to pesticides through

applications of a fluorescent tracer (sodium

‐ fluorescein) to flowering apple orchards and Phacelia

tanacetifolia (commonly referred to as Phacelia ) fields located in Germany. The mass of tracer present

on bees was measured after foraging in flowering apple orchard (n=9) or Phacelia fields (n=5) for various

time intervals after spray application at 0.018

‐ 0.020 lb a.i./A (0.020

‐ 0.022 kg a.i./ha). Bees were foraging

during spray application, so they could have been directly sprayed with the tracer. For the apple

orchards, the mean

‐ measured amount of tracer per bee ranged 1.62

‐ 20.84 ng a.i./bee for the nine trials,

and an overall average of 6.33 ng a.i./bee. The range of averages is equivalent to 0.079

‐ 1.02 μ g a.i./bee

when normalized to 1 lb a.i./A and the normalized overall average is 0.33 μ g a.i./bee. For the Phacelia

fields, the amount of tracer per bee was higher than those from the apple orchards, with mean

measured values for the 5 trials ranging 6.34 to 35.77 ng/bee, and an overall average of 18.19 ng/bee.

The range of averages is equivalent to 0.31 to 1.75 μ g a.i./bee when normalized to 1 lb a.i./A and the

normalized overall average is 0.89 μ g a.i./bee. The tracer was detected at >2.4 μ g a.i./bee per 1 lb a.i./A

in 3% of individuals foraging in apple orchards and 14% foraging in Phacelia fields. The maximum

concentration reported in the study was 2.7 μ g a.i./bee per 1 lb a.i./A. A detailed description of this

study is provided in Appendix 4

.

3.1.2.2 Alternative method that was considered: T

‐ REX arthropod residue value

The upper

‐ bound pesticide residue value for arthropods used in T

‐ REX is from an analysis completed by

EPA that involved compiling empirical data from the scientific literature and registrant

‐ submitted

studies. This analysis included data from 14 studies that involved 13 pesticides (primarily

organophosphate and carbamate insecticides). These studies measured pesticide residues on various

arthropods, including moth and beetle larvae, crickets, grasshoppers, beetles and unidentified

arthropods captured in treated areas. The use of the arthropod residue value relies upon measured

residues of pesticides in and on arthropods that were located on treated fields at the time the field was

sprayed. It is assumed that the residues are predominantly based on direct spray; however, they mayPage 71 of 275 also incorporate some contact of arthropods with pesticide residues on treated foliage as well as

consumption. A detailed description of the analysis completed by EPA to determine an upper

‐ bound

arthropod residue value for use in T

‐ REX is available in Appendix B of the T

‐ REX user’s guide (USEPA

2012 e ).

The extent to which these data are reflective of residues which are likely to occur in or on honey bees is

uncertain as honey bees would likely be in flight during pesticide applications as opposed to crawling on

soil/leaves. However, the data do represent actual measured residue levels for insects and are expected

to be representative of residues on honey bees and other non

‐ Apis bees that share the same habitats

and behavior. Contact

‐ based toxicity values are expressed on a dose basis ( μ g a.i./bee). The upper

‐

bound arthropod residue concentration of 94 mg a.i./kg can be converted to a dose of 12 μ g a.i./bee

(normalized to 1 lb a.i./A or 1.12 kg a.i./ha) by multiplying the concentration by the average body weight

of an adult worker bee (0.128 g from Mayer and Johansen 1990).

3.1.2.3 Comparison of Koch and Weisser 1997 and T

‐ REX values to empirical data

The two approaches described above can be evaluated to a limited extent using empirically based

measures of pesticides on honey bees foraging in fields sprayed with pesticides. Only two studies have

been identified in the literature where chemical concentrations were quantified on bees after foraging

in treated fields that were treated foliar spray applications (Hanny and Harvey 1982, Delabie et al.

1985). Unfortunately, these authors did not provide maximum residue values; however, they did

present mean residues. These values, which are provided in Table 7

, can be compared to the mean

residue values of the T

‐ REX arthropod residue value and Koch and Weisser 1997.

The mean residue value incorporated into T

‐ REX is an order of magnitude above the mean residue

values reported by Hanny and Harvey 1982 and a factor of 3 higher than the mean from Delabie et al.

1985. The two mean residue values of Koch and Weisser 1997 are higher, but on the same order of

magnitude as the residues reported by Hanny and Harvey 1982 and are lower than the mean reported

by Delabie et al. 1985. Conclusions of the appropriateness of either the T

‐ REX arthropod residue value

or Koch and Weisser 1997 for representing upper

‐ bound contact exposures to bees should be limited

due to the limited amount of empirical data from other sources; however, the available information

suggests that the upper

‐ bound arthropod residue value and the Koch and Weisser 1997 values mayPage 72 of 275 represent reasonable surrogates for estimating exposures to bees directly sprayed with pesticides.

Given that the mean data from Delabie et al

. 1985 is higher than the means from Koch and Weisser

1997 but not greater than the T

‐ REX arthropod residue mean, the T

‐ REX value may provide a more

conservative estimate of exposure.

Table 7. T

‐ REX screen compared to residue data for contact with adult forager bees. Exposure through

direct spray on to forager bees measured within 24 hours of application.

Citation Concentration

(mg/kg

per 1 lb a.i./A) Dose

( μ g a.i./bee per 1 lb

a.i./A) Chemical Crop

Upper

bound Mean Upper

bound Mean

T

‐ REX (arthropod) 94 65 12 8.3 All All

Delabie et al

. 1985 NA 21 NA 2.7 cypermethrin Oilseed rape

Koch and Weisser 1997 19 6.94 2.7 0.89 sodium

‐ fluorescein * Phacelia

Koch and Weisser 1997 19 2.54 2.0 0.33 sodium

‐ fluorescein * Apple

Hanny and Harvey 1982 NA 1.19 NA 0.152 carbaryl ** Corn

Hanny and Harvey 1982 NA 0.36 NA 0.045 carbaryl ** Corn

*This chemical is a fluorescent tracer, not a pesticide.

**Two different experiments were conducted using different formulations. The results of the two formulations are presented separately.

NA = not available

3.1.2.4 Assumptions and uncertainties associated with contact exposure methods for foliar

applications

There are a limited number of studies available to evaluate the two approaches that could potentially

represent the Tier I exposure value for direct spray. Of the two studies that are available, only mean

‐

measured concentrations on bees are reported. Since the Tier I exposure value is intended to represent

an upper

‐ bound exposure, not a mean, the utility of the empirically based means in evaluating the

upper

‐ bound values is limited. In addition, because there are only two studies available for evaluation

purposes, it is unclear how representative these data are related to other locations, crops and field

conditions. It is unknown whether or not the data from these two studies are representative of high

‐ end

exposures that other bees may receive under different conditions.

According to Atkins et al. 1981, when pesticides are applied to crop foliage via spray, the application

rate expressed as lb a.i./A is equivalent to the dose received by bees foraging on the treated field, which

is expressed as μ g a.i./ bee. This conclusion was drawn from comparisons of honey bee mortalitiesPage 73 of 275 observed on treated fields to contact LD 50 values that were generated in the lab for the same chemicals

(N

= 65). In the field studies, pesticides were applied to crops in bloom. According to the Atkins’

conclusions, an application rate of 1 lb a.i./A or 1.12 kg a.i./ha is equivalent to a contact exposure of 1 μ g

ai/ bee. The Atkins’ value appears to be on the same order of magnitude as the measured chemical

residues on bees reported by Koch and Weisser 1997.

3.1.3 Summary of Proposed Tier I Exposure Assessment Method for Foliar Applications

For foliar applications, the proposed method includes two exposure routes: dietary and contact. Table 8

summarizes the initial residue values expressed in units of μ g a.i./bee per 1 lb a.i./A, which are used by

the EPA and in units of μ g a.i./bee per 1 kg a.i./ha, which are used by other countries, including Canada.

As discussed below, these values should be adjusted to account for the application rate of the chemical.

For generating RQs, dietary based exposure values can be compared to oral toxicity data for larvae and

adult worker bees while contact exposure values can be compared to acute contact toxicity data for

adult worker bees.

Table 8. Tier I exposure values used to estimate high end exposures of honey bees to pesticides

applied via foliar applications.

Life

Stage Exposure Type Dose ( μ g a.i./bee

per 1 lb a.i./A) Dose ( μ g a.i./bee

per 1 kg a.i./ha)

Adult Diet (nectar + pollen) 32 29

Larvae Diet (nectar + pollen) 13 12

Adult Direct spray (T

‐ REX) 12 11

Adult Direct spray (Koch and Weisser 1997) 2.7 2.1Page 74 of 275 For dietary exposure, the proposal includes the use of the upper

‐ bound residue for tall grass used in the

T

‐ REX model. T

‐ REX is already used for ecological risk assessments of pesticides (for establishing

screening

‐ level exposures to terrestrial vertebrates). The current proposal is to use the upper

‐ bound

residue for tall grass to represent an upper

‐ bound residue value for pollen and nectar. This value would

then be converted to a dietary dose received by adult and larval worker bees using pollen and nectar

consumption rates for these two life stages, which are 292 and 120 mg/day, respectively ( Appendix 1 ).

Therefore, the dietary based exposure value for larvae is 13 μ g a.i./bee per 1 lb a.i./A and the value for

adults is 32 μ g a.i./bee per 1 lb a.i./A. Estimated exposure values can be scaled to an application rate by

multiplying by the application rate. In practice, the peak exposure value generated by T

‐ REX accounts for

multiple applications and dissipation of the chemical in between applications (based on the foliar

dissipation half

‐ life of the chemical). The T

‐ REX user manual (USEPA 2012) provides a detailed

explanation of how multiple applications and foliar dissipation are accounted for in the final estimate of

exposure.

In order to quantify contact exposures due to direct spray, the proposed upper bound value is 2.7 μ g

a.i./bee per 1 lb a.i./A, based on data published by Koch and Weisser 1997. As with the dietary exposure,

the contact exposure value can be adjusted to account for application rate and multiple applications;

however, if the applications are spaced such that it is not reasonable that the same forager bees would

be exposed during multiple applications ( i.e.

, >5 days 8 ), the direct spray exposure value should be based

on only a single application.

3.1.4 Potential options for characterizing exposure (resulting from foliar applications) using

available data when Tier I screen is failed

For pesticides that are applied via foliar spray and exceed levels of concern for dietary exposures to

bees, it may be possible to refine dietary

‐ based estimates of exposure. This refinement could be

accomplished using pesticide

‐ specific measurements on treated foliage (of crops) taken within 24 hours

of the application on foliage of crops. The following registrant

‐ submitted magnitude of the residue

(MOR) studies may be used for this refinement: crop field trials (OCSPP Guideline 860.1500; USEPA

1996 b ), field rotational crops (OCSPP Guideline 860.1900; USEPA 1996 d ), and irrigated crops (OCSPP

Guideline 860.1400; USEPA 1996 e ). In addition, cropped TFD studies (OCSPP guideline 835.6100; USEPA

8 According to Winston 1987, the lifespan of a forager bee is 4

‐ 5 days.Page 75 of 275 2008) may also be used. Three limitations exist for this refinement option. First, the MOR studies often

do not include measures of pesticide residues on foliage within 24 hours of the application. This is

because the studies are in intended to quantify pesticide residues in food items potentially consumed by

humans, which would typically be harvested more than 24 hours after the application. However, in

situations where residue

‐ decline studies are triggered, day zero data may be available. Second, in order

to rely on these chemical

‐ specific data, a sufficient number of residue values from different studies

would be required to confidently represent the chemical

‐ specific exposure values. Third, this refinement

applies only to dietary

‐ based exposures for bees. If the Tier I methods for both dietary and contact

exposures described above yield exposure values that are of concern, then the risk conclusions would

not change due to refinement of the dietary

‐ based exposure alone. There is no proposed method for

refining exposure estimates for contact.

A potential application of MOR data is explored using MOR data for imidacloprid and unnamed chemical

1, respectively. Table 9 and Table 10 contain Day 0 residues values in various crops for imidacloprid and

unnamed chemical 1, respectively. Compared to the T

‐ REX screening

‐ residue value for tall grass (110

mg/kg per 1 lb a.i./A), the highest residue value for unnamed chemical 1 is within a factor of 2 and the

highest residue for imidacloprid is less than a factor of 2. Importantly, a substantial amount of

variability in day 0 residues is seen for unnamed chemical 1, both within a crop matrix (but across

different field trials) and among crop matrices. Specifically, up to a 4

‐ fold range in day 0 residues can be

seen for wheat (forage: 6.8

‐ 27.6 mg ai/kg per lb ai/A across two trials in Brazil) and canola (forage: 11.6

‐

42.4 mg ai/kg per lb ai/A across three trials in Australia 

### ). The source of this variability is not known, but

may reflect subtle differences in pesticide application techniques, climate, crop conditions, and chemical

measurements among different residue trials. For these reasons, sufficient day 0 magnitude of residue

data on foliage should be available to characterize this variability before such data is used to refine the

Tier 1 T

‐ REX foliar residue estimates.Page 76 of 275 Table 9. Day 0 residue values for imidacloprid (and degradates), and normalized residues to 1 lb a.i./A

Total

Application

Rate

(lb a.i./A) Day 0

Residues

(ppm) Crop Matrix MOR Day 0 Residues

normalized to 1 lb Total

Application Rate

(mg/kg per 1 lb a.i./A) Country Citation

0.275 24 Soybean Hay 87.27 USA MRID 46785002

0.463 0.19 Succulent beans Lima beans 0.41 USA MRID 45046401

Table 10. Unnamed chemical 1 Day 0 residue values, and normalized residues to 1 lb a.i./A

Total

Application

Rate*

(lb a.i./A) Day 0

Residues*

*

(ppm) Crop Matrix MOR Day 0 Residues

normalized to 1 lb Total

Application Rate

(mg/kg per 1 lb a.i./A) Country Citation

0.086 4.3

Barley Straw 49.9

Australia

Study 1

‐ 4 3.3 Forage 38.3

0.085 1.3 Straw 15.3 New

Zealand 1.1 Forage 13.0

0.086

‐

0.087 1.0

‐ 3.7 Canola Forage 11.6

‐ 42.4 (n=3) Australia Study 1

‐ 5

0.084

‐

0.087 1.8

‐ 2.4

Wheat Straw 20.8

‐ 27.7 (n=2)

Australia

Study 1

‐ 6 1.6 Forage 18.5

0.091

‐

0.093 2.0

‐ 3.1 Hay 22.4

‐ 33.5 (n=2)

Brazil

0.085

‐

0.094 0.6

‐ 2.6 Forage 6.8

‐ 27.6 (n=2)

0.087 1.8 Straw 20.8 New

Zealand

* sum of two applications

** residue samples collected on day of second application

In addition to comparing day 0 MOR data to the T

‐ REX foliar application Tier 1 screening value, it is also

instructive to compare MOR data to available data on residues in pollen and nectar for the same

chemical ( Table 11 ). When the MOR Day 0 studies are compared to available pollen and nectar data for

unnamed chemical 1, the highest Day 0 MOR residue (49.9 mg ai/kg per lb ai/A) is slightly higher than

the highest measured concentration of the chemical in pollen (31.3 mg ai/kg per lb ai/A), suggesting that

foliar residues may be a reasonable surrogate for residues in pollen (at least on day 0). While the higher

day 0 residue values measured in the MOR studies exceed residues measured in pollen for unnamed

chemical 1, many MOR day 0 values are below the maximum residue measured in pollen. Again, this

indicates the importance of obtaining sufficient MOR data to characterize the variability in residue

values among different crops, plant matrices and fields. Otherwise, the available MOR Day 0 value may

not be protective for pollen. When compared to the highest concentration of unnamed chemical 1 in

nectar (2.2 mg ai/kg per lb ai/A), the MOR Day 0 values are all above the day 0 residue value for nectarPage 77 of 275 (overall range: 6.8

‐ 49.9 mg ai/kg per lb ai/A). This suggests that MOR day 0 residue data may be more

conservative (i.e., more protective) in relation to residues in nectar compared to pollen, at least for

unnamed chemical 1.

Table 11. Unnamed chemical 1 pollen and nectar data compared to magnitude of residue Day 0

residues.

Citation Crop Matrix Concentration

(mg/kg

‐ plant per 1 lb a.i./A)

Study 1

‐ 1 Cotton

– pollen 1 4.2

‐ 31.3

Cotton

‐ nectar 1 <0.22

‐ 0.83

Phacelia –pollen 2 13.5

‐ 18.9

Study 1

‐ 2 Phacelia

– nectar 2 2.1

‐ 2.2

Study 1

‐ 6 Wheat

‐ forage 6.8

Study 1

‐ 4 Barley

‐ straw 49.9

1 values represent range in maximum measured residues in forager

‐ collected pollen and nectar on day 0 from 4 tunnel trials with cotton.

2 values represent range in maximum measured residues in pollen collected from traps and forager

‐ collected nectar from 2 tunnel trials with

Phacelia

.

In summary, this example indicates that day 0 residue measurements from MOR studies have the

capacity to provide some additional refinement of the Tier 1 pollen and nectar screening value from T

‐

REX described earlier. However, use of day 0 MOR data is subject to uncertainty related to the limited

available data from which to characterize variability in foliar residues among crops. Furthermore, use of

chemical

‐ specific foliar residue data still does not address uncertainties when extrapolating foliar

residues to those expected in pollen and nectar.Page 78 of 275 3.2 Quantifying Exposure Resulting from Soil Applications

For soil treatments, it is assumed that bees will be exposed via dietary consumption of pollen and nectar

that are contaminated as a result of systemic transport of pesticides from soil. For these application

types, it is assumed that honey bees will not be directly sprayed because they are not expected to be

present on the surface of the soil. The proposed method for estimating dietary exposures to bees

resulting from soil treatments is based on an empirically based model developed by Briggs et al

. 1982

and 1983, with modifications (referred to in this white paper as “the Briggs’ Model”). This model relates

the Log K

ow of a chemical to its concentration in plant shoots, which (like the T

‐ REX approach for foliar

spray applications) can be used as a surrogate for concentrations in nectar and in pollen. In comparison

to the dietary

‐ based exposure values proposed for foliar spray applications ( i.e.

, based on the tall grass

upper

‐ bound value), the Briggs’ model generates exposure values that are two orders of magnitude

lower. Also discussed in the section below is another method that was considered in the development of

the Tier I exposure method for soil applications: the EPPO screening value of 1 mg a.i./kg in pollen and

nectar of treated crops. EPPO’s screening value is based on data compiled by Alix et al. 2009, including

pesticide residues measured in different plant parts (leaves, fruit, green part, inflorescence, whole plant,

and grain) following applications to soil or seed treatments. Evaluations of both the Briggs’ Model and

the 1 mg a.i./kg screening value are described and evaluated below. These approaches are evaluated

using empirical measurements of pesticides in pollen and nectar of crops that received soil treatments

of the pesticides. These data are provided in Table 12 and summaries of the studies used to generate

the empirical data are provided for reference purposes in Appendices 3 and 4

.Page79of275Table12.Measuredconcentrationsofpesticidesinpollenandnectarofcropstreatedwithpesticidesviasoilapplications.Concentrationunitsareunnormalized(i.e.,expressedasmga.i./kg)andnormalizedtoapplicationrate(i.e.,expressedasmga.i./kgper1lba.i./A).ChemicalCitationCropApplicationrate(lba.i./A)Concentration(mga.i./kg)NormalizedConcentration(mga.i./kgper1lba.i./A)NectarPollenNectarPollenDimethoateLordetal.1968Fuchsia174.82NA0.283NADimethoateLordetal.1968Nasturtium171.26NA0.0726NADinotefruan*DivelyandKamel2012Pumpkin0.2700.01540.08120.0570.301Dinotefruan*DivelyandKamel2012Pumpkin0.2700.01090.02690.0400.100ImidaclopridStonerandEitzer2012Squash0.3440.0140.0280.0410.081Imidacloprid*BeedleandHarbin2011Cotton0.0630.066NA1.1NAImidacloprid*FreesemanandHarbin2011Tomato0.25NA0.054NA0.22Imidacloprid*DivelyandKamel2012Pumpkin0.0270.00070.00940.0260.351Imidacloprid*DivelyandKamel2012Pumpkin0.2510.01130.05670.0450.226Imidacloprid*DivelyandKamel2012Pumpkin0.3770.01780.1090.0470.288Imidacloprid*DivelyandKamel2012Pumpkin0.3770.02310.1290.0610.341Imidacloprid*DivelyandKamel2012Pumpkin0.027NDNDNDNDImidacloprid*DivelyandKamel2012Pumpkin0.2510.00670.02390.0270.0953Imidacloprid*DivelyandKamel2012Pumpkin0.3770.0160.04400.0420.117OxamylDivelyandKamel2012Pumpkin0.125NDNDNDNDOxamylDivelyandKamel2012Pumpkin0.125NDNDNDNDThiamethoxamStonerandEitzer2012Squash0.1270.0200.0140.160.11Thiamethoxam*DivelyandKamel2012Pumpkin0.1710.01860.1320.1090.768Thiamethoxam*DivelyandKamel2012Pumpkin0.1710.01510.04200.0880.245Unnamedchem.#2Study2‐4Melon0.0890.0260.0120.290.14NA=notavailableND=notdetected*representstotalresidues(i.e.,parentanddegradates)Page 80 of 275 3.2.1 Proposed method for estimating exposures from soil applications: Briggs’ Model

3.2.1.1 Method Description and Evaluation

In 2006, the United Kingdom’s Environment Agency reviewed several models that predict chemical

concentrations in plants that result from uptake from soil (Environment Agency 2006). Models that were

reviewed included those published by Chiou et al

. 2001, Hung and MacKay 1997, Briggs et al

. 1982 and

1983 with additions by Ryan et al

. 1988, Topp et al

. 1986, Trapp and Matthies 1995 and Travis and Arms

1988. This review involved comparisons of model

‐ estimated concentrations of chemicals to five sets of

empirical data from different studies. These datasets included 13 chemicals with the Log K

ow values

ranging 0.18

‐ 6.5. Of the six models that were evaluated, the model by Briggs et al

. 1982 and 1983 with

modification by Ryan et al. 1988 estimated chemical concentrations in shoots that were closest to

empirical data, with estimated values being more conservative, and yet within an order of magnitude of

empirical values. The model by Briggs et al

. 1982 and 1983 is also of interest because it is incorporated

into the Pesticide Root Zone Model (PRZM; Carosusel et al

., 2006). Although PRZM is used in this paper

to estimate pesticide runoff from treated fields, the plant uptake portion of the model is not

incorporated into the calculations. The model developed by Briggs et al

. 1982 andd 1983 and as revised

by Ryan et al

. 1988 is considered further as a potential Tier I screening method for estimating pesticide

exposures to honey bees feeding upon crops where the soil was treated with pesticides.

The Briggs’ model is based on an empirical relationship between measured K

ow values and stem

concentration factors (SCFs). The SCFs are ratios of concentrations in plant stems ( i.e.

, shoots) to

measured concentrations in the aqueous medium from which plant roots uptake the chemical. They are

calculated using empirically based transpiration stream concentration factors (TSCFs), which are a ratio

of the concentration of the chemical in the xylem to the concentration in the aqueous medium. The

relationship between K

ow and TSCF, was derived using data for 17 pesticides, including carbamate

insecticides and phenylurea herbicides with Log K

ow values ranging from

‐ 0.57 to 4.6. All of the pesticides

included in the dataset used to develop the Briggs’ model were non

‐ ionic organic chemicals. The

regression is based on empirically based K

ow values and measures of concentrations in steams and

aqueous medium ( Equation 1 ).Page 81 of 275 Equation 1

ܵܨܥ ൌ ൣ10 ሺ ଴.ଽହכ௢௄௚௢௅௪ିଶ.଴ହ ሻ ൅ 0.82൧ כ ܶܵܨܥ

Where: ܶܵܨܥ ൌ ൣ0.784 כ 10 ି଴.ସଷସכሺ௅௢௚௄௢௪ିଵ.଻଼ሻ మ /ଶ.ସସ ൧

The Briggs’ approach was modified by Ryan et al

. 1988 by adding an equilibrium partitioning component

that allows for estimation of pesticide concentrations from soil ( Equation 2 ), rather than aqueous

medium as was used by Briggs. Equation 2

, which is the Briggs’ model with the modification by Ryan et

al. 1988, can be rearranged to estimate the pesticide concentration in plant shoots using three input

parameters: K

ow , K

oc and application rate. Note that if K

oc is not available or appropriate for a chemical,

the K d can be used by replacing the following portion of Equation 2: K

oc * f

oc .

Equation 2

ܵܨܥ ᇱ

ൌ ܥ ௦௧௘௠

ܥ

௟௜௢௦ ൌ ൣ10 ሺ ଴.ଽହכ௢௄௚௢௅௪ିଶ.଴ହ ሻ ൅ 0.82൧ כ ܶܵܨܥ כ ൤ ߩ

ܿ݋݂ כ ܿ݋ܭ כ ߩ ൅ ߠ ൨

Where:

Where: SCF’

= stem concentration factor based on uptake from soil

C

stem = concentration in stems ( μ g a.i./g plant)

C soil = concentration in soil ( μ g a.i./g soil)

f

oc = fraction of organic carbon in soil

θ

= soil

‐ water content by volume (cm 3 /cm 3 )

ρ = soil bulk density (g

‐ dw/cm 3 )

K

oc = soil organic carbon

‐ water partitioning coefficient (cm 3 /g

‐ oc or L/kg

‐ oc)

The Briggs’ model with the modifications by Ryan et al

. 1988 is evaluated here by comparing estimated

concentrations in stems to empirical data available for pesticide concentrations in nectar and pollen in

studies where pesticides were applied to soil and transported into these matrices. Data are available for

five chemicals, including dimethoate (Lord et al

. 1968), dinotefuran, imidacloprid, thiamethoxam (Dively

and Kamel 2012), and unnamed chemical #2 ( Table 13 ). For these chemicals, K

ow and K

oc data were

collected from registrant

‐ submitted data. For the parameters in Equation 2 that define the properties of

the soil, conservative values were chosen to maximize the concentration in soil pore water and thus

maximize the amount of chemical available for uptake into stems. Values were chosen to be consistent

with standard scenarios used to run PRZM. For the fraction of organic carbon in soil (f

oc ), a value of 0.01

is used. A value of 1.5 g

‐ dw/cm 3 is selected to represent bulk density ( ρ ). Soil water content ( θ ) is set to

0.2 cm 3 /cm 3

. Equation 2 can be used to calculate the concentration of a chemical in stems using thePage 82 of 275 parameter values above and the application rate. For consistency among the reviewed methods, 1 lb

a.i./A (1.12 kg a.i./ha) is used as an application rate. If it is assumed that this application rate is

homogenously distributed throughout the top 6 inches (15 cm) of the treated soil (based on the

assumption that the majority of the pesticide will remain in this portion of the soil), this rate is

equivalent to 0.50 μ g a.i./g

‐ soil (using the bulk density).

The resulting estimated values of pesticides are generally within an order of magnitude of empirical data

of the same pesticides in nectar and pollen ( Table 13 ). This indicates that the Briggs’ model with

modifications by Ryan et al

. 1988 may provide reasonable estimates of pesticides in nectar and pollen

due to systemic transport after applications to soil. For the Tier I screen, which is intended to be

conservative, this approach is limited because the estimated values are not always higher than the

empirical data. This is not surprising since the relationship between K

ow and SCF that was derived by

Briggs et al

. 1982 and 1983 is based on a median estimate of the SCF value, not an upper bound.

Table 13. Comparison of estimated concentrations of pesticides in plant stems (based on Equation 2)

and empirical data measurements in nectar and pollen. Concentration units are mg a.i./kg

‐ plant

matrix normalized to 1 lb a.i./A.

Chemical Log K

ow K

oc Estimated

concentration

in stems (mg

a.i./kg per 1 lb

a.i./A) Empirical concentrations (mg a.i./kg per 1 lb

a.i./A)

Nectar Pollen

Dimethoate 0.78 30 0.52 0.0726

‐ 0.283 NA

Dinotefruan*

‐ 0.55 6 0.18 0.040

‐ 0.057 0.100

‐ 0.301

Imidacloprid* 0.57 178 0.10 0.026

‐ 1.1 0.081

‐ 0.351

Thiamethoxam*

‐ 0.13 33.1 0.16 0.088

‐ 0.16 0.11

‐ 0.768

Unnamed chem. #2 1.94 241 0.039 0.29 0.14

*Empirical data include parent and degradates.

**Residues measured in tomato anthers

NA = not availablePage 83 of 275 In order to use of the Briggs’ model for deriving more conservative estimates of concentrations of

pesticides in plants, the original relationship between TSCF and Log K

ow was recalculated. This was done

using the SAS® version 9.2 (SAS Institute, Cary, NC) nonlinear regression procedure (PROC NLIN) with the

Gauss method of optimization to solve for the three unknown parameters relevant to the equation. This

method differs from the original approach reported by Briggs et al

. 1982 and 1983 in that they did not

use the non

‐ linear regression method of analysis. In addition, the recalculated relationship includes all of

the TSCF values reported by Briggs et al

. 1982 and 1983 while the original equation excluded one value

that was assumed by the authors to be an outlier. In addition to the mean estimates of TSCF values, the

current analysis involved the calculation of the 95 th percentile upper

‐ bound estimates of TSCF values

( Figure 9 ). 9 The median and 95 th percentile estimated TSCFs (based on Log K

ow ) are provided in Appendix

5

. The 95 th percentile TSCF values can be used in Equation 2 to calculate upper

‐ bound estimates of

pesticide concentrations in stems. The resulting estimates are conservative and are generally within an

order of magnitude of empirical estimates of pesticides in nectar and pollen ( Table 14).

Figure 9. Median and 95 th percentile upper

‐ bound TSCF data fit to Briggs et al. (1982 and 1983) empirically

‐ based

TSCF data for 18 pesticides.

9 It should be noted that in the original TSCF equation reported by Briggs et al

. (1982) was based on n = 17 because one

“outlier” was excluded. The recalculated median and 95 th percentile TSCF values are based on all of the empirical data reported

by Briggs et al

. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

‐ 10 1 2 3 4 5 Median TSCF

95th percentile TSCF

Empirical data

Log KowTSCFPage 84 of 275 Table 14. Comparison of estimated concentrations of pesticides in plant stems (based on Equation 2

and 95 th percentile TSCF) and empirical data measurements in nectar and pollen. Concentration units

are mg a.i./kg

‐ plant matrix normalized to 1 lb a.i./A (1.12 kg a.i./ha).

Chemical Log K

ow K

oc Upper

‐ bound

TSCF Estimated

concentration

in stems (mg

a.i./kg per 1 lb

a.i./A) Empirical concentrations

(mg a.i./kg per 1 lb a.i./A)

Nectar Pollen

Dimethoate 0.78 30 0.726615 1.46 0.0726

‐ 0.283 NA

Dinotefuran*

‐ 0.55 6 0.417797 1.78 0.040

‐ 0.057 0.100

‐

0.301

Imidacloprid* 0.57 178 0.67502 0.30 0.026

‐ 1.1 0.081

‐

0.351

Thiamethoxam*

‐ 0.13 33.1 0.503762 0.90 0.088

‐ 0.16 0.11

‐ 0.768

Unnamed chem. #2 1.94 241 0.875448 0.026 0.29 0.14

*Empirical data include parent and degradates.

NA = not available

3.2.1.2 Assumptions and Uncertainties

There are five notable limitations to using the modified Briggs’ model approach. The first is that this

methodology is based on empirical data from only one type of plant. Uptake may be different for

different types of plants (Ryan et al

. 1988); however, the species used by Briggs et al. 1982 ( i.e., barley)

is representative of a crop species that is likely grown on soils where pesticides are applied.

The second limitation is that the data set used to derive Equation 1 is based on a limited number of

chemicals that represent only two classes of pesticides. Despite this limitation, the pesticides that were

used in this approach are systemic and cover a wide range of Log K

ow values ( i.e., range is

‐ 0.57

‐ 4.6),

leading to an approach that is applicable to a wide range of pesticides with the potential to be

systemically transported by plants.

The third limitation is that this approach is based on data from non

‐ ionic organic chemicals and may

have limited utility for ionic chemicals that whose transport may not be predicted well using K

ow and K

oc .

Generally, compounds that are lipophilic and non

‐ polar will cross plant cell membranes more readily

than charged compounds do. Nevertheless, charged compounds may enter a cell via active transport,

which requires energy, or passive transport, which is a diffusive process (Trapp 2004). Thus, the ion

trapping effect of weak electrolytes ( i.e.

, most pesticides, particularly herbicides) leads to movement

through the phloem and equations used to model this process are based on two primary concepts: weakPage 85 of 275 acid and intermediate permeability theories (Trapp 2004) which are represented in the model by acid

strength (pKa) and polarity/lipophilicity (K

ow ), respectively (Bromilow et al. 1990).

The fourth limitation of the Briggs’ model is that it is based on passive transport of chemicals into xylem

of barley. This approach does not directly estimate pesticide concentrations in plants that are the result

of phloem transport. One possible assumption that could be made in applying this model for estimating

pesticide concentrations in plants would be to assume that chemical transport via phloem is equivalent

to that of transport via xylem. An alternative approach would be to use different models for neutral and

ionic compounds, such as those described by Briggs et al. 1987, Kleier 1988 and Trapp et al. 1995.

The fifth limitation involves the use of estimated pesticide concentrations in vegetative plant matrix ( i.e.

,

shoots) as a surrogate for nectar and pollen. At this point, the relative concentrations of a chemical in

pollen and nectar to plant shoots is unknown; however, estimated concentrations of a select number of

pesticides in shoots appear to be protective of values in pollen and nectar ( Table 14 ). Data reported by

Dively and Kamel 2012 indicate that for soil drench, transplant dip and drip applications of imidacloprid,

dinotefuran and thiamethoxam, pesticide (plus degradate) concentrations in foliage were higher but on

the same order of magnitude compared to concentrations in pollen and an order of magnitude higher

than concentrations in nectar than concentrations in nectar.

3.2.2 

### Alternative method that was considered : EPPO’s 1 ppm default value

3.2.2.1 Method Description and Evaluation

In the screening

‐ level pollinator exposure assessment for pesticides applied via soil treatments

described by EPPO, if pesticide

‐ specific residues in pollen and nectar are not available, a screening value

of 1 mg ai/kg plant matrix is the assumed exposure (EPPO, 2010). This screening value was considered

when developing the proposed method to represent a Tier I exposure value for soil applications. EPPO’s

screening value of 1 mg a.i./kg is based on the maximum value from data compiled by Alix et al. 2009,

including pesticide residues measured in different plant parts (leaves, fruit, green part, inflorescence,

whole plant, and grain) following applications to soil or seed treatments.

When the 1 mg a.i./kg default value is compared to maximum residue values from available studies

where pesticides were applied to soil, all residue values for pollen are below the screening value ( Table

12 ). There is one residue value (from Lord et al

. 1968) for nectar that exceeds the screen by a factor ofPage 86 of 275 almost 5 ( Table 12 ); however, this value was from a study where the application rate of dimethoate was

equivalent to 17 lb a.i./A, which is much higher than currently registered application rates of this

chemical (and most other insecticides) in North America. With the exception of the dimethoate data, all

of the measured concentrations of pesticides (values not normalized) in pollen and nectar of plants

(receiving soil applications) are 2 orders of magnitude below the 1 mg a.i./kg screening value. Based on

this information, it appears that the 1 mg a.i./kg default value is a reasonably conservative estimate of

exposure for pollen and nectar of crops treated with pesticides from soil applications.

3.2.2.2 Assumptions and Uncertainties

The proposed use of the EPPO screening

‐ level exposure value of 1 mg a.i./kg for pollen and nectar of

seed treated crops does not account for differences in application rate. There is uncertainty in not

accounting for the mass of the chemical applied since it is expected that the magnitude of the pesticide

concentration in the plant will be influenced by the magnitude of the application. This is illustrated by

the dimethoate data published by Lord et al

. (1968), where a high application rate ( i.e.

, 17 lb a.i./A)

resulted in concentrations in nectar that exceeded 1 mg a.i./kg. Although this application rate is not

relevant to current registrations for dimethoate or most insecticides, some pesticides are currently

registered for soil applications at similar rates. In addition, this approach does not account for physical

‐

chemical properties of a pesticide that may influence its potential systemic transport throughout the

plant and into the pollen and nectar. For example, a chemical with low mobility (high K

oc , high K

ow ) may

be expected have lower concentrations in nectar when compared to a chemical with high mobility ( i.e.

,

low K

oc , low K

ow ).Page 87 of 275 3.2.3 Summary of Tier I Method for Soil Applications

Despite the limitations discussed above, both the EPPO 1 mg a.i./kg screening and the Briggs’ model

modified by Ryan et al

. 1988 and with the further modifications proposed in this white paper ( i.e., with

95 th percentile TSCF values) appear to be useful tools for generating reasonably conservative estimates

of pesticide concentrations in plants after systemic transport from soil. These approaches are designed

to estimate pesticide concentrations in plants after translocation from soil; therefore, they could

potentially be used for pesticides that are applied directly to the soil. The approaches are consistent

with a screening

‐ level assessment because they are both simple and efficient, and are empirically based

on data for pesticides. The Briggs’ model has the advantage that it relies upon some basis physical

‐

chemical properties of pesticides and also accounts for the mass applied to the soil; therefore, the

Briggs’ model is proposed for estimating pesticide concentrations in pollen and nectar of crops as a

result of systemic transport of pesticides following applications to soil.

In using the Briggs’ model, the proposed approach is to use Equation 2

, with the 95 th percentile TSCF

value that is specific to an assessed pesticide’s Log K

ow ( Appendix 5 ). It is assumed that the resulting

value is equivalent to pesticide concentrations in pollen and nectar of crops receiving soil treatments of

the pesticide. The estimated concentration in pollen and nectar would be converted to dietary

‐ based

exposures for adult and larval bees using the consumption rates for pollen and nectar as discussed in

Appendix 1 ( i.e., 292 and 120 mg/day, respectively).

In the Tier I approach, it is assumed that all chemicals may be systemically transported. This assumption

may be limited based on Log K

ow (Ryan et al

. 1988). Therefore, it could be limited to the bounds of the

Log K

ow values used in the empirical data set generated by the Briggs’ model ( i.e.

, for chemicals with Log

K

ow <5). Whether a chemical is transported systemically in plants could potentially be confirmed using

empirical data submitted to EPA ( e.g., plant metabolism studies); however, it would be up to pesticide

registrants to submit sufficient data to demonstrate that a pesticide is not systemic.Page 88 of 275 3.3 Potential options for characterizing exposure using available data when Soil Treatment Tier I

screen is failed

In the development of the Tier I method for assessing honey bee dietary exposures to pesticides

resulting from soil applications, the potential utility of empirical data that are required by the EPA for

pesticide registrations was explored. Five sets of data were investigated, including: terrestrial field

dissipation (TFD) studies with cropped fields (OCSPP guideline 835.6100; USEPA 2008), crop field trials

(OCSPP guideline 860.1500; USEPA 1996 a ), nature of the residue in plants (OSCP Guideline 860.1300;

USEPA 1996 b ) and confined as well as field accumulation in rotational crop studies, i.e

., OCSPP guideline

860.1850 (USEPA 1996 c ) and OSCP Guideline 860.1900 (USEPA 1996 d ). A basic description of each of

these studies is provided below along with their limitations for use in the Tier I screen. In summary,

none of these studies are specifically designed to measure high

‐ end pesticide concentrations in pollen

and nectar at times when bees may be exposed. With some modification to the study designs, some of

these studies may be used to generate chemical

‐ specific measures of pesticides in nectar and pollen.

This is a potential consideration for refined exposure assessments for chemicals that do not pass the Tier

I screen. Although it was determined that these studies are not appropriate for development of the

exposure method for the Tier I screen, some of these studies may be useful for characterization of

potential exposures for pesticides that fail the screen. A discussion of the potential utility of these

studies for characterizing exposures is provided below.

3.3.1 Terrestrial Field Dissipation Studies for a Cropped Field

TFD studies are required for pesticide active ingredients with outdoor use patterns. The purpose of a

TFD study is to provide data on the dissipation of a pesticide under actual use patterns which are

expected to account for multiple transport ( e.g., volatilization, leaching) and degradation processes

( e.g., metabolism, hydrolysis). TFD studies report the amounts of the applied pesticide active ingredient

(and its degradates) in samples collected from different environmental compartments ( e.g., soil, air and

vegetation) over time. For the most part, submitted TFD studies involve bare ground applications of

pesticides ( i.e.

, no crop is present on the study site); therefore, the existing studies that have been

submitted to support pesticide registrations are not useful for estimating pesticide concentrations in

plants as a result of systemic transport from soil. The existing guideline (OCSPP 835.6100; USEPA 2008)

does include guidance for registrants to conduct the study with a standing crop if it is believe thatPage 89 of 275 uptake may be an important route of removal from the field. However, the sampling design for the

cropped field studies does not provide an adequate upper

‐ bound estimate of pesticide exposure.

For example, one of the few TFD studies submitted that was conducted using unnamed chemical #1

applied as three foliar sprays to spinach measured residues in whole spinach plants 8, 11, and 14 days

after the initial application (3 days after 2 nd application, and 3 days after 3 rd application). Residues were

also measured in two rotational crops with a 77

‐ day plant

‐ back (cantaloupe) and a 245

‐ day plant

‐ back

(wheat). Although these data may provide some information about the relative persistence of a

chemical and provide information on the systemic nature of a chemical, they are not necessarily

designed to obtain upper

‐ bound estimates of pesticide concentrations in plants. In the future, TFD

studies could be designed on a chemical

‐ by

‐ chemical basis to provide empirical measurements of

pesticide residues in plant foliage (that can be used as a surrogate for nectar and pollen) or designed to

directly measure pesticide residues in pollen and nectar. Specifically, the TFD study is conducted for at

least 365 days (one year), where for regions that are productive year round ( e.g.

, California) multiple

crops in rotation (see crop rotational studies below) could be studied and residues measured in plant

matrices relevant for bee exposure.

3.3.1.1 Crop Field Trials (magnitude of residue studies)

Crop field trials, which are also referred to as magnitude of residue (MOR) studies, measure residue

levels in or on raw agricultural commodities (RACs; e.g., fruit, lettuce leaves, grain) relative to actual

application methodologies and practices in the field. The pesticide is applied under conditions expected

to result in the highest residues on the RACs, and sampling techniques are representative of field

growing and harvesting conditions for the crop, and crops are harvested at commercial maturity of the

RAC. The residues are used to evaluate the potential dietary exposure for human health risk

assessments, and to set tolerances for the chemical on different crops. For the Tier I exposure

assessment method, there are several limitations that prevent the use of empirical data from MOR

studies. The sampling methodologies that are used the residues estimated for the RACs from the crop

field trials do not necessarily generate conservative exposure values for dietary items relevant to bees;

however, this is not a defect of the study design for its intended purpose, which is to derive

measurements of pesticide concentrations that may be present on food items for human consumption

collected from fields treated with pesticides. Measurements are taken days, weeks, and sometimes

months (for soil treatments) after application; therefore, the amount of pesticide residue has decreasedPage 90 of 275 over time ( i.e., lost to degradation to breakdown products), and may not be representative of the

residues during time of bloom when flowers are present and honey bees may be foraging on the crop.

3.3.1.2 Nature of Residue in Plants (Plant metabolism studies)

The plant metabolism studies are required whenever a pesticide use is determined to be a food use in

order to quantify the metabolic fate of the parent active ingredient using radioactive compounds. These

studies are intended to characterize the distribution of pesticide residues (including parent and

degradates) in treated crops. Metabolism studies are versatile in that residues may be estimated from

portions of the plant considered edible to humans ( e.g., lettuce leaves, apple fruits, tomato pulp and

peel), as well as inedible portions of the plant such as cotton bolls, apple tree leaves, tomato stems, and

roots. Despite this versatility, most studies are designed to capture pre

‐ harvest applications in order to

measure residues in fruit and mature plant parts relevant to human consumption through diet.

Therefore, the sampling strategy ( i.e.

, the timing of sampling) of this study is not designed to generate

conservative exposure values at times when residues in plants may be highest for bees and on dietary

items that are relevant to bees.Page 91 of 275 3.3.1.3 Accumulation in Rotational Crops

Submitted studies on pesticide accumulation in rotational crops, both confined accumulation and field

accumulation (OSCP Guideline 860.1500; USEPA 1996 a ) are conditionally required when a pesticide is

proposed for rotational crop use ( e.g., any field vegetable crop or when it is reasonably foreseeable that

any food or feed crop may be planted after harvest of the treated crop). These studies are designed to

determine the nature and amount of pesticide residues that are taken up by rotational food crops. A

confined rotational crop study is first conducted using small plots and radiolabeled pure active

ingredient. Based on the results of the confined study, a field rotational crop study may be required. The

field rotational crop study is conducted using the end

‐ use product and is designed to determine

whether residues occur in rotational crops grown under actual field conditions. Both rotational crop

studies are conducted on food crops that belong to three rotated crop categories: 1) small grain 2) leafy

vegetable or soybean; and, 3) root crop. Following application of the pesticide to bare soil, untreated

rotational crops are planted following three soil aging intervals (30, 120, and 365 days), or plant

‐ back

intervals (PBI) 10

. Residues are quantified plant tissues considered relevant to human and livestock

consumption. Based on the results from the rotational crop studies, the Agency may establish

appropriate crop rotation restrictions (time from application to planting of rotational crop) and

determine the need for tolerances on the rotated crops.

Although the rotational crop studies are designed to address human health risk assessment needs, the

Agency believes these studies also have utility in assessing risks to insect pollinators (honeybees).

Specifically, results from rotational crop studies can be used in assessing the extent to which a pesticide

is systemic. Whether a compound is systemic is an important consideration in assessing the potential of

pesticide to accumulate in pollen and nectar. Although in many cases the systemic nature of a pesticide

is known based on the nature of its proposed uses, its chemical class, or other information submitted by

the registrant; in other cases, direct knowledge of whether a compound is systemic may not be known.

Because the pesticide in question is applied to bare soil prior to planting the rotated crops, the residues

quantified in plant tissue are an indication of systemically

‐ transported chemical, since chemical

transport from the soil into the plant is required. In addition to the magnitude of the parent pesticide,

the amount of pesticide metabolites is also quantified by these studies, which is equally relevant to

pollinator risk assessment. An illustration of results from confined rotational crop studies is provided in

10 The PBIs reflect various potential rotational crop circumstances, such as crop failure (30 days), immediate crop rotation

following primary crop harvest (120 days) and annual crop rotation (365 days).Page 92 of 275 Table 15 and Table 16 for two pesticides: clothianidin and unnamed “chemical 1”. Had the systemic

nature of these pesticides not already been known, such information could have been considered by the

Agency as an indication of systemic transport in plants and potential transport to pollen and nectar.

Another way in which results from rotational crop studies can be used in pollinator risk assessment

pertains to the extent that seasonal pesticide “carry over” becomes an important consideration in

exposure assessment. If pesticide residues of concern are found to accumulate in rotated crops at

toxicologically

‐ relevant levels, then the pollinator exposure assessment may need to account for

pesticide accumulation by plants (pollen and nectar) over multiple growing seasons. In these situations,

adjustment of exposure model input parameters may be required to account for prior accumulated

pesticide residues and field study designs may need to be altered to include multiple seasons of

pesticide application. Data presented in Table 15 indicate that soil

‐ applied clothianidin can persist and

accumulate in rotational food crops for periods of one year, although residues decline over time and the

lowest residues are found in seed (grain). For unnamed “chemical 1,” rotational crop data indicate that

the parent chemical may persist and accumulation in some portions of rotational crops, but the primary

metabolite persists and accumulates in rotational crops at higher levels ( Table 16 ). Importantly, in order

to evaluate the need to address multi

‐ season carryover in the context of pollinator assessment for

either of these two chemicals, residues would need to be considered in the context of potential

exposure and toxicity to pollinators.

Although data from rotational crop studies can have utility in pollinator risk assessment, a number of

limitations are apparent which require careful consideration. First, these studies do not quantify

residues in pollen and nectar which are the primary routes of oral exposure to honeybees. Thus, there is

considerable uncertainty in the extent to which residues in various plant tissues can be used to

represent potential residue in pollen and nectar. Second, in terms of evaluating the extent to which a

compound is systemic, the shortest PBI used in typical rotational crop studies is 30 days. Therefore, the

extent to which less persistent pesticides ( e.g.

, soil half

‐ life <10 days) are systemic in plants may not be

readily identified. It is further noted that results from rotational crop studies are likely to be most

applicable to pesticides which are applied via soil application, as the nature of systemic transport in

plants can depend on the application method (foliar, soil, seed) and point of contact with the plant

(roots, stem, leaves).Page 93 of 275 Table 15. Sample parent compound residue data for a confined rotational study (860.1850, MRID

45422618) using soluble concentrate formulation of clothianidin. A single application of 0.293 lbs

a.i./A, applied directly to bare soil prior to planting rotation.

Plant back interval (days) Turnip

Swiss Chard Wheat

Top Root Forage Hay Straw Grain

29 0.114 0.006 0.054 0.137 0.091 0.327 0.003

153 0.067 0.004 0.056 0.126 0.057 0.136 <0.002

314 0.023 0.002 0.025 0.072 0.046 0.089 <0.002

Table 16. Sample residue data for a confined rotational study (860.1850) of unnamed chemical 1

applied to directly to bare soil using a single application of 0.535 lbs a.i./A.

Plant back interval (days) Mature Radish Mature

Lettuce Wheat

Top Root Lea f Forage Hay Straw Grain

Parent

30 0.042 <0.001 0.013 0.010 0.004 0.020 <0.001

120 0.009 <0.001 <0.001 <0.001 0.018 0.106 <0.001

365 0.021 <0.001 0.007 <0.001 <0.001 0.009 <0.001

Primary Metabolite

30 0.773 0.135 0.365 0.835 0.658 0.952 0.046

120 0.329 0.077 0.286 0.183 1.076 1.452 0.050

365 0.610 0.047 0.176 0.138 0.455 0.330 0.012

3.4 Quantifying Exposure Resulting from Seed Treatments

3.4.1 Method Description and Evaluation

The proposed Tier I exposure method for seed treatments is based on the EPPO 2010 screening value of

1 mg a.i./kg in pollen and nectar of plants to which seed treatments were made. For these application

types, it is assumed that bees will only be exposed through the diet. A discussion of other potential

exposure routes, such as dust is provided in the section below, but is not quantified in the Tier I method

for assessing pesticide exposures to bees from seed treatments. In the Tier I approach, it is assumed that

all pesticides that are applied to seeds are systemic, and therefore can be transported into pollen and

nectar that may be consumed by honey bees. This approach may be used for all pesticides that are

applied via seed treatment, by assuming that the upper

‐ bound exposure value for pollen and nectar is 1

mg a.i./kg. This value can be multiplied by nectar and pollen consumption for adult and larval worker

bees to determine the upper

‐ bound doses potentially received by bees. This method can be applied to

all chemicals that are applied to seeds, with no need for adjustment based on application rate or

chemical properties.Page 94 of 275 The effectiveness of the EPPO’s screen was evaluated using empirical data describing pesticide

concentrations in pollen and nectar of crops whose seeds were treated with the pesticide. Data were

located in nine different studies, representing six different chemicals, the majority of which were

neonicotinoid insecticides, which are known to be systemic compounds ( Table 17 ). The highest residue

value measured in pollen and nectar samples was 0.036 mg a.i./kg, which a factor of 28 lower than the

EPPO screen ( i.e.

, 1.0 mg a.i./kg). As a result, EPPO’s screening value of 1 mg a.i./kg appears to be a

reasonably conservative estimate of pesticide residues in pollen and nectar of crops to which pesticides

are applied via seed treatment.

Table 17. Measured concentrations of pesticides in pollen and nectar of plants where seeds were

treated. Data are not normalized to app rate.

Chemical Rate (mg

a.i./seed) Crop Measured concentration

(mg a.i./kg) Citation

Pollen Nectar

Imidacloprid 1 Sunflower 0.036 * NA Laurent and Rathahao 2005

Imidacloprid 1 Corn 0.018 * NA Bonmatin et al. 2005b

Imidacloprid 0.7 Sunflower 0.011 * NA Bonmatin et al. 2005a

Imidacloprid 0.7 Sunflower 0.0039 ** 0.0019 ** Schmuck et al

. 2001

Clothianidin 1.25 Corn 0.0039 ** NA Krupke et al. 2012

Clothianidin 1.25 Corn 0.0039** NA Staedtler 2009

Clothianidin 1.25 Corn 0.0039** NA Staedtler 2009

Clothianidin 1.25 Corn 0.0034** NA Staedtler 2009

Metalaxyl NA Corn 0.0031 ** NA Krupke et al. 2012

Clothianidin 1.25 Corn 0.0029** NA Staedtler 2009

Clothianidin 1.25 Corn 0.0029** NA Staedtler 2009

Clothianidin NA Oilseed rape <0.001+ 0.003 Wallner 2009

Clothianidin NA Canola 0.00259 * 0.00224 * Cutler et al. 2007

Thiamethoxam NA Corn 0.0017 ** NA Krupke et al. 2012

Trifloxystrobin NA Corn 0.0017 ** NA Krupke et al. 2012

Unnamed Chem # 2 NA Canola <0.00125 + <0.00125 + Study 2

‐ 5

Thiamethoxam 0.75 Pumpkin <0.0002 + <0.0002 + Dively and Kammel 2012

Imidacloprid NA Mustard <0.010 + <0.010 + Choudhary and Sharma 2008

Clothianidin 1.0 Melon ND ND Bocksch 2010

imidacloprid 0.33 Melon ND ND Bocksch 2010

NA = not available ND= non

‐ detectable

* Represents maximum

** Represents mean

+ Chemical was not detected. Level of detection is set as upper bound.Page 95 of 275 Table 18 includes the proposed residue values expressed as a dose ( μ g a.i./bee). The dietary

‐ based

exposure value for adults is 0.29 μ g a.i./kg for exposure through consumption of nectar and pollen. The

dietary

‐ based exposure value for larvae is 0.120 μ g a.i./bee. The strengths of this approach are that it is

empirically based and simple to use. At the Tier I screening level, the values in Table 18 can be applied

to all chemicals that are applied to seeds, with no need for adjustment based on application rate or

chemical properties.

Table 18. Tier I exposure values used to estimate high end exposures of honey bees to pesticides

applied via seed treatments.

Life Stage Exposure Type Dietary Dose

( μ g a.i./bee)

Adult Diet (nectar + pollen) 0.29

Larvae Diet (nectar + pollen) 0.12

3.4.1.1 Assumptions and Uncertainties

As indicated above, the proposed use of the EPPO screening

‐ level exposure value of 1 mg a.i./kg for

pollen and nectar of seed treated crops does not account for differences in application rate ( i.e.

, the

mass of the pesticide applied to the seed). There is uncertainty in not accounting for the mass of the

chemical applied since it is expected that the magnitude of the pesticide concentration in the plant will

be influenced by the magnitude of the application; however the evaluation of this screen indicates that

is protective for rates that are currently being used for seed treatments. The pesticide application rates

of the available data used to evaluate the 1 mg a.i./kg screening value range 0.7

‐ 1.25 mg a.i./seed.

Therefore, the 1 mg a.i./kg screening value is expected to apply to pesticide treatments that are at a

similar rate. If application rates are much greater, pesticide concentrations in pollen and nectar may be

higher than the empirical data provided in Table 17

. However, even if pesticide concentrations of seed

treatments exceed 1.25 mg a.i/seed, it seems unlikely that pesticide concentrations in nectar and pollen

would exceed the 1 mg a.i./kg value since 1) the screening value is a factor of 28 greater than the

highest residue measured in pollen and nectar, and 2) pesticide mass applied to seeds is not expected to

be much greater than 1.25 mg a.i/seed since this value is already similar to the overall weight of the

seed itself.Page 96 of 275 In addition to the EPPO (2010) screening value, available uptake models were considered. No uptake

models were identified where pesticide concentrations could be estimated in plants after seed

treatments. As an alternative, the soil

‐ plant uptake model developed by Briggs et al

. 1982 and 1983,

with modifications by Ryan et al

. (1988) was considered as a potential method that may be used to

estimate upper

‐ bound exposures of bees to pesticides applied as seed treatments. In order to apply

such a model, it is necessary to estimate the amount of pesticide present in the soil of the treatment

site. For pesticide applications that are made via seed treatments, which are expressed on a mass of

pesticide per seed ( i.e.

, mg a.i./seed) or mass of pesticide per mass of seed ( e.g.

, mg a.i./cwt seeds)

basis, several assumptions would need to be made to determine the mass of pesticide available in a

volume of soil. For example, it would be necessary to determine how deep the seed is planted, how

many seeds are planted per area, and how much mass of pesticide is available for uptake. Given that

these parameters are expected to vary by crop and even by region, there would be a great deal of

uncertainty in assigning representative parameter values to represent these variables. Therefore, it was

determined that the application of the soil to plant uptake model developed by Briggs et al

. 1982 and

1983, with modifications by Ryan et al

. 1988 would generate estimates of pesticide concentrations in

nectar and pollen that have greater uncertainty when compared to the EU’s screening value, which is

based on empirical measures in plants following both soil and seed treatments.

3.5 Quantifying Pesticide Exposure from Tree Injections and Trunk Drenches

3.5.1 Method Description and Evaluation

No empirically based models were identified to estimate pesticide concentrations in pollen, nectar or

leaves of trees injected or drenched with pesticides. For screening

‐ level purposes, the proposed method

is to estimate the concentration in the vegetative part of the treated tree by dividing the mass of the

pesticide applied to the tree by the mass of tree vegetation represented primarily by leaves, but also by

flowers. This approach assumes that the applied pesticide is homogenously distributed in the tree’s

leaves and flowers and is not present in other parts of the tree. This approach can be evaluated using

data from two registrant submitted studies involving trunk drench applications of imidacloprid to citrus

trees (Bryne et al

. 2011) and Horse Chestnut trees ( Aesculus hippocastanum ) (Maus et al. 2004).Page 97 of 275 The highest measured concentration of total imidacloprid (parent and degradates) in citrus nectar, in

Bryne et al

. 2011, was 0.11 μ g a.i./L, which is approximately 0.18 mg a.i./kg. In this study, imidacloprid

was applied at a rate of 0.0016 lb a.i./tree (726 mg a.i./tree). The approach described above can be

applied to these application scenarios using the weight of citrus leaves and the weight of hardwood tree

leaves. For citrus trees, Alva et al. 2003 reported that 32 month old orange trees had 0.600

‐ 1.000 kg of

leaves (dry weight) per tree. If it is assumed that leaves are represented by 80% water (see T

‐ REX user’s

guide), this is equivalent to 3

‐ 5 kg wet weight of leaves per tree. If the mass of imidacloprid applied to

the citrus trees included in the study conducted by Bryne et al

. 2011 ( i.e.

, 726 mg a.i./tree) is divided by

the mass of leaves per tree, the result is an estimated concentration of 242

‐ 145 mg a.i./kg. This value is

three orders of magnitude above the reported concentration of imidacloprid in nectar ( i.e.

, 0.18 mg

a.i./kg).

The highest measured concentration of total imidacloprid (parent and degradates) in chestnut tree

leaves (Maus et al. 2004) was 4.64 mg a.i./kg. In this study, imidacloprid was applied at a rate of 0.06 g

a.i. per centimeter of stem diameter. Applications for the four trees in the study ranged from 0.003

–

0.004 lb a.i./tree (1320

– 1760 mg a.i./tree, depending on the tree diameter). For chestnut trees, leaf

biomass for hardwoods can be estimated using general hardwood leaf biomass equation from Sollins et

al. 1973, as adapted by Jenkins et al. 2003. Using this equation, the equivalent wet weight of leaves per

tree ranges from 31

– 50 kg. If the mass of imidacloprid applied to the chestnut trees included in the

study conducted by Maus et al

. 2004 ( i.e.

, 1320

– 1760 mg a.i./tree) is divided by the mass of leaves per

tree, the result is an estimated concentration ranging from 38

– 47 mg a.i./kg. The measured value of

imidacloprid in leaves is one order of magnitude higher than the highest measured concentration of

total imidacloprid in leaves.



### 3.5.1.1 Assumptions and Uncertainties

One major uncertainty associated with this approach is the estimate of the vegetative mass of the tree,

which can vary greatly based on species, age, time of the year and geography. In addition, in this

approach, it is assumed that 100% of the active ingredient is taken up into the tree and moved into the

leaves and flowers. It is unlikely that all of the pesticide mass applied to the tree actually enters the tree,

or that all of this is moved solely to the leaves.Page 98 of 275 .

3.6 Discussion of Potential Exposures through Other Routes besides Diet and Direct Spray

3.6.1 Contact with Dust

Another potentially important route of exposure for bees is contact with dusts that occurs from direct

application of a pesticide. Bees could be exposed to pesticides in dust while in flight (contact exposure),

or through flowers where dust has been deposited (contact or dietary exposure). Possible sources

include dust generated through abraded seed coat dust during planting, disruption of treated soil

through farming activity, and generation of contaminated dusts through the harvesting of treated

plants. Of these potential sources of contaminated dust, only the exposure to dusts generated through

abrasion of treated seed coat has been associated with incidents of honey bee mortality (Pistorius et al

.

2009, Forster et al

. 2009) and is the focus of considerable research ( e.g.

, Taparro et al

. 2012, Krupke et

al

. 2012). EFSA 2012 as well as the recent SETAC Pellston workshop (Fischer and Moriarty 2012) also

identified exposure to dust drift from sowing treated seed as a relevant exposure route for bees.

However, methods of consistently quantifying exposures to dusts have not been well defined. The

extent to which honey bees are exposed via contact with abraded seed coat dust is determined by many

factors, including the physico

‐ chemical properties of the seed coating, seed planting equipment, use of

seed delivery agents ( e.g.

, talc and graphite), environmental conditions (wind speed, humidity), and hive

location in relation to sowing. These factors influence the amount of dust formed, the pesticide

concentration in the dust, and depending on dust particle size and weather, the extent to which the dust

may move.

EPA, PMRA and CalDPR are not aware of any validated models to assess this exposure route. A field

‐

scale model for dust emissions from planting of treated seed could potentially be developed using the

AERMOD model (USEPA 2012 f

). Parameterization of this model is complicated because of the number

and variety of variables that determine the importance of this exposure route and associated risks ( e.g.,

type of equipment, use of talc, seed coating material, climatological conditions, and particle size). An

empirically based approach involving the available studies documenting incidents associated with dust

exposures (Taparro et al

. 2012, Krupke et al

. 2012) could be used to derive parameters for AERMOD;

however, given the extremely high concentrations of insecticides found in solid lubricants for seedPage 99 of 275 planting equipment, it is unlikely that a screening model based on such source terms would be useful to

discriminate among insecticides’ risks to bees.

Relative to potential exposure through drift of abraded seed coat, the EPA and PMRA are currently

investigating the mortality incidents that have occurred in the U.S. and Canada in 2012 to determine

whether the exposures through dust can be associated with specific application and/or weather

conditions. This work may be used to inform future assessment methods of this route of exposure. At

this time, EPA and PMRA are working with pesticide registrants, seed treatment companies and seeding

equipment manufacturers to better ensure the development and use of appropriate sticking agents,

seeding equipment lubricating agents, and more effective venting/filtering options for such equipment

to minimize the evolution of dust at the time of planting so that exposure to honey bees through

contaminated dust is minimized.

3.6.2 Consumption of Contaminated Drinking Water

In order to understand the potential pesticide exposure to bees through consumption of water, it is

important to be able to estimate pesticide concentrations in the different types of water bodies from

which bees may collect water, as well as the amounts of water bees will collect from each source.

Pesticide concentrations are expected to vary among potential sources of drinking water. An analysis

discussed in detail in Appendix 2 includes estimated pesticide concentrations in ponds, puddles and

dew, which are all considered relevant sources of water for bees. In estimating the total pesticide

exposure a single bee may get from drinking water, there are two major sources of uncertainty: 1) the

major source(s) of water used by bees are unknown, and 2) the amount of water a bee will actually drink

is unknown. Appendix 2 includes an analysis of two potential drinking water intake rates for bees using

two different methods, where one is based on behavioral observations of honey bees and the other is

based on direct measurements of water flux rates of a similar species (i.e., the brown paper wasp). The

analysis showed that the two methods generate results that differ by orders of magnitude, and

concluded that the method based on water flux rates in the brown paper wasp is more reliable. Upper

bound pesticide concentrations were estimated in various sources of water that bees potentially use for

drinking water, including ponds (adjacent to treated fields), on

‐ field puddles and dew (present on crops)

In addition, potential exposures through contaminated guttation fluid are also discussed using available

empirical measures of pesticide concentrations in this fluid. Guttation fluid has been discussed in thePage 100 of 275 scientific literature in recent years as being a potential drinking water exposure route of concern due to

its high (ppm level) concentrations of some pesticides following pesticide treatments.

Based on estimated pesticide concentrations in various sources of water potentially consumed by bees

and a water flux rate from the brown paper wasp, the results of this analysis indicate that if bees

consume the majority of their water from puddles or ponds, the exposures relative to dietary and direct

spray are insignificant. If bees drink a substantial amount of water from guttation fluid or dew,

conservative exposures may be similar to or even exceed pesticide exposures through the diet or direct

spray. Because this indicates a potential concern for assessing exposures of honey bees to pesticides,

potential exposures through drinking dew and guttation fluid were investigated further. This

investigation concluded that pesticide exposures through dew and guttation fluid are not expected to be

as significant when compared to diet because of two primary reasons. First, although the importance of

dew and guttation fluid to bees as a source of drinking water is unknown, dew and guttation fluid are

only expected to be present during a portion of the morning which would prevent bees from drinking a

substantial amount of water from these sources. Second, for many worker bees, pesticide doses through

consumption of dew and guttation fluid may be much less due to lower or non

‐ existent drinking water

consumption rates (because of higher amounts of water consumed through food). Therefore, pesticide

exposure through drinking water is not included in the proposed Tier I exposure route for bees.

3.7 Tier II Exposure Assessment for Honey Bees

As discussed previously, the risk assessment process is intended to be iterative. At the Tier I level,

exposure values used to estimate RQs are intended to be conservative; however, for chemicals which do

not pass the Tier I level, increasingly refined estimates of exposure may be needed to provide a more

realistic understanding of pesticide exposures to honey bees under actual use conditions. Therefore, the

purpose of the Tier II exposure assessment is to obtain pesticide specific, empirically

‐ based exposure

data that potentially represent doses received by bees. Like the Tier I exposure method, potential

exposures should be assessed through quantification of doses through direct spray or diet. Specifically,

studies should be designed to quantify pesticide residues in pollen, nectar and on bees located on

treated fields.Page 101 of 275 The strength of basing the Tier II exposure approach on empirical data from field studies is that some of

the uncertainties associated with the Tier I exposure method are reduced or eliminated. For instance,

the assumption incorporated into the Tier I exposure assessment for foliar applications that pesticide

concentrations on grass are equivalent to nectar and pollen is not necessary if pesticide concentrations

are quantified directly in nectar and pollen. Also, the Tier I use of upper

‐ bound exposure values based

on a large set of chemicals is not necessary if pesticide

‐ specific residue levels can be established under

field conditions that are representative of the pesticide’s intended use patterns. One remaining

uncertainty that is associated with chemical

‐ specific measures of pesticides in nectar and pollen is that

the actual pesticide doses received by bees through diet are still unknown due to the need to convert

measured concentrations in pollen and nectar to dose using food consumption data ( i.e., Rortais et al

.

2005).

This section describes the basic objectives and design elements of two types of field studies that can be

used to quantify pesticide concentrations in matrices that are relevant to bees. The first study is a

targeted field study where pesticide residues are quantified in pollen and nectar collected from treated

crops. This study could be specifically designed with the sole intent of quantifying pesticide residues in

matrices relevant to honey bees, or it could be incorporated into a study design of an existing study

required for pesticides, such as a cropped TFD study (OCSPP guideline 835.6100; USEPA 2008). The

second study is referred to as a tunnel study because the treated crop is kept under an enclosure

(referred to as a tunnel) along with a nucleus hive of bees. Pesticide concentrations can be quantified

from the stomach contents, pollen sacs, pollen traps attached to the hive and comb of the bees and

their hive which is maintained within the tunnel. Depending on the plant species, both pollen and

nectar may be readily available for direct collection by researchers. In these cases, the targeted field

study may be preferred for generating Tier II exposure data. In cases where it is logistically difficult for

researchers to collect pollen and nectar samples by hand, honey bees may serve as a reasonable means

to collect samples. In those cases, tunnel studies may be preferred.

The sampling scheme is an important consideration in the design of the study. Sampling should target

the maximum concentrations found in pollen and nectar; therefore, these studies should sample pollen

and/or nectar at different time points during bloom to evaluate the presence and the decline of the

compound over time. The sampling should also be specific to the target crop and application method.

For foliar applications, studies should be designed such that pesticide concentrations are quantified onPage 102 of 275 the day of application (presumably to quantify the highest pesticide residues) and during subsequent

days that are selected to represent the concentrations on bees and relevant plant samples over time.

These sampling dates should be selected based on consideration of two factors: dissipation of the

chemical through degradation and transport away from the plant ( e.g., via volatilization) as well as

systemic transport through the plant to pollen and nectar. For soil and seed treatments, pesticide

concentrations should also be quantified in nectar, pollen, at the time of flowering. In these studies,

pesticide degradates of concern should also be quantified. These degradates can be identified using

available registrant

‐ submitted environmental fate and plant metabolism studies (Guideline 860.1300;

USEPA 1996 b ).

There are several sources of variation related to the anticipated use of the pesticide that should be

considered when designing studies for the Tier II exposure assessment, including application methods,

crops and location. Application methods employed for a chemical can vary. Because different

application methods may result in different levels of exposure, application practices that are expected to

generate high

‐ end exposure concentrations for the pesticide should be selected. In determining which

crops to include in field studies, a focus should be placed on registered uses for crops that are highly

attractive to pollinators and the crops that have the highest application rates as well as the potential to

be visited by pollinators. For systemic pesticides in particular, different plant species may have

differences in uptake, distribution, and metabolism of the test substance. In addition, different plants

may have different matrices that represent food sources for bees. For example, corn only produces

pollen that bees will collect as a food source; canola produces both nectar and pollen (in flowers). Some

plants, such as cotton also produce extra

‐ floral nectaries which may be present when flowers are not.

Different field locations have different climates, soils, and therefore growing conditions at a spatial

scale. The locations of the studies should be selected so that they represent the anticipated use sites of

the chemical. If possible, like with MOR studies, pollinator

‐ specific studies could be conducted in

multiple regions that are identified to be of concern. As a result of these considerations, it may be

necessary to conduct targeted studies using multiple application methods, crops and locations in order

to quantify potential pesticide exposures to bees under conditions that are representative of the

anticipated use of the chemical. Because field residue data may exhibit extreme spatially and temporal

variability, it is necessary to collect a sufficient number of data from different studies in order to obtain

a sample that represents the potential environmental exposures. A sufficient number of studies should

be collected to allow the risk assessor to have an understanding of the central tendency and upper

‐Page 103 of 275 bound exposure values. The upper

‐ bound exposure values ( e.g.

, 90 th or 95 th percentiles) from Tier II

exposure values may be used for risk assessment purposes.

The field and tunnel (semi

‐ field) residue studies can provide important information regarding the levels

of contamination potentially encountered by bees in the field; however, there are a number of

uncertainties associated with these studies. One major uncertainty is that the levels of residues inside

the hive may be different than those first encountered by bees in the field. Bee bread and honey that

form the basis of the hive diet undergo processing by bees with various enzymes and microorganisms

prior to feeding most of the castes of hive bees. Only foragers will consume nectar directly as a major

food source to meet their metabolic requirements during foraging. Consequently, measurements of

residue levels in pollen and nectar collected directly from the flower or from returning bees may be

different from the exposure to individuals within the hive. Furthermore, bees will typically collect a

variety of food sources that would dilute any contamination from the test crop, unless other sources of

contamination are also brought into the hive. Therefore, pesticide concentrations in freshly collected

nectar and pollen could differ from potential residue levels in honey and bee bread, respectively.

In addition, one notable limitation to the Tier II exposure assessment method is that it will require a

substantial amount of time and resources to complete. In the future, it is possible that the data acquired

from targeted monitoring studies may be used to establish patterns of exposure based on application

methods, crops and location. These patterns could then be used to develop a refined method where the

data could be extrapolated to other chemicals. In addition, some field and tunnel residue studies that

are currently being conducted also involve the collection of data on residues in leaves and blossoms.

These data could potentially provide a more complete dataset from which to evaluate the ability of

blossom or leaf residues to provide a conservative measure of residues in pollen and nectar.Page 104 of 275 

### 4 Characterization of Ecological Effects

At this time, the first tier of testing in North America consists of an acute contact toxicity test, i.e

.,

OCSPP Guideline 850.3020 (USEPA 2012 a ) and OECD Guideline 214 (OECD 1998 a ) on adult honey bees

that provides a median Lethal Dose (LD

50 ), i.e

, the lethal dose that causes the death of 50% of the

exposed organisms. In addition to a contact toxicity test, an acute oral toxicity test is also required in

Canada that provides information on the oral LD 50 from a single dose of the test compound, along with

any sublethal effects (OECD Guideline 213; OECD 1998 b ). Depending on the outcome of these toxicity

tests, pesticides are classified as practically non

‐ toxic (LD

50 >11 μ g a.i./bee), moderately toxic

(2<LD

50 <10.99 μ g a.i./bee), or highly toxic (LD

50 <2 μ g a.i./bee) to bees on an acute exposure basis. If the

acute contact LD 50 is less than 11 μ g a.i./bee, additional testing may be required in the form of a foliar

residue study (OCSPP Guideline 850.3030; USEPA 2012 b ) to determine the duration over which field

‐

weathered foliar residues remain toxic to honey bees. On a case

‐ by

‐ case basis, EPA and PMRA may also

require additional higher

‐ tiered studies such as hive studies and semi

‐ field and field pollinator studies

( i.e

., OCSPP Guideline 850.3040 (USEPA 2012 c), OECD Guidance Document 75 (OECD 2007), and the

EPPO Standards PP1/170 (EPPO 2010) if the data from toxicity studies indicate potential chronic effects

or adverse effects on colonies. The ecological risk assessment method proposed in this white paper

would expand the current data requirements substantially.

Expansion of Tier I testing for pollinators was proposed in a USDA

‐ led workshop, as well as in two recent

publications. In 2009, EPA hosted a USDA

‐ led workshop to discuss the development of new toxicity

testing protocols for determining the potential effects of pesticides on bees. This workshop included

representatives of CalDPR, PMRA, the French Ministry of Agriculture, and university researchers and

focused on laboratory, semi

‐ field, and field toxicity testing protocols for honey bees and other non

‐

target invertebrate pollinators. Proceedings from the workshop (USDA 2010) indicated the need for

acute oral toxicity testing, effects testing with non

‐ adult bees, better standardization of sublethal effects

to be included in toxicity tests, and the development of standardized methods for higher tiered studies

using colonies under more realistic field conditions. In addition, the summary of a 2011 SETAC Pellston

Workshop (Fischer and Moriarty 2011) recommended inclusion of both oral and contact acute toxicity

testing on adult bees in Tier I, as well as larval testing where larval exposure is possible. Another

recommendation was to include chronic toxicity testing with adult bees “as soon as test methodologies

can be verified.” EFSA 2012 made similar recommendations and also noted the importance of makingPage 105 of 275 precise observations of sublethal effects that might occur during current testing, ultimately

recommending that tests to evaluate sublethal effects should be considered for future Tier I testing.

4.1 Tier I Effects Assessment

The purpose of the ecological effects assessment is to describe the effects elicited by a stressor ( i.e

.,

pesticide active ingredient). This involves linking observed effects to the assessment endpoints and

evaluating whether a cause

‐ effect relationship exists (USEPA 1998). During the Tier 1 stage of the risk

assessment, effects data are evaluated to determine whether they are consistent with assessment

endpoints and to confirm that the conditions under which they occur are consistent with the conceptual

model depicted in the problem formulation. This section of the white paper discusses the ecological

effect studies which are proposed for the screening

‐ level (Tier 1) risk assessment. While many of the

studies discussed in this section are routinely required by the EPA and PMRA, other studies are relatively

new and in some cases, the testing procedures for these newer studies ( e.g

., acute larval and chronic

toxicity tests) are still in development. As discussed in the exposure characterization (Section 3), the

major routes of exposure are expected to be contact and dietary ( i.e., oral). Therefore, at the Tier I level,

toxicity tests are intended to assess the effects of a chemical when bees are exposed in this manner.

4.1.1 Acute Toxicity

The proposed Tier I method for characterizing effects of pesticides on bees following acute exposures is

based on three types of studies. These include: acute contact toxicity for adult worker bees, acute oral

toxicity for adult worker bees and acute oral toxicity for worker larvae. The most sensitive LD 50 values

representing each of these exposure routes and life stages are intended to be used in combination with

the appropriate estimated exposure value to derive RQs for the Tier I risk assessment. In cases where a

pesticide is not applied via foliar spray, the acute contact toxicity data may not be necessary for

assessing the risks of a chemical because for soil applications, seed treatments and tree trunk

applications, dietary exposure is expected to be the predominant route.Page 106 of 275 4.1.1.1 Contact Exposure to Adult Worker Bees

As noted previously, the current screening

‐ level toxicity study for bees that is required in the U.S. is an

acute contact toxicity test. According to 40CFR158 data requirements for pesticides (CFR 40 2012),

these data are required when the pesticide is applied to crops which are known to be attractive to bees,

or has other outdoor use patterns (such as forestry or residential) which may result in bee exposure. The

test design and method for deriving acute contact toxicity data for adult worker bees are well

established, with guidelines available through EPA Guideline 850.3020 (USEPA 2012 a ) and OECD Test

Number 214 (OECD 1998 a ). The purpose of this study is to quantify the dose

‐ response relationship, with

the response measured by bee mortality following contact exposures to a pesticide. The standard acute

contact toxicity test methodology involves application of the technical grade active ingredient in a

solvent (or in a formulated product), applied by microapplicator to the thorax of adult test bees. Adult

worker bees are exposed to five doses in a geometric series of the test substance. A minimum of three

replicate test groups are used for each test concentration, with each replicate containing ten bees. Each

test includes a control group (treated with the solvent). In addition, a positive control involving

exposures of bees to a toxic standard ( e.g

., dimethoate) is typically included in the test. The test

duration is 48 hrs; however, if the mortality rate continues to increase between 24 and 48 hrs, the test

may be extended to 96 hrs. The primary measurement endpoint is the 48

‐ hr LD 50 (expressed in units of

μ g a.i./bee). Sublethal effects are also reported in acute contact studies; therefore, depending on the

nature of those effects and whether a dose

‐ response relationship is observed, the study may yield a

median effect dose (ED

50 ). Because the focus of this test is on quantifying the mortality, the dose levels

of these tests are not designed to quantify an ED 50 with confidence (EFSA 2012).

Based on the median lethal contact dose for 50% of the bees tested ( i.e

., contact LD 50 ) from this study,

the pesticide is classified as practically non

‐ toxic (LD

50 ≥ 11 μ g/bee), moderately toxic (10.9>LD

50 > 2

μ g/bee), or highly toxic (<2 μ g/bee). The proposed Tier I risk assessment method involves using the 48

or 96 hour LD 50 generated from an acute contact laboratory study to derive RQs representing risks to

forager bees exposed to pesticides from contact (following foliar spray applications). Unless the

pesticide is determined to be practically non

‐ toxic, EPA would then typically require a study on the

toxicity of residues on foliage to honey bees (OSCPP 850.3030; USEPA 2012 b ).Page 107 of 275 The current OSCPP guideline study 850.3030 (USEPA 2012 b ) evaluates the toxicity of residues on foliage.

In this study, a formulated product of a chemical is applied to a bee attractive plant ( e.g

., clover or

alfalfa) at the maximum application rate with the minimum application interval. The crop is then

harvested in a manner that provides different age residues ( e.g., 0, 24, 48, 72 and 96 hours after

application). The harvested foliage is brought back to the laboratory where it is placed into cages. Adult

bees are placed in each cage with the foliage and allowed to come into contact with the foliage. Food

and water are provided ad libitum

. The bees are monitored for a period of time until mortality declines

to below 25%. The measurement endpoint derived from this study is the RT 25 , which is defined as the

time needed to reduce the “residual toxicity” of the test substance, as measured by mortality, to 25%.

The time period determined by this toxicity value is considered to be the time that the test substance is

expected to remain toxic to bees in the field from the residual exposure of the test substance on

vegetation at an expressed rate of application (typically expressed in terms of lb a.i./A). Although the

results of this study would be useful in the characterization of effects and risks of a pesticide to honey

bees ( e.g., through a discussion of how long a pesticide may be expected to present a risk to foraging

honey bees), the proposed Tier I risk assessment method does not include using the data generated

from this type of study to derive RQs.

4.1.1.2 Oral (Dietary) Exposure to Adult Worker Bees

Although not currently required by EPA, PMRA and the EU routinely require acute oral toxicity data for

adult worker bees. The acute oral toxicity study is a laboratory test method, designed to assess the

mortality of young adult worker bees following a single oral dose of a pesticide. A standard OECD

guideline exists for this study, i.e

., OECD Guideline 213 (OECD 1998 b ). According to the OECD guideline,

adult worker honey bees are exposed to five doses in a geometric series of the test substance dispersed

in sucrose solution. A minimum of three replicate test groups, each with ten bees, is then dosed with

each test concentration. As with the acute contact study, a toxic standard (usually dimethoate) is

typically included in the test series to serve as a reference (positive) control. The bees are then fed the

same diet, free of the test substance. Similar to the acute contact toxicity test required by EPA, the study

provides a 48

‐ hr LD 50 value and can be extended to include a 96

‐ hr LD 50 value if mortality is

demonstrated to increase between 24 and 48 hours. In addition, the study should also generate some

observations of sublethal effects that could potentially be used to develop an ED 50 value. The proposedPage 108 of 275 Tier I risk assessment method involves using the 48 or 96 hour LD 50 generated from an acute oral

laboratory study to derive RQs representing risks to adult bees exposed to pesticides through the diet

(following foliar spray applications, soil application, seed treatments and tree trunk treatments of

pesticides).

4.1.1.3 Oral Exposure to Larval Worker Bees

Unlike with the acute contact and oral toxicity studies for adult worker bees, established regulatory test

guidelines for assessing larval toxicity do not exist. Given that larvae may be exposed to pesticides

through the diet, the lack of understanding of the toxicity of a chemical on larvae is considered to be a

significant data gap. Therefore, efforts are underway to identify critical design elements of an acute oral

larval toxicity study that may be used to understand the toxicity of a chemical to this life stage of worker

bees. Several larval toxicity studies have been published in the literature that may be useful in

establishing an acceptable acute oral larval toxicity studies ( e.g., Aupinel et al

. 2007 and OECD 2007).

One option is an in vitro study designed to measure the acute toxicity of a technical grade active

ingredient directly applied to honey bee larvae. Assays where individual bees have been removed from

their colonies can be thought of as in vitro studies since the bees are outside of the ‘superorganism’.

Aupinel et al

. 2007 proposed an in vitro method for assessing the effect of pesticides to honey bee

brood which allows the quantification of doses ingested by larvae and the assessment of larval and

pupal mortality as well as sublethal effects. In this method, bee larvae (1 st instar) are transferred into 48

‐

well plates and fed royal jelly containing known quantities of the test chemical over the 48

‐ hr study

period. The test provides LD 50 values and depending on the extent of replication can provide NOAEC

values as well. The methodology was subsequently ring tested and a review (Aupinel et al. 2009)

indicated that additional adjustment in the method may be needed to address potential sources of

variability ( e.g

., colony origin of the brood, season and larval heterogeneity at grafting).

Alternatively, the screening

‐ level assessment of larval toxicity could be based on a honey bee brood

study using in

‐ hive exposure of brood fed by adults. The methodology for this in vivo study ( i.e

., within

the “superorganism”) was first described by Oomen et al

. 1992 and later developed into an OECD

Guidance document (OECD 2007) based on Schur et al

. 2003 using whole colonies. These methods

however, are not considered to be laboratory

‐ based studies. Rather they are considered semi

‐ fieldPage 109 of 275 studies since they entail using honey bee colonies and are conducted in a tunnel even though the

original study method by Oomen et al

. 1992 involved bee colonies which were not confined to a tunnel.

The OECD guidance (OECD 2007) suggests that an in vitro laboratory

‐ based study may be an appropriate

first step in determining whether the colony study is warranted. Consistent with this recommendation,

for assessing the acute toxicity of a pesticide to larvae that are exposed through the diet, the proposed

Tier I risk assessment method is to use an LD 50 value generated from an in vitro test with honey bee

larvae, similar to the method used by Aupinel et al

. 2009.

One notable uncertainty associate with the Aupinel et al

. 2009 method, as well as existing chronic

toxicity tests with larvae, is that they rely on a synthetic food matrix to which the test chemical is added.

The uncertainty surrounds the extent to which this food is reflective of the brood food and/or royal jelly

that developing larvae are typically fed. The manipulation of bee larvae outside of the colony

environment also introduces uncertainties regarding how such manipulations affect bees in terms of the

trophallaxis, i.e

., sharing of food, on which they naturally depend. This is particularly true for bee larvae

which consume a highly processed food as opposed to the matrix provided in some of the currently

available study methods.

4.1.1.4 Other Acute Toxicity Data

Additional data are often available that may be useful in characterizing the effects of a pesticide on

bees. The scientific literature often includes toxicity data involving bees. In cases where more sensitive

acute toxicity data for honey bees are available in scientifically valid studies from the scientific literature,

these values may be used to derive RQs (instead of less sensitive endpoints from registrant

‐ submitted

studies). In addition, there are cases where data that are not used to derive RQs are considered in the

effects characterization. For example, data are often available from registrant

‐ submitted acute toxicity

studies involving other non

‐ target arthropods ( e.g., green lacewing, non

‐ parasitoid wasps). Also, reports

of honey bee mortality events (beekill incident reports) following pesticide exposures may be useful in

evaluating the available laboratory toxicity data.Page 110 of 275 4.1.2 Chronic Toxicity

Chronic laboratory

‐ based toxicity testing has not been routinely required on individual bees in the US;

information on the potential chronic toxicity of pesticides to bees has historically been discerned from

whole colony studies conducted under semi

‐ field or full field conditions. The USDA Technical Working

Group report (USDA 2009) noted that chronic toxicity tests with individual adult bees have been

conducted to support registrations in the EU. These tests consist of collecting newly emerged (juvenile)

bees from comb and placing 50 of these bees per cage. A sufficient number of replicates are run along

with suitable negative and reference controls to conduct hypothesis testing to establish a NOAEL and

LOAEL. The bees are fed a sucrose solution; however, no protein source (pollen) is provided. Exposure is

through spikes made to the sucrose solution; cages of bees are provided 10 mL of test solution every

other day. Bees are observed for a total of 10 days and mortality (chronic survival) is the primary

measurement endpoint. In discussions at the USDA workshop, participants suggested that the exposure

source to the bees could vary depending on the primary route of exposure expected for the chemical,

i.e

., either through nectar (sucrose) or through pollen. However, exposure through pollen can present

logistical challenges as bees may inadvertently walk through the treated pollen where exposure may be

extended to contact rather than simply through oral ingestion. Although the primary endpoint

measured has been mortality, additional measurement endpoints may include adult bee longevity, body

weight, and immune response.

The proposed Tier I risk assessment method for bees includes quantification of the toxicity of larvae and

adult worker bees following chronic exposures to pesticides; however, no formal guidelines have been

developed to date for conducting chronic toxicity tests with either adult or larval bees. Therefore at this

time, evaluation of the potential chronic effects of pesticides on individual bees is not possible;

however, efforts are underway to identify design elements of a chronic larval and adult toxicity studies

that may be used to understand the toxicity of a chemical to worker bees. Critical questions related to

this process include:

 What is an appropriate duration to represent a chronic exposure to larval and adult worker

bees?

 What is an achievable duration of exposure given husbandry limitations of honey bees?Page 111 of 275  What endpoints are meaningful for understanding the potential impacts of a pesticide on the

assessment endpoints ( i.e

., maintaining honey bee population size, stability of managed bees,

quality and quantity of hive products, species richness and abundance)?

 How should those endpoints be quantified ( e.g., EC 10 , EC 20 , EC

x , NOAEC, LOAEC)?

These questions are explored in the following sections that focus on the duration of chronic effects tests

and the endpoints measured in those tests. Also discussed below are some uncertainties in conducting

laboratory toxicity tests with bees that may be necessary to consider when designing chronic toxicity

studies.

4.1.2.1 Duration of Chronic Tests

The term “chronic” is intended to represent repeated exposures that occur over an extended period of

time, generally representing a substantial portion of an organism’s life. Because different organisms

have different life expectancies, the actual duration of a chronic exposure may differ by organism. For

example, because a summer worker bee lives for a period of several weeks, a chronic exposure for a

worker bee would be measured by weeks. Because a queen bee may live for years, a chronic exposure

for the queen may be measured by months or years. Therefore, in establishing a chronic test for worker

bees, it is necessary to select appropriate test durations so that the end result represents an exposure

that is chronic.

As indicated in the problem formulation, the proposed Tier I method for assessing risks of pesticides to

bees involves establishing separate endpoints for worker larvae and adults. The duration of the worker

larvae life stage is approximately 7 days (Wilson 1987), with the first 5 days occurring while the larvae

are uncapped and fed by nurse bees. Chronic larvae toxicity tests have been carried out where bees

were observed for 7 or more days following exposures. Efforts to extend larval toxicity tests beyond 7

days to include the pupation stage have frequently resulted in increased mortality (>10%) of the

developing bees. Therefore, the 7

‐ day exposure period appears to be an appropriate duration of

exposure as it covers a large proportion of the larval life stage and extension into the pupal life stage

may not be possible due to husbandry concerns. The proposed Tier I method for deriving chronic RQs

for larvae worker bees would be based on toxicity tests with 7

‐ day exposure periods.

The lifespan of an adult, summer worker bee is several weeks. Some laboratory tests have been

developed where the toxicity of adult bees has been examined over a 10

‐ day exposure period (EFSA

2012). This also appears to be an appropriate duration of exposure to represent chronic effects to adult

worker bees as it represents 25

‐ 50% of the adult lifespan. Therefore, the proposed Tier I method forPage 112 of 275 deriving chronic RQs for adult worker bees would be based on toxicity tests with 10

‐ day exposure

periods.

4.1.2.1.1 Mortality

As discussed in the problem formulation, the assessment endpoints for the ecological risk assessment of

bees involve maintaining honey bee population size, stability of managed bees, quality and quantity of

hive products, species richness and abundance. It is important to identify specific endpoints which will

be measured in toxicity tests. These measurement endpoints must have clear linkages to assessment

endpoints if they are to be used quantitatively ( i.e

., to calculate RQs) in risk assessment. As indicated in

Table 1

, at the individual bee level (which is the focus of the Tier I assessment), measurement endpoints

relevant to these assessment endpoints include: individual survival, adult bee longevity, brood size,

brood success, and queen fecundity. Chronic toxicity tests with larvae and adults can be used to quantify

effects of pesticides on all of these endpoints, with the exception of queen fecundity (which would

require an egg laying study involving the queen). Therefore, the focus of the chronic toxicity tests with

larvae and adults is on mortality that may occur during the tests. Potential impacts of a pesticide on

brood size and success can be assessed by determining whether there is decrease in the number of

brood ( i.e

., larvae) following a chronic exposure of larvae to that pesticide. Potential impacts of a

pesticide on adult survival and longevity can be assessed by determining the mortality and the decrease

in the life spans of adult bees following chronic exposures to the pesticide. The notable limitation to the

proposed chronic toxicity endpoints is that they do not include measures of queen fecundity.

In order to derive a RQ, the measurement endpoint must be quantified. For chronic toxicity studies,

endpoints are traditionally represented by the No Observed Adverse Effects Concentration or Level

(NOAEC or NOAEL). These values are calculated from hypothesis

‐ based tests where the Lowest

Observed Adverse Concentration or Level (LOAEC or LOAEL), which is determined from statistical or

biological significance, is then used to establish the NOAEC or NOAEL. The NOAEC[L] is assumed to

represent a lower

‐ bound dose where no significant mortality is expected to occur in larvae or adults.

The proposed method for establishing RQs for the Tier 1 assessment is based on NOAEL values from 7

‐ d

larval and 10

‐ d adult studies, where the LOAELs are based on mortality or longevity (of adults).Page 113 of 275 As an alternative, regression

‐ based approaches have been proposed for quantifying measurement

endpoints. Use of a regression

‐ based endpoint would require determining what level of effect is

relevant for a chronic exposure ( i.e., the percent decrease must be established). Since the Level of

Concern for acute exposure has been established based on 10% mortality to foragers, a comparable

endpoint for the chronic test would be an LD 10

. In acute toxicity tests, the endpoint is the median lethal

dose (LD

50 ), which is the most confident estimate of mortality generated from an acute toxicity study

given that confidence intervals around the regression line (mean) are minimized at its center

‐ most

point, i.e

., the LD 50

. Predictions of LD

x values that are on the low or high ends of the dose

‐ response curve

( e.g., LD 10 and LD 90 , respectively), become less confident. This can be attributed to the selected test

concentrations of the acute toxicity test, which are based on the goal of quantifying the LD 50

. If the

desired endpoint for a chronic study is an LD

x value, it would be necessary to select test doses that

would be sufficient to confidently estimate that LD

x value.

4.1.2.1.2 Sublethal Effects

Although the proposed measurement endpoints for chronic exposures are based on mortality, there are

an increasing number of sublethal endpoints reported in Apis and non

‐ Apis toxicity studies which may

be conceptually relevant to the proposed assessment endpoints. Sublethal effects have been defined as

effects which do not directly cause the death of an individual organism (Thompson and Maus 2007);

however, these same authors differentiate effects on reproduction ( e.g

., death of larvae, failure of

pupation or malformations during pre

‐ imaginal development) as brood effects rather than sublethal

effects. Examples of reported sublethal endpoints include impairments to movement and

communication, foraging and homing behavior, recruitment of fellow foragers, health of the queen,

detoxification pathways, immunodeficiency, and colony maintenance activity. Additional examples of

sublethal effects reported in the open literature include data from proboscis extension response (PER)

tests and flight tunnel studies. Proboscis extension response tests serve to show an insect’s learning

ability through olfactory conditioning, i.e., a bee will learn to extend its proboscis when a sugar stimulus

is touched to its antennae but will not when given a repellant stimulus. When a bee is unable to learn

this behavior, this response may reflect an impairment of the bee’s ability to forage and provide food for

itself and the hive. Additionally, flight tunnel studies have been used to assess the homing ability of the

bee, as well as a bee’s ability to communicate a food source to other bees through the “waggle dance.”

EFSA’s risk assessment of plant protection products on bees (EFSA, 2012) identified sublethal endpoints

as being inadequately covered by existing test designs. Desneux et al. 2007 identified physiological andPage 114 of 275 behavioral symptoms that can be observed and reported in laboratory studies. EFSA recommended that

neurotoxicity symptoms ( e.g

., movement and coordination), cleaning behavior, olfactory conditioning

(learning and memory), enzymatic functions, and development of the hypopharyngeal gland tissues

(responsible for producing brood/royal jelly) are important indicators of bee health and vitality and must

be accounted for in the risk assessment process.

Before using these endpoints to derive RQs, the relevancy of these sublethal measurement endpoints

for evaluating assessment endpoints needs to be established through quantitative links to colony

strength and survival. Although such endpoints may not be used quantitatively to estimate potential

effects on assessment endpoints, they could potentially be used qualitatively to characterize potential

effects which may not have clearly established relationships to colony

‐ level effects and for explaining

mechanisms by which colony

‐ level effects might occur. The SETAC Pellston workshop (Fischer and

Moriarty 2011) as well as the EFSA opinion (EFSA 2012) both identified sublethal effects of pesticides on

adult and larval bees as an important priority area for future research, especially in terms of developing

tiered species

‐ specific tests. Future work involving sublethal effects should also investigate their

quantitative relationships with the assessment endpoints for risk assessment ( Table 1 ).

Key science issues that must be addressed when considering sublethal effects include whether the

length of a given study is sufficient to determine if a sublethal effect will lead to a survival, growth,

and/or reproductive effect(s). Other factors to consider include the extent to which sublethal effects,

observed in the laboratory and field, occur at environmentally relevant concentrations. While these are

important areas for further evaluation, the scope of this white paper is intended to focus on the basic

risk assessment process and underlying studies for determining whether exposure to a single chemical

may result in adverse effects to the whole colony under actual use conditions. If there is sufficient

information regarding a sublethal effect that provide evidence of a dose response and plausible linkages

can be developed between the measurement endpoint and agency assessment endpoints, then such

effects may have both qualitative and quantitative utility in a risk assessment. Where such information

is not available, measurement endpoints may only have utility in qualitatively describing potential

effects which have been associated with the pesticide. With respect to mixtures, while multiple stressors

and the interactive effects of pesticides and/or other environmental stressors are important issues, they

will not be examined at this time.

Sublethal effects measured in studies can span a broad range of biological levels of organization

including altered biochemical pathways, genetic aberrations, changes in gut microflora, and behavioralPage 115 of 275 effects on the whole organism. However, there is an increasing need to understand how measurement

endpoints at one level of biological organization relate to specific molecular initiating events (modes of

action) and are part of a sequence of key events, which ultimately lead to an adverse outcome. Although

adverse effects on survival, growth and reproduction in the intact animal may be more readily used in

risk assessment, there is currently limited information available to quantitatively link many of the

sublethal measurement endpoints with these apical endpoints.

Developing plausible hypotheses for potential mechanisms of action, which include key events and

adverse outcomes, is increasingly important to understand and interpret the range of effects that may

result across multiple levels of biological organization. These uncertainties underscore the need to

delineate AOPs to better enable the extrapolation of measurement endpoints from one level of

biological organization to another. Although it may not be necessary to completely delineate AOP(s),

depending on the endpoint, there may be insufficient information available even on partially complete

AOPs to enable risk assessors to reliably extrapolate the results to apical assessment endpoints on which

the EPA, PMRA and CalDPR regulate. The need to identify a plausible modes of action is consistent with

global efforts to identify and characterize molecular initiating events and other key events at various

levels of biological organization that ultimately result in an adverse effect on the whole

organism/population/community.

In order to link behavioral and physiological effects to survival, growth, and reproduction, there are

threshold and temporal considerations. Threshold considerations include qualitative and quantitative

measurements, such as the percent reduction in a particular measurement endpoint that will affect the

vitality of an individual bee and ultimately that of the colony. Temporal considerations include transient

effects, which must take into consideration the functions of different castes and life spans of bees and

whether such transient effects are ultimately compensated for by the colony.

Although there is uncertainty in how sublethal measurement endpoints may be related to assessment

endpoints, the EPA Overview Document (USEPA 2004) indicates that sublethal effect data may be

considered in the assessment. This option is exercised on a case

‐ by

‐ case basis and with careful

consideration of the nature of the sublethal effect measured and the extent and quality of available data

to support establishment of a plausible relationship between the measure of effect (sublethal endpoint)

and the assessment endpoints. This option includes a determination of whether there are clear,

reasonable, and plausible links between the sublethal effect and survival or reproductive capacity of

organisms in the field in accordance with the screening assessment endpoints of survival andPage 116 of 275 reproduction capacity. Although a number of statistically significant sublethal effects may be identified

in a study between treated and untreated test organisms, these endpoints may have limited utility in a

risk assessment unless they can be quantitatively linked to adverse effects for the whole organism, i.e

.,

colony, or population compared with an unaffected one (Thompson and Maus 2007). In the context of

sublethal effects, there are no generally accepted thresholds of these effects, in terms of magnitude (EC

x

versus NOAEC) for risk assessment. Thompson and Maus 2007 have discussed the need for validated

assays for measuring such effects to achieve greater consistency and reliability in the results; however,

at this time appropriate validation criteria have not been identified nor have appropriate reference

standards for eliciting such effects been reported.

4.2 Tier II Effects Assessment

At a screening level ( i.e

., Tier 1), effects data may primarily be based on laboratory studies while

exposure estimates may be based on models and/or conservative default values. These studies are

evaluated using relatively conservative assumptions to provide high

‐ end estimates of potential risk such

that if a chemical passes the screen, the presumption of low risk to non

‐ target organisms is considered

protective. As discussed in the section on problem formulation and which will be discussed in greater

detail in the risk characterization section to follow, estimated exposure concentrations (EECs) are

compared to acute toxicity endpoints ( i.e

., LD 50 values) from laboratory studies with individual bees. For

those chemicals which fail to pass the screen ( i.e

., the RQ value>LOC), additional testing/refinement may

be required. As part of the tiered process, higher tier studies are intended to reflect increasing levels of

realism, albeit from a less controlled testing environment, in terms of how organisms may be exposed

and the nature of the effects which result from such exposure. This section discusses existing tests

which have been developed and which could be used to refine the screening

‐ level assessment.

As part of the tiered testing process for evaluating potential effects on bees, higher

‐ tier refinements

shift the focus from exposure and effects on individual bees to that of the intact colony. As noted in the

preceding section, the transition to higher

‐ tier toxicity tests may occur in advance of a formal risk

assessment and may be predicated on the toxicity thresholds that were discussed earlier, i.e

., 40CFR158

testing requirements. As indicated in the 40CFR158, higher

‐ tier testing of pollinators is currently

triggered when specific criteria are met. For pesticides with an acute LD 50 <11 μ g a.i./bee and with

extended residual toxicity ( e.g

., RT 25 >8 hrs) and/or data indicating the potential for adverse effects at

the colony level, higher

‐ tier (Tier III) field pollinator testing may be required. The decision to requirePage 117 of 275 additional toxicity testing is based on risk management needs and is typically made in the context of a

risk assessment. However, the regulated community may elect to proceed with completing such studies

based on their understanding of data requirements specified in the 40CFR158.

Unlike the screening

‐ level studies, Tier II studies with bees are not typically conducted in the laboratory

but rather focus on relatively controlled field studies (referred to as semi

‐ field”) where the movement of

the bees to and from the colony is restricted through the use of enclosures containing treated crops

and/or pesticide exposure is facilitated by feeding bees a pesticide

‐ spiked diet. Compared to laboratory

studies where exposure is to individual organisms and typically through a single route ( i.e

., contact or

oral), semi

‐ field studies can provide clearer lines of evidence for linking multiple routes of exposure to

adverse ecological effects. Semi

‐ field studies are also useful for determining the extent to which effects

on individual bees identified in Tier I laboratory studies are expressed at the colony level. Although the

current EPA data requirements for pesticides identified in the 40CFR158 do not specifically include semi

‐

field studies, the OCSPP Guideline 850.3040 on field testing for pollinators is general and would

accommodate semi

‐ field studies. Semi

‐ field studies represent a reasonable and cost

‐ effective means of

addressing uncertainties and/or minimally refining the concerns regarding specific risks to bees in

advance or in lieu of full

‐ field testing.

The social nature of honey bees requires extensive interaction ( e.g

., trophallaxis) and communication

among various castes of bees for successful development and propagation of colonies. As a result,

numerous linkages exist between pesticide exposure and effects on individual bees and their collective

impact on honey bee colony health which are not readily assessed in the laboratory. Additionally, the

Tier II semi

‐ field studies provide a means of assessing both exposure and effects that are less

conservative and more realistic than the methods used in laboratory testing. For example, the Tier 1

laboratory larval toxicity studies typically feed larvae with spiked sucrose solutions; however, early in

their development, bee larvae do not typically consume pollen or nectar directly but rather consume

brood food from the hypopharyngeal and mandibular glands of nurse bees. Thus, Tier II studies may be

conducted if concerns remain from Tier I levels of the risk assessment and further information is needed

for a more refined estimate of potential risk to honey bees. An example of such a refinement is the

transition from in vitro testing of bee larvae proposed by Aupinel et al

. 2007 to the in vivo colony

‐ level

brood study described in the OECD Guidance document (OECD 2007).Page 118 of 275 The design of the Tier II semi

‐ field study is based upon the specific issues raised at the initial tiers of risk

assessment. Consequently, the endpoints measured in the Tier II test should attempt to focus on those

specific measureable endpoints that are of concern based on screening

‐ level studies. For example, if a

chemical is toxic to the brood but not to adults, then the Tier II study would focus on endpoints related

to brood strength. Likewise, if a chemical is identified to be persistent, systemic, and can be brought

back to the hive, then an overwintering design may be the most appropriate.

Typical designs of semi

‐ field studies include a crop (or surrogate species such as Phacelia spp. ) that is

grown outdoors in an enclosed system with controlled or confined exposure. The test crop is grown

under good agricultural practices, and the maximum application rate with the minimum application

interval is used. The test crop provides the source of pollen and nectar. The design could also be

structured to reflect a desired exposure system or foraging environment such as a mixture of crops and

weeds or flowering margins, although such designs can introduce additional variables which may be

difficult to control across replicates and treatments. A study can also be designed with a more artificial

exposure system that makes use of spiked food sources to evaluate effects. As is evident, there are a

range of different designs, and the actual test structure will depend on the specific question(s) asked in

the risk assessment



### .

In the following section, aspects of the Tier II toxicity tests are discussed in terms of various methods

which have been proposed to examine the toxicity of chemicals to bees under confined test conditions.

4.2.1 Toxicity directed to brood

Chemicals may be transported back to the hive thereby creating an exposure pathway for the brood

within the hive. Sublethal concentrations of a toxic chemical in pollen and nectar may not impair adult

foraging behavior and can allow foragers to collect contaminated food and return to the hive. This food

may then be stored and residues may be translocated within the colony to areas where developing

brood are present and serve as a route of exposure to those brood (Wu et al

. 2011). The primary

measurement endpoints for this type of study include egg abundance, larval abundance, pupal

abundance, brood pattern, and emergence success (by following a cohort through development) and

are typically expressed in terms of a statistically significant difference between treated and negative

control hives. If studies have sufficient replication and multiple treatment groups are included, then the

study design may be sufficient to yield NOAEC/LOAEC values.Page 119 of 275 4.2.1.1 Semi

‐ Field Tunnel Study (OECD 75; EPPO 170)

This study design is currently described by EPPO 2010 and OECD 2007 test guidelines. The test design

employs a tunnel/tent in which small nucleus hives containing about 5,000 worker bees, brood in all

stages, and stores of pollen and nectar are placed. A test crop is used at or near full bloom that is

representative of high end exposure conditions for the given type of application method and rate. The

hives are introduced in the tunnels 2

‐ 3 days before pesticide application (for foliar sprays) and hive

condition is assessed while the bees acclimate to the enclosure. Pesticides can then be applied at peak

bee foraging activity to maximize exposure to foraging bees. Hives are kept in the tunnel for a period of

7

– 10 days after the first pesticide application and allowed to forage on the treated crop. The hives are

then removed from the tunnel and allowed to forage on untreated areas with continued monitoring

(usually up to a total study period of 28 days, but sometimes longer). A schematic of the OECD semi

‐ field

tunnel study milestones and measurement endpoints is shown in Figure 10

.

Figure 10. Schematic of Honey bee Semi

‐ Field Tunnel Study Design (Source: OECD 2007). BFD = brood

fixing day ( i.e

., day colonies are introduced to the tunnel).

Measurement endpoints include brood condition (percent of brood area as eggs, larvae, capped brood)

and associated indices (brood termination rate 11

, brood index 12

, and compensation index 13 ), hive

strength (percent of comb area covered with bees), queen health, adult bee mortality, and flight

activity, which are measured at various points over the 28

‐ d study. Results are evaluated by comparing

the treated colonies with the controls (treated with water only). A reference chemical is also frequently

11 Brood termination rate is determined by the number of brood in designated cells that do not reach the expected brood stage

at a specified time.

12 Brood index is calculated by scoring each cell development stage, totaling the scores and then dividing by the number of

initially marked egg cells.

13 Brood compensation index, considered an indicator of the recovery potential of the colony, is determined by calculating the

number of identified cells where new brood cells are laid down after loss of the first effort.Page 120 of 275 tested to demonstrate that the application method used actually results in exposure to bees as well as

the ability of the study to detect/quantify effects. Reference chemical selection is dependent on the

expected mode of action of the pesticide under consideration, but should be a chemical with known

toxicity to the hive. For pesticides that are expected to directly impact brood development, an insect

growth regulator (IGR) is typically used ( e.g

., fenoxycarb). For pesticides that are expected to elicit

effects directly on adults (and indirectly on brood) through rapid ‘knock down’, a reference chemical

that acts similarly ( e.g

., dimethoate) is often selected.

The semi

‐ field study design allows for an evaluation of the hive when it forages on an actual crop under

a realistic application scenario. This enables the combined impact of lethal and sublethal effects on

multiple castes of exposed bees to be ascertained at the hive level ( e.g

., colony strength, brood

development). These endpoints are more closely aligned to the proposed assessment endpoints ( Table

1 ) compared to effects measured on individual bees. Furthermore, exposure to brood is realistic in the

sense that larvae are exposed to the chemical in processed brood food rather than artificial matrices

used in laboratory studies ( e.g

., sucrose solution). Although typically used to assess foliar pesticide

application methods, the semi

‐ field tunnel study design is amenable to other application methods ( e.g

.,

seed treatment of systemic pesticides) provided that exposure is adequately demonstrated ( e.g

., plants

are grown to full bloom and test chemical residues are quantified in pollen and nectar).

The semi

‐ field tunnel design also contains limitations that relate to the known stress that the tunnels

place on the foragers and consequently, the hives. Since the studies are conducted outside of a

laboratory environment, they are subject to the vagaries of weather. As noted in the OECD guidance

(OECD 2007), the test cannot be performed under adverse weather conditions; low temperatures can

limit bee foraging activity and thereby affect potential exposure, while high temperatures can affect the

extent to which nectar may be available as well as affect bee foraging activity. Because bees are not

allowed to forage freely over large areas, the quantity and quality of food they are able to gather may be

compromised compared to open field conditions (free foraging). Experience with these studies indicates

that confinement stress on foraging bees, among other factors, often leads to adverse effects on egg,

larval, and pupal abundance with increasing duration of confinement in the tunnels. As a result, hives

can only be kept in tunnels for a relatively short period of time (7

‐ 10 days) to ensure adequate condition

of controls for comparison. Even during this time, declines in control hive condition are commonly

encountered. This relatively short exposure period introduces uncertainty when attempting to relatePage 121 of 275 results to longer

‐ term exposures that bees may experience in the field ( e.g

., commercial hives being

moved to multiple crops in a growing season). Furthermore, effects at other critical time periods of hive

development ( e.g

., overwintering) are not addressed, although such effects could be assessed if the

post

‐ tunnel observation period were extended and colonies were of sufficient strength to support

overwintering.

As noted, the duration of semi

‐ field studies has typically been limited to less than 2 weeks since

bees/colonies begin to exhibit stress from being confined to tented enclosures/ tunnels for prolonged

periods of time. However, while controlled exposure through confinement to a tunnel may be limited to

≤ 2 wks, observation periods for such studies may be extended for considerably longer periods of time

provided that the colonies have sufficient forage. Colonies used in tunnel studies are typically reduced in

size and may not have sufficient strength (in terms of overall numbers of bees) to collect the necessary

stores to over

‐ winter; however, it may be possible to build nucleus colonies up to sufficient strength

following the enclosure period such that they could survive over

‐ wintering. What is frequently done

though is to transition to full field studies where full size colonies (~10,000

‐ 50,000 bees) are used to

simulate more realistic conditions. Such studies are discussed in greater detail in the following section.

4.2.2 Field Feeding design

Although the feeding study does not involve enclosures/tunnels and bees are allowed to forage freely,

the study is considered a semi

‐ field study since test organisms are provided pesticide

‐ spiked food, which

does not occur in full

‐ field studies.

The Oomen et al

. 1992 method was developed in order to test for the possibility of adverse effects on

the brood within a honey bee hive. The basic design uses 1 liter of a sucrose solution that is placed in a

feeder near a full size honey bee hive. The sucrose solution is either a control without contamination or

a solution spiked with a known concentration of a pesticide. The solution is provided as a food source,

and once the hive has collected all of the solution, weekly measurements are taken on brood

development within the hives; development is monitored by tracking the sequential development of

brood through the use of acetate sheet overlays of the brood comb. In addition to the data on brood

development, data on adult and brood mortality estimates can be obtained through counts of bees

collected in dead bee traps placed at the entrance to the colony. Sublethal effect data on bee behavior

and queen performance can also be included in the study. It is important to note that the hives are notPage 122 of 275 placed within tunnels but are allowed to freely forage. Further, the test solution represents a single

exposure scenario that is expected to be consumed within one day. The test chemical concentrations

are not meant to be reflective of environmentally relevant concentrations but rather may be

exaggerated in order to screen chemicals for possible impacts to the brood when in a whole hive

situation. An advantage of this type of study is that the hives are not confined within a tunnel as such

confinement can put stress on the colony. The Oomen et al

. 1992 method is, however, considered a

qualitative assessment (OECD 2007) of effects to brood since the actual exposure to brood cannot be

quantified.

EPA has considered further refinements in the semi

‐ field study designs; however, these refinements

have not been well vetted. The previous semi

‐ field study methods have undergone review by the ICP

‐ BR

and the larger EU community and studies following these methods are becoming increasingly common

in support of pesticide registrations in both the EU and to a lesser extent in the US. However, for the

sake of completeness, other possible refinements in the semi

‐ field study methods are discussed below

and within the context of their strengths and limitations (summarized in Table 22 ). For systemic

pesticides or those with a specific use pattern that can potentially contaminate food resources of bees,

an alternative feeding design may further inform the risk assessment.

For this type of study, spiked food (pollen patties, sucrose solution, or both) can be fed ad libitum to full

size honey bee hives of at least 10,000 bees over a specified duration of time and during a specified time

of the year. Different concentrations of the chemical in food are used with the intent of establishing a

NOAEC/LOAEC. The treatment levels are either selected from concentrations of concern that have been

identified in the body of literature or by environmentally relevant concentrations found in targeted

monitoring studies for residues in pollen and nectar. The design does not employ tunnels, so the bees

are allowed to forage freely. While the potential exists for bees to forage in areas where other pesticides

may be in use, test site selection is intended to minimize the potential for such exposure(s).

Consequently, the test hives are placed in an area with minimal or no other crops present in order to

minimize any exposure to other plant protection products. Consumption of the food sources provided to

the hives are measured, as are the residues of concern ( i.e

., parent and toxic metabolites) in the spiked

food samples and hive stored pollen and honey to ascertain whether the spiked food is being stored.

Measurements of residues potentially include other matrices such as bees ( i.e

., to ascertain exposure of

adult bees), royal jelly and brood food ( i.e

., to ascertain exposure to the brood), and residues in bee

‐

collected pollen and nectar from outside the hive ( i.e

., to assess exposure to contaminants from sourcesPage 123 of 275 other than the spiked food). The measurement endpoints for this type of study are determined by the

protection goals as well as remaining uncertainties highlighted by the lower

‐ tier laboratory studies with

individual bees as well as other lines of evidence ( e.g

., open literature and/or incident data). Therefore,

this type of study may be amenable to protocol modification to suit the current understanding of the

pesticide chemistry, application methodology, and expected effect on the brood.

During a field

‐ feeding study, exposure can be controlled to a greater extent than a full

‐ field study

through the use of pollen traps to limit incoming pollen and encourage feeding on the treated food

source. As previously mentioned the enclosures used in the typical semi

‐ field design introduce stress to

the colony and affect the brood performance of the colonies in as little as 7 to 10 days. The open field

feeding design allows a colony to freely forage thereby eliminating the stress of the enclosure.

Furthermore, the study hives can be large enough to survive through overwintering, unlike the smaller

nucleus hives typically used in the tunnel studies. The use of larger colonies enables the study to

proceed over a longer duration than other semi

‐ field studies in an enclosure. With this flexibility, the

full

‐ field feeding design can be used to evaluate a colony’s response to either short

‐ term or long

‐ term

exposure conditions to evaluate overwintering performance of the hives. Consequently, this study can

address the uncertainties identified at the lower

‐ tier levels or even at the initial stages of the Tier II

toxicity assessment with other semi

‐ field studies specifically when oral exposure is the pathway of

concern.

The results of this study are intended to reflect the extent to which specific effects may occur in

response to various pesticide levels in pollen and nectar when compared to measured residue levels in

pollen and nectar from targeted monitoring studies. Assuming that targeted monitoring studies are

available to design the feeding study or are available following the completion of the feeding study,

these data would provide a measure of the level of residues in pollen and nectar from various crops.

This feeding study could then provide a context for these residue data at the whole

‐ hive level and reveal

to what extent effects would be anticipated from the residue levels in the different crops via

comparisons between the NOAEC and measured concentrations in pollen and nectar. The comparison

between this NOAEC and the residue levels is not a quantitative comparison, but rather a means of

understanding the potential impacts on the colony given realistic residue levels in pollen and/or nectar

from various crops. No RQ’s are generated with this study at the Tier II level, but the risk assessor could

identify those residue levels above the NOAEC to be of concern; although such comparisons would have

to account for the extent of the effects ( i.e

., percent difference from controls) measured at the LOAEC.Page 124 of 275 These comparisons could then inform the risk assessor as to which crops or crop groups that were

assessed in the tier II exposure analysis may be of most concern, and which crops could be excluded

from further analysis based on the absence of an effect at similar residue levels. Consequently, this type

of feeding study can reduce the number of crops for which a Tier III field pollinator study would be

necessary or provide enough information at the Tier II level to remove the need for a Tier III study.

It is also important to note that there are a number of uncertainties with the field feeding study design

( Table 22 ). First, the bees are allowed to freely forage, so consumption of the spiked food provided to

the colony depends on a range of factors that may not be constant for all experimental situations. For

example, if alternative forage is ample, then the bees may use less spiked sucrose solution or spiked

pollen than when outside forage sources are scarce. Thus, this type of study, which does not use an

enclosure, is subject to numerous sources of variability at the field level including weather, disease,

parasites, and exposure to other pesticides present in the environment. Second, the feeding aspect is

artificial and may not accurately mimic typical foraging behavior by honey bees or the stability of the

test material in such matrices under natural conditions ( e.g

., full sunlight for photolytic compounds).

Third, use of an internal spiked food source can confound efforts to use foraging behavior as a

measurement endpoint. A lack of foraging on the spiked food source could be interpreted as either

intoxication, or alternatively as avoidance of the contaminated food item or simply that an alternative

source of food was preferred. Fourth, the performance of the colonies has not been evaluated in

relation to long

‐ term continual use of the artificial food sources. Finally, as the purpose of the study is to

identify a NOAEC, the study should be designed to provide sufficient statistical power to detect the

desired level of biological significance, based on the potential impact to the assessment endpoints of

concern. However, at this time, a level of biological significance for survival, growth, and reproduction at

the whole hive level is unknown.Page125of275Table19.ComparisonofTierIISemi‐FieldStudyDesignsTestTypeStudyObjectivesTypicalEndpointsStrengthsLimitationsUtilityinRiskAssessmentTierIIIStudyRefinementsaSemi‐field(Tunnel)OECD75;EPPO170Toxicitytodevelopingbroodunderwhole‐hiveexposureconditionstopesticidetreatedcropinsemi‐fieldtunnels.BrooddevelopmentBroodterminationrateBroodindexBroodcompensationindexColonystrengthQueenhealthPollen&nectarstorageAdultmortalityForaging/flightactivityAbnormalbehaviorWorkerlongevityWholehiveresponseisquantifiedinadditiontodifferentbeecastes/lifestagesImpactondevelopingbroodareassessedMultipleexposureroutescanbeassessed(directcontact,oralexposure)ActualpesticideapplicationmethodsandratesareassessedExposuredurationislimited(~7daysofdirectexposure)duetotunnelstressLong‐termeffects(overwintering)arenottypicallyaddressedExposuremaynotrepresentrealisticcropexposurescenarios(croptype,temporalandspatialscales)Identifyarangeofpotentialwholehiveeffectsofshort‐termexposurescenariobasedonworst‐caserealisticmaximumapplicationstoaspecificcropEvaluationofeffectstobroodandadultcastesrelatedsurvival,growth,reproduction,honeyproduction,andpollinationservicesAllowsalongerdurationofstudyAssesslong‐termwholehiveeffectsfollowingchronicexposuretoatestcropRefinetheeffectscharacterizationbyevaluatingtheimpactedmeasurementendpointswithouttheaddedstressofthetunnelSemi‐field,Open(feeding)Toxicitytodevelopingbroodunderwhole‐hive,freeforagingexposureconditionsviarepeatedexposuretospikeddiet(e.g.,pollencakes)BrooddevelopmentBroodterminationrateBroodindexBroodcompensationindexColonystrengthQueenhealthPollen&nectarstorageAdultmortalityFreeforagingdesignreducesstressoncoloniescomparedtotunnelsExposurecanbeevaluatedoverlongtimeperiodsOraldosecanbecontrolledandquantifiedWholehiveresponseisquantifiedinadditiontodifferentUncertaintyregardingtheextenttowhichbeeswillconsumestoredspikedfood.Residuesonalternativesourcesofpollen/nectarExposuremaynotrepresentrealisticcropexposureEvaluationofarangeofpotentialeffectstoadultsand/orbroodbasedonuncertaintiesfromlowertierstudiesorothersemi‐fieldstudiesNOAELbasedonconcentrationswithinin‐hivefoodsources(surrogatesforpollenandnectar)AbilitytointerpretNaturalconditionsallowforrealisticexposurepathwayAbilitytoassesswholehiveeffectswhenforagingactivityisaknownendpointofconcernRefinetheeffectscharacterizationforcropswhoseresiduelevelsfallbetweentheLOAELandtheNOAELPage126of275Foraging/flightactivityOverwinteringWorkerlongevitybeecastes/lifestagesImpactondevelopingbroodareassessedPotentialforknownexposuretoresiduelevelsmeasuredinpollen/nectarNOAELindependentofcropinordertobebroadlycomparableforcomparisonwithallcropsDoesnotrequirelargefieldplotsscenarios(croptype,temporalandspatialscales)DoesnotcaptureeffectstoforagingbehavioroncontaminatedfoodsourcesresiduelevelsinpollenandnectarfromtargetedfieldresiduemonitoringstudiesaThiscolumnprovidesthatpotentialrationalesforprogressingtoatierIIIfieldstudyinordertoaddressanyremaininguncertainty.Page 127 of 275 

### 4.3 Tier III Effects Assessment

4.3.1 Refinement of Tiers I and II

The results of the field study should be linked to the regulatory concerns identified by risk managers. It

is also important to note that the full field study is intended to represent a highly refined level of

assessment and one that addresses specific uncertainties regarding the potential for colony

‐ level

effects. The information obtained from a field study must be considered in the context of the full body

of information available for a chemical; therefore, the risk assessment must consider the entire weight

of evidence. As discussed previously though, a field study may initially be intended to provide a general

understanding of whether a chemical results in adverse impacts to an entire colony. If there is an effect

in the screen, a definitive study may be needed to quantify the magnitude of the effect in order to

better inform risk management decisions. As risk management decisions are made, various forms of

mitigation may be considered and those efforts may reduce potential risks. The effects of mitigation

should be evaluated through each step of the refinement process such that field studies do not need to

be repeated to better account for such efforts.

Although this section is included under the effects characterization, Tier III effects assessments are

intended to represent highly refined studies that address specific uncertainties and/or risks identified in

lower

‐ tier studies. Current U.S. testing requirements to support pesticide registrations indicate that if

certain conditions are met, refined testing may be required. According to the 40CFR158, field testing for

pollinators (OCSPP Guideline 850.3040; USEPA 2012 c ) using a technical end

‐ use product (TEP) is

required if data from other sources indicate potential effects on colonies particularly if those effects

involve endpoints other than acute mortality. Field testing may also be required if toxicity of residues on

foliage tests ( e.g

., OCSPP Guideline 850.3030; USEPA 2012 b ) indicate that the pesticide exhibits

extended residual toxicity ( i.e

., pesticides with residues which remain toxic to >25% of the organisms

tested for periods >8 hrs) or if data from studies with terrestrial arthropods other than bees indicated

potential chronic, reproductive or behavioral effects. A field study serves as a means of addressing

uncertainties raised in lower

‐ tier studies conducted either in a laboratory or under restricted conditions

inside an enclosure/tunnel or with an artificial food source and can be useful in examining the effects of

pesticides with extended residual toxicity. However, a full field study is intended to represent realistic

application conditions whereby the test substance is applied to a specific crop on which bees are

foraging freely without the use of an enclosure and whereby test colonies are primarily exposed throughPage 128 of 275 residues carried back to the hive by the worker bees. Consequently, a full

‐ field study is also the most

resource intensive (relative to studies used for the Tier I and II assessments) to conduct in terms of both

time and money; these studies can also be the most difficult to interpret given the level of associated

variability. Therefore, they can represent a significant investment to regulatory agencies in terms of the

resources required to review these studies, but these studies also provide context relative to other lines

of evidence. The weight attributed to these studies as to how they influence regulatory decisions varies

widely across regulatory authorities; however, well conducted studies can be useful in determining,

under actual use conditions and typical foraging activity of bees, whether a compound represents a

significant risk to bees.

Given the expense to conduct full

‐ field studies and that they are typically conducted on the basis of

results from lower

‐ tier studies, the availability of appropriate guidance is important to ensure that the

studies will be useful in a regulatory context. In the following sections, study design elements for full

field pollinator studies are discussed. As with the semi

‐ field study, the length of the full

‐ field study is

dictated by the specific questions and uncertainties which the study is intended to address. Although

the exposure component of semi

‐ field studies is frequently limited to a maximum of 2 weeks,

observation periods can extend beyond that time. However, with full

‐ field studies, even the exposure

period can be extended to reflect actual use conditions and for those studies which are examining over

‐

wintering success, the study duration can range from several months to a year. It is important to note

that increasing the duration of a study can also increase the likelihood of confounding effects such as

disease, pests, nutritional deficits, and weather.

The results of the field study should be linked to the regulatory concerns identified by risk managers. It

is also important to note that the full

‐ field study is intended to represent a highly refined level of

assessment and one that addresses specific uncertainties regarding the potential for colony

‐ level

effects. The information obtained from a field study must be considered in the context of the full body

of information available for a chemical; therefore, the risk assessment must consider the entire weight

of evidence. As discussed previously though, a field study may initially be intended to provide a general

understanding of whether a chemical results in adverse impacts to an entire colony. If there is an effect

in the screen, a definitive study may be needed to quantify the magnitude of the effect in order to

better inform risk management decisions. As risk management decisions are made, various forms of

mitigation may be considered and those efforts may reduce potential risks. The effects of mitigationPage 129 of 275 should be evaluated through each step of the refinement process such that field studies do not need to

be repeated to better account for such efforts.

4.3.2 Design of a Field Study for A. mellifera

The actual design of the field study should be based upon both the uncertainties identified at the lower

tiers as well as protection goals that are identified at the problem formulation phase of the assessment.

For example, if a protection goal is to maintain pollination services, then the field study can be tailored

to address pollination activity based on specific adverse effects ( e.g.

, impacts to behavior, adult bee

longevity, etc .) identified in the lower tier registrant

‐ submitted studies and/or open literature and the

properties of the chemical ( e.g.

, extended residues on foliage, systemic distribution in plants, etc .).

Information contained in the targeted monitoring studies examining residues in pollen and/or nectar

coupled with effects observed in acute toxicity tests with adults/larvae and/or colony level effects from

Tier II tunnel/feeding studies can inform the design of the field study by focusing future studies on the

primary routes of exposure or crops that represent the worst case exposure scenarios and on measuring

particular effects. The aim of the field study is to test the potential effects of a specific chemical under

actual use conditions and compare the results with those obtained in laboratory and semi

‐ field studies

in order to better characterize potential risks to pollinators from the use of a chemical.

A full

‐ field study should be designed based upon specific questions identified from the lower tiers to

reflect a range of exposure scenarios. Although a single field study may not address all uncertainties

and/or use scenarios, it should be designed to represent relatively worst case conditions that are

considered protective for other uses. In focusing the study design on specific concerns, crops that are

good sources of both pollen and nectar, and have a high treatment rate, in addition to being highly

attractive to honey bees, may serve as an ideal target crop. Currently, the EPA has a guideline for

conducting the pollinator field study, which is under 40CFR158 as guideline number 850.3040 (USEPA

2012 c ). Similarly, the EPPO guideline 1/170 (4) (EPPO 2010) also addresses pollinator field study

guidelines. The EPA field pollinator guideline does not provide much detail, but rather provides flexibility

for designing a study based on the needs of the risk assessment. However, EPPO 1/170 (4) (EPPO 2010)

provides more detail that can be used to design a field pollinator study with honey bees in terms of

experimental conditions ( e.g

., selection of the crop, placement of colonies relative to the treated field,Page 130 of 275 size of the colonies, plot size), application of treatments ( e.g

., positive/negative controls, timing of

application, rates) and mode of assessment ( e.g

., timing and frequency of assessments).

Although intended as guidance for conducting field studies with mammals and birds, the EPA Guideline

850.2500 (Field Testing for Terrestrial Wildlife; USEPA 2012 d ) provides very useful information on study

design elements to consider for field testing. This guidance discusses the importance of clearly

articulating the purpose of a field study in advance since its purpose should dictate how the study is

ultimately designed. According to the guidance, the purpose of a field study is to either refute the

assumption that risks to wildlife will occur under conditions of actual use of the pesticide or to provide

some quantification of the risk that may occur. To address these concerns, field studies can be either

screening or definitive. If laboratory toxicity data on individual bees and/or colony level data are

available from semi

‐ field studies, these data coupled with measured residue data from target

monitoring studies to estimate risk and a screening field study may be appropriate to determine if

impacts occur at the colony level under actual use conditions. In this case, the screening field study

would be intended to confirm or refute an assumption of risk to honey bee colonies. If a screening

‐ level

study and/or incident data indicate that impacts are occurring, or if other available data (reliable open

literature data) suggest that deleterious effects have occurred or are likely, the guideline recommends

that the study design should be quantitative, thereby evaluating the magnitude of the impacts in a

definitive study. Therefore, screening field studies may provide “pass/fail” information as to whether

colonies are being adversely affected by an application; however, definitive studies should provide

information on the magnitude of effects ( e.g

., acute mortality of forage bees, the extent of effects on

brood production/survival).

An important consideration in the selection of a study site is the geographic area. Although

representation of a wide range of biogeographic areas is ideal, it may not be practical. Therefore, to

keep the number of study sites practical while still accomplishing the purpose of the study, site selection

should be based on those areas that are likely to represent the greatest risk or highest concern, are

representative of an area/crop which bees are likely to frequent in the highest density, and consider the

extent to which untreated alternative forage is available and can be minimized. The extent and nature of

alternative forage in the vicinity of the study site(s) should also be considered in site selection.Page 131 of 275 Additionally, the number of study sites is also an important consideration and OCSPP Guideline

850.2500 (USEPA 2012 d ) provides guidance on methods used to determine necessary sample sizes

depending on the level of confidence required in the study result. However, there can be practical

limitations to the number of study sites and as noted in the guidance, the number of sites may be

reduced if study site selection is biased toward worst case, i.e

., conditions which are considered likely to

result in relatively high exposures. Statistical analysis should be conducted for all field studies; therefore,

field studies should also determine the power of the test to adequately assess the endpoints relative to

the appropriate level of biological significance. A power analysis should accompany each protocol

proposal in order to determine the number of hives necessary for the conduct of the study. In addition,

the final report should contain a power analysis to determine if the appropriate level of statistical power

has been achieved.

Another important consideration is the size of the study sites. The size of the study site must ensure

adequate exposure of bees to the test material and reduce the opportunity for bees to forage on

untreated sites. The OCSPP Guideline 850.2500 (USEPA 2012 d ) notes that the study should never be less

than an individual field in which a crop is planted. Distance between study sites is also an important

design element. Although the guidance indicates that sites should be separated adequately to ensure

independence, it notes that this is dependent mainly on the range of the species. Distance between

treated and control sites is important given that honey bees can forage for considerable distances

depending on the availability of food. Winston 1987 reports that the median foraging radius of honey

bee colonies in agricultural areas is a few hundred meters and is 1.7 km in forested areas; however,

distances up to 11 km (7 miles) have been reported when no other competing food sources are

available. Again, there can be practical constraints to achieving ideal distances to ensure absolute

independence of study sites.

As noted, test conditions should resemble the conditions likely to be encountered under actual use of

the end

‐ use product; in most instances the pesticide should be applied at the maximum treatment rate

and reapplication frequency specified on the label. If more than a single method is specified on the

label, then the method that maximizes exposure of bees should be used.

Based on the information contained in the aforementioned guidelines, a field study for honey bees in

general should be conducted under actual use conditions in which a typical end

‐ use product is used atPage 132 of 275 its maximum application rate, frequency, and method as described on the label for a specific crop. The

evaluation of the hive should ideally proceed for a minimum of 50 days based on the lifecycle of the

honey bee within the colony. For the EPPO 2010 study protocol, the following description applies. The

study is conducted on a crop grown outdoors without enclosures; consequently the primary exposure

route is to foragers. The crop is subject to good agricultural practices, and the bees are allowed to freely

fly and forage. The design should reflect a realistic foraging environment and exposure system in the

field where the pesticide is to be applied. Hives (each with a minimum of ~10,000 bees) are placed

within or at the edge of treated or control flowering plots and monitored for a specified period of time

depending on the concerns of the chemical ( e.g

., systemic compound that is persistent in hive food

stores). Because hives may differ in size, colonies should be distributed as equitably as is possible

between treatments. The colonies should be in position approximately 2–3 days before the trial in order

to acclimate to the study site. If the chemical is a foliar spray, treatments should be applied when the

test crop is in full flower during the daytime when bees are demonstrated to be actively foraging on the

test crop. Hives should be queen

‐ right ( i.e

., have a single functional queen), use sister queens ( i.e

.,

common maternal source), and be of similar strength at the start of the study. The EPPO guidance notes

that during spray applications, the test hives should be protected from spray drift. Equipment should be

free of other chemicals, and the hives should have low incidences of disease and parasites, though it is

nearly impossible to use hives completely free of disease/pests. Unlike semi

‐ field enclosure studies, in

the case of a field study, a toxic reference standard ( e.g

., dimethoate) is usually not suitable. The test

system includes the treated plot and an untreated control and ideally should be suitably separated to

minimize the extent to which bees from either group can interact. Although very desirable, the EPPO

guidance suggests that replication is often not feasible because of the requirements for adequate

separation of replicates to ensure independence. The potential endpoints that can be measured in a

field study include the following:

 Colony strength

 Weight of the hive

 Pollen, honey, nectar stores

 Mortality at the hive

 Mortality of drones and pupae

 Mortality in the crop

 Presence of the same queen

 Ability of the colony to replace the queen (requeen) naturally

 Foraging activity in the crop

 The returning of foraging bees

 Behavioral abnormalitiesPage 133 of 275  Residues in pollen, nectar, pollen pellets, wax, honey, bee bread, and dead bees

 Assessment of the brood, including an estimate of adults, the area containing cells, eggs, larvae,

and capped cells

 Disease resistance or pest levels

 Worker longevity

 Over

‐ wintering success

These endpoints represent the range of endpoints available for measurement in full

‐ field studies;

however, actual measurement endpoints should be dictated by the stated objective of the study and the

specific uncertainties it is intended to address. The study protocol must articulate the objective of the

study, its measures of effect and how these data will be collected and analyzed. In terms of protocol

development, any number of endpoints may be selected for measurement and analysis. However, the

endpoints selected should be based on those that are of concern as identified in the lower tier or open

literature studies and the fate properties of the chemical. Therefore, one field study likely would not

measure all of the endpoints identified above. Field studies may also focus on different time periods

during a hive’s lifecycle in the year for more rigorous sampling depending on the chemical. For example,

if overwintering is a concern, then the period of intense sampling could be immediately before and after

overwintering. The extent to which a colony is manipulated during a study can affect the study outcome;

therefore, consideration must be given to the extent to which a colony must be opened and the nature

of the measurement endpoints that will be collected at that time to determine the most efficient and

least invasive means of collecting those data.Page 134 of 275 4.3.3 Analysis and Interpretation of Effects

The analysis and interpretation of full field studies must consider a variety of factors which include how

and the extent to which the bees/colonies are exposed and the nature of the effect(s) both in terms of

the statistical and biological significance. Additionally, these studies are intended to be refinements

based on lower

‐ tier studies of effects (at both the individual bee and colony level) and based on lower

‐

tier exposure estimates (based on modeled or measured residues). Therefore the design, conduct and

analysis of full

‐ field studies must be considered in the context of studies which served as a foundation

for requiring such studies. Given the shorter duration of lower

‐ tier studies and their oftentimes

exaggerated exposure conditions, the full

‐ field studies offer an opportunity to understand the potential

effects of a pesticide on bees/colonies under actual use conditions and to determine whether effects

observed in lower

‐ tier studies occur to an extent which would result in sufficient adverse effects to

warrant mitigation. Understanding the nature of the measurement endpoints and their relationship to

assessment endpoints along an AOP should influence the extent to which impairments can or should be

weighted. The previous discussion of sublethal measurement endpoints, where the relevancy of a

particular effect to the overall impaired survival of the colony must be considered as well as the extent

to which a colony may recover from such effects. As discussed earlier, the determination as to whether

the likelihood and magnitude of an adverse effect ( i.e

., risk) is sufficient to warrant mitigation is

dependent on a number of factors and these decisions extend beyond risk assessment.

In the following sections, some of the factors associated with the analysis and interpretation of full field

studies are discussed. These sections and the factors discussed therein are not intended to be

exhaustive; however, they are intended to provide an indication of factors that should be considered in

advance of conducting studies in order to facilitate their eventual analysis/interpretation.

4.3.3.1 Exposure

The analysis of the data obtained from field studies involves comparisons of the hives on treated plots

with those on untreated (negative control) plots. It can be difficult to evaluate the effect of the test

chemical within a full field study because the design allows for bees to forage freely and to obtain food

both on site and off the treated site. In studies with limited bloom density on the treated crop and

extensive alternative forage in the vicinity, bees may actually forage very little in the treated crop and

this may lead to an underestimation of potential effects on the hive due to reduced exposure or

alternatively to an inability to detect treatment effects due to control contamination.Page 135 of 275 Consequently, it is important to obtain accurate estimates of exposure to the test crop. This estimate

can be informed by several lines of evidence. First, pollen traps affixed at the entrances of the hives

force pollen off of the legs of a percentage of foragers when they return to the hive. This pollen can then

be analyzed for residues in addition to the floral origin of the pollen as well. Second, qualitative

measures of foraging activity, potential quantitative measures of the return of forager bees ( e.g

.,

measured through the use of radiofrequency identification tags; RFID), and quantitative measures of

bloom density can provide information on the potential attractiveness of the crop of interest. Third,

freshly stored nectar and pollen or stored bee bread and honey can be measured in the hives to

determine the level of residues that the bees are storing for consumption or processing prior to feeding

of the queen and brood. When nectar is collected, some pollen from the flowers may also be collected

on the bees and mixed with the nectar when it is stored. So in addition to sampling residues in freshly

stored nectar, pollen could potentially be obtained from within this collected nectar to determine the

origin of the nectar as well. Measures of residues in the blossoms would provide a comparison of stored

concentrations with those collected from the blossoms.

The estimates of exposure can be difficult to obtain, however. If older comb is used in the study, it is

possible that the hives will enter the study with reserves of bee bread and honey, or deposited pollen

and nectar. If this is the case, exposure may be difficult to ascertain as the bees could consume older

stores first. Another possibility is the sampling of the residues inside the hive may not be representative

of the residue levels from the incoming stored food as the food reserves are spatially and temporally

diverse.

Typical methods of statistical analysis for the measurement endpoints include measures of central

tendency ( e.g

., means) and measures of dispersion ( e.g

., variance, standard error) and comparison of

treatment means to those of controls. All of the toxicity endpoints are typically analyzed separately

using statistics, primarily through group comparisons with a single variable to test for differences from

the control group. For example, a decrease in brood may be related to a number of factors, including

the amount of food resources coming into the hive. Worker mortality, foraging intensity, disease and

parasite prevalence, and honey production, all of which may impact the hive level food resources, would

be analyzed individually in order to help explain possible changes in brood abundance. However, this

approach requires the collection of many additional endpoints and may also be stressful to the hive due

to the invasive nature required by the collection methods. Therefore, the field study should focus onPage 136 of 275 only the parameters of interest as the field study design should address the potential for an effect as

opposed to the mechanistic causes of an effect.

Time trends are not routinely reported, but they can be qualitatively evaluated in order to identify

changes in the hive condition over time. These should be evaluated carefully however, as hive

parameters naturally change over time. Therefore it is essential that control colonies perform

adequately in order to provide a concurrent measure of performance.

4.3.4 Biological Significance

Higher

‐ tier studies can provide information on a wide

‐ range of effects and depending on the study

design and the amount of variability associated with a particular measurement endpoint, there can be

uncertainty regarding the interpretation of whether a statistically

‐ significant effect is biologically

significant. Low variability may enable the statistical detection of subtle changes in a measurement

endpoint; however, there can be uncertainty whether the extent of the change is of sufficient

magnitude to be of concern. Conversely, there may be uncertainty whether an effect, that is not

statistically significant but occurs in a consistent and elevated frequency in treated colonies compared to

negative controls, is biologically significant. Additionally, time series analyses may reveal short

‐ lived

effects that may be statistically significant and of sufficient magnitude to be of concern; however, if the

colony is able to compensate for these effects, there is uncertainty regarding the extent to which such

effects should influence a risk assessment. Therefore, reconciling statistical and biological significance

can be a challenge.

4.3.5 Experimental Design Weaknesses

The preceding discussion of factors associated with the analysis and interpretation of full

‐ field studies is

further informed by considering some of the limitations that have been associated with previous field

studies submitted in support of pesticide registrations. In the following sections, issues associated with

the level of exposure to the pesticide of interest as well as to other pesticides with which the test

bees/colonies may have come in contact are discussed. Also, colony size, distance between study plots,

colony health (pests and diseases) and study duration are discussed in terms of some of the issues

associated with previously submitted studies. All of these factors can potentially confound efforts to

interpret study results and can quickly render full field studies of low utility to risk assessment.Page 137 of 275 4.3.5.1 Level of Exposure

Honey bees as the test species within a field study are able to forage freely. Consequently, the study has

limited ability to control where the bees forage since it does not confine them to only the test crop as a

food source. Considering the number of factors that affect the foraging intensity on the test crop ( e.g.

,

attractiveness of crop to bees, weather), this aspect of the study design is one of the most difficult to

address at the full field study level.

A field study that was conducted with a relatively new class of insecticides/miticides serves as an

example of the challenge related to honey bee foraging behavior. One of the representative chemicals

was evaluated in foliar applications to citrus and the subsequent effect on honey bee brood and the hive

was assessed. The study used a single orchard for the treatment and a single orchard for the control;

therefore, neither group was replicated. The treated and control orchards were 0.53 ha (1.3 acres) in

size. The hives were equipped with a minimum of food at the start of the study; however, the study

report did not state how much food ( i.e

., stored pollen and honey) the hives were allowed to carry into

the test at the initiation of the study. Data on the amount of stored food at test initiation is essential as

it provides insight into the potential for alternative sources of untreated food. The study report also

stated that the orchards were isolated and not in close proximity to other flowering crops or extensive

blooming weeds, though it was not mentioned how far from the test orchards the study authors had

accounted for.

This study included pollen identification and examined the source of pollen entering the hives. A

comparison of the sources of pollen is shown in Tables 20 and 21

. These data suggest differences

between control and the treated plots as well as differences between hives within a treatment and the

differences over time within the same hives. During the peak period of foraging on the control plot, 24%

of pollen, on average, originated from citrus. However, in the treatment hives, only 2.2% of the pollen

on average originated from citrus. The majority of pollen entering the treatment hives came from a

variety of other plant species. While the amount of pollen originating from citrus changed over time in

both the control and treatment group, this relationship of higher level of citrus pollen entering control

hives was consistent over the course of the sampling period. This study reveals that the assumption of

equal foraging of treated and untreated plots does not always hold and highlights the importance of

pollen identification analysis. It also reinforces the uncertainty in conclusions from the field study design

when bees are allowed to freely forage as there is a high level of variability over time given likelyPage 138 of 275 differences in bloom intensity (surrogate for attractiveness) and differences between where different

hives forage. It also highlights the question as to what is a worst

‐ case scenario for the foraging on a

specific crop. This study provides an example of an important consideration for a field study, i.e

., that

the field study should have a sufficiently attractive test crop to meet the foraging needs of the test

hives.

Table 20. Results of a pollen identification analysis (palynology) in a field study on citrus with a test

chemical

– treated plots.

Sampling

Date DAA2 1 Hive

Number Pollen Source [%]

Citrus sp. Hypecoum

sp Quercus

ilex Helianthemum

sp. Olea

europaea Other 2

18 Apr 2008 +7 1 4

‐ 16 64

‐ 16

18 Apr 2008 +7 2 1 22

‐ 52

‐ 25

18 Apr 2008 +7 3 0

‐ ‐ ‐ ‐ 100

18 Apr 2008 +7 4 9

‐ ‐ 72

‐ 19

18 Apr 2008 +7 5 <1

‐ ‐ 92

‐ 8

18 Apr 2008 +7 6 1

‐ ‐ 86

‐ 13

18 Apr 2008 +7 Mean 2.7 3.7 2.7 61

‐ 30.2

25 Apr 2008 +14 1 0

‐ 53 27

‐ 20

25 Apr 2008 +14 2 1

‐ ‐ 78

‐ 21

25 Apr 2008 +14 3 0

‐ 48 39

‐ 13

25 Apr 2008 +14 4 2

‐ 25 64

‐ 9

25 Apr 2008 +14 5 <1

‐ 26 68

‐ 6

25 Apr 2008 +14 6 9

‐ 26 49

‐ 16

25 Apr 2008 +14 Mean 2.2

‐ 29.7 54.2

‐ 14.2

07 May 2008 +26 1 1

‐ 59 22

‐ 18

07 May 2008 +26 2 <1

‐ 60

‐ 33 7

07 May 2008 +26 3 <1

‐ 37 38

‐ 25

07 May 2008 +26 4 <1

‐ 45 25 12 17

07 May 2008 +26 5 1

‐ 35 47 9 8

07 May 2008 +26 6 1

‐ 38 15 40 6

07 May 2008 +26 Mean 1

‐ 45.7 24.5 15.7 13.5

1 DAA2: days after the second application of the test substance

2 Anacardiaceae, Asteriaceae, Lamiaceae, Papaveraceae, Cistaceae, Fabaceae, Brassicaceae, Fumariaceae, Resedaceae, Rosaceae, Salicaceae,

Pinaceae, Oxalidaceae, Lauraceae, Euphorbiaceae, Mimosaceae, Rhamnaceae, Ranunculaceae, Boraginaceae, Clusiaceae, ConvolvulaceaePage 139 of 275 Table 21. Results of a pollen identification analysis (palynology) in a field study on citrus with a test chemical

–

control plots

.

Sampling

Date DAA2 1 Hive

Number Pollen Source [%]

Citrus sp. Hypecoum

sp Quercus

ilex Helianthemum

sp. Olea

europaea Other 6

18 Apr 2008 +7 1 35 24 22

‐ ‐ 19

18 Apr 2008 +7 2 5 28 21

‐ ‐ 45

18 Apr 2008 +7 3 14

‐ 35 37

‐ 14

18 Apr 2008 +7 4 3 30

‐ 20

‐ 46

18 Apr 2008 +7 5 19 9 25 17

‐ 30

18 Apr 2008 +7 6 3 14

‐ ‐ ‐ 83

18 Apr 2008 +7 Mean 13.2 17.5 17.2 12.3

‐ 39.8

25 Apr 2008 +14 1 5 34 13 7

‐ 41

25 Apr 2008 +14 2 31

‐ 24

‐ ‐ 45

25 Apr 2008 +14 3 21

‐ 19

‐ 39 21

25 Apr 2008 +14 4 49

‐ ‐ 10

‐ 41

25 Apr 2008 +14 5 23

‐ 19 13

‐ 45

25 Apr 2008 +14 6 13 25 31

‐ ‐ 31

25 Apr 2008 +14 Mean 23.7 9.8 17.6 5.0 6.5 37.3

07 May 2008 +26 1 3

‐‐ ‐ 62 35

07 May 2008 +26 2 5

‐‐ ‐ 41 54

07 May 2008 +26 3 5

‐‐ ‐ 81 14

07 May 2008 +26 4 19

‐‐ ‐ 55 26

07 May 2008 +26 5 2

‐‐ ‐ 54 44

07 May 2008 +26 6 18

‐ 14

‐ 23 45

07 May 2008 +26 Mean 8.7

‐ 2.3

‐ 52.7 36.3

1 DAA2: days after the second application of the test substance

2 Anacardiaceae, Asteriaceae, Lamiaceae, Papaveraceae, Cistaceae, Fabaceae, Brassicaceae, Fumariaceae, Resedaceae, Rosaceae, Salicaceae,

Pinaceae, Oxalidaceae, Euphorbiaceae, Rhamnaceae, Ranunculaceae, Boraginaceae, Clusiaceae, Asphodelaceae, Plantaginaceae,

Scrophulariaceae

The bloom intensity of the crop and level of alternative forage can impact the amount of foraging on the

treated crop as well. Fields of a given crop with determinate blooming ( i.e.

, having a defined bloom

period) will roughly bloom at the same time. However, peak bloom intensity may vary, in some cases by

up to a couple of weeks. Figure 11 illustrates a field study conducted with a test chemical on citrus in

Argentina. Four treated citrus orchards and four control citrus orchards were separated by 3 km or more

from each other. In this study, the authors conducted an assessment in which the blooming on the

different plots was categorized on different days during the study. The study authors estimated the

number of blossoms on each plot on 2 randomly chosen branches of 5 impartially pre

‐ selected citrus

trees within the orchard that were also “in proximity” to the hives. The study authors did not provide a

distance between the selected citrus trees and the hives. A bloom category was assigned to each

branch, and the category values of all branches were summed to provide an overall bloom category for a

plot (possible range from 0 to 40). The bloom category was based on the number of flowers in variousPage 140 of 275 stages from no completely open flowers to flowers that were in full bloom and completely open. The

categories were as follows based on the number of completely opened flowers:

 Category 0: no completely open flowers per branch

 Category 1: >25 completely open flowers per branch

 Category 2: 25<x<50 completely open flowers per branch

 Category 3: 50<x<75 completely open flowers per branch

 Category 4: >100 completely open flowers per branch

As Figure 11 shows, the plots varied in bloom intensity over the course of the study, and they also varied

by treatment. The control plot appeared to bloom earlier than the treatment plots, perhaps due to site

characteristics and/or the varieties of citrus, which also varied in the study, i.e

., the control plots were

primarily Okitsu versus the W. Murcott or Lanelate varieties in the treated plot.

0 5 10 15 20 25 30 35 40OverallBloomCategory 

## Overall Bloom Category at Control Plots

Plot 1

Plot 5

Plot 6

Plot 7Page 141 of 275 Figure 11. Classification of blooming intensity in a field study with citrus showing

differences in blooming timing.

Hives were placed in the field at the same time, so differences in bloom intensity can affect the

attractiveness of the forage crop, or shift the focus of honey bee foraging to alternative forage sources.

Given the range of foraging distance for a honey bee, bees may utilize variety of alternative sources if

the test plot is inadequate in terms of area or extent of bloom.

Another consideration is that colonies may contain significant reserves of untreated pollen and honey

that serve as food as identified earlier in the section related to exposure. Furthermore, all or part of the

test substance

‐ containing nectar and pollen that are collected by foragers may be stored in the colony

and may not be used immediately. Therefore, during a study with a relatively short duration, the extent

to which residue

‐ containing food is consumed may be an uncertainty. Furthermore, the test substance

in pollen and/or nectar may break down to less toxic, or potentially more toxic, metabolites as it is

stored and not immediately consumed. This aspect of storage also presents an uncertainty for the

exposure of the colony to a test chemical.

Relating exposure to weather, bees will not forage during rain storms or when temperatures are too low

for bee flight. This may affect the activity and the strength of the hives, as well as exposure to the test

chemical. Furthermore, in temperate areas, an unseasonably cold winter may also affect loss of hives in

both control and treated groups due to overwintering stress and confound the ability of the study to

detect treatment effects. 0 5 10 15 20 25 30 35 40OverallBloomCategory

Date of Assessment 

## Overall Bloom Category at Treated Plots

Plot 2

Plot 3

Plot 4

Plot 8Page 142 of 275 4.3.5.2 Exposure to Other Pesticides

Agricultural fields are rarely separated in space and time from other pesticides. These products may be

applied to the same crop as part of grower standard practice, or they may be applied on neighboring

fields on which honey bees may forage or from which the pesticides may drift into the test or control

plots of a full field study. In the case of persistent plant protection products, these chemicals may

remain on the test field or in the adjacent field well after the application. These products may then be

collected by honey bees and brought back to the hive thereby serving as a source of exposure to other

chemicals or even the chemical under study.

4.3.5.3 Colony size

The size of the test colonies is an important consideration for the design of the field study. According to

EFSA recommendations, a population of 10,000 individuals is identified as the minimum size. However,

this size is not representative of a normal colony during the spring and summer seasons, which is

typically between 20,000 (spring) and 60,000 or more (June

‐ July) individuals. Considering the minimum

size of 10,000 individuals, this size corresponds to the beginning of hive development at the end of the

over

‐ wintering period just before the hive starts rapid expansion in the early spring. Exposure from

different types of applications may occur not only in the early spring ( e.g.

, foliar spray to tree crops) but

also later in spring and during summer ( e.g.

, seed treatment to corn or canola) when larger

‐ sized

colonies are more representative.

EFSA 2012 also notes several points related to the advantages of different sizes of the hives for the field

study. Larger populations may be more efficient honey producers and produce more honey on a per bee

basis while using less per bee over the winter. Thus, a larger colony with more foragers and in

‐ nest bees

could provide a better observation of the effects of the pesticide because of the higher number of bees

involved. However, EFSA also notes that smaller colonies may be more sensitive due to reduced

resilience to replace foragers with nurse bees and a smaller brood area.

4.3.5.4 Separation of treatment from control plots

The separation of the treated plot from the control plot is essential considering the foraging range of the

honey bee. For control and treatment plots with inadequate separation where honey bees can forage

freely, bees from control plots may forage on treated plots and bees from treated plots may forage on

control plots thereby bringing contaminated food into the control hives. Conversely, treatment plot hivePage 143 of 275 bees may forage in control plots as well, thereby decreasing the extent of exposure. This movement of

control and treatment bees between plots can confound the interpretation of the field study.

In one case study, a chemical was applied as a seed treatment in a 130

‐ day study under open field

conditions at four test sites. Each site contained one 1

‐ ha field planted with canola seed that had been

treated with the test substance and one 1

‐ ha field with untreated seed to serve as a control. Each of the

treated and control fields were separated by at least 250 m. Four honey bee colonies were placed in the

middle of each of the eight fields (n=32) during a 3

‐ week canola bloom period beginning on July.

According to the study authors, no other flowering crops or corn grown from seed treated with the test

substance were planted within a 1

‐ km radius of any of the test plots. In addition, the authors also stated

that the availability of alternative forage within 1 km of the test plots was minimal. While potential

forage crops such as soybean, corn, and alfalfa were located within 1 km of some of the test plots, none

were in bloom while the honey bee colonies were in the canola test plots.

The residue analysis of bee

‐ collected nectar and pollen, as well as honey and beeswax, indicated that

bees from the control and treatment groups were cross

‐ foraging in both the control and treatment

fields. The majority of samples (>75%) collected had no detectable residues, whether from colonies in

treated or control fields. Residues of the test material were detected in treatment plot nectar at

concentrations ranging from 0.521 to 2.24 ppb, while concentrations ranged from 0.535 to 0.969 ppb in

control hives when detected. These detections in the control hives indicate that bees from these hives

likely foraged on test plots, which may be a function of inadequate separation between control and

treated test plots or because the forage in some control fields was of lower quality (due to insect

damage and lower rates of plant emergence). It is also possible that treated bees were able to forage in

control plots, which contributes to the uncertainty associated with inadequate separation between

control and treated test plots.

4.3.5.5 Parasites and Pests

There are a number of parasites and diseases that afflict honey bee hives. These include varroa mites,

the microsporidian Nosema spp

., bacteria ( e.g

., American foulbrood; Paenibacillus larvae ), small hive

beetle ( Aethina tumida ), wax moth ( Galleria mellonella ), fungi ( e.g

., chalkbrood; Ascosphaera apis ), and

viruses ( e.g

., deformed wing virus and Israeli acute paralysis virus; IAPV). Some of these pests/diseases,

in particular varroa mites and Nosema spp

, can affect some of the endpoints of concern as well as the

survival of the hives themselves. It is also nearly impossible to eliminate infection/infestation completelyPage 144 of 275 from hives. Therefore, field studies are likely to have some level of infection/infestation involved in the

conduct of the study. Treatments for the pests may occur before the conduct of the study, though this

should not impact the performance of the test hives. If the disease or parasite occurrence is too high,

the ability of the study to detect potential treatment effects may be limited. Furthermore, the condition

of the test hives after any hive maintenance treatments should be evaluated in the acclimation phase of

the test hives prior to exposure to the test substance.

4.3.5.6 Duration of the study

The duration of the study should be based on the persistence of the chemical of interest. However, as

EFSA discusses in their report (EFSA 2012), the maximum duration of the experiment in the guideline

EPPO 170 (28 days) is not adapted to assess the effects of systemic and persistent pesticides on all the

categories of bees inside the colony. Considering that the life of a bee is about 50 days or more during

spring and summer (21 days of pre

‐ imaginal development and at least 28 days for the adult life), and

several months in winter, a longer duration of assessment may be required depending on the chemical

of interest.

4.3.6 Suggested modifications

EPA, PMRA and CalDPR have identified a number of ways to improve upon the basic design of a full field

study. These suggestions are based on field study reviews by EPA, PMRA and CalDPR and

recommendations proposed in EFSA review of scientific methods for evaluating risk concerns to

pollinators (EFSA 2012). These additions or changes are as follows:

 Increase the sizes of the test plots. The size of the plots should reflect typical North American

agricultural areas. The size of the areas should meet the nutritional requirements of the hive for

the duration of the study, in addition to reflecting typical agricultural crop acreage in North

America. It is recognized however, that the bloom period of the test crop may be short relative

to the overall study period, but the study design must consider that the nutritional requirements

of the study colonies are met.

 Statistical analysis should be conducted for all field studies; therefore, field studies should also

determine the power of the test to adequately assess the endpoints relative to the appropriate

level of biological significance. A power analysis should accompany each protocol proposal in

order to determine the number of hives necessary for the conduct of the study. In addition, the

final report should contain a power analysis to determine if the appropriate level of statistical

power has been achieved. While this is the ideal, it is recognized that there can be practicalPage 145 of 275 constraints to achieving a desired statistical power. Actual study designs for higher tier studies

are case

‐ by

‐ case and are intended to address specific uncertainties identified in lower

‐ tier

and/or open literature studies.

 Measures of exposure are essential to the interpretation of the field study. Consequently, the

study should measure residues in incoming pollen (through the use of pollen traps) and/or

nectar (sampling of the bee honey stomach), measure residues in stored pollen and nectar pre

‐

exposure and at various times post

‐ exposure, measure bloom intensity and duration of that

intensity, and identify sources of pollen and nectar (through pollen trapped in in

‐ coming nectar).

 Limit disease/pests using new hive equipment or source bees from low pest incidence sources.

 A field study should provide for adequate separation of treated and control plots. However,

climatic and landscape variations may preclude greater distances between plots depending on

these variations over varying geographic scales. According to EFSA (EFSA 2012), the field tests

should use areas with similar environmental conditions, where possible with at least 4

– 6 km

(2.5

– 3.7 miles) between treated and control plots.

 The duration of the study should match the concern of the chemical. According to the EFSA

2012 scientific opinion, the colonies that are used in the experiment should be monitored for a

period of time covering all of the flowering period and beyond and last at least two brood

cycles. When residues are persistent, monitoring should continue through winter to after the

over

‐ wintering period. For the over

‐ wintering monitoring, the test and control colonies should

be placed in an area far from intensive agriculture in order to limit the exposure of hives to

pesticides other than from the test crop. This aspect of the design is necessary not only for the

treatment groups but also for the control groups as exposure to the latter may lead to an

underestimation of the toxic effect of the test material.

 Finally, EFSA 2012 proposes other recommendations to control or increase exposure of bees to

test material. The study should use highly attractive crops and regular assessments of foraging.

Pollen traps should be used to determine the pollen levels coming in. The pollen should be

analyzed not just for color but for floral origin (microscopic examination of pollen) as well. The

pollen should be subjected to a residue analysis to confirm that the bees near the treated field

have been exposed and to confirm that the control colonies have not been contaminated. Other

means of assessing or controlling exposure include the following:Page 146 of 275  Foragers on the flowers

– a quantitative assessment of observations of foraging bees on

the flowers of the treated or control field, in combination with a qualitative assessment

of the behavior of the foraging bees

 Foragers on the flight back to the hive– a qualitative assessment of observations of

foraging bees returning to the hives, including aggressive interactions with the guard

bees

 The in

‐ nest bees

– quantitative assessment of hive brood following OECD 75 guidance

document (OECD 2007)

 Residue analysis of in

‐ hive bees (in particular nurse bees)/dead bees from bee trap

 Evaluation of the storage of food and the residues contained in the food stores (bee

bread, honey)

 Removal of food stocks

Implementation of the above additions or modifications would be part of the study development phase.

As has been the approach to date for pollinator field studies, EPA as well as PMRA and CalDPR have

requested that registrants submit protocols for such studies prior to study initiation. This white paper

proposes to continue this approach in order to promote efficiency and increase the potential for success

given the cost of pollinator field studies in terms of time and resources.

Because of the expense involved in conducting and reviewing full field pollinator studies, this paper has

gone into detail on study design elements to consider when conducting such tests. However, the

ultimate design of the study will be dictated by the specific uncertainties it is intended to address. While

these studies are conducted in response to concerns identified in lower tier studies, the full field study is

ultimately intended to address whether risks estimated using lower tier exposure and effect data occur

under actual use conditions. The results of full field studies can have an important effect on the

understanding of lower tier data and of the potential risks.Page 147 of 275 

### 5 Risk Characterization

Risk characterization represents the final step in the risk assessment process and is intended to

integrate estimates from the exposure and effect characterizations to derive quantitative estimates of

risk (USEPA 2000). Similar to the process used for evaluating potential risks to other taxa and as

discussed earlier in this document, screening

‐ level ( i.e

., Tier I) risk assessments typically rely on point

estimates of potential exposure and effects which are considered conservative. The risk characterization

is also intended to articulate assumptions, limitations and uncertainties associated with the risk

assessment. In clearly articulating each step of the risk assessment and its underlying

process/assumptions, it assures that the process is transparent and the assessment itself is clear,

consistent and reasonable. That is to say that the risk manager and any stakeholder can understand how

risk conclusions were arrived at, that the approach used is consistent with processes identified in EPA

guidelines, that it could be readily repeated and that relatively similar conclusions would be reached

given the same underlying data and assumptions.

The risk characterization consists of two components, i.e

., risk estimation and risk description. In risk

estimation exposure and effects data are compared in the form of a ratio ( i.e

., RQ) and within the

context of regulatory levels of concern. In the risk discussion section, multiple lines of evidence are

considered to further qualitatively describe potential risks. These lines of evidence can include incident

data and studies reported in open literature which have been determined to be of sufficient quality. The

risk description section then evaluates lines of evidence, in terms of the adequacy and quality of data as

well as the degree and type of uncertainty, used in support of the risk assessment. To the extent

possible, the risk description should attempt to articulate the nature and intensity of effects as well as

their temporal ( e.g

., whether recovery occurs) and spatial scale. In addition, this risk description should

evaluate the effects of potential mitigation on risk estimates.

As depicted earlier in Figures 2 and 3

, the proposed risk assessment process is intended to be iterative.

At a screening level (Tier 1) RQ values are estimated and if they exceed LOCs, estimates of exposure may

be refined based on measured rather than modeled residue levels. If LOCs are still exceeded based on

laboratory

‐ derived toxicity endpoints for individual bees, higher tier refinements can be considered to

determine whether the whole colony may be affected under semi

‐ field (Tier II) or full

‐ field (Tier III)

testing conditions. In the following sections the risk estimation and risk description components of risk

characterization are discussed.Page 148 of 275 5.1 Risk Estimation

RQ values represent the ratio of point estimates for exposure (numerator) and effects (denominator). As

discussed in the exposure characterization section, the screening

‐ level risk assessment relies on

exposure estimates for foliarly applied compounds based on T

‐ REX (dietary exposure) and Koch and

Weisser 1997 (contact exposure) while dietary exposure estimates for soil treatments are estimated

using the Briggs’ model and seed treatments are based on ICP

‐ BR’s 1 mg a.i./kg default ( Table 22 ). As

with all risk assessments, one of the most relevant questions early in the assessment is whether

exposure to bees is likely. This question represented one of the earliest phases of the risk assessment

process depicted earlier in Boxes 2a and 2b of Figures 2 and 3 of Section 1

. For those products for which

bees are likely to be exposed, the screening

‐ level (Tier 1) assessment considers potential risks from both

contact exposure (depicted in Figure 2 of Section 1 ) and ingestion of residues (depicted in Figure 3 of

Section 1 ). Effects estimates for use in adult honey bee RQ values rely on the acute contact ( Box 4a of

Figure 2 of Section 1 ) and oral LD 50 values for young adult bees ( Box 4b of Figure 2 and Box 4a of Figure

3 of Section 1 ) while potential risks to bee larvae are estimated using the LD 50 value for larvae ( Box 4c of

Figure 2 and Box 4b of Figure 3 of Section 1 ). Although the risk assessment process developed through

the SETAC Pellston workshop (Fischer and Moriarty 2011) recommended the use of a NOAEC value for

determining acute risk to larvae, these values are not routinely measured in the current acute toxicity

tests, as the studies have regression

‐ based designs and do not typically have sufficient replication to

support the hypothesis testing needed to support the development of a reliable NOAEC. Additionally, as

discussed in the effects characterization section of this white paper, chronic toxicity tests have not been

sufficiently vetted at this time for consistently measuring such effects; therefore, while this white paper

discusses the role of such measurements in the risk assessment process, suitable tests have yet to be

developed to allow the calculation of chronic RQ values.

Table 22 summarizes the exposure and effect estimates used in developing the Tier 1 screening

‐ level RQ

values for individual adult bees and larvae for foliar spray applications, soil applications, seed treatments

and tree trunk applications. The appropriate exposure values and effect endpoints would be used in

Boxes 4a

, 4b and 4c of Figure 2 of Section 1 and Boxes 4a and 4b of Figure 3 of Section 1

. The resulting

RQ values would then be compared to the LOC which is discussed in the next section.Page 149 of 275 Table 22. Summary of exposure and effect estimates used in deriving risk quotients for Tier I risk assessments.

Measurement

Endpoint Exposure

Route Exposure Estimate a Acute Effect

Endpoint Chronic Effect Endpoint

Foliar Applications

Individual Survival

(adults) Contact (2.7 μ g a.i./bee)*AR Acute contact

LD 50 None

Individual Survival

(adults) Diet (32 μ g a.i./bee)*AR c Acute oral LD 50 Acute adult oral NOAEL (effects to

survival or longevity)**

Brood size and

success Diet (13 μ g a.i./bee)*AR c Larval LD 50 ** Acute oral NOAEL for larvae (effects

to survival, amount of brood) d

Soil Treatments

Individual Survival

(adults) Diet (Briggs EEC b )(0.29

g/day) Acute oral LD 50 Acute adult oral NOAEL (effects to

survival or longevity) d

Brood size and

success Diet (Briggs EEC b )(0.12

g/day) Larval LD 50 Acute oral NOAEL for larvae (effects

to survival, amount of brood) d

Seed Treatments

Individual Survival

(adults) Diet 0.29 μ g a.i./bee Acute oral LD 50 Acute adult oral NOAEL (effects to

survival or longevity) d

Brood size and

success Diet 0.12 μ g a.i./bee Larval LD 50 Acute oral NOAEL for larvae (effects

to survival, amount of brood) d

Tree Trunk Applications

Individual Survival

(adults) Diet 0.29*( μ g a.i.

applied to tree/g of

foliage) Acute oral LD 50 Acute adult oral NOAEL (effects to

survival or longevity) d

Brood size and

success Diet 0.12*( μ g a.i.

applied to tree/g of

foliage) Larval LD 50 Acute oral NOAEL for larvae (effects

to survival, amount of brood) d

AR = application rate (in lbs a.i./A)

a Based on food consumption rates for larvae and adult worker bees as described in Appendix 1.

b Exposure to soil treatments is based on the modified Briggs plant uptake model; whereas seed treatment exposure is based on the

assumption of 1 mg/kg concentration of a pesticide in pollen and/or nectar

c See T

‐ REX manual for method of calculating the exposure value when there are multiple applications.

d Test is currently under development.

5.1.1 Levels of Concern (LOCs)

Historically, the EPA has not quantified risk to honey bees using the deterministic RQ

‐ based approach

used for other taxa. In the current approach, the potential hazard to bees is qualitatively based on the

outcome of acute contact toxicity tests. This has typically resulted in recommended label language

similar to that contained in the Environmental Hazards section of the Label Review Manual for honey

bee hazard statement (USEPA 2012). Although the language is intended to address potential acute

effects, the guidance indicates that label language to reduce potential chronic hazards to bees will be

dealt with on a case

‐ by

‐ case basis.Page 150 of 275 One method which has been used to determine whether the potential risk from pesticides is sufficiently

low such that either additional refinements in risk estimates or mitigation is not needed, is through the

use of monitoring data. In the European Union, hazard quotients (HQ) derived by dividing the maximum

application rate for a sprayed product by the acute median lethal dose (LD

50 based on oral or contact

toxicity) are compared to a value of 50. If the HQ is less than 50, the product is considered to be safe for

bees (Thompson and Thorbahn 2009, Mineau et al

. 2008) unless the product is an insect growth

regulator. The value of 50 is based on an analysis of more than 20 years of honey bee colony incident

data from the Wildlife Incident Investigation Scheme (WIIS), which were suspected to be associated with

pesticide applications and represented a total of 234 poisonings (Mineau et al

. 2008). The WIIS is a

voluntary reporting system in the United Kingdom, and it relies on beekill incident reports from

beekeepers and other interested organizations. Information on pesticide use was obtained from the UK

Pesticide Usage Survey data which are obtained from crop surveys conducted every 2

– 6 years over a

21

‐ year period. Based on this analysis, it was determined that HQ values <50 posed negligible risk of

hive mortality incidents. The paper by Mineau et al

. 2008 acknowledges the potential for under

‐

reporting of beekill incidents in the field, i.e

., acute bee mortality is not pronounced at the hive itself and

goes unobserved and that the predictive models developed in their analysis may not account for all of

the factors that could influence the likelihood that the use of a pesticide would result in acute mortality.

Although the risks to honey bees have not been quantified in past pesticide assessments conducted by

EPA, assessments conducted in response to litigation under the Endangered Species Act have required

the quantification of potential direct and indirect effects to federally

‐ listed threatened or endangered

species (hereafter referred to as “listed”). For the purposes of these assessments, OPP developed

interim guidance (dated May 15, 2007) recommending that acute RQ values are compared to a level of

concern (LOC) of 0.05 to determine whether the use of a chemical is likely to result in adverse acute

effects to listed terrestrial invertebrate species (CFR40 2012 b ) 14

. According to the interim guidance, the

LOCs for birds, mammals and aquatic species were based on 1975 regulations for the enforcement of

FIFRA (40 CFR Part 154: 49005, 49007 and 49016). For birds and mammals, the following regulation

applied:

“[I]t can be estimated that a dose or exposure of 10 times lower than the LD 50 or LC 50 would be expected

to lead to mortality rates of about 0.01 percent (assuming a dose

‐ response slope of 4.5) and 4 percent

(assuming a dose

‐ response slope of 2), respectively. A dose 5 times lower than the LD 50 or LC 50 would be

14 For the current list of Federally endangered and threatened arthropod species, see:

http://www.fws.gov/endangered/species/us

‐ species.htmlPage 151 of 275 expected to lead to mortality rates of about 0.1 percent and 10 percent, respectively. These values were

used as a basis for selecting safety factors of 5 to 10X for setting the classification criteria for protecting

wildlife.” (CFR40)

A safety factor of 10, which results in an acute LOC of 0.1, was established for birds and mammals;

however, an acute LOC of 0.05 was set for aquatic species as an added measure of protection since

these organisms were considered confined to a particular location and did not have the same

opportunity as birds and mammals to avoid exposure. This same rationale was applied to terrestrial

invertebrates as well.

In the following section, an approach is described for developing LOCs with which to compare the acute

and chronic RQ values for bees proposed in this white paper. The approach follows a similar process

used previously but takes greater advantage of insect pollinator

‐ specific toxicity data, including slope

information from acute toxicity studies with bees, and the extent to which bee colonies may be able to

sustain/accommodate acute mortality events.

Although EPA typically relies on a default acute dose

‐ response slope of 4.5 (95% confidence interval: 2

–

9), an analysis of the slope values for registrant

‐ submitted honey bee acute contact and oral toxicity

data indicates mean (95% confidence interval) values of 3.93 (1.32

– 9.56) and 3.40 (1.33

– 7.18)

respectively ( Table 23 ). These values are relatively close to the default value used for other taxa. Based

on various slope values and the likelihood of individual mortality estimated using the EPA Microsoft®

Excel

‐ based Individual Effect Chance Model (IEC; Version 1.1), which allows for such calculations by

entering slope estimates, various LOCs have been derived ( Table 24 ). Depending on the slope of the

dose response curve from acute contact and oral toxicity studies and on the level of mortality, LOC

values range from 0.02 for 1% mortality with a slope of 1.4 to an LOC of 0.64 for 10% mortality based on

a slope of 6.6.Page 152 of 275 Table 23. Statistics associated with slopes of acute oral and contact toxicity data available in EFED’s toxicity

database for pesticides.

Statistic Contact Oral

number 69 26

min 0.8 1.2

max 18.2 8.5

average 3.9 3.4

5 th percentile 1.3 1.3

10 th percentile 1.5 1.4

25 th percentile 2.1 2.1

median 3.0 3.2

75 th percentile 4.9 4.4

90 th percentile 6.6 5.0

95 th percentile 9.6 7.2

Table 24. Levels of concern associated with different slope values and different levels of mortality in worker

bees.

Slope Comment 1% mortality 5% mortality 10% mortality

1.4 10 th percentile slope (oral) 0.022 0.067 0.12

1.5 10 th percentile slope (contact) 0.028 0.08 0.14

3 Median slope (contact) 0.17 0.28 0.37

3.2 Median slope (oral) 0.19 0.31 0.40

5 90 th percentile slope (oral) 0.34 0.47 0.55

6.6 90 th percentile slope (contact) 0.45 0.56 0.64

As discussed throughout this document, the loss of individual bees is an important indicator of toxicity

and useful for estimating risk; however, the protection goals identified in the problem formulation

section of this white paper as they relate to honey bees depend on the proper functioning of the colony

itself. Discussions with beekeepers who have reported incidents to the EPA indicate that colonies can

(depending on the time of year) sustain sudden losses of 30

– 40% and recover, although losses at such

levels may compromise honey production for a period of 6 weeks. If the colony were to experience such

losses during a period of dearth ( i.e

., when pollen and nectar sources may not be readily available to

foraging bees), the effects could be more protracted and result in substantially reduced food reserves

and potentially increased vulnerability to disease. The reasonable level of mortality on which to set the

LOC is based on the amount of mortality test guidelines allowed in control groups ( i.e

., 10%). The

proposed Tier I method includes a LOC of 0.4 for acute exposure. This value is based on a median slope

of 3.2 and a limit of 10% mortality ( Table 27 ) for both estimating risk from either an acute oral or

contact exposure of larvae or adult bees. Although chronic toxicity test guidelines are not currently

available for bees, the evaluation of potential chronic risk for other taxa is typically conducted byPage 153 of 275 comparing the NOAEC from such studies to EECs to ensure that that ratio is below 1, i.e

., the LOC for

chronic risk is set to 1.0.

With reference to the decision trees depicted in Figures 2 and 3 of this document, the screening

‐ level

assessment compares RQ values to LOCs ( Box 5 of Figures 2 and 3 ) to determine whether there is a

presumption of minimal risk or whether additional refinements may be needed. Based on the preceding

discussion, the LOC to which the screening

‐ level acute RQ values would be compared is 0.40. For

pesticide uses where RQ values exceed the LOC, refinement options for both exposure and effect

numbers have been discussed in this white paper. These include the use of measured residues values

rather than modeled estimates to decrease the magnitude of the RQ’s numerator; such refinements in

the exposure estimates (Tier II exposure; Box 9a of Figures 2 and 3 ) are considered to be part of the Tier

I risk assessment process since the result of such refinements enables the calculation of an RQ value

( Box 6 of Figures 2 and 3 ). Refinements to Tier I effects endpoints are based on the understanding that

the Tier I studies are on individual bees and the assessment endpoint for honey bees is at the colony

level. Therefore, when Tier II semi

‐ field studies (depicted in Box 9b of Figures 2 and 3) and/or Tier III

full

‐ field studies (depicted in Box 12 of Figures 2 and 3 ) are available, they should be evaluated to

determine whether effects to individual bees translate into adverse effects at the colony level, the

nature of those effects and the duration of such effects. Placing RQ values into context through the use

of additional lines of evidence is conducted in the risk description step of the risk assessment and is

discussed in the next section.

5.2 Risk Description

Screening

‐ level estimates of risk at Tier I are intended to be conservative and therefore protective for

taxa for which the test organism serves as a surrogate. As with other taxa, the reliance on conservative

estimates of exposure and effects for bees needs to be appropriately characterized. The risk description

section is intended to evaluate all of the lines of evidence available for a particular chemical in

conjunction with the quantitative RQ values. Information from open literature (including peer reviewed

journals and “grey literature”, such as published dissertations) and registrant

‐ submitted studies which

meet the standard for inclusion in risk assessments can be used to qualitatively describe potential

adverse effects which are expected to result from the use of the chemical and can represent useful

sources of information on the toxicity of pesticides to non

‐ Apis bees as well. Given the nature of the

current guideline study designs, the risk estimation section places considerable emphasis on survival asPage 154 of 275 a measurement endpoint; however, it is recognized that the absence of statistically significant levels of

mortality in either individual bees or in colonies cannot be construed as the absence of adverse effects

(Thompson 2002). Thus, this section should include a discussion of sublethal effects which may have

been reported. Additionally, relevant ecological incident data from the EPA EIIS and the National

Pesticide Information Center (NPIC 2012) and through the Canadian PMRA Pesticide Product

Information Database (Health Canada 2012) should be used as lines of evidence to support risk

estimates. Risk assessors should evaluate the multiple lines of evidence in determining whether

additional data should be recommended.

As discussed throughout this document, the risk assessment process is intended to be iterative and

requires that risk assessors and risk managers collaborate to identify potential risk management options

which will dictate the risk assessment process. At the end of Tier I testing, if the pesticide is determined

to be potentially hazardous to bees two options are available. One option is to proceed to Tier II for

further effects testing (depicted as Box 9b of Figures 2 and 3 ) or refinement of exposure estimates

(depicted as Box 9a of Figures 2 and 3 ). The other option is to employ risk mitigation measures ( Boxes 8

and 11 of Figures 2 and 3 ). As noted in the discussion above, these measures usually seek to limit bee

exposure via the use of label restrictions. Where mitigation measures result in reduced loading ( i.e

.,

lower application rates or number of applications), the effect of reduced loading on exposure estimates

should be re

‐ evaluated through available models. Since the lower proposed rates may not have higher

‐

tier monitoring data available with which to calibrate estimated exposure values, it may not be possible

to determine the extent to which modeled estimates reflect actual exposure levels.

Where the focus of Tier I effects testing is on individual bees, Tier II effects testing focuses on the whole

hive. Tier II and III testing may be conducted if concerns are identified in Tier I testing or from other

sources such as published literature. Tier II and III testing is more complex and endpoints identified

relate to impacts at the whole hive level. Thus, mitigation based on this level of testing will be more

refined and will reflect specific issues identified in the tests. However, in general the Tier II ( Box 9b of

Figures 2 and 3 ) tests are the first to examine potential effects to the whole colony and provide an

opportunity to understand whether effects observed on individual bees under laboratory test conditions

extend to the whole colony. In considering the higher tier colony

‐ level toxicity tests, the risk assessment

should include a description of the nature and duration of the anticipated effect and whether there isPage 155 of 275 evidence to support that protection goals may not be attained as a result of the proposed use of a

pesticide.

At this time, the proposed risk assessment method for bees includes qualitative use of Tier II and Tier III

effect studies to characterize potential risks to the whole colony that may result from the proposed use

of a compound. Since these studies are typically conducted at maximum proposed application rates and

on vulnerable crops ( i.e

., crops which are considered to be particularly attractive to bees and serve as

good sources of both pollen and nectar), they are not used quantitatively to generate RQ values.

Studies conducted at Tier II under semi

‐ field (tunnel/feeding) studies may not fully reflect the effects of

chemicals on honey bees which are free foraging and able to select from a broader range of food

sources; however, these studies provide additional information with which to characterize effects on the

whole colony and better enable risk assessor/risk managers to determine whether additional data

and/or mitigation measures are necessary.

The risk assessment process is intended to integrate multiple lines of evidence. The process used for

evaluating these multiple lines of evidence has not been specifically articulated for honey bees;

however, it has been described in a draft guidance developed for evaluating the weight of evidence for

the Endocrine Disruptor Screening Program (EDSP) Tier 1 screening to identify candidate chemicals for

Tier 2 testing (USEPA 2010 b ). In the draft guidance, it indicates that the body of available data is taken

into account for consistency, coherence, and biological plausibility. This analysis not only applies to the

outcome of guideline studies but also other scientifically relevant information, which in the case of risk

assessments for bees would include targeted residue monitoring studies, open literature studies, and

incident reports. More specifically, the evaluation of individual studies includes the characterization of:

 Quality of data and the extent to which effects can be replicated within a laboratory and across

different laboratories;

 nature of the effect(s) seen in the study(ies) ( e.g.

, were the effects seen in studies persistent or

t ransient changes; were sublethal changes or adverse outcomes);

 dose

‐ and time

‐ dependent changes, if available;

 strengths and limitations of results;

 number and type of effects induced and magnitude, and severity of effects;

 consistency, pattern, range, and interrelationships of effects observed across studies, species,

strains, and castes/sexes;

 conditions under which effects occur ( e.g.

, dose, route, duration);

 understanding of MOA and biological plausibility of responses; and,

 specificity and sensitivity of the effect(s).Page 156 of 275 Effects observed in studies are considered in the context of both statistical and biological significance;

the level of confidence is determined by the strengths as well as the limitations and uncertainties

associated with the study.

Since the overall risk assessment integrates information across multiple assessment tiers, key questions

which should be asked include but are not limited to the following.

 How do exposures used to quantify effects compare to those expected in the field? Specifically,

are the duration, frequency and magnitude of exposures used to quantify effects likely to be

higher or lower than those expected in the real world settings?

 How do the spatial scale of exposures used to quantify effects compare to those expected in the

field?

 What is the nature of effects observed across multiple Tiers ( e.g

., duration, magnitude,

frequency)? Are observed effects consistent across multiple Tiers? If not, why not?

 What are major sources of uncertainty and bias in exposure and effects assessment across

different tiers? To what extent can additional studies reduce this uncertainty?

 Are observed effects consistent with what is known about the pesticide’s mode of action?

 What is the biological and statistical significance associated with effects observed in the various

studies? How closely are the observed effects related to the assessment endpoints?

 To what extent do other lines of evidence ( e.g

., incident reports, open literature studies)

support or detract from the exposure and effects reported in submitted studies?

Although this list is not intended to be exhaustive, it demonstrates how information from the different

tiers of the assessment must be integrated to provide reasonable understanding of how the exposures

and effects evaluated at the individual bee and whole colony level relate and how risk estimates are

affected by refinements.

The risk characterization draws on multiple lines of evidence on both exposure and effects (to individual

bees and the colony) based on guideline exposure and toxicity studies as well as open literature studies

and incident data. In doing so, the risk characterization should relate back to the risk hypothesis

articulated and conceptual model depicted in the problem formulation to inform the risk manager of

whether the analysis has led to a presumption of minimal risk or not and identify uncertainties

associated with either conclusion. Ideally, there should be congruence between the different lines of

evidence considered in the assessment; however, where there are differences and/or seeming

contradictions, the characterization should attempt to provide some explanation or identify these as

uncertainties.Page 157 of 275 One area of uncertainty is the extent to which the risk assessment may be protective for non

‐ Apis bees.

Some of the considerations associated with the extent to which honey bees serve as reasonable

surrogates for non

‐ Apis bees have been discussed previously; however, a more detailed discussion

follows.

5.3 Consideration of Non

‐ Apis Bees in Ecological Risk Assessment

The focus of this white paper and proposed risk assessment process on the honey bee, A. mellifera

,

reflects two important factors: 1) honey bees are considered the most important pollinator in most

regions of the world from both a commercial and ecological perspective; 15 and 2) standardized test

methods for evaluating exposure and effects of chemicals in a regulatory context are much more

developed with the honey bee compared to non

‐ Apis bees (EFSA 2012). Nonetheless, within North

America alone, there are an estimated 4,000 species of bees (Michner 2007) and this number rises to

more than 20,000 worldwide (Fischer and Moriarty 2011). Several species of non

‐ Apis bees are

commercially managed for their pollination services, including bumble bees ( Bombus spp.)

, leaf cutting

bees ( Megachile rotundata ), alkalai bees ( Nomia melanderi ), and blue orchard bees ( Osmia lignaria)

,

and the Japanese horn

‐ faced bee ( O. cornifrons ). Importantly, a growing body of information indicates

native bees (in addition to other insect pollinators such as flies, moths, butterflies, beetles, wasps, and

ants) play an important role in crop and native plant pollination, besides their overall ecological

importance via maintaining biological diversity. Although the proposed method does not include a

formal risk assessment process that is specific to non

‐ Apis bees, the need for such a process is clear

owing to potential differences in sensitivity and exposure compared to honey bee. The purpose of this

section is to briefly summarize the aspects of non

‐ Apis bees which require additional consideration in

the context of the current proposed risks assessment process for honey bee. Potential avenues for

modifying the proposed risk assessment process are also explored.

5.3.1 Biological and Ecological Considerations

Several aspects of the biology and ecology of non

‐ Apis bees lead to important differences in the route

and extent to which they may be exposed to pesticides compared to honey bees. These aspects have

been recently reviewed (EFSA 2012, Fisher and Moriarty 2011) and are summarized here briefly.

15 According to Tautz, J. (2008), approximately 80% of the world’s flowing plants are pollinated by insects and 85% of these by

honey bees. In all, the list of flowering plants pollinated by honey bees includes 170,000 species.Page 158 of 275 Specifically, many non

‐ Apis bees are smaller in size and thus, would receive a higher dose on a contact

exposure basis ( i.e

., greater surface area to volume ratio) via intercepting droplets of sprayed pesticide.

Most non

‐ Apis bees are solitary nesting species 16 and therefore, loss of a single nesting adult would have

a much greater consequence on reproduction (at least for that nest) compared to the loss of a single

adult foraging honey bee. Furthermore, the foraging range of non

‐ Apis bees tends to be much smaller

than that of honey bees. As a consequence, non

‐ Apis bees that occupy areas adjacent to treated fields

may be exposed to pesticides at a higher proportion of their foraging area compared to honey bees,

which can forage over long distances (~7 km) in which they are more likely to encounter untreated

forage areas. For ground nesting bees, exposure via direct contact with soil (and inhalation of volatile

soil

‐ applied fumigants) may be a major route of exposure unlike that for the honey bee. Soil and leaf

material are known to be used extensively by some non

‐ Apis bees for nest construction, which may lead

to different types of exposures ( e.g., prolonged contact exposure with contaminated residues on treated

foliage).

5.3.2 Exposure Considerations

The above discussion indicates that differences in biology and life history among Apis and non

‐ Apis bees

will lead to differential exposure to pesticides. To investigate the extent to which exposure estimates for

honey bees may serve as a surrogate for non

‐ Apis bees, comparisons were made in the daily

consumptions rates of pollen and nectar available from the literature as compiled by EFSA (2012).

Although there are a number of uncertainties associated with these consumption estimates, the data in

Tables 25 and 26 suggest that proposed food consumption rate for adult honey bee workers (292

mg/bee/day) is similar to that for adult bumble bee (210

‐ 402 mg/bee/day) and is greater than that of

adult female European mason bee and alfalfa leaf cutting bees (45

‐ 193 and 110

‐ 165 mg/bee/day,

respectively). Food consumption rates estimated for 5

‐ day old honey bee larvae (120 mg/bee/day) are

greater than rates for larvae of the other non

‐ Apis bees (7.8

‐ 83 mg/bee/day) shown in Table 26

. This

suggest that the proposed Tier 1 exposure assessment for food consumption by adult honey bees would

be representative (and somewhat protective) for adults these particular non

‐ Apis bees.

16 Colonies of the social non

‐ Apis bees ( e.g

., bumble bees and stingless bees) tend to be smaller than honey bees.Page 159 of 275 Table 25. Comparison of Oral Exposure to Pollen and Nectar for adult Apis and Non

‐ Apis Bees

Species Nectar consumption rate

(mg/bee/day)* Pollen consumption

rate (mg/bee/day) Total food consumption

rate (mg/bee/day)

Honey bee worker

(A. mellifera) 292 0.04 292

Bumblebee

(Bombus spp.) 183

‐ 372 27

‐ 30 210

‐ 402

European mason bee

(Osmia cornuta) 45

‐ 193 na 45

‐ 193

Alfalfa leaf

‐ cutting bee

( Megachile rotundata ) 110

‐ 165 na 110

‐ 165

na = not applicable

Table 26. Comparison of Oral Exposure to Pollen and Nectar for larval Apis and Non

‐ Apis Bees

Species Male/

female Nectar consumption

rate (mg/bee/day) * Pollen consumption

rate (mg/bee/day) * Total food consumption

rate (mg/bee/day)

Honey bee

(A. mellifera) Female 117 2.7 120

Bumblebee

(Bombus spp.) unknown 60 22

‐ 23 82

‐ 83

European mason bee

(Osmia cornuta) Female 1.8 16.3 18

Male 1.1 9.5 11

Alfalfa leaf

‐ cutting bee

( Megachile rotundata ) Female 6.2 3.1 9.3

Male 5.2 2.6 7.8

* from stored provisions

As discussed previously, non

‐ Apis bees are expected to be exposed to pesticides via soil and plant

material used for nest construction. For the European mason bee, contact exposure to mud by adult

females has been estimated at 200

– 400 mg/bee/day. Similarly, contact exposure of alfalfa leaf cutting

bees has been estimated at 173 mg/bee/day. The current proposed risk assessment process for honey

bee would have to be modified to address soil and foliar contact exposure to non

‐ Apis bees. As

described in the following section, such modification depends not only on the availability of data from

which to estimate exposure, but also adverse effects.

5.3.3 Toxicity Testing Considerations

The main design elements of available laboratory, semi

‐ field and open

‐ field studies of non

‐ Apis bees

( e.g

., Bombus spp

. and solitary bees) was recently reviewed by EFSA 2012. This review identified

published laboratory test methods for:Page 160 of 275  Bumble bees ( B. terrestris, B. impatiens )

 Mason bees ( O. lignaria, O. bicornis)

 Leaf

‐ cutting bees ( M. rotundata )

 Alkali bees ( N. melanderi )

These test protocols mostly involved acute (<96

‐ h) exposures to adult life stages, although some

involved longer exposures to larvae during development. Exposure routes included contact, oral,

residual contact and inhalation. While many of these test methods show promise for assessing risks to

non

‐ Apis bees, EFSA 2012 concluded that additional validation is needed prior to adoption of these test

protocols into a regulatory risk assessment scheme. While many of the non

‐ Apis species identified in the

EFSA report are commercially available, it is uncertain whether suppliers would be able to meet year

‐

round demand for these species should they be needed to support regulatory toxicity testing.

Semi

‐ field protocols were reported as available for bumble bees ( B. terrestris ) and two species of

solitary bees ( M. rotundata and O. lignaria ). As with laboratory tests of non

‐ Apis bees, no formal

guidelines have been established for semi

‐ field tests with these species either. EFSA 2012 noted that

protocol development for B. terrestris requires attention to the life cycle of the colonies (with colonies

active for only 5

‐ 6 weeks) as well as nest construction in the context of evaluating effects on individual

larvae. Further work on establishing reference toxicant and control acceptability criteria was also

recommended to enable semi

‐ field guidelines to be developed.

Open field study protocols were identified by EFSA 2012 for Bombus spp

. and M. rotundata following

similar conceptual approaches as that for A. mellifera

. With Bombus spp

., however, EFSA noted that

additional development is required to assess the effects of pesticides at the colony level. Due to the

construction of the hive, current methods do not enable assessment of the starting condition of the

colonies to be assessed. Furthermore, only an invasive approach is available for assessing effects on the

numbers of brood and adults (killing the colonies at test termination). Additionally, determining

appropriate study conditions for native species such as B. terrestris has been a challenge in designing

studies (Laycock et al

. 2012 and Whitehorn et al

. 2012).Page 161 of 275 5.4 Colony Models

As discussed in the problem formulation, the risk assessment process is meant to be iterative, moving

from a conservative screening level, to increased levels of refinement that focus on areas where specific

risks may exist. These higher tiers are intended to be increasingly representative of actual exposures and

effects. The interpretation of the potential risk of pesticides on bees is complicated by several factors

that are related to the nature of biology of the honey bee and of the proposed tiered system.

The Tier I method is intended to efficiently distinguish between those pesticides that do not pose a risk

to bees and those pesticides where additional characterization and/or data may be required to

understand the potential risks to bees. The proposed Tier I method is designed to represent upper

‐

bound exposures to bees, and does so by estimating exposures and effects to the larvae and worker

bees that receive the greatest doses of pesticides through contact or diet. Although this method is

assumed to be conservative, linking potential decreases in survival of individual forager bees or larvae to

the ultimate survival of the colony may be complicated due to highly dynamic and complex interactions

between honey bee castes and different worker bees ( e.g., foragers, nurse bees, etc.). Among these

complexities is the existence of multiple feedback mechanisms upon which honey bees detect and

respond to natural and anthropogenic perturbations. For example, excessive loss of older forager bees

can result in changes in the age

‐ dependent polyethism ( i.e

., task allocation among social insects) in

which younger worker bees are prematurely recruited for foraging activities in order to maintain

adequate resources for the colony. Although not without their own associated costs ( e.g

., recruitment

of younger foragers can lead to less successful foraging and fewer hive bees to rear developing brood),

such compensatory mechanisms can temporally mask longer

‐ term effects on the hive should effects of

the stressor continue.

Although Tier II semi

‐ field and Tier III full

‐ field studies provide more realistic exposures as well as

endpoints relevant to the entire colony, they are complicated by multiple confounding factors that are

not captured by lab studies. In addition, in order to measure the impacts of a pesticide on the ultimate

survival of colonies, studies require a large number of hives in order to have sufficient replication. As an

alternative, many semi

‐ field or field studies measure sublethal effects ( e.g., amount of brood, changes in

behavior), that like with the Tier I assessment may be difficult to link to the ultimate survival of the

colony. Furthermore, temporal and spatial factors such as season and availability of alternate foragePage 162 of 275 habitat can strongly influence the colony

‐ level response to a given stressor. These factors complicate

extrapolation of adverse effects beyond the conditions associated with the study.

Given these and other challenges in the application of empirically

‐ derived stressor

‐ response

relationships in honey bee risk assessment, it seems that at least conceptually, the use of colony

‐ level

ecological models would offer many advantages for assessing risks of pesticides. For example, by

modeling the feedback loops inherent among various groups of worker bees that are differentiated by

their tasks in the hive, the compensatory responses to perturbations can be explicitly assessed. In

addition, pesticide effects can be considered in the context of seasonal variation in colony dynamics and

spatial variation in forage habitat. Critical factors that determine colony success ( e.g

., number of over

‐

wintering bees and associated food reserves) can be integrated into the modeling framework and

output. Importantly, such colony

‐ level ecological models can serve as a platform for integrating the

multiple measures of effects and exposure available from lower tier studies and quantitatively link these

measures to endpoints that are closely aligned with the proposed assessment endpoints, which include

population size and stability of managed bees and quantity and quality of hive products, such as honey.

5.4.1 Description of Desired Characteristics of Models that May be used for Risk Assessments of

Bees

In order to use a colony level model for pesticide risk assessments, a desirable model would have the

ability to derive measurement endpoints that are consistent with the protection goals stated in the

problem formulation of this white paper. These protection goals include provision of pollination

services, production of hive products ( e.g

., honey) and contribution to pollinator biodiversity. The

measurement endpoints that have been identified for use in risk assessment to meet those goals

include: 1) colony strength, 2) colony survival, 3) quantity and quality of honey and 4) species richness

and abundance. The first three measurement endpoints can be assessed using a colony

‐ level model for

honey bees; therefore, a desirable colony model would estimate the number of worker bees in a colony,

the amount of honey the hive produces, and predict the likelihood that a colony would survive.

As discussed above, the biology of the honey bee is complex; however, in order to function, a simulation

model must simplify the system which it represents. A colony

‐ level simulation model should account for

the key interactions among bees and biological characteristics ( e.g

., diet requirements, temperature ofPage 163 of 275 the hive, unique reproductive strategy) that may impact the interpretation of pesticide effects measured

as mortality to a group of bees ( e.g

., foragers) or sublethal effects. When considering other bees or

insects, the honey bee is probably the most studied. A simple search of ScienceDirect® 17 for the term

“ Apis mellifera ” yields thousands of scientific articles related to various aspects of the biology of this

single species. Although the biology of the honey bee is highly complex, the extent to which it has been

studied suggests that realistic parameterization of a colony level model with relevant endpoints, such as

queen fecundity and worker longevity, should be possible using available empirical data.

To be used for pesticide risk assessments, a model should be parameterized using exposure and toxicity

data that are representative of an assessed pesticide. As discussed in the exposure characterization,

dietary exposure can be estimated using Tier I methods, such as the T

‐ REX model for foliar spray

applications or the Briggs’ model for soil applications. Exposures to different groups of worker bees as

described by their tasks could be assessed by considering their specific dietary requirements and food

consumption rates (as described in detail in Appendix I). Acute mortality data from Tier I tests or effects

to brood from Tier II or Tier III studies could be incorporated into a colony level model to investigate

impacts of mortality or changes in the amount of brood on the colony (as defined by survival, number of

worker bees or amount of honey).

In addition, because pesticides may be applied throughout the country and throughout the year, an

ideal colony model should be representative of national level usage of a pesticide ( e.g

., application rate,

frequency and timing). In cases where factors related to season or weather ( e.g

., rainfall, temperature)

impact a colony, these factors should be considered, perhaps on a regional basis. Also, the duration of

the simulation should be long enough to reliably predict effects to the desired measurement endpoints.

Finally, in order to be used for risk assessment, an available model should be scientifically defensible and

transparent. A desirable model would be clearly described, peer reviewed, and publically available. The

sensitivity of model results to key assumptions and input parameters would also be documented along

with associated uncertainties. Importantly, the results of a desirable model would be extensively

evaluated using empirical results from colonies exposed to pesticides.

17 Copyright© 2012 Elsevier B.V. All rights reserved. SciVerse ® is a registered trademark of Elsevier Properties S.A, used under

license. ScienceDirect ® is a registered trademark of Elsevier B.V.Page 164 of 275 5.4.2 Description of Selected Colony

‐ level Models

A number of colony

‐ level models with A. mellifera have been described in the published literature in

order to address specific research objectives (e.g

., investigate impacts of temperature and mites on

colonies); however, a recent review of these models concluded that existing models of honey bee

colonies did not seem ready for immediate use in ecological risk assessments because they do not

simultaneously consider multiple stressors and link an explicit representation of foragers to the

landscape, many of the models are not transparent, and sensitivity analyses and evaluations to

empirically based outcomes have not been conducted for most models (Fisher and Moriarty 2011). With

modifications, there are several colony

‐ level models that have potential to be used for ecological risk

assessments. Perhaps in the future, some models may be adapted for use in assessing the risks of

pesticides. In order to provide background on the potential use of colony

‐ level models to assess risks of

pesticides to honey bees, a sampling of honey bee colony models obtained from the published literature

are summarized below. It should be noted that this section is not intended to be comprehensive of all

available colony

‐ level models for honey bees, nor is it intended to propose and/or endorse the use of a

particular model for future use.

Becher et al

. 2010 recently developed a honey bee colony

‐ level model to investigate the impact of

brood temperature on the age at first foraging of worker bees and subsequent effects on colony

dynamics. In their model, Becher et al

. 2010 conducted a deterministic simulation of the temperature

gradient across a single brood comb of unlimited size ( e.g

., warmer areas near the center of the comb

where brood were concentrated and cooler regions towards the periphery of the comb). This

temperature gradient was informed by empirical measurements of hive temperature and was assumed

to impact brood survival and development rate. Task

‐ specific division of labor was addressed by

considering three age classes of bees: developing brood up to 21 days old, hive bees up to 15 days old

and foraging bees up to 10 days old. A maximum queen egg laying rate of 1,500 eggs/day was modified

by the availability of empty cells in the comb available for depositing eggs, which in turn was determined

by temperature effects on developing brood. A critical component of the model is the number of hive

“heater bees” available for regulating temperature during overwintering. Under different thermal

regimes, colony size was not particularly sensitive to the early onset of foraging and loss of hive bees.

However, the model results indicate that colony survival depends heavily on the minimum number of

bees available for heating the hive during winter. This endpoint is one of the key measures of effectPage 165 of 275 identified earlier for the design of field studies. Although informed by a number of empirical

relationships, output from the model was not compared to field data on colony dynamics nor was the

impact of pesticides included.

Khoury et al

. 2011 developed a compartmental model based on simple mathematical relationships of

worker bee division of labor, bee longevity and colony growth. In their model, Khoury et al

. 2011

hypothesized that increased forager death rates would reduce social inhibition of foraging by older

foragers and consequently, result in increased recruitment of younger bees into the forager workforce.

This premature recruitment of foragers in turn would lead to fewer hive bees available for brood rearing

and create a feedback mechanism leading to greater impacts on brood rearing. Due to the much greater

mortality rate of foragers compared to in

‐ hive bees, continued loss of foragers would result in additional

demand for young foragers and subsequent reduction of hive bees. Khoury et al

. 2011 concluded that

when sustained indefinitely, a reduction in forager longevity by 2.8 days (corresponding to a death rate

of 0.355 bees/day) represented a threshold for long

‐ term colony survival. The authors caution, however,

that their model was specifically developed to investigate how changes in forager mortality rate affected

division of labor and colony growth and not intended for application beyond these questions.

Schmickl and Crailsheim 2007 developed one of the more complex models of honey bee colony

dynamics called “HoPoMo.” This model considers many factors that influence colony size dynamics

including: seasonal variation in queen egg laying rate, availability of pollen stores, temporal polyethism

among worker bees in relation to division of labor, three stages of brood development (egg, larvae, and

pupae), larval cannibalism by workers, availability of empty comb for egg laying, efficiency of brood

nursing as influenced by the ratio of larvae to nurse bee, regulation of foraging demand for nectar and

pollen resources, influence of weather conditions (temperature and precipitation)and even swarming

activity. Several of the model algorithms were verified with empirical measurements from previous

studies ( e.g

., overall worker bee life expectancy, seasonal growth of adult bees and brood cells, growth

of pollen stores and overall colony weight). A sensitivity analysis indicated that mortality rates were

among the most influential set of input parameters. Due to its complexity, HoPoMo uses 60 differential

equations in order to describe the multiple feedback loops and hive dynamics. While the overall pattern

of colony dynamics simulated by HoPoMo appears to agree with empirical measurements of colony

strength, application of the model for evaluating the effects of pesticide exposure was not evaluated at

the time of its publication. Thus, the extent to which HoPoMo would accommodate laboratory and field

‐Page 166 of 275 derived exposure and effects data without significant modification is unclear.

DeGrandi

‐ Hoffman et al. 1989 published a model called “BeePop,” which models the dependency of

queen egg

‐ laying rate on the size of the adult population, temperature and photo

‐ period. The task

division among bee castes is represented by a fixed age of bees and is not altered by outside

perturbations. The model consists of two major components, i.e

., EGGLAY and COLPOP; EGGLAY

determines the number of eggs laid by the queen at any time ( t ) and the percentage of eggs that will

develop into workers and drones is assigned in a subroutine. COLPOP tracks the development of eggs to

adults and predicts the colony’s adult population size at any time ( t ). The BEEPOP model estimates the

number of eggs laid by the queen per day at any time ( t ) as a function of ambient temperature,

photoperiod and adult population size. The proportion of eggs developing into drones is determined by

the amount of sperm remaining in the queen’s spermatheca (which is a function of the age of the queen

and the number of drones with which she mated) as well the photoperiod and the foraging population

size in the colony. The model then estimates the number of larvae that develop into adults (assuming

that only 85% emerge as adults) and the number of adults which become foragers in 21 days after

emergence. The user specifies the number of days which forager bees live and the model estimates the

colony size in terms of total number of adult bees.

Thompson et al

. 2005 is one of the few publications to model the impacts of pesticides on colony

dynamics that adapted the model of Varroa mite ( Varroa spp .) infestation growth by Wilkinson and

Smith 2002 to predict the impacts of four pesticides on colony size. In their model, the authors

considered the impact of pesticides including an insect growth regulator, chiton synthesis inhibitor,

ecdysteroid synthesis inhibitor, and an ecdysteroid analogue on queen egg laying rate, brood

development and two castes of adult workers ( i.e

., younger nurse bees and older forager bees).

Seasonal aspects of queen egg laying and adult mortality rates were incorporated into model

predictions. In addition to direct effects on bee survival, indirect effects were simulated based on

reduced availability of nurse bees for rearing brood via premature recruitment of foragers and increased

mortality rates of foragers. Importantly, the authors demonstrated the different effects of forager and

brood mortality which occurred at different times of the year. The primary output of the model was the

size of the colony available at over wintering which is considered an important indicator of hive survival

during the following spring.Page 167 of 275 Makela et al

. 1993 developed a model that included colony

‐ level and Apis population level dynamics

that could be applied spatially to a landscape to predict the spread of African honey bees. This model

accounted for intra

‐ colonial parameters, including ages of worker and queen bees, numbers of eggs laid,

amounts of food stored in the hive as well as genetics as they could distinguish between a European and

Africanized hive. The model distinguished between different types of worker bees (according to their

job). Colony

‐ level outputs included several that are relevant to the defined protection goals, including

number of worker bees, amount of brood, mass of honey and pollen stored in the hive and age of

worker bees. The model also predicted population

‐ level outcomes, including the spatial distribution of

colonies over time and the genetic make

‐ up of the colony ( i.e., the extent to which a colony was

Africanized). The model was not evaluated using empirical results relevant to the colony

‐ level and

population

‐ level predictions. Also, this model does not consider impacts of pesticide exposures on the

intra

‐ colonial parameters. Despite those limitations, this model presents an interesting example of how

a simulation model can generate spatially explicit, population

‐ level outcomes that reflect colony

dynamics and the reproduction of bees.

5.4.3 Summary Regarding Colony

‐ level Models

Although current models of honey bee colonies appear to require additional development and

evaluation prior to their immediate use in ecological risk assessments for pesticides, a number of the

available colony

‐ level models (or portions of them) have potential to be used for ecological risk

assessments. At this time, EPA is considering the potential utility of these models in integrating diverse

types of information related to pesticide exposures and effects in the context of the complex biology of

a honey bee colony and the desired protection goals. The discussion above focused on some

considerations that may determine the utility of a model for ecological risk assessments followed by a

description of a subset of models that are available in the published literature. The EPA is interested in

the SAP’s thoughts on the potential utility of colony level models, including future considerations for

developing these types of models into useful tools for pesticide risk assessment purposes.Page 168 of 275 

### 6 Epilogue

This white paper has provided a description of the proposed process depicted earlier in Figures 2 and 3

for evaluating the potential risks of pesticides to honey bees. The proposed process is relatively

consistent with the process used to evaluate risk to other taxa in that it is both tiered and iterative and it

makes use of similar models for estimating exposure. At Tier I, the processes serves as a screen that is

based on relatively conservative estimates of exposure that reduce the likelihood of making a Type II

error, i.e

., concluding that there is not an effect when there is, while attempting to minimize Type I

errors, i.e

., concluding that there is an effect when there is not. Achieving this balance can be

challenging since the Tier I screen is based on effects to individual bees in a laboratory while the actual

effect of interest is whether there are adverse effects to the colony as a whole. A screen that is too

conservative/protective will not serve as an effective screen but rather as a wall requiring that all

chemicals undergo higher

‐ tier exposure and/or effect testing. This would result in an unreasonable level

of resources both on the part of the agencies tasked with reviewing such data as well as on the

regulated community tasked with conducting such studies. Although the process is characterized as

quantitative, deterministic RQ values are only calculated at a screening level while Tier II and Tier III

assessments are intended to be qualitative and will rely on best professional judgment regarding the

integration of a wide range of data rather than an LOC. Measured residue values from Tier II and III

exposure studies are proposed for use in refining RQ values in the screening

‐ level assessment process;

however, colony

‐ level effects studies at Tier II and III are not proposed for use in refining RQ values. The

proposed process provides information on exposure and effects for both individual bees and at the

colony level; however, there is uncertainty as to the extent that the process can be considered

representative of native species. In the absence of an alternative approach for the non

‐ Apis bees, the

proposed process for honey bees may represent the best available science at this time. The science

underlying the proposed process has evolved considerably over the past 10 years and it is likely that it

will continue to evolve and become more inclusive of non

‐ Apis species. Once exposure and effect

studies are available for native species, the regulatory process will likely accommodate these changes

particularly if it becomes apparent that the current process for evaluating risk is inadequate.

The use of colony

‐ level modeling is expected to enable risk assessors to estimate potential effects on

honey bee colonies using lower

‐ tier effect and exposure estimates and will better enable the results

from multiple lines of evidence to be integrated. Once sufficiently developed and vetted, the models canPage 169 of 275 be used to characterize the likelihood that a colony may not survive given Tier I RQ values that exceed

the LOC. These models could identify which endpoints should be the focus of Tier II semi

‐ field studies

and where both Tier I and Tier II data are available, what is the likelihood of colony loss. Such modeling

efforts could help identify remaining uncertainties that should be the focus of Tier III studies if required

to support risk management decisions.

Significant challenges remain in interpreting the results of full field studies. For example, Figures 12 and

13 depict the pattern of honey bee mortality (mean number of dead adults, larvae and pupae combined)

and overall colony strength (in mean number of bees per hive), respectively, following the planting of

treated seed. Figure 12 depicts a series of spikes in mortality; however, the majority of the peak

mortality events were associated with periods when the colonies are being assessed (opened) either by

the beekeeper or the researchers. This represents an important consideration in terms of how much

colonies have to be manipulated in order to collect measurement endpoints and how these

measurements impact the proper maintenance and function of the colony. Although there are multiple

peaks in mortality throughout the course of the study, the highest levels of mean mortality occurred

prior to initiation of the study in both treated and control colonies. While treated colonies appear to

exhibit higher levels of mortality during each peak mortality event, it is uncertain whether the average

level of mortality is having a detrimental effect on the colonies. Figure 13 depicting colony strength

suggests roughly a two week period when treated colonies exhibited fewer bees relative to control

colonies; however, afterwards there was no apparent difference between controls and treated colonies

based on this measurement endpoint. Although it is difficult to interpret the overall outcome of this

study by examining these two measurement endpoints alone, the data underscore the challenges in

understanding the extent to which a particular parameter would have to change to have a meaningful

impact at the colony level. While there are transient effects during the study, these effects (taking the

graphs at face value) do not appear to have affected the overwintering success of the colonies.Page 170 of 275 Figure 12. Adult honey bee mortality following planting of treated seed

.

Figure 13. Overall honey bee colony strength (mean number of bees per hive) following planting of

treated seed.Page 171 of 275 As indicated in this paper, the science of evaluating the potential effects of pesticides on honey bees and

more broadly on non

‐ Apis bees, continues to evolve. It is important that sufficient guidance is developed

to assist in the uniform conduct and interpretation of studies; however, as studies become more

environmentally relevant, the ability to maintain uniformity is reduced. With increased realism, the field

studies are subject to a number of confounding effects that have already been discussed; however, to

the extent that the Tier III studies can be contoured to address specific uncertainties identified in lower

tiers and to the extent that the results of these studies are considered in the context of lower

‐ tier

studies and other scientifically relevant information, risk assessors will be able to utilize a weight

‐ of

‐

evidence approach to develop consistent and plausible risk assessments for bees.Page 172 of 275 7 References

Alix, A., M. P. Chauzat, S. Duchard, G. Lewis, C. Maus, M. J. Miles. E. Pilling, H. M. Thompson, K. Wallner.

2009. Environmental Risk Assessment Scheme for Plant Protection Products

– Chapter 10:

Honey bees

– Proposed Scheme. Pages 27

‐ 33 in P. A. Oomen and H. M. Thompson (editors)

International Commission for Plant

‐ Bee Relationships Bee Protection Group 10 th International

Symposium Hazards of Pesticides to Bees, Buscharest (Romania). October 8

– 10, 2008. Julius

–

Kühn Archiv

Alix, A., M. P. Chauzat, S. Duchard, G. Lewis, C. Maus, M. J. Miles, E. Piling, H. M. Thompson, and K.

Wallner. 2009. Guidance for the assessment of risks to bees from the use of plant protection

products applied as seed coating and soil applications

– conclusions of the ICP

‐ BR dedicated

working group. Pages 15

– 27 in P. A. Oomen and H. M. Thompson (editors) Hazards of

Pesticides to Bees, 10 th International Symposium of the ICP

‐ BR Bee Protection Group, Bucharest

(Romania), October 8

– 10, 2008. Julus Kühn Arch 423.

Alva, A.K.; Fares, A. and H. Dou. 2003. Managing citrus trees to optimize dry mass and nutrient

partitioning. Journal of Plant Nutrition, 26 (8): 1541

‐ 1559.

Ankley, G. T.

, R. S. Bennett, R. J. Erickson, D. J. Hoff, M. W. Hornung, R. D. Johnson, D. R. Mount, J. W.

Nichols, C. L. Russom, P. K. Schmieder, J. A Serrano, J. E. Tietge, and D. L. Villeneuve. 2009.

Adverse Outcome Pathways: A Conceptual Framework to Support Exotoxicology Research and

Risk Assessment. Environmental Toxicology and Chemistry 29(3): 730

– 741.

Atkins, E. L., D. Kellum and K. W. Atkins. 1981. Reducing Pesticide Hazards to Honey Bees: Mortality

Prediction Techniques and Integrated Management Strategies. Division of Agricultural Sciences

University of Calornia. Leaflet 2883.

Aupinel, P. D. Fortini, B. Michaud, P. Mderzycki, E. Padovani, D. Przygoda, C. Maus, J

‐ D. Charriere, V.

Kilchenmann, U. Riessberger

‐ Galle, J. J. Vollmann, L Jeker, M. Janke, J

‐ F. Odoux, J

‐ N. 2009.

Pesticide toxicity on honebee brood in laboratory conditions. 2009. Pages 96

– 102 in P. A.

Oomen and H. M. Thompson (editors) Hazards of Pesticides to Bees 10 th International

Symposium of the ICP

‐ BR Bee Protection Group. Bucharest (Romania), October 8

– 9, 2008.

Julius Kühn Archiv 423.

Aupinel, P., D. Fortini, B. Michaud, F. Marolleau, J

‐ N. Tasei and J

‐ F. Odoux. 2007. Toxicity of dimethoate

and fenoxycarb to honey bee brood ( Apis mellifera ), using a new in vitro standardized feeding

method. Pest. Manag. Sci 63: 1090

– 1094.

Babendreier, D., Kalberer, N., Romeis, J., Fluri, P. and F. Bigler. 2004. Pollen consumption in honey bee

largae: a step forward in the risk assessment of transgenic plants. Apidologie, 35: 293

‐ 300.

Balderrama N.M., Almeida L.O., Núñez J.A. 1992. Metabolic rate during foraging in the honeybee,

J.Comp. Physiol. B 162: 440–447

Beedle, E.C. and Harbin,A.M. 2011. Determination of residues of imidacloprid and its metabolites 5

‐ hydroxy imidacloprid and imidacloprid olefin in bee relevant matricies collected from cotton,Page 173 of 275 grown at locations treated with imidacloprid at least once per year during two consecutive

years. Bayer CropScience, Research Triangle Park, NC. Report Number: EBNTL056

‐ 01.

Becher, M. A., H. Hildenbrandt, C. K. Hemelrijk, and R. F. A. Moritz. 2010. Brood temperature, task

division and colony survival in honeybees: A model. Ecological Modelling 221:769

‐ 776.

Biesmeijer, J. C., S. P. M. Roberts, M. Reemer, R. Ohlemuller, M. Edwards, T. Peeters, A. P. Schaffers, S.

G. Potts, R. Kleukers, C. D. Thomas, J. Settele and W. E. Kunin. 006. Parallel Declines in

Pollinators and Insect

‐ Pollinated Plants in Britain and the Netherlands. Science 21: 351

– 354.

http://www.sciencemag.org/c ontent/313/5785/351.abstract

Bishop, G. 1961. Growth rates of honey bee larva. J. Exp. Zool. 146, 11

‐ 20

Briggs G.G., R.L.O. Rigitano, and R.H. Bromilow. 1987. Physico

‐ chemical factors affecting uptake by roots

and translocation to shoots of weak acids in barley. Pestle Sci 19, 101

‐ 112

Briggs, G., Bromilow, R., Evans, A., Williams, M., 1983. Relationships between lipophilicity and the

distribution of non

‐ ionised chemicals in barley shoots following uptake by the roots. Pesticide

Science

, 14, 492

‐ 500.

Briggs, G.G., Bromilow, R.H., Evans, A.A., 1982. Relationships between lipophilicity and root uptake and

translocation of non

‐ ionised chemicals by barley. Pesticide Science

, 13, 495

‐ 504.

Bromilow, R.H., K. Chamberlain, and A.A. Evans. 1990. Physicochemical aspects of phloem translocation

of herbicides. Weed Science 38: 305

‐ 314.

Bryne, F.J., J.G. Morse, P.K. Vischer, E.E. Grafton

‐ Cardwell, W. Leimkuehler. 2011. Determination of

exposure levels of honey bees foraging on flowers of citrus trees previously treated with

imidacloprid. Bayer CropScience, performing laboratory University of California Riverside. Bay

Report # EBNTL056

‐ 7. March 20, 2011.

Butler, C.G. 1944. The influence of various physical and biological factors of the environment on

honeybee activity. An examination of the relationship between activity and nectar

concentration and abundance. The Journal of Experimental Biology, 21:5

‐ 21.

CAPPA. 2010 report: http://capabees.com/main/files/pdf/2010winterloss.pdf

CAPPA. 2011 report: http://www.capabees.com/mai n/files/pdf/canwinloss.pdf

Carousel, R.F., J.C. Imhoff, P.R. Hummel, J.M. Cheplick, A.S. Donigian, Jr., L.A. Suarez. 2006. PRZM

‐ 3, A

Model for Predicting Pesticide and Nitrogen Fate in the Crop Root and Unsaturated Soil Zones:

Users Manual for Release 3.12.2. National Exposure Research Laboratory, Office of Research and

Development, U.S. Environmental Protection Agency, Athens, GA; AQUA TERRA Consultants,

Mountain View, CA; Waterborne Environmental, Inc., Leesburg, VA.

CFR40. 2012. Code of Federal Regulations Title 40 (Protection of Environment), Chapter I (Environmental

Protection Agency), Subchapter E (Pesticide Programs), Part 158 (Data Requirements for

Pesticides), Subpart G (Ecological Effects) §158.630 (Terrestrial and aquatic nontarget organismsPage 174 of 275 data requirements table.) http://ecfr.gpoaccess.gov/cgi/t/text/text

‐

idx?c=ecfr&sid=66fb614e5 148c846d08b5230c1c07526&tpl=/ecfrbro wse/Title40/40cfr158_main

_02.tpl

CFR40. 2012 b Code of Federal Regulations 40 (Protection of the Environment) Part 154

Chiou, C.T., G. Y. Sheng, and M. Manes. 2001. A partition

‐ limited model for the plant uptake of organic

contaminants from soil and water. Environmental Science and Technology

, 35, 1437

‐ 1444

Choudhary, A. and D. C. Sharma. 2008. Dynamics of pesticide residues in nectar and pollen of mustard

( Brassica juncea (L.) Czern.) grown in Himachal Pradesh (India). Environ Monit Assess, 144: 143

‐

150.

Cornell Law School. 2012. Legal Information Institute. http://www.law.cornell. edu/uscode/text/7/136

Crailsheim, K.; Hrassnigg, N.; Gmeinbauer, R.; Szolderits, M.J.; Schneider, L.H.W. and U. Brosch. 1993.

Pollen utilization in non

‐ breeding honeybees in winter. J. Insect Physiol. 39 (5): 369

‐ 373.

Crailsheim, K.,Schneider, L.H.W; Hrassnigg, N.; Bühlmann, G.; Brosch, U.; Gmeinbauer, R.; and B.

Schöffmann. 1992. Pollen consumption and utilization in worker honeybees ( Apis mellifera

carnica ): dependence on individual age and function. J. Insect Physiol., 38 (6): 409

‐ 419.

Das Graças Vidal, M., de Jong, D., Wien, H.C. and R.A. Morse. 2006. Nectar and pollen production in

pumpkin ( Cucurbita pepo L.). Revista Brasileira de Botânica: 29(2).

Davis, A.R. and R.W. Shuel. 1988. Distribution of C(14)

‐ Labeled carbofuran and dimethoate in royal jelly,

queen larvae and nurse honeybees. Apicologie, 19 (1): 37

‐ 50.

DeGrandi

‐ Hoffman, G., S. A. Roth, G. L. Loper, and E. H. Erickson. 1989. Beepop

‐ A Honeybee

Population

‐ Dynamics Simulation

‐ Model. Ecological Modelling 45:133

‐ 150.

DeGrandi

‐ Hoffman, G., Y. Chen, E. Huang and M. H. Huang. 2010. The effect of diet on protein

concentration, hypopharyngeal gland development and virus load in worker honey bees ( Apis

mellifera L.). Journal of Insect Physiology 56: 1184

– 1191.

Deitz, A. and E.N. Lambremont. 1970. Caste Determination in Honey Bees. II. Food Consumption of

Individual Honey Bee Larvae, Determined with 32 P

‐ Labeled Royal Jelly. Annals of

the Entomological Society of America, 63 (5): 1342

‐ 1345.

Delabie, J.; Bos, C.; Fonta, C. and C. Masson. 1985. Toxic and repellent effects of cypermethrin on the

honeybee: laboratory, glasshouse and field experiments. Pestic. Sci, 16: 409

‐ 415.

Desneux, N., A. Decourtye, and J

‐ M Delpuech. 2007. The Sublethal Effects of Pesticides on Beneficial

Arthropods. Annu. Rev. Entomol. 52: 81

‐ 106

Dively, G.P. and A. Kamel. 2012. Insecticide residues in pollen and nectar of a cucurbit crop and their

potential exposure to pollinators. Journal of Agricultural and Food Chemistry. 60 (18): 4449–

4456.Page 175 of 275 Donahue, R.L., R.W. Miller, J.C. Shickluna. 1983. Soils, an Introduction to Soils and Plant growth, 5th

edition. Prentice Hall, Inc., Englewood Cliffs, NJ.

EFSA. 2012. Scientific Opinion on the science behind the development of a risk assessment of Plant

Protection Products on bees ( Apis mellifera

, Bombus spp. and solitary bees). EFSA Journal 2012;

10(5) 2668. [275 pp.] doi:10.2903/j.efsa.2012.2668. Available online:

www.efsa.europa.eu/efsajournal

Environment Agency. 2006. Evaluation of models for predicting plant uptake of chemicals from soil.

Science report SC050021/SR. Available online at:

http://www.environmentagency.gov.uk/s tatic/documents/Research/sc050021_2029764.pdf

EPPO. 2010. Efficacy Evaluation of Plant Protection Products: Side

‐ effects on Honey bees. PP 1/170 (4).

OEPP/EPPO Bulletin 40: 313–319

EPPO. 2010. Environmental risk assessment scheme for plant protection products: Chapter 10, Honey

bees. Euopean and Mediterranean Plant Protection Organization (EPPO). Bulletin OEPP/EPPO

Bulletin 40, 323–331.

Escalante

‐ Pérez, M. and M. Heil. 2012. Nectar secretion: its ecological context & physiological

regulation. In: J.M. Vivanco and F. Baluška (eds.), Signaling and communication in Plants,

Secretions and Exudates in biological systems. Springer

‐ Verlag, Berlin, Heidelberg.

FAO. 2000. Appendix 2. Parameters of pesticides that influence processes in the soil. In FAO Information

Division Editorial Group (Ed.), Pesticide Disposal Series 8. Assessing Soil Contamination. A

Reference Manual. Rome: Food & Agriculture Organization of the United Nations (FAO).

Federal Office of Agriculture (FOAG). 2009. Bee Health Monitoring in Switzerland (Translation):

Clothianidin. Project Number: G202097, M/356596/01/3. Unpublished study prepared by

Switzerland, Pesticides Dept., and Federal Office of Economic Affairs. 15 p. MRID 47881701

Fischer, D. and T. Moriarty. 2011. Pesticide risk assessment for pollinators: summary of a SETAC

Pellston Workshop. Edited by D. Fischer and T. Moriarty. SETAC Press.

http://www.setac.org/sites/default/file s/executivesummarypollinators_20sep2011.pdf

Fletcher, J.S., J.E. Nellesson and T. G. Pfleeger. 1994. Literature review and evaluation of the EPA food

‐

chain (Kenaga) nomogram, an instrument for estimating pesticide residues on plants. Environ.

Tox. And Chem. 13(9):1383

‐ 1391.

Forster, R. 2009. Bee poisoning caused by insecticidal seed treatment of maize in Germany in 2008.

Pages 126

– 131 in P. A. Oomen and H. M. Thompson (editors) Hazards of Pesticides to Bees,

10 th International Symposium of the ICP

‐ BR Bee Protection Group, Bucharest (Romania),

October 8

– 10, 2008. Julius Kühn Arch 423.

Freeseman, P.L. and Harbin, A.M. 2011. Determination of the Residues of Imidacloprid and its

Metabolites 5

‐ Hydroxy Imidacloprid and Imidacloprid Olefin in Bee Relevant Matrices Collected

from Tomato, a Fruiting Vegetable, Grown at Locations Treated with Imidacloprid at Least OncePage 176 of 275 Per Year During Two Successive Years. March 31, 2011. Bayer CropScience Report #: EBNTL056

‐

05.

Fries, I and K. Wibran. 1987. Effects on honey bee colonies following application of the pyrethroids

cypermethrin and PP 321 in flowering oilseed rape. American Bee Journal, 127(4): 266

‐ 269.

Gary, N.E., P.C. Witherell, K. Lorenzen. 1978. Distribution of Honey bees During Water Collection.

Journal of Apicultural Research. 18, 26

‐ 29.

Girolami, V., L. Mazzon, A. Squartini, N. Mori, M. Marzaro, A. Di Bernardo, M. Greatti, C. Giorio, and A.

Tapparo. 2009. Translocation of Neonicotinoid Insecticides From Coated Seeds to Seedling

Guttation Drops: A Novel Way of Intoxication for Bees. Journal of Economic Entomology 102(5):

1808

‐ 1815.

Gmeinbauer, R. and K. Crailsheim. 1993. Glucose utilization during flight of honeybee ( Apis mellifera )

workers, drones and queens. J. Insect Physiol. 39(11): 959

‐ 967.

Hanny, B., and J. Harvey. 1982. Sevin Sprayable Versus Sevin XLR Applied to Field Corn ( Zea mays L.) at

Pine Bluffs, Wyoming

‐ Effects on Honey Bees ( Apis mellifera L.). Am.Bee J. 122(7):506

‐ 508.

Health Canada. 2003. Regulatory Directive: Organizing and Formatting a Complete Submission for Pest

Control Products. DIR2003

‐ 01. http://www.hc

‐ sc.gc.ca/cps

‐ spc/pubs/pest/_pol

‐ guide/dir2003

‐ 01/index

‐ eng.php

Health Canada. 2012. Consumer Product Safety Pesticide Incident Reporting. http://www.hc

‐ sc.gc.ca/cps

‐ spc/pest/part/protect

‐ proteger/incident/index

‐ eng.php

Health Canada. 2012. Consumer Product Safety. Use Site Category Data Code (DACO) Tables.

http://www.hc

‐ sc.gc.ca/cps

‐ spc/pest/registrant

‐ titulaire/prod/_daco

‐ codo/index

‐ eng.php

Hoerger, F. and E.E. Kenaga. 1972. Pesticide residues on plants: correlation of representative data as a

basis for estimation of their magnitude in the environment. IN: F. Coulston and F. Corte, eds.,

Environmental Quality and Safety: Chemistry, Toxicology and Technology. Vol 1. George Theime

Publishers, Stuttgart, Germany. pp. 9

‐ 28.

http://www.nass.usda.gov/Surveys/Guide_to_NASS_Surveys/Bee_and_Honey/index.asp

http://www.plosone.org/article/info %3Adoi%2F10.1371%2Fjournal.pone.0004071

Hoffmann, E.J. and S.J. Castle. 2012. Imidacloprid in melon guttation fluid: a potential mode of exposure

for pest and beneficial organisms. Journal of Economic Entomology, 105(1): 67

‐ 71.

Hung, H. and D. Mackay. 1997. A novel and simple model of the uptake of organic chemicals by

vegetation from air and soil. Chemosphere

, 35, 959

‐ 977.

Jenkins, J., D. Chojinacky, L. Heath, and R. Birdsey. 2003. Comprehensive database of diameter

‐ based

biomass regressions for North American tree species. General Technical Report NE

‐ 319, Forest

Service, United States Department of Agriculture.

Johansson. T.S.K. and M.P. Johansson. 1978. Providing Honey bees with Water. Bee World. 59, 11

‐ 17.Page 177 of 275 Kamel, A.; Dively, G.; Hawthorn, D. and J. Pettis. Unpublished research examining the fate of

imidacloprid and its metabolites in honey bee hives.

Khoury DS, Myerscough MR, Barron AB. 2011. A Quantitative Model of Honey Bee Colony Population

Dynamics. PLoS ONE 6(4): e18491. doi:10.1371/journal.pone.0018491

Kleier, D.A. 1988. Phloem mobility of xenobiotics I. Mathematical model unifying the weak acid and

intermediate permeability theories. Plant Physiol 86,803

‐ 810

Koch, H. and P. Weißer. 1997. Exposure of honey bees during pesticide application under field

conditions. Apidologie, 28: 439

‐ 447.

Kovac, H., Stabentheiner, and S. Schmaranzer. 2010. Thermoregulation of water foraging honey bees

‐ Balancing of endothermic activity with radiative heat gain and functional requirements. Journal

of Insect Physiology. 56, 1834

‐ 1845.

Krupke, C. H., G. J. Hunt, B. D. Eitzer, G. Andino, K. Given. 2012. Multiple routes of pesticide exposure for

honey bees living near agricultural fields. PLoSONE 7(1): e29268.

Doi:10.1371/journal.pone.0029268

Kühnholz, S. and T.D. Seeley. 1997. The control of water collection in honey bee colonies. Behavioral

Ecology and Sociobiology. 41: 407

– 422.

Laycock, I., K. M. Lenthall, A. T. Barratt, J. E. Cresswell. 2012. Effects of imidacloprid, a neonicotinoid

pesticide, on reproduction in work bumble bees (Bombus terrestris). Ecotoxicology

Lewis, G., M. Coulson, C. Vergnet, C. Maus, H. M. Thompson, R. Becker, D. Rogers, and I. Tornier. 2009.

Proposed revision of the higher tier testing requirements for EPPO Standard PP1/170: Test

Methods for evaluating the side

‐ effects of plant protection products on honey bees. Pages 34

–

42 in P. A. Oomen and H. M. Thompson (editors) International Commission for Plant

‐ Bee

Relationships Bee Protection Group 10 th International Symposium Hazards of Pesticides to Bees,

Bucharest (Romania). October 8

– 10, 2008. Julius –Kühn Archiv

Lord, K.A., May, M.A. and J.H. Stevenson. 1968. The secretion of the systemic insecticides dimethoate

and phorate into nectar. Ann. Appl. Biol. 61, 19

‐ 27.

Lückmann, J., Hofmann, S., Munderle, M.; et al

. 2010 Field Survey on Guttation of Maize Seedlings under

Agronomic Use Conditions in Austria and Assessment of the Relevance of Guttation Fluid for

Honey bees: Final Report. Project Number: M/355018/02/1, R09105, EBTIL065. Unpublished

study prepared by RIFCon GmbH. 251 p. (MRID 479753

‐ 01)

Makela, M. E., G. A. Rowell, W. J. Sames IV, and L.T. Wilson. 1993. An object

‐ oriented intracolonial and

population level model of honey bees based on behaviors of European and Africanized

subspecies. Ecological Modelling 67: 259

– 284.

Maus, C., C. Anderson and L. Doering, 2004. Determination of the Residue Levels of Imidacloprid and Its

Relevant Metabolites in Nectar, Pollen and Other Plant Material of Horse Chestnut TreesPage 178 of 275 (Aesculus hippocastanum) After Trunk Injection Application and Sampling 2001. Project

Number: MAUS/AM023, E/370/2057/4. Unpublished study prepared by Bayer CropScience. 27

p.

Mayer, D. & C. Johansen. 1990. Pollinator Protection: A Bee & Pesticide Handbook

. Wicwas Press.

Cheshire, Conn. p. 161

Menkene, G., L. Frey and G. Johnson. 1989. Monitor 4 Spray: A Residue Monitoring Study in Sugar Beets

to Assess Exposure to Avian Species under Standard Agricultural Use Conditions in California:

Lab Project Number: 162

‐ 180. Unpublished study pre ‐ pared by Wildlife International Ltd. 90 p.

(MRID 41548802)

Michener, C. 2007. The bees of the World. 2 nd Ed. Johns Hopkins University Press. Baltimore. 953 pp.

Mineau, P., K. M. Harding, M. Whiteside, M. H. Fletcher, D. Garthwaite, and L. D. Knopper. 2008. Using

reports of bee mortality in the field to calibrate laboratory

‐ derived pesticide risk indices.

Environmental Entomology 37(2): 546

– 554.

Mullin, C. A., M. Frazier, J. L. Frazier, S. Ashcraft, R. Simonds, D. vanEngelsdorp, and J. S. Pettis. 1010.

High Levels of Miticides and Agrochemicals in North American Apiaries: Implications for Honey

Bee Health. PLoSONE 5(3): e9754.doi:10.1371/journal.pone.0009754.

http://www.plosone.org/article/info %3Adoi%2F10.1371%2Fjournal.pone.0009754

Nagy, K.A. and C.C. Peterson. 1988. Scaling of Water Flux Rate in Animals. Zoology, vol. 120. University

of California Press. 172 pp.

National Academies of Science. 2008. Science and Decisions: Advancing Risk Assessment. Committee on

Improving Risk Analysis Approaches Used by the U. S. EPA, National Research Council ISBN: 0

‐

309

‐ 12047

‐ 0. http://www.nap.edu/catalog/12209.html

National Research Council. 2007. Toxicity Testing in the 21 st Century: A Vision and a Strategy. National

Academy of Sciences, Washington DC.

Nelson, J.A. 1924. Growth and feeding of honeybee larvae. United States Department of Agriculture,

Dept. Bull. No. 1222: 1

‐ 37.

Nicolson, S.W. 2009. Review: Water homeostasis in bees, with emphasis on sociality. Journal of

Experimental Biology. 212, 429

‐ 434.

NPIC. 2012. National Pesticide Information Center http://npic.orst.edu/

OECD. 1998 a

. OECD Guidelines for the Testing of Chemicals. Test Number 214, Acute Contact Toxicity

Test. http://www.oecd

‐ ilibrary.org/environment/test

‐ no

‐ 214

‐ honey bees

‐ acute

‐ contact

‐

toxicity

‐ test_9789264070189

‐ en;jsessionid=43gvto47wnue9.delta

OECD. 1998 b

. OECD Guidelines for the Testing of Chemicals. Test Number 213: Honey bees, Acute Oral

Toxicity Test. http://www.oecd

‐ ilibrary.org/environment/test

‐ no

‐ 213

‐ honey bees

‐ acute

‐ oral

‐

toxicity

‐ test_9789264070165

‐ en;jsessionid=5p2ngklfmv8p4.epsilonPage 179 of 275 OECD. 2007. Guidance document on the honey bee ( Apis mellifera ) brood test under semi

‐ field

conditions. OECD Environment, Health and Safety Publications. Series on Testing and

Assessment No. 75. ENV/JM/MONO (2007) 22.

OECD. 2010. Environment Directorate Joint Meeting of the Chemicals Committee and the Working Party

on Chemicals, Pesticides and Biotechnology. OECD Survey of Pollinator Testing, Research,

Mitigation and Information Management: Survey Results. ENV/JM/MONO(2010)24.

http://www.oecd.org/dataoecd/19/27/45275778.pdf

OEPP/EPPO. 2010. EPPA Standards PP1/170(3) Test methods for evaluating the side

‐ effects of plant

protection products on honey bees. Bulletin OEPP/EPPO Bulletin 31: 323

‐ 330.

Oomen, P. A. A. DeRuijter and J. Van der Steen. 1992. Method for honey bee brood feeding tests with

insect growth

‐ regulating inseciticides. Bul OEPP/EPPO Bulletin 22: 613

– 616.

Perritt, J.; Palmer, D.; Krueger, H.; et al

. 

### 1990. An Evaluation of Its Effects upon Birds and other Wildlife

on and around Cotton Fields in East

‐ central Alabama: Wildlife. (MRID 41548803)

Pettis, J. S. and K. S. Delaplane. 2010. Coordinated responses to honey bee deline in the USA. Apidologie

41: 256

‐ 263).

Pham

‐ Delègue M. and A. Bonjean. 1983. La pollinisation du tournesol en production de semence

hybride, Bull. Tech. Agric. 10, 211–218

Pistorius, J., G. Bischoff, U. Heimbach, and M. Stähler. 2009. Bee poisoning incidents in Germany in

spring 2008 caused by abrasion of active substance from treated seeds during sowing of maize.

Pages 118

– 126 in P. A. Oomen and H. M. Thompson (editors) Hazards of Pesticides to Bees,

10 th International Symposium of the ICP

‐ BR Bee Protection Group, Bucharest (Romania),

October 8

– 10, 2008. Julius Kühn Arch 423

Raven, P.H., R.F. Evert and S.E. Eichhorn. 1992. Biology of Plants. Worth Publishers, New York.

Reetz, J.E., Zühlke, S., Spiteller, M. and K. Wallneer. 2011. Neonictinoid insecticides translocated in

guttated droplets of seed

‐ treated maize and wheat: a threat to honey bees? Apidologie, 42:

596

‐ 606.

Riedl, H. E. Johansen, L. Brewer and J. Barbour. 2006. How to Reduce Bee Poisoning from Pesticides. A

Pacific Northwest Extension Publication. Oregon State University, University of Idaho, and

Washington State University. http://extension.oregonstate .edu/catalog/pdf/pnw/pnw591.pdf

Rortais, A., Arnold, G., Halm, M.P., F. Touffet

‐ Briens. 2005. Modes of honey bees exposure to systemic

insecticides:estimated amounts of contaminated pollen and nectar consumed by different

categories of bees. Apidologie, 36: 71

‐ 83.

Ryan, J.A., R. M. Bell, J. M. Davidson and G. A. Oconnor. 1988. Plant Uptake of Non

‐ ionic organic

chemicals from soils. Chemosphere

, 17, 2299

‐ 2323.Page 180 of 275 Schmaranzer, S. 2000. Thermoregulation of water collecting honey bees ( Apis mellifera ). Journal of

Insect Physiology. 46, 1187

‐ 1194.

Schmickl, T., and K. Crailsheim. 2007. HoPoMo: A model of honeybee intracolonial population dynamics

and resource management. Ecological Modelling 204:219

‐ 245.

Schönherr, J. and M. Riederer. 1989. Foliar penetration and accumulation of organic chemicals in plant

cuticles. Reviews of Environmental contamination and Toxicology, 108:1

‐ 70.

Schur, A., I. Tornier, D. Brasse, W. Muhlen, W. Von der Ohe, K Waller and M. Wehling. 2003. Honey bee

brood ring

‐ test in 2002: method for the assessment of side effects of plant protection products

on the honey bee brood under semi

‐ field conditions. Bull Insect 56(1): 91



### – 96

Seeley, T. 1995. The Wisdom of the Hive: the Social Physiology of Honey Bee Colonies. Harvard

University Press, Cambridge, MA. 295 pp.

Simpson, J. 1955. The significance of the presence of pollen in the food of worker larvae of the honey

bee. Quarterly Journal of Microscopical Science, 96(1): 117

‐ 120.

Škerl, M.I.S; Bolta, ŠV.; Č esnik, H.B. and A. Gregorc. 2009. Residues of pesticides in honeybee ( Apis

mellifera carnica ) bee bread and in pollen loads from treated apple orchards. Bull Environ

Contam Toxicol, 83: 374

‐ 377.

Sollins, P., D. E. Reichle, and J. S. Olson. 1973. Organic matter budget and model for a southern

Appalachian Lirodendron forest. Publication EDFB

‐ IBP

‐ 73

‐ 2. Oak Ridge, TN: Oak Ridge National

Laboratory.

Staedtler, T. 2009. Determination of residue levels of clothianidin and its metabolites TZMU and TZNG in

pollen harvested from maize plants grown in commercial practice from Poncho Pro® dressed

seeds (nominally 1.25 mg clothianidin/seed) in the Upper Rhine Valley in Germany. Bayer

CropScience. Report No. R08188

‐ 2.

Szolderits, M..J. and K. Crailsheim. 1993. A comparison of pollen consumption and digestion in honey

bee (Apis mellifera carnica) drones and workers. J. insect Physiol., 39 (10): 877

‐ 881.

Tapparro, A., C. Giorio, M. Marzaro, D. Marton, L. Soldá, and V. Girolami. 2011. Rapid analysis of

neonicotinoid insecticides in guttation drops of corn seedlings obtained from coated seeds.

Journal of Environmental Monitoring. 13: 1564

– 1568.

Tapparo, A., D. Marton, C. Giorio, A. Zanella, L. Soldà, M. Marzaro, L. Vivan, V. Girolami. 2012.

Assessment of the environmental exposure of honey bees to particulate matter containing

neonicotinoid insecticides coming from corn coated seeds. Environmental Science and

Technology 46: 2592

‐ 2599.

Tautz, J. 2008. The Buzz About Bees: Biology of a Superorganism

. Springer

‐ Verlag Berlin.Page 181 of 275 Thompson, H. M. 2002. Behavioral Effects of Pesticides in Bees—their Potential for Use in Risk

Assessment. Ecotoxicology 12: 317

– 330.

Thompson, H.M. 2010. Risk assessment for honey bees and pesticides

‐ recent developments and ‘new

issues. Pest Manag. Sci. 66, 1157

‐ 1162.

Thompson, H. M. and C. Maus. 2007. Perspective: the relevance of sublethal effects in honey bee testing

for pesticide risk assessment. Pest Manag. Sci. 63: 1058

– 1061.

Thompson, H. M. and D. Thorbahn. 2009. Review of honey bee pesticide poisoning incidents in Europe

–

evaluation of the hazard quotient approach for risk assessment. In P. A. Oomen and H. M.

Thompson (editors) Hazards of Pesticides to Bees 10 th International Symposium on the ICP

‐ BR

Bee Protection Group. Bucharest (Romania), October 8

– 10, 2008. Julius Kühn Arch

Thompson, H. M., S. Wilkins, A. H. Battersby, R. J. Waite, and D. Wilkinson. 2005. The effects of four

insect growth

‐ regulating (IGR) insecticides on honeybee ( Apis mellifera l. ) colony development,

queen rearing and drone sperm production. Ecotoxicology 14:757

‐ 769.

Topp, E., I. Scheunert, A. Attar and F. Korte. 1986. Factors affecting the uptake of C

‐ 14

‐ labelled organic

chemicals by plants from soil. Ecotoxicology and Environmental Safety

, 11, 219

‐ 228.

Trapp, S. 2004. Plant uptake and transport models for neutral and ionic chemicals. Environ Sci & Pollut

Res. 11(1): 33

‐ 39.

Trapp, S. and M. Matthies. 1995. Generic one

‐ compartment model for uptake of organic chemicals by

foliar vegetation. Environmental Science and Technology

, 29, 2333

‐ 2338.

Travis, C.C. and A. D. Arms. 1988. Bioconcentration of organics in beef, milk, and vegetation.

Environmental Science and Technology

, 22, 271

‐ 274.

USDA. 2007. Colony Collapse Disorder Action Plan. CCD Steering Committee. June 20, 2007.

http://www.ars.usda.gov/is/ br/ccd/ccd_actionplan.pdf

USDA. 2009. USDA Technical Working Group Report on Honey Bee Toxicity Testing, July 8 and 9, 2009.

http://www.aphis.usda.gov/pla nt_health/plant_pest_info/hon ey_bees/downloads/twg_report_

july_2010.pdf

USDA. 2010. USDA Technical Working Group Report on Honey Bee Toxicity Testing. July 8 and 9, 2009.

http://www.aphis.usda.gov/pla nt_health/plant_pest_info/hon ey_bees/downloads/twg_report_

july_2010.pdf

USDA. 2012. Honey. National Agricultural Statistics Service (NASS), Agricultural Statistics Board, United

States Department of Agriculture (USDA). ISSN: 1949

– 1492

http://usda01.library.cornell.edu/usda/current/Hone/Hone

‐ 03

‐ 30

‐ 2012.pdf

USEPA. 1993. Wildlife Exposure Factors Handbook

. EPA/600/R

‐ 93/187. Office of Research and

Development. December 1993.Page 182 of 275 USEPA. 1996 a

. Residue Chemistry Test Guidelines: OCSPP 860. 1500 Crop Field Trials. EPA 712

‐ C

‐ 96

‐ 183

. http://www.regulations.gov/#!documentDetail;D=EPA

‐ HQ

‐ OPPT

‐ 2009

‐ 0155

‐ 0013

USEPA. 1996 b

. Residue Chemistry Test Guidelines: OCSPP 860.1300 Nature of the Residues Plants,

Livestock. EPA 712

‐ C

‐ 96

‐ 172. http://www.regulations.gov/#!documentDetail;D=EPA

‐ HQ

‐ OPPT

‐

2009

‐ 0155

‐ 0006

USEPA. 1996 c

. Residue Chemistry Test Guidelines: OCSPP 860.1850 Confined Accumulation in

Rotational Crops. EPA

‐ 712

‐ C

‐ 96

‐ 188 http://www.regulations.gov/#!documentDetail;D=EPA

‐ HQ

‐

OPPT

‐ 2009

‐ 0155

‐ 0018

USEPA. 1996 d

. Residue Chemistry Test Guidelines: OCSPP 860.1900 Field Accumulation in Rotational

Crops. EPA

‐ 712

‐ C

‐ 96

‐ 189 http://www.regulations.gov/#!documentDetail;D=EPA

‐ HQ

‐ OPPT

‐

2009

‐ 0155

‐ 0019

USEPA. 1996 e

. Residue Chemistry Test Guidelines. OPPTS 860.1400 Water, Fish, and Irrigated Crops.

EPA 712

‐ C

‐ 96

‐ 178. August 1996. http://www.regulations.gov /#!documentDetail;D=EPA

‐ HQ

‐

OPPT

‐ 2009

‐ 0155

‐ 0010

USEPA. 1998. Guidelines for Ecological Risk Assessment. Published on May 14, 1998, Federal Register

63(93): 26846

– 26924. http://www.epa.gov/raf/public ations/pdfs/ECOTXTBX.PDF

USEPA. 2000. Science Policy Council Handbook Risk Characterization. Office of Science Policy. Office of

Research and Development, Washington DC. EPA 100

‐ B

‐ 00

‐ 002. December 2000.

http://www.epa.gov/spc/pdfs/rchandbk.pdf

USEPA. 2001. A Probabilistic Model and Process to Assess Acute Lethal Risks to Birds. Prepared for the

March 13

– 16, 2001 Scientific Advisory Panel Meeting. February 16, 2001. Prepared by Edward

Fite, M.S., EFED/OPP; Ed Odenkirchen, Ph.D., EFED/OPP; and Timothy Barry, Sc.D., Office of the

Administrator/Office of Policy, Economics and Innovation, US EPA. Available online at:

http://www.epa.gov/scipoly/sa p/meetings/2001/031301_mtg.htm

USEPA. 2004. Overview of the Ecological Risk Assessment Process in the Office of Pesticide Programs

, U.

S. Environmental Protection Agency, Endangered and Threatened Species Effects

Determinations.

USEPA. 2007. Tier I Rice Model for Estimating Pesticide concentrations in Rice Paddies. United States

Environmental Protection Agency, Office of Pesticide Programs, Environmental Fate and Effects

Division. Available online at: http://www.epa.gov/oppefed1/models/water/index.htm#rice

.

USEPA. 2008. Fate, Transport and Transformation Test Guidelines: OCSPP 835.6100 Terrestrial Field

Dissipation. United Stated Environmental Protection Agency, Washington, DC. EPA 712

‐ C

‐ 08

‐

020, October 2008.

USEPA. 2009 a

. ECOTOXicology Database, United States Environmental Protection Agency (USEPA).

Available at http://cfpub.epa.gov/ecotox/

.Page 183 of 275 USEPA. 2010. Fiscal Year 2011

– 2015 EPA Strategic Plan Achieving our Vision. U.S. Environmental

Protection Agency September 30.2010.

http://nepis.epa.gov/Exe/ZyNET.exe/P1008YOS.t xt?ZyActionD=ZyDocument&Client=EPA&Index

=1995%20Thru%201999%7C1976%20Thru%201980%7C2006%20Thru%202010%7C1991%20Thr

u%201994%7C2000%20Thru%202005%7C1986%20Thru%201990%7C1981%20Thru%201985%7

CPrior%20to%201976&Docs=&Query=190r10002%20 &Time=&EndTime=&SearchMethod=2&To

cRestrict=n&Toc=&TocEntry=&Q Field=&QFieldYear=&QFieldMonth=&QFieldDay=&UseQField=&

IntQFieldOp=0&ExtQFieldOp=0&XmlQuery=&F ile=D%3A%5CZYFILES%5CINDEX%20DATA%5C06T

HRU10%5CTXT%5C00000021%5CP1008YOS.txt&Us er=ANONYMOUS&Password=anonymous&S

ortMethod=

‐ %7Ch&MaximumDocuments=15&FuzzyDegree= 0&ImageQuality=r85g16/r85g16/x150y150g16/i

500&Display=hpfr&DefSeekPage=x&SearchBack=Z yActionL&Back=ZyActionS&BackDesc=Results

%20page&MaximumPages=1&ZyEntry=3

USEPA. 2010 b

. Weight of Evidence Guidance: Evaluating Results of EDSP Tier 1 Screening to Identify

Candidate Chemicals for Tier 2 Testing. Draft for Public Comment.

http://www.epa.gov/endo/pubs/EDSP_Draft_ WoE_Paper_%20for_Public_Comments%20_2010.

pdf

USEPA. 2011. Evaluation Guidelines for Ecological Toxicity Data in the Open Literature.

http://www.epa.gov/pesticides /science/efed/policy_guidance/ team_authors/endangered_spec

ies_reregistration_workgroup/esa_evaluation_open_literature.htm

USEPA. 2011. Interim Guidance on Honey Bee Data Requirements. Memorandum dated October 19,

2011.

http://www.epa.gov/pesticides/s cience/efed/policy_guidance/te am_authors/terrestrial_biolog

y_tech_team/honey bee_data_interim_guidance.htm

USEPA. 2012. Label Review Manaual. http://www.epa.gov/oppfead1/labeling/lrm/

USEPA. 2012 a

. Ecological Effects Test Guidelines. OCSPP 850.3020 Honey Bee Acute Contact Toxicity.

EPA 712

‐ C

‐ 019. January 2012. http://www.regulations.gov /#!documentDetail;D=EPA

‐ HQ

‐ OPPT

‐

2009

‐ 0154

‐ 0016

USEPA. 2012 b

. Ecological Effects Test Guidelines OCSPP 850.3030 Honey Bee Toxicity of Residues on

Foliage. EPA 712

‐ C

‐ 018. January 2012. http://www.regulations.gov /#!documentDetail;D=EPA

‐

HQ

‐ OPPT

‐ 2009

‐ 0154

‐ 0017

USEPA. 2012 c

. Ecological Effects Test Guidelines OCSPP 850.3040. Field Testing for Pollinators. EPA

712

‐ C

‐ 017. January 2012 http://www.regulations.gov /#!documentDetail;D=EPA

‐ HQ

‐ OPPT

‐

2009

‐ 0154

‐ 0018

USEPA. 2012 d

. Ecological Effects Test Guidelines. OCSPP 850.2500 Field Testing for Terrestrial Wildlife.

Office of Chemical Safety and Pollution Prevention (formerly the Office of Prevention, Pesticides

and Toxic Substances. EPA 712

‐ C

‐ 021. January 2012.

http://www.regulations.gov/#!documentDetail;D=EPA

‐ HQ

‐ OPPT

‐ 2009

‐ 0154

‐ 0014Page 184 of 275 USEPA. 2012 

### e

. User’s guide for T

‐ REX version 1.5 (Terrestrial Residue EXposure model). United States

Environmental Protection Agency, Office of Pesticide Programs, Environmental Fate and Effects

Division. Available online at:

http://www.epa.gov/oppefed1/models/ terrestrial/trex/t_rex_user_guide.htm

USEPA 2012 f

. AERMOD Modelling System

http://www.epa.gov/ttn/scram/dispersion_prefrec.htm#aermod

vanEngelsdorp, D., J Hayes (Jr), R. M. Underwood, J. Pettis. 2008. A Survey of Honey Bee Colony Losses

in the U. S., Fall 2007 to Spring 2008. PLoSONE 3(12): e4071.doi:10.1371/journal.pone.0004071.

vanEngelsdorp, D., J. D. Evans, C. Saegerman, C. Mullin, E. Haubruge, B. K, Nguyen, M. Frazier, J. Frazier,

D. Cox

‐ Foster, Y. Chen, R. Underwood, D. R. Tarpy, J. S. Pettis. 2009. Colony Colapse Disorder: A

Descriptive Study. PLoSONE 4(8): e6481. Doi:10.1371/journal.pone.0006481

http://www.plosone.org/article/info %3Adoi%2F10.1371%2Fjournal.pone.0006481

Visscher, P.K., Crailsheim, K. and G. Sherman. 1996. How do honey bees ( Apis mellifera ) fuel their water

foraging flights? Journal of Insect Physiology. 42, 1089

‐ 1094.

Whitehorn, P. R., S. O’Connor, F.L. Wackers, D. Goulson. 2012. Neonicotinoid pesticide reduces

bumblebee colony growth and queen production. Science 336: 351

– 352.

Williard, T.R. 2012. Thiamethoxam Magnitude of Residues in Leaves and Flowers from Tomato in

California. Final Report. Syngenta Crop Protection. Report Number TK0025811.

Winston, M. L. 1987. The Biology of the Honey Bee. Harvard University Press, Cambridge, MA. ISBN 0

‐

674

‐ 07409

‐ 2.

Wu, J.Y., C. M. Anelli, and W. S. Sheppard. 2011. Sub

‐ Lethal Effects of Pesticide Residues in Brood Comb

on Worker Honey Bee ( Apis mellifera ) Development and Longevity. PLoS ONE 6(2): e14720.

doi:10.1371/journal.pone.0014720

Wykes, G.R. 1953. The sugar content of nectars. Biochemical journal, 53 (2): 294

‐ 296

Yang, X and D. L. Cox

‐ Foster. 2005. Impact of an ectoparasite on the immunity and pathology of an

invertebrate: evidence for host immunosuppression and viral amplification. Proceedings of the

National Academy of Sciences. 102 (21): 7470

– 7475Page 185 of 275 

### Appendix 1. Estimation of food consumption rates for worker larvae and adults

As discussed in the effects characterization, acute oral toxicity data are necessary for adult and larvae in

order to characterize the risks of a pesticide. Because these toxicity data are expressed on a dose basis

( i.e., μ g a.i./bee), it is necessary to convert estimated concentrations of pesticides in bee food

(expressed as mg a.i./kg) into doses. Honey bees fulfill their nutritional requirements through

consumption of nectar, honey, pollen and bee bread. In addition, bees require royal jelly and brood food

(also referred to as brood jelly) which are composed primarily of glandular secretions produced by nurse

bees, mixed with some honey. In the proposed approach, pesticide doses received by bees can be

calculated using nectar and pollen consumption rates for larval and adult worker bees. In this approach,

it is assumed that exposures through consumption of nectar and pollen are conservative

representations of potential exposures through consumption of honey and bee bread, respectively. This

approach is likely to be conservative because it assumes that pesticides do not degrade while honey and

bee bread are stored in the hive. For bees that consume honey, it is assumed that the estimated

pesticide exposures can be related back to the original concentration in nectar by accounting for the

amount of sugar consumed by bees. It is also assumed that pollen and nectar consumption rates and

resulting exposures are protective of exposures of bees to pesticides through consumption of royal jelly

and brood food, which are expected to have lower concentrations of pesticides compared to pollen and

nectar.

Because the diet of adult worker bees changes in content and amount as they age and their tasks

evolve, the doses of adult bees are expected to vary. For the Tier I risk assessment method, which is

intended to generate conservative, exposure estimates are intended to be based on food consumption

rates for the age/task of bees expected to receive the greatest exposures. This appendix discusses pollen

and nectar consumption rates for different types of bees, building 

### upon work published by Rortais et

al. (2005), Crailshaim et al

. (1992 and 1993) and others. Food consumption rates are proposed for

larvae and the most susceptible adult worker bees. In addition, comparisons are made between the

proposed food consumption rates and other bee castes ( i.e., drones and queens).Page 186 of 275 Food consumption of larval worker bees

The average duration of the larval life stage of honey bee workers is 5

‐ 6 days (Winston 1987). Over the

course of the life stage, larvae consume different types and amounts of food. During the first three days,

larvae consume a total of 30 mg (Nelson 1924 as described by Rortais et al

. 2005) of royal jelly and

brood food, both of which are produced by adult nurse bees (Winston 1987). The daily food

consumption rate during the first three days of the larval stage is not likely to be equivalent from one

day to the next since larvae are growing exponentially. Therefore, if the food intake rate is also

exponential, approximate food consumption rates of larvae would be 3.75, 7.5 and 15 mg on days 1, 2

and 3, respectively. During the fourth and fifth days of the larval life stage, larvae are fed bee bread (or

pollen) and honey (or nectar). The nectar and pollen consumption rates of larvae during days 4 and 5 of

the uncapped phase are discussed below.

Nectar (and honey)

Rortais et al

. 2005 based their estimated sugar consumption rates of larvae (age 4 and 5 days), on data

published by Bishop 1961) indicating that they consume 120 mg food over this two day period. If it is

assumed that 5.4 mg of this is represented by pollen (see next section), then it can be assumed that

larvae consume 115 mg honey during this time period. As discussed above, the growth rate of larvae is

exponential; therefore, the honey consumption rate of larvae is expected to increase exponentially each

day to support that growth rate. Based on that assumption, the honey consumption rate for days 4 and

5 of the larval period can be estimated as 37 and 78 mg. If it is assumed that larvae are fed honey

diluted with water so that the food of larvae is 45% sugar (Winston 1987, Rortais et al

. 2005) and nectar

contains an average of 30% sugar (See Table 1

‐ 4), this is equivalent to consumption of 56 and 117 mg of

nectar for days 4 and 5, respectively. The estimated nectar consumption rate of 5 day old larvae is

greater than the amount of royal jelly and brood food that they consume during the entire first three

days of the larval life stage, which suggests that the use of nectar consumption rates of 5 day old larvae

are protective of consumption rates of royal jelly and brood food.Page 187 of 275 Pollen (and bee bread)

Rortais et al

. 2005 suggest that worker larvae consume 5.4 mg pollen during larval development. This

value is based on data reported by Simpson 1955 that corresponded to consumption of red clover

pollen. Simpson 1955 observed a large amount of variability in the amount of pollen consumed by

larvae, with some larvae consuming more than 5.4 mg pollen and some larvae consuming none.

Babendreier et al

. 2004 reported that larvae consumed 1.5

‐ 2 mg of maize pollen during the larval

development stage, noting significant differences in the masses of pollen consumed by larvae from

different colonies. Since larvae only consume pollen during the last two days of this life stage (Winston

1987), available information indicate that pollen consumption rates are 0.75 to 2.7 mg ( Table 1

‐ 1 ).

Table 1

‐ 1. Daily pollen consumption rates of larval worker bees during the last two days of the larval life stage.

Source Pollen consumption rate (mg/day)* Pollen species

Simpson (1955) 2.7 Red clover

Babendreier et al

. (2004) 0.75

‐ 1 corn

*Calculated by dividing amount of pollen consumed over entire life stage by 2 days.

Proposed food consumption rate for larval worker bees

As described in the exposure section of this white paper, for each type of application ( i.e.

, foliar spray,

soil treatment, seed treatment and tree injection/drench), a pesticide residue concentration in one

plant matrix (plant leaves) is proposed to represent residues in both pollen and in nectar. Effectively, the

proposed Tier I methods for these types of application assumes that pesticide concentrations in pollen

and in nectar are equivalent. Thus, the Tier I estimated daily dose received by larvae can be calculated

by multiplying the pesticide concentration by the food consumption rate of larvae as represented by the

sum of daily pollen and nectar consumption ( i.e.

, 2.7 and 117 mg/day, respectively). Therefore, the

proposed food consumption rate of larvae is 120 mg/day.

Discussion

The purpose of this analysis is to derive food consumption rates that can be related to pesticide

concentrations in food sources collected by bees ( i.e., nectar and pollen). On the surface, the proposed

food consumption rate of 120 mg/day for 5 day old larvae appears to be equivalent to the food

consumption rate larvae over days 4 and 5 combined (as suggested by Rortais et al. 2005); however, thePage 188 of 275 proposed value is different because it is adjusted to represent consumption of the original source

material ( i.e., nectar) for the honey that the larvae are actually eating. As discussed above, the

adjustments are made by assuming that the sugar contents and pesticide concentrations in the food

consumed by the larvae are constant for honey and nectar and only the water contents change.

Although exposures through consumption of royal jelly and brood food are not explicitly accounted for

in the proposed Tier I exposure method, the proposed method is expected to be conservative for two

primary reasons. First, because the daily food consumption rate proposed for larvae is much larger than

the consumption rate of brood food, which is 30 mg over three days. Second, because pesticide

concentrations in pollen and nectar are expected to be greater than those in royal jelly and brood food.

This is supported by work by Davis and Shuel 1988 and Kamel et al. (unpublished) that demonstrated

that pesticide concentrations in food consumed by nurse bees were 2

‐ 4 orders of magnitude higher than

concentrations measured in royal jelly. Davis and Shuel 1988 dosed nurse bees with 13 ppb carbofuran

or 12 ppb dimethoate, and measured concentrations of 0.0020 ppb carbofuran and 0.0029 ppb

dimethoate in royal jelly of exposed nurse bees. Kamel et al

. (unpublished) exposed nurse bees to 100

ppb imidacloprid in pollen and measured imidacloprid concentrations of 0.27

‐ 1.02 ppb in royal jelly.

Although royal jelly differs in composition from brood food, both consist of secretions from the

hypopharyngeal and mandibular glands of nurse bees. Therefore, the observations that pesticide

concentrations in royal jelly may be orders of magnitude lower than the diet of the nurse bees, apply to

the food consumed by larvae during the first 3 days of the uncapped life stage.

Food consumption of adult worker bees

Adult worker bees consume nectar, honey, pollen and bee bread. Over the course of the adult life stage,

an adult worker bee will require different portions of these food items in order to meet the demands of

the different tasks a worker must fulfill. For example, as the metabolic requirements of a bee increase,

for example while foraging

, the bee will require more nectar (or honey) to meet the energy demands of

flying. Also, bees that produce brood food and royal jelly ( i.e

., nurse bees) require the most pollen

(compared to other bees engaged in different tasks) because their glandular secretions provide protein

and lipids to larvae and the queen (Winston 1987).Page 189 of 275 Nectar

Nectar consumption rates for different types of worker bees were calculated by Rortais et al. (2005)

( Table 1

‐ 2 ). The estimated nectar consumption rate of nectar foraging bees ( i.e.

, 107

‐ 428 mg/day) is of

interest for the Tier I exposure assessment method because it represents the highest estimated nectar

consumption rate of adult worker bees. This suggests that nectar foraging bees would be potentially

receive the highest pesticide exposures through consumption of contaminated nectar.

Table 1

‐ 2. Estimated nectar consumption rates of adult worker bees by task and approximate age.

Task* Rortais et al

. (2005)

task description Average age

(in days)* Nectar

consumption rate

(mg/day)**

Cell cleaning and capping Wax producer 0

‐ 10 60

Brood and queen tending (nurse bees) Brood attending 6

‐ 17 113

‐ 167

Comb building, cleaning and food handling Wax producer 11

‐ 18 60

Foraging (pollen) Forager >18 35

‐ 52

Foraging (nectar) Forager >18 107

‐ 428

*From Winston 1987

**Adapted from Rortais et al. 2005 using sugar consumption rates and assuming that the sugar content of nectar is 30% (average of data in

Table 1

‐ 4).

The nectar consumption rate of nectar foraging worker bees is based on Equation 1

‐ 1



### . Because there

are 5 different variables that influence the calculation of the nectar consumption rate for nectar

foragers, there is potentially a great deal of variability that influences this estimate. In addition, this

approach is limited in that it does not account for the energy ( i.e., sugar) requirements of bees while

they are at rest.



### Equation 1

‐ 1 ܦ ௖௘௡௧௔௥ ൌ ்כௌ ಷ כ஽כி

௉

Where (values used by Rortais et al

. 2005 in parentheses):

S F is the amount of sugar required for flight (8

‐ 12 mg/hr)

T is the average number of foraging trips made per day (10)

D is the duration of each foraging trip (0.5

‐ 1.33 hr)

F is the fraction of the foraging trip spent flying (0.8)

P is the amount of sugar present in nectar (1 mg sugar per 2.5 mg nectar or 40% sugar)

An analysis was conducted by EPA to explore the ranges of nectar consumption rates when the

variability in parameter values is considered along with the influence of the resting metabolic

requirements of bees. This analysis was based on a Monte Carlo simulation (conducted using Crystal Ball

2000), where 10,000 nectar forager honey bees were simulated using randomly selected values fromPage 190 of 275 distributions of the 5 variables identified above. In addition, this approach accounted for account for the

energy requirements of bees while they are at rest.

For this simulation, the nectar consumption rate of an individual bee was calculated using Equation 1

‐ 2

,

which represents a modification to the approach used by Rortais et al

. (2005). The approach depicted by

this equation is as follows. For each simulated bee, the number of trips (T) made in one day was

randomly selected. Then, for each trip, the duration (D i ), fraction of each trip spent flying (F i ) and the

sugar content of the collected nectar (P i ) was randomly selected. The assumptions associated with each

of the variables are provided in Table 1

‐ 3

. This approach assumes that the bees will forage at different

locations throughout the day. The amount of sugar required for each trip (to meet the energetic

demands of flying) was calculated by multiplying the duration of the individual foraging trip by the

fraction of the trip spent flying by the bee’s sugar requirement for flying. The total amount of sugar

required for flying over the entire day was calculated by adding up the amount of sugar required for all

of the day’s trips. In addition, the amount of nectar required to meet the bee’s energy demands while

resting during the day was calculated by multiplying the amount of time in the day when the bee was

not flying by the amount of sugar required per hour to meet the bee’s resting metabolic rate (S

R ). A

value of 0.7 mg/hour was selected based on Winston (1987). This value was converted to the amount of

nectar required to meet the bee’s resting metabolic rate by dividing S R by the average sugar content of

nectar from various plants ( i.e., 30%, Table 1

‐ 4 ). Based on this analysis, the median and 95 th percentile

estimates of total nectar consumption by nectar forager bees are 292 and 499 mg/day, respectively

( Table 1

‐ 5 ). The median estimate of 292 mg/day is proposed for use in estimating pesticide doses to

honey bees.Page 191 of 275 Equation 1

‐ 2 ܦ ௖௘௡௧௔௥ ൌ ܵ ி כ ∑ ቀ ஽ ೔ ிכ ೔

௉ ೔ ቁ ்

௜ୀ଴ ൅ ௌ ೃ כൣଶସି൫ ∑ ஽ ೔ כி ೔ ೅

೔సబ ൯൧

௉

ೌೡ೐

Where:

S F is the amount of sugar required for flight

S R is the amount of sugar required to meet resting metabolic rate

F i is the fraction of time spent flying during trip i

D i is the duration of foraging trip i

P i is the amount (mg) of sugar in nectar collected during trip i

P

ave is the average sugar content in nectar

Table 1

‐ 3. Parameter assumptions used in Monte Carlo analysis of nectar consumption rates of nectar

foraging bees.

Variable Distribution

assumption

* Mean Standard

Deviation Minimum Maximum Source(s)

Number of trips per day Lognormal 10 3 1 150 Winston 1987

Sugar requirement during

flight (mg/hr) uniform NA NA

7 12 Balderrama et a l

. 1992;

Gmeinbauer and

Crailsheim 1993

Duration of single

foraging trip (hr) uniform NA NA 0.5 1.33 Winston 1987

Fraction of trip spent

flying uniform NA NA

0.5 0.9 It is assumed that the bee

will spend a portion of

the foraging trip flying

and the rest of the time

collecting nectar. Rortais

et al

. 2005 assumed a

value of 0.8.

Sugar content of nectar lognormal 0.3 0.1 0.1 0.6 Table 1

‐ 4

NA = not applicable

*In cases were no information were available to inform the distribution shape assumption, a uniform distribution was selected.Page 192 of 275 Table 1

‐ 4. Sugar content in nectar of different plants.

Plant species (common name) * % sugar in nectar Source

Raphanus raphanistrum (charlock) 25

‐ 58 Butler 1944

Malus domestica (Apple) 32

‐ 56 Butler 1944**

Tilia platyphyllos (lime) 33

‐ 55 Butler 1944

Taraxacum dens

‐ leonis (Dandelion) 15

‐ 55 Butler 1944

Prunus cerasus (wild cherry) 21

‐ 52 Butler 1944

Cucurbita pepo ( Pumpkin) 40.5

‐ 50.5 Das Graças Vidal et al

. 2006

Helianthus annuus (sunflower) 40 Pham Delègue and Bonjean

(1983) as cited by Rortais et al.

(2005)

Brassica sp. (savoy cabbage) 39 Butler 1944

Rubus idaeus (raspberry) 19

‐ 39 Butler 1944

Prunus sp. (plum) 7

‐ 38 Butler 1944

Trifolium repens (wild white clover) 12

‐ 36 Butler 1944

Trifolium repens (white clover) 32.4 Wykes 1952

Tilia x vulgaris (common lime) 32.3 Wykes 1952

Rubus fruticosus (blackberry) 11

‐ 31 Butler 1944

Onobrychis viciaefolia (sainfoin) 17

‐ 30 Butler 1944

Rubus loganobaccus (loganberry) 30.0 Wykes 1952

Lavandula spica (lavender) 28.2 Wykes 1952

Chamaenerion angustifolium (rosebay willow

‐ herb) 26.2 Wykes 1952

Borago officinalis (borage) 26.1 Wykes 1952

Lotus corniculatus (bird’s foot trefoil) 26 Butler 1944

Rubus fruticosus (blackberry) 25.7 Wykes 1952

Ribes nigrum (black currant) 25 Butler 1944

Crataegus oxyacantha (hawthorn) 2

‐ 22 Butler 1944

Pycnanthemum pilosum (mountain mint) 19.8 Wykes 1952

Trifolium pratense (red clover) 17.9 Wykes 1952

Pyrus sp. (Pear) 15 Butler 1944

Sinapis alba (white mustard) 13.9 Wykes 1952

Hedera heli x (ivy) 11.0 Wykes 1952

*The average sugar content in nectar from these genera (multiple values for the same genus are averaged) is approximately 30% (stdev

= 10%).

**Butler observed that sugar content for the same type of plant varied among individuals and among samples collected from the same

individual.

Table 1

‐ 5. Results of Monte Carlo analysis for nectar consumption rates of nectar foraging bees.

Percentile Time spent (hr) Amount of nectar consumed (mg)

Flying Resting While flying While resting Over entire day

5% 4.9 19.1 128 44 173

10% 6.3 17.7 150 41 192

25% 8.3 15.7 195 37 232

50% 8.9 15.1 256 35 292

75% 9.9 14.1 333 33 366

90% 8.7 15.3 415 36 451

95% 11.8 12.2 471 28 499Page 193 of 275 Pollen (and bee bread)

Crailsheim et al

. 1992 measured pollen contents in gastrointestinal tracts (including crops, midguts and

recta) of adult worker bees aged 1, 4, 9, 16, 23 and 30 days. These were used to represent the daily

pollen consumption rates of individual bees. Consumption rates of the different ages and their

corresponding tasks (based on Winston 1987) are provided in Table 1

‐ 6. Based on this information,

young bees and nurse bees consume the largest amounts of pollen. According to Crailsheim et al

. 1992,

forager bees consume a “negligible” amount of pollen.

Table 1

‐ 6. Pollen consumption rates of adult worker bees by task and approximate age.

Task* Average age of

task

(days) * Age where pollen

consumption was

reported (days)** Pollen

consumption rate

(mg/day) **

Cell cleaning and capping 0

‐ 10 1 2.2

4 8.2 ***

Brood and queen tending (nurse bees) 6

‐ 17 9 9.5

16 1.7

Comb building, cleaning and food handling 11

‐ 18 16 1.7

Foraging >18 23, 30 0.041

* From Winston 1987

** Estimated from Table 4 of Crailsheim et al. (1992). Consistent with Table 1 of Szolderits and Crailsheim 1993.

*** This may also include nurse bees.

Proposed food consumption rate for adult worker bees

As discussed above, the proposed Tier I methods for estimating dietary exposure assume that pesticide

concentrations in pollen and in nectar are equivalent. Thus, the most susceptible group of adult worker

bees, as defined by task, can be identified by considering the total amount of pollen and nectar

consumed by the different groups of bees. When nectar and pollen consumption rates are considered

for the different tasks of worker bees, nectar foraging bees have the highest food consumption rates

among adult worker bees ( Figure 1

‐ 1 ). Based on this information, the food consumption rate of 292

mg/bee is proposed for incorporation into the Tier I exposure method to derive dose

‐ based estimates of

exposure to adult worker bees consuming pollen and nectar.Page 194 of 275 Figure 1

‐ 1. Food consumption rates of adult worker bees by task.



### Discussion

The proposed food consumption rate for adult worker bees is based on the median estimate of nectar

consumption of nectar foraging bees. The median value is selected because it is considered most likely

to represent food consumption of this group of bees. In addition, use of the median value avoids

compounding conservative assumptions in the overall tier I exposure method. For example, if an upper

bound food consumption rate for nectar foragers was proposed, the overall exposure value would most

likely exceed the desired 95 th percentile for adult worker bees due to the combination of an upper

bound estimate of residues in pollen and nectar with an upper

‐ bound estimate of consumption rates.

This method described above focuses on exposures to honey bees using food consumption rates that

correspond to summer bees. Winter bees may also be exposed to pesticides through consumption of 0 50 100 150 200 250 300

Cell cleaning and

capping Brood and queen

tending (nurse

bees) Comb building,

cleaning and food

handling Foraging (pollen) Foraging (nectar) pollen

nectar

TaskFoodconsumption (mg/day)Page 195 of 275 food that is stored in the hive. Rortais et al

. 2005 estimated sugar consumption rates of 8.8 mg

sugar/day for winter bees. Although these bees are consuming sugar through honey, this value can be

correlated to a concentration in the nectar used to create the honey by considering the sugar content of

nectar (average value of 30%). This yields a relative nectar consumption rate of 29 mg nectar/day, which

is an order of magnitude below the proposed food consumption rate for adult worker bees ( i.e., 292

mg/day). Crailsheim et al

. 1993 compared pollen consumption of winter and summer bees. When

compared to summer nurse bees, which have the highest pollen consumption rates among summer

nurse bees, winter bees consumed an order of magnitude less pollen on a daily basis, which is higher

than the pollen consumption rate of nectar foraging bees. Despite that, the total food consumption rate

of nectar foraging bees is protective of daily estimated exposures potentially received by winter bees

consuming food stored in the hive.

The Tier I exposure method proposes to use a total food consumption rate to estimate dietary

exposures to larvae and adult bees because the Tier I methods assume that pesticide concentrations in

pollen and nectar are equivalent ( i.e., there is effectively one EEC for bee food). On a case

‐ by case basis,

the risk assessor may choose to use alternative food consumption rates or to separate out consumption

rates for pollen and nectar. If data are available where the assessed pesticide is measured in pollen and

in nectar and the concentration in pollen is much higher than nectar, the risk assessor may choose to

break out the consumption rates. In that case, nurse bees may be at a greater risk than the nectar

foragers. In addition, there may be interest in estimating exposures to different groups of worker bees

for colony

‐ level modeling. This can be accomplished using food consumption rates for different worker

bees or castes. The latter is discussed in the following section. Table 1

‐ 7 summarizes the food

consumption rates for larvae and adult worker bees (described above) and drones and queens

(described below).

Comparison of proposed consumption rates and resulting exposures to other castes

Although the focus on the current Tier I exposure method is on worker bees, it is also expected to

generate estimates of exposure that are protective for drones and queens. Table 1

‐ 8 provides an

illustration of the relative exposures of larvae of different castes and different adult worker bees by

tasks and drones. Pesticide doses in this table are estimated using food consumption rates included in

Table 1

‐ 7

. To illustrate the relative doses potentially received by bees consuming food with the samePage 196 of 275 level of pesticide contamination, doses are calculated for nectar and pollen that contain concentrations

of 100 μ g a.i./kg. It is assumed that the concentration in brood food and royal jelly is 1 μ g a.i./kg ( i.e

.,

100x lower than pollen and nectar) based on Davis and Shuel 1988 and Kamel et al

. (unpublished). The

results of this analysis are discussed below.Page197of275Table1‐7.Estimatedfoodconsumptionratesofbees.LifeStageCaste(taskinhive*)Averageage(indays)*Dailyconsumptionrate(mg/day)Broodfood/royaljellyNectar**Pollen***TotalfoodLarvalWorker13.75nonenone3.7527.5nonenone7.5315nonenone154none372.7405none1172.7120Drone5none52unknown>526none100unknown>100Queen19.4nonenone9.4219nonenone19338nonenone384100nonenone1005203nonenone203AdultWorker(cellcleaningandcapping)0‐10none602.2‐8.247‐53Worker(broodandqueentending,nursebees)6‐17none113‐1678.2‐9.593‐135Worker(combbuilding,cleaningandfoodhandling)11‐18none601.747Worker(foragingforpollen)>18none35‐520.04126‐39Worker(foragingfornectar)>18none292+0.041292Worker(maintenanceofhiveinwinter0‐90none29231Drone>10none133‐337+0.0002133‐337Queen0+UnknownunknownNoneunknownNA=notapplicable*FromWinston1987**FromRortaisetal.2005.Assumesthataveragesugarcontentofnectaris30%.***FromCrailsheimetal.(1992,1993).+CalculatedbyUSEPA,asdescribedinthisappendix.Page198of275Table1‐8.Pesticidedosesreceivedbydifferentbeesbasedonestimatedfoodconsumptionratesofbees(fromTable1‐7),assumptionthatpesticideconcentrationinpolleninnectaris100μga.i./kgandthatconcentrationinbroodfoodandroyaljellyis1μga.i./kg(i.e.,100xlowerthanpollenandnectar).LifeStageCaste(taskinhive*)Averageage(indays)*Dailydose(μga.i./beeperday)Broodfood/royaljellyNectar**Pollen***TotalfoodLarvalWorker10.00375000.0037520.0075000.007530.015000.015403.70.74.05011.70.2712Drone5052unknown>5.260100unknown>10Queen10.0094000.009420.019000.01930.038000.03840.100000.10050.203000.203AdultWorker(cellcleaningandcapping)0‐1004.50.22‐0.824.7‐5.3Worker(broodandqueentending,nursebees)6‐1708.5‐12.50.82‐0.959.3‐13.5Worker(combbuilding,cleaningandfoodhandling)11‐1804.50.174.7Worker(foragingforpollen)>1802.6‐3.90.00412.6‐3.9Worker(foragingfornectar)>18029.20.004129.2Worker(maintenanceofhiveinwinter0‐9002.90.23.1Drone>10013.3‐33.70.0000213.3‐33.7Queen0+unknownunknownnoneunknownPage 199 of 275 Drones

According to Rortais et al

. 2005, drone larvae consume 46.2 mg sugar during the last two days of the

larval life stage ( i.e., days 5 and 6). If it is assumed that drone larvae grow exponentially and that their

food consumption rates increase each day, then estimated sugar consumption during days 5 and 6 of

the larval period would be 15.5 and 30.7 mg, respectively. If it is assumed that this sugar originates from

honey where the original nectar source had 30% sugar (based on the average concentration of sugar in

nectar from Table 1

‐ 4 this is equivalent to 52 and 100 mg nectar on days 5 and 6, respectively. The

highest of these values is similar to the nectar consumption rate for workers ( i.e., 117 mg/day). A

literature search conducted in July 2012 by EPA was unsuccessful at locating pollen consumption data

for drone larvae. Despite the lack of pollen consumption data for drone larvae, if the pollen

consumption rate for drones is on the same order of magnitude as workers, the proposed food

consumption rate based on larval workers is expected to be protective for drone larvae.

Adult drones consume honey and pollen. Drones spend the majority of their time resting in the hive,

with younger drones (8 days old) rarely leaving the hive except perhaps for orientation flights that last

only a few minutes. Older drones make an average of 3

‐ 5 mating flights that last 25

‐ 32 minutes. The

energy requirements of adult drones are 1

‐ 3 mg sugar/hr while resting and 14 mg sugar/hour while

flying (Winston 1987). Therefore, the food requirements and potential dietary pesticide exposures of

older drones that make mating flights are expected to be greater than younger adult drones. When

considering the amount of time flying per day and the amount of time resting, older adult drones

require 40

‐ 101 mg sugar per day. If it is assumed that the honey the drones consume originally came

from a nectar source with an average sugar content ( i.e., 30%), the nectar requirements of drones is

133

‐ 337 mg/day. According to Szolderits and Crailsheim 1993, individual drones consume 0.2275

mg/day when they are 3 days old and older drones (20 days old) consume 0.0002 mg/day. Therefore,

the average amount of nectar and pollen consumed by adult drones is 133

‐ 337 mg/day. This is similar to

the proposed food consumption rate used to represent adult worker bees ( i.e., 292 mg/day), suggesting

that the adult worker value will also be representative of exposures to adult drones.Page 200 of 275 Queens

Queen larvae consume royal jelly that is composed of glandular secretions of nurse bees. According to

Dietz and Lambremont 1970, queen larvae consume 40% more food compared to worker larvae during

the first 6 days of the larval life stage. Although the consumption rate of queen larvae is higher than

worker larvae by a factor of 2.5, the overall exposure to queen larvae is expected to be less than worker

larvae because the proposed exposure value is based on the assumption that worker larvae consume

pollen and nectar, which has a concentration that is >100 times greater than the expected concentration

in food of queen larvae, i.e., royal jelly (Davis and Shuel 1988, Kamel et al

. (unpublished)) ( Table 1

‐ 8 ).

As adults, queens are fed a mixture of brood food and honey. Information on the food consumption

rates of adult queen bees have not been located. As discussed above, because pesticide concentrations

in brood food are expected to be orders of magnitude below concentrations in pollen and nectar, the

food consumption rate of adult queens would have to be more than a factor of 100 greater than worker

bees in order for the adult worker bee exposure value to be under protective for adult queen bees.Page 201 of 275 

### Appendix 2. Discussion of Potential Pesticide Exposures through Consumption of

Contaminated Drinking Water

In order to understand the potential pesticide exposure to bees through consumption of water, it is

important to be able to estimate pesticide concentrations in the different types of water bodies from

which bees may collect water, as well as the amounts of water bees will collect from each source.

Pesticide concentrations are expected to vary among potential sources of drinking water. This appendix

includes estimated pesticide concentrations in ponds, puddles and dew, which are all considered

relevant sources of water for bees. In estimating the total pesticide exposure a single bee may get from

drinking water, there are two major sources of uncertainty: 1) the major source(s) of water used by bees

are unknown, and 2) the amount of water a bee will actually drink is unknown. This section estimates

potential drinking water intake rates for bees using two different methods that generate results that

differ by orders of magnitude, concluding that the method based on water flux rates in a similar species

is more reliable. In addition, this section discusses potential exposures through contaminated guttation

fluid, which has been examined in the literature in recent years as being a potential drinking water

exposure route of concern due to its high (ppm level) concentrations of some pesticides following

pesticide treatments. No method has been identified for estimating pesticide concentrations in

guttation fluid, so measured concentrations are considered.

Water Uses and Sources

Honey bees collect water for two primary reasons: first, to regulate the temperature within the hive

through evaporative cooling to maintain conditions for healthy brood; and second, to use as a part of

the preparation of larval brood food by nurse bees (Gary et al.

, 1978; Winston, 1987; Seeley, 1995;

Visscher et al. 1996, Kühnholz and Seeley 1997, Nicolson 2009, Kovac et al. 2010). It is assumed that

when an individual bee’s daily water requirement is not met by food ( i.e., consumption of honey and

nectar), it will drink water to meet that requirement. An average colony will collect approximately 25 L

of water annually (Seeley 1995). Unlike pollen and nectar (more precisely, bee bread and honey), water

is not stored within the hive (Kovac et al. 2010).

Therefore, water must be collected by foraging bees when needed by the colony.Page 202 of 275 There is no indication that bees prefer a particular water source; however, they are often observed at

water sources closest to their hive (Gary et al.

, 1978). Honey bees have been observed collecting water

from a variety of sources, including: streams, ponds, lakes, creeks, marshes and puddles. Bees have also

been observed collecting water from grass and plant stalks (Gary et al. 1978, Seeley 1995, Kühnholz and

Seeley 1997, Schmaranzer, 2000), suggesting that they may collect dew and guttation fluid present on

plants. Guttation fluid has been observed during times of honey bee foraging during morning hours

(Girolami et al. 2009, Lückmann et al. 2010) and has been documented as a potential source of water for

honey bees (Reetz et al

. 2011). It is unknown whether honey bees utilize guttation fluid as a primary

source of water.

Drinking Water Consumption Rates

This section presents two methods for estimating daily drinking water consumption rates for individual

bees. One method estimates the amount of water consumed by forager bees based on observations of

bee behavior. The other method uses the water flux rate of the brown paper wasp ( Polistes fuscatus ), a

species that is similar to the honey bee, to represent the water flux rate of bee. These two methods

yield very different results and have their own strengths and limitations. Table 2

‐ 1 summarizes the two

methods, which are also described below.

Table 2

‐ 1. Summary of two methods used to estimate drinking water consumption rates of honey

bees.

Method Drinking water

consumption

(mL/day) Strengths Limitations

Estimate from water

forager honey bee

observations

(# trips, amount of water

collected and kept) 0.45

‐ 1.8 Based on observations for

honey bees Not based on direct

measurements, but on

observations of honey bee

behaviors. Focuses only on

one type of worker bee ( i.e.,

water foragers). Parameter

values are uncertain. Estimate

is 5

‐ 20x the amount of water

in the body of a bee.

Flux rate of brown paper

wasps 0.047 Based on direct measures of

the water requirements of

wasps, which are assumed to

be an appropriate surrogate for

the honey bee. Not based on honey bee data.Page 203 of 275 Method based on Honey Bee Observations

The first method for estimating daily water consumption incorporates information describing the

amount of water collected and kept by water forager bees in order to estimate their daily needs. In this

approach, average amount of water collected per day is calculated by multiplying the average amount of

water collected per trip, i.e

., 0.030

– 0.060 mL (Visscher et al

. 1996, Thompson 2010, Seeley 1995) by

the number of trips per day, i.e

., 50

‐ 100 (Seeley, 1995, Johansson and Johansson 1978). The resulting

estimate of water collected by water foragers per day is 1.5

‐ 6 mL. The estimated amount of water

consumed by water foraging bees is calculated by multiplying the amount of water collected by these

bees by 30% (this approximate value was reported by Johansson and Johansson 1978). The estimated

amount of water consumed by water forager bees is 0.45

‐ 1.8 mL/day.

The strength of this range of estimated drinking water rates is that it is based on observations of honey

bees foraging for water; however, the estimated range is limited in that it is dependent upon several

parameters that are highly variable and uncertain ( e.g., Johansson and Johansson 1978 reported an

approximate value for how much water a water forager bee will keep). Also, this analysis focuses only

on data for water forager bees. It does not consider the water consumption rates of other types of

worker bees. In addition, these variables are expected to be impacted by time of year and temperature.

For instance, water loads have been documented to increase with increasing ambient temperature

(Kovac et al. 2010). A final limitation of this approach can be illustrated by comparing the estimated

daily water consumption rate to the water content of a bee. The estimate water content of a bee, 0.088

mL, can be estimated by multiplying the body weight, i.e., 0.128 g (Mayer and Johansen 1990) by the

water content of an insect, i.e., 69% (USEPA 1993), and the standard density of water, i.e., 1 g = 1 mL.

Comparison of the water content of a bee to the water consumption rate estimated using this method

indicates that the bees would consume 5 to 21X the amount of water in their bodies. Despite the fact

that bees eliminate water during the day, it is unlikely that bees would have to replace the entire

amount of water in their bodies 5

‐ 20 times a day.

Method based on Brown Paper Wasp Flux Rate

The second method for estimating the amount of water consumed by an individual bee is to use the

daily water intake rate (flux rate) of bees, which accounts for water intake from all sources (primarilyPage 204 of 275 from food and drinking). No flux data have been located for honey bees; however, direct measurements

of flux data are available for the brown paper wasp. The brown paper wasp is considered an appropriate

surrogate for honey bees for several reasons. First, they are taxonomically related ( i.e.

, both species are

in the Hymoneptera order). Second, they are similar in weight to honey bees (worker wasps weigh

0.122 g (Nagy and Peterson 1988), while worker bees weigh 0.128 g (Mayer and Johansen 1990)).

Finally, both species are social (colonial) and utilize water for cooling their nests (Nicolson 2009).

According to Nagy and Peterson 1988, brown paper wasp workers require 0.050 mL/day of water and

their queens require 0.067 mL/day. The drinking water intake rate can be estimated by subtracting the

water intake rate from food from the flux rate. As discussed in Appendix 1

, adult worker honey bees

meet their dietary needs through consumption of nectar, honey, pollen and bee bread. Since the focus

of Appendix 1 was on deriving food consumption rates that could be related to pesticide concentrations

in food sources collected by bees ( i.e., nectar and pollen), honey and bee bread were assumed to be

represented by nectar and pollen. In this case, because the water contents of the different food items

differ substantially, it is necessary to distinguish between the food items when estimating the amount of

water a bee will consume through food. In this case, it is assumed that pollen and bee bread contribute

a negligible amount of water because they have much lower water contents and are consumed at lower

rates compared to nectar and pollen. If it is assumed that 1) worker bees consume nectar with 30%

sugar content (Table 1

‐ 4 of Appendix 1 ) and honey with 82% sugar content (Winston 1987), 2) that the

remainder of nectar ( i.e., 70%) and honey ( i.e., 18%) is represented by water, and that 3) worker bees

consume nectar and honey at the rates provided in Table 2

‐ 2

, then the amount of water consumed in

the diet differs based on the task of the worker bee and (as expected) whether a bee is consuming

honey or nectar. If a worker bee requires 0.050 mL water/day (based on the brown paper wasp flux

rate), then food may make up anywhere from 7% to >100% of the worker bee’s daily water

requirement. Therefore, in order to fulfill their daily water requirements, worker bees must drink a

maximum of 0.047 mL per day.

The strength of this estimate is that is based on direct measurements of water requirements of a closely

related insect. Although the flux rate incorporated into this method is not based on data for the honey

bee, it appears that the brown paper wasp shares enough characteristics with the honey bee to

represent an appropriate surrogate. Unlike the first method described in this section, this estimatedPage 205 of 275 value appears to be reasonable, given that it is less than, but still on the same order of magnitude as the

water content of a bee ( i.e., 0.088 mL).Page206of275Table2‐2.Estimatedfoodconsumptionratesandresultingamountsofwaterconsumedwithfoodforadultworkerbeesbytask.WorkerTaskNectarHoneyFoodconsumptionrate(mg/day)*Waterconsumedwithfood(mL/day)PercentdailywaterfluxconsumedwithfoodFoodconsumptionrate(mg/day)**Waterconsumedwithfood(mL/day)PercentdailywaterfluxconsumedwithfoodCellcleaningandcapping600.04284%220.00408%Broodandqueentending(nursebees)1670.1169234%610.011022%Combbuilding,cleaningandfoodhandling600.04284%220.00408%Foraging(pollen)520.036473%190.00347%Foraging(nectar)292+0.2044409%1070.019238%*AdaptedfromRortaisetal.2005usingsugarconsumptionratesandassumingthatthesugarcontentofnectaris30%(averageofdatainTable1‐4).**AdaptedfromRortaisetal.2005usingsugarconsumptionratesandassumingthatthesugarcontentofhoneyis82%(Winston1987).+AnalysisdescribedinAppendix1.Page 207 of 275 Preferred method for estimating drinking water intake rate

When considering the strengths and limitations of the two methods for estimating a drinking water

intake rate for worker bees, the method based on the brown paper wasp flux rate appears to generate a

more reliable estimate. Therefore, a drinking water intake rate of 0.047 mL/day will be used in the

following section of this appendix to estimate pesticide doses through various sources drinking water.

Estimation of pesticide doses through consumption of different sources of contaminated water

This section explores potential pesticide exposures to honey bees through drinking contaminated water

from ponds (adjacent to fields treated with pesticides), puddles (located on treated fields), dew (on

crops sprayed with pesticides), and guttation fluid in crops. Concentrations in ponds, puddles and dew

are typically estimated using conservative modeling approaches that are based on equilibrium

partitioning. In this section, pesticide concentrations are calculated based on an assumed application

rate of 1 lb a.i./A (per year). No method has been identified for estimating pesticide concentrations in

guttation fluid, so only measured concentrations are considered below. In order to compare the

potential pesticide doses received by bees through drinking water to doses received through diet and

direct spray, concentrations are translated to a daily dose by multiplying by the daily drinking water

intake rate ( i.e., 0.047 mL/day). In this approach, for each drinking water source, it is assumed that the

bees are consuming 100% of their drinking water from that source. The resulting doses are expressed as

μ g a.i./bee per 1 lb a.i./A.

Ponds

The GENEEC2 ( GEN eric E stimated E nvironmental C oncentration) model (version 2) was used to estimate

pesticide concentrations in ponds adjacent to treated fields 18

. This model uses the K

oc and degradation

kinetic data to estimate runoff from a ten

‐ hectare (10

‐ ha) field into a 1

‐ ha by 2

‐ m deep (20,000 m 3 )

"standard" pond. This model is designed for use as a coarse screen for risk assessments and estimates

conservative pesticide concentrations in surface water from a few basic chemical parameters and

pesticide label use and application information. In order to derive conservative pesticide concentrations

in ponds for a variety of chemicals with different mobilities, it was assumed that the pesticides were

18 A detailed description of this model is available online at:

http://www.epa.gov/oppefed1/model s/water/geneec2_description.htm

.Page 208 of 275 applied via aerial applications and had no degradation. Different K

oc values were selected according to

the Food and Agriculture Organization (FAO) mobility classification (FAO 2000) in order to estimate

concentrations in ponds for chemicals with different mobilities (ranging from highly mobile to immobile;

Table 2

‐ 3 ). Input parameters for GENEEC2 are provided in 

### Table 2

‐ 4

.

Table 2.3. FAO mobility classifications and corresponding K

oc ranges.

FAO mobility class K

oc (L/kg)

Highly mobile <10

Mobile 10

‐ 100

Moderately mobile 100

‐ 1,000

Slightly mobile 1,000

‐ 10,000

Hardly mobile 10,000

‐ 100,000

Immobile >100,000

Table 2

‐ 4. Input parameters used to run GENEEC2 for estimating pesticide concentrations in ponds adjacent to

fields.

Parameter Description Parameter Value Comments

Application Rate (lb a.i./A) 1 None

Maximum number of

applications per year 1 None

K

oc (L/kg

‐ oc) 1

10

100

1000

10,000

100,000

1,000,000 Only one K

oc value selected for each model run

Soil aerobic metabolic half

‐ life

(days) 0 Assume chemical is stable

Is pesticide wetted in? No None

Method of application Aerial Most conservative

Droplet size distribution Very fine to fine Most conservative

Width of no spray zone (feet) 0 Most conservative

Solubility (ppm) 100,000 Set high value to prevent influence of solubility.

Aerobic Aquatic Metabolism

half

‐ life (days) 0 Assume chemical is stable

Hydrolysis half

‐ life (days) 0 Assume chemical is stable

Photolysis half

‐ life (days) 0 Assume chemical is stable

Estimated concentrations range from 1.8 to 67 μ g a.i./L for chemicals with K

oc values ranging 1

‐

1,000,000 L/kg

‐ oc ( Table 2

‐ 5 ). Assuming that 100% of the bees’ drinking water comes from ponds thePage 209 of 275 corresponding dose range is 0.00008

‐ 0031 μ g a.i./bee. These values are 3 or more orders of magnitude

below the estimated contact or dietary doses proposed for the Tier I screen for foliar spray applications.

For soil and seed treatments, the doses through drinking water are 2 orders of magnitude below the

dietary doses generated using the recommended Tier I method for estimating exposures resulting from

these applications. Based on this information, it appears that pesticide doses received by bees drinking

from ponds are insignificant relative to doses received through consumption of food or through direct

spray onto the bees.

Table 2

‐ 5. Peak estimated concentrations in pond (in μ g a.i./L) as estimated using GENEEC2 and resulting doses

from 100% of drinking water from ponds.

K

oc (L/kg) Estimated

concentration

in pond water

(μg a.i./L) Dose ( μ g a.i./bee)

Drinking

water Foliar spray:

contact Foliar spray:

diet Soil

application:

diet * Seed

treatment:

diet

1 67 0.0031

2.7 32 0.62

0.29 10 66 0.0031 0.56

100 59 0.0028 0.29

1,000 33 0.0016 0.15

10,000 11 0.00052 0.084

100,000 5.2 0.00024 <0.063

1,000,000 1.8 0.00008 <0.056

* Calculated assuming K

ow = K

oc /0.35.

Puddles

Pesticide concentrations in puddles located on pesticide

‐ treated fields can be estimated using the Tier I

rice model (v. 1.0) (USEPA 2007), with modifications to simulate an on

‐ field puddle rather than a rice

paddy. The Tier I rice model relies on an equilibrium partitioning concept to provide conservative

estimates of environmental concentrations resulting from application of pesticides to rice paddies.

When a pesticide is applied to a rice paddy, the model assumes that it will instantaneously partition

between a water phase and a sediment phase, independent of the size of the puddle. The Tier I rice

model is represented by Equation 2

‐ 1

. OPP uses this model with parameters that represent a rice paddy

( i.e., sediment depth, water depth, bulk density and porosity). This model can be modified to represent

a puddle on a treated field using values for these parameters that represent on

‐ field puddles. In this

application, it is assumed that puddles may be directly sprayed with a pesticide and that equilibrium is

reached instantaneously.Page 210 of 275 Equation 2

‐ 1 

##  

d b sed sed w ai

w 

### K d d m C

     '

Where: C

w = water concentration (μg/L)

m ai '

= mass applied per unit area (kg/ha)

K d = water

‐ soil partitioning coefficient (L/kg) (equivalent to Koc *0.01)

d sed =sediment depth (m)

d

w = water depth (m)

ρ b = bulk density (kg/m 3 )

θ sed =porosity (unitless)

In order to allow for comparisons of pesticide doses through consumption of puddle water to other

doses discussed in this paper, values are normalized to 1 lb a.i./A. Therefore, a value of 1.12 kg/ha is

used to represent the mass applied per unit area (m ai ’) for all estimates of the concentration in water

(C

w ) generated using Equation 2

‐ 1

. A sensitivity analysis was conducted to identify the parameters that

would yield high

‐ end estimates of exposure. In this analysis, puddle water depths (d

w ) ranged from 0.5

to 6 inches ( i.e., 0.013

‐ 0.15 m), sediment depth (d sed ) was 10

‐ 200% of the puddle water depth, bulk

density ( ρ b ) was set to 1400

‐ 1800 kg/m 3 (which is within the range of the standard PRZM scenarios used

by EPA to represent fields through the US). Porosity did not influence the estimated concentration in

water, therefore it was set to a value of 0.509, which is the default used by the Tier I Rice model. Soil

partitioning coefficient (K d ) values were selected to be consistent with the values used to estimate

pesticide concentrations in ponds and dew (assuming K d = 0.01*K

oc ). The C

w values for different

parameter values and are provided in Table 2

‐ 6

. In this table, a K d of 0.1 L/kg is used to illustrate the

difference in sensitivity of model results to the different puddle parameters.Page 211 of 275 Table 2

‐ 6. Sensitivity analysis of puddle model parameters. Highly

‐ mobile chemical (K d = 0.1 L/kg) used as an

example.

d

w (m) d sed (m) porosity pb (kg/m 3 ) k d (L/kg) C

w (ug/L) D

water (low)

( u g a.i./bee) D

water (high)

( u g

a.i./bee)

0.013 0.0013 0.509 1400 0.1 5.724166 1.4E

‐ 04 1.0E

‐ 02

0.013 0.013 0.509 1400 0.1 0.608822 1.5E

‐ 05 1.1E

‐ 03

0.013 0.026 0.509 1400 0.1 0.305491 7.6E

‐ 06 5.5E

‐ 04

0.013 0.0013 0.509 1800 0.1 4.522298 1.1E

‐ 04 8.1E

‐ 03

0.013 0.013 0.509 1800 0.1 0.474653 1.2E

‐ 05 8.5E

‐ 04

0.013 0.026 0.509 1800 0.1 0.237982 5.9E

‐ 06 4.3E

‐ 04

0.15 0.015 0.509 1400 0.1 0.496094 1.2E

‐ 05 8.9E

‐ 04

0.15 0.15 0.509 1400 0.1 0.052765 1.3E

‐ 06 9.5E

‐ 05

0.15 0.3 0.509 1400 0.1 0.026476 6.6E

‐ 07 4.8E

‐ 05

0.15 0.015 0.509 1800 0.1 0.391932 9.8E

‐ 06 7.1E

‐ 04

0.15 0.15 0.509 1800 0.1 0.041137 1.0E

‐ 06 7.4E

‐ 05

0.15 0.3 0.509 1800 0.1 0.020625 5.2E

‐ 07 3.7E

‐ 05

The high end estimates for all of the selected K

oc values (0.01*K d ) are provided in Table 2

‐ 7, along with

conversions of these values to doses received by bees consuming 100% of their daily drinking water

from contaminated puddles. Estimated concentrations in puddles are on the same order of magnitude

or below that of concentrations estimated in ponds using GENEEC2. Therefore, similar to bees that drink

water from ponds, bees that meet 100% of their drinking water needs from puddles (located on treated

fields) are expected to receive pesticide doses that are insignificant relative to doses received through

consumption of food or through direct spray onto the bees.

Table 2

‐ 7. Peak estimated concentrations in puddle (in μ g a.i./L) as estimated using modified Tier I rice model

and resulting doses from 100% of drinking water from puddle.

K

oc (L/kg) Estimated

concentration

in puddle

water ( μ g

a.i./L) Dose ( μ g a.i./bee)

Drinking

water Foliar

spray:

contact Foliar

spray: diet Soil

application:

diet* Seed

treatment:

diet

1 57 0.0027

2.7 32 0.62

0.29 10 5.7 0.00027 0.56

100 0.61 0.000029 0.29

1,000 0.061 0.0000029 0.15

10,000 0.0061 0.00000029 0.084

100,000 0.00061 0.000000029 <0.063

1,000,000 0.000061 0.0000000029 <0.056

*Calculated assuming Kow = Koc/0.35.Page 212 of 275 Dew

This analysis relies upon a simple equilibrium partition model based on K

oc and the carbon content of

plants. The partitioning model, which is depicted in Equation 2

‐ 2

, is a two

‐ compartment model including

water and leaf organic carbon into which the pesticide may associate. In past discussions with the SAP,

this approach was considered appropriate by the SAP when discussing methods for estimating pesticide

exposure to birds (USEPA 2001). The value C dew(t) is the concentration of dissolved pesticide in dew

(mg/L). The value C plant(t) is the concentration of pesticide on and in plant leaves (mg/kg (fresh weight))

at time t after application and can be derived from the T

‐ REX model. For a single application of 1 lb a.i./A

to foliage, the most conservative C plant(t=0) derived from T

‐ REX is 240 mg a.i./kg

‐ foliage, which

corresponds to residues on short grass. The K

oc is the organic carbon:water partition coefficient of the

pesticide being modeled (in L/kg

‐ oc) and f

oc is the fraction of organic carbon in leaves. Donahue et al

.

(1983) have estimated the fraction of organic carbon in dry leaves to be 0.40 for alfalfa, clover,

bluegrass, corn stalk, and small grain straw. If it is assumed that the water content of plants is 90%

(Raven et al

. 1999), this is equivalent to 0.04 on a fresh weight basis. Therefore, for this analysis, f

oc is set

to a value of 0.04.

Equation 2

‐ 2

oc oc t plant

t dew 

### f K

C

C

* ) (

) ( 

### 

Ranges of C

w values in dew generated using different K

oc values are provided in Table 2

‐ 8 along with the

estimated doses received by forager bees consuming 100% of their daily drinking water from

contaminated dew. When considering the proposed Tier I method for estimating exposures of bees to

pesticides applied via foliar spray, estimated doses received through consumption of dew are equivalent

to diet and contact for chemicals that are mobile to highly mobile (i.e., Koc <100 L/kg, Table 2

‐ 3 ). For

chemical that are moderately mobile to immobile ( i.e., K

oc >100 L/kg, Table 2

‐ 3 ), the dose received

through drinking dew is less substantial compared to doses through the diet or contact. Since this

method assumes that the pesticide is applied directly to the foliage and then partitions into dew, this

exposure route is not relevant to soil or seed treatments. The results of this analysis indicate that dew

may represent a substantial exposure pathway for bees in cases where the following conditions are met:

1) the bees drink 100% of their daily drinking water from contaminated dew, 2) bees consume only 7%

of their daily water needs through food, 3) the dew contains a pesticide with a K

oc <100 L/kg and 4) the

pesticide is applied via foliar spray. Although these results are initially of concern relevant to assessingPage 213 of 275 exposures of honey bees to pesticides, this exposure route is not incorporated into the Tier I exposure

method because the first condition listed above (for describing when dew exposure is substantial) is

expected to be unlikely. This is because dew is only expected to be present during a portion of the

morning (Kovac et al. 2010), and would not be available during the majority of the day. Also, dew is not

expected to be present during the hottest parts of the day, which are expected to represent the times

when water demand of the individual bee would be at its greatest. In addition, this approach assumes

that bees only acquire 7% of their daily water needs from food (condition 2 above). As indicated in Table

2

‐ 2

, bees may consume anywhere from 7% to >100% of their daily water requirement through food.

Therefore, for many bees, pesticide doses through consumption of dew may be much less due to lower

or non

‐ existent drinking water consumption rates.

Table 2

‐ 8. Peak estimated concentrations in dew (in μ g a.i./L) as estimated using K

oc partitioning

model and resulting doses from 100% of drinking water from dew.

K

oc (L/kg) Estimated

concentration in

dew ( μ g a.i./L) Dose ( μ g a.i./bee)

Drinking

water Foliar

spray:

contact Foliar

spray: diet Soil

application:

diet Seed

treatment:

diet

1 6000000 280

2.7 32 Not

applicable Not

applicable 10 600000 28

100 60000 2.8

1,000 6000 0.28

10,000 600 0.028

100,000 60 0.0028

1,000,000 6 0.00028

Guttation Fluid

Methods are not available to estimate pesticide concentrations in guttation fluid. Although the Briggs’

model was considered, comparison of estimated pesticide concentrations in the transpiration stream to

empirical measures of pesticides in guttation fluid indicate that this approach is not suitable (because

the empirical concentrations are orders of magnitude above the Briggs’ estimates). This difference may

be attributed to concentration of chemicals in guttation fluid on the outside of the plant due to

evaporation of the water content of the fluid and the basis of the Briggs’ model on the internal fluid of

the plant, which is not expected to concentrate a chemical due to evaporation. Therefore, this section

focuses on available empirical data where pesticide concentrations were measured in guttation fluid.Page 214 of 275 Empirical measurements of pesticide concentrations in guttation fluid are available from 4 studies in the

scientific literature as well as 4 registrant

‐ submitted studies. The majority of the data were generated

from studies where crops received seed treatments of pesticides; however data are available where

pesticide concentrations in guttation fluid were measured from crops where pesticides were applied via

foliar spray or soil treatment. Descriptions of these studies are provided in Appendices 3 and 4

. All but

two studies involved seed treatments with neonicotinoid insecticides (specifically clothianidin,

imidacloprid and thiamethoxam) and monitoring of pesticides in guttation fluid of young and mature

plants. The highest pesticide concentrations reported in these studies ranged from 8100 to 39,000,000

μ g a.i./ L ( Table 2

‐ 9 

### )

. These concentrations are orders of magnitude above the estimated

concentrations in ponds, puddles and dew. Individual bees drinking all of their daily drinking water from

guttation fluid would receive pesticide doses that are equivalent to or higher than those doses received

through direct spray from foliar applications. This would suggest that pesticide exposures through

drinking guttation fluid of crops that received treatments of pesticides may be substantial for bees.Page 215 of 275 Table 2

‐ 9. Pesticide concentrations reported in guttation fluid of crops treated with pesticides and

corresponding doses in bees consuming 100% of their daily drinking water from this source.

Chemical Application

method Crop Measured

concentration

( μ g a.i./ L) Dose ( μ g

a.i./bee) Citation **

Thiamethoxam Seed treatment Corn 8,740,000 411 Tapparo et al

. 2011

Clothianidin Seed treatment Corn 6,400,000 301 Lückmann et al. 2010

Clothianidin Seed treatment Corn 2,240,000 105 Lückmann et al. 2010

Imidacloprid Seed treatment Corn 2,100,000 99 Tapparo et al

. 2011

Thiamethoxam Seed treatment Corn 1,920,000 90 Tapparo et al

. 2011

Imidacloprid Seed treatment Corn 1,230,000 58 Tapparo et al

. 2011

Thiamethoxam Seed treatment Corn 1,170,000 55 Tapparo et al

. 2011

Thiamethoxam Seed treatment Corn 1,110,000 52 Tapparo et al

. 2011

Imidacloprid Seed treatment Corn 712,000 33 Girolami et al

. 2009

Clothianidin Seed treatment Corn 616,000 29 Tapparo et al

. 2011

Clothianidin Seed treatment Corn 561,000 26 FOAG 2009

Clothianidin Seed treatment Corn 379,000 18 FOAG 2009

Clothianidin Seed treatment Corn 218,000 10 Tapparo et al

. 2011

Thiamethoxam Seed treatment Corn 184,000 8.6 Tapparo et al

. 2011

Clothianidin Seed treatment Corn 141,000 6.6 Girolami et al

. 2009

Clothianidin Seed treatment Corn 110,000 5.2 Reetz et al

. 2011

Imidacloprid Soil treatment Melon 99,100 4.7 Hoffman and Castle 2012

Thiamethoxam Seed treatment Corn 90,200 4.2 Girolami et al

. 2009

Unnamed

chemical #2 Foliar spray Oilseed rape 38,650 * 1.8 Study 2

‐ 6

Unnamed

chemical #2 Foliar spray Oilseed rape 8,100 * 0.38 Study 2

‐ 7

*Normalized to 1 lb a.i./A.

**Summaries of these studies are provided in Appendices 3 and 4.

Although these preliminary conclusions represent a concern for assessing exposures of honey bees to

pesticides, there are several factors that limit incorporation of this exposure route into the Tier I screen

for all pesticides. First, the conclusion that guttation fluid could represent a substantial exposure route

for bees is based on the assumption that bees are collecting 100% of their daily drinking water from this

source. As discussed above when considering exposure through consumption of dew, this assumption is

unlikely given that guttation fluid is only present during the morning hours of the day (Reetz et al

. 2011),

which are not expected to be the times when the water requirements of an individual bee would be the

highest. Second, as discussed in the dew section above, this analysis is based on the assumption that

bees only acquire 7% of their daily water needs from food; however, many bees are expected to

consume much more water with their food. Therefore, for many bees, pesticide doses throughPage 216 of 275 consumption of guttation fluid may be much less due to lower or non

‐ existent drinking water

consumption rates. Third, the importance of guttation fluid to bees as a source of drinking water is

unknown. Perhaps guttation fluid will be more important to bees located in areas where surface water is

not available ( e.g., arid climates), or when it is the closest source of water to the hive in times of need.

Finally, it is unclear which pesticides would be present in guttation fluid. Physical chemical properties of

a chemical are expected to impact the mobility of a chemical within a plant. The three pesticides that

have been reported in guttation fluid after seed or soil treatments all have high solubility and high

mobility. Some of the same studies also included seed treatments of fipronil (solubility

= 4.2 mg/L; Log

K

ow = 3.5; K

oc = 727 L/kg

‐ oc), which has lower solubility and mobility compared to clothianidin,

imidacloprid and thiamethoxam (solubility values range: 327

– 4100 mg/L; Log K

ow values range: ‐ 0.13 to

1.12; K

oc values range: 33

‐ 345 L/kg

‐ oc). In these studies, fipronil was not detected in guttation fluid of

plants (Girolami et al

. 2009, Tapparo et al

. 2011). Therefore, although some but not all pesticides may

be present in guttation fluid ( e.g., following seed treatments).

Summary of Exposure through Drinking Water

Based on estimated pesticide concentrations in various sources of water potentially consumed by bees

and a water flux rate from the brown paper wasp, the results of this analysis indicate that if bees

consume the majority of their water from puddles or ponds, the exposures relative to dietary and direct

spray are insignificant. If bees drink a substantial amount of water from guttation fluid or dew,

conservative exposures may be similar to or even exceed pesticide exposures through the diet or direct

spray. Because this indicates a potential concern for assessing exposures of honey bees to pesticides,

potential exposures through drinking dew and guttation fluid were investigated further. This

investigation concluded that pesticide exposures through dew and guttation fluid are not expected to be

as significant when compared to diet because of two primary reasons. First, although the importance of

dew and guttation fluid to bees as a source of drinking water is unknown, dew and guttation fluid are

only expected to be present during a portion of the morning which would prevent bees from drinking a

substantial amount of water from these sources. Second, for many worker bees, pesticide doses through

consumption of dew and guttation fluid may be much less due to lower or non

‐ existent drinking water

consumption rates (because of higher amounts of water consumed through food). Therefore, pesticide

exposure through drinking water is not included in the proposed Tier I exposure route for bees.Page 217 of 275 

### Appendix 3. Summaries of empirical studies from unpublished, registrant

‐ submitted studies

that were used to evaluate Tier I methods for estimating pesticide exposures

This appendix contains summaries of empirical studies from unpublished, registrant

‐ submitted study

reports focusing on pesticide concentrations in pollen, nectar and guttation fluid. The maximum

concentrations from these studies were used to evaluate the draft tier I modeling approaches included

in the white paper.

Beedle and Harbin 2011

Beedle and Harbin 2011 quantified total imidacloprid residues (imidacloprid + 5

‐ hyrdoxy imidacloprid +

imidacloprid olefin) in nectar and leaves collected from cotton plants grown at five locations treated

with imidacloprid. The five trials were conducted in clay soils with a drainage classification of “heavy”,

defined as slow drainage capacity. Plant parts were quantified by HPLC/triple stage quadrupole mass

spectrometry (LC/MS/MS) using stable isotope

‐ labeled internal standards. Composite samples were

collect of cotton nectar and cotton leaves seven days prior to application of imidacloprid and six days

following imidacloprid. Admire Pro® (imidacloprid) was applied to the study sites by drip

‐ line

chemigation in 2009 at a rate ranging from 0.18 to 0.38 lbs a.i./A. Each study site received one aerial

foliar spray of imidacloprid (Provado® 1.6F) in 2010 during flowering at a rate of 0.063 lbs a.i./A.

Residues are summarized in Table 3

‐ 1

.

Table 3

‐ 1. Summary of total imidacloprid residues measured in cotton nectar and leaves (ppm)

Media Application

rate

(lb a.i./A) Sampling timing DAT Total imidacloprid

(ppm) Maximum

residue (mg/kg

per 1 lb a.i./A)

Nectar NA (

‐ 7 to

‐ 2 DBT) 0.0043

‐‐

Nectar 0.063 6 DA T 0.066 1.1

Leaves NA (

‐ 7 to

‐ 2 DBT) 0.025

‐‐

Leaves 0.063 6 DA T 1.9 30

DAT = days after treatmentPage 218 of 275 Bocksch 2010

In a registrant

‐ submitted study (MRID 47961202), residues of clothianidin and imidacloprid and their

metabolites were measured in melon following application as seed treatment. The objective of the

study was to determine the levels of clothianidin residues and those of its metabolites

thiazolylmethylurea (TZMU) and thiazolylnitroguianidine (TZNG), as well as of imidacloprid and its

metabolites imidacloprid

‐ 5

‐ hydroxy and imidaclodprid

‐ olefin in melon grown from treated seed.

Attention was paid to residues in flower, nectar and pollen. The treatment group received a nominal

seed dressing rate of 1.0 mg a.i. clothianidin + 0.33 mg a.i. imidacloprid per seed. Samples of entire

young melon plants (above ground plant material) were obtained at the time of transplanting from the

greenhouse to the field. Additionally, flowers as well as nectar and pollen from combs were collected

during flowering, starting at transplanting and continuing during flowering of the crop. In young

treatment plants, residues of c1othianidin, TZMU, TZNG, imidacloprid, imidacloprid

‐ 5

‐ hydroxy and

imidacloprid

‐ olefin ranged from 0.93 to 11 mg/kg for c1othianidin, from 0.10 to 0.50 mg/kg for TZMU,

from 0.05 to 0.2 mg/kg for TZNG, from 0.46 to 2.6 mg/kg for imidacloprid, from 0.03 to 0.16 mg/kg for

imidacloprid

‐ 5

‐ hydroxy and from 0.02 to 0.06 mg/kg for imidacloprid

‐ olefin. In treated flower samples,

the residues of clothianidin ranged from < LOD to 0.0030 mg/kg, the residues of imidacloprid ranged

from < LOD to 0.0095 mg/kg. No quantifiable residues of TZMU, TZNG, imidacloprid

‐ olefin, except for a

single detection of imidacloprid

‐ 5

‐ hydroxy at 0.0010 mg/kg that was reported in the treated flower

samples. No quantifiable residues were reported in samples of treated pollen or nectar/honey;

however, a phylogenetic analysis was not completed on the pollen sampled from the hives to determine

the source of the pollen. Also, it is important to note that residues of clothianidin were detected in

control flowers and control young plant ranging from 0.0011 mg/kg to 0.0032 mg/kg, and <LOD to

0.0025, respectively. Table 3

‐ 2 summarizes detected residues in flowers, pollen, nectar, and young

plants.Page 219 of 275 Table 3

‐ 2. Summary of Clothianidin, imidacloprid and their metabolites in melon flowers, pollen,

nectar and young plant grown from treated melon seed.

Sample Matrix Range Clothianidin TZMU TZNG Imidacloprid Olefin 5

‐ OH Total Residue

CNI's (mg/kg)

Flowers Min 0.001 <LOD <LOD 0.0011 <LOD <LOD <LOD

Max 0.003 <LOD <LOD 0.0095 <LOD <LOD 0.0126

Pollen Min <LOD <LOD <LOD <LOD <LOD <LOD <LOD

Max <LOD <LOD <LOD <LOQ <LOQ <LOQ <LOQ

Nectar Min <LOD <LOD <LOD <LOD <LOD <LOD <LOD

Max <LOD <LOD <LOD <LOD <LOD <LOD <LOD

Plant Min 0.0011 0.1 0.05 0.0012 0.02 0.03 0.0022

Max 11 0.5 0.2 2.6 0.06 0.16 14

LOD = level of detection

Bryne et al

. 2011

Byrne et al. 2011 quantified total imidacloprid concentrations (imidacloprid + 5

‐ hyrdoxy imidacloprid +

imidacloprid olefin) in nectar that was regurgitated from foraging bees returning to the hive, and nectar

that was stored in new comb. This study was conducted in two portions, where the first part confined

honey bee colonies within tunnel cage enclosures each containing three flowering citrus trees. The

second part of the study situated Honey bee colonies within a large area of treated commercial citrus.

Admire Pro® (imidacloprid) was applied at the maximum label rate of 14 fl oz per acre (0.104 fl oz per

tree; 0.5 lbs a.i./A) as a trunk drench for both the tunnel and open

‐ field studies. Nectar was sampled

from individual flowers using a micro

‐ capillary tube and drawn into the tube by capillary action. For the

regurgitated nectar samples, bees were intercepted at the entrance to the hives using a small net and

were anesthetize on dry ice, after which individual bees were forced to regurgitate the contents of their

crops by pressing gently on the lateral sides of their abdomen region with paddle forceps.

Nectar was also collected from deposits made to new comb within each hive. Hive deposited nectar

(uncapped honey) was sampled during the afternoon of day 3 after bees were released within the

tunnels. Imidacloprid, imidacloprid olefin and 5

‐ hydroxy imidacloprid were measured in nectar and

pollen. Nectar was measured directly by dilution with HPLC mobile phase and the pollen was extracted

using QuEChERS methodology. The final extracts were analyzed by LC/MS/MS employing stable isotope

internal standards. Samples were not taken overtime, but represent exposure during a 3

‐ day period in

April when trees were in bloom. The highest measured concentration of 0.11 ppb was in stored nectar.Page 220 of 275 Federal Office of Agriculture (FOAG) 2009

In a registrant

‐ submitted study (MRID 47881701) monitoring bee health in Switzerland, residues of

clothianidin were measured in guttation fluid collected from corn grown from seeds treated with

Poncho® (0.5 mg a.i./seed). In the two trials that were conducted guttation fluid was collected 15 to 50

days and 8

‐ 38 days after the seeds were sown. Residues were analyzed using an LC

‐ MS/MS method,

and the detection limit in water was 0.10 μ g/L. Clothianidin residues in guttation fluid were greatest

during the early growth stages of the corn plants, when there was more guttation fluid being released,

and decreased as the plants grew. Concentrations of clothianidin ranged from 0.05 to 37 μ g a.i./mL.

Freeseman and Harbin 2011

Nine trials were conducted to determine imidacloprid, 5

‐ hydroxy imidacloprid, and imidacloprid olefin

residues in pollen and leaves of tomatoes treated by drip chemigation with Admire Pro® in 2009 and in

2010. The sites were planted in soils classified as either heavy (4 sites) or medium (5 sites). Six of nine

sites were treated twice in 2010, once at or closely following transplantation, and a second 52 to 57

days later. The application rate was 3.5 fl. oz./acre (0.13 lbs. a.i./acre) per treatment for a total

application rate of 7.0 fl. oz./acre (0.25 lbs. a.i./acre). The remaining three sites were treated with 5.0 fl.

oz./acre (0.18 lbs. a.i./acre). These applications were made 2 to 25 days following transplanting. All

applications were made through drip chemigation via buried lines. Residues were measured in anthers

and leaves grown in each soil type. It is important to note that Admire Pro was applied at a use rate of

5.0 to 7.0 fl. oz. acre (0.18

– 0.25 lbs. a.i./acre) or 47% to 66% of the maximum label rate. Results are

summarized in Table 3

‐ 3.

Table 3

‐ 3. Summary of Total Imidacloprid in soil (unadjusted) (total imidacloprid includes 5

‐ OH

imidacloprid and imidacloprid olefin)

Media Soil Class

(drainage) Application

Rate (lbs

ai/A) Sample

Timing

(DAT) Total Imidacloprid Residue (ppm)

n Min Max highest

average

site

residue Median Mean STDEV

tomato anthers Heavy Soil 0.25 72

‐ 79 8 0.014 0.03 0.027 0.022 0.021 0.005

tomato anthers Medium Soil 0.25 79

‐ 102 10 0.016 0.054 0.046 0.036 0.034 0.012

Tomato leaves Heavy Soil 0.25 72

‐ 79 8 0.057 0.057 0.12 0.089 0.093 0.026

Tomato leaves Medium Soil 0.25 79

‐ 102 10 0.038 0.038 0.2 0.1 0.11 0.061

Heavy Soil represents soil with slow drainage capacity, medium has moderate drainage capacityPage 221 of 275 Lückmann et al

. 2010

In a registrant

‐ submitted study (MRID 479753

‐ 01) residues of clothianidin were measured in guttation

fluid of corn seedlings grown from seeds treated with Poncho® (0.5 mg a.i./seed) or Poncho Pro® (1.25

mg a.i./seed) under field conditions in Austria. Two regions in Austria were monitored, Baumgartenberg

(with 5 sub

‐ regions) and Jennersdorf (with 4 sub

‐ regions), each region containing separate study fields.

The seeding rate was approximately 2 units/ha (1 unit = 50,000 corn seeds); therefore, the nominal

application rates were 50 g a.i./ha (0.0446 lbs a.i./A) for Poncho®, and 125 g a.i./ha (0.112 lbs a.i./A) for

Poncho Pro®, respectively. Each study region was monitored regularly for guttation fluid and/or dew,

when guttation fluid was observed; up to 3 samples (1 mL volume) were collected. Guttation fluid was

observed ≥ 97% of observation days in the morning in both study regions (May

‐ June 2009);

observations of guttation fluid in the evening was much lower ( approximately 50% of observation days).

During the morning hours the presence of guttation fluid and honeybee forager activity coincided, and

occurs throughout the period of seedling growth. When an alternative source of water is not accessible

to honeybees, they were seen resting on the soil and corn plants close to their hive, perhaps to forage.

A small number of honeybees (3

‐ 8) were observed collecting guttation fluid from corn plants, more

honeybees were observed collecting guttation fluid and/or dew from other plants within the field or

along the margin of the field. Residues of clothianidin in guttation fluid collected from both regions

typically ranged from 100

‐ 200 μ g a.i./mL, and the highest concentration of clothianidin in guttation

fluid was 717 μ g a.i./mL.

Maus et al

. 2004

In this field study, Horse Chestnut trees ( Aesculus hippocastanum ) were injected with Imidacloprid SL

200. Applications were based on the stem diameters and ranged from 1.32

‐ 1.76 g ai/tree. No residues

of Imidacloprid or the two metabolites were detected above the LOQ in control leaves, control nectar,

control fruit or treatment fruit. Imidacloprid and both metabolites were detected in all treatment leaf

samples from 2 to 124 days after treatment; peak levels occurred 61 days after treatment and were

2200  g/kg of imidacloprid, 2313  g/kg of Hydroxy

‐ metabolite, and 259  g/kg of Olefin

‐ metabolite

(parent maximum was measured in T3, maximum degradate concentrations were measured in a

different treatment group, T1). Total imidacloprid (imidacloprid + 5

‐ OH + olefin) in T1 was at a

maximum 61 DAT, 4642 μ g/kg. Imidacloprid was detected in one replicate control blossom sample (8

μ g/kg) 7 days after application; no explanation was provided for this contamination. Residues ofPage 222 of 275 imidacloprid were detected in one treatment blossom sample (7 μ g/kg) two days after treatment and

for imidacloprid (up to 283 μ g/kg) and the Hydroxy

‐ metabolite (up to 7 μ g/kg) 7 days after treatment.

Table 3

‐ 4 summarizes the total imidacloprid residues in tree leaves in the days following the application.

Table 3

‐ 4. Summary of Total imidacloprid residues in Horse Chesnut leaves of trees receiving tree

injections.

Replicate Treatment

(g a.i./tree) † DBH

(cm) ln(Leaf

biomass)

(kg, dry

weight) * Leaf biomass

(dry weight) ** Leaf Biomass

(wet weight) *** DAT Total

Imidacloprid

( μ g/kg)

T1 1.45 23.00 1.59 6.60 32.98 1 ND

T2 1.32 21.00 1.44 5.63 28.15 1 ND

T3 1.51 24.00 1.67 7.10 35.51 1 ND

T4 1.76 28.00 1.94 9.29 46.44 1 ND

T1 1.45 23.00 1.59 6.60 32.98 2 1349

T2 1.32 21.00 1.44 5.63 28.15 2 27

T3 1.51 24.00 1.67 7.10 35.51 2 276

T4 1.76 28.00 1.94 9.29 46.44 2 14

T1 1.45 23.00 1.59 6.60 32.98 33 3102

T2 1.32 21.00 1.44 5.63 28.15 33 1666

T3 1.51 24.00 1.67 7.10 35.51 33 2238

T4 1.76 28.00 1.94 9.29 46.44 33 2882

T1 1.45 23.00 1.59 6.60 32.98 61 4642

T2 1.32 21.00 1.44 5.63 28.15 61 287

T3 1.51 24.00 1.67 7.10 35.51 61 2832

T4 1.76 28.00 1.94 9.29 46.44 61 2156

T1 1.45 23.00 1.59 6.60 32.98 96 1292

T2 1.32 21.00 1.44 5.63 28.15 96 36

T3 1.51 24.00 1.67 7.10 35.51 96 2208

T4 1.76 28.00 1.94 9.29 46.44 96 1396

T1 1.45 23.00 1.59 6.60 32.98 124 615

T2 1.32 21.00 1.44 5.63 28.15 124 454

T3 1.51 24.00 1.67 7.10 35.51 124 625

T4 1.76 28.00 1.94 9.29 46.44 124 1086

† adjusted for trunk diameter (0.06 g a.i./cm stem)

* According to Sollins et al., 1973 (hardwoods)

** Correction for bias when converting from ln to arithmetic units (Sollins et al

. 1973)

*** assuming 80% wet weight is H 2 0Page 223 of 275 Staedtler 2009

In a registrant submitted study (MRID 48298801) residues of clothianidin, and its metabolites

thiazolylmethylurea (TZMU) and thiazolylnitroguianidine (TZNG), were measured in pollen harvested

from maize plants grown in commercial practice from treated seeds (1.25 mg a.i./seed) in the Upper

Rhine Valley in Germany. The objective of the study was to determine the levels of clothianidin residues

and those of its metabolites (TZMU and TZNG) in corn pollen collected from plants grown from seeds

dressed with clothianidin (Poncho Pro®). Samples were collected approximately 72 days after planting

treated seed. A total of 252 samples of pollen were taken from five corn fields planted with clothianidin

treated corn seed 19

, and an additional 193 samples from bee

‐ derived matricies (bee bread, pollen from

pollen traps, and dead bees) were collected during the field sampling phase of the study. Corn pollen

sample analysis resulted in 237 samples that contained clothianidin residues >LOQ (94% detection

frequency, mean residue of 3.4 μ g/kg; 90 th %

‐ tile residue of 5.2 μ g/kg). The maximum detection in corn

pollen was 10.4 μ g/kg. Residue analysis of pollen from pollen traps placed at the entrance to bee hives

resulted in 28 samples that contained clothianidin residues > LOQ (n=118, 24% detection frequency,

mean residue of 1.1 μ g/kg, 90 th %

‐ tile residue of 2.2 μ g/kg). The maximum detection in pollen from

pollen traps was 11.4 μ g/kg. Dead bee residue analysis resulted in two samples >LOQ (1.2 and 1.1

μg/kg, n=39, mean residue of 0.5 μ g/kg, 90 th %

‐ tile residue of 1.0 μ g/kg). Both of these samples were

obtained from hives that were designated to the same field region (Müllheim); it is unclear if the bees

were from the same hive. Residue analysis of bee bread resulted in 7 samples >LOQ (n=36, mean

residue of 1.0 μ g/kg, 90 th %

‐ tile residue of 2.0 μ g/kg). Residues in pollen are summarized in Table 3

‐ 5

.

Table 3

‐ 5. Measured concentrations ( μ g/kg) of clothianidin in pollen collected in the field and in traps

placed at hive entrances.

Site Mean Clothianidin, μ g/kg pollen

(ppb)

(Field Pollen) Mean Clothianidin, μ g/kg pollen(ppb)

(Pollen Traps)

Buhl

‐ Oberbruch 3.906 1.142

Schwanau 2.854 NS

Kippenheim 3.358 1.605

Herbolzheim 3.942 NS

Mullheim 2.942 0.73

19 Bühl

‐ Oberbruch, Schwanau, Kippenheim, Herbolzheim, Müllheim

.Page 224 of 275 Study 1

‐ 1

In a registrant submitted semi

‐ field study, residues of unnamed chemical #1 were measured in matrices

relevant to exposure of honeybees. Unnamed chemical #1 (49.1 % w/w) was diluted in water and

applied with a foliar spray application to cotton in California at full bloom at three test rates, separately:

0.045 lb a.i./acre (1 or 2 applications at 5

‐ day interval), 0.089 lb a.i./acre (2 applications at 5 day

‐

interval) and 0.134 lb a.i./acre (2 applications at 5

‐ day interval). When spray residues were dry,

honeybee colonies were exposed to the treated crop in tunnels (two bee hives per tunnel) for 10 days.

Samples of pollen (extracted from flowers), forager honeybees (for subsequent extraction of pollen

loads and nectar) and pollen and larvae from the combs were taken and analyzed for unnamed chemical

#1 and its major metabolite. Additional measurements on the general conditions and weight of the

colonies were recorded before and after exposure to ensure the colonies were in a suitable condition to

fulfill the purpose of the experiment. Since bees were not present in the field during application, they

were exposed to the treated field following application via contact with foliar residues and oral exposure

via consumption, transport and processing of pollen and nectar. Table 3

‐ 6 summarizes the residue data

in pollen and nectar measured in cotton. Day zero residue values were used to calculate doses from

each food source.Page 225 of 275 Table 3

‐ 6. Unnamed chemical #1 residues (mg ai/kg) in pollen collected from treated cotton plants,

pollen collected from foragers, and nectar collected from foragers

Application Rate (lb

ai/A) Application Days Non

‐ Application Days

(D1

‐ D4 & D6

‐ D10)

D0 D5 Min Max

Pollen & Stamen from Plants (mg ai/kg)

0.045 x 1 1.263 n/a <0.010 <0.010

0.045 x 2 1.077 2.540 <0.010 0.061

0.089 x 2 6.656 0.691 <0.010 0.057

0.134 x 2 2.612 0.075 0.013 0.070

Pollen from Foragers (mg ai/kg) *

0.045 x 1 0.127

– 0.187 n/a 0.013 0.222

0.045 x 2 0.173

– 0.226 0.192

– 0.830 0.032 0.296

0.089 x 2 0.512

– 2.782 0.787

– 1.146 0.100 2.262

0.134 x 2 1.209

– 2.218 1.150

– 1.420 0.129 2.226

Nectar from Foragers (mg ai/kg) *

0.045 x 1 0.022

– 0.033 n/a <0.010 0.126

0.045 x 2 <0.010 0.045

– 0.049 <0.010 0.043

0.089 x 2 <0.010

– 0.074 0.022

– 0.023 <0.010 0.036

0.134 x 2 0.052

– 0.109 0.022

– 0.044 <0.010 1.006

* range in values from bees collected from hive #1 and #2. n/a

= not applicable (no pesticide application)

Study 1

‐ 2

In a registrant submitted semi

‐ field study residues of unnamed chemical #1 and its metabolite were

investigated in honeybee products (pollen and nectar) and plants following spray applications to

Phacelia tanacetifolia grown on plots in Niefern

‐ Öschelbronn, Germany. Five treatment groups were

investigated in separated tunnels; four treated plots and one control. Unnamed chemical #1 was applied

as a foliar spray at rates of 24 g a.i./ha (Treatments 1 and 3; 0.021 lbs a.i./A), and 48 g a.i./ha

(Treatments 2 and 4; 0.043 lbs a.i./A). Treatments 1 and 2 were conducted before flowering; treatments

3 and 4 were conducted during flowering and daily bee flight/foraging. The condition of the colonies and

the development of the bee brood was assessed before the start of exposure of the honeybees in the

tunnels

, and determined to be acceptable. For treatments T1 and T2, samples were collected 10, 15, and

16 days after application to the Phacelia crop. For treatments T3 and T4, samples were collected at 0, 5

and 6 days after application of the test item. Nectar samples obtained from forager bees (regurgitation

samples) and pollen samples were collected via pollen traps at the hive entrance. Results from

treatment groups 1 and 2 (T1 and T2; before flowering) showed no residues of unnamed chemical #1.

Samples from T3 and T4 showed measureable residues and were conducted during flowering. The

results of the analysis of nectar samples from controls, T3 and T4 treatments are provided in Table 3

‐ 7

.Page 226 of 275 Results of the analyses of pollen samples from pollen traps in these treatments are provided in Table 3

‐

8.

Table 3

‐ 7. Unnamed Chemical #1 results of nectar analysis at 0 DAT for treatments receiving

applications during flowering and foraging.

Timing (DAT) Treatment Application rate

(g a.i./ha) Application rate (lb a.i./A) Residues (mg/kg)

0 C

‐‐  ‐‐ ND

0 T3 (during flight) 24 0.021 0.0438

0 T3 (during flight) 24 0.021 0.0462

0 T3 (during flight) 24 0.021 0.0424

0 T4 (during flight) 48 0.043 0.0889

0 T4 (during flight) 48 0.043 0.0548

0 T4 (during flight) 48 0.043 0.0503

DAT = Days after treatment

Table 3

‐ 8. Results of analysis of pollen samples from pollen traps 0 DAT

Timing

(DAT) Treatment Application rate

(g a.i./ha) Application rate

(lb a.i./A) Residues (mg/kg)

0 C

‐‐  ‐‐ ND

0 T3 24 0.021 0.29

0 T4 48 0.043 0.809

DAT = Days after treatment

Study 1

‐ 3

The nature of the radioactive residue of 14 C

‐ unnamed chemical #1 in succeeding crops was studied in a

confined crop rotational study. In outdoor test plots in California, 14 C

‐ unnamed chemical #1 was applied

to bare sandy loam soil at a nominal rate of 600 g a.i./ha. Following aging for plant back intervals (PBIs)

of 30, 120, and 365 days, radishes (variety cherry belle), lettuce (variety butter crunch), and wheat

(variety summit) were planted. Plots were maintained using standard agricultural practices. Immature

radish tops, wheat forage, immature lettuce, and wheat hay (fresh cut and dry cut) were harvested 30,

120, and 365 days after treatment (DAT). Mature radish tops and roots, mature lettuce, wheat straw,

and wheat grain were also harvested 30, 120, and 365 DAT.

Samples were extracted and analyzed within ~1.1

‐ 4.7 months of harvest. Since they were stored at

‐

20 o C and analyzed within 6 months of harvest, supporting freezer storage stability are not requiredPage 227 of 275 according to OECD guidelines. Final extraction and analysis of stored samples was conducted to establish

stability of samples that were analyzed by GC

‐ MS beyond 6 months of harvest. Selected results from this

study for the unnamed chemical #1 and its primary metabolite are shown in Table 3

‐ 9

.

Table 3

‐ 9. Sample residue data (mg/kg) for a confined rotational study (860.1850) of CHEMICAL 1 applied to

directly to bare soil using a single application of 0.535 lbs a.i./A.

Plant back interval (days) Mature Radish Mature

Lettuce Wheat

Top Root Lea f Forage Hay Straw Grain

Parent (mg/kg)

30 0.042 <0.001 0.013 0.010 0.004 0.020 <0.001

120 0.009 <0.001 <0.001 <0.001 0.018 0.106 <0.001

365 0.021 <0.001 0.007 <0.001 <0.001 0.009 <0.001

Primary Metabolite (mg/kg)

30 0.773 0.135 0.365 0.835 0.658 0.952 0.046

120 0.329 0.077 0.286 0.183 1.076 1.452 0.050

365 0.610 0.047 0.176 0.138 0.455 0.330 0.012

Study 1

‐ 4

The registrant submitted field trial data for unnamed chemical #1 in/on barley (hay, grain, straw, and

forage) from 27 field trials conducted during 2008, 2009, and 2010 in Australia and New Zealand,

Europe, and the USA. Each field trial included one untreated plot and one or two treated plots in which

unnamed chemical #1, formulated as a 240 g ai/L suspension concentrate (SC), was applied to barley as

two foliar broadcast applications at a target rate of 0.043

‐ 0.045 lb a.i./A/application (48

‐

50 g a.i./ha/application), for a total seasonal rate of 0.086

‐ 0.090 lb ai/A (96

‐ 100 g a.i./ha). Separate

plots were established for hay and for grain and straw in the EU and US trials.

To assess residue decline, samples of grain and straw were harvested at 0, 6

‐ 7, 13

‐ 15, 20

‐ 22 and/or 27

‐

28 days (1 Australian, 1 New Zealand, and 7 EU trials, and 1 US trial); residue decline in forage was

investigated at one Australian and one New Zealand trial at 0

‐ , 7

‐ , 14

‐ , and 28

‐ day pre

‐ harvest intervals

(PHIs).

Barley samples were analyzed for residues of unnamed chemical #1 and its metabolites #1 and #2 using

a liquid chromatographic system with tandem mass spectrometers (LC/MS/MS). The method was

adequately validated prior to, and in conjunction with, the analysis of the field trial samples, using

untreated samples fortified separately with unnamed chemical #1 and metabolites at 0.010

‐ 5.0 ppm.Page 228 of 275 The validated limit of quantitation (LOQ) was 0.010 ppm, and the calculated limit of detection (LOD) was

0.003 ppm (EU and US studies) or 0.005

‐ 0.006 ppm (Australia and New Zealand studies) for each analyte

in barley grain, straw, forage, and hay.

The maximum storage duration from harvest to extraction for analysis was 281 days. Adequate storage

stability data are available to support the storage conditions and durations of barley samples. Results

from only trials that included day 0 residue samples are shown in Table 3

‐ 10.

Table 3

‐ 10. Summary of results residue

‐ decline trials for unnamed chemical #1 applied to barley which

included day 0 measurements.

Trial (City,

State/Province,

Country, Year) Crop/

Variety Commodity or

Matrix Total Rate

lb a.i./A

(g a.i./ha) PHI a

(day) Residues (ppm fresh weight) b,c

Chemical 1 Metabolite #1 metabolite #2

Trial B

‐ 1

(Feilding,

Manawatu, New

Zealand, 2009) Barley /

Putney Forage

0.086

(97) 0 1.1 0.02 0.17

7 0.48, 0.39 0.02, 0.01 0.18, 0.13

14 0.02 ND 0.02

28 0.01 ND (0.006)

Grain 0 0.12 ND 0.01

7 0.06 ND 0.01

14 0.02, 0.03 ND, ND 0.02, 0.02

28 0.01 ND 0.01

Straw 0 1.3 0.02 0.19

7 1.0 0.04 0.37

14 0.03, 0.03 ND, ND 0.03, 0.02

28 (0.006) ND ND

Trial B

‐ 2

(Cambooya,

Queensland,

Australia, 2008) Barley /

Fitzroy Forage

0.086

(96.6) 0 3.3 0.09 0.07

7 0.22, 0.18 0.03, 0.03 0.02, 0.02

14 0.16 0.04 0.02

Grain 0 0.27 0.01 0.01

7 0.05 ND 0.01

14 0.05, 0.06 ND, (0.009) 0.01, 0.02

Straw 0 4.3 0.06 0.11

7 0.24 0.02 0.03

14 0.18, 0.25 0.03, 0.03 0.03, 0.03

a PHI = Pre

‐ harvest Interval; the number of days hay samples were allowed to dry in the field is presented in parentheses.

b ND = Nondetectable (residues <LOD). LOQ = 0.010 ppm; LOD = 0.005

‐ 0.006 ppm (Australia/New Zealand studies) or 0.003 ppm (EU and US

studies). Residues between the LOD and LOQ are presented in parentheses.

c Results expressed on a fresh weight basis.

Study 1

‐ 5

The registrant

‐ submitted field trial data for unnamed chemical #1 in/on canola/oil seed rape from 21

field trials conducted in Australia, Europe, and the USA and Canada (North American Free Trade area;

NAFTA): Each field trial included one treated plot in which unnamed chemical #1, formulated as a 240 g

a.i./L suspension concentrate (SC), was applied to canola as two foliar broadcast applications at a targetPage 229 of 275 rate of 0.043

‐ 0.045 lb a.i./A/application (~48

‐ 50 g a.i./ha/application), for a total seasonal target rate of

0.086

‐ 0.090 lb a.i./A (~96

‐ 100 g a.i./ha).

To assess residue decline, samples of canola seed were collected from five EU trials (in France, Germany,

and Spain), and one US trial (MI) at PHIs of approximately 0, 7, 14, 21, and/or 28 days; additional forage

samples were collected from three Australian trials at PHIs of 0, 7, and 14 days.

Samples were analyzed for residues of unnamed chemical #1 and its metabolites metabolite #1 and

metabolite #2 using a liquid chromatographic system with tandem mass spectrometers (LC/MS/MS).

The method was adequately validated prior to and in conjunction with the analysis of the field trial

samples, using untreated samples fortified separately with unnamed chemical #1 and metabolites at

0.01

‐ 5.0 ppm for seed, and 0.01

‐ 0.65 ppm for forage and stubble. The estimated limit of quantitation

(LOQ) was 0.010 ppm and the calculated limit of detection (LOD) was 0.003 ppm (EU and NAFTA studies)

or 0.005

‐ 0.007 ppm (Australia studies) for each analyte.

The maximum storage duration from harvest to extraction for analysis was 296 days. Adequate storage

stability data are available to support the storage conditions and durations of the samples. Results from

only trials that included day 0 residue samples are shown in Table 3

‐ 11

.Page 230 of 275 Table 3

‐ 11. Summary of results residue

‐ decline trials for unnamed chemical #1 applied to canola

which included day 0 measurements.

Trial (City,

State/Province,

Country, Year) Crop/

Variety Commodity

or Matrix Total Rate

lb a.i./A

(g a.i./ha) PHI a

(day) Residues (ppm fresh weight) b

Chemical 1 metabolite #1 metabolite #2

Trial C

‐ 1

(Wunghnu,

Victoria,

Australia,

2008) Canola /

Pioneer

46Y20 Forage 0.086

(97.0) 0 1.0 0.02 0.03

7 0.06 0.03 0.02

13 0.03, 0.07 0.05, 0.07 0.01, 0.04

Trial C

‐ 2

(Neville, New

South Wales,

Australia,

2009) Canola /

Hyola 502RR Forage 0.086

(96.8) 0 1.3 0.04 0.16

7 0.49 0.04 0.11

14 0.12, 0.17 0.08, 0.07 0.05, 0.06

Trial C

‐ 3

(Hamilton, South

Australia,

Australia,

2009) Canola /

Hurricane Forage 0.087

(97.7) 0 3.7 0.04 0.08

7 1.1 0.01 0.07

14 1.1, 1.5 0.01, 0.02 0.08, 0.11

Trial C

‐ 4

(Arradon,

Brittany,

France,

2010) Canola /

Cokpico Seed 0.078

(88.1) 0 0.464, 0.683 0.021, 0.026 0.013, 0.016

7 0.079, 0.061 0.026, 0.017 0.013, (0.007)

15 0.039, 0.047 0.021, 0.019 0.010, 0.011

21 0.031, 0.029 0.012, 0.013 ND, ND

28 0.018, 0.022 (0.008), (0.010) ND, ND

a PHI = Pre

‐ harvest Interval; the number of days hay samples were allowed to dry in the field is presented in parentheses.

b ND = Non

‐ detectable (residues <LOD). LOQ = 0.010 ppm; LOD = 0.005

‐ 0.007 ppm (Australia studies) or 0.003 ppm (EU and US studies). Residues

between the LOD and LOQ are presented in parentheses.

Study 1

‐ 6

The registrant

‐ submitted field trial data for unnamed chemical #1 in/on wheat (forage, hay, grain, and

straw) from 38 trials conducted during the 2008

‐ 2009 season in Australia, New Zealand, Brazil, Europe,

the USA and Canada. Each field trial included one or three treated plots in which unnamed chemical #1,

formulated as a 240 g a.i./L suspension concentrate (SC), was applied to wheat as two foliar broadcast

applications at a target rate of 0.043

‐ 0.045 lb a.i./A/application (48

‐ 50 g a.i./ha/application), for a total

seasonal rate of 0.086

‐ 0.090 lb ai/A (96

‐ 100 g a.i./ha). Separate plots were established for forage, hay,

and for grain and straw in the Brazil, EU, and US/Canadian trials. In addition, three separate plots were

established in two EU trials (Germany and France) to examine residue decline in grain and straw; at

these plots, applications were made earlier in the season to plants at earlier growth stages. At three

Australian sites, two Brazilian sites, eight EU sites (France, Germany, Italy, Poland, Spain, and the UK),

and two US/Canadian sites (NE and ON), samples were collected at PHIs of 0, 3

‐ 4, 6

‐ 10, 13

‐ 16, 20

‐ 22,

and/or 27

‐ 29 days to assess residue decline.Page 231 of 275 Wheat samples were analyzed for residues of unnamed chemical #1 and its metabolites metabolite #1

and metabolite #2 using a liquid chromatographic system with tandem mass spectrometers

(LC/MS/MS). The method was adequately validated prior to and in conjunction with the analysis of the

field trial samples, using untreated samples fortified separately with unnamed chemical #1 and

metabolites at 0.01

‐ 5.00 ppm. The calculated limit of quantitation (LOQ) was 0.010 ppm, and the

calculated limit of detection (LOD) was 0.003 ppm (EU, Brazil, and US and Canada studies) or 0.005

‐

0.006 ppm (Australia and New Zealand studies) for each analyte in wheat forage, hay, grain, and straw.

The maximum storage duration from harvest to extraction for analysis was 354 days, with the exception

of two forage samples (one treated, one untreated) which were analyzed 737 days after harvest.

Adequate storage stability data are available to support the storage conditions and durations of wheat

samples. Results from only trials that included day 0 residue samples are shown in Table 3

‐ 12

.

Table 3

‐ 12. Results from Study 1

‐ 6.

Trial ID (City,

State/

Province,

Country, Year) Crop/

Variety Commodity or

Matrix Total Rate

lb a.i./A

(g a.i./ha) PHI a

(day) Residues (ppm fresh weight) b,c

Chemical 1 metabolite #1 metabolite #2

Trial W

‐ 1

(Maules

Creek,

Australia,

2008) Wheat/

Sunstate Forage

0.086

(97) 0 1.6 0.02 0.05

8 1.4 f

, 0.50 f 0.03 f

, (0.009) f 0.16 f

, 0.08 f

16 0.16 (0.008) 0.02

28 0.10 (0.006) 0.02

Grain 0 0.16 ND (0.006)

8 0.03 ND (0.009)

16 0.02, 0.01 ND, ND 0.01, (0.008)

28 0.01 ND 0.01

Straw 0 2.4 f 0.03 f 0.07 f

8 1.7 0.02 0.21

16 0.20, 0.37 (0.006), 0.01 0.03, 0.03

28 0.09 ND 0.02

Trial W

‐ 2

(York,

Australia,

2008) Wheat/

Yitpi Grain

0.084

(94.3) 0 0.04 ND ND

7 0.05 ND (0.005)

14 0.04, 0.03 ND, ND (0.007), (0.006)

28 0.03 ND 0.01

Straw 0 1.8 0.04 0.09

7 1.8 0.06 0.11

14 1.4, 1.4 0.04, 0.04 0.12, 0.11

28 1.2 0.04 0.10Page 232 of 275 Trial ID (City,

State/

Province,

Country, Year) Crop/

Variety Commodity or

Matrix Total Rate

lb a.i./A

(g a.i./ha) PHI a

(day) Residues (ppm fresh weight) b,c

Chemical 1 metabolite #1 metabolite #2

Trial W

‐ 3

(Palmerston

North,

Manawatu,

New Zealand,

2009) Wheat/

Sage Grain

0.086

(97) 0 0.24 (0.005) (0.007)

7 0.01 ND (0.007)

14 0.01, (0.007) ND, ND (0.007), ND

28 (0.008) ND 0.01

Straw 0 1.8 0.06 0.05

7 0.11 (0.006) 0.02

14 0.06, 0.05 ND, ND 0.03, 0.02

28 0.06 ND (0.006)

Trial W

‐ 4

(Mogi Mirim,

SP, Brazil,

2008) Wheat/

IAC 375 Forage 0.085

(95.7) 0 0.5818 0.0342 0.0588

3 0.2899 0.0440 0.0964

7 0.0949 0.0365 0.1321

14 0.0106 0.0326 0.1092

21 ND 0.0255 0.0787

Hay 0.091

(102) 0 (2) 2.0328 0.0694 0.1596

3 (1) 0.6782 0.0745 0.2221

7 (2) 0.2604, 0.2651 0.0859, 0.0830 0.2771, 0.2661

14 (2) 0.0472 0.1055 0.2562

21 (2) 0.0202 0.0821 0.2430

Trial W

‐ 5

(Rolandia, PR,

Brazil, 2008) Wheat/

Coodetec

104 Forage 0.093

(105) 0 2.5874 0.0328 0.1519

3 0.8017 0.0393 0.2702

7 0.3829, 0.3945 0.0240, 0.0344 0.3594, 0.3414

14 0.0218 (0.0065) 0.3138

21 0.0104 0.0154 0.1792

Hay 0.093

(105) 0 (2) 3.1285 0.0726 0.1671

3 (2) 0.9807 0.0491 0.2645

7 (2) 0.5048, 0.5600 0.0358, 0.0393 0.2625, 0.2719

14 (2) 0.1962 0.0211 0.1499

21 (2) 0.0513 (0.0087) 0.0466

a PHI = Pre

‐ harvest Interval; the number of days hay samples were allowed to dry in the field is presented in parentheses.

b ND = Non

‐ detectable (residues <LOD). LOQ = 0.010 ppm; LOD = 0.005

‐ 0.006 ppm (Australia/New Zealand studies) or 0.003 ppm (EU, Brazil and

US/Canada studies). Residues between the LOD and LOQ are presented in parentheses.

c Samples were harvested late due to late

‐ season rainfall which slowed ripening/dry

‐ down of the crop.

Study 2

‐ 1

In a registrant

‐ submitted semi

‐ field study, residues of unnamed chemical #2 were investigated in nectar

and pollen from honey bees in Phacelia tanacetifolia (referred to as “ Phacelia ”) fields treated with foliar

applications of unnamed chemical #2. The study consisted of three treatment (tunnels) groups (two

treatments and one control). The tunnel tents (5.0 m  20.0 m and a height of 3.5 m) were placed overPage 233 of 275 plots of Phacelia and were covered with light plastic gauze. Honey bee colonies were established in the

enclosures prior to the second applications of T1 and T2. A Phacelia crop area of 90 m 2 was available for

foraging in each tunnel. T1 was applied at a rate of 10 g a.i./ha (0.0089 lb a.i./A) and T2 was applied at a

rate of 100 g a.i./ha (0.089 lb a.i./A). Two applications were applied to the crop in each treatment

group; the first was approximately two weeks before flowering. The second applications in T1 and T2

and the water control applications were performed during full flowering and during daily bee flight ( i.e.,

when foraging activity was ≥ 5 bees/m 2 ). One healthy, disease

‐ free and queen

‐ right bee hive with 10

combs was used for each treatment group and for the controls. The colonies were set up in the tunnels

four days before the second applications in T1 and T2. Tables 3

‐ 13 and 3

‐ 14 summarize the maximum

residues measured in pollen and nectar 1 day after the second application.

Table 3

‐ 13. Summary of unnamed chemical #2 residues measured in pollen collected from forager

bees sampled 1 day after the 2 nd application.

Treatment Application rate in g

a.i./ha (lb a.i./A) Concentration of unnamed

chemical 2 ( μ g a.i./kg) Concentration of unnamed

chemical 2 (mg/kg per 1 lb a.i./A)

C 0 ND

‐‐

T1 10 (0.0089) * 338.0 38

T2 100 (0.089) * 1718 19

a DAA = days after 2 nd application

ND = not detected

* Applications made during full flowering and during daily bee flight.

Table 3

‐ 14. Summary of unnamed chemical #2 measured in nectar collected from forager bees

(collected 1 day after the second application).

Treatment Application rate in

g a.i./ha (lb a.i./A) Concentration of unnamed

chemical 2 ( μ g a.i./kg) Concentration of unnamed

chemical 2 (mg/kg per 1 lb a.i./A)

C 0 ND ND

T1 10 (0.0089) * NA NA

T2 100 (0.089) * 54.25 0.61

NA = not analyzed

– sample collection vessel failure.

ND = not detected

* Applications made during full flowering and during daily bee flight.

Study 2

‐ 2

In a registrant

‐ submitted field study, residues of unnamed chemical #2 were measured in pollen

(anthers) and nectar in melon flowers treated by foliar spray to the flowering melon crop at a rate of 90

g a.i./ha (0.08 lb a.i./A). Two treatment groups were conducted with a separate control, both received

the same application rate and same interval between each application (7 days), however T1 received

applications during an early growth stage prior to flowering, and T2 during early flowering. The tables

below summarize the results of residues measured in pollen and nectar samples collected 1 day afterPage 234 of 275 the second application. The nectar was extracted from the flowers with the capillary method using

micropipettes (10  L). The pollen was sampled by cutting the anthers from blossoms with scissors. In

nectar samples from T1, there were no detected residues of unnamed chemical #2 or of any of the

metabolites. There were no detected residues in any of the nectar control samples. In the anther

samples (for pollen) from T1, there were no residues above LOQ of unnamed chemical #2 or any

metabolite in the test item treated samples or the control samples. In T2, unnamed chemical #2 was

detected in nectar samples at 6.084 and 13.36  g ai/kg (median

= 9.722  g ai/kg), and at 20.25 and

97.01  g ai/kg (median

= 58.63  g ai/kg) in pollen (anther) samples. There were also no detections of

parent or metabolites in the control. Tables 3

‐ 15 and 3

‐ 16 summarize the results of the analysis of

nectar and pollen samples taken 1 day after the 2 nd application.

Table 3

‐ 15. Residues of unnamed chemical #2 in nectar from melon flowers (  g/kg) 1 day after 2 nd

application

Treatment Application rate

in g a.i./ha (lb a.i./A) Concentration of

unnamed chemical

2 ( μ g a.i./kg) Concentration of

unnamed chemical 2

(mg/kg per 1 lb a.i./A)

C

‐‐ ND

‐‐

T1 90 (0.08) ND

‐‐

T2 90 (0.08) 13.36 0.167

Table 3

‐ 16. Residues of unnamed chemical #2 in pollen (anthers) from melon flowers (  g/kg) 1 day

after 2 nd application

Treatment Application rate

in g a.i./ha (lb a.i./A) Concentration of

unnamed chemical

2 ( μ g a.i./kg) Concentration of

unnamed chemical 2

(mg/kg per 1 lb a.i./A)

C

‐‐ ND

‐‐

T1 90 (0.08) ND

‐‐

T2 90 (0.08) 97.01 1.21

Study 2

‐ 3

In a registrant

‐ submitted field study, the magnitude of potential residues of unnamed chemical #2 in

pollen was assessed in tomato by residue analysis of anthers from tomato flowers. The study was

conducted in Quinto, Province Zaragoza in Spain in May and June 2010. The study included three

treatment groups: two treatment groups (T1 and T2) with two applications of the test item 48 and

40 days before first sampling in T1, and 6 and 1 days before first sampling in T2, respectively. The

applications were carried out at a rate of 90 g ai/ha. (0.08 lb a.i./A). The third group was the control. The

applications were performed 48 days and 40 days before first sampling in T1 (representing treatment atPage 235 of 275 early growth stage) and 6 days and 1 day before first sampling of tomato anthers in T2 (representing

treatment just before flowering). Residue analysis was carried out on samples of anthers from the

treated tomato flowers. Samples of anthers were taken on two sampling days after the second

application in T1 and T2. The first samples were taken at the beginning of flowering; the second

samples were taken two days later. The anthers (for pollen) were sampled by cutting entire anthers

from the flowers with scissors. Early growth stage sampling (T1) showed no residues of unnamed

chemical #2 or its metabolites. In T2 (early flowering), unnamed chemical #2 was detected at 15.58 and

103.1  g/kg (median

= 59.34  g/kg). No parent or metabolite residues were detected in the control

samples. Table 3

‐ 17 summarizes the results of samples taken after the 2 nd application in both treatment

groups and the control group.

Table 3

‐ 17. Residues of unnamed chemical #2 in pollen (anthers) from tomato flowers (  g/kg) 1 day

after 2 nd application

Treatment Application rate in g a.i./ha

(lb a.i./A) Concentration of

unnamed chemical 2

( μ g a.i./kg) Concentration of unnamed

chemical 2 (mg/kg per 1 lb

a.i./A)

C

‐‐ ND

‐‐

T1 90 (0.08) ND

‐‐

T2 90 (0.08) 103.1 1.29

Study 2

‐ 4

In a registrant submitted semi

‐ field study, residues of unnamed chemical #2 were measured in nectar

and pollen collected from honey bees in melon fields treated with three drip irrigation applications of

unnamed chemical #2 at a rate of 100 g a.i./ha (0.089 lb a.i./A). The study consisted of one treatment

(with three replicates) and one control (no replicates). The first drip application was performed on

flowering melon, followed by two more applications with 7 day application intervals between the three

applications. The first application was made at 8 am, which may coincide with bee flight. The second

application was made between 10:30 and 11 am, which corresponded with bee flight. The third

application was made between 9:30 and 10 pm, which does not correspond to bee flight. Honeybee

colonies were placed inside tunnels containing treated or untreated melon at early flowering, 5 days

before the third application. Table 3

‐ 18 and 3

‐ 19 summarize the results of the residue analysis of nectar

and pollen sampled from foraging bees.Page 236 of 275 Table 3

‐ 18. Summary of unnamed chemical #2 residues measured in pollen from forager bees (g/kg)

sampled after the 2 nd application.

Treatment Application rate

g a.i./ha

(lb a.i./A) DAA a Concentration of unnamed

chemical 2 ( μ g a.i./kg) Concentration of

unnamed chemical 2

(mg/kg per 1 lb a.i./A)

Control

‐‐ +4 ND

‐‐

Replicate 1 100

(0.089) +4 5.126 0.06

Replicate 2 100

(0.089) +4 7.999 0.09

Replicate 3 100

(0.089) +4 12.06 0.14

Control

‐‐ ND

‐‐

Replicate 1 100

(0.089) +7 8.455 0.10

Replicate 2 100

(0.089) +7 11.17 0.13

Replicate 3 100

(0.089) +7 5.631 0.06

a DAA = days after third application

ND = Not detected (<LOD, level of detection, <1.25  g/kg)

Table 3

‐ 19. Summary of unnamed chemical #2 measured in nectar from forager bee stomach contents

(honey stomach contents).

Treatment Application rate

g a.s./ha (lb

a.i./A) DAA a Concentration of unnamed

chemical 2 ( μ g a.i./kg) Concentration of

unnamed chemical 2

(mg/kg per 1 lb a.i./A)

Control

‐‐ +3 ND

‐‐

Replicate 1 100 (0.089) +3 <LO Q

‐‐

Replicate 2 100 (0.089) +3 <LO Q

‐‐

Replicate 3 100 (0.089) +3 ND

‐‐

Control

‐‐ +7 ND

‐‐

Replicate 1 100 (0.089) +7 26.23 0.26

Replicate 2 100 (0.089) +7 <LO Q

‐‐

Replicate 3 100 (0.089) +7 13.16 0.15

LOQ = Level of quantification, 5.0  g/kg

ND = Not detected (<LOD, level of detection, <1.25  g/kg)

Study 2

‐ 5

In a registrant

‐ submitted field study, residues of unnamed chemical #2 were investigated in nectar and

pollen of canola following seed treatments with unnamed chemical #2. Pollen and nectar were collected

from the plots during the flowering stage for the canola plants. Each treated plot was planted to treated

canola seed at a rate of approximately 7.9 kg/ha (7.0 lb/acre), equivalent to 0.07 lb ai/A

(78.6 g ai/ha/season). Four treatment trials were conducted in Canada. No residues of unnamed

chemical #2 or its metabolites were detected in pollen or nectar sampled during flower of canola grown

from seeds treated with unnamed chemical #2.Page 237 of 275 Study 2

‐ 6

In a registrant

‐ submitted study, concentrations of unnamed chemical #2 in guttation fluid were studied

under field conditions following two applications of unnamed chemical #2 applied at 90 g a.i./ha (0.08 lb

a.i./A). The study was conducted near Tübingen in Southern Germany. The study included three

treatment groups, T1 and T2 receiving applications during different times of bee flight, and the control

which received no treatment. The first spray application in the test item treatments T1 and T2 was

performed onto fields of nonflowering B. napus L. The second application in these groups was

performed on flowering B. napus L. In the treatment T1 the second application was carried out in the

evening, after daily honey bee

‐ flight. In the treatment T2 the second application was carried out on the

following day during daily honey bee

‐ flight. Samples of guttation fluid were taken from surface of leaves

of treated plants. The results from the analysis of the guttation fluid are summarized below. In the

treatment T1, unnamed chemical #2 was found in guttation fluid at levels of 41.09 to 2117  g ai/kg

(median

= 1079  g ai/kg) after the first spray application and at levels of 212.7 to 3092  g ai/kg

(median

= 1652  g ai/kg) after the 2 nd spray application. The metabolites were observed at

concentrations ranging 7.2

‐ 45.82  g/kg. In the treatment T2, unnamed chemical #2 was found in

guttation fluid at levels of 16.13 to 1355  g ai/kg (median

= 1277  g ai/kg) after the first spray

application and at levels of 278.6 to 1641  g ai/kg (median

= 959.8  g ai/kg) after the 2 nd spray

application ( Table 3

‐ 20 ). The metabolites were observed at levels that ranged from one order of

magnitude to greater than two orders of magnitude below the parent. There were no detectable

residues of unnamed chemical #2 or any of the metabolites in guttation fluid from the control at any of

the sampling days.Page 238 of 275 Table 3

‐ 20. Summary of residues of unnamed chemical #2 in guttation fluid.

Treatment Application rate

g a.i./ha (lb a.i./A) DAA a Concentration of

Unnamed chemical

#2 ( μ g/kg) Concentration of unnamed

chemical #2 (μg/L per 1 lb

a.i./A) **

Sampling after 1 st application

C

‐‐ +2 ND b

‐‐

C

‐‐ +3 ND

‐‐

C

‐‐ +20 ND

‐‐

T1 90 (0.08) +2 2117 26,000

T1 90 (0.08) +3 NS

‐‐

T1 90 (0.08) +20 41.09 510

T2 90 (0.08) +2 1355 17,000

T2 90 (0.08) +3 1277 16,000

T2 90 (0.08) +20 16.13 200

Sampling after 2 nd application

C

‐‐ +2 ND

‐‐

C

‐‐ +3 ND

‐‐

T1 90 (0.08) +2 3092 39,000

T1 90 (0.08) +3 212.7 2,700

T2 90 (0.08) +2 1641 21,000

T2 90 (0.08) +3 278.6 3,500

a DAA = days after application

ND = <1.25  g/kg

NS = Not sampled

** Assumes that guttation fluid density is equivalent to that of water (i.e., 1 kg

= 1 L)

Study 2

‐ 7

In a registrant

‐ submitted study, concentrations of unnamed chemical #2 in guttation fluid were studied

under field conditions following two applications of unnamed chemical #2 applied as a foliar spray at 90

g a.i./ha (0.08 lb a.i./A). The study was conducted Benfeld in Northern France. The study included three

treatment groups, T1 and T2 receiving applications during different times of bee flight, and the control

which received no treatment. The first spray application in the test item treatments T1 and T2 was

performed onto fields of non

‐ flowering B. napus L. The second application in these groups was

performed on flowering B. napus L. In the treatment T1, the second application was carried out in the

evening, after daily honey bee

‐ flight. In the treatment T2, the second application was carried out on the

following day during honey bee

‐ flight. Samples of guttation fluid were taken from surfaces of leaves of

treated plants. These results are described below. In treatment 1 (T1) residues of unnamed chemical 2 in

guttation fluid were at 349.5  g ai/kg five days after the first spray of the test item and at 534.8 and

194.3  g ai/kg (median

= 364.55  g ai/kg) at the samplings on DAA+2 and DAA+6, after the second spray

of the test item. Quantifiable residues of two of six metabolites were found only at the sampling on

DAA+2 at low levels, slightly above the LOQ (approximately 5  g/kg). In treatment 2 (T2) residues ofPage 239 of 275 unnamed chemical 2 in guttation fluid were at 585.2  g ai/kg at the first sampling and at 647.1 and

108.8  g ai/kg (median

= 377.95  g ai/kg) on DAA+2 and DAA+6, respectively ( Table 3

‐ 21 ). Residues of

three of six metabolites were found after the first sampling at low levels, slightly above the LOQ.

Residues of two of six metabolites were found on DAA+2 at low levels, slightly above the LOQ.

Table 3

‐ 21. Summary of residues of unnamed chemical #2 in guttation fluid.

Treatment Application rate

g a.i./ha (lb a.i./A) DAA a Concentration of

Unnamed chemical #2

( μ g/kg) Maximum residue (μg/L

per 1 lb a.i./A) **

Sampling after 1 st application

C

‐‐ +5 ND

‐‐

T1 90 (0.08) +5 349.5 4,400

T2 90 (0.08) +5 585.2 7,300

Sampling after 2 nd application

C

‐‐ +1 ND

‐‐

T1 90 (0.08) +2 534.8 6,700

T1 90 (0.08) +6 194.3 2,400

T2 90 (0.08) +2 647.1 8,100

T2 90 (0.08) +6 108.8 1,400

a DAA = days after application

ND = <1.25  g/kg

NS = Not sampled

** Assumes that guttation fluid density is equivalent to that of water (i.e., 1 kg

= 1 L)Page 240 of 275 

### Appendix 4. Summaries of empirical studies from the scientific literature that were used to

evaluate Tier I methods for estimating pesticide exposures

This appendix contains summaries of empirical studies from the scientific literature reporting pesticide

concentrations in pollen, nectar, on bees and in guttation fluid. The maximum concentrations from

these studies were used to evaluate the draft tier I modeling approaches included in the white. These

studies are listed in Table 4

‐ 1.Page241of275Table4‐1.Studiesfromthescientificliteraturethataresummarizedinthisappendix.DatafromthesestudiesareusedtoevaluateTierIexposureassessmentmethodsdescribedinthewhitepaper.CitationSamplescollectedChemical(s)ApplicationType(s)Crop(s)ChoudharyandSharma(2008)Nectar,pollenEndosulfan,lambdaCyhalothrinandSpiromesifenFoliarsprayMustardChoudharyandSharma(2008)Nectar,pollenImidaclopridSeedtreatmentMustardDelabieetal.(1985)Bees,flowers,leavesCypermethrinFoliarsprayOilseedrapeDivelyandKamel(2012)Nectar,pollen,leavesImidacloprid,Dinotefuran,ThiamethoxamorOxamylSoilapplication,foliarspray,seedtreatmentPumpkinFriesandWibran(1987)PollenCypermethrinandunidentifiedpyrethroidFoliarsprayOilseedrapeGirolamietal.(2009)GuttationfluidImidacloprid,Clothianidin,Thiamethoxam,andFipronilSeedtreatmentCornHannyandHarvey(1982)Pollen,beesCarbarylFoliarsprayCornHoffmanandCastle(2012)GuttationfluidImidaclopridSoilapplicationCantaloupesKochandWeisser(1997)BeesSodium‐fluorescein(tracer)FoliarsprayAppleandPhaceliatanacetifoliaLordetal.(1968)NectarDimethoateSoildrenchFuchsiasp.,Nasturtium(Tropaeolumsp.)Reetzetal.(2011)GuttationfluidClothianidinSeedtreatmentCornŠkerletal.(2009)PollenDiazinon,ThiaclopridFoliarsprayAppleStonerandEitzer2012Pollen,nectar,leavesImidacloprid,ThiamethoxamSoilapplicationSquashTapparoetal.(2011)GuttationfluidThiamethoxam,Clothianidin,Imidacloprid,andFipronilSeedtreatmentCornWallner(2009)Pollen,nectarBoscalid,ProthioconazolFoliarsprayOilseedrapeWallner(2009)Pollen,nectarClothianidinseedtreatmentOilseedrapePage 242 of 275 Choudhary and Sharma 2008

In a field study involving applications of several pesticides to flowering mustard ( Brassica juncea ),

Choudhary and Sharma 2008 quantified concentrations of those pesticides in nectar and pollen.

Endosulfan, lambda cyhalothrin and spiromesifen were applied via foliar spray at rates of 0.468, 0.067

and 0.201 lb a.i./A, respectively. In addition, imidacloprid was applied as a seed treatment at a rate of

0.019 lb a.i./A ( Table 4

‐ 2 ). Foliar spray applications were made during two separate years (2003

‐ 2004

and 2004

‐ 2005) when 50% of mustard plants were flowering. Nectar was collected from bees that were

observed foraging on the treated fields (3 replicate fields). Samples were collected at the time of the

application and several time periods (1, 4, 8, 24, 48, 72, 120 and 240 hours after application).

Concentrations of endosulfan, lambda cyhalothrin and spiromesifen were quantified using gas

chromatographic analysis; while HPLC was used to quantify imidacloprid. Measured residues of

endosulfan, lambda cyhalothrin and spiromesifen were highest at the time of application ( Tables 4

‐ 3

, 4

‐

4 and 4

‐ 5 ). Imidacloprid was not detected in pollen or nectar at any time point included in the study

(Level of Detection = 10 ppb).

Table 4

‐ 2. Application rates and methods of four chemicals included in this study.

Chemical Application

method Application rate (lb a.i./A) Application rate (kg a.i./ha)

Endosulfan Foliar spray 0.468 0.525

Lambda cyhalothrin Foliar spray 0.067 0.075

Spiromesifen Foliar spray 0.201 0.225

Imidacloprid Seed treatment 0.019 0.021Page 243 of 275 Table 4

‐ 3. Total endosulfan (includes endosulfan I and II) residues measured in nectar during two

different field trials. Values represent average of 3 samples measured using Gas Chromatograph

Method.

Time (hour after

application) Reported Concentration

(mg a.i./kg) Normalized concentration

(mg a.i./kg per 1 lb a.i./A)

Nectar Pollen Nectar Pollen

2003

‐ 2004

0 1.825 2.224 3.90 4.75

1 1.551 1.553 3.31 3.32

4 1.333 1.235 2.85 2.64

8 1.098 0.892 2.35 1.91

24 0.919 0.745 1.96 1.59

48 0.715 0.398 1.53 0.850

72 0.313 0.125 0.669 0.267

120 0.010 ND 0.021 ND

240 ND ND ND ND

2004

‐ 2005

0 1.614 2.127 3.45 4.54

1 1.493 1.412 3.19 3.02

4 1.214 1.102 2.59 2.35

8 0.998 0.792 2.13 1.69

24 0.865 0.592 1.85 1.26

48 0.600 0.308 1.28 0.658

72 0.286 0.097 0.611 0.207

120 0.011 ND 0.024 ND

240 ND ND ND ND

ND = not detectedPage 244 of 275 Table 4

‐ 4. Lambda cyhalothrin residues measured in nectar during two different field trials. Values

represent average of 3 samples measured using Gas Chromatograph Method.

Time (hour after

application) Reported Concentration

(mg a.i./kg) Normalized concentration

(mg a.i./kg per 1 lb a.i./A)

Nectar Pollen Nectar Pollen

2003

‐ 2004

0 0.909 1.672 13.6 25.0

1 0.691 1.264 10.3 18.9

4 0.567 1.023 8.46 15.3

8 0.453 0.806 6.76 12.0

24 0.271 0.437 4.04 6.52

48 0.163 0.197 2.43 2.94

72 0.012 0.043 0.18 0.64

120 ND ND ND ND

240 ND ND ND ND

2004

‐ 2005

0 0.836 1.612 12.5 24.1

1 0.660 1.161 9.85 17.3

4 0.540 0.951 8.06 14.2

8 0.450 0.771 6.72 11.5

24 0.253 0.442 3.78 6.60

48 0.134 0.163 2.00 2.43

72 0.004 0.011 0.06 0.16

120 ND ND ND ND

240 ND ND ND ND

ND = not detectedPage 245 of 275 Table 4

‐ 5. Spiromesifen residues measured in nectar during two different field trials. Values represent

average of 3 samples measured using Gas Chromatograph Method.

Time (hour after

application) Reported Concentration

(mg a.i./kg) Normalized concentration

(mg a.i./kg per 1 lb a.i./A)

Nectar Pollen Nectar Pollen

2003

‐ 2004

0 1.452 2.101 7.22 10.5

1 1.121 1.437 5.58 7.15

4 0.801 1.151 3.99 5.73

8 0.705 0.950 3.51 4.73

24 0.508 0.719 2.53 3.58

48 0.257 0.317 1.28 1.58

72 0.094 0.002 0.47 0.01

120 ND ND ND ND

240 ND ND ND ND

2004

‐ 2005

0 1.413 1.827 7.03 9.09

1 1.011 1.242 5.03 6.18

4 0.784 1.037 3.90 5.16

8 0.594 0.900 2.96 4.48

24 0.491 0.592 2.44 2.95

48 0.224 0.296 1.11 1.47

72 0.083 0.004 0.41 0.02

120 ND ND ND ND

240 ND ND ND ND

ND = not detectedPage 246 of 275 Delabie et al

. 1985

In this field study, 8 hives, divided into 2 apiaries, were located adjacent to an oilseed rape ( Brassica sp. )

field treated with 0.045 lb a.i./A (0.050 kg a.i./ha) cypermethrin. Dead bees were collected using Todd

traps placed at the hive entrances. Bees from two hives were analyzed for cypermethrin residues on the

day of the application (day 0) and for the following 3 days. The reported data are provided in Table 4

‐ 6

.

The highest reported concentration in bees was 0.95 mg a.i./kg, which translates to a dose of 2.7 μ g

a.i./bee per 1 lb a.i./A. The results of this study are limited by a small number of replicates ( i.e

., only two

apiaries) and variability among individual samples (it is presumed that the reported values represent

means of samples collected from hives); however, these data are useful in characterizing the

concentration of cypermethrin on bees that were potentially directly sprayed and foraged in the treated

area.

Table 4

‐ 6. Concentrations of cypermethrin measured on dead bees after foraging on treated field.

Day Apiary # Highest reported

Concentration*

(mg a.i./kg) Normalized

concentration

(mg a.i./kg per 1 lb

a.i./A) Normalized dose**

( μ g a.i./bee per 1 lb

a.i./A)

0 1 0.74 16 2.1

0 2 0.63 14 1.8

1 1 0.95 21 2.7

1 2 0.54 12 1.5

2 1 0.10 2.2 0.28

3 1 0.01 0.2 0.03

*The numerical values were not provided in the article. These values were estimated from Figure 4 of the article.

**Calculated by multiplying normalized dose by the weight of individual bee ( i.e

., 0.128 g). Note that mg/kg is equivalent to μ g/g

.

In addition, cypermethrin residues were measured on flowers and leaves of the treated crop. On

samples collected within one day after the application, residues were approximately 2 mg/kg on flowers

and 1.5 mg/kg on leaves. This translates to 44 and 33 mg/kg per 1 lb a.i./A for flowers and leaves,

respectively. As with the residues on bees, these data were extracted from a figure provided in the

article (Figure 5), therefore, the residue values are approximate.Page 247 of 275 Dively and Kamel 2012

Dively and Kamel 2010 quantified levels of several insecticides in pumpkin ( Cucurbita pepo ) nectar after

pesticides were applied to soil via chemigation. Pesticide residues in nectar are the result of systemic

transport through pumpkin plants located in the treated soil. In 2009 and 2010, pumpkin plants located

in the field were treated with imidacloprid, dinotefuran, thiamethoxam or oxamyl. Rates and application

methods used for the different treatment plots are provided in Tables 2

‐ 7 and 2

‐ 8

. There were 4

replicates for each application scenario and a control for each study year. Prior to blooming, paper bags

were placed over flower buds. When the flowers bloomed, they were removed and their nectar was

collected using syringes. Concentrations of the pesticides and their degradates were quantified using

liquid chromatography

‐ tandem mass spectrometry. Dively and Kamel 2012 reported concentrations of

thiamethoxam, dinotefuran, imidacloprid and oxamyl in pollen collected during the same time as nectar

in the study described above. Residues of thimethoxam, dinotefuran and imidacloprid were an order of

magnitude above those reported in nectar. Oxamyl was not detected in pollen or nectar samples.

Reported maximum concentrations of pesticides plus their degradates in nectar and pollen are provided

in Tables 2

‐ 7 and 2

‐ 8

, respectively.

Table 4

‐ 7. Pesticide concentrations measured in nectar.

Chemical* Year application type Total

application rate

(lb/A) Maximum

concentration *

(mg/kg) Normalized

concentration *

(mg a.i./kg per

1 lb a.i./A)

Imidacloprid 2009 bedding drench 0.027 0.0007 0.026

Imidacloprid 2009 transplant 0.251 0.0113 0.045

Imidacloprid 2009 transplant 0.377 0.0178 0.047

Imidacloprid 2009 water/drip 0.377 0.0231 0.061

Dinotefruan 2009 transplant drip 0.270 0.0154 0.057

Thiamethoxam 2009 transplant 0.171 0.0186 0.109

dinotefuran 2009 Foliar spray 0.270 0.0216 0.0801

thiamethoxam 2009 Foliar spray 0.171 0.0124 0.0723

Imidacloprid 2010 bedding drench 0.027 ND ND

Imidacloprid 2010 transplant 0.251 0.0067 0.027

Imidacloprid 2010 water/drip 0.377 0.016 0.042

Dinotefruan 2010 transplant drip 0.270 0.0109 0.040

Thiamethoxam 2010 transplant 0.171 0.0151 0.088

dinotefuran 2010 foliar spray 0.270 0.005 0.0185

dinotefuran 2010 foliar spray 0.270 0.016 0.0593

thiamethoxam 2010 foliar spray 0.171 0.0025 0.0146

thiamethoxam 2010 foliar spray 0.171 0.007 0.0408

*Represents parent and degradatesPage 248 of 275 Table 4

‐ 8. Pesticide concentrations measured in pollen



### .

Chemical* Year application type Total

application

rate (lb/A) Maximum

concentration*

(mg/kg) Normalized

concentration*

(mg a.i./kg per

1 lb a.i./A)

Imidacloprid 2009 bedding drench 0.027 0.0094 0.351

Imidacloprid 2009 transplant 0.251 0.0567 0.226

Imidacloprid 2009 transplant 0.377 0.1085 0.288

Imidacloprid 2009 water/drip 0.377 0.1285 0.341

Dinotefruan 2009 transplant drip 0.270 0.0812 0.301

Thiamethoxam 2009 transplant 0.171 0.1316 0.768

dinotefuran 2009 foliar spray 0.270 0.168 0.623

thiamethoxam 2009 foliar spray 0.171 0.162 0.946

Imidacloprid 2010 bedding drench 0.027 ND ND

Imidacloprid 2010 transplant 0.251 0.0239 0.0953

Imidacloprid 2010 water/drip 0.377 0.0440 0.117

Dinotefruan 2010 transplant drip 0.270 0.0269 0.100

Thiamethoxam 2010 transplant 0.171 0.0420 0.245

dinotefuran 2010 foliar spray 0.270 0.0221 0.082

dinotefuran 2010 foliar spray 0.270 0.114 0.424

thiamethoxam 2010 foliar spray 0.171 0.0191 0.111

thiamethoxam 2010 foliar spray 0.171 0.0378 0.221

*Represents parent and degradates1

Fries and Wibran 1987

In this study, cypermethrin and an unnamed pyrethroid insecticide, referred to in the article as “PP 321”

were applied to oilseed rape ( Brassica campestris ). On the day of the application, residues of the two

insecticides were measured in pollen. Cypermethrin and PP321 were applied to one field via foliar spray

at respective rates of 0.039 and 0.004 lb a.i./A (0.044 and 0.005 kg a.i./ha). Two honey bee colonies

were placed at each field and pollen samples were collected from bees entering the hives using pollen

traps. The reported concentrations of the pesticides in pollen (measured on the day of application),

application rate normalized concentrations are provided in Table 4

‐ 9

. One notable limitation of this

study is a lack of replication; however, this study provides information that is useful for characterizing

the concentrations of pesticides in pollen. The reported concentrations will be treated as maximum,

rather than mean values.Page 249 of 275 Table 4

‐ 9. Reported concentrations of cypermethrin and PP321 in pollen.

Chemical Application rate (lb a.i./A) Reported concentration

(mg/kg) Normalized concentration

(mg/kg per 1 lb a.i./A)

Cypermethrin 0.039 1.9 49

PP 321 0.004 0.2 50

Girolami et al

. 2009

Girolami et al. 2009 quantified imidacloprid, clothianidin, thiamethoxam, and fipronil residues in

guttation fluid. Guttation fluid was collected from corn seedlings grown in the field from seeds coated

with imidacloprid (Gaucho® 0.5 mg a.i./seed), clothianidin (Poncho® 1.25 mg a.i./seed), thiamethoxam

(Cruiser® 1.0 mg a.i./seed), and fipronil (Regent® 1.0 mg a.i./seed). Guttation fluid was collected in the

field from 8

‐ 9 a.m. daily until a volume of 5 mL was reached, beginning at seedling emergence and

continuing for the first 3 weeks after emergence. Concentrations of chemicals in guttation fluid were

measured using high

‐ performance liquid chromatography (HPLC). In corn, it was observed that

guttation fluid can flow down the plant into the crown cup and persist throughout the day. Guttation

fluid was observed until 9

‐ 10 a.m. throughout April and May. The reported mean measured

concentrations for each chemical are reported in Table 4

‐ 8

.

Table 4

‐ 8. Measured concentrations in guttation fluid ( μ g a.i./mL)

Clothianidin

(1.25 mg ai/seed) Imidacloprid

(0.5mg ai/seed) Thiamethoxam

(1.0 mg ai/seed)

23.3 47 11.9

Hanny and Harvey 1982

The purpose of this study was to compare effects of two carbaryl formulations (Sevin® Sprayable and

Sevin® XLR) on honey bees ( A. mellifera ) under field conditions. Two separate fields of corn ( Zea mays ;

40 A each) located in Wyoming were treated with a single application of 2.0 lb a.i./A carbaryl via aerial

spray. Six honey bee colonies were located adjacent to each treated field. Dead bees were collected

twice a day from 3 colonies adjacent to each treated field using “Todd dead bee traps.” Pollen samples

were collected from the other 3 colonies using “modified OAC pollen traps.”

Reported mean concentrations of carbaryl on dead bees from the three colonies located adjacent to

each site are provided in Table 4

‐ 10

. These values were normalized to application rate and converted toPage 250 of 275 a dose basis by multiplying by the body weight of a worker bee ( i.e., 0.128 g). Pesticide residues on bees

near fields treated with the Sevin® Sprayable formulation appear to be higher compared to the Sevin

XLR formulation. Carbaryl residues were highest on bees found dead on the day of the application. For

the Sevin® Sprayable Formulation, the mean concentration was 1.19 mg a.i./kg per 1 lb a.i./A, which

corresponds to a dose of 0.152 μ g a.i./bee per 1 lb a.i./A. For the Sevin® XLR Formulation, the mean

concentration was 0.36 mg a.i./kg per 1 lb a.i./A, which corresponds to a dose of 0.152 μ g a.i./bee per 1

lb a.i./A. One notable limitation of this study report is that it does not include standard deviations or

maximum concentrations, which prevents understanding of the variability associated with the measured

concentrations of carbaryl on bees.

Table 4

‐ 10. Mean number of dead bees, carbaryl concentrations (from application of Sevin ® Sprayable

and Sevin ® XLR formulations) on dead bees collected from bee traps located adjacent to treated fields

(from Hanny and Harvey 1982), and normalized to 1 lb a.i./A and to dose.

Day after

application Reported mean concentration of

carbaryl on bees (mg/kg) Normalized mean concentration

on bees (mg/kg per 1 lb a.i./A) Normalized dose (μg a.i./bee

per 1 lb a.i./A)

Sevin

Sprayable Sevin XLR Sevin

Sprayable Sevin XLR Sevin

Sprayable Sevin XLR

0* 2.37 0.71 1.19 0.36 0.152 0.045

1 1.75 0.3 0.88 0.15 0.112 0.019

2 0.5 0.09 0.25 0.05 0.0320 0.0058

3 0.36 0.1 0.18 0.05 0.0230 0.0064

4 0.17 NA 0.09 NA 0.0109 NA

5 0.27 NA 0.14 NA 0.0173 NA

6 0.32 NA 0.16 NA 0.0205 NA

*represents day of treatment

NA = not available

Mean

‐ measured concentrations (based on data from 3 colonies) of carbaryl in pollen are provided in

Tables 4

‐ 11 and 4

‐ 12

. In addition, this table provides the author reported proportions of each pollen

sample that were represented by corn, which allows for normalization of reported concentrations of

carbaryl in pollen samples to represent 100% corn. Tables 4

‐ 11 and 4

‐ 12 also include concentrations of

carbaryl normalized to 1 lb a.i./A and 100% corn pollen.Page 251 of 275 Table 4

‐ 11. Daily concentrations of carbaryl (from Sevin ® Sprayable formulation) in pollen collected

from bees foraging near corn fields treated with carbaryl and fraction of pollen represented by corn.

Day after

application Reported mean

concentration of

carbaryl in pollen

(mg/kg) Mean % of pollen

represented by corn Normalized concentration

(mg/kg per 1 lb a.i./A based on

100% corn pollen)

0 1.9 30.5% 3.1

1 0.51 31.5% 0.080

2 1.22 26.2% 0.16

3 0.32 22.3% 0.036

4 0.15 62.3% 0.047

5 0.14 53.7% 0.038

6 0.14 49.3% 0.035

Table 4

‐ 12. Daily concentrations of carbaryl (From Sevin ® XLR formulation) in pollen collected from

bees foraging near corn fields treated with carbaryl and fraction of pollen represented by corn.

Day after

application Reported mean

concentration of

carbaryl in pollen

(mg/kg) Mean % of pollen

represented by corn Normalized concentration

(mg/kg per 1 lb a.i./A based on

100% corn pollen)

0 0.95 30.5% 1.6

1 0.18 31.5% 0.028

2 0.08 26.2% 0.010

3 0.02 22.3% 0.002

Hoffman and Castle 2012

Hoffman and Castle 2012 quantified imidacloprid residues in guttation fluid from cantaloupes grown in

fields. Imidacloprid was applied as a soil drip to the sixth node stage of young cantaloupe plants that

had already begun to bloom at an application rate of 767.3 mL/ha. Four days after application,

guttation fluid was observed and collected from 5 treated plants. The guttation fluid evaporated from

the leaves of the cantaloupe by 8:00 a.m. Imidacloprid residues were detected at a mean concentration

of 2.2 ± 0.55 μ g a.i./L.

In a targeted monitoring study involving guttation fluid, cantaloupe were planted in the early fall

(September

‐ October), and imidacloprid was applied to young plants as a soil drench application at rates

of 282 or 422 g a.i./ha (0.25 or 0.377 lbs a.i./A, respectively). Imidacloprid residues were measured, andPage 252 of 275 ranged from 0.04 to 37.4 μ g a.i./mL. The highest concentration detected corresponds to the higher

application rate (0.377 lbs a.i./A).

Koch and Weisser 1997

This study investigated potential contact based exposures of bees to pesticides through applications of a

fluorescent tracer (sodium

‐ fluorescein) to flowering apple ( Malus domestica ) orchards and Phacelia

tanacetifolia (commonly referred to as Phacelia ) fields located in Germany. From 1992

‐ 1997, nine trials

(spraying/sampling events) were conducted in the orchards and five trails were conducted in the

Phacelia fields. The sizes of the treated ground areas ranged 0.42

‐ 1.64 ha for the apple orchards and

0.25

‐ 1 ha for Phacelia fields. The tracer was applied at a rate of 20 g/10000 m 2 of vertical spray area for

orchards and ground spray area for Phacelia fields. For orchards the ground based application rate

ranged 0.018

‐ 0.020 lb a.i./A (average: 0.019) and for Phacelia fields, the rate was equivalent to 0.018 lbs

a.i./A. The tests were conducted during the flowing period of the orchard and Phacelia fields, therefore

the number of trials was limited to the duration of the flowering period. Following each application,

bees were collected at closed hive entrances over a period of 20

‐ 30 min in 5 min intervals. Sample size

varied per trial but was approximately 100 bees per sampling point (4316 bees were analyzed in the

nine orchard trials; 1724 bees in the five Phacelia trials).

For the apple orchards, the mean measured amount of tracer per bee ranged 1.62

‐ 20.84 ng/bee for the

nine trials, and an overall average of 6.33 ng/bee ( Table 4

‐ 13 ). The range of averages is equivalent to

0.079

‐ 1.02 μ g a.i./bee when normalized to 1 lb a.i./A and the normalized overall average is 0.33 μ g

a.i./bee. Table 4

‐ 13 presents the mean residues measured for each trail on bees as reported by the

authors and normalized to application rate. Values are normalized to mass pesticide per bee and mass

chemical per kg of bee, the latter is calculated by dividing the mass of pesticide per bee by the weight of

the bee, which is 0.128 g. In 8 of the 9 trials, the majority (>57%) of individual bees had ≤ 5 ng tracer

( ≤ 0.26 μ g a.i./bee normalized to 1 lb a.i./A); however, for all but one of the trials, several bees had

residues that exceeded 45 ng tracer (>2.4 μ g a.i./bee normalized to 1 lb a.i./A). Of all the bees used to

quantify levels of the tracer, approximately 3% (113 out of 4316) had levels that exceeded 45 ng

tracer 20

. Figure 4

‐ 1 presents the frequency distribution of measured residues on bees measured in the 9

trials with apple orchards.

20 This was determined by first multiplying number of bees in each trial that exceeded 45 ng tracer by the number of bees inPage 253 of 275 Table 4

‐ 13. Mean residues measured residues on bees for apple orchard trials.

Trial Number of bees Mean

deposit*

(ng/bee) Mass applied per

ground area

(lb/A) Normalized mean

deposit (μg a.i./bee

per 1 lb a.i./A) Normalized mean

deposit (mg a.i./kg

per 1 lb a.i./A)**

1 239 3.81 0.018 0.21 1.61

2 227 2.21 0.018 0.12 0.94

3 498 7.8 0.020 0.38 2.98

4 480 1.62 0.020 0.079 0.62

5 490 3.39 0.020 0.17 1.29

6 600 20.84 0.020 1.02 7.95

7 594 8.51 0.020 0.42 3.25

8 488 4.44 0.018 0.25 1.94

9 700 4.42 0.018 0.25 1.93

all 4316 6.33 0.019 0.33 2.54

*As reported by the authors.

**Calculated by dividing the normalized mean deposit per bee by the body weight of an adult worker bee, which is 0.128 g.

Figure 4

‐ 1. Frequency distribution of measured tracer on individual bees during 9 trials with apple

orchards. Concentrations are normalized to application rate.

each trial to calculate the number of bees per trial that exceeded this level. The values for each trial were then added to

estimate the total number of bees in the study exceeding 45 ng tracer. 0 10 20 30 40 50 60 70 80 90 100

<=0.26 >0.26 to

<=0.53 >0.53 to

<=0.79 >0.79 to

<=1.1 >1.1 to

<=1.3 >1.3 to

<=1.6 >1.6 to

<=1.8 >1.8 to

<=2.1 >2.1 to

<=2.4 >2.4 Trial 1

Trial 2

Trial 3

Trial 4

Trial 5

Trial 6

Trial 7

Trial 8

Trial 9Percent ofbeesintrial

Mass of tracer measured on individual bees (ug/bee per 1 lb/A)Page 254 of 275 For the Phacelia fields, the amount of tracer per bee was higher than those from the apple orchards,

with mean measured values for the 5 trials ranging 6.34 to 35.77 ng/bee, and an overall average of

18.19 ng/bee. The range of averages is equivalent to 0.31 to 1.75 μ g a.i./bee when normalized to 1 lb

a.i./A and the normalized overall average is 0.89 μ g a.i./bee. Table 4

‐ 14 presents the mean residues

measured for each trail on bees as reported by the authors and normalized to application rate. For the

Phaceila trials, there were more bees with residues that exceeded 45 ng tracer (>2.4 μg a.i./bee

normalized to 1 lb a.i./A) when compared to the apple trials. Of all the bees used to quantify levels of

the tracer, approximately 14% (244 out of 1724) had levels that exceeded 45 ng tracer 21

. Figure 4

‐ 2

presents the frequency distribution of measured residues on bees measured in the 5 trials with Phacelia

fields. The highest residue detected in the Phacelia trials was 48 ng/bee, which is equivalent to 2.7 μ g

a.i./bee normalized to 1 lb a.i./A.

21 This was determined by first multiplying number of bees in each trial that exceeded 45 ng tracer by the number of bees in

each trial to calculate the number of bees per trial that exceeded this level. The values for each trial were then added to

estimate the total number of bees in the study exceeding 45 ng tracer.Page 255 of 275 Table 4

‐ 14. Mean residues measured residues on bees for apple orchard trials.

Trial Number of bees Mean deposit *

(ng/bee) Mass applied per

ground area

(lb/A) Normalized mean

deposit ( μ g a.i./bee

per 1 lb a.i./A) Normalized mean

deposit (mg a.i./kg

per 1 lb a.i./A) **

1 150 11.39 0.020 0.56 4.35

2 200 24.41 0.020 1.19 9.32

3 360 6.34 0.020 0.31 2.42

4 499 35.77 0.020 1.75 13.7

5 515 13.07 0.020 0.64 4.99

all 1724 18.19 0.020 0.89 6.94

*As reported by the authors.

**Calculated by dividing the normalized mean deposit per bee by the body weight of an adult worker bee, which is 0.128 g.

Figure 2

‐ 2. Frequency distribution of measured tracer on individual bees during 5 trials with Phacelia

fields. Concentrations are normalized to application rate.

Lord et al

. 1968

Lord et al. 1968 conducted a series of experiments that involved measurements of dimethoate or

phorate in nectar of Fuchsia sp. and nasturtium ( Tropaeolum sp. ) plants after applications to soil. Plants

were contained in 5 inch diameter pots (surface area = 0.013 m 2 ) located in a greenhouse. For both

dimethoate and phorate, 25 mg a.i. were applied per pot, which is equivalent to a field application rate

of 19 kg a.i./ha (or 17 lb a.i./A). According the Registration Eligibility Decision Documents for these 0 10 20 30 40 50 60 70 80 90 100

<=0.26 >0.26 to

<=0.53 >0.53 to

<=0.79 >0.79 to

<=1.1 >1.1 to

<=1.3 >1.3 to

<=1.6 >1.6 to

<=1.8 >1.8 to

<=2.1 >2.1 to

<=2.4 >2.4 Trial 1

Trial 2

Trial 3

Trial 4

Trial 5Percent ofbeesintrial

Mass of tracer measured on individual bees (ug/bee per 1 lb/A)Page 256 of 275 pesticides, this application rate is an order of magnitude higher than currently registered applications in

the US.

In this experiment, pesticide concentrations were quantified in nectar 6 days after application because

toxicity tests conducted with fruit flies ( Drosphila melanogaster ) over time established the highest

mortality rates at this time point, suggesting that pesticide concentrations were at their peaks in nectar.

Phorate was detected at approximately 0.100 μg a.i./mL, which was below the level where residues

could be accurately quantified. Given the uncertainty associated with the phorate concentrations, these

data will not be considered further. For dimethoate, mean concentrations in nectar were 0.741±0.259

μ g a.i./mL for nasturtium and 2.890±0.550 μ g a.i./mL for Fuchsia

. These values were converted to a

mass of pesticide per mass of nectar. First, these values were converted to a mass of pesticide per mass

of sugar present in the nectar using the average of reported sugar contents of nectar from different

plant species as reported by Wykes 1953 ( i.e., 240 mg sugar/mL nectar). The resulting values were

divided by 2.5 mg nectar/mg sugar (Rortais et al

. 2005) in order to translate the residues into a mass of

pesticide per mass of nectar. These values were also normalized to 1 lb a.i./A by dividing by the

application rate ( Table 4

‐ 15 ).

Table 4

‐ 15. Dimethoate concentrations in nectar.

Plant

species Concentration Normalized concentration

(mg/kg per 1 lb a.i./A) ( μ g a.i./mL nectar) ( μ g a.i./mg sugar) (mg/kg nectar)

nasturtium 0.741 0.00309 1.26 0.0726

Fuchsia 2.89 0.0120 4.82 0.283Page 257 of 275 Reetz et al

. 2011

Reetz et al. 2011 quantified clothianidin residues in guttation fluid collected from maize (corn) plants

grown from seed under field conditions in Germany. Four different seed treatments were planted in

April 2009; one seed treatment did not include clothianidin, and the remaining three seed treatments

used one of two formulations of clothianidin (Poncho® (0.5 mg clothianidin a.i. /seed, equivalent to

0.006 or 0.067 lb a.i./A for seeding rates of 5321 and 60896 seeds/A, respectively)) or Poncho Pro® (1.25

mg clothianidin a.i./seed, equivalent to 0.015 or 0.168 lb a.i./A for seeding rates of 5321 and 60896

seeds/A, respectively)). Guttation fluid was collected May to July 2009, with sampling occurring every 2

days when weather conditions were good. Residue analysis was conducted using 50 μL of guttation fluid

mixed with a 10 μL internal reference standard imidacloprid

‐ d4. Concentrations were identified and

quantified using a LC

‐ HR

‐ MS (LTQTM

‐ Orbitrap spectrometer). Honeybees were observed collecting

exuded fluids from Potenilla plants along the edge of the plot of seed treated maize (corn) plants when

guttation fluid was being collected on the field, although none were observed on the field plot.

Concentrations of 7.4 to 8 μ g a.i./mL (equivalent to 47.6 to 110.4 μ g a.i./mL, when normalized to 1 lb

a.i./A) clothianidin were measured in the guttation fluid that was collected from maize plants. The

greatest concentrations of clothianidin measured in guttation fluid occurred on the first day of sampling.

Škerl et al

. 2009

This study involved applications of formulated products containing diazinon (Oleodiazinon), thiacloprid

(Calypso SC 480) and difenoconazole (Score 250 EC) to apple ( Malus domestica ) orchards located in

Slovenia in Spring of 2007. Application rates of the products were given as 15 L/ha Oleodiazinon, 0.2

L/ha Calypso SC 480 and 0.2 L/ha Score 250 EC. The application rates of the active ingredients were not

included in the report; however, the mass of diazinon in Oleodiazinon (95 g/L) and the mass of

thiacloprid in Calypso 480 SC (480 g/L) were both located on support documentation from the pesticide

registrants’ websites 22,23

. Therefore, the application rates of diazinon and thiacloprid used in this study

were 1.3 and 0.09 lb a.i./A, respectively. The percent a.i. present in Score 250 EC was not located;

therefore data for difenoconazole are not considered further. Pollen samples were collected using

pollen traps located at two hives that were adjacent to a treated apple orchard. The concentrations of

diazinon and thiacloprid in pollen are provided in Table 4

‐ 16

. One notable limitation of this study is a

22 Oleodiazinon: http://www.staehler.ch/pdf /tmb/oleodiazinons_d.pdf

23 Calypso 480 SC: http://www.bayercropscience.com.a u/resources/uploads/label/file7439.pdfPage 258 of 275 lack of replication; however, this study provides information that is useful for characterizing the

concentrations of pesticides in pollen. The reported concentrations will be treated as maximum, rather

than mean values.

Table 4

‐ 16. Diazinon and thiacloprid concentrations in pollen.

Chemical Application rate (lb a.i./A) Average Concentration

(mg a.i./kg) Normalized concentration

(mg a.i./kg per 1 lb a.i./A)

Diazinon 1.3 1.98 1.5

Thiacloprid 0.09 0.09 1

Stoner and Eitzer 2012

The purpose of this study was to measure concentrations of imidacloprid and thiamethoxam in the

pollen and nectar of squash ( Cucurbita pepo ) that was located in soil treated with these two pesticides.

Applications were made via spray onto the soil or drip irrigation during 2009 and 2010 (on different

fields). Application rates are provided in Table 4

‐ 17

. Over the two years, the average application rates

of imidacloprid and thiamethoxam were 0.344 and 0.127 lb a.i./A, respectively. The concentrations of

imidacloprid and thiamethoxam in pollen and nectar were reported as a combination of both years and

both application methods. The reported data are provided in Table 4

‐ 18 and Table 4

‐ 19

, respectively

along with concentrations normalized to application rate.

Table 4

‐ 17. Application rates included in study conducted by Stoner and Eitzer (2012).

Chemical Year Application method Application rate

(lb a.i./A) Application rate

(kg a.i./ha)

Imidacloprid 2009 Soil spray (incorporation) 0.320 0.358

Imidacloprid 2009 Drip irrigation 0.320 0.358

Thiamethoxam 2009 Soil spray (incorporation) 0.125 0.140

Thiamethoxam 2009 Drip irrigation 0.125 0.140

Imidacloprid 2010 Soil spray (incorporation) 0.367 0.411

Imidacloprid 2010 Drip irrigation 0.367 0.411

Thiamethoxam 2010 Soil spray (incorporation) 0.128 0.143

Thiamethoxam 2010 Drip irrigation 0.128 0.143Page 259 of 275 Table 4

‐ 18. Reported and normalized concentrations of imidacloprid and thiamethoxam in pollen

samples collected in 2009 and 2010.

Chemical Average

application

rate (lb

a.i./A) Concentration (mg a.i./kg) Number of

samples Normalized Concentration (mg

a.i./kg per 1 lb a.i./A)

Mean (SD) Min Max Mean (SD) Min Max

Imidacloprid 0.344 0.014 (0.008) 0.006 0.028 12 0.041 (0.023) 0.017 0.081

thiamethoxam 0.127 0.012 (0.009) 0.005 0.014 6 0.094 (0.071) 0.039 0.11

Table 4

‐ 19. Reported and normalized concentrations of imidacloprid and thiamethoxam in nectar

samples collected in 2009 and 2010.

Chemical Average

application

rate (lb

a.i./A) Concentration (mg a.i./kg) Number

of

samples Normalized Concentration (mg

a.i./kg per 1 lb a.i./A)

Mean (SD) Min Max Mean (SD) Min Max

Imidacloprid 0.344 0.010 (0.003) 0.005 0.014 6 0.029 (0.009) 0.015 0.041

thiamethoxam 0.127 0.011 (0.006) 0.005 0.020 6 0.087 (0.047) 0.039 0.16

Tapparo et al

. 2011

Tapparo et al. 2011 quantified thiamethoxam, clothianidin, imidacloprid, and fipronil residues in

guttation fluid from corn seedlings grown in both an open field and greenhouse experiments. Seeds

were coated with thiamethoxam (Cruiser® 0.6 or 1.0 mg a.i./seed), clothianidin (Poncho® 1.25 mg

a.i./seed), imidacloprid (Gaucho® 0.5, 1.0, or 1.25 mg a.i./seed), and fipronil (Regent® 0.5, 0.75, or 1.0

mg a.i./seed). Guttation fluid was collected every morning beginning the day after seedling emergence

and continuing for 20 days after emergence. Residues of thiamethoxam, clothianidin, imidacloprid, and

fipronil were analyzed using ultra high performance liquid chromatography (UHPLC). Measured fipronil

concentrations were below the detection limit (LOD

= 5.1 μ g/L). In the greenhouse experiment, residues

of thiamethoxam, clothianidin, and imidacloprid were the greatest on the first day after seedling

emergence, decreasing over the first 10 days, and increased during the last 10 days of measurement

( Table 4

‐ 12 ). Due to decrease in guttation fluid release with growth of the plant, concentration of the

chemical may be highly concentrated within the limited water released by the plant. Also noted in a

second greenhouse experiment was that the total concentrations of imidacloprid, clothianidin, and

thiamethoxam measured in guttation fluid collected confirmed high peak concentrations occurring after

seedling emergence ( Table 4

‐ 12 ). In a third greenhouse experiment using thiamethoxam only, soil

conditions (wetness) were used to measure the effects of chemical concentration within guttation fluid.

The results indicated that under drier soil conditions the concentration of thiamethoxam is more

concentrated ( Table 4

‐ 12 ). During an open field experiment that was ran concurrently with thePage 260 of 275 greenhouse experiments, the measured concentrations of thiamethoxam, clothianidin, and imidacloprid

also resulted in peaks in residue levels the first day after seedling emergence ( Table 4

‐ 12 ).

Table 4

‐ 12. Measured concentrations in guttation fluid (μg a.i./mL)

Greenhouse Experiment 1

Time Thiamethoxam

(1.0 mg ai/seed)

μ g a.i./mL Clothianidin

(1.25 mg ai/seed)

μ g a.i./mL Imidacloprid

(0.5mg ai/seed)

μ g a.i./mL

First day after SE 24.3 36.0 80.9

8

‐ 10 days after SE 3.6 8.8 17.3

11

‐ 20 days after SE 8.3 31.6 60.1

Greenhouse Experiment 2

Thiamethoxam

(1.0 mg ai/seed)

μ g a.i./mL Clothianidin

(1.25 mg ai/seed)

μ g a.i./mL Imidacloprid

(1.25 mg ai/seed)

μ g a.i./mL

146.0 101.7 345.8

Greenhouse Experiment 3 (Thiamethoxam only;1.0 mg a.i./seed)

Conditions grown under Thiamethoxam

μ g a.i./mL

wet (near saturation) 155

moderate 253

dry 1154

Field Experiment 1

Time Thiamethoxam

(1.0 mg ai/seed)

μ g a.i./mL Clothianidin

(1.25 mg ai/seed)

μ g a.i./mL Imidacloprid

(0.5 mg ai/seed)

μ g a.i./mL

First day after SE 227 46 222

SE = seedling emergence

1 Assumptions made on mass a.i./seed

Wallner 2009

In this study, boscalid and prothioconazol were sprayed onto an oilseed rape ( Brassica sp. ) field at rates

of 0.45 and 0.22 lb a.i./A (0.500 and 0.250 kg a.i./ha), respectively. In addition, the seeds were treated

with clothianidin. Honey bees from two apiaries located adjacent to the treated field were used to

collect pollen and nectar. Samples were collected over a 7 day period, including the day before the

application, the day of the application, and the following 5 days. Prothioconazol and clothianidin were

not detected in the pollen collected by the honey bees. Boscalid was detected in all pollen samples. The

average boscalid concentration in pollen was 13.9 mg/kg on the day of the application and 26.2 mg/kg

on the day after application ( Table 4

‐ 20 ). All three pesticides were detected in nectar collected fromPage 261 of 275 bees. On the day of the application, boscalid and prothioconazol were detected at 1.43 and 0.69 mg/kg,

respectively ( Tables 4

‐ 21 and 4

‐ 22 ). Clothianidin was detected at a maximum of 0.003 mg/kg.

Table 4

‐ 20. Boscalid concentrations in pollen.

Day Average Concentration

(mg a.i./kg) Normalized concentration

(mg a.i./kg per 1 lb a.i./A)

0 13.9 31

1 26.2 58

2

‐ 5 4.7 10

Table 4

‐ 21. Boscalid concentrations in nectar.

Day Average Concentration

(mg a.i./kg) Normalized concentration

(mg a.i./kg per 1 lb a.i./A)

0 1.43 3.2

1 0.13 0.29

2 0.017 0.038

Table 4

‐ 22. Prothioconazol concentrations in nectar.

Day Average Concentration

(mg a.i./kg) Normalized concentration

(mg a.i./kg per 1 lb a.i./A)

0 0.69 3.2

1 0.06 0.27

2 0.017 0.077Page 262 of 275 

### Appendix 5. Transpiration Stream Concentration Factors (TSCFs)

Median and 95 th percentile estimates of TSCF values calculated by EPA using empirical data reported

by Briggs 1982 and 1983.

Log K

ow Empirical TSCFs (Briggs 1982) Median TSCF estimate Upper 95 th percentile TSCF estimate

‐ 1 0.060662 0.351114

‐ 0.99 0.061637 0.352286

‐ 0.98 0.062624 0.353473

‐ 0.97 0.063624 0.354673

‐ 0.96 0.064635 0.355888

‐ 0.95 0.06566 0.357116

‐ 0.94 0.066696 0.358358

‐ 0.93 0.067746 0.359614

‐ 0.92 0.068807 0.360885

‐ 0.91 0.069882 0.362169

‐ 0.9 0.070969 0.363467

‐ 0.89 0.072069 0.36478

‐ 0.88 0.073182 0.366106

‐ 0.87 0.074308 0.367447

‐ 0.86 0.075447 0.368802

‐ 0.85 0.076599 0.370171

‐ 0.84 0.077765 0.371554

‐ 0.83 0.078943 0.372951

‐ 0.82 0.080135 0.374363

‐ 0.81 0.08134 0.375789

‐ 0.8 0.082559 0.377229

‐ 0.79 0.083791 0.378683

‐ 0.78 0.085037 0.380152

‐ 0.77 0.086297 0.381634

‐ 0.76 0.08757 0.383131

‐ 0.75 0.088857 0.384642

‐ 0.74 0.090158 0.386167

‐ 0.73 0.091472 0.387707

‐ 0.72 0.092801 0.38926

‐ 0.71 0.094143 0.390828

‐ 0.7 0.0955 0.392409

‐ 0.69 0.09687 0.394005

‐ 0.68 0.098255 0.395614

‐ 0.67 0.099654 0.397238

‐ 0.66 0.101067 0.398876

‐ 0.65 0.102494 0.400527

‐ 0.64 0.103936 0.402193

‐ 0.63 0.105391 0.403872

‐ 0.62 0.106862 0.405565Page 263 of 275 Log K

ow Empirical TSCFs (Briggs 1982) Median TSCF estimate Upper 95 th percentile TSCF estimate

‐ 0.61 0.108346 0.407272

‐ 0.6 0.109845 0.408992

‐ 0.59 0.111358 0.410726

‐ 0.58 0.112886 0.412473

‐ 0.57 0.19 0.114429 0.414235

‐ 0.57 0.114429 0.414235

‐ 0.56 0.115985 0.416009

‐ 0.55 0.117557 0.417797

‐ 0.54 0.119143 0.419598

‐ 0.53 0.120743 0.421412

‐ 0.52 0.122358 0.42324

‐ 0.51 0.123988 0.425081

‐ 0.5 0.125632 0.426934

‐ 0.49 0.127291 0.428801

‐ 0.48 0.128964 0.43068

‐ 0.47 0.21 0.130652 0.432572

‐ 0.47 0.130652 0.432572

‐ 0.46 0.132355 0.434477

‐ 0.45 0.134072 0.436395

‐ 0.44 0.135804 0.438325

‐ 0.43 0.13755 0.440267

‐ 0.42 0.139311 0.442222

‐ 0.41 0.141087 0.444189

‐ 0.4 0.142877 0.446168

‐ 0.39 0.144681 0.448158

‐ 0.38 0.1465 0.450161

‐ 0.37 0.148334 0.452176

‐ 0.36 0.150182 0.454202

‐ 0.35 0.152044 0.45624

‐ 0.34 0.153921 0.45829

‐ 0.33 0.155812 0.46035

‐ 0.32 0.157718 0.462422

‐ 0.31 0.159637 0.464506

‐ 0.3 0.161571 0.4666

‐ 0.29 0.163519 0.468705

‐ 0.28 0.165482 0.47082

‐ 0.27 0.167458 0.472947

‐ 0.26 0.169448 0.475084

‐ 0.25 0.171452 0.477231

‐ 0.24 0.173471 0.479389

‐ 0.23 0.175502 0.481556

‐ 0.22 0.177548 0.483734

‐ 0.21 0.179607 0.485922

‐ 0.2 0.18168 0.488119

‐ 0.19 0.183767 0.490326

‐ 0.18 0.185867 0.492542

‐ 0.17 0.18798 0.494768Page 264 of 275 Log K

ow Empirical TSCFs (Briggs 1982) Median TSCF estimate Upper 95 th percentile TSCF estimate

‐ 0.16 0.190107 0.497003

‐ 0.15 0.192246 0.499247

‐ 0.14 0.194399 0.5015

‐ 0.13 0.28 0.196565 0.503762

‐ 0.13 0.196565 0.503762

‐ 0.12 0.051 0.198744 0.506032

‐ 0.12 0.198744 0.506032

‐ 0.11 0.200935 0.508311

‐ 0.1 0.203139 0.510598

‐ 0.09 0.205356 0.512894

‐ 0.08 0.207585 0.515197

‐ 0.07 0.209826 0.517509

‐ 0.06 0.21208 0.519828

‐ 0.05 0.214345 0.522155

‐ 0.04 0.216623 0.524489

‐ 0.03 0.218912 0.526831

‐ 0.02 0.221213 0.52918

‐ 0.01 0.223526 0.531536

7.53E

‐ 16 0.22585 0.533899

0.01 0.228185 0.536268

0.02 0.230531 0.538645

0.03 0.232889 0.541027

0.04 0.235257 0.543416

0.05 0.237635 0.545811

0.06 0.240024 0.548213

0.07 0.242424 0.55062

0.08 0.244833 0.553032

0.09 0.247253 0.555451

0.1 0.249682 0.557875

0.11 0.252121 0.560304

0.12 0.254569 0.562738

0.13 0.257027 0.565177

0.14 0.259493 0.567621

0.15 0.261969 0.570069

0.16 0.264453 0.572522

0.17 0.266945 0.574979

0.18 0.269446 0.577441

0.19 0.271955 0.579906

0.2 0.274471 0.582376

0.21 0.276995 0.584849

0.22 0.279527 0.587326

0.23 0.282065 0.589806

0.24 0.284611 0.592289

0.25 0.287163 0.594776

0.26 0.289722 0.597265

0.27 0.292287 0.599757

0.28 0.294859 0.602252Page 265 of 275 Log K

ow Empirical TSCFs (Briggs 1982) Median TSCF estimate Upper 95 th percentile TSCF estimate

0.29 0.297435 0.604749

0.3 0.300018 0.607249

0.31 0.302606 0.609751

0.32 0.305198 0.612255

0.33 0.307796 0.61476

0.34 0.310398 0.617268

0.35 0.313004 0.619777

0.36 0.315615 0.622287

0.37 0.318229 0.624798

0.38 0.320846 0.627311

0.39 0.323467 0.629824

0.4 0.326091 0.632338

0.41 0.328717 0.634853

0.42 0.331346 0.637368

0.43 0.333977 0.639883

0.44 0.33661 0.642398

0.45 0.339244 0.644913

0.46 0.34188 0.647428

0.47 0.344516 0.649942

0.48 0.347154 0.652456

0.49 0.349791 0.654969

0.5 0.352429 0.657481

0.51 0.355067 0.659991

0.52 0.357704 0.662501

0.53 0.36034 0.665008

0.54 0.362975 0.667514

0.55 0.365608 0.670018

0.56 0.36824 0.67252

0.57 0.37087 0.67502

0.58 0.373498 0.677517

0.59 0.376122 0.680011

0.6 0.378744 0.682503

0.61 0.381363 0.684991

0.62 0.383978 0.687477

0.63 0.386588 0.689958

0.64 0.389195 0.692436

0.65 0.391797 0.69491

0.66 0.394394 0.69738

0.67 0.396986 0.699846

0.68 0.399572 0.702307

0.69 0.402152 0.704763

0.7 0.404727 0.707214

0.71 0.407294 0.70966

0.72 0.409855 0.712101

0.73 0.412408 0.714535

0.74 0.414954 0.716964

0.75 0.417491 0.719387Page 266 of 275 Log K

ow Empirical TSCFs (Briggs 1982) Median TSCF estimate Upper 95 th percentile TSCF estimate

0.76 0.420021 0.721803

0.77 0.422542 0.724212

0.78 0.425054 0.726615

0.79 0.427557 0.72901

0.8 0.47 0.43005 0.731398

0.8 0.43005 0.731398

0.81 0.432533 0.733778

0.82 0.435006 0.736151

0.83 0.437468 0.738514

0.84 0.439919 0.74087

0.85 0.442359 0.743216

0.86 0.444787 0.745554

0.87 0.447204 0.747882

0.88 0.449608 0.750201

0.89 0.451999 0.752509

0.9 0.454377 0.754807

0.91 0.456742 0.757095

0.92 0.459094 0.759372

0.93 0.461431 0.761638

0.94 0.463754 0.763892

0.95 0.466062 0.766135

0.96 0.468355 0.768365

0.97 0.470633 0.770583

0.98 0.472895 0.772788

0.99 0.475142 0.774981

1 0.477371 0.77716

1.01 0.479584 0.779325

1.02 0.48178 0.781476

1.03 0.483959 0.783612

1.04 0.47 0.48612 0.785734

1.04 0.48612 0.785734

1.05 0.488263 0.787841

1.06 0.490388 0.789933

1.07 0.492494 0.792008

1.08 0.54 0.494581 0.794068

1.08 0.494581 0.794068

1.09 0.496649 0.796111

1.1 0.498697 0.798137

1.11 0.500726 0.800147

1.12 0.502734 0.802138

1.13 0.504722 0.804112

1.14 0.506689 0.806067

1.15 0.508634 0.808004

1.16 0.510559 0.809922

1.17 0.512461 0.811821

1.18 0.514342 0.8137

1.19 0.5162 0.815558Page 267 of 275 Log K

ow Empirical TSCFs (Briggs 1982) Median TSCF estimate Upper 95 th percentile TSCF estimate

1.2 0.518035 0.817397

1.21 0.519848 0.819214

1.22 0.521637 0.821011

1.23 0.523403 0.822786

1.24 0.525146 0.824539

1.25 0.526864 0.82627

1.26 0.528558 0.827978

1.27 0.530227 0.829663

1.28 0.531872 0.831325

1.29 0.533491 0.832963

1.3 0.535085 0.834578

1.31 0.536653 0.836168

1.32 0.538196 0.837733

1.33 0.539712 0.839273

1.34 0.541202 0.840788

1.35 0.542666 0.842277

1.36 0.544102 0.843739

1.37 0.545512 0.845176

1.38 0.546894 0.846586

1.39 0.548249 0.847968

1.4 0.549576 0.849323

1.41 0.550875 0.850651

1.42 0.552145 0.85195

1.43 0.553388 0.853222

1.44 0.554601 0.854464

1.45 0.555786 0.855678

1.46 0.556942 0.856862

1.47 0.558069 0.858017

1.48 0.559166 0.859142

1.49 0.67 0.560234 0.860237

1.49 0.560234 0.860237

1.5 0.561272 0.861301

1.51 0.56228 0.862335

1.52 0.563259 0.863339

1.53 0.564206 0.864311

1.54 0.565124 0.865251

1.55 0.566011 0.86616

1.56 0.566867 0.867038

1.57 0.22 0.567692 0.867883

1.57 0.567692 0.867883

1.58 0.568487 0.868696

1.59 0.56925 0.869477

1.6 0.569982 0.870225

1.61 0.570682 0.87094

1.62 0.571352 0.871622

1.63 0.571989 0.872271

1.64 0.572595 0.872887Page 268 of 275 Log K

ow Empirical TSCFs (Briggs 1982) Median TSCF estimate Upper 95 th percentile TSCF estimate

1.65 0.573169 0.873469

1.66 0.573711 0.874018

1.67 0.574221 0.874533

1.68 0.574699 0.875014

1.69 0.575145 0.875461

1.7 0.575559 0.875874

1.71 0.57594 0.876253

1.72 0.576289 0.876597

1.73 0.576606 0.876907

1.74 0.57689 0.877183

1.75 0.577141 0.877424

1.76 0.57736 0.877631

1.77 0.577546 0.877803

1.78 0.5777 0.87794

1.79 0.577821 0.878043

1.8 0.5 0.577909 0.878111

1.8 0.577909 0.878111

1.81 0.577965 0.878145

1.82 0.577988 0.878144

1.83 0.577978 0.878108

1.84 0.577935 0.878038

1.85 0.57786 0.877933

1.86 0.577752 0.877794

1.87 0.577611 0.877621

1.88 0.577438 0.877413

1.89 0.577232 0.87717

1.9 0.576994 0.876894

1.91 0.576723 0.876583

1.92 0.576419 0.876239

1.93 0.576083 0.87586

1.94 0.575714 0.875448

1.95 0.575314 0.875002

1.96 0.574881 0.874523

1.97 0.574415 0.87401

1.98 0.55 0.573918 0.873465

1.98 0.573918 0.873465

1.99 0.573389 0.872886

2 0.572827 0.872274

2.01 0.572234 0.87163

2.02 0.571609 0.870953

2.03 0.570952 0.870244

2.04 0.570264 0.869503

2.05 0.569545 0.86873

2.06 0.568794 0.867926

2.07 0.568012 0.86709

2.08 0.567199 0.866223

2.09 0.566355 0.865326Page 269 of 275 Log K

ow Empirical TSCFs (Briggs 1982) Median TSCF estimate Upper 95 th percentile TSCF estimate

2.1 0.56548 0.864397

2.11 0.564575 0.863438

2.12 0.563639 0.86245

2.13 0.562673 0.861431

2.14 0.561677 0.860383

2.15 0.560651 0.859305

2.16 0.559595 0.858199

2.17 0.558509 0.857063

2.18 0.557394 0.8559

2.19 0.55625 0.854708

2.2 0.555076 0.853489

2.21 0.553874 0.852242

2.22 0.552643 0.850968

2.23 0.551384 0.849667

2.24 0.550096 0.84834

2.25 0.54878 0.846986

2.26 0.547437 0.845607

2.27 0.546065 0.844202

2.27 0.94 0.546065 0.844202

2.28 0.544667 0.842772

2.29 0.543241 0.841318

2.3 0.541788 0.839838

2.31 0.540308 0.838335

2.32 0.538802 0.836808

2.33 0.53727 0.835257

2.34 0.535712 0.833683

2.35 0.534128 0.832087

2.36 0.532519 0.830468

2.37 0.530884 0.828827

2.38 0.529225 0.827165

2.39 0.527541 0.825481

2.4 0.525832 0.823776

2.41 0.524099 0.82205

2.42 0.522343 0.820305

2.43 0.520563 0.818539

2.44 0.518759 0.816754

2.45 0.516933 0.814949

2.46 0.515084 0.813126

2.47 0.513212 0.811284

2.48 0.511318 0.809424

2.49 0.509402 0.807546

2.5 0.507465 0.80565

2.51 0.505507 0.803737

2.52 0.503527 0.801808

2.53 0.501527 0.799862

2.54 0.499507 0.7979

2.55 0.497467 0.795922Page 270 of 275 Log K

ow Empirical TSCFs (Briggs 1982) Median TSCF estimate Upper 95 th percentile TSCF estimate

2.56 0.495406 0.793928

2.57 0.493327 0.791919

2.58 0.491228 0.789896

2.59 0.489111 0.787858

2.6 0.486975 0.785805

2.61 0.484821 0.783739

2.62 0.482649 0.781659

2.63 0.48046 0.779566

2.64 0.478254 0.777459

2.64 0.37 0.478254 0.777459

2.65 0.476031 0.77534

2.66 0.473791 0.773209

2.67 0.471535 0.771065

2.68 0.469264 0.76891

2.69 0.466977 0.766743

2.7 0.464674 0.764565

2.71 0.462357 0.762375

2.72 0.460025 0.760175

2.73 0.45768 0.757965

2.74 0.45532 0.755744

2.75 0.452947 0.753513

2.76 0.450561 0.751273

2.77 0.448162 0.749023

2.78 0.44575 0.746764

2.79 0.443327 0.744496

2.8 0.440891 0.742219

2.8 0.47 0.440891 0.742219

2.81 0.438445 0.739933

2.82 0.435987 0.73764

2.83 0.433518 0.735338

2.84 0.431039 0.733029

2.85 0.42855 0.730712

2.86 0.426051 0.728387

2.87 0.423542 0.726056

2.88 0.421025 0.723717

2.89 0.418499 0.721372

2.89 0.51 0.418499 0.721372

2.9 0.415964 0.71902

2.91 0.413422 0.716662

2.92 0.410871 0.714298

2.93 0.408314 0.711928

2.94 0.405749 0.709552

2.95 0.403177 0.707171

2.96 0.4006 0.704784

2.97 0.398016 0.702392

2.98 0.395426 0.699995

2.99 0.392831 0.697593Page 271 of 275 Log K

ow Empirical TSCFs (Briggs 1982) Median TSCF estimate Upper 95 th percentile TSCF estimate

3 0.390231 0.695187

3.01 0.387626 0.692776

3.02 0.385017 0.690361

3.03 0.382404 0.687942

3.04 0.379787 0.685519

3.05 0.377166 0.683092

3.06 0.374542 0.680661

3.07 0.371916 0.678227

3.08 0.369287 0.67579

3.09 0.366656 0.67335

3.1 0.364023 0.670907

3.11 0.361388 0.66846

3.12 0.358753 0.666012

3.12 0.26 0.358753 0.666012

3.13 0.356116 0.663561

3.14 0.353479 0.661107

3.15 0.350841 0.658652

3.16 0.348203 0.656194

3.17 0.345566 0.653735

3.18 0.342929 0.651273

3.19 0.340293 0.648811

3.2 0.337658 0.646347

3.21 0.335025 0.643881

3.22 0.332393 0.641415

3.23 0.329763 0.638948

3.24 0.327136 0.63648

3.25 0.324511 0.634011

3.26 0.321889 0.631542

3.27 0.31927 0.629072

3.28 0.316654 0.626602

3.29 0.314042 0.624133

3.3 0.311434 0.621663

3.31 0.308831 0.619194

3.32 0.306231 0.616725

3.33 0.303637 0.614257

3.34 0.301047 0.611789

3.35 0.298462 0.609323

3.36 0.295883 0.606857

3.37 0.29331 0.604393

3.38 0.290742 0.60193

3.39 0.288181 0.599468

3.4 0.285626 0.597008

3.41 0.283078 0.59455

3.42 0.280536 0.592094

3.43 0.278002 0.58964

3.44 0.275475 0.587188

3.45 0.272955 0.584739Page 272 of 275 Log K

ow Empirical TSCFs (Briggs 1982) Median TSCF estimate Upper 95 th percentile TSCF estimate

3.46 0.270443 0.582293

3.47 0.267939 0.579849

3.48 0.265443 0.577408

3.49 0.262956 0.57497

3.5 0.260477 0.572535

3.51 0.258007 0.570104

3.52 0.255546 0.567676

3.53 0.253094 0.565252

3.54 0.250651 0.562831

3.55 0.248218 0.560415

3.56 0.245795 0.558003

3.57 0.243381 0.555596

3.58 0.240978 0.553192

3.59 0.238585 0.550794

3.6 0.236202 0.5484

3.61 0.23383 0.546011

3.62 0.231468 0.543628

3.63 0.229117 0.54125

3.64 0.226778 0.538877

3.65 0.224449 0.53651

3.66 0.222132 0.534148

3.67 0.219826 0.531793

3.68 0.217532 0.529444

3.69 0.21525 0.527101

3.7 0.21298 0.524764

3.7 0.11 0.21298 0.524764

3.71 0.210721 0.522434

3.72 0.208475 0.520111

3.73 0.206241 0.517795

3.74 0.20402 0.515486

3.75 0.20181 0.513184

3.76 0.199614 0.51089

3.77 0.19743 0.508603

3.78 0.195259 0.506324

3.79 0.193101 0.504052

3.8 0.190956 0.501789

3.81 0.188825 0.499534

3.82 0.186706 0.497288

3.83 0.184601 0.495049

3.84 0.182509 0.49282

3.85 0.180431 0.490599

3.86 0.178366 0.488388

3.87 0.176315 0.486185

3.88 0.174277 0.483992

3.89 0.172254 0.481808

3.9 0.170244 0.479634

3.91 0.168248 0.477469Page 273 of 275 Log K

ow Empirical TSCFs (Briggs 1982) Median TSCF estimate Upper 95 th percentile TSCF estimate

3.92 0.166266 0.475314

3.93 0.164299 0.473169

3.94 0.162345 0.471035

3.95 0.160405 0.468911

3.96 0.15848 0.466797

3.97 0.156569 0.464693

3.98 0.154672 0.462601

3.99 0.152789 0.460519

4 0.150921 0.458448

4.01 0.149068 0.456389

4.02 0.147228 0.45434

4.03 0.145403 0.452303

4.04 0.143593 0.450277

4.05 0.141797 0.448263

4.06 0.140016 0.446261

4.07 0.138249 0.44427

4.08 0.136497 0.442292

4.09 0.134759 0.440325

4.1 0.133036 0.438371

4.11 0.131328 0.436429

4.12 0.129634 0.434499

4.13 0.127955 0.432582

4.14 0.12629 0.430678

4.15 0.12464 0.428786

4.16 0.123005 0.426907

4.17 0.121384 0.42504

4.18 0.119778 0.423187

4.19 0.118186 0.421347

4.2 0.116609 0.41952

4.21 0.115046 0.417707

4.22 0.113498 0.415906

4.23 0.111965 0.414119

4.24 0.110446 0.412346

4.25 0.108941 0.410586

4.26 0.107451 0.408839

4.27 0.105975 0.407107

4.28 0.104513 0.405388

4.29 0.103066 0.403683

4.3 0.101633 0.401992

4.31 0.100214 0.400314

4.32 0.09881 0.398651

4.33 0.09742 0.397002

4.34 0.096043 0.395367

4.35 0.094681 0.393746

4.36 0.093333 0.392139

4.37 0.091999 0.390546

4.38 0.090679 0.388968Page 274 of 275 Log K

ow Empirical TSCFs (Briggs 1982) Median TSCF estimate Upper 95 th percentile TSCF estimate

4.39 0.089373 0.387403

4.4 0.08808 0.385854

4.41 0.086802 0.384318

4.42 0.085537 0.382797

4.43 0.084285 0.38129

4.44 0.083048 0.379797

4.45 0.081824 0.378319

4.46 0.080613 0.376856

4.47 0.079416 0.375406

4.48 0.078232 0.373971

4.49 0.077061 0.372551

4.5 0.075904 0.371145

4.51 0.07476 0.369753

4.52 0.073629 0.368376

4.53 0.07251 0.367013

4.54 0.071405 0.365665

4.55 0.070313 0.364331

4.56 0.069233 0.363011

4.57 0.068166 0.361706

4.58 0.067112 0.360415

4.59 0.066071 0.359138

4.6 0.065042 0.357876

4.6 0.06 0.065042 0.357876

4.61 0.064025 0.356627

4.62 0.06302 0.355393

4.63 0.062028 0.354173

4.64 0.061048 0.352967

4.65 0.06008 0.351776

4.66 0.059124 0.350598

4.67 0.05818 0.349434

4.68 0.057248 0.348284

4.69 0.056328 0.347148

4.7 0.055419 0.346026

4.71 0.054522 0.344918

4.72 0.053636 0.343823

4.73 0.052762 0.342742

4.74 0.051899 0.341675

4.75 0.051047 0.340621

4.76 0.050206 0.33958

4.77 0.049377 0.338553

4.78 0.048558 0.337539

4.79 0.04775 0.336539

4.8 0.046953 0.335551

4.81 0.046167 0.334577

4.82 0.045392 0.333616

4.83 0.044626 0.332667

4.84 0.043872 0.331732Page 275 of 275 Log K

ow Empirical TSCFs (Briggs 1982) Median TSCF estimate Upper 95 th percentile TSCF estimate

4.85 0.043127 0.330809

4.86 0.042393 0.329899

4.87 0.041669 0.329001

4.88 0.040955 0.328116

4.89 0.040251 0.327244

4.9 0.039557 0.326383

4.91 0.038872 0.325535

4.92 0.038197 0.324699

4.93 0.037532 0.323875

4.94 0.036877 0.323063

4.95 0.03623 0.322263

4.96 0.035593 0.321475

4.97 0.034966 0.320698

4.98 0.034347 0.319933

4.99 0.033737 0.319179

5 0.033137 0.318436